Electronically submitted to SF Bay Regional Water Board ... · • Data Management & QA/QC – Coordination and implementation of the SMCWPPP Water Quality Monitoring Data Management

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Electronically submitted to SF Bay Regional Water Board ftp Site (c/o Zachary Rokeach) and to Regional Data Center (Moss Landing) on 3/31/2020. March 31, 2020 Mr. Michael Montgomery Executive Officer San Francisco Bay Region Regional Water Quality Control Board 1515 Clay Street, Suite 1400 Oakland, CA 94612 Subject: Submittal of SMCWPPP Integrated Monitoring Report for Water Years 2014 through

2019 and Electronic Monitoring Data Submittal for Water Year 2019. Dear Mr. Montgomery: On behalf of all San Mateo Countywide Water Pollution Prevention Program (SMCWPPP) Permittees, I am pleased to submit SMCWPPP’s Integrated Monitoring Report (IMR) for water quality monitoring conducted Water Year (WY) 2014 through WY 2019 (October 1, 2013 through September 30, 2019) and Electronic Monitoring Data for water quality monitoring conducted in WY 2019. SMCWPPP is a program of the City/County Association of Governments of San Mateo County (C/CAG). The IMR is submitted in compliance with Provision C.8.h.v. of the Municipal Regional Permit regulating discharge of stormwater runoff from Bay Areas municipal agencies (MRP 2.0, NPDES No. CAS612008, Order R2-2015-0049, effective date January 1, 2016). The IMR contains summaries of monitoring conducted in WY 2014 - WY 2019 pursuant to Provision C.8 of the MRP, including: Creek Status Monitoring (Provision C.8.d.), Stressor/Source Identification Projects (Provision C.8.e.), Pollutants of Concern Monitoring (Provision C.8.f.), and Pesticides and Toxicity Monitoring (C.8.g.). The IMR consists of a comprehensive Executive Summary and five Parts:

• Part A – San Francisco Estuary Receiving Water Monitoring (C.8.c.) • Part B – Creek Status (C.8.d) and Pesticides & Toxicity (C.8.g.) Monitoring • Part C – Stressor Source Identification (SSID) Projects (C.8.e.) • Part D – Pollutants of Concern Monitoring (C.8.f.) • Part E – Budget Summary (C.8.h.v.(4))

Electronic Monitoring Data are submitted in compliance with Provision C.8.h.ii. of the MRP. Please note that although the IMR summarizes data collected by SMCWPPP and third-party

555 County Center Redwood City, CA 94063

P 650.599.1406 F 650.361.8227 flowstobay.org

Mr. Michael Montgomery March 31, 2020 Page 2

organizations1, the electronic data files include only those data collected by SMCWPPP pursuant to the MRP provisions listed in Table 1. Table 1. Project, date range, and applicable MRP provision for data included in the Water Year 2019 Electronic Monitoring Data submittal.

Project Date Range MRP Provision

Creek Status Monitoring April - September 2019 C.8.d.

Pollutants of Concern Monitoring May - July 2019 C.8.f.

Pesticides and Toxicity Monitoring July 2019 C.8.g.

The quality of all Creek Status Monitoring (MRP Provision C.8.d.), Pesticides and Toxicity Monitoring (MRP Provision C.8.g.) data, and Pollutants of Concern (MRP Provision C.8.f.) nutrient and copper data in the electronic data files was evaluated consistent with the Bay Area Stormwater Management Agencies Association (BASMAA) Regional Monitoring Coalition’s Creek Status Monitoring Program Quality Assurance Project Plan (QAPP), which is comparable with the latest version of the State of California’s Surface Water Ambient Monitoring Program (SWAMP) Quality Assurance Program Plan (QAPrP). The quality of all Pollutants of Concern Monitoring (MPR Provision C.8.f.) PCBs and mercury data in the electronic data files was evaluated consistent with the Clean Watersheds for Clean Bay (CW4CB) project QAPP. In compliance with Provision C.8.h.ii. (Electronic Reporting) of the MRP, all CEDEN-acceptable data (i.e., data collected from a receiving water) were provided to the Regional Data Center for the California Environmental Data Exchange Network (CEDEN), located at Moss Landing Marine Laboratory. These data are submitted in a format comparable with the SWAMP database. Pollutants of Concern Monitoring data collected in non-receiving waters are included in the attached electronic files but will not be submitted to the Regional Data Center at this time. For more details on the non-receiving waters data submittal to CEDEN see the BASMAA letter to CEDEN (dated March 20, 2017) which was cc’d to several of your staff. Monitoring data included in this submittal suggest that water quality conditions in San Mateo County creeks vary substantially among sites and between monitoring events. Temporal and spatial variability adds to the challenge of interpreting and evaluating the data and using it to help identify potential persistent water quality issues warranting a programmatic response from stormwater agencies. The IMR includes detailed analyses of the monitoring data. We look forward to discussing the findings, conclusions, and recommended next steps included in the IMR and to continuing to work with you and your staff to successfully address new challenges regarding water quality monitoring. Please contact me if you have any comments or questions.

1 See the Third-Party Monitoring Statement attached to this letter.

Mr. Michael Montgomery March 31, 2020 Page 3

Certification Regarding SMCWPPP Program Urban Creeks Monitoring Report "I certify, under penalty of law, that this document and all attachments were prepared under my direction or supervision in accordance with a system designed to assure that qualified personnel properly gathered and evaluated the information submitted. Based on my inquiry of the person or persons who managed the system, or those persons directly responsible for gathering the information, the information submitted, is, to the best of my knowledge and belief, true, accurate, and complete. I am aware that there are significant penalties for submitting false information, including the possibility of fine and imprisonment for knowing violations.” Sincerely,

Matthew Fabry, P.E. Program Manager . Attachments: SMCWPPP IMR Water Year 2014 through Water Year 2019 (uploaded to ftp site).

Electronic Data Report for Water Year 2019 Creek Status Monitoring, Pollutants of Concern Monitoring, and Pesticides and Toxicity Monitoring (uploaded to ftp site).

Third Party Monitoring Statement.

________________________________________________________________ Third Party Monitoring Statement Please note that consistent with Provision C.8.a.iii. of the MRP, one water quality monitoring requirement was partially fulfilled by third party monitoring in Water Year 2019:

• The Regional Monitoring Program for Water Quality in San Francisco Bay (RMP) conducted a portion of the data collection in Water Year 2018 on behalf of Permittees, pursuant to MRP Provision C.8.f – Pollutants of Concern Monitoring. The results of that monitoring are summarized in Section 5 of the attached UCMR. Data collected from stations monitored by the RMP will be submitted to the California Environmental Data Exchange Network directly by the RMP following completion of their quality assurance review.

• Data collected by the State of California's Surface Water Ambient Monitoring Program (SWAMP) through its Stream Pollutant Trend (SPoT) Monitoring Program at the San Mateo location is used to partially fulfill MRP Provision C.8.f - Pollutants of Concern Monitoring requirements addressing trends evaluation. Data collected from stations monitored by the SPoT Program will be submitted directly to the California Environmental Data Exchange Network according to the SWAMP schedule for review and reporting of data, which may not occur for several years.

March 31, 2020

A Program of the City/County Association of Governments

FOR SAN MATEO COUNTY MRP PERMITTEES

Water Years 2014 – 2019

EXECUTIVE SUMMARY

Submitted in Compliance with NPDES Permit No. CAS612008 (Order No. R2-2015-0049)

Provision C.8.h.v

CREDITS

This report is submitted by the participating agencies in the

Town of Atherton City of Foster City City of San Bruno City of Belmont City of Half Moon Bay City of San Carlos City of Brisbane Town of Hillsborough City of San Mateo City of Burlingame City of Menlo Park City of South San Francisco Town of Colma City of Millbrae Town of Woodside City of Daly City City of Pacifica County of San Mateo City of East Palo Alto Town of Portola Valley SMC Flood Control District City of Redwood City

Prepared for: San Mateo Countywide Water Pollution Prevention Program (SMCWPPP)

555 County Center, Redwood City, CA 94063 A Program of the City/County Association of Governments (C/CAG)

Prepared by:

EOA, Inc. 1410 Jackson St., Oakland, CA 94610

SMCWPPP WYs 2014 – 2019 IMR: Executive Summary

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Table of Contents Table of Contents ........................................................................................................................................... i List of Figures ................................................................................................................................................. i List of Tables .................................................................................................................................................. i INTRODUCTION AND BACKGROUND ............................................................................................................ 1 PART A: SAN FRANCISCO ESTUARY RECEIVING WATER MONITORING ........................................................ 2 PART B: CREEK STATUS AND PESTICIDES & TOXICITY MONITORING ............................................................ 2

Bioassessment ................................................................................................................................... 2 Continuous Temperature and Water Quality Monitoring ................................................................. 3 Pathogen Indicator Monitoring ......................................................................................................... 6 Chlorine Monitoring .......................................................................................................................... 6 Pesticides & Toxicity Monitoring ....................................................................................................... 6 Creek Status and Pesticides & Toxicity Monitoring Recommendations ........................................... 7

PART C: STRESSOR/SOURCE IDENTIFICATION (SSID) PROJECTS ................................................................... 8 PART D: POLLUTANTS OF CONCERN (POC) MONITORING............................................................................ 9

PCBs and Mercury ............................................................................................................................. 9 Copper ............................................................................................................................................. 15 Nutrients .......................................................................................................................................... 16 Emerging Contaminants .................................................................................................................. 16

PART E: MONITORING BUDGET SUMMARY ................................................................................................ 17 REFERENCES ................................................................................................................................................ 22

List of Figures Figure E-1. Biological condition category based upon CSCI scores from 90 bioassessment sites in San Mateo County, WY 2012 – WY 2019. ............................................................................................................ 4

Figure E-2. Continuous temperature and water quality stations in San Mateo County, WY 2014 – WY 2019. ............................................................................................................................................................. 5

Figure E-3. POC Monitoring Stations in San Mateo County (includes all PCBs and mercury samples from early 2000s to WY 2019). ............................................................................................................................ 11

Figure E-4. San Mateo County WMA Status Based upon Total PCBs Concentration in Sediment and/or PCBs Particle Ratio in Stormwater Runoff Samples Collected through WY 2019. ...................................... 14

List of Tables Table E-1. Water Year 2019 Creek Status Monitoring Station Summary Table ............................................ ii

Table E-2. Water quality monitoring cost summary for implementing MRP Provision C.8 during WYs 2014 – 2019. ........................................................................................................................................................ 19

Table E-3. Qualitative cost-benefit evaluation of MRP 2.0 Provision C.8 water quality monitoring. ......... 20


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Table E-1. Water Year 2019 Creek Status Monitoring Station Summary Table In compliance with Provision C.8.h.v(1), this table of all creek status monitoring stations sampled in Water Year 2019 is provided immediately following the Table of Contents.

Map ID 1 Station ID

Bayside or

Coastside Watershed Creek Name Land

Use Latitude Longitude Probabilistic Targeted

Bioassessment, Nutrients,

General WQ Chlorine Pesticides

& Toxicity Temp 2 Cont. WQ 3

Pathogen Indicators

030 202TUN030 Coastal Tunitas Creek Tunitas Creek NU 37.37940 -122.3748 X X X X 040 202TUN040 Coastal Tunitas Creek Tunitas Creek NU 37.38847 -122.3709 X X X 005 204BEL005 Bayside Belmont Creek Belmont Creek U 37.51778 -122.26914 X X 4280 204R04280 Bayside Belmont Creek Belmont Creek U 37.45434 -122.20118 X X 4428 204R04428 Bayside Cordilleras Creek Cordilleras Creek U 37.55466 -122.35632 X X 4160 204R04160 Bayside Burlingame Creek Burlingame Creek U 37.51480 -122.28340 X X 3635 204R03635 Bayside Atherton Creek Atherton Creek U 37.47975 -122.25986 X X 4600 204R04600 Bayside Atherton Creek Atherton Creek U 37.43671 -122.21467 X X 4056 205R04056 Bayside San Francisquito Cr Dry Creek U 37.43885 -122.26506 X X 5044 205R05044 Bayside San Francisquito Cr Dry Creek U 37.42803 -122.25148 X X 010 204PUL010 Bayside Pulgas Creek Pulgas Creek U 37.50195 -122.25238 X 138 202PES138 Coastside Pescadero Creek Pescadero Creek NU 37.27410 -122.28860 X 142 202PES142 Coastside Pescadero Creek McCormick Creek NU 37.27757 -122.28635 X 144 202PES144 Coastside Pescadero Creek Pescadero Creek NU 37.27592 -122.28550 X 150 202PES150 Coastside Pescadero Creek Jones Gulch NU 37.27424 -122.26811 X 154 202PES154 Coastside Pescadero Creek Pescadero Creek NU 37.27446 -122.26798 X 5 202TUN005 Coastside Tunitas Creek Tunitas Creek NU 37.36202 -122.39062 X 25 202TUN025 Coastside Tunitas Creek Tunitas Creek NU 37.37735 -122.37413 X 35 202TUN035 Coastside Tunitas Creek Tunitas Creek NU 37.38425 -122.37323 X 60 202TUN060 Coastside Tunitas Creek Tunitas Creek NU 37.40476 -122.35711 X

Notes: U = urban, NU = non-urban 1 Map ID applies to Figure 1.1 in Part A of this Integrated Monitoring Report 2 Temperature monitoring was conducted continuously (i.e., hourly) April through September. 3 Continuous water quality monitoring (temperature, dissolved oxygen, pH, specific conductivity) was conducted during two 2-week periods (spring and late summer).


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INTRODUCTION AND BACKGROUND This Integrated Monitoring Report (IMR), Water Year1 (WY) 2014 through WY 2019, was prepared by the San Mateo Countywide Water Pollution Prevention Program (SMCWPPP). SMCWPPP is a program of the City/County Association of Governments (C/CAG) of San Mateo County. Each incorporated city and town in the county and the County of San Mateo share a common National Pollutant Discharge Elimination System (NPDES) stormwater permit for Bay Area municipalities referred to as the Municipal Regional Permit (MRP). The MRP was first adopted by the San Francisco Regional Water Quality Control Board (Regional Water Board) on October 14, 2009 as Order R2-2009-0074 (SFRWQCB 2009; referred to as MRP 1.0). On November 19, 2015, the Regional Water Board updated and reissued the MRP as Order R2-2015-0049 (SFRWQCB 2015; referred to as MRP 2.0). This IMR, including all appendices and attachments, fulfills the requirements of Provision C.8.h.v. of MRP 2.0 for comprehensively interpreting and reporting all monitoring data collected since the previous IMR. The previous IMR included data collected during WY 2012 and WY 2013 (SMCWPPP 2014) and the time period addressed by this report includes WY 2014 – WY 2019. However, please note that:

• For PCBs, this report focuses on progress to-date towards identifying source areas and properties in San Mateo County. In this context, it evaluates all the relevant and readily available sediment and stormwater runoff chemistry data collected in San Mateo County, ranging back to the early 2000s.

• Some sections and summary tables in this report focus on summarizing compliance with MRP 2.0 requirements and thus focus on the Pollutant of Concern (POC) monitoring and related activities conducted during WY 2016 – WY 2019.

On behalf of San Mateo County MRP Permittees and pursuant to Provision C.8.h.ii of MRP 2.0, SMCWPPP also submitted the data presented in this report in electronic SWAMP-comparable formats to the Regional Water Board. Provision C.8.a (Compliance Options) of the MRP allows Permittees to address monitoring requirements through a “regional collaborative effort,” their countywide stormwater program, and/or individually. On behalf of San Mateo County Permittees, SMCWPPP conducts creek water quality monitoring and monitoring projects in collaboration with the Bay Area Stormwater Management Agency Association (BASMAA) Regional Monitoring Coalition (RMC), and actively participates in the Regional Monitoring Program for Water Quality in San Francisco Bay (RMP), which focuses on assessing Bay water quality and associated impacts. Monitoring data were collected in accordance with the BASMAA RMC Quality Assurance Project Plan (QAPP; BASMAA 2016a) and the BASMAA RMC Standard Operating Procedures (SOPs; BASMAA 2016b). Where applicable, and in compliance with Provision C.8.b. of MRP 2.0, methods described in the QAPP and SOP are comparable with methods specified by the California Surface Water Ambient Monitoring Program (SWAMP) Quality Assurance Program Plan (QAPrP).

1 The water quality monitoring described in this report was conducted on a Water Year basis. A Water Year begins on October 1 and ends on September 30 of the named year. For example, Water Year 2019 (WY 2019) began on October 1, 2018 and concluded on September 30, 2019.


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The IMR consists of five “Parts” that address the major sub-provisions of MRP Provision C.8 (Water Quality Monitoring). The following sections summarize each Part and, in compliance with Provision C.8.h.v(4), include recommendations for monitoring during future permit terms:

• Part A: San Francisco Estuary Receiving Water Monitoring

• Part B: Creek Status and Pesticides & Toxicity Monitoring

• Part C: Stressor/Source Identification Projects

• Part D: Pollutants of Concern Monitoring

• Part E: Budget Summary PART A: SAN FRANCISCO ESTUARY RECEIVING WATER MONITORING In accordance with Provision C.8.b. of MRP 1.0 and Provision C.8.c. of MRP 2.0, Permittees are required to provide financial contributions towards implementing an Estuary receiving water monitoring program on an annual basis that, at a minimum, is equivalent to the monitoring conducted via the Regional Monitoring Program for Water Quality in San Francisco Bay (RMP). SMCWPPP Permittees comply with this provision by making financial contributions to the RMP via SMCWPPP. Additionally, SMCWPPP Program staff and other BASMAA RMC representatives actively participate in RMP committees, workgroups, and strategy teams, such as the Small Tributaries Loading Strategy (STLS) to help oversee RMP activities and provide input, consistent with MRP Permittee interests. PART B: CREEK STATUS AND PESTICIDES & TOXICITY MONITORING Part B of the IMR comprehensively interprets and reports all Creek Status and Pesticides & Toxicity monitoring data collected since the previous IMR (SMCWPPP 2014). As such, Part B includes data collected during WY 2014 through WY 2019, with bioassessment and chlorine data also inclusive of WY 2012 and WY 2013. The RMC’s creek status monitoring strategy includes both a regional ambient/probabilistic monitoring design and a local “targeted” monitoring design. The probabilistic monitoring design selects sites randomly and was developed to remove bias from site selection such that ecosystem conditions can be objectively assessed on local (i.e., San Mateo County) and regional (i.e., RMC) scales. The targeted monitoring design focuses on sites selected based on the presence of significant fish and wildlife resources as well as historical and/or recent indications of water quality concerns. Monitoring results are compared to “triggers” listed in Provision C.8.d. of MRP 2.0. Some triggers are equivalent to regulatory Water Quality Objectives (WQOs); others are not WQOs but are thresholds above (or below) which potential impacts to aquatic life or other beneficial uses may occur. Sites where triggers are exceeded (or not met) are considered for future stressor/source identification (SSID) projects. Bioassessment Bioassessments include the collection of benthic macroinvertebrate and algae samples, physical habitat measurements, collection of grab creek water samples for water chemistry (i.e., nutrient analyses), and measurement of general water quality parameters using a pre-calibrated multi-parameter field probe. During WY 2012 - WY 2019, SMCWPPP conducted biological assessments at 80 sites in San Mateo County. An additional 10 sites were monitored by the Regional Water Board for a total of 90 sites in the


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County, of which 87 were selected using the probabilistic design and three were targeted. The California Stream Condition Index (CSCI), a statewide tool that translates benthic macroinvertebrate data into an overall measure of stream health, was used to assess biological condition:

• Of the 90 bioassessment sites (including three targeted sites), 60 received CSCI scores that were below the MRP trigger (0.795), corresponding to the two lower condition categories (likely altered and very likely altered). Fifty-five of the 60 low-scoring sites were classified as urban. The proportion of sites with good biological conditions was much higher in watersheds draining to the Pacific Ocean compared to sites located in watersheds draining to San Francisco Bay. Figure E-1 shows condition categories based upon CSCI scores at the 90 bioassessment sites.

• Cumulative frequency functions for CSCI scores indicate there is a 48% probability that a random site in San Mateo County will have a CSCI score below 0.795 (i.e., likely altered or very likely altered). There is an 86% probability that a random urban site in San Mateo County will have a CSCI score below 0.795 and there is a 22% probability that a random non-urban site will have a CSCI score below 0.795.

Ancillary parameters, such as physical habitat, nutrient concentrations, and general water quality measurements, along with land use data, were analyzed using Spearman’s rank correlation and random forest models to identify stressors that are likely to pose the greatest risk to stream health:

• The random forest model of CSCI scores indicates that landscape, habitat, and water-quality stressors, specifically road density, impervious area, and total nitrogen, were the best predictors of biological condition. Results of the San Mateo stressor assessment differ from an assessment of the RMC regional dataset from WY 2012 – WY 2016. Although both San Mateo and regional CSCI scores are strongly influenced by imperviousness in the contributing area, the regional assessment did not identify nutrients as an important predictor of CSCI scores (BASMAA 2019).

• It should be noted that despite these apparent relationships to stressors, these analyses do not determine causation, particularly as stressors from habitat/landscape factors are often present at the same sites that exhibit water quality impairment.

Continuous Temperature and Water Quality Monitoring Continuous monitoring of water temperature and general water quality in WY 2014 through WY 2019 was conducted in compliance with Provisions C.8.c. of MRP 1.0 and C.8.d.iii. – iv. of MRP 2.0. Hourly temperature measurements were recorded at a minimum of four sites each year from April through September. Continuous (15-minute) general water quality measurements (pH, DO, specific conductance, temperature) were recorded at two sites each year during two 2-week periods in spring (Event 1) and summer (Event 2). Monitoring stations (Figure E-2) were selected based on the presence of significant fish and wildlife resources as well as historical and/or recent indications of water quality concerns. The same sites were often monitored for multiple years to gain a better understanding of the range of water quality conditions that may occur over time. In some years, continuous monitoring data were used to support or follow-up on SSID investigations.


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Figure E-1. Biological condition category based upon CSCI scores from 90 bioassessment sites in San Mateo County, WY 2012 – WY 2019.


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Figure E-2. Continuous temperature and water quality stations in San Mateo County, WY 2014 – WY 2019.


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Overall, continuous monitoring results typically indicate that temperature and specific conductivity increase in the downstream direction, which in San Mateo County watersheds is characterized by increasing urbanization. In addition, the MRP maximum weekly average temperature (MWAT) trigger threshold of 17°C was often exceeded. These exceedances resulted in sites being placed on the list of candidate SSID projects, but were usually explained by lack of continuous flow in the late summer. Other locations where the MWAT trigger was exceeded were in reaches that cold-water fish migrate through rather than reside or rear in. Pathogen Indicator Monitoring From WY 2014 through WY 2019, in compliance with Provisions C.8.c. of MRP 1.0 and C.8.d.v. of MRP 2.0, SMCWPPP collected five grab samples per year for pathogen indicator bacteria analysis. Monitoring was conducted in three areas at sites selected to inform bacteria SSID investigations and/or follow-up on reports (e.g., by San Mateo County Parks staff) of potential high bacteria in creeks where water contact recreation (REC-1) is likely. The overall goal of pathogen indicator monitoring is to assess whether WQOs are being met and whether creeks are supportive of REC-1 Beneficial Uses. Overall, pathogen indicator monitoring results from San Mateo County were highly variable and sometimes exceed WQOs. It is important to recognize that pathogen indicators do not directly represent actual pathogen concentrations and do not distinguish among sources of bacteria. Sources of pathogen indicator bacteria include wildlife, livestock, pets, leaking septic systems/sanitary sewers, homeless encampments, and regrowth of bacteria in biofilms. Bacteria from human sources are more likely to be associated with human health risks during water contact recreation. In addition, WQOs were derived based upon studies conducted at bathing beaches, not creeks where lower levels of human exposure generally occur. As a result, the comparison of pathogen indicator results to WQOs may not be appropriate and should be interpreted cautiously. Chlorine Monitoring From WY 2012 through WY 2019, in compliance with Provision C.8.c of MRP 1.0 and Provision C.8.d.ii of MRP 2.0, SMCWPPP collected field measurements of total and free chlorine residual in creeks where bioassessments were conducted. While chlorine residual has generally not been a concern in San Mateo County creeks, WY 2019 and prior monitoring results suggest there are occasional trigger exceedances of free chlorine and/or total chlorine residual in the County. Trigger exceedances may be the result of one-time potable water discharges, and it is generally challenging to determine the source of elevated chlorine from such episodic discharges. Furthermore, chlorine in surface waters can dissipate from volatilization and reaction with sediment and organic matter. Over the past eight years of monitoring (WY 2012 – WY 2019), of the 80 stations where chlorine was measured, a total of 11 exceeded the MRP chlorine trigger. The appropriate municipalities were informed of the exceedances at the time they happened but efforts by municipal staff to track down the sources were generally unsuccessful. Chlorine sources are generally transient and challenging to trace. Pesticides & Toxicity Monitoring Toxicity testing, sediment chemistry monitoring, and water column pesticides monitoring, collectively referred to as pesticides and toxicity monitoring, was conducted during WY 2014 through WY 2019 in


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compliance with Provisions C.8.c of MRP 1.0 and C.8.g of MRP 2.0. There were slight differences between the two permit terms regarding the required number of samples, toxicity test organisms, chemical constituents, and MRP triggers. Toxicity and chemistry data from WY 2014 through WY 2019 were reviewed for overall findings and evidence of trends. There were 18 test results that had significant toxicity, but with a Percent Effect that did not exceed the MRP trigger thresholds. A majority of these toxicity results were found in water samples and were associated with either C. dubia reproduction (six samples), a chronic toxicity endpoint, or H. azteca survival (six samples), an acute toxicity endpoint. Five of the six water samples with toxicity to H. azteca were collected during wet season sampling events, suggesting that stormwater runoff is impacting this organism. The water samples with toxicity to C. dubia were more evenly divided between wet and dry season sampling events. Probable effect concentration (PEC) quotients calculated based upon chemical analysis results from sediment samples collected in San Mateo County from WY 2014 through WY 2019 did not exceed the applicable threshold (≥ 1.0) for any analytes except chromium and nickel. Excluding these metals which occur naturally in San Mateo County geologic materials, there were four samples with threshold effect concentration (TEC) quotients ≥ 1.0; the more conservative of the two evaluation criteria. These included legacy insecticide DDT compounds in Laurel Creek and Atherton Creek, individual PAHs in Laurel Creek and Atherton Creek, and copper and zinc in Pulgas Creek. Overall, detection frequencies for pesticides bifenthrin and fipronil were on par with results from the DPR Northern California study (Ensminger 2019) and H. azteca toxicity responses were similar to SPoT monitoring in San Mateo Creek (Phillips et al. 2014). Creek Status and Pesticides & Toxicity Monitoring Recommendations Impacts to urban streams identified through creek status monitoring are likely the result of long-term changes in stream hydrology, channel geomorphology, in-stream habitat complexity, and other modifications associated with urban development and associated impervious surfaces, and, to a lesser extent, pollutants typically found in urban watersheds. San Mateo County MRP Permittees are actively implementing many stormwater runoff management programs to address these stressors and pollutants found in local creeks and the Bay, with the goal of protecting these natural resources. Through the continued implementation of MRP-associated and other watershed stewardship programs, SMCWPPP anticipates that channel conditions and water quality in local creeks and the Bay will continue to improve over time. The following recommendations were developed based upon the Creek Status and Pesticides & Toxicity monitoring data and are directed towards the next iteration of the MRP, which is referred to as MRP 3.0 (MRP 3.0 is currently under development and will likely become effective in WY 2022):

• Transition from the current ambient probabilistic biological condition assessment monitoring design to a targeted design that would focus on specific watersheds or reaches of interest. A targeted watershed approach would provide SMCWPPP and San Mateo County Permittees with more flexibility to evaluate areas that are priority to stakeholders to study, improve, and/or protect and where it is anticipated that the associated resources may be available.


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• Continuous monitoring for temperature and general water quality under MRP 1.0 and 2.0 has been an effective tool in supporting SSID studies and evaluating cold water habitat. It could also complement targeted biological condition assessments. Continued implementation of this approach during MRP 3.0 is recommended.

• Pathogen indicator monitoring has generally not been very informative due to the prevalence of uncontrollable sources, such as wildlife, and sources outside the scope of stormwater management programs, such as homeless encampments. Monitoring efforts for pathogen indicators should instead be used to support investigations and implementation plans for bacteria Total Maximum Daily Load (TMDL) or related water quality restoration projects.

• Although chlorine monitoring can be an important tool when needed in investigating fish kills, continued periodic reconnaissance chlorine monitoring is not recommended for MRP 3.0. Chlorine sources are generally transient and challenging to trace. In addition, the most common source, discharges of potable water, is already addressed by other MRP provisions and other NPDES permits.

• The current Pesticides & Toxicity monitoring requirements should remain unchanged until a statewide monitoring program, currently under development by the State Water Board, is in place.

PART C: STRESSOR/SOURCE IDENTIFICATION (SSID) PROJECTS SSID projects identify potential sources and/or stressors associated with observed water quality impacts. In compliance with MRP 1.0 (Provision C.8.d.) and MRP 2.0 (Provision C.8.e.), Permittees were required to initiate a minimum number of SSID projects during the permit term. The projects are intended to be oriented towards taking action(s) to alleviate stressors and reduce sources of pollutants. During MRP 1.0 (WY 2012 – WY 2015), SMCWPPP initiated three SSID projects as part of a regional collaborative. During MRP 2.0 (WY 2016 – WY 2019), SMCWPPP initiated one individual project and participated in one regional project. Brief summarizes of these projects are as follow:

• The San Mateo Creek Low Dissolved Oxygen (DO) SSID Project was triggered by low DO measurements in 2003 and 2013. However, low concentrations of DO were not observed during implementation of the work plan in WY 2014, WY 2015, or WY 2016. A new schedule of dry season releases from the upstream Crystal Springs Reservoir appears to alleviate low DO in the impacted creek reaches (SMCWPPP 2016).

• The San Mateo Creek Pathogen Indicator SSID Project was triggered by fecal indicator bacteria (FIB) at densities exceeding WQOs in 2003 and 2012. Several bacteria control measures were recommended including pet waste cleanup outreach and additional measures to improve the sanitary sewer conveyance system (SMCWPPP 2016).

• The Pillar Point Harbor Watershed Pathogen Indicator SSID Project investigated FIB sources from the MS4 to receiving waters. Results showed that FIB densities are highly variable and do not follow predictable patterns. Furthermore, very few human or dog markers were present, suggesting that FIB conveyed by the MS4 may be challenging to control. However, the data available at this time are limited, introducing uncertainty into the conclusions reached to-date. The project report recommended additional outreach and measures to reduce standing water in the MS4 which is assumed to create conditions suitable for biofilm regrowth of FIB. The final project report was submitted with this IMR.


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• Electrical Utilities as a Potential PCBs Source to Stormwater in the San Francisco Bay Area is a Regional SSID Project. PCBs monitoring by the BASMAA RMC partners and the RMP suggests that diffuse sources of PCBs are present throughout the region. One potential diffuse source is releases and spills from electrical utility equipment. The work plan, developed in WY 2018, presents a framework to investigate electrical utility equipment as a source of PCBs to urban stormwater runoff and identify appropriate actions and control measures to reduce the water quality impacts of this source. The RMC partners are currently gathering information from municipally owned electrical utilities to improve current estimates of PCBs loadings to MS4s and to identify opportunities to reduce releases (e.g., develop improved spill response and reporting procedures).

Overall, Permittees have found that SSID monitoring projects provided valuable information. Although the SSID studies have often found that the primary stressor sources are unrelated to municipal stormwater runoff and/or the source/stressor identification effort is inconclusive, they have resulted in a greater understanding of hydrology, water quality, and land use in the targeted watersheds, and the findings inform other aspects of stormwater runoff management. Continuation of SSID monitoring projects in the next permit should be considered, with a level-of-effort that does not increase the overall costs of Provision C.8 monitoring beyond current costs. PART D: POLLUTANTS OF CONCERN (POC) MONITORING In compliance with MRP Provision C.8.f. of MRP 2.0, SMCWPPP conducted POC monitoring for PCBs, mercury, copper, and nutrients over the permit term. The MRP-required yearly minimum number of samples was met or exceeded for all POCs. In addition, the MRP-required minimum number of samples addressing each Management Question specified in Provision C.8.f. by the end of year four of the permit term was met or exceeded for all POCs. Of the WY 2016 through WY 2019 POC monitoring analytes, promulgated WQOs for the protection of aquatic life only exist for total mercury, dissolved copper, and unionized ammonia. None of the SMCWPPP or third-party water samples collected in San Mateo County over this time period exceeded applicable water quality objectives (WQOs). PCBs and Mercury This section focuses on progress to-date towards identifying PCBs source areas and properties in San Mateo County. Consistent with MRP requirements, the focus has been on PCBs, with ancillary and secondary benefits assumed to be realized for controlling mercury. Highlights from the PCBs and mercury monitoring conducted to-date included the following:

• SMCWPPP’s PCBs and mercury monitoring commenced in the early 2000s and has generally focused on San Mateo County WMAs containing high interest parcels with land uses potentially associated with PCBs.


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• In 2014, SMCWPPP worked with San Mateo County MRP Permittees to conduct a process to screen for “high interest parcels” for PCBs in the county. The screening covered all land areas in the county that drain to the Bay, focusing on about 160,000 urban parcels. Parcels were identified that were industrialized in 1980 or earlier (i.e., old industrial parcels) or have other land uses associated with PCBs (i.e., electrical, recycling, and military). SMCWPPP then worked with municipal staff to prioritize these parcels based on the evaluation of existing information on current land uses and practices (e.g., redevelopment status, extent and quality of pavement, level of current housekeeping, any history of stormwater violations, and presence of electrical or heavy equipment, storage tanks, or stormwater treatment), local institutional/historical knowledge, and surveys of site conditions (windshield, Google Street View, and/or aerial photograph). The prioritization resulted in a list of about 1,600 high interest parcels for PCBs in San Mateo County.

• The above 1,600 high interest parcels are almost entirely located within 105 “catchments of interest” with high interest parcels comprising at least 1% of their area (and usually with existing pollutant controls). In FY 2016, SMCWPPP implemented a process to identify Watershed Management Areas (WMAs) and prioritize them based on the potential for controls (especially source property referrals) to reduce PCBs loads. WMAs were defined as the sum of the 105 catchments of interest and an additional 25 catchments with existing or planned stormwater pollutant controls (e.g., GI implemented on parcels per Provision C.3 requirements, built on public lands such as parks, or retrofitted into the public ROW), for a total of about 130 catchments designated as WMAs. WMA catchments are stormwater runoff hydrologic catchments in San Mateo County that drain to 24-inch or larger diameter outfalls.

• Each water year, SMCWPPP designed and implemented a PCBs and mercury monitoring plan based on the 2014 desktop screening (which was revisited and refined each year as needed) and all sampling results available at that time. Stormwater runoff monitoring was coordinated with RMP STLS reconnaissance monitoring, with SMCWPPP providing sample station locations to SFEI staff.

• To-date, about 60 composite samples of stormwater runoff2 have been collected from the bottom of San Mateo County WMAs and about 400 individual and composite grab samples of sediment have been collected within priority WMAs to help characterize the catchments and identify source areas and properties (Figure E-3). Most samples were collected in the public ROW. The grab sediment samples were collected from a variety of types of locations, including manholes, storm drain inlets, driveways, streets, and sidewalks, often adjacent to or nearby high interest parcels with land uses associated with PCBs and/or other characteristics potentially associated with pollutant discharge (e.g., poor housekeeping, unpaved areas).

2 Not including about 30 additional stormwater runoff samples collected at the Pulgas Creek pump station stormwater loading station.


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Figure E-3. POC Monitoring Stations in San Mateo County (includes all PCBs and mercury samples from early 2000s to WY 2019).


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• SMCWPPP’s PCBs and mercury monitoring program included collecting sediment samples in the public ROW (e.g., from streets and the MS4) by every known PCBs remediation site in San Mateo County, to the extent applicable and feasible.

• When a previously unknown potential source property was revealed via the PCBs and mercury monitoring program, SMCWPPP conducted a follow-up review of current and historical records regarding site occupants and uses, hazardous material/waste use, storage, and/or release, violation notices, and any remediation activities. Apart from databases such as EPA’s Toxic Release Inventory (TRI) and Envirofacts, and the State of California’s Geotracker and Envirostor, the most useful records were often kept by San Mateo County Department of Environmental Health. Four previously unknown potential source properties have been identified in San Mateo County, all in WMA 210 (Pulgas Creek Pump Station South) in the City of San Carlos. SMCWPPP is working with the City of San Carlos to determine next steps for these properties, including potential referral to the Regional Water Board.

• SMCWPPP’s PCBs and mercury monitoring program has resulted in SMCWPPP referring four properties (two sets of two adjacent properties, all in San Carlos) to the Regional Water Board for potential further PCBs investigation and abatement.

• Sediment monitoring in the Redwood City MS4 (WMA 379) conducted in 2014 and 2017 in the vicinity of 2201 Bay Road identified an additional source area. This area includes two properties listed for PCBs on GeoTracker: Tyco Engineering Products and an adjacent railroad spur. The Tyco site was remediated and redeveloped (MRP Provision C.3 compliant) and is currently a parking lot for Stanford Hospital. A total of 43 sediment samples and 2 composite stormwater runoff samples have been collected to-date in WMA 379 by SMCWPPP and others, but the only potential PCBs source area that has been identified is the former Tyco site and adjacent historical railroad spur. In April 2019, Regional Water Board staff informed SMCWPPP that they plan to require a clean out the storm drain as part of approving a proposed cap modification and redevelopment of the property and may have the opportunity to request additional post-cleanout monitoring. SMCWPPP will continue to track these efforts and will request PCBs load reduction credit as appropriate.

• Low PCBs concentrations in composite stormwater runoff samples from the bottom of WMA catchments have suggested that either PCBs sources are not prevalent in the catchment or the samples are “false negatives.” False negatives could be the result of low rainfall/runoff rates failing to mobilize sediments from source areas and/or other factors. Only a few stormwater runoff sampling stations in San Mateo County have been resampled, but the results from two such stations in South San Francisco suggested small storm sizes may have resulted in false negatives. SMCWPPP, in collaboration with the SCVURPPP, has recently preliminarily developed a method to normalize results from this type of stormwater runoff monitoring based upon storm intensity. However, the high variability in many of the parameters involved led to a high degree of uncertainty in the evaluation results. SMCWPPP and the SCVURPPP will continue to evaluate normalization methods and results as more data become available in future years, in coordination with related efforts by the RMP (referred to as the RMP’s “Advanced Data Analysis”).


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• Figure E-4 is a map illustrating the current status of WMAs in San Mateo County, based upon the monitoring data collected through WY 2019. Based upon total PCBs concentration in sediment and/or PCBs particle ratio in stormwater runoff samples, each WMA is placed in one of the following categories:

1. Samples > 0.5 mg/kg PCBs, source properties identified.

2. Samples > 0.5 mg/kg PCBs, source properties not identified.

3. Samples 0.2 – 0.5 mg/kg PCBs.

4. Samples <0.2 mg/kg PCBs.

5. No samples collected.

• The most recent two years of POC monitoring data, WY 2018 (n = 50) and WY 2019 (n = 25), suggest that the PCBs monitoring program in the public ROW in San Mateo County may be approaching diminishing returns in terms of finding PCBs and potentially identifying new source areas, based upon the following:

o The sediment sampling design continued to target locations thought to have the greatest possibility of having elevated PCBs, with an overall goal of attempting to locate source properties.

o The mean PCBs concentrations in WY 2018 and WY 2019 sediment samples were about an order of magnitude lower than the entire PCBs data set.

o The median PCBs concentrations in WY 2018 and WY 2019 sediment samples were about 50% lower than the entire data set.

o In WY 2018, only 1 of the 50 sediment samples collected had a PCBs concentration that exceeded 1.0 mg/kg. One other sample had a PCBs concentration between 0.5 and 1.0 mg/kg. All of the remaining samples had a PCBs concentration below 0.5 mg/kg.

o In WY 2019, none of the 25 sediment samples collected had a PCBs concentration that exceeded 1.0 mg/kg. One sample had a PCBs concentration between 0.5 and 1.0 mg/kg. All of the remaining samples had a PCBs concentration below 0.5 mg/kg.

• SMCWPPP participated in a BASMAA monitoring study that satisfied the MRP Provision C.12.e requirement to collect 20 composite caulk/sealant samples throughout the MRP permit area. The final project report was included with the SMCWPPP’s FY 2017/18 Annual Report, submitted to the Regional Water Board on September 30, 2018.

• SMCWPPP participated in a BASMAA regional study that was developed to satisfy MRP Provision C.8.f requirements to collect at least eight PCBs and mercury samples that address Management Question No. 3 (Management Action Effectiveness). The study investigated the effectiveness of hydrodynamic separator (HDS) units and various types of biochar-amended bioretention soil media (BSM) at removing PCBs and mercury from stormwater. Results of the study are summarized by BASMAA reports that are appended to SMCWPPP’s WY 2018 UCMR.


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Figure E-4. San Mateo County WMA Status Based upon Total PCBs Concentration in Sediment and/or PCBs Particle Ratio in Stormwater Runoff Samples Collected through WY 2019.


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• MRP Provision C.12.g requires Permittees to conduct or cause to be conducted studies concerning the fate, transport, and biological uptake of PCBs discharged from urban runoff to San Francisco Bay margin areas. The provision states: “the specific information needs include understanding the in-Bay transport of PCBs discharged in urban runoff, the sediment and food web PCBs concentrations in margin areas receiving urban runoff, the influence of urban runoff on the patterns of food web PCBs accumulation, especially in Bay margins, and the identification of drainages where urban runoff PCBs are particularly important in food web accumulation.” C.12.g requires Permittees to report in this IMR “the findings and results of the studies completed, planned, or in progress as well as implications of studies on potential control measures to be investigated, piloted or implemented in future permit cycles.” Attachment 1 provides a summary of a multi-year project by the San Francisco Bay (Bay) Regional Monitoring Program (RMP) that is addressing the requirements of Provision C.12.g. The project:

o Identified four Priority Margin Units (PMUs) for initial study that are located downstream of urban watersheds where PCBs management actions are ongoing and/or planned;

o Is developing conceptual and PCBs mass budget models for each of the four PMUs; and

o Is conducting monitoring in the PMUs to evaluate trends in pollutant levels and track responses to pollutant load reductions.

• In WY 2020, SMCWPPP will continue to collect samples for PCBs and mercury analysis in compliance with Provision C.8.f of MRP 2.0.

• SMCWPPP will develop a control measures plan, including a schedule and corresponding RAA, which demonstrates quantitatively that sufficient control measures will be implemented to attain the San Mateo County portions of the mercury and PCBs TMDL wasteload allocations by 2028 and 2030, respectively. Per the requirements in MRP Provisions C.11/12.d., this control measures plan is due in September 2020. As part of this effort, SMCWPPP and San Mateo County Permittees will continue planning scenarios for control measure implementation in priority WMAs in San Mateo County. The plan will be informed by the PCBs and mercury monitoring data summarized in this report. High priority will continue to be given to the Pulgas Creek pump station north and south drainages (WMA 31 and WMA 210), which are the two WMAs in San Mateo County with the greatest number of samples with elevated concentrations of PCBs in sediment and stormwater runoff samples to-date.

Copper In WY 2019, SMCWPPP continued to collect and analyze copper samples in compliance with Provision C.8.f of MRP 2.0. The yearly minimum of two samples was satisfied and the requirement to have a cumulative total of four samples addressing Management Question No. 4 (Loads and Status) and No. 5 (Trends) by year four of the Permit (i.e., WY 2019) was also satisfied. A review of the WY 2016 through WY 2019 copper dataset suggests that relatively low levels of copper are being conveyed to receiving waters from urban areas during stormwater runoff events and there have not been any exceedances of an applicable WQO for copper in a receiving water sample. However, although WQOs do not apply to stormwater runoff samples collected from the MS4, these data were also compared to the hardness dependent acute WQOs and three samples from the MS4 exceeded the WQO. It is uncertain what the copper concentration would have been after mixing with the receiving water. Furthermore, if the hardness of the receiving water was higher, a higher WQO would have been calculated.


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SMCWPPP will continue to collect samples for copper analysis in compliance with Provision C.8.f of MRP 2.0 with a goal of at least three samples in WY 2020 to meet the requirement of 20 samples by year five of the Permit (i.e., WY 2020). Copper data collected under MRP 2.0 have been of limited value to SMCWPPP. Copper data collected in San Francisco Bay through the RMP Status and Trends Program are more useful in tracking the effectiveness of the copper control measures required by Provision C.13 of MRP 2.0 and, more importantly, the success of the Brake Pad Partnership and Senate Bill (SB) 346 which addresses the largest source of copper by requiring brake pad manufacturers to reduce the use of copper in brake pads sold in California. However, the copper data collected in compliance with Provision C.8.f of MRP 2.0 can provide a relatively cost-effective check on copper discharges to tributaries to the Bay. SMCWPPP recommends maintaining the same overall copper monitoring requirements (i.e., 20 total samples) in MRP 3.0, but an elimination of the yearly minimums could result in a more effective monitoring design. Nutrients In WY 2019, SMCWPPP continued to collect and analyze nutrient samples in compliance with Provision C.8.f of MRP 2.0. The yearly minimum of two samples was satisfied and the requirement to have a cumulative total of 20 samples addressing Management Question No. 4 (Loads and Status) by year four of the Permit (i.e., WY 2019) was also satisfied. A review of the WY 2016 through WY 2019 nutrient dataset suggests that nutrient concentrations are highest during storm events and generally higher at stations lower in the watershed. In addition, the highest nitrogen concentrations were found in Atherton Creek and the highest phosphorus concentrations were found in Redwood Creek. In WY 2020, SMCWPPP will continue to collect samples for nutrient analysis in compliance with Provision C.8.f of MRP 2.0. Although nutrient data can be useful in supporting some types of Stressor/Source Identification projects initiated in compliance with Provision C.8.e. of MRP 2.0, SMCWPPP recommends that the requirement for nutrient monitoring be removed from the POC Monitoring provision under MRP 3.0. The original need for nutrient sampling in tributaries to the Bay to support Regional Water Board efforts to develop nutrient numeric endpoints for the San Francisco Bay Estuary no longer exists. This effort has now been captured and superseded by the State Water Board Biostimulatory Substances and Biological Integrity Project3 which is proposing to adopt a statewide water quality objective for biostimulatory substances (such as nitrogen and phosphorus) along with a program of implementation as an amendment to the Water Quality Control Plan for Inland Surface Water, Enclosed Bays and Estuaries of California (ISWEBE Plan). Emerging Contaminants During MRP 2.0, SMCWPPP has leveraged its participation in these RMP special studies to satisfy the POC monitoring requirement for Contaminants of Emerging Concern (CECs) within Provision C.8.f. SMCWPPP recommends that MRP 3.0 provisions continue to support special studies that address data gaps and the scientific understanding of fate and transport of stormwater-related CECs in the Bay. In

3 https://www.waterboards.ca.gov/water_issues/programs/biostimulatory_substances_biointegrity/


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particular, SMCWPPP is supportive of continued coordination through the STLS to identify the appropriate watersheds and sampling sites for monitoring CECs through RMP special studies. SMCWPPP is also supportive of further developing conceptual and empirical models to better evaluate the distribution and sources of CECs of interest within a stormwater and watershed context. SMCWPPP further recommends including requirements to “conduct or cause to be conducted a special study that addresses relevant management information needs for emerging contaminants;” however, these requirements should allow more flexibility with respect to the classes of compounds identified in the permit, allowing easier alignment with RMP special studies that may address a variety of stormwater-related CECs as the science is advanced over the coming years. PART E: MONITORING BUDGET SUMMARY SMCWPPP developed a water quality monitoring cost summary for San Mateo County, WYs 2014 through 2019, in accordance with the requirements of Provision C.8.h.v(4) of MRP 2.0, which requires the IMR to include a “budget summary for each monitoring requirement”. Water quality monitoring in compliance with MRP 2.0 Provision C.8 is conducted by SMCWPPP on behalf of San Mateo County MRP Permittees. This report summarizes the approximate budget expended by SMCWPPP for its water quality monitoring conducted from WY 2014 through WY 2019, a six-year period. The previous SMCWPPP IMR was submitted to the Regional Water Board in March 2014 and summarized approximate costs for water quality monitoring conducted by SMCWPPP during WY 2012 and WY 2013 in compliance with MRP 1.0. Water quality monitoring required by Provision C.8 of MRP 2.0 is intended to assess the condition of water quality in Bay Area receiving waters (creeks and Bay); identify and prioritize stormwater associated impacts, stressors, sources, and loads; identify appropriate management actions; and detect trends in water quality over time and the effects of stormwater control implementation. SMCWPPP conducts creek water quality monitoring and monitoring projects in San Mateo County in collaboration with the RMC, and actively participates in the RMP, which focuses on assessing Bay water quality and associated impacts. This report provides a summary of monitoring costs expended by SMCWPPP to comply with MRP 2.0 and provides qualitative estimates of the water quality benefits realized. Table E-2 presents approximate costs expended by SMCWPPP to comply with Provision C.8 of MRP 2.0 during WYs 2014 – 2019.4 Costs presented include all aspects of implementing Provision C.8, including:

• Monitoring program and project planning,

• Monitoring program coordination and management,

• Fieldwork to collect data,

• Laboratory analysis,

• Quality assurance / quality control (QA/QC),

• Data evaluation, analysis, and interpretation,

4 Costs presented do not include costs incurred by Permittees to implement other water quality monitoring activities and programs required by other NPDES permits issued to Permittees (e.g., POTW monitoring, aquatic pesticide application monitoring, stream maintenance program monitoring).


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• Data management, and

• Data and reporting. Direct financial contributions to the RMP by SMCWPPP on behalf of San Mateo County Permittees and the NPDES permit fee surcharges that were paid by Permittees during that time frame (and used by the State and/or Regional Water Board to fund its Surface Water Ambient Monitoring Program (SWAMP)) are also included in the reported costs. The costs listed in Table E-2 show the considerable resources (~$3.7 million) that SMCWPPP expended over the course of WYs 2014 – 2019 towards complying with water quality monitoring requirements described in MRP 2.0 Provision C.8. Average annual costs to San Mateo County Permittees during this six-year timeframe were roughly $620,000. The costs are associated with the following monitoring activities:

• San Francisco Bay Estuary Receiving Water Monitoring (RMP) – Permittee monetary contributions and SMCWPPP and Permittee staff time spent actively participating in the RMP, including participation in several workgroups and strategy teams, in compliance with MRP 2.0 Provision C.8.c.

• Creek Status Monitoring – Preparation, coordination, management and implementation of the SMCWPPP’s Creek Status Monitoring Program, which is implemented in compliance with MRP 2.0 Provision C.8.d.

• Stressor/Source Identification (SSID) Projects – Preparation, coordination, management and implementation of SSID projects that were implemented in compliance with MRP 2.0 Provision C.8.e.

• Pollutants of Concern Monitoring – Preparation, coordination, management and implementation of the SMCWPPP Pollutants of Concern (POC) Monitoring Program that was implemented in compliance with MRP 2.0 Provision C.8.f., including investigations conducted to attempt to find properties that are sources of PCBs to the storm drain system.

• Pesticides and Toxicity Monitoring – Preparation, coordination, management and implementation of the SMCWPPP Pesticides and Toxicity Monitoring Program that was implemented in compliance with MRP 2.0 Provision C.8.g.

• Data Management & QA/QC – Coordination and implementation of the SMCWPPP Water Quality Monitoring Data Management and Quality Assurance Program, which implements all aspect of data management and quality assurance procedures required by MRP 2.0 Provision C.8.b, and consistent with approved Standard Operating Procedures (SOPs) and Quality Assurance Project Plans (QAPPs).

• Reporting – Analysis, interpretation and reporting of all data collected via the SMCWPPP’s Creek Status Monitoring, SSID projects, POC Monitoring, and Pesticides and Toxicity Monitoring Programs, consistent with MRP 2.0 Provision C.8.h.

• NPDES Surcharge: SWAMP – Monetary contributions provided by Permittees to the State of California as part of the SWAMP surcharge issued to Permittee as part of their annual NDPES fee.


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Table E-2. Water quality monitoring cost summary for implementing MRP Provision C.8 during WYs 2014 – 2019.

MRP 2.0 Sub-provision

Approximate Total Costs

WYs 2014 - 2019 (6 years)

Approximate Average Costs

per Water Year

Percent of Total Costs

C.8.b Data Management & Quality Assurance/Quality Control (QA/QC) $200,000 $33,333 5%

C.8.c San Francisco Bay Estuary Receiving Water Monitoring (RMP) $600,000 $100,000 16%

C.8.d Creek Status Monitoring $1,300,000 $216,667 35%

C.8.e Stressor/Source Identification (SSID) Projects $220,000 $36,667 6%

C.8.f Pollutants of Concern (POC) Monitoring $800,000 $133,333 22%

C.8.g Pesticides and Toxicity Monitoring $200,000 $33,333 5%

C.8.h Reporting $250,000 $41,667 7%

NA NPDES Surcharge - Surface Water Ambient Monitoring Program (SWAMP) $150,000 $25,000 4%

Total $3,720,000 $620,000 100% Note: see above bullets for the activities that are included in each monitoring line item. SMCWPPP’s water quality monitoring program generates data designed to answer core management questions outlined in MRP 2.0. In many instances, these management questions are further delineated into scientific monitoring questions, which assist in developing and implementing appropriate monitoring designs. This section provides a qualitative cost-benefit evaluation of the water quality monitoring data collection programs implemented by SMCWPPP to comply with MRP Provision C.8. The cost-benefit evaluation was conducted based on the ability of SMCWPPP to answer core management and scientific monitoring questions using the water quality monitoring data collected. Table E-3 presents the results of the evaluation, which informed SMCWPPP’s recommendations for water quality monitoring under MRP 3.0.


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Table E-3. Qualitative cost-benefit evaluation of MRP 2.0 Provision C.8 water quality monitoring.


Relative Cost of

Implementing ($ - $$$$)3

Benefit Towards Answering Core

Management Questions

( - )

Evaluation Summary

C.8.c

San Francisco Bay Estuary Receiving Water Monitoring (RMP)

$$$$

Contributions to the RMP provided useful information on the status and trends of water quality in the Bay and provided supplemental information to help SMCWPPP identify PCBs and mercury source areas for management actions. Attempts to focus RMP-led monitoring on high priority issues remains an on-going challenge due to competing interests and information needs. Overall, the RMP provides useful information to track water quality conditions in the Bay and help inform broad-scale management and policy directions based on science, but at a relatively high cost.

C.8.d Creek Status Monitoring $$$$

Creek status monitoring continued to provide useful information on the status of water quality in urban creeks that receive stormwater discharges, and the biological condition of those creeks. Many parameters were monitored, however, the utility of the data that the MRP requires to be collected is variable among parameters. Some parameters have provided valuable, baseline data or helped identify concerns that should be addressed. Other parameters were less useful and did not directly assist stormwater managers in validating, refining, or adjusting current practices. The high relative costs and the variability in the usefulness of data collected via this provision suggest that refinements are needed to improve the cost-effectiveness of Creek Status Monitoring during MRP 3.0.

C.8.e Stressor/Source Identification (SSID) Projects

$$

SSID studies have provided useful information that is needed to help better define potential water quality concerns and identify sources of pollutants or environmental stress occurring in San Mateo County streams. SSID projects have been challenging due to the lack of methods available to differentiate the causes of stress and sources of pollutants/stress, due to the complex and overlapping watershed/runoff processes observed in streams. The relatively moderate costs and moderate/high benefits of data collected via this provision suggest that SSID projects are cost-effective. However, refinements are needed to the study methods and endpoint expectations to improve the utility of the data collected via Provision C.8.e during MRP 3.0.


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Relative Cost of




( - )

Evaluation Summary

C.8.f Pollutants of Concern (POC) Monitoring

$$$$

Monitoring conducted under Provision C.8.f provided valuable data on potential sources of PCBs in Watershed Management Areas (WMAs) and helped prioritize land areas for further source property evaluations. Additionally, the data collected under this provision helped further understand the geographical distribution of POCs in the urban portion of San Mateo County that drains to the Bay. Although the costs associated with POC monitoring are relatively high, the PCBs data collected during MRP 2.0 have helped to characterize the urban landscape and identify some source areas. However, recent monitoring data suggest that the PCBs monitoring program in the public ROW in San Mateo County may be approaching diminishing returns in terms of finding PCBs and potentially identifying new source areas. Thus PCBs monitoring would show a lower benefit towards answering core management questions (one or two stars) if evaluated solely on potential future benefit. In addition, nutrient and copper monitoring data collected during MRP 2.0 were not particularly useful in answering monitoring questions associated with these pollutants and would show a lower benefit towards answering core management questions (one or two stars) if evaluated individually.

C.8.g Pesticides and Toxicity Monitoring $$

SMCWPPP expended a relatively low level of budget for Pesticides and Toxicity Monitoring during MRP 2.0. Data collected via the statewide SPoT program provided important information on trends in pesticides and toxicity in stream sediments over time. Low costs and low/moderate benefits suggest that refinements are needed to improve the cost-benefits of the data collected via Provision C.8.g during MRP 3.0. Currently a statewide effort to develop an Urban Pesticide Coordinated Monitoring Program is underway, and SMCWPPP is actively participating in this process. For SMCWPPP, the goal is to stabilize costs for pesticide/toxicity monitoring, while improving and enhancing coordination of data collection efforts on a statewide basis with the California Department of Pesticide Regulation (DRP) to fill important information gaps that will improve the regulation of pesticides that effect stormwater quality.

NA

NPDES Surcharge - Surface Water Ambient Monitoring Program (SWAMP)

$

The costs to SMCWPPP for this program were relatively low, but benefits to local stormwater programs and managers were not readily apparent.

3 Qualitative cost categories were based on the relative percentage of total costs for each major monitoring component shown above, with data management, QA/QC and reporting costs incorporated into the appropriate component costs. Cost categories were defined as: $ = <5%, $$ = 5 - 10%; $$$ = 10 - 15%; $$$$ = >15%.


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REFERENCES Bay Area Stormwater Management Agency Association (BASMAA) Regional Monitoring Coalition (RMC).

2016a. Creek Status and Pesticides & Toxicity Monitoring Quality Assurance Project Plan, Final Version 3. Prepared for BASMAA by EOA, Inc. on behalf of the Santa Clara Urban Runoff Pollution Prevention Program and the San Mateo Countywide Water Pollution Prevention Program, Applied Marine Sciences on behalf of the Alameda Countywide Clean Water Program, and Armand Ruby Consulting on behalf of the Contra Costa Clean Water Program. 83 pp plus appendices.

Bay Area Stormwater Management Agency Association (BASMAA) Regional Monitoring Coalition (RMC).

2016b. Creek Status and Pesticides & Toxicity Monitoring Standard Operating Procedures, Final Version 3. Prepared for BASMAA by EOA, Inc. on behalf of the Santa Clara Urban Runoff Pollution Prevention Program and the San Mateo Countywide Water Pollution Prevention Program, Applied Marine Sciences on behalf of the Alameda Countywide Clean Water Program, and Armand Ruby Consulting on behalf of the Contra Costa Clean Water Program. 190 pp.

Bay Area Stormwater Management Agency Association (BASMAA). 2019. BASMAA Regional Monitoring

Coalition Five-Year Bioassessment Report, Water Years 2012-2016. Ensminger, M. 2019. Ambient and Mitigation Monitoring in Urban Areas in Northern California FY

2017/2018. Prepared by California Department of Pesticide Regulation Environmental Monitoring Branch.

Phillips, B.M., Anderson, B.S., Siegler, K., Voorhees, J., Tadesse, D., Weber, L., Breuer, R. 2014. Trends in

Chemical Contamination, Toxicity and Land Use in California Watersheds: Stream Pollution Trends (SPoT) Monitoring Program. Third Report – Five-Year Trends 2008-2012. California State Water Resources Control Board, Sacramento, CA.

San Francisco Regional Water Quality Control Board (SFRWQCB). 2009. Municipal Regional Stormwater

NPDES Permit. Order R2-2009-0074, NPDES Permit No. CAS612008. 125 pp plus appendices. San Francisco Regional Water Quality Control Board (SFRWQCB). 2015. Municipal Regional Stormwater

NPDES Permit. Order R2-2015-0049, NPDES Permit No. CAS612008. 152 pp plus appendices. San Mateo Countywide Water Pollution Prevention Program (SMCWPPP). 2014. Part A of the Integrated

Monitoring Report. Water Quality Monitoring. Water Years 2012 and 2013 (October 2011 – September 2013). March 15, 2014.

San Mateo Countywide Water Pollution Prevention Program (SMCWPPP). 2016. Urban Creeks

Monitoring Report, Water Quality Monitoring Water Year 2015. March 31, 2016.

PART A: SAN FRANCISCO ESTUARY RECEIVING WATER MONITORING

Water Year 2014 through Water Year 2019

Submitted in compliance with Provision C.8.h.v of NPDES Permit No. CAS612008 (Order No. R2-2015-0049)

March 31, 2020

SMCWPPP IMR Part A RMP Participation, Water Years 2014 – 2019

i

LIST OF ACRONYMS

AFR Alternative Flame Retardant

BAHM Bay Area Hydrological Model

BASMAA Bay Area Stormwater Management Agency Association

BOD Board of Directors

C/CAG City/County Association of Governments

CEC Contaminant of Emerging Concern

ECWG Emerging Contaminant Workgroup

IMR Integrated Monitoring Report

MPC Monitoring and Pollutants of Concern Committee

MRP Municipal Regional Permit

MS4 Municipal Separate Stormwater Sewer System

NPDES National Pollution Discharge Elimination System

PBDE Polybrominated Diphenyl Ether

PCBs Polychlorinated Biphenyls

PFAS Polyfluoroalkyl Sulfonate Substances

POC Pollutant of Concern

RAA Reasonable Assurance Analysis

RMC Regional Monitoring Coalition

RMP Regional Monitoring Program for Water Quality in the San Francisco Bay

RWSM Regional Watershed Spreadsheet Model

S&T Status and Trends

SMCWPPP San Mateo Countywide Water Pollution Prevention Program

SFEI San Francisco Estuary Institute

SFRWQCB San Francisco Bay Regional Water Quality Control Board

SPLWG Sources, Pathways and Loadings Workgroup

SSC Suspended Sediment Concentration

STLS Small Tributary Loading Strategy

TMDL Total Maximum Daily Load

TRC Technical Review Committee

USGS United States Geological Survey

WMA Watershed Management Area

WY Water Year


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TABLE OF CONTENTS

List of Acronyms .......................................................................................................................................... i

Table of Contents ........................................................................................................................................ ii

List of Figures ............................................................................................................................................. ii

List of Tables ............................................................................................................................................... ii

1.0 Introduction........................................................................................................................................ 1

2.0 RMP Status and Trends Monitoring Program ................................................................................ 2

3.0 RMP Pilot and Special Studies ....................................................................................................... 3

4.0 Participation in Committees, Workgroups and Strategy Teams ................................................. 4

5.0 Small Tributaries Loading Strategy ................................................................................................. 5

5.1 Wet Weather Reconnaissance Monitoring ................................................................................ 5

5.2 STLS Trends Strategy ............................................................................................................... 6

5.3 Advanced Data Analysis ............................................................................................................ 8

5.3.1 Site Inter-Comparison Methodologies .......................................................................... 8

5.3.2 PCB Congener Profile Comparisons ............................................................................ 8

5.4 Alternative Flame Retardant Conceptual Model ...................................................................... 10

5.5 Regional Watershed Spreadsheet Model ................................................................................ 10

6.0 Recommendations .......................................................................................................................... 11

7.0 References ....................................................................................................................................... 12

LIST OF FIGURES

Figure 5.1. Aroclor fractions in stormwater at the outlet of Pulgas Pump Station South over time (figure produced by SFEI, 2018). ........................................................................................................................... 10

LIST OF TABLES

Table 1.1. RMP Status and Trends Monitoring Schedule. ............................................................................ 2


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1.0 INTRODUCTION

This Integrated Monitoring Report (IMR) Part A: San Francisco Estuary Receiving Water Monitoring, Water Year1 (WY) 2014 through WY 2019 was prepared by the San Mateo Countywide Water Pollution Prevention Program (SMCWPPP or Program). SMCWPPP is a program of the City/County Association of Governments (C/CAG) of San Mateo County. Each incorporated city and town in the county and the County of San Mateo share a common National Pollutant Discharge Elimination System (NPDES) stormwater permit for Bay Area municipalities referred to as the Municipal Regional Permit (MRP). The MRP was first adopted by the San Francisco Regional Water Quality Control Board (SFRWQCB or Regional Water Board) on October 14, 2009 as Order R2-2009-0074 (SFRWQCB 2009; referred to as MRP 1.0). On November 19, 2015, the Regional Water Board updated and reissued the MRP as Order R2-2015-0049 (SFRWQCB 2015; referred to as MRP 2.0). This report fulfills the requirements of provision C.8.h.v of MRP 2.0 for comprehensively interpreting and reporting all monitoring data collected since the previous IMR. As such, this report describes monitoring activities conducted during WY 2014 through WY 2019. The previous IMR described monitoring conducted during WY 2012 and WY 2013 (SMCWPPP 2014). As described in provision C.8.c of MRP 2.0 and provision C.8.b of MRP 1.0, Permittees are required to provide annual financial contributions towards implementing an Estuary receiving water monitoring program that at a minimum is equivalent to the Regional Monitoring Program for Water Quality in the San Francisco Bay (RMP). Since the adoption of the 2009 MRP, SMCWPPP has complied with this provision by making financial contributions to the RMP. Additionally, SMCWPPP staff actively participates in RMP committees, workgroups, and strategy teams as described in the following sections. The RMP is a long-term (1993 – present) monitoring program that is discharger-funded and shares direction and participation by regulatory agencies and the regulated community with the goal of assessing water quality in the San Francisco Bay. The regulated community includes municipal separate stormwater sewer systems (MS4s), publicly owned treatment works, dredgers, flood control districts, and industrial dischargers. The San Francisco Estuary Institute (SFEI) is the implementing entity for the RMP and the fiduciary agent for RMP stakeholder funds. SFEI does not provide direct oversight of the RMP but does help identify stakeholder information needs, develop workplans that address these needs, and implement the workplans. The RMP is intended to answer the following core management questions:

1. Are chemical concentrations in the Estuary potentially at levels of concern and are associated impacts likely?

2. What are the concentrations and masses of contaminants in the Estuary and its segments?

3. What are the sources, pathways, loadings, and processes leading to contaminant related impacts in the Estuary?

1 Most hydrologic monitoring occurs for a period defined as a Water Year, which begins on October 1 and ends on

September 30 of the named year. For example, Water Year 2019 (WY 2019) began on October 1, 2018 and concluded on September 30, 2019.


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4. Have the concentrations, masses, and associated impacts of contaminants in the Estuary increased or decreased?

5. What are the projected concentrations, masses, and associated impacts of contaminants in the Estuary?

The RMP budget is generally divided between two major program elements: Status and Trends and Pilot/Special Studies. The following sections provide a brief overview of these programs. The 2019 Annual Update to the RMP Multi-year Plan2 provides more details and establishes deliverables for each component of the current RMP budget. The RMP publishes annual summary reports. In odd years, the Pulse of the Bay Report focuses on Bay water quality and summarizes information from all sources. In even years, the RMP Update Report has a narrower and specific focus. The 2019 Pulse of the Bay3 includes: an overview of the various pollutant sources and pathways to the Bay with feature articles on four sources/pathways (municipal and industrial wastewater, stormwater, dredging and dredged sediment disposal) and updates on priority pollutants, such as nutrients, contaminants of emerging concern, mercury, Polychlorinated Biphenyls (PCBs), selenium, bacteria, and copper.

2.0 RMP STATUS AND TRENDS MONITORING PROGRAM

The Status and Trends Monitoring Program (S&T Program) is the long-term contaminant-monitoring component of the RMP. The S&T Program was initiated as a pilot study in 1989, implemented thereafter, and was redesigned in 2007 based on a more rigorous statistical design that enables the detection of trends. The RMP Technical Review Committee (TRC) continues to assess the efficacy and value of the various elements of the S&T Program and to recommend modifications to S&T Program activities based on ongoing findings. The current S&T sampling schedule, established in 2014, is listed in Table 2.1 with 2014 - 2019 accomplishments and 2020 goals. A redesign of the S&T Program is anticipated in 2020 reflecting on 20 years of monitoring, continued need for legacy contaminant monitoring, on-ramping of contaminants of emerging concern (CECs), and future collaboration with the United States Geological Survey (USGS).

Table 1.1. RMP Status and Trends Monitoring Schedule.

Program Element Schedule 2014 2015 2016 2017 2018 2019 2020 (Plan)

Water Every 2 years X X X

Bird Eggs Every 3 years X X

Sediment Every 4 years X X

Bay Margin Sediments Random X X X

Sport Fish Every 5 years X X

Bivalves Every 2 years X X X X

Support to the USGS for suspended sediment, nutrients, and phytoplankton monitoring

Every year X X X X X X X

2 https://www.sfei.org/sites/default/files/biblio_files/2019%20Multi-Year%20Plan%20-%20SC%20Approved%2020190430%20-%20050119.pdf

3 https://www.sfei.org/documents/2019-pulse-bay-pollutant-pathways

https://www.sfei.org/sites/default/files/biblio_files/2019%20Multi-Year%20Plan%20-%20SC%20Approved%2020190430%20-%20050119.pdf

https://www.sfei.org/sites/default/files/biblio_files/2019%20Multi-Year%20Plan%20-%20SC%20Approved%2020190430%20-%20050119.pdf

https://www.sfei.org/documents/2019-pulse-bay-pollutant-pathways


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Additional information on the S&T Program and associated monitoring data are available for download via the RMP website at http://www.sfei.org/content/status-trends-monitoring.

3.0 RMP PILOT AND SPECIAL STUDIES

The RMP also conducts Pilot and Special Studies on an annual basis. Studies are typically designed to investigate and develop new monitoring measures related to anthropogenic contamination or contaminant effects on biota in the Estuary. Special Studies address specific scientific issues that RMP committees, workgroups, and strategy teams identify as priority for further study. These studies are developed through an open selection process at the workgroup level and selected for funding through the TRC and the Steering Committee. The amount of RMP budget allocated to Special Studies has increased over the years and is expected to continue this trend into the future. In 2019, Pilot and Special Studies focused on the following topics:

• Small Tributary Loading Strategy (see Section 5.0 below and IMR Part D: Pollutants of Concern Monitoring for more details)

o Watershed characterization reconnaissance monitoring for pollutants of concern

o Advanced analysis of PCBs data

o Development of a trends strategy

• Emerging Contaminant Strategy (see IMR Part D: Pollutants of Concern Monitoring for more information)

o Ongoing review and update of the RMP’s Tiered Risk and Management Action Framework

o Contaminants of emerging concern monitoring (imidacloprid, fragrance ingredients, Polyfluoroalkyl Sulfonate Substances (PFAS), nonionic surfactants, pharmaceuticals) in water, sediment, and/or wastewater

o Non-targeted analysis of Bay sediment to help identify new CECs

o Increased focus on studying CECs in stormwater runoff

• Nutrients Management Strategy

o Continued coordination with USGS for monitoring of nutrients, phytoplankton biomass, and dissolved oxygen at moored sensors in the Bay

o Ship-based nutrient sampling

o Study of harmful algae and toxin accumulation in small fish and mussels using Supplemental Environmental Project funds

• Microplastics

o Review and update of the RMP’s Microplastic Strategy

o Monitoring of microplastics in sport fish

o Development of several documents synthesizing microplastic research, including a factsheet and a draft manuscript on microplastics in urban stormwater runoff (Werbowski et al. in review)

http://www.sfei.org/content/status-trends-monitoring


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• Development of conceptual PCB models for prioritized Bay margin units

• Sediment

o Development of the Sediment Monitoring Strategy

o Review of beneficial reuse and strategic placement projects

o Bathymetric change studies

o Hosting and support for Dredged Material Management Office database

• Selenium monitoring in sturgeon plugs and North Bay clams and water

Results and summaries of the most pertinent Pilot and Special Studies can be found on the RMP website (http://www.sfei.org/rmp/rmp_pilot_specstudies). During WY 2014 – WY 2019, a considerable amount of RMP and Stormwater Program staff time was spent overseeing and implementing Special Studies associated with the RMP’s Small Tributary Loading Strategy (STLS). Pilot and Special Studies associated with the STLS are intended to fill data gaps associated with loadings of Pollutants of Concern (POC) from relatively small tributaries to the San Francisco Bay. Additional information on STLS-related studies is included in Section 5.0 of this report (IMR Part A) and in IMR Part D: POC Monitoring.

4.0 PARTICIPATION IN COMMITTEES, WORKGROUPS AND STRATEGY TEAMS

During WY 2014 - WY 2018, SMCWPPP and the BASMAA RMC partners actively participated in the following RMP committees, workgroups, and strategy teams:

• Steering Committee

• Technical Review Committee (TRC)

• Sources, Pathways and Loadings Workgroup (SPLWG)

• Emerging Contaminant Workgroup (ECWG)

• Nutrient Technical Workgroup

• Strategy Teams (e.g., Small Tributaries, PCBs, Microplastics, Dioxins, Selenium) Committee, workgroup, and strategy team representation was provided by Permittee staff, Stormwater Program staff, and/or individuals designated by the Bay Area Stormwater Management Agency Association (BASMAA) Regional Monitoring Coalition (RMC) participants and the BASMAA Board of Directors (BASMAA BOD). Representation included participating in meetings, reviewing technical reports and work products, co-authoring or reviewing articles and publications, and providing general program direction to RMP staff. Representatives of the RMC also provided timely summaries and updates to and received input from, Stormwater Program representatives (on behalf of Permittees) during BASMAA Monitoring and Pollutants of Concern Committee (MPC) and/or BASMAA BOD meetings to ensure that MRP Permittees’ interests were represented.

http://www.sfei.org/rmp/rmp_pilot_specstudies


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5.0 SMALL TRIBUTARIES LOADING STRATEGY

The RMP Small Tributaries Loading Strategy was developed in 2009 by the STLS Team, which includes representatives from BASMAA, Regional Water Board staff, RMP staff, and technical advisors and is overseen by the Sources, Pathways, and Loadings Workgroup. The objective of the STLS is to develop a comprehensive planning framework to coordinate POC monitoring/modeling between the RMP and RMC participants. The following management policies and decisions guide the STLS:

• Refining pollutant loading estimates for future Total Maximum Daily Load (TMDL) updates,

• Informing provisions of the current and future versions of the MRP,

• Identifying small tributaries to prioritize for management actions, and

• Informing decisions on the best management practices for reducing concentrations and loads.

Work conducted by the STLS in 2014 - 2019 is framed by the same five priority POC management information needs identified in MRP 2.0.

1. Source Identification – identifying which sources or watershed source areas provide the greatest opportunities for reductions of POCs in urban stormwater runoff;

2. Contributions to Bay Impairment – identifying which watershed source areas contribute most to the impairment of San Francisco Bay beneficial uses (due to source intensity and sensitivity of discharge location);

3. Management Action Effectiveness – providing support for planning future management actions or evaluating the effectiveness or impacts of existing management actions;

4. Loads and Status – providing information on POC loads, concentrations, and presence in local tributaries or urban stormwater discharges; and

5. Trends – evaluating trends in POC loading to the Bay and POC concentrations in urban stormwater discharges or local tributaries over time.

The sections below describe the tasks implemented by the RMP STLS in 2014 - 2019 to address the relevant management policies.

5.1 Wet Weather Reconnaissance Monitoring

With a goal of identifying watershed sources of PCBs and mercury, STLS field monitoring in WYs 2015 - 2019 focused on collection of storm composite samples in the downstream reaches of catchments located throughout the Bay Area. A total of 66 sites (21 in San Mateo County) were sampled during at least one storm event. The contributing areas for these sites range in size from 0.02 km2 to 233 km2 and typically represent small engineered MS4 drainage areas (75% were smaller than 5.2 km2). Storm composite water samples were analyzed for concentrations of PCBs (i.e., RMP 40 congeners), total mercury, and suspended sediment concentration (SSC). In addition, a pilot study was continued at a subset of locations (two stations) to collect fine sediments using specialized settling chambers. A full description of the methods and results from WY 2015 through WY 2018 monitoring is included in Gilbreath et al. (in preparation) which is provided as Attachment D.3 to IMR Part D: POC Monitoring Report.


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Data generated through STLS reconnaissance monitoring are used by SMCWPPP to prioritize Watershed Management Areas (WMAs) where PCB source investigations will be conducted. Reconnaissance monitoring by the STLS is planned to continue in WY 2020 and possibly into future years.

The RMP STLS has a growing database, now consisting of 88 stations regionwide that have been sampled during wet weather events for PCBs, mercury, and SSC since 2003. Some stations have also been sampled for a larger suite of constituents. Prior to WY 2015, most of the stations were located in natural creeks. Since that time, most of the stations were located in small MS4 catchments draining primarily old industrial land uses. At three of these sites, collection was conducted using a remote sediment sampler only – a method that was pilot tested during WYs 2015-2018 and approved by the SPLWG in spring 2018 for use in reconnaissance monitoring. Acknowledging that dynamic climatic conditions and individual storm characteristics may affect data interpretation, the following broad conclusions and recommendations were identified as a result of wet weather reconnaissance monitoring conducted via the STLS (Gilbreat et al. in preparation):

• PCBs positively correlate with impervious cover, old industrial land use, and mercury. They inversely correlate with watershed area. Although mercury and PCBs positively correlate, the relationship is relatively weak, probably due to the larger role of atmospheric recirculation in the mercury cycle and the differences in use history of each POC.

• Neither PCBs nor mercury have strong correlations with other trace metals (As, Cu, Cd, Pb, and Zn). Therefore, there is no support for the use of trace metals as surrogate investigative tools for either PCBs or mercury sources.

• Continued focus on resampling of some stations (i.e., those that return lower than expected concentrations) is recommended to test for false negatives.

5.2 STLS Trends Strategy

The STLS Trends Strategy was initiated in 2015 at the recommendation of the SPLWG which advised the STLS to define where and how trends may be most effectively measured in relation to management effort so that data collection methods deployed over the next several years will support this management information need. The STLS Trends Strategy team is comprised of SFEI staff, RMC participants, and Regional Water Board staff. Invitations to key meetings are extended to additional interested parties (e.g., USEPA), and technical advisors (e.g., USGS) are consulted to review specific technical work products. The Trends Strategy document (and Technical Appendix), initially drafted in WY 2016, serves as a foundation for this team. The main document summarizes the background, management questions, and guiding principles of the Trends Strategy. It also describes coordination between the RMP and BASMAA within the context of the MRP, proposed tasks to answer management questions, anticipated deliverables, and the overall timeline. The current priority POCs are PCBs and mercury and trend indicators under consideration (i.e., PCB concentrations and particle-ratios) were identified within the context of existing datasets (e.g., POC loading stations) and TMDL timelines. However, the Strategy recognizes that priorities can change in the future. The Technical Appendix (Melwani et al. 2016) presents an evaluation of variability and statistical


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power4 for detecting trends based on POC loading station PCBs data. It presents sample size and revisit frequency scenarios needed to detect declining trends in PCBs in 25 years with > 80% statistical power. Due to high variability in baseline PCB concentrations, the modeled sampling scenarios would likely not be practical to implement. Therefore, the Technical Appendix recommends additional analyses and monitoring that should be considered prior to developing a trends monitoring design. In 2018, the STLS Trends Strategy team followed up on some of the recommendations from the Technical Appendix. A statistical model for trends in PCB loads in the Guadalupe River (as a case study) was finalized. The model incorporates the significant turbidity-PCB relationships that exist and evaluates climatic, seasonal, and inter-annual factors as potential drivers of PCB loads. More intensive review of the Guadalupe River dataset resulted in two main findings: 1) No trends in PCB loads were apparent for the period of 2003 through 2014: 2) A monitoring design that includes sampling at least two storms in 13 out of 20 years (with 4 to 6 grab samples per storm) would detect inter-annual trends of 25% or more over 20 years with > 80% power (Melwani et al. 2018). Results of the statistical analyses were presented at key stages in the analysis to USGS technical advisors with expertise in trends analysis of water data. It is uncertain how the Guadalupe River model and analysis could be applied to other watersheds which have distinct characteristics. In 2018, the Trends Strategy team updated the Trends Strategy document to include an evaluation of how various tasks to date have and could be used to address the five POC information needs from the MRP (Wu et al. 2018). This review included empirical data collection (i.e., loading stations, wet weather reconnaissance monitoring, BASMAA source identification and BMP effectiveness monitoring, SPoT monitoring) and modeling approaches (i.e., RWSM, the Guadalupe River statistical analysis, Reasonable Assurance Analysis). The 2018 Trends Strategy describes the pros and cons of various methods available to identify and predict trends. Due to concerns about the limitations of extrapolating monitoring results from a relatively small number of watersheds to the entire region, regional modeling was proposed as the most efficient tool to estimate POC loading over time and space for trends evaluation at the desired spatial scales. The 2018 Trends Strategy reviews and compares currently available models and modeling platforms relative to their ability to answer key management questions, including Countywide Reasonable Assurance Analysis (RAA) modeling efforts, the Bay Area Hydrological Model (BAHM), the Regional Watershed Spreadsheet Model (RWSM), and HSPF and SWMM platforms. Based on the goals of the STLS Trends Strategy team, the BAHM, which is based on the HSPF platform, was recommended as the most suitable starting point to develop a regional POC trends model. This recommendation was documented in the Modeling Implementation Plan (Wu and McKee 2019). Beyond questions about PCBs and mercury, the new regional model will also be used to support other RMP workgroups that have similar management questions. For example, the model could be used to estimate sediment and CEC loads to the Bay. In 2020, the STLS plans to develop a regional model for hydrology following procedures outlined in the Modeling Implementation. The hydrology model, once established, will be used as a basis for modeling sediment, PCBs, CECs, and other POCs in subsequent years.

4 Power is defined as the probability of detecting a trend of a certain magnitude during a specified monitoring period

(years), where a Type I error rate is set at 5%.


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5.3 Advanced Data Analysis

In 2018, the STLS began a new task to provide a deeper analysis of the growing set of PCBs data collected by BASMAA and the RMP. The Advanced Data Analysis task includes two parallel lines of investigation: site inter-comparison methodologies and PCB congener profile comparisons. 5.3.1 Site Inter-Comparison Methodologies

Most of the wet weather characterization data used by the Program and other BASMAA RMC partners to identify and prioritize WMAs where PCB source investigations will be conducted are based on composite samples collected during a single storm event. See IMR Part D for more information on the wet weather sampling programs implemented by the Program and the WMA characterization process. The data collected through these sampling techniques is valuable, but some aspects of its interpretation have been challenging. Since only one storm was sampled at most sites, differing storm characteristics (intensity, duration, antecedent rainfall conditions) interplay with differing PCBs source characteristics to confound comparisons among watersheds. For example, if the targeted storm was relatively small, it is possible that measured PCBs concentrations (and/or PCBs particle ratios) will be lower than they would be in a sample collected at the same station during a larger storm, when larger amounts of sediments and attached pollutants may be mobilized. The main goal of this investigation was to develop a method to account for the differences in targeted storm characteristics at the various sampled stations. In 2019, the STLS developed a method to generate comparable yield estimates for small industrial watersheds where only a single storm has been sampled. The method includes four steps:

1. Estimate storm runoff volume in the sampled watershed.

2. Compute estimates of storm load for the sampled storm.

3. Adjust estimates of storm load to a standard sized storm.

4. Normalize standardized storm loads to the watershed area of interest to generate storm yields.

This stepwise method was developed using Santa Clara County as a case study and pilot tested with a focus on nested sites within the Guadalupe River watershed. McKee et al. (2019) describes the loads-based site inter-comparison method. Further development and testing in a great number of areas with a wider range of conditions was recommended for 2020. 5.3.2 PCB Congener Profile Comparisons

PCBs samples collected by BASMAA and the STLS are routinely analyzed for 40 individual PCBs congeners (i.e., the “RMP 40”). Although most data analyses are conducted using the sum of those congeners, BASMAA and the STLS recognize the value of generating the more robust RMP 40-based dataset with individual congener results and the potential for future data exploration possibilities. For example, PCBs congener profiles can inform efforts to identify source areas that contribute most to the PCBs mass exported from the watershed via stormwater, and to help illustrate variability in PCBs mobilization from source areas over time.


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In 2018, the STLS began development of a method to estimate the contributions of different Aroclor5 mixtures to the congener profiles of samples of stormwater and sediment. The method is based on the use of indicator congeners that are representative of each of the four most commonly used Aroclors. Data from the Pulgas Pump Station watershed in the City of San Carlos were used to pilot test the method. At this station, stormwater and sediment had high concentrations with a relatively unique pattern, dominated by congeners indicative of a combination of Aroclors 1242 and 1260. The concentrations and congener profiles in sediment suggest that there are two distinct source areas in the watershed that combine to create the mix of 1242 and 1260 that is dominant in stormwater at the Pump Station (Figure 5.1). The data suggest that if PCB flux from one of these areas could be eliminated, loads from the watershed would be reduced by 50% or more. For the Coyote Creek watershed in Santa Clara County, the similarity in congener profiles between the highest concentration sediment samples and the stormwater samples suggest that the important source areas in the watershed have been identified, and that reduction of loading from an area at the south end of the Charcot Avenue Storm Drain watershed would yield the greatest reduction in export at the Coyote Creek station. The concentrations and congener profiles in stormwater and sediment from the Guadalupe River watershed indicate the presence of one source area that is likely a significant contributor to PCBs export from the watershed, but suggest that all of the significant sources areas may not yet have been identified. A report describing the PCBs congener profile comparison method was published in 2019 (Davis and Gilbreath 2019).

5 PCBs were manufactured and used as complex mixtures of individual PCBs (referred to as PCB congeners). In

North America, the only producer was the Monsanto Company, which marketed PCBs under the trade name Aroclor from 1930 to 1977. A series of different Aroclor mixtures was produced, with varying degrees of overall chlorine content, and these different mixtures were used for different purposes. The congener composition of the various Aroclor mixtures has been reported in the literature (e.g., Schulz et al. 1989, Frame et al. 1996a,b). As a consequence of the use of Aroclor mixtures, PCBs are also present in the environment as complex mixtures of congeners.


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Figure 5.1. Aroclor fractions in stormwater at the outlet of Pulgas Pump Station South over time (figure produced by SFEI, 2018).

5.4 Alternative Flame Retardant Conceptual Model

Alternative flame retardants (AFRs) came into use following state bans and nationwide phase-outs of polybrominated diphenyl ether (PBDE) flame retardants in the early 2000’s. They include many categories of compounds, including organophosphate esters. In 2018 the RMP STLS and the Emerging Contaminant Workgroup worked together to conduct a special study to inform ECWG’s planning activities related to AFRs. The special study compiled and reviewed available data and previously developed conceptual models for PBDE to support a stormwater-related AFR conceptual model being developed by the ECWG. Organophosphate esters were prioritized for further investigation due to their increasing use, persistent character, and ubiquitous detections at concentrations exceeding PBDE concentrations in the Bay. Limited stormwater data from two watersheds in Richmond and Sunnyvale suggest that urban runoff may be an important source of these compounds. Additional monitoring and modeling were recommended in the AFR Technical Report (Lin and Sutton 2018).

5.5 Regional Watershed Spreadsheet Model

The Regional Watershed Spreadsheet Model is a land use based planning tool for estimation of annual POC loads from small tributaries to San Francisco Bay at a regional scale. Development of the RWSM began in 2010 and the RWSM tool-kit that was published in 2017. The RWSM is based on the idea that to accurately assess total contaminant loads entering San Francisco Bay, it is necessary to estimate loads from local watersheds. “Spreadsheet models” of stormwater quality provide a useful and relatively inexpensive means of estimating regional scale watershed loads. Spreadsheet models have advantages over mechanistic models because the data for many of the input parameters required by mechanistic models may not currently exist, and also require large calibration datasets which take money and time to collect.


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The RWSM is based on the assumption that an estimate of mean annual stormwater runoff volume for each land use type within a watershed can be combined with an estimate of mean annual pollutant concentration for that same land use type to derive a pollutant load which can be aggregated for a watershed or many watersheds within a region of interest. It may be used to provide hypotheses about which sub-regions or watersheds export relatively higher or lower loads to the Bay relative to area. It can also serve as a baseline for analyzing changes in loadings due to large scale changes in land use (e.g., associated with redevelopment and new development) and runoff (e.g., associated with climate change and changes in impoundment). However, the RWSM is less reliable for predicting loadings from individual watersheds and for estimating load changes in relation to implementation of stormwater runoff treatment BMPs. The RWSM beta tool-kit, published in June 2017 includes:

• Hydrology Model coded using ArcPy and drawing on a user interface accessible through ArcGIS;

• Pollutant Model Spreadsheet for taking the outputs from the Hydrology Model and inputting land use coefficients to estimate pollutant loads;

• Two optional calibration tools – a spreadsheet for manual calibration, and an R script for an optimized automated calibration; and

• User Manual

As described in Section 5.2, a more sophisticated dynamic simulation model (i.e., SWMM, HSPF) is currently under development.

6.0 RECOMMENDATIONS

Overall, SMCWPPP has found that the RMP’s work helps support certain aspects of San Mateo County Permittee efforts to comply with MRP requirements. SMCWPPP recommends that RMP funding continues to be directed towards the following high priority topics:

• Bay status and trends monitoring focused on current and emerging pollutants;

• Wet weather monitoring in small tributaries for high priority pollutants; and

• Conceptual and quantative modeling to further understand the sources, transport, and fates of current and emerging pollutants.


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7.0 REFERENCES

Davis, J.A. and Gilbreath, A.N. 2019. Small Tributaries Pollutants of Concern Reconnaissance Monitoring: Pilot Evaluation of Source Areas Using PCB Congener Data. SFEI Contributin No. 956. San Francisco Estuary Institute, Richmond, CA.

Gilbreath, A., Hunt, J., and McKee, L. in preparation. Pollutants of Concern Reconnaissance Monitoring Progress Report, Water Years 2015 – 2019. SFEI Controbution No. XXX. San Francisco Estuary Institute, Richmond, CA.

Lin, D. and Sutton, R. 2018. Alternative Flame Retardants in San Francisco Bay: Synthesis and Strategy. SFEI Contribution No. 885. San Francisco Estuary Institute, Richmond, CA.

McKee, L.J., Gilbreath, A.N., Hunt, J.A., Wu, J., Yee, D. and Davis, J.A. 2019. Small Tributaries Pollutants of Concern Reconnaissance Monitoring: Loads and Yields-based Prioritization Methodology Pilot Study. SFEI Contributin No. 817. San Francisco Estuary Institute, Richmond, CA

Melwani, A., Yee, D., McKee, L., Gilbreath, A., Trowbridge, P., and Davis, J. 2018. DRAFT Statistical Methods Development and Sampling Design Optimization to Support Trends Analysis for Loads of Polychlorinated Biphenyls from the Guadalupe River in San José, California, USA.

Melwani, A.R., Yee, D., Gilbreath, A., McKee, L.M. 2016. Technical Appendix to the Small Tributaries Trend Design. San Francisco Estuary Institute.

San Francisco Bay Regional Water Quality Control Board (SFBRWQCB). 2009. San Francisco Regional Water Quality Control Board Municipal Regional Stormwater NPDES Permit. Order R2-2009-0074, NPDES Permit No. CAS612008. 125 pp plus appendices.

San Francisco Bay Regional Water Quality Control Board (SFBRWQCB). 2015. San Francisco Region Water Quality Municipal Regional Stormwater NPDES Permit. Order R2-2015-0049, NPDES Permit No. CAS612008. 152 pp plus appendices.

San Francisco Bay Regional Water Quality Control Board (SFRWQCB). 2017. Water Quality Control Plan (Basin Plan) for the San Francisco Bay Region. San Francisco Regional Water Quality Control Board. Updated to reflect amendments adopted up through May 4, 2017. http://www.waterboards.ca.gov/sanfranciscobay/basin_planning.shtml.

San Mateo Countywide Water Pollution Prevention Program (SMCWPPP). 2014. Part A of the Integrated Monitoring Report. Water Quality Monitoring. Water Years 2012 and 2013 (October 2011 – September 2013). March 15, 2014.

Werbowski, L. M., Gilbreath, A. N., Munno, K, Zhu, X., Grbic, J., Wu, T., Sutton, R., Sedlak, M. D., Deshpande, A. D., and Rochman, C. M. in review. Urban stormwater runoff: a major pathway for anthropogenic particles, black rubbery fragments, and other types of microplastics to urban receiving waters. Submitted to Journal of Integrated Environmental Assessment and Management.

Wu, J. and McKee, L. 2019. Modeling and Trends Implementation Plan – Version 1.0, A technical report prepared for the Regional Monitoring Program for Water Quality in San Francisco Bay (RMP). Contribution No. 942. San Francisco Estuary Institute, Richmond, CA.

Wu, J., Trowbridge, P., Yee, D., McKee, L., and Gilbreath, A. 2018. RMP Small Tributaries Loading Strategy: Modeling and Trends Strategy 2018. Contribution No. 886. San Francisco Estuary Institute, Richmond, CA.

http://www.waterboards.ca.gov/sanfranciscobay/basin_planning.shtml

SMCWPPP IMR Part B: Creek Status Monitoring, WY 2014 – WY 2019

Cover Page

DRAFT

PART B: CREEK STATUS MONITORING



March 31, 2020


i

Preface

In early 2010, several members of the Bay Area Stormwater Agencies Association (BASMAA) joined together to form the Regional Monitoring Coalition (RMC), to coordinate and oversee water quality monitoring required by the Municipal Regional National Pollutant Discharge Elimination System (NPDES) Stormwater Permit (in this document the permit is referred to as MRP)1. The RMC is comprised of the following participants:

• Alameda Countywide Clean Water Program (ACCWP)

• Contra Costa Clean Water Program (CCCWP)

• San Mateo Countywide Water Pollution Prevention Program (SMCWPPP)

• Santa Clara Valley Urban Runoff Pollution Prevention Program (SCVURPPP)

• Fairfield-Suisun Urban Runoff Management Program (FSURMP)

• City of Vallejo and Vallejo Flood and Wastewater District (Vallejo)

This Integrated Monitoring Report (IMR) Part B: Creek Status Monitoring, Water Year (WY) 2014 – WY 2019 complies with Provision C.8.h.v of the MRP for reporting of all data collected since the previous IMR which was submitted on March 31, 2014. It includes data collected in WY 2014 through WY 2019 (October 1, 2013 through September 30, 2019). Data were collected pursuant to Creek Status Monitoring and Pesticides & Toxicity Monitoring requirements of MRP Provision C.8. Data presented in this report were developed under the direction of the RMC and the San Mateo Countywide Water Pollution Prevention Program (SMCWPPP) using probabilistic and targeted monitoring designs as described herein.

Consistent with the RMC Creek Status and Long-Term Trends Monitoring Plan (BASMAA 2012), monitoring data were collected in accordance with the most recent versions of the BASMAA RMC Quality Assurance Project Plan (QAPP; BASMAA, 2016a) and BASMAA RMC Standard Operating Procedures (SOPs; BASMAA, 2016b). Where applicable, monitoring data were derived using methods comparable with methods specified by the California Surface Water Ambient Monitoring Program (SWAMP) Quality Assurance Program Plan (QAPrP)2. Data presented in this report were also submitted in electronic SWAMP-comparable formats by SMCWPPP to the San Francisco Bay Regional Water Quality Control Board on behalf of San Mateo County Permittees and pursuant to Provision C.8.h.ii of the MRP.

1 The San Francisco Bay Regional Water Quality Control Board (SFRWQCB or Regional Water Board) issued the MRP to 76 cities, counties and flood control districts (i.e., Permittees) in the Bay Area on October 14, 2009 (SFRWQCB 2009). On November 19, 2015, the Regional Water Board updated and reissued the MRP (SFRWQCB 2015). The BASMAA programs supporting MRP Regional Projects include all MRP Permittees as well as the cities of Antioch, Brentwood, and Oakley, which are not named as Permittees under the MRP but have voluntarily elected to participate in MRP-related regional activities.

2 The current SWAMP QAPrP is available at: https://www.waterboards.ca.gov/water_issues/programs/swamp/qapp/swamp_QAPrP_2017_Final.pdf


ii

List of Acronyms

ACCWP Alameda Countywide Clean Water Program AFDM Ash Free Dry Mass AFS American Fisheries Society ASCI Algae Stream Condition Index BASMAA Bay Area Stormwater Management Agency Association BMI Benthic Macroinvertebrate BMP Best Management Practices C/CAG City/County Association of Governments CCCWP Contra Costa Clean Water Program CDF Cumulative Distribution Function CEDEN California Data Exchange Network COLD Cold Freshwater Habitat CSBP California Bioassessment Protocol CSCI California Stream Condition Index DF Detection Frequency DO Dissolved Oxygen DPR Department of Pesticide Regulation EMAP Environmental Monitoring and Assessment Program FSURMP Fairfield Suisun Urban Runoff Management Program GIS Geographic Information Systems GM Geometric Mean GRTS Generalized Random Tessellation Stratified GSI Green Stormwater Infrastructure HDI Human Disturbance Index IBI Index of Biological Integrity IDDE Illicit Discharge Detection and Elimination IMR Integrated Monitoring Report IPI Index Physical Habitat Integrity IPM Integrated Pest Management LID Low Impact Development MDL Method Detection Limit MIGR Fish Migration MMI Multimetric Index MPC Monitoring and Pollutants of Concern Committee MPN Most Probable Number MRP Municipal Regional Permit MS4 Municipal Separate Storm Sewer System MUN Municipal and Domestic Water Supply Beneficial Use MWAT Maximum Weekly Average Temperature MWMT Maximum Weekly Maximum Temperature NMFS National Marine Fisheries Service NPDES National Pollutant Discharge Elimination System O/E Observed to Expected PAH Polycyclic Aromatic Hydrocarbons PCBs Polychlorinated Biphenyls PEC Probable Effects Concentrations PHAB Physical Habitat Assessments pMMI Predictive Multimetric Index PSA Perennial Streams Assessment QAPP Quality Assurance Project Plan QAPrP Quality Assurance Program Plan QA/QC Quality Assurance/Quality Control RARE Preservation of Rare and Endangered Species


iii

RM Reporting Module RMC Regional Monitoring Coalition RWB Reachwide Benthos RWQC Recreation Water Quality Criteria SAFIT Southwest Association of Freshwater Invertebrate Taxonomists SCAPE Stream Classification and Priority Explorer SCCWRP Southern California Coastal Water Research Project SCVURPPP Santa Clara Valley Urban Runoff Pollution Prevention Program SFRWQCB San Francisco Bay Regional Water Quality Control Board SMC Southern California Monitoring Coalition SMCWPPP San Mateo County Water Pollution Prevention Program SOP Standard Operating Protocol SPoT Stream Pollution Trends Program SPWN Fish Spawning SRP Stormwater Resource Plan SSID Stressor/Source Identification STORMS Strategy to Optimize Resource Management of Storm Water SWAMP Surface Water Ambient Monitoring Program SWPP Surface Water Protection Program TEC Threshold Effects Concentrations TKN Total Kjeldahl Nitrogen TMDL Total Maximum Daily Load TOC Total Organic Carbon TST Test of Significant Toxicity TU Toxicity Unit UCMR Urban Creeks Monitoring Report USEPA Environmental Protection Agency WARM Warm Freshwater Habitat WQO Water Quality Objective

WY Water Year


iv

Table of Contents

Preface ........................................................................................................................................ i List of Acronyms ......................................................................................................................... ii Table of Contents ....................................................................................................................... iv List of Figures ............................................................................................................................ vi List of Tables ........................................................................................................................... viii List of Attachments ..................................................................................................................... x 1.0 Introduction ..................................................................................................................... 1

1.1 Monitoring Goals ..................................................................................................... 2 1.2 Regional Monitoring Coalition .................................................................................. 2 1.3 Monitoring and Data Assessment Methods ............................................................. 4

1.3.1 Monitoring Methods ........................................................................................ 4 1.3.2 Laboratory Analysis Methods ......................................................................... 5 1.3.3 Data Analysis Methods ................................................................................... 5

1.4 Setting ..................................................................................................................... 5 1.4.1 Designated Beneficial Uses ......................................................................... 9 1.4.2 Climate ........................................................................................................ 9

1.5 Statement of Data Quality ......................................................................................12 2.0 Biological Condition Assessment ....................................................................................13

2.1 Introduction ............................................................................................................13 2.2 Methods .................................................................................................................14

2.2.1 Probabilistic Survey Design ...........................................................................14 2.2.2 Site Evaluations .............................................................................................15 2.2.3 Field Sampling Methods ................................................................................15 2.2.4 Data Analysis ................................................................................................16

2.3 Results and Discussion ..........................................................................................24 2.3.1 Bioassessment Results (WY 2019) ...............................................................25 2.3.2 Evaluation of Countywide Bioassessment Results (WY 2012 – WY 2019) ....35 2.3.3 Trend Analysis...............................................................................................44

2.4 Considerations for Future Bioassessment Monitoring ...............................................48 2.4.1 Investigate Sites with Reduced Biological Conditions ....................................48 2.4.2 Evaluate Sources and Impacts of Potential Stressors ....................................51 2.4.3 Evaluate Effectiveness of BMP/Restoration Projects .....................................53 2.4.4 Evaluate Sites in Good Condition ..................................................................53

3.0 Continuous Water Quality Monitoring .............................................................................56 3.1 Introduction ............................................................................................................56 3.2 Methods .................................................................................................................56

3.2.1 Continuous Temperature ...............................................................................56 3.2.2 Continuous General Water Quality ................................................................57 3.2.3 Data Evaluation .............................................................................................57

3.3 WY 2014 – WY 2019 Overview ..............................................................................57 3.3.1 San Mateo Creek Watershed (WY 2014 – WY 2015) .................................58 3.3.2 San Francisquito Creek Watershed (WY 2014 – WY 2016) ........................58 3.3.3 San Pedro Creek Watershed (WY 2017 – WY 2018) ..................................59

3.4 WY 2019 Results (Tunitas Creek Watershed) ........................................................62 3.4.1 Tunitas Creek Study Area ...........................................................................62 3.4.2 WY 2019 Continuous Temperature Results and Discussion .......................65 3.4.3 WY 2019 Continuous General Water Quality Results and Discussion ........68

4.0 Pathogen Indicator Monitoring ........................................................................................72


v

4.1 Introduction ............................................................................................................72 4.2 Methods .................................................................................................................72

4.2.1 Sample Collection .......................................................................................72 4.2.2 Data Evaluation ..........................................................................................72

4.3 Study Area .............................................................................................................73 4.3.1 WY 2018 – WY 2019: Pescadero Creek Watershed ...................................75 4.3.2 WY 2017: Pillar Point Harbor Watershed ....................................................75 4.3.3 WY 2014 – WY 2016: San Mateo Creek Watershed ...................................75

4.4 Results and Discussion ..........................................................................................76 5.0 Chlorine Monitoring ........................................................................................................78

5.1 Introduction ............................................................................................................78 5.2 Methods .................................................................................................................78 5.3 Results and Discussion ..........................................................................................78

6.0 Toxicity and Sediment Chemistry Monitoring ..................................................................82 6.1 Introduction ............................................................................................................82 6.2 Methods .................................................................................................................83

6.2.1 Site Selection ..............................................................................................83 6.2.2 Sample Collection .......................................................................................84 6.2.3 Data Evaluation ..........................................................................................86

6.3 Results and Discussion ..........................................................................................87 6.3.1 Toxicity .......................................................................................................88 6.3.2 Sediment Chemistry ...................................................................................91 6.3.3 Pesticides in Water .....................................................................................97 6.3.4 Additional Pesticide Monitoring Efforts ........................................................97

7.0 Conclusions and Recommendations ............................................................................ 100 7.1 Conclusions ......................................................................................................... 101

7.1.1 Biological Condition Assessment ................................................................. 101 7.1.2 Continuous Monitoring for Temperature and General Water Quality ............ 104 7.1.3 Pathogen Indicator Monitoring .................................................................. 106 7.1.4 Chlorine Monitoring ................................................................................... 106 7.1.5 Pesticides and Toxicity Monitoring ............................................................ 107

7.2 Trigger Assessment ............................................................................................. 109 7.3 Recommendations ............................................................................................... 111

7.3.1 Biological Condition Assessment .............................................................. 111 7.3.2 Continuous Monitoring for Temperature and General Water Quality ......... 112 7.3.3 Pathogen Indicator Monitoring .................................................................. 112 7.3.4 Chlorine Monitoring ................................................................................... 113 7.3.5 Pesticides and Toxicity Monitoring ............................................................ 113

8.0 Summary of Stormwater Management Programs by San Mateo County Permittees .... 114 9.0 References ................................................................................................................... 117


vi

List of Figures

Figure 1.1. Map of SMCWPPP sites monitored in WY 2019. ...................................................... 8

Figure 1.2. Average annual precipitation in San Mateo County, modeled by the PRISM Climate Group for the period of 1981-2010. ...........................................................................................11

Figure 1.3. Annual rainfall recorded at the San Francisco International Airport, WY 1946 – WY 2019. .........................................................................................................................................12

Figure 2.1. Examples of benthic macroinvertebrates. ................................................................17

Figure 2.2. Examples of soft algae and diatoms. .......................................................................18

Figure 2.3. Condition category as represented by CSCI, hybrid ASCI and IPI Scores for ten bioassessment sites sampled in San Mateo County in WY 2019. .............................................28

Figure 2.4. Bioassessments stations in the Tunitas Creek Watershed, San Mateo County, WY 2015 – WY 2019. ......................................................................................................................33

Figure 2.5. Bioassessments stations in the Belmont Creek Watershed, San Mateo County, WY 2012 – WY 2019. ......................................................................................................................34

Figure 2.6. Biological conditions categories for CSCI at 90 bioassessment sites in San Mateo County sampled between WY 2012 and WY 2019. ...................................................................37

Figure 2.7. All San Mateo County biological condition scores (WY 2012 – WY 2019) grouped by condition category and strata (n=90, including 3 targeted WY 2019 sites) ................................38

Figure 2.8. Cumulative distribution functions of CSCI, ASCI-D, ASCI-H and ASCI-SB scores in San Mateo County (n = 87) .......................................................................................................40

Figure 2.9. Percent Impervious Area in 5 km vs. CSCI and ASCI-H scores. Data grouped by strata: Pacific Ocean vs. San Francisco Bay .............................................................................43

Figure 2.10. Box plots showing the distribution of CSCI and ASCI-H scores for 90 bioassessment sites in San Mateo County, grouped by three classes of percent watershed imperviousness. ........................................................................................................................44

Figure 2.11. Box plots showing the distribution of CSCI scores at urban sites in San Mateo County, WY 2012 – WY 2019. ..................................................................................................45

Figure 2.12. Distribution of CSCI scores at sites in four San Mateo County watersheds that were sampled during Pre-MRP and MRP time periods. ............................................................47

Figure 2.13. Comparison of CSCI scores for 16 sites in the San Francisquito Creek watershed with predicted model of CSCI scores based on developed landscapes (Beck et al. 2020) ........50

Figure 2.14. Total Nitrogen vs. CSCI and ASCI-H scores. Data grouped by strata: Pacific Ocean vs. San Francisco Bay ..............................................................................................................51

Figure 2.15. Total nitrogen concentrations measured at 90 bioassessment sites in San Mateo County sampled between WY 2012 and WY 2019. ...................................................................52

Figure 2.16. Protected areas in San Mateo County and bioassessment sites with scores in the top two condition categories for CSCI and ASCI-H. ..................................................................55

Figure 3.1. Continuous temperature and water quality stations in San Mateo County, WY 2014 – WY 2019. ..................................................................................................................................61


vii

Figure 3.2. Continuous temperature, continuous water quality, and bioassessment stations in the Tunitas Creek Watershed, San Mateo County, WY 2019. ...................................................63

Figure 3.3. Photo of Tunitas Creek approximately 0.15 mile upstream from the convergence with East Fork Tunitas Creek tributary (Photo by SMCWPPP field staff). ..................................64

Figure 3.4. Weekly average temperature values calculated for water temperature collected at five sites in Tunitas Creek over 24 weeks of monitoring in WY 2019. The MRP trigger (17°C) is shown for comparison. ..............................................................................................................67

Figure 3.5. Water temperature, shown as daily average, collected between April and September 2019 at five sites in Tunitas Creek, San Mateo County. ..........................................67

Figure 4.1. Pathogen indicator monitoring sites in WY 2014- WY 2019, San Mateo County. Exceedances of E. coli WQOs are shown in red. ......................................................................74

Figure 5.1 Chlorine sample stations and results WY 2012 – WY 2019 in San Mateo County. ...81

Figure 6.1 Pesticide and toxicity sampling locations in San Mateo County during WY 2014 through WY 2019. .....................................................................................................................85


viii

List of Tables

Table 1.1. Regional Monitoring Coalition participants. ................................................................ 3

Table 1.2. Monitoring parameters of MRP 2.0 Provisions C.8.d (Creek Status Monitoring) and C.8.g (Pesticides & Toxicity Monitoring) and associated monitoring component. ....................... 4

Table 1.3. Sites and parameters monitored in WY 2019 in San Mateo County........................... 7

Table 2.1. Condition categories used to evaluate CSCI, ASCI, and IPI scores. .........................20

Table 2.2. Physical habitat metrics used to assess physical habitat data collected at bioassessment sites in San Mateo County, WY 2012 through WY 2019. The five metrics used to calculate IPI scores are also shown. .....................................................................................21

Table 2.3. MRP trigger thresholds for nutrient and general water quality variables. ..................22

Table 2.4. Bioassessment sampling locations and dates in San Mateo County in WY 2019. ....25

Table 2.5. Biological condition scores, presented as CSCI and ASCI (diatom, soft algae and hybrid) for ten sites sampled in San Mateo during WY 2019. Site characteristics related to percent impervious watershed area and channel modification are also presented. Bold shaded values indicate scores in the two highest condition categories for each indicator. .....................26

Table 2.6. IPI scores for ten probabilistic sites sampled by SMCWPPP in WY 2019. Qualitative PHAB scores are also listed. CSCI and hybrid ASCI scores are provided for comparison. ......27

Table 2.7. General water quality measurements for ten bioassessment sites in San Mateo County sampled in WY 2019. ....................................................................................................29

Table 2.8. Nutrient and conventional constituent concentrations in water samples collected at ten sites in San Mateo County during WY 2019. Water quality objectives were not exceeded. See Table 2.1 for WQO values. ................................................................................................30

Table 2.9. Biological condition presented as CSCI and ASCI (hybrid) scores and Physical Habitat conditions presented as IPI Score and Total PHAB in Tunitas Creek. Bold values indicate scores in the top two condition categories. ...................................................................32

Table 2.10. Number of sites grouped by land use classification for each condition category for the four biological indicators. .....................................................................................................36

Table 2.11. Summary statistics for the CSCI random forest model. Ranking of most influential predictor variables are colored according to: physical habitat (green), land use (orange), and water quality (blue). ...................................................................................................................41

Table 2.12. Summary statistics for the ASCI-H random forest model. Ranking of most influential predictor variables are colored according to: land use (orange) and water quality (blue). ..........42

Table 2.13. Total number of bioassessment surveys conducted by SMCWPPP, grouped by watershed, during MRP and Pre-MRP. .....................................................................................46

Table 2.14. CSCI scores for bioassessments conducted at three San Mateo County sites during two different monitoring periods: Pre-MRP and MRP. ...............................................................48

Table 2.15. Location and ownership of bioassessment sites that had scores in the top two condition categories for CSCI and ASCI-H. ...............................................................................54

Table 3.1. Water Quality Objectives and trigger thresholds used for evaluation of continuous temperature and water quality data. ..........................................................................................57


ix

Table 3.2 Descriptive statistics for continuous water temperature measured from April 3 through September 18, 2019 at five sites in Tunitas Creek, San Mateo County. ....................................65

Table 3.3. Weekly average temperature values for water temperature data collected at five stations monitored in the Tunitas Creek Watershed, WY 2019. The MRP MWAT trigger is 17°C. .................................................................................................................................................66

Table 3.4. Descriptive statistics for continuous water temperature, dissolved oxygen, pH, and specific conductance measured at two Tunitas Creek sites in San Mateo County during WY 2019. Data were collected every 15 minutes over a two 2-week time periods during May (Event 1) and September (Event 2). .....................................................................................................68

Table 4.1. Bacteriological trigger thresholds and WQOs for water contact recreation in freshwater. ................................................................................................................................73

Table 4.2. Enterococci and E. coli levels measured in San Mateo County during WY 2018 and WY 2019 (August 1, 2019). .......................................................................................................76

Table 5.1. Summary of SMCWPPP chlorine testing results compared to MRP trigger of 0.1 mg/L, WY 2019. ........................................................................................................................79

Table 6.1. Summary of SMCWPPP dry weather water and sediment toxicity results, Pulgas Creek, WY 2019. .......................................................................................................................88

Table 6.2. Toxicity test result summary, WY 2014 – WY 2019, SMCWPPP. The Percent Effect is indicated for test results with toxicity relative to the lab control. Test results with toxicity exceeding the MRP 1.0 and MRP 2.0 trigger thresholds are shaded. .......................................90

Table 6.3. Threshold Effect Concentration (TEC) quotients for WY 2019 sediment chemistry constituents. Bolded and shaded values indicate TEC quotient ≥ 1.0. ......................................91

Table 6.4. Probable Effect Concentration (PEC) quotients for WY 2019 sediment chemistry constituents. Bolded and shaded values indicate PEC quotient ≥ 1.0. ......................................92

Table 6.5. Pesticide concentrations and calculated toxicity unit (TU) equivalents, WY 2019. ....93

Table 6.6. Summary of grain size for site 204PUL010 in San Mateo County, WY 2019. ...........94

Table 6.7. TU equivalent summary for San Mateo County sediment samples, WY 2014 – WY 2019. .........................................................................................................................................96

Table 7.1. Percent of sites in San Mateo County in each condition category, WY 2012 – WY 2019. ....................................................................................................................................... 103

Table 7.2. Summary of SMCWPPP MRP trigger threshold exceedance analysis, WY 2019. “No” indicates samples were collected but did not exceed the MRP trigger; “Yes” indicates an exceedance of the MRP trigger. .............................................................................................. 110


x

List of Attachments

Attachment 1. QA/QC Report

Attachment 2. Bioassessment Data, WY 2012 – WY 2019

Attachment 3. Technical Report: SMCWPPP Creek Status Bioassessment Monitoring 2012 Through 2019

Attachment 4. Trigger Exceedances WY 2014 – WY 2019


1

1.0 Introduction

This Integrated Monitoring Report (IMR) Part B: Creek Status Monitoring, Water Year3 (WY) 2014 through WY 2019 was prepared by the San Mateo Countywide Water Pollution Prevention Program (SMCWPPP or Program). SMCWPPP is a program of the City/County Association of Governments (C/CAG) of San Mateo County. Each incorporated city and town in the county and the County of San Mateo share a common National Pollutant Discharge Elimination System (NPDES) stormwater permit for Bay Area municipalities referred to as the Municipal Regional Permit (MRP). The MRP was first adopted by the San Francisco Regional Water Quality Control Board (SFRWQCB or Regional Water Board) on October 14, 2009 as Order R2-2009-0074 (SFRWQCB 2009; referred to as MRP 1.0). On November 19, 2015, the Regional Water Board updated and reissued the MRP as Order R2-2015-0049 (SFRWQCB 2015; referred to as MRP 2.0). This report fulfills the requirements of Provision C.8.h.v of MRP 2.0 for comprehensively interpreting and reporting all Creek Status and Pesticides & Toxicity monitoring data collected since the previous IMR. As such, this report includes data collected during WY 2014 through WY 2019.4 The previous IMR included data collected during WY 2012 and WY 2013 (SMCWPPP 2014). Data presented in this report from WY 2014 and WY 2015 were collected pursuant to water quality monitoring requirements in provisions C.8.c (Creek Status Monitoring) of MRP 1.0 and provisions C.8.d (Creek Status Monitoring) and C.8.g (Pesticides & Toxicity Monitoring) of MRP 2.0.5 Data presented in this report were submitted electronically to the Regional Water Board by SMCWPPP and may be obtained via the California Environmental Data Exchange Network (CEDEN).

Sections of this report are organized according to the following topics:

• Section 1.0 – Introduction including overview of the Program goals, background, monitoring approach, and statement of data quality

• Section 2.0 – Biological condition assessment and stressor analysis at probabilistic sites

• Section 3.0 – Continuous water quality monitoring (temperature, general water quality)

• Section 4.0 – Pathogen indicators

• Section 5.0 – Chlorine monitoring

• Section 6.0 – Pesticides & Toxicity monitoring

• Section 7.0 – Conclusions and recommendations

• Section 8.0 – Summary of stormwater management programs


September 30 of the named year. For example, Water Year 2019 (WY 2019) began on October 1, 2018 and concluded on September 30, 2019. 4 The exception is biological condition data, which are reported for the WY 2012 through WY 2019 period of record. 5 Monitoring data collected pursuant to other C.8 provisions (e.g., Pollutants of Concern Monitoring, Stressor/Source Identification Monitoring Projects) are reported in other Parts of the SMCWPPP Integrated Monitoring Report (IMR) for WY 2014 through WY 2019.


2

1.1 Monitoring Goals

Provision C.8.d of MRP 2.0 (and Provision C.8.c of MRP 1.0) requires Permittees to conduct creek status monitoring that is intended to answer the following management questions:

1. Are water quality objectives, both numeric and narrative, being met in local receiving waters, including creeks, rivers, and tributaries?

2. Are conditions in local receiving water supportive of or likely supportive of beneficial uses?

The first management question is addressed primarily through the evaluation of probabilistic and targeted monitoring data with respect to the triggers defined in the MRP. Sites where triggers are exceeded may indicate potential impacts to aquatic life or other beneficial uses and are considered for future evaluation via Stressor/Source identification (SSID) projects.

The second management question is addressed by assessing indicators of aquatic life beneficial uses, such as the indices of biological integrity based on benthic macroinvertebrate and algae data. Continuous monitoring data (temperature, dissolved oxygen, pH, and specific conductance) are evaluated with respect to COLD and WARM Beneficial Uses. Pathogen indicator data are used to assess REC-1 (water contact recreation) Beneficial Uses.

Creek Status and Pesticides & Toxicity monitoring parameters, methods, occurrences, durations and minimum number of sampling sites are described in Provisions C.8.d and C.8.g of the MRP, respectively. The monitoring requirements in the 2015 MRP (MRP 2.0) are similar to the 2009 MRP (MRP 1.0) requirements (which began implementation on October 1, 2011) and build upon earlier monitoring conducted by SMCWPPP. Creek Status and Pesticides & Toxicity monitoring is coordinated through the Bay Area Stormwater Agencies Association (BASMAA) Regional Monitoring Coalition (RMC). Monitoring results are evaluated to determine whether triggers are met, and further investigation is warranted as a potential SSID Project, as described in Provision C.8.e of the MRP. Results of Creek Status and Pesticides & Toxicity Monitoring conducted in Water Years 2012 through 2018 are summarized in this report and were detailed in prior reports (SMCWPPP 2019a, SMCWPPP 2018, SMCWPPP 2017, SMCWPPP 2016, SMCWPPP 2015, SMCWPPP 2014).

1.2 Regional Monitoring Coalition

Provision C.8.a (Compliance Options) of the MRP allows Permittees to address monitoring requirements through a regional collaborative effort, their Stormwater Program, and/or individually. The RMC was formed in early 2010 as a collaboration among a number of the BASMAA members and MRP Permittees (Table 1.1) to develop and implement a regionally coordinated water quality monitoring program to improve stormwater management in the region and address water quality monitoring required by the MRP6. Implementation of the RMC’s Creek Status and Long-Term Trends Monitoring Plan (BASMAA 2012) allows Permittees and

6 The Regional Water Board issued the first five-year MRP to 76 cities, counties and flood control districts (i.e., Permittees) in the Bay Area on October 14, 2009 (MRP 1.0; SFRWQCB 2009). On November 19, 2015, the Regional Water Board updated and reissued the MRP (MRP 2.0; SFRWQCB 2015). The BASMAA programs supporting MRP Regional Projects include all MRP Permittees as well as the cities of Antioch, Brentwood, and Oakley which are not named as Permittees under the MRP but have voluntarily elected to participate in MRP-related regional activities.


3

the Regional Water Board to improve their ability to collectively answer core management questions in a cost-effective and scientifically rigorous way. Participation in the RMC is facilitated through the BASMAA Monitoring and Pollutants of Concern (MPC) Committee.

Table 1.1. Regional Monitoring Coalition participants.

Stormwater Programs RMC Participants

Santa Clara Valley Urban Runoff Pollution Prevention Program (SCVURPPP)

Cities of Campbell, Cupertino, Los Altos, Milpitas, Monte Sereno, Mountain View, Palo Alto, San Jose, Santa Clara, Saratoga, Sunnyvale, Los Altos Hills, and Los Gatos; Santa Clara Valley Water District; and, Santa Clara County

Alameda Countywide Clean Water Program (ACCWP)

Cities of Alameda, Albany, Berkeley, Dublin, Emeryville, Fremont, Hayward, Livermore, Newark, Oakland, Piedmont, Pleasanton, San Leandro, and Union City; Alameda County; Alameda County Flood Control and Water Conservation District; and, Zone 7

Contra Costa Clean Water Program (CCCWP)

Cities of Antioch, Brentwood, Clayton, Concord, El Cerrito, Hercules, Lafayette, Martinez, Oakley, Orinda, Pinole, Pittsburg, Pleasant Hill, Richmond, San Pablo, San Ramon, Walnut Creek, Danville, and Moraga; Contra Costa County; and, Contra Costa County Flood Control and Water Conservation District

San Mateo County Wide Water Pollution Prevention Program (SMCWPPP)

Cities of Belmont, Brisbane, Burlingame, Daly City, East Palo Alto, Foster City, Half Moon Bay, Menlo Park, Millbrae, Pacifica, Redwood City, San Bruno, San Carlos, San Mateo, South San Francisco, Atherton, Colma, Hillsborough, Portola Valley, and Woodside; San Mateo County Flood Control District; and, San Mateo County

Fairfield-Suisun Urban Runoff Management Program (FSURMP)

Cities of Fairfield and Suisun City

Vallejo Permittees City of Vallejo and Vallejo Flood and Wastewater District

The goals of the RMC are to:

1. Assist Permittees in complying with requirements in MRP Provision C.8 (Water Quality Monitoring);

2. Develop and implement regionally consistent creek monitoring approaches and designs in the Bay Area, through the improved coordination among RMC participants and other agencies (e.g., Regional Water Board) that share common goals; and

3. Stabilize the costs of creek monitoring by reducing duplication of effort and streamlining reporting.

The RMC’s monitoring strategy for complying with Creek Status Monitoring is described in the RMC Creek Status and Long-Term Trends Monitoring Plan (BASMAA 2012). The strategy includes regional ambient/probabilistic monitoring and local “targeted” monitoring. The combination of these two components allows each individual RMC participating program to assess the status of beneficial uses in local creeks within its jurisdictional area, while also contributing data to answer management questions at the regional scale (e.g., differences between aquatic life condition in urban and non-urban creeks). The 2015 MRP (MRP 2.0) specifically prescribes the probabilistic/targeted approach and most of the other details of the RMC Creek Status and Long-Term Trends Monitoring Plan. Table 1.2 provides a list of which monitoring parameters are included in the probabilistic versus the targeted programs in MRP


4

2.0. This report includes data collected in San Mateo County under both monitoring components. Data are organized into report sections that reflect the format of monitoring requirements in the MRP.

Table 1.2. Monitoring parameters of MRP 2.0 Provisions C.8.d (Creek Status Monitoring) and C.8.g (Pesticides & Toxicity Monitoring) and associated monitoring component.

Monitoring Elements

Monitoring Component

Report Section

Regional Ambient

(Probabilistic)

Local (Targeted)

Creek Status Monitoring (C.8.d)

Bioassessment & Physical Habitat Assessment X (X)1 2.0

Nutrients X (X)1 2.0

General Water Quality (Continuous) X 3.0

Temperature (Continuous) X 3.0

Pathogen Indicators X 4.0

Chlorine X (X)2 5.0

Pesticides & Toxicity Monitoring (C.8.g)

Water Toxicity X 6.0

Water Chemistry X 6.0

Sediment Toxicity X 6.0

Sediment Chemistry X 6.0 Notes: 1 Provision C.8.d.i.(6) allows for up to 20% of sample locations to be selected on a targeted basis. 2 Provision C.8.d.ii.(2) provides options for probabilistic or targeted site selection. In WY 2012 - 2019, chlorine was measured at probabilistic sites.

1.3 Monitoring and Data Assessment Methods

1.3.1 Monitoring Methods

Water quality data were collected in accordance with California Surface Water Ambient Monitoring Program (SWAMP) comparable methods and procedures described in the BASMAA RMC Standard Operating Procedures (SOPs; BASMAA 2016b) and the associated Quality Assurance Project Plan (QAPP; BASMAA 2016a). These documents are updated as needed to stay current and optimize applicability. Where applicable, monitoring data were collected using methods comparable to those specified by the SWAMP Quality Assurance Program Plan (QAPrP)7, and were submitted in SWAMP-compatible format to the Regional Water Board. The SOPs were developed using a standard format that describes health and safety cautions and considerations, relevant training, site selection, and sampling methods/procedures, including

7The current SWAMP QAPrP is available at: https://www.waterboards.ca.gov/water_issues/programs/swamp/qapp/swamp_QAPrP_2017_Final.pdf


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pre-fieldwork mobilization activities to prepare equipment, sample collection, and de-mobilization activities to preserve and transport samples.

1.3.2 Laboratory Analysis Methods

RMC participants, including SMCWPPP, agreed to use the same laboratories for individual parameters (except pathogen indicators), developed standards for contracting with the labs, and coordinated quality assurance samples. All samples collected by RMC participants that were sent to laboratories for analysis were analyzed and reported per SWAMP-comparable methods as described in the RMC QAPP (BASMAA 2016a). Analytical laboratory methods, reporting limits and holding times for chemical water quality parameters are also described in BASMAA (2016a). Analytical laboratory contractors in WY 2019 included:

• BioAssessment Services, Inc. – Benthic macroinvertebrate (BMI) identification

• EcoAnalysts, Inc. – Algae identification

• CalTest, Inc. – Sediment chemistry, nutrients, chlorophyll a, ash free dry mass

• Pacific EcoRisk, Inc. – Water and sediment toxicity

• Alpha Analytical – Pathogen indicators

1.3.3 Data Analysis Methods

Monitoring data generated during WY 2014 through WY 2019 were analyzed and evaluated to identify potential stressors that may be contributing to degraded or impacted biological conditions, including exceedances of water quality objectives (WQOs). Creek Status Monitoring and Pesticides & Toxicity Monitoring data are evaluated with respect to numeric thresholds (i.e., triggers) specified in the MRP (SFRWQCB 2015, SFRWQCB 2009). Sites with monitoring data that do not meet WQOs and/or exceed MRP trigger thresholds require consideration for further evaluation as part of a Stressor/Source Identification project. SSID projects are intended to be oriented toward taking action(s) to alleviate stressors and reduce sources of pollutants. A stepwise process for conducting SSID projects is described in Provision C.8.e.iii of MRP 2.0.

In compliance with Provision C.8.e.i of MRP 2.0, all monitoring results exceeding trigger thresholds are added to a list of candidate SSID projects that will be maintained throughout the permit term. Follow-up SSID projects are selected from this list.

1.4 Setting

There are 34 watersheds in San Mateo County draining an area of about 450 square miles. The San Mateo Range, which runs north/south, divides the county roughly in half. The eastern half (“Bayside”) drains to San Francisco Bay and is characterized by relatively flat, urbanized areas along the Bay. To varying degrees, portions of all Bayside watersheds within the urban zone have been engineered or placed within underground culverts. The western half of the county (“coastside”) drains to the Pacific Ocean and consists of approximately 50 percent parkland and open space, with agriculture and relatively small urban areas.

The complete list of probabilistic and targeted monitoring sites sampled by SMCWPPP in WY 2019 in compliance with Provisions C.8.d (Creek Status Monitoring) and C.8.g (Pesticides and Toxicity Monitoring) is presented in Table 1.3. Probabilistic station numbers, generated from the RMC Sample Frame, are provided for all bioassessment locations. Targeted stations numbers,


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based on SWAMP station numbering methods (BASMAA 2016b), are provided for all targeted monitoring sites. Monitoring locations with monitoring parameter(s) from WY 2019 are mapped in Figure 1.1. Monitoring locations from WY 2014 through WY 2018 are mapped in the various parameter-specific sections of this report.


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Table 1.3. Sites and parameters monitored in WY 2019 in San Mateo County.

Map ID 1

Station ID Bayside

or Coastside

Watershed Creek Name Land Use

Latitude Longitude

Probabilistic Targeted

Bioassessment, Nutrients,

General WQ Chlorine

Pesticides & Toxicity

Temp 2 Contin-uous WQ 3

Pathogen Indicators

030 202TUN030 Coastal Tunitas Creek Tunitas Creek NU 37.37940 -122.3748 X X X X

040 202TUN040 Coastal Tunitas Creek Tunitas Creek NU 37.38847 -122.3709 X X X

005 204BEL005 Bayside Belmont Creek Belmont Creek U 37.51778 -122.26914 X X

4280 204R04280 Bayside Belmont Creek Belmont Creek U 37.45434 -122.20118 X X

4428 204R04428 Bayside Cordilleras Creek Cordilleras Creek U 37.55466 -122.35632 X X

4160 204R04160 Bayside Burlingame Creek Burlingame

Creek U 37.51480 -122.28340 X X

3635 204R03635 Bayside Atherton Creek Atherton Creek U 37.47975 -122.25986 X X

4600 204R04600 Bayside Atherton Creek Atherton Creek U 37.43671 -122.21467 X X

4056 205R04056 Bayside San Francisquito Cr Dry Creek U 37.43885 -122.26506 X X

5044 205R05044 Bayside San Francisquito Cr Dry Creek U 37.42803 -122.25148 X X

010 204PUL010 Bayside Pulgas Creek Pulgas Creek U 37.50195 -122.25238 X

138 202PES138 Coastside Pescadero Creek Pescadero Creek NU 37.27410 -122.28860 X

142 202PES142 Coastside Pescadero Creek McCormick

Creek NU 37.27757 -122.28635

X


150 202PES150 Coastside Pescadero Creek Jones Gulch NU 37.27424 -122.26811 X


5 202TUN005 Coastside Tunitas Creek Tunitas Creek NU 37.36202 -122.39062 X




U = urban, NU = non-urban 1 Map ID applies to Figure 1.1. 2 Temperature monitoring was conducted continuously (i.e., hourly) April through September. 3 Continuous water quality monitoring (temperature, dissolved oxygen, pH, specific conductivity) was conducted during two 2-week periods (spring and late summer).


8

Figure 1.1. Map of SMCWPPP sites monitored in WY 2019.


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1.4.1 Designated Beneficial Uses

Beneficial Uses in San Mateo County creeks are designated by the Regional Water Board for specific water bodies and serve as the basis for establishing WQOs designed to protect those uses (SFBRWQCB 2017). All creeks in San Mateo County, except a few coastal creeks, are designated as having warm freshwater habitat (WARM) beneficial uses. Nearly all coastal creeks and a few bayside creeks, such as San Mateo Creek and San Francisquito Creek, are designated as having cold freshwater habitat (COLD) beneficial uses, meaning they generally support trout, anadromous salmon, and/or steelhead fisheries. Dissolved oxygen WQOs are more stringent in creeks with COLD beneficial uses because these species are relatively intolerant to environmental stresses. Virtually all creeks in the region are designated as having water contact recreations (REC-1) beneficial uses such as swimming and wading where ingestion of water is considered reasonably possible. Fecal indicator bacteria WQOs are identified to protect REC-1 beneficial uses. Several coastal creeks, as well as Bear Gulch Creek and Crystal Springs Reservoir in the San Mateo Creek watershed, are designated as having municipal and domestic supply (MUN) beneficial uses, due to the presence of drinking water reservoirs and/or diversions for these purposes. The Basin Plan identifies WQOs for several constituents of concern that apply only to waters with MUN beneficial uses.

1.4.2 Climate

San Mateo County experiences a Mediterranean climate with cool, wet winters and hot, dry summers. The area is characterized by microclimates created by topography, ocean currents, fog exposure, and onshore winds. The wet season typically extends from October through April with local long-term, mean annual precipitation ranging from 20 inches near the Bay to over 40 inches along the highest ridges of the San Mateo Mountain Range (PRISM Climate Group 30-year normals, 1981-20108). Figure 1.2 illustrates the geographic variability of mean annual precipitation in the area. It is important to understand that mean annual precipitation depths are statistically calculated or modeled; actual measured precipitation each year rarely equals the statistical average. Figure 1.3 illustrates the temporal variability in annual precipitation measured at the San Francisco International Airport (SFO) from WY 1946 to WY 2019. This record illustrates that extended periods of drought are common and often punctuated by above average years. Creek Status Monitoring in compliance with the MRP began in WY 2012 which was the first year of a severe statewide drought that persisted through WY 2016. WY 2018 rainfall was below average at SFO, but it was preceded by a wet year in WY 2017.

The overall Bay Area climate and the specific conditions within any given year are influenced by global climate change. The Climate Change Assessment report for the Bay Area highlights several impacts of climate change that are already being felt: the Bay Area’s average annual maximum temperature increased by nearly 1°C from 1950 – 2005, coastal fog along the coast may be less frequent, and sea level in the Bay Area has risen over 8 inches (Ackerly et al. 2018). These changes are projected to increase significantly in the coming decades. As a consequence, heat extremes, high year-to-year variability in precipitation, droughts, intense storms, and other events will likely also increase.

Climate patterns (e.g., extended droughts) and individual weather events (e.g., extreme storms, hot summers) influence biological communities (i.e., vegetation, wildlife) and their surrounding physical habitat and water quality. They should therefore be considered when evaluating the

8 http://www.prism.oregonstate.edu/normals/


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type of data collected by the Creek Status Monitoring Program. For example, periods of drought (rather than individual dry years) can result in changes in riparian and upland vegetation communities. Long drought periods are associated with increased streambed sedimentation, which can persist directly or indirectly for many years, depending on the occurrence and magnitude of flushing flow events. Furthermore, in response to prolonged drought, the relative proportion of pool habitat can increase at the expense of riffle habitat.

It is uncertain what effect these factors have on indices of biotic integrity (IBIs) that are calculated using data collected by the Creek Status Monitoring Program, such as benthic macroinvertebrates or algae. A study evaluating 20 years of bioassessment data collected in northern California showed that, although benthic macroinvertebrate taxa with certain traits may be affected by dry (and wet) years and/or warm (and cool) years, IBIs based on these organisms appear to be resilient (Mazor et al. 2009, Lawrence et al. 2010). However, this study did not specifically examine the impact of longer periods of extended drought or heat on IBIs, which would require analysis of a dataset with a much longer period of record. The Herbst Lab at the Sierra Nevada Aquatic Research Laboratory, University of California Santa Barbara recently completed a study exploring how flooding and droughts vary taxa metrics in the Sierra Nevada streams. While species diversity and density remained relatively unchanged during flooding, extreme dry weather conditions significantly impacted benthic macroinvertebrate population structure. These differences were exacerbated with continued exposure to drought (Herbst et al. 2019). Similar changes to the benthic macroinvertebrate community in San Mateo County streams may have occurred during the WY 2012 – WY 2016 drought but have not been evaluated.


11

Figure 1.2. Average annual precipitation in San Mateo County, modeled by the PRISM Climate Group for the period of 1981-2010.


12

Figure 1.3. Annual rainfall recorded at the San Francisco International Airport, WY 1946 – WY 2019.

1.5 Statement of Data Quality

A comprehensive Quality Assurance/Quality Control (QA/QC) program was implemented by SMCWPPP covering all aspects of the probabilistic and targeted monitoring. In general QA/QC procedures were implemented as specified in the BASMAA RMC QAPP (BASMAA 2016a), which was adapted from the methods detailed by the SWAMP QAPrP9. The QAPP was revised twice –in 2014 and again in 2016 – to conform to changes in the MRP reissuance and changes made to the SWAMP QAPrP. The revisions were minor, and overall methods and protocols remain similar. Each year’s monitoring data were compared against objectives in the governing QAPP. Monitoring was performed according to protocols specified in the BASMAA RMC SOPs (BASMAA 2016b), which were also revised with the QAPP.

Overall, the results of the QA/QC reviews suggest that the Creek Status Monitoring data generated during WY 2012 – WY 2019 were of sufficient quality for the purposes of this evaluation. Some data were flagged in accordance with QA/QC protocols, but none were rejected. A detailed QA/QC report for WY 2019 data is included as Attachment 1. Detailed QA/QC reports for monitoring data collected during past water years are included with previous reports (SMCWPPP 2019a, SMCWPPP 2018, SMCWPPP 2017, SMCWPPP 2016, SMCWPPP 2015, SMCWPPP 2014).

9 The current SWAMP QAPrP is available at: http://www.waterboards.ca.gov/water_issues/programs/swamp/docs/qapp/swamp_qapp_master090108a.pdf

0

5

10

15

20

25

30

35

40

45

1945 1950 1955 1960 1965 1970 1975 1980 1985 1990 1995 2000 2005 2010 2015

An

nu

al P

reci

pit

atio

n (

in.)

Water Year

Average annual precipitation = 19.5 in.MRP (WY 2012 - WY 2019)

2020

http://www.waterboards.ca.gov/water_issues/programs/swamp/docs/qapp/swamp_qapp_master090108a.pdf


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2.0 Biological Condition Assessment

2.1 Introduction

In compliance with Creek Status Monitoring Provisions C.8.c of MRP 1.0 and C.8.d.i of MRP 2.0, SMCWPPP has conducted bioassessment monitoring since WY 2012. Nearly all bioassessment monitoring has been performed at sites selected randomly using the probabilistic monitoring design. The probabilistic monitoring design allows each individual RMC participating program to objectively assess stream ecosystem conditions within its program area (i.e., county jurisdictional area) while contributing data to answer regional management questions about water quality and beneficial use condition in San Francisco Bay Area creeks. The survey design provides an unbiased framework for condition assessment of ambient aquatic life uses within known estimates of precision. The monitoring design was developed to address management questions for RMC participating counties and the overall RMC area:

1. What is the condition of aquatic life in creeks in the RMC area; are water quality objectives met and are beneficial uses supported?

i. What is the condition of aquatic life in the urbanized portion of the RMC area; are water quality objectives met and are beneficial uses supported?

ii. What is the condition of aquatic life in RMC participant counties; are water quality objectives met and are beneficial uses supported?

iii. To what extent does the condition of aquatic life in urban and non-urban creeks differ in the RMC area?

iv. To what extent does the condition of aquatic life in urban and non-urban creeks differ in each of the RMC participating counties?

2. What are major stressors to aquatic life in the RMC area?

i. What are major stressors to aquatic life in the urbanized portion of the RMC area?

3. What are the long-term trends in water quality in creeks over time? The first question (i.e., What is the condition of aquatic life in creeks in the RMC?) is addressed by assessing indicators of aquatic biological health at probabilistic sampling locations. Once a sufficient number of samples have been collected, ambient biological condition can be estimated for streams at a regional (or countywide) scale. Over the past eight years (WY 2012 through WY 2019), SMCWPPP and the Regional Water Board have sampled 90 probabilistic sites in San Mateo County, providing a sufficient sample size to estimate ambient biological condition for urban and non-urban streams countywide. There is still an insufficient number of samples to accurately assess the biological condition of individual watersheds or smaller jurisdictional areas (i.e., cities).10

The second question (i.e., What are major stressors to aquatic life in the RMC area?) is addressed by evaluation of physical habitat and water chemistry data collected at the probabilistic sites, as potential stressors to biological health. The stressor levels can be

10 For each of the strata, it is necessary to obtain a sample size of at least 30 in order to evaluate the condition of

aquatic life within known estimates of precision. This estimate is defined by a power curve from a binomial distribution (BASMAA 2012).


14

compared to biological indicator data through correlation and random forest models. Assessing the extent and relative importance of stressors in predicting biological condition can help prioritize stressors at a regional scale and inform local management decisions.

The third question (i.e., What are the long-term trends in water quality in creeks over time?) is addressed by assessing the change in biological condition over several years. Changes in biological condition over time can help evaluate the effectiveness of management actions. Although, long-term trend analysis for the RMC probabilistic survey will require more than eight years of data collection, preliminary trend analysis of biological condition may be possible for some stream reaches using a combination of historical targeted data with the probabilistic data.

MRP 2.0 allows for up to 20% of bioassessment surveys at targeted sites to address other types of management questions, provided a statistically representative dataset (i.e., 30 samples) has already been collected. Under MRP 2.0, SMCWPPP conducted bioassessment surveys at three targeted sites. Two sites on Tunitas Creek were selected to follow-up on previous monitoring results that suggested lower than expected biological health (202TUN030 and 202TUN040). One site on Belmont Creek provides baseline data in a creek where a multi-benefit project is planned (204BEL005).

This section of the report presents bioassessment results from WY 2019, as well as a comprehensive evaluation of the probabilistic bioassessment data collected in San Mateo County from WY 2012 through WY 2019. In addition, in compliance with Provision C.8.d.i.(8) of MRP 2.0, WY 2019 data are compared to triggers and WQOs identified in the MRP. Sites with results exceeding trigger thresholds are added to the list of candidate SSID projects.

2.2 Methods

2.2.1 Probabilistic Survey Design

The RMC probabilistic design was created using the Generalized Random Tessellation Stratified (GRTS) approach developed by the United States Environmental Protection Agency (USEPA) and Oregon State University (Stevens and Olsen 2004). GRTS offers multiple benefits for coordinating among monitoring entities, including the ability to develop a spatially balanced design that produces statistically representative data with known confidence intervals. The GRTS approach has been implemented in California by several organizations including the statewide Perennial Streams Assessment (PSA) conducted by Surface Water Ambient Monitoring Program (Ode et al. 2011) and the Southern California Stormwater Monitoring Coalition’s (SMC) regional monitoring program conducted by municipal stormwater programs in Southern California (SCCWRP 2007).

Sample sites were selected using the GRTS approach from a sample frame consisting of a creek network geographic information system (GIS) data set within the 3,407-square mile RMC area (BASMAA 2012). The sample frame includes non-tidally influenced perennial and non-perennial creeks within five management units representing areas managed by the stormwater programs associated with the RMC (listed in Table 1.1). There is approximately one site for every stream kilometer in the sample frame. The National Hydrography Plus Dataset (1:100,000) was selected as the creek network data layer to provide consistency with both the Statewide PSA and the SMC, and the opportunity for future data coordination with these programs.


15

Once the master draw was performed, the list of sites was classified by county and land use (i.e., urban and non-urban) to allow for comparisons between these strata. Urban areas were delineated by combining urban area boundaries and city boundaries defined by the U.S. Census (2000). Non-urban areas were defined as the remainder of the RMC area. Some sites classified as urban fall near the non-urban edge of the city boundaries and have little upstream development. For consistency, these urban sites were not re-classified. Therefore, data values within the urban classification represent a wide range of conditions.

The RMC participants weight their annual sampling efforts so that approximately 80% are in in urban areas and 20% in non-urban areas. In addition, between WY 2012 and WY 2015, SWAMP conducted 34 bioassessments throughout the RMC region at non-urban sites selected from the sample frame, including 10 sites in San Mateo County11.

2.2.2 Site Evaluations

Sites identified in the regional sample draw are evaluated by each RMC participant in chronological order using the process described in RMC Standard Operating Procedure FS-12 (BASMAA 2016a) which is consistent with the procedure described by Southern California Coastal Water Research Project (SCCWRP 2012). Each site is evaluated to determine if it meets RMC sampling location criteria (e.g., not tidally influenced, sufficient flow, safe accessibility, landowner permission to access site). Site evaluation information is stored in a database and analyzed to determine the statistical significance of local and regional average ambient conditions calculated from the multi-year dataset.

2.2.3 Field Sampling Methods

Bioassessment survey methods were consistent with the BASMAA RMC QAPP (BASMAA 2016a) and SOPs (BASMAA 2016b). In accordance with the RMC QAPP (BASMAA 2016a) bioassessments were planned during the spring index period (approximately April 15 – July 15) with the goal to sample a minimum of 30 days after any significant storm (defined as at least 0.5-inch of rainfall within a 24-hour period). The 30-day grace period allows diatom and soft algae communities to recover from peak flows that may scour benthic algae from the bottom of the stream channel.12

Over the eight-year monitoring period, one or two small but significant storms (i.e., 0.5 inches in 24-hour period) typically occurred during the first two weeks of April. Generally, bioassessment sampling was conducted 20-30 days following the storm event to allow recovery time for the algal community. However, due to drought or below average rainfall conditions that characterized the WY 2012 – WY 2016 period, bioassessments at selected sites (i.e., small streams less impacted by storm) were conducted approximately 10 days after the significant storm event to ensure that flow would still be present. During WY 2019, a significant storm occurred late in the season (May 20, 2019), after bioassessment fieldwork had commenced. Bioassessment sampling was paused until June 3, 2019 (approximately two weeks after the storm). All algae data collected prior to the 30-day grace period were flagged.

11 SFRWQCB SWAMP staff stated that they will not conduct RMC related bioassessment monitoring during MRP 2.0. 12 The BASMAA 30-day grace period is more conservative than the 21-day grace period described in the SWAMP SOP (Ode et al. 2016).


16

Each bioassessment sampling site consisted of an approximately 150-meter stream reach that was divided into 11 equidistant transects placed perpendicular to the direction of flow. Benthic macroinvertebrate (BMI) and algae samples were collected at 11 evenly spaced transects using the Reachwide Benthos (RWB) method described in the SWAMP SOP (Ode et al. 2016). The most recent SWAMP SOP (i.e., Ode et al. 2016) combines the BMI and algae methods referenced in the MRP (Ode et al. 2007, Fetscher et al. 2009), provides additional guidance, and adds two new physical habitat analytes (assess scour and engineered channels). The full suite of physical habitat data was collected within the sample reach using methods described in Ode et al. (2016).

Immediately prior to biological and physical habitat data collection, water samples were collected for nutrients, conventional analytes, ash free dry mass, and chlorophyll a analysis using the Standard Grab Sample Collection Method as described in SOP FS-2 (BASMAA 2016b). Water samples were also collected and analyzed in the field for free and total chlorine using a Pocket ColorimeterTM II and DPD Powder Pillows according to SOP FS-3 (BASMAA 2016b) (see Section 5.0 for chlorine monitoring results). In addition, general water quality parameters (dissolved oxygen, pH, specific conductivity and temperature) were measured at or near the centroid of the stream flow using a pre-calibrated multi-parameter probe.

Biological and water samples were sent to laboratories for analysis. The laboratory analytical methods used for BMIs followed Woodard et al. (2012), using the Southwest Association of Freshwater Invertebrate Taxonomists (SAFIT) Level 1 Standard Taxonomic Level of Effort, with the additional effort of identifying chironomids (midges) to subfamily/tribe instead of family (Chironomidae). Soft algae and diatom samples were analyzed following SWAMP protocols (Stancheva et al. 2015). The taxonomic resolution for all data was compared SWAMP master taxonomic list. All BMI and algal taxa identified in samples collected over the eight-year monitoring period were consistent with the taxa listed on the SWAMP Master List, which was then included in the data submittal each year.

2.2.4 Data Analysis

Biological condition indicator data and stressor data for all bioassessment sites surveyed in WY 2012 – WY 2019 were compiled into a master spreadsheet for data analyses. The master spreadsheet is included with this report as Attachment 2. BMI and algae data were analyzed to assess the biological condition (i.e., aquatic life Beneficial Uses) of the sampled reaches using condition index scores. Physical habitat data were used to assess biological condition and were evaluated as potential stressors. Water chemistry data were evaluated as potential stressors to biological health using triggers and WQOs identified in the MRP (see Stressor Variable section below). Data analysis methods for biological indicators and stressors are described below.

2.2.4.1 Biological Indicators

Benthic Macroinvertebrates

The benthic (i.e., bottom-dwelling) macroinvertebrates collected through this monitoring program are organisms that live on, under, and around the rocks and sediment in the stream bed. Examples include dragonfly and stonefly larvae, snails, worms, and beetles (Figure 2.1). Each BMI species has a unique response to water chemistry and physical habitat condition. Some are relatively sensitive to poor habitat and pollution; others are more tolerant. Therefore, the abundance and variety of BMIs in a stream is an indicator of the biological condition of the stream.


17

The California Stream Condition Index (CSCI) is an assessment tool that was developed by the State Water Resources Control Board (State Water Board) to support the development of California’s statewide Biological Integrity Plan13. The CSCI translates benthic macroinvertebrate data into an overall measure of stream health. The CSCI was developed using a large reference data set that represents the full range of natural conditions in California and site-specific models for predicting biological communities. The CSCI combines two types of indices: 1) taxonomic completeness, as measured by the ratio of observed-to-expected taxa (O/E); and 2) ecological structure and function, measured as a predictive multimetric index (pMMI) that is based on reference conditions. The CSCI score is computed as the average of the sum of the O/E and pMMI.

Figure 2.1. Examples of benthic macroinvertebrates.

CSCI scores for each station are calculated using a combination of biological and environmental data following methods described in Rehn et al. (2015). Biological data consist of the BMI data collected and analyzed using the protocols described in the previous section. Environmental predictor data are generated in GIS using drainage areas upstream of each BMI sampling location. The environmental predictors and BMI data were formatted into comma delimited files and used as input for the RStudio statistical package and the necessary CSCI program scripts,

13 The Biological Integrity Assessment Implementation Plan has been combined with the Biostimulatory Substances

Amendment project. The State Water Board is proposing to adopt statewide WQOs for biostimulatory substances (e.g., nitrate) in freshwater along with a program of implementation. A draft policy document for public review is anticipated in late 2019.


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developed by Southern California Coastal Water Research Project (SCCWRP) staff (Mazor et al. 2016).

The State Water Board is continuing to evaluate the performance of CSCI in a regulatory context. In Provision C.8.d of MRP 2.0, the Regional Water Board defines a CSCI score of 0.795 as a trigger threshold for identifying sites with potentially degraded biological condition that may be considered as candidates for a Stressor/Source Identification project.

Benthic Algae

Similar to BMI’s, the abundance and type of benthic algae species living on a streambed are an indicator of stream health. When evaluated with the CSCI, biological indices based on benthic algae can provide a more complete picture of the stream’s biological condition because algae respond more directly to nutrients and water chemistry. In contrast, BMIs are more responsive to physical habitat. Figure 2.2 shows examples of benthic algae common in Bay Area streams.

Figure 2.2. Examples of soft algae and diatoms.


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The State Water Board and SCCWRP recently developed the draft Algae Stream Condition Index (ASCI) which uses benthic algae data as a measure of biological condition for streams in California (Theroux et al. in prep.). The ASCI includes both predictive14 and non-predictive multimetric indices (MMI) used to evaluate ecological conditions. There are three versions of the ASCI MMI: an index for diatoms, one for soft-bodied algae and a hybrid index using both assemblages. Using a statewide data set, all three indices were evaluated by the State Water Board for precision, accuracy, responsiveness, and regional bias. The hybrid ASCI was found to be the most sensitive to anthropogenic stressor gradients, emphasizing the value of combining multiple algal assemblages (diatoms and soft bodied algae) to provide a more comprehensive assessment of biological condition (Theroux et al. in prep).

Additional study is needed to determine the best approach to apply the ASCI tools to evaluate bioassessment data. For example, it is not clear if the ASCI should be used as a second line of evidence to understand CSCI scoring results, or if it would be more effective as an independent indicator to evaluate different types of stressors (e.g., nutrients) to which BMIs are not very responsive. The ASCI is currently under review by the Biostimulatory-Biointegrity Policy Science Advisory Panel and the State Water Board.

The algae data collected at 90 bioassessment sites in San Mateo County between 2012 and 2019 were evaluated using the three ASCI MMIs (diatom, soft algae and hybrid). ASCI scores were generated using the beta version reporting module developed by SCCWRP. These scores are considered provisional until the ASCI has been fully evaluated and finalized.

2.2.4.2 Physical Habitat Indicators

The condition of physical habitat is a major contributor to stream ecosystem health. Physical habitat components such as streambed substrate, channel morphology, microhabitat complexity, in-stream cover-type complexity, and riparian vegetation cover contribute to the overall physical and biological integrity of a stream. The physical characteristics of a stream reach are affected by both natural factors (e.g., climate, slope, geology) and human disturbance (e.g., channelization, development, stream crossings, hydromodification). Physical habitat conditions are generally evaluated using endpoint variables, or metrics, which are calculated using reach-scale averages of transect-based measurements and observations. The State Water Board has developed a SWAMP Bioassessment Reporting Module (SWAMP RM), a custom Microsoft AccessTM application, that produces approximately 170 different metrics that are based on physical habitat measurements collected using both USEPA’s Environmental Monitoring and Assessment Program (EMAP) for freshwater wadeable streams (Kaufmann et al. 1999) and the SWAMP “Full” habitat protocol (Ode et al 2007) that was implemented by SMCWPPP at bioassessment stations. The metrics are classified into five thematic groups representing different physical attributes: substrate, riparian vegetation (including structure and shading), flow habitat variability, in-channel cover, and channel morphology.

14 Predictive indices utilize environmental variables that characterize immutable natural gradients as predictors for biological conditions. A predictive algal O/E model was developed and tested, but ultimately not recommended due to low precision and accuracy. Predictive metrics were used for both diatom and hybrid MMIs, but the soft body algae MMI did not incorporate predictive metrics.


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The State Water Board recently developed the Index of Physical Habitat Integrity (IPI) as an overall measure of physical habitat condition. Similar to the CSCI, the IPI is calculated using a combination of physical habitat data collected in the field and environmental data generated in GIS following the methods described in Rehn et al. (2018). The IPI is based on five of the metrics generated by the SWAMP RM. The metrics were selected for their ability to discriminate between reference and stressed sites and provide unbiased representation of waterbodies across the different ecoregions of California. Scoring for these metrics were then calibrated using environmental variables that were associated with drainage areas for each sampling location. 2.2.4.3 Biological and Physical Habitat Condition Thresholds

Existing thresholds for CSCI scores (Mazor 2015) and ASCI scores (Theroux et al. in prep) were used to evaluate the BMI and algae data collected in San Mateo County and analyzed in this report (Table 2.1). Provisional thresholds for IPI scores (Rehn et al. 2018) were used to evaluate physical habitat conditions. The thresholds for all three indices were based on the distribution of scores for data collected at reference calibration sites located throughout California. Four condition categories are defined by these thresholds: “likely intact” (greater than 30th percentile of reference site scores); “possibly intact” (between the 10th and the 30th percentiles); “likely altered” (between the 1st and 10th percentiles); and “very likely altered” (less than the 1st percentile). A CSCI score below 0.795 is referenced in MRP 2.0 as a threshold indicating a potentially degraded biological community, and thus should be considered for a SSID Project. The MRP threshold is at the division between the “possibly intact” and “likely altered” condition categories described in Mazor (2015). Further investigation is needed to evaluate the applicability of this threshold to sites in highly urban watersheds and/or modified channels that are common throughout the Bay Area.

Table 2.1. Condition categories used to evaluate CSCI, ASCI, and IPI scores.

Biological Indicator

Tool Likely Intact Possibly Intact Likely Altered Very Likely

Altered

BMI CSCI > 0.92 > 0.79 to < 0.92 > 0.63 to < 0.79 < 0.63

Diatoms

ASCI

> 0.92 > 0.81 to < 0.92 > 0.66 to < 0.81 < 0.66

Soft Algae > 0.92 > 0.80 to < 0.92 > 0.65 to < 0.80 < 0.65

Hybrid > 0.95 > 0.88 to < 0.95 > 0.78 to < 0.88 < 0.78

Physical Habitat IPI > 0.94 > 0.84 to < 0.94 > 0.71 to < 0.83 < 0.70

2.2.4.4 Stressor Variables

Physical habitat, landscape characteristics, general water quality, and water chemistry data collected during the bioassessment surveys were compiled and evaluated as potential stressor variables affecting biological condition.


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Physical habitat stressor variables include 11 of the metrics developed by the SWAMP RM (described above) that were selected based on their ability to discriminate between reference and stressed sites and also showed little bias among ecoregions (Andy Rehn, personal communication, 2017) (Table 2.2). Additional physical habitat variables include the reachwide qualitative assessment (PHAB) that consists of three separate attributes: channel alteration, epifaunal substrate, and sediment deposition. Each attribute is individually scored on a scale of 0 to 20, with a score of 20 representing good condition. The total PHAB score is the sum of three individual attribute scores with a score of 60 representing the highest possible score.

Table 2.2. Physical habitat metrics used to assess physical habitat data collected at bioassessment sites in San Mateo County, WY 2012 through WY 2019. The five metrics used to calculate IPI scores are also shown.

Type Variable Name Variables used

for IPI Score

Channel Morphology Evenness of Flow Habitat Types x

Percent Fast Water of Reach

Habitat Complexity and Cover

Mean Filamentous Algae Cover

Natural Shelter cover - SWAMP

Shannon Diversity (H) of Aquatic Habitat Types x

Riparian Cover Sum of Three Layers x

Human Disturbance Combined Riparian Human Disturbance Index - SWAMP

Substrate Size and Composition

Evenness of Natural Substrate Types

Percent Gravel - coarse

Percent Substrate Smaller than Sand (<2 mm) x

Shannon Diversity (H) of Natural Substrate Types x

Landscape variables were generated in GIS using three different scales of drainage area upstream of each sampling location: 1 km, 5 km, and entire watershed. Land use and transportation data were overlaid with the drainage areas to calculate landscape variables, including percent urban area, percent impervious area, total number of road crossings, and road density.

Water quality stressor variables include the general parameters measured in the field (i.e., dissolved oxygen, pH, temperature and specific conductivity, free chlorine and total chlorine residual) and water chemistry analyzed at laboratories (nutrients and anions). Additional water quality variables included chlorophyll a and ash free dry mass, both measured from filtration of the benthic algae composite samples.

Some of the water quality stressor variables used in the analysis were calculated or converted from other analytes or units of measurement:

• Conversion of measured total ammonia to the more toxic form of unionized ammonia was calculated to compare with the 0.025 mg/L annual median standard provided in the San Francisco Basin Water Quality Control Plan (Basin Plan) (SFRWQCB 2017). The conversion was based on a formula provided by the American Fisheries Society (AFS; https://fisheries.org/wp-content/uploads/2016/03/Copy-of-pub_ammonia_fwc.xls). The


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calculation requires total ammonia and field-measured values of pH, temperature, and specific conductance.

• Total nitrogen concentration was calculated by summing nitrate, nitrite, and Total Kjeldahl Nitrogen concentrations.

• The volumetric concentrations (mass/volume) for ash free dry mass and chlorophyll a (as measured by the laboratory) were converted to an area concentration (mass/area). Calculations required using both algae sampling grab size and composite volume.

Another potential stressor is climate. During the first five years of probabilistic sampling (WY 2012 – WY 2016), annual precipitation was lower than average. The drought ended with an above average wet season in WY 2017, followed by an average season in WY 2018 and above average in WY 2019. Comparison of sampling results from wet and dry years will provide useful information to evaluate the impacts of drought on biological integrity of the streams.

2.2.4.5 Trigger Thresholds

In compliance with Provision C.8.h.iii.(4) of MRP 2.0, water chemistry data collected at the bioassessment sites during WY 2019 were compared to MRP trigger thresholds and applicable water quality standards (Table 2.3). Thresholds for pH, specific conductance, dissolved oxygen (DO), and temperature (for waters with COLD Beneficial Use only) are listed in Provision C.8.d.iv of MRP 2.0. Except for temperature and specific conductance, these conform to WQOs in the Basin Plan (SFRWQCB 2017). Of the eleven nutrients analyzed synoptically with bioassessments, WQOs only exist for three: ammonia (unionized form), and chloride and nitrate (for waters with MUN Beneficial Use only).

Table 2.3. MRP trigger thresholds for nutrient and general water quality variables.

Units Threshold Direction Source

Nutrients and Ions

Nitrate as N a mg/L 10 Increase Basin Plan

Un-ionized Ammonia b mg/L 0.025 Increase Basin Plan

Chloride a mg/L 250 Increase Basin Plan

General Water Quality

Oxygen, Dissolved mg/L 5.0 or 7.0 Decrease Basin Plan

pH 6.5 and 8.5 Basin Plan

Temperature, instantaneous maximum c °C 24 Increase MRP

Specific Conductance c µS/cm 2000 Increase MRP a Nitrate and chloride WQOs only apply to waters with MUN designated Beneficial Uses. b This threshold is an annual median value and is not typically applied to individual samples. c The MRP thresholds (or triggers) for temperature and specific conductance apply when 20 percent of instantaneous results are in exceedance. Application to individual samples is provisional.


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2.2.4.6 Stressor Assessment

The association of stressors (physical habitat, landscape, chemistry) with biological indicator scores (CSCI and ASCI) was evaluated using eight years (WY 2012 – 2019) of bioassessment data collected in San Mateo County. Spearman’s rank correlation analyses and random forest statistical models were applied to the dataset. A summary of these analyses is provided in Section 2.3.2. A technical report detailing the methods and results is provided in Attachment 3.

2.2.4.7 SCAPE Modeling to Assess CSCI Scores

Biological conditions, based on CSCI scores, for the 90 bioassessment sampling locations in San Mateo County were compared to a landscape model developed for streams in California that estimates ranges of likely scores for CSCI scores based on the level of landscape alteration contributing to the sampling reach (Beck et al. 2020). The landscape model was created using data from StreamCat, which is a national dataset that includes attributes characterizing watershed development (Hill at al. 2015).

The predictive model was developed to support management decisions, such as identifying reaches for restoration or enhanced protection based on how observed scores relate to the model expectation. It has been integrated into a publicly available web-based application called the Stream Classification and Priority Explorer (SCAPE). The SCAPE tool can be used to compare measured/calculated CSCI scores with the predictive scores produced by the model (https://sccwrp.shinyapps.io/scape/). The SCAPE model was obtained from SCCWRP as a GIS shapefile. Stream/channel attributes in the shapefile include stream classifications using three thresholds for CSCI (1st, 10th, and 30th percentile of reference sites) and a prediction interval (ranging from the 10th to the 90th percentiles of the quantile predictions). There are four possible stream classifications in the model: “likely unconstrained”, “possibly constrained”, “possibly unconstrained” and “likely unconstrained”. The model predicts a range of CSCI scores for each stream reach and an expected median score. Observed CSCI scores at a site are compared to the model expectations and characterized as over-scoring, expected or under-scoring. See section 2.4.1 for application of the SCAPE model to CSCI scores at bioassessment sites in San Mateo County.

https://sccwrp.shinyapps.io/scape/


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2.3 Results and Discussion

The results for bioassessment monitoring in WY 2019, as well as the previous eight years (WY 2012 – WY 2019), are presented in the section below.

• Section 2.3.1 presents results of biological assessments conducted at ten sites in San Mateo County during WY 2019. The bioassessment monitoring conducted at the three targeted sites is described in greater detail.

• Section 2.3.2 provides an overall summary of biological conditions (CSCI and ASCI scores) and exceedances of stressor thresholds for the 90 bioassessment sites sampled in San Mateo County between WY 2012 and WY 2019. The association between biological conditions and stressor data (land use, water chemistry, physical habitat) for 87 probabilistic sites is evaluated using statistical analyses. The evaluation of bioassessment data is consistent with the approach used in the RMC 5-year Bioassessment Report (BASMAA 2019). A complete description of methods and results for the countywide analysis of bioassessment data is provided in Attachment 3.

• Section 2.3.3 presents a comparison of historical BMI data (Pre-MRP; WY 2002 – WY 2007) and BMI data collected in compliance with the MRP (WY 2012 – WY 2019), based on CSCI scores, as one approach using existing data sources to evaluate trends in biological conditions. The Program conducted bioassessments in four watersheds in San Mateo County between 2002 and 2007.

• Section 2.4 provides potential approaches to consider for future monitoring design to address requirements for Provision C.8 of the next MRP (i.e., MRP 3.0) that is currently under development and will likely become effective in WY 2022. One approach would be to conduct targeted monitoring at specific watershed/reaches that have identified water quality problems or reduced biological conditions and high potential to mitigate stressor impacts through management actions. Another approach is to identify healthy stream reaches and focus efforts on protecting those resources.

Conclusions and recommendations for this section are presented in Section 7.0.


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2.3.1 Bioassessment Results (WY 2019)

This section documents the biological condition and stressor data collected in WY 2019. In WY 2019, the Program conducted bioassessments at seven probabilistic sites and three targeted sites in San Mateo County15. The WY 2019 bioassessment sites are listed in Table 2.4 and mapped in Figure 2.3. The probabilistic sites were derived from the RMC Sample Frame. Two of the targeted sites were selected to follow-up on previous monitoring results that suggested lower than expected biological health. The remaining targeted site provides baseline data in a creek where a multi-benefit project is planned. Targeted monitoring results are provided below in Section 2.3.1.3.

Table 2.4. Bioassessment sampling locations and dates in San Mateo County in WY 2019.

Station Code

Drainage Area

Creek Name Land Use

Sample Date

Latitude Longitude Targeted Probabilistic

202TUN030 Pacific Ocean

Tunitas Creek NU 14-May-19 37.37940 -122.37483 x

202TUN040 Tunitas Creek NU 14-May-19 37.38840 -122.37090 x

204BEL005

San Francisco Bay

Belmont Creek U 13-May-19 37.51770 -122.26910 x

204R03635 Atherton Creek U 10-Jun-19 37.51522 -122.28230 x

204R04160 Burlingame Creek U 15-May-19 37.47890 -122.26126 x

204R04280 Belmont Creek U 13-May-19 37.55342 -122.35830 x

204R04428 Cordilleras Creek U 15-May-19 37.45430 -122.20001 x

204R04600 Atherton Creek U 10-Jun-19 37.43610 -122.21560 x

205R04056 Dry Creek U 11-Jun-19 37.43903 -122.26530 x

205R05044 Dry Creek U 11-Jun-19 37.42820 -122.25179 x

Land use: NU = non-urban, U = urban

2.3.1.1 Biological and Physical Habitat Conditions (WY 2019)

Biological condition for the ten sites sampled by SMCWPPP in WY 2019, as represented by CSCI and ASCI (diatom, soft algae, and hybrid) scores, is shown in Table 2.5. Physical habitat condition, as represented by IPI Scores, is shown in Table 2.6. Scores in the two highest condition categories (i.e., above the 10th percentile of reference sites) for each indicator are shown in shaded cells with bold text.

CSCI Scores The CSCI scores ranged from 0.46 to 0.94 across the ten bioassessment sites sampled in WY 2019 (Table 2.5). Two of ten (20%) of the sites had CSCI scores in the two highest condition categories: “possibly intact” and “likely intact”. These classifications are above the MRP trigger threshold value of 0.795. Both sites were located in Tunitas Creek, which is a relatively undeveloped watershed that drains to the Pacific Ocean (Figure 2.3). See Section 2.3.1.3 for more information on Tunitas Creek bioassessment monitoring.

15 MRP 2.0 allows for up to 20% of bioassessment locations (2 sites) to be targeted each year. Prior to WY 2019, targeted sites were not selected for monitoring. During WY 2019, bioassessments were conducted at three targeted sites.


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The remaining eight sites had CSCI scores in the “very likely altered” condition category (<0.63). These sites are located in developed (13% - 41% impervious area) watersheds draining to San Francisco Bay. Sites with CSCI scores below 0.795 will be considered as candidates for SSID projects.

Table 2.5. Biological condition scores, presented as CSCI and ASCI (diatom, soft algae and hybrid) for ten sites sampled in San Mateo during WY 2019. Site characteristics related to percent impervious watershed area and channel modification are also presented. Bold shaded values indicate scores in the two highest condition categories for each indicator.

Station Code

Creek Impervious Watershed Area (%)

Modified Channel

CSCI Score

ASCI Score

Diatom Soft

Algae Hybrid

202TUN030 Tunitas Creek 1% N 0.84 0.79 0.37 0.82

202TUN040 Tunitas Creek 1% N 0.94 0.61 0.54 0.78

204BEL005 Belmont Creek 41% Y 0.46 0.63 0.81 0.67

204R03635 Atherton Creek 24% Y 0.49 0.35 0.55 0.46

204R04160 Burlingame Creek 37% N 0.51 0.66 0.81 0.72

204R04280 Belmont Creek 39% N 0.55 0.43 1.07 0.86

204R04428 Cordilleras Creek 19% N 0.60 0.85 0.81 0.73

204R04600 Atherton Creek 24% N 0.53 0.57 1.28 0.67

205R04056 Dry Creek 14% N 0.49 0.58 0.37 0.63

205R05044 Dry Creek 13% N 0.48 0.47 1.28 0.51 1 Highly modified channel is defined as having armored bed and banks (e.g., concrete, gabion, rip rap) for majority of the reach or characterized as highly channelized earthen levee.

ASCI Scores

The benthic algae taxa identified in the samples collected in San Mateo County were used to calculate scores for the provisional statewide ASCI. Scores for three ASCI indices (diatoms, soft algae and hybrid) are shown in Table 2.5. In general, ASCI scores were lower for the diatom and hybrid indices compared to the soft algae index. Only one site in Cordilleras Creek (204R04428) scored in either of the top two condition categories for either index. In contrast, six of the ten sites had scores in top two condition categories for the soft algae index. There is no MRP trigger for any of the ASCI index scores.

IPI Scores

Physical habitat conditions, as represented by IPI scores, are listed in Table 2.6 along with CSCI scores and hybrid ASCI scores. The top two condition categories for all three indices (i.e., above the 10th percentile of reference sites) are shown in shaded cells with bold text. Six of the ten sites had IPI scores that were in the top two condition categories (> 0.83). Interestingly, IPI scores for the two sites on Tunitas Creek were two condition categories apart, despite their proximity (Figure 2.3). Differences in IPI scores between the two sites appear to be mostly associated with differences in the amount of riparian canopy cover.


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The qualitative habitat (PHAB) scores, including individual scores for channelization, epifaunal substrate and sedimentation attributes16, and total PHAB (sum of the three attributes scores) are also presented in Table 2.6. Total PHAB scores ranged from 16 to 47 (total possible is 60). In contrast to IPI scores, the total PHAB scores were nearly identical for the two sites on Tunitas Creek. Biological condition scores for CSCI and the hybrid ASCI are included in the table for comparison.

Table 2.6. IPI scores for ten probabilistic sites sampled by SMCWPPP in WY 2019. Qualitative PHAB scores are also listed. CSCI and hybrid ASCI scores are provided for comparison.

Station Code Creek CSCI Score

ASCI Hybrid

IPI Score

Channel Alteration

Epifaunal Substrate

Sediment Deposition

Total PHAB Score

202TUN030 Tunitas Creek 0.84 0.82 1.09 19 17 11 47

202TUN040 Tunitas Creek 0.94 0.78 0.77 20 17 9 46

204BEL005 Belmont Creek 0.46 0.67 0.63 9 5 4 18

204R03635 Atherton Creek 0.49 0.46 0.49 0 1 15 16

204R04160 Burlingame Creek 0.51 0.72 0.92 13 11 6 30

204R04280 Belmont Creek 0.55 0.86 0.84 6 8 7 21

204R04428 Cordilleras Creek 0.60 0.73 0.84 10 9 6 25

204R04600 Atherton Creek 0.53 0.67 0.40 2 1 16 19

205R04056 Dry Creek 0.49 0.63 0.92 15 14 9 38

205R05044 Dry Creek 0.48 0.51 0.87 15 10 6 31

Overall Condition

The condition categories for each site based on two of the biological indicators (CSCI and hybrid ASCI) and physical habitat indicator (IPI) are listed in Table 2.6 and mapped in Figure 2.3. There were no WY 2019 sites that received scores in the top two condition categories for all three indicators (CSCI, ASCI, and IPI). Both of the targeted sites on Tunitas Creek had CSCI scores in the top two condition categories (≥ 0.795); however, only one of the sites was in the top two condition categories for the other indices; 202TUN030 had an IPI score in the highest category (Table 2.6, Figure 2.3). The remaining eight WY 2019 bioassessment sites were located in urban watersheds draining to San Francisco Bay and had CSCI and ASCI hybrid scores that were in the lower two condition categories.

16 Channelization is measure of extent of reach that is armored/modified; Epifaunal substrate is measure of quantity and quality of physical habitat features (substrate, wood) that provides structure for colonization of biological communities; Sedimentation is a measure of the amount of sediment that has accumulated in the reach.


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Figure 2.3. Condition category as represented by CSCI, hybrid ASCI and IPI Scores for ten bioassessment sites sampled in San Mateo County in WY 2019.


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2.3.1.2 Stressor Assessment (WY 2019)

This section presents results for stressor data collected at the ten bioassessment sites in WY 2019. The comparison of WY 2019 stressor data to associated MRP triggers and/or WQOs is also documented for the purposes of maintaining the list of sites with trigger exceedances for SSID project consideration.

General Water Quality

General water quality measurements sampled at the ten bioassessment sites in WY 2019 are listed in Table 2.7. The MRP trigger threshold for specific conductance was exceeded at site 204R03635 in Atherton Creek. No other triggers or WQOs were exceeded.

Table 2.7. General water quality measurements for ten bioassessment sites in San Mateo County sampled in WY 2019.

Station Code Creek Name Temp

(C) DO

(mg/L) pH

Specific Conductance

(uS/cm)

202TUN030 Tunitas Creek 12.7 9.8 8.3 756

202TUN040 Tunitas Creek 11.9 10.3 8.3 601

204BEL005 Belmont Creek 16.9 10.7 8.3 1224

204R03635 Atherton Creek 20.4 11.5 8.4 2113a

204R04160 Burlingame Creek 13.7 7.2 8.2 1160

204R04280 Belmont Creek 13.3 7.7 8.0 1343

204R04428 Cordilleras Creek 14.7 8.2 8.2 918

204R04600 Atherton Creek 17.4 10.1 8.1 2102

205R04056 Dry Creek 17.0 7.1 7.9 799

205R05044 Dry Creek 20.2 6.7 7.9 722

a. The MRP trigger of 2000 µS/cm was exceeded at this site.

Water Chemistry (Nutrients)

Nutrient and conventional analyte concentrations measured in water samples collected at ten bioassessment sites in San Mateo County during WY 2019 are listed in Table 2.8. Water quality objectives were not exceeded for water chemistry parameters. Total nitrogen concentrations ranged from 0.2 to 1.2 mg/L. The two highest total nitrogen concentrations (1.2 mg/L) were measured at the two sites on Atherton Creek. Total phosphorus concentrations ranged from 0.04 to 0.18 mg/L. The two highest total phosphorus concentrations (0.17 and 0.18 mg/L) were also measured at two sites on Atherton Creek.

Chlorophyll a and ash free dry mass are two indicators of biomass. The highest concentration of chlorophyll a (260 mg/m2) occurred at the upper elevation site on Tunitas Creek (site 202TUN040). In contrast, the lowest concentration of chlorophyll a (10 mg/m2) occurred at the lower elevation site on Tunitas Creek (site 202TUN030). Similar discrepancies in chlorophyll a concentrations were observed between the two sites on Atherton Creek.


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Table 2.8. Nutrient and conventional constituent concentrations in water samples collected at ten sites in San Mateo County during WY 2019. Water quality objectives were not exceeded. See Table 2.1 for WQO values.

Station Code

Creek

Ammonia as N

Unionized Ammonia

(as N) Chloride AFDM

Chlorophyll a

Nitrate as N

Nitrite as N

Total Kjeldahl Nitrogen

As N

Total Nitrogen

Ortho-phosphate

as P

Phosphorus as P

Silica as

SiO2

mg/L mg/L mg/L g/m2 mg/m2 mg/L mg/L mg/L mg/L mg/L mg/L mg/L

WQO: NA 0.025 b 250 a NA NA 10 a NA NA NA NA NA NA

202TUN030 Tunitas Creek 0.28 0.010 72 55 10 0.07 0.002 J 0.08 J 0.2 0.04 0.05 22

202TUN040 Tunitas Creek 0.22 0.008 69 143 260 < 0.02 0.001 J 0.14 0.2 0.04 0.04 22

204BEL005 Belmont Creek 0.24 0.013 140 111 53 0.33 0.004 J 0.52 0.9 0.06 0.07 20

204R03635 Atherton Creek 0.11 0.009 220 145 220 0.5 0.005 0.66 1.2 0.15 0.18 24

204R04160 Burlingame Creek 0.15 0.005 60 390 132 0.15 < 0.001 0.69 0.8 0.13 0.16 47

204R04280 Belmont Creek 0.39 0.009 130 41 59 0.33 0.002 J 0.52 0.9 0.04 0.05 20

204R04428 Cordilleras Creek 0.08 0.003 58 83 49 0.18 0.004 J 0.44 0.6 0.04 0.05 20

204R04600 Atherton Creek 0.13 0.004 170 51 10 0.53 0.004 J 0.63 1.2 0.15 0.17 30

205R04056 Dry Creek 0.11 0.003 43 87 51 0.23 0.004 J 0.3 0.5 0.07 0.08 30

205R05044 Dry Creek 0.08 0.003 58 117 37 0.16 0.013 0.47 0.6 0.09 0.11 18

Number of exceedances NA 0 0 NA NA 0 NA NA NA NA NA NA

NA = Not Applicable J = The reported result is an estimate. a Chloride and nitrate WQOs only apply to waters with MUN designated Beneficial Uses. b This threshold is an annual median value and is not typically applied to individual samples.


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2.3.1.3 Targeted Sites (WY 2019)

Tunitas Creek

The Tunitas Creek watershed was targeted for bioassessment in WY 2019 to follow-up on prior CSCI scores that were relatively low for an undeveloped watershed. The SCAPE model (discussed in Section 2.2.4.7) classifies Tunitas Creek as a “possibly unconstrained” channel with CSCI scores expected to range between 0.55 and 1.05 and median expected scores of 0.83 to 1.02. However, in WY 2013, a CSCI score of 0.51 was calculated for a station on the Dry Creek tributary to Tunitas Creek (202R00268) and in WY 2016, a CSCI score of 0.55 was calculated for a station in the upper watershed (202R00488) (Figure 2.4). Both scores are in the very likely altered condition category. One hypothesis for this result is that lower than expected scores at site 202R00488 were the result of land uses in the watershed that are not well captured by the SCAPE model (i.e., timber growing/harvesting, ranching, and road crossings). The Tunitas Creek watershed was also targeted in WY 2019 for continuous temperature and water quality monitoring (see Section 3.4.1).

Site Selection

In WY 2019, the Program targeted two sites along the mainstem of Tunitas Creek for bioassessment sampling.

• 202TUN030. Station 202R00488 was resampled and was given a new station code (202TUN030) to distinguish the results from the probabilistic dataset. It was assumed that the CSCI score from WY 2019 at 202TUN030 would be similar to the CSCI score from WY 2016 at 202R00488 (0.55). This assumption was based on another assumption that there were no changes in land uses between WY 2016 and WY 2019. The BMI communities upon which CSCI scores are based are generally responsive to land use and resilient to year-to-year changes in precipitation.

• 202TUN040. The second station (202TUN040) was located approximately 0.7 miles upstream of 202TUN030. This location is 0.25 miles downstream of Station 202R00376, which was sampled in WY 2015 and had a CSCI score of 0.92. Station 202TUN040 is also just downstream of Rings Gulch, a relatively small (150-acre) catchment area with cattle ranching land uses. This location was intended to provide higher resolution of the CSCI score gradient along Tunitas Creek and to test whether drainage from Rings Gulch was responsible for low CSCI scores observed in Tunitas Creek at station 202R00488 / 202TUN030.

Results

Biological condition at sites in the Tunitas Creek watershed, as represented by CSCI and ASCI (hybrid) scores, are listed in Table 2.9. Physical habitat conditions, as represented as IPI Scores and Total PHAB Score (channel alteration, epifaunal substrate, and sediment deposition), are also listed in Table 2.9. Scores in the top two condition categories (likely intact and possibly intact) for each predictive indicator are shown with bold text. See Table 2.1 for condition category thresholds.


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Table 2.9. Biological condition presented as CSCI and ASCI (hybrid) scores and Physical Habitat conditions presented as IPI Score and Total PHAB in Tunitas Creek. Bold values indicate scores in the top two condition categories.

Physical Habitat

Station Code Year CSCI Score

ASCI Hybrid

IPI Score

Channel Alteration

(0 – 20)

Epifaunal Substrate

(0 – 20)

Sediment Deposition

(0 – 20)

Total PHAB Score

(0 – 60)

202R00376 2015 0.92 0.69 --1 18 15 16 49

202TUN040 2019 0.94 0.78 0.77 20 17 9 46

202R00488 / 202TUN030

2016 0.55 0.75 1.04 20 17 9 46

2019 0.84 0.82 1.09 19 17 11 47

202R00268 2013 0.51 0.99 --1 17 10 7 34 1 Riparian data required for the calculation of IPI Scores was not collected prior to 2016.

CSCI Scores

The CSCI score in WY 2019 was 0.84 (possibly intact condition category), which is within the range predicted by the SCAPE model. This CSCI score is almost 0.3 points higher than CSCI score at same location sampled during WY 2016. The reason for the change in score between the two years is uncertain. There are no known changes in land use. For example, no large developments were constructed during this time period. However, there could have been changes in land management practices on ranches, private forests, and open space areas in the watershed. A large portion of the upper watershed is within the County Timberland Preserve Zone (TPZ; see Figure 2.4). Timber growing and harvesting uses can be permitted in the TPZ area.

Another possible explanation for the change in CSCI scores is a change in drought condition. Water Year 2016, when the score of 0.55 was observed, was the fifth year of an extended dry period that began in WY 2012 and ended with a very wet year in WY 2017. Although the CSCI tool is reported to be resilient to drought conditions (Mazor et al. 2009), it may be more susceptible to wet and dry years in some types of watersheds, such as Tunitas Creek which is perennial with large areas of protected open space, privately-owned redwood forests (TPZ areas), and small ranching operations (Figure 2.4).

These results suggest that there may be year-to-year variability in CSCI scores. Additional bioassessment monitoring over time at this site may provide additional insights into overall variability in conditions and/or changes in stressor levels.

ASCI Scores

A comparison of the ASCI hybrid scores for WY 2016 and WY 2019 at Station 202TUN030 also show variation. Scores for this index increased from 0.75 in WY 2016 to 0.82 in WY 2019.

Physical Habitat Scores

In contrast to CSCI and ASCI scores, there was very little change in any of the physical habitat measures between WY 2016 and WY 2019 at Station 202TUN030. IPI scores in both years were above 0.94 and thus in the likely intact condition category. The qualitative habitat (PHAB)


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scores were also similar between the two years. Channel alteration and epifaunal substrate were both in the optimal range; however, sediment deposition (9 in WY 2016 and 11 in WY 2019) indicated marginal performance.

Figure 2.4. Bioassessments stations in the Tunitas Creek Watershed, San Mateo County, WY 2015 – WY 2019.


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Belmont Creek

The purpose for conducting targeted bioassessment sampling on Belmont Creek was to establish baseline data on biological and physical habitat conditions prior to potential construction of a flood control project. The San Mateo County Flood and Sea Level Rise Resiliency Agency is coordinating with the Cities of Belmont and San Carlos on the Belmont Creek Watershed Management Plan (Watershed Plan) to address flooding issues in lower Belmont Creek (creek reach between Old County Road and Industrial Road) (Erika Powell, San Mateo County PWD, personal communication, 2019). In addition to flood protection, the Watershed Plan would include measures to reduce impacts to the channel (e.g., prevent erosion in upstream reaches), incorporate green infrastructure and improve water quality.

A flood by-pass channel, just upstream of Old County Road, is one of the alternatives being considered for the Watershed Plan. The targeted sampling location was in the reach that historically floods, just downstream of Old County Road (Figure 2.5). Figure 2.5 shows the sampling location, CSCI score, and additional bioassessment monitoring data collected by SMCWPPP at three probabilistic sites in Belmont Creek from WY 2012 to WY 2019.

Bioassessments were conducted at the targeted monitoring site in Belmont Creek on May 13, 2019 and results are presented in Sections 2.3.1.1 and 2.3.1.2. Additional bioassessment monitoring at the targeted site may occur in the future, following the completion of different components of the Watershed Plan.

Figure 2.5. Bioassessments stations in the Belmont Creek Watershed, San Mateo County, WY 2012 – WY 2019.


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2.3.2 Evaluation of Countywide Bioassessment Results (WY 2012 – WY 2019)

This section addresses the first two bioassessment monitoring management questions presented at the beginning of Section 2.0 by summarizing WY 2012 – WY 2019 biological condition data and comparing condition scores to synoptically-collected stressor data.

The first bioassessment monitoring management question (i.e., What is the condition of aquatic life in creeks in the RMC?) is addressed by assessing indicators of aquatic biological health at probabilistic sampling locations. As part of the RMC Bioassessment Monitoring Program, 90 sites were sampled by SMCWPPP (n=80) and SWAMP (n=10) in San Mateo County from WY 2012 to WY 2019. SMCWPPP sampled 65 urban and 15 non-urban sites and SWAMP sampled 10 non-urban sites. All monitoring sites were derived from the RMC sample frame, except for three targeted sites that were sampled during WY 2019.

The bioassessment data collected at probabilistic sites (n=87) were evaluated to assess the current condition of water bodies in the County and to identify stressors that are likely to pose the greatest risk to the health of those streams. The methods used to evaluate bioassessment data are consistent with the approach used to develop the RMC Five-Year Bioassessment Report (BASMAA 2019). A detailed description of the methods and results used for the countywide analysis of bioassessment data is provided in Attachment 3. A summary of the methods and results is presented below.

The overall extent of biological conditions in San Mateo County streams was estimated using cumulative distribution functions (CDFs). CDF sample weights were calculated as the total stream length in the RMC sample frame divided by the stream length evaluated in each land use category (urban and non-urban). The adjusted sample weights were used to estimate the proportion of stream length (average and 95% confidence interval) represented by CSCI and hybrid ASCI scores for all SMCWPPP sites combined, as well as urban and non-urban sites only. All calculations were conducted using the R-package spsurvey (Kincaid and Olsen 2016).

The second bioassessment monitoring management question (i.e., What are major stressors to aquatic life in the RMC area?) is addressed by evaluation of physical habitat, land use, and water chemistry data collected at the probabilistic sites, as potential stressors to biological health. Potential stressors to biological condition were evaluated using Spearman’s rank correlation analysis and random forest statistical models. The stressor variable list consisted of 50 quantitative environmental variables, related to water quality, physical habitat, and land use factors that could potentially influence biological condition scores. In addition, two categorical factors (Land Use and Strata) were used to evaluate condition scores for different types of streams. Watershed drainage location (Pacific Ocean versus San Francisco Bay) was used as the strata to evaluate regional differences in biological conditions.


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2.3.2.1 Site Evaluations

A total of 514 randomly selected monitoring sites were sampled in the RMC region between 2012 and 2019. Eighty-seven of these sites (17%) were located in San Mateo County, representing a total stream length of 91.6 km. Sixty-four of the sites in San Mateo County (74%) were from urban streams and channels.

A total of 250 sites were initially evaluated to obtain the 87 sites that were ultimately sampled. This equates to a rejection rate of about 65%, which can largely be attributed to sites corresponding to areas that were not sampleable, e.g. due to lack of flow, lack of creek, tidal influence, or accessibility. As of the beginning of WY 2020, there are 500 sites remaining in the RMC sample draw for San Mateo County. The majority of these sites correspond to the non-urban portion of the County (472 of 500 sites). Assuming a rejection rate of 65%, the sample draw will likely only be sufficient to provide new sites through WY 2020.

2.3.2.2 Countywide Biological Conditions (WY 2012 – WY 2019)

A summary of CSCI and three ASCI index scores from bioassessment sites in San Mateo County sampled between 2012 and 2019 is presented in Table 2.10. A total of 60 of the 90 (67%) bioassessment sites (including three targeted sites) received CSCI scores below the MRP trigger of 0.795, which corresponds to the two lower condition categories (likely altered and very likely altered) (Table 2.10 and Figure 2.6). Fifty-five of the 60 sites with CSCI scores below 0.795 were in streams classified as urban. As a reminder, urban areas were delineated by combining urban area boundaries and city boundaries defined by the U.S. Census (2000). Non-urban areas were defined as the remainder of the RMC area. Some sites classified as urban fall near the non-urban edge of the city boundaries and have little upstream development.

Table 2.10. Number of sites grouped by land use classification for each condition category for the four biological indicators.

Index1 Sites with

index score2 Land Use

Likely Intact

Possibly Altered

Likely Altered Very Likely

Altered

CSCI 90 Urban 6 4 8 47

Non-Urban 13 7 2 3

ASCI-D 90 Urban 7 7 12 39

Non-Urban 10 7 5 3

ASCI-SB 85 Urban 29 16 5 10

Non-Urban 15 5 3 2

ASCI-H 85 Urban 1 5 8 47

Non-Urban 10 3 9 3 1 Indices: CSCI = California Stream Condition Index; ASCI-D = Diatom Algae Stream Condition Index; ASCI-SB = Soft Bodied Algae Stream Condition Index; ASCI-H = Hybrid Algae Stream Condition Index. 2 Index scores for ASCI-SB and ASCI-H were not calculated for 5 bioassessment sites due to insufficient soft algae taxa needed to calculate score.


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Figure 2.6. Biological conditions categories for CSCI at 90 bioassessment sites in San Mateo County sampled between WY 2012 and WY 2019.


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The number of sites receiving scores within the lower two condition categories (likely altered and very likely altered) for the diatom and hybrid ASCI indices was similar to the CSCI index, with 59 and 67 sites, respectively. The number of sites in the two lower condition categories for the soft-bodied algae ASCI was substantially less (20) than the other three indices (Table 2.10). There were proportionally more urban sites that had ASCI-SB index scores in the two higher condition categories (75%) compared to the other three indices (ranged 10-23%), indicating that the soft-bodied algae index may not respond well to urban disturbance gradients.

Figure 2.7 shows the proportion of scores in each condition category for the four indicators (CSCI, ASCI-D, ASCI-SB, ASCI-H) for two strata: creeks flowing to the Pacific Ocean and creeks flowing to San Francisco Bay. Overall, the proportion of sites with good biological condition (likely intact) was higher in creeks draining in the Pacific Ocean compared to sites located in watersheds draining into the San Francisco Bay. Within each of the strata, CSCI, ASCI-D, and ASCI-H indicator scores showed a high degree of similarity. In contrast, the soft-bodied algae index scores (ASCI-SB) had a higher proportion of sites in the highest condition category (likely intact). The contrast is particularly evident in creeks flowing to San Francisco Bay. This is consistent with the observation described above that the ASCI-SB may not respond well to urban disturbance gradients, as a greater number of the bayside creek sites are urban.

Figure 2.7. All San Mateo County biological condition scores (WY 2012 – WY 2019) grouped by condition category and strata (n=90, including 3 targeted WY 2019 sites)


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Figure 2.8 includes four plots showing cumulative distribution functions for the four biological condition indicators (CSCI, ASCI-D, ASCI-SB, and ASCI-H). Each plot shows CDFs for urban sites (n=64) all sites (n=8717), and non-urban sites (n=23). The CDFs can be used to determine the probability that a random observation will be less than or equal to a certain value. The dashed lines show the uncertainty around these estimates. For example, there is a 48% probability that a random site in San Mateo County will have a CSCI score below 0.79 (i.e., likely altered or very likely altered). There is an 86% probability that a random urban site in San Mateo County will have a CSCI score below 0.79 and there is a 22% probability that a random non-urban site will have a CSCI score below 0.79.

The CDFs for the various indicators are shaped differently illustrating the differences in scores in the San Mateo County dataset. The responsiveness or lack of responsiveness of the indicator to urban land uses is illustrated by the separation of the “urban” line from the “all sites” and “non-urban” lines. The CSCI CDFs have the greatest degree of separation between the urban line and the others; whereas, the ASCI-SB CDFs have the smallest degree of separation. As stated previously, the ASCI-SB may not respond to urban disturbance gradients in San Mateo County streams.

17 CDFs were developed using only the 87 probabilistic sites.


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Figure 2.8. Cumulative distribution functions of CSCI, ASCI-D, ASCI-H and ASCI-SB scores in San Mateo County (n = 87)


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2.3.2.3 Stressor Association with Biological Conditions (WY 2012 – WY 2019)

To evaluate the association of stressors with biological condition, Spearman’s rank correlation analyses and random forest modeling was performed. The correlation was conducted first to prune the list of 50 potential stressor variables. Nineteen of the 50 variables exhibited a statistically significant correlation with at least one of the four biological indices (CSCI, ASCI-D, ASCI-SB, ASCI-H). Many of the correlated variables were representative of land use (i.e., road density, road crossings, impervious and urban area). Three variables were related to water quality (Total Nitrogen, Total Kjeldahl Nitrogen, and temperature); and four variables were representative of physical habitat (filamentous algae cover, channel alteration, epifaunal substrate and combined human disturbance index).

The prioritized list of 19 variables was used in random forest model development for CSCI and ASCI-H. Random forest model results indicated better relationships between stressors and CSCI scores compared to stressors and ASCI-H scores. The random forest models showed that 64% of the variability in CSCI scores could be explained with eight predictor (stressor) variables; whereas, only 43% of the variability ASCI-H scores could be explained with seven predictors.

The CSCI random forest model indicated that land use and physical habitat variables were most influential in predicting biological condition as expressed by CSCI scores (Table 2.11). The ASCI-H random forest model indicated that land use and water quality variables were most influential in predicting biological condition as expressed by ASCI-H scores (Table 2.12). The percent impervious and percent urban in 5k area were top stressor variables for CSCI and ASCI-H, respectively (see Section 2.3.2.4 for more discussion on urban influence on biological conditions).

Table 2.11. Summary statistics for the CSCI random forest model. Ranking of most influential predictor variables are colored according to: physical habitat (green), land use (orange), and water quality (blue).

Stressor Variable % Increase

MSE Increase Node

Purity

Percent Impervious Area in 5km (PctImp_5K) 15.8 0.81

Road Density in 1 km (RdDen_1K) 15.5 0.72

Total Nitrogen (TotalN) 15.2 0.33

Road Density in Watershed (RdDen_W) 15.1 0.43

Percent Impervious Areas of Reach (PctImp) 14.1 0.44

Total Kjeldahl Nitrogen (TKN) 12.1 0.32

Epifaunal Substrate Score 12.1 0.36

Percent Urban in 5 km (PctUrb_5K) 10.9 0.39


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Table 2.12. Summary statistics for the ASCI-H random forest model. Ranking of most influential predictor variables are colored according to: land use (orange) and water quality (blue).

Stressor Variable % Increase MSE Increase Node Purity

Percent Urban Area in 5 km (PctUrb_5K) 15.5 0.39



Road Crossings in 5 km (RdCrs_5K) 11.7 0.23

Temperature (Temp) 11.6 0.28



The stressor analysis of biological condition in San Mateo County streams using BMIs (CSCI scores) and algae (ASCI scores) has shown that both types of assemblages correlate with landscape factors, as well as unique sets of water quality and habitat stressors. It should be acknowledged that despite these apparent relationships to stressors, these analyses do not determine causation, particularly as stressors from habitat/landscape factors are often present at the same sites that exhibit water quality impairment.

Comparison of San Mateo Stressor Assessment to RMC Regional Assessment

The analyses of stressor data association for San Mateo bioassessment sites was similar to findings in the RMC Five-Year Bioassessment Report (BASMAA 2019). Regional biological condition, based on CSCI scores, was strongly influenced by physical habitat variables and land use within the vicinity of the site. The percent of the land area within a 5 km radius of a site that is impervious appears to have the largest influence on CSCI scores based on the regional random forest model results. Regional biological condition, based on an algae diatom index, was moderately correlated with water quality variables, and less associated with physical habitat or landscape variables (note: the algae index used for the RMC report was different than the ASCI index used in this report). However, the RMC report showed that nutrient variables (e.g., nitrate, total nitrogen, orthophosphate, phosphorus) did not correlate strongly with CSCI scores in the Bay Area, nor were nutrients ranked as important variables explaining CSCI scores via the random forest model. These results are in contrast to the San Mateo stressor data analyses, which found that total nitrogen and TKN were significantly correlated to CSCI scores.


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2.3.2.4 Biological Conditions in Urban Landscapes

The previous section shows that biological indicator scores appear highly sensitive to urbanization. The correlation and random forest analyses of stressor association with biological conditions indicated that percent impervious area has the greatest influence on both CSCI and ASCI-H scores. The relationship is strongest for sites in the San Francisco Bay strata (Figure 2.9).

Figure 2.9. Percent Impervious Area in 5 km vs. CSCI and ASCI-H scores. Data grouped by strata: Pacific Ocean vs. San Francisco Bay

The distribution of CSCI and ASCI-H scores for the 90 bioassessment sites in San Mateo County, presented as box plots, is shown for three classes of urbanization, represented by percent watershed imperviousness (Figure 2.10). For highly developed watersheds (>10% impervious area), the median CSCI score was below 0.5 and all but three sites were below the MRP trigger (0.795). The variability in CSCI scores was lower at sites in highly developed watersheds, indicating that BMI communities are consistently impacted by stressors associated with urbanization.


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Sites with a moderate level of urbanization (3-10%) had a median CSCI score of about 0.75. These sited ranged 0.5 to 1.2 in CSCI scores, indicating a much wider range of stressor impacts associated with urbanization (and other factors). Sites with a low level of urbanization (<3% impervious area) were generally in good biological conditions, with a median CSCI score of about 0.83. However, there were some low scoring sites, indicating non-urban stressors were impacting those sites.

The ASCI hybrid scores showed very similar patterns with regards to three classes of urbanization (Figure 2.10). A wider range in scores at sites in highly developed watersheds may indicate a wider range of responses to urban stressors (i.e., algae may have less sensitivity to habitat modification).

Figure 2.10. Box plots showing the distribution of CSCI and ASCI-H scores for 90 bioassessment sites in San Mateo County, grouped by three classes of percent watershed imperviousness.

2.3.3 Trend Analysis

This section addresses the third bioassessment monitoring management question presented at the beginning of Section 2.0 (i.e., What are the long-term trends in water quality in creeks over time?) by assessing the change in biological condition over several years. Changes in biological condition over time can help evaluate the effectiveness of management actions. For example, control measures, such as green stormwater infrastructure (GSI), implemented to disconnect impervious areas, should result in improved stream condition and water quality. Many of these measures are still in the planning phase and it may take decades to see their benefits. Therefore, more than eight years of data will be required to evaluate trends. In the interim, a preliminary trend analysis is presented using probabilistic data and historic data collected prior to adoption of MRP 1.0.

Figure 2.11 shows the distribution of CSCI scores calculated for urban sites for each year of MRP monitoring (WY 2012 – WY 2019). Over the eight-year monitoring period, biological conditions, based on CSCI scores, have been relatively consistent at urban sites (median scores around 0.5), with the exception of a couple of years (WY 2012 and WY 2018) when median CSCI scores ranged from 0.6 to 0.7 (Figure 2.11)


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Figure 2.11. Box plots showing the distribution of CSCI scores at urban sites in San Mateo County, WY 2012 – WY 2019.

Comparison of MRP Data to Historic Data

The Program conducted bioassessments in four watersheds between 2002 and 2007 as part of watershed assessment and monitoring requirements in its municipal stormwater NPDES Permit (Pre-MRP). Bioassessments were conducted in San Pedro Creek (2002 and 2003), San Mateo Creek (2004), Cordilleras Creek (2005 and 2006) and Belmont Creek (2006 and 2007). The total number of bioassessment surveys conducted in San Mateo County watersheds during Pre-MRP and MRP time periods is shown in Table 2.13. Watersheds with less than four sampling locations were combined into two groups: “Tributaries to San Francisco Bay” and “Tributaries to Pacific Ocean”.

Bioassessment sampling during the Pre-MRP time period was conducted using the California Stream Bioassessment Protocol (CSBP), a different methodology than the SWAMP Bioassessment Reachwide Protocol which was implemented during the MRP. The CSBP was the standardized methodology developed by California Department Fish and Wildlife for bioassessments conducted in streams throughout California. BMI samples collected using the CSBP method have been shown to produce taxonomic metric scores similar to the SWAMP protocol. In addition, the level of taxonomic identification (SAFIT Level 1+) is consistent for samples collected and analyzed during Pre-MRP and MRP, which allows for a consistent and standard approach to calculate CSCI scores.


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Table 2.13. Total number of bioassessment surveys conducted by SMCWPPP, grouped by watershed, during MRP and Pre-MRP.

Watershed Bioassessment Sites

MRP Pre-MRP

Monitoring Period 2012-2019 2002 - 2007

San Francisco Bay

Atherton Creek 5 0

Belmont Creek 4 5

Cordilleras Creek 4 7

Redwood Creek 8 0

San Francisquito Creek 16 0

San Mateo Creek 6 6

Tributaries to SF Bay 11 0

Pacific Ocean

Pescadero Creek 8 0

Pilarcitos Creek 6 0

San Gregorio Creek 7 0

San Pedro Creek 4 7

Tributaries to Ocean 6 0

Tunitas Creek 4 0

Total 90 25

Biological conditions based on CSCI scores during the Pre-MRP and MRP time periods were compared for four San Mateo County watersheds. Bioassessment sites were well distributed throughout each watershed for both monitoring periods. The sample time frames were approximately 7-10 years apart. During the Pre-MRP monitoring period, some sites were sampled two consecutive years; average CSCI scores were used for these sites. The distribution of CSCI scores for each watershed, grouped by the Pre-MRP and MRP time periods is shown in Figure 2.12. Median CSCI scores were consistently higher in all four watersheds during the MRP sample period.


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Figure 2.12. Distribution of CSCI scores at sites in four San Mateo County watersheds that were sampled during Pre-MRP and MRP time periods.

A comparison of CSCI scores at individual sites that were sampled during the two sampling periods is shown in Table 2.14. SWAMP Station codes were used for sites sampled during Pre-MRP. The CSCI scores for both sampling events that occurred during Pre-MRP time period are shown. Similar to the overall watershed results previously discussed, the CSCI scores were higher for sampling events that occurred during the MRP time period.

The higher CSCI scores observed at watershed/sites during the MRP time period may be an indication that biological conditions have improved since the Pre-MRP time period. It is important to note that biological condition, based on CSCI scores, can be influenced by many factors, including: timing and magnitude of storm events during sampling index period, variable antecedent conditions (e.g., precipitation, temperature), and changes in management actions (e.g., operations related to water releases from reservoirs or diversions). It is not clear, especially with such a small sample size, what factors might be associated with general trend of improved biological conditions at these watersheds/sites.


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Table 2.14. CSCI scores for bioassessments conducted at three San Mateo County sites during two different monitoring periods: Pre-MRP and MRP.

Site Name Status Station ID Sampling

Date CSCI Score

San Pedro Creek at Sanchez Art Center

Pre-MRP 202SPE040 15-May-02 0.65

202SPE040 27-Apr-03 0.62

MRP 202R03404 17-May-18 0.65

Belmont Creek at Misty Lane Pre-MRP

204BEL030 04-May-06 0.33

204BEL030 04-Apr-07 0.29

MRP 204R00520 28-May-13 0.52

Cordilleras Creek downstream of El Camino Real

Pre-MRP 204COR010 25-Apr-05 0.35

204COR010 02-May-06 0.38

MRP 204R02548 17-May-16 0.41

2.4 Considerations for Future Bioassessment Monitoring

The RMC bioassessment dataset provides a comprehensive survey of stream health throughout the San Francisco Bay Area. The probabilistic design allows for an evaluation of ambient stream conditions using biological indicators and stressor data at regional and countywide scales. These data provide stormwater programs with an understanding of existing stream conditions that can assist in the decision-making process for future management actions, as well as baseline conditions that can be re-evaluated over time to assess trends. The urban sites for most counties within the RMC sample frame will be exhausted (i.e., either sampled or evaluated) following Creek Status Monitoring during WY 2020. As result, the RMC is currently evaluating options for revising the monitoring design to address the Creek Status Monitoring requirements anticipated under MRP 3.0. One of the options under consideration by the RMC is to implement a targeted monitoring design that would focus on specific watersheds or reaches of interest. A watershed approach would provide stormwater programs more flexibility to evaluate priority areas that stakeholders want to improve, protect, or learn more about. The following objectives could be used to guide the monitoring design:

• Address existing problems (i.e., poor biological conditions, water quality issues) at locations where implementation of potential management actions is practical and feasible.

• Evaluate changes in biological conditions and/or water quality at locations that are likely affected by planned management activities.

• Actively monitor and manage areas that are in good condition. Examples for each of these approaches are provided in section below: 2.4.1 Investigate Sites with Reduced Biological Conditions

The RMC dataset indicates majority of urban streams are in poor condition. Biological conditions were poor (i.e., lowest two condition categories) at 85% and 90% of the bioassessment sites classified as urban, based on CSCI and ASCI-H scores, respectively. This


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suggests that stressors associated with urban streams (e.g., poor physical habitat, hydromodification) may create too many constraints to achieve good biological conditions (as defined in the MRP. As a result, stormwater programs may want to focus resources on better understanding the biological conditions of urban sites/reaches that have a higher potential for improved conditions due to management actions. The SCAPE tool (discussed in Section 2.2.4.7) is an existing channel model that provides a context for evaluating stream health by estimating an expectation of biological condition at a given stream reach relative to landscape constraints. Biological condition, based on CSCI scores, can be compared to the reach expectation. As an example, CSCI scores for 16 sites sampled in San Francisquito Creek watershed over an eight-year period were compared to the range of scores predicted by the landscape model (Figure 2.13). The predicted range of CSCI scores for these sites are fall into two stream classifications: possibly constrained (light red), and possibly unconstrained (light blue). The CSCI scores for bioassessment sites (i.e., Relative Site Score) are represented by either circles or triangles superimposed over the predicted range of CSCI scores estimated by the model. Sites that have CSCI scores higher than model predictions are depicted by an up-pointing triangle symbol (i.e., “over scoring”); sites with CSCI scores lower than model predictions were depicted by an inverted triangle (i.e., under scoring”).

There was one “under scoring site” in San Francisquito Creek watershed. Dry Creek, tributary to Bear Creek, (site 205R01704) was below the predicted range for “possibly constrained” stream segment. A second site on Dry Creek (site 205R02728) was also on the very low end of the predicted range for CSCI scores. These two sites indicate biological conditions that may have impacts beyond what is predicted by the model for the developed landscape. Follow-up monitoring could be implemented at these sites to evaluate stressors impacting conditions. The over-scoring sites depicted in the figure are examples of bioassessment locations that are in good condition relative to channel constraints associated with development. Sites 205R00088 and site 205R01816 are located in the upper reaches of Corte Madera Creek that have rural land uses. Stormwater program efforts could be made to work with private landowners to implement projects (e.g., bank stabilization) that protect biological conditions at these sites.


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Figure 2.13. Comparison of CSCI scores for 16 sites in the San Francisquito Creek watershed with predicted model of CSCI scores based on developed landscapes (Beck et al. 2020)


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2.4.2 Evaluate Sources and Impacts of Potential Stressors

Targeted monitoring design could also focus on site) where bioassessment data exceeded stressor thresholds or WQOs. For example, nutrient concentrations (e.g., total nitrogen, total phosphorus) are potential water quality stressors that can impact biological conditions (Sutula et al 2018). Comparisons of total nitrogen concentrations to CSCI and ASCI-H scores for sites in San Mateo County that drain to San Francisco Bay and the Pacific Ocean are presented in Figure 2.14. This figure illustrates that the association between total nitrogen concentrations and CSCI scores is stronger than the association between total nitrogen and ASCI-H scores.

Figure 2.14. Total Nitrogen vs. CSCI and ASCI-H scores. Data grouped by strata: Pacific Ocean vs. San Francisco Bay

Follow-up monitoring could be conducted at selected watersheds to evaluate sites with elevated concentrations of nutrients. Total nitrogen concentrations for the 90 bioassessment sites sampled in San Mateo County are shown in Figure 2.15. The highest concentration (< 3.0 mg/L)) occurred in Calera Creek (tributary to the Pacific Ocean). Moderately high concentrations (1.5 – 3.0 mg/L) occurred primarily in San Francisco Bay watersheds, including Atherton, Redwood, Laurel and Colma Creeks. Future monitoring in these watersheds may be directed at identifying sources of nutrients and implementing controls to reduce those concentrations.


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Figure 2.15. Total nitrogen concentrations measured at 90 bioassessment sites in San Mateo County sampled between WY 2012 and WY 2019.


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2.4.3 Evaluate Effectiveness of BMP/Restoration Projects

Bioassessment monitoring could be conducted at stream locations that may be impacted by stream restoration or best management practices (BMPs), such as Green Stormwater Infrastructure (GSI) projects. Benthic macroinvertebrate indicators are often used to assess success of stream restoration projects (Rubin et al. 2017). BMIs can be good indicators that show response to changes in physical habitat, as well as water quality. The CSCI score provides an overall measure of biological conditions, however, individual BMI metrics can provide useful information related to presence or absence of specific stressors (e.g., fine sediment). The RMC dataset may provide information on baseline conditions at locations where projects are currently being planned. Stormwater programs could conduct future bioassessments following construction of restoration or GSI projects to evaluate if conditions have improved. Effectiveness monitoring should be included in all restoration projects as one measure of success.

2.4.4 Evaluate Sites in Good Condition

Many of the bioassessment sites sampled by SMCWPPP and SWAMP are located in publicly protected lands that have limited or no urban development. These lands include State Parks, County Parks, Open Space Districts, and watersheds protected by public utility agencies that provide water supply (e.g., Coastside County Water District). Thirteen of the 90 bioassessment sites sampled in San Mateo County received high biological condition scores for both BMI data (CSCI scores > 0.79) and algae data (ASCI-H scores > 0.88). Nine of the thirteen sites were in publicly protected lands and four sites were located in privately owned land18. Site location information and ownership is provided in Table 2.15. Site locations are provided in Figure 2.16.

Stormwater programs should ensure that information and data on high quality sites are made available to park and land managers so that these areas can be managed in a way that protects stream health and water quality. Follow-up monitoring may also be conducted to evaluate whether biological conditions are changing over time. Trends monitoring at minimally disturbed sites may provide useful information related to climate change.

18 Field crews obtained permission to access private property to conduct bioassessment sampling.


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Table 2.15. Location and ownership of bioassessment sites that had scores in the top two condition categories for CSCI and ASCI-H.

Station Code Creek Name Watershed Ownership

202R00312 Mills Creek Mills Creek State Park

202R00166 Little Butano Creek Pescadero Creek State Park

202R00038 Little Butano Creek Pescadero Creek State Park

202R00214 Tarwater Creek Pescadero Creek County Park

202R00550 Jones Gulch Pescadero Creek County Park

202R00150 Butano Creek Pescadero Creek Midpeninsula Regional Open Space District

202R00378 Pescadero Creek Pescadero Creek Private

202R00072 Pilarcitos Creek Pilarcitos Creek County Water District

202R00440 Purisima Creek Purisima Creek Midpeninsula Regional Open Space District

205R03624 Bear Creek San Francisquito Creek Private

205R01816 Corte Madera Creek San Francisquito Creek Private

202R03880 La Honda Creek San Gregorio Creek Midpeninsula Regional Open Space District

202R00280 Tributary to Alpine Creek San Gregorio Creek Private


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Figure 2.16. Protected areas in San Mateo County and bioassessment sites with scores in the top two condition categories for CSCI and ASCI-H.


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3.0 Continuous Water Quality Monitoring

3.1 Introduction

This section describes continuous water temperature and general water quality data that were collected during WY 2014 through WY 2019. These parameters were monitored in compliance with Creek Status Monitoring Provisions C.8.c of MRP 1.0 and C.8.d.iii – iv of MRP 2.0. Monitoring was conducted at selected sites using a targeted design based on the directed principle19 to address the following management questions:

1. What is the spatial and temporal variability in water quality conditions during the spring and summer season?

2. Do general water quality measurements indicate potential impacts to aquatic life?

The first management question is addressed primarily through evaluation of water quality results in the context of existing aquatic life uses. Temperature and general water quality data were evaluated for potential impacts to different life stages and overall population of fish community present within monitored reaches.

The second management question is addressed primarily through the evaluation of targeted data with respect to WQOs and thresholds from published literature. Sites where exceedances occur may indicate potential impacts to aquatic life or other beneficial uses and are considered as candidates for future Stressor/Source Identification projects.

The sections below summarize methods and results from continuous temperature and water quality monitoring conducted in WY 2014 – WY 2019. Conclusions and recommendations for continuous monitoring are presented in Section 7.0.

3.2 Methods

In compliance with MRP 1.0 and MRP 2.0, temperature was monitored at a minimum of four sites each year and general water quality was monitored at two sites each year. Continuous temperature and water quality data were collected in accordance with SWAMP-comparable methods and procedures described in the BASMAA RMC SOPs (current version is BASMAA 2016a) and associated QAPP (current version is BASMAA 2016b). Data were evaluated with respect to MRP 1.0 Provision C.8.c and MRP 2.0 Provision C.8.d triggers for each parameter.

3.2.1 Continuous Temperature

Digital temperature loggers (Onset HOBO Water Temp Pro V2) were programmed to record data at 60-minute intervals. The loggers were deployed at targeted sites from April through September. Procedures used for calibrating, deploying, programming and downloading data are described in RMC SOP FS-5 (BASMAA 2016a). SMCWPPP typically deploys temperature loggers at more than the minimum number of sites in anticipation of field equipment being stolen or washed downstream.

19 Directed Monitoring Design Principle: A deterministic approach in which points are selected deliberately based on

knowledge of their attributes of interest as related to the environmental site being monitored. This principle is also known as "judgmental," "authoritative," "targeted," or "knowledge-based."


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3.2.2 Continuous General Water Quality

Water quality monitoring equipment recording dissolved oxygen, temperature, conductivity, and pH (YSI 6600 data sondes) were programmed to record data at 15-minute intervals. The sondes were deployed at targeted sites for two 1 to 2-week events each year: spring season (Event 1) and late-summer season (Event 2). Procedures for calibrating, deploying, programming and downloading data are described in RMC SOP FS-4 (BASMAA 2016a).

3.2.3 Data Evaluation

Continuous temperature and water quality data are analyzed and evaluated to identify potential stressors that may be contributing to degraded or impacted biological conditions, including exceedances of WQOs. The relevant trigger criteria for continuous temperature and water quality data are listed in Table 3.1. Sites with continuous monitoring results exceeding the trigger criteria are identified as candidate SSID projects.

Table 3.1. Water Quality Objectives and trigger thresholds used for evaluation of continuous temperature and water quality data.

Monitoring Parameter

Objective/Trigger Threshold Units Source

Temperature

Two or more weekly average temperatures exceed the Maximum Weekly Average Temperature (MWAT) threshold of 17.0°C for a Steelhead stream, or 20% of the results at one sampling station exceed the instantaneous maximum of 24°C.

⁰C MRP 2.0 Provision C.8.d.iii

Sullivan et al. 2000

General Water Quality Parameters

20% of results at each monitoring site exceed one or more established standard or threshold - applies individually to each parameter

Conductivity 2000 μS/cm

MRP 2.0 Provision C.8.d.iii

Dissolved Oxygen WARM < 5.0, COLD < 7.0 mg/L SF Bay Basin Plan Ch. 3, p. 3-4

pH > 6.5, < 8.5 1 pH SF Bay Basin Plan Ch. 3, p. 3-4

Temperature Same as Temperature (See Above) 1. Special consideration will be used at sites where imported water is naturally causing higher pH in receiving waters.

3.3 WY 2014 – WY 2019 Overview

Continuous temperature and water quality monitoring sites were selected based on the presence of significant fish and wildlife resources as well as historical and/or recent indications of water quality concerns. The same sites were often monitored for multiple years to gain a better understanding of the range of water quality conditions that may occur over time. In some years, continuous monitoring data were used to support or follow-up on SSID investigations. It is important to note that in San Mateo County, there are limited number of urban watersheds that support steelhead populations or other cold-water fish communities.


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Figure 3.1 maps the stations where continuous temperature and water quality monitoring was conducted during WY 2014 through WY 2019. For reference, Figure 3.1 shows which creeks are designated in the Basin Plan has having COLD Beneficial Uses. Continuous monitoring stations were focused at stream reaches within the San Mateo Creek Watershed (WY 2014 – WY 2015; water quality sondes only), San Francisquito Creek Watershed (temperature only: WY 2014 – WY 2015; WY 2016: temperature and water quality), San Pedro Creek Watershed (WY 2017 – WY 2018), and Tunitas Creek Watershed (WY 2019).

The sections below summarize monitoring results from WY 2014 – WY 2018. Details are available in the respective UCMRs (SMCWPPP 2015, 2016, 2017, 2018, and 2019a). Results from WY 2019 continuous monitoring are detailed in this IMR in Section 3.4.

3.3.1 San Mateo Creek Watershed (WY 2014 – WY 2015)

Continuous general water quality data (dissolved oxygen, specific conductance, pH, and temperature) were recorded at two locations in the San Mateo Creek Watershed (Figure 3.1). during two two-week deployments in WY 2014 and WY 2015 (four total events) (SMCWPPP 2015 and SCVURPPP 2016). The data were used to support an SSID study investigating low dissolved oxygen concentrations in San Mateo Creek. The sampling stations were located downstream of the juvenile steelhead rearing and spawning habitat that occurs within a two-mile reach of San Mateo Creek below the Crystal Springs Reservoir (Brinkerhoff, SFPUC, personal communication 2013). WY 2015 station locations were located within 0.15 miles upstream of the El Camino Real culvert which functions as a grade control structure within the creek, decreasing upstream channel slope and velocity, and causing fine sediments to accumulate. Although these characteristics have caused low concentrations of dissolved oxygen in prior years, The SSID Project Report, included as Appendix B to the WY 2015 UCMR (SMCWPPP 2016) concluded that previously recorded low dissolved oxygen levels are no longer likely as a result of increased dry season releases from Crystal Springs Reservoir which is owned and operated by the San Francisco Public Utilities Commission (SFPUC). MRP triggers for dissolved oxygen, specific conductivity, pH, and instantaneous maximum temperature were not exceeded in either year of monitoring. The Maximum Weekly Average Temperature (MWAT) trigger of 17°C was exceeded in WY 2015; however, the 17°C threshold is based on streams of the Pacific Northwest and may not be an appropriate trigger for Bay Area streams.

3.3.2 San Francisquito Creek Watershed (WY 2014 – WY 2016)

In WY 2014 – WY 2016, continuous temperature monitoring (hourly) was conducted from April through September at stations in the San Francisquito Creek Watershed. Although the MRP requires a minimum of four temperature stations, six stations were monitored in WY 2014 to mitigate for potential equipment loss. One of the WY 2016 stations (located in upper West Union Creek) was eliminated after WY 2014 due to challenging access and low temperature results in WY 2014 suggesting that the reach was supportive of cold-water aquatic life. Although creeks in the watershed typically cease to have continuous flow through the dry season, all sites were located in pools that have historically remained wet throughout the summer. These pools may provide refuge for cold water fish prior to the onset of wet season flows.

The MRP trigger threshold for instantaneous maximum temperature was not exceeded in any of the three years at any of the targeted temperature stations. However, in WY 2015, one of the stations on Bear Creek (at Sand Hill Road; Station 205BRC010) had an unusual pattern of daily temperature spikes for a portion of the record. The daily spikes would begin at 8:00 AM with a quick temperature increase of 5 to 10 ºC that disappeared from the records by 10:00 AM. The


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temperature then decreased steadily over the remainder of the day. In very dry years such as WY 2015 when flows are extremely low, it is difficult to determine the cause of the temperature spikes. Possible explanations include: sunshine hitting the instrument, warm overland flows from nearby properties, or temporary diversions from the creek causing water levels to drop below the instrument. As a result of the unexplained spikes, the station was added to the list of candidate SSID studies. WY 2016 temperature monitoring results from the same station did not have any unexplained spikes.

In all three years of monitoring, the MRP MWAT trigger of 17°C was exceeded at one or more station. It was noted in the WY 2016 UCMR that, due to persistent drought conditions, the WY 2014 – WY 2016 monitoring period likely represents a worst-case scenario for summer temperatures (SMCWPPP 2017). Furthermore, alternative data evaluation thresholds, such as the Maximum Weekly Maximum Temperature (MWMT) threshold of 20°C used by the National Marine Fisheries Service (NMFS) in the Central Coast Steelhead Recovery Plan (NMFS 2016) were not exceeded. In compliance with the MRP, stations with MWAT trigger exceedances were added to the list of candidate SSID projects.

In WY 2016, the Bear Creek tributary to San Francisquito Creek was targeted for general water quality monitoring, in part to support exploration of daily temperature spikes observed in WY 2015, but also to evaluate water quality in reaches that have historically supported juvenile steelhead rearing and spawning habitat (Leidy et al. 2005). MRP triggers for specific conductivity, pH, and instantaneous maximum temperature were not exceeded at either station. However, dissolved oxygen concentrations at both stations were below the WQOs for WARM and COLD freshwater habitat during the August 20 – September 5, 2016 deployment. During the dry season, the sampling locations had become isolated pools with just a trickle or no surface flow entering the pool from the upstream channel. These pools would provide the only refugia for juvenile steelhead and other native fishes. Thus, the measured low DO levels would not likely support steelhead, especially if they were cut off from reaches with better habitat and water quality. Both sites were added to the list for potential SSID projects for low DO.

3.3.3 San Pedro Creek Watershed (WY 2017 – WY 2018)

In WY 2017 and WY 2018 the San Pedro Creek Watershed was targeted for continuous temperature and water quality monitoring because it contains the northern-most population of naturally producing steelhead trout (Oncorhynchus mykiss) in San Mateo County (Titus et al. 2010). Fish habitat is supported by perennial flow resulting from multiple springs located in the upper watershed. Land uses are mixed, with urban communities in the valleys and along the coast, and undeveloped and public lands in the upper areas of the watershed. Although degradation of physical habitat and the presence of fish barriers such as bridge culverts may threaten the steelhead population in San Pedro Creek, restoration efforts are helping to reestablish and enhance habitat.

The same set of five temperature stations and two water quality stations were monitored in both years. Water temperatures generally increased in the downstream direction (spatial pattern) and increased through the summer (temporal pattern) but remained well below the MRP instantaneous maximum and MWAT triggers. Likewise, general water quality parameters (temperature, pH, dissolved oxygen, and specific conductance) did not exceed MPR trigger thresholds.

Continuous monitoring data were supplemented by other types of Provision C.8 data collected in the San Pedro Creek Watershed. Bioassessments were conducted at a total of four


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probabilistic monitoring stations from WY 2015 to WY 2018. CSCI scores along the mainstem of San Pedro Creek were in the “likely altered” condition category; whereas CSCI scores farther up in the watershed along the Middle Fork of San Pedro Creek (within County Park land) were in the “possibly intact” and likely intact” stream condition categories. In compliance with Provision C.8.g of MRP 2.0, the mouth of San Pedro Creek was also monitored for dry weather Pesticides and Toxicity monitoring in WY 2017 and wet weather Pesticides and Toxicity monitoring in WY 2018 Although dry weather water samples were significantly toxic to Pimephales promelas (fathead minnow) and Ceriodaphnia dubia (water flea) and wet weather water samples were significantly toxic to Pimephales promelas and Hyalella azteca (amphipod), the percent effect of all samples was below the threshold (50%) for additional investigation. Pesticide concentrations in the dry weather sediment samples and wet weather water samples were all very low and the cause of the toxicity is unknown.


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Figure 3.1. Continuous temperature and water quality stations in San Mateo County, WY 2014 – WY 2019.


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3.4 WY 2019 Results (Tunitas Creek Watershed)

In WY 2019, continuous (hourly) temperature measurements were recorded from April 3 through September 18, 2019, at five locations in the Tunitas Creek watershed. Continuous (15-minute) general water quality measurements (temperature, dissolved oxygen, pH, specific conductance) were recorded at two stations during two two-week sampling events (Events 1 and 2). Sample Event 1 occurred from May 17 through May 31, 2019. Sample Event 2 occurred from September 5 through September 18, 2019. The two general water quality stations were also targeted for bioassessment surveys in WY 2019. Temperature, general water quality, and bioassessment monitoring stations are illustrated in Figure 3.2.

3.4.1 Tunitas Creek Study Area

The Tunitas Creek watershed was targeted for continuous monitoring in WY 2019 to supplement targeted bioassessment monitoring also conducted in Tunitas Creek in WY 2019. The targeted bioassessment monitoring, discussed in more detail in Section 2.3.1 of this IMR, follows up on prior CSCI scores that were relatively low for an undeveloped watershed.

Tunitas Creek is a coastal stream in western San Mateo County, with a generally undeveloped watershed draining 11.5 square-miles of the Santa Cruz Mountains (MidPeninsula Regional Open Space District 2015) (Figure 3.3). The creek originates in the forested peaks of Kings Mountain, at an elevation of 1,860 feet (570 meters). After flowing southwest 6.6 miles (10.6 km) alongside Tunitas Creek Road, Tunitas Creek drains into the Pacific Ocean. The upper portion of the watershed includes Rings Gulch which is drained by East Fork Tunitas Creek. The lower portion of the watershed contains scattered farms and ranches, and the Dry Creek tributary. Tunitas Creek passes under the Cabrillo Highway and empties into the Pacific Ocean via the Tunitas Creek Beach in Half Moon Bay.

The Basin Plan designates Tunitas Creek as having COLD Beneficial Uses (SFBRWQCB 2017). The watershed historically provided suitable habitat for steelhead trout with “good spawning” habitat in the lower two miles of Tunitas Creek and “excellent” steelhead habitat in the East Fork tributary as identified by the California Department of Fish and Game (DFG) in the 1960s (DFG 1962, DFG 1964). Forty years later, the DFG observed relatively low steelhead populations in the watershed. However, the greatest density of steelhead juveniles was found in the upper reach of Tunitas Creek (Becker et al. 2010, National Marine Fisheries Service 2016).

Riparian species of interest and special concern are also found in the Tunitas Creek watershed. Among the most notable are the California red legged frog (Rana draytonii), the San Francisco Garter Snake (Thamnophis sirtalis tetrataenia), the California giant salamander (Dicamptodon ensatus) and the Santa Cruz black salamander (Aneides niger) (California State Coastal Conservancy 2019, MidPeninsula Regional Open Space District 2015).

WY 2019 results for continuous temperature and general water quality, described in the sections below, appear to support COLD Beneficial Uses in Tunitas Creek.


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Figure 3.2. Continuous temperature, continuous water quality, and bioassessment stations in the Tunitas Creek Watershed, San Mateo County, WY 2019.


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Figure 3.3. Photo of Tunitas Creek approximately 0.15 mile upstream from the convergence with East Fork Tunitas Creek tributary (Photo by SMCWPPP field staff).


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3.4.2 WY 2019 Continuous Temperature Results and Discussion

Temperature loggers were deployed at five sites in the Tunitas Creek watershed on April 3, field checked on July 19, and removed on September 18, 2019. Summary statistics for continuous water temperature data collected at the five sites are listed in Table 3.2. None of the recorded temperatures exceeded the instantaneous maximum temperature trigger of 24°C.

Table 3.2 Descriptive statistics for continuous water temperature measured from April 3 through September 18, 2019 at five sites in Tunitas Creek, San Mateo County.

Site ID 202TUN005 202TUN025 202TUN030 202TUN035 202TUN060

(downstream --------------------------------------------- upstream)

Start Date 4/3/2019 4/3/2019 4/3/2019 4/3/2019 4/3/2019

End Date 9/18/2019 9/18/2019 9/18/2019 9/18/2019 9/18/2019

Tem

pera

ture

(ºC

)

Minimum 9.6 8.7 9.1 9.2 9.8

Median 13.8 13.5 13.7 13.6 13.0

Mean 13.8 13.4 13.6 13.5 12.9

Maximum 17.8 16.5 17.9 17.5 15.9

Max 7-day mean 16.1 15.3 16.2 15.9 14.7

N (# individual measurements)

4028 4028 4028 4028 4027

# Measurements > 24°C 0 0 0 0 0

Weekly average temperature values calculated for each of the five monitoring sites are listed Table 3.3 and graphed in Figure 3.4. Consistent with MRP requirements, the weekly average temperature was calculated for non-overlapping, seven-day periods. The values across all the sites ranged from lows of 10.9°C to 11.3°C recorded in April or May highs to 14.7°C to 16.2°C recorded in September (Table 3.3). The MWAT trigger was never exceeded at any of the sites.


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Table 3.3. Weekly average temperature values for water temperature data collected at five stations monitored in the Tunitas Creek Watershed, WY 2019. The MRP MWAT trigger is 17°C.

Station 202TUN005 202TUN025 202TUN030 202TUN035 202TUN060

Date Weekly Average Temperature (°C)

4/3/2019 12.6 12.3 12.3 12.1 11.8

4/10/2019 11.3 11.0 11.1 10.9 11.0

4/17/2019 12.0 11.7 11.7 11.6 11.6

4/24/2019 12.6 12.2 12.3 12.2 11.9

5/1/2019 11.6 11.2 11.3 11.1 10.9

5/8/2019 12.6 12.3 12.3 12.1 11.5

5/15/2019 12.0 11.7 11.7 11.6 11.2

5/22/2019 12.2 12.1 12.0 11.8 11.5

5/29/2019 12.5 12.3 12.3 12.1 11.9

6/5/2019 13.5 13.2 13.5 13.4 13.2

6/12/2019 14.2 13.9 14.0 13.9 13.3

6/19/2019 14.1 13.6 13.8 13.7 13.1

6/26/2019 13.9 13.4 13.6 13.5 12.6

7/3/2019 13.9 13.5 13.6 13.5 12.6

7/10/2019 14.7 14.3 14.6 14.5 13.6

7/17/2019 15.2 14.7 15.0 15.0 13.8

7/24/2019 14.4 14.0 14.3 14.3 13.8

7/31/2019 15.2 14.8 15.1 15.0 13.9

8/7/2019 15.6 15.1 15.4 15.3 14.2

8/14/2019 15.2 14.7 15.1 15.0 14.7

8/21/2019 15.6 14.9 15.5 15.4 14.5

8/28/2019 15.5 14.8 15.4 15.2 14.6

9/4/2019 16.1 15.3 16.2 15.9 14.5

9/11/2019 15.0 14.7 15.3 15.0 14.7

9/18/2019 14.1 14.3 14.3 14.1 14.3

Total Weeks 25 25 25 25 25

MWAT >17 0 0 0 0 0

% Exceed 0% 0% 0% 0% 0%

> MRP Trigger No No No No No


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Time series plots of the weekly average temperature values and daily averages are shown for all five sites in Figures 3.4 and 3.5, respectively. Water temperatures increased gradually over the monitoring period, with lowest temperatures measured at the most upstream station (202TUN060) and increasing slightly with decreasing site elevation. A temperature peak recorded at all stations in the early-June record coincided with an historic heat wave caused by unseasonable offshore air flows20.

Figure 3.4. Weekly average temperature values calculated for water temperature collected at five sites in Tunitas Creek over 24 weeks of monitoring in WY 2019. The MRP trigger (17°C) is shown for comparison.

Figure 3.5. Water temperature, shown as daily average, collected between April and September 2019 at five sites in Tunitas Creek, San Mateo County.

20 https://www.weather.gov/mtr/HeatWave_6_9-11_2019

https://www.weather.gov/mtr/HeatWave_6_9-11_2019


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3.4.3 WY 2019 Continuous General Water Quality Results and Discussion

Summary statistics for general water quality measurements collected at the two stations in Tunitas Creek are listed in Table 3.4. Station locations are mapped in Figure 3.2. For Event 1, sondes were deployed on May 17 and retrieved on May 31, 2019. For Event 2, sondes were deployed on September 5 and retrieved on September 18, 2019. Time series plots of the data for Event 1 and Event 2 are shown in Figures 3.6 and 3.7, respectively. MRP trigger thresholds are shown for reference.

Table 3.4. Descriptive statistics for continuous water temperature, dissolved oxygen, pH, and specific conductance measured at two Tunitas Creek sites in San Mateo County during WY 2019. Data were collected every 15 minutes over a two 2-week time periods during May (Event 1) and September (Event 2).

Parameter Data Type 202TUN030 202TUN040

Event 1 WY19

Event 2 WY19

Event 1 WY19

Event 2 WY19

Temperature (°C)

Minimum 9.8 12.7 10.2 12.9

Median 11.7 15.7 11.6 15.9

Mean 11.7 15.6 11.6 15.6

Maximum 12.9 19.2 12.9 17.6

% > 24 0% 0% 0% 0%

Dissolved Oxygen (mg/L)

Minimum 10.5 7.9 10.5 6.2

Median 11.0 9.1 10.8 7.9

Mean 11.0 9.4 10.9 7.9

Maximum 11.4 11.1 11.4 8.8

% < 7 0% 0% 0% 4%

pH

Minimum 7.8 7.4 8.0 7.4

Median 8.0 7.9 8.2 7.9

Mean 8.0 7.9 8.2 7.9

Maximum 8.2 8.5 8.3 8.0

% < 6.5 or > 8.5 0% 0% 0% 0%

Specific Conductivity

(uS/cm)

Minimum 528 1057 478 1122

Median 594 1075 552 1139

Mean 608 1079 562 1143

Maximum 741 1117 697 1170

% > 2000 0% 0% 0% 0%

Total number of data points (N) 1341 1233 1341 1233


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Figure 3.6. Continuous water quality data (temperature, specific conductance, pH, and dissolved oxygen) collected during May 17-31, 2019 (Event 1) at two sites in the Tunitas Creek watershed.


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Figure 3.7. Continuous water quality data (temperature, specific conductance, pH, and dissolved oxygen) collected during September 5-18, 2019 (Event 2) at two sites in the Tunitas Creek watershed.


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Temperature

Water temperatures recorded by the sondes never exceeded the 24°C MRP trigger threshold for instantaneous maximum temperature at either site for either sampling event (Table 3.4, Figures 3.6 and 3.7). The water temperature data collected by temperature loggers were used to evaluate MWAT (see previous section). As previously stated, the MWAT threshold of 17°C was not exceeded at any of the Tunitas Creek stations (Table 3.3, Figure 3.4).

Specific Conductivity Specific conductance measurements did not exceed the MRP trigger of 2000 µs/cm during either sampling event. Conductivity was similar at the two stations and was lower during Event 1 compared to Event 2 (Figures 3.6 and 3.7). The increase in specific conductance over the summer may be the result of the increasing importance of higher conductivity groundwater contributions after a long period with little to no precipitation.

pH

During the two sampling events, all pH measurements fell within the Basin Plan WQOs for pH (< 6.5 and/or > 8.5). The pH measurements were similar at both stations and similar during both events.

Dissolved Oxygen

During Event 1, dissolved oxygen (DO) concentrations at both stations were similar in daily patterns and magnitude, remaining well above the Basin Plan WQO for COLD freshwater habitat (7.0 mg/L) (Figure 3.6). Event 2 DO concentrations were lower at both stations compared to Event 1 (Table 3.4). During Event 2, DO was higher at the downstream station (202TUN030) with greater diurnal fluctuations compared to the upstream station (202TUN040) (Figure 3.7) and all recorded concentrations at the downstream station were above the WQO of 7 mg/L. During Event 2, 4% of the recorded DO concentrations at the upstream station (202TUN040) dropped below the WQO of 7 mg/L; however, this did not result in an exceedance of the MRP trigger which would require 20% of the record being below the WQO.

The cause of the decrease in DO from Event 1 to Event 2 is likely due to increases in water temperatures during Event 2. Warmer water has less capacity to absorb oxygen. The cause of the differences in DO between the two stations during Event 2 is unknown. It may be related to differences in land use within the catchment areas and/or it may be related to differences in physical habitat at the two stations. The downstream station (202TUN030) is in a deep canyon with a thick riparian canopy blocking sunlight; whereas, the upper station (202TUN040) has more exposure to sunlight. DO during Event 2 may also have been responding to small algae bloom/decomposition associated with warm weather. Above average air temperatures were recorded on September 13, 2019 at Half Moon Bay (reaching 81°F, or 15 °F warmer than the historical average high temperature for that time of year) (AccuWeather 2019).


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4.0 Pathogen Indicator Monitoring

4.1 Introduction

This section describes the results of pathogen indicator monitoring that was conducted during WY 2014 through WY 2019 in compliance with Creek Status Monitoring Provisions C.8.c of MRP 1.0 and C.8.d.v of MRP 2.0. Monitoring was conducted at selected sites using a targeted design based on the directed principle to address the following management question(s):

1. What are the pathogen indicator concentrations at creek sites where there is potential for water contact recreation to occur?

2. What are the pathogen indicator concentrations in catchment areas that drain to creek or ocean sites where there is potential for water contact recreation to occur?

These management questions are addressed primarily through the evaluation of data with respect to trigger thresholds identified in the MRP and WQOs adopted by the State Water Board. Sites where exceedances occur may indicate potential impacts to water contact recreation (REC-1) or other beneficial uses and are considered as candidates for future Stressor Source Identification projects.

In compliance with MPR 1.0 and 2.0, five samples were collected each year for a cumulative total of 30 samples for the WY 2014 through WY 2019 IMR reporting period. The sections below summarize methods and results from pathogen indicator monitoring conducted during WY 2014 through WY 2019. Conclusion and recommendations for this section are presented in Section 7.0.

4.2 Methods

4.2.1 Sample Collection

Pathogen indicator samples were collected during the dry season in accordance with SWAMP-comparable methods and procedures described in the BASMAA RMC SOPs (BASMAA 2016b) and associated QAPP (BASMAA 2016a). Sampling techniques for pathogen indicators (E. coli, enterococci and fecal coliform) include direct filling of sterile containers and transfer of samples to the analytical laboratory within specified holding time requirements. Procedures for sampling and transporting samples are described in RMC SOP FS-2 (BASMAA 2016b).


During MRP 1.0 (WY 2014 and WY 2015) E. coli and fecal coliform were monitored as pathogen indicators. During MRP 2.0 (WY 2016 – WY 2019), E. coli and enterococcus were monitored as pathogen indicators. Pathogen indicator data were evaluated with respect to trigger thresholds identified in the MRP and WQOs adopted by the State Water Board on August 7, 2018 and approved by the USEPA on March 22, 2019. Pathogen indicator trigger thresholds and WQOs are listed in Table 4.1.

The MRP triggers and adopted WQOs are both based on the 2012 USEPA recommended recreational water quality criteria (RWQC). The 2012 RWQC offer two sets of numeric concentration thresholds for E. coli and enterococci designed to protect all types of water contact recreation in freshwaters where immersion and ingestion are likely. The two sets of


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criteria are based on estimated rates of gastrointestinal illness (estimated illness rate of 36 per 1,000 recreators and estimated illness rate of 32 per 1,000 recreators). MPR 2.0 specified the illness rate of 36/1000 as a trigger threshold; whereas, the State Water Board adopted the more conservative set of criteria based on the illness rate of 32/1000. The 2012 RWQC consist of both a geometric mean (GM) and a Statistical Threshold Value (STV). The GM criteria are applied when there are at least five samples distributed over a six-week period. The STV criteria should not be exceeded by more than 10 percent of the samples taken in a month, and therefore approximate a single sample maximum. Because pathogen indicator samples collected in compliance with the MRP are not repeated, results are compared to the STV criteria. Also, in this evaluation, the Most Probable Number (MPN) of bacteria colonies given by the analytical method is compared directly with the Colony Forming Units (CFU) of the USEPA recommendations.

The 2012 USEPA RWQC do not recommend using fecal coliform as a pathogen indicator. Therefore, fecal coliform data collected during MRP 1.0 were compared to WQOs listed in the Basin Plan (2017).

Table 4.1. Bacteriological trigger thresholds and WQOs for water contact recreation in freshwater.

State Water Board WQO

(Estimated Illness Rate 32/1,000) MRP 2.0 Trigger Threshold

(Estimated Illness Rate 36/1,000) Basin Plan WQO

Pathogen Indicator GM STV GM STV GM STV

Fecal Coliform (MPN/100 mL) NA NA NA NA 200 400

E. coli (cfu/100 mL) 100 320 125 410 NA NA

Enterococci (cfu/100 mL) 30 110 35 130 NA NA

4.3 Study Area

Pathogen indicator monitoring sites have been selected to inform bacteria SSID investigations and follow-up on anecdotal reports of high bacteria in creeks where water contact recreation (REC-1) is likely. Figure 4.1 includes an overview of San Mateo County showing where pathogen indicator monitoring has been conducted from WY 2014 – WY 2019. Inset maps in Figure 4.1 show details of the three targeted areas that are described in the sections below.


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Figure 4.1. Pathogen indicator monitoring sites in WY 2014- WY 2019, San Mateo County. Exceedances of E. coli WQOs are shown in red.


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4.3.1 WY 2018 – WY 2019: Pescadero Creek Watershed

In WY 2019, pathogen indicator samples were collected during one sampling event (August 1, 2019) from five sites chosen in coordination with San Mateo County Parks (Figure 4.1). The sample stations for WY 2019 were the same sample stations that were monitored for pathogen indicators in WY 2018. Repeat sampling can provide information (albeit limited) on variability at the sites. All sites were located in the Pescadero Creek watershed in the vicinity of Memorial County Park. Two sites were located on tributaries to Pescadero Creek: Jones Gulch (202PES150) and McCormick Creek (202PES142). Three sites were located on the main stem of Pescadero Creek upstream and downstream of the confluences with the two sampled tributaries. The sites were selected in coordination with San Mateo County Parks staff to characterize geographic patterns of pathogen indicator densities within the Pescadero Creek watershed.

Results of WY 2018 and WY 2019 pathogen indicator monitoring in the Pescadero Creek watershed are described in Section 4.4.

4.3.2 WY 2017: Pillar Point Harbor Watershed

In WY 2017, pathogen indicator samples were collected during one sampling event (August 28, 2017) from five sites in the Pillar Point Harbor watershed. Two of the sites were located on Denniston Creek, upstream and downstream of an MS4 outfall, and the MS4 draining to Denniston Creek was sampled. Two samples were collected from the MS4 in the “Capistrano Catchment”, a 15-acre area with no creeks that is drained entirely by the MS4 and discharges directly to one of the beaches along Pillar Point Harbor. Monitoring results were used to develop a Stressor Source Identification work plan that was implemented in WY 2018 and WY 2019. The Final Pillar Point Harbor Watershed Pathogen Indicator SSID Project Report is included as an appendix to Part C (SSID Status Report) this IMR.

The SSID project included microbial source tracking (MST) techniques such as development of a geodatabase to map potential bacteria sources and analysis of samples for human and dog genetic markers. Results of the SSID study showed that E. coli densities were highly variable and did not follow predictable seasonal patterns. Furthermore, an overall lack of human and dog markers suggests that bacteria sources may not be associated with anthropogenic sources that can be easier to control than wildlife sources and that are more likely to impact REC-1 beneficial uses. See Part C of this IMR (SSID Status Report) for more information.

4.3.3 WY 2014 – WY 2016: San Mateo Creek Watershed

San Mateo Creek and its tributary, Polhemus Creek, were targeted for MRP required pathogen indicator monitoring in WY 2014 through WY 2016. Results were used to support monitoring conducted as part of the San Mateo Creek Pathogen Indicator SSID Project. The SSID project followed up on SWAMP monitoring in the watershed that identified exceedances of E. coli WQOs in 2003 and MRP monitoring that confirmed E. coli exceedances in 2012. The SSID Project investigated the magnitude, seasonal variability, and predominant sources of pathogen indicators in the watershed using MST approaches consisting of both field and desktop methods. E. coli results followed predictable seasonal and spatial patterns, with higher densities observed during wet season monitoring events and at stations lower in the watershed. Genetic marker analyses suggested year-round human sources in lower reaches of San Mateo Creek and dog sources during wet weather. The San Mateo Creek Pathogen Indicator SSID Final Project Report, submitted with the WY 2015 UCMR, recommended that local municipalities


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work together to increase public education and outreach targeting pet waste cleanup. In addition, planned improvements to the sanitary sewer conveyance system in response to a Cease and Desist Order are anticipated to improve water quality by reducing leakage. There were no exceedances of the E. coli WQO observed in follow-up monitoring conducted in WY 2016, suggesting that the control actions had a beneficial impact on water quality in San Mateo Creek.


Pathogen indicator (E. coli and enterococci) densities measured in grab samples collected from the Pescadero Creek watershed in WY 2018 on July 27, 2018 and WY 2019 on August 1, 2019 are listed in Table 4.2. Stations are mapped in Figure 4.1. There were no measurements that exceeded the MRP 2.0 trigger threshold or State Water Board WQO for E. coli in either year. Two samples exceeded the criteria for enterococci in WY 2018 but not in WY 2019. The WY 2018 enterococci trigger exceedances were observed in McCormick Creek (202PES142) and Pescadero Creek at Memorial Park downstream of the McCormick confluence (202PES138). It appears likely that McCormick Creek discharges were affecting water quality in Pescadero Creek on July 27, 2018 but not on August 1, 2019. This high year to year variability in pathogen indicator density is consistent with findings reported by the USEPA – indicator density can fluctuate on both short (hourly) and long temporal scales in river and stream sites (USEPA 2010). Potential sources of pathogen indicators in the Pescadero Creek watershed include, but are not limited to, pet waste, wildlife, bacterial growth within the creek bed and conveyance systems, and leaking public and private sewer lines or onsite wastewater treatment systems.

Table 4.2. Enterococci and E. coli levels measured in San Mateo County during WY 2018 and WY 2019 (August 1, 2019).

Site ID Site Name

Enterococci

(MPN/100ml)

Enterococci

(MPN/100ml)

E. Coli

(MPN/100ml)

E. Coli

(MPN/100ml)

WY 2018 WY 2019 WY 2018 WY 2019

202PES154 Pescadero Creek Upstream of Jones Gulch

43 16 30 13

202PES150 Jones Gulch Upstream of Confluence

36 35 ND 31

202PES144 Pescadero Creek Upstream of McCormick Creek

42 12 19 26

202PES142 McCormick Creek Upstream of Confluence

816 82 153 57

202PES138 Pescadero Creek at Memorial Park

435 23 14 29

WQO (based on 32 per 1000 recreators) 110 320

MRP 2.0 Trigger (36 per 1000 recreators) 130 410


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All monitoring data collected between WY 2014 and WY 2019 were compared to the MRP trigger thresholds and State Water Board WQOs. The results for E. coli during this period of record are shown in Figure 4.1 with samples exceeding the WQO of 320 cfu/100 mL shown in red. There were three exceedances in WY 2017 at MS4 stations in the Pillar Point Harbor watershed. The data were used to support an SSID study investigation that was completed in WY 2019. There were three exceedances in two years (WY 2014 and WY 2015) in San Mateo Creek at two of the more urbanized sites on the creek.

It is important to recognize that pathogen indicators do not directly represent actual pathogen concentrations and do not distinguish among sources of bacteria. Testing water samples for specific pathogens is generally not practical for a number of reasons (e.g., concentrations of pathogens from fecal contamination may be small and difficult to detect but still of concern, laboratory analysis is often difficult and expensive, and the number of possible pathogens to potentially test for is large). Therefore, the presence of pathogens is inferred by testing for “pathogen indicator” organisms. The USEPA recommends using E. coli and enterococci as indicators of fecal contamination based on historical and recent epidemiological studies (USEPA 2012). The USEPA pathogen indicator thresholds were derived based on human recreation at beaches receiving bacteriological contamination from human wastewater and may not be applicable to conditions in urban creeks which do not receive wastewater treatment plant discharges. Furthermore, although animal fecal waste contributes to the pathogen indicator load, it is much less likely to contain pathogens of concern to human health than human sources. In most cases, it is the human sources that are associated with REC-1 health risks rather than wildlife or domestic animal sources (USEPA 2012). As a result, the comparison of pathogen indicator results to pathogen indicator thresholds may not be appropriate and should be interpreted cautiously.


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5.0 Chlorine Monitoring

5.1 Introduction

Chlorine is added to potable water supplies and wastewater to kill microorganisms that cause waterborne diseases. However, the same chlorine can be toxic to the aquatic species. Chlorinated water may be inadvertently discharged to the MS4 and/or urban creeks from residential activities, such as pool dewatering, car washing, and over-watering landscaping, or from municipal activities, such as hydrant flushing and water main breaks.

From WY 2012 through WY 2019, in compliance with Provision C.8.c of MRP 1.0 and Provision C.8.d.ii of MRP 2.0, and to assess whether chlorine in receiving waters is present at concentrations potentially toxic to aquatic life, SMCWPPP field staff measured total and free chlorine residual in creeks where bioassessments were conducted. Total chlorine residual is comprised of “combined” chlorine and free chlorine and should always be greater than or equal to the free chlorine residual. Combined chlorine is chlorine that has reacted with ammonia or organic nitrogen to form chloramines, while free chlorine is chlorine that remains unbound. Both can be toxic to aquatic life, but chlorine dissipates into the atmosphere more quickly than chloramine.

5.2 Methods

In accordance with the BASMAA RMC Creek Status and Long-Term Trends Monitoring Plan (BASMAA 2012), field testing for free chlorine and total chlorine residual was conducted at ten sites each year concurrent with spring bioassessment sampling. From WY 2012 through WY 2018, all sites were selected using the probabilistic design described in Section 2.0. In WY 2019, three of the ten sites were selected on a targeted basis to address bioassessment management questions. Probabilistic and targeted site selection methods are described in Section 2.0.

Field testing for free chlorine and total chlorine residual conformed to methods and procedures described in the BASMAA RMC SOPs (BASMAA 2016b), which are comparable to those specified in the SWAMP QAPP. Per SOP FS-3 (BASMAA 2016b), water samples were collected and analyzed for free and total chlorine using a Pocket ColorimeterTM II and DPD Powder Pillows, which has a manufacturer reported method detection limit of 0.02 mg/L. If concentrations exceeded the MRP trigger, the site was immediately resampled. The MRP 1.0 trigger criterion, implemented in WY 2012 through WY 2015, was 0.08 mg/L. The MRP 2.0 trigger criterion, implemented in WY 2016 through WY 2019, was 0.1 mg/L. If the resample also exceeds the trigger, the site is added to the list of candidate SSID projects. Provision C.8.d.ii(4) of MRP 2.0 also specifies that “Permittees report the observation to the appropriate Permittee central contact point for illicit discharges so that the illicit discharge staff can investigate and abate the associated discharge in accordance with its Provision C.5.e – Spill and Dumping Complaint Response Program.”


The section below summarizes results from chlorine monitoring conducted during WY 2012 through WY 2018 and details results from WY 2019. Conclusion and recommendations are presented in Section 7.0.


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In WY 2019, SMCWPPP monitored ten sites for free chlorine and total chlorine residual. The measurements were compared to the MRP 2.0 trigger threshold of 0.1 mg/L. Results are listed in Table 5.1. The trigger threshold for total chlorine residual was exceeded in the initial sample and the immediate resample at one station (204R04160), Burlingame Creek in the Town of Hillsborough. The field crew noted a pipe discharging a trickle of water to the creek just upstream of the sampling site. The 4-inch PVC pipe was unburied suggesting that it was a temporary and private installation, not part of the MS4. The exceedance and the presence of the pipe were immediately reported to the Hillsborough illicit discharge contact. In WY 2019, at one station (202R03624), the free chlorine result (0.09 mg/L) was greater than the total residual chlorine result (0.06 mg/L). Inverted results such as this have been occasionally noted throughout the WY 2012 – WY 2019 monitoring program. Potential causes for these inverted results include matrix interferences, colorimeter user error, and concentrations near the detection limit. According to Hach, the supplier of the equipment and reagents, the free chlorine could have false positive results due to a pH exceedance of 7.6 and/or an alkalinity exceedance of 250 mg/L. The pH was measured concurrently with the chlorine sample, but alkalinity was not measured. The pH measured concurrently with chlorine at station 202R03624 exceeded 7.6, which may have resulted in a false positive for the free chlorine measurement. It is unlikely that the higher free chlorine readings were caused by user error. The field crew is well trained and aware of potential problems with this testing method, such as wait times between adding reagents and taking the readings and separating the free chlorine and total residual chlorine samples. The cause of the inverted free chlorine and total chlorine residual results (compared to expected) is unknown. However, it should be noted that colorimetric field instruments are generally not considered capable of providing accurate measurements of free chlorine and total chlorine residual below 0.13 mg/L, regardless of the method detection limit provided by the manufacturer. For this reason, the Statewide General Permit for drinking Water Discharges (Order WQ 2014-0194-DWQ) uses 0.1 mg/L as a reporting limit for field measurements of total chlorine residual.

Table 5.1. Summary of SMCWPPP chlorine testing results compared to MRP trigger of 0.1 mg/L, WY 2019.

Station Code

Date Creek Free

Chlorine (mg/L)1,2

Total Chlorine Residual (mg/L)1,2

Exceeds Trigger

Threshold (0.1 mg/L)2

202TUN030 05/14/19 Tunitas Creek <0.02 <0.02 No

202TUN040 05/14/19 Tunitas Creek <0.02 <0.02 No

204BEL005 05/13/19 Belmont Creek <0.02 <0.02 No

204R03635 06/10/19 Atherton Creek 0.01 0.06 No

204R04160 05/19/19 Burlingame Creek <0.02 0.1 / 0.2 Yes

204R04280 05/13/19 Belmont Creek <0.02 <0.02 No

204R04428 05/15/19 Cordilleras Creek 0.1 0.1 No

204R04600 06/10/19 Atherton Creek 0.03 0.04 No

205R04056 06/11/19 Dry Creek 0.03 0.03 No

205R05044 06/11/19 Dry Creek 0.09 0.06 No

1 The method detection limit is 0.02 mg/L; however, the Statewide General Permit for Drinking Water Discharges (Order WQ 2014-0194-DWQ) uses 0.1 mg/L as a reporting limit (minimum level) for field measurements of total chlorine residual. 2 The MRP trigger threshold of 0.1 mg/L applies to both free chlorine and total chlorine residual measurements


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A total of 80 stations have been monitored by SMCWPPP for free chlorine and total chlorine residual between WY 2012 and WY 2019 in compliance with MRP 1.0 and MRP 2.0. Occasional exceedances were recorded throughout the years and addressed by the appropriate follow-up process. Figure 5.1 maps of all the samples stations with their associated results. Each sample station has two symbols; free chlorine in the left square and total chlorine residual on the right. Larger symbols are used to represent WY 2019 results. The results exceeding the MRP 2.0 trigger threshold of 0.1 mg/L are shown in red. The results exceeding the MRP 1.0 trigger threshold of 0.08 mg/L (but below the MRP 2.0 trigger) are shown in orange. All results equal to or below 0.08 mg/L are shown in green. Since WY 2012 there have been 11 stations with exceedances of either the free chlorine (9 exceedances) or total chlorine (6 exceedances) residual trigger. Trigger exceedances tend to occur in high order streams that flow through populated areas. The values range from non-detectable levels of chlorine to 0.58 mg/L. The two highest results occurred on Redwood Creek in WY 2017.


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Figure 5.1 Chlorine sample stations and results WY 2012 – WY 2019 in San Mateo County.


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6.0 Toxicity and Sediment Chemistry Monitoring

6.1 Introduction

This section describes the results of toxicity testing, sediment chemistry monitoring, and water column pesticides monitoring, collectively referred to as pesticides and toxicity monitoring, conducted during WY 2014 through WY 2019 in compliance with Provisions C.8.c of MRP 1.0 and C.8.g of MRP 2.0. The following discussion uses the pesticides and toxicity monitoring results and data from projects external to the RMC, to inform management efforts for San Mateo County urban creeks with respect to achievement of WQOs and support of beneficial uses.

Toxicity testing provides a tool for assessing the toxic effects (acute and chronic) of all chemicals in samples of receiving waters or sediments and allows the cumulative effect of the pollutant present in the sample to be evaluated. Because different test organisms are sensitive to different classes of chemicals and pollutants, several different organisms are monitored. Sediment and water chemistry monitoring for a variety of potential pollutants conducted synoptically with toxicity monitoring provides preliminary insight into the possible causes of toxicity should they be found.

Wet and dry weather monitoring of pesticides and toxicity in urban creeks was required during both the MRP 1.0 and MRP 2.0 permit terms. However, there were slight differences between the two permit terms with regard to the required number of samples, toxicity test organisms, and chemical constituents.

Dry Weather

In WY 2014 and WY 2015, Provision C.8.c of MRP 1.0 required that two sites be sampled for pesticides and toxicity each year during the dry weather period. SMCWPPP selected these two sites from the list of ten probabilistic sites where bioassessment was conducted during the same WY. MRP 1.0 dry weather monitoring included:

• Toxicity testing in water using four species: Ceriodaphnia dubia (chronic survival and reproduction), Pimephales promelas (larval survival and growth), Selenastrum capricornutum (growth), and Hyalella azteca (survival).

• Toxicity testing in sediment using one species: Hyalella azteca (survival)21.

• Sediment chemistry analysis for pyrethroids, chlordane, dieldrin, endrin, heptachlor epoxide, lindane, dichlorodiphenyltrichloroethanes (DDT), metals, polycyclic aromatic hydrocarbons (PAHs), total organic carbon (TOC), and sediment grain size.

In WY 2016 through WY 2019, Provision C.8.g of MRP 2.0 required SMCWPPP to sample one site each year during the dry season for pesticides and toxicity. The permit provides examples of possible monitoring location types, including sites with suspected or past toxicity results,

21 Although the chronic (growth) endpoint for Hyalella azteca was not required by the MRP, it was provided by the

laboratory and reported in the UCMRs.


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existing bioassessment sites, or creek restoration sites. MRP 2.0, dry weather monitoring includes:

• Toxicity testing in water using five species: Ceriodaphnia dubia (chronic survival and reproduction), Pimephales promelas (larval survival and growth), Selenastrum capricornutum (growth), Hyalella azteca (survival) and Chironomus dilutus (survival).

• Toxicity testing in sediment using two species: Hyella azteca (survival) and Chironomus dilutus (survival).

• Sediment chemistry analysis for pyrethroids, fipronil, carbaryl, total polycyclic aromatic hydrocarbons (PAHs), metals, TOC, and sediment grain size.

Wet Weather

In WY 2014 and WY 2015, MRP 1.0 required wet weather toxicity testing at the same two sites where dry season toxicity and sediment chemistry monitoring was conducted. The wet weather toxicity monitoring was based on the same four species as was used in the dry season monitoring. No wet weather water chemistry monitoring for pesticides or other potential pollutants was required during MRP 1.0.

Provision C.8.g.iii.(3) of MRP 2.0, covering WY 2016 through WY 2019, requires a collective total of ten wet weather toxicity and water chemistry samples if the wet weather monitoring is conducted by the RMC on behalf of all Permittees. MRP 2.0 states that the monitoring locations should be representative of urban watersheds (i.e., at the bottom of watersheds). At the RMC Monitoring Workgroup meeting on January 25, 2016, RMC members agreed to collaborate on implementation of the wet weather monitoring requirements. MRP 2.0 wet weather monitoring requirements include collection of water column samples during storm events for toxicity testing using the same five organisms required for dry weather testing and analysis of pyrethroids, fipronil, imidacloprid, and indoxacarb22. All ten wet weather samples were collected in WY 2018 during a single storm event on January 8, 2018. SCVURPPP and ACCWP each collected three samples, and SMCWPPP and CCCWP each collected two samples.

6.2 Methods

6.2.1 Site Selection

In WY 2014 and WY 2015, under MRP 1.0, the two annual pesticides and toxicity monitoring sites were selected from the list of ten probabilistic sites where bioassessment surveys were conducted. See Section 2.2 of this report for a description of the probabilistic survey design. Sites were identified based on the likelihood that they would be safe to access during storm events and that fine depositional sediments would be present during the dry season.

In WY 2016 through WY 2019, under MRP 2.0, sites were selected to represent mixed-land use in urban watersheds not already being monitored for toxicity or pesticides by other programs, such as the SWAMP Stream Pollution Trends (SPoT) program. A different watershed was targeted each year with the goal of eventually developing a geographically diverse dataset.

22 Standard analytical methods for indoxacarb are not currently available. Indoxacarb analysis will not be required

until the water year following notification by the Executive Officer that a method is available.


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Specific monitoring locations within the identified creeks were based on the likelihood that they would contain fine depositional sediments during the dry season and would be safe to access during wet weather sampling, if relevant. During WY 2019, Pulgas Creek in the City of San Carlos (see Figure 6.1) was selected for monitoring.

In WY 2018, in compliance with Provision C.8.g.iii of MRP 2.0, water toxicity and pesticides samples were collected from two sites during wet weather: San Pedro Creek in the City of Pacifica and Cordilleras Creek near the City of San Carlos. San Pedro Creek was selected because it was monitored for dry weather pesticides and toxicity in WY 2017. Cordilleras Creek was selected because it was targeted for dry weather monitoring in WY 2018. The goal was to compare dry and wet weather monitoring results.

All stations monitored by SMCWPPP for wet and dry weather pesticides and toxicity during WY 2014 through WY 2019 are mapped in Figure 6.1. The SPoT station on San Mateo Creek is also mapped.

6.2.2 Sample Collection

Water and sediment samples for pesticides and toxicity monitoring were collected in accordance with SWAMP-comparable methods and procedures described in the BASMAA RMC SOPs (BASMAA 2016a) and the associated QAPP (BASMAA 2016b). Before sampling, field personnel conduct a qualitative assessment of the proposed sampling site to identify appropriate sampling locations. This is particularly necessary for sediment sampling, which requires the presence of fine-sediment depositional areas that can support at least five sub-sites within a 100-meter reach.

Water samples were collected using standard grab sampling methods. The required number of labeled amber glass bottles were filled and placed on ice to cool to < 6C. The laboratory was notified of the impending sampling delivery to meet sample hold times. Procedures used for sampling and transporting water samples are described in SOP FS-2 (BASMAA 2016a).

Sediment samples were collected after any water samples were collected. Sediment samples were collected from the top 2 cm at each sub-site beginning at the downstream-most location and continuing upstream. Field staff walk in an upstream direction, carefully avoiding disturbance of sediment at collection sub-sites. Sediment samples were placed in a compositing container, thoroughly homogenized, and then aliquoted into separate jars for chemical or toxicological analysis using standard clean sampling techniques (see SOP FS-6, BASMAA 2016a).

Samples were submitted to respective laboratories under RMC SOP FS-9 Chain of Custody procedures and field data sheets were reviewed per SOP FS-13 (BASMAA 2016a).


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Figure 6.1 Pesticide and toxicity sampling locations in San Mateo County during WY 2014 through WY 2019.


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Water and Sediment Toxicity

Toxicity data evaluation required by MRP 1.0 and MRP 2.0 involves first assessing whether the samples are toxic to the test organisms relative to the laboratory control treatment via statistical comparison. MRP 2.0 specifies using the Test of Significant Toxicity (TST) statistical approach to compare the sample to the laboratory control. For samples with toxicity (i.e., those that “failed” the TST), the Percent Effect is evaluated. The Percent Effect compares sample endpoints (survival, reproduction, growth) to the laboratory control endpoints. Both the statistical comparison (e.g., TST) and the comparison of the sample results to the laboratory control (e.g., Percent Effect) are determined by the laboratory.

For WY 2014 and WY 2015 data, Table 8.1 of MRP 1.0 identified toxicity results of less than 50% of the laboratory control as requiring follow-up action for water toxicity tests. For sediment toxicity tests in these years, MRP 1.0 Table H-1 identified toxicity results of greater than 20% less than the control as requiring follow-up action.

For WY 2016 through WY 2019 data, Provision C.8.g of MRP 2.0 identified toxicity results reported as “fail” via the TST approach and a Percent Effect of ≥ 50% as requiring follow-up action for water and sediment tests.

MRP 2.0 (WY 2016 – WY 2019) requires that the site is resampled if any toxicity test result exceeds the threshold. MRP 1.0 (WY 2014 and WY 2015) required resampling for water toxicity tests only, not sediment tests. If both the initial and follow-up sample exceed the threshold, the site is added to the list of candidate SSID projects.

Sediment Chemistry

Sediment chemistry results were evaluated using three criteria: Probable Effects Concentration (PEC) quotients, Threshold Effects Concentration (TEC) quotients and Toxicity Unit (TU) equivalents. PEC and TEC quotients are calculated as the ratio of the measured concentration to the respective PEC and TEC values from MacDonald et al. (2000). TU equivalents are calculated for individual pyrethroid pesticide results based on available LC5023, values from the literature. Because organic carbon mitigates the toxicity of pyrethroid pesticides and the LC50 values are derived on the basis of TOC-normalized concentrations, the pyrethroid concentrations as reported by the lab were divided by the measured TOC concentration at each site, and the TOC-normalized concentrations were then used to compute TU equivalents for each pyrethroid.

Under MRP 1.0 (WY 2014 and WY 2015), sites were added to the list of candidate SSID projects if three or more TEC quotients were ≥ 1.0, if the site had a mean PEC quotient ≥ 0.5, or if the sum of TU equivalents for all measured pyrethroids was ≥ 1.0.

MRP 2.0 requires that all sites where a PEC or TEC quotient is ≥ 1.0 are added to the list of candidate SSID projects. MRP 2.0 does not require consideration of pyrethroid, fipronil, or carbaryl24 sediment chemistry data for follow-up SSID projects, perhaps because pyrethroids

23 The LC50 is the concentration of a given chemical that is lethal on average to 50% of test organisms. 24 No LC50 is published for carbaryl in sediment.


87

are ubiquitous in the urban environment and little is known about fipronil and carbaryl distribution.

Evaluation of sediment chemistry data and calculation of PEC/TEC quotients and TU equivalents is based in several assumptions and considerations, including:

• For PAHs in sediment, the laboratory reports concentrations for 24 individual PAHs; whereas, PECs and TECs are listed in MacDonald et al. (2000) for total PAHs. Total PAH concentrations were calculated by summing the concentrations of 24 individual PAHs.

• Concentrations equal to one-half of the respective laboratory method detection limits were substituted for non-detect data so that calculations and statistics could be computed. Therefore, some of the TEC and PEC quotients and TU equivalents may be artificially elevated (and contribute to trigger exceedances) due to the method used to account for non-detect data.

• The TECs for bedded sediments are very conservative values that do not consider site specific background conditions and are therefore not very useful in identifying real water quality concerns in receiving waters in San Mateo County. All sites in the County are likely to have at least one TEC quotient equal to or greater than 1.0. This is due to high levels of naturally occurring chromium and nickel in geologic formations (i.e., serpentine) and soils that contribute to TEC and PEC quotients.

Water Chemistry

Provision C.8.g.iv of MRP 2.0 requires that chemical pollutant data from water and sediment monitoring be compared to the corresponding WQOs in the Basin Plan for each analyte sampled. If concentrations in the samples exceed their WQOs, then the site at which the exceedances were observed will be added to the list of candidate SSID projects. However, the Basin Plan does not contain numeric WQOs for the chemical analytes encompassed within the wet weather pesticide monitoring.

Water chemistry analysis was not part of pesticides and toxicity monitoring under MRP 1.0.


In WY 2014 and WY 2015, a total of four sites (two sites per year) were monitored for water and sediment toxicity and sediment chemistry during the wet and dry seasons. In WY 2014, sites in the Laurel Creek and Pilarcitos Creek watersheds were selected for monitoring. In WY 2015, sites in the Laurel Creek and Atherton Creek watersheds were selected for monitoring. The monitoring sites were selected from a list of locations where bioassessment surveys had been conducted. The results of these monitoring efforts were compared to MRP 1.0 trigger thresholds.

WY 2016 through WY 2019 dry weather water and sediment toxicity and sediment chemistry monitoring was conducted to satisfy the requirements specified in MRP 2.0. Dry weather monitoring took place at one site per year and was located in varying watersheds throughout San Mateo County to shed light on spatial variations in water quality present within the County. The monitored sites from WYs 2016, 2017, 2018, and 2019 were located in Laurel Creek, San Pedro Creek, Cordilleras Creek, and Pulgas Creek, respectively. In WY 2018, wet weather


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toxicity and water chemistry monitoring was conducted in San Pedro Creek and Cordilleras Creek to satisfy Provision C.8.g.iii of MRP 2.0.

Toxicity and pesticides monitoring results are described in the sections below. Conclusions and recommendations are provided in section 7.0.

6.3.1 Toxicity

WY 2019 Results

Details of the WY 2019 toxicity tests are listed in Table 6.1. Based on the WY 2019 toxicity test results, it is not necessary to add Pulgas Creek to the list of potential SSID projects. The sediment sample was not toxic to either of the test organisms. The water sample was significantly toxic to one of the five test organisms (C. dubia reproduction); however, the Percent Effect did not exceed the 50% threshold for follow-up. The cause of the water toxicity is unknown. Consistent with MRP requirements, no water chemistry samples were collected with the toxicity samples. The sediment chemistry, described in more detail in Section 6.3.2, suggests that copper and zinc may be slightly elevated in the Pulgas Creek watershed and could have caused toxicity to this test orgasms that is sensitive to a broad range of aquatic contaminants. Pyrethroids were also elevated in the sediment sample; however, no sediment toxicity to H. azteca which was observed is a test organism known to be sensitive to pyrethroid pesticides.

Table 6.1. Summary of SMCWPPP dry weather water and sediment toxicity results, Pulgas Creek, WY 2019.

Site Organism Test Type Unit

Results

% Effect

TST Value

Follow up needed

(TST "Fail" and

≥50%)

Lab Control

Organism Test

204P

UL

010

Pu

lgas

Cre

ek

July

23,

201

9

Water

Ceriodaphnia dubia Survival % 100 100 0% NA1 No

Reproduction Num/Rep 32 25.6 20.0% Fail No

Pimephales promelas Survival % 100 87.5 12.9% Pass No

Growth mg/ind 0.767 0.73 4.73% Pass No

Chironomus dilutus Survival % 100 97.5 13% Pass No

Hyalella azteca Survival % 100 100 0.00% Pass No

Selenastrum capricornutum Growth cells/ml 3560000 8000000 -125% Pass No

Sediment

Chironomus dilutus Survival % 80 80 0.00% Pass No

Hyalella azteca Survival % 97.5 90 7.69% Pass No

1 TST analysis is not performed for survival endpoint - a percent effect <25% is considered a "Pass", and a percent effect ≥25% is considered a "Fail"


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WY 2014 – WY 2019 Summary

Toxicity results for the IMR reporting period, WY 2014 through WY 2019, are summarized in Table 6.2. Details of the WY 2014 to WY 2018 toxicity tests can be found in the Urban Creeks Monitoring Reports for each year (SMCWPPP 2019a, SMCWPPP 2018, SMCWPPP 2017, SMCWPPP 2016, SMCWPPP 2015).

During WY 2014 through WY 2019, there were three toxicity tests with sample results having toxicity relative to the laboratory control and a Percent Effect exceeding the MRP trigger threshold (see Section 6.2.3 for an explanation of MRP 1.0 and 2.0 triggers). All three of these tests with trigger exceedances were conducted in WY 2014 and WY 2015 for the growth (chronic) endpoint of H. azteca, a test that was not required by the MRP but was reported by the analytical laboratory. With one exception, where the Percent Effect was below the MRP trigger threshold, the associated tests for the survival (acute) endpoint did not cause toxicity to H. azteca. H. azteca is known to be sensitive to pyrethroid pesticides and these pesticides are commonly detected in urban creek sediment samples throughout San Mateo County. Long-term monitoring of San Mateo Creek by the SPoT program suggests that pyrethroid concentrations in sediment have decreased since 2011/2012 (SMCWPPP 2019b), which may explain why MRP 2.0 sediment samples were not toxic to H. azteca.

There were 18 test results that had significant toxicity, but with a Percent Effect that did not exceed the MRP trigger thresholds. A majority of these toxicity results were found in water samples and were associated with either C. dubia reproduction (six samples), a chronic toxicity endpoint, or H. azteca survival (six samples), an acute toxicity endpoint. Five of the six water samples with toxicity to H. azteca were collected during wet season sampling events suggesting that stormwater runoff is affecting H. azteca. The water samples with toxicity to C. dubia were more evenly dispersed between wet and dry season sampling events.


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Table 6.2. Toxicity test result summary, WY 2014 – WY 2019, SMCWPPP. The Percent Effect is indicated for test results with toxicity relative to the lab control. Test results with toxicity exceeding the MRP 1.0 and MRP 2.0 trigger thresholds are shaded.

Station ID Creek Date Season

Sediment Water

C. dilutus 2 H. azteca C. dubia P. promelas C. dilutus 2 H. azteca S. capricornutum

Survival Survival Growth 2 Survival Reproduction Survival Growth Survival Survival Growth

MRP 1.0

204R01288 Laurel Cr 2/8/2014 Wet -- -- -- No No No No -- Yes (16%) No

204R01288 Laurel Cr 6/4/2014 Dry -- Yes (18%) Yes (50%) No No No No -- No No

204R01308 Pilarcitos Cr 2/8/2014 Wet -- -- -- No No No No -- No No

204R01308 Pilarcitos Cr 6/4/2014 Dry -- No Yes (43%) No Yes (33%) 1 No No -- No No

204R01448 Atherton Cr 2/6/2015 Wet -- -- -- No Yes (30%) No No -- Yes (24%) No

204R01448 Atherton Cr 7/7/2015 Dry -- No No No No No No -- No No

204R02056 Laurel Cr 2/6/2015 Wet -- -- -- No Yes (22%) No No -- Yes (45%) No

204R02056 Laurel C 7/7/2015 Dry -- No Yes (31%) No No No No -- No No

MRP 2.0

205LAU010 Laurel Cr 7/11/2016 Dry Yes (14%) No -- No Yes (31%) No No Yes (10%) Yes (29%) No

202SPE005 San Pedro Cr 7/13/2017 Dry No No -- No Yes (46%) Yes (18%) No No No No

202SPE005 San Pedro Cr 1/20/2018 Wet -- -- -- No No No Yes (23%) No Yes (16%) No

204COR010 Cordilleras Cr 7/17/2018 Dry No No -- No No No No Yes (11%) No No

204COR010 Cordilleras Cr 1/18/2018 Wet -- -- -- No No No No No Yes (20%) No

204PUL010 Pulgas Cr 7/23/2019 Dry No No -- No Yes (20%) No No No No No

Notes: 1 - The test response in one of the replicates for this test treatment was determined to be a statistical outlier; the results reported above are for the analysis of the data excluding the outlier. 2 - Chironomus dilutus testing was not required by MRP 1.0. Hyalella azteca growth was not required by either permit but is included here when reported by the lab.


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6.3.2 Sediment Chemistry

Sediment chemistry results are evaluated as potential stressors based on TEC quotients and PEC quotients according to trigger thresholds listed in MRP 1.0 and MRP 2.0 (see Section 6.2.3). Evaluation of TU equivalents was required under MRP 1.0 and, although not required under MRP 2.0, used to inform stormwater management.

WY 2019 Results

Table 6.3 lists concentrations and TEC quotients for sediment chemistry constituents (metals and total PAHs) collected in WY 2019 from Pulgas Creek. TEC quotients are calculated as the measured concentration divided by the highly conservative TEC value, per MacDonald et al. (2000)25. TECs are extremely conservative and are intended to identify concentrations below which harmful effects on sediment-dwelling organisms are unlikely to be observed. The site on Pulgas Creek exceeded the trigger threshold from MRP 2.0 of having at least one result with a TEC quotient ≥ 1.0 and will therefore be added to the list of potential SSID projects. Two of the exceedances were for copper and zinc, while the other was for nickel. Exceedances for chromium and nickel are expected in watersheds draining hillsides underlain by serpentine formations. The reasons for the copper and zinc exceedances are unknown; however, these metals are commonly found in runoff from roads and other urban uses. The 2,200-acre Pulgas Creek watershed is almost entirely urban (84%) with an impervious surface area of 48%.

Table 6.4 lists concentrations and PEC quotients for sediment chemistry constituents (metals and total PAHs) collected in WY 2019 from Pulgas Creek. PECs are intended to identify concentrations above which toxicity to benthic-dwelling organisms are predicted to be probable. No PEC quotients were greater than 1.0.

Table 6.3. Threshold Effect Concentration (TEC) quotients for WY 2019 sediment chemistry constituents. Bolded and shaded values indicate TEC quotient ≥ 1.0.

204PUL010

TEC Pulgas Creek

Metals (mg/kg DW) Concentration Quotient

Arsenic 9.79 3.6 0.37

Cadmium 0.99 0.16 0.16

Chromium 43.4 37 0.9

Copper 31.6 33.6 1.06

Lead 35.8 19 0.53

Nickel 22.7 34 1.5

Zinc 121 130 1.07

PAHs (ug/kg DW)

Total PAHs 1610 334.2 0.21 a

a. Total calculated using 1/2 MDLs for some individual PAHs.

25 MacDonald et al. (2000) does not provide TEC or PEC values for pyrethroids, fipronil, or carbaryl. Pesticides are compared to LC50 values in Table 6.5.


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Table 6.4. Probable Effect Concentration (PEC) quotients for WY 2019 sediment chemistry constituents. Bolded and shaded values indicate PEC quotient ≥ 1.0.

PEC

204PUL010

Pulgas Creek

Metals (mg/kg DW) Concentration Quotient

Arsenic 33.0 3.6 0.11

Cadmium 4.98 0.16 0.03

Chromium 111 37 0.33

Copper 149 33.6 0.23

Lead 128 19 0.15

Nickel 48.6 34 0.7

Zinc 459 130 0.28

PAHs (ug/kg DW)

Total PAHs 22,800 334.2 0.02 a

a. Total calculated using ½ MDLs for some individual PAHs.


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Table 6.5 lists the concentrations of pesticides measured in sediment samples taken in WY 2019, TOC-normalized concentrations, and TU equivalents for the pesticides for which there are published LC50 values in the literature. Many of the pesticides measured were below method detection limits (MDLs) and TU equivalents were calculated using ½ the MDL concentration. The highest TU equivalent was for bifenthrin (0.56) which is considered to be the leading cause of pyrethroid-related toxicity in urban areas (Ruby 2013) and the most-commonly detected insecticide monitored by the California Department of Pesticide Regulation (DPR) Surface Water Protection Program Monitoring (SWPP) (Ensminger 2017). The sum-of-pyrethroids TU equivalent is 1.2 which exceeds the MRP 1.0 trigger threshold. With the exception of fipronil sulfone, all other pesticides were below the MDL; therefore, the TU equivalents calculated using ½ the MDL are not very informative.

Table 6.5. Pesticide concentrations and calculated toxicity unit (TU) equivalents, WY 2019.

204PUL010

Pulgas Creek

Unit LC50

Concentration

Normalized to TOC

TU Equivalent

Total Organic Carbon % NA 1.2 NA NA

Pyrethroid

Bifenthrin µg/g dw 0.52 0.0035 0.29 0.56

Cyfluthrin, total µg/g dw 1.08 0.0009 b 0.07 0.07

Cypermethrin, total µg/g dw 0.38 0.0003 a 0.02 0.06

Deltamethrin/Tralomethrin µg/g dw 0.79 0.004 0.33 0.42

Esfenvalerate/Fenvalerate, total µg/g dw 1.54 0.0003 a 0.03 0.02

Cyhalothrin, Total lambda- µg/g dw 0.45 0.0002 a 0.01 0.02

Permethrin, Total µg/g dw 10.83 0.0028 0.23 0.02

Sum of TU Equivalents 1.2

Other MRP Pesticides of Concern

Carbaryl mg/Kg NA 0.01 a, c 0.8 NA

Fipronil ng/g dw 306 0.26 a 21.3 0.07

Fipronil Desulfinyl ng/g dw NA c 0.26 a 21.3 NA

Fipronil Sulfide ng/g dw 435 0.26 a 21.3 0.05

Fipronil Sulfone ng/g dw 158 0.62 b 51.7 0.33 a. Concentration was below the method detection limit (MDL). TU equivalents calculated using 1/2 MDL. b. TU equivalents calculated from concentration below the reporting limit but above the MDL (J-flagged). c. Sources: Amweg et al. 2005 and Maund et al. 2002 for pyrethroids; Maul et al. for fipronil compounds d. No available LC50 value for Carbaryl or Fipronil Desulfinyl.


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In compliance with the MRP, a grain size analysis was conducted on the sediment sample (Table 6.6). The sample was 5.5% fines (i.e., 1.5% clay and 4.0% silt).

Table 6.6. Summary of grain size for site 204PUL010 in San Mateo County, WY 2019.

Grain Size (%) 204PUL010

Pulgas Creek

Clay <0.0039 mm 1.5%

Silt 0.0039 to <0.0625 mm 4.0%

Sand

V. Fine 0.0625 to <0.125 mm 6.2%

Fine 0.125 to <0.25 mm 23.3%

Medium 0.25 to <0.5 mm 32.1%

Coarse 0.5 to <1.0 mm 19.2%

V. Coarse 1.0 to <2.0 mm 13.7%

Granule 2.0 to <4.0 mm 9.3%

Pebble

Small 4 to <8 mm 6.6%

Medium 8 to <16 mm 3.3%

Large 16 to <32 mm 0%

V. Large 32 to <64 mm 0% Note: Sum of grain size values for both sites is greater than 100% due to the laboratory analytical methods used.

WY 2014 – WY 2019 Summary

Between WY 2014 and WY 2019, there were no PEC quotients calculated for the SMCWPPP sediment chemistry dataset that were ≥ 1.0 for analytes other than chromium and nickel. Chromium and nickel are excluded from this PEC/TEC analysis because they are contributed primarily by serpentine formations present in the watersheds where monitoring occurred. Excluding chromium and nickel, there were four samples with TEC quotients ≥ 1.0; the more conservative of the two evaluation criteria. The constituents and locations with TEC quotients ≥ 1.0 included:

• Legacy insecticide DDT compounds, which were monitored under MRP 1.0 but not under MRP 2.0, and exceeded the TEC in Laurel Creek WY 2014 and WY 2015 and in Atherton Creek in WY 2015;

• Individual PAHs, pyrene and chlordane, in Atherton Creek in WY 2015 and chlordane in Laurel Creek in WY 2015; and

• Copper and zinc in Pulgas Creek in WY 2019.

Table 6.7 lists TU equivalents for pesticides with LC50s available in the literature and concentrations for pesticides without LC50s for sediment samples collected in WY 2014 – WY 2019. The sum-of-pyrethroids TU equivalents ranged from 0.08 (San Pedro Creek in WY 2017) to 7.9 (station 204R01288 on Laurel Creek in WY 2014). The Laurel Creek sediment sample with the high pyrethroid TU equivalent was collected from a location relatively high in the watershed (Figure 6.1). Subsequent sampling at stations near the bottom of the Laurel Creek watershed in WY 2015 and WY 2016 had lower TU equivalents of 0.07 and 2.6, respectively. All three of these Laurel Creek sediment samples also had sediment toxicity (Table 6.2). The WY 2014 and WY 2015 samples had chronic (growth) toxicity to the pyrethroid-sensitive test


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organism, H. azteca, with Percent Effects exceeding the MRP 1.0 trigger threshold. The WY 2016 Laurel Creek sample was not toxic to H. azteca but was toxic to C. dilutus with a Percent Effect that did not exceed the MRP 2.0 trigger threshold. Four samples had sum-of-pyrethroid TU equivalents that exceeded the MRP 1.0 trigger threshold of 1.0: Pilarcitos Creek in WY 2014, Laurel Creek in WY 2014 and WY 2015, and Pulgas Creek in WY 2019.

Sampling for fipronil and carbaryl pesticides began in WY 2016 with adoption of MRP 2.0 and the fipronil degradates were added in WY 2017. Carbaryl has not been detected in any of the sediment samples (Table 6.7). Fipronil and/or fipronil sulfone were detected in San Pedro Creek and Pulgas Creek at TOC normalized concentrations below the LC50.


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Table 6.7. TU equivalent summary for San Mateo County sediment samples, WY 2014 – WY 2019.

Analyte Pyrethroids Other MRP Pesticides of Concern

Bifenthrin Cyfluthrin Cypermethrin Deltamethrin Esfenvalerate Lambda-

cyhalothrin Permethrin

Sum Pyrethroids

Carbaryl Fipronil Fipronil

desulfinyl Fipronil sulfide

Fipronil sulfone

LC50 c 0.52 µg/g dw

1.08 µg/g dw

0.38 µg/g dw

0.79 µg/g dw

1.54 µg/g dw

0.45 µg/g dw

10.83 µg/g dw

- NA d 306 ng/g

dw NA d

435 ng/g dw

158 ng/g dw

Station ID Creek Date

MRP 1.0

202R01308 Pilarcitos 6/4/2014 1.06 0.24 <MDL 0.22 b <MDL <MDL 0.15 1.9 a - - - - -

204R01288 Laurel 6/4/2014 5.19 1.02 0.58 0.66 <MDL <MDL 0.32 7.9 a - - - - -

204R01448 Atherton 7/7/2015 0.56 0.06 <MDL <MDL <MDL <MDL 0.03 0.7 a - - - - -

204R02056 Laurel 7/7/2015 0.51 0.07 <MDL <MDL <MDL <MDL <MDL 0.7 a - - - - -

MRP 2.0

204LAU010 Laurel 7/11/2016 1.37 0.36 0.23 b 0.51 <MDL 0.09 b 0.05 2.6 a <MDL <MDL - - -

202SPE005 San Pedro 7/13/2017 0.04 <MDL <MDL <MDL <MDL <MDL 0.001 b 0.08 a <MDL 0.02 b <MDL <MDL 0.08 b

204COR010 Cordilleras 7/17/2018 0.25 b <MDL <MDL 0.10 b <MDL <MDL 0.08 b 0.52 a <MDL <MDL <MDL <MDL <MDL

204PUL010 Pulgas 7/23/2019 0.56 0.07 b <MDL 0.42 <MDL <MDL 0.02 1.2 a <MDL <MDL <MDL <MDL 0.33 b

a. TU equivalent calculated using 1/2 MDL and total calculated using 1/2 MDLs for some individual pyrethroids. b. TU equivalents calculated from concentration below the reporting limit (J-flagged). c. Sources: Amweg et al. 2005 and Maund et al. 2002 for pyrethroids; Maul et al. 2008 for fipronil compounds d. No available LC50 value for Carbaryl or Fipronil Desulfinyl.


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6.3.3 Pesticides in Water

During WY 2018, wet weather water samples were collected for pesticide analysis at two sites in San Mateo County (San Pedro Creek and Cordilleras Creek) to fulfill Provision C.8.g.iii.(3) of MRP 2.0. Results were reported in the WY 2018 UCMR (SMCWPPP 2019a). The concentrations of most pesticides analyzed were below the MDL, meaning that these analytes were reported as non-detects. The neonicotinoid, imidacloprid was found at detectable levels at one of the two sites (Cordilleras Creek). Additionally, detectable levels of fipronil and its degradation products were found at both sites. There are no WQOs specified in the San Francisco Bay Basin Plan for the water column pesticide analytes. As a result, no WQO or MRP trigger threshold exceedance analysis was performed on wet weather pesticide data.

6.3.4 Additional Pesticide Monitoring Efforts

Throughout the monitoring period associated with the sampling results described in this report, several additional programs external to SMCWPPP and the RMC conducted similar pesticides and toxicity studies within California. These studies provide valuable data for comparison against RMC findings to view regional water quality in a broader spatial and temporal context, ultimately providing more accurate and complete answers to the management questions set forth by the MRP.

DPR SWPPP Monitoring

Mentioned previously in this document, the DPR SWPP is one of the largest pesticide monitoring and management efforts currently being undertaken in California. Pesticide studies conducted by the DPR SWPP evaluate the frequency of pesticide detections at any concentration and make use of USEPA aquatic benchmarks for many pesticide compounds. DPR provides web access to a number of their monitoring reports which contain detailed analyses of USEPA aquatic benchmark exceedance rates. DPR also maintains the Surface Water Database (SURF) to provide public access to quantitative pesticide data from a wide array of surface water monitoring studies. This database could be queried in the future to allow for the leverage of DPR monitoring data in more complex analyses of MRP pesticide data.

In WY 2017, DPR conducted two studies in Northern and Southern California that involved pesticides and toxicity monitoring at urban sites in Alameda, Contra Costa, Placer, Sacramento, Santa Clara (Guadalupe River – see Figure 6.1), Los Angeles, Orange, and San Diego Counties. Both water and sediment samples were collected and analyzed for a wide range of pesticide compounds. In both the Northern and Southern California studies, bifenthrin and fipronil were found to be among the most frequently detected pesticides. Additionally, pyrethroid concentrations were found to be above their USEPA minimum benchmarks for toxicity to aquatic life for the majority of samples with the exception of cyfluthrin. The studies also state that the detection frequencies of most pyrethroids have remained consistent over recent years. (Budd 2018 and Ensminger 2017)

In WY 2018, DPR again conducted two urban monitoring studies in Northern and Southern California that targeted watersheds in the same counties sampled during WY 2017 and involved the collection of water and sediment samples. Similar to WY 2017, bifenthrin was among the most frequently detected insecticides in water samples from both the Northern and Southern California WY 2018 studies. In the Northern California study, bifenthrin was the most frequently


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detected insecticide and second most frequently detected compound in water samples with a detection frequency (DF) of 76%. In the Southern California study, bifenthrin was the most frequently detected pyrethroid insecticide and the fifth most frequently detected compound in water samples with a DF of 72%. Fipronil and its degradates were also detected at high rates in water samples from the Northern and Southern California studies. While fipronil itself only had a DF of 48% in the Northern California study, fipronil and its degradates collectively had a DF of 72%. Out of these compounds, fipronil sulfone was found at the highest rate with a DF of 70%. Fipronil was also found at a high rate during the Southern California study with a DF of 76%. Its degradates were also found in a large portion of samples, with fipronil sulfone again being the most found with a DF of 67%. Sediment samples from Northern and Southern California were collected and analyzed for bifenthrin and eight other pyrethroids, but concentrations of fipronil and its degradates were not measured. In both studies, bifenthrin was detected in all samples and was also responsible for the greatest magnitude of TUs. (Budd 2019 and Ensminger 2019)

Findings from the WY 2017 and WY 2018 DPR studies generally corroborate the results garnered from SMCWPPP pesticides monitoring. In particular, bifenthrin has been the most frequently detected pesticide in samples collected by SMCWPPP from WYs 2014 through 2019 and responsible for the high-magnitude TU equivalents. Similarly, fipronil and/or its degradates were found at detectable levels in 50% of SMCWPPP sediment samples.

SPoT Monitoring Program

The SPoT Monitoring Program conducts annual dry season monitoring (subject to funding constraints) of sediments collected from a statewide network of large rivers. The goal of the SPoT Program is to investigate long-term trends in water quality. Sites are targeted in bottom-of-the-watershed locations with slow water flow and appropriate micromorphology to allow deposition and accumulation of sediments, including a station near the mouth of San Mateo Creek (Figure 6.1). In most years, sediments are analyzed for toxicity, pesticides, metals, PCBs, mercury, and organic pollutants (Phillips et al. 2014). The most recent technical report prepared by SPoT program staff was published in 2016 and describes seven-year trends from the initiation of the program in 2008 through 2014 (Phillips et al. 2016). An update to the report is anticipated in the near future.

Toxicity testing was conducted by SPoT in San Mateo County using H. azteca as the indicator organism and the TST statistical approach, similar to a subset of the toxicity testing completed by SMCWPPP. SPoT samples were characterized as highly toxic if the percent survival was lower than the threshold of 38.6% survival identified as the lower limit survival rate threshold for high toxicity (Anderson et al. 2011). SPoT reported that H. azteca toxicity responses have been consistent over the seven-year monitoring period with toxic and highly toxic samples accounting for an average of 18.6% of the samples tested (Phillips et al. 2014). This average aligns relatively closely with the total amount of toxicity exceedances attributed to H. azteca survival found during SMCWPPP monitoring from WY 2014 through WY 2019, which was 12.5%. The SPoT study also calculated five-year rolling averages of toxicity results from 2008 to 2012 and again from 2010 to 2014 to resolve temporal trends in the data. It was found that while the total number of sites exhibiting no toxicity increased from the first averaging period to the second, the number of sites exhibiting moderate to high toxicity also increased during this time (Phillips et al. 2014).

During SPoT sediment chemistry monitoring, the average total pyrethroid concentrations were shown to have doubled from 2010 to 2013. The SPoT analysis identified urban monitoring sites


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as the exclusive cause of the increase in average pyrethroid concentrations, as pyrethroid concentrations in agricultural and other land use areas remained consistently low throughout the entirety of the monitoring period. The study identified bifenthrin as the primary driver of the increase in average pyrethroid concentrations with a DF of 73% throughout the extent of the monitoring period (Phillips et al. 2014). These findings contrast with the results of the most recent SMCWPPP monitoring period, which have not shown a measurable increase in pyrethroid-related water quality impairment. Additionally, results from SPoT testing for fipronil at urban sites in 2013 and 2014 showed that the DF of fipronil and its degradates in addition to their average and maximum concentrations increased between the two years.


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7.0 Conclusions and Recommendations

This section includes conclusions and recommendations from the review of WY 2014 through WY 2019 Creek Status and Pesticides & Toxicity Monitoring data that were presented in the preceding chapters. In addition, it evaluates probabilistic bioassessment data collected in San Mateo County from WY 2012 through WY 2019.

In WY 2019, in compliance with Provisions C.8.d and C.8.g of MRP 2.0 and the BASMAA RMC Creek Status and Long-Term Trends Monitoring Plan (BASMAA 2012), SMCWPPP continued to implement a two-component monitoring design that was initiated in WY 2012. The strategy includes a regional ambient/probabilistic bioassessment monitoring component and a component based on local targeted monitoring for general water quality parameters and pesticides/toxicity. The combination of these monitoring designs allows each individual RMC participating program (including SMCWPPP) to assess the status of Beneficial Uses in local creeks within its jurisdictional area, while also contributing data to eventually answer management questions at the regional scale (e.g., differences between aquatic life condition in urban and non-urban creeks).

Conclusions from Creek Status and Pesticides/Toxicity Monitoring conducted during WY 2014 through WY 2019 (WY 2012 through WY 2019 for bioassessment) in San Mateo County are based on the management questions from the MRP presented in Section 1.0 of this report:

1) Are water quality objectives, both numeric and narrative, being met in local receiving waters, including creeks, rivers, and tributaries?

2) Are conditions in local receiving water supportive of or likely supportive of beneficial uses?

The first management question was addressed primarily through the evaluation of probabilistic and targeted monitoring data with respect to WQOs and triggers defined in the MRP. A summary of trigger exceedances observed for each WY 2019 site is presented in Table 7.2. Trigger exceedances from WY 2014 through WY 2018 are summarized in this IMR, described in prior annual monitoring reports (SMCWPPP 2019a, SMCWPPP 2018, SMCWPPP 2017, SMCWPPP 2016, SMCWPPP 2015), and listed in Attachment 4. In compliance with Provision C.8.e.i of MRP 2.0, SMCWPPP coordinates with the RMC to maintain a comprehensive list of all monitoring results from the region (since WY 2016) exceeding trigger thresholds. Sites where triggers are exceeded may indicate potential impacts to aquatic life or other beneficial uses and are considered for future evaluation via Stressor/Source Identification projects.

The second management question was addressed primarily by assessing indicators of aquatic biological health using benthic macroinvertebrate and algae data. The indices of biological integrity based on BMI and algae data (i.e., CSCI and ASCI) are direct measures of aquatic life beneficial uses. Biological condition scores were compared to physical habitat and water quality data collected synoptically with bioassessments to evaluate whether any correlations exist that may help explain the variation in biological condition scores. Continuous monitoring data (temperature, dissolved oxygen, pH, and specific conductance) were evaluated with respect to COLD and WARM Beneficial Uses. Finally, pathogen indicator data were used to assess REC-1 (water contact recreation) Beneficial Uses.


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All monitoring and data validation were conducted using methods consistent with the BASMAA RMC QAPP (BASMAA 2016a) and SOPs (BASMAA 2016b). Recommendations for future monitoring are described in Section 7.3.

7.1 Conclusions

7.1.1 Biological Condition Assessment

In WY 2012 through WY 2019, bioassessment monitoring was conducted in compliance with Provisions C.8.c of MRP 1.0 and C.8.d.i of MRP 2.0. Nearly all bioassessment monitoring (87 of 90 sites) was performed at sites selected randomly using the regional probabilistic monitoring design. The probabilistic monitoring design allows each individual RMC participating program to objectively assess stream ecosystem conditions within its jurisdictional area while contributing data to answer regional management questions about water quality and beneficial use condition in San Francisco Bay Area creeks. The monitoring design was developed to address the following management questions from the BASMAA RMC Creek Status and Long-Term Trends Monitoring Plan (BASMAA 2012):

1. What is the condition of aquatic life in creeks in the RMC area; are water quality objectives met and are beneficial uses supported?

2. What are major stressors to aquatic life in the RMC area?

3. What are the long-term trends in water quality in creeks over time?

The first question (i.e., What is the condition of aquatic life in creeks in the RMC area; are water quality objectives met and are beneficial uses supported?) was addressed by assessing indicators of aquatic biological health at probabilistic sampling locations. Over the past eight years (WY 2012 through WY 2019), SMCWPPP and the Regional Water Board have sampled 87 probabilistic sites in San Mateo County, providing a sufficient sample size to estimate ambient biological condition for urban streams countywide within known estimates of precision. Stream condition was assessed using three different types of indices/tools: the BMI-based CSCI, the draft benthic algae-based ASCI (diatom, soft algae, and hybrid), and the physical habitat-based IPI. Of these three, the CSCI is the only tool with an MRP trigger threshold for follow-up SSID consideration.

The second question (i.e., What are major stressors to aquatic life in the RMC area?) was addressed by the evaluation of physical habitat and water chemistry data collected at the probabilistic sites, as potential stressors to biological health. Assessing the extent and relative risk of stressors can help prioritize stressors and inform local management decisions. The stressor levels were compared to biological indicator data (i.e., CSCI and ASCI scores) through correlation and random forest models. The methods were consistent with the approach used in the RMC Five-Year Bioassessment Report (BASMAA 2019) which analyzed the first five years (WY 2012 – WY 2016) of regional bioassessment data. Results from SMCWPPP’s assessment are compared to the regional assessment. A detailed description of the methods and results for the countywide analysis of bioassessment data is provided in Attachment 3.

The third question (i.e., What are the long-term trends in water quality in creeks over time?) was addressed by assessing the change in biological condition over several years. Changes in biological condition over time can help evaluate the effectiveness of management actions.


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7.1.1.1 Bioassessment Data (WY 2019)

In WY 2019, ten sites were sampled for benthic macroinvertebrates, benthic algae, and nutrients. Physical habitat was also assessed at each of the ten stations, and general water quality parameters were measured using a pre-calibrated multi-parameter field probe. Seven of the ten sites were randomly selected using the probabilistic monitoring design and three were targeted. Two of the targeted sites were in Tunitas Creek and were selected to follow-up on previous CSCI scores that were lower than expected. The third targeted site, in Belmont Creek, was selected to provide baseline data in a creek where a multi-benefit project is planned.

CSCI scores and water quality data were compared to applicable WQOs and triggers identified in the MRP. Sites with results that exceed WQOs and triggers are considered as candidates for SSID projects, consistent with Provision C.8.e of MRP 2.0.

• All seven of the probabilistic sites and the site in Belmont had CSCI scores below the MRP trigger threshold of 0.795. These eight sites were all classified as urban and were located in creeks that drain to San Francisco Bay.

• One site on Atherton Creek had a specific conductance level that exceeded the MRP trigger threshold. Other nutrient or general water quality parameters were not measured at concentrations exceeding WQOs or MPR trigger thresholds.

Tunitas Creek

With redwood forest dominated headwaters, the Tunitas Creek watershed is relatively undeveloped and drains to the Pacific Ocean. In WY 2016, the CSCI score from a site in Tunitas Creek was 0.55, which is in the lowest stream condition category (very likely altered). Targeted monitoring at the same site in WY 2019 resulted in a CSCI score of 0.84, which is in the second highest condition category (possibly intact) and above the MRP trigger threshold. It is unknown why the CSCI score increased in WY 2019, when there were no known changes in land use or land management. It is possible that the CSCI tool produces results that are more variable than expected under changing weather conditions. Water Year 2016 was the fifth year in a row with below average rainfall; whereas, WY 2019 had above average rainfall and was within two years of the very wet year of WY 2017.

7.1.1.2 Countywide Bioassessment Data (WY 2012 – WY 2019)

The bioassessment data collected at probabilistic sites from WY 2012 to WY 2019 (n=87) were evaluated to assess the current condition of streams in the County and to identify stressors that are likely to pose the greatest risk to stream health. The methods used to evaluate bioassessment data were consistent with the approach used to develop the RMC Five-Year Bioassessment Report (BASMAA 2019).

Biological Condition Assessment

Four biological indicators (CSCI and ASCI-D, ASCI-SB, ASCI-H) were used to assess stream conditions. Results of the analysis indicate that much of the stream length in San Mateo County is in poor biological condition. Aquatic life uses may not be fully supported at most sites sampled by SMCWPPP. These findings should be interpreted with the understanding that the survey focused on urban streams. Approximately 65% of the samples (60 of 90) were collected at urban sites. Although the low non-urban sample size precludes making any definitive


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comparisons, bioassessment scores at non-urban sites were generally higher than scores at urban sites:

• A total of 60 of the 90 (67%) bioassessment sites (including three targeted sites) received CSCI scores that were below the MRP trigger (0.795), corresponding to the two lower condition categories (likely altered and very likely altered). Fifty-five of the 60 low scoring sites were classified as urban. The proportion of sites with good biological conditions was much higher in watersheds draining to the Pacific Ocean compared to sites located in watersheds draining to San Francisco Bay.

• Cumulative frequency functions (CDFs) for CSCI scores indicate there is a 48% probability that a random site in San Mateo County will have a CSCI score below 0.79 (i.e., likely altered or very likely altered). There is an 86% probability that a random urban site in San Mateo County will have a CSCI score below 0.79 and there is a 22% probability that a random non-urban site will have a CSCI score below 0.79.

• Biological conditions based on algae data (ASCI) differ from the conditions based on BMI data (CSCI). The ASCI-H tool applies diatom and soft bodied algae data, the ASCI-D tool applies diatom data, and the ASCI-SB tool applies soft bodied algae data. Table 7.1 summarizes the percent of sites in each condition category for each of the four indices. The percent of sites in each category based on ASCI-D and ASCI-H were similar in magnitude to the CSCI results. The ASCI-SB scores do not appear to be associated with the urban disturbance gradient. A higher percent of sites was in the likely intact condition category based on ASCI-SB scores, and a lower percent was in the very likely altered category. Table 7.1. Percent of sites in San Mateo County in each condition category, WY 2012 – WY 2019.

Index 1

Sites with

index score 2

Likely Intact

Possibly Altered


Altered

Benthic Macroinvertebrates (BMI)

CSCI 90 21% 12% 11% 56%

Benthic Algae

ASCI-D 90 19% 16% 19% 47%

ASCI-SB 85 52% 25% 9% 14%

ASCI-H 85 13% 9% 20% 59%

1 Indices: CSCI = California Stream Condition Index; ASCI-D = Diatom Algae Stream Condition Index; ASCI-SB = Soft Bodied Algae Stream Condition Index; ASCI-H = Hybrid Algae Stream Condition Index. 2 Index scores for ASCI-SB and ASCI-H were not calculated for 5 bioassessment sites due to insufficient soft algae taxa.

Stressor Assessment

The association between biological indicators (CSCI and ASCI-H) and stressor data was evaluated in the using Spearman’s rank correlation and random forest models. The results indicate that each of the biological indicators respond to different types of stressors:


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• The random forest model of CSCI scores indicates that landscape, habitat, and water-quality stressors, specifically road density, impervious area, and total nitrogen were the best predictors of biological condition. The random forest model of ASCI-H scores indicates habitat and water-quality stressors, specifically temperature, channel alteration, and combined human disturbance (HDI) were the best predictors of biological condition.

• In the Spearman’s rank correlation analysis, nutrients (Total Nitrogen and Total Kjeldahl Nitrogen) correlated better with CSCI scores from sites on creeks flowing to San Francisco Bay than sites on creeks flowing to the Pacific Ocean. Nutrient stressors (Total Kjeldahl Nitrogen and Total Nitrogen) also ranked as important variables in the random forest models.

• Results of the San Mateo County stressor assessment differ from the RMC regional assessment. Although both San Mateo County and regional CSCI scores are strongly influenced by imperviousness in the contributing area, the regional assessment did not identify nutrients as important predictors of CSCI scores (BASMAA 2019).

It should be noted that despite these apparent relationships to stressors, these analyses do not determine causation, particularly as stressors from habitat/landscape factors are often present at the same sites that exhibit water quality impairment.

7.1.1.3 Trend Assessment

Based on a review of the probabilistic data collected in San Mateo County, it appears that analysis of long-term trends using the probabilistic dataset will require more than eight years of monitoring. For example, annual median CSCI scores at urban sites were similar during all years of MRP monitoring (WY 2012 – WY 2019).

Comparison of the probabilistic dataset with targeted data collected prior to adoption of the MRP (i.e., WY 2002 to WY 2007) allows for a semi-quantitative trends assessment. SMCWPPP conducted bioassessments in four San Mateo County watersheds between 2002 and 2007 as part of watershed assessment and monitoring requirements in its municipal stormwater NPDES Permit (Pre-MRP). Biological conditions, based on CSCI scores, during Pre-MRP and MRP time periods were compared for all sites within each watershed. Median CSCI scores were consistently higher for sampling events occurring during the MRP. Likewise, comparison of data from three individual sites that were monitored during each of the time periods shows higher CSCI scores for the MRP time period.

7.1.2 Continuous Monitoring for Temperature and General Water Quality

Continuous monitoring of water temperature and general water quality in WY 2012 through WY 2019 was conducted in compliance with Provisions C.8.c of MRP 1.0 and C.8.d.iii – iv of MRP 2.0. Hourly temperature measurements were recorded at a minimum of four sites each year from April through September. Continuous (15-minute) general water quality measurements (pH, DO, specific conductance, temperature) were recorded at two sites each year during two 2-week periods in spring (Event 1) and summer (Event 2). Monitoring was conducted to address the following management questions from the BASMAA RMC Creek Status and Long-Term Trends Monitoring Plan (BASMAA 2012):

1. What is the spatial and temporal variability in water quality conditions during the spring and summer season?


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2. Do general water quality measurements indicate potential impacts to aquatic life?

Monitoring sites were selected based on the presence of significant fish and wildlife resources as well as historical and/or recent indications of water quality concerns. The same sites were often monitored for multiple years to gain a better understanding of the range of water quality conditions that may occur over time. In some years, continuous monitoring data were used to support or follow-up on SSID investigations. The results from continuous monitoring of water temperature and general water quality in San Mateo County in WY 2014 through WY 2019 may be summarized as follows:

• The San Mateo Creek watershed, which supports steelhead rearing and spawning habitat, was targeted for general water quality monitoring in WY 2014 and WY 2015. Data were used to support an SSID study investigating low dissolved oxygen concentrations. The study confirmed that historic low dissolved oxygen concentrations were likely caused by low dry season flows which no longer occurred as a result of increased dry season releases from the Crystal Springs Reservoir. However, the MRP MWAT trigger of 17°C was exceeded in WY 2015.

• The San Francisquito Creek watershed was targeted for temperature monitoring in WY 2014 through WY 2016, which represents a worst-case scenario for summer temperatures due to persistent drought conditions. Creeks in the watershed typically cease to have continuous flow through the dry season, even during non-drought years. Therefore, all sites were located in pools that have historically remained wet throughout the summer and likely provide refuge for cold water fish. In all three years of monitoring, the MRP MWAT trigger of 17°C was exceeded at one or more station.

• The Bear Creek tributary to San Francisquito Creek was targeted for general water quality monitoring in WY 2016 to evaluate water quality in reaches that have historically supported juvenile steelhead rearing and spawning habitat. Both stations had become isolated pools by the summer monitoring event but would not have provided good refuge for cold water fish due to low dissolved oxygen concentrations that were below WQOs for WARM and COLD freshwater habitat.

• The San Pedro Creek watershed, which contains the northern-most population of naturally producing steelhead trout in San Mateo County, was targeted for temperature and general water quality monitoring in WY 2017 and WY 2018. No WQOs or MRP trigger thresholds were exceeded in either year.

• The Tunitas Creek watershed, which currently supports steelhead populations, was targeted for temperature and general water quality monitoring in WY 2019 to supplement targeted bioassessment monitoring also conducted in Tunitas Creek. As described above, in WY 2016 the CSCI score for a site on Tunitas Creek was in the very likely altered condition category, an unexpectedly low score considering the low level of urban development in the watershed. In WY 2019, the CSCI score was much higher, in the possibly intact category. In addition, no WQOs or MPR trigger thresholds were exceeded in the continuous monitoring dataset.

Overall, continuous monitoring results typically reveal that temperature and specific conductivity increase in the downstream direction which is also characterized by increasing urbanization in San Mateo County watersheds. In addition, the MRP MWAT trigger threshold of 17°C is often


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exceeded. These exceedances result in sites being placed on the list of candidate SSID projects, but are usually explained by lack of continuous flow in the late summer. Other locations where the MWAT trigger is exceeded are in reaches that cold water fish travel through rather than reside.

7.1.3 Pathogen Indicator Monitoring

From WY 2014 through WY 2019, in compliance with Provisions C.8.c of MRP 1.0 and C.8.d.v of MRP 2.0, SMCWPPP collected five grab samples per year for pathogen indicator bacteria analysis. Monitoring was conducted in three areas at sites selected to inform bacteria SSID investigations and/or follow-up on reports of potential high bacteria in creeks where water contact recreation (REC-1) is likely (e.g., by San Mateo County Parks staff). The overall goal of pathogen indicator monitoring is to assess whether WQOs are being met, i.e., supportive of REC-1 Beneficial Uses. The results from pathogen indicator monitoring in San Mateo County in WY 2014 through WY 2019 may be summarized as follows:

• The San Mateo Creek watershed was targeted in WY 2014 through WY 2016 to support and follow-up on the San Mateo Creek Pathogen Indicator SSID Project (SMCWPPP 2016). The study found that E. coli followed predictable seasonal and spatial patterns, with higher densities observed during wet season monitoring events and at stations lower in the watershed. There were no exceedances of the E. coli WQO in WY 2016 samples, suggesting that the control actions had a beneficial impact on water quality in San Mateo Creek.

• Creek and MS4 stations were sampled in WY 2017 to support the Pillar Point Harbor Watershed Pathogen Indicator SSID Project. Results of the SSID study showed that E. coli densities were highly variable and did not follow predictable seasonal patterns. The Final Project report is included as an appendix to Part C (SSID Status Report) of this IMR.

• The Pescadero Creek watershed, in the vicinity of Memorial County Park, was targeted in WY 2018 and WY 2019 to characterize geographic patterns of pathogen indicator densities in the area. Two of the WY 2018 grab samples exceeded the enterococci WQO and the pattern suggested a bacterial source in the McCormick Creek tributary. In WY 2019, the McCormick Creek station had the highest bacteria densities of the monitored stations but no WQOs were exceeded.

Overall, pathogen indicator monitoring results from San Mateo County are highly variable and sometimes exceed WQOs. It is important to recognize that pathogen indicators do not directly represent actual pathogen concentrations and do not distinguish among sources of bacteria. Sources of pathogen indicator bacteria include homeless encampments, wildlife, livestock, pets, leaking septic systems/sanitary sewers, and regrowth of bacteria in the environment. Bacteria from human sources are more likely to be associated with human health risks during water contact recreation. As a result, the comparison of pathogen indicator results to WQOs may not be appropriate and should be interpreted cautiously.

7.1.4 Chlorine Monitoring

From WY 2012 through WY 2019, in compliance with Provision C.8.c of MRP 1.0 and Provision C.8.d.ii of MRP 2.0, SMCWPPP collected field measurements of total and free chlorine residual in creeks where bioassessments were conducted.


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While chlorine residual has generally not been a concern in San Mateo County creeks, WY 2019 and prior monitoring results suggest there are occasional trigger exceedances of free chlorine and/or total chlorine residual in the County. Trigger exceedances may be the result of one-time potable water discharges, and it is generally challenging to determine the source of elevated chlorine from such episodic discharges. Furthermore, chlorine in surface waters can dissipate from volatilization and reaction with sediment and organic matter. In WY 2019, there was one exceedance of the MRP trigger for chlorine (0.1 mg/L). Over the past eight years of monitoring (WY 2012 – WY 2019), there have been a total of 11 sites with chlorine trigger exceedances (including the one site in WY 2019).

7.1.5 Pesticides and Toxicity Monitoring

Toxicity testing, sediment chemistry monitoring, and water column pesticides monitoring, collectively referred to as pesticides and toxicity monitoring, was conducted during WY 2014 through WY 2019 in compliance with Provisions C.8.c of MRP 1.0 and C.8.g of MRP 2.0. There were slight differences between the two permit terms regarding the required number of samples, toxicity test organisms, chemical constituents, and MRP triggers.

Data Evaluation Summary

There are five toxicity test species for water samples and two test species for sediment samples. The test organism H. azteca, required for water and sediment is known to be sensitive to pyrethroid pesticides. The test organism C. dilutus, added in MRP 2.0, is known to be sensitive to neonicotinoids. A two-tiered approach is applied to assess toxicity. First, organism responses from ambient samples are compared to responses from appropriate laboratory control samples using a statistical comparison. This is followed by a comparison to a “threshold value” or “Percent Effect” that indicates the magnitude of the difference in response. The MRP 2.0 trigger threshold is 50 Percent Effect in the initial sample and a second, follow-up sample for both water and sediment toxicity tests. The MRP 1.0 trigger threshold was 20 Percent Effect in sediment samples with no follow-up required and 50 Percent Effect in the initial and follow-up water samples.

Sediment chemistry data for metals, PAHs, and legacy pesticides (MRP 1.0 only) are compared to Threshold Effect Concentrations (TECs) and Probably Effect Concentrations (PECs) published by MacDonald et al. (2000). Most samples in San Mateo County have chromium and nickel concentrations that exceed the TEC and PEC. These metals are naturally occurring in the serpentine formations that underly mountains and hills in the region. Sediment chemistry data for pyrethroid and fipronil (MRP 2.0 only) pesticides are compared to TOC-normalized LC50s, calculated at Toxicity Unit equivalents. There are no WQOs for the suite of monitored constituents for comparison to water chemistry data.

Under MRP 1.0 (WY 2014 and WY 2015), pesticides and toxicity monitoring stations were selected from the list of bioassessment stations surveyed those years. Under MRP 2.0 (WY 2016 – WY 2019), bottom-of-the-watershed stations in different creeks were monitored each year with the goal of eventually developing a geographically diverse dataset.

WY 2019 Results

In WY 2019, SMCWPPP conducted dry weather pesticides and toxicity monitoring at one station on Pulgas Creek in the City of San Carlos. Statistically significant toxicity to C. dubia was observed in the water sample collected from Pulgas Creek. However, the magnitude of the toxic


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effects in the samples compared to laboratory controls did not exceed MRP trigger criteria of 50 Percent Effect. The cause of the observed toxicity is unknown; however, sediment concentrations of copper and zinc were slightly elevated (i.e., exceeded the PEC) and could have caused toxicity to this test orgasm that is sensitive to a broad range of aquatic contaminants.

Pesticide concentrations in the WY 2019 Pulgas Creek sediment sample were all very low, most below the MDL, and TU equivalents were calculated using ½ the MDL concentration. The exceptions were bifenthrin (with a TU equivalent of 0.56), deltamethrin/tralomethrin (with a TU equivalent of 0.42), and fipronil sulfone (with a TU equivalent of 0.33). Bifenthrin is considered to be the leading cause of pyrethroid-related toxicity in urban areas (Ruby 2013) and the most-commonly detected insecticide monitored by the California DPR SWPP (Ensminger 2017).

WY 2018 Wet Weather Monitoring

During WY 2018, wet weather water samples were collected for pesticide analysis at two sites in San Mateo County (San Pedro Creek and Cordilleras Creek) to fulfill Provision C.8.g.iii.(3) of MRP 2.0, in coordination with the RMC partners. Results were reported in the WY 2018 UCMR (SMCWPPP 2019a). The concentrations of most pesticides analyzed were below the MDL. However, the neonicotinoid, imidacloprid was found at detectable levels at one of the two sites (Cordilleras Creek). Additionally, detectable levels of fipronil and its degradation products were found at both sites.

WY 2014 – WY 2019 Data Summary

Toxicity and chemistry data from WY 2014 through WY 2019 were reviewed for overall findings and evidence of trends. There were 18 test results that had significant toxicity, but with a Percent Effect that did not exceed the MRP trigger thresholds. A majority of these toxicity results were found in water samples and were associated with either C. dubia reproduction (six samples), a chronic toxicity endpoint, or H. azteca survival (six samples), an acute toxicity endpoint. Five of the six water samples with toxicity to H. azteca were collected during wet season sampling events suggesting that stormwater runoff is affecting H. azteca. The water samples with toxicity to C. dubia were more evenly divided between wet and dry season sampling events.

Between WY 2014 and WY 2019, there were no PEC quotients calculated for the SMCWPPP sediment chemistry dataset that were ≥ 1.0 for analytes other than chromium and nickel. Excluding these naturally occurring metals, there were four samples with TEC quotients ≥ 1.0, the more conservative of the two evaluation criteria. These included legacy insecticide DDT compounds in Laurel Creek and Atherton Creek, individual PAHs in Laurel Creek and Atherton Creek, and copper and zinc in Pulgas Creek in WY 2019. Overall, detection frequencies for bifenthrin and fipronil were on par with results from the DPR Northern California study (Ensminger 2019) and H. azteca toxicity responses were similar to SPoT monitoring in San Mateo Creek (Phillips et al. 2014).

The pesticides and toxicity data collected from WYs 2014 through 2019 provide a reference to inform management decisions regarding water quality improvement in San Mateo County watersheds and guide the planning of future monitoring in the area.


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7.2 Trigger Assessment

The MRP requires analysis of the monitoring data to identify candidate sites for SSID projects. Trigger thresholds against which to compare the data are provided for most monitoring parameters in the MRP and are described in the foregoing sections of this report. Stream condition was assessed based on CSCI scores that were calculated using BMI data. Nutrient data were evaluated using applicable water quality standards from the Basin Plan (SFRWQCB 2017). Water and sediment chemistry and toxicity data were evaluated using numeric trigger thresholds specified in the MRP. In compliance with Provision C.8.e.i of the MRP, all monitoring results exceeding trigger thresholds are added to a list of candidate SSID projects that will be maintained throughout the permit term. Follow-up SSID projects will be selected from this list. Table 7.2 lists candidate SSID projects based on WY 2019 Creek Status and Pesticides/Toxicity monitoring data. Trigger and WQO exceedances from WY 2014 through WY 2018 were reported in the respective UCMRs (SMCWPPP 2015, 2016, 2017, 2018, and 2019a) and are summarized in the tables included in Attachment 4.

Additional analysis of the data is provided in the previous sections of this report and should be considered prior to selecting and defining SSID projects. The analyses include review of physical habitat and water chemistry data to identify potential stressors that may be contributing to degraded or diminished biological conditions. Analyses in this report also include historical and spatial perspectives that help provide context and deeper understanding of the trigger exceedances.


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Table 7.2. Summary of SMCWPPP MRP trigger threshold exceedance analysis, WY 2019. “No” indicates samples were collected but did not exceed the MRP trigger; “Yes” indicates an exceedance of the MRP trigger.

Station Number Creek Name B

ioas

sess

men

t 1

Nu

trie

nts

2

Ch

lori

ne

3

Wat

er T

oxi

city

4

Sed

imen

t

To

xici

ty 4

Sed

imen

t

Ch

emis

try

5

Co

nti

nu

ou

s

Tem

per

atu

re 6

Dis

solv

ed

Oxy

gen

7

pH

8

Sp

ecif

ic

Co

nd

uct

ance

9

Pat

ho

gen

Ind

icat

ors

10

202TUN030 Tunitas Creek No No No -- -- -- No No No No --

202TUN040 Tunitas Creek No No No -- -- -- No No No No --

204BEL005 Belmont Creek Yes No No -- -- -- -- -- -- -- --

204R04280 Belmont Creek Yes No No -- -- -- -- -- -- -- --

204R04428 Cordilleras Creek Yes No No -- -- -- -- -- -- -- --

204R04160 Burlingame Creek Yes No Yes -- -- -- -- -- -- -- --

204R03635 Atherton Creek Yes No No -- -- -- -- -- -- -- --


205R04056 Dry Creek Yes No No -- -- -- -- -- -- -- --


204PUL010 Pulgas Creek -- -- -- No No Yes -- -- -- -- --

202PES138 Pescadero Creek -- -- -- -- -- -- -- -- -- -- No

202PES142 McCormick Creek -- -- -- -- -- -- -- -- -- -- No


202PES150 Jones Gulch -- -- -- -- -- -- -- -- -- -- No


202TUN005 Tunitas Creek -- -- -- -- -- -- No -- -- -- --




1. CSCI score ≤ 0.795. 2. Unionized ammonia (as N) ≥ 0.025 mg/L, nitrate (as N) ≥ 10 mg/L, chloride > 250 mg/L. 3. Free chlorine or total chlorine residual ≥ 0.1 mg/L. 4. Test of Significant Toxicity = Fail and Percent Effect ≥ 50 %. 5. TEC or PEC quotient ≥ 1.0 for any constituent. 6. Two or more weekly average temperatures exceed the MWAT of 17.0°C or 20% of results ≥ 24°C. 7. Twenty percent of results = DO < 7.0 mg/L in COLD streams or DO < 5.0 mg/L in WARM streams. 8. Twenty percent of results = pH < 6.5 or pH > 8.5. 9. Twenty percent of results = specific conductance > 2000 uS. 10. Enterococcus ≥ 130 cfu/100ml or E. coli ≥ 410 cfu/100ml.


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7.3 Recommendations

The recommendations presented in this section are directed towards the next iteration of the MRP (MRP 3.0) that is currently under development and will likely become effective in WY 2022. In WY 2020 and WY 2021, SMCWPPP will continue to coordinate with RMC partners on implementation of monitoring requirements in Provisions C.8.d and C.8.g of MRP 2.0.

The following recommendations are based on findings from six years (WY 2014 through WY 2019) of Creek Status and Pesticides and Toxicity monitoring conducted by SMCWPPP, as well as reflections on other monitoring, data analysis, and policy development projects being conducted in the region and statewide.

7.3.1 Biological Condition Assessment

The Program is currently working with RMC partners and Regional Water Board staff to evaluate options for revising the Biological Condition Assessment monitoring design to address Creek Status Monitoring requirements anticipated under MRP 3.0. One of the options under consideration is a targeted monitoring design that would focus on specific watersheds or reaches of interest. A watershed approach would provide stormwater programs more flexibility to evaluate priority areas that stakeholders want to improve, protect, or study. This approach was developed in response to the following findings:

• Baseline ambient conditions in San Mateo County urban creeks are described within known estimates of precision using probabilistic data generated through MRP 1.0 and MRP 2.0 monitoring and assessment tools such as the CSCI and ASCI. Continuing to build the dataset at a countywide scale is unlikely to provide additional benefit to local stormwater management programs.

• The probabilistic sample draw will likely only be sufficient to provide new sites through WY 2020. A re-design of the sample draw could provide more sites and address some of the lessons learned about the current sample draw; however, this effort would only be warranted if ambient probabilistic monitoring is desired.

• Stakeholders, such as municipal stormwater programs, land managers, creek groups, and other interested individuals and organizations have expressed that the probabilistic monitoring data results do not adequately provide information needed to identify and address site-specific water quality problems. They prefer for the Program to conduct monitoring activities in creeks of high interest.

The following objectives could be used to guide future monitoring design:

• Conduct monitoring within watersheds or subwatersheds of interest. Watershed(s) could be selected based on known water quality concerns, existing aquatic and riparian resources, planned management activities, or stakeholder interest. Monitoring could be used to develop a high-resolution longitudinal profile of CSCI scores and potential stressors with the goal of identifying sources of stressors and implementing control actions. In addition to bioassessment surveys, monitoring could include creek walks using established protocols and desktop watershed mapping.

• Re-assess sites that have lower or higher biological condition than expected. Use the SCAPE model (discussed in Section 2.2.4.7) to prioritize sites for follow-up assessment. The SCAPE model that provides a context for evaluating stream health by estimating an


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expectation of biological condition along a given stream reach relative to landscape constraints. Biological condition, based on CSCI scores, can be compared to the reach expectation.

• Evaluate the effectiveness of BMPs or restoration projects. Conduct annual or biannual monitoring in creek reaches where biological condition and/or water quality are likely to be improved by planned management actions such as GSI implementation, flood control projects, and creek restoration. Baseline data generated through MRP 2.0, MRP 1.0, and Pre-MRP monitoring can be used for comparison.

• Actively monitor and manage stream segments where monitoring data have indicated good water quality and biological condition.

7.3.2 Continuous Monitoring for Temperature and General Water Quality

Continuous monitoring for temperature and general water quality has been an effective tool in supporting SSID studies and evaluating cold water habitat. The Program recommends continued implementation of this approach in MRP 3.0.

7.3.3 Pathogen Indicator Monitoring

Pathogen indicator monitoring is a relatively small part of the overall Creek Status and Pesticides & Toxicity monitoring program. Nonetheless, the Program recommends discontinuing this monitoring in MRP 3.0. This recommendation is based on several factors:

• Wildlife, a likely source of pathogen indicator bacteria in creeks, tend to congregate in urban creek corridors. It would be difficult and undesirable to restrict their use of creeks. Furthermore, bacteria from wildlife sources do not generally pose a risk to REC-1 Beneficial Uses.

• Homeless encampments are another common source of bacteria in receiving waters. Although this human source of bacteria does pose a risk to REC-1 Beneficial Uses, control options are challenging and generally not within the scope of stormwater management programs. The issue of homelessness is being dealt with through a patchwork of public and private programs aimed at housing people, preventing homelessness, law enforcement, and other measures.26

• Bacteria densities in freshwater creeks are highly variable and single grab samples are not very useful in identifying problems or making decisions about stormwater management.

Monitoring efforts for pathogen indicators should instead be used to support bacteria Total Maximum Daily Load (TMDL) action plans such as the San Pedro Creek and Pacifica State Beach Bacteria TMDL which is implemented through Provision C.14 of MRP 2.0.

26 https://calmatters.org/explainers/californias-homelessness-crisis-explained/

https://calmatters.org/explainers/californias-homelessness-crisis-explained/


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7.3.4 Chlorine Monitoring

Although chlorine monitoring can be an important tool in investigating fish kills, continued reconnaissance chlorine monitoring is not recommended in the next MRP. Based on the chlorine data from WY 2012 – WY 2019, little value is added to the Creek Status Monitoring program by this monitoring.

• The sources of chlorine detected through Creek Status Monitoring are generally transient and challenging to trace.

• Discharges of drinking water are the most likely source of free chlorine and total chlorine residual. These discharges are already addressed by MRP Provisions C.5 (IDDE) and C.15 (Exempted and Conditionally Exempted Discharges) and the NPDES General Permit for Drinking Water Systems (Order WQ 2014-0194-DWQ).

• Available field equipment does not provide reliable results below 0.13 mg/L, a concentration higher than the MRP trigger resulting in uncertainty of exceedances. False positives can result in wasted efforts trying to track down non-existent sources.

7.3.5 Pesticides and Toxicity Monitoring

The Strategy to Optimize Resource Management of Storm Water (STORMS), adopted by the State Water Board in January 2016, is developing a statewide framework for urban pesticides reduction (Urban Pesticides Amendments). The primary goal of the statewide Urban Pesticides Amendments is to improve collaboration among regulators, leading to better management of pesticides in urban runoff. The Urban Pesticides Amendments will also organize coordinated pesticides and toxicity monitoring and data sharing. The Urban Pesticides Amendments team is proposing a statewide monitoring program that will substitute for pesticides and toxicity monitoring requirements in MS4 permits, such as the MRP. The goal is to generate useful data at minimal cost and standardize information at the statewide level. The Draft Amendments will likely be released for public review in early 2021 with adoption anticipated in mid-2021. Currently, the mechanism for implementing the statewide monitoring program is uncertain.

The Program recommends no changes to the current Provision C.8.g Pesticides and Toxicity monitoring requirements until the statewide monitoring program is in place.


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8.0 Summary of Stormwater Management Programs by San Mateo County Permittees

The Creek Status and Pesticides and Toxicity Monitoring programs (consistent with MRP Provisions C.8.d and C.8.g of MRP 2.0, respectively) focus on assessing the water quality condition of urban creeks in San Mateo County and identifying stressors and sources of impacts observed.

This Integrated Creek Status Monitoring Report presents a comprehensive review of bioassessment and stressor data collected in WY 2012 through WY 2019. Data suggest that most urban streams have likely altered or very likely altered populations of aquatic life indicators (e.g., benthic macroinvertebrates). These poor stream conditions are likely the result of long-term changes in stream hydrology, channel geomorphology, in-stream habitat complexity, and other modifications to the watershed and riparian areas associated with the urban development that has occurred over the past 50 plus years. Additionally, episodic or site-specific increases in temperature (particularly in lower creek reaches or reaches directly below reservoirs) may not be optimal for aquatic life in some local creeks.

SMCWPPP Permittees are actively implementing many stormwater management programs to address these and other stressors and associated sources of water quality conditions observed in local creeks, with the goal of protecting these natural resources. For example:

• In compliance with Provision C.3 of MRP 2.0, new and redevelopment projects in the Bay Area are now designed to more effectively reduce water quality and hydromodification impacts associated with urban development. Low impact development (LID) methods, such as rainwater harvesting and use, infiltration and biotreatment are required as part of development and redevelopment projects. In addition, Green Infrastructure planning is now part of all municipal projects. These LID measures are expected to reduce the impacts of urban runoff and associated impervious surfaces on stream health.

• In compliance with Provision C.7 of MRP 2.0, SMCWPPP and the San Mateo County Permittees are implementing stormwater outreach activities. Some of SMCWPPP’s recent accomplishments include a County campaign to reduce littering of cigarette butts, Coastal Cleanup Day events, increased social media presence, participation in the Our Water Our World (OWOW) program, publication of newsletters, launching of a countywide school outreach program that asked students to submit proposal to green up their school campus, a K-12 teacher fellowship program for developing units related to stormwater pollution prevention, and a countywide rain barrel rebate program. The overarching goal of these actions is to reduce stormwater pollution by educating and motivating residents.

• In compliance with MRP Provision C.9, Permittees are implementing pesticide toxicity control programs that focus on source control and pollution prevention measures. The control measures include the implementation of integrated pest management (IPM) policies/ordinances, public education and outreach programs, pesticide disposal programs, supporting the adoption of formal State pesticide registration procedures, and sustainable landscaping requirements for new and redevelopment projects. These efforts will eventually be supplemented by the statewide Urban Pesticides Amendments which will seek to manage pesticide usage via state and federal pesticide regulatory


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authorities such as DPR and USEPA. The anticipated result is a reduction in pyrethroids and other pesticides in urban stormwater runoff and a reduction in the magnitude and extent of toxicity in local creeks.

• Trash loadings to local creeks have been reduced through implementation of new control measures in compliance with Provision C.10 of MRP 2.0 and other efforts by Permittees to reduce the impacts of illegal dumping directly into waterways. These actions include the installation and maintenance of trash capture systems, the adoption of ordinances to reduce the impacts of litter prone items, enhanced institutional controls such as street sweeping, and the on-going removal and control of direct dumping. MRP 2.0 establishes a mandatory trash load reduction schedule, minimum areas to be treated by full trash capture systems, and requires development and implementation of receiving water monitoring programs for trash.

• In compliance with Provisions C.2 (Municipal Operations), C.4 (Industrial and Commercial Site Controls), C.5 (Illicit Discharge Detection and Elimination), and C.6 (Construction Site Controls) of MRP 2.0, Permittees continue to implement Best Management Practices (BMPs) that are designed to prevent non-stormwater discharges during dry weather and reduce the exposure of stormwater runoff to contaminants during rainfall events.

• In compliance with Provision C.13 of MRP 2.0, copper in stormwater runoff is reduced through implementation of controls such as architectural and site design requirements, prohibition of discharges from water features treated with copper, and industrial facility inspections.

• Mercury and polychlorinated biphenyls (PCBs) in stormwater runoff are being reduced through implementation of the respective TMDL water quality restoration plans. In compliance with Provisions C.11 (mercury) and C.12 (PCBs) of MRP 2.0, the Countywide Program will continue to identify sources of these pollutants and will implement control actions designed to achieve load reduction goals. Monitoring activities conducted in WY 2014 through WY 2019 that specifically target mercury and PCBs are described in the Integrated Pollutants of Concern Monitoring Data Report that is included as Part D of this IMR.

In addition to controls implemented in compliance with the MRP, numerous other efforts and programs designed to improve the biological, physical and chemical condition of local creeks are underway. For example, in 2017 C/CAG developed the San Mateo Countywide Stormwater Resource Plan (SRP) to satisfy state requirements and guidelines to ensure C/CAG and San Mateo county MRP Permittees are eligible to compete for future voter-approved bond funds for stormwater or dry weather capture projects. The SRP identifies and prioritizes opportunities to better utilize stormwater as a resource in San Mateo County through a detailed analysis of watershed processes, surface and groundwater resources, input from stakeholders and the public, and analysis of multiple benefits that can be achieved through strategically planned stormwater management projects. These projects aim to capture and manage stormwater more sustainably, reduce flooding and pollution associated with runoff, improve biological functioning of plants, soils, and other natural infrastructure, and provide many community benefits, including cleaner air and water and enhanced aesthetic value of local streets and neighborhoods.

Through the continued implementation of MRP-associated and other watershed stewardship programs, SMCWPPP anticipates that stream conditions and water quality in local creeks will continue to improve overtime. In the near term, toxicity observed in creeks should decrease as


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pesticide regulations better incorporate water quality concerns during the pesticide registration process. In the longer term, control measures implemented to “green” the “grey” infrastructure and disconnect impervious areas constructed over the course of the past 50 plus years will take time to implement. Consequently, it may take several decades to observe the benefits of these important, large-scale improvements to our watersheds in our local creeks. Long-term creek status monitoring programs designed to detect these changes over time are therefore beneficial to our collective understanding of the condition and health of our local waterways.


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9.0 References

Ackerly, D., Jones, A., Stacey, M., Riordan, B. 2018. San Francisco Bay Area Summary Report. California’s Fourth Climate Change Assessment. Publication number: CCCA4-SUM-2018-005.

Amweg, E.L., Weston, D.P., and Ureda, N.M. 2005. Use and toxicity of pyrethroid pesticides in the Central Valley, California, USA. Environmental Toxicology and Chemistry: 24(4): 966-972.

Anderson, B.S., Hunt, J.W., Markewicz, D., Larsen, K., 2011. Toxicity in California Waters, Surface Water Ambient Monitoring Program. California Water Resources Control Board. Sacramento, CA.

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Maund, S.J., Hamer, M.J., Lane, M.C., Farrelly, C., Rapley, J.H., Goggin, U.M., Gentle, W.E. 2002. Partitioning, bioavailability, and toxicity of the pyrethroid insecticide cypermethrin in sediments. Environmental Toxicology and Chemistry: 21 (1): 9-15.

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Mazor, R.D. 2015. Bioassessment of Perennial Streams in Southern California: A Report on the First Five Years of the Stormwater Monitoring Coalition’s Regional Stream Survey. Prepared by Raphael D. Mazor, Southern California Coastal water Research Project. Technical Report 844. May 2015.

Mazor, R.D., A. Rehn, P.R. Ode, M. Engeln, K. Schiff, E. Stein, D. Gillett, D. Herbst, C.P. Hawkins. In review. Bioassessment in complex environments: Designing an index for consistent meaning in different settings.

Mazor, R.D., Purcell, A.H., and Resh, V.H. 2009. Long-term variability in bioassessments: a twenty-year study from two northern California streams. Environmental Management 43:129-1286.

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Phillips, B.M., Anderson, B.S., Siegler, K., Voorhees, J., Tadesse, D., Weber, L., Breuer, R. 2016. Spatial and Temporal Trends in Chemical Contamination and Toxicity Relative to Land Use in California Watersheds: Stream Pollution Trends (SPoT) Monitoring Program. Fourth Report – Seven-Year Trends 2008-2014. California State Water Resources Control Board, Sacramento, CA.

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https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/aquatic-life-benchmarks-and-ecological-risk

ATTACHMENTS

Attachment 1

QA/QC Report

Quality Assurance/Quality Control Report

Creek Status and Pesticides & Toxicity Monitoring, Water Year 2019

Prepared by:

EOA, Inc 1410 Jackson Street Oakland, CA 94612

Prepared for:

March 31, 2020

SMCWPPP WY 2019 Creek Status Monitoring and Pesticides & Toxicity QA/QC Report

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TABLE OF CONTENTS 1. Introduction ............................................................................................................................................ 5

1.1. Data Types Evaluated ................................................................................................................... 5

1.2. Laboratories .................................................................................................................................. 5

1.3. QA/QC Attributes .......................................................................................................................... 6

1.3.1. Representativeness .............................................................................................................. 6 1.3.2. Comparability ........................................................................................................................ 6 1.3.3. Completeness ....................................................................................................................... 6 1.3.4. Sensitivity .............................................................................................................................. 6 1.3.5. Accuracy ................................................................................................................................ 6 1.3.6. Precision ................................................................................................................................ 7 1.3.7. Contamination ....................................................................................................................... 7

2. Methods ................................................................................................................................................. 8

2.1. Representativeness ...................................................................................................................... 8

2.2. Comparability ................................................................................................................................ 8

2.3. Completeness ............................................................................................................................... 8

2.3.1. Data Collection ...................................................................................................................... 8 2.3.2. Field Sheets .......................................................................................................................... 9 2.3.3. Laboratory Results ................................................................................................................ 9

2.4. Sensitivity ...................................................................................................................................... 9

2.4.1. Biological Data ...................................................................................................................... 9 2.4.2. Chemical Analysis ................................................................................................................. 9

2.5. Accuracy ........................................................................................................................................ 9

2.5.1. Biological Data ...................................................................................................................... 9 2.5.2. Chemical Analysis ................................................................................................................. 9 2.5.3. Water Quality Data Collection ............................................................................................. 10

2.6. Precision ...................................................................................................................................... 10

2.6.1. Field Duplicates ................................................................................................................... 10 2.6.2. Chemical Analysis ............................................................................................................... 10

2.7. Contamination ............................................................................................................................. 10

3. Results ................................................................................................................................................. 11

3.1. Overall Project Representativeness ............................................................................................ 11

3.2. Overall Project Comparability ...................................................................................................... 11

3.3. Bioassessments and Physical Habitat Assessments.................................................................. 11

3.3.1. Completeness ..................................................................................................................... 11 3.3.2. Sensitivity ............................................................................................................................ 11 3.3.3. Accuracy .............................................................................................................................. 11 3.3.4. Precision .............................................................................................................................. 12 3.3.5. Contamination ..................................................................................................................... 13

3.4. Field Measurements .................................................................................................................... 13

3.4.1. Completeness ..................................................................................................................... 13 3.4.2. Sensitivity ............................................................................................................................ 13 3.4.3. Accuracy .............................................................................................................................. 13 3.4.4. Precision .............................................................................................................................. 14

3.5. Water Chemistry.......................................................................................................................... 14

3.5.1. Completeness ..................................................................................................................... 14 3.5.2. Sensitivity ............................................................................................................................ 14

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3.5.3. Accuracy .............................................................................................................................. 14 3.5.4. Precision .............................................................................................................................. 15 3.5.5. Contamination ..................................................................................................................... 15

3.6. Pathogen Indicators .................................................................................................................... 16


3.7. Continuous Water Quality ........................................................................................................... 17


3.8. Continuous Temperature Monitoring .......................................................................................... 17


3.9. Sediment Chemistry .................................................................................................................... 18


3.10. Toxicity Testing ........................................................................................................................... 22

3.10.1. Completeness ..................................................................................................................... 22 3.10.2. Sensitivity and Accuracy ..................................................................................................... 22 3.10.3. Precision .............................................................................................................................. 22 3.10.4. Contamination ..................................................................................................................... 23

4. Conclusions ......................................................................................................................................... 23

5. References ........................................................................................................................................... 24


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LIST OF TABLES

Table 1. Quality control metrics for taxonomic identification of benthic macroinvertebrates collected in San Mateo County in WY 2019 compared to measurement quality objectives. ................................................ 12

Table 2. Field duplicate water chemistry results for sites 205R04056, collected on June 11, 2019 .......... 12

Table 3. Target and actual reporting limits for nutrients analyzed in SMCWPPP creek status monitoring. Data in highlighted rows exceed monitoring quality objectives in RMC QAPP. ......................................... 14

Table 4. Field duplicate water chemistry results for site 205R04056, collected on June 11, 2019. Data in highlighted rows exceed measurement quality objectives in RMC QAPP. ................................................. 15

Table 5. Lab and field duplicate pathogen results collected on August 1, 2019. ........................................ 16

Table 6. Drift measurements for two continuous water quality monitoring events in San Mateo County urban creeks during WY 2019. Bold and highlighted values exceeded measurement quality objectives. 17

Table 7. Comparison of target and actual reporting limits for sediment analytes where reporting limits exceeded target limits. Sediment samples were collected in San Mateo County creeks in WY 2019. ...... 19

Table 8. Sediment chemistry duplicate field results for site 544MSH045, collected on July 23, 2019 in San Mateo County. Data in highlighted rows exceed monitoring quality objectives in RMC QAPP. .......... Error! Bookmark not defined.

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LIST OF ACRONYMS

BASMAA Bay Area Stormwater Management Agencies Association

BMI Benthic Macroinvertebrates

CDFW California Department of Fish and Wildlife

DPD Diethyl-p-phenylene Diamine

DQO Data Quality Objective

EDDs Electronic data deliverables

EV Expected Value

KLI Kinnetic Laboratories, Inc.

LCS Laboratory Control Sample

LCSD Laboratory Control Sample Duplicate

MPN Most Probably Number

MQO Measurement Quality Objective


MS Matrix Spike

MSD Matrix Spike Duplicate

MV Measured Value

ND Non-detect

NIST National Institute of Standards and Technology

NPDES National Pollution Discharge Elimination System

NV Native Value

PAH Polycyclic Aromatic Hydrocarbon

PR Percent Recovery

QA Quality Assurance

QAPP Quality Assurance Project Plan

QC Quality Control

RL Reporting Limit


RPD Relative Percent Difference

SAFIT Southwest Association of Freshwater Invertebrate Taxonomists

SCCWRP Southern California Coastal Water Research Project

SFRWQCB San Francisco Regional Water Quality Control Board

SMCWPPP San Mateo County Urban Pollution Prevention Program

SOP Standard Operating Procedures

STE Standard Taxonomic Effort

SV Spike Value

SWAMP Surface Water Ambient Monitoring Program

TKN Total Kjeldahl Nitrogen

WY Water Year


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1. INTRODUCTION

In Water Year 2019 (WY 2019; October 1, 2018 through September 30, 2019), the San Mateo County Water Pollution Prevention Program (SMCWPPP) conducted Creek Status Monitoring in compliance with Provision C.8.d and Pesticide & Toxicity Monitoring in compliance with Provision C.8.g of the National Pollutant Discharge Elimination System (NPDES) stormwater permit for Bay Area municipalities, referred to as the Municipal Regional Permit (MRP). The monitoring strategy includes regional ambient/probabilistic monitoring and local “targeted” monitoring as described in the Bay Area Stormwater Management Agencies Association (BASMAA) Regional Monitoring Coalition (RMC) Creek Status and Long-Term Trends Monitoring Plan (BASMAA 2012). SMCWPPP implemented a comprehensive data quality assurance and quality control (QA/QC) program, covering all aspects of the probabilistic and targeted monitoring. QA/QC for data collected was performed according to procedures detailed in the BASMAA RMC Quality Assurance Project Plan (QAPP) (BASMAA 2016a) and the BASMAA RMC Standard Operating Procedures (SOP; BASMAA 2016b), SOP FS-13 (Standard Operating Procedures for QA/QC Data Review). The BASMAA RMC QAPP and SOP are based on the QA program developed by the California Surface Water Ambient Monitoring Program (SWAMP 2017).

Based on the QA/QC review, no WY 2019 data were rejected, and some data were flagged. Overall, WY 2019 data met QA/QC objectives. Details are provided in the sections below.

1.1. DATA TYPES EVALUATED

During creek status monitoring (MRP Provision C.8.d), several data types were collected and evaluated for quality assurance and quality control. These data types include the following:

1. Bioassessment data a. Benthic Macroinvertebrates (BMI) b. Algae

2. Physical Habitat Assessment 3. Field Measurements 4. Water Chemistry 5. Pathogen Indicators 6. Continuous Water Quality (2-week deployment; 15-minute interval)

a. Temperature b. Dissolved Oxygen c. Conductivity d. pH

7. Continuous Temperature Measurements (5-month deployment; 1-hour interval)

During pesticide & toxicity monitoring the following data types were collected and evaluated for quality assurance and quality control:

1. Water Toxicity (dry weather; MRP Provision C.8.g.i) 2. Sediment Toxicity (dry weather; MRP Provision C.8.g.ii) 3. Sediment Chemistry (dry weather; MRP Provision C.8.g.ii)

1.2. LABORATORIES

Laboratories that provided analytical and taxonomic identification support to SMCWPPP and the RMC were selected based on demonstrated capability to adhere to specified protocols. Laboratories are certified and are as follows:

• Caltest Analytical Laboratory (nutrients, chlorophyll a, ash free dry mass, sediment chemistry)

• Pacific EcoRisk, Inc. (water and sediment toxicity)

• Alpha Analytical Laboratories, Inc. (pathogen indicators)

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• BioAsessment Services (benthic macroinvertebrate (BMI) identification)

• Jon Lee Consulting (BMI identification Quality Control)

• EcoAnalysts, Inc. (algae identification)

1.3. QA/QC ATTRIBUTES

The RMC SOP and QAPP identify seven data quality attributes that are used to assess data QA/QC. They include (1) Representativeness, (2) Comparability, (3) Completeness, (4) Sensitivity, (5) Precision, (6) Accuracy, and (7) Contamination. These seven attributes are compared to Data Quality Objectives (DQOs), which were established to ensure that data collected are of adequate quality and sufficient for the intended uses. DQOs address both quantitative and qualitative assessment of the acceptability of data – representativeness and comparability are qualitative while completeness, sensitivity, precision, accuracy, and contamination are quantitative assessments.

Specific DQOs are based on Measurement Quality Objectives (MQOs) for each analyte. Chemical analysis relies on repeatable physical and chemical properties of target constituents to assess accuracy and precision. Biological data are quantified by experienced taxonomists relying on organism morphological features.

1.3.1. Representativeness

Data representativeness assesses whether the data were collected so as to represent actual conditions at each monitoring location. For this project, all samples and field measurements are assumed to be representative if they are performed according to protocols specified in the RMC QAPP and SOPs.

1.3.2. Comparability

The QA/QC officer ensures that the data may be reasonably compared to data from other programs producing similar types of data. For RMC Creek Status monitoring, individual stormwater programs try to maintain comparability within the RMC. The key measure of comparability for all RMC data is the California Surface Water Ambient Monitoring Program.

1.3.3. Completeness

Completeness is the degree to which all data were produced as planned; this covers both sample collection and analysis. For chemical data and field measurements an overall completeness of greater than 90% is considered acceptable for RMC chemical data and field measurements. For bioassessment-related parameters – including BMI and algae taxonomy samples/analysis and associated field measurement – a completeness of 95% is considered acceptable.

1.3.4. Sensitivity

Sensitivity analysis determines whether the methods can identify and/or quantify results at low enough levels. For the chemical analyses in this project, sensitivity is considered to be adequate if the reporting limits (RLs) comply with the specifications in RMC QAPP Appendix E: RMC Target Method Reporting Limits. For benthic macroinvertebrate data, taxonomic identification sensitivity is acceptable provided taxonomists use standard taxonomic effort (STE) Level I as established by the Southwest Association of Freshwater Invertebrate Taxonomists (SAFIT). There is no established level of sensitivity for algae taxonomic identification.

1.3.5. Accuracy

Accuracy is assessed as the percent recovery of samples spiked with a known amount of a specific chemical constituent. Chemistry laboratories routinely analyze a series of spiked samples; the results of these analyses are reported by the laboratories and evaluated using the RMC Database QA/QC Testing Tool. Acceptable levels of accuracy are specified for chemical analytes and toxicity test parameters in


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RMC QAPP Appendix A: Measurement Quality Objectives for RMC Analytes, and for biological measurements in Appendix B: Benthic Macroinvertebrate MQOs and Data Production Process.

1.3.6. Precision

Precision is nominally assessed as the degree to which replicate measurements agree, nominally determined by calculation of the relative percent difference (RPD) between duplicate measurements. Chemistry laboratories routinely analyze a series of duplicate samples that are generated internally. The RMC QAPP also requires collection and analysis of field duplicate samples at a rate of 5% of all samples for all parameters1. The results of the duplicate analyses are reported by the laboratories and evaluated using RMC Database QA/QC Testing Tool. Results of the Tool are confirmed manually. Acceptable levels of precision are specified for chemical analytes and toxicity test parameters in RMC QAPP Appendix A: Measurement Quality Objectives for RMC Analytes, and for biological measurements in Appendix B: Benthic Macroinvertebrate MQOs and Data Production Process.

1.3.7. Contamination

For chemical data, contamination is assessed as the presence of analytical constituents in blank samples. The RMC QAPP requires collection and analysis of field blank samples at a rate of 5% for orthophosphate.

1 The QAPP also requires the collection of field duplicate samples for 10% of biological samples (BMI and algae). However, there are no prescribed methods for assessing the precision of these duplicate samples.

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2. METHODS

2.1. REPRESENTATIVENESS

To ensure representativeness, each member of the SMCWPPP field crew received and reviewed all applicable SOPs and the QAPP. Most field crew members also attended a two-day bioassessment and field sampling training session from the California Water Boards Training Academy. The course was taught by California Department of Fish and Wildlife, Aquatic Bioassessment Laboratory staff and covered procedures for sampling benthic macroinvertebrates, algae, and measuring physical habitat characteristics using the applicable SWAMP SOPs. As a result, each field crew member was knowledgeable of, and performed data collection according to the protocols in the RMC QAPP and SOPs, ensuring that all samples and field measurements are representative of conditions in San Mateo County urban creeks.

2.2. COMPARABILITY

In addition to the bioassessment and field sampling training, SMCWPPP field crew members participated in an inter-calibration exercise with other stormwater programs prior to field assessments at least once during the permit term. During the inter-calibration exercise, the field crews also reviewed water chemistry (nutrient) sample collection and water quality field measurement methods. Close communication throughout the field season with other stormwater program field crews also ensured comparability.

Sub-contractors collecting samples and the laboratories performing analyses received copies of the RMC SOP and QAPP and have acknowledged reviewing the documents. Data collection and analysis by these parties adhered to the RMC protocols and was included in their operating contracts.

Following completion of the field and laboratory work, the field data sheets and laboratory reports were reviewed by the SMCWPPP Program Quality Assurance staff, and were compared against the methods and protocols specified in the SOPs and QAPP. Specifically, staff checked for conformance with field and laboratory methods as specified in SOPs and QAPP, including sample collection and analytical methods, sample preservation, sample holding times, etc.

Electronic data deliverables (EDDs) were submitted to the San Francisco Regional Water Quality Control Board (SFRWQCB) in Microsoft Excel templates developed by SWAMP, to ensure data comparability with the SWAMP program. In addition, data entry followed SWAMP documentation specific to each data type, including the exclusion of qualitative values that do not appear on SWAMP’s look up lists2 such as field crew member names and site IDs. Completed templates were reviewed using SWAMP’s online data checker3, further ensuring SWAMP-comparability.

2.3. COMPLETENESS

2.3.1. Data Collection

All efforts were made to collect 100% of planned samples. Upon completion of all data collection, the number of samples collected for each data type was compared to the number of samples planned and the number required by the MRP, and reasons for any missed samples were identified. When possible, SMCWPPP staff resampled sites if missing data were identified prior to the close of the monitoring period. Specifically, continuous water quality data were reviewed immediately following deployment for adherence to MQOs. If data were rejected, samplers were redeployed immediately.

2 Look up lists available online at http://swamp.waterboards.ca.gov/swamp_checker/LookUpLists.php 3 Checker available online at http://swamp.waterboards.ca.gov/swamp_checker/SWAMPUpload.php

http://swamp.waterboards.ca.gov/swamp_checker/LookUpLists.php

http://swamp.waterboards.ca.gov/swamp_checker/SWAMPUpload.php


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For bioassessments, the SMCWPPP field crew made all efforts to collect the required number of BMI and algae subsamples per site; in the event of a dry transect, the samples were slid to the closest sampleable location to ensure 11 total subsamples in each station’s composite sample.

2.3.2. Field Sheets

Following the completion of each sampling event, the field crew leader/local monitoring coordinator reviewed any field generated documents for completion, and any missing values were entered. Once field sheets were returned to the office, a second SMCWPPP staff member reviewed the field sheets again and noted any missing data.

2.3.3. Laboratory Results

SMCWPPP staff assessed laboratory reports and EDDs for the number and type of analysis performed to ensure all sites and samples were included in the laboratory results.

2.4. SENSITIVITY

2.4.1. Biological Data

Benthic macroinvertebrates were identified to SAFIT STE Level I, with the additional effort of identifying chironomids (midges) to subfamily/tribe instead of family (Chironomidae).

2.4.2. Chemical Analysis

The reporting limits for analytical results were compared to the target reporting limits in Appendix E (RMC Target Method Reporting Limits) of the RMC QAPP. Results with reporting limits that exceeded the target reporting limit were flagged.

2.5. ACCURACY

2.5.1. Biological Data

Ten percent of the total number of BMI samples collected was submitted to a separate taxonomic laboratory, Jon Lee Consulting, for independent assessment of taxonomic accuracy, enumeration of organisms, and conformance to standard taxonomic level. For SMCWPPP, one sample was evaluated for QC purposes. Results were compared to MQOs in Appendix B (Benthic macroinvertebrate MQOs and Data Production Process).


Caltest evaluated and reported the percent recovery (PR) of laboratory control samples (LCS; in lieu of reference materials) and matrix spikes (MS), which were recalculated and compared to the applicable MQOs set by Appendix A (Measurement Quality Objectives for RMC Analytes) of the RMC QAPP MQOs. If a QA sample did not meet MQOs, all samples in that batch for that particular analyte were flagged.

For reference materials, percent recovery was calculated as:

PR = MV / EV x 100%

Where: MV = the measured value EV = the expected (reference) value

For matrix spikes, percent recovery was calculated as:

PR = [(MV – NV) / SV] x 100%

Where: MV = the measured value of the spiked sample NV = the native, unspiked result SV = the spike concentration added

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2.5.3. Water Quality Data Collection

Accuracy for continuous water quality monitoring sondes was assured via continuing calibration verification for each instrument before and after each two-week deployment. Instrument drift was calculated by comparing the instrument’s measurements in standard solutions taken before and after deployment. The drift was compared to measurement quality objectives for drift listed on the SWAMP calibration form, included as an attachment to the RMC SOP FS-3.

Temperature data were checked for accuracy by comparing measurements taken by HOBO temperature loggers with NIST thermometer readings in room temperature water and ice water prior to deployment. The mean difference and standard deviation for each HOBO was calculated, and if a logger had a mean difference exceeding 0.2 ºC, it was replaced.

2.6. PRECISION

2.6.1. Field Duplicates

For creek status monitoring, duplicate biological samples were collected at 10% (one) of the 10 sites and duplicate water chemistry samples were collected at 10% (one) of the sites sampled to evaluate precision of field sampling methods. The RPD for water chemistry field duplicates was calculated and compared to the MQO (RPD < 25%) set by Table 26-1 in Appendix A of the RMC QAPP. If the RPD of the two field duplicates did not meet the MQO, the results were flagged.

The RMC QAPP requires collection and analysis of duplicate sediment chemistry and toxicity samples at a rate of 5% of total samples collected for the project. Responsibility for the collection of the field duplicate rotates each year amongst Alameda County Clean Water Program (ACCWP), Contra Costa Clean Water Program (CCCWP), Santa Clara Valley Urban Runoff Pollution Prevention Program (SCVURPPP), and SMCWPPP.

The sediment sample and field duplicate were collected together using the Sediment Scoop Method described in the RMC SOP, homogenized, and then distributed to two separate containers. For sediment chemistry field duplicates, the RPD was calculated for each analyte and compared to the MQOs (RPD < 25%) set by Tables 26-7 through 26-11 in Appendix A of the RMC QAPP. For sediment and water toxicity field duplicates, the RPD of the batch mean was calculated and compared to the recommended acceptable RPD (< 20%) set by Tables 26-12 and 26-13 in Appendix A. If the RPD of the field duplicates did not meet the MQO, the results were flagged.

The RPD is calculated as:

RPD = ABS ([X1-X2] / [(X1+X2) / 2])

Where: X1 = the first sample result X2 = the duplicate sample result

No field duplicate is required for pathogen indicators.


Caltest evaluated and reported the RPD for laboratory duplicates, laboratory control duplicates, and matrix spike duplicates. The RPDs for all duplicate samples were recalculated and compared to the applicable MQO set by Appendix A of the RMC QAPP. If a laboratory duplicate sample did not meet MQOs, all samples in that batch for that particular analyte were flagged.

2.7. CONTAMINATION

Blank samples were analyzed for contamination, and results were compared to MQOs set by Appendix A of the RMC QAPP. For creek status monitoring, the RMC QAPP requires all blanks (laboratory and field) to be less than the analyte reporting limits. If a blank sample did not meet this MQO, all samples in that batch for that particular analyte were flagged.


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3. RESULTS

3.1. OVERALL PROJECT REPRESENTATIVENESS

The SMCWPPP staff and field crew members were trained in SWAMP and RMC protocols, and received significant supervision from the local monitoring coordinator and QA officer. As a result, creek status monitoring data were considered to be representative of conditions in San Mateo County Creeks.

3.2. OVERALL PROJECT COMPARABILITY

SMCWPPP creek status monitoring data were considered to be comparable to both other agencies in the RMC and to SWAMP due to a shared QAPP and SOP, trainings, use of the same electronic data templates, and close communication.

3.3. BIOASSESSMENTS AND PHYSICAL HABITAT ASSESSMENTS

In addition to algae and BMI taxonomic samples, the SMCWPPP field crew collected chlorophyll a and ash free dry mass samples during bioassessments. The BMI taxonomic laboratory, BioAssessment Services, confirmed that the laboratory QA/QC procedures aligned with the procedures in Appendices B through D of the RMC QAPP and met the BMI MQOs in Appendix B.

3.3.1. Completeness

SMCWPPP completed bioassessments and physical habitat assessments for 10 of 10 planned/required sites for a 100% sampling completion rate. However, physical habitat assessments could not be taken at several transects due to inaccessibility.

3.3.2. Sensitivity

The BMI taxonomic identification met sensitivity objectives; the taxonomy laboratory, BioAssessment Services, and QC laboratory, Jon Lee Consulting, confirmed that organisms were identified to SAFIT STE Level I, with the exception of Chironomidae which was analyzed to SAFIT level 1a.

The analytical RL for ash free dry mass analysis (8 mg/L) was much higher than the RMC QAPP target RL of 2 mg/L due to high concentrations requiring large dilutions. The results were several orders of magnitude higher than the actual and target reporting limit and were not affected by the higher RL. While the chlorophyll a analyses also required large dilutions due to high concentrations within the samples, the chlorophyll a analytical RL was below that of the RMC QAPP target RL.

Note that the target RLs in the RMC QAPP are set by the SWAMP, but there are currently no appropriate SWAMP targets for either ash free dry mass or chlorophyll a. Limits in the RMC QAPP are meant to reflect current laboratory capabilities. At lower analyte concentrations where a dilution would not be necessary, the analytical RLs would have met the target RLs.

3.3.3. Accuracy

The BMI sample that was submitted to an independent QC taxonomic laboratory had one instance of a lower taxonomic resolution discrepancy, however these discrepancies are not considered to be misidentifications according to the individual error rate MQO. One minor counting error was also found. The QC laboratory calculated sorting and taxonomic identification metrics, which were compared to the measurement quality objectives in Table 27-1 in Appendix B of the RMC QAPP. All MQOs were met. A comparison of the metrics with the MQOs is shown in Table 1. A copy of the QC laboratory report is available upon request.

There is currently no protocol for evaluating the accuracy of algae taxonomic identification.

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Table 1. Quality control metrics for taxonomic identification of benthic macroinvertebrates collected in San Mateo County in WY 2019 compared to measurement quality objectives.

Quality Control Metric MQO Error Rate Exceeds MQO?

Recount Accuracy > 95% 99.84% No

Taxa ID ≤ 10% 0% No

Individual ID ≤ 10% 0% No

Low Taxonomic Resolution Individual ≤ 10% 3.70% No

Low Taxonomic Resolution Count ≤ 10% 0.16% No

High Taxonomic Resolution Individual ≤ 10% 0.16% No

High Taxonomic Resolution Count ≤ 10% 3.70% No

3.3.4. Precision

Field blind duplicate chlorophyll a and ash free dry mass samples were collected at one site in WY 2019 and were sent to the laboratory for analysis.

Duplicate field samples do not provide a valid estimate of precision in the sampling and are of little use to assessing precision, because there is no reasonable expectation that duplicates will produce identical data. Nonetheless, the RPD of the chlorophyll a and ash free dry mass duplicate results were calculated and compared to the MQO (< 25%) for conventional analytes in water (Table 26-1 in Appendix B of the RMC QAPP). Due to the nature of chlorophyll a and ash free dry mass collection, the RPDs for both parameters are expected to exceed the MQO. The field duplicate results and their RPDs are shown in Table 2. As expected, exceedances were observed for both analytes.

Again, discrepancies were expected due to the potential natural variability in algae production within the reach and the collection of field duplicates at different locations along each transect (as specified in the protocol). As a result, both parameters have frequently exceeded the field duplicate RPD MQOs during past years’ monitoring efforts.

Table 2. Field duplicate water chemistry results for sites 205R04056, collected on June 11, 2019

Analyte Units

205R04056 June 11, 2019

Original Result

Duplicate Result

RPD Exceeds MQO

(>25%)a

Chlorophyll a mg/m2 640 910 35% Yes

Ash Free Dry Mass g/m2 1090 1760 47% Yes

aIn accordance with the RMC QAPP, if the native concentration of either sample is less than the reporting limit, the RPD is not applicable

Laboratory duplicates were also collected for chlorophyll a and ash free dry mass samples. The RPD for ash free dry mass was found to be above the MQO limit, and the corresponding samples were flagged.


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All field collection equipment was decontaminated between sites in accordance with the RMC SOP FS-8 and CDFW Aquatic Invasive Species Decontamination protocols. As a result, it is assumed that samples were free of biological contamination.

3.4. FIELD MEASUREMENTS

Field measurements of temperature, dissolved oxygen, pH, specific conductivity, and chlorine residual were collected concurrently with bioassessments and water chemistry samples. Chlorine residual was measured using a HACH Pocket ColorimeterTM II, which uses the DPD method. All other parameters were measured with a YSI Professional Plus or YSI 600XLM-V2-S multi-parameter instrument. All data collection was performed according to RMC SOP FS-3 (Performing Manual Field Measurements).

3.4.1. Completeness

Temperature, dissolved oxygen, pH, specific conductivity, free and total chlorine residual were collected at all 10 bioassessment sites for a 100% completeness rate.

3.4.2. Sensitivity

Free and total chlorine residual were measured using a HACH Pocket ColorimeterTM II, which uses the DPD method. For this method, the estimated detection limit for the low range measurements (0.02-2.00 mg/L) was 0.02 mg/L. There is, however, no established reporting limit. Colorimetric field instruments are generally not considered capable of providing accurate measurements of free chlorine and total chlorine residual below 0.13 mg/L (Missouri Department of Natural Resources 2004), due to analytical noise, regardless of the method detection limit provided by the manufacturer. For this reason, the Statewide General Permit for drinking Water Discharges (SWRCB 2014) and other recently issued NPDES permits, use 0.1 mg/L as a reporting limit for field measurements of total chlorine residual.

SMCWPPP also uses this threshold as a reporting limit for MRP chlorine residual monitoring. All measurements between 0.02 and 0.1 mg/L have been flagged as “detected, not quantified”. The adopted SMCWPPP reporting limit is still much lower than the target reporting limit of 0.5 mg/L listed in the RMC QAPP for free and total chlorine residual.

There are no reporting limits for temperature, dissolved oxygen, pH, and conductivity measurements, but the actual measurements are much higher than target reporting limits in the RMC QAPP, so it is assumed that the target reporting limits are met for all field measurements.

3.4.3. Accuracy

Data collection occurred Monday through Thursday, and the multi-parameter instrument was calibrated at most 12 hours prior to the first sample on Monday, with the dissolved oxygen sensor calibrated every morning to ensure accurate measurements. Calibration solutions are certified standards, whose expiration dates were noted prior to use. The chlorine kit is factory-calibrated and is sent into the manufacturer every other year to be calibrated.

Free chlorine was measured to be higher than total chlorine at one of the ten sites sampled in WY 2019. In past years, free chlorine has also occasionally been measured as higher than total chlorine. Theoretically, the free chlorine measurement should always be less than or equal to the total chlorine measurement, as the total chlorine concentration in water encompasses the free chlorine concentration in addition to any other chlorine species. The reason for free chlorine concentrations exceeding total chlorine concentrations at a sample site has not been definitively established. Potential causes for these inverted results include matrix interferences, colorimeter user error, and uncertainty associated with low concentrations below the reporting limit. According to Hach, the manufacturer of the equipment and reagents, the free chlorine could have false positive results due to a pH exceedance of 7.6 and/or an alkalinity exceedance of 250 mg/L. It is unlikely that the higher free chlorine readings were caused by user error. The field crew is well trained and aware of potential problems with this testing method, such as wait times between adding reagents and taking the readings and separating the free chlorine and total

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residual chlorine samples. When free chlorine was observed to be higher than total chlorine at a sample site, the free chlorine measurement was retaken with a new water sample and recorded on the field form. It was deemed unnecessary to flag free chlorine measurements that were higher than total chlorine measurements.

3.4.4. Precision

Precision could not be measured as no duplicate field measurements are required or were collected.

3.5. WATER CHEMISTRY

Water chemistry samples were collected by SMCWPPP staff concurrently with bioassessment samples and analyzed by Caltest Analytical Laboratory within their respective holding times. Caltest performed all internal QA/QC requirements as specified in the QAPP and reported their findings to the RMC. Key water chemistry MQOs are listed in RMC QAPP Table 26-2.

3.5.1. Completeness

SMCWPPP collected 100% of planned/required water chemistry samples at the 10 bioassessment sites including one field duplicate sample. Samples were analyzed for all requested analytes, and 100% of results were reported. Water chemistry data were flagged when necessary, but none were rejected.

3.5.2. Sensitivity

Laboratory reporting limits met or were lower than target reporting limits for all nutrients except chloride and nitrate. The reporting limit for all chloride samples exceeded the target reporting limit, but concentrations were much higher than reporting limits, and the elevated reporting limits do not decrease confidence in the measurements.

The reporting limit (0.05 mg/L) and method detection limit (0.02 mg/L) for nitrate samples were higher than the target reporting limit (0.01 mg/L). As a result, the nitrate concentration at one site was measured to be below the method detection limit but may have been quantified if the detection and reporting limit. SMCWPPP has discussed the reporting limits with Caltest, and due the methodology, lower limits cannot currently be achieved. Target and actual reporting limits are shown in Table 3.

Table 3. Target and actual reporting limits for nutrients analyzed in SMCWPPP creek status monitoring. Data in highlighted rows exceed monitoring quality objectives in RMC QAPP.

Analyte Target RL

mg/L Actual RL

mg/L

Ammonia 0.02 0.02

Chloride 0.25 1-50

Total Kjeldahl Nitrogen 0.5 0.1

Nitrate 0.01 0.05

Nitrite 0.01 0.005

Orthophosphate 0.01 0.01-0.05

Silica 1 0.1-1

Phosphorus 0.01 0.01

3.5.3. Accuracy

The RMC QAPP lists a target range of 90-110% for nutrient laboratory control samples (LCS), and 80-120% for nutrient matrix spike and matrix spike duplicates (MS/MSD). For other conventional analytes (i.e., silica and chloride), both the LCS and MS/MSD MQO for recovery is 80-120%.


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Recoveries on most LCS and MS/MSD samples were within the MQO target range. An LCS collected for silica exceeded the MQO range listed in the RMC QAPP, and six MS/MSD PRs collected for silica exceeded the MQO range. The QA samples affected eight sites, whose results have been assigned the appropriate SWAMP flag. Though the data were flagged, none of the analytical data were rejected due to accuracy. The target PR ranges on laboratory reports differed from the RMC QAPP PR for several LCS and MS/MSD samples. As a result, some QA samples that exceeded RMC MQOs were flagged, but not by the laboratory and vice versa.

3.5.4. Precision

The RPD for all laboratory control sample and MS/MSD pairs were consistently below the MQO target of < 25%. Please note that the laboratory used a lower threshold of 20% for all analytes. However, all RPDs were much lower than 20% and no samples were flagged by the laboratory or the QA officer for exceeding the RPD MQO. Water chemistry field duplicates were collected at one site in San Mateo County and were compared against the original samples. For WY 2019, the Total Kjeldahl Nitrogen and ammonia duplicate samples exceeded the RPD MQO. In past years of sampling, Total Kjeldahl Nitrogen has been common among the analytes that exceed the field duplicate RPD MQOs. Field crews will continue to make an effort in subsequent years to collect the original and duplicate samples in an identical fashion. The field duplicate water chemistry results and their RPDs are shown in Table 4. Because of the variability in reporting limits, values less than the RL were not evaluated for RPD. For those analytes whose RPDs could be calculated and did not meet the RMC MQO, they were assigned the appropriate SWAMP flag.

Table 4. Field duplicate water chemistry results for site 205R04056, collected on June 11, 2019. Data in highlighted rows exceed measurement quality objectives in RMC QAPP.

Analyte Name Fraction Name Unit Original Result

Duplicate Result

RPD Exceeds

MQO (>25%)a

Ammonia as N Total mg/L 0.11 0.078 34% Yes

Chloride None mg/L 38 38 0% No

Nitrate as N None mg/L 0.06 ND N/A N/A

Nitrite as N None mg/L J 0.004 J 0.004 N/A N/A

Nitrogen, Total Kjeldahl None mg/L 0.3 0.47 44% Yes

Orthophosphate as P Dissolved mg/L 0.068 0.067 2% No

Phosphorus as P Total mg/L 0.076 0.076 0% No

Silica as SiO2 Total mg/L 30 30 0% No

aIn accordance with the RMC QAPP, if the native concentration of either sample is less than the reporting limit, the RPD is not applicable


None of the target analytes were detected in any of the laboratory blanks at levels above their reporting limit. All analytes were non-detect in the laboratory blanks. The RMC QAPP does not require field blanks to be collected, and possible contamination from sample collection was not assessed. However, the SMCWPPP field crew takes appropriate precautions to avoid contamination, including wearing gloves

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during sample collection and rinsing sample containers with stream water when preservatives are not needed.

3.6. PATHOGEN INDICATORS

Pathogen indicator samples were collected by SMCWPPP staff and were analyzed by Alpha Analytical Laboratories, Inc for E. coli and enterococcus. Samples were collected on August 1, 2019.

3.6.1. Completeness

All five required/planned pathogen indicator samples were collected for a 100% completeness rate. However, the samples taken at site 202PES150 and 202PES154 were not analyzed within the eight-hour hold time specified by the RMC QAPPP. The sample from site 202PES150 was analyzed 70 minutes after the eight-hour hold time limit, and the sample from site 202PES154 was analyzed 60 minutes after the limit. These hold time limit exceedances are not expected to have affected the integrity of the sample results. As a result, these were flagged but not rejected.

3.6.2. Sensitivity

The reporting limits for E. coli and enterococcus (1 MPN/100mL and 2 MPN/100mL, respectively) met the target RL of 2 MPN/100mL listed in the project QAPP.

3.6.3. Accuracy

Negative and positive laboratory controls were run for microbial media. A negative response was observed in the negative control and a positive response was observed in the positive control required by the project QAPP Table 26-4.

3.6.4. Precision

The RMC QAPP requires one laboratory duplicate to be run per 10 samples or per analytical batch, whichever is more frequent. However, determining precision for pathogen indicators requires 15 duplicate sets. Due to the small number of samples collected for this project, there were not enough laboratory duplicates to determine precision. In WY 2019, only one laboratory duplicate was run and is not sufficient to determine precision. The RMC QAPP does not require a field duplicate to be collected for pathogen indicators. However, one field duplicate was collected in WY 2019 by the field crew for a different project. The RPD for E. coli was 0% and 113% for enterococcus. Since there is no requirement for pathogen field duplicates, there is no corresponding MQO, and the precision could not be assessed. See Table 5 for the field duplicate results.

Table 5. Lab and field duplicate pathogen results collected on August 1, 2019.

Duplicate Type Analyte Original Result

(MPN/100mL) Duplicate Result

(MPN/100mL) RPD

Lab Duplicate E. coli 29.4 27.9 NA

Lab Duplicate Enterococcus 23.1 16.0 NA

Field Duplicate E. coli 7.5 7.5 0%

Field Duplicate Enterococcus 14.8 4.1 113%


One method blank (sterility check) was run in the batch for E. coli and enterococcus. No growth was observed in the blank.


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3.7. CONTINUOUS WATER QUALITY

Continuous water quality measurements were recorded at two sites during the spring (May 2019), concurrent with bioassessments, and again in the summer (September 2019) in compliance with the MRP. Temperature, pH, dissolved oxygen, and specific conductivity were recorded once every 15 minutes for approximately two-weeks using a multi-parameter water quality sonde (YSI 6600-V2).

3.7.1. Completeness

The MRP requires one to two-week deployments, and both deployments exceeded the one week minimum. The first deployment lasted 14 days, while the second deployment lasted 13 days. All sondes collected data for 100% of the planned deployments, and no data were rejected. However, the pH sensor for the sonde deployed at site 202TUN030 failed the post-calibration drift check during the summer deployment. These data were flagged but not rejected.

3.7.2. Sensitivity

There are no method reporting limits for temperature, dissolved oxygen, pH, and conductivity measurements, but the actual measurements are much higher than target reporting limits in the RMC QAPP, so it is assumed that target reporting limits are met for all field measurements.

3.7.3. Accuracy

The SMCWPPP staff conduct pre- and post-deployment sonde calibrations for the two sondes used during monitoring events and calculate the drift during the deployments. During the second monitoring event, the sonde deployed at 202TUN030 exceeded both the pH 7 and pH 10 MQOs. The pH results at this site were subsequently flagged for this deployment, but not rejected. A summary of the drift measurements is shown in Table 6.

3.7.4. Precision

There is no protocol listed in the RMC QAPP for measuring the precision of continuous water quality measurements.

3.8. CONTINUOUS TEMPERATURE MONITORING

Continuous temperature monitoring was conducted from April through September 2019 at five sites in San Mateo County. Onset HOBO Water Temperature data loggers recorded one measurement per hour.

3.8.1. Completeness

The MRP requires SMCWPPP to monitor four stream reaches for temperature each year but anticipating the potential for a HOBO temperature logger to be lost during such a long deployment, SMCWPPP deployed one extra temperature logger for a total of five loggers. In the middle of the deployment,

Table 6. Drift measurements for two continuous water quality monitoring events in San Mateo County urban creeks during WY 2019. Bold and highlighted values exceeded measurement quality objectives.

Parameter Measurement

Quality Objectives

202TUN030 202TUN040

Event 1 Event 2 Event 1 Event 2

Dissolved Oxygen (mg/l) ± 0.5 mg/L

or 10% 0.13 0.05 -0.09 0.07

pH 7.0 ± 0.2 0.08 0.20 0.04 -0.04

pH 10.0 ± 0.2 -0.10 0.43 0.06 -0.01

Specific Conductance (uS/cm)

± 10% -3.9% -0.2% -0.6% -0.1%

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SMCWPPP staff checked the loggers to ensure that they were still present and recording. If a logger was missing during the mid-deployment field check, it would be replaced with a new logger. During the field check, staff also downloaded the existing data and redeployed the other loggers. All temperature loggers were recovered at the end of the deployment, resulting in a completion rate of over 100%.

3.8.2. Sensitivity

There is no target reporting limit for temperature listed in the RMC QAPP, thus sensitivity could not be evaluated for continuous temperature measurements.

3.8.3. Accuracy

A pre-deployment accuracy check was run on the temperature loggers in March 2019. None of the loggers exceeded the 0.2 ºC mean difference threshold for either the room temperature bath or the 0.2 ºC mean difference for the ice bath. The loggers were subsequently deployed, and no flagging of the data was necessary.

3.8.4. Precision

There are no precision protocols for continuous temperature monitoring.

3.9. SEDIMENT CHEMISTRY

The dry season sediment chemistry sample was collected by Kinnetic Laboratories, Inc (KLI) concurrently with the dry season toxicity sample on July 23, 2019. Inorganic and synthetic organic compounds were analyzed by Caltest and grain size distribution was analyzed by Soil Control Laboratories, a subcontractor laboratory. Caltest conducted all QA/QC requirements as specified in the RMC QAPP and reported their findings to the RMC. Key sediment chemistry MQOs are listed in RMC QAPP Tables 26-9 through 26-11. Sediment chemistry data were flagged when necessary, but none were rejected

3.9.1. Completeness

The MRP requires a sediment chemistry sample to be collected at one location each year. In WY 2019, SMCWPPP collected the sediment chemistry sample at 204PUL010. The laboratories analyzed samples within the one year holding time for analytes in sediment, set by the RMC SOP, and reported 100% of the required analytes.

3.9.2. Sensitivity

A comparison of target and actual reporting limits for those parameters is shown in Table 7. For sediment chemistry analysis conducted in WY 2019, laboratory reporting limits were higher than RMC QAPP target reporting limits for 29 analytes. Since reporting limits for a sample are dependent on the percent solids of that sample, it is likely that the amount of solids in the sample resulted in these exceedances.


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Table 7. Comparison of target and actual reporting limits for sediment analytes where reporting limits exceeded target limits. Sediment samples were collected in San Mateo County creeks in WY 2019.

Analyte Target RL Actual RL

Unit

Arsenic 0.3 0.51 mg/Kg

Cadmium 0.01 0.08 mg/Kg

Chromium 0.1 1 mg/Kg

Copper 0.01 0.41 mg/Kg

Lead 0.01 0.08 mg/Kg

Nickel 0.02 0.08 mg/Kg

Zinc 0.1 0.81 mg/Kg

Benzo(a)pyrene 20 36 ng/g

Benzo(b)fluoranthene 20 36 ng/g

Benzo(e)pyrene 20 36 ng/g

Benzo(g,h,i)perylene 20 36 ng/g

Benzo(k)fluoranthene 20 36 ng/g

Chrysene 20 36 ng/g

Dibenz(a,h)anthracene 20 36 ng/g

Indeno(1,2,3-c,d)pyrene 20 36 ng/g

Perylene 20 36 ng/g

Bifenthrin 0.33 1.3 ng/g

Cyfluthrin 0.33 1.3 ng/g

Total Lambda-cyhalothrin 0.33 1.3 ng/g

Total Cypermethrin 0.33 1.3 ng/g

Total Deltamethrin 0.33 1.3 ng/g

Total Esfenvalerate/Fenvalerate 0.33 1.3 ng/g

Permethrin 0.33 1.3 ng/g

Carbaryl 30 31 ng/g

Fipronil 0.33 1.3 ng/g

Fipronil Desulfinyl 0.33 1.3 ng/g

Fipronil Sulfide 0.33 1.3 ng/g

Fipronil Sulfone 0.33 1.3 ng/g

Total Organic Carbon 0.01 0.05 % dw

3.9.3. Accuracy

Inorganic Analytes No QA samples exceeded the QAPP MQO for LCS percent recovery (PR) for inorganic analytes (75-125%), but the MS samples for arsenic and lead exceeded the PR MQO. These samples were flagged but not rejected.

Synthetic Organic Compounds The RMC QAPP lists the percent recovery MQO for pyrethroids and other synthetic organic compounds in sediment as 50-150%. However, the PR MQOs listed in the laboratory reports for synthetic organic compounds varied by analyte and were much larger than PR ranges listed in the QAPP. The MQOs

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ranged from 1 to 275% in certain cases. As a result, several analytes were flagged by the local QA officers, but not by the laboratory.

None of the LCS PRs exceeded the RMC MQO range. However, the MS/MSD PRs exceeded the RMC MQO range for seven PAHs in addition to fipronil, fipronil desulfinyl, fipronil sulfide, and fipronil sulfide. The PAH MS/MSD samples that exceeded the PR MQO include benzo(a)pyrene, benzo(g,h,i)perylene, fluoranthene, indeno(1,2,3-cd)pyrene, naphthalene, perylene, and pyrene.

3.9.4. Precision

Inorganic Analytes The RMC QAPP lists the maximum RPD for inorganic analytes (metals) as 25%. All MS/MSD sets for metals were well below the RMC RPD MQO of 25%.

Synthetic Organic Compounds The maximum RPD for synthetic organics listed in the sediment laboratory report lists ranges from 30 to 50% for most analytes. However, the RMC QAPP lists the MQO as < 25% RPD for most synthetic organics, < 35% for pyrethroids and fipronil, and < 40% for carbaryl. None of the MS/MSD pairs or LCS duplicates exceeded the RPD MQO.

Field Duplicates A sediment sample field duplicate was collected in Contra Costa County on July 23, 2019 and evaluated for precision. The field duplicate sample and corresponding RPDs are shown in Table 8. Because of the variability in reporting limits, values less than the RL were not evaluated for RPD. The measured concentrations of a majority of analytes from the original and duplicate samples were below the method detection limit and therefore reported as “ND”. As a result, the RPDs were non-calculable. All calculable RPDs were below the MQO limits. Analytes that exceeded the MQO of RPD < 25% were medium sand (0.25 to <0.5 mm); total cyfluthrin; total lambda-cyhalothrin; deltamethrin/tralomethrin. Given the inherent variability associated with sediment sample field duplicates, the number of analytes with RPDs outside of the MQO limits is acceptable. The method used to collect sediment field duplicates provides more insight to laboratory precision than precision of field methods; however, the results do suggest that field methods are precise.


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Table 9. Sediment chemistry duplicate field results for site 544MSH045, collected on July 23, 2019 in Contra Costa County. Data in highlighted rows exceed monitoring quality objectives in RMC QAPP.

Analyte Unit Original Duplicate RPD Exceeds MQO?

(<25%)a

Gra

in S

ize

Dis

trib

uti

on

Clay: <0.0039 mm % 25.14 23.22 7.9% No

Silt: 0.0039 to <0.0625 mm % 60.97 60.13 1.4% No

Sand: V. Fine 0.0625 to <0.125 mm % 7.36 8.39 13.1% No

Sand: Fine 0.125 to <0.25 mm % 3.4 4.19 20.8% No

Sand: Medium 0.25 to <0.5 mm % 1.54 2.37 42.5% Yes

Sand: Coarse 0.5 to <1.0 mm % 0.8 1 22.2% No

Sand: V. Coarse 1.0 to <2.0 mm % 0.8 0.7 13.3% No

Granule: 2.0 to <4.0 mm % 0.38 0.36 5.4% No

Pebble: Small 4 to <8 mm % 0.09 ND N/A N/A

Pebble: Medium 8 to <16 mm % 1.68 ND N/A N/A

Pebble: Large 16 to <32 mm % ND ND N/A N/A

Pebble: V. Large 32 to <64 mm % ND ND N/A N/A

Met

als

Arsenic mg/Kg dw 6.4 6.1 4.8% No

Cadmium mg/Kg dw 0.14 0.17 19.4% No

Chromium mg/Kg dw 31 32 3.2% No

Copper mg/Kg dw 34 34 0% No

Lead mg/Kg dw 9.3 10 7.3% No

Nickel mg/Kg dw 43 45 4.5% No

Zinc mg/Kg dw 130 140 7.4% No

Pyr

eth

roid

s (M

QO

<35

%)

Bifenthrin ng/g dw 18 17 5.7% No

Cyfluthrin ng/g dw 2.5 1.7 38.1% Yes

Lambda-Cyhalothrin ng/g dw 1 1.3 26.1% No

Cypermethrin ng/g dw ND ND N/A ND

Deltamethrin/Tralomethrin ng/g dw 6.8 4.8 34.5% No

Esfenvalerate/Fenvalerate ng/g dw ND ND N/A N/A

Permethrin ng/g dw 0.77 0.83 7.5% No

Total Organic Carbon % 2.6 2.4 8.0% No

Carbaryl mg/Kg dw ND ND N/A N/A

Fip

ron

il

Fipronil ng/g dw ND ND N/A N/A

Fipronil Desulfinyl ng/g dw ND ND N/A N/A

Fipronil Sulfide ng/g dw ND ND N/A N/A

Fipronil Sulfone ng/g dw ND ND N/A N/A

Po

lycy

clic

Aro

mat

ic H

ydro

carb

on

s

Acenaphthene ng/g dw ND ND N/A N/A

Acenaphthylene ng/g dw ND ND N/A N/A

Anthracene ng/g dw ND ND N/A N/A

Benz(a)anthracene ng/g dw ND ND N/A N/A

Benzo(a)pyrene ng/g dw ND ND N/A N/A

Benzo(b)fluoranthene ng/g dw ND ND N/A N/A

Benzo(e)pyrene ng/g dw ND ND N/A N/A

Benzo(g,h,i)perylene ng/g dw ND ND N/A N/A

Benzo(k)fluoranthene ng/g dw ND ND N/A N/A

Biphenyl ng/g dw ND ND N/A N/A

Chrysene ng/g dw ND ND N/A N/A

22

Table 9. Sediment chemistry duplicate field results for site 544MSH045, collected on July 23, 2019 in Contra Costa County. Data in highlighted rows exceed monitoring quality objectives in RMC QAPP.

Analyte Unit Original Duplicate RPD Exceeds MQO?

(<25%)a

Dibenz(a,h)anthracene ng/g dw ND ND N/A N/A

Dibenzothiophene ng/g dw ND ND N/A N/A

Dimethylnaphthalene, 2,6- ng/g dw 11 11 0% No

Fluoranthene ng/g dw ND ND N/A N/A

Fluorene ng/g dw ND ND N/A N/A

Indeno(1,2,3-c,d)pyrene ng/g dw ND ND N/A N/A

Methylnaphthalene, 1- ng/g dw ND ND N/A N/A

Methylnaphthalene, 2- ng/g dw ND ND N/A N/A

Methylphenanthrene, 1- ng/g dw ND ND N/A N/A

Naphthalene ng/g dw ND ND N/A N/A

Perylene ng/g dw ND ND N/A N/A

Phenanthrene ng/g dw ND ND N/A N/A

Pyrene ng/g dw ND ND N/A N/A a MQO for pyrethroids is <35%. In accordance with the RMC QAPP, if the native concentration of either sample is less than the reporting limit, the RPD is not applicable

Laboratory Duplicates Laboratory duplicates were collected and analyzed for grain sizes and total organic carbon. All RPDs were below the MQO limits except for medium (8 to <16 mm) pebbles in addition to granules (2.0 to <4.0 mm). As a result, the associated samples were flagged.


All instrument (lab) blanks had concentrations below the reporting limit, and no data were flagged or rejected.

3.10. TOXICITY TESTING

Dry season water and sediment toxicity samples were collected by KLI concurrently with dry season sediment chemistry samples at one San Mateo County site on July 23, 2019. All toxicity tests were performed by Pacific EcoRisk. The water samples were analyzed for toxicity to five organisms (Selenastrum capricornutum, Ceriodaphnia dubia, Pimephales promelas, Hyalella azteca, and Chironomus dilutus) and the sediment samples were analyzed for toxicity to Hyalella azteca and Chironomus dilutus.

3.10.1. Completeness

The MRP requires the collection of dry season water and sediment toxicity samples at one site per year in San Mateo County. Pacific EcoRisk tested the required organisms for toxicity, and 100% of results were reported.

3.10.2. Sensitivity and Accuracy

Internal laboratory procedures that align with the RMC QAPP, including water and sediment quality testing and reference toxicant testing, were performed and submitted to SMCWPPP. The laboratory data QC checks found that all conditions and responses were acceptable. A copy of the laboratory QC report is available upon request.

3.10.3. Precision

Field duplicates for sediment toxicity were not taken during the dry weather sampling. This oversight was the result of a misunderstanding of the conflict between the 2016 version of the RMC QAPP (V3.0) and


23

the SWAMP requirements for toxicity sample field duplicates that were revised in 2018. As such, 2019 RMC toxicity data are SWAMP comparable, but were flagged with the “VQCP” qualifier to indicate a discrepancy with the RMC QAPP. The RMC QAPP has been updated to reflect the recent revisions to the SWAMP MQOs.


There are no QA/QC procedures for contamination of toxicity samples, but staff followed applicable RMC SOPs to limit possible contamination of samples.

4. CONCLUSIONS

Sample collection and analysis followed MRP and RMC QAPP requirements and data that exceeded measurement quality objectives were flagged. However, no data were rejected.

24

5. REFERENCES

Bay Area Stormwater Management Agency Association (BASMAA). 2012. Regional Monitoring Coalition Final Creek Status and Long-Term Trends Monitoring Plan. Prepared By EOA, Inc. Oakland, CA. 23 pp.

Bay Area Stormwater Management Agency Association (BASMAA) Regional Monitoring Coalition. 2016a. Creek Status Monitoring Program Quality Assurance Project Plan, Final Draft Version 3. Prepared for BASMAA by EOA, Inc. on behalf of the Santa Clara Urban Runoff Pollution Prevention Program and the San Mateo Countywide Water Pollution Prevention Program, Applied Marine Sciences on behalf of the Alameda Countywide Clean Water Program, and Armand Ruby Consulting on behalf of the Contra Costa Clean Water Program. 128 pp.

Bay Area Stormwater Management Agency Association (BASMAA) Regional Monitoring Coalition. 2016b. Creek Status Monitoring Program Standard Operating Procedures Version 3. Prepared for BASMAA by EOA, Inc. on behalf of the Santa Clara Urban Runoff Pollution Prevention Program and the San Mateo Countywide Water Pollution Prevention Program, Applied Marine Sciences on behalf of the Alameda Countywide Clean Water Program, and Armand Ruby Consulting on behalf of the Contra Costa Clean Water Program. 192 pp.

Missouri Department of Natural Resources. 2004. Water Pollution Control Permit Manual, Appendix T: Total Chlorine Residual Study. 2 pp.

State Water Resources Control Board (SWRCB). 2014. Statewide National Pollutant Discharge Elimination System (NPDES) Permit for Drinking Water System Discharges to Waters of the United States. Order WQ 2014-0194-DWQ. General Oder No. CAG140001. 111 pp.

Surface Water Ambient Monitoring Program (SWAMP). 2017. SWAMP Quality Assurance Program Plan. May. 140 pp.

Attachment 2

SMCWPPP Bioassessment Data, WY 2012 – WY 2019

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202R00072 Pilarcitos Creek 37.51509 -122.38599 NU 5/29/12 11.3 202 11.61 6.01 17 16 50 12 DNQ 0.06 DNQ 0.0 0.33 = -0.002 ND 0.14 = 0.01 DNQ 0.02 = 0.5 0.02 0.94 0.94 0.90 0.74 NR 20 16 14 0.47 0.75 30 1.6 NR 1.5 1% 0% 0.3

202R00087 Milagra Creek 37.64487 -122.48103 U 5/30/12 10.5 510 10.97 7.63 69 25 17 -6 ND -0.04 ND 0.2 0.19 = -0.002 ND 0.16 = 0.03 = 0.03 = 0.4 0.03 0.75 0.68 0.89 1.20 NR 19 13 10 1.5 0.67 14 1.6 NR 1.7 27% 43% 7.7

204R00180 Sanchez Creek 37.57358 -122.36900 U 5/30/12 9.0 555 15.85 7.61 47 20 79 8 DNQ -0.04 ND 0.2 0.44 = -0.002 ND 0.4 = 0.11 = 0.13 = 0.8 0.13 0.47 0.79 0.66 0.76 NR 3 3 8 4.96 0.56 14 1.8 NR 1.4 31% 68% 7.2

204R00200 Polhemus Creek 37.52355 -122.34064 U 5/31/12 9.3 1090 14.06 7.84 85 23 231 -50 ND 0.07 DNQ 1.0 0.43 = 0.002 DNQ 0.55 = 0.12 = 0.17 = 1.0 0.17 0.43 0.55 0.51 1.00 NR 16 7 13 2.14 0.61 12 1.7 NR 1.7 41% 66% 8.9

205R00168 Corte Madera Creek 37.39619 -122.23464 U 6/4/12 9.4 664 14.96 7.86 41 21 38 -5 ND 0.10 DNQ 1.7 0.14 = 0.008 DNQ 0.37 = 0.18 = 0.17 = 0.5 0.17 0.93 0.52 0.55 0.91 NR 20 11 12 1.68 0.8 40 1.8 NR 1.4 7% 27% 3.2

205R00088 Corte Madera Creek 37.37287 -122.22002 U 6/4/12 10.2 618 13.76 7 33 21 138 7 DNQ 0.12 = 0.3 0.06 DNQ -0.002 ND 0.16 = 0.31 = 0.08 = 0.2 0.08 1.19 0.65 0.67 0.56 NR 19 15 15 2.58 0.99 24 1.8 NR 1.5 4% 11% 2.5

202R00024 Woodhams Creek 37.32503 -122.24861 NU 6/6/12 9.7 835 10.8 8.09 13 34 235 -21 ND 0.11 = 2.4 0.06 DNQ 0.003 DNQ 0.45 = 0.16 = 0.20 = 0.5 0.20 0.85 0.75 0.87 0.96 NR 20 12 15 0.21 0.31 16 1.7 NR 1.9 1% 0% 0.0

204R00232 Arroyo Ojo De Agua 37.46381 -122.25059 U 6/12/12 9.6 928 15.19 7.91 30 68 103 -6 ND -0.04 ND 0.4 1.50 = -0.002 ND 0.46 = 0.10 = 0.11 = 2.0 0.11 0.43 0.83 0.80 0.48 NR 19 14 15 3.08 0.94 23 1.6 NR 1.8 41% 88% 10.3

204R00244 Trib to Arroyo Ojo De Agua 37.47191 -122.24306 U 6/12/12 10.7 1117 23.76 8.22 53 19 122 = 0.06 DNQ 3.8 0.16 = 0.002 DNQ 0.58 = 0.08 = 0.11 = 0.7 0.11 0.41 0.37 0.53 0.46 NR 0 1 17 3.93 0 3 0.3 NR 1.0 43% 96% 12.1

202R00104 La Honda Creek 37.38878 -122.284299 NU 6/13/12 7.1 238 12 7.24 21 NR 57 9 = 0.11 = 0.4 0.28 = 0.001 ND 0.188 = 0.09 = 0.06 = 0.5 0.06 0.70 1.00 0.86 0.83 NR 15 8 4 0.52 0.68 49 1.7 NR 1.7 5% 7% 2.3

202R00284 Denniston Creek 37.50515 -122.48723 U 6/15/12 10.7 265 12.5 6.81 27 23 56 -6 ND 0.13 = 0.2 0.16 = 0.004 DNQ 0.15 = 0.03 = 0.05 = 0.3 0.05 0.79 0.96 0.83 1.08 NR 15 6 2 2.83 0.8 55 1.5 NR 1.3 3% 3% 1.0

202R00166 Little Butano Creek 37.213634 -122.314113 NU 6/25/12 10.1 235 12.15 7.72 17 NR 31 9 = 0.46 = 5.0 0.24 = 0.001 ND 0.235 = 0.10 = 0.05 = 0.5 0.05 1.16 0.99 1.17 0.97 NR 19 15 8 0 0.45 28 1.6 NR 1.7 8% 0% 1.2

202R00038 Little Butano Creek 37.215901 -122.307277 NU 6/26/12 10.2 230 12.84 7.67 15 NR 17 9 = 0.15 = 1.5 0.20 = 0.001 ND 0.19 = 0.09 = 0.04 = 0.4 0.04 1.07 1.06 1.11 0.77 NR 19 15 12 0 0.97 16 1.9 NR 1.6 6% 0% 1.4

205R00296 West Union Creek 37.45211 -122.29852 NU 5/6/13 5.6 475 12.26 6.73 41 NR 31 3 = 0.04 DNQ 0.0 0.00 ND 0.001 ND 0.331 = 0.00 ND 0.01 = 0.3 0.01 0.72 0.77 1.04 1.28 NR 18 8 4 0.91 0.56 59 1.4 NR 1.5 2% 2% 0.3

204R00436 Easton Creek 37.58017 -122.37242 U 5/20/13 8.1 661 20.62 7.12 72 25 78 115 = -0.04 ND 0.1 0.29 = 0.003 DNQ 0.57 = 0.07 = 0.08 = 0.9 0.08 0.35 0.57 0.43 0.69 NR 0 6 7 4.36 0.51 17 1.4 NR 1.4 42% 98% 11.7

204R00884 Easton Creek 37.57729 -122.38584 U 5/20/13 9.2 945 12.96 7.39 70 19 57 115 = -0.04 ND 0.1 0.34 = -0.002 ND 0.75 = 0.14 = 0.16 = 1.1 0.16 0.42 0.33 0.49 0.62 NR 8 11 19 4.33 0.37 2 1.6 NR 1.6 44% 98% 12.2

202R00908 Calera Creek 37.61230 -122.49411 U 5/21/13 8.6 697 21.56 7.51 130 9 243 84 = 0.09 DNQ 1.1 8.10 = 0.018 DNQ 1.4 = 3.00 = 3.00 = 9.5 3.00 0.17 0.60 0.48 0.87 NR 18 11 6 1.15 0.99 50 1.0 NR 1.5 10% 15% 2.9

204R00807 Colma Creek 37.65251 -122.42241 U 5/21/13 13.6 1386 14.24 7.75 110 25 364 25 = -0.04 ND 0.2 2.40 = 0.037 = 0.48 = 0.01 DNQ 0.02 = 2.9 0.02 0.25 0.42 0.55 0.58 NR 0 0 13 4.99 0.69 2 0.0 NR 0.0 45% 62% 10.6

202R00280 Tributary to Alpine Creek 37.29444 -122.22163 NU 5/22/13 11.1 703 9.75 7.59 15 30 160 152 = -0.04 ND 0.1 0.16 = -0.002 ND -0.14 ND 0.38 = 0.40 = 0.2 0.40 0.85 0.85 0.96 1.00 NR 20 19 14 0 0.64 13 0.8 NR 1.8 1% 0% 1.2

202R00248 San Gregorio Creek 37.31983 -122.34070 NU 5/23/13 10.4 886 11.65 7.6 56 27 228 10 = -0.04 ND 0.1 -0.01 ND -0.002 ND -0.14 ND 0.15 = 0.16 = 0.1 0.16 1.08 0.40 0.45 0.65 NR 18 16 13 1.69 0.91 28 1.3 NR 1.5 2% 3% 1.7

205R00872 Bear Gulch Creek 37.41986 -122.24529 U 5/27/13 9.4 683 13.86 7.85 56 17 136 17 = -0.04 ND 0.3 0.08 = -0.002 ND -0.14 ND 0.03 = 0.04 = 0.2 0.04 0.67 0.71 0.51 0.89 NR 14 12 10 2.41 0.78 29 1.7 NR 1.8 6% 25% 2.4

205R00984 Bear Gulch Creek 37.58017 -122.37242 U 5/27/13 8.1 661 20.62 7.12 36 17 245 36 = 0.08 DNQ 0.4 0.11 = -0.002 ND -0.14 ND 0.03 = 0.05 = 0.2 0.05 0.89 0.46 0.50 0.66 NR 0 6 7 1.18 0.63 27 1.5 NR 1.5 4% 12% 1.4

204R00680 Redwood Creek 37.43783 -122.24151 U 5/28/13 8.2 1595 16.41 7.74 100 45 481 36 = -0.04 ND 0.3 -0.01 ND -0.002 ND -0.14 ND 0.07 = 0.09 = 0.1 0.09 0.51 0.35 0.34 1.01 NR 7 6 9 2.31 0.39 35 1.5 NR 1.6 23% 81% 8.4

204R00520 Belmont Creek 37.51221 -122.29034 U 5/28/13 8.4 1041 15.06 7.1 130 18 135 41 = 0.06 DNQ 0.2 0.13 = -0.002 ND -0.14 ND 0.04 = 0.05 = 0.2 0.05 0.52 0.53 0.48 0.87 NR 6 11 10 3.36 0.75 17 1.5 NR 1.7 35% 62% 8.5

202R00150 Butano Creek 37.22664 -122.2412 NU 6/4/13 10.1 333 11.68 7.95 15 NR 42 7 = 0.015 ND NR 0.00 ND 0.001 ND 0.172 = 0.00 ND 0.01 DNQ 0.2 0.01 1.00 0.91 0.93 1.00 NR 20 19 18 0 0.63 29 1.4 NR 1.9 11% 0% 1.5

202R00268 Dry Creek 37.35917 -122.39124 NU 6/5/13 9.2 1035 11.91 7.65 132 NR 34 22 = 0.015 ND NR 0.25 = 0.001 ND 0.543 = 0.03 = 0.05 = 0.8 0.05 0.51 0.87 0.99 0.83 NR 17 10 7 0 0.37 30 1.8 NR 1.5 1% 0% 0.7

202R00214 Tarwater Creek 37.26166 -122.24082 NU 6/6/13 9.3 1540 12.34 7.88 286 NR 18 10 = 0.015 ND NR 0.11 = 0.001 ND 0.267 = 0.31 = 0.36 = 0.4 0.36 0.91 1.08 1.08 0.83 NR 19 18 17 1.12 0.46 23 1.7 NR 1.9 1% 0% 1.5

204R01460 Sanchez Creek 37.57670 -122.36803 U 4/28/14 9.2 88 13.52 7.49 57 21 62 39 = -0.04 ND 0.1 0.18 = -0.005 ND 0.22 = 0.08 = 0.08 = 0.4 0.08 0.42 0.29 0.61 0.97 NR 11 8 7 2.25 0.42 14 1.8 NR 1.8 31% 69% 7.2

204R01204 Burlingame Creek 37.55699 -122.35379 U 4/28/14 8.6 79 11.19 7.87 59 33 31 56 ND 0.06 DNQ 0.8 0.23 = -0.005 ND 0.53 = 0.21 = 0.21 = 0.8 0.21 0.53 0.54 0.60 0.85 NR 18 15 14 2.4 0.53 27 1.7 NR 1.6 36% 94% 10.3

204R01012 Cordilleras Creek 37.47381 -122.26848 U 4/29/14 96.7 872 16.02 7.9 67 15 27 3 = -0.04 ND 0.4 -0.01 ND -0.005 ND 0.31 = 0.05 = 0.06 = 0.3 0.06 0.58 0.83 0.71 0.89 NR 18 6 4 2.36 0.46 18 1.7 NR 1.6 16% 36% 5.0

204R01288 Laurel Creek 37.52342 -122.31235 U 4/29/14 7.9 729 11.54 7.55 81 18 29 38 = 1.20 = 8.0 0.33 = 0.029 DNQ 1.7 = 0.14 = 0.17 = 2.1 0.17 0.34 0.75 0.66 0.87 NR 18 7 6 2.36 0.36 16 1.4 NR 1.7 33% 62% 10.6

204R01256 Arroyo Ojo de Agua 37.45444 -122.25038 U 5/6/14 9.1 988 14.34 7.92 32 67 88 32 = -0.04 ND 0.4 0.43 = 0.005 DNQ 0.53 = 0.31 = 0.12 = 1.0 0.12 0.51 0.81 0.75 1.07 NR 16 17 15 1.66 0.72 17 1.8 NR 1.7 34% 79% 9.6

204R01268 Redwood Creek 37.46835 -122.23270 U 5/6/14 14.9 851 23.93 9.46 52 19 255 240 = 0.12 = 68.8 0.12 = 0.010 DNQ 1.7 = 0.27 = 0.32 = 1.8 0.32 0.46 0.55 0.58 0.48 NR 0 0 5 4.76 0.11 8 0.3 NR 0.7 22% 81% 8.0

202R00312 Mills Creek 37.452328 -122.392423 NU 5/7/14 9.5 761 11.9 8.32 56 23 98 11 = 0.02 DNQ 0.8 0.08 = 0.003 DNQ 0.286 = 0.20 = 0.21 = 0.4 0.21 0.82 0.86 0.95 1.10 NR 19 15 14 0.14 0.38 28 1.6 NR 1.5 2% 3% 0.7

202R00328 Pilarcitos Creek 37.50722 -122.38654 NU 5/7/14 10.4 298 10.82 7.54 20 14 670 30 = -0.04 ND 0.1 0.27 = -0.005 ND 0.35 = 0.02 = 0.02 = 0.6 0.02 1.01 0.95 0.80 1.00 NR 19 14 3 0.97 0.62 35 1.4 NR 1.4 1% 0% 0.4

202R01308 Pilarcitos Creek 37.46831 -122.43627 U 5/7/14 9.4 300 14.23 7.39 33 18 70 4 = -0.04 ND 0.1 0.59 = -0.005 ND 0.44 = 0.10 = 0.10 = 1.0 0.10 0.31 0.89 0.77 0.97 NR 14 5 3 2 0.78 60 1.7 NR 1.3 3% 3% 0.9

202R00972 Arroyo de en Medio 37.51374 -122.45084 U 5/8/14 10.5 251 11.41 7.56 36 28 170 17 = 0.23 = 1.6 0.53 = -0.005 ND 1.2 = 0.03 = 0.10 = 1.7 0.10 0.48 0.26 0.62 0.97 NR 20 16 10 0 0.6 67 1.2 NR 1.1 1% 0% 2.0

202R00376 Tunitas Creek 37.390839 -122.368261 NU 5/12/14 10.7 1189 12.1 8.15 195 20 106 16 = 0.015 ND NR 0.03 = 0.012 ND 0.104 = 0.07 = 0.06 = 0.1 0.06 0.92 0.81 0.69 1.05 NR 18 15 16 0.85 0.47 34 1.6 NR 1.6 1% 0% 1.6

205R01192 Corte Madera Creek 37.39096 -122.23115 U 5/13/14 7.9 897 13.05 7.23 58 21 134 10 = -0.04 ND 0.1 -0.01 ND -0.005 ND 0.22 = 0.06 = 0.06 = 0.2 0.06 0.60 0.40 0.55 0.83 NR 11 9 6 2.87 0.44 39 1.6 NR 1.5 7% 26% 3.2

205R01704 Dry Creek 37.43389 -122.26094 U 4/22/15 9.5 875 11.8 7.98 42 24 342 18 = 0.12 = 2.2 -0.01 ND -0.005 ND 0.75 = 0.12 = 0.10 = 0.8 0.10 0.32 0.64 0.78 1.28 NR 12 9 7 1.88 0.56 22 1.9 NR 1.8 13% 61% 6.3

204R01448 Atherton Creek 37.43459 -122.21776 U 4/22/15 12.4 2801 16.4 8.42 250 23 59 101 = 0.15 = 9.3 0.31 = -0.005 ND 1.1 = 0.10 = 0.12 = 1.4 0.12 0.40 0.32 0.50 0.86 NR 2 1 9 2.36 0.26 10 0.7 NR 1.2 17% 48% 5.4

202R00378 Pescadero Creek 37.21994 -122.16385 NU 4/23/15 10.0 830 10.8 8.16 55 29 69 -3 ND 0.14 = 3.5 -0.01 ND -0.005 ND 0.53 = 0.13 = 0.14 = 0.5 0.14 0.93 1.05 1.03 1.28 NR 18 15 8 0.23 0.73 32 1.7 NR 1.9 17% 0% 0.5

205R01816 Corte Madera Creek 37.36615 -122.21570 U 4/30/15 10.8 928 11.7 8.21 40 19 24 6 = 0.04 DNQ 1.3 -0.01 ND -0.005 ND 0.31 = 0.07 = 0.07 = 0.3 0.07 1.18 0.69 1.03 1.28 NR 14 15 16 2.48 1 13 1.8 NR 1.6 3% 8% 2.4

202R01612 MF San Pedro Cr 37.57810 -122.47139 U 5/11/15 10.6 398 11.7 8.11 23 16 96 30 = 0.35 = 8.7 0.02 DNQ -0.005 ND -0.07 ND 0.02 = 0.02 = 0.1 0.02 0.90 0.81 NA NA NR 18 18 8 0.47 0.74 18 1.8 NR 1.5 1% 0% 0.6

202R01356 MF San Pedro Cr 37.57524 -122.46105 U 5/11/15 10.5 458 11.1 7.81 27 17 50 11 = 0.23 = 2.7 -0.01 ND -0.005 ND -0.07 ND 0.01 = 0.01 = 0.0 0.01 0.91 1.14 NA NA NR 20 17 13 0.15 0.48 9 1.4 NR 1.6 1% 0% 0.9

204R02056 Laurel Creek 37.53342 -122.30243 U 5/12/15 9.2 1129 13.2 8.3 120 16 22 11 = -0.08 ND 1.6 0.67 = 0.010 DNQ 0.83 = 0.09 = 0.10 = 1.5 0.10 0.45 0.54 0.75 1.28 NR 7 5 6 4.14 0.37 17 1.8 NR 1.4 39% 74% 11.7

204R02248 Laurel Creek 37.52659 -122.32286 U 5/12/15 6.7 1179 12.2 7.72 91 19 206 34 = -0.04 ND 0.2 0.16 = -0.005 ND 0.4 = 0.06 = 0.07 = 0.6 0.07 0.33 0.62 0.69 1.28 NR 14 10 8 3.76 0.47 10 1.9 NR 1.8 41% 72% 13.0

202R00440 Purisima Creek 37.43417 -122.34959 NU 5/13/15 10.7 665 10.7 8.45 26 20 11 45 = 0.04 DNQ 2.1 0.11 = -0.005 ND 0.75 = 0.12 = 0.06 = 0.9 0.06 1.16 0.85 1.17 1.28 NR 18 18 10 1.22 0.51 17 1.4 NR 1.8 4% 11% 2.7

204R01972 Cordilleras Creek 37.48375 -122.25730 U 5/13/15 10.1 1115 12.2 8.22 86 13 45 4 = -0.04 ND 0.6 -0.01 ND -0.005 ND 0.48 = 0.06 = 0.16 = 0.5 0.16 0.41 0.53 0.61 0.81 NR 9 11 10 4.04 0.78 9 1.5 NR 1.6 19% 45% 5.5

202R00408 Langley Creek 37.33100 -122.27439 NU 5/27/15 8.6 893 11.6 7.32 42 33 49 14 = 0.07 = 0.3 0.01 DNQ -0.002 ND 0.189 = 0.13 = 0.12 = 0.2 0.12 0.50 0.91 0.96 1.03 NR 18 11 14 0.27 0.61 17 2.0 NR 1.4 1% 0% 1.7

202R00506 Peters Creek 37.28940 -122.17619 NU 5/9/16 10.5 412 11.2 8.19 86 22 28 5 = 0.06 = 1.7 -0.02 ND 0.001 DNQ 0.48 = 0.05 = 0.04 = 0.5 0.04 1.11 0.92 0.80 1.28 1.06 20 18 16 0.27 0.66 14 0.7 200 1.9 19% 3% 2.6

202R00488 Tunitas Creek 37.38001 -122.37482 NU 5/10/16 10.3 8 12.1 8.25 45 22 39 -3 ND 0.03 = 1.0 0.95 = 0.002 DNQ 0.26 = 0.05 = 0.05 = 1.2 0.05 0.55 0.46 0.75 1.07 0.68 18 16 6 1.13 0.5 35 1.8 187 1.9 1% 0% 1.6

202R02332 Pilarcitos Creek 37.47000 -122.44116 U 5/10/16 10.3 570 12.7 7.89 16 23 112 11 = 0.03 = 0.5 0.03 DNQ 0.001 DNQ 0.31 = 0.08 = 0.09 = 0.3 0.09 0.51 0.78 NA NA 1.04 15 5 2 2.46 0.05 60 1.7 138 1.1 3% 4% 1.0

204R02228 San Mateo Creek 37.56114 -122.33698 U 5/11/16 10.7 309 15.5 8.08 20 10 99 26 = 0.05 = 1.5 0.04 DNQ 0.002 DNQ 0.4 = 0.02 = 0.02 = 0.4 0.02 0.56 0.56 0.56 1.15 0.91 11 7 5 2.67 0.76 35 1.6 137 1.5 9% 14% 2.8

204R02504 Polhemus Creek 37.53015 -122.34871 U 5/11/16 11.0 1077 13 8.02 79 21 148 189 = 0.03 = 0.7 -0.02 ND 0.002 DNQ 0.57 = 0.08 = 0.08 = 0.6 0.08 0.45 0.43 0.46 0.96 0.88 7 15 8 2.76 0.27 14 1.5 137 1.8 33% 54% 6.9

205R02728 Dry Creek 37.42452 -122.24954 U 5/12/16 8.8 9 14.3 8.18 45 18 54 27 = 0.06 = 2.2 0.23 = 0.001 DNQ 0.4 = 0.03 = 0.14 = 0.6 0.14 0.39 0.36 0.67 0.96 0.98 19 12 8 1.82 0.29 30 1.8 140 1.8 13% 64% 6.4

205R02920 Bear Gulch Creek 37.42376 -122.25112 U 5/12/16 10.3 585 16.1 7.97 69 18 42 72 = 0.05 = 1.1 -0.02 ND 0.002 DNQ 0.57 = 0.08 = 0.10 = 0.6 0.10 0.75 0.61 0.83 1.07 0.94 19 11 7 1.1 0.46 22 1.9 131 1.8 5% 16% 1.5

Attachment 2: Eight years of bioassessment data (WY 2012- WY 2019) used for analyses in the Integrated Monitoring Report

Land Use

VariablesSite Information Water Quality Water Chemistry (nutrients)

Biological and Physical Habitat

Indicator ScoresPhysical Habitat

Sta

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Te

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Ch

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As

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Ma

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(g

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Ch

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mg

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Am

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205R03032 West Union Creek 37.43720 -122.28319 U 5/16/16 10.0 532 12.9 7.77 44 15 64 3 = 0.03 = 0.3 -0.02 ND -0.001 ND 0.44 = -0.01 ND -0.01 ND 0.5 0.00 0.58 0.84 0.96 1.28 0.97 15 16 9 1.9 0.37 21 1.9 137 1.7 2% 3% 0.5

204R02548 Cordilleras Creek 37.49544 -122.24336 U 5/17/16 7.8 976 18.1 8.01 75 17 48 64 = 0.02 DNQ 0.6 0.22 = 0.001 DNQ 0.97 = 0.11 = 0.12 = 1.2 0.12 0.41 0.60 0.57 1.07 0.89 12 7 6 4.22 0.05 17 1.4 89 1.4 28% 60% 7.8

205R02408 Bull Run Creek 37.38400 -122.23499 U 5/17/16 10.1 1106 12.9 8.25 66 19 263 6 = 0.02 DNQ 0.7 0.06 = -0.001 ND 0.53 = 0.09 = 0.09 = 0.6 0.09 0.64 0.69 0.84 1.28 0.65 19 14 4 1.78 0.76 22 0.9 125 1.7 6% 29% 4.9

204R03496 Redwood Creek 37.44775 -122.23470 U 5/22/17 9.3 922 15.61 8.07 54 33 84 24 = 0.09 = 2.7 0.37 = 0.034 = 0.83 = 0.14 = 0.15 = 1.2 0.15 0.60 0.56 0.64 1.12 0.06 12 8 8 3.72 0.61 59 1.5 143 1.5 19% 79% 7.6

204R02472 Redwood Creek 37.46516 -122.23462 U 5/22/17 13.1 945 26.2 8.86 58 30 179 292 = 0.09 = 24.5 0.47 = 0.032 = 1.9 = 0.18 = 0.23 = 2.4 0.23 0.54 0.40 0.65 0.73 0.85 0 1 15 4.36 0 30 0.1 26 0.5 22% 81% 8.0

204R03240 Atherton Creek 37.42732 -122.22682 U 5/23/17 7.2 5322 14.4 7.87 440 19 546 20 = 0.03 = 0.5 -0.02 ND 0.002 DNQ 1.2 = 0.05 = 0.06 = 1.2 0.06 0.41 0.52 0.59 1.28 0.34 12 8 12 3.3 0.34 78 1.8 138 1.2 12% 30% 3.7

204R02611 Atherton Creek 37.45083 -122.20592 U 5/23/17 13.9 2626 20.55 8.55 230 14 297 203 = 0.04 = 4.2 -0.02 ND -0.001 ND 1 = 0.01 DNQ 0.01 = 1.0 0.01 0.43 0.29 0.48 0.57 0.74 0 0 18 3.96 0 6 0.6 119 0.5 24% 70% 7.1

202R00552 Lawrence Creek 37.38846 -122.31340 NU 5/24/17 10.4 443 11.9 8.21 32 20 276 5 = 0.02 DNQ 0.5 -0.02 ND -0.001 ND 0.22 = 0.01 = 0.02 = 0.2 0.02 1.16 0.95 0.84 1.28 0.81 20 17 6 0 0.72 50 1.6 93 1.7 2% 1% 0.4

204R03316 Arroyo Ojo De Agua 37.48119 -122.23427 U 5/24/17 9.2 778 21.8 8.55 54 59 158 193 = 0.05 = 6.1 1.80 = 0.011 = 0.53 = 0.01 = 0.11 = 2.3 0.11 0.42 0.36 0.62 0.54 0.18 0 0 18 5.51 0 14 0.7 23 0.7 45% 93% 12.2

204R03336 Belmont Creek 37.51628 -122.27867 U 5/25/17 8.2 1324 14.6 7.76 160 22 57 8 = 0.02 DNQ 0.2 0.31 = -0.001 ND 0.66 = 0.05 = 0.05 = 1.0 0.05 0.50 0.57 0.47 1.28 0.72 14 9 7 3.15 0.48 37 1.5 86 1.5 40% 73% 9.1

202R00550 Jones Gulch 37.27880 -122.26832 NU 5/30/17 10.1 519 11.69 7.96 46 50 206 -4 ND 0.05 = 1.7 0.23 = 0.006 = 0.62 = 0.32 = 0.34 = 0.9 0.34 0.87 0.94 1.00 1.28 1.03 20 15 8 0.23 0.62 40 1.7 158 1.8 1% 0% 2.1

204R03252 San Mateo Creek 37.56313 -122.32754 U 5/31/17 10.0 336 15.82 8.07 24 11 163 10 = 0.05 = 1.6 0.05 DNQ 0.004 DNQ 0.4 = 0.01 = 0.03 = 0.5 0.03 0.67 0.80 0.68 1.07 0.87 11 6 4 4.58 0.85 33 1.3 120 1.3 8% 13% 2.7

204R03272 San Mateo Creek 37.53385 -122.35018 U 5/31/17 9.6 249 14.71 7.92 24 11 48 40 = 0.05 = 1.6 0.05 DNQ 0.003 DNQ 0.4 = 0.01 DNQ 0.02 = 0.4 0.02 0.63 0.97 0.78 0.87 0.83 14 13 9 2.69 0.75 46 1.6 107 1.6 7% 8% 2.2

202R00614 Pescadero Creek 37.27410 -122.28860 NU 5/14/18 10.5 644 13.7 7.95 38 23 63 39 = 0.05 = 0.9 0.06 = 0.001 DNQ 0.44 = 0.10 = -0.01 ND 0.5 0.00 1.17 1.16 0.80 0.87 1.04 14 13 13 1.72 0.64 19 1.9 105 1.6 1% 0% 1.4

202R03656 Pilarcitos Creek 37.46781 -122.42269 U 5/15/18 10.3 379 12.8 7.66 33 18 40 -3 ND 0.05 = 0.4 0.85 = 0.003 DNQ 0.35 = 0.04 = 0.08 = 1.2 0.08 0.71 0.82 NA NA 0.8 14 4 3 3.12 0.54 65 1.5 96 1.2 2% 1% 0.7

202R00584 Pilarcitos Creek 37.49547 -122.38512 NU 5/15/18 9.5 321 13.3 7.54 16 16 114 9 = 0.03 = 0.2 0.46 = 0.003 DNQ 0.35 = 0.02 = 0.08 = 0.8 0.08 0.86 0.75 0.83 0.81 0.7 12 15 10 2.67 0.74 42 1.6 45 1.7 1% 0% 0.4

204R03508 Mills Creek 37.59105 -122.37406 U 5/16/18 10.0 664 13.2 7.88 51 23 24 54 = 0.07 = 0.6 0.11 = 0.001 DNQ 0.4 = 0.01 = 0.03 = 0.5 0.03 0.35 0.78 0.80 0.81 0.62 6 7 14 5.34 0 17 0.9 59 1.4 47% 91% 11.8

204R03528 San Mateo Creek 37.54808 -122.34661 U 5/16/18 10.7 244 13.2 7.77 16 6 72 31 = 0.07 = 0.9 0.14 = 0.001 DNQ 0.35 = -0.01 ND -0.01 ND 0.5 0.00 0.60 0.96 0.88 0.78 0.92 16 14 7 2.26 0.75 44 1.8 71 1.5 7% 10% 2.4

202R03404 San Pedro Creek 37.58203 -122.48719 U 5/17/18 10.0 459 13.5 7.92 26 20 119 27 = 0.07 = 0.8 0.49 = 0.007 = 0.35 = 0.02 = -0.01 ND 0.8 0.00 0.65 0.73 0.65 0.96 1 15 16 6 2.47 0.82 29 1.7 94 1.7 13% 23% 2.8

202R03916 San Pedro Creek 37.59144 -122.50333 U 5/17/18 10.1 456 14.1 8.07 28 19 159 25 = 0.07 = 2.0 0.50 = 0.006 = 0.35 = 0.03 = -0.01 ND 0.9 0.00 0.68 1.01 0.74 0.81 1.06 17 9 10 2.5 0.63 32 1.6 102 1.5 15% 26% 3.5

205R03624 Bear Creek 37.41883 -122.26498 U 5/21/18 10.5 562 12.7 8.22 17 18 53 26 = 0.07 = 1.1 0.16 = -0.001 ND 0.35 = 0.09 = 0.10 = 0.5 0.10 1.20 1.01 0.96 1.28 1.21 11 15 12 4.33 0.99 22 1.6 157 1.6 3% 6% 1.1

205R03864 Hamms Gulch 37.36498 -122.22906 U 5/22/18 10.4 694 10.9 7.83 33 21 92 -3 ND 0.77 = 9.3 0.10 = -0.001 ND 0.35 = 0.02 = 0.09 = 0.4 0.09 1.14 0.78 NA NA 1.15 20 15 11 0 0.94 27 1.4 145 1.7 1% 0% 0.9

202R03880 La Honda Creek 37.38759 -122.27219 U 5/22/18 12.5 497 10.7 8.19 22 16 39 24 = 0.77 = 21.0 0.09 = -0.001 ND 0.13 = 0.02 = 0.01 = 0.2 0.01 0.99 1.09 0.90 0.96 1.16 18 17 9 0.6 0.99 32 1.5 234 1.5 4% 10% 2.4

204R04280 Belmont Creek 37.55342 -122.35830 U 5/13/19 7.7 1343 13.3 8.04 130 20 41 59 = 0.39 = 8.8 0.33 = 0.002 DNQ 0.52 = 0.04 = 0.05 = 0.9 0.05 0.55 0.43 0.86 1.07 0.63 6 8 7 2.06 0.53 20 1.4 133 1.3 39% 72% 9.3

204BEL005 Belmont Creek 37.51770 -122.26910 U 5/13/19 10.7 1224 16.9 8.31 140 20 111 53 = 0.24 = 12.8 0.33 = 0.004 DNQ 0.52 = 0.06 = 0.07 = 0.9 0.07 0.46 0.63 0.67 0.81 0.84 9 5 4 4.09 0.33 51 1.4 94 1.2 41% 75% 9.6

202TUN040 Tunitas Creek 37.38840 -122.37090 NU 5/14/19 10.3 601 11.9 8.26 69 22 143 260 = 0.22 = 7.6 -0.02 ND 0.001 DNQ 0.14 = 0.04 = 0.04 = 0.2 0.04 0.94 0.61 0.78 0.54 1.09 20 17 9 0.29 0.5 50 1.6 111 1.7 1% 0% 1.7

202TUN030 Tunitas Creek 37.37940 -122.37483 NU 5/14/19 9.8 756 12.7 8.26 72 22 55 10 = 0.28 = 10.2 0.07 = 0.002 DNQ 0.083 DNQ 0.04 = 0.05 = 0.2 0.05 0.84 0.79 0.82 0.37 0.77 19 17 11 1.08 0.59 35 1.7 209 1.7 1% 0% 1.6

204R04160 Burlingame Creek 37.47890 -122.26126 U 5/15/19 7.2 1160 13.7 8.23 60 47 390 132 = 0.15 = 5.4 0.15 = -0.001 ND 0.69 = 0.13 = 0.16 = 0.8 0.16 0.51 0.66 0.72 0.81 0.92 13 11 6 1.41 0.44 34 1.9 146 1.8 37% 93% 10.8

204R04428 Cordilleras Creek 37.45430 -122.20001 U 5/15/19 8.2 918 14.7 8.15 58 20 83 49 = 0.08 = 2.5 0.18 = 0.004 DNQ 0.44 = 0.04 = 0.05 = 0.6 0.05 0.60 0.85 0.73 0.81 0.84 10 9 6 4.7 0.73 44 1.7 122 1.5 19% 46% 5.5

204R04600 Atherton Creek 37.43610 -122.21560 U 6/10/19 10.1 2102 17.4 8.1 170 30 51 10 = 0.13 = 4.4 0.53 = 0.004 DNQ 0.63 = 0.15 = 0.17 = 1.2 0.17 0.53 0.57 0.67 1.28 0.49 2 1 16 3.66 0 2 1.0 101 1.0 24% 61% 6.6

204R03635 Atherton Creek 37.51522 -122.28230 U 6/10/19 11.5 2113 20.4 8.43 220 24 145 220 = 0.11 = 9.3 0.50 = 0.005 = 0.66 = 0.15 = 0.18 = 1.2 0.18 0.49 0.35 0.46 0.55 0.4 0 1 15 4.64 0 0 0.5 206 0.0 24% 71% 7.0

205R05044 Dry Creek 37.42820 -122.25179 U 6/11/19 6.7 722 20.2 7.94 58 18 117 37 = 0.08 = 2.5 0.16 = 0.013 = 0.47 = 0.09 = 0.11 = 0.6 0.11 0.48 0.47 0.51 1.28 0.92 15 10 6 4.18 0.21 34 1.8 136 1.7 13% 63% 6.4

205R04056 Dry Creek 37.43903 -122.26530 U 6/11/19 7.1 799 17 7.92 43 30 87 51 = 0.11 = 2.5 0.23 = 0.004 DNQ 0.3 = 0.07 = 0.08 = 0.5 0.08 0.49 0.58 0.63 0.37 0.87 15 14 9 3.65 0.29 23 1.7 146 1.7 14% 62% 6.3

Land Use: U - Urban, NU - Non-urban

QA Flag: ND - Non-detect (used ½ value of the method detection limit), DNQ - Detected Not Quantifiable (used measured value)

UIA- Un-ionized Ammonia

TKN - Total Kjeldahl Nitrogen

Site Information Water Quality Water Chemistry (nutrients)Biological and Physical Habitat

Indicator ScoresPhysical Habitat

Land Use

Variables

CSCI - California Stream Index

ASCI_D - Algae Stream Condition Index (Diatoms)

IPI - Index Physical Habitat Integrity

ASCI_SB - Algae Stream Condition Index (Soft Body)

ASCI_H - Algae Stream Condition Index (Hybrid)

NR - Not Recorded

Attachment 3

Technical Report: SMCWPPP Creek Status Bioassessment Monitoring, 2012 Through 2019

Prepared by Melwani, A., Applied Marine Sciences

====8Type equation here.

SAN MATEO COUNTYWIDE WATER POLLUTION PREVENTION PROGRAM CREEK STATUS BIOASSESSMENT MONITORING 2012 THROUGH 2019 TECHNICAL REPORT

Report prepared by Aroon Melwani, Ph.D.

Applied Marine Sciences

4749 Bennett Drive, Suite L Livermore, California 94511

Submitted to:

EOA, Inc.

DRAFT January 14, 2020

Creek Status Bioassessment Summary Report January 14, 2020

Page i

List of Acronyms Acronym Definition

ASCI Algae Stream Condition IndexBASMAA Bay Area Stormwater Management Agencies Association BMI Benthic MacroinvertebrateCDF Cumulative Distribution FunctionCSCI California Stream Condition IndexMDL Method Detection LimitMRP, MRP 2.0 Municipal Regional Stormwater PermitMSE Mean Square ErrorND Non-detect NPDES National Pollutant Discharge Elimination SystemQAPP Quality Assurance Project PlanRF Random Forest RMC Regional Monitoring CoalitionSFBRWQCB San Francisco Bay Regional Water Quality Control Board (California

Regional Water Quality Control Board, San Francisco Bay Region)SMCWPPP San Mateo Countywide Water Pollution Prevention Program SOP Standard Operating ProcedureSWAMP Surface Water Ambient Monitoring ProgramWY Water Year


Page ii

Table of Contents List of Acronyms ............................................................................................................................. i Table of Contents ............................................................................................................................ ii List of Figures ................................................................................................................................ iii List of Tables ................................................................................................................................. iv 1 Introduction ............................................................................................................................. 5 2 Statistical Analyses .................................................................................................................. 5

2.1 Estimating Extent of Healthy Streams ............................................................................ 6 2.2 Biological Indicator Thresholds ...................................................................................... 6 2.3 Stressor Analysis ............................................................................................................. 7

3 Results ..................................................................................................................................... 8 3.1 SMCWPPP Site Evaluations........................................................................................... 8 3.2 Biological Condition of Streams in San Mateo County.................................................. 8 3.3 Stressors Associated with Biological Conditions ......................................................... 15

4 Summary ................................................................................................................................ 31 4.1 Biological Conditions of Streams ................................................................................. 31 4.2 Stressors Associated with Biological Conditions ......................................................... 32

5 Recommendations ................................................................................................................. 32 6 References ............................................................................................................................. 33


Page iii

List of Figures Figure 1. Cumulative Distribution Function of CSCI Scores in San Mateo County (n = 87) 10 Figure 2. Cumulative Distribution Function of ASCI-D Scores in San Mateo County (n = 87) 11 Figure 3. Cumulative Distribution Function of ASCI-SB Scores in San Mateo County (n = 87) 12 Figure 4. Cumulative Distribution Function of ASCI-H Scores in San Mateo County (n = 87) 13 Figure 5. All San Mateo County Biological Condition Scores (2012-2019) Grouped by Condition Category and Strata (including 3 targeted WY2019 sites) 14 Figure 6. Percent Urban Area vs. CSCI and ASCI-H scores. Data grouped by strata: Pacific Ocean vs. San Francisco Bay 19 Figure 7. Percent Impervious Area in 5 km vs. CSCI and ASCI-H scores. Data grouped by strata: Pacific Ocean vs. San Francisco Bay 20 Figure 8. Number of Road Crossings in 5 km vs. CSCI and ASCI-H scores. Data grouped by strata: Pacific Ocean vs. San Francisco Bay 21 Figure 9. Road Density in 5 km vs. CSCI and ASCI-H scores. Data grouped by strata: Pacific Ocean vs. San Francisco Bay 22 Figure 10. Total Nitrogen vs. CSCI and ASCI-D scores. Data grouped by strata: Pacific Ocean vs. San Francisco Bay 23 Figure 11. Total Kjeldahl Nitrogen vs. CSCI and ASCI-D scores. Data grouped by strata: Pacific Ocean vs. San Francisco Bay 24 Figure 12. Temperature vs. CSCI and ASCI-H scores. Data grouped by strata: Pacific Ocean vs. San Francisco Bay 25 Figure 13. Mean Filamentous Algae Cover vs. CSCI and ASCI-H scores. Data grouped by strata: Pacific Ocean vs. San Francisco Bay 26 Figure 14. Channel Alteration Score vs. CSCI and ASCI-H scores. Data grouped by strata: Pacific Ocean vs. San Francisco Bay 27 Figure 15. Epifaunal Substrate Score vs. CSCI and ASCI-H scores. Data grouped by strata: Pacific Ocean vs. San Francisco Bay 28 Figure 16. Combined Human Disturbance Index vs. CSCI and ASCI-H scores. Data grouped by strata: Pacific Ocean vs. San Francisco Bay 29


Page iv

List of Tables Table 1. Biological Condition Indices, Categories and Thresholds 6 Table 2. Site Evaluations for San Mateo County (2012-2019) 8 Table 3. Probabilistic Sites Remaining in San Mateo County 8 Table 4. Number of Sites in each CSCI Condition Class by Strata and Land Use (including 3 targeted WY2019 sites) 10 Table 5. Number of SMCWPPP Sites in each ASCI-D Condition Class by Strata and Land Use (including 3 targeted WY2019 sites) 11 Table 6. Number of SMCWPPP Sites* in each ASCI-SB Condition Class by Strata and Land Use (including 3 targeted WY2019 sites) 12 Table 7. Number of SMCWPPP Sites* in each ASCI-H Condition Class by Strata and Land Use (including 3 targeted WY2019 sites) 13 Table 8. Spearman’s rank correlation (rho) statistics for stressor variables with < -0.5 or > 0.5 with one or more biological indices (CSCI, ASCI-D, ASCI-SB, or ASCI-H). Data for both urban and non-urban land use and Pacific Ocean and San Francisco Bay strata were pooled. Variables are color coded according to: physical habitat (green), land use (orange), and water quality (blue). All rho values were statistically significant, except those marked with ‘ns’. 16 Table 9. Spearman’s rank correlation (rho) statistics for stressor variables with < -0.5 or > 0.5 with CSCI or ASCI-H for the Pacific Ocean and San Francisco Bay strata. Data for both urban and non-urban sites were pooled. Variables are color coded according to: physical habitat (green), land use (orange), and water quality (blue). All rho values were statistically significant, except those marked with ‘ns’. 18 Table 10. Summary statistics for the CSCI random forest model. Ranking of most influential predictor variables are colored according to: physical habitat (green), land use (orange), and water quality (blue). Variables were trimmed based on a cutoff of >=10% increase MSE. 30 Table 11. Summary statistics for the ASCI-H random forest model. Ranking of most influential predictor variables are colored according to: physical habitat (green), land use (orange), and water quality (blue). The rank correlation coefficient (rho) for each stressor variable is also presented based on the training dataset used for model development. Variables were trimmed based on a cutoff of >=10% increase MSE. 30


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1 Introduction The San Mateo Countywide Water Pollution Prevention Program (SMCWPPP) conducts Creek Status Monitoring as required by Provision C.8 of the Municipal Regional National Pollutant Discharge Elimination System (NPDES) Stormwater Permit (SFRWQCB 2009, 2015). Since 2012, SMCWPPP has worked with several members of the Bay Area Stormwater Agencies Association (BASMAA) to form the Regional Monitoring Coalition (RMC), to coordinate and oversee water quality monitoring required by the Municipal Regional NPDES Stormwater Permit (in this document the permit is referred to as MRP). A key component of the coordinated effort has been to implement bioassessment monitoring in accordance with standardized methodologies established by the California Surface Water Ambient Monitoring Program (SWAMP), including sampling of benthic macroinvertebrates (BMIs), benthic algae (i.e., diatoms and soft algae), water chemistry, and the characterization of physical habitat (BASMAA 2016a, 2016b).

In coordination with the BASMAA RMC, the SMCWPPP is required to conduct bioassessments at 10 monitoring sites at streams/channels in San Mateo County each year. The vast majority of sites were probabilistically (i.e., randomly) selected from a sample frame comprising perennial and non-perennial streams, channels and rivers within the five participating counties that overlap with the San Francisco Bay Regional Water Quality Control Board (Region 2) boundary and the eastern portion of Contra Costa County that drains to the Central Valley region (Region 5). Bioassessments were conducted at three targeted sites in Water Year (WY) 2019 (October 1, 2018 through September 30, 2019). Over the past eight years (WY 2012 through WY 2019), SMCWPPP has sampled a total of 87 probabilistic sites and 3 targeted sites.

The goal of this report is to summarize the key results on a regional (i.e., countywide) basis from SMCWPPP’s bioassessment data collection over the past eight years of MRP monitoring. The findings are intended to help the SMCWPPP better understand the current condition of water bodies in the County, identify stressors that are likely to pose the greatest risk to the health of those streams, and suggest key characteristics of streams that may help prioritize locations for future monitoring.

2 Statistical Analyses All statistical analyses described in this report were conducted in R Studio, running R version 3.5.0 (R Core Team 2018). SMCWPPP data were assembled for analysis by querying a relational database consisting of bioassessment data collected in San Mateo County over an eight-year period (WY 2012 and WY 2019). Prior to all analyses, censored water quality results (i.e., non-detects) were substituted with 50% of the method detection limit (MDL). Biological data were then graphed and used to generate cumulative distribution functions (CDFs) and evaluate correlational statistics. Subsequently, missing values were estimated by imputation to facilitate use of all sites in the multivariate analysis. Silica and unionized ammonia (UIA) were found to have a handful of missing values and were subject to imputation using the R-package mice (van Buuren and Groothuis-Oudshoorn, 2011). In this method, replacement values were randomly selected from the distribution of observed data. Overall, 11 datapoints were imputed (Silica, n = 7; UIA, n = 4).


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Spatial patterns were evaluated by grouping the results by watershed/sub-watersheds and strata (Pacific Ocean or San Francisco Bay). Due to the large number of relatively small sub-watersheds in San Mateo County, statistical analyses focused on evaluations by strata only.

2.1 Estimating Extent of Healthy Streams

Stream health was evaluated using biological indices developed for the State of California. The California Stream Condition Index (CSCI) (Mazor et al. 2016) was used to assess benthic macro-invertebrate data and the Algae Stream Condition Index (ASCI) (Theroux et al. in prep) was used to assess benthic algae data. Three different indices were used for ASCI representing diatoms (ASCI-D); soft-bodied algae (ASCI-SB) and hybrid of both diatoms and soft-bodies algae (ASCI-H).

Following the methods described in BASMAA (2019), the overall extent of biological conditions in San Mateo County streams was estimated using cumulative distribution functions (CDFs). CDF sample weights were calculated as the total stream length in the RMC sample frame, and divided by the stream length evaluated in each land use category (urban or non-urban). The adjusted sample weights were used to estimate the proportion of stream length (average and 95% confidence interval) represented by CSCI and ASCI scores for all SMCWPPP sites combined, and then for urban and non-urban sites separately. Post-stratification of San Mateo County data was performed to generated the county-specific CDFs. All calculations were conducted using the R-package spsurvey (Kincaid and Olsen 2016).

2.2 Biological Indicator Thresholds

Existing thresholds for the CSCI (Mazor et al. 2016) and ASCI (Theroux et al. in prep) were used to evaluate bioassessment data compiled and analyzed in this report (Table 1). The thresholds for each index were based on the distribution of scores for data collected at reference calibration sites in California. Four condition categories are defined by these thresholds: “likely intact” (greater than 30th percentile of calibration reference site scores); “possibly altered” (between the 10th and the 30th percentiles); “likely altered” (between the 1st and 10th percentiles; and “very likely altered” (less than the 1st percentile).

Table 1. Biological Condition Indices, Categories and Thresholds

Index Likely Intact

Possibly Altered


Altered

Benthic Macroinvertebrates (BMI)

CSCI > 0.92 > 0.79 to < 0.92 > 0.63 to < 0.79 < 0.63

Benthic Algae

ASCI-D (Diatom) > 0.92 > 0.81 to < 0.92 > 0.66 to < 0.81 < 0.66

ASCI-SB (Soft Bodied Algae)

> 0.92 > 0.80 to < 0.92 > 0.65 to < 0.80 < 0.65

ASCI-H (Hybrid) > 0.95 > 0.88 to < 0.95 > 0.78 to < 0.88 < 0.78


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2.3 Stressor Analysis

Stressors related to biological condition scores were evaluated using Spearman’s rank correlation analysis and random forest (RF) statistical models. The stressor variable list (Appendix 1) consisted of 50 quantitative environmental variables that are related to water quality, physical habitat, and landscape factors that could potentially influence biological condition scores, and two categorical factors (Land Use and Strata) that could potentially differentiate condition scores for different types of streams.

Correlation statistics were employed as a variable reduction technique to isolate a shortened list of explanatory variables that significantly correlate with biological condition scores (each of the four biological indicators were evaluated separately). Correlation analysis was performed with all sites pooled, as well as by stratification of sites that are in watersheds that drain either to the Pacific Ocean or San Francisco Bay. Subsequently, CSCI and the ASCI-H were prioritized for RF analysis as they exhibited the best correlation statistics with stressor variables. Of the three algae indices, ASCI-H was reported to exhibit the best response to environmental disturbance gradients for the statewide dataset (Theroux et al. in prep).

RF regression tree methods were used to evaluate the relative importance of the selected explanatory variables for CSCI and ASCI-H, and create an ordered list of explanatory variables related to biological condition scores. Both sets of analyses initially were run with the same sets of optimized predictor variables based on the correlation results, and then further refined during model runs. RF models were developed using the R-package randomForest. Data were standardized (i.e. scaled to account variables having different units and ranges) and partitioned into training (80%) and validation (20%) sets for model testing. A random selection of samples was generated by sub-sampling from both the San Francisco Bay and Pacific Ocean strata to maintain a regional balance of samples within the partitioned datasets. The training dataset for CSCI modeling had 72 sites, while the validation data encompassed 18 sites. Due to a few sites where ASCI-H scores could not be calculated due to insufficient number of soft-algae taxa needed to calculate index scores, the training dataset for ASCI-H modeling had 70 sites, while the validation data had 15 sites.

Several runs of the model procedure were performed with the training data set to optimize the random forests, including tuning the model to the maximum number of predictors per branch, the number of trees to build, and validation of the predictions. The final set of models evaluated a maximum of 4 predictor interactions and 1000 trees. Two variable importance statistics were used to estimate the relative influence of predictor variables: (1) % Increase in MSE = percent increase in mean-square-error of predictions as a result of variable values being permuted; (2) Increase in Node Purity = difference between the residual sum-of-squares before and after a split in the tree. Variables of greater importance to predictions will exhibit larger changes in MSE and values of node purity.

Random forest tuning was performed by evaluating the corresponding variable importance scores, partial dependency plots, and the change in R2 once the variable was excluded. Partial dependency plots show the predicted biological response based on an individual explanatory variable with all other variables removed. No variable with less than 10% influence on CSCI or ASCI-H predictions was retained in the final models. Finally, the random forest models were


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used to predict CSCI and ASCI-H scores for the validation data set. Appendix 2 presents the observed and predicted values for the final validation models.

3 Results

3.1 SMCWPPP Site Evaluations

A total of 514 randomly selected monitoring sites were sampled in the RMC region between 2012 and 2019. Eighty-seven of these sites (17%) were assessed in San Mateo County, representing a total stream length of 91.6 km (Table 2Table 2). Sixty-four of the sites in San Mateo County (74%) were from urban streams and channels of the County.

A total of 250 sites were initially evaluated to obtain the 87 sites that were ultimately sampled. This equates to a rejection rate of ~ 65%, which can largely be attributed to sites corresponding to areas that were not sampleable, e.g. due to lack of flow, lack of creek, tidal influence, or accessibility. As of WY 2020, there are 500 sites remaining in the RMC sample draw for San Mateo County (Table 3). The vast majority of these sites correspond to the non-urban portion of the County (472 of 500 sites). Therefore, assuming a rejection rate of 65%, the sample draw will likely only be sufficient to complete the current MRP term through 2020.

Table 2. Site Evaluations for San Mateo County (2012-2019)

Urban Non-Urban Total

Sites Stream Length

(km)Sites

Stream Length (km)

Sites Stream

Length (km)

Target 64 67.4 23 24.2 87 91.6Non-Target 62 65.3 10 10.5 72 75.8Target Not-Sampled (denial, physical barrier, other)

68 71.6 23 24.2 91 95.8

Total 194 204.3 56 59.0 250 263.3

Table 3. Probabilistic Sites Remaining in San Mateo County

Sites

Stream Length (km)

Urban 28 29.5Non-Urban 472 497.0Total 500 526.5

3.2 Biological Condition of Streams in San Mateo County

The distribution of biological index scores observed in San Mateo County during 2012-2019 suggests that many streams do not support healthy biological conditions. Figures Figure 1Figure 2Figure 3Figure 4 show cumulative distribution functions of the BMI and algae index scores for the entire SMCWPPP dataset (i.e., urban and non-urban sites combined) and the urban and non-urban dataset separately. The median (50th percentile) CSCI score based on all 87 probabilistic sites was estimated at 0.82, which corresponds to the Possibly Altered condition class. However, the 50th percentile of CSCI scores from urban sites was 0.51, corresponding to the Very Likely


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Altered and substantially below the MRP trigger (0.795) for potential follow-up studies. The non-urban sites indicated a median score of 0.92 but this estimate is highly variable (wide confidence intervals) due to the fewer number of sites (n = 23 of 87) represented.

ASCI scores based on diatoms and the hybrid method exhibited similar CDFs to CSCI, with urban sites scoring much lower than non-urban sites. The median ASCI-D and ASCI-H scores at urban sites was 0.60 and 0.64 (both Very Likely Altered) compared to 0.91 (Possibly Altered) and 0.92 (Likely Intact), respectively. Overall, when all sites are combined the median was 0.83 (Possibly Altered) for ASCI-D and 0.82 (Likely Altered) for ASCI-H. In contrast, the ASCI scores based on soft-algae (ASCI-SB) did not vary greatly between urban and non-urban sites. Considering all sites together, the median ASCI-SB score was 0.97 (Likely Intact) compared to the 50th percentile for urban sites of 0.90 (Possibly Altered) and non-urban sites of 0.98 (Likely Intact).

Comparison of the number of sites in each biological condition class by strata and land use shows that most of the urban waterbodies in poor condition are associated with San Francisco Bay watersheds (TablesTable 4, Table 5, Table 6, and Figure 5). All together 50 sites were characterized as corresponding to the Very Likely Altered condition category, with 86% (n = 43) representing sites from the San Francisco Bay / Urban strata. Conversely, 19 sites were characterized as Likely Intact based on BMIs, with 68% (n = 13) representing sites from the Pacific Ocean / Non-urban strata.

CSCI scores and the diatom and hybrid ASCI indicator scores showed a high degree of similarity when data were stratified by San Francisco Bay or the Pacific Ocean (Figure 5). For example, the Pacific Ocean strata exhibited 13 of 23 (57%) sites from non-urban areas as Likely Intact and 4 of 12 (33%) sites from urban area as Very Likely Altered. This was very similar to the pattern of ASCI-D and ASCI-H condition scores, where 9 of 23 (39%) and 8 of 23 (35%), respectively from non-urban areas were scored as Likely Intact, and 2 of 12 (17%) and 5 of 12 (42%) from urban areas were scored as Very Likely Altered. This trend was also apparent for San Francisco Bay, where the vast majority of urban sites coincided. Based on CSCI scoring, 43 of 53 sites from urban areas were scored as Very Likely Altered, compared to 37 of 53 (70%) sites using ASCI-D and 42 of 53 (79%) sites using ASCI-H.

The ASCI-SB index showed congruency with CSCI scoring at non-urban sites, but not for urban sites. For example, exactly the same number of non-urban sites from the Pacific Ocean strata were scored as Likely Intact in the CSCI and ASCI-SB. However, none of the urban sites were similarly scored as Very Likely Altered, when the CSCI scoring identified four sites in this condition class. For San Francisco Bay sites, only 10 of 53 (19%) urban sites were in the poorest condition category, which was significantly less than was found for CSCI scoring (86%).


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Figure 1. Cumulative Distribution Function of CSCI Scores in San Mateo County (n = 87) Table 4. Number of Sites in each CSCI Condition Class by Strata and Land Use (including 3 targeted WY2019 sites)

Strata Land Use Likely Intact Possibly Altered

Likely Altered

Very Likely Altered

Pacific Ocean Urban 1 3 4 4San Francisco Bay Urban 5 1 4 43Pacific Ocean Non-Urban 13 6 1 3San Francisco Bay Non-Urban 0 1 1 0


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Figure 2. Cumulative Distribution Function of ASCI-D Scores in San Mateo County (n = 87) Table 5. Number of SMCWPPP Sites in each ASCI-D Condition Class by Strata and Land Use (including 3 targeted WY2019 sites)


Likely Altered

Very Likely Altered



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Figure 3. Cumulative Distribution Function of ASCI-SB Scores in San Mateo County (n = 87) Table 6. Number of SMCWPPP Sites* in each ASCI-SB Condition Class by Strata and Land Use (including 3 targeted WY2019 sites)


Likely Altered

Very Likely Altered


* Insufficient data for scoring at 4 Pacific, Urban sites and 1 SF Bay, Urban site


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Figure 4. Cumulative Distribution Function of ASCI-H Scores in San Mateo County (n = 87) Table 7. Number of SMCWPPP Sites* in each ASCI-H Condition Class by Strata and Land Use (including 3 targeted WY2019 sites)

Land Use Likely Intact

Possibly Altered

Likely Altered

Very Likely Altered


* Insufficient data for scoring at 4 Pacific, Urban sites and 1 SF Bay, Urban site


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Figure 5. All San Mateo County Biological Condition Scores (2012-2019) Grouped by Condition Category and Strata (including 3 targeted WY2019 sites)


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3.3 Stressors Associated with Biological Conditions

To evaluate stressors associated with biological condition, correlation analysis and random forest modeling was performed. Correlation results indicated 19 of 50 quantitative variables exhibited a statistically significant correlation with one or more of the biological indices (Table 8). A Spearman’s rho value of < -0.5 or > 0.5 was considered the minimum cutoff for correlated variables. Many of the correlated variables were representative of land use (i.e., road density, road crossings, impervious and urban area; FiguresFigure 6,Figure 7, Figure 8 and Figure 9). Additionally, three variables were related to water quality (Total Nitrogen, TKN, and temperature; FiguresFigure 10,Figure 11 ,Figure 12) and four variables were representative of physical habitat (filamentous algae cover, channel alteration, epifaunal substrate and combined human disturbance index; Figures Figure 13,Figure 14,Figure 15, and 16).

The number of significant variables that met the cutoff of +/- 0.5 varied among indices. All 19 variables were significant for CSCI and ASCI-D. Seventeen variables (except TKN and Total Nitrogen) were statistically significant for the ASCI-H index. However, only Temperature and filamentous algae cover was significantly correlated to ASCI-SB. Most of the variables that were found to be significantly correlated with index scores were negatively correlated. Only the Channel Alteration and Epifaunal Substrate physical habitat scores were positively correlated with CSCI and ASCI indices.

Correlation analyses were also performed to evaluate stressors that associated with CSCI and ASCI-H scores in watersheds draining to the Pacific Ocean compared to San Francisco Bay (


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Table 9). CSCI and the ASCI-H were prioritized for this comparison as they exhibited the best correlation statistics when all data were pooled, and since the indices were designed to be relatable with respect to environmental disturbance gradients (Theroux et al. in prep). The results of this analysis suggest that most of the significant correlations were as a result of the sites in the San Francisco Bay region. A notable spatial difference in the stressor correlations was that CSCI and ASCI-H were only significantly correlated with 5 and 9 variables, respectively in the Pacific Ocean stratum, while these two indices were correlated with 16 variables and 12 variables each in the San Francisco Bay stratum. This observation may suggest spatial differences to stressor impacting upon stream health, as well as the larger sample size of San Francisco Bay sites for evaluating correlations.

The prioritized list of 19 variables were used in random forest model development for CSCI and ASCI-H. RF model results clearly indicated better relationships to stressors using CSCI scores compared to the ASCI-H index. Validation of the final random forest models showed that the CSCI model explained 64% of the variance using eight predictor (stressor) variables, while the ASCI-H model only explained 43% of the variance using seven predictors.

The CSCI RF model indicated that land use and physical habitat variables were most influential to most biological condition (Table 10). Of the eight variables in the final CSCI model, five consisted of landscape variables calculated on various spatial scales (e.g., PctImp_5K and PctImp), three variables were associated with water quality (Total Nitrogen, TKN, temperature), and one was a habitat variable (Epifaunal Substrate cover). The landscape variables that were most influential to CSCI scores were associated with the degree of human impact/imperviousness and the water quality variables were associated with nutrient dynamics.

Overall, three groups of stressor variables exhibited the largest influence on the CSCI random forest model (14.1 – 15.8%). These variables were percent impervious area (reach and 5 km scale), road density (1 km and watershed scale), and total Nitrogen. The remaining variables in the final model exerted a lesser degree of influence (10.1 – 12.1%) on CSCI scores.

The ASCI-H RF model indicated that land use and water quality variables were most influential to biological condition (Table 11). Of the seven variables in the final ASCI-H model, five were comprised of landscape variables at various spatial scales (e.g., PctImp_5K, PctImp_1K, PctImp) and two were water quality variables (TKN and temperature). It was notable that several of the same variables were indicated in this model to the CSCI RF. Overall, three stressor variables exhibited the largest influence on the ASCI-H scores (13.9– 15.5%). These variables were percent impervious area (reach), road density (1 km), and percent urban area (5 km). The other four variables exerted a similar degree of influence (10.0 – 11.7%) on ASCI-H scores.

Table 8. Spearman’s rank correlation (rho) statistics for stressor variables with < -0.5 or > 0.5 with one or more biological indices (CSCI, ASCI-D, ASCI-SB, or ASCI-H). Data for both urban and non-urban land use and Pacific Ocean and San Francisco Bay strata were pooled. Variables are color coded according to: physical habitat (green), land use (orange), and water quality (blue). All rho values were statistically significant, except those marked with ‘ns’.

Stressor CSCI ASCI-D ASCI-SB ASCI-H


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Algae Cover -0.50 -0.44 -0.37 -0.45ChanAlt 0.56 0.51 0.33 ns 0.55EpiSub 0.63 0.46 0.19 ns 0.51HDI -0.55 -0.46 -0.25 ns -0.57PctImp -0.64 -0.49 -0.17 ns -0.54PctImp_1K -0.62 -0.37 -0.13 ns -0.50PctImp_5K -0.69 -0.54 -0.19 ns -0.60PctUrb -0.68 -0.62 -0.21 ns -0.63PctUrb_1K -0.64 -0.56 -0.20 ns -0.61PctUrb_5K -0.68 -0.60 -0.19 ns -0.63RdCrs_1K -0.56 -0.43 -0.16 ns -0.51RdCrs_5K -0.42 -0.40 -0.21 ns -0.58RdCrs_W -0.36 -0.36 -0.20 ns -0.56RdDen_1K -0.71 -0.49 -0.18 ns -0.57RdDen_5K -0.69 -0.56 -0.19 ns -0.58RdDen_W -0.68 -0.59 -0.21 ns -0.59Temp -0.51 -0.48 -0.34 -0.63TKN -0.56 -0.37 0.08 ns -0.28 ns

Total N -0.59 -0.35 -0.06 ns -0.29 ns


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Table 9. Spearman’s rank correlation (rho) statistics for stressor variables with < -0.5 or > 0.5 with CSCI or ASCI-H for the Pacific Ocean and San Francisco Bay strata. Data for both urban and non-urban sites were pooled. Variables are color coded according to: physical habitat (green), land use (orange), and water quality (blue). All rho values were statistically significant, except those marked with ‘ns’.

Stressor CSCI ASCI-H CSCI ASCI-H

Pacific Ocean San Francisco Bay

Algae Cover -0.14 ns -0.20 ns -0.40 -0.26 ns

ChanAlt 0.35 0.20 ns 0.39 0.46EpiSub 0.52 0.16 ns 0.43 0.39HDI -0.24 ns -0.48 -0.39 -0.28PctImp -0.08 ns -0.04 ns -0.72 -0.35PctImp_1K -0.27 ns -0.33 ns -0.44 -0.18 ns

PctImp_5K -0.16 ns -0.32 ns -0.67 -0.33PctUrb -0.20 ns -0.40 -0.61 -0.35PctUrb_1K -0.39 -0.48 -0.36 -0.28PctUrb_5K -0.22 ns -0.38 -0.58 -0.34RdCrs_1K -0.23 ns -0.40 -0.44 -0.23 ns

RdCrs_5K -0.25 ns -0.47 -0.09 ns -0.26 ns

RdCrs_W -0.15 ns -0.47 -0.01 ns -0.21 ns

RdDen_1K -0.39 -0.59 -0.59 -0.16RdDen_5K -0.22 ns -0.33 ns -0.66 -0.28RdDen_W -0.17 ns -0.34 ns -0.70 -0.30Temp -0.33 ns -0.37 -0.13 ns 0.46TKN -0.21 ns -0.05 ns -0.46 -0.12 ns

Total N -0.34 -0.17 ns -0.53 -0.11 ns


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Figure 6. Percent Urban Area vs. CSCI and ASCI-H scores. Data grouped by strata: Pacific Ocean vs. San Francisco Bay


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Figure 7. Percent Impervious Area in 5 km vs. CSCI and ASCI-H scores. Data grouped by strata: Pacific Ocean vs. San Francisco Bay


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Figure 8. Number of Road Crossings in 5 km vs. CSCI and ASCI-H scores. Data grouped by strata: Pacific Ocean vs. San Francisco Bay


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Figure 9. Road Density in 5 km vs. CSCI and ASCI-H scores. Data grouped by strata: Pacific Ocean vs. San Francisco Bay


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Figure 10. Total Nitrogen vs. CSCI and ASCI-H scores. Data grouped by strata: Pacific Ocean vs. San Francisco Bay


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Figure 11. Total Kjeldahl Nitrogen vs. CSCI and ASCI-H scores. Data grouped by strata: Pacific Ocean vs. San Francisco Bay


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Figure 12. Temperature vs. CSCI and ASCI-H scores. Data grouped by strata: Pacific Ocean vs. San Francisco Bay


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Figure 13. Mean Filamentous Algae Cover vs. CSCI and ASCI-H scores. Data grouped by strata: Pacific Ocean vs. San Francisco Bay


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Figure 14. Channel Alteration Score vs. CSCI and ASCI-H scores. Data grouped by strata: Pacific Ocean vs. San Francisco Bay


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Figure 15. Epifaunal Substrate Score vs. CSCI and ASCI-H scores. Data grouped by strata: Pacific Ocean vs. San Francisco Bay


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Figure 16. Combined Human Disturbance Index vs. CSCI and ASCI-H scores. Data grouped by strata: Pacific Ocean vs. San Francisco Bay


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Table 10. Summary statistics for the CSCI random forest model. Ranking of most influential predictor variables are colored according to: physical habitat (green), land use (orange), and water quality (blue). Variables were trimmed based on a cutoff of >=10% increase MSE.


MSE Increase

Node Purity

Percent Impervious Area in 5km (PctImp_5K) 15.8 0.81


Total Nitrogen (TotalN) 15.2 0.33

Road Density in Watershed (RdDen_W) 15.1 0.43



Epifaunal Substrate Score 12.1 0.36

Percent Urban in 5 km (PctUrb_5K) 10.9 0.39

Table 11. Summary statistics for the ASCI-H random forest model. Ranking of most influential predictor variables are colored according to: physical habitat (green), land use (orange), and water quality (blue). The rank correlation coefficient (rho) for each stressor variable is also presented based on the training dataset used for model development. Variables were trimmed based on a cutoff of >=10% increase MSE.


MSE Increase

Node Purity

Percent Urban Area in 5 km (PctUrb_5K) 15.5 0.39



Road Crossings in 5 km (RdCrs_5K) 11.7 0.23

Temperature (Temp) 11.6 0.28




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4 Summary

4.1 Biological Conditions of Streams

During WY 2012 – WY 2019, the ecological health of San Mateo County streams was assessed using standardized multi-metric indices for two biological indicators: BMIs and algae. Sites were selected from a probabilistic sample frame of San Francisco Bay area streams to provide estimates of biological condition for the region and at the countywide scale. In San Mateo County, bioassessment survey results indicate that a moderate proportion of the total stream length are in poor biological condition:

The CSCI scores for BMI assemblages indicate that 37% of stream length are in the Very Likely Altered (lowest) condition category and 32% of stream length are in the Likely Intact (or highest) condition category. Nearly half (48%) of the stream length exhibit CSCI scores below 0.795, the MRP trigger for potential follow-on activity.

The ASCI-D scores for diatoms indicates that 29% of the stream length are in the Very Likely Altered (lowest) condition category and 30% of stream length are in the Likely Intact (or highest) condition category. MRP triggers have yet to be established for ASCI indices.

The ASCI-SB scores for soft-bodied algae indicates that only 7% of the stream length are in the Very Likely Altered (or lowest) condition category and 59% of stream length are in the Likely Intact (or highest) condition category. This was notable different distribution of scores to the other biological indices.

The ASCI-H scores for diatoms and soft-algae combined indicates that 38% of the stream length are in the Very Likely Altered (lowest) condition category and 28% of stream length are in the Likely Intact (or highest) condition category. MRP triggers have yet to be established for ASCI indices.

Comparison among indices by strata in San Mateo County indicated that BMIs and algae often scored in different condition classes:

About 40% of sites were classed as Very Likely Altered using CSCI and one or more of the algae indices. These streams predominantly occurred in the urban area of San Francisco Bay watersheds.

Only 10% of sites were classed as Likely Intact using CSCI and one or more the algae indices. These sites may already support beneficial uses and should be considered as the highest priorities for protection. These streams predominantly occurred in non-urban area of Pacific Ocean watersheds.

CSCI and ASCI-D scores were consistently lower at urban than non-urban sites, indicating that sites with a higher levels of landscape influences may not support healthy biological communities.

ASCI-SB scores were similar between urban and non-urban sites, indicating that the soft-bodied algae index may not respond well to urban disturbance gradients.


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4.2 Stressors Associated with Biological Conditions

The stressor analysis of BMIs and algae condition in San Mateo County streams has shown that both types of stream assemblages correlate with landscape factors, as well as unique sets of water quality and habitat stressors. These observations support the need for multiple indicators to assess potential causes of biological conditions in streams.

Nutrients (total Nitrogen and TKN) correlated better with CSCI scores from the San Francisco Bay region than sites from the Pacific Ocean stratum. TKN or Total Nitrogen also ranked as important variables in the RF models.

The RF model of CSCI scores indicates that landscape, habitat, and water-quality stressors, specifically road density, impervious area, and total nitrogen were the best predictors of biological condition.

The RF model of ASCI-H scores indicates habitat and water-quality stressors, specifically temperature, channel alteration, and combined human disturbance (HDI) were the best predictors of biological condition.

CSCI scores appear highly sensitive to physical habitat degradation, which occurs frequently in the many highly modified urban streams monitored by the SMCWPPP. It is not clear how well the CSCI tool can demonstrate responses to stressors associated with water quality, when physical habitat is usually the primary factor affecting BMI communities.

The ASCI-D and ASCI-H indices were similarly responsive to environmental-stressor gradients, and were particularly associated with water quality and habitat factors.

The ASCI-SB index was not a reliable indicator of streams in poor condition due to high scoring at both disturbed and undisturbed sites throughout San Mateo County. In addition, ASCI-SB scores could not be calculated in a few streams due to insufficient number of soft algae taxa to calculate the index, resulting in data gaps and lack of utility of the ASCI-SB index for model testing. Additional evaluations of the ASCI-SB index performance are needed to assess the utility of this indicator in future bioassessment surveys.

It should be acknowledged that despite these apparent relationships to stressors, these analyses do not determine causation, particularly as stressors from habitat/landscape factors are often present at the same sites that exhibit water quality impairment.

5 Recommendations Based on this evaluation of data collected during the eight years of SMCWPPP bioassessment surveys, three design option are briefly introduced below for consideration during future monitoring design discussions:


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1. The SMCWPPP could continue to sample probabilistic sites in coordination with the RMC by selecting sites from a revised sample frame to establish baseline conditions over finer spatial scales. The current RMC sample frame will likely be exhausted for San Mateo County sites after WY 2020, which provides an opportunity to implement an improved sample frame / draw. In this manner, estimates of biological condition may continue at the regional or countywide scale, as well as for finer scales (e.g., watersheds) of interest. Smaller, more focused geographic scales of probabilistic assessments may provide stronger associations between biological conditions and stressors, while continuing to report on ambient stream health. Watershed or other spatial strata may also provide managers opportunities to focus on improved understanding of spatial patterns, track temporal trends, and build a weight-of-evidence for stressors influencing specific areas or stream types in the County. 2. Alternatively, targeted studies could become the focus of future bioassessment surveys. Targeted studies may be used in several ways to support Creek Status Monitoring requirements. For example, targeted studies could evaluate the effectiveness of stream restoration or BMP projects; monitor sources or stressors at impaired sites or watersheds; establish baseline conditions at reference sites; and investigate variability in biological conditions over time. 3. The SMCWPPP should also consider the management needs for implementing a hybrid monitoring approach that combines random and targeted surveys. In such a design, probabilistic sites could be used to evaluate the status of ambient stream condition, while supplemented by targeted assessments in streams requiring follow-up action. RMC participants could evaluate similar objectives at the regional scale while the SMCWPPP could use its own assessment for identifying targeted sites/watersheds in need of focused investigation.

6 References Bay Area Stormwater Management Agencies Association (BASMAA). 2016a. Regional Monitoring Coalition Creek Status Monitoring Standard Operating Procedures. Version 3, March 2016.

Bay Area Stormwater Management Agencies Association (BASMAA). 2016b. Regional Monitoring Coalition Creek Status Monitoring Program Quality Assurance Project Plan. Version 3, March 2016.

Bay Area Stormwater Management Agencies Association (BASMAA). 2019. Regional Monitoring Coalition Five Year Bioassessment Report., Final Report, March 2019.

Beck, M.W., Mazor, R. D., Johnson, S., Wisenbaker, K., Westfall, J., Ode, P. R. Hill, R. Loflen, C., Sutula, M. and Stein, E. D., 2019. Prioritizing management goals for stream biological integrity within the developed landscape context},Freshwater Science Vol. 38 (4): 883-898

Kincaid, T. M. and Olsen, A. R. 2016. spsurvey: Spatial Survey Design and Analysis. R package version 3.3.

Mazor, R., Ode, P.R., Rehn, A.C., Engeln, M., Boyle, T., Fintel, E., Verbrugge, S., and Yang, C. 2016. The California Stream Condition Index (CSCI): Interim instructions for calculating scores using GIS and R. SWAMP-SOP-2015-0004. Revision Date: August 5, 2016.


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R Core Team. 2018. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (https://www.R-project.org/).

Rehn, A.C., Mazor, R.D. and Ode, P.R. 2015. The California Stream Condition Index (CSCI): A new statewide biological scoring tool for assessing the health of freshwater streams. California State Water Resources Control Board Surface Water Ambient Monitoring Program (SWAMP) TM-2015-0002. September 2015.

San Francisco Bay Regional Water Quality Control Board (SFBRWQCB). 2015. California Regional Water Quality Control Board San Francisco Bay Region Municipal Regional Stormwater NPDES Permit (MRP 2.0). Order No. R2-2015-0049. NPDES Permit No. CAS612008. November 19, 2015.

San Francisco Bay Regional Water Quality Control Board (SFBRWQCB). 2009. California Regional Water Quality Control Board San Francisco Bay Region Municipal Regional Stormwater NPDES Permit (MRP 1.0). Order No. R2-2009-0049. NPDES Permit No. CAS612008. October 14, 2009.

Stevens, D.L., Jr., and Olsen, A.R. 2004. Spatially-balanced sampling of natural resources. Journal of the American Statistical Association 99: 262-278.

Theroux, S., Mazor, R.D., Beck, M.W., Ode, P., Stein, E.D., Sutula, M. (in prep).

Predictive multimetric indices for algae populations in diverse landscapes, Ecological Indicators

van Buuren, S. and Groothuis-Oudshoorn, K. 2011. mice: Multivariate Imputation by Chained Equations in R. Journal of Statistical Software, 45(3), 1–67.


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Appendix 1 Stressor Variables Evaluated in Correlation and Random Forest Models Table 1-A. Description of response variables and explanatory environmental variables used for model development.

Variable Name Description

CSCI California Stream Condition Index

ASCI-H Hybrid Algae Stream Condition Index

Strata Site Classification: Pacific Ocean or San Francisco Bay

Land Use Site Classification: Urban or Non-urban

Avg Algae Cover Mean Filamentous Algae Cover

Avg Boulders Mean Boulders cover

Avg Wetted Width Mean Wetted Width/Depth Ratio

Avg Wood Debris Mean Woody Debris <0.3m cover

Chan-Alt Channel Alteration Score

Epi-Sub Epifaunal Substrate Score

Sed-Dep Sediment Deposition Score

Flow Hab Evenness of Flow Habitat Types

Nat Shelt Natural Shelter cover - SWAMP

Nat Sub Evenness of Natural Substrate Types

Pct Bold_L Percent Boulders - large

Pct Bold_LS Percent Boulders - large & small

Pct Bold_S Percent Boulders - small

Pct Fines Percent Fines

Pct Fst Water Percent Fast Water of Reach

Pct Gravel Percent Gravel - coarse

Pct Slow Water Percent Slow Water of Reach

Pct Smaller Sand Percent Substrate Smaller than Sand (<2 mm)

Pct Sand Percent Sand

ShD Aquatic Habitat Shannon Diversity (H) of Aquatic Habitat Types

ShD Natural Substrate

Shannon Diversity (H) of Natural Substrate Types

HDI Combined Riparian Human Disturbance Index - SWAMP

Pct Impervious Percent Impervious Area of Reach

Pct Impervious 1K Percent Impervious Area in 1km

Pct Impervious 5K Percent Impervious Area in 5km


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Variable Name Description

Pct Urban Percent Urban Area of Reach

Pct Urban 1K Percent Urban Area in 1km

Pct Urban 5K Percent Urban Area in 5km

Rd Crossings 1K Number Road Crossings in 1km

Rd Crossings 5K Number Road Crossings in 5km

Rd Crossings W Number Road Crossings in watershed

Rd Density 1K Road Density in 1km

Rd Density 5K Road Density in 5km

Rd Density W Road Density in watershed

AFDM Ash Free Dry Mass

Ammonia Ammonia

Chla Chlorophyll a

Chloride Chloride

DO Dissolved oxygen concentration

Nitrate Nitrate

Nitrite Nitrite

OP Orthophosphate

pH pH

Phosphorus Total Phosphorus

Silica Silica

Specific Cond Specific conductivity

Temp Temperature

TKN Total Kjeldahl Nitrogen

Total N Total Nitrogen

UIA Unionized Ammonia


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Appendix 2 Predicted Condition Scores Based Upon Random Forest Models

Figure 1-A. Relationship of observed to predicted CSCI and ASCI-H scores in the validation dataset using the most influential

explanatory variables shown in Tables 10 and 11, respectively.

Attachment 4

MRP Trigger Exceedance Tables WY 2014 Through WY 2019

Summary of SMCWPPP MRP trigger threshold exceedance analysis, WY 2014. “No” indicates samples were collected but did not exceed the MRP trigger; “Yes” indicates an exceedance of the MRP trigger.

Station Number Creek Name

Probabilistic Sites Targeted Sites

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202R00328 Pilarcitos Creek No No No -- -- -- -- -- -- --

202R00972 Arroyo de en Medio Yes No No -- -- -- -- -- -- --

202R01308 Pilarcitos Creek Yes No No No No Yes -- -- -- --

204R01012 Cordilleras Creek Yes Yesa Yes -- -- -- -- -- -- --

204R01204 Burlingame Creek Yes No No -- -- -- -- -- -- --

204R01256 Arroyo Ojo de Agua Yes No No -- -- -- -- -- -- --

204R01268 Redwood Creek Yes No No -- -- -- -- -- -- --

204R01288 Laurel Creek Yes No Yes No No Yes -- -- -- --

204R01460 Sanchez Creek Yes No No -- -- -- -- -- -- --

204SMA059 San Mateo Creek -- -- -- -- -- -- No No No --

204SMA060 San Mateo Creek -- -- -- -- -- -- -- -- -- Yes

204SMA080 San Mateo Creek -- -- -- -- -- -- No No No No

204SMA100 San Mateo Creek -- -- -- -- -- -- -- -- -- No

204SMA110 Polhemus Creek -- -- -- -- -- -- -- -- -- No

204SMA119 San Mateo Creek -- -- -- -- -- -- -- -- -- No

205ALA015 Alambique Creek -- -- -- -- -- -- No -- -- --

205BCR010 Bear Creek -- -- -- -- -- -- No -- -- --

205BCR050 Bear Creek -- -- -- -- -- -- No -- -- --

205BCR060 Bear Creek -- -- -- -- -- -- No -- -- --

205R01192 Corte Madera Creek Yes No No -- -- -- -- -- -- --

205WUN150 West Union Creek -- -- -- -- -- -- No -- -- --

205WUN650 West Union Creek -- -- -- -- -- -- No -- -- -- a The unionized ammonia concentration was flagged as questionable due to an unusually high field pH used in the calculation.

Summary of SMCWPPP trigger threshold exceedance analysis, WY 2015. “No” indicates samples were collected but did not exceed the MRP trigger; “Yes” indicates an exceedance of an MRP trigger.

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202R00378 Pescadero Creek No No No -- -- -- -- -- -- -- --

202R00440 Purisima Creek No No No -- -- -- -- -- -- -- --

202R01356 Middle Fork San Pedro Creek No No No -- -- -- -- -- -- -- --

202R01612 Middle Fork San Pedro Creek No No No -- -- -- -- -- -- -- --

204R01448 Atherton Creek Yes No Yes No No Yes -- -- -- -- --


204R02056 Laurel Creek Yes No No No Yes Yes -- -- -- -- --

204R02248 Laurel Creek Yes No Yes -- -- -- -- -- -- -- --


205R01816 Corte Madera Creek No No No -- -- -- -- -- -- -- --

204SMA058 San Mateo Creek -- -- -- -- -- -- Yes No No No --

204SMA059 San Mateo Creek -- -- -- -- -- -- Yes No No No --

204SMA060 San Mateo Creek -- -- -- -- -- -- -- -- -- -- Yes


204SMA100 San Mateo Creek -- -- -- -- -- -- -- -- -- -- No

204SMA110 Polhemus Creek -- -- -- -- -- -- -- -- -- -- No


205ALA015 Alambique Creek -- -- -- -- -- -- No -- -- -- --

205BCR010 Bear Creek -- -- -- -- -- -- Yes -- -- -- --



205WUN150 West Union Creek -- -- -- -- -- -- No -- -- -- --



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202R00488 Tunitas Creek Yes No Yes -- -- -- -- -- -- -- --

202R00506 Peters Creek No No No -- -- -- -- -- -- -- --

202R02332 Pilarcitos Creek Yes No Yes -- -- -- -- -- -- -- --

204R02228 San Mateo Creek Yes No No -- -- -- -- -- -- -- --

204R02504 Polhemus Creek Yes No No -- -- -- -- -- -- -- --


205R02408 Bull Run Creek Yes No Yes -- -- -- -- -- -- -- --


205R02920 Bear Creek Yes No No -- -- -- -- -- -- -- --

205R03032 West Union Yes No No -- -- -- -- -- -- -- --

204LAU010 Laurel Creek -- -- -- No No Yes -- -- -- -- --





204SMA110 Polhemus Creek -- -- -- -- -- -- -- -- -- -- No

205ALA015 Alambique Creek -- -- -- -- -- -- No -- -- -- --

205BCR010 Bear Creek -- -- -- -- -- -- Yes Yes No No --


205BCR060 Bear Creek -- -- -- -- -- -- No -- -- -- --

205WUN150 West Union Creek -- -- -- -- -- -- No Yes Yes No -- Notes: 1. CSCI score ≤ 0.795. 2. Unionized ammonia (as N) ≥ 0.025 mg/L, nitrate (as N) ≥ 10 mg/L, chloride > 250 mg/L. 3. Free chlorine or total chlorine residual ≥ 0.1 mg/L. 4. Test of Significant Toxicity = Fail and Percent Effect ≥ 50 %. 5. TEC or PEC quotient ≥ 1.0 for any constituent. 6. Two or more MWAT ≥ 17.0°C or 20% of results ≥ 24°C. 7. DO < 7.0 mg/L in COLD streams or DO < 5.0 mg/L in WARM streams. 8. pH < 6.5 or pH > 8.5. 9. Specific conductance > 2000 uS. 10. Enterococcus ≥ 130 cfu/100ml or E. coli ≥ 410 cfu/100ml.



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202R00550 Jones Gulch No No No -- -- -- -- -- -- -- --

202R00552 Lawrence Creek No No No -- -- -- -- -- -- -- --

204R02472 Redwood Creek Yes No Yes -- -- -- -- -- Yes -- --

204R02611 Atherton Creek Yes No No -- -- -- -- -- Yes -- --




204R03316 Arroyo Ojo de Agua Yes No No -- -- -- -- -- Yes -- --

204R03336 Belmont Creek Yes No No -- -- -- -- -- -- -- --

204R03496 Redwood Creek Yes No No -- -- -- -- -- -- -- --

202SPE005 San Pedro Creek -- -- -- No No Yes -- -- -- -- --

202DEN017 NA (MS4) -- -- -- -- -- -- -- -- -- -- NA

202DEN005 Denniston Creek -- -- -- -- -- -- -- -- -- -- No

202DEN020 Denniston Creek -- -- -- -- -- -- -- -- -- -- Yes

202CAP001 NA (MS4) -- -- -- -- -- -- -- -- -- -- NA

202CAP025 NA (MS4) -- -- -- -- -- -- -- -- -- -- NA

202SPE019 San Pedro Creek -- -- -- -- -- -- No -- -- -- --

202SPE040 San Pedro Creek -- -- -- -- -- -- No No No No --

202SPE050 San Pedro Creek -- -- -- -- -- -- No -- -- -- --

202SPE070 San Pedro Creek -- -- -- -- -- -- No No No No --

202SPE085 San Pedro Creek -- -- -- -- -- -- No -- -- -- -- 1. CSCI score ≤ 0.795. 2. Unionized ammonia (as N) ≥ 0.025 mg/L, nitrate (as N) ≥ 10 mg/L, chloride > 250 mg/L. 3. Free chlorine or total chlorine residual ≥ 0.1 mg/L. 4. Test of Significant Toxicity = Fail and Percent Effect ≥ 50 %. 5. TEC or PEC quotient ≥ 1.0 for any constituent. 6. Two or more MWAT ≥ 17.0°C or 20% of results ≥ 24°C. 7. DO < 7.0 mg/L in COLD streams or DO < 5.0 mg/L in WARM streams. 8. pH < 6.5 or pH > 8.5. 9. Specific conductance > 2000 uS. 10. Enterococcus ≥ 130 cfu/100ml or E. coli ≥ 410 cfu/100ml.



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202R00584 Pilarcitos Creek No No No -- -- -- -- -- -- -- -- --

202R00614 Pescadero Creek No No No -- -- -- -- -- -- -- -- --

202R03404 San Pedro Creek Yes No No -- -- -- -- -- -- -- -- --

202R03656 Pilarcitos Creek Yes No No -- -- -- -- -- -- -- -- --

202R03880 La Honda Creek No No No -- -- -- -- -- -- -- -- --

202R03916 San Pedro Creek Yes No No -- -- -- -- -- -- -- -- --

204R03508 Mills Creek Yes No No -- -- -- -- -- -- -- -- --

204R03528 San Mateo Creek Yes No No -- -- -- -- -- -- -- -- --

205R03624 Bear Creek No No No -- -- -- -- -- -- -- -- --

205R03864 Hamms Gulch No No No -- -- -- -- -- -- -- -- --

202SPE005 San Pedro Creek -- -- -- No No -- -- -- -- -- --

204COR010 Cordilleras Creek -- -- -- No No No Yes -- -- -- -- --

202PES138 Pescadero Creek -- -- -- -- -- -- -- -- -- -- -- Yes

202PES142 McCormick Creek -- -- -- -- -- -- -- -- -- -- -- Yes

202PES144 Pescadero Creek -- -- -- -- -- -- -- -- -- -- -- No

202PES150 Jones Gulch -- -- -- -- -- -- -- -- -- -- -- No

202PES154 Pescadero Creek -- -- -- -- -- -- -- -- -- -- -- No

202SPE019 San Pedro Creek -- -- -- -- -- -- -- No -- -- -- --

202SPE040 San Pedro Creek -- -- -- -- -- -- -- No No No No --

202SPE050 San Pedro Creek -- -- -- -- -- -- -- No -- -- -- --

202SPE070 San Pedro Creek -- -- -- -- -- -- -- No No No No --

202SPE085 San Pedro Creek -- -- -- -- -- -- -- No -- -- -- -- 1. CSCI score ≤ 0.795. 2. Unionized ammonia (as N) ≥ 0.025 mg/L, nitrate (as N) ≥ 10 mg/L, chloride > 250 mg/L. 3. Free chlorine or total chlorine residual ≥ 0.1 mg/L. 4. Test of Significant Toxicity = Fail and Percent Effect ≥ 50 %. 5. TEC or PEC quotient ≥ 1.0 for any constituent. 6. Two or more MWAT ≥ 17.0°C or 20% of results ≥ 24°C. 7. DO < 7.0 mg/L in COLD streams or DO < 5.0 mg/L in WARM streams. 8. pH < 6.5 or pH > 8.5. 9. Specific conductance > 2000 uS. 10. Enterococcus ≥ 130 cfu/100ml or E. coli ≥ 410 cfu/100ml.


1

Cover Page

DRAFT March 31, 2020


PART C: STRESSOR/SOURCE IDENTIFICATION PROJECTS



SMCWPPP IMR Part C: SSID Projects, WY 2014 – WY 2019

i

List of Acronyms

ACCWP Alameda Countywide Clean Water Program

BASMAA Bay Area Stormwater Management Agency Association

BMP Best Management Practices

C/CAG City/County Association of Governments

CCCWP Contra Costa Clean Water Program

CEDEN California Data Exchange Network

DO Dissolved Oxygen

FIB Fecal Indicator Bacteria

FSURMP Fairfield Suisun Urban Runoff Management Program

IMR Integrated Monitoring Report


MS4 Municipal Separate Storm Sewer System

MST Microbial Source Tracking

NPDES National Pollutant Discharge Elimination System

OES Office of Emergency Services

OFEE Oil Filled Electrical Equipment

PCBs Polychlorinated Biphenyls PG&E Pacific Gas and Electric Company

QAPP Quality Assurance Project Plan

QAPrP Quality Assurance Program Plan

QA/QC Quality Assurance/Quality Control


SCVURPPP Santa Clara Valley Urban Runoff Pollution Prevention Program

SFRWQCB San Francisco Bay Regional Water Quality Control Board

SMCWPPP San Mateo County Water Pollution Prevention Program

SOP Standard Operating Protocol

SSID Stressor/Source Identification

SWAMP Surface Water Ambient Monitoring Program

TMDL Total Maximum Daily Load

UCMR Urban Creeks Monitoring Report

USEPA Environmental Protection Agency

WQO Water Quality Objective

WY Water Year


ii

Table of Contents

List of Acronyms .......................................................................................................................... i Table of Contents ........................................................................................................................ ii List of Figures ............................................................................................................................. ii List of Tables .............................................................................................................................. ii List of Attachments ..................................................................................................................... ii 1.0 Introduction ..................................................................................................................... 1

1.1 SSID Requirements................................................................................................. 1 2.0 SSID Projects Initiated by SMCWPPP ............................................................................. 4

2.1 San Mateo Creek Low Dissolved Oxygen SSID Project .......................................... 4 2.2 San Mateo Creek Pathogen Indicator SSID Project................................................. 6 2.3 Pillar Point Watershed Pathogen Indicator SSID Project ......................................... 6

3.0 Regional PCBs from Electrical Utility Equipment ............................................................. 9 3.1 Overview of the Regional SSID Work Plan .............................................................. 9

3.1.1 Task 1: Desktop Analysis ...........................................................................10 3.1.2 Task 2: Develop Source Control Framework ..............................................11 3.1.3 Task 3: Develop methodologies to account for PCBs load reductions from

new source control measures .....................................................................12 3.1.4 Task 4: Develop SSID Project Report ........................................................12

3.2 Current Status of the Regional SSID Project ..........................................................12 4.0 Recommendations .........................................................................................................14 5.0 References .....................................................................................................................15

List of Figures

Figure 2.1. San Mateo Creek watershed, San Mateo County, CA. ............................................. 5

Figure 2.2. Pillar Point Watershed Pathogen Indicator SSID Project monitoring stations. .......... 8

List of Tables

Table 1.1. BASMAA Regional Monitoring Coalition (RMC) participants. .................................... 2

List of Attachments

Attachment 1. BASMAA RMC Regional SSID Report

Attachment 2. Final Pillar Point Harbor Watershed Pathogen Indicator SSID Project Report


1

1.0 Introduction

This Integrated Monitoring Report (IMR) Part C: Stressor/Source Identification Projects, Water Year1 (WY) 2014 through WY 2019 was prepared by the San Mateo Countywide Water Pollution Prevention Program (SMCWPPP or Program). SMCWPPP is a program of the City/County Association of Governments (C/CAG) of San Mateo County. Each incorporated city and town in the county and the County of San Mateo share a common National Pollutant Discharge Elimination System (NPDES) stormwater permit for Bay Area municipalities referred to as the Municipal Regional Permit (MRP). The MRP was first adopted by the San Francisco Regional Water Quality Control Board (SFRWQCB or Regional Water Board) on October 14, 2009 as Order R2-2009-0074 (SFRWQCB 2009; referred to as MRP 1.0). On November 19, 2015, the Regional Water Board updated and reissued the MRP as Order R2-2015-0049 (SFRWQCB 2015; referred to as MRP 2.0). This report fulfills the requirements of Provision C.8.h.v of MRP 2.0 for comprehensively interpreting and reporting all Stressor/Source Identification (SSID) monitoring data collected since the previous IMR was submitted in 2014. As such, this report includes data collected during WY 2014 through WY 2019. The previous IMR included data collected during WY 2012 and WY 2013 (SMCWPPP 2014).

Monitoring data were collected in accordance with the Bay Area Stormwater Management Agencies Association (BASMAA) Regional Monitoring Coalition (RMC) Quality Assurance Project Plan (QAPP; BASMAA 2016a) and Standard Operating Procedures (SOPs; BASMAA 2016b). Where applicable, monitoring data were derived using methods comparable with those specified by the California Surface Water Ambient Monitoring Program (SWAMP) Quality Assurance Program Plan (QAPrP)2. The BASMAA QAPP and SOPs were revised twice, once in 2014 and again in 2016, to conform to MRP 2.0 and changes made to the SWAMP QAPrP. The changes made were minor, and overall methods and protocols remain similar.

Data presented in this report were submitted electronically to the Regional Water Board by SMCWPPP and may be obtained via the California Environmental Data Exchange Network (CEDEN).

1.1 SSID Requirements

Provision C.8 of the MRP requires that Permittees evaluate Creek Status and Pesticides and Toxicity monitoring data with respect to triggers defined in the MRP. Sites where triggers are exceeded may indicate potential impacts to Aquatic Life or other Beneficial Uses and are therefore considered as candidates for SSID projects. SSID projects are selected from the list of trigger exceedances based on criteria such as magnitude of threshold exceedance, parameter, and likelihood that stormwater management action(s) could address the exceedance. Pollutants of Concern monitoring results may be considered as appropriate.


September 30 of the named year. For example, Water Year 2019 (WY 2019) began on October 1, 2018 and concluded on September 30, 2019. 2 The current SWAMP QAPrP is available at: https://www.waterboards.ca.gov/water_issues/programs/swamp/qapp/swamp_QAPrP_2017_Final.pdf


2

Both MRP 1.0 (SFRWQCB 2009) and MRP 2.0 (SFRWQCB 2015) allow Permittees to comply with the SSID requirements of Provision C.8 through a regional collaborative effort, their Stormwater Program, and/or individually. In June 2010, Permittees notified the Water Board in writing of their agreement to participate in a regional monitoring collaborative to address requirements in Provision C.8. The regional monitoring collaborative is referred to as the BASMAA RMC. In a November 2, 2010 letter to the Permittees, the Regional Water Board’s Assistant Executive Officer (Dr. Thomas Mumley) acknowledged that all Permittees have opted to conduct monitoring required by the MRP through a regional monitoring collaborative, the BASMAA RMC. Participants in the BASMAA RMC are listed in Table 1.1.

Table 0.1. BASMAA Regional Monitoring Coalition (RMC) participants.

Stormwater Programs RMC Participants

Santa Clara Valley Urban Runoff Pollution Prevention Program (SCVURPPP)

Cities of Campbell, Cupertino, Los Altos, Milpitas, Monte Sereno, Mountain View, Palo Alto, San Jose, Santa Clara, Saratoga, Sunnyvale, Los Altos Hills, and Los Gatos; Santa Clara Valley Water District; and, Santa Clara County

Clean Water Program of Alameda County (ACCWP)

Cities of Alameda, Albany, Berkeley, Dublin, Emeryville, Fremont, Hayward, Livermore, Newark, Oakland, Piedmont, Pleasanton, San Leandro, and Union City; Alameda County; Alameda County Flood Control and Water Conservation District; and, Zone 7

Contra Costa Clean Water Program (CCCWP)

Cities of Antioch, Brentwood, Clayton, Concord, El Cerrito, Hercules, Lafayette, Martinez, Oakley, Orinda, Pinole, Pittsburg, Pleasant Hill, Richmond, San Pablo, San Ramon, Walnut Creek, Danville, and Moraga; Contra Costa County; and, Contra Costa County Flood Control and Water Conservation District

San Mateo County Wide Water Pollution Prevention Program (SMCWPPP)

Cities of Belmont, Brisbane, Burlingame, Daly City, East Palo Alto, Foster City, Half Moon Bay, Menlo Park, Millbrae, Pacifica, Redwood City, San Bruno, San Carlos, San Mateo, South San Francisco, Atherton, Colma, Hillsborough, Portola Valley, and Woodside; San Mateo County Flood Control District; and, San Mateo County

Fairfield-Suisun Urban Runoff Management Program (FSURMP)

Cities of Fairfield and Suisun City

Vallejo Permittees City of Vallejo and Vallejo Sanitation and Flood Control District

The MRP requires that Permittees initiate a minimum number of SSID projects during the permit term. During MRP 1.0 (WY 2012 – WY 2015), as a regional collaborative, SMCWPPP and its RMC partners were required to collectively initiate a region-wide minimum of ten SSID projects, with a minimum of two assessing toxicity. During MRP 2.0, SMCWPPP and its RMC partners were required to collectively initiate a region-wide minimum of eight SSID projects, with a minimum of one assessing toxicity. During both permit terms, the RMC partners agreed to a population-based distribution of the required number of SSID projects among the Programs, with most projects conducted by individual Programs addressing local needs and one MRP 2.0 project conducted regionally. Through these agreements, SMCWPPP initiated two projects during MRP 1.0, one project during MRP 2.0, and participated in one regional project during MRP 2.0.


3

Provision C.8.e.ii of MRP 2.0 requires that all SSID project reports initiated during MRP 2.0 are presented in a unified, regional-level report. As such, the BASMAA RMC Regional SSID Report is included as Attachment 1.

SSID projects must identify and isolate potential sources and/or stressors associated with observed water quality impacts. They are intended to be oriented to taking action(s) to alleviate stressors and reduce sources of pollutants. Provision C.8.e.iii of MRP 2.0 describes a stepwise process for conducting SSID projects:

• Step 1: Develop a work plan for each SSID project that defines the problem to the extent known, describes the SSID project objectives, considers the problem within a watershed context, lists candidate causes of the problem, and establishes a schedule for investigating the cause(s) of the trigger. The MRP recommends study approaches for specific triggers. For example, toxicity studies should follow guidance for Toxicity Reduction Evaluations (TRE) or Toxicity Identification Evaluations (TIE), physical habitat and conventional parameter (e.g., dissolved oxygen, temperature) studies should generally follow Step 5 (Identify Probable Causes) of the Causal Analysis/Diagnosis Decision Information System (CADDIS), and pathogen indicator studies should generally follow the California Microbial Source Identification Manual (Griffith et al. 2013).

• Step 2: Conduct SSID investigation according to the schedule in the SSID work plan and report on the status of SSID investigations annually.

• Step 3: Conduct follow-up actions based on SSID investigation findings. These may include development of an implementation schedule for new or improved best management practices (BMPs). If a Permittee determines that municipal separate storm sewer system (MS4) discharges are not contributing to an exceedance of a water quality standard, the Permittee may end the SSID project upon written concurrence of the Executive Officer. If the SSID investigation is inconclusive, the Permittee may request that the Executive Officer consider the SSID project complete.3

3 MRP 1.0 did not require that the Executive Officer concur in writing before an SSID project is

determined to be completed.


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2.0 SSID Projects Initiated by SMCWPPP

This section summarizes the results of SSID projects initiated or completed by SMCWPPP during WY 2014 through WY 2019.

During MRP 1.0, SMCWPPP initiated and completed two SSID projects in San Mateo Creek investigating low dissolved oxygen and pathogen indicators.

During MRP 2.0, SMCWPPP initiated one SSID project addressing pathogen indicators in the Pillar Point Harbor watershed. The Program also participated in a regional project addressing releases and spills of PCBs from electrical utility equipment (see Section 3.0).

2.1 San Mateo Creek Low Dissolved Oxygen SSID Project

Historical (2003) and more recent (2013) monitoring data collected in the vicinity of De Anza Park in San Mateo Creek showed dissolved oxygen (DO) concentrations below the water quality objective (WQO) of 7 mg/L for waters designated as cold water habitat. The affected creek reach is located in the lower 5-square mile portion of the watershed, which is characterized by residential and urban land uses, as well as modified creek conditions. In contrast, the upper 29-square mile watershed area (88% of the total watershed area) is generally undeveloped and drains to a system of reservoirs that are owned and operated by the San Francisco Public Utilities Commission (SFPUC) (Figure 2.1). Flow in the lower reaches of San Mateo Creek are controlled by releases from the Lower Crystal Springs Reservoir dam.

Dissolved oxygen concentrations measured in 2003 by the California Surface Water Ambient Monitoring Program (SWAMP) showed wide diurnal fluctuations in lower San Mateo Creek, consistent with excessive photosynthesis (SFBRWQCB 2007). Continuous water quality monitoring conducted by SMCWPPP in WY 2013 in compliance with MRP 1.0 confirmed wide daily fluctuations in DO at De Anza Park; however, the daily pattern was not consistent with excessive photosynthesis. Instead, the DO pattern suggested daily stratification of the pool, possibly as a result of low streamflow, high air temperatures, and cold groundwater seepage (SMCWPPP 2014).

The Program conducted a SSID project to address the potential water quality concerns related to low DO. Field investigations included continuous monitoring of DO and a creek walk to measure pool depths. Low concentrations of DO were not observed in San Mateo Creek at the De Anza Park station during WY 2014, WY 2015 (SMCWPPP 2016), or during follow-up monitoring in WY 2016 (SMCWPPP 2017). Results of the SSID investigation suggest that low DO conditions are no longer present or expected in the lower reach of San Mateo Creek along De Anza Park due to a new schedule of increased dry season releases of water from the upstream Crystal Springs Reservoir. These findings were confirmed through Creek Status Monitoring conducted in WY 2015. No additional management measures were recommended and the SSID project is considered complete.

The Final San Mateo Creek Low Dissolved Oxygen SSID Project Report was submitted to the Regional Water Board on July 9, 2015 and was also included with the Program’s WY 2015 Urban Creeks Monitoring Report (UCMR; SMCWPPP 2016).


5

Figure 2.1. San Mateo Creek watershed, San Mateo County, CA.


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2.2 San Mateo Creek Pathogen Indicator SSID Project

Monitoring data collected in 2003 and 2012 at stations in San Mateo Creek showed fecal indicator bacteria (FIB) at densities exceeding WQOs for waters designated as having water contact recreation (REC-1) Beneficial Uses. During WY 2014 and WY 2015 SMCWPPP conducted a SSID project to address this potential water quality concern. Results of the SSID investigation suggest that FIB are present at densities exceeding REC-1 WQOs in San Mateo Creek reaches downstream of Sierra Drive. However, noncontact recreation (REC-2) Beneficial Use WQOs are not exceeded. Microbial source tracking (MST) techniques suggest that human sources are present year-round and dog sources are present during and shortly after wet weather. Many other potential sources of FIB are present in the watershed and likely contribute to the FIB densities measured at sampling stations. These include uncontrollable sources such as wildlife and natural bacterial growth in the creek bed and conveyance system. A number of management actions designed specifically or opportunistically to control bacterial sources are currently planned or are being implemented by municipalities in the San Mateo Creek watershed. These include control measures for pet waste (signage and public education), trash reduction efforts that may reduce nuisance wildlife, programs to address homeless encampments, and several improvements to the sanitary sewer conveyance system in response to a Cease and Desist Order. In addition, the City of San Mateo, Town of Hillsborough, San Mateo County, and SMCWPPP are working together to increase public education and outreach targeting pet waste in the San Mateo Creek watershed. Examples of recommended measures include installation of additional cleanup signs, dog bag dispensers, and trash receptacles at creekside parks. Local municipalities are also continuing actions to address homelessness begun through the Housing Our People Effectively strategy and Homeless Outreach Team program. However, even if human and dog sources are better controlled, results could still exceed WQOs due to uncontrollable sources such as wildlife and natural bacterial growth. The Final San Mateo Creek Pathogen Indicator SSID Project Report was included with the WY 2015 UCMR (SMCWPPP 2016).

2.3 Pillar Point Watershed Pathogen Indicator SSID Project

The Pillar Point Watershed Pathogen Indicator SSID Project was triggered by fecal indicator bacteria (FIB) densities exceeding WQOs that have been measured in receiving waters and tributaries to Pillar Point Harbor. A SSID work plan (SMCWPPP 2018) was submitted with the SMCWPPP WY 2017 UCMR dated March 31, 2018. The work plan describes steps to investigate urban sources of fecal indicator bacteria in the Pillar Point Watershed. SMCWPPP implemented the work plan in WY 2018 and WY 2019 with assistance from and in close coordination with the San Mateo County Resource Conservation District (RCD). Consistent with Provision C.8.e.iii.(1)(g) of MRP 2.0, the study generally follows the California Microbial Source Identification Manual (Griffith et al. 2013). The objective of the SSID study is to build on a Proposition 50 Clean Beaches Initiative Grant-funded study that was conducted by the RCD and University of California, Davis (UCD) in 2008 and 2011-12 (RCD 2014). The Proposition 50 Pillar Point Harbor Source Identification Project consisted of extensive water quality and hydrologic monitoring in the Harbor and its watershed, including collection of water, sediment, and biofilm samples during wet and dry weather for analysis of FIB (E. coli and enterococci) and bacteroidales associated with human, bovine, dog, horse, and avian sources. The RCD/UCD study indicated that high FIB measured at Pillar Point


7

beaches was likely due to influences from storm drains and creeks rather than from sources at the beaches and within the harbor itself. The Pillar Point SSID project follows up on the Proposition 50 Pillar Point Harbor Source Identification Project and focuses on identifying spatial and temporal (seasonal) information about FIB sources from the MS4 through desktop and field investigations. Field investigations included grab samples collected at 14 stations located in five subwatersheds draining to Pillar Point Harbor (Figure 2.2). In most subwatersheds, the sample design included stations upstream of the MS4, within the MS4, and at the outlet to the Harbor. Sampling was conducted during two storm events and two dry season events in WY 2018. All samples were analyzed for FIB (E. coli) and human and dog bacteroidales genetic markers. Human and dog markers were selected to represent the most likely controllable anthropogenic sources. Desktop investigations conducted in WY 2018 and WY 2019 included development of a geodatabase to map potential bacteria sources and review of monitoring data collected by San Mateo County Environmental Health Services.

Results showed E. coli densities often exceed recommended WQOs for freshwaters designated as having water contact recreation (REC-1) Beneficial Uses (i.e., 320 cfu/100mL). However, FIB densities are highly variable and do not follow predictable seasonal patterns across all subwatersheds investigated. For example, two of the subwatersheds did not have higher wet weather FIB densities compared to dry season densities. A dearth of human and dog markers detected in this SSID study (particularly during the dry season) suggests that FIB conveyed by the MS4 may not be controllable. Instead, the primary sources of FIB within and conveyed by the MS4 appear to be uncontrollable wildlife (i.e., raccoons, deer, rodents) that are present in the MS4 and contributing areas. Regrowth of FIB in biofilms within the MS4, and subsequent shearing off of these materials is another likely source of FIB to receiving waters, though data limitations in this study preclude making evidence-based conclusions.

The Final Pillar Point Harbor Watershed Pathogen Indicator SSID Project Report was submitted to the Regional Water Board on October 28, 2019 and is included with this IMR as Attachment 2. The report documented management actions that are already being implemented along the coast and throughout the County that specifically or opportunistically reduce bacterial sources in stormwater runoff. These actions include stormwater and sewer infrastructure improvements, prohibition of non-stormwater runoff, trash controls, pet waste ordinances, pet waste cleanup stations, stormwater education and outreach, confined animal facility best management practices, and beach clean-ups.

Several additional bacterial control measures were recommended in the Final Project Report. Potential measures include installation of additional pet waste cleanup stations; continued education and outreach; investigations to identify locations within the MS4 where groundwater infiltration may be occurring (and subsequent repair); outreach to the owner(s)/operator(s) of the sewage collection system to understand and potentially improve operations, monitoring, and maintenance; and continued technical assistance to farms and ranches to promote water quality protection.

It is important to acknowledge that a) WQOs for FIB do not distinguish among sources of FIB and b) FIB detections do not necessarily correlate well with the presence of pathogens. Animal fecal waste is much less likely to contain pathogens of concern to human health than human sources, and FIB associated with biofilms likely does not indicate that pathogens are present. In most cases, human sources of fecal contamination are associated with REC-1 health risks rather than wildlife or domestic animal sources (USEPA 2012). Furthermore, even if controllable


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bacteria sources (i.e., human and dog sources) are eliminated, FIB densities in receiving waters could still exceed WQOs due to wildlife and natural FIB growth in biofilms, sediment, and organic matter.

Figure 2.2. Pillar Point Watershed Pathogen Indicator SSID Project monitoring stations.


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3.0 Regional PCBs from Electrical Utility Equipment

In late-2018, BASMAA contracted with EOA, Inc. to develop a work plan for a regional SSID project addressing releases and spills of PCBs from electrical utility equipment. The Regional SSID Project - Electrical Utilities as a Potential PCBs Source to Stormwater in the San Francisco Bay Area – was triggered by fish tissue monitoring in the Bay that led to the Bay being designated as impaired on the Clean Water Act (CWA) Section 303(d) list and the adoption of a TMDL for PCBs in 2008. Subsequent PCBs monitoring by the BASMAA RMC partners and the Regional Monitoring Program for Water Quality in San Francisco Bay (RMP) suggests that diffuse sources of PCBs are present throughout the region. One potential source of PCBs to stormwater is releases and spills from electrical utility equipment.

PCBs were historically used in several types of electrical utility equipment, some of which still contain PCBs. Although much of the PCB-containing equipment has been removed from service, some remains in use, and releases and spills from the equipment may be occurring at levels approaching the TMDL waste load allocation. However, the information currently available is not adequate to fully quantify the scope and magnitude of electrical utility applications as a source of PCBs to stormwater. The information gap is partially due to state and federal regulatory levels for reporting and clean-up of PCBs spills that are higher than the PCB levels needed to comply with the PCBs TMDL requirements. Furthermore, stormwater Programs have neither the authority to compel electrical utilities to provide information about spills, equipment replacement programs, and clean-up protocols, nor the authority to require additional controls. Therefore, BASMAA identified a need to develop and implement a regional SSID work plan to further understand the magnitude and extent of this potential PCBs source, and identify controls (if necessary) that could be put into place to reduce the water quality impacts of this source.

The work plan was submitted with each countywide stormwater program’s WY 2018 UCMR. It presents a framework for working with the Regional Water Board, which does have jurisdictional authority over electrical utility companies. The overall goal for the regional SSID project is to investigate electrical utility equipment as a source of PCBs to urban stormwater runoff and identify appropriate actions and control measures to reduce this source. Building on the information presented by SCVURPPP (2018b), this project is designed to achieve the following three objectives:

1. Gather information from Bay Area utilities to improve estimates of current PCBs loadings to municipal separate storm sewer systems (MS4s) from electrical utility equipment, and document current actions conducted by utilities to reduce or prevent release of PCBs from their equipment;

2. Identify opportunities to improve spill response, cleanup protocols, or other programs designed to reduce or prevent releases of PCBs from electrical utility equipment to MS4s;

3. Develop an appropriate mechanism for municipalities to ensure adequate clean-up, reporting and control measure implementation to reduce urban stormwater loadings of PCBs from electrical utility equipment.

The information gained during this project will also provide data that municipalities can use to provide better estimates of PCBs load reductions that can be achieved through implementation of a regional control measure program for electrical utilities.


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3.1 Overview of the Regional SSID Work Plan

The work plan identified 4 project tasks: (1) conduct a desk top analysis; (2) propose a source control framework for electrical utility equipment to reduce ongoing PCBs loads to the Bay in stormwater runoff; (3) Develop data inputs to better estimate PCBs load reductions that can be achieved via new source controls; and (4) develop an SSID project report. Each of these tasks are described in more detail below. Progress made to date is provided in Section 3.2.

3.1.1 Task 1: Desktop Analysis

The desktop analysis is designed to gather and evaluate information on electrical utility equipment in the Bay Area to provide the foundation for development of a comprehensive regional control measure program to reduce PCBs loads from this source. The desktop analysis includes the following five sub-tasks:

• Subtask 1.1 Request information from electrical utility companies. This task will seek the assistance and support of the Regional Water Board to: obtain information from non-municipally owned utility companies that is not publicly available but is needed to better understand the extent and magnitude of PCBs releases from Oil Filled Electrical Equipment (OFEE); identify the most appropriate actions to prevent or reduce releases from this source; and develop and implement effective reporting and control measures. For this task, the Regional Water Board will be asked to assist BASMAA in compelling electrical utility companies (e.g., PG&E) to provide the necessary information. A preliminary list of information that will be requested includes the following:

o Spill reporting and notification procedures (both region-wide and location-specific);

o Spill records NOT reported to the California Governor’s Office of Emergency Services (Cal OES);

o SOPs and other documentation used by electrical utilities and their contractors to guide spill response and cleanup actions when releases from OFEE occur;

o SOPs and documentation, including analytical methods for PCBs used by electrical utilities and their contractors to identify and clean up regular leaks from OFEE during regular maintenance activities;

o Measurement data on concentrations of PCBs in OFEE; o Maintenance records that document when and where PCBs-containing OFEE

are removed from the system and how often PCBs containing equipment is inspected for leaks or spills;

o Documentation of past programs to voluntarily remove PCBs-containing oils or OFEE – including what equipment was removed, and the locations from which it was removed; and

o Documentation of where PCBs-containing OFEE were located in the past, and where they are currently located across the Bay Area.

Additional data gaps may also be identified and added to the data request based on discussions with Regional Water Board staff and/or preliminary information provided by utilities.


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• Subtask 1.2 Assess current electrical utility data. This task will review, tabulate and analyze the information provided by electrical utilities as a result of the Regional Water Board’s request for information, in order to document the following:

o Measurement data on PCBs concentrations and/or mass in OFEE; o Locations of PCBs-containing OFEE; o Quantity of PCBs-containing OFEE removed from service annually; o Occurrences of spills or releases from OFEE; o Current PCBs spill and cleanup reporting requirements; and o Current PCBs cleanup protocols.

• Subtask 1.3 Improve estimates of PCBs loadings. This task will combine the information provided in Subtask 1.2 with all existing data in order to develop improved estimates of current PCBs loadings from electrical utility equipment to MS4s in the study area. The quality of these estimates will partly depend on the quality of the data received from the utilities.

• Subtask 1.4 Refine PCBs reporting requirements This task will review all current reporting and notification requirements to identify any improvements or clarifications that the Regional Water Board could require of electrical utilities to provide the type of data needed to better quantify the amount of PCBs released from OFEE spills, and to help ensure that adequate cleanup actions are being implemented.

• Subtask 1.5 Evaluate PCBs cleanup protocols This task will review all documented cleanup protocols that are currently used by electrical utilities in order to identify any changes or improvements that could be recommended to further reduce the discharge of PCBs to the MS4 when releases occur.

3.1.2 Task 2: Develop Source Control Framework

Based on the results of the desktop analysis, this task will propose an appropriate framework for managing and implementing control measures to reduce PCBs from electrical utility equipment. The framework should include prescribed methods and procedures for unplanned spills and releases from OFEE, as well as a plan for continued reduction of PCBs from in-use OFEE, and potentially further identification and cleanup of historic release sites. The framework will likely include the following elements:

• Summary of the outcomes of the desktop analysis results, including:

o Summary of information provided by electrical utilities; o Improved estimates of current PCBs loadings from electrical utility equipment

based on information received; o Documentation of current spill clean-up and reporting actions, and existing

programs for proactive removal of PCBs-containing oils and equipment conducted by electrical utilities;

o Recommended PCBs spill and cleanup reporting requirements that the Regional Water Board could require of electrical utilities;


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o Recommended improvements to PCBs spill cleanup protocol(s) that would reduce the discharge of PCBs to MS4s that the Regional Water Board could require of electrical utilities.

o Recommended approach to manage and control releases of PCBs from electrical utilities. The approach may include requirements the Regional Water Board could impose on electrical utilities in the Bay Area, such as new spill reporting and cleanup protocols.

3.1.3 Task 3: Develop methodologies to account for PCBs load reductions from new

source control measures

BASMAA will further apply the results of the desktop analysis to develop data inputs to better account for the PCBs load reductions that can be achieved via the new clean-up and reporting protocols identified above in Task 2.

3.1.4 Task 4: Develop SSID Project Report

BASMAA will prepare a report describing the desktop analysis and outcomes. The report will summarize the information provided by electrical utilities and identify recommendations to modify or improve current control measures or management actions that will reduce PCBs released to MS4s. The Management Questions that will be addressed include:

1. What is the current magnitude and extent of PCBs stormwater loadings from electrical utility equipment and operations in the San Francisco Bay Area region?

2. Are there aspects of equipment or operational procedures that electrical utilities should be required to report to the Regional Water Board?

3. Are there additional spill and clean-up controls needed to reduce water quality impacts from the release of PCBs in electrical utility equipment?

4. Are there additional proactive activities needed to avoid releases of PCBs from electrical utility equipment?

5. What are the PCBs load reductions that can be achieved through implementation of a regional reporting and control measure program?

3.2 Current Status of the Regional SSID Project

Implementation of the regional SSID work plan began in WY 2019. The Work Plan focused on Pacific Gas and Electric Company (PG&E), the largest electrical utility operating in the MRP area, and the only utility that is not owned by a municipality. The work plan outlined a 2-step process to (1) conduct a desktop analysis using data from PG&E in order to better understand the extent and magnitude of PCBs releases from oil-filled electrical equipment (OFEE) and document current and past efforts to reduce PCBs in OFEE, and (2) propose a source control framework to potentially reduce ongoing PCBs loads to the Bay from electrical utility equipment. The project team developed a letter requesting assistance from the Regional Water Board and outlining the specific data that is needed from PG&E to complete this project. However, PG&E is currently in bankruptcy proceedings, and the outcomes of that process have not yet been determined.


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Because of the current situation with PG&E, BASMAA developed a revised approach to the SSID project in early WY 2020 that focuses on municipally-owned electrical utilities in the MRP area. Although these municipally-owned electrical utilities represent a fraction of the electrical utility equipment and properties in the MRP area, BASMAA member agencies have a better opportunity to work with these utilities and gather the type of information needed to conduct the desktop analysis, albeit at a smaller scale. The revised approach will continue to implement the Regional SSID work plan but will focus exclusively on municipally-owned electrical utilities in the Bay Area. The revised approach implements the SSID work plan objectives to develop an appropriate source control framework to inform the development of practices to potentially reduce the release of PCBs from electrical utility equipment; and to develop estimates of PCBs load reductions that could be achieved through implementation of revised management practices, such as improved clean-up and reporting procedures.

In November and December 2019, BASMAA held a series of meetings with representatives from municipally-owned electrical utilities and associated municipal staff in the MRP area to discuss the project and information needs. Based on input provided during these meetings, BASMAA developed an information request for municipally-owned electrical utilities that was similar to the request sent to the Regional Water Board for PG&E data.

BASMAA intends to continue this project during WY 2020. The new request for information will be submitted to each of the municipally-owned electrical utilities in the MRP area in the near future. The BASMAA project team will proceed with the desktop analysis upon receipt of data from these utility partners. It is anticipated that the final project report will be submitted to the Regional Water Board with the Program’s WY 2020 UCMR by March 31, 2021.


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4.0 Recommendations

Overall, Permittees find that SSID monitoring provides valuable information to assist with identifying stressors and sources of water quality impacts. Although the SSID studies have found that the primary stressor sources are unrelated to municipal stormwater runoff and/or are inconclusive, they have resulted in a greater understanding of hydrology, water quality, and land use in the targeted watersheds, and the findings inform other aspects of management to improve the condition of local receiving waters. Continuation of SSID monitoring in the next permit should be considered, with the level-of-effort determined in a manner mindful of the overall costs of implementing Provision C.8 monitoring requirements and other provisions.


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5.0 References

Bay Area Stormwater Management Agencies Association (BASMAA) 2017. Interim Accounting Methodology for TMDL Loads Reduced. Prepared by Geosyntec Consultants and EOA, Inc. March 2017.

Bay Area Stormwater Management Agency Association (BASMAA) Regional Monitoring Coalition (RMC). 2016a. Creek Status and Pesticides & Toxicity Monitoring Quality Assurance Project Plan, Final Version 3. Prepared for BASMAA by EOA, Inc. on behalf of the Santa Clara Urban Runoff Pollution Prevention Program and the San Mateo Countywide Water Pollution Prevention Program, Applied Marine Sciences on behalf of the Alameda Countywide Clean Water Program, and Armand Ruby Consulting on behalf of the Contra Costa Clean Water Program. 83 pp plus appendices.

Bay Area Stormwater Management Agency Association (BASMAA) Regional Monitoring Coalition (RMC). 2016b. Creek Status and Pesticides & Toxicity Monitoring Standard Operating Procedures, Final Version 3. Prepared for BASMAA by EOA, Inc. on behalf of the Santa Clara Urban Runoff Pollution Prevention Program and the San Mateo Countywide Water Pollution Prevention Program, Applied Marine Sciences on behalf of the Alameda Countywide Clean Water Program, and Armand Ruby Consulting on behalf of the Contra Costa Clean Water Program. 190 pp.

Griffith, J.F., Blythe, A.L., Boehm, A.B., Holden, P.A., Jay, J.A., Hagedorn, C., McGee, C.D., and Weisberg, S.B. 2013. The California Microbial Source Identification Manual: A Tiered Approach to Identifying Fecal Pollution Sources to Beaches. Southern California coastal Water Research Project Technical Report 804.


San Francisco Regional Water Quality Control Board (SFRWQCB). 2017. Water Quality Control Plan (Basin Plan) for the San Francisco Bay Region. Updated to reflect amendments adopted up through May 4, 2017. http://www.waterboards.ca.gov/sanfranciscobay/basin_planning.shtml.


San Mateo County Resource Conservation District (RCD). 2014. Final Project Report. Pillar Point Harbor Source Identification Project. Clean Beaches Grant Program, Proposition 50. Agreement 07-574-550-2. January 2014.







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Santa Clara Valley Urban Runoff Pollution Prevention Program (SCVURPPP). 2018. Potential Contributions of PCBs to Stormwater from Electrical Utilities in the San Francisco Bay Area. Overview and Information Needs. Prepared by EOA, Inc. September 2018.

USEPA. 2012. Recreational Water Quality Criteria. Office of Water 820-F-12-058. A Non-Predictive Algal Index for Complex Environments.


ATTACHMENTS


Attachment 1

BASMAA RMC Regional SSID Report

BASMAA Regional Monitoring Coalition Regional Stressor/Source Identification (SSID) Report, prepared in compliance with Municipal Regional Stormwater NPDES Permit (MRP; Order No. R2-2015-0049) Provision C.8.e.iii MRP 2.0 SSID Project Locations, Rationales, Status Updated March 25, 2020

Page 2 of 2

SSID

Project

ID

Date

Updated

County/

Program

Creek/

Channel

Name

Site

Code(s) or

Other Site

ID

Project Title

Primary Indicator(s) Triggering Stressor/Source ID Project

Indicator Result Summary Rationale for Proposing/Selecting

Project

Current Status of SSID

Project or Date

Completed

EO Concurrence

of project

completion (per

C.8.e.iii.(b))

Bio

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AL-1 3/23/20 ACCWP Palo Seco Creek

Exploring Unexpected CSCI Results and the Impacts of Restoration Activities

X

Sites where there is a substantial difference in CSCI score observed at a location relative to upstream or downstream sites, including sites on Palo Seco Creek upstream of the Sausal Creek restoration-related sites, that had substantial and unexpected differences in CSCI scores.

The project will provide additional data to aid consideration of unexpected and unexplained CSCI results from previous water year sampling on Palo Seco Creek, enable a more focused study of monitoring data collected over many years in a single watershed, and allow analysis of before and after data at sites upstream and downstream of previously completed restoration activities.

In WY 2019, nutrient sampling, bioassessment, and additional DO and temperature monitoring were conducted. The final SSID progress report is included in ACCWP’s March 2020 IMR, recommending project completion.

AL-2 1/22/20 ACCWP Arroyo Las Positas

Arroyo Las Positas Stressor Source Identification Project

X

CSCI scores below the threshold were recorded on Arroyo Las Positas in WYs 2016 and 2017. In 2017, one site exceeded the Basin Plan threshold for chloride. The creek is also listed on the 303(d) list for eutrophication and has an approved TMDL for Diazinon.

The Water Board is conducting sampling in the watershed as part of their TMDL development efforts and an SSID project will supplement those efforts and generate a better overall picture of stressors impacting the waterbody.

In WY 2019, ACCWP conducted bioassessments, nutrient sampling, and continuous monitoring at multiple locations within the watershed over the course of spring and summer months. The first SSID progress report is included in ACCWP’s March 2020 IMR.

CC-1 1/27/20 CCCWP Lower Marsh Creek

Marsh Creek Stressor Source Identification Study

X

10 fish kills have been documented in Marsh Creek between September 2005 and September 2019. Low dissolved oxygen was proved to be the cause in the most recent (9/17/19) event; circumstances indicate low DO was a likely cause in many if not all of the prior events.

This SSID study addresses the root causes of fish kills in Marsh Creek. Monitoring data collected by CCCWP and other parties are being used to investigate multiple potential causes, including low dissolved oxygen, warm temperatures, daily pH swings, fluctuating flows, physical stranding, and pesticide exposure. During year 2 a pilot test of water storage and night-time flow augmentation was conducted by the City of Brentwood Wastewater Treatment Plant (WWTP).

The CCCWP SSID work plan was submitted in 2018. The Year 2 Status Report is included in CCCWP’s March 2020 IMR. The study successfully concluded in Year 2. The final report will recommend project completion. Flow augmentation appears to be a viable means of avoiding lethally low DO in portions of the creek downstream of the WWTP.


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Site

Code(s) or

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ID

Project Title



Project


Project or Date

Completed

EO Concurrence

of project

completion (per

C.8.e.iii.(b))

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SC-1 3/24/20 SCVURPPP Coyote Creek NA Coyote Creek Toxicity SSID Project

X

The SWRCB recently added Coyote Creek to the 303(d) list for toxicity.

This SSID study investigated the extent and magnitude of toxicity in an urban reach of Coyote Creek. Sediment samples (n=8) were collected during the dry season of 2018 and 2019. Samples were generally not toxic, with the exception of one sample that had low levels of toxicity (subsequent re-test of sample was not toxic). Sediment chemistry results were inconclusive (i.e., pesticide concentrations were not at levels suspected of causing toxicity). SSID Project results support similar findings from long term monitoring conducted by the SWAMP SPoT Program of reduced acute toxicity in Coyote Creek over the past 10 years.

The work plan was submitted with SCVURPPP's WY 2017 UCMR. A project report describing the results of the WY 2018 and WY 2019 monitoring and recommending project completion will be submitted with the WY 2019 IMR.

SC-2 1/29/20 SCVURPPP Lower Silver-Thompson Creek

NA Lower Silver SSID Project

X X

Low CSCI scores and high nutrient concentrations at a majority of bioassessment locations.

Evaluate potential causes of reduced biological conditions in Lower Silver-Thompson Creek. The SSID Project is investigating sources of nutrients and assessing the range and extent of eutrophic conditions (if present). The Project will evaluate association between stressor data (e.g., water chemistry, dissolved oxygen and physical habitat) and biological condition indicators (i.e., CSCI and ASCI scores)

The work plan was submitted with SCVURPPP's FY 18-19 Annual Report. It is anticipated to be a two-year project with the project report to be submitted with the WY 2020 UCMR.

SM-1 3/24/20 SMCWPPP

Pillar Point / Deer Creek / Denniston Creek

NA Pillar Point Harbor Bacteria SSID Project

X

FIB samples from 2008 and 2011-2012 exceeded WQOs.

A grant-funded Pillar Point Harbor MST study conducted by the RCD and UC Davis in 2008, 2011-2012 pointed to urban runoff as a primary contributor to bacteria at Capistrano Beach and Pillar Point Harbor. The study, however, did not identify the specific urban locations or types of bacteria. This SSID project investigated bacteria contributions from the urban areas within the watershed. In WY 2018, Pathogen indicator and MST monitoring was conducted at 14 freshwater sites during 2 wet and 2 dry events. Very few samples contained “controllable” source markers (i.e., human and dog). Additional field studies

The work plan was submitted with SMCWPPP’s WY 2017 UCMR. A project report describing the results of the WY 2018 and WY 2019 investigations was submitted on October 28, 2019. RWQCB staff requested minor report changes prior to Executive Officer concurrence regarding project completion. A TMDL addressing bacteria in Pillar Point


Page 2 of 2

SSID

Project

ID

Date

Updated

County/

Program

Creek/

Channel

Name

Site

Code(s) or

Other Site

ID

Project Title



Project


Project or Date

Completed

EO Concurrence

of project

completion (per

C.8.e.iii.(b))

Bio

asse

ss

Gen

eral

WQ

Ch

lori

ne

Tem

p

Wat

er T

ox

Sed

To

x

Sed

Ch

em

Pat

ho

gen

Ind

icat

ors

Oth

er

were conducted in WY 2019 to understand hydrology and specific source areas.

Harbor is currently under development.

FSV-1 1/16/20

City of Vallejo in assoc. with FSURMP

Rindler Creek 207R03504 Rindler Creek Bacteria and Nitrogen Study

X E. coli result of 2800 MPN/100mL in Sept. 2017.

A source identification study is warranted in Rindler Creek due to the elevated FIB result, other (non-RMC) monitoring indicating elevated ammonia levels, and the presence of a suspected pollutant source upstream of the data collection point. Rindler Creek is a highly urbanized and modified creek that originates in open space northeast of the City of Vallejo. Monitoring is conducted just downstream of the creek crossing under Columbus Parkway; upstream of this site there is City-owned land that is grazed by cattle roughly from December-June.

Additional monitoring in the spring and summer of 2019 revealed consistently high levels of E coli and enterococci when cattle are present. A Work Plan is in development and will be submitted with the IMR in March 2020.

RMC-1 3/24/20 RMC/ Regional

NA (entire RMC area)

NA

Regional SSID Project: Electrical Utilities as a Potential PCBs Source to Stormwater in the San Francisco Bay Area

X

Fish tissue monitoring in San Francisco Bay led to the Bay being designated as impaired on the CWA 303(d) list and the adoption of a TMDL for PCBs in 2008. POC monitoring suggests diffuse PCBs sources throughout region.

PCBs were historically used in electrical utility equipment, some of which still contain PCBs. Although much of the equipment has been removed from services, ongoing releases and spills may be occurring at levels approaching the TMDL waste load allocation. This regional SSID project is investigating opportunities for BASMAA RMC partners to work with RWQCB staff to: 1) improve knowledge about the extent and magnitude of PCB releases and spills, 2) improve the flow of information from utility companies, and 3) compel cooperation from utility companies to implement improved control measures.

The work plan was submitted with each Program’s WY 2018 UCMR and implementation began in WY 2019. The work plan outlined a process for BASMAA RMC partners to work with RWQCB staff to better understand PCB releases from electrical utility equipment owned by PG&E and to propose a source control framework. Ongoing bankruptcy proceedings at PG&E stalled the process. Therefore, BASMAA is now reaching out to municipally-owned utilities. The SSID project is anticipated to be completed in June 2020.


Attachment 2

Final Pillar Point Harbor Watershed Pathogen Indicator SSID Project Report

PILLAR POINT HARBOR

WATERSHED PATHOGEN

INDICATOR STRESSOR/SOURCE

IDENTIFICATION (SSID)

Project Report

Prepared in support of Provision C.8.e of the Municipal Regional Permit (NPDES Permit # CAS612008)

October 18, 2019

SMCWPPP Pillar Point Harbor Watershed Pathogen Indicator SSID –Project Report

i

EXECUTIVE SUMMARY

This report presents the results and recommendations of the Pillar Point Harbor Watershed Pathogen Indicator Stressor/Source Identification project. The project was conducted to address requirements in the San Francisco Bay Region Municipal Regional Permit (MRP) for discharges of stormwater runoff. Per MRP Provision C.8.e.ii, Stressor/Source Identification (SSID) projects are designed to identify and isolate potential sources and/or stressors associated with observed or potential impacts to aquatic life or other Beneficial Uses. SSID projects are intended to be oriented toward taking action(s) to alleviate stressors and reduce sources of pollutants.

This SSID project was triggered by fecal indicator bacteria (FIB) densities exceeding Water Quality Objectives (WQOs) in receiving waters and tributaries to Pillar Point Harbor. In an effort to understand the primary sources of fecal contamination at Pillar Point Harbor and to identify potential remediation strategies, the San Mateo Resource Conservation District (RCD) and University of California, Davis (UCD) implemented a Proposition 50 Clean Beaches Initiative Grant-funded study in 2008 and 2011-12 (RCD 2014). The Proposition 50 Pillar Point Harbor Source Identification Project consisted of extensive water quality and hydrologic monitoring in the Harbor and its watershed, including collection of water, sediment, and biofilm samples during wet and dry weather for analysis of FIB (E. coli and enterococci) and bacteroidales associated with human, bovine, dog, horse, and avian sources. The RCD (2014) study indicated that high FIB in the Harbor was likely due, in part, to influences from storm drains and creek.

This SSID project follows up on the Pillar Point Harbor Source Identification Project and focuses on identifying spatial and temporal (seasonal) information about FIB sources from the municipal separate storm sewer system (MS4) through desktop and field investigations. Field investigations included grab samples collected at 14 stations located in five subwatersheds draining to Pillar Point Harbor. In most subwatersheds, the sample design included stations upstream of the MS4, within the MS4, and at the outlet to the Harbor. Sampling was conducted during two storm events and two dry season events in Water Year 2018. All samples were analyzed for FIB (E. coli) and human and dog bacteroidales genetic markers. Human and dog markers were selected to represent the most likely controllable anthropogenic sources. Desktop investigations conducted in Water Years 2018 and 2019 included development of a geodatabase to map potential bacteria sources and review of monitoring data collected by San Mateo County Environmental Health Services. Results showed E. coli densities often exceed recommended WQOs for freshwaters designated as having water contact recreation (REC-1) Beneficial Uses (i.e., 320 cfu/100mL). However, FIB densities are highly variable and do not follow predictable seasonal patterns across all subwatersheds investigated. For example, two of the subwatersheds did not have higher wet weather FIB densities compared to dry season densities. A dearth of human and dog markers detected in this SSID study (particularly during the dry season) suggests that FIB conveyed by the MS4 may not be controllable. Instead, the sources of FIB within and conveyed by the MS4 appear to be primarily


ii

uncontrollable wildlife (i.e., raccoons, deer, rodents) that are present in the MS4 and contributing areas. Regrowth of FIB in biofilms within the MS4, and subsequent shearing off of these materials is another likely source of FIB to receiving waters. Stakeholders in the Pillar Point Harbor watershed, such as the San Mateo Countywide Water Pollution Prevention Program (SMCWPPP), the County of San Mateo, the RCD, and the Harbor District are already implementing many management actions along the coast and throughout the County that specifically or opportunistically reduce bacterial sources in stormwater runoff. These actions include stormwater and sewer infrastructure improvements, prohibition of non-stormwater runoff, trash controls, pet waste ordinances, pet waste cleanup stations, stormwater education and outreach, confined animal facility best management practices, and beach clean-ups. Stakeholders may wish to consider additional bacterial control measures. Potential measures include installation of additional pet waste cleanup stations; continued education and outreach; investigations to identify locations within the MS4 where groundwater infiltration may be occurring (and subsequent repair); outreach to the owner(s)/operator(s) of the sewage collection system to understand and potentially improve operations, monitoring, and maintenance; and continued technical assistance to farms and ranches to promote water quality protection. It is important to acknowledge that a) WQOs for FIB do not distinguish among sources of FIB and b) FIB detections do not necessarily correlate well with the presence of pathogens. Animal fecal waste is much less likely to contain pathogens of concern to human health than human sources, and FIB associated with biofilms likely does not indicate that pathogens are present. In most cases, human sources of fecal contamination are associated with REC-1 health risks rather than wildlife or domestic animal sources (USEPA 2012). Furthermore, even if controllable bacteria sources (i.e., human and dog sources) are eliminated, FIB densities in receiving waters could still exceed WQOs due to wildlife and natural FIB growth in biofilms, sediment, and organic matter.


iii

TABLE OF CONTENTS

EXECUTIVE SUMMARY ............................................................................................................................ I

TABLE OF CONTENTS ........................................................................................................................... III

LIST OF FIGURES .................................................................................................................................... IV

LIST OF TABLES ........................................................................................................................................ V

LIST OF APPENDICES .............................................................................................................................. V

1.0 INTRODUCTION ......................................................................................................................... 1 1.1 WORK PLAN SUMMARY AND SSID STUDY MANAGEMENT QUESTIONS .................................... 1

2.0 METHODS ..................................................................................................................................... 5 2.1 DESKTOP ANALYSIS ................................................................................................................................ 5 2.2 FIELD INVESTIGATION ........................................................................................................................... 5

2.2.1 E. Coli Monitoring .................................................................................................................................. 6 2.2.2 Microbial Source Tracking ................................................................................................................ 6

2.3 STATEMENT OF DATA QUALITY ....................................................................................................... 10

3.0 RESULTS .................................................................................................................................... 12 3.1 MAPPING ................................................................................................................................................ 12

3.1.1 Geographic Setting Layers .............................................................................................................. 12 3.1.2 Potential Bacteria Source Layers ................................................................................................. 13

3.2 FIB AND MST MONITORING ............................................................................................................. 15 3.3 BEACH DATA REVIEW ......................................................................................................................... 20

4.0 DISCUSSION .............................................................................................................................. 22 4.1 PILLAR POINT MARSH ........................................................................................................................ 23 4.2 PRINCETON-BY-THE-SEA ................................................................................................................... 24 4.3 DENNISTON CREEK .............................................................................................................................. 24 4.4 CAPISTRANO CATCHMENT ................................................................................................................. 26 4.5 ST. AUGUSTINE CREEK ........................................................................................................................ 29 4.6 DEER CREEK .......................................................................................................................................... 32

5.0 CONCLUSIONS AND RECOMMENDATIONS ..................................................................... 36 5.1 OVERALL FINDINGS ............................................................................................................................. 36

5.1.1 Bacteria Fate and Transport .......................................................................................................... 38 5.2 CURRENT AND RECOMMENDED MANAGEMENT ACTIONS .......................................................... 38

5.2.1 Biofilms ..................................................................................................................................................... 38 5.2.2 Trash .......................................................................................................................................................... 39 5.2.3 Pet Waste ................................................................................................................................................. 40 5.2.4 Wastewater ............................................................................................................................................ 41 5.2.5 Direct Sources of Wastes .................................................................................................................. 41 5.2.6 Livestock .................................................................................................................................................. 42 5.2.6 Wildlife Waste ....................................................................................................................................... 42

6.0 REFERENCES............................................................................................................................. 44


iv

LIST OF FIGURES

Figure 1.1. Pillar Point Harbor watershed and subwatersheds. .......................................... 4

Figure 2.1. Pillar Point Harbor Watershed Pathogen Indicator SSID Project monitoring stations. ............................................................................................................................... 9

Figure 3.1. Cumulative precipitation at Half Moon Bay, CA (source: U.S. Climate Data) and SSID project sample dates, WY 2018. ....................................................................... 15

Figure 3.2. Enterococci densities measured in the surf zone at Capistrano Beach (Pillar Point #5) and Beach House Beach (Pillar Point #9) by County Environmental Health Services, WY 2018. .......................................................................................................... 20

Figure 3.3. Enterococci densities measured in the surf zone at Capistrano Beach (Pillar Point #5) and Beach House Beach (Pillar Point #9) by County Environmental Health Services, WYs 2013 – 2018. ............................................................................................. 21

Figure 4.1. Pillar Point Harbor beaches and County Environmental Health Services sample stations. ................................................................................................................. 22

Figure 4.2. Photo showing RVs parked along Airport Road and trash in nearby ditch. ... 23

Figure 4.3. E. coli densities measured at Denniston Creek watershed stations. ............... 25

Figure 4.4. Capistrano catchment outfall at Capistrano Beach (station CPDS), August 2017................................................................................................................................... 27

Figure 4.5. Capistrano catchment MS4 and SSID sample stations with drainage areas (MS4 based on BKF 2013 and differs from County storm drain GIS layer in geodatabase and RCD investigations). .................................................................................................. 28

Figure 4.6. E. coli densities measured in Capistrano Catchment stations. ....................... 29

Figure 4.7. St. Augustine Creek outfall at Capistrano Beach (station AGDS). ................ 31

Figure 4.8. E. coli densities measured in St. Augustine Creek watershed stations. ......... 32

Figure 4.9. Deer Creek at Valencia Ave (Station DRVL) showing constrained channel, January 2018. .................................................................................................................... 34

Figure 4.10. Deer Creek outfall (station DRDS) with boat launch in background, January 9, 2018............................................................................................................................... 34

Figure 4.11. E. coli densities measured in Deer Creek watershed stations, by site, upstream to downstream. .................................................................................................. 35

Figure 4.12. E. coli densities measured in Deer Creek watershed stations, by date. ........ 35


v

LIST OF TABLES

Table 1.1. Potential sources of pathogen indicators in the Study Area within the Pillar Point Harbor watershed....................................................................................................... 3

Table 2.1. Pillar Point Harbor Watershed Pathogen Indicator SSID Project sample stations. ............................................................................................................................... 7

Table 3.1. Bacteria monitoring results and field observations, Pillar Point Harbor Watershed SSID Project. E. coli results exceeding the freshwater single sample WQO of 320 cfu/100mL at ambient water station (i.e., not MS4 stations) are shown in bold. ...... 17

Table 3.2. Human and dog marker results, Pillar Point Harbor Watershed SSID Project.19

LIST OF APPENDICES

Appendix A Pillar Point Harbor Pathogen Indicator SSID Project Geodatabase Metadata

Appendix B. Geodatabase Figures


1

1.0 INTRODUCTION

This report presents the results of the Pillar Point Harbor Watershed Pathogen Indicator Stressor/Source Identification Project which was initiated in 2017 to address requirements listed under Provision C.8.e of the San Francisco Bay Region National Pollutant Discharge Elimination System (NPDES) stormwater Municipal Regional Permit (MRP) (Order R2-2015-0049). The San Mateo Countywide Water Pollution Prevention Program (SMCWPPP), a program of the City/County Association of Governments (C/CAG), is working with Bay Area Stormwater Management Agencies Association (BASMAA) Regional Monitoring Coalition (RMC) members to collectively initiate eight Stressor/Source Identification (SSID) projects during the five-year term of the MRP (i.e., 2016 – 2020). The Pillar Point Watershed Pathogen Indicator SSID Project satisfies SMCWPPP’s contribution toward the eight SSID projects, as agreed upon by the RMC. Per MRP Provision C.8.e.ii, SSID projects are designed to identify and address potential sources and/or stressors associated with observed or potential impacts to aquatic life or other Beneficial Uses. SSID projects are intended to be oriented toward taking action(s) to alleviate stressors and reduce sources of pollutants.

SMCWPPP initiated the Pathogen Indicator SSID Project in the Pillar Point Harbor watershed in response to historical pathogen indicator bacteria data collected from Pillar Point Harbor and its tributaries indicating potential water quality issues. Implementation of the SSID Project is consistent with the Pillar Point Watershed Pathogen Indicator SSID Project Work Plan (Work Plan; SMCWPPP 2018) which was submitted to the San Francisco Bay Regional Water Quality Control Board (Regional Water Board) with the SMCWPPP Urban Creeks Monitoring Report dated March 31, 2018. SMCWPPP implemented the Work Plan with assistance from and in close coordination with the San Mateo Resource Conservation District (RCD) and the County of San Mateo. RCD work on this project was supported by funding provided by the San Mateo County Harbor District.

This introduction (Section 1.0) provides background on pathogen indicators and water quality objectives (WQOs) developed to protect recreational beneficial uses. Section 2.0 of this report describes methods used in the investigation. Section 3.0 presents results of the SSID project desktop and field investigations. Section 4.0 discusses the results within the context of each of the subwatersheds. Section 5.0 summarizes the results and recommends management actions. References are listed in Section 6.0.

1.1 Work Plan Summary and SSID Study Management Questions

This SSID project was designed to identify whether the areas drained by the municipal separate storm sewer system (MS4) that discharge to Pillar Point Harbor (i.e., Study Area) are an important source of bacteria to Pillar Point Harbor and whether the sources of bacteria in the Study Area are controllable (especially human and dog). These are key steps towards the longer-term goal of reducing fecal indicator bacteria (FIB)1 densities in Pillar Point Harbor and, more specifically, reducing the risk of illness for recreators at the local beaches. In this effort, it is important to understand the regulatory context of the FIB WQOs, the behavior of these bacteria in the environment, and risks associated with FIB.

1 Fecal indicator bacteria (FIB) are also referred to as “pathogen indicators”.


2

The core of the SSID Project is identification of FIB sources through information gathering. This includes development of a geodatabase with data layers related to the environmental setting and potential FIB sources and working with the RCD and County to gain a better understanding of the tributaries and catchments draining to Pillar Point Harbor.

Consistent with MRP Provision C.8.e.iii, the Work Plan for the SSID Project (SMCWPPP 2018) contains the following elements:

• Problem definition. Historic samples collected in receiving waters and tributaries to Pillar Point Harbor had FIB densities that exceed Water Quality Objectives (WQOs) established by the State and Regional Water Boards to protect water contact recreation (REC-1) and noncontact water recreation (REC-2) Beneficial Uses. A Proposition 50 funded study conducted by the RCD and University of California, Davis (UCD) in 2008 and 2011/2012 suggested that high FIB observed at the beaches was likely influenced by storm drains and creeks more than from sources at the beaches and within the harbor itself (Kim and Wuertz 2014).

• Study objectives. The objective of the SSID study is to build on the RCD/UCD Proposition 50 Pillar Point Harbor Source Identification Project by focusing on bacteria sources to specific creeks and storms drains that discharge to Pillar Point Harbor. The SSID study is designed to identify spatial, temporal (seasonal), and species-specific information regarding sources of bacteria to Pillar Point Harbor from the community of El Granada and surrounding areas, which are part of unincorporated San Mateo County, an MRP Permittee.

• Watershed context. The watershed draining to Pillar Point Harbor is approximately 3,923 acres and consists of several subwatershed areas (Figure 1.1). The Study Area for the SSID project consists of the Pillar Point Marsh, Denniston Creek, Capistrano Catchment, St. Augustine Creek, and Deer Creek subwatershed areas that are drained by the MS4 and regulated through the MRP.

• Candidate causes. Table 1.1 lists potential sources of FIB that may be present in the Study Area. Potential sources are grouped into two categories: controllable and uncontrollable. Controllable sources are those that could be reduced through management actions implemented by municipalities; however, the magnitude of reduction may be constrained. Uncontrollable sources occur naturally and would be difficult or impossible to reduce through the types of management actions available to municipalities.


3

Table 1.1. Potential sources of pathogen indicators in the Study Area within the Pillar Point Harbor watershed.

Controllable Sources Pet waste (cats, dogs, chickens) Wildlife waste (birds, rodents, deer, raccoons), associated with human activities, such as littering

and exposed trash receptacles, which can attract wildlife by creating scavenging areas. (Some wildlife waste is controllable; however, most is probably not. Nor would it be desirable to eliminate wildlife from creek corridors.)

Trash receptacle leachate. Trash bins may also contain discarded pet waste or diapers.

Illicit connections. Power-washing of mats, containers, and impervious surfaces into the MS4. Human waste discharges (homeless encampments, RV discharges, dumping, leaking sewer lines

and septic systems). Domestic animals and livestock (cattle, horses, chickens, goats). Likely not present in the area

drained by the MS4 (i.e., the Study Area) but may be present within upper watershed. Uncontrollable Sources Wildlife waste (birds, rodents, deer, raccoons, ground squirrels, rabbit, skunk, opossum, coyotes,

wild turkey) from wildlife in open space, creek corridors, beaches, stormwater conveyance systems, and forested areas.

Bacteria naturally present in the environment (e.g., biofilms, organic matter, soils, and sediments in the watershed, creek, and conveyance system).

• Study design and schedule. The SSID investigation, conducted during Water Years 2018 and 2019, combined desktop approaches and water sample analyses that generally follow the California Microbial Source Identification Manual: A Tiered Approach for Identifying Fecal Pollution Sources to Beaches (Griffith et al. 2013). The desktop analysis was based primarily on information gathering and development of a geodatabase. The field investigation consisted of sample collection during two wet weather and two dry season events at fourteen stations. Stations were selected to characterize specific land uses and contributing areas. Samples were analyzed for FIB (E. coli) and bacteroidales associated with humans and dogs.

Three Management Questions were identified in the Work Plan:

1. Are there specific areas within the Study Area that are contributing FIB to receiving waters during wet and dry conditions?

2. Are there downstream trends in FIB densities in creeks that flow through urban areas to Pillar Point Harbor during wet and dry conditions?

3. Are controllable sources of bacteria (especially human and dog) present in the urban areas?

The Work Plan also describes the potential need to identify management actions to control bacteria densities in receiving waters depending on results of the SSID investigation.


4

Figure 1.1. Pillar Point Harbor watershed and subwatersheds.


5

2.0 Methods In accordance with the Pillar Point Harbor Watershed SSID Work Plan (SMCWPPP 2018), both desktop and field methods based on the tiered approach described in the California Microbial Source Identification Manual: A Tiered Approach to Identifying Fecal Pollution Sources to Beaches (Griffith et al. 2013) were implemented. Study methods are summarized below; see the SSID Project Work Plan (SMCWPPP 2018) for detailed method descriptions.

2.1 Desktop Analysis

Existing and new data related to potential sources of FIB were collated in a geographic information system (GIS) database using ArcGIS software. The geographic extent of the GIS data layers in the database is generally limited to the portions of the Pillar Point Harbor watershed mapped in Figure 1.1 which includes the following subwatersheds/catchments: Pillar Point Marsh, Denniston Creek, Capistrano Drainage, St. Augustine Creek, and Deer Creek. Data layers were created and digitized by EOA or acquired from public sources such as San Mateo County Information Services Department. The database metadata (Appendix A) contains information on the methodology for creating/compiling individual GIS layers and attribute details for each layer.

The desktop analysis also included review of beach monitoring data collected by County Environmental Health Services and other relevant information gathered by RCD staff. RCD staff conducted targeted reconnaissance surveys of the watershed to understand local hydrology and identify FIB sources such as areas with wildlife, pet waste, and homeless encampments. RCD staff also conducted dye testing within limited portions of the sanitary sewer system to assess whether leaks were reaching the MS4.

2.2 Field Investigation

Fourteen monitoring stations were selected to characterize background water quality upstream of the areas drained by the MS4 and specific catchments within the Study Area. Table 2.1 lists the station locations, goals for each station, and bacteria sampling history. Stations are mapped in Figure 2.1. Samples were collected during four sampling events: two events within 48 hours of rainfall totaling at least 0.20 inches over a 6-hour period (January 9, 2018 and March 2, 2018) and two events during the dry season (June 27, 2018 and July 18, 2018). All stations were sampled during each of the four events unless flowing water was not observed.

All grab samples were analyzed by Cel Analytical in San Francisco, CA for:

• E. coli using Standard Method (SM) 9223B

• Universal bacteroidales gene markers (UniB) using quantitative polymerase chain reaction (qPCR)

• Human bacteroidales gene markers (HF183) using qPCR

• Dog bacteroidales gene markers (DogBac) using qPCR Field measurements of pH, dissolved oxygen, specific conductance, temperature, and ammonia were also collected and recorded with each grab sample.


6

2.2.1 E. Coli Monitoring

E. coli is the FIB recommended by USEPA (2012) for freshwater and was recently adopted by the State Water Resources Control Board as the applicable bacteria indicator, replacing fecal and total coliform. E. coli results from this SSID Project are compared to the statistical threshold value (STV) WQO of 320 cfu/100 mL2 for freshwater (i.e., waters were the salinity is equal to or less than 1 parts per thousand3 [ppth] 95 percent or more of the time). In this report, the STV is used synonymously with the concept of the single sample maximum (SSM) criteria. The E. coli six-week geometric mean (GM) WQO for freshwater is 100 cfu/100 mL.

Water quality objectives for water contact in ocean waters are based on enterococci and fecal coliform. The enterococci SSM WQO in ocean waters is 110 cfu/100 mL. The enterococci six-week GM WQO is 30 cfu/100 mL.

It is important to recognize that pathogen indicator thresholds and WQOs were derived based on human recreation at beaches receiving bacteriological contamination from human wastewater and may not be applicable to conditions in urban creeks which do not receive wastewater treatment plant discharges. Pathogen indicators (i.e., FIB) observed at the Pillar Point Harbor stations may not be associated with human sources and therefore may pose a relatively low threat to human health compared to human sources. As a result, the comparison of FIB results to WQOs may not be appropriate and should be interpreted cautiously.

2.2.2 Microbial Source Tracking

The presence of E. coli in Pillar Point Harbor tributaries may indicate fecal contamination; however, E. coli results alone do not indicate whether the fecal contamination is associated with a potentially controllable source such as human or pet waste. Therefore, microbial source tracking (MST) techniques were applied to begin characterizing which individual animal species are contributing to fecal contamination in the creeks and MS4. The Pillar Point Harbor SSID Project included sampling for the bacterial group of bacteroidales as an MST approach. Bacteroidales are an abundant bacteria found in human and animal feces that have been found to survive for up to six days in the environment. Similar to pathogens, bacteroidales have little potential for growth outside of the digestive tract of animals. Bacteroidales have a high degree of host specificity and therefore can be used to distinguish between human and other sources of fecal contamination by analyzing gene markers using qPCR.

The microbial source tracking (MST) markers (human and dog) were selected for this SSID Project because they are the most likely “controllable” bacteria sources associated with urban development.

2 In this document, bacteria colony forming units (cfu) per 100 milliliters (mL) (i.e., cfu/100 mL) are used interchangeably with most probably number (MPN) per 100 mL. 3 A salinity of 1 ppth is roughly equal to a specific conductance of 1,968 microSiemens (uS) per centimeter (cm).


7

Table 2.1. Pillar Point Harbor Watershed Pathogen Indicator SSID Project sample stations.

Station ID Latitude Longitude Location Goal Bacteria Sampling History

Pillar Point Marsh Subwatershed

ARPT 37.50638 -122.49473 Swale draining Airport St. Captures runoff from Airport St. and Pillar Ridge Mobile Home Park

None

Denniston Creek Subwatershed

DNUS 37.51618 -122.48781 End of Bridgeport Dr./Cabrillo Farms Background station (upstream of MS4). Approximate location of Kim and Wuertz (2014) station PPH-DN4. Sampled for FIB and MST in Dec 2012. Limited sampling by RCD.

DNCS 37.50798 -122.48503 Drainage swale below MS4 outfall near Sonora Ave/Coral Reef Ave in upper watershed.

Isolates residential areas in upper watershed. Likely dry during dry weather.

None

DNPC 37.50471 -122.48657 Manhole near Prospect Way/Capistrano Rd.

Includes majority of MS4 discharges in watershed.

Limited sampling by RCD and SMCWPPP

DNDS 37.50522 -122.48700 Creek downstream of Prospect Way. Downstream station near mouth of creek. Kim and Wuertz (2014) station PPH-4. Sampled biweekly for FIB in 2011 and 2012. Sampled for MST in 2008, 2011, and 2012 (n=20). Sampling by RCD (2014-2017) and during first flush events.

Capistrano Catchment CPNO 37.50447 -122.48576 Manhole on Capistrano Rd in front of

HMB Brewing Company North portion of catchment. GW infiltration causes perennial flow.

Limited sampling by RCD and SMCWPPP

CPSO 37.50367 -122.48520 Inlet on Capistrano Rd in front of Barbara's Fishtrap

South portion of catchment. Limited sampling by RCD and SMCWPPP

CPDS 37.50381 -122.48590 Outfall to beach Captures entire Capistrano Catchment. Kim and Wuertz (2014) station PPH-1. Sampled biweekly for FIB in 2011 and 2012. Sampled for MST in 2008, 2011, and 2012 (n=20). Sampling by RCD (2014-2017) and during first flush events (2008-2017). Some Surfrider data.


8

Station ID Latitude Longitude Location Goal Bacteria Sampling History

St. Augustine Creek Subwatershed

AGUS 37.50989 -122.47706 Creek at end of Montecito Ave Background station (upstream of MS4). Kim and Wuertz (2014) station PPH-2B. Limited sampling by RCD.

AGCH 37.50548 -122.4814 Manhole on north side of Capistrano Rd/Hwy 1

Captures engineered creek and entire residential MS4 (upstream of Harbor District property).

Limited sampling by RCD

AGDS 37.50330 -122.4845 Outfall to beach at "Bathhouse" Downstream station. Sampling only possible during low tide.

Kim and Wuertz (2014) station PPH-2. Sampled biweekly for FIB in 2008, 2011, and 2012. Sampled for MST in 2008, 2011, and 2012 (n=18). Sampling by RCD (2014-2017) and during first flush events.

Deer Creek Subwatershed

DRUS 37.50987 -122.47223 Creek near end of San Juan Ave Background station (upstream of MS4). Approximate location of Kim and Wuertz (2014) station PPH-DR6. Sampled for FIB and MST in Dec 2012.

DRVL 37.50642 -122.47670 Creek at Valencia Ave crossing Captures creek and majority of non-engineered MS4 draining residential area.

Approximate location of Kim and Wuertz (2014) station PPH-DR4. Sampled for FIB and MST in Dec 2012.

DRDS 37.50272 -122.47710 Outfall to beach on east side of Launch Ramp

Downstream station. Sampling only possible during low tide.

Kim and Wuertz (2014) station PPH-8. Sampled biweekly for FIB in 2011 and 2012. Sampled for MST in 2008, 2011, and 2012 (n=14). Sampling by RCD (2014-2017) and during first flush events.


9

Figure 2.1. Pillar Point Harbor Watershed Pathogen Indicator SSID Project monitoring stations.


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2.3 Statement of Data Quality

Data Quality Assurance/Quality Control (QA/QC) for the SSID Project was performed in accordance with procedures established in the BASMAA RMC Quality Assurance Project Plan (QAPP) (BASMAA 2016a) and Standard Operating Procedures (SOPs; BASMAA 2016b), SOP FS-13 (Standard Operating Procedures for QA/QC Data Review). The BASMAA RMC SOPs and QAPP are based on the SOPs and QAPP developed by the Surface Water Ambient Monitoring Program (SWAMP). Data collected during the Pillar Point Harbor SSID Project were compared to seven Data Quality Objectives (DQOs) defined in the BASMAA RMC QAPP that evaluate whether collected data are of adequate quality for their intended use. These DQOs include both quantitative and qualitative assessments of data acceptability. The qualitative goals include representativeness and comparability, and the quantitative goals include completeness, sensitivity, precision, accuracy, and contamination. Overall, the data collected for the SSID Project met the QA/QC objectives.

Representativeness – The representativeness of data is the ability of the sampling locations and sampling procedures to adequately represent the true conditions found at the sample sites. For this project, all samples are assumed to be representative as they were taken according to the protocols specified in the BASMAA RMC SOP and QAPP. All field and laboratory personnel were familiarized with the monitoring methods used in the project and followed prescribed protocols, including laboratory methods to ensure the collection of representative, uncontaminated samples.

Comparability – Comparability is the degree to which data can be compared directly to other studies. The SSID Project data were determined to have sufficient comparability to other similar studies The SSID Project used the same methods for field collection and laboratory analysis of FIB samples as is normally used for FIB sampling carried out as a component of the Creek Status Monitoring projects conducted by the BASMAA RMC. Additionally, the data generated from the SSID Project were entered into Microsoft Excel templates developed by SWAMP and reviewed using SWAMP’s online data checker to ensure that the data were formatted correctly. Electronic data deliverables (EDDs) containing the project data were submitted to the San Francisco Bay Regional Water Quality Control Board on March 31, 2019.

Completeness – Completeness is the degree to which all data were generated as planned in terms of both data collection and subsequent analysis. The BASMAA RMC QAPP defines an acceptable completeness threshold for FIB sampling as greater than or equal to 90%, meaning that the SSID Project met the completeness objective.

Sensitivity – Sensitivity is determined by the ability of an analytical method to measure a target parameter at a specified concentration and can be characterized using such metrics as instrument detection limits (IDLs), method detection limits (MDLs), and reporting limits (RLs). For SWAMP-comparable programs, RLs are the measurement of primary interest. The BASMAA RMC QAPP defines the target reporting limit for E. coli as 2 MPN/100 mL, meaning that any analytical reporting limit used by the laboratory less than or equal to this endpoint indicates an acceptable sensitivity. The SSID Project E. coli samples were analyzed with a reporting limit of 10 MPN/100 mL, meaning the samples did not meet the Sensitivity DQO. However, the exceeded RLs were due to a 10x dilution being necessary for all samples to be quantified, and the RLs would have been less than the target RL if dilution had not been necessary. The


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BASMAA RMC QAPP does not specify target reporting limits for bacteroidales analytes, meaning that no sensitivity analysis could be performed for these samples.

Precision – Precision describes the variation in measurement between multiple samples of the same analyte. The BASMAA RMC QAPP requires one laboratory duplicate to be run per 10 samples or per analytical batch, whichever is more frequent. However, determining precision for pathogen indicators requires 15 duplicate sets. Due to the small number of samples collected for this project, there were not enough laboratory duplicates to determine precision.

Accuracy – Accuracy describes the conformity of a measurement to an acceptable reference or true value. The BASMAA RMC QAPP states that positive and negative laboratory controls are to be used to evaluate the accuracy of FIB analysis. Positive laboratory control samples spiked with a known concentration and laboratory blanks were collected and analyzed for all analytes within each lab batch. All positive control samples were found to have detectable concentrations of the target analyte, and all laboratory blanks were measured as non-detect.

Contamination – Contamination determines whether the analyte concentration in a given sample has originated from the target matrix in the sample environment itself or was artificially increased due to factors of the collection or analytical process. Field and laboratory blanks are used to assess potential contamination during sample collection, preparation, and analysis. According to the BASMAA RMC QAPP, laboratory blanks must be collected whenever a sample dilution was necessary, but field blanks are not required. However, both field and laboratory blanks were collected and analyzed during each sample event of the SSID Project, and all were measured as non-detect.


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3.0 Results 3.1 Mapping

Geographic information was collected and digitized to identify potential FIB sources and to interpret and visualize relationships between different variables. Most information falls into one of two categories: (1) providing information on the geographic setting of the Study Area and its surroundings, or (2) descriptive of potential bacteria sources. Metadata are included in Appendix A and maps compiling the data layers are included in Appendix B.

3.1.1 Geographic Setting Layers

Geographic setting layers provide the context within which potential bacteria sources and their fate and transport should be evaluated.

• Creeks (Creeks) – This layer was derived from the National Hydrography Dataset and edited using the Midcoast Storm Drain Inventory Project, prepared by BKF Engineers (2013) (Storm Drain Inventory Project). It shows surface water flows in the region and includes Denniston Creek, St. Augustine Creek, and Deer Creek.

• Pillar Point Harbor Watersheds (Watersheds) – This layer contains the watersheds that drain to Pillar Point Harbor including Pillar Point Marsh, Denniston Creek, St. Augustine Creek, and Deer Creek watersheds, and the Capistrano MS4 catchment. The watersheds were delineated by EOA using the Storm Drain Inventory Project (BKF Engineers 2013) and digital elevation models to improve accuracy. The delineated subwatersheds create a basic hydrologic model of the Study Area.

• Stormwater System (Storm_Drain_Lines) – This layer, provided by San Mateo County and prepared by FUGRO, shows stormwater conveyance systems such as storm drain lines and ditches which discharge to creeks within the Study Area. For enhanced accuracy, this layer was compared to the Storm Drain Inventory Report (BKF Engineers 2013) and a separate storm drain layer provided by the RCD.

• Land Use Parcels (Parcels) – This layer is maintained by San Mateo County and includes all parcels within the study. The land use of each parcel provides insight on potential sources draining to Pillar Point Harbor.

• EOA Land Use Parcels (EOA_Landuse) – This layer was first created by EOA with the help of San Mateo County for a County trash project. Land use was further collated and truthed for parcels within the Study Area by EOA for this report. This layer has fewer land use designations than the original County layer. The land use of each parcel provides insight on potential sources draining to Pillar Point Harbor.

• SSID Sites (PPHSSIDSites) – This layer includes the sites where bacteria samples were collected for this SSID study.

• Other Sampling Locations (Bacteria_Sampling Stations) – This layer contains locations where bacteria samples have been collected on the beaches by the San Mateo County Health Department.


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• SSID Watersheds (SSID_Watersheds) – This layer maps the catchments that drain to each of the SSID Sites and helps outline sources for each site. Catchments were delineated using the Watersheds layer, digital elevation models, as well as aerial and Google Street View imagery.

3.1.2 Potential Bacteria Source Layers

Potential bacteria sources (listed in Table 1.1) are mapped to gain an understanding of where they occur within the Study Area, how they relate to monitoring results, and where control actions could be targeted.

• Potential Direct Human Sources (Direct_Human_Sources) – This layer shows current and historic locations of likely direct human sources. The locations were noted by RCD staff. A location is considered a direct source if human waste is observed on the ground and/or there is evidence of people residing there, which could range from obvious garbage and tents to people being there at the time of inspection. One small (i.e., single occupancy) homeless camp has been noted in the woody vegetation on the beach in front of Half Moon Bay Brewing Company at the Capistrano Catchment outfall. Transient homeless have been sighted behind the Mezza Luna restaurant along Denniston Creek. There is evidence of people living in RVs and cars along Airport Street and in Princeton (which borders the Study Area), which suggests that human waste could be present in the vicinity and is why station ARPT was selected (see Table 2.1 and Figure 2.1).

• Pets (Pets) – Pet waste, when left on the ground, can be a major source of bacteria to MS4s and receiving waters. Residential areas, parks, and favorite dog walking routes are the most likely areas where pet waste is found. The RCD assisted in identifying areas where dog waste was observed. These areas include the open space off Sevilla Avenue, the French and Clipper Ridge trailheads, and the upper watershed of Deer Creek.

• Dog Waste Stations (Dog_Waste_Stations) – These stations were identified by the RCD as locations where dog waste can be disposed of properly. These points help in considering areas where dogs might frequent, but also where dog waste is managed.

• Trails (Trails) – This layer was developed by the National Park Service and clipped to include relevant trails in the Pillar Point Harbor watershed. Trails receive frequent use from dog walkers and equestrians and, if waste from these animals is not managed, could act as avenues for contaminated runoff.

• Sanitary Sewer System (Sanitary_Sewer_Lines) – This layer shows the infrastructure network used to transport wastewater from properties near Pillar Point Harbor to the wastewater treatment facility (Sewer Authority Mid-Coastside) in Half Moon Bay. While this layer was utilized in the source tracking analysis, it is not available to the public and is not included in the geodatabase. Occasional or undiscovered leaks could be a potential source of bacteria to MS4s and receiving waters in the Study Area. The RCD performed three dye tests at targeted locations in the Capistrano Catchment during wet and dry weather with no response observed in the MS4 (i.e., inconclusive results).


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• Sanitary Sewer Overflow Events (SSO) – This point layer maps sanitary sewer overflow (SSO) events that occurred within the Study Area. Category 1 events are discharges of untreated or partially treated wastewater that reach surface water, are not fully captured, and/or are not disposed of properly. Category 2 and Category 3 events do not reach surface waters and therefore are not included. SSOs and leaking conveyance lines can contribute bacteria to MS4s and receiving waters through surface and subsurface pathways. Data were obtained from the State Water Resources Control Board Sanitary Sewer Overflow Reduction Program website. There was one event in the Study Area near the east side of Pillar Point Marsh in 2012; however, no events of any category are listed during the SSID study sampling period (i.e., Water Year 2018).

• Septic Tanks (Septic_Tanks) – Failing septic systems (i.e., onsite wastewater treatment systems [OWTS]) can allow untreated human waste to flow into drainage ditches and MS4s. The County reports that there is one septic system in the vicinity of the Study Area, on the Half Moon Bay Airport property. While this layer was utilized in the source tracking analysis, it is not available to the public and is not included in the geodatabase.

• Livestock Areas (Livestock) – Livestock manure, if not properly managed, is potentially a significant source of FIB in the study area. The polygons in this layer show areas that contain or potentially contain livestock such as goats, cattle, or horses. Livestock areas were identified though aerial photo interpretation and local knowledge of the study area. A cattle ranch is located in the Deer Creek watershed. It was also noted that equestrian activity regularly occurs on trails in the upper reaches of Denniston Creek on GGNRA trails.

• Business sources (Business_Sources) – Disposal of greywater and wastewater from commercial enterprises could introduce bacteria to the MS4. Anecdotal observations and relevant stormwater violations and complaints recorded by the County were compiled to create a GIS layer for these business locations.

• Storm Drain Issues (Storm_Drain_Issues) –This layer maps probable leaking or damaged infrastructure as noted by RCD staff. Damaged storm lines could be infiltrated by contaminated groundwater. Also, they may not drain properly resulting in pools of stagnant water that attract wildlife and provide habitat for bacteria growth (i.e., biofilms). This layer is a subset of the Storm_Drain_Lines layer and was created with direction from RCD staff.

• Wildlife (Wildlife) – Birds, seagulls, opossums, raccoons, skunks, squirrels, deer, ducks, rodents, pigeons, snakes, woodrats, bobcats, mountain lions, coyotes, and other wildlife are often the primary source of bacteria in creeks. Locations where raccoons were entering the storm drain system and where birds were flocking are included in this layer.


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3.2 FIB and MST Monitoring

In accordance with the SSID Work Plan, four sampling events were conducted during Water Year4 2018 (WY 2018). Figure 3.1 illustrates the sampling events within the perspective of rainfall recorded at Half Moon Bay, CA approximately 2.5 miles south of the Pillar Point Harbor watershed. The January 9, 2018 sampling event was conducted during a 2-inch storm event resulting in a seasonal total of 9 inches of rainfall. The March 2, 2018 sampling event was conducted during a 0.38-inch storm event. The June 27, 2018 sampling event was conducted during the dry season 18 days after the last recorded rainfall event exceeding 0.08 inch. Rainfall was recorded during two days between the June sampling event and the July 18, 2018 sampling event; however, both of the recorded amounts were less than 0.04 inch. A total of 18.4 inches of rainfall were recorded in WY 2018 which is about 10 inches less than the annual average rainfall of 28.98 inches.

Figure 3.1. Cumulative precipitation at Half Moon Bay, CA (source: U.S. Climate Data) and SSID project sample dates, WY 2018.

4 Most hydrologic monitoring is conducted on a “water year” basis, which is generally consistent with wet and dry season precipitation patterns. A water year begins on October 1 and ends on September 30 of the named year. For example, WY 2018 began on October 1, 2017 and ended on September 30, 2018.

9-Jan-18

2-Mar-18

27-Jun-18 18-Jul-18

0

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1-Oct-17 29-Oct-17 26-Nov-17 24-Dec-17 21-Jan-18 18-Feb-18 18-Mar-18 15-Apr-18 13-May-18 10-Jun-18 8-Jul-18 5-Aug-18 2-Sep-18 30-Sep-18

Cum

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Table 3.1 lists the analytical results, field measurements, and field observations for the 14 stations targeted in this study. Table 3.1 is organized according to the five subwatersheds where samples were collected. Table 3.2 summarizes human and dog marker results only. E. coli densities measured during the Pillar Point Harbor Watershed SSID project ranged from 10 MPN/100 mL during the dry season at upstream stations to 24,196 MPN/100 mL during the January 9, 2018 storm event in the Capistrano Catchment. Many of the samples exceeded the newly adopted statewide SSM WQO for E. coli in freshwater of 320 MPN/100 mL. E. coli densities were generally lower at stations upstream of the MS4 compared to downstream stations and were generally higher during storm events compared to the dry season. However, as the sections below detail, these general patterns were not consistent in each of the sampled subwatersheds. Human and dog markers were present in very few of the samples. Human markers were only detected during the January 9, 2018 storm event in the Capistrano Catchment. Dog markers were more widespread (Denniston Creek, Capistrano Catchment, and St. Augustine Creek) and were detected during both of the storm sampling events, but not during the dry season.


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Table 3.1. Bacteria monitoring results and field observations, Pillar Point Harbor Watershed SSID Project. E. coli results exceeding the freshwater single sample WQO of 320 cfu/100mL at ambient water station (i.e., not MS4 stations) are shown in bold.

Date Time E. Coli B. (Human

marker) B. (Dog marker)

Estimated flow pH Temp.

Specific Cond. DO DO

Ammonium (NH4+) Comments

(MPN/100mL) (gc/mL) (gc/mL) (C°) (μS/cm) (% sat.) (mg/L) (mg/L) Pillar Point Marsh Subwatershed ARPT - Swale draining Airport Street

01/09/18 9:30 1,014 0 0 5-20 cfs 6.62 12.9 352 79 7.8 1.2

Lots of trash in drainages and ditches. More drainage from RV (southeast) side of the street, but most from culvert under airport field. Geese and other birds here. Sampled after crew member fell in and disturbed the water.

03/02/18 7:05 96 0 0 1-5 cfs 7.03 9.5 527 87 9.9 0.22 Trash present.

06/27/18 9:42 NS NS NS Isolated Pool 7.41 15.5 645 59 5.8 0.09 Not sampled due to lack of flow.

07/18/18 12:05 NS NS NS Isolated Pool 6.75 16.8 731 62 5.9 0.09 Stagnant. Not sampled due to lack of

flow.

Denniston Creek Subwatershed DNUS - Background station (upstream of MS4)

01/09/18 11:20 292 0 0 1-5 cfs 6.72 12.4 261 100 10.7 0.28

Small tributaries to creek discharging downstream of sampling station have higher flow than usual and some foam. Dog waste on trail near the tributary.

03/02/18 7:55 63 0 0 1-5 cfs 7.15 9.4 264 99 11.3 0.11 Active erosion occurring upstream and at sample site.

06/27/18 9:00 36 0 0 0.1-1 cfs 7.54 13.5 343 83 8.5 0.12 NR

07/18/18 11:45 10 0 0 0.1-1 cfs 6.93 15.1 366 36 3.6 0.9 No flow at sample site, sample collected just upstream, on other side of road crossing.

DNCS - Residential catchment (WQOs do not apply to stations within the MS4)

01/09/18 11:00 7,550 0 617 0.1-1 cfs 6.67 11.4 309 95 9.9 0.45 Trash present.

03/02/18 7:45 17,329 0 0 Trickle (<0.1 cfs) 7.03 11.9 327 79 8.5 0.21 Trash and surface scum present.

Stagnant, suds on the street.

06/27/18 8:50 NS NS NS Isolated Pool 7.18 16.4 1092 36 3.7 2.62 Trash present.

07/18/18 11:40 NS NS NS Dry NR NR NR NR NR NR Too dry to sample.

DNPC - Main MS4 in Denniston watershed (WQOs do not apply to stations within the MS4)

01/09/18 10:40 4,160 0 188 1-5 cfs 6.52 13.1 348 93 9.7 0.61

Flow entering from both sides into the manhole (DNPC) but most from the east from residential MS4 that flows to open ditch then back underground behind Seville.

03/02/18 7:30 15,531 0 8 Trickle (<0.1 cfs) 6.93 10.7 427 90 10.0 0.16 Stagnant.

06/27/18 8:15 10 0 0 Isolated Pool 7.17 16.3 937 86 8.3 0.26

Leaf litter present. Grate upstream (behind Seville) has stains, possibly oil or other dumping.

07/18/18 11:30 377 0 0 Trickle (<0.1 cfs) 6.76 18 963 82 7.5 0.16

Water present in grate behind Seville (groundwater infiltration?), oil stains sign of dumping?

DNDS - Downstream station near mouth of creek

01/09/18 10:20 771 0 22 5-20 cfs 6.7 12.5 282 96 10.2 0.35 RV parked directly over storm drain. Approx. 70% of flow at station is from creek; 30% from MS4 outfall at bridge.

03/02/18 7:15 386 0 5 1-5 cfs 7.25 9.5 306 102 11.5 0.1 NR

06/27/18 8:05 279 0 0 0.1-1 cfs 7.43 14.5 570 83 8.4 0.16 NR

07/18/18 10:50 552 0 0 0.1-1 cfs 6.99 15.2 584 89 8.8 0.24

Downstream water appears very anoxic, blue sheen on water. Dog feces, trash above outfall upstream of DNDS. Creek upstream of road crossing is entirely subsurface.


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(MPN/100mL) (gc/mL) (gc/mL) (C°) (μS/cm) (% sat.) (mg/L) (mg/L) Capistrano Catchment CPNO - North portion of catchment (WQOs do not apply to stations within the MS4)

01/09/18 1:30 231 30 0 0.1-1 cfs 6.78 15.2 968 75 7.5 0.23 Oily sheen upstream. Sulfide odor.

03/02/18 8:25 496 0 0 Trickle (<0.1 cfs) 6.86 12.9 910 65 6.9 0.16 Sulfide odor.

06/27/18 7:30 10 0 0 Trickle (<0.1 cfs) 7.34 18 966 73 6.9 0.36 Debris, dirt and leaf litter in pipe.

07/18/18 10:30 10 0 0 0.1-1 cfs 6.86 20 957 67 6.2 0.39 Feces above water in pipe (raccoon?).

CPSO - South portion of catchment (WQOs do not apply to stations within the MS4)

01/09/18 2:00 24,196 8 597 Trickle (<0.1 cfs) 6.89 13.9 679 89 9.1 0.28 Trash cans nearby. Very little flow,

almost stagnant.

03/02/18 8:40 6,867 0 0 Trickle (<0.1 cfs) 7.16 10.4 446 87 9.9 0.14 Unknown odor.

06/27/18 NR NS NS NS Dry NR NR NR NR NR NR

Trash present. Too little water to sample. Scum on surface, wet on north side wall appears to be groundwater infiltration.

07/18/18 10:45 NS NS NS Isolated Pool NR NR NR NR NR NR Unable to sample, stagnant and very

murky, lots of leaf litter.

CPDS - Entire Capistrano catchment (WQOs do not apply to stations within the MS4)

01/09/18 1:10 1,850 0 0 0.1-1 cfs 6.7 15.4 887 83 8.3 0.33 Sulfide odor. Not much flow. Wood debris right in front of outfall. Sediment in outfall.

03/02/18 8:15 399 0 0 0.1-1 cfs 6.78 14.4 846 80 8.1 0.23 Trash present.

06/27/18 7:20 1,720 0 0 Trickle (<0.1 cfs) 7.42 18 828 63 5.9 0.42 Trash present

07/18/18 10:20 1,291 0 0 Trickle (<0.1 cfs) 6.87 20.2 802 72 6.5 0.35 Wood in pipe. Animal tracks nearby.

St. Augustine Creek Subwatershed AGUS - Background station (upstream of MS4)

01/09/18 12:50 259 0 0 1-5 cfs 6.74 12 467 85 9.1 0.19 Side drainage ditch connects under road downstream of sample site.

03/02/18 6:50 161 0 0 0.1-1 cfs 7.09 8.8 504 93 10.6 0.18 NR

06/27/18 6:20 148 0 0 0.1-1 cfs 7.99 12.6 539 89 9.3 0.47 NR

07/18/18 9:07 10 0 0 0.1-1 cfs 7.47 13.4 580 78 7.9 0.21 Strong odor, possibly decomposing plant materials.

AGCH - Upstream of Harbor District (WQOs do not apply to stations within the MS4)

01/09/18 12:40 557 0 17 1-5 cfs 7.02 12.8 495 98 10.3 0.25 NR

03/02/18 6:35 364 0 0 1-5 cfs 7.55 10 520 100 11.5 0.17 NR

06/27/18 6:10 NS NS NS NR NR NR NR NR NR NR Too little water to sample. Sand bags placed within MS4 at AGCH. Racoon tracks.

07/18/18 9:04 NS NS NS Dry NR NR NR NR NR NR Too little water to sample, very strong odor of decomposition.

AGDS - Downstream station (WQOs do not apply to stations within the MS4)

01/09/18 12:25 703 0 0 1-5 cfs 7.03 12.9 526 99 10.4 0.31

Tide up in rocks at the outfall, but not inside. Sample taken from east outfall. There are 2 outfalls. This one had more flow. Could not get sample once they combined; other side had little flow. Would need a negative tide to collect a combined sample; outfalls hit rocks with biofilm.

03/02/18 6:20 459 0 0 0.1-1 cfs 7.19 11.1 1047 102 11.2 0.4 NR

06/27/18 5:55 862 0 0 Trickle (<0.1 cfs) 8.05 15.6 6752 89 8.8 5.73 NR.

07/18/18 8:46 12,033 0 0 Trickle (<0.1 cfs) 6.77 16.3 10653 89 8.3 7.36 Very low flow.


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(MPN/100mL) (gc/mL) (gc/mL) (C°) (μS/cm) (% sat.) (mg/L) (mg/L) Deer Creek Subwatershed DRUS - Background station (upstream of MS4)

01/09/18 12:15 173 0 0 1-5 cfs 6.97 12.3 360 97 10.4 0.29 Foam in creek.

03/02/18 5:55 122 0 0 0.1-1 cfs 7.31 8 422 105 12.3 0.28 NR

06/27/18 7:05 473 0 0 0.1-1 cfs 7.76 13 451 100 10.4 0.31 Dog waste on roadside.

07/18/18 9:50 471 0 0 0.1-1 cfs 7.26 14 461 93 9.5 0.16 Many plants at site.

DRVL - Middle station

01/09/18 12:00 563 0 0 1-5 cfs 7.07 12.5 406 101 10.7 0.3 NR

03/02/18 5:35 238 0 0 0.1-1 cfs 7.32 8.4 436 110 12.9 0.34 NR

06/27/18 6:50 657 0 0 0.1-1 cfs 8.06 13.3 468 104 10.8 0.31 Racoon prints in stream; algae present.

07/18/18 9:42 882 0 0 0.1-1 cfs 7.47 14.6 490 102 10.3 0.15 NR

DRDS - Downstream station (WQOs do not apply to stations within the MS4)

01/09/18 11:45 663 0 0 1-5 cfs 6.9 12.5 413 101 10.6 0.35

Tide close to outfall (a few meters) so Deer creek needs a tide no more than about 1.5 feet. No tidal influence though.

03/02/18 5:15 754 0 0 1-5 cfs 7.41 8.7 471 108 12.5 0.4 NR

06/27/18 6:35 2,224 0 0 0.1-1 cfs 7.85 14.1 727 98 10.1 0.67 Trash present. Lots of gull prints in and out of the water, bird feces on the sand downstream.

07/18/18 9:28 557 0 0 0.1-1 cfs 7.08 15.9 867 84 8.2 0.9 Bird feces near site. Many dead fish on ground near fish cleaning station (drains to beach).

NR = Not Recorded NS = Not Sampled cfs = cubic feet per second gc/mL = gene copies per milliliter

Table 3.2. Human and dog marker results, Pillar Point Harbor Watershed SSID Project.

Human Bacteroidales (gc/mL)

Pillar Point Marsh Denniston Creek Capistrano Catchment St. Augustine Creek Deer Creek

Date ARPT DNUS DNCS DNPC DNDS CPNO CPSO CPDS AGUS AGCH AGDS DRUS DRVL DRDS January 2018 0 0 0 0 0 30 8 * 0 0 0 0 0 0 0 March 2018 0 0 0 0 0 0 0 0 0 0 0 0 0 0 June 2018 NS 0 NS 0 0 0 NS 0 0 NS 0 0 0 0 July 2018 NS 0 NS 0 0 0 NS 0 0 NS 0 0 0 0

Dog Bacteroidales (gc/mL)

Pillar Point

Marsh Denniston Creek Capistrano Catchment St. Augustine Creek Deer Creek Date ARPT DNUS DNCS DNPC DNDS CPNO CPSO CPDS AGUS AGCH AGDS DRUS DRVL DRDS

January 2018 0 0 617 188 22 0 597 0 0 17 0 0 0 0 March 2018 0 0 0 8 5 0 0 0 0 0 0 0 0 0 June 2018 NS 0 NS 0 0 0 NS 0 0 NS 0 0 0 0 July 2018 NS 0 NS 0 0 0 NS 0 0 NS 0 0 0 0

NS = not sampled * Sample results below reporting limit but above method detection limit


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3.3 Beach Data Review

Water samples from recreational waters in San Mateo County are sampled each week for FIB by County staff and volunteers from the Surfrider Foundation. The data are compared by County Environmental Health Services staff to WQOs and advisory or warning signs are posted (online and at the beach) if the samples exceed WQOs. Samples are typically collected every Monday morning in the surf zone at the beach. A total of 43 beaches are sampled countywide, including four stations in Pillar Point Harbor (Figure 4.1). Figure 3.2 shows enterococci values and the running geometric means for samples collected during WY 2018 from Capistrano Beach (station Pillar Point #5) and Beach House Beach (Pillar Point #9). Although these two beaches are on opposite sides of the Inner Harbor, enterococci densities follow roughly the same pattern, suggesting that circulation and mixing within Pillar Point Harbor may be more influential on beach FIB than local freshwater sources. Occasional high single samples elevate the calculated 6-week geometric mean for several weeks. The highest densities at the beaches in WY 2018 were measured on January 8, 2018 just prior to the January 9 sampling event. Water quality objectives for enterococci and sampling event dates are shown in Figure 3.2 for reference.

Figure 3.2. Enterococci densities measured in the surf zone at Capistrano Beach (Pillar Point #5) and Beach House Beach (Pillar Point #9) by County Environmental Health Services, WY 2018.

10

100

1,000

10,000

100,000

10/1/2017 11/20/2017 1/9/2018 2/28/2018 4/19/2018 6/8/2018 7/28/2018 9/16/2018

Ente

roco

cci (

MPN

/100

mL)

Pillar Point #5 (value)


Pillar Point #5 (6-week GM)


Sample Date

WQO (SSM)

WQO (6-week GM)

Jan. 9, 2018 Mar. 2, 2018 Jul. 18, 2018Jun. 27, 2018


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Figure 3.2 shows that there were seasonal differences in enterococci densities at the two beach stations during WY 2018. Densities are lowest during the very dry months of September and October at the start and end of the water year. The highest densities are measured during the wet season followed by a drop in densities in the spring as rainfall becomes less frequent. There was also a mid-summer “peak” in June and July. This bimodal pattern is observed in some, but not all, years in the long-term record. Figure 3.3 plots enterococci densities at Capistrano Beach and Beach House Beach for WY 2013 through WY 2018.

Figure 3.3. Enterococci densities measured in the surf zone at Capistrano Beach (Pillar Point #5) and Beach House Beach (Pillar Point #9) by County Environmental Health Services, WYs 2013 – 2018.

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10/1/2012 10/2/2013 10/3/2014 10/4/2015 10/4/2016 10/5/2017

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WQO (SSM)

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4.0 Discussion Pillar Point Harbor (Figure 4.1) is an embayment located along the San Mateo County coastline that is enclosed by two sets of breakwaters (Wuertz et al. 2011). The inner breakwaters, constructed in 1982, protect the 45-acre inner boat harbor which contains an active marina with approximately 400 slips and an adjacent beach (Inner Harbor Beach). The outer breakwaters, constructed in 1961, protect an additional 280 acres of ocean waters with five beaches (Mavericks Beach, Pillar Point Marsh Beach, Yacht Club Beach, Capistrano Beach, and Beach House Beach). The breakwaters mitigate the exchange of harbor water with the open ocean. Results of a circulation study using fluorescent dyes and drogues (i.e., floating citrus fruit) conducted in September 2008 (dry conditions with low stormwater inflow) suggest that Pillar Point Harbor is somewhat isolated from surrounding beaches and coastal features (Wuertz et al. 2011). Circulation within the harbor is in a clockwise pattern driven primarily by the tidal prism with wind as a secondary mechanism. The breakwaters are relatively impermeable and flushing of the outer harbor occurs within 2.5 days (for the northern zone) to over 3 days (for the shallow waters of the northwestern side of the harbor).

Figure 4.1. Pillar Point Harbor beaches and County Environmental Health Services sample stations.


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The watershed draining to Pillar Point Harbor is approximately 3,923 acres and consists of several subwatershed areas (Figure 1.1), which are described below. The Study Area for this SSID project is limited to the Pillar Point Marsh, Denniston Creek, Capistrano Catchment, St. Augustine Creek, and Deer Creek subwatershed areas that are drained by the MS4 and regulated through the MRP. The small community of Princeton-by-the-Sea is not included in the Study Area; it drains directly to Yacht Club Beach and does not contain natural waterbodies. 4.1 Pillar Point Marsh

This 850-acre subwatershed includes a protected 66-acre brackish marsh that conveys runoff from the Half Moon Bay Airport, the Pillar Ridge Mobile Home Park, and several agricultural fields to Pillar Point Harbor (Flint 1977). Although the mouth of Pillar Point Marsh at the beach is sampled weekly for FIB (station Pillar Point 8), upstream locations (such as station ARPT from this study) have not been previously targeted. Station ARPT was included in the PPH SSID study because it is located within a swale downstream of MS4 discharges from the Pillar Ridge Mobile Home Park and a section of Airport Street where recreational vehicles (RVs) are often parked (Figure 4.2). There have been anecdotal reports of dumping from the RVs and trailers into roadside ditches and observations of trash at the monitoring station. Furthermore, large numbers of birds have been observed in the fields south of the airport runway. Flow was only present at the station during the two wet season sampling events. During the dry season events, only stagnant, isolated pools were observed. The swale at the ARPT station had high E. coli densities during the January storm event (1,014 MPN/100 mL) but relatively low densities during the March storm event (96 MPN/ 100mL) (Table 3.1) suggesting that, even during runoff events, bacteria loading from this swale is variable. Human and dog markers were not observed in either of the storm event samples (Table 3.2) suggesting that RVs may not be contributing these sources, at least not during events of this magnitude. A lack of flow at ARPT during the June and July monitoring events suggests that this swale is not contributing to dry season observations of elevated FIB at Pillar Point March Beach.

Figure 4.2. Photo showing RVs parked along Airport Road and trash in nearby ditch.


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4.2 Princeton-by-the-Sea

Princeton-by-the-Sea is a small community located between the Pillar Point Marsh and Denniston Creek subwatersheds. It is densely developed with residential, commercial, and industrial uses. Runoff from this area drains down roadside ditches that drain directly to Yacht Club Beach. The ditches are generally dry, except during storm events. There is little to no upstream contributing watershed area and no creeks or other natural waterbodies. No samples were collected in Princeton-by-the-Sea as part of this SSID study; however, the County conducts weekly FIB sampling in the surf zone on the western end of Yacht Club Beach (station Pillar Point 7, Figure 4.1). The RCD also collects samples during the first flush from several outfalls in this area. 4.3 Denniston Creek

At approximately 2,500 acres, the Denniston Creek watershed is the largest watershed draining to Pillar Point Harbor (Figure 1.1). The upper watershed is undeveloped and much of it lies within the protected Golden Gate National Recreation Area (GGNRA). This part of the GGNRA is known as the Rancho Corral de Tierra and was acquired in 2011. Multi-use trails (hiking, horses, bikes) run along the ridgetops on both sides of Denniston Creek with some trailheads near residential areas in the watershed. Other land uses in the upper watershed include approximately 38 acres (2% of watershed) of agricultural fields and Coastside County Water District (CCWD) properties. CCWD owns and operates the Denniston Reservoir (an onstream, regulating reservoir), a point of diversion for municipal water supply (located approximately 4,300-feet upstream of the reservoir), the Denniston Water Treatment Plant, and nearby storage tanks. Although operated for water storage, the reservoir also functions to capture sediment from Denniston Creek and must be periodically dredged to maintain capacity. Flow in Denniston Creek below the reservoir consists of spillage over and seepage through the dam. Downstream of the CCWD properties, on the coastal plain, land uses in the watershed consist of agricultural fields and residential subdivisions in El Granada which are drained by an engineered MS4. Several commercial properties (primarily restaurants), also drained by the MS4, are located near the mouth of the creek. Denniston Creek is a relatively natural channel with a thickly wooded riparian corridor through the coastal plain. Culverted sections are limited to major road crossings (e.g., Highway 1, Prospect Way). There are four MS4 outfalls to the creek with small catchment areas upstream of Highway 1. The majority of the MS4 within the Denniston Creek watershed enters the creek at Prospect Way, just upstream of the bottom-of-the-watershed station (DNDS). The monitoring network in Denniston Creek watershed includes a station in the creek upstream of the MS4 (DNUS); a downstream, bottom-of-the-watershed station (DNDS); and two stations that characterize the MS4:

• Station DNCS is located in a swale that is part of the MS4 and receives runoff via a culvert from approximately 32-acres of residential land use. An open space area above station DNCS appears to be privately owned but contains what appear to be dirt bike trails. RCD staff observed several piles of dog waste in the vicinity. Flow was only observed at this station during the storm events.


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• Station DNPC, accessed via a manhole, is located within the main MS4 pipe draining the watershed. Samples are collected downstream of “confluence” of the two pipes that meet at this point. One of the pipes drains a small amount of roadway and enters from the north; the other consists of the main MS4 and enters from the east. The catchment area for DNPC is approximately 130 acres and consists of residential and park areas east of Highway 1 (including the DNCS catchment area), Highway 1, agricultural fields west of Highway 1, and commercial properties with parking areas along Capistrano Road. During the dry season, the ditch upstream of DNPC that conveys the majority of the MS4 runoff through an agricultural field is consistently dry. Flows observed at DNPC during the dry season appear to be the result of underground infiltration into the MS4 pipe in the immediate vicinity of DNPC. This hypothesis is consistent with findings from a closed-circuit television (CCTV) investigation conducted by the RCD which found multiple cracks and fractures in the MS4. Relatively high dry season specific conductance levels (937 and 963 µS/cm) suggest that sea water intrusion may be part of the dry season groundwater signature. Racoons have been observed in the MS4 at DNPC.

Figure 4.3. E. coli densities measured at Denniston Creek watershed stations.

Figure 4.3 shows that E. coli densities measured at the upstream station on Denniston Creek (DNUS) were consistently lower than other stations in the Denniston Creek watershed. Although densities at DNUS were higher during the two storm events compared to the two dry season events, they never exceeded the single sample maximum WQO of 320 cfu/100mL.

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E. coli densities measured at the downstream station on Denniston Creek (DNDS) were consistently higher than those measured at DNUS and exceeded the SSM WQO during three of the four sampling events (both of the storm events and the July dry season event). During storm events high E. coli densities discharged from the MS4 appear to be diluted by flows in the creek. For example, on March 2, 2018, E. coli was 386 MPN/100mL at DNDS and over 15,000 MPN/100mL at the MS4 stations (DNCS and DNPC). During the dry season, E. coli densities at DNDS were higher than those measured at DNPC (DNCS was too dry to sample in June and July), suggesting that bacteria sources outside of the MS4 are also important. Evidence of a transient use is occasionally observed in the creek upstream of station DNDS; however, human markers were not detected at this station or any other station in the Denniston Creek watershed. Dog markers were detected at the two MS4 stations (DNCS and DNPC) and the bottom-of-the-watershed station (DNDS) during storm events (Tables 3.1 and 3.2). The higher quantity of dog marker detected at the upper MS4 station (DNCS; 617 gc/mL) compared to the lower MS4 station (DNPC; 188 gc/mL) and bottom-of-the-watershed stations (DNDS; 22 gc/mL) on January 9, 2018 suggests that the primary source of dog-specific bacteria is in the residential part of the watershed. A small park (Princeton by the Sea Park) that drains directly to the creek contains a designated “Dog Area” that has a dog bag dispenser and a waste receptible; however, some users may neglect to use the supplies, as RCD staff observed a pile of waste in the park. There was no overall correlation between dog marker concentrations and E. coli densities at the Denniston Creek stations. 4.4 Capistrano Catchment

The 15-acre Capistrano catchment is a piped system that does not contain any natural waterbodies. It is almost entirely impervious and contains hotels, shops, restaurants, brew pubs, and large parking lots (Figure 4.5). Many of the businesses have private storm drains that connect directly to the underground MS4. The Capistrano catchment MS4 network discharges directly to Capistrano Beach via a 24-inch reinforced concrete pipe (RCP) (Figures 4.4 and 4.5). This outfall flows year-round and can be inundated by water and sediment during high tides. There are anecdotal reports that a homeless individual is regularly observed resting in the vegetation above the outfall. Through its Business and Commercial Inspection Program, the County has had to repeatedly instruct restaurants in this catchment that wash water from cleaning floor mats, garbage cans, and sidewalks/patios should not be allowed to flow into storm drains. Wash water can convey fats, oils, and grease (FOG) and other pollutants into the storm drain.5 FOG in storm drains and sanitary sewer lines can build up causing blockages that constrict flow and may eventually result in breaks. FOG may also attract wildlife, such as rats and raccoons, to storm drains. It is unknown what role if any FOG plays in biofilms. The SSID sampling design for this catchment includes the outfall at the beach (CPDS) and two sub-catchments: the north area (CPNO) and the south area (CPSO) (Figure 4.5). RCD investigations suggest that a portion of the south sub-catchment shown in Figure 4.5 drains to the St. Augustine system. Storm drains at the south sub-catchment station (CPSO) were not flowing

5 County Ordinance (4.28.110) prohibits discharge of wastewater containing more than 300 mg/L of FOG into the collection system. Grease traps and interceptors are required at new restaurants to prevent FOG discharges.


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during the dry season; whereas, storm drains in the north sub-catchment (CPNO) have a small amount of year-round flow. The year-round flow at CPNO and CPDS appear to be largely the result of groundwater seepage into the pipes, via cracks and breaks observed by the RCD using CCTV. Consistently high (relative to fresh water) specific conductance levels suggest that, similar to the lower Denniston Creek watershed, sea water may be part of the groundwater signature in the Capistrano catchment. Low dry season flows at private MS4 connections in both the CPNO and CPSO sub-catchments have also been observed.

Figure 4.4. Capistrano catchment outfall at Capistrano Beach (station CPDS), August 2017.


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Figure 4.5. Capistrano catchment MS4 and SSID sample stations with drainage areas (MS4 based on BKF 2013 and differs from County storm drain GIS layer in geodatabase and RCD investigations).

The highest E. coli densities from this SSID study were measured in the south sub-catchment (station CPSO) on January 9, 2018 (24,196 MPN/100mL) (Table 3.1 and Figure 4.6). Both human and dog markers were detected in this sample (Table 3.2). Although, the south sub-catchment is a source of high E. coli, as well as potential human and dog bacteria sources during storm events, there is little to no flow during the dry season (Table 2.1). The catch basin at CPSO was dry during the June event and contained a stagnant puddle in July. The small amount of dry season water appears to originate from groundwater infiltration into the MS4 and from private storm drain connections to the MS4. The source of the human marker is unknown. As follow-up to the human marker detection, the RCD conducted limited dye tests during wet and dry weather to investigate whether sanitary sewer lines in the south sub-catchment leak into the MS4; however, results were inconclusive. Although the dye added to the sanitary system was not observed in the MS4, the test did not rule out the possibility of a connection. E. coli densities measured at the Capistrano catchment outfall (CPDS) were consistently above the SSM WQO (320 cfu/100mL) (Figure 4.6). During storm events, it is likely that primary sources of FIB are conveyed through the MS4 (especially from the south sub-catchment [CPSO]), with contributions from pet waste and an unknown human source. During the dry


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season, no flow was observed in the south sub-catchment and E. coli densities measured in the north sub-catchment (CPNO) were very low (10 MPN/100mL). It is possible that wildlife (rodents and raccoons) waste and growth of FIB in biofilms (supported by groundwater infiltration) in the lower end of the pipe system are the primary source of dry season FIB at CPDS. This suggestion is supported by the presence of animal tracks that have been observed nearby (Table 3.1).

Figure 4.6. E. coli densities measured in Capistrano Catchment stations.

4.5 St. Augustine Creek

The St. Augustine Creek watershed is approximately 310 acres. Land uses consist of open space (including GGNRA) in the upper watershed, residential uses east of Highway 1, and Pillar Point Harbor District property in the lower watershed. St. Augustine Creek is contained within the engineered storm drain system through the entire developed portion of the watershed. Pillar Point Harbor District includes parking areas, Harbor Master offices, maintenance buildings, public restrooms, and several small business leases (e.g., surf shop, restaurants, bait and tackle). The Harbor District is drained by a private storm drain system, parts of which discharge directly to the inner harbor and parts of which connect underground to St. Augustine Creek.

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The RCD is assisting the Harbor District in investigating their private storm drain system using techniques such as CCTV, flow observations, and dye testing. FOG and sediment were found to be partially blocking the lower 500 feet of the storm drain through which St. Augustine Creek flows on Harbor District property. The primary source of FOG to the system has been addressed by sealing off a misplaced wash water connection to the storm drain. Management practices have also been discussed with Harbor District tenants and staff, and all storm drains have been marked with “Flows to Ocean” stickers. In the summer of 2020, the FOG and sediment will be cleaned out of the storm drain line and the remainder of the line will be inspected using CCTV. In addition to the work on the St. Augustine line, the Harbor District replaced or repaired compromised storm drain pipes flowing to the inner harbor on the north end of the property in September 2018. Potential improvements on the south end of Harbor District property, near the Deer Creek outfall, are still being considered. The SSID sampling design for the St. Augustine Creek watershed includes three stations:

• An upstream station (AGUS) flows year-round and is located near the point where the creek enters the engineered storm drain system that conveys it all the way to the beach.

• Station AGCH is located within the underground portion of the creek, downstream of the residential area and upstream of the Harbor District area. Station AGCH is accessed via a vault and, although dry season flow was observed, it was very low and unsafe to sample. There is a linear park located upstream of station AGCH along Ave Granada; it contains a pet waste cleanup station (bag dispenser and waste bin) and was observed to be free of pet waste by RCD staff during one watershed survey.

• The downstream station (AGDS) is located at the double 48-inch RCP outfall to the beach (Figure 4.7). The outfall is inundated with sea water during high tides and, although sampling events were timed to coincide with low tide when inundation was less likely, specific conductance levels suggest that most samples collected at AGDS were influenced by sea water (Table 3.1). The sea water may have entered the system via the outfalls due to tidal action and/or via sea water influenced groundwater seepage into the pipes.

E. coli densities measured at the St. Augustine stations increase in the downstream direction, especially during the dry season (Figure 4.8). At the upstream background station (AGUS), E. coli densities were higher during storm events compared to the dry season but remained below the SSM WQO. Although flow at station AGCH (downstream of the residential area) was observed during all four sampling events, the field crew was unable to safely collect samples from the vault during the June and July events. During storm events, E. coli densities measured at station AGCH were higher than densities measured upstream at AGUS and exceeded the SSM WQO. Dog-specific bacteroidales markers were also present at AGCH during the January storm event. The dog marker was not detected during any other sampling event or at any other St. Augustine station, and human markers were not detected at any of the St. Augustine stations. E. coli densities at the downstream station (AGDS) were higher than the two upstream stations, and the E. coli pattern differed from the upstream station (AGUS). At the downstream station, E. coli densities were higher during the dry season; whereas, they were lower during the dry season


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at the upstream station. The source of the dry season FIB is difficult to assess with the limited sampling data. Harbor District sewer lines are ruled out as a likely source based on dye testing that did not show a connection to the creek. Therefore, dry season bacteria sources at AGDS are likely due to wildlife within the system (confirmed by observations of raccoon feces) and/or biofilm growth within the MS4 supported by groundwater infiltration. Residential areas draining to the MS4 are a likely contributor of FIB from pet waste at AGDS during storm events.

Figure 4.7. St. Augustine Creek outfall at Capistrano Beach (station AGDS).


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Figure 4.8. E. coli densities measured in St. Augustine Creek watershed stations.

4.6 Deer Creek

The Deer Creek watershed is approximately 309 acres. Land uses consist of open space (including GGNRA) and a cattle ranch in the upper watershed, and residential uses east of Highway 1. West of Highway 1, there is a parking area, launch ramp, and adjacent fish cleaning station, restrooms, and a portable waste dump station on Harbor District property. Deer Creek flows in a natural but highly constrained channel through the residential area (Figure 4.9). The MS4 in the residential area consists primarily of roadside ditches; an engineered pipe system is absent. Deer Creek enters a culvert just east of Highway 1 and remains in the engineered storm drain system until its discharge point at the beach. Historical imagery and pipeline investigations suggest that Deer Creek formerly discharged to the Inner Harbor but is now diverted to the Outer Harbor just south of the boat launch (Figure 2.1). The outfall, a 36-inch RCP, is inundated with sea water at high tide. The RCD conducted CCTV scoping in parts of the Deer Creek pipe on Harbor District property as part of their larger investigation. A considerable amount of sediment was observed relatively high up in the system, presumably pushed in by the tides. The presence of an old buoy and lifejackets in abandoned pipelines seems to confirm the tidal influence. Above ground, in the Harbor District parking lot near the outfall, newly installed educational signage is present with messaging about pollution, conservation, fishing, and cleaning up

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cigarette butts and after pets. There is also a pet waste cleanup station and a sewage discharge station for small boats and RVs (plumbed directly into the sanitary sewer line) in this location. The SSID sampling design for the Deer Creek watershed includes a station upstream of the MS4 (DRUS) which flows year-round, a station within the residential area (DRVL; Figure 4.9), and a downstream station at the outfall to the beach (DRDS) (Figures 2.1 and 4.10). During storm events, E. coli densities increased in the downstream direction, with upstream densities below the SSM WQO and densities measured at downstream stations generally above the SSM WQO (Figure 4.11). However, this pattern was observed in only one of the two dry season events (June). In July, E. coli densities measured at the downstream station (DRDR) (557 MPN/100mL) were lower than those measured at the residential station (DRVL; 882 MPN/100mL). E. coli densities at all stations in Deer Creek were generally higher during the dry season compared to the wet season (Table 3.1, Figure 4.12). This pattern differs from other watersheds sampled in this SSID study, which generally had lower dry season E. coli densities compared to those measured during storm events, particularly at upstream stations. Dry season E. coli densities at upstream stations in Denniston Creek and St. Augustine Creek were relatively low, ranging from 10 MPN/100 mL to 148 MPN/100 mL; whereas, dry season E. coli densities at the upstream station on Deer Creek ranged from 471 MPN/100 mL to 473 MPN/100 mL, which is above the WQO. There appears to be a dry season FIB source in the upper watershed of Deer Creek, perhaps associated with the cattle ranch and/or wildlife. This is supported by findings from Kim and Wuertz (2014) which found that bovine waste (including cattle and/or deer) was the main source of bacteria at the Deer Creek outlet. No human or dog markers were detected in any of the Deer Creek samples (Table 3.2).


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Figure 4.9. Deer Creek at Valencia Ave (Station DRVL) showing constrained channel, January 2018.

Figure 4.10. Deer Creek outfall (station DRDS) with boat launch in background, January 9, 2018.


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Figure 4.11. E. coli densities measured in Deer Creek watershed stations, by site, upstream to downstream.

Figure 4.12. E. coli densities measured in Deer Creek watershed stations, by date.

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5.0 Conclusions and Recommendations 5.1 Overall Findings

The Pillar Point Harbor Watershed Pathogen Indicator SSID Project implemented desktop and field investigations to answer the three Management Questions identified in the Work Plan (SMCWPPP 2018). Desktop analysis included development of a geodatabase with layers showing potential bacteria sources as well as the geographic setting and review of recent and historical monitoring data collected at local beaches by County Environmental Health Services. Field investigations included grab samples collected at 14 freshwater stations for analysis of FIB (E. coli) and human and dog genetic markers. Sampling was conducted during two wet weather events and two dry season events. These investigations were supplemented by field reconnaissance and limited dye testing of the sanitary sewer system conducted by the RCD. 1. Are there specific areas within the Study Area (i.e., the area drained by the MS4 that

discharges to Pillar Point Harbor) that are contributing FIB to receiving waters during wet and dry conditions? As described in the preceding sections of this report, FIB densities in the Study Area are highly variable and do not follow predictable patterns across all subwatersheds investigated.

• Wet vs. Dry Tributary Source Areas. E. coli densities measured at bottom-of-the-watershed stations in Denniston Creek (DNDS) and the Capistrano Catchment (CPDS) were roughly in the same range during wet conditions compared to dry conditions. In contrast, E. coli densities measured at bottom-of-the-watershed stations in St. Augustine Creek (AGDS) and Deer Creek (DRDS) were lower during wet conditions compared to dry conditions (Table 3.1).

• Wet vs. Dry MS4 Source Areas. MS4 (or mid-channel) stations in the Pillar Point Marsh subwatershed (station ARPT), Denniston Creek, the Capistrano Catchment, and St. Augustine Creek had higher E. coli densities in wet conditions compared to dry conditions. Some of the MS4 stations were dry or not flowing during dry weather, suggesting that the MS4 draining residential areas in these subwatersheds is not the primary contributor of FIB during the dry season. In contrast, the mid-channel station in Deer Creek (DRVL) had lower E. coli densities during wet conditions compared to dry conditions (Figure 4.11 and Table 3.1).

• Wet vs. Dry Conditions in the Harbor. FIB (i.e., enterococci) densities measured in the surf zone at beaches on opposite sides of the Inner Harbor (Capistrano Beach and Beach House Beach) by County Environmental Health Services were very similar in magnitude and pattern. This suggests good mixing of all FIB sources within the Harbor, including those originating on land (e.g., urban runoff, riparian wildlife, biofilm growth within MS4) and those originating on the beach and in the Harbor (e.g., boats, biofilm growth on beach sediments, wildlife). Although wet season FIB densities were higher than dry season FIB densities at both beaches, a dry season peak was also observed some years (Figures 3.2 and 3.3).


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2. Are there downstream trends in FIB densities in creeks that flow through urban areas to Pillar Point Harbor during wet and dry conditions? All tributaries had lower E. coli densities at stations upstream of the MS4 compared to bottom-of-the-watershed stations (see Table 3.1 and Figures 4.3, 4.6, 4.7, and 4.10), suggesting that FIB sources are present in the urban areas draining to the MS4. However, as discussed above and in Section 4, because MS4 discharges are limited during the dry season (i.e., some of the MS4 stations were dry or not flowing), some of the FIB increases at downstream stations appear to be caused by sources other than the urban areas (e.g., biofilm growth in the MS4 due to groundwater infiltration, wildlife).

3. Are controllable sources of bacteria (especially human and dog) present in the urban areas? The geodatabase GIS layers show potential controllable and uncontrollable bacteria sources such as direct human sources (e.g., homeless encampments and RVs), areas where pet waste is observed, areas where pet waste and horse droppings are likely (e.g., trails), livestock areas, public and private sanitary systems, and wildlife (see Section 3.1). Many of these potential sources are present throughout the Study Area (Appendix B). However, the overall dearth of human and dog markers present in the MST samples suggest that controllable sources, and especially human sources, may be limited. Human markers were only detected during the January 9, 2018 storm event in the Capistrano Catchment. Dog markers were more widespread (Denniston Creek, Capistrano Catchment, and St. Augustine Creek) and were detected during both of the storm sampling events.

Overall, FIB (E. coli) in freshwater tributaries to Pillar Point Harbor exhibit complex patterns that differ depending on the subwatershed. The MS4 appears to be a source of FIB to local creeks and Pillar Point Harbor during storm events; however, the relatively limited flow from the MS4 during the dry season limits its year-round importance. Furthermore, the overall lack of human and dog markers detected in this SSID study (particularly during the dry season) suggests that FIB conveyed by the MS4 may not be controllable. Instead, the sources of FIB within and conveyed by the MS4 appear to be primarily the result of wildlife (i.e., raccoons, deer, rodents) that are present in the MS4 and contributing areas. FIB growth within biofilms in the MS4 may be another likely source of FIB, particularly during the dry season. Two of the creeks that discharge to Pillar Point Harbor (St. Augustine Creek and Deer Creek) are diverted to underground culverts east of Highway 1 and remain underground until they discharge at the beach. Both of these creeks have higher FIB levels at their beach outfalls compared to upstream stations and both have relatively high dry season FIB levels. These findings further support biofilm growth within the MS4 as a potential source. Results showed E. coli densities often exceed recommended WQOs for freshwaters designated as having water contact recreation (REC-1) Beneficial Uses (i.e., 320 cfu/100mL). It is important to acknowledge that a) WQOs for FIB do not distinguish among sources of FIB and b) FIB detections do not necessarily correlate well with the presence of pathogens. Animal fecal waste is much less likely to contain pathogens of concern to human health than human sources, and FIB associated with biofilms likely does not indicate that pathogens are present. In most cases, human sources of fecal contamination are associated with REC-1 health risks rather than wildlife


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or domestic animal sources (USEPA 2012). Furthermore, even if controllable bacteria sources (i.e., human and dog sources) are eliminated, FIB densities in receiving waters could still exceed WQOs due to wildlife and natural FIB growth in biofilms.

5.1.1 Bacteria Fate and Transport

The fate and transport of bacteria in the system is an important factor that affects the concentration of FIB measured at any one location. Removal mechanisms include inactivation (i.e., loss in viability of the microorganism) and physical transport (either downstream or into bed sediments). Inactivation or die-off is dependent on several factors, including temperature, pH, salinity, nutrient concentrations, predation, and ultraviolet (UV) irradiance. Bacteria can attach to sediment particles even under flowing or turbulent conditions resulting in removal from the water column and the formation of biofilms. However, bacteria colonies can grow in the sediment and biofilms and later become resuspended in the water column. Bed sediments thereby can transition from a sink to a source. Modeling of these mechanisms is difficult because the conditions (physical and chemical variables) under which bacteria attach or detach from particles are not fully understood (Walters et al. 2013).

5.2 Current and Recommended Management Actions

Stakeholders in the Pillar Point Harbor watershed such as SMCWPPP, the County, the RCD, Surfrider, the Harbor District, and the Granada Community Services District6 implement many management actions along the coast and throughout the County to address the water quality impacts of stormwater runoff. Some of these actions are required or recommended by permits, plans, and ordinances such as the MRP, Local Coastal Program (LCP), and the County Stormwater Management and Discharge Control Ordinance (Chapter 4.100) and Prohibition on Use of Vehicle for Human Habitation (Chapter 7.96). Other actions are volunteer efforts often coordinated by the RCD. Management actions designed specifically or opportunistically to control bacterial sources are discussed in the sections below according to the primary bacteria source that they address.

The stakeholders may wish to consider additional management actions to further control bacterial discharges from the MS4 to receiving waters. However, it should be noted that, even if controllable bacteria sources (i.e., primarily human and dog sources) are eliminated, FIB densities in receiving waters could still exceed WQOs due to wildlife and natural FIB growth in biofilms.

5.2.1 Biofilms

Biofilms in storm drains and creeks were identified as a potential source of FIB in the Pillar Point Harbor watershed. Biofilms are communities of microorganisms surrounded by extracellular polymeric substances (i.e., sticky substances, slime). Biofilms can form on any solid surface that has contact with water and nutrients, including storm drain pipes and sediment particles. The public and privately-owned storm drain pipes in the lower watershed provide a particularly good environment for biofilm growth. Water is present year-round, often as a result

6 The Granada Community Services District oversees parks in the unincorporated areas of El Granada and Princeton-by-the-Sea.


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of perennial creeks flowing through the system, seawater entering the pipes during high tides, and groundwater infiltration into the pipes where cracks and leaks are present. Furthermore, the underground systems protect the bacterial colonies from temperature fluctuations and UV irradiance that might otherwise cause die-off. Detached FIB from biofilms likely contribute to concentrations measured at sampling stations, even though these FIB would not be associated with recent fecal contamination. Recent studies conducted in Newport Beach concluded that biofilm regrowth of FIB in street gutters and storm drains may explain high levels of FIB in runoff from residential areas where extensive measures have been conducted to eliminate sewage contributions from the sanitary sewer system (Skinner et al. 2010). Biofilms as a source of FIB could be reduced by controlling nuisance runoff (and other dry weather water sources), nutrient loading, and the debris/trash that provide additional surfaces for biofilm growth.

• Nuisance runoff (i.e., non-stormwater discharges) is prohibited under Provision A.1 of the MRP. The County’s Illicit Discharge Detection and Elimination (IDDE) Program and Enforcement Response Plan (ERP) provide a system for active surveillance (i.e., business inspections) and complaint response. County staff regularly inspect businesses in the Pillar Point Harbor watershed and frequently inform business owners and operators that equipment wash water should not be allowed to drain into the MS4.

• The RCD and the Harbor District will be cleaning FOG from one of the Harbor District stormwater lines that could be harboring biofilms. They will also continue to make improvements to stormwater infrastructure as needed and implement measures to control and discourage nuisance runoff.

• Other dry weather water sources are more difficult to control. For example, it is unlikely that seawater inundation during high tide could be eliminated. However, cracks in the MS4 and private storm drains that allow groundwater infiltration could be repaired. Several such cracks have been identified by the RCD using CCTV and communicated to the County.

Potential Additional Management Actions

• The County and Harbor District could work with the RCD to understand where storm drain repairs are warranted. Work to conduct the repairs should be considered through the capital improvement process.

• Cracks and leaks already identified in the MS4 in the Capistrano Catchment should be repaired.

5.2.2 Trash

Unmanaged trash can be a source of bacteria to creeks, both directly and by attracting birds, rodents, and other wildlife. It may also play a role in the formation of biofilms by providing surfaces on which biofilms can adhere. In compliance with Provision C.10 of the MRP, the County is implementing a Long-Term Trash Load Reduction Plan and Assessment Strategy (County of San Mateo 2014). The Long-Term Plan identifies and maps trash generating areas


40

and trash sources, delineates and prioritizes Trash Management Areas (TMAs), and describes current and future control measures. Although the Long-Term Plan is focused on reducing the impacts of discharges from the MS4, it also addresses direct dumping and wind dispersion of trash where possible.

Areas of Moderate trash generation were mapped in the Pillar Ridge Mobile Home Park (Pillar Point Marsh watershed), Princeton-by-the-Sea, and some commercial areas in El Granada. The remainder of the watershed is mapped as Low trash generation. Management actions being implemented in these areas include installation and maintenance of several full trash capture devices, enhanced street sweeping, beach and roadway cleanup events, outreach to businesses and residents, improved bins/container management, and bag and polystyrene bans. The goal is achieve full trash capture equivalency by 2022.

5.2.3 Pet Waste

Pet waste left on sidewalks, streets, yards, trails, and open space areas can enter the creek during runoff events (e.g., storms, sidewalk washing, irrigation). Even in the absence of other fecal sources, pet waste from dogs, cats, and other domestic animals contains FIB in quantities that can cause exceedances of WQOs in receiving water. Control measures for pet waste currently include enforcement of County ordinances that limit the number of allowable pets, complaint response, pet waste cleanup signage and dog bag dispensers, and public outreach and education.

• The RCD identified seven pet waste cleanup stations in the watershed: o Tide pools parking lot near Pillar Point Marsh,

o Park off Bridgeport Ave in the Denniston Creek watershed,

o Park on El Granada Ave in the St., Augustine Creek watershed,

o North Harbor District parking lot,

o Harbor District Johnston Pier,

o Harbor District Residential Pier

o South Harbor District parking lot near the Deer Creek outfall.

• Public outreach and education is conducted by the County, SMCWPPP, the RCD, the Harbor District, Surfrider, and the Sewer Authority Mid-Coastside. These agencies have also come together in a group called Coastside One Water to discuss local water issues and pool resources for education and outreach initiatives that include pet waste pollution messaging. Proximity of Pillar Point Harbor to the Fitzgerald Area of Special Biological Significance (ASBS) provides local residents and visitors exposure to the stormwater and pet waste education and outreach programs that are part of the Fitzgerald Pollution Reduction Program and the San Vicente Creek Bacteria Water Quality Improvement Plan (WQIP). Efforts include websites hosted by the County, Harbor District, and RCD, special edition newsletters, community and school presentations, tabling events, flyers, mailers, factsheets, and a pet waste pledge.


41

However, detection of dog markers in storm samples collected in Denniston Creek, Capistrano Catchment, and St. Augustine Creek suggests that further efforts to control dog waste could potentially help reduce concentrations of FIB. Furthermore, feeding of feral cats is known to occur in some creekside locations. Potential Additional Management Actions

• Install pet waste cleanup stations and/or signage at GGNRA trailheads. Dog waste has been observed at these locations.

• The Granada Community Service District could consider installing a pet waste cleanup station complete with signage, bag dispenser, and waste bin at or near Capistrano Beach.

• Several hotels in the watershed allow pets. The County, RCD, and/or other stakeholders could reach out to these hotels to identify ways of educating out-of-town guests who may not be as informed or conscientious as local residents.

• The County or other stakeholders could consider developing outreach materials that target individuals who encourage the presence of feral cats. If warranted, feral cat populations could be controlled through sterilization programs.

5.2.4 Wastewater

Leaks and overflows from the public sanitary sewer conveyance system and private laterals can convey human waste directly or indirectly to the MS4 and receiving waters. Although SSOs in the Study Area are rare, human markers have rarely been found in the Study Area, and targeted dye testing conducted in the Capistrano Catchment by the RCD did not identify any sanitary system leaks, this potential bacteria source is still considered a priority due to potential risks to human health in recreational waters contaminated with human fecal material. Furthermore, human markers were detected in two samples collected during the January storm event in the Capistrano Catchment.

There is one septic tank (OWTS) in the Pillar Point Harbor watershed, at the Half Moon Bay Airport. Per the County’s Individual Sewage Disposal Systems Ordinance (Chapter 4.84), this OWTS is subject to a triennial inspection by the County Health Officer to ensure its continued proper functioning.


• A deeper understanding of the operations, monitoring, and maintenance schedule and procedures for the collection system in the Study Area could be developed.

• Sewer districts and other stakeholders could explore opportunities to conduct outreach to owners/operators of private laterals.

5.2.5 Direct Sources of Wastes

Although homelessness in the Pillar Point Harbor watershed is not as extensive as it is in other parts of the County, homeless encampments are considered a priority bacteria source because


42

they can result in the direct discharge of human fecal material and trash to receiving waters. Transient individuals have been observed in Denniston Creek, at the Capistrano Catchment outlet, and along Airport Road (i.e., RVs). The issue of homelessness is complex and politically sensitive. Potential solutions to address discharge from encampments to the watershed are generally resource intensive and beyond the scope of stormwater programs. This is complicated by the fact that ordinances relied upon to enforce cleanup activities were developed for businesses and landowners, not individuals trying to reside in public areas and along creeks. Therefore, municipalities may not have legal standing to remove encampments. Furthermore, cleanup activities can be dangerous to personnel due to encounters with individuals/dogs and the presence of hazardous materials. Nonetheless, the County Department of Human Services, Center on Homelessness is actively involved in efforts to prevent encampments by helping to move homeless people into housing. The Strategic Plan: Ending Homelessness in San Mateo County (2016) articulates a path to achieve that goal by 2020. Interventions include outreach and engagement, emergency shelters, transitional housing, and prevention programs. The County also has an ordinance prohibiting overnight parking of RVs and the use of vehicles for human habitation (Chapter 7.96).

5.2.6 Livestock

There is only one known location with livestock in the Pillar Point Harbor watershed, a cattle ranch located upstream of the MS4 in the Deer Creek watershed. Monitoring results suggest that this ranch may be contributing wet and dry season FIB to Deer Creek.

Trails in the GGNRA receive frequent use from equestrians (and dog walkers). If waste from these animals is not managed, it could contribute FIB to the Pillar Point Harbor watershed.


• The RCD could continue to reach out to the cattle ranch owner and offer technical assistance as well as highlight funding sources to install fencing to prevent cattle from entering the creek.

• The County and/or RCD could consider working with GGNRA to add educational signage to GGNRA trails about the Pillar Point Harbor watershed and the need to control waste from horses and pets.

5.2.6 Wildlife Waste

Wildlife sources of bacteria are generally considered uncontrollable. The riparian corridors along the Study Area creek, parks (including the GGNRA), beaches, and large residential lots provide desirable habitat for attracting and sustaining many wildlife and avian populations. These include raccoons, skunks, squirrels, deer, ducks, rodents, pigeons, snakes, woodrats, bobcats, mountain lions and coyotes. Raccoons and skunks were observed in the storm drain system during the SSID Project site visits. Furthermore, rodents and pigeons are common nuisance wildlife in commercial areas.


43

Control measures for wildlife waste are generally limited to trash reduction efforts and trash receptable management to prevent scavenging. Reducing non-stormwater flows in the MS4 may also make pipes less attractive to wildlife if they are using those flows as a water source.


44

6.0 References

Bay Area Stormwater Management Agencies Association (BASMAA). 2016a. Regional Monitoring Coalition Creek Status Monitoring Program Quality Assurance Project Plan.

Bay Area Stormwater Management Agencies Association (BASMAA). 2016b. Regional Monitoring Coalition Creek Status Monitoring Program Standard Operating Procedures.

BKF Engineers. 2013. Drainage Report. Midcoast Storm Drain Inventory and Assessment Project. Prepared for the County of San Mateo. February 8, 2013.

County of San Mateo. 2014. Long-Term Trash Load Reduction Plan and Assessment Strategy. In compliance with Provision C.10.c of Order R2-2009-0074. February 1, 2014.

Flint, P.S. 1977. Environmental Study of the Pillar Pt. Marsh, San Mateo County, California. Part I. Baseline Data. Consulting report prepared for Coastside County Water District.

Griffith, J.F., Blythe, A.L., Boehm, A.B., Holden, P.A., Jay, J.A., Hagedorn, C., McGee, C.D., and Weisberg, S.B. 2013. The California Microbial Source Identification Manual: A Tiered Approach to Identifying Fecal Pollution Sources to Beaches. Southern California coastal Water Research Project Technical Report 804.

Kim, M. and Wuertz, S. 2014. Identification of Sources of Fecal Pollution Impacting Pillar Point Harbor. A Final Report Submitted to San Mateo Resource Conservation District. January 2014.

San Mateo County Resource Conservation District (RCD). 2014. Final Project Report. Pillar Point Harbor Source Identification Project. Clean Beaches Grant Program, Proposition 50. Agreement 07-574-550-2. January 2014.

San Mateo Countywide Water Pollution Prevention Program (SMCWPPP). 2018. Pillar Point Watershed Pathogen Indicator Stressor/Source Identification Project Work Plan. Prepared for SMCWPPP by EOA, Inc., Oakland, California. March 31.

Skinner, J.f., Kappeler, J., and Guzman, J. 2010. Regrowth of enterococci and fecal coliform in biofilm. Stormwater Magazine, June 30, 2010.

United States Environmental Protection Agency (USEPA). 2012. Recreational Water Quality Criteria. Office of Water 820-F-12-058.

Urban Water Resources Research Council (UWRRC). 2014. Pathogens in Urban Stormwater Systems. Prepared by UWRRC Pathogens in Wet Weather Flows Technical Committee, Environmental and Water Resources Institute, American Society of Civil Engineers with support from Urban Drainage and Flood Control District, Denver, CO and Urban Watersheds Research Institute. August 2014. Accessible at: http://www.asce-pgh.org/Resources/EWRI/Pathogens%20Paper%20August%202014.pdf

http://www.asce-pgh.org/Resources/EWRI/Pathogens%20Paper%20August%202014.pdf

http://www.asce-pgh.org/Resources/EWRI/Pathogens%20Paper%20August%202014.pdf


45

Walters, E., Schwarzwalder, K., Rutschmann, P., Muller, E., and Horn, H. 2013. Influence of resuspension on the fate of fecal indicator bacteria in large-scale flumes mimicking an oligotrophic river. Water Research, http://dx.doi.org/10.1016/j.watres.2013.10.002.

Wuertz, S., Wang, D., Zamani, K., and Bombardelli, F. 2011. An Analysis of Water Circulation in Pillar Point Harbor, Half Moon Bay, California, based on the Dye Distribution Study of September 27, 2008. Report prepared for San Mateo County Resource Conservation District.

http://dx.doi.org/10.1016/j.watres.2013.10.002

Appendix A

Geodatabase Metadata

Pillar Point Harbor Bacteria Geodatabase

A-1

Pillar Point Harbor Bacteria Geodatabase Metadata Geodatabase: PillarPointBacteria.gdb Publication Date: October 15, 2019 Datum: D_North_American_1983 Projected Coordinate System: NAD_1983_StatePlane_California_III_FIPS_0403_Feet Attribute: the title of the field/column Attribute Type: The data type stored in the geodatabase. ““Double” and “Float” refer to a numeric attribute, while “String” is a text attribute. Description: Brief explanation of the attribute as well as a note if it was created by an organization other than EOA. *All original attributes have been left in each geodatabase layer, however not all are listed in this appendix. All fields created by EOA have been included. Primary Contact: Alec Arditti EOA, Inc. 1410 Jackson St. Oakland, CA [email protected] (510)832-2852 ext. 114 Disclaimer: The GIS data layers described below should be considered in draft form and are subject to change. These data are a work in progress and will be updated as more information is gathered. EOA and SMCWPPP staff do not take responsibility for any errors in the data, or any analysis that may result.


A-2

Geodatabase Layers: Point Layers Business_Sources - Businesses cited as having potential illicit discharges to creeks. Businesses were also included that had relevant stormwater violations or complaints per the San Mateo County Stormwater Program. Layer digitized by EOA (2019). Attribute Attribute Type Description Comments String Description of potential runoff issue Business String Name of business associated with the violation or complaint Lat Double Latitude (Decimal Degrees) Long Double Longitude (Decimal Degrees) Watershed String Watershed business falls within

Direct_Human_Sources – Locations identified by the San Mateo County Resource Conservation District as suspected or confirmed human source sites. Sites digitized by EOA (2019). Attribute Attribute Type Description

Permanency String If the source is continuous and if not the frequency at which it occurs

Scale String The extent of the potential human source (single vs. multiple)

Comments String Description of the location

Year String The year or time descriptor the human source has been observed

Lat Double Latitude (Decimal Degrees) Long Double Longitude (Decimal Degrees) Watershed String Watershed the human source falls within

Dog_Waste_Stations – Locations within the study area that have stations for proper disposal of dog waste. The stations were identified by the RCD. Attribute Attribute Type Description Name String Location of the station Lat Double Latitude (Decimal Degrees) Long Double Longitude (Decimal Degrees) Watershed String Watershed the station falls within


A-3

Other_Bacteria_Monitoring_Sites – Locations of bacteria sampling stations maintained by the County health department and Snapshot Day. Locations and information pertaining to the sites is taken from the County Health website and the state Waterboard’s safe to swim website: http://maps.smcgov.org/beachmonitoring/ https://mywaterquality.ca.gov/safe_to_swim/interactive_map/ Layer created by EOA (2019) Attribute Attribute Type Description Name String ID used by the agency who maintains the station Lat Double Latitude (Decimal Degrees) Long Double Longitude (Decimal Degrees) Watershed String Watershed the site falls within

PPHSSIDSites – Locations of bacteria sampling stations. The locations were selected as representative of important subwatersheds of the study area. Layer created by EOA (2019). Attribute Attribute Type Description Name String Abbreviation that serves as an identifier for the point Lat Double Latitude (Decimal Degrees) Long Double Longitude (Decimal Degrees) Watershed String Watershed the site falls within

SSO – Category 1 Sanitary Sewer Overflow (SSO) events that have occurred within the MS4 permit area. Category 1 events are discharges of untreated or partially treated wastewater that reach surface water, are not fully captured, or are not disposed of properly. Locations and information pertaining to the events is taken from the State Water Resources Control Board Sanitary Sewer Overflow Reduction Program website: https://www.waterboards.ca.gov/water_issues/programs/sso/sso_map/sso_pub.shtml Layer created by EOA (2019).

Attribute Attribute Type Description

Date String Month, Day, Year and Time of overage SSO_Event_ID String

SSO event identification number, linked to report on SWRCB website

Site_Name String Specifies location of spill Volume String Total spill volume Recovered String Spill volume recovered Type String Category 1, 2 or 3 Reason String The cause of the spill Lat Double Latitude (Decimal Degrees) Long Double Longitude (Decimal Degrees) Watershed String Watershed the SSO occurred within

http://maps.smcgov.org/beachmonitoring/

https://mywaterquality.ca.gov/safe_to_swim/interactive_map/

https://www.waterboards.ca.gov/water_issues/programs/sso/sso_map/sso_pub.shtml


A-4

Wildlife – Locations identified by the RCD as suspected or confirmed wildlife source sites. Digitized by EOA (2019).

Attribute Attribute

Type Description Comments String Summary of applicable wildlife locations and activities Latitude Double Latitude (approximate) Longitude Double Longitude (approximate) Watershed String Watershed the wildlife were noted within

Polyline Layers: Creeks – Creeks that drain into Pillar Point Harbor, including tributaries. Taken from the National Hydrography Dataset, which is maintained by USGS. Then modified using the Storm Drain Inventory Project to improve accuracy. Layer created by EOA (2019).

Attribute Attribute Type Description

Creek_name String Name of the creek that the line segment is a part of FEET Double Length of segment in feet

Storm_Drain_Lines – Locations of the storm drain network, including storm drain lines, ditches, swales and gutters that fall within the study area. Data was gathered by FUGRO. (2013). Attribute Attribute Type Description PIPEDIAM Double Diameter of the pipe (attribute created by FUGRO)

UNITTYPE String Specifies pipe, gutter, ditch, swale etc. (attribute created by FUGRO)

Display String Categorization of storm lines for map displaying purposes Feet Float Length of segment in feet

Storm_Drain_Issues – Locations of potential issues with the storm drain system, identified by the RCD. Line segments were extracted by EOA from the existing Storm_Drain_Lines (2019). Attribute Attribute Type Description Comments String Description of issue Watershed String Watershed the issue falls within FEET Double Length of segment in feet


A-5

Trails – Extent of trails that pass through the study area. The source data were provided by the National Park Service and relevant trails were extracted by EOA (2019). Attribute Attribute Type Description TRLNAME String Name of the trail TRLUSE String Specifies whether the trail allows hiking, biking or horses FEET Double Length of segment in feet

Polygon Layers Livestock – Locations of livestock within the study. Data collected by EOA via aerial imagery Digitized by EOA (2019). Attribute Attribute Type Description Comments String Description of livestock Watershed String Watershed livestock are located within ACRES Double Size of livestock area in acres

Parcels – The parcel boundaries of the parcels that intersect the study area, available publicly online from San Mateo County and downloaded by EOA in 2014. Also included in this GIS layer is the owner information and land use code of every parcel. Data was obtained from the San Mateo County Recorder’s office in 2014. All attributes are original to the layer.

Attribute Attribute

Type Description APN String Assessor’s parcel number Acres Float Acreage OWNER String Property owner Mailing_Address String Owner mailing address Site_Address String Site address

PUC_CODE Double Parcel Land Use Code, as provided the County Recorder’s office

Landuse String Land Use


A-6

EOA_Landuse – The parcel boundaries of the parcels that intersect the study area, taken from ABAG (Association of Bay Area Governments) and modified by EOA. Landuse was modified by EOA for a trash project and approved by the county. Landuse was further modified within the study area by EOA staff to reduce the number of categories and improve accuracy. Attribute Attribute Type Description

Landuse String EOA landuse designation with County approval for county trash project

acres Float Acreage

Reclassified String EOA landuse designation specifically for Pillar Point Harbor

Pets – Locations within the study area that have potential sources of bacteria from pet waste. Locations were identified with the help of the RCD. Digitized by EOA (2019). Attribute Attribute Type Description Comments String Description of pet and location ACRES Double Size of area in acres

Watersheds – Watershed boundaries for surface waters, creek and storm drain networks that drain to Pillar Point Harbor. Watersheds were created and verified through a combination of Storm Drain Inventory Project, digital elevation models, and aerial and Google Street View imagery. Layer created by EOA (2018). Attribute Attribute Type Description

NAME String Name of creek or surface water associated with watershed

ACRES Double Watershed acreage WATERSHED String Name of watershed

SSID_Watersheds– Catchment boundaries for surface waters, creek and storm drain networks that drain to the SSID Sites. Catchments were delineated through a combination of the Storm Drain Inventory Project, digital elevation models and aerial and streetview imagery. Layer created by EOA (2019). Attribute Attribute Type Description NAME String Name of creek or surface water associated with watershed ACRES Double Watershed acreage Catchment String Name of SSID site that catchment drains to

Appendix B

Geodatabase Figures

March 31, 2020



Provision C.8.h.v

PART D: POLLUTANTS OF CONCERN MONITORING REPORT

Data Collected in San Mateo County through Water Year 2019

CREDITS


Town of Atherton City of Foster City City of San Bruno City of Belmont City of Half Moon Bay City of San Carlos City of Brisbane Town of Hillsborough City of San Mateo City of Burlingame City of Menlo Park City of South San Francisco Town of Colma City of Millbrae Town of Woodside City of Daly City City of Pacifica County of San Mateo City of East Palo Alto Town of Portola Valley SM County Flood Control District City of Redwood City



Prepared by:


SMCWPPP POC Monitoring Report – Data Collected in San Mateo County through WY 2019

i

TABLE OF CONTENTS TABLE OF CONTENTS ........................................................................................................................................... i LIST OF TABLES ....................................................................................................................................................ii LIST OF FIGURES ..................................................................................................................................................ii LIST OF ATTACHMENTS ...................................................................................................................................... iii LIST OF ABBREVIATIONS ..................................................................................................................................... iv 1.0 INTRODUCTION ......................................................................................................................................... 1

1.1. URBAN CREEKS MONITORING REPORT AND INTEGRATED MONITORING REPORT ........................................................... 1 1.2. POC MONITORING APPLICABLE PERMIT REQUIREMENTS .......................................................................................... 2

1.2.1. MRP 1.0 POC Monitoring Requirements (WY 2014 and WY 2015) ..................................................... 2 1.2.2. MRP 2.0 POC Monitoring Requirements (WY 2016 - WY 2019) .......................................................... 3

1.3. THIRD-PARTY DATA ........................................................................................................................................... 5 1.3.1. RMP STLS ............................................................................................................................................. 6 1.3.2. SPoT Monitoring Program ................................................................................................................... 6

2.0 POLLUTANT LOADING STATION (WYS 2014 AND 2015) .............................................................................. 8 3.0 WY 2016 – 2019 POC MONITORING ACCOMPLISHMENTS .......................................................................... 9 4.0 SUMMARY OF DATA QUALITY FOR WYS 2016 – 2019 ................................................................................. 9 5.0 PROGRESS TO-DATE IDENTIFYING PCBS AND MERCURY SOURCES ............................................................. 15

5.1. SAMPLING SUMMARY AND CHRONOLOGY ............................................................................................................ 15 5.1.1. WY 2000 through WY 2014 ............................................................................................................... 16 5.1.2. WY 2015 ............................................................................................................................................ 17 5.1.3. WY 2016 ............................................................................................................................................ 17 5.1.4. WY 2017 ............................................................................................................................................ 18 5.1.5. WY 2018 ............................................................................................................................................ 18 5.1.6. WY 2019 ............................................................................................................................................ 18

5.2. SAN MATEO COUNTY STORMWATER RUNOFF MONITORING FOR PCBS AND MERCURY ................................................ 19 5.3. REGIONAL STORMWATER RUNOFF MONITORING FOR PCBS AND MERCURY ............................................................... 20 5.4. SAN MATEO COUNTY SEDIMENT MONITORING FOR PCBS AND MERCURY ................................................................. 24 5.5. WATERSHED MANAGEMENT AREA STATUS .......................................................................................................... 25

5.5.1. City of Brisbane ................................................................................................................................. 28 5.5.2. City of South San Francisco ............................................................................................................... 30 5.5.3. City of Burlingame ............................................................................................................................. 38 5.5.4. City of San Mateo .............................................................................................................................. 41 5.5.5. City of Belmont .................................................................................................................................. 43 5.5.6. City of San Carlos ............................................................................................................................... 45 5.5.7. City of Redwood City ......................................................................................................................... 52 5.5.8. City of East Palo Alto ......................................................................................................................... 58

6.0 COPPER, NUTRIENTS, AND EMERGING CONTAMINANTS (WYS 2016 – 2019) ............................................. 60 6.1. COPPER ......................................................................................................................................................... 61 6.2. NUTRIENTS .................................................................................................................................................... 62 6.3. EMERGING CONTAMINANTS .............................................................................................................................. 67

7.0 COMPLIANCE WITH APPLICABLE WATER QUALITY OBJECTIVES ................................................................. 68 8.0 SUMMARY AND DISCUSSION ................................................................................................................... 69

8.1. PCBS AND MERCURY ....................................................................................................................................... 71 8.2. COPPER ......................................................................................................................................................... 75


ii

8.3. NUTRIENTS .................................................................................................................................................... 75 8.4. EMERGING CONTAMINANTS .............................................................................................................................. 76

9.0 REFERENCES ............................................................................................................................................. 77

LIST OF TABLES Table 1. MRP Provision C.8.f Pollutants of Concern Monitoring Requirements. .......................................................... 7

Table 2. SMCWPPP/BASMAA and Third-Party POC Monitoring Accomplishments, PCBs, WYs 2016 - 2019. ............. 10

Table 3. SMCWPPP/BASMAA and Third-Party POC Monitoring Accomplishments, Mercury, WYs 2016 - 2019. ....... 11

Table 4. SMCWPPP/BASMAA and Third-Party POC Monitoring Accomplishments, Copper, WYs 2016 - 2019. ......... 12

Table 5. SMCWPPP/BASMAA and Third-Party POC Monitoring Accomplishments, Nutrients, WYs 2016 - 2019. ..... 13

Table 6. Descriptive Statistics – PCBs and Mercury Concentrations in San Mateo County Stormwater Runoff and Natural Waterway Water Samples through WY 2019a ................................................................................................ 20

Table 7. Descriptive Statistics – PCBs and Mercury Concentrations in Bay Area Stormwater Runoff and Natural Waterway Water Samples through WY 2019a ............................................................................................................. 24

Table 8. Descriptive Statistics – San Mateo County Sediment Sample PCBs and Mercury Concentrations ................ 25

Table 9. Total and Dissolved Copper Concentrations in SMCWPPP Water Samples, WYs 2016 – 2019. .................... 63

Table 10. Nutrient Concentrations in SMCWPPP Water Samples, WYs 2016 – 2019. ................................................ 66

Table 11. Comparison of SMCWPPP copper monitoring data to WQOs, WYs 2016 – 2019. ...................................... 70

LIST OF FIGURES Figure 1. POC Monitoring Stations in San Mateo County (includes all samples from early 2000s to WY 2019). ........ 14

Figure 2. PCBs Concentrations in Stormwater Runoff Samples Collected in MS4s and Natural Waterways in the Bay Area. ............................................................................................................................................................................ 21

Figure 3. PCBs Particle Ratio in Stormwater Runoff Samples Collected in Large MS4s and Natural Waterways in the Bay Area. ...................................................................................................................................................................... 22

Figure 4. Mercury Concentrations in Stormwater Runoff Samples Collected in MS4s and Natural Waterways in the Bay Area. ...................................................................................................................................................................... 23

Figure 5. Mercury Particle Ratio in Stormwater Runoff Samples Collected in Large MS4s and Natural Waterways in the Bay Area. ............................................................................................................................................................... 23

Figure 6. San Mateo County WMA Status Based upon Total PCBs Concentration in Sediment and/or PCBs Particle Ratio in Stormwater Runoff Samples Collected through WY 2019. ............................................................................ 27

Figure 7. WMAs 17, 350, and 1004. ............................................................................................................................. 29

Figure 8. WMAs 313, 314, 315, and 1002 .................................................................................................................... 33

Figure 9. WMA 319 ...................................................................................................................................................... 34

Figure 10. WMAs 293, 294, and 357 ............................................................................................................................ 35

Figure 11. WMAs 316, 317, 358, 359, and 1001 .......................................................................................................... 36


iii

Figure 12. WMAs 291, 292, 316, and 1001 .................................................................................................................. 37

Figure 13. WMAs 85 and 164 ...................................................................................................................................... 39

Figure 14. WMAs 141, 142, and 1006 .......................................................................................................................... 40

Figure 15. WMAs 156 and 408 .................................................................................................................................... 42

Figure 16. WMA 60 ...................................................................................................................................................... 44

Figure 17. WMAs 59, 75, and 1011 .............................................................................................................................. 48

Figure 18. WMA 31 ...................................................................................................................................................... 49

Figure 19. WMA 210 .................................................................................................................................................... 50

Figure 20. WMA 210 – Enlargement of Sampled Area ................................................................................................ 51

Figure 21. WMA 379 (northwest portion) ................................................................................................................... 54

Figure 22. WMAs 254 and 379 (southeast portion) .................................................................................................... 55

Figure 23. WMAs 269, 405, 1000 ................................................................................................................................. 56

Figure 24. WMA 239 .................................................................................................................................................... 57

Figure 25. WMAs 70, 72, 1015 ..................................................................................................................................... 59

Figure 26. Copper and nutrient monitoring stations, San Mateo County, WY 2016 – WY 2019. ................................ 60

LIST OF ATTACHMENTS Attachment 1 – Fate and Transport Study of PCBs: Urban Runoff Impact On San Francisco Bay Margins

Attachment 2 – WY 2019 Quality Assurance / Quality Control Report

Attachment 3 – Results of Monitoring San Mateo County Stormwater Runoff for PCBs and Mercury

Attachment 4 – Results of Monitoring San Mateo County Sediments for PCBs and Mercury

Attachment 5 – Summary of PCBs and Mercury Monitoring Results in San Mateo County WMAs


iv

LIST OF ABBREVIATIONS BASMAA Bay Area Stormwater Management Agency Association BMP Best Management Practice CEC Contaminants of Emerging Concern CEDEN California Environmental Data Exchange Network CSCI California Stream Condition Index CW4CB Clean Watersheds for Clean Bay DTSC California Department of Toxic Substances Control ECWG Emerging Contaminants Work Group of the RMP MRP Municipal Regional Permit MS4 Municipal Separate Storm Sewer System NPDES National Pollution Discharge Elimination System PBDEs Polybrominated Diphenyl Ethers PCBs Polychlorinated Biphenyls PFAS Perfluoroalkyl Sulfonates PFOS Perfluorooctane Sulfonates POC Pollutant of Concern RMC Regional Monitoring Coalition RMP San Francisco Bay Regional Monitoring Program RWSM Regional Watershed Spreadsheet Model SAP Sampling and Analysis Plan SMCWPPP San Mateo Countywide Water Pollution Prevention Program (Countywide Program) SFEI San Francisco Estuary Institute SPoT Statewide Stream Pollutant Trend Monitoring SSC Suspended Sediment Concentration SSID Stressor/Source Identification STLS Small Tributary Loading Strategy TOC Total Organic Carbon UCMR Urban Creeks Monitoring Report USEPA US Environmental Protection Agency WLA Wasteload Allocation WQO Water Quality Objective WY Water Year


1

1.0 INTRODUCTION This Pollutants of Concern (POC) monitoring report was prepared by the San Mateo Countywide Water Pollution Prevention Program (SMCWPPP or Countywide Program), as part of SMCWPPP’s March 2020 Integrated Monitoring Report (IMR). SMCWPPP is a program of the San Mateo County City/County Association of Governments (C/CAG). SMCWPPP prepared this report on behalf of San Mateo County local municipal agencies subject to the regional stormwater National Pollutant Discharge Elimination System (NPDES) permit for Bay Area municipalities issued by the San Francisco Regional Water Quality Control Board (Regional Water Board). The stormwater permit is usually referred to as the Municipal Regional Permit (MRP). The version reissued on November 19, 2015 is referred to as MRP 2.0 (Regional Water Board 2015). This report fulfills the requirements of MRP 2.0 Provision C.8.h.iii for reporting a comprehensive analysis of Provision C.8.f. POC Monitoring data collected pursuant to Provision C.8. since the previous IMR. The previous SMCWPPP IMR addressed Water Year (WY) 2011 – WY 2013 (SMCWPPP 2014) and the time period addressed by this report includes WY 2014 – WY 20191. However, please note that:

• For PCBs, this report focuses on progress to-date towards identifying source areas and properties in San Mateo County. In this context, it evaluates all the relevant and readily available sediment and stormwater runoff chemistry data collected in San Mateo County, ranging back to the early 2000s.

• Some sections and summary tables in this report focus on summarizing compliance with MRP 2.0 requirements and thus focus on the POC monitoring and related activities conducted during WY 2016 – WY 2019.

This POC monitoring report is included as an appendix to SMCWPPP’s WY 2014 – 2019 IMR. In addition, consistent with MRP Provision C.8.h.ii., POC monitoring data generated from sampling of receiving waters (e.g., creeks) were submitted to the San Francisco Bay Area Regional Data Center for upload to the California Environmental Data Exchange Network (CEDEN)2. 1.1. Urban Creeks Monitoring Report and Integrated Monitoring Report Per MRP requirements, SMCWPPP submits a comprehensive Urban Creeks Monitoring Report (UCMR) by March 31 of each year, reporting on all data collected during the foregoing October 1 – September 30 period. The UCMR contains summaries of Creek Status, Stressor/Source Identification (SSID) Projects, and POC Monitoring. By March 31 of the fifth year of the Permit term (2020), MRP Permittees submit this IMR in lieu of the annual Urban Creeks Monitoring Report. The IMR is part of the next Report of Waste Discharge for the reissuance of the MRP. The IMR reports on all the data collected since the previous IMR and contains the following:

• A Water Year Summary Table, as described in Provision C.8.h.iii, containing information pertaining to the fourth-year monitoring data;

1 The water quality monitoring described in this report was conducted on a Water Year basis. A Water Year begins on October 1 and ends on September 30 of the named year. For example, Water Year 2019 (WY 2019) began on October 1, 2018 and concluded on September 30, 2019. 2 CEDEN has historically only accepted and shared data collected in streams, lakes, rivers, and the ocean (i.e., receiving waters). In late-2016, we were notified that there were changes to the types of data that CEDEN would accept and share. However, pending further clarification, SMCWPPP will continue to submit only receiving water data to CEDEN.


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• A comprehensive analysis of all data collected pursuant to Provision C.8 since the previous IMR, (and may include other pertinent studies);

• For POCs, methods, data, calculations, load estimates, and source estimates for each POC parameter, as applicable; and

• A budget summary for each monitoring requirement and recommendations for future monitoring.

In accordance with MRP requirements, this POC monitoring report includes the following standard monitoring report content:

• The purpose of the monitoring and brief descriptions of study design rationale;

• Quality Assurance/Quality Control summaries for sample collection and analytical methods, including a discussion of any limitations of the data;

• Brief descriptions of sampling protocols and analytical methods;

• Sample location description, including water body name and segment and location coordinates;

• Sample ID, collection date (and time if relevant), and media;

• Concentrations detected, measurement units, and detection limits;

• Assessment, analysis, and interpretation of the data for each monitoring program component;

• A listing of non-Permittee entities whose data are included in the report; and

• Assessment of compliance with applicable water quality standards. 1.2. POC Monitoring Applicable Permit Requirements The following sections summarize the POC Monitoring requirements in MRP 1.0 and MRP 2.0, except reporting requirements, which were summarized above. 1.2.1. MRP 1.0 POC Monitoring Requirements (WY 2014 and WY 2015) Provision C.8.e.i of MRP 1.0 required POC loads monitoring to assess inputs of POCs to the Bay from local tributaries and urban runoff, assess progress toward achieving wasteload allocations (WLAs) for TMDLs, and help resolve uncertainties associated with loading estimates for these pollutants. An RMP Small Tributaries Loading Strategy (STLS) developed a comprehensive planning framework to coordinate POC loads monitoring/modeling between the RMP and RMC participants. With concurrence of participating Regional Water Board staff, the framework presented an alternative approach to the POC loads monitoring requirements described in MRP Provision C.8.e.i, as allowed by Provision C.8.e. The STLS loads monitoring framework included intensive monitoring at six bottom-of-the watershed stations, including the Pulgas Creek Pump Station south drainage station in San Mateo County, which was operated by SMCWPPP. See section 2.0 and Gilbreath et al. (2016) for additional information.


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1.2.2. MRP 2.0 POC Monitoring Requirements (WY 2016 - WY 2019) Provision C.8.f of the MRP 2.0 (POC Monitoring) requires monitoring of several POCs including polychlorinated biphenyls (PCBs), mercury, copper, emerging contaminants3, and nutrients. Provision C.8.f specifies yearly (i.e., WY) and total (i.e., permit term) minimum numbers of samples for each POC. In addition, POC monitoring must address the five priority management information needs (i.e., Management Questions) identified in C.8.f:

1. Source Identification – identifying which sources or watershed source areas provide the greatest opportunities for reductions of POCs in urban stormwater runoff;

2. Contributions to Bay Impairment – identifying which watershed source areas contribute most to the impairment of San Francisco Bay beneficial uses (due to source intensity and sensitivity of discharge location);

3. Management Action Effectiveness – providing support for planning future management actions or evaluating the effectiveness or impacts of existing management actions;

4. Loads and Status – providing information on POC loads, concentrations or presence in local tributaries or urban stormwater discharges; and

5. Trends – providing information on trends in POC loading to the Bay and POC concentrations in urban stormwater discharges or local tributaries over time.

The MRP specifies the minimum number of samples for each POC that must address each Management Question. For example, over the first five years of the permit, a minimum total of 80 PCBs samples must be collected and analyzed. At least eight PCB samples must be collected each year. By the end of year four4 of the permit term, each of the five Management Questions must be addressed with at least eight PCB samples. It is possible that a single sample can address more than one information need. The MRP’s POC Monitoring requirements are summarized in Table 1. The requirements in MRP 2.0 Provision C.8.f. (POC Monitoring) have been met through a variety of water quality programs and studies:

• SMCWPPP collects POC samples as part of its own water quality monitoring program to directly meet C.8.f. requirements.

3 Emerging contaminant monitoring requirements will be met through participation in the Regional Monitoring Program for Water Quality in San Francisco Bay (RMP) special studies. The special studies will account for relevant contaminants of emerging concern (CECs) in stormwater and will address at least PFOS, PFAS, and alternative flame retardants being used to replace PBDEs. 4 Note that the minimum sampling requirements addressing information needs must be completed by the end of year four of the permit (i.e., WY 2019); however, the minimum number of total samples does not need to be met until the end of year five of the permit (i.e., WY 2020).


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• Other MRP provisions require studies or have information needs that are consistent with Provision C.8.f. requirements. The associated POC monitoring is credited towards these other provisions and Provision C.8.f.:

o MRP Provisions C.11/12.a. require that Permittees develop and maintain a list of management areas (referred to as Watershed Management Areas or WMAs) in which mercury and PCBs control measures will be implemented during the permit term, as well as the monitoring data and other information used to select the WMAs. Updated lists with identified control measures are provided with each of SMCWPPP’s Annual Reports. Provision C.8.f supports C.11/12.a. requirements by requiring monitoring directed towards mercury and PCBs source identification.

o MRP Provision C.12.e requires that Permittees sample caulk and other sealants used in storm drain or roadway infrastructure in the public right-of-way to investigate whether PCBs are present in such material and in what concentrations. SMCWPPP worked with other MRP Permittees through the Bay Area Stormwater Management Agencies Association (BASMAA) to complete a regional investigation that addressed this requirement. 54 samples of caulk and sealant materials from ten types of roadway and storm drain infrastructure were collected throughout the MRP area and combined into 20 composites that were tested for PCBs. Results of the investigation were documented by BASMAA (2018), a report submitted with the Countywide Program’s FY 2017/18 Annual Report.

• To learn more about the effectiveness of selected stormwater treatment controls, SMCWPPP participated in two additional BASMAA regional projects. The studies were developed to satisfy Provision C.8.f requirements for SMCWPPP and other Bay Area stormwater programs to each collect at least eight PCBs and mercury samples that address Management Question No. 3 (Management Action Effectiveness). The studies investigated the effectiveness of hydrodynamic separator (HDS) units and various types of biochar-amended bioretention soil media (BSM) at removing PCBs and mercury from stormwater runoff:

o A regional study evaluated the effectiveness of biochar-amended bioretention soil media (BSM) to remove PCBs and mercury from stormwater runoff collected in the MRP region. Twenty-six samples consisting of influent/effluent pairs from bench scale column tests of biochar-enhanced BSM were analyzed. Stormwater runoff was run through six columns with five different biochar-enhanced BSM mixes and one standard BSM as a control to evaluate which mix was most effective at removing PCBs and mercury. All five biochar-BSM blends showed evidence of overall improved PCBs and mercury performance compared to the standard BSM; however, the increased benefit relative to increased cost was not analyzed. Hydraulics were found to be a critical factor in achieving good pollutant removal in the columns suggesting that outlet controls could be used to enhance the performance of BMPs. Furthermore, this study suggested that an irreducible minimum concentration of PCBs may be 1,000 pg/L (BASMAA 2019a).

o A regional study collected samples of the solids captured and removed from eight HDS unit sumps during cleanouts and analyzed for mercury and PCBs. Maintenance records and construction plans were reviewed to develop estimates of the average volume of solids removed per cleanout. This information was combined with the monitoring data to estimate the mass of pollutant removed. Across all eight units, the median percent PCBs removed ranged from 5% - 32% of the catchment pollutant load (BASMAA 2019b).


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SMCWPPP also works collaboratively with our water quality monitoring partners to find mutually beneficial monitoring approaches. MRP Provision C.8.a.iii allows Permittees to use data collected by third-party organizations to fulfill monitoring requirements, provided the data are demonstrated to meet the required data quality objectives. For example, samples collected in San Mateo County through the Regional Monitoring Program for Water Quality in the San Francisco Estuary (RMP), the Clean Watersheds for a Clean Bay (CW4CB) EPA grant-funded project, and the State’s Stream Pollution Trends (SPoT) Monitoring Program are used by SMCWPPP to help address Provision C.8.f monitoring requirements. Finally, MRP Provision C.12.g requires Permittees to conduct or cause to be conducted studies concerning the fate, transport, and biological uptake of PCBs discharged from urban runoff to San Francisco Bay margin areas. The provision states: “the specific information needs include understanding the in-Bay transport of PCBs discharged in urban runoff, the sediment and food web PCBs concentrations in margin areas receiving urban runoff, the influence of urban runoff on the patterns of food web PCBs accumulation, especially in Bay margins, and the identification of drainages where urban runoff PCBs are particularly important in food web accumulation.” C.12.g requires Permittees to report in this IMR “the findings and results of the studies completed, planned, or in progress as well as implications of studies on potential control measures to be investigated, piloted or implemented in future permit cycles.” Attachment 1 provides a summary of a multi-year project by the San Francisco Bay (Bay) Regional Monitoring Program (RMP) that is addressing the requirements of Provision C.12.g. by identifying, modeling, and investigating embayments along the Bay shoreline designated “Priority Margin Units” (PMUs). The project:

Identified four PMUs for initial study that are located downstream of urban watersheds where PCBs management actions are ongoing and/or planned;

Is developing conceptual and PCBs mass budget models for each of the four PMUs; and

Is conducting monitoring in the PMUs to evaluate trends in pollutant levels and track responses to pollutant load reductions.

1.3. Third-Party Data The Countywide Program strives to work collaboratively with water quality monitoring partners to develop mutually beneficial monitoring approaches. Provision C.8.a.iii of the MRP allows Permittees to use data collected by third-party organizations to fulfill monitoring requirements, provided the data are demonstrated to meet the required data quality objectives. As such, samples collected in San Mateo County through two ongoing programs, (1) the Small Tributary Loading Strategy (STLS) of the Regional Monitoring Program for Water Quality in San Francisco Bay (RMP) and (2) the State’s Stream Pollution Trends (SPoT) Monitoring Program, supplement the Countywide Program’s efforts towards achieving Provision C.8.f monitoring requirements. In addition, Clean Watersheds for a Clean Bay (CW4CB), a BASMAA project that was funded by a grant from USEPA and ended in 2017, provided data from WY 2012, WY 2013, and WY 2016. Third party monitoring conducted by the RMP, SPoT, and CW4CB also provides context for reviewing and interpreting Countywide Program monitoring results. As in past years, this POC Data Report evaluates PCBs and mercury data from third-party POC monitoring efforts, along with data collected by the Countywide Program.


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1.3.1. RMP STLS The RMP’s Small Tributary Loading Strategy (STLS) Team typically conducts annual monitoring for POCs on a region-wide basis (Gilbreath et al., in preparation). SMCWPPP is an active participant in the STLS and works with other Bay Area municipal stormwater programs to identify opportunities to direct RMP funds and monitoring activities towards addressing both short- and long-term municipal stormwater permit management questions. POC monitoring activities conducted by the STLS in recent years focused on pollutant loading monitoring at six region-wide stations (WY 2012 – WY 2014) (see Section 2.0), and wet weather characterization monitoring in catchments of interest (WY 2015 – present). The wet weather characterization sampling uses a similar approach to the PCBs and mercury sampling that has been implemented by SMCWPPP, and Countywide Program staff has assisted the STLS to select all of its PCBs and mercury monitoring stations in San Mateo County (see Section 4.2). RMP STLS monitoring in WY 2020 is continuing to focus on wet weather characterization, targeting two stations in San Mateo County. Like WY 2019, in WY 2020 STLS monitored stations that were previously sampled but did not have elevated PCBs concentrations. Additional stations may be monitored using un-manned “remote” samplers that capture suspended sediment from the water column throughout the duration of their deployment which is typically during one storm event. The STLS Team has been pilot testing these devices since WY 2015 and recently concluded that they generate data adequate for evaluating whether a WMA should be prioritized for source property investigations. In future years, RMP STLS monitoring is expected to shift towards Management Question #5 (Trends). The STLS Trends Strategy Team, initiated in WY 2015, is currently developing a regional monitoring and modeling program to assess trends in POC loading to San Francisco Bay from small tributaries. The STLS Trends Strategy will initially focus on PCBs and mercury but will not be limited to those POCs. The preliminary monitoring design concept included additional monitoring at one or two of the region-wide loadings stations to gain a better understanding of the variability in PCBs concentrations/loadings in the existing dataset. However, uncertainties about the utility of developing a trends monitoring program that targets just one or two watersheds coupled with unknowns about how to extrapolate findings to the region has prompted the Trends Strategy Team to delay monitoring and focus instead on identifying practical modeling approaches. STLS Trends monitoring is not anticipated to commence before WY 2021. 1.3.2. SPoT Monitoring Program SPoT conducts annual dry season monitoring (subject to funding constraints) of sediments collected from a statewide network of selected rivers and streams throughout California. The goal of the SPoT monitoring program is to investigate long-term trends in sediment toxicity and sediment contaminant concentrations and relate contaminant concentrations and toxicity to watershed land uses. Sites are targeted in bottom-of-the-watershed locations with slow water flow and appropriate micromorphology to allow deposition and accumulation of sediments, including a station (204SMA020) near the mouth of San Mateo Creek in the City of San Mateo. The SPoT Monitoring Program conducts annual dry season monitoring (subject to funding constraints) of sediments collected from a statewide network of large rivers. The goal of the SPoT Program is to investigate long-term trends in water quality (Management Question #5 – Trends). Sites are targeted in bottom-of-the-watershed locations with slow water flow and appropriate micromorphology to allow deposition and accumulation of sediments, including a station near the mouth of San Mateo Creek. In most years, sediments are analyzed for PCBs, mercury, metals (including copper) toxicity, pesticides, and


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organic pollutants. In WY 2019, SPoT monitoring in San Mateo Creek did not include mercury or copper but samples were analyzed for PCBs, pesticides, organic pollutants, and toxicity. It is likely that SPoT monitoring in WY 2020 program will include mercury, copper, pesticides, and toxicity, but not PCBs (K. Siegler personal communication, August 2019). The most recent technical report prepared by SPoT program staff was published in 2016 and describes seven-year trends from the initiation of the program in 208 through 2014 (Phillips et al. 2016). This report will be updated in the future. Table 1. MRP Provision C.8.f Pollutants of Concern Monitoring Requirements.

Pollutant of Concern Media

Total Samples

by the End of Year

Fived Yearly

Minimum

Minimum Number of Samples That Must Be Collected for Each Information Need by the End of

Year Four

Sour

ce Id

entif

icatio

n

Cont

ribut

ions

to B

ay

Impa

irmen

t

Man

agem

ent A

ctio

n Ef

fect

iven

ess

Load

s and

Stat

us

Tren

ds

PCBs Water or sediment 80 8 8 8 8 8 8

Total Mercury Water or sediment 80 8 8 8 8 8 8

Total & Dissolved Copper Water 20 2 -- -- -- 4 4

Nutrientsa Water 20 2 -- -- -- 20 --Emerging Contaminantsb -- -- -- -- -- -- -- --

Ancillary Parametersc -- -- -- -- -- -- -- --

Notes: a Ammonium5, nitrate, nitrite, total Kjeldahl nitrogen, orthophosphate, total phosphorus (analyzed concurrently in each nutrient sample). b Must include perfluorooctane sulfonates (PFOS, in sediment), perfluoroalkyl sulfonates (PFAS, in sediment), alternative flame retardants. The Permittee shall conduct or cause to be conducted a special study that addresses relevant management information needs for emerging contaminants. The special study must account for relevant Contaminants of Emerging Concern (CECs) in stormwater and would address at least PFOS, PFAS, and alternative flame retardants being used to replace PBDEs. c Total Organic Carbon (TOC) should be collected concurrently with PCBs data when normalization to TOC is deemed appropriate. Suspended sediment concentration (SSC) should be collected in water samples used to assess loads, loading trends, or BMP effectiveness. Hardness data are used in conjunction with copper concentrations collected in fresh water. d Total samples that must be collected over the five-year Permit term.

5 There are several challenges to collecting samples for “ammonium” analysis. Therefore, samples are analyzed for total ammonia which is the sum of un-ionized ammonia (NH3) and ionized ammonia (ammonium, NH4+). Ammonium concentrations are calculated by subtracting the calculated concentration of un-ionized ammonia from the measured concentration of total ammonia. Un-ionized ammonia concentrations are calculated using a formula provided by the American Fisheries Society that includes field pH, field temperature, and specific conductance. This approach was approved by Regional Water Board staff in an email dated June 21, 2016.


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2.0 POLLUTANT LOADING STATION (WYs 2014 AND 2015) Provision C.8.e.i of MRP 1.0 required POC loads monitoring to assess inputs of POCs to the Bay from local tributaries and urban runoff, assess progress toward achieving wasteload allocations (WLAs) for TMDLs, and help resolve uncertainties associated with loading estimates for these pollutants. An RMP Small Tributaries Loading Strategy (STLS) was developed in 2009 by the STLS Team, which included representatives from BASMAA, Regional Water Board staff, RMP staff, and technical advisors. The objective of the STLS was to develop a comprehensive planning framework to coordinate POC loads monitoring/modeling between the RMP and RMC participants. With concurrence of participating Regional Water Board staff, the framework presented an alternative approach to the POC loads monitoring requirements described in MRP Provision C.8.e.i, as allowed by Provision C.8.e. The STLS loads monitoring framework included intensive monitoring at six bottom-of-the watershed stations over several years to collect data needed in developing loading estimates from small tributaries for priority POCs. Four stations were set up and monitored beginning in October 2011. The monitoring was extended to include two additional stations, including the Pulgas Creek Pump Station south drainage station in San Mateo County (Figure 1), which were established in October 2012 to complete the monitoring network:

1. Lower Marsh Creek (Contra Costa County), established WY 2012

2. Guadalupe River (Santa Clara County), established WY 2012

3. Lower San Leandro Creek (Alameda County), established WY 2012

4. Sunnyvale East Channel (Santa Clara County), established WY 2012

5. North Richmond Pump Station (Contra Costa County), established WY 2013

6. Pulgas Creek Pump Station south drainage (San Mateo County), established WY 2013 The Pulgas Creek Pump Station south drainage station was operated by SMCWPPP. Discrete and composite stormwater runoff samples were collected over the rising, peak and falling stages of the hydrographs. Samples collected were analyzed PCBs, mercury, and other analytes consistent with MRP 1.0 Provision C.8.e., and turbidity was recorded continuously. A total of 33 stormwater runoff samples have been collected from this location, including four samples collected in WY 2011 before this location was established as a pollutant loading station:

• WY 2011 – four samples collected in February and March 2011.

• WY 2013 – four samples collected in March 2013.

• WY 2014 – 25 samples collected from November 2013 through March 2014. The 33 samples had a highly elevated average PCBs particle ratio of 8,220 ng/g (see Section 4.2 for further details regarding San Mateo County stormwater runoff monitoring for PCBs and mercury). Gilbreath et al. (2016) provides complete details about the methods used and monitoring results for all six of the pollutant loading stations.


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3.0 WY 2016 – 2019 POC MONITORING ACCOMPLISHMENTS This section summarizes POC monitoring accomplishments during WY 2016 – WY 2019 in compliance with MRP 2.0 requirements. In compliance with MRP Provision C.8.f. of MRP 2.0, SMCWPPP conducted POC monitoring for PCBs, mercury, copper, and nutrients in WY 2019. The MRP-required yearly minimum number of samples was met or exceeded for all POCs. The total number of samples collected for each POC in WY 2019, the agency conducting the monitoring, and the Management Questions addressed are listed in Table 2 (PCBs), Table 3 (mercury), Table 4 (copper), and Table 5 (nutrients). These tables also include this information for WY 2016 through WY 2019 and show cumulative progress towards the Provision C.8.f minimum sample requirements. Tables 2 through 5 show that the MRP-required minimum number of samples addressing each Management Question by the end of year four of the permit term was met or exceeded for all POCs. Section 4.0 summarizes the QA/QC program that was implemented by the Countywide Program covering all aspects of POC monitoring during WYs 2014 – 2019. Figure 1 shows the POC monitoring stations in San Mateo County. Figure 1 includes all the relevant and readily available sediment and stormwater runoff sample stations in San Mateo County where PCBs were collected, ranging back to the early 2000s, in the context of evaluating progress to-date towards identifying PCBs source areas and properties in San Mateo County (discussed in Section 5.0). Section 6.0 discusses the results for monitoring for copper, nutrients, and emerging contaminants. Compliance with applicable water quality objectives (WQOs) is discussed in Section 7.0. Section 8.0 summarizes and discusses the POC monitoring data presented in this report.

4.0 SUMMARY OF DATA QUALITY FOR WYs 2016 – 2019 In accordance with MRP 1.0 and MRP 2.0 requirements, a comprehensive QA/QC program was implemented by the Countywide Program covering all aspects of POC monitoring conducted during WYs 2016 – 2019. The QA/QC protocols have been described in previous SMCWPPP UCMRs (SMCWPPP 2017a, 2018a, 2019a) and continued to be based upon the Quality Assurance Project Plan (QAPP) developed for the CW4CB project (AMS 2012) and the BASMAA Regional Monitoring Coalition (RMC) QAPP (BASMAA 2016). Data were assessed for seven data quality attributes: (1) Representativeness, (2) Comparability, (3) Completeness, (4) Sensitivity, (5) Contamination, (6) Accuracy, and (7) Precision. Data Quality Objectives (DQOs) related to these categories were established to ensure that the data collected are of adequate quality for the intended uses. Attachment 2 contains a report summarizing the results of the WY 2019 data validation. Validation of data collected during WYs 2016 – 2018 is described in previous SMCWPPP UCMRs (SMCWPPP 2017a, 2018a, 2019a). Overall, the results of the QA/QC reviews suggest that the POC monitoring data generated during WYs 2016 – 2019 were of sufficient quality for the purposes of the POC monitoring program.


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Table 2. SMCWPPP/BASMAA and Third-Party POC Monitoring Accomplishments, PCBs, WYs 2016 - 2019.

Management Question Addresseda

Pollutant of Concern/

Organization

Number of PCBs

Samples 1. S

ourc

e Id

entif

icatio

n

2. C

ontr

ibut

ions

to B

ay

Impa

irmen

t

3. M

anag

emen

t Act

ion

Effe

ctiv

enes

s

4. Lo

ads a

nd S

tatu

s

5. T

rend

s

Sample Type and Comments WY 2019 SMCWPPP 25 25 -- -- -- -- Urban sediment samples to identify source areas RMP STLS 2 2 2 -- 2 2 Stormwater runoff samples to characterize WMAs SPoT 1 -- -- -- -- 1 Creek bed sediment sample to assess trends (PCBs only, no mercury) WY 2018 SMCWPPP 13 13 13 -- 13 13 Stormwater runoff samples to characterize WMAs SMCWPPP 57 57 -- -- -- -- Urban sediment samples to identify source areas BASMAA 5 5 -- -- -- -- Regional public infrastructure caulk/sealant samples (1/4 of project total) BASMAA 8 -- -- 8 -- -- Regional HDS unit & biochar effectiveness study (1/4 of project total) RMP STLS 2 2 2 -- 2 2 Stormwater runoff samples to characterize WMAs SPoT -- -- -- -- -- -- Creek bed sediment sample to assess trends WY 2017 SMCWPPP 17 17 17 -- 17 17 Stormwater runoff samples to characterize WMAs SMCWPPP 67 67 -- -- -- -- Urban sediment samples to identify source areas RMP STLS 4 4 4 -- 4 4 Stormwater runoff samples to characterize WMAs SPoT 1 -- -- -- -- 1 Creek bed sediment sample to assess trends (PCBs only, no mercury) WY 2016 SMCWPPP 8 8 8 -- 8 8 Stormwater runoff samples to characterize WMAs RMP STLS 7 7 7 -- 7 7 Stormwater runoff samples to characterize WMAs CW4CB -- -- -- 3 -- -- BMP effectiveness samples at Bransten Road bioretention facilities

Total / MRP Minimumb 217 / 80 207 / 8 53 / 8 11 / 8 53 / 8 55 / 8

a Individual samples can address more than one Management Question simultaneously. b The MRP overall minimum number of POC samples must be met by the end of the five-year permit term. The MRP minimum number of samples for each Management Question must be met by the end of year four of the permit.


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Table 3. SMCWPPP/BASMAA and Third-Party POC Monitoring Accomplishments, Mercury, WYs 2016 - 2019.



Organization

Number of Mercury Samples 1.

Sou

rce

Iden

tifica

tion

2. C

ontr

ibut

ions

to B

ay

Impa

irmen

t

3. M

anag

emen

t Act

ion

Effe

ctiv

enes

s

4. Lo

ads a

nd S

tatu

s

5. T

rend

s

Sample Type and Comments WY 2019 SMCWPPP 25 25 -- -- -- -- Urban sediment samples to identify source areas RMP STLS 2 2 2 -- 2 2 Stormwater runoff samples to characterize WMAs SPoT -- -- -- -- -- -- Creek bed sediment sample to assess trends WY 2018 SMCWPPP 13 13 13 -- 13 13 Stormwater runoff samples to characterize WMAs SMCWPPP 57 57 -- -- -- -- Urban sediment samples to identify source areas BASMAA 8 -- -- 8 -- -- Regional HDS unit & biochar effectiveness study (1/4 of project total) RMP STLS 2 2 2 -- 2 2 Stormwater runoff samples to characterize WMAs SPoT 1 -- -- -- -- 1 Creek bed sediment sample to assess trends (mercury only, no PCBs) WY 2017 SMCWPPP 17 17 17 -- 17 17 Stormwater runoff samples to characterize WMAs SMCWPPP 67 67 -- -- -- -- Urban sediment samples to identify source areas RMP STLS 4 4 4 -- 4 4 Stormwater runoff samples to characterize WMAs SPoT -- -- -- -- -- -- Creek bed sediment sample to assess trends WY 2016 SMCWPPP 8 8 8 -- 8 8 Stormwater runoff samples to characterize WMAs RMP STLS 7 7 7 -- 7 7 Stormwater runoff samples to characterize WMAs CW4CB -- -- -- 3 -- -- BMP effectiveness samples at Bransten Road bioretention facilities

Total / MRP Minimumb 211 / 80 202 / 8 53 / 8 11 / 8 53 / 8 54 / 8

a Individual samples can address more than one Management Question simultaneously. b The MRP overall minimum number of POC samples must be met by the end of the five-year permit term. The MRP minimum number of samples for each Management Question must be met by the end of year four of the permit.


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Table 4. SMCWPPP/BASMAA and Third-Party POC Monitoring Accomplishments, Copper, WYs 2016 - 2019.



Organization Number of

Samples 1. S

ourc

e Id

entif

icatio

n

2. C

ontr

ibut

ions

to B

ay

Impa

irmen

t

3. M

anag

emen

t Act

ion

Effe

ctiv

enes

s

4. Lo

ads a

nd S

tatu

s

5. T

rend

s

Sample Type and Comments WY 2019 SMCWPPP 2 -- -- -- 2 -- Dry season creek water samples from mixed-use watersheds WY 2018 SMCWPPP 4 -- -- -- 4 4 Creek water samples collected during storm event and spring base flows SPoT 1 -- -- -- -- 1 Creek bed sediment samples to assess trends WY 2017 SMCWPPP 1 -- -- -- 1 -- Copper analyzed on a subset of PCBs/Hg stormwater runoff samples SMCWPPP 5 -- -- -- 5 2 Creek water samples collected during storm event and spring base flowsc SPoT 1 -- -- -- -- 1 Creek bed sediment samples to assess trends WY 2016 SMCWPPP 3 -- -- -- 3 -- Copper analyzed on a subset of PCBs/Hg stormwater runoff samples

Total / MRP Minimumb 17 / 20 NA NA NA 15 / 4 8 / 4

NA = Not Applicable. For this pollutant, the MRP does not require sampling to address the management question. a Individual samples can address more than one Management Question simultaneously. b The MRP overall minimum number of POC samples must be met by the end of the five-year permit term. The MRP minimum number of samples for each Management Question must be met by the end of year four of the permit. c One of these five samples was a PCBs/Hg stormwater runoff sample that was also analyzed for copper.


13

Table 5. SMCWPPP/BASMAA and Third-Party POC Monitoring Accomplishments, Nutrients, WYs 2016 - 2019.



Organization Number of

Samples 1. S

ourc

e Id

entif

icatio

n

2. C

ontr

ibut

ions

to B

ay

Impa

irmen

t

3. M

anag

emen

t Act

ion

Effe

ctiv

enes

s

4. Lo

ads a

nd S

tatu

s

5. T

rend

s

Sample Type and Comments WY 2019 SMCWPPP 9 -- -- -- 9 -- Dry season creek samples at stations also sampled during spring base flows WY 2018 SMCWPPP 4 -- -- -- 4 -- Creek water samples collected during storm event and spring base flows WY 2017 SMCWPPP 5 -- -- -- 5 -- Creek water samples collected during storm event and spring base flows WY 2016 SMCWPPP 2 -- -- -- 2 -- Creek water samples collected from bottom-of-the-watershed stations

Total / MRP Minimumb 20 / 20 NA NA NA 20 / 20 NA

NA = Not Applicable. For this pollutant, the MRP does not require sampling to address the management question. a Individual samples can address more than one Management Question simultaneously. b The MRP overall minimum number of POC samples must be met by the end of the five-year permit term. The MRP minimum number of samples for each Management Question must be met by the end of year four of the permit.


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Figure 1. POC Monitoring Stations in San Mateo County (includes all samples from early 2000s to WY 2019).


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5.0 PROGRESS TO-DATE IDENTIFYING PCBS AND MERCURY SOURCES The below sections summarize progress to-date using POC monitoring (informed by records reviews) to identify sources of PCBs and mercury in San Mateo County stormwater runoff. The Countywide Program’s PCBs and mercury monitoring has been focused on catchments in San Mateo County (referred to as Watershed Management Areas or WMAs) containing high interest parcels with land uses potentially associated with PCBs such as old industrial, electrical and recycling. PCBs and mercury monitoring conducted by SMCWPPP has primarily focused on addressing Management Question No. 1 (Source Identification), while contributing to the regional dataset being used to address Management Questions No. 2 (Contributions to Bay Impairment) and No. 3 (Loads and Status). In addition to the efforts described in the below sections, during the past several years the RMP has conducted stormwater runoff monitoring in San Mateo County and other parts of the Bay Area through the STLS. As described earlier (Section 1.3.1), the STLS monitoring in San Mateo County was coordinated with SMCWPPP, with SMCWPPP staff assisting with selection of sampling stations and coordination with staff from local agencies. Monitoring objectives have included characterizing PCBs and mercury concentrations in stormwater runoff from the bottom of selected urban catchments with potential pollutant source areas. SMCWPPP (2017a, 2018a, and 2019a) include additional information on the STLS efforts in San Mateo County. 5.1. Sampling Summary and Chronology The following sections summarize the general chronology of PCBs and mercury monitoring conducted in San Mateo County to characterize pollutant concentrations across the urban landscape and to identify source areas and properties. To-date, about 60 composite samples of stormwater runoff6 have been collected from the bottom of San Mateo County WMAs and about 400 individual and composite grab samples of sediment have been collected within priority WMAs to help characterize the catchments and identify source areas and properties. Most samples were collected in the public ROW. The grab sediment samples were collected from a variety of types of locations, including manholes, storm drain inlets, driveways, streets, and sidewalks, often adjacent to or nearby high interest parcels with land uses associated with PCBs and/or other characteristics potentially associated with pollutant discharge (e.g., poor housekeeping, unpaved areas). SMCWPPP’s PCBs and mercury monitoring program also included collecting sediment samples in the public ROW (e.g., from streets and the MS4) by every known PCBs remediation site in San Mateo County, to the extent applicable and feasible. When a previously unknown potential source property was revealed via the PCBs and mercury monitoring program, SMCWPPP conducted a follow-up review of current and historical records regarding site occupants and uses, hazardous material/waste use, storage, and/or release, violation notices, and any remediation activities. Apart from databases such as EPA’s Toxic Release Inventory (TRI) and Envirofacts, and the State of California’s Geotracker and Envirostor, the most useful records were often kept by San Mateo County Department of Environmental Health. Four previously unknown potential source properties have been identified in San Mateo County, all in WMA 210 (Pulgas Creek Pump Station South) in the City of San Carlos. SMCWPPP is working with the City of San Carlos to determine next steps for these properties, including potential referral to the



16

Regional Water Board (see Section 5.5.6 for more details). In addition, SMCWPPP’s PCBs and mercury monitoring program has resulted in SMCWPPP referring four properties (two sets of two adjacent properties, all in San Carlos) to the Regional Water Board for potential further PCBs investigation and abatement (see Section 5.5.6). 5.1.1. WY 2000 through WY 2014 From 2000 to 2015, SMCWPPP and others conducted periodic sediment sampling programs in San Mateo County to begin to characterize the distribution of PCBs in various land uses throughout the urban landscape and identify catchments and properties within catchments that are potential sources of PCBs to the MS4. During this period, over 270 sediment samples were collected in San Mateo County, mainly from streets and MS4s in the public right-of-way (e.g., storm drain lines accessed via manholes, storm drain inlets, drainage channels, and pump station sumps). The samples were analyzed for PCBs congeners, total mercury, and ancillary analytes (KLI and EOA 2002, SMSTOPPP 2002, 2003, and 2004, Yee and McKee 2010, SMCWPPP 2015, and CW4CB 2017a). The initial step in the sediment sampling programs was a 2000 and 2001 collaborative project among SMCWPPP and other Bay Area countywide stormwater programs referred to as the Joint Stormwater Agency Project (JSAP). The JSAP measured concentrations of PCBs, mercury and other pollutants in sediments collected from stormwater conveyance systems in San Mateo County and other parts of the Bay Area (KLI and EOA 2002). The primary goal was to characterize the distribution of pollutants among land uses in watersheds draining to the Bay. In follow-up to the JSAP regional survey, SMCWPPP and other Bay Area countywide stormwater programs began performing “case studies” in some areas where relatively elevated PCBs were found during the JSAP. The primary goals were to develop methods to identify PCBs sources and begin to identify measures to address any controllable sources found. The techniques employed included collection and analysis of stormwater conveyance sediment samples and research on historical and current land use. In the early 2000s, SMCWPPP completed PCBs case study work in four San Mateo County areas where elevated levels of PCBs were found during the JSAP survey. The case studies investigated the Bradford and Broadway pump station drainages in Redwood City, the South Maple pump station drainage in South San Francisco, an area in the vicinity of Colma Creek, and the Pulgas Creek pump station drainage in San Carlos (SMSTOPPP 2002, 2003, and 2004). In 2007, a State of California Proposition 13 grant-funded study by the San Francisco Estuary Institute (SFEI) collected street dirt and MS4 sediment samples in the City of San Carlos in San Mateo County and other parts of the Bay Area (Yee and McKee 2010). In addition, beginning in 2010 SMCWPPP partnered with the Bay Area Stormwater Management Agencies Association (BASMAA) in the USEPA grant-funded Clean Watersheds for a Clean Bay (CW4CB) project to conduct additional investigation of PCBs sources to the MS4 in the Pulgas Creek pump station drainage in San Carlos (CW4CB 2017a). In WY 2014, SMCWPPP worked with San Mateo County MRP Permittees to conduct a process to screen for “high interest parcels” for PCBs in the county. The process was generally consistent with a framework developed through a collaboration of SMCWPPP and the other Bay Area countywide stormwater programs in consultation with Regional Water Board staff. The screening covered all land areas in the county that drain to the Bay, focusing on about 160,000 urban parcels. Parcels were identified that were industrialized in 1980 or earlier (i.e., old industrial parcels) or have other land uses associated with PCBs (i.e., electrical, recycling, and military). SMCWPPP then worked with municipal


17

staff to prioritize these parcels based on the evaluation of existing information on current land uses and practices (e.g., redevelopment status, extent and quality of pavement, level of current housekeeping, any history of stormwater violations, and presence of electrical or heavy equipment, storage tanks, or stormwater treatment), local institutional/historical knowledge, and surveys of site conditions (windshield, Google Street View, and/or aerial photograph). The prioritization resulted in a list of about 1,600 high interest parcels for PCBs in San Mateo County (SMCWPPP 2015). 5.1.2. WY 2015 In January and February 2015, SMCWPPP designed a monitoring plan based on the results of the 2014 screening for high interest parcels. SMCWPPP then collected 101 sediment samples from the urban storm drainage system (e.g., manholes, storm drain inlets) and public right-of-way surfaces (e.g., street gutters). The general goal was to continue attempting to identify potential source areas for PCBs. Samples were distributed among the nine municipalities that collectively encompass 93% of the old industrial land use in San Mateo County that drains to San Francisco Bay (SMCWPPP 2015). 5.1.3. WY 2016 Provisions C.11.a.iii and C.12.a.iii require that Permittees provide a list of management areas in which new PCBs and mercury control measures will be implemented during the permit term. These management areas were designated Watershed Management Areas (WMAs). In FY 2016, SMCWPPP implemented a process to identify WMAs and prioritize them based on the potential for controls (especially source property referrals) to reduce PCBs loads. Progress toward developing the list was initially submitted in a report dated April 1, 2016 (SMCWPPP 2016a) and the initial list was submitted with SMCWPPP’s FY 2015/16 Annual Report (SMCWPPP 2016b). The 1,600 high interest parcels described above are almost entirely located within 105 “catchments of interest” with high interest parcels comprising at least 1% of their area (and usually with existing pollutant controls). WMAs were defined as the sum of the 105 catchments of interest and an additional 25 catchments with existing or planned stormwater pollutant controls (e.g., GI implemented on parcels per Provision C.3 requirements, built on public lands such as parks, or retrofitted into the public ROW), for a total of about 130 catchments designated as WMAs (SMCWPPP 2016a and b). It should be noted that WMA catchments are stormwater runoff hydrologic catchments in San Mateo County that drain to 24-inch or larger diameter outfalls. These urban catchments were originally delineated at this geographical scale as part of SMCWPPP’s program to help local agencies develop trash controls in San Mateo County (SMCWPPP 2014).7 Finally, during the WY 2016 rainy season SMCWPPP collected eight composite samples of stormwater runoff. The samples were collected from outfalls at the bottom of WMAs that contain high interest parcels (i.e., with land uses associated with PCBs such as old industrial, electrical and recycling, as described above). The RMP STLS collected an additional seven stormwater runoff composite samples in

7The WMA numbering system starts with the numerical designations (ranging from 0 to 408) used by SMCWPPP (2014). Additional WMAs were delineated for areas that contain parcels of interest but were not delineated in 2014, with numerical designations ranging from 1000 to 1017. These 18 WMAs were not necessarily hydrologic catchments. They combine areas that drain to outfalls ≥ 24-inches, drain directly to natural waterways including the Bay, and/or private drainages. Finally, additional WMAs were delineated that lack parcels of interest but include pollutant controls (mainly GI in old urban parcels that were redeveloped). These WMAs are not hydrologic catchments and were delineated for each Permittee that drains to the Bay. They were designated “Other –” followed by three letters representing the jurisdiction (e.g., Other – SSF for South San Francisco).


18

San Mateo County in coordination with SMCWPPP. Composite samples consisting of four to eight aliquots collected during the rising limb and peak of the storm hydrograph (as determined through field observations) were analyzed for PCBs congeners, total mercury, and other analytes (SMCWPPP 2017a). 5.1.4. WY 2017 SMCWPPP’s major WY 2017 POC monitoring efforts included the following:

Collected 17 composite samples of stormwater runoff from outfalls at the bottom of WMAs that contain high interest parcels with land uses associated with PCBs. The RMP STLS collected an additional four stormwater runoff composite samples in San Mateo County in coordination with SMCWPPP. Composite samples consisting of four to eight aliquots collected during the rising limb and peak of the storm hydrograph (as determined through field observations) were analyzed for PCBs congeners, total mercury, and other analytes (SMCWPPP 2018a).

Collected 61 sediment samples as part of the program to attempt to identify source properties within WMAs. These samples were collected in the public ROW, including locations adjacent to high interest parcels. Individual and composite sediment samples collected from manholes, storm drain inlets, driveways, and sidewalks were analyzed for PCBs congeners, total mercury, and other analytes (SMCWPPP 2018a).

Continued updating and prioritizing the list of WMAs in San Mateo County (SMCWPPP 2018b). 5.1.5. WY 2018 SMCWPPP’s major WY 2018 POC monitoring efforts included the following:

Collected 13 composite samples of stormwater runoff from outfalls at the bottom of WMAs that contain high interest parcels with land uses associated with PCBs. The RMP STLS collected an additional two stormwater runoff composite samples in San Mateo County in coordination with SMCWPPP. Composite samples consisting of four to eight aliquots collected during the rising limb and peak of the storm hydrograph (as determined through field observations) were analyzed for PCBs congeners, total mercury, and other analytes (SMCWPPP 2019a).

Collected 50 sediment samples as part of the program to attempt to identify source properties within WMAs. These samples were collected in the public ROW, including locations adjacent to high interest parcels. Individual and composite sediment samples collected from manholes, storm drain inlets, driveways, and sidewalks were analyzed for PCBs congeners, total mercury, and other analytes (SMCWPPP 2019a).

Continued updating and prioritizing the list of WMAs in San Mateo County (SMCWPPP 2019b). 5.1.6. WY 2019 During WY 2019, SMCWPPP collected 25 sediment samples as part of the program to attempt to identify source properties within WMAs. These samples were collected in the public ROW, including locations adjacent to high interest parcels. Individual and composite sediment samples collected from manholes, storm drain inlets, driveways, and sidewalks were analyzed for PCBs congeners, total mercury, and other analytes. In addition, the RMP STLS collected two stormwater runoff composite samples in San Mateo County in coordination with SMCWPPP. The results of the WY 2019 and prior PCBs and mercury monitoring are summarized in the following sections.


19

As part of continuing to develop strategies for reducing PCBs and mercury loads in stormwater runoff, the Countywide Program evaluated its WY 2019 PCBs and mercury stormwater runoff and sediment data, additional WY 2019 stormwater runoff sample data collected through the RMP STLS (see Section 1.3.1), and similar data from previous water years collected by the Countywide Program and through the STLS. Objectives included attempting to identify source areas and properties within WMAs, identifying which WMAs provide the greatest opportunities for implementing cost-effective PCBs controls, and prioritizing WMAs for potential future investigations. The results of the evaluation are described in the remaining subsections in Section 5.0. 5.2. San Mateo County Stormwater Runoff Monitoring for PCBs and Mercury To prioritize WMAs for stormwater sampling, SMCWPPP evaluated several types of data, including: land use, PCBs and mercury concentrations from prior sediment and stormwater runoff sampling efforts, municipal storm drain maps showing pipelines and access points (e.g., manholes, outfalls, pump stations), and logistical/safety considerations. Composite samples, consisting of four to eight aliquots collected during the rising limb and peak of the storm hydrograph (as determined through field observations), are analyzed for the 40 PCBs congeners used by the RMP for Bay samples8 (method EPA 1668C), total mercury (method EPA 1631E), and suspended sediment concentration (SSC; method ASTM D3977-97). During WYs 2016 – 2018, SMCWPPP collected 38 composite samples of stormwater runoff from outfalls at the bottom of WMAs that contain high interest parcels (SMCWPPP did not collect stormwater runoff samples in WY 2019). From WYs 2015 – 2019, an additional 21 composite stormwater samples were collected through the RMP’s STLS, with four of the RMP’s STLS samples being at previously sampled sites. Prior to that, from WYs 2011 – 2014, the RMP STLS collected 43 grab samples at four sites, with the majority being at the Pulgas Creek Pump Station south catchment loading station. The total of about 100 samples (at 59 unique sites) primarily help address Management Questions No. 1 (Source Identification) and Management Question No. 4 (Loads and Status). These data will also be used by the RMP STLS to improve calibration of the Regional Watershed Spreadsheet Model (RWSM), which is a land use based planning tool for estimation of overall POC loads from small tributaries to San Francisco Bay at a regional scale. PCBs and mercury stormwater runoff sampling results are summarized in Attachment 3. Of the 59 stormwater runoff samples collected in San Mateo County from WY 2015 - 2019 by the Countywide Program and the RMP, ten samples had PCBs particle ratios greater than 0.5 mg/kg, twelve were between 0.2 and 0.5 mg/kg, and the remainder were below 0.2 mg/kg. Table 6 summarizes PCBs, mercury, and SSC monitoring results for stormwater runoff samples collected in San Mateo County (by the Countywide Program and RMP STLS) through WY 2019. “Total PCBs” was calculated as the sum of the RMP 40 congeners. Particle ratio is calculated by dividing the total pollutant (PCBs or mercury) concentration by SSC. Assuming a pollutant is entirely bound to suspended sediments in the water sample, particle ratios estimate the average concentration of pollutant on the suspended sediment and are sometimes referred to as particle concentration. Since PCBs and mercury are hypothesized to primarily be bound to sediment in aquatic environments, particle ratios are often used to normalize pollutant concentrations in samples with varying levels of suspended sediment.

8 PCBs congeners 8, 18, 28, 31, 33, 44, 49, 52, 56, 60, 66, 70, 74, 87, 95, 97, 99, 101, l05, 110, 118, 128, 132, 138, 141, 149, l51, 153, 156, 158, 170, 174, 177, 180, 183, 187, 194, 195, 201, 203.


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Table 6. Descriptive Statistics – PCBs and Mercury Concentrations in San Mateo County Stormwater Runoff and Natural Waterway Water Samples through WY 2019a

PCBs (ng/L)b Hg (ng/L) SSC (mg/L) PCBs Particle

Ratio (ng/g)c

Hg Particle Ratio

(ng/mg)c Min NDd 0.44 3.20 ND 0.008 10th Percentile 1.79 2.86 13.9 30.3 0.03 25th Percentile 1.82 3.33 14.2 34.3 0.04 50th Percentile 6.20 12.6 43.7 167 0.31 75th Percentile 13.0 20.5 68 365 0.48 90th Percentile 49.5 40.2 148 936 0.74 Max 448 71.1 719 8,222 2.28 Mean 22.0 16.4 76.3 524 0.39 a Based upon 59 PCBs sampling stations and 38 mercury sampling stations. Results have been averaged for stations with more than one sample. b Total PCBs calculated as sum of RMP 40 congeners. c PCBs and Hg Particle Ratios calculated by dividing total PCBs and Hg concentrations by SSC, respectively. d Not Detected.

For sites with more than one sample, total PCBs concentrations were averaged in Table 6. In addition, for sites with multiple samples, particle ratios in Table 6 were calculated by dividing the sum of PCBs concentrations by the sum of suspended sediment concentrations. This averaging is essentially equivalent to compositing all the individual samples that have been collected at a site. This is consistent with the RMP STLS approach to data evaluation (Gilbreath et al., in preparation). Low PCBs concentrations in composite stormwater runoff samples from the bottom of WMA catchments have suggested that either PCBs sources are not prevalent in the catchment or the samples are “false negatives.” False negatives could be the result of low rainfall/runoff rates failing to mobilize sediments from source areas and/or other factors. Only a few stormwater runoff sampling stations in San Mateo County have been resampled, but the results from two such stations in South San Francisco, as described by SMCWPPP (2018), suggested small storm sizes may have resulted in false negatives. SMCWPPP, in collaboration with the SCVURPPP, has recently preliminarily developed a method to normalize results from this type of stormwater runoff monitoring based upon storm intensity. However, the high variability in many of the parameters involved led to a high degree of uncertainty in the evaluation results. SMCWPPP and the SCVURPPP will continue to evaluate normalization methods and results as more data become available in future years, in coordination with related efforts by the RMP (referred to as the RMP’s “Advanced Data Analysis”). 5.3. Regional Stormwater Runoff Monitoring for PCBs and Mercury This section evaluates data collected by the Countywide Program to-date on PCBs concentrations in stormwater runoff and natural waterways in the context of similar data collected regionally. The analysis included data from other Bay Area countywide stormwater programs and the RMP STLS (Gilbreath et al., in preparation). The dataset includes stormwater runoff samples collected from 138 municipal separate storm sewer system (MS4) bottom of catchment stations and water samples from 28 natural waterways (usually creeks with generally natural channels) throughout the Bay Area. The MS4 catchment sites


21

included storm drain manholes, outfalls, pump stations, and artificial channels.9 Many of the sites have been sampled more than once and/or have multiple sample results reported for individual storm events. Nineteen of the 138 MS4 sites have multiple sample results (sample counts of 2 to 80) and 18 of the 28 natural waterway sites have multiple sample results (sample counts of 2 to 126). PCBs concentrations in Bay Area stormwater runoff and natural waterway samples (n=166) are shown in Figure 2. PCBs particle ratios are shown in Figure 3. Figures 2 and 3 compare samples collected in San Mateo County to samples collected outside of the County. Two of the four highest PCBs concentrations in the overall stormwater runoff sample dataset are for samples collected in San Mateo County, with Pulgas Creek Pump Station South having the highest PCBs concentration (average 448 ng/L) and SM-SCS-75A (Industrial Road Ditch) having the fourth highest concentration (160 ng/L). The 33 samples collected at Pulgas Creek Pump Station South station had very elevated PCBs concentrations. The site has had the two highest PCBs concentrations (6,669 ng/L and 4,084 ng/L) measured out of 748 total individual samples collected regionally and the four highest PCBs particle ratios (37 mg/kg, 21 mg/kg, 15 mg/kg, and 15 mg/kg).

Figure 2. PCBs Concentrations in Stormwater Runoff Samples Collected in MS4s and Natural Waterways in the Bay Area.

9 Stormwater runoff samples have also been collected from inlets and/or treatment systems (e.g., bioretention) during special studies. However, those are not included in this analysis.

0.0

0.1

1.0

10.0

100.0

1,000.0

0 50 100 150

PCB

Conc

entr

atio

n (n

g/L)

PCB Concentration Rank

Samples Outside of San Mateo CountySamples Within San Mateo County


22

Table 7 provides descriptive statistics for PCBs and mercury concentrations in the Bay Area stormwater runoff and natural waterway dataset (n=166). The median PCB concentration is 6.98 ng/L and the mean is 18.9 ng/L. The median PCB particle ratio is 0.01 mg/kg and the mean is 0.37 mg/kg. As can be seen in Figures 2 and 3, which are plotted on a log scale, there are a few catchments with highly elevated in PCBs (such as the Pulgas Creek Pump Station catchments) that greatly influence the mean concentration relative to the median (i.e., 50th percentile).

Figure 3. PCBs Particle Ratio in Stormwater Runoff Samples Collected in Large MS4s and Natural Waterways in the Bay Area.

1

10

100

1,000

10,000

0 50 100 150

PCB

Part

icle

Ratio

(ng/

g)

PCB Particle Ratio Rank



23

Figure 4. Mercury Concentrations in Stormwater Runoff Samples Collected in MS4s and Natural Waterways in the Bay Area.

Figure 5. Mercury Particle Ratio in Stormwater Runoff Samples Collected in Large MS4s and Natural Waterways in the Bay Area.

0.0

0.1

1.0

10.0

100.0

1,000.0

10,000.0

0 20 40 60 80 100 120

Hg C

once

ntra

tion

(ng/

L)

Hg Concentration Rank


0.0

0.1

1.0

10.0

0 20 40 60 80 100 120

Hg P

artic

le R

atio

(ng/

mg)

Hg Particle Ratio Rank



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Table 7. Descriptive Statistics – PCBs and Mercury Concentrations in Bay Area Stormwater Runoff and Natural Waterway Water Samples through WY 2019a

PCBs (ng/L)b

Hg (ng/L)

SSC (mg/L)

PCBs Particle Ratio (ng/g)c

Hg Particle Ratio (ng/mg)c

Min NDd 0.44 3.20 ND 0.008 10th Percentile 1.37 4.21 15.7 17.8 0.12 25th Percentile 2.70 7.36 29.2 46.7 0.20 50th Percentile 6.98 15.8 55.0 110 0.34 75th Percentile 16.1 35.4 112 222 0.53 90th Percentile 38.1 67.2 244 690 0.75 Max 448 1,053 2,630 9,343 5.29 Mean 18.9 39.4 126 367 0.46 a Based upon 166 PCBs sampling stations and 113 mercury sampling stations. Results have been averaged for stations with more than one sample. b Total PCBs calculated as sum of RMP 40 congeners. c PCBs and Hg Particle Ratios calculated by dividing Total PCBs and Hg concentrations by SSC, respectively. d Not Detected.

5.4. San Mateo County Sediment Monitoring for PCBs and Mercury Since WY 2001, about 400 sediment samples have been collected in San Mateo County as part of investigations to identify source properties within WMAs, potentially for referral to the Regional Water Board for further investigation and potential abatement. These samples were collected in the public right-of-way (ROW), including locations adjacent to high interest parcels. Individual and composite sediment samples were collected from manholes, storm drain inlets, driveways, streets, and sidewalks. Each sample was analyzed for the RMP 40 PCBs congeners and total mercury. Total PCBs was calculated as the sum of the 40 congeners. The laboratory passed all samples through a 2 mm sieve before analysis to remove gravel and cobbles. Table 8 compares the descriptive statistics for POC sediment samples that have been collected in San Mateo County up to WY 2019, WY 2019 samples, and all Bay Area wide samples. The mean and median PCBs concentrations were lower in WY 2019 than previous years. For the WY 2019 PCBs samples, no samples were above 1.0 mg/kg, one was between 0.5 and 1.0 mg/kg, two were between 0.2 and 0.5 mg/kg and 22 were below 0.2 mg/kg. The median was 0.024 mg/kg and the mean was 0.087 mg/kg. For the WY 2019 mercury samples, none was above 1.0 mg/kg, three were between 0.3 and 1.0 mg/kg, and 22 were below 0.3 mg/kg. The median was 0.120 mg/kg and the mean was 0.155 mg/kg. Attachment 4 summarizes PCBs and mercury sediment monitoring locations and analytical results. The results are discussed by selected WMA in the following sections, along with sediment data from previous Water Years and the stormwater runoff data collected to-date.


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Table 8. Descriptive Statistics – San Mateo County Sediment Sample PCBs and Mercury Concentrations

Bay Area All Samples To-date

San Mateo County WYs 2001-2018

San Mateo County WY 2019

Number of Samples 1535 1349 379b 327 25 25

PCBs (mg/kg)a

Hg (mg/kg)

PCBs (mg/kg)a

Hg (mg/kg)

PCBs (mg/kg)a

Hg (mg/kg)

Min ND ND ND 0.006 ND 0.010 10th Percentile ND 0.054 0.003 0.047 ND 0.018 25th Percentile 0.009 0.086 0.014 0.064 0.004 0.056 50th Percentile 0.041 0.149 0.044 0.100 0.024 0.120 75th Percentile 0.161 0.294 0.132 0.177 0.131 0.203 90th Percentile 0.773 0.740 0.565 0.333 0.332 0.461 Max 193 20.6 193 3.93 0.556 0.561 Mean 0.652 0.410 1.00 0.211 0.087 0.155

a Total PCBs calculated as sum of RMP 40 congeners. b Includes 26 samples from three PCBs cleanup reports in San Carlos and Redwood City. 5.5. Watershed Management Area Status The Countywide Program evaluated the monitoring data to-date to help categorize WMAs by level of PCBs in existing stormwater runoff and sediment samples.10 Based upon the data collected in San Mateo County to-date by the Countywide Program and other parties (e.g., the RMP’s STLS), catchments of interest were categorized into the following five groups:

1. One or more sediment and/or stormwater runoff samples with PCBs concentrations (particle ratios for stormwater runoff) greater than 0.5 mg/kg (500 ng/g) and source properties have been identified within the catchment.

2. One or more sediment and/or stormwater runoff samples with PCBs concentrations (particle ratios for stormwater runoff) greater than 0.5 mg/kg (500 ng/g) and source properties have not been identified within the catchment.

3. One or more sediment and/or stormwater runoff samples with PCBs concentrations (particle ratios for stormwater runoff) between 0.2 – 0.5 mg/kg (200 – 500 ng/g).

4. One or more sediment and/or stormwater runoff samples with PCBs concentrations (particle ratios for stormwater runoff) less than 0.2 mg/kg (200 ng/g).

5. No samples collected to-date.

10 This section focuses on “catchments of interest,” which as described earlier (Section 5.1) are a subset of the list of San Mateo County WMAs. The list of 130 WMAs includes 105 “catchments of interest” with high interest parcels for PCBs comprising at least 1% of their area. The remaining 25 WMAs include PCBs and mercury controls such as green infrastructure on parcels but lack high interest parcels.


26

Figure 6 is a map illustrating the current status of WMAs in San Mateo County, based on the sediment and stormwater runoff monitoring results to-date.11 Only WMAs with high interest parcels were included in Figure 6. Attachment 5 provides a summary of PCBs and mercury monitoring results for San Mateo county WMAs. For each WMA, Attachment 5 includes:

• The WMA area, the area of high interest parcels in the WMA, and the percent of the total WMA area that is comprised of high interest parcels;

• A summary of the number of stormwater runoff and sediment samples collected to-date in the WMA; and

• The median and range of PCBs concentrations in the samples collected to-date in the WMA (median and range of PCBs particle ratio for stormwater runoff samples).

Attachments 3, 4 and 5 summarize PCBs and mercury monitoring results for stormwater runoff and sediment samples collected in San Mateo County.12 Based on the available data to-date (e.g., sediment and stormwater runoff monitoring and land use research through WY 2019), WMAs with stormwater runoff sample PCBs particle ratios and/or sediment sample PCBs concentrations ≥0.2 mg/kg, and/or other features relevant to PCBs investigations, are described in the following sections, which are organized by the applicable municipalities.

11 Where sediment and stormwater runoff particle ratio analysis results conflict, the higher result was conservatively applied. 12 The WMA IDs in San Mateo County are numerical (1 – 1017). Sample names consist of a prefix for the county (SM), followed by a three-letter prefix for the Permittee where the sample was collected (e.g., SSF for South San Francisco, SCS for San Carlos), followed by the WMA ID, and followed by a letter (e.g., A, B, C) to distinguish the sampling site from the WMA in which that sample was collected. Samples collected previously may have a different sample naming convention.


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Figure 6. San Mateo County WMA Status Based upon Total PCBs Concentration in Sediment and/or PCBs Particle Ratio in Stormwater Runoff Samples Collected through WY 2019.


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5.5.1. City of Brisbane WMAs in the City of Brisbane with PCBs particle ratios over 0.2 mg/kg in stormwater runoff samples, elevated concentrations of PCBs in sediment samples, and/or other features relevant to investigating sources of PCBs are shown in Figure 7 and briefly described below. It should be noted that the industrial area in the northeast corner of Figure 7 drains to San Francisco’s combined sewer and is therefore considered non-jurisdictional. WMA 17

WMA 17 is a large catchment that corresponds to the watershed of the now underground Guadalupe Creek. It contains a large industrial area developed mostly in the 1960s and buildings of the type that would be expected to potentially have PCBs in building materials. Several old railroad lines used to support the industries. A sediment sample collected during WY 2015 in one of the two main lines under Valley Drive had elevated levels of PCBs (1.22 mg/kg) despite potential dilution due to the large size of the watershed. A stormwater runoff sample collected by the RMP in WY 2016 (SM-BRI-17A or Valley Dr SD) had a relatively low PCBs particle ratio of 0.11 mg/kg. Six additional sediment samples were collected in WY 2018, with one of the samples having elevated PCBs (1.02 mg/kg), and the remaining samples all under 0.2 mg/kg. The elevated sample was collected from an inlet that drains a portion of one of the old railroad lines. Another four sediment samples were collected in WY 2019 along the old railroad line with one of the samples having an elevated PCBs concentration (0.56 mg/kg), and the other three being below 0.2 mg/kg PCBs. Despite the above attempts to iteratively hone in on a source area in this WMA, none of the sediment samples collected to-date with elevated PCBs appears appear to be associated with a specific parcel. However, it is possible that additional sediment sampling could lead to identifying specific source property(ies) (e.g., within the railroad ROW). WMA 1004

WMA 1004 is located along Tunnel Avenue in the Brisbane Baylands area. Stormwater runoff sample SM-BRI-1004A (Tunnel Avenue Ditch) was collected by the RMP in WY 2016 and had a relatively low PCBs particle ratio of 0.11 mg/kg. The catchment has a high proportion of high interest properties, including containing all of the Brisbane Baylands old railyard and a large PG&E property on Geneva Avenue. The Baylands area is an active cleanup site (although not for PCBs) and will eventually be redeveloped. Several sediment samples collected in past years in the vicinity of the PG&E property and historical railroad lines had relatively low PCBs concentrations (<0.2 mg/kg PCBs). WMA 350

WMA 350 is upstream of WMA 1004, and contains a PCBs cleanup site (Bayshore Elementary) that was redeveloped in 2017. The PCBs were associated with the original building materials and it therefore appears unlikely that there is an ongoing source of PCBs to the MS4. One sediment sample collected downstream of the school in WY 2018 had a relatively low concentration of PCBs.


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Figure 7. WMAs 17, 350, and 1004.


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5.5.2. City of South San Francisco WMAs in the City of South San Francisco with PCBs particle ratios over 0.2 mg/kg in stormwater runoff samples, elevated concentrations of PCBs in sediment samples, and/or other features relevant to investigating sources of PCBs are shown in Figures 8 through 12 and briefly described below. WMA 291

WMA 291 is a relatively large catchment that is comprised almost entirely of old industrial land uses. A stormwater runoff sample collected by the RMP in WY 2017 had an elevated PCB particle ratio (0.74 mg/kg). A 2002 sediment sample at 245 S. Spruce Avenue had an elevated PCBs concentration of 2.72 mg/kg and this property was referred to the Regional Water Board in June 2003. However, since that time, investigations have not shown further evidence that this property is a source of PCBs to the MS4. Sediment samples in WY 2015 and WY 2017 on Linden Avenue near Dollar Avenue were also moderately elevated for PCBs (0.48 and 0.44 mg/kg). Two sediment samples were collected near 245 S. Spruce Avenue in WY 2018, one of which was moderately elevated for PCBs (0.21 mg/kg). The moderately elevated sample was collected from the boundary of the property and a historical railroad, which now is part of the current BART right-of-way. Investigations in this WMA have iteratively collected a total of 19 sediment samples, but except for the tentative identification of 245 S. Spruce Avenue, source properties have not been identified. WMA 294

WMA 294 is a 67-acre catchment that drains into Colma Creek at Mitchell Avenue. Within the WMA is 166 Harbor Way, designated in the Department of Toxic Substances Control (DTSC) Envirostor database as “Caltrans/SSF Maintenance Station.” This property was purchased by Caltrans which tested the soil and found several contaminants including PCBs. The contaminated soil has been capped since at least 2005 and the property is currently mostly vacant with a small portion devoted to k-rail storage. A sediment sample was collected in the driveway of this property in WY 2017 had a moderately elevated PCBs concentration of 0.28 mg/kg. A stormwater runoff sample collected in WY 2017 also had a moderately elevated PCBs particle ratio (0.37 mg/kg). WMA 314

WMA 314 is a 66-acre catchment located near Oyster Point that is comprised of light industrial land uses along with an old railroad right-of-way. Site SM-SSF-314A (Gull Dr. SD) was sampled by the RMP STLS in WY 2015 and resampled in WY 2018 and had an elevated PCBs particle ratio in both samples (0.95 and 0.86 mg/kg, respectively). The WY 2018 sample had a total PCBs concentration (71 ng/L) that was about an order of magnitude higher than the WY 2015 sample (8.6 ng/L). Two sediment samples collected in WY 2017 both had relatively low (urban background) concentrations of PCBs, with the highest concentration being 0.15 mg/kg. Another sediment sample taken in WY 2019 also had a low PCBs concentration of 0.02 mg/kg. Thus, the efforts to-date have not identified any source area(s) associated with the elevated PCBs particle ratios in the stormwater runoff samples. However, it is possible that additional sediment sampling could lead to identifying specific source property(ies) (i.e., within the railroad ROW). WMA 315

WMA 315 is a 108-acre catchment with an outfall very close to the outfall for WMA 314. WMA 315 is comprised almost entirely of light industrial land uses. The RMP STLS collected a stormwater runoff


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sample at the bottom of this catchment in WY 2016 and then resampled the same station in WY 2018 (Gull Drive station). Total PCBs (5.8 ng/L) and PCBs particle ratio (0.18 mg/kg) were relatively low in the WY 2016 sample, but roughly an order of magnitude higher in the WY 2018 sample (total PCBs = 93.2 ng/L and PCBs particle ratio = 1.02 mg/kg). Five sediment samples were collected in this catchment in WY 2019, with two of the samples having moderately elevated PCBs concentration (0.27 and 0.43 mg/kg). Both samples were along railroads, one active and one historic. Thus, the efforts to-date have not identified any source area(s) associated with the elevated PCBs particle ratios in the stormwater runoff sample. However, it is possible that additional sediment sampling could lead to identifying specific source property(ies) (e.g., within the railroad ROW). WMA 319

WMA 319 is also located near Oyster Point. Sample SM-SSF-319A (Forbes Blvd Outfall) was collected by the RMP STLS in WY 2016 and had a relatively low PCBs particle ratio of 0.08 mg/kg. Although the catchment was historically industrial, it is now mostly redeveloped and composed of biotechnology corporations. A sediment sample in WY 2017 also had a relatively low (0.06 mg/kg) PCBs concentration. WMA 358

WMA 358 is a small 32 acre catchment that drains into Colma Creek at Utah Avenue. A sediment sample collected in WY 2015 had an elevated PCBs concentration (1.46 mg/kg). Three follow-up sediment samples collected in WY 2017 all had relatively low (urban background) levels of PCBs, with the highest concentration being 0.09 mg/kg. Another follow-up sediment sample collected in WY 2019 also had a low concentration ( 0.03 mg/kg). Stormwater runoff samples have not been collected from this catchment and would be challenging to collect because of tidal inundation. The attempts to-date to identify a source area in this WMA have not succeeded. However, it is possible that additional sediment sampling could be more fruitful. WMA 359

WMA 359 is a small 23 acre catchment that drains into Colma Creek behind 222 Littlefield Avenue. In WY 2017 the RMP STLS collected a stormwater runoff sample with a somewhat elevated PCBs particle ratio of 0.79 mg/kg. The catchment is composed of all old industrial land uses including old railroad tracks. In WY 2018, three follow-up sediment samples collected in the catchment all had relatively low PCBs concentrations (less than 0.2 mg/kg). Another follow-up sediment sample collected in WY 2019 also had a low PCBs concentration (0.13 mg/kg). Based on the work conducted to-date, it appears that identifying any source areas via additional sediment sampling in this WMA’s public ROW would be challenging. WMA 1001

WMA 1001 is a large 345-acre catchment that is composed of all the non-contiguous small catchments along Colma Creek that have outfall diameters of 18-inches and smaller. In WY 2018, a stormwater runoff sample collected from this catchment had a relatively low total PCBs concentration of 1,100 ng/L, but a moderately elevated PCBs particle ratio of 0.35 mg/kg. Six sediment samples collected in 2015 and 2018 had relatively low concentrations ( ≤ 0.09 mg/kg). WMA 1001B

In WY 2017, a stormwater runoff sample (SM-SSF-1001B) collected on Shaw Road near this catchment’s outall to Colma Creek had an elevated PCBs particle ratio (1.7 mg/kg). This catchment is very small and


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only drains about five light industrial properties along Shaw Road including historical rail lines. A sediment sample collected in this catchment in WY 2015 had a concentration of 0.46 mg/kg. Five additional sediment samples were collected in this catchment in WY 2018, with one having a moderately elevated PCBs concentration of 0.35 mg/kg, and the other five all having relatively low concentrations ( ≤ 0.06 mg/kg). During WY 2019, two sediment samples were also collected along Shaw Road in WMA 362 (just south of WMA 1001) to investigate an electrical property and another property that straddles both WMAs. Both had low concentrations of PCBs ( ≤ 0.07 mg/kg). WMA 1001D

Between 2000 and 2015, seven samples were collected in this catchment with two of the samples (from 2000 and 2007) having a moderately elevated PCBs concentration (0.23 and 0.43 mg/kg). The remaining five samples all had low concentrations of PCBs (< 0.04 mg/kg). During an attempt in WY 2017 to sample stormwater runoff near the outfall of this catchment, field workers observed that this catchment likey drains to the south to WMA 291.


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Figure 8. WMAs 313, 314, 315, and 1002


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Figure 9. WMA 319


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Figure 10. WMAs 293, 294, and 357


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Figure 11. WMAs 316, 317, 358, 359, and 1001


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Figure 12. WMAs 291, 292, 316, and 1001


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5.5.3. City of Burlingame WMAs in the City of Burlingame with PCBs particle ratio over 0.2 mg/kg in stormwater runoff samples, elevated concentrations of PCBs in sediment samples, and/or other features relevant to investigating sources of PCBs are shown in Figures 13 and 14 and briefly described below. WMA 85

WMA 85 is a 121-acre catchment northwest of Highway 101 in Burlingame that is comprised mostly of light industrial land uses. A stormwater sample collected in WY 2018 had a slightly elevated PCBs particle ratio of 0.24 mg/kg, and a repeat sample of the same location by the RMP in WY 2019 had a PCBs particle ratio of 0.33 mg/kg and a relatively high total PCBs concentration of 31.1 ng/l. Two previous sediment samples collected in this WMA had relatively low concentrations (less than 0.2 mg/kg), including one at a pump station. WMA 142

WMA 142 is a small 20-acre catchment that is comprised mostly of industrial land uses. Sample SM-BUR-142A was part of a trio of stormwater runoff samples collected at the forebay of the Marsten Road pump station. It had an elevated PCBs particle ratio (0.67 mg/kg). SM-BUR-1006A, which was collected at the same location but drains adjacent WMA 1006, had a moderately elevated PCBs particle ratio (0.37 mg/kg). Seven sediment samples collected in or very close to WMA 142 in WY 2018 all had low PCBs concentrations (less than 0.2 mg/kg). WMA 164

WMA 164 is a 241-acre catchment. The lower half of this catchment has mostly light industrial land uses and the upper half has mostly residential and commercial land uses. A stormwater runoff sample collected in WY 2018 had a moderately elevated PCBs particle ratio of 0.45 mg/kg, although another sample collected by the RMP in WY 2019 had a low PCBs particle ratio of 0.05 mg/kg. This site is downstream of a pump station where sediments may settle out of the stormwater runoff flows. Four sediment samples collected in this catchment in WYs 2002 and 2015 had relatively low PCBs concentrations (less than 0.2 mg/kg).


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Figure 13. WMAs 85 and 164


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Figure 14. WMAs 141, 142, and 1006


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5.5.4. City of San Mateo WMAs in the City of San Mateo with PCBs particle ratio greater than 0.2 mg/kg in stormwater runoff samples, elevated concentrations of PCBs in sediment samples, and/or other features relevant to investigating sources of PCBs are shown in Figure 15 and briefly described below. WMA 156

WMA 156 is a 40-acre catchment that flows north into the 16th Street Channel at Delaware Street. Historically it contained old industrial land uses. It drains Caltrain property including the Hayward Park Station. There is a major retail redevelopment project currently underway in this WMA. A stormwater runoff sample collected in WY 2017 near the catchment outfall had a slightly elevated PCB particle ratio (0.2 mg/kg) but a sediment sample collected upstream did not have an elevated PCBs concentration. WMA 408

WMA 408 is a 43-acre catchment next to WMA 156. It is comprised of a mix of retail, commercial and residential land uses, with a relatively low proportion (16%) of high interest parcels (see Attachment 5). A stormwater runoff sample collected in WY 2017 had a relatively high PCBs particle ratio (1.9 mg/kg). This result was notable given the lack of industrial land uses and low percentage of high interest parcels. Seven follow-up sediment samples collected from this WMA in WY 2018 all had relatively low PCBs concentrations (less than 0.2 mg/kg). Given the high previous result and low concentrations in multiple sediment samples, it may be advisable to resample the stormwater runoff station.


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Figure 15. WMAs 156 and 408


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5.5.5. City of Belmont WMAs in the City of Belmont with PCBs particle ratio greater than 0.2 mg/kg in stormwater runoff samples, elevated concentrations of PCBs in sediment samples, and/or other features relevant to investigating sources of PCBs are shown in Figure 16 and briefly described below. WMA 60

WMA 60 is a 298-acre catchment that drains north into Laurel Creek. Two stormwater runoff samples were collected in the catchment in WY 2017 (SM-BEL-60A and SM-BEL-60B). Sample SM-BEL-60A was not elevated but SM-BEL-60B had a relatively high PCBs particle ratio (1.0 mg/kg). This result was noteworthy since the sample catchment is mostly residential with few high interest parcels. In WY 2018, seven sediment samples were collected in this catchment, all of which had relatively low PCBs concentrations (less than 0.2 mg/kg). In WY 2019 an additional sediment sample was collected that also had a very low PCBs concentration (0.002 mg/kg). Given the previous elevated stormwater runoff sample result and the low concentrations in the sediment samples, it may be advisable to resample the stormwater runoff station.


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Figure 16. WMA 60


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5.5.6. City of San Carlos WMAs in the City of San Carlos with PCBs particle ratios greater than 0.2 mg/kg in stormwater runoff samples, elevated concentrations of PCBs in sediment samples, and/or other features relevant to investigating sources of PCBs are shown in Figure 17 – 20 and briefly described below. WMA 75

WMA 75 is a 66-acre catchment comprised entirely of old industrial land uses. Sample SM-SCS-75A (Industrial Rd Ditch) was collected by the RMP in WY 2016 and had a PCBs particle ratio of 6,140 ng/g, which is among the highest levels found in Bay Area stormwater samples collected to-date. The sample station is located where the MS4 daylights into a ditch on the east side of Industrial Road downstream of the adjacent Delta Star and Tiegel Manufacturing properties. The Countywide Program collected seven sediment samples in WY 2017 in the area. Two of these samples were collected near the Delta Star and Tiegel properties. One was collected in the storm drain line directly downstream of both properties and had a very elevated PCBs concentration (49.4 mg/kg). The other was also elevated, with a PCBs concentration of 1.20 mg/kg, and was collected from surface sediments at the location where the Tiegel property drains into the public right-of-way. In WY 2018, SMCWPPP collected a sample across the street from Delta Star in front of the PG&E property. The sample had a PCBs concentration of 0.76 mg/kg. It is not believed that the PCBs in this sample originated from the PG&E property given that the sample only drained a portion of the front parking lot. Rather, the PCBs were more likely present at this location due to a halo effect around Delta Star. For example, groundwater has been observed in the MS4 in this area due to a high-water table, tidal effects, and infiltration. PCBs-containing sediments potentially could have been conveyed upstream in the storm drain line by groundwater that infiltrated into the pipe. The remainder of the PG&E property drains toward the east. The remaining samples were not elevated, suggesting that there are no other sources of PCBs in this WMA other than Delta Star and Tiegel properties (Figure 17). Delta Star manufactures transformers, including transformers with PCBs historically (from 1961 to 1974). This is a cleanup site with elevated PCBs found in on-site soil and groundwater samples. PCBs migrated to the adjacent Tiegel property at 495 Bragato Road, a roughly three-acre site that is largely unpaved. A “Removal Action” under DTSC oversight was implemented between June 1989 and January 1991 to remove soil impacted with PCBs exceeding 25 ppm. The Delta Star and Tiegel properties currently meet public health, safety, and the environmental cleanup goals based on human exposure at the site. However, based on the PCBs concentrations in the sediment and stormwater runoff samples, the site appears to be a source of PCBs to the MS4 and San Francisco Bay at levels that are a concern from the standpoint of the Bay PCBs TMDL (i.e., contribute to bioaccumulation in Bay fish and other wildlife). The Countywide Program recently worked with the City of San Carlos to refer these properties to the Regional Water Board for potential additional investigation and abatement. WMA 31 (Pulgas Creek Pump Station North)

WMA 31 is a 99-acre catchment that drains to the Pulgas Creek pump station from the north. In addition to elevated sediment samples collected by SMCWPPP from the pump station sump, the RMP collected four stormwater runoff samples from the bottom of catchment (i.e., where flows enter the pump station from the north) during two storms in WY 2011. The samples were all elevated, with an average PCBs particle ratio of 893 ng/g. In addition, street dirt and sediment samples with elevated PCBs have been collected in front of and in the vicinity of 977 Bransten Road, a property within WMA 31 (Figure 18). The current occupant of this property is GC Lubricants. 977 Bransten Road is a DTSC cleanup site


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due to soil and groundwater contamination with PCBs and other pollutants associated with activities at GC Lubricants and California Oil Recyclers, Inc., a previous tenant at the site. 1007/1011 Bransten Road is the property located adjacent to and immediately north of 977 Bransten Road and designated the “Estate of Robert E. Frank.” A DTSC “Site Screening Form” describes PCBs in the subsurface on both sides of border between the two properties and states there may have been a historic source on both sides of the property line. Abatement measures have been implemented to reduce movement of contaminated soils from the properties, including a concrete cap over contaminated areas. However, the available information suggests that soils/sediments with PCBs are migrating from these properties into the public ROW, including the street and the MS4. The Countywide Program recently worked with the City of San Carlos to refer these properties to the Regional Water Board for potential additional investigation and abatement. WMA 210 (Pulgas Creek Pump Station South)

WMA 210 is a 141-acre catchment that drains to the Pulgas Creek pump station from the south (Figures 19 and 20). In addition to elevated sediment samples collected by SMCWPPP from the pump station sump, the RMP’s STLS has collected 33 storm samples at the bottom of this catchment (i.e., where flows enter the pump station from the south):

• WY 2011 – four samples collected in February and March 2011.

• WY 2013 – four samples collected in March 2013.

• WY 2014 – 25 samples collected from November 2013 through March 2014. The 33 samples had an average PCBs particle ratio of 8,220 ng/g, the highest of any stormwater runoff sampling location in the Bay Area. There appear to be several sources of PCBs within this WMA. The best documented of these sites is the property at 1411 Industrial Road. A sediment sample with a very elevated PCBs concentration (193 mg/kg) was previously collected from a storm drain inlet located in the parking lot of this about 1.3-acre property. The property drains to the MS4 at a sidewalk manhole where other elevated sediment samples have been collected. Since 2012 the occupant of this property has been a Habitat for Humanity Re-Store. Based upon records from the San Mateo County Department of Environmental Health, before that the property was occupied by an auto body shop and an automotive paint company. Between 1958 and 1994, Adhesive Engineering / Master Builders, Inc. was the occupant and conducted manufacturing, research and development of construction grade epoxy resin and products. Adhesive Engineering / Master Builders, Inc. had a history of violations for leaky wastewater drums and improper storage of hazardous wastes in the late 1980s and early 1990s, and PCBs were reportedly used on the site in the past. An environmental assessment report conducted as part of a business closure in 1994 revealed that 93 mg/kg PCBs was found in a soil sample collected in 1987. The soil sample was collected beneath an aboveground tank that was heated by oil-containing PCBs circulating in coils around the tank. The report also described the removal in 1987 of 44 cubic yards of contaminated soil from the area where the tank was located. As part of the 1994 environmental assessment, a soil sample was collected from the same area and PCBs were not detected at that time, but soil samples from other areas on the property were not collected and tested for PCBs. The above information suggests that the 1411 Industrial Road property is a source of PCBs to the MS4. Regional Water Board staff is currently working with the property owner to investigate and cleanup the site. The Countywide Program is currently working with the City of San Carlos to explore the possibility of referring this property to the Regional Water Board for potential additional investigation and abatement.


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In WY 2017, the Countywide Program collected ten sediment samples from the WMA 210 to better delineate the sources of PCBs in this catchment. Three samples were collected in the vicinity of 1411 Industrial Road to help rule out that neighboring properties are PCBs sources. All three of these samples had relatively low PCBs concentrations, with the highest having a PCBs concentration of 0.07 mg/kg, which helps to verify that the properties to the east and south are not also sources. Multiple sediment samples previously collected around the PG&E substation across the street also had relatively low levels of PCBs, suggesting that this property is not a source. PCBs from unknown sources were previously found in inlets and manholes in the vicinity of Center, Washington and Varian Streets and Bayport Avenue (Figure 20). The PCBs in these samples could have originated from any of about 20 small industries on these streets. During WY 2017, seven additional samples were collected in this area. The results suggest that three small properties may be PCBs sources. Two samples collected from the driveways of 1030 Washington Street, a construction business, had elevated PCBs (1.29 and 3.73 mg/kg). A sample from the driveway of 1029 Washington Street was also elevated with a concentration of 5.64 mg/kg. In addition, samples from the driveway of 1030 Varian Street, an unpaved lot used for storage, had an elevated PCBs concentration of 1.84 mg/kg. It should be noted that all the buildings in this area appear to be of the type and age that could potentially have PCBs in building materials. In WY 2018, the Countywide Program collected two sediment samples along Washington Street. The first sample was from the gutter upstream of 1030 Washington Street and had a PCBs concentration of 0.25 mg/kg. The second sample was from the gutter upstream of 1029 Washington Street and had a PCBs concentration of 0.06 mg/kg. These relatively low concentrations suggest that the sources of PCBs are not upstream of the two properties of interest along Washington Street. The Countywide Program is currently working with the City of San Carlos to determine next steps for these properties. When a previously unknown potential source property is revealed via the PCBs and mercury monitoring program, SMCWPPP conducts a follow-up review of current and historical records regarding site occupants and uses, hazardous material/waste use, storage, and/or release, violation notices, and any remediation activities. Apart from databases such as EPA’s Toxic Release Inventory (TRI) and Envirofacts, and the State of California’s Geotracker and Envirostor, the most useful records were often kept by San Mateo County Department of Environmental Health. In contrast to 1411 Industrial Road (see above), the review of records for 1030 Washington Street, 1029 Washington Street, and 1030 Varian Street did not reveal any obvious use or release of PCBs in the past.


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Figure 17. WMAs 59, 75, and 1011


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Figure 18. WMA 31


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Figure 19. WMA 210


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Figure 20. WMA 210 – Enlargement of Sampled Area


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5.5.7. City of Redwood City WMAs in the City of Redwood City with PCBs particle ratio greater than 0.2 mg/kg in stormwater runoff samples, elevated concentrations of PCBs in sediment samples, and/or other features relevant to investigating sources of PCBs are shown in Figure 21 – 24 and briefly described below. WMA 379

WMA 379 (Figures 21 and 22) is an 802-acre catchment located in Redwood City and the unincorporated North Fair Oaks census-designated place (CDP). The catchment is divided into a northerly half (A) and a southerly half (B), each with a distinct MS4 outfall. Both outfalls were sampled by the Countywide Program in WY 2016. Sample SM-RCY-379A had a relatively low PCBs particle ratio (105 ng/g). Sample SM-RCY-379B also had a relatively low PCBs particle ratio (182 ng/g). In WY 2017, the Countywide Program collected fifteen samples in WMA 379 in an attempt to identify PCBs source along Bay Road and Spring Street, in follow-up to elevated sediment samples collected during previous years, including a sediment sample with an elevated PCBs concentration (6.93 mg/kg) collected in 2014 from a storm drain inlet on Spring Street (Amec 2015). None of nine samples collected in the Bay Road near Hurlingame Avenue area was elevated, with the highest PCBs concentration being 0.14 mg/kg. A single sample collected by SMCWPPP from an inlet at the back of the sidewalk in front of 2201 Bay Road had an elevated PCBs concentration of 1.97 mg/kg. This area includes two properties listed for PCBs on GeoTracker13: Tyco Engineering Products and an adjacent railroad spur. The Tyco site was remediated and redeveloped (MRP Provision C.3 compliant) and is currently a parking lot for Stanford Hospital. Four sediment samples were collected on Spring Street in WY 2017. None was elevated, with the highest PCBs concentration being 0.08 mg/kg. In WY 2018, two additional samples were collected to further verify the lower results along Spring Street, and to test for the presence of any PCBs sources along Charter Street on the south side of the old Tyco property. Both samples had low concentrations of PCBs (less than 0.2 mg/kg). A total of 43 sediment samples and 2 composite stormwater runoff samples have been collected to-date in WMA 379 by SMCWPPP and others, but the only potential PCBs source area that has been identified is the former Tyco site and adjacent historical railroad spur. In April 2019, Regional Water Board staff informed SMCWPPP that they plan to include a conditional requirement to clean out the storm drain as part of the proposed cap modification and redevelopment of the property, and may have the opportunity to request additional post-cleanout monitoring. SMCWPPP will continue to track these efforts and will request PCBs load reduction credit as appropriate. WMA 405/1000

WMA 405 (Figure 23) consists almost entirely of SIMS Metal Management at the Port of Redwood City. Samples collected in WYs 2015 and 2017 from the driveway of SIMS and in close proximity to the site but another catchement (WMA 1000) had elevated PCBs concentrations of 0.57 and 0.75 mg/kg, respectively. Sims has implemented practices to prevent metal fluff potentially containing a variety of contaminants (including PCBs) from entering the Bay.

13 GeoTracker is the State Water Resources Control Board’s Internet-accessible database system used to track and archive compliance data from authorized or unauthorized discharges of waste to land, or unauthorized releases of hazardous substances from underground storage tanks.


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WMA 239

WMA 239 (Figure 24) is a 36-acre mostly industrial catchment that is half in Redwood City and half in Menlo Park. In WY 2015, SMCWPPP collected a sediment sample in this catchment that had an elevated PCBs concentration of 0.57 mg/kg. Four additional sediment samples were collected in WY 2017, all of which had relatively low (urban background) PCBs concentrations, with the highest concentration being 0.16 mg/kg. Currently in this WMA there is a large housing redevelopment that is almost complete. One of the areas that was redeveloped (Haven Avenue Industrial Condominiums) at 3633 Haven Avenue was remediated for PCBs contamination in 2006. Stormwater runoff sampling has not been conducted in this catchment due to a lack of public access to the catchment outfall (which discharges to the Bay).


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Figure 21. WMA 379 (northwest portion)


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Figure 22. WMAs 254 and 379 (southeast portion)


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Figure 23. WMAs 269, 405, 1000


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Figure 24. WMA 239


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5.5.8. City of East Palo Alto WMAs in the City of East Palo Alto with PCBs particle ratios greater than 0.2 mg/kg in stormwater runoff samples, elevated concentrations of PCBs in sediment samples, and/or other features relevant to investigating sources of PCBs are shown in Figure 25 and briefly described below. WMA 70

WMA 70 is a 490-acre catchment. A stormwater runoff sample collected by the RMP in WY 2015 had an elevated total PCBs concentration (28.5 ng/L) but a relatively low PCBs particle ratio (108 ng/g). Three sediment samples collected by SMCWPPP in the area in WY 2017 had relatively low PCBs concentrations, with the highest having a concentration of 0.03 mg/kg. WMA 1015/72

WMA 1015 consists of multiple catchments in the City of East Palo Alto. This WMA contains Romic Environmental Technologies Corporation, a property that is known to be contaminated with PCBs and has been vacant for many years. A stormwater runoff sample and two sediment samples in close proximity to the Romic driveway but in another catchement (WMA 72) all had relatively low concentrations of PCBs. WMA 1015 also contains 391 Demeter, a property that formerly was used to stockpile soils with PCBs that were removed from a separate remediation site. The site is expected to be redeveloped. This property drains directly to the Bay, and is all private property and inaccessible. A sediment sample from an inlet at the north end of Demeter Street (WMA 67) was moderately elevated in PCBs with a concentration of 0.21 mg/kg.


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Figure 25. WMAs 70, 72, 1015


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6.0 COPPER, NUTRIENTS, AND EMERGING CONTAMINANTS (WYs 2016 – 2019)

The below sections on copper, nutrients, and emerging contaminants focus on summarizing compliance with MRP 2.0 requirements and thus focus on the POC monitoring and related activities conducted during WY 2016 – WY 2019. Copper and nutrient monitoring stations are shown in Figure 26.

Figure 26. Copper and nutrient monitoring stations, San Mateo County, WY 2016 – WY 2019.


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6.1. Copper The Water Quality Control Plan for the San Francisco Bay Basin (Basin Plan) includes a Water Quality Attainment Strategy (WQAS) to support copper site-specific objectives for San Francisco Bay (SFBRWQCB 2017). The WQAS for copper states that NPDES permits for urban runoff management agencies must require implementation of best management practices (BMPs) and control measures designed to prevent urban runoff discharges from causing or contributing to exceedances of copper water quality objectives. These measures are included in Provision C.13 of MRP 2.0. Additionally, the WQAS requires that NPDES permits contain requirements to conduct or cause to be conducted monitoring of copper loading to the Bay. The RMP Status and Trends Monitoring Program currently collects water and sediment samples from San Francisco Bay every two or three years for analysis of a large suite of toxic contaminants, including copper. In addition to the RMP studies, copper monitoring is required by Provision C.8.f of MRP 2.0. From WY 2016 through WY 2019, in compliance with Provision C.8.f of MRP 2.0, the Countywide Program collected at least two samples per year for copper analysis (Table 4). The goal of the monitoring was to address Management Question No. 4 (Loads and Status) and/or No. 5 (Trends). A different copper monitoring approach was followed each year, often with the objective of opportunistically conducting copper analyses on samples collected for other purposes. In addition, trends samples collected in San Mateo Creek by the SPoT program were used to supplement SMCWPPP monitoring efforts (see Section 1.3.2. for a description of the SPoT program and SPoT data results). As shown in Table 4, a total of 17 San Mateo County samples were analyzed for copper from WY 2016 through WY 2019. Fifteen of these samples address Management Question No. 4 (Loads and Status) and eight address Management Question No. 5 (Trends). The bullets below describe SMCWPPP approaches to copper monitoring in WY 2016 through WY 2019 and findings based on the laboratory results. Monitoring stations are mapped in Figure 26. All SMCWPPP samples were analyzed for total and dissolved copper14 (method EPA 200.8) and hardness (method SM 2340C). Results are summarized in Table 9. Comparisons to Water Quality Objectives are included in Section 7.0.

• WY 2016 and WY 2017: Storm Samples from High Interest WMAs. In WY 2016, SMCWPPP conducted copper analyses on a subset (three) of the 13 composite samples of stormwater runoff from outfalls at the bottom of WMAs containing parcels of high interest with respect to PCBs (i.e., generally old industrial land uses). In WY 2017, one of 17 stormwater composite samples from WMA outfalls was analyzed for copper. See Section 5.0 (Progress To-date Identifying PCBs and Mercury Sources) for a discussion of WMAs and high interest parcels.

o Results summarized in Table 9 suggest that copper concentrations in stormwater runoff composite samples collected at outfalls draining WMAs with old industrial land uses are generally higher than those collected in creeks with a greater mix of land uses.

14 In order to simplify the field effort and reduce the risk of sample contamination, SMCWPPP requested that the analytical laboratory conduct the sample filtration required for dissolved copper analysis.


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• WY 2017: Wet Weather Upstream/Downstream Comparison. SMCWPPP sampled two locations (upstream and downstream) on two creeks (Atherton Creek and Redwood Creek) during a storm event to compare copper concentrations above and below dense urban land uses. One of the downstream stations (Redwood Creek) was also sampled in the spring to compare seasonal differences. The downstream location on Atherton Creek could not be sampled in the spring due to dry conditions.

o Copper concentrations measured during the January storm event were higher at the bottom-of-the-watershed stations (204ATH010 and 204RED010) compared to the upstream stations (204ATH050 and 204RED050). This suggests that stormwater runoff from the urban land uses between the two stations was contributing copper to the creeks.

o At the bottom-of-the-watershed station on Redwood Creek (204RED010), copper concentrations in the January storm event were similar to concentrations measured during spring baseflow; however, the higher water hardness during spring baseflow reduces the bioavailability of the copper.

• WY 2018: Wet Weather/Spring Baseflow Comparison. SMCWPPP sampled two bottom-of-the-watershed locations (San Pedro Creek and Cordilleras Creek) during a storm event and during spring baseflow to compare seasonal differences in copper concentrations. Mobilization for storm samples collection in WY 2018 was driven by Provision C.8.g (Pesticides & Toxicity) monitoring requirements.

o Copper concentrations at both stations (202SPE005 and 204COR010) were higher during the January storm event compared to the spring base flow event, suggesting an influence by stormwater runoff. In addition, the dissolved portion of the total copper concentration was higher in the spring baseflow samples compared to the storm samples. This finding illustrates coppers affinity to suspended sediment which is higher during storm events.

• WY 2019: Dry Season Concentrations. SMCWPPP conducted copper analyses on a subset (two) of the nine dry season (June 31, 2019) samples that were collected for nutrient analysis.

o Copper concentrations in these dry season baseflow samples were generally lower than all other samples analyzed from WY 2014 through WY 2018.

6.2. Nutrients Nutrients were included in the MRP 2.0 POC monitoring requirements to support Regional Water Board efforts to develop nutrient numeric endpoints (NNE) for the San Francisco Bay Estuary. The “San Francisco Bay Nutrient Management Strategy” (NMS) is part of a statewide initiative to address nutrient over-enrichment in State waters (Regional Water Board 2012). Its goal is to lay out a well-reasoned and cost-effective program to generate the scientific understanding needed to fully support major management decisions such as establishing/revising WQOs for nutrients and dissolved oxygen, developing/implementing a nutrient monitoring program, and specifying nutrient limits in NPDES permits. The NMS monitoring program currently focuses on stations located within San Francisco Bay rather than freshwater tributaries.


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Table 9. Total and Dissolved Copper Concentrations in SMCWPPP Water Samples, WYs 2016 – 2019.

Station Code Creek/Location Sample Date

Total Copper (μg/L)

Dissolved Copper (μg/L)

Hardness as CaCO3

(mg/L)

WY 2016 & WY 2017 Stormwater Runoff Composite Samples from WMA Stormwater Outfalls with High-interest Parcels SM-MPK-71 Stormwater outfall in Menlo Park 2/17/2016 23.1 14.8 450 SM-RCY-327 Stormwater outfall in Redwood City 2/17/2016 19.7 9.3 38 SM-RCY-388 Stormwater outfall in Redwood City 2/17/2016 27.0 9.2 28 SM-SSF-316A Stormwater outfall in South San Francisco 12/10/2016 12.7 6.5 345 WY 2017 Wet Weather Upstream/Downstream Comparison 204ATH050 Atherton – upstream (wet weather) 1/9/2017 8.4 6.2 200 204ATH010 Atherton – downstream (wet weather) 1/9/2017 12 9.8 260 204RED050 Redwood – upstream (wet weather) 1/9/2017 8.1 6.4 260 204RED010 Redwood – downstream (wet weather) 1/9/2017 13 11 260 204RED010 Redwood – downstream (spring baseflow) 5/22/2017 14 12 380 WY 2018 Wet Weather/Spring Baseflow Comparison 202SPE005 San Pedro – wet weather flow 1/8/2018 9.5 2.7 50 202SPE005 San Pedro – spring baseflow 5/17/2018 0.84 0.41 J 190 204COR010 Cordilleras – wet weather flow 1/8/2018 8.4 4.3 76 204COR010 Cordilleras – spring baseflow 5/21/2018 1.7 1.2 380 WY 2019 Dry Season Concentrations 204BEL005 Belmont 7/31/2019 0.72 0.93 a 270 204R04428 Cordilleras 7/31/2019 0.48 J 0.75 a 400

Notes:

J-flagged data are above the detection limit but less than the reporting limit and are therefore considered estimated. a The total and dissolved copper concentration from July 31, 2019 samples were flagged as questionable. Dissolved copper, by definition, must be ≤ total copper, which was not the case for these samples. The data validation process did not find any other concerns with the copper results. It is possible that contamination was introduced during the laboratory filtration process for this sample. Furthermore, all results from this sampling event were close to the reporting limit and therefore subject to higher uncertainty.

Provision C.8.f of MRP 2.0 requires monitoring for a suite of nutrients (i.e., ammonium, nitrate, nitrite, total Kjeldahl nitrogen (TKN), orthophosphate, and total phosphorus). This list is similar to the list of analytes measured by the RMP and BASMAA partners at the six regional loading stations (including a San Mateo County station at the Pulgas Creek Pump Station in the City of San Carlos) monitored in WY 2012 - WY 2014. The prior data collected in freshwater tributaries to San Francisco Bay were used by the Nutrient Strategy Technical Team to develop and calibrate nutrient loading models. In WY 2016 through WY 2019, in compliance with Provision C.8.f of MRP 2.0, the Countywide Program collected at least two samples per year for nutrient analysis. The goal of the monitoring was to address


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Management Question No. 4 (Loads and Status) for which at least 20 samples needed to be collected by year four of the permit (i.e., WY 2019). The analytes and chemical analysis methods were ammonia (SM 4500 C), nitrate (EPA 300.0), nitrite (SM 4500 B), TKN (SM 4500 C), orthophosphate (SM 4500 E), and total phosphorus (SM 4500 E). The bullets below describe SMCWPPP approaches to nutrient monitoring in WY 2016 through WY 2019. Monitoring stations are mapped in Figure 26. Results are summarized in Table 10. Comparisons to applicable WQOs are described in Section 7.0.

• WY 2016. Monitoring for nutrients was conducted at two bottom-of-the-watershed stations with mixed land uses (San Mateo Creek and Bear Creek) during the dry season when eutrophication, if present, would be most likely to result in impacts to beneficial uses.

• WY 2017. SMCWPPP sampled two locations (upstream and downstream) on two creeks (Atherton Creek and Redwood Creek) during a storm event to compare nutrient concentrations above and below dense urban land uses. Follow-up monitoring at all four stations was attempted during spring baseflows (concurrent with bioassessment monitoring); however, the downstream Atherton Creek station was dry when the field crew returned in the spring. Although data are included in this report (Table 10), two of the three dry season samples are not counted towards Provision C.8.f POC monitoring requirements because they apply instead to Provision C.8.d Creek Status Monitoring. These were stations 204ATH050 (bioassessment station 204R03240) and 204RED050 (bioassessment station 20403496).

• WY 2018. SMCWPPP sampled two bottom-of-the-watershed locations (San Pedro Creek and Cordilleras Creek) during a storm event and during spring baseflow to compare seasonal differences in nutrient concentrations. Mobilization for storm samples collection in WY 2018 was driven by Provision C.8.g (Pesticides & Toxicity) monitoring requirements.

• WY 2019. SMCWPPP conducted POC monitoring for nutrients at nine creek stations on July 31, 2019. These stations were also sampled for nutrients as part of the bioassessment survey protocol that was conducted in May 2019. The stations were selected using a probabilistic monitoring design established for creek status monitoring. Comparison of nutrient concentrations for the two WY 2019 time periods is provided in Table 10.

Based on the laboratory results summarized in Table 10, the following findings were noted:

• Concentrations of total nitrogen and phosphorus are generally higher at downstream stations compared to upstream stations during storm events and baseflow conditions.

• Nutrient concentrations are higher during storm runoff sampling events than spring and summer baseflow events. This finding is consistent with the draft conceptual model developed by the NMS which suggests that nutrient loads to San Francisco Bay from creeks are highest during the wet season, although considerably less than loads from publicly owned wastewater treatment works (POTWs) (Senn and Novick 2014).

• The highest concentration of total nitrogen was measured in Atherton Creek (3.91 mg/L at 204ATH010) during the January 9, 2017 storm runoff sampling event. The highest concentration of phosphorus was measured in Redwood Creek (0.36 mg/L at 204RED010) during the January 9, 2017 storm runoff sampling event.


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• Under baseflow conditions sampled in May, June, and July, the highest total nitrogen concentration was measured in Atherton Creek (1.95 mg/L at 204R04600) on July 31, 2019. The highest baseflow phosphorus concentrations were measured in Atherton Creek (0.17 mg/L at 20404600 on June 10, 2019) and Burlingame Creek (0.17 mg/L at 204R04160 on July 31, 2019).


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Table 10. Nutrient Concentrations in SMCWPPP Water Samples, WYs 2016 – 2019.

Station Code Creek/ Location Date Ni

trat

e as

N

Nitr

ite a

s N

Tota

l Kje

ldah

l Ni

trog

en (T

KN)

Amm

onia

as N

Un-io

nize

d Am

mon

ia a

s N1

Amm

oniu

m2

Tota

l Nitr

ogen

3

Diss

olve

d Or

thop

hosp

hate

as

P

Phos

phor

us a

s P

WY 2016 204SMA060 San Mateo 6/23/16 0.063 0.002 0.48 0.024 0.0004 0.024 0.545 0.014 0.011 205BRC010 Bear Creek 6/23/16 ND 0.001 0.26 0.048 0.001 0.047 0.271 0.034 0.042 WY 2017 204ATH010 Atherton 1/9/2017 2.2 0.006 1.7 0.057 0.0013 0.06 3.91 0.19 0.29 204ATH050 Atherton 1/9/2017 1.5 0.006 1.8 0.064 0.0007 0.06 3.31 0.1 0.24 204ATH050 4 Atherton 5/23/2017 ND 0.002 1.2 0.034 0.0005 0.03 1.21 0.05 0.06 204RED010 Redwood 1/9/2017 1.5 0.005 1.6 0.046 0.0017 0.04 3.11 0.27 0.36 204RED010 Redwood 5/22/2017 0.57 0.028 1.3 0.069 0.0122 0.06 1.9 0.11 0.16 204RED050 Redwood 1/9/2017 1.2 0.004 1.4 0.038 0.0011 0.04 2.6 0.2 0.27 204RED050 4 Redwood 5/22/2017 0.37 0.034 0.83 0.093 0.0027 0.09 1.23 0.14 0.16 WY 2018 202SPE005 San Pedro 1/8/2018 0.81 0.009 0.88 0.077 J 0.0008 0.076 1.70 0.062 0.22 202SPE005 San Pedro 5/17/2018 0.47 0.004 J 0.18 0.05 0.0013 0.049 0.65 0.033 0.025 204COR010 Cordilleras 1/8/2018 0.54 0.012 0.88 0.077 J 0.001 0.076 1.43 0.13 0.21 204COR010 Cordilleras 5/21/2018 0.11 J ND 0.70 0.055 0.0008 0.054 0.81 0.089 0.11 WY 2019 202TUN030 4 Tunitas 5/14/2019 0.066 0.002 J 0.083 J 0.28 0.0102 0.27 0.15 0.044 0.047 202TUN030 Tunitas 7/31/2019 0.066 J 0.001 J 0.17 0.11 0.0038 0.106 0.24 0.05 0.045 202TUN040 4 Tunitas 5/14/2019 ND 0.001 J 0.14 0.22 0.0076 0.21 0.15 0.044 0.039 202TUN040 Tunitas 7/31/2019 ND ND ND 0.056 0.0024 0.054 0.05 0.041 0.036 204BEL005 4 Belmont 5/13/2019 0.33 0.004 J 0.52 0.24 0.0128 0.227 0.85 0.058 0.067 204BEL005 Belmont 7/31/2019 0.066 J 0.001 J 0.41 0.062 0.0034 0.059 0.48 0.062 0.064 204R04160 4 Burlingame 5/15/2019 0.15 ND 0.69 0.15 0.0054 0.145 0.84 0.13 0.16 204R04160 Burlingame 7/31/2019 0.097 J 0.001 J 0.44 0.08 0.0015 0.078 0.54 0.17 0.17 204R04280 4 Belmont 5/13/2019 0.33 0.002 J 0.52 0.39 0.0088 0.381 0.85 0.044 0.053 204R04280 Belmont 7/31/2019 0.14 0.003 J 0.28 ND 0.0002 0.007 0.42 0.051 0.058 204R04428 4 Cordilleras 5/15/2019 0.18 0.004 J 0.44 0.077 0.0025 0.075 0.62 0.038 0.052 204R04428 Cordilleras 7/31/2019 0.099 J 0.001 J 0.19 0.059 0.0018 0.057 0.29 0.052 0.05 204R04600 4 Atherton 6/10/2019 0.53 0.004 J 0.63 0.13 0.0044 0.126 1.16 0.15 0.17 204R04600 Atherton 7/31/2019 0.94 0.005 1.0 0.065 0.0097 0.055 1.95 0.12 0.15 205R04056 4 Dry Creek 6/11/2019 0.23 0.004 J 0.30 0.11 0.0025 0.108 0.53 0.068 0.076 205R04056 Dry Creek 7/31/2019 0.074 J 0.003 J 0.58 0.079 0.0043 0.075 0.66 0.089 0.11 205R05044 4 Dry Creek 6/11/2019 0.16 0.013 0.47 0.082 0.0025 0.080 0.64 0.093 0.11 205R05044 Dry Creek 7/31/2019 0.14 0.005 0.28 0.052 0.0010 0.051 0.43 0.064 0.071

Notes:

All constituents reported as mg/L.

J-flagged data are above the detection limit but less than the reporting limit and are therefore considered estimated.

ND = Not Detected 1 Un-ionized ammonia calculated using formula provided by the American Fisheries Society Online Resources. Formula requires field measurements of temperature, pH, and specific conductance, which were not recorded for the Jan. 8, 2018 event. Specific conductance and pH values for Jan. 8, 2018 samples were estimated based on laboratory intake measurements reported for the concurrent toxicity samples. Temperature was estimated to be 12°C. Un-ionized ammonia calculated using ½ method detection limit for non-detect ammonia measurements. 2 Ammonium = ammonia – un-ionized ammonia. 3 Total nitrogen = TKN + nitrate + nitrite. Non-detects valued at ½ method detection limit in calculation. 4 Some samples were analyzed in compliance with Provision C.8.d Creek Status Monitoring requirements. They do not count towards Provision C.8.f POC Monitoring requirements but are included for comparison purposes.


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6.3. Emerging Contaminants Emerging contaminant monitoring is being addressed through SMCWPPP’s participation in the RMP. The RMP has investigated Contaminants of Emerging Concern (CECs) since 2001 and established the RMP Emerging Contaminants Work Group (ECWG) in 2006. The purpose of the ECWG is to identify CECs that might impact beneficial uses in the Bay and to develop cost-effective strategies to identify, monitor, and minimize impacts. The RMP published a CEC Strategy “living” document in 2013, completed a full revision in 2017 (Sutton et al. 2013, Sutton and Sedlak 2015, Sutton et al. 2017), and made minor updates in 2018 (Lin et al. 2018). The CEC Strategy document guides RMP special studies on CECs using a tiered risk and management action framework. Provision C.8.f of MRP 2.0 identifies three emerging contaminants that at a minimum must be addressed through POC monitoring: Perfluorooctane Sulfonate Substances (PFOS), Perfluoroalkyl and Polyfluoroalkyl Sulfonate Substances (PFAS), and Alternative Flame Retardants (AFRs). PFAS is a broad class of chemicals used in industrial applications and consumer goods primarily for their ability to repel oil and water. PFOS are a subgroup within the PFAS umbrella and are identified in the CEC Strategy as “moderate” concern due to Bay occurrence data suggesting a high probability of a low-level effect on Bay wildlife. Other PFAS and AFRs are mostly identified as “possible” concern due to uncertainties in measured or predicted Bay concentrations or in toxicity thresholds. RMP staff recently published reports summarizing PFOS and PFAS monitoring results in the Bay (Houtz et al. 2016, Sedlak et al. 2017, Sedlak et al. 2018). Organophosphate esters (OPEs), which are a class of AFRs widely used in plastic and polymer additives for their flame retardant properties, have recently been elevated to “moderate” concern by the ECWG due to their presence in the Bay at levels comparable or exceeding protective thresholds, the potential for cumulative endocrine disrupting effects, lack of understanding of fate and transport, and likelihood of increased use as replacement compounds (Shimabuku et al. 2019 draft). Bisphenols (another class of plastic additives with endocrine-disrupting properties) have also been elevated to “moderate” concern based on recent Bay monitoring results, and in 2019, the ECWG recommended further monitoring of OPEs and bisphenols, including stormwater runoff monitoring. AFRs came into use following state bans and nationwide phase-outs of polybrominated diphenyl ether (PBDE) flame retardants in the early 2000s. They include many categories of compounds, including OPEs. In 2018 the RMP STLS and ECWG worked together to conduct a special study to inform ECWG’s planning activities related to AFRs. The special study compiled and reviewed available data and previously developed conceptual models for PBDE to support a stormwater related AFR conceptual model being developed by the ECWG. Organophosphate esters were prioritized for further investigation due to their increasing use, persistent character, and ubiquitous detections at concentrations exceeding PBDE concentrations in the Bay. Limited stormwater data from two watersheds in Richmond and Sunnyvale suggest that urban runoff may be an important source of these compounds. Additional monitoring and modeling were recommended in the special study (Lin and Sutton 2018). In 2019, based on recent results from the 2017 RMP Status and Trends Water Cruise on OPE detections, and with the opportunity to advance monitoring of OPEs and other CECs via the multi-year non-targeted analysis of stormwater-related CECs initiated in 2018, the ECWG agreed to prioritize monitoring AFRs for RMP special studies. Additional funds were recommended to supplement the Emerging Contaminants Strategy in support of developing CECs conceptual models more broadly as part of the longer-term CECs Modeling Strategy. In 2018, the RMP’s ECWG initiated a multi-year special study to analyze stormwater samples collected from urban watersheds for a large suite of CECs. The list of CECs to be analyzed is based on recent work conducted in Puget Sound streams and is intended to target urban runoff constituents rather than those


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found in wastewater (e.g., pharmaceuticals). In addition to vehicle tire chemicals and imidacloprid (a neonicotinoid insecticide), the list includes the CECs specifically identified in Provision C.8.f of MRP 2.0 (PFOSs, PFASs, and AFRs). Pilot sampling began in 2019 in close coordination with the STLS and preliminary results were shared with the ECWG. Year-two of this three-year study was approved in 2019, with the inclusion of additional CECs, including OPEs and bisphenol A and S. The final reports and manuscripts for this study are anticipated in fall 2021.

7.0 COMPLIANCE WITH APPLICABLE WATER QUALITY OBJECTIVES

Provision C.8.h.i of MRP 2.0 requires RMC participants to assess all data collected pursuant to Provision C.8 for compliance with applicable water quality objectives (WQOs). In compliance with this requirement, POC monitoring water sampling data collected in WY 2016 through 2019 by the Countywide Program were compared to applicable numeric WQOs. There were no exceedances of applicable WQOs. The comparison to applicable WQOs considered the following:

• Discharge vs. Receiving Water – WQOs apply to receiving waters, not discharges such as stormwater runoff. A WQO generally represents the maximum concentration of a pollutant that can be present in the water column without adversely affecting organisms using the aquatic system as habitat, people consuming those organisms or water, and/or other current or potential beneficial uses. During WY 2016 through WY 2019, nutrient and copper data were collected in receiving waters by SMCWPPP. PCBs and mercury samples were collected within the engineered storm drain network where WQOs do not apply. Dilution is likely to occur when the MS4 discharges urban stormwater (and non-stormwater) runoff into local receiving waters. Therefore, it is unknown whether discharges that exceed WQOs result in exceedances in the receiving water itself, the location where there is the potential for aquatic life to be exposed to a pollutant.

• Freshwater vs. Saltwater - POC monitoring samples were collected from freshwater (i.e., above tidal influence in creeks) and therefore comparisons were made to freshwater WQOs.

• Aquatic Life vs. Human Health - Comparisons were primarily made to WQOs for the protection of aquatic life, not WQOs for the protection of human health to support the consumption of water or organisms. The rationale is that water and organisms are not likely consumed by humans at the locations of the monitoring stations.

• Acute vs. Chronic Objectives/Criteria – All monitoring of stormwater runoff for PCBs and mercury and several of the copper/nutrient creek sampling events were conducted during episodic storm events. Storm episode monitoring results likely do not represent long-term concentrations of the monitored constituents in receiving waters. Therefore, storm monitoring data were compared to acute WQOs for aquatic life that represent the highest concentrations of a pollutant to which an aquatic community can be exposed for a short period of time (e.g., one hour) without resulting in an unacceptable effect. Spring and summer baseflow creek monitoring data were compared to chronic WQOs developed to assess longer-term exposure.


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Of the WY 2016 through WY 2019 POC monitoring analytes, promulgated WQOs for the protection of aquatic life only exist for total mercury, dissolved copper, and unionized ammonia:

• Total Mercury. All water samples collected in San Mateo County watersheds by SMCWPPP from WY 2016 through WY 2019 for mercury analysis were stormwater runoff within the MS4, not receiving water. Stormwater runoff results are not directly comparable to WQOs, as described above. However, all WY 2016 through WY 2019 mercury concentrations in stormwater runoff (Attachment 3) were well below the freshwater acute objective for mercury of 2.4 μg/L (2,400 ng/L).

• Dissolved Copper. Acute (1-hour average) and chronic (4-day average) WQOs for copper are expressed in terms of the dissolved fraction of the metal in the water column and are hardness dependent15. The copper WQOs were calculated using the base e exponential functions described in the California Toxics Rule (40 CFR 131.38) which apply hardness values measured at the sample station. Dissolved copper concentrations were compared to the calculated WQOs. Per the above discussion, storm monitoring data was compared to acute WQOs and spring baseflow creek monitoring data were compared to chronic WQOs. Three of the 15 samples had dissolved copper concentrations that exceeded the calculated WQO (Table 11). However, as stated above, the samples were collected in the MS4, not the receiving water. Furthermore, it is unknown whether the receiving water had the same hardness as the discharge. If the hardness in the receiving water was higher, a higher WQO would have been applicable.

• Nutrients. The un-ionized ammonia concentrations calculated based on measured concentrations of ammonia in Countywide Program samples (Table 10) were all well below the annual median WQO for un-ionized ammonia of 0.025 mg/L.

8.0 SUMMARY AND DISCUSSION This IMR fulfills the requirements of MRP 2.0 Provision C.8.h.iii for reporting a comprehensive analysis of Provision C.8.f. POC Monitoring data collected pursuant to Provision C.8. since the previous IMR. The previous SMCWPPP IMR addressed WYs 2011 – WY 2013 (SMCWPPP 2014) and the time period addressed by this report includes WY 2014 – WY 2019. However, please note that for PCBs, this report focuses on progress to-date towards identifying source areas and properties in San Mateo County. In this context, it evaluates all the relevant and readily available sediment and stormwater runoff PCBs chemistry data collected in San Mateo County, ranging back to the early 2000s. This includes POC monitoring data collected directly by the Countywide Program and appropriate data collected by third parties such as the RMP’s STLS. Yearly minimum sampling requirements specified in Provision C.8.f. were met for all POC monitoring parameters.

15 The current copper standards for freshwater in California do not account for the effects of pH or natural organic matter and can be overly stringent or under-protective (or both, at different times). Therefore, the California Stormwater Quality Association (CASQA) has asked the USEPA to considering updating the California Toxics Rule standards for copper using the Biotic Ligand Model (BLM) which accounts for the effect of water chemistry in addition to hardness (i.e., temperature, pH, dissolved organic carbon, major cations and anions).


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Table 11. Comparison of SMCWPPP copper monitoring data to WQOs, WYs 2016 – 2019.

Station Code Sample Date Measured Dissolved

Copper (μg/L)

Measured Hardness as

CaCO3 (mg/L)

Acute WQO for Dissolved Copper

at Measured Hardness (μg/L)

Chronic WQO for Dissolved Copper

at Measured Hardness (μg/L)

WY 2016 SM-MPK-71 2/17/2016 14.8 450 55.4 32.4 (NA) SM-RCY-327 2/17/2016 9.34 38.4 5.5 4.0 (NA) SM-RCY-388 2/17/2016 9.24 28.0 4.1 3.0 (NA)

WY 2017 SM-SSF-316A 12/10/2016 6.5 34.8 5.0 3.6 (NA) 204ATH010 1/9/2017 9.8 260 33.1 20.3 (NA) 204ATH050 1/9/2017 6.2 200 25.8 16.2 (NA) 204RED010 1/9/2017 11 260 33.1 20.3 (NA) 204RED010 5/22/2017 12 380 47.3 28.0 204RED050 1/9/2017 6.4 260 33.1 20.3 (NA)

WY 2018 202SPE005 1/8/2018 2.7 50 7.0 5.0 (NA) 202SPE005 5/17/2018 0.41 J 190 24.6 15.5 204COR010 1/8/2018 4.3 76 10.4 7.1 (NA) 204COR010 5/21/2018 1.2 380 47.3 28.0

WY 2019 204BEL005 7/31/2019 0.93 270 34.3 20.9 204R04428 7/31/2019 0.75 400 49.6 29.3

J-flagged data are above the detection limit but less than the reporting limit and are therefore considered estimated. NA = Not applicable. Chronic WQOs are not applicable to storm event grab samples.


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8.1. PCBs and Mercury This report focuses on progress to-date towards identifying PCBs source areas and properties in San Mateo County. Consistent with MRP requirements, the focus has been on PCBs, with ancillary and secondary benefits assumed to be realized for controlling mercury. Highlights from the PCBs and mercury monitoring conducted to-date included the following:

• The Countywide Program’s PCBs and mercury monitoring commenced in the early 2000s and has generally focused on San Mateo County WMAs containing high interest parcels with land uses potentially associated with PCBs.

• In 2014, SMCWPPP worked with San Mateo County MRP Permittees to conduct a process to screen for “high interest parcels” for PCBs in the county. The screening covered all land areas in the county that drain to the Bay, focusing on about 160,000 urban parcels. Parcels were identified that were industrialized in 1980 or earlier (i.e., old industrial parcels) or have other land uses associated with PCBs (i.e., electrical, recycling, and military). SMCWPPP then worked with municipal staff to prioritize these parcels based on the evaluation of existing information on current land uses and practices (e.g., redevelopment status, extent and quality of pavement, level of current housekeeping, any history of stormwater violations, and presence of electrical or heavy equipment, storage tanks, or stormwater treatment), local institutional/historical knowledge, and surveys of site conditions (windshield, Google Street View, and/or aerial photograph). The prioritization resulted in a list of about 1,600 high interest parcels for PCBs in San Mateo County (SMCWPPP 2015).

• The above 1,600 high interest parcels are almost entirely located within 105 “catchments of interest” with high interest parcels comprising at least 1% of their area (and usually with existing pollutant controls). In FY 2016, SMCWPPP implemented a process to identify Watershed Management Areas (WMAs) and prioritize them based on the potential for controls (especially source property referrals) to reduce PCBs loads. WMAs were defined as the sum of the 105 catchments of interest and an additional 25 catchments with existing or planned stormwater pollutant controls (e.g., GI implemented on parcels per Provision C.3 requirements, built on public lands such as parks, or retrofitted into the public ROW), for a total of about 130 catchments designated as WMAs (SMCWPPP 2016a and b). WMA catchments are stormwater runoff hydrologic catchments in San Mateo County that drain to 24-inch or larger diameter outfalls.

• Each water year, SMCWPPP designed and implemented a PCBs and mercury monitoring plan based on the 2014 desktop screening (which was revisited and refined each year as needed) and all sampling results available at that time. Stormwater runoff monitoring was coordinated with RMP STLS reconnaissance monitoring, with SMCWPPP providing sample station locations to SFEI staff.


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• To-date, about 60 composite samples of stormwater runoff16 have been collected from the bottom of San Mateo County WMAs and about 400 individual and composite grab samples of sediment have been collected within priority WMAs to help characterize the catchments and identify source areas and properties. Most samples were collected in the public ROW. The grab sediment samples were collected from a variety of types of locations, including manholes, storm drain inlets, driveways, streets, and sidewalks, often adjacent to or nearby high interest parcels with land uses associated with PCBs and/or other characteristics potentially associated with pollutant discharge (e.g., poor housekeeping, unpaved areas).

• SMCWPPP’s PCBs and mercury monitoring program included collecting sediment samples in the public ROW (e.g., from streets and the MS4) by every known PCBs remediation site in San Mateo County, to the extent applicable and feasible.

• When a previously unknown potential source property was revealed via the PCBs and mercury monitoring program, SMCWPPP conducted a follow-up review of current and historical records regarding site occupants and uses, hazardous material/waste use, storage, and/or release, violation notices, and any remediation activities. Apart from databases such as EPA’s Toxic Release Inventory (TRI) and Envirofacts, and the State of California’s Geotracker and Envirostor, the most useful records were often kept by San Mateo County Department of Environmental Health. Four previously unknown potential source properties have been identified in San Mateo County, all in WMA 210 (Pulgas Creek Pump Station South) in the City of San Carlos. SMCWPPP is working with the City of San Carlos to determine next steps for these properties, including potential referral to the Regional Water Board. The four properties are located at the following San Carlos addresses (see Section 5.5.6 for more details):

1. 1411 Industrial Road

2. 1030 Washington Street

3. 1029 Washington Street

4. 1030 Varian Street

• SMCWPPP’s PCBs and mercury monitoring program has resulted in SMCWPPP referring four properties (two sets of two adjacent properties, all in San Carlos) to the Regional Water Board for potential further PCBs investigation and abatement (see Section 5.5.6):

o 270 Industrial Road (Delta Star) / 495 Bragato Road (Tiegel)

o 977 and 1007/1011 Bransten Road



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• Sediment monitoring in the Redwood City MS4 (WMA 379) conducted in 2014 and 2017 in the vicinity of 2201 Bay Road identified an additional source area (see Section 5.5.7). This area includes two properties listed for PCBs on GeoTracker: Tyco Engineering Products and an adjacent railroad spur. The Tyco site was remediated and redeveloped (MRP Provision C.3 compliant) and is currently a parking lot for Stanford Hospital. A total of 43 sediment samples and 2 composite stormwater runoff samples have been collected to-date in WMA 379 by SMCWPPP and others, but the only potential PCBs source area that has been identified is the former Tyco site and adjacent historical railroad spur. In April 2019, Regional Water Board staff informed SMCWPPP that they plan to require a clean out the storm drain as part of approving a proposed cap modification and redevelopment of the property and may have the opportunity to request additional post-cleanout monitoring. SMCWPPP will continue to track these efforts and will request PCBs load reduction credit as appropriate.

• Low PCBs concentrations in composite stormwater runoff samples from the bottom of WMA catchments have suggested that either PCBs sources are not prevalent in the catchment or the samples are “false negatives.” False negatives could be the result of low rainfall/runoff rates failing to mobilize sediments from source areas and/or other factors. Only a few stormwater runoff sampling stations in San Mateo County have been resampled, but the results from two such stations in South San Francisco, as described by SMCWPPP (2018), suggested small storm sizes may have resulted in false negatives. SMCWPPP, in collaboration with the SCVURPPP, has recently preliminarily developed a method to normalize results from this type of stormwater runoff monitoring based upon storm intensity. However, the high variability in many of the parameters involved led to a high degree of uncertainty in the evaluation results. SMCWPPP and the SCVURPPP will continue to evaluate normalization methods and results as more data become available in future years, in coordination with related efforts by the RMP (referred to as the RMP’s “Advanced Data Analysis”).

• Figure 6 is a map illustrating the current status of WMAs in San Mateo County, based upon the monitoring data collected through WY 2019. Based upon total PCBs concentration in sediment and/or PCBs particle ratio in stormwater runoff samples, each WMA is placed in one of the following categories:

1. Samples > 0.5 mg/kg PCBs, source properties identified.

2. Samples > 0.5 mg/kg PCBs, source properties not identified.

3. Samples 0.2 – 0.5 mg/kg PCBs.

4. Samples <0.2 mg/kg PCBs.

5. No samples collected.

• The most recent two years of POC monitoring data, WY 2018 (n = 50) and WY 2019 (n = 25), suggest that the PCBs monitoring program in the public ROW in San Mateo County may be approaching diminishing returns in terms of finding PCBs and potentially identifying new source areas, based upon the following:

o The sediment sampling design continued to target locations thought to have the greatest possibility of having elevated PCBs, with an overall goal of attempting to locate source properties.

o The mean PCBs concentrations in WY 2018 and WY 2019 sediment samples were about an order of magnitude lower than the entire PCBs data set.


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o The median PCBs concentrations in WY 2018 and WY 2019 sediment samples were about 50% lower than the entire data set.

o In WY 2018, only 1 of the 50 sediment samples collected had a PCBs concentration that exceeded 1.0 mg/kg. One other sample had a PCBs concentration between 0.5 and 1.0 mg/kg. All of the remaining samples had a PCBs concentration below 0.5 mg/kg.

o In WY 2019, none of the 25 sediment samples collected had a PCBs concentration that exceeded 1.0 mg/kg. One sample had a PCBs concentration between 0.5 and 1.0 mg/kg. All of the remaining samples had a PCBs concentration below 0.5 mg/kg.

• Of the WY 2016 through WY 2019 POC monitoring analytes, promulgated WQOs for the protection of aquatic life only exist for total mercury, dissolved copper, and unionized ammonia. None of the SMCWPPP or third-party water samples collected in San Mateo County over this time period exceeded applicable water quality objectives (WQOs).

• SMCWPPP participated in a BASMAA monitoring study that satisfied the MRP Provision C.12.e requirement to collect 20 composite caulk/sealant samples throughout the MRP permit area. The final project report was included with the Countywide Program’s FY 2017/18 Annual Report, submitted to the Regional Water Board on September 30, 2018 (BASMAA 2018).

• SMCWPPP participated in a BASMAA regional study that was developed to satisfy MRP Provision C.8.f requirements to collect at least eight PCBs and mercury samples that address Management Question No. 3 (Management Action Effectiveness). The study investigated the effectiveness of hydrodynamic separator (HDS) units and various types of biochar-amended bioretention soil media (BSM) at removing PCBs and mercury from stormwater. Results of the study are summarized by BASMAA (2019a and b), reports that are appended to SMCWPPP’s WY 2018 UCMR.

• MRP Provision C.12.g requires Permittees to conduct or cause to be conducted studies concerning the fate, transport, and biological uptake of PCBs discharged from urban runoff to San Francisco Bay margin areas. The provision states: “the specific information needs include understanding the in-Bay transport of PCBs discharged in urban runoff, the sediment and food web PCBs concentrations in margin areas receiving urban runoff, the influence of urban runoff on the patterns of food web PCBs accumulation, especially in Bay margins, and the identification of drainages where urban runoff PCBs are particularly important in food web accumulation.” C.12.g requires Permittees to report in this IMR “the findings and results of the studies completed, planned, or in progress as well as implications of studies on potential control measures to be investigated, piloted or implemented in future permit cycles.” Attachment 1 provides a summary of a multi-year project by the San Francisco Bay (Bay) Regional Monitoring Program (RMP) that is addressing the requirements of Provision C.12.g. The project:

o Identified four PMUs for initial study that are located downstream of urban watersheds where PCBs management actions are ongoing and/or planned;

o Is developing conceptual and PCBs mass budget models for each of the four PMUs; and

o Is conducting monitoring in the PMUs to evaluate trends in pollutant levels and track responses to pollutant load reductions.


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• In WY 2020, the Program will continue to collect samples for PCBs and mercury analysis in compliance with provision C.8.f of MRP 2.0.

• SMCWPPP will develop a control measures plan, including a schedule and corresponding RAA, which demonstrates quantitatively that sufficient control measures will be implemented to attain the San Mateo County portions of the mercury and PCBs TMDL wasteload allocations by 2028 and 2030, respectively. Per the requirements in MRP Provisions C.11/12.d., this control measures plan is due in September 2020. As part of this effort, SMCWPPP and San Mateo County Permittees will continue planning scenarios for control measure implementation in priority WMAs in San Mateo County. The plan will be informed by the PCBs and mercury monitoring data summarized in this report. High priority will continue to be given to the Pulgas Creek pump station north and south drainages (WMA 31 and WMA 210), which are the two WMAs in San Mateo County with the greatest number of samples with elevated concentrations of PCBs in sediment and stormwater runoff samples to-date.

8.2. Copper In WY 2019, the Countywide Program continued to collect and analyze copper samples in compliance with Provision C.8.f of MRP 2.0. The yearly minimum of two samples was satisfied and the requirement to have a cumulative total of four samples addressing Management Question No. 4 (Loads and Status) and No. 5 (Trends) by year four of the Permit (i.e., WY 2019) was also satisfied. A review of the WY 2016 through WY 2019 copper dataset suggests that relatively low levels of copper are being conveyed to receiving waters from urban areas during stormwater runoff events and there have not been any exceedances of an applicable WQO for copper in a receiving water sample. However, although WQOs do not apply to stormwater runoff samples collected from the MS4, these data were also compared to the hardness dependent acute WQOs and three samples from the MS4 exceeded the WQO. It is uncertain what the copper concentration would have been after mixing with the receiving water. Furthermore, if the hardness of the receiving water was higher, a higher WQO would have been calculated. The Program will continue to collect samples for copper analysis in compliance with Provision C.8.f of MRP 2.0 with a goal of at least three samples in WY 2020 to meet the requirement of 20 samples by year five of the Permit (i.e., WY 2020). Copper data collected under MRP 2.0 have been of limited value to the Program. Copper data collected in San Francisco Bay through the RMP Status and Trends Program are more useful in tracking the effectiveness of the copper control measures required by Provision C.13 of MRP 2.0 and, more importantly, the success of the Brake Pad Partnership and Senate Bill (SB) 346 which addresses the largest source of copper by requiring brake pad manufacturers to reduce the use of copper in brake pads sold in California. However, the copper data collected in compliance with Provision C.8.f of MRP 2.0 can provide a relatively cost-effective check on copper discharges to tributaries to the Bay. The Program recommends maintaining the same overall copper monitoring requirements (i.e., 20 total samples) in MRP 3.0, but an elimination of the yearly minimums could result in a more effective monitoring design. 8.3. Nutrients In WY 2019, the Countywide Program continued to collect and analyze nutrient samples in compliance with Provision C.8.f of MRP 2.0. The yearly minimum of two samples was satisfied and the requirement to have a cumulative total of 20 samples addressing Management Question No. 4 (Loads and Status) by


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year four of the Permit (i.e., WY 2019) was also satisfied. A review of the WY 2016 through WY 2019 nutrient dataset suggests that nutrient concentrations are highest during storm events and generally higher at stations lower in the watershed. In addition, the highest nitrogen concentrations were found in Atherton Creek and the highest phosphorus concentrations were found in Redwood Creek. In WY 2020, the Program will continue to collect samples for nutrient analysis in compliance with Provision C.8.f of MRP 2.0. Although nutrient data can be useful in supporting some types of Stressor/Source Identification projects initiated in compliance with Provision C.8.e of MRP 2.0, the Program recommends that the requirement for nutrient monitoring be removed from the POC Monitoring provision under MRP 3.0. The original need for nutrient sampling in tributaries to the Bay to support Regional Water Board efforts to develop nutrient numeric endpoints for the San Francisco Bay Estuary no longer exists. This effort has now been captured and superseded by the State Water Board Biostimulatory Substances and Biological Integrity Project17 which is proposing to adopt a statewide water quality objective for biostimulatory substances (such as nitrogen and phosphorus) along with a program of implementation as an amendment to the Water Quality Control Plan for Inland Surface Water, Enclosed Bays and Estuaries of California (ISWEBE Plan). 8.4. Emerging Contaminants During MRP 2.0, SMCWPPP has leveraged its participation in these RMP special studies to satisfy the POC monitoring requirement for CECs within Provision C.8.f. SMCWPPP recommends that MRP 3.0 provisions continue to support special studies that address data gaps and the scientific understanding of fate and transport of stormwater-related CECs in the Bay. In particular, SMCWPPP is supportive of continued coordination through the STLS to identify the appropriate watersheds and sampling sites for monitoring CECs through RMP special studies. SMCWPPP is also supportive of further developing conceptual and empirical models to better evaluate the distribution and sources of CECs of interest within a stormwater and watershed context. SMCWPPP further recommends including requirements to “conduct or cause to be conducted a special study that addresses relevant management information needs for emerging contaminants;” however, these requirements should allow more flexibility with respect to the classes of compounds identified in the permit, allowing easier alignment with RMP special studies that may address a variety of stormwater-related CECs as the science is advanced over the coming years.

17 https://www.waterboards.ca.gov/water_issues/programs/biostimulatory_substances_biointegrity/


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9.0 REFERENCES Amec Foster Wheeler Environment & Infrastructure, Inc., 2015. Storm Drain Investigation Completion Report. Prepared for: Pentair Thermal Management, Redwood City, California. Oakland, California. Project No. 0124420040.04.3. January 2015. AMS, 2012. Quality Assurance Project Plan. Clean Watersheds for a Clean Bay – Implementing the San Francisco Bay’s PCB and Mercury TMDL with a Focus on Urban Runoff. EPA San Francisco Bay Water Quality Improvement Fund Grant # CFDA 66.202. Prepared for Bay Areas Stormwater Management Agencies Association (BASMAA) by Applied Marine Sciences. BASMAA, 2016. Creek Status Monitoring Program Quality Assurance Project Plan, Final Draft Version 3. Prepared for Bay Area Stormwater Management Agency Association (BASMAA) Regional Monitoring Coalition by EOA, Inc. on behalf of the Santa Clara Urban Runoff Pollution Prevention Program and the San Mateo Countywide Water Pollution Prevention Program, and Applied Marine Sciences on behalf of the Alameda Countywide Clean Water Program and the Contra Costa Clean Water Program. BASMAA, 2018. Evaluation of PCBs in Caulk and Sealants in Public Roadway and Storm Drain Infrastructure. Prepared for Bay Area Stormwater Management Agency Association (BASMAA) by EOA, Inc., KLI, and SFEI. August 16, 2018. BASMAA, 2019a. Pollutant Removal from Stormwater with Biochar Amended Bioretention Soil Media (BSM). Prepared for Bay Area Stormwater Management Agency Association (BASMAA) by OWP at Sacramento State, EOA Inc., KLI, and SFEI. March 2019. BASMAA, 2019b. Pollutant of Concern Monitoring for Management Action Effectiveness – Evaluation of Mercury and PCBs Removal Effectiveness of Full Trash Capture Hydrodynamic Separator Units. Prepared for Bay Area Stormwater Management Agency Association (BASMAA) by OWP at Sacramento State, EOA Inc., KLI, and SFEI. March 2019. Gilbreath, A.N., Hunt, J.A., Wu, J., Kim, P.S., and McKee, L.J., 2016. Pollutants of concern (POC) loads monitoring progress report, water years (WYs) 2012, 2013, and 2014. A technical report prepared for the Regional Monitoring Program for Water Quality in San Francisco Bay (RMP), Sources, Pathways and Loadings Workgroup (SPLWG), Small Tributaries Loading Strategy (STLS). Contribution No. 741. San Francisco Estuary Institute, Richmond, California. Gilbreath, A.N., Hunt, J.A., and McKee, L.J., in preparation. Pollutants of Concern Reconnaissance Monitoring Progress Report, Water Years 2015-2019. SFEI Contribution No. XXX. San Francisco Estuary Institute, Richmond, California. Houtz, E.F., Sutton, R., Park, J-S., and Sedlak, M., 2016. Poly- and perfluoroalkyl substances in wastewater: Significance of unknown precursors, manufacturing shifts, and likely AFFF impacts. Water Research v. 95, pp. 142-149. Lin, D. Sutton, R., Shimabuku, I., Sedlak, M., Wu, J., and Holleman, R. 2018. Contaminants of Emerging Concern in San Francisco Bay: A Strategy for Future Investigations 2018 Update. SFEI Contribution No. 873. San Francisco Estuary Institute, Richmond, CA.


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Lin, D. and Sutton, R. 2018. Alternative Flame Retardants in San Francisco Bay: Synthesis and Strategy. SFEI Contribution No. 885. San Francisco Estuary Institute, Richmond, CA. Phillips, B.M., Anderson, B.S., Siegler, K., Voorhees, J., Tadesse, D., Webber, L., Breuer, R. 2016. Spatial and Temporal Trends in Chemical Contamination and Toxicity Relative to Land Use in California Watersheds: Stream Pollution Trends (SPoT) Monitoring Program. Fourth Report – Seven-Year Trends 2008-2014. California State Water Resources Control Board, Sacramento, CA. Regional Water Board, 2012. San Francisco Bay Nutrient Management Strategy. San Francisco Regional Water Quality Control Board. Regional Water Board, 2015. San Francisco Bay Region Municipal Regional Stormwater NPDES Permit. San Francisco Regional Water Quality Control Board Order R2-2015-0049, NPDES Permit No. CAS612008. November 19, 2015. Sedlak, M.D., Benskin, J.P., Wong, A., Grace, R., and Greig, D.J., 2017. Per and polyfluoroalkyl substances (PFASs) in San Francisco Bay wildlife: Temporal trends, exposure pathways, and notable presence of precursor compounds. Chemosphere v. 185, pp. 1217-1226. Sedlak, M.D., Sutton, R., Wong, A., Lin, D. 2018. Per and polyfluoroalkyl substances (PFASs) in San Francisco Bay: Synthesis and Strategy. San Francisco Estuary Institute, Richmond, CA. Contribution # 867. 130 pages. Siegler, K. 2018. The UC Davis Marine Pollution Studies Laboratory at Granite Canyon. Personal communication, August 2018. Senn, D.B. and Novick, E., 2014. Scientific Foundation for the San Francisco Bay Nutrient Management Strategy. Draft FINAL. October 2014. SMCWPPP, 2014. Integrated Monitoring Report. Prepared for San Mateo Countywide Water Pollution Prevention Program (SMCWPPP) by EOA, Inc., Oakland, California. March 2014. SMCWPPP, 2015. PCBs and Mercury Source Area Identification, Water Year 2015 POC Monitoring Report. Prepared for San Mateo Countywide Water Pollution Prevention Program (SMCWPPP) by EOA, Inc., Oakland, California. September 2015. SMCWPPP, 2016a. Progress Report: Identification of Watershed Management Areas for PCBs and Mercury. San Mateo Countywide Water Pollution Prevention Program. April 1, 2016. SMCWPPP, 2016b. Identifying Management Areas and Controls for Mercury and PCBs in San Mateo County Stormwater Runoff. San Mateo Countywide Water Pollution Prevention Program. September 30, 2016. SMCWPPP, 2017a. Urban Creeks Monitoring Report, Water Quality Monitoring, Water Year 2016 (October 2015 – September 2016). Prepared for San Mateo Countywide Water Pollution Prevention Program (SMCWPPP) by EOA, Inc., Oakland, California. March 31, 2017.


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SMCWPPP, 2017b. Control Measures Plan for PCBs and Mercury in San Mateo County Stormwater Runoff. San Mateo Countywide Water Pollution Prevention Program. September 30, 2017. SMCWPPP, 2018a. Urban Creeks Monitoring Report, Water Quality Monitoring, Water Year 2017 (October 2016 – September 2017). Prepared for San Mateo Countywide Water Pollution Prevention Program (SMCWPPP) by EOA, Inc., Oakland, California. March 31, 2018. SMCWPPP, 2018b. Updated Control Measures Plan for PCBs and Mercury in San Mateo County Stormwater Runoff. San Mateo Countywide Water Pollution Prevention Program. September 30, 2018. SMCWPPP, 2018c. Pollutants of Concern Monitoring Report. Water Year 2018 Accomplishments and Water Year 2019 Planned Allocation of Effort. San Mateo Countywide Water Pollution Prevention Program. October 15, 2018. SMCWPPP, 2019a. Urban Creeks Monitoring Report, Water Quality Monitoring, Water Year 2018 (October 2017 – September 2018). Prepared for San Mateo Countywide Water Pollution Prevention Program (SMCWPPP) by EOA, Inc., Oakland, California. March 31, 2019. SMCWPPP, 2019b. Updated Control Measures Plan for PCBs and Mercury in San Mateo County Stormwater Runoff. San Mateo Countywide Water Pollution Prevention Program. September 30, 2019. Sutton, R., Sedlak, M., and Yee, D., 2013. Contaminants of Emerging Concern in San Francisco Bay: A Strategy for Future Investigations. San Francisco Estuary Institute, Richmond, CA. Contribution # 700. Sutton, R. and Sedlak, M., 2015. Contaminants of Emerging Concern in San Francisco Bay: A Strategy for Future Investigations. 2015 Update. San Francisco Estuary Institute, Richmond, CA. Contribution # 761. Sutton, R., Sedlak, M., Sun, J. and Lin, D., 2017. Contaminants of Emerging Concern in San Francisco Bay: A Strategy for Future Investigations. 2017 Revision. San Francisco Estuary Institute, Richmond, CA. Contribution # 851. San Francisco Bay Regional Water Quality Control Board (SFBRWQCB). 2017. Water Quality Control Plan (Basin Plan) for the San Francisco Bay Basin. https://www.waterboards.ca.gov/sanfranciscobay/basin_planning.html. May 4, 2017. Shimabuku, Ila, Sutton, R., Chen, D., Wu, Y., Sun, J. 2019. Draft Report. Flame Retardants and plastic additives in San Francisco Bay: Targeted monitoring of organophosphate esters and bisphenols. San Francisco Estuary Institute, Richmond, CA. Contribution #925. 48 pages.

Attachment 1 Fate and Transport Study of PCBs: Urban Runoff Impact On San Francisco Bay Margins

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MRP PROVISION C.12.g. FATE AND TRANSPORT STUDY OF PCBS: URBAN RUNOFF IMPACT ON SAN FRANCISCO BAY MARGINS

Background

MRP Provision C.12.g requires Permittees to conduct or cause to be conducted studies concerning the fate, transport, and biological uptake of PCBs discharged from urban runoff to San Francisco Bay margin areas. The provision states: “the specific information needs include understanding the in-Bay transport of PCBs discharged in urban runoff, the sediment and food web PCBs concentrations in margin areas receiving urban runoff, the influence of urban runoff on the patterns of food web PCBs accumulation, especially in Bay margins, and the identification of drainages where urban runoff PCBs are particularly important in food web accumulation.” Conceptually, advances in this type of knowledge could allow the Regional Water Board to explore revising the PCBs TMDL to incentivize implementing PCBs management actions in such drainages that drain to sensitive Bay margin areas. Prioritizing actions in these drainages could possibly facilitate reaching TMDL goals more efficiently, though establishing this type of prioritization process would involve many challenges. Provision C.12.g. is being addressed through a multi-year project by the San Francisco Bay (Bay) Regional Monitoring Program (RMP) to identify, model, and investigate embayments along the Bay shoreline designated “Priority Margin Units” (PMUs). The project:

Identified four PMUs for initial study that are located downstream of urban watersheds where PCBs management actions are ongoing and/or planned;

Is developing conceptual and PCBs mass budget models for each of the four PMUs; and

Is conducting monitoring in the PMUs to evaluate trends in pollutant levels and track responses to pollutant load reductions.

The objectives of this effort to model and investigate Bay PMUs include:

Characterizing concentrations and the spatial distribution of PCBs in sediment and food web biota in PMUs, including establishing baseline data on PCBs concentration and loading;

Evaluating the response of PMU receiving waters over time to load reduction efforts in the watershed, such as remediation of PCBs-contaminated properties, including tracking PCBs in sport fish as the ultimate indicator of progress in reduction of impairment; and

Informing the review and possible revision of the PCBs TMDL and the reissuance of the MRP, both of which were initially tentatively scheduled to occur in 2020 (while the MRP reissuance process in underway and is anticipated to be completed in 2021, the status of evaluating and possibly revising the Bay PCBs TMDL remains uncertain at this time).

A general description and multi-year budget for this project is in the “PCBs” section of the RMP Multi-Year Plan, 2020 Annual Update, dated January 2020 (sfei.org/documents/2020-rmp-multi-year-plan). The RMP PCBs Workgroup, which includes representative from BASMAA, the Regional Water Board, and other RMP stakeholders, provides oversight over the project, including reviewing and commenting on

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draft conceptual model reports and plans for PMU-related RMP Special Studies (e.g., PMU monitoring plans). In accordance with MRP Provision C.12.g., Permittees submitted in their FY 2016/17 Annual Reports a workplan for meeting the above information needs, which included descriptions of studies proposed or underway and a preliminary schedule. Permittees then reported on the status of the studies in their FY 2017/18 Annual Reports. In their Integrated Monitoring Reports (IMRs), due by March 30, 2020, Permittees are required to report the findings and results of the studies completed, planned, or in progress as well as implications of the studies on potential control measures to be investigated, piloted, or implemented in future permit cycles. The four PMUs initially selected were:

Emeryville Crescent (Alameda County)

San Leandro Bay (Alameda County)

Steinberger Slough (San Mateo County)

Richmond Harbor (Contra Costa County) The PMU conceptual models are intended to provide a foundation for future monitoring to track responses to load reductions and may eventually help guide planning of management actions. Three of the selected embayments (all except San Leandro Bay) receive drainage from pilot watersheds that were included in BASMAA’s Clean Watersheds for a Clean Bay project (basmaa.org/Clean-Watersheds-for-a-Clean-Bay-Project). Status of PMU Conceptual Models

The following sections summarize the status of conceptual model development in each of the four PMUs. Emeryville Crescent

A final conceptual model report (dated April 2017) is available on the San Francisco Estuary Institute (SFEI) website: sfei.org/sites/default/files/biblio_files/Emeryville%20Crescent%20Draft%20Final%20Report%2005-02-17%20Final%20Clean_0.pdf. The report’s key finding, which was based on a simple one-box pollutant fate model and dependent on assumptions made for the model’s input parameters, was that PCBs concentrations in sediment and the food web could potentially decline fairly quickly (within 10 years) in response to load reductions from the watershed. San Leandro Bay

A conceptual model for San Leandro Bay was developed in three phases, with reports available on the SFEI website. The Phase 1 report (dated June 2017) presented analyses of watershed loading, initial retention, and long-term fate, including results of sediment sampling in 2016:

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sfei.org/sites/default/files/biblio_files/Yee%20et%20al%202017%20Conceptual%20Model%20Report%20San%20Leandro%20Bay%20Phase%201.pdf. The Phase 2 report (dated December 2017) is designated a data report and documented the methods, quality assurance, and all of the results of the 2016 field study: sfei.org/sites/default/files/biblio_files/San%20Leandro%20Bay%20PCB%20Study%20Data%20Report%20Final.pdf The Phase 3 report (dated November 2019) was recently completed and is available here: sfei.org/sites/default/files/biblio_files/San%20Leandro%20Bay%20PCBs%20Phase%203%20Final%20Report%20_0.pdf This final report incorporates all of the results of the 2016 field study, and includes additional discussion of the potential influence of contaminated sites in the watershed and the results of passive sampling by Stanford researchers. It also includes a comparative analysis of long-term fate in San Leandro Bay and the Emeryville Crescent, a section on bioaccumulation, and a concluding section with answers to the management questions that were the impetus for the work. The report included a discussion of the results of mass budget modeling that illustrated one type of challenge encountered during the PMU conceptual modeling effort. A wetland sediment core profile at Damon Slough indicated a substantial reduction in PCBs between the 1970s and the early 2000s. The simple mass budget model developed during this study suggested continued reductions in PCBs. However, a comparison of the results of extensive sampling of San Leandro Bay surface sediment in 1998 and in 2016 suggested minimal decline in PCBs over this more recent 18 year period. This finding may suggest that continuing PCBs inputs from the watershed are greater than estimated as part of the mass budget modeling and are slowing the recovery of San Leandro Bay. It is important to note that numerous uncertainties associated with the model and its parameters influence projected system response time. Steinberger Slough / Redwood Creek

A conceptual model for Steinberger Slough / Redwood Creek is currently under development. SFEI staff released a draft report in February 2020. Like the other conceptual models, it includes results of existing monitoring efforts in the PMU and watershed, analyses of watershed loading, development of a mass budget, and long-term fate modeling, including projected PCBs concentrations in sediment and the food web in response to load reductions from the watershed. Richmond Harbor

Due to budget limitations and because other RMP efforts were deemed higher priority, a conceptual model for the Richmond Harbor PMU is not yet under development.

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RMP Special Studies Related to PMUs

In addition to ongoing conceptual model development (as described above), and continuing technical and logistical support for the RMP PCBs Workgroup, various types of RMP Special Studies18 related to PMUs are ongoing, including the following:

Shiner Surfperch PCBs Monitoring in PMUs – shiner surfperch is a crucial indicator of impairment, due to its explicit inclusion as an indicator species in the TMDL, importance as a sport fish species, tendency to accumulate high concentrations, site fidelity, and other factors. The conceptual site models recommend periodic monitoring of shiner surfperch to track trends in the PMUs, and as the ultimate indicator of progress in reduction of impairment. A coordinated sampling of PCBs in shiner surfperch in PMUs is being conducted as an add-on to RMP Status and Trends (S&T) sport fish sampling. A dataset for shiner surfperch will be developed that is directly comparable across the PMUs and the five locations that are sampled in S&T monitoring.

Stormwater Runoff PCBs Monitoring in PMUs – this study is collecting information on PCBs concentrations and particle ratios in stormwater in watersheds draining to the PMUs to better estimate current PCBs loads into the PMUs (a critical component of the PMU mass budgets) and to help track the effectiveness of PCBs controls such as remediation of PCBs-contaminated properties.

Assess Loading and Spatial Distribution of PCBs in Steinberger Slough / Redwood Creek PMU – this study will address information gaps in the conceptual model for this area and establish baseline data for evaluating the response of these receiving waters to load reduction efforts in the watershed. Passive sampling devices (PSDs) will be deployed to assess spatial patterns in dissolved PCBs in pore water and surface water, providing information on spatial patterns in an index of current biotic exposure. In addition, analysis of depth profiles of pore water with PSDs, accompanied by bulk sediment chemistry in cores, will provide information on the chronology of loading and exposure over the past 50 years. This study is being conducted in collaboration with Stanford researchers.

Discussion

As of the end of calendar year 2019, the PMU conceptual modeling and associated special studies are continuing to progress. Four PMUs for initial study, characterization, and tracking have been identified, and conceptual models have been completed for two of the PMUs, the Emeryville Crescent and San Leandro Bay. A draft conceptual model for a third PMU, Steinberger Slough / Redwood Creek, is under development. In conjunction with the modeling, RMP Special Studies are characterizing concentrations and the spatial distribution of PCBs in sediment and food web biota in PMUs and establishing baseline data on PCBs concentration and loading, and will help evaluate the response of the PMUs to load reduction efforts in their watersheds. The efforts to model and investigate the PMUs are generating valuable new data and knowledge that will inform future revisions of the PCBs TMDL. However, it would be premature to propose major changes to the TMDL at this time, such as revising the stormwater allocation (e.g., assigning allocations to watershed areas that vary depending upon the sensitivity of the Bay margin area to which they drain). Similarly, additional work should be completed before attempting to project any implications of the modeling and studies on potential control measures to be investigated, piloted, or implemented in

18These efforts are partly funded by Supplemental Environmental Projects (SEPs).

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future stormwater permit cycles. BASMAA representatives will continue to participate in the RMP PCBs Workgroup to help oversee this work and guide it towards developing information that will inform implementing controls for PCBs in stormwater runoff and reducing the Bay’s PCBs impairment.

Attachment 2 WY 2019 Quality Assurance / Quality Control Report

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Pollutants of Concern Monitoring - Quality Assurance/Quality Control Report, WY 2019 1.0 INTRODUCTION The San Mateo Countywide Pollution Prevention Program (SMCWPPP) conducted Pollutants of Concern (POC) Monitoring in Water Year (WY) 2019 to comply with Provision C.8.f (Pollutants of Concern Monitoring) of the National Pollutant Discharge Elimination Program (NPDES) Municipal Regional Permit for the San Francisco Bay Area (i.e., MRP). Monitoring included analysis for polychlorinated biphenyls (PCBs), total mercury, total and dissolved copper, suspended sediment concentration (SSC), and nutrients (i.e., ammonia, nitrate, nitrite, total Kjeldahl nitrogen, orthophosphate, and total phosphorus).

This project utilized the Clean Watersheds for Clean Bay Project (CW4CB) Quality Assurance Project Plan (QAPP; BASMAA 2013) as a basis for Quality Assurance and Quality Control (QA/QC) procedures. Missing components were supplemented by the Bay Area Stormwater Management Agencies Association (BASMAA) Regional Monitoring Coalition (RMC) QAPP (BASMAA 2016) and the QAPP for the California Surface Water Ambient Monitoring Program (SWAMP), specifically for nutrient and copper samples, respectively. Data were assessed for seven data quality attributes, which include (1) Representativeness, (2) Comparability, (3) Completeness, (4) Sensitivity, (5) Contamination, (6) Accuracy, and (7) Precision. These seven attributes were compared to Data Quality Objectives (DQOs), which were established to ensure that data collected are of adequate quality and sufficient for the intended uses. DQOs address both quantitative and qualitative assessment of the acceptability of data – representativeness and comparability are qualitative while completeness, sensitivity, precision, accuracy, and contamination are quantitative assessments. Specific DQOs are based on Measurement Quality Objectives (MQOs) for each analyte.

The MQOs for each of the POC analytes are summarized in Table 1 for water and Table 2 for sediment. As there was no reporting limit listed in the QAPP for copper, results were compared to the SWAMP recommended reporting limits for inorganic analytes in freshwater. Overall, the results of the QA/QC review suggest that the data generated during this study were of sufficient quality for the purposes of the project. While some data were flagged based on the MQOs and DQOs identified in the QAPPs, none of the data was rejected. Further details regarding the QA/QC review are provided in the sections below.

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Table 1. Measurement quality objectives for analytes in water from the Clean Watersheds for a Clean Bay (CW4CB) Quality Assurance Project Plan (BASMAA 2013) and BASMAA RMC Quality Assurance Project Plan (BASMAA 2016).

Sample Nutrients1 Hardness1 SSC2 Copper2 Mercury2 PCBs2

Laboratory Blank < RL <RL < RL < RL < RL < RL

Reference Material

(Laboratory Control Sample)

Recovery

90-110% 80-120% NA 75-125% 75-125% 50-150%

Matrix Spike Recovery

80-120% 80-120% NA 75-125% 75-125% 50-150%

Duplicates (Matrix Spike,

Field, and Laboratory)3

RPD < 25% RPD < 25% RPD < 25% RPD < 25% RPD < 25% RPD < 25%

Reporting Limit

0.01mg/L

except for:

Ammonia (0.02mg/L)

TKN4 (0.5mg/L)

1 mg/L5 0.5 mg/L 0.10 μg/L6 0.0002 μg/L

(0.2 ng/L)

0.002 µg/L

(2000 pg/L)

RL = Reporting Limit; RPD = Relative Percent Difference

1 From the BASMAA QAPP 2 From the CW4CB QAPP 3 NA if native concentration for either sample is less than the reporting limit 4 TKN = Total Kjeldahl Nitrogen

5 No hardness RL listed in either QAPP. Value is from SWAMP-recommended reporting limits for conventional analytes in freshwater. (https://www.waterboards.ca.gov/water_issues/programs/swamp/docs/tools/19_tables_fr_water/1_conv_fr_water.pdf)

6 No copper RL listed in either QAPP. Value is from SWAMP-recommended reporting limits for inorganic analytes in freshwater. (http://www.waterboards.ca.gov/water_issues/programs/swamp/docs/tools/19_tables_fr_water/4_inorg_fr_water.pdf)

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Table 2. Measurement quality objectives for analytes in sediment from the Clean Watersheds for a Clean Bay (CW4CB) Quality Assurance Project Plan (BASMAA 2013).

Sample Total Solids Mercury PCBs

Laboratory Blank < RL < RL < RL

Reference Material (Laboratory Control Sample)

Recovery N/A 75-125% 50-150%

Matrix Spike Recovery

N/A 75-125% 50-150%

Duplicates 1 (Matrix Spike, Field, and

Laboratory) RPD < 25% RPD < 25% RPD < 25%2

Reporting Limit 0.1%3

30 μg/kg

0.03 mg/kg

30,000 ng/kg

0.2 µg/kg

0.0002 mg/kg

200 ng/kg

RL = Reporting Limit; RPD = Relative Percent Difference

1 NA if native concentration for either sample is less than the reporting limit 2 Only applicable for matrix spike duplicates. Method specific for field and laboratory duplicates

3 RL for total solids in water

2.0 REPRESENTATIVENESS Data representativeness assesses whether the data were collected so as to represent actual conditions at each monitoring location. For this project, all samples were assumed to be representative if they were collected and analyzed according to protocols specified in the CW4CB QAPP and RMC QAPP. All field and laboratory personnel received and reviewed the QAPPs, and followed prescribed protocols including laboratory methods.

3.0 COMPARABILITY The QA/QC officer ensures that the data may be reasonably compared to data from other programs producing similar types of data. For POC monitoring, individual stormwater programs try to maintain comparability within the RMC. The key measure of comparability for all RMC data is the California Surface Water Ambient Monitoring Program.

Electronic data deliverables (EDDs) were submitted to the San Francisco Bay Regional Water Quality Control Board (SFRWQCB) in Microsoft Excel templates developed by SWAMP, to ensure data comparability with SWAMP. In addition, data entry followed SWAMP documentation specific to each

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data type, including the exclusion of qualitative values that do not appear on SWAMP’s look up lists19. Completed templates were reviewed using SWAMP’s online data checker20, further ensuring SWAMP-comparability.

All WY 2019 data were considered comparable to SWAMP data and other RMC data.

4.0 COMPLETENESS Completeness is the degree to which all data were produced as planned; this covers both sample collection and analysis. An overall completeness of greater than 90% is considered acceptable for RMC chemical data and field measurements.

During WY 2019, SMCWPPP collected 100% of planned samples. Nine aqueous samples were collected and analyzed for nutrients (ammonia, nitrate, nitrite, total Kjeldahl nitrogen, phosphorus, and orthophosphate). Two aqueous samples were collected and analyzed for copper and hardness. Twenty-five sediment samples were collected and analyzed for PCBs and mercury.

5.0 SENSITIVITY 5.1. Water Sensitivity analysis determines whether the methods can identify and/or quantify results at low enough levels. For the aqueous chemical analyses in this project, sensitivity is considered to be adequate if the reporting limits (RLs) comply with the specifications in RMC QAPP Appendix E (RMC Target Method Reporting Limits) and the CW4CB QAPP Appendix B (CW4CB Target Method Reporting Limits).

A summary of the target and actual reporting limits for each analyte is shown in Table 3. The reporting limits for hardness, nitrate, ammonia, and copper samples exceeded their respective target reporting limits. While the hardness concentrations were well above the reporting limit, several of the nitrate concentrations that were reported as “detected, not quantified” would have been quantified had the target reporting limit been met. The analytical laboratory has conveyed that it is not currently possible to lower the nitrate reporting limit due to the analytical protocol used to measure the constituent. One of the ammonia samples was non-detect and one copper sample was “detected, not quantified, but these concentrations were much lower than the target reporting limit. As a result, a lower reporting limit would not have an impact on these results.

19 Look up lists available online at http://swamp.waterboards.ca.gov/swamp_checker/LookUpLists.php 20 Checker available online at http://swamp.waterboards.ca.gov/swamp_checker/SWAMPUpload.php

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Table 3. Target and actual reporting limits for SMCWPPP pollutants of concern monitoring in water in WY 2019.

Analyte Unit Target Actual Exceeds Target RL?

Ammonia mg/L 0.02 0.05 Yes

Nitrate mg/L 0.01 0.1 Yes

Nitrite mg/L 0.01 0.005 No

Total Kjeldahl Nitrogen mg/L 0.5 0.1 No

Phosphorus mg/L 0.01 0.01 No

Orthophosphate mg/L 0.01 0.01 No

Copper μg/L 0.1 0.1-0.5 Yes

Hardness mg/L 1 5-10 Yes

5.2. Sediment Analysis The reporting limits for all sediment PCB samples exceeded the CW4CB reporting limit requirement of 200 ng/kg. The target reporting limit for mercury (0.03 mg/kg) was also exceeded for most samples. SMCWPPP will inquire with the analytical laboratory if lower reporting limits are possible for future analysis.

6.0 CONTAMINATION For chemical data, contamination is assessed as the presence of analytical constituents in blank samples.

6.1. Water Analysis Several laboratory and equipment (filter) blanks were run during the nutrient, copper, and hardness analyses. All associated blanks were non-detect.

6.2. Sediment Analysis Several laboratory blanks were analyzed during sediment analysis for mercury and PCBs. All of the laboratory blanks for mercury non-detect, but one PCB blank (PCB 118) was detected in a laboratory blank at a concentration above the reporting limit. As a result, all PCB 118 results have been flagged, but not rejected. No other PCBs were detected in the laboratory blanks.

7.0 ACCURACY Accuracy is assessed as the percent recovery of samples spiked with a known amount of a specific chemical constituent. The analytical laboratory evaluated and reported the Percent Recovery (PR) of Laboratory Control Samples (LCS; in lieu of reference materials) and Matrix Spikes (MS)/Matrix Spike Duplicates (MSD), which were recalculated and compared to the target ranges in the RMC and CW4CB QAPPs. If a QA sample did not meet MQOs, all samples in that batch for that analyte were flagged.

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7.1. Water Analysis All laboratory LCS and MS/MSD samples for nutrients, copper, and hardness were within their respective MQOs.

7.2. Sediment Analysis All laboratory control and matrix spike samples for sediment mercury and PCBs met their corresponding MQOs.

8.0 PRECISION Precision is the repeatability of a measurement and is quantified by the Relative Percent Difference (RPD) of two duplicate samples. Three measures of precision were used for this project – matrix spike duplicates, laboratory duplicates, and field duplicates. The MQO for RPD specified by both the CW4CB QAPP and the BASMAA QAPP is <25%.

8.1. Water Analysis

8.1.1. Laboratory Duplicates Matrix spike duplicates and laboratory control sample duplicates for nutrients, copper, and hardness were well below the targeted range of < 25%.

8.1.2. Field Duplicates One nutrient field duplicate was collected during WY 2019 POC monitoring. The field duplicate sample met the RPD MQO for all analytes.

8.2. Sediment Analysis

8.2.1. Laboratory Duplicates The majority of matrix spike duplicates analyzed for mercury and PCBs were well below the RPD MQO (< 25%). However, the following PCBs did exceed the MQO during a matrix spike duplicate:

• PCB 44 • PCB 52 • PCB 66 • PCB 77 • PCB 101 • PCB 105 • PCB 118 • PCB 126 • PCB 128 • PCB 180 • PCB 187

8.2.2. Field Duplicates Three sediment field blind duplicates were collected in WY 2019. The field duplicates exceeded the RPD MQO for mercury and nine PCB congeners. The sample taken at SM-BEL-01-A had the fewest number of analytes exceeding the MQO with one total exceedance. The sample taken at SM-BRI-02-J had the

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highest number of analytes exceeding the MQO with nine total exceedances. The analytes that exceeded the MQO include the following (the number of samples that exceeded the MQO for that analyte are included in parentheses):

• Mercury (1) • PCB 95 (1) • PCB 118 (1) • PCB 123 (1) • PCB 132/153 (1) • PCB 138/158 (1) • PCB 141 (1) • PCB 149 (1) • PCB 151 (1) • PCB 180 (1)

9.0 REFERENCES Bay Area Stormwater Management Agency Association (BASMAA). 2013. Quality Assurance Project Plan. Clean Watersheds for a Clean Bay – Implementing the San Francisco Bay’s PCB and Mercury TMDL with a Focus on Urban Runoff. Revision Number 1. EPA San Francisco Bay Water Quality Improvement Fund Grant # CFDA 66.202. Prepared for Bay Area Stormwater Management Agencies Association (BASMAA) by Applied Marine Sciences (AMS). August 2013. Bay Area Stormwater Management Agency Association (BASMAA) Regional Monitoring Coalition. 2016. Creek Status Monitoring Program Quality Assurance Project Plan, Final Draft Version 3. Prepared for BASMAA by EOA, Inc. on behalf of the Santa Clara Urban Runoff Pollution Prevention Program and the San Mateo Countywide Water Pollution Prevention Program, Applied Marine Sciences on behalf of the Alameda Countywide Clean Water Program and the Contra Costa Clean Water Program. 128 pp. Surface Water Ambient Monitoring Program (SWAMP). 2018. Quality Assurance Program Plan. May 2018. 140 pp.

Attachment 3 Results of Monitoring San Mateo County Stormwater Runoff for PCBs and Mercury

1

Site Name (RMP Site Name in Parentheses) Permittee Sample Type Latitude Longitude Water Year Sample Date SSC (mg/L) Total PCBs

(ng/L) Total PCBs

(ng/g)

Total Hg

(ng/L)

Total Hg

(ng/g)

RMP STLS Stormwater Runoff Samples Borel Creek Receiving Water WY 2011 2/16/2011 239 3.41 14.3 -- -- SM-SCS-210A (Pulgas Creek PS S) San Carlos MS4 37.50456 -122.24898 WY 2011 2/17/2011 49.7 19.1 384 -- -- SM-SCS-210A (Pulgas Creek PS S) San Carlos MS4 37.50456 -122.24898 WY 2011 2/17/2011 42.3 53.9 1,273 -- -- SM-SCS-31A (Pulgas Creek PS N) San Carlos MS4 37.50462 -122.24905 WY 2011 2/17/2011 105 43.3 411 -- -- SM-SCS-31A (Pulgas Creek PS N) San Carlos MS4 37.50462 -122.24905 WY 2011 2/17/2011 83.6 46.9 561 -- -- SM-SCS-210A (Pulgas Creek PS S) San Carlos MS4 37.50456 -122.24898 WY 2011 3/18/2011 24.7 21.9 884 -- -- SM-SCS-210A (Pulgas Creek PS S) San Carlos MS4 37.50456 -122.24898 WY 2011 3/18/2011 17.4 31.0 1,782 -- -- SM-SCS-31A (Pulgas Creek PS N) San Carlos MS4 37.50462 -122.24905 WY 2011 3/18/2011 31.0 66.6 2,148 -- -- SM-SCS-31A (Pulgas Creek PS N) San Carlos MS4 37.50462 -122.24905 WY 2011 3/18/2011 50.3 84.5 1,681 -- -- Belmont Creek Receiving Water WY 2011 3/18/2011 148 2.83 19.1 -- -- Belmont Creek Receiving Water WY 2011 3/18/2011 209 3.06 14.6 -- -- Belmont Creek Receiving Water WY 2011 3/18/2011 448 4.91 10.9 -- -- Borel Creek Receiving Water WY 2011 3/18/2011 372 6.30 16.9 -- -- Borel Creek Receiving Water WY 2011 3/18/2011 628 8.67 13.8 -- -- SM-SCS-210A (Pulgas Creek PS S) San Carlos MS4 37.50456 -122.24898 WY 2013 3/6/2013 7.09 15.1 2,125 -- -- SM-SCS-210A (Pulgas Creek PS S) San Carlos MS4 37.50456 -122.24898 WY 2013 3/6/2013 30.8 28.5 925 -- -- SM-SCS-210A (Pulgas Creek PS S) San Carlos MS4 37.50456 -122.24898 WY 2013 3/6/2013 40.1 32.5 809 -- -- SM-SCS-210A (Pulgas Creek PS S) San Carlos MS4 37.50456 -122.24898 WY 2013 3/6/2013 61.2 62.7 1,025 -- -- SM-SCS-210A (Pulgas Creek PS S) San Carlos MS4 37.50456 -122.24898 WY 2014 11/19/2013 22.5 467 20,733 -- -- SM-SCS-210A (Pulgas Creek PS S) San Carlos MS4 37.50456 -122.24898 WY 2014 11/19/2013 47.3 731 15,447 -- -- SM-SCS-210A (Pulgas Creek PS S) San Carlos MS4 37.50456 -122.24898 WY 2014 11/19/2013 277 4,084 14,744 -- -- SM-SCS-210A (Pulgas Creek PS S) San Carlos MS4 37.50456 -122.24898 WY 2014 11/19/2013 179 6,669 37,363 -- -- SM-SCS-210A (Pulgas Creek PS S) San Carlos MS4 37.50456 -122.24898 WY 2014 2/6/2014 10.1 35.3 3,493 -- -- SM-SCS-210A (Pulgas Creek PS S) San Carlos MS4 37.50456 -122.24898 WY 2014 2/6/2014 33.0 50.1 1,519 -- -- SM-SCS-210A (Pulgas Creek PS S) San Carlos MS4 37.50456 -122.24898 WY 2014 2/6/2014 65.0 64.1 987 -- -- SM-SCS-210A (Pulgas Creek PS S) San Carlos MS4 37.50456 -122.24898 WY 2014 2/6/2014 32.0 143 4,481 -- -- SM-SCS-210A (Pulgas Creek PS S) San Carlos MS4 37.50456 -122.24898 WY 2014 2/6/2014 50.9 211 4,153 -- -- SM-SCS-210A (Pulgas Creek PS S) San Carlos MS4 37.50456 -122.24898 WY 2014 2/8/2014 27.0 25.1 931 -- -- SM-SCS-210A (Pulgas Creek PS S) San Carlos MS4 37.50456 -122.24898 WY 2014 2/8/2014 42.0 29.1 692 -- -- SM-SCS-210A (Pulgas Creek PS S) San Carlos MS4 37.50456 -122.24898 WY 2014 2/8/2014 29.0 35.4 1,221 -- -- SM-SCS-210A (Pulgas Creek PS S) San Carlos MS4 37.50456 -122.24898 WY 2014 2/8/2014 14.0 37.4 2,672 -- -- SM-SCS-210A (Pulgas Creek PS S) San Carlos MS4 37.50456 -122.24898 WY 2014 2/26/2014 43.6 48.3 1,108 -- -- SM-SCS-210A (Pulgas Creek PS S) San Carlos MS4 37.50456 -122.24898 WY 2014 2/26/2014 27.0 69.5 2,574 -- -- SM-SCS-210A (Pulgas Creek PS S) San Carlos MS4 37.50456 -122.24898 WY 2014 2/26/2014 91.4 172 1,886 -- --

2


(ng/L) Total PCBs

(ng/g)

Total Hg

(ng/L)

Total Hg

(ng/g) SM-SCS-210A (Pulgas Creek PS S) San Carlos MS4 37.50456 -122.24898 WY 2014 2/26/2014 131 660 5,057 -- -- SM-SCS-210A (Pulgas Creek PS S) San Carlos MS4 37.50456 -122.24898 WY 2014 3/26/2014 42.0 61.6 1,467 -- -- SM-SCS-210A (Pulgas Creek PS S) San Carlos MS4 37.50456 -122.24898 WY 2014 3/26/2014 38.2 63.0 1,648 -- -- SM-SCS-210A (Pulgas Creek PS S) San Carlos MS4 37.50456 -122.24898 WY 2014 3/26/2014 23.7 74.2 3,125 -- -- SM-SCS-210A (Pulgas Creek PS S) San Carlos MS4 37.50456 -122.24898 WY 2014 3/26/2014 120 505 4,196 -- -- SM-SCS-210A (Pulgas Creek PS S) San Carlos MS4 37.50456 -122.24898 WY 2014 3/31/2014 84.8 16.9 200 -- -- SM-SCS-210A (Pulgas Creek PS S) San Carlos MS4 37.50456 -122.24898 WY 2014 3/31/2014 21.6 28.5 1,318 -- -- SM-SCS-210A (Pulgas Creek PS S) San Carlos MS4 37.50456 -122.24898 WY 2014 3/31/2014 31.2 85.5 2,741 -- -- SM-SCS-210A (Pulgas Creek PS S) San Carlos MS4 37.50456 -122.24898 WY 2014 3/31/2014 41.8 151 3,616 -- -- SM-RCY-267A (Oddstad PS) Redwood City MS4 37.49172 -122.21886 WY 2015 12/2/2014 148 9.20 62.4 54.8 372 SM-RCY-337A (Veterans PS) Redwood City MS4 37.49723 -122.23693 WY 2015 12/15/2014 29.2 3.52 121 13.7 469 SM-EPA-70A (Runnymede Ditch) East Palo Alto MS4 37.46883 -122.12701 WY 2015 2/6/2015 265 28.55 108 51.5 194 SM-EPA-72A (SD near Cooley Landing) East Palo Alto MS4 37.47492 -122.12640 WY 2015 2/6/2015 82.0 6.47 78.9 35.0 427 SM-SSF-306A (South Linden PS) South San Francisco MS4 37.65017 -122.41127 WY 2015 2/6/2015 43.0 7.81 182 29.2 679 SM-SSF-293A (Gateway Blvd SD) South San Francisco MS4 37.65244 -122.40257 WY 2015 2/6/2015 45.0 5.24 117 19.6 436 SM-SSF-319A (Forbes Blvd Outfall) South San Francisco MS4 37.65889 -122.37996 WY 2016 3/5/2016 23.0 1.84 80.0 14.7 639 SM-SSF-315A (Gull Dr Outfall) South San Francisco MS4 37.66033 -122.38502 WY 2016 3/5/2016 33.0 5.77 175 10.4 315 SM-SSF-314A (Gull Dr SD) South San Francisco MS4 37.66033 -122.38510 WY 2016 3/5/2016 10.0 8.59 859 5.62 562 SM-BRI-17A (Valley Dr SD) Brisbane MS4 37.68694 -122.40215 WY 2016 3/5/2016 96.0 10.4 109 26.5 276 SM-BRI-1004A (Tunnel Ave Ditch) Brisbane MS4 37.69490 -122.39946 WY 2016 3/5/2016 96.0 10.5 109 71.1 741 SM-SCS-32A (Taylor Way SD) San Carlos MS4 37.51320 -122.26466 WY 2016 3/11/2016 25.0 4.23 169 28.9 1156 SM-SCS-75A (Industrial Rd Ditch) San Carlos MS4 37.51831 -122.26371 WY 2016 3/11/2016 26.0 160 6,139 13.9 535 SM-SSF-291A (S Linden Ave SD (291)) South San Francisco MS4 37.64327 -122.41066 WY 2017 1/8/2017 16.0 11.8 736 12.4 775 SM-SSF-296A (S Spruce Ave SD at Mayfair Ave (296)) South San Francisco MS4 37.65084 -122.41811 WY 2017 1/8/2017 111 3.36 30.3 38.9 350

SM-SSF-359A (Outfall to Colma Ck on service road near Littlefield Ave. (359)) South San Francisco MS4 37.64290 -122.39677 WY 2017 2/7/2017 43.0 33.9 788 9.05 210

Colma Ck at S. Linden Blvd (Colma Ck at S. Linden Blvd) South San Francisco Receiving Water 37.65017 -122.41189 WY 2017 2/7/2017 71.0 2.65 37.3 15.3 215

SM-SSF-315A (Gull Dr Outfall) South San Francisco MS4 37.66033 -122.38502 WY 2018 1/8/18 91.0 93 1,024 4.74 52.1 SM-SSF-314A (Gull Dr SD) South San Francisco MS4 37.66033 -122.38510 WY 2018 1/9/18 75.0 71.0 946 5.10 68.0 SM-BUR-164A Burlingame MS4 37.59960 -122.37526 WY 2019 11/28/2018 80.0 3.87 48.4 22.1 276 SM-BUR-85A Burlingame MS4 37.60194 -122.37499 WY 2019 11/28/2019 93.0 31.1 334 40.9 440

SMCWPPP Stormwater Runoff Samples

SM-MPK-71A Menlo Park MS4 37.48361 -122.14507 WY 2016 2/17/2016 13.7 0.59 43.2 6.80 496

3


(ng/L) Total PCBs

(ng/g)

Total Hg

(ng/L)

Total Hg

(ng/g) SM-RCY-327A Redwood City MS4 37.48868 -122.22823 WY 2016 2/17/2016 43.7 5.70 130 14.9 341 SM-RCY-388A Redwood City MS4 37.48877 -122.22665 WY 2016 2/17/2016 49.5 2.49 50.3 15.4 311 SM-MPK-238A Menlo Park MS4 37.48480 -122.17445 WY 2016 3/5/2016 80.1 3.19 39.8 12.7 159 SM-MPK-238B Menlo Park MS4 37.48489 -122.17380 WY 2016 3/5/2016 51.3 6.20 121 8.90 173 SM-RCY-379A Redwood City MS4 37.48908 -122.20648 WY 2016 3/5/2016 123 13.0 106 18.3 149 SM-RCY-379B Redwood City MS4 37.48910 -122.20647 WY 2016 3/5/2016 43.3 7.87 182 10.9 252 SM-RCY-254A Redwood City MS4 37.48916 -122.20651 WY 2016 3/5/2016 13.9 1.57 113 9.90 712 SM-SSF-317A South San Francisco MS4 37.64707 -122.39230 WY 2017 12/10/2016 5.80 2.61 450 0.82 141 SM-SSF-316A South San Francisco MS4 37.64767 -122.39192 WY 2017 12/10/2016 44.1 4.25 96.4 1.80 40.8 SM-SSF-318A South San Francisco MS4 37.64787 -122.38723 WY 2017 12/10/2016 8.50 2.26 266 5.42 638 SM-BUR-142A Burlingame MS4 37.59183 -122.36623 WY 2017 12/15/2016 51.5 34.5 670 2.27 44.1 SM-BUR-141A Burlingame MS4 37.59184 -122.36626 WY 2017 12/15/2016 51.3 8.48 165 7.79 152 SM-BUR-1006A Burlingame MS4 37.59185 -122.36629 WY 2017 12/15/2016 51.8 18.9 365 6.44 124 SM-SSF-1001B South San Francisco MS4 37.64076 -122.40637 WY 2017 12/15/2016 32.2 55.2 1,714 2.44 75.8 SM-SSF-292A South San Francisco MS4 37.64126 -122.40866 WY 2017 12/15/2016 719 7.89 11.0 0.95 1.32 SM-SSF-294A South San Francisco MS4 37.64886 -122.40160 WY 2017 12/15/2016 28.6 10.5 367 1.80 62.9 SM-RCY-324A Redwood City MS4 37.48358 -122.22763 WY 2017 1/8/2017 44.0 7.43 169 26.3 598 SM-RCY-323A Redwood City MS4 37.48500 -122.23281 WY 2017 1/8/2017 8.10 1.55 191 12.7 1568 SM-SMO-89A San Mateo MS4 37.54877 -122.30450 WY 2017 1/10/2017 27.8 4.03 145 2.32 83.5 SM-BEL-60B Belmont MS4 37.52746 -122.27434 WY 2017 2/9/2017 36.4 37.2 1,022 3.98 109 SM-BEL-60A Belmont MS4 37.52887 -122.27821 WY 2017 2/9/2017 34.3 6.11 178 4.83 141 SM-SMO-156A San Mateo MS4 37.55661 -122.30842 WY 2017 2/20/2017 90.6 19 204 12.7 140 SM-SMO-408A San Mateo MS4 37.55918 -122.30479 WY 2017 2/20/2017 29.1 55.3 1,900 5.5 189 SM-MPK-66A Menlo Park MS4 37.48079 -122.14498 WY 2017 3/24/2017 21.4 8.35 390 3.55 166 SM-SCS-1011B San Carlos MS4 37.51692 -122.25373 WY 2018 1/8/2018 15.0 2.50 167 6.12 408 SM-SCS-1011A San Carlos MS4 37.51701 -122.25379 WY 2018 1/8/2018 59.7 10.8 181 3.94 66.0 SM-SMO-25A San Mateo MS4 37.57970 -122.31911 WY 2018 1/8/2018 14.8 2.22 150 3.10 209 SM-SMO-149A San Mateo MS4 37.58710 -122.33222 WY 2018 1/8/2018 17.0 1.79 105 5.24 308 SM-BUR-164A Burlingame MS4 37.59960 -122.37526 WY 2018 1/8/2018 9.9 4.43 447 5.27 532 SM-BUR-85A Burlingame MS4 37.60194 -122.37499 WY 2018 1/8/2018 15.2 3.67 241 5.55 365 SM-SSF-356A South San Francisco MS4 37.64851 -122.40913 WY 2018 1/24/2018 55.8 4.89 88 0.44 7.89 SM-RCY-266A Redwood City MS4 37.49483 -122.21869 WY 2018 3/1/2018 21.6 0.11 4.91 4.06 188 SM-RCY-333A Redwood City MS4 37.49549 -122.21984 WY 2018 3/1/2018 417 6.30 15.1 4.43 10.6 SM-SCS-1011D San Carlos MS4 37.51238 -122.25777 WY 2018 3/1/2018 25.3 5.82 230 0.66 26.1 SM-SCS-1011C San Carlos MS4 37.51246 -122.25781 WY 2018 3/1/2018 28.5 5.80 204 0.72 25.3

4


(ng/L) Total PCBs

(ng/g)

Total Hg

(ng/L)

Total Hg

(ng/g) SM-SSF-1001C South San Francisco MS4 37.64309 -122.39930 WY 2018 3/1/2018 3.20 1.13 353 7.31 2284 SM-SSF-306B (South Linden PS) South San Francisco MS4 37.65025 -122.41170 WY 2018 4/6/2018 14.5 2.51 173 4.68 323

Notes: SSC – Suspended Sediment Concentration. Total PCBs = sum of the 40 PCBs congeners analyzed by the RMP for Bay samples. PCBs and mercury results with units of ng/g are particle ratios.

Attachment 4 Results of Monitoring San Mateo County Sediments for PCBs and Mercury

1

Permittee WMA Sample ID Sample Date Latitude Longitude Total PCBs (mg/kg)

Mercury (mg/kg)

Belmont 60

SM-BEL-60-A 5/22/2018 37.52699 -122.27609 0.00 0.21 SM-BEL-60-B 5/22/2018 37.52667 -122.27568 0.00 0.02 SM-BEL-60-C 5/22/2018 37.52297 -122.27790 0.01 0.17 SM-BEL-60-D 5/22/2018 37.52281 -122.27776 0.02 0.23 SM-BEL-60-E 5/22/2018 37.52200 -122.27684 0.02 0.09 SM-BEL-60-F 5/22/2018 37.52295 -122.27849 0.02 0.12 SM-BEL-60-G 5/22/2018 37.52701 -122.27293 0.01 0.08 SM-BEL-60-J 5/13/2019 37.52585 -122.27464 0.00 0.01

77 SM-BEL-01-A 5/13/2019 37.52513 -122.26635 0.01 0.24

Brisbane

1004

SMC025 9/20/2001 37.70673 -122.39801 0.14 1.73 SM-BRI-01-A 2/18/2015 37.70150 -122.40867 0.04 0.17 SM-BRI-01-B 2/18/2015 37.70102 -122.40810 0.01 0.04 SM-BRI-01-C 2/18/2015 37.69897 -122.40682 0.04 0.06 SM-BRI-01-D 2/18/2015 37.70024 -122.40736 0.01 0.04

17

SM-BRI-02-A 2/18/2015 37.68805 -122.40444 1.22 0.07 SM-BRI-02-B 5/29/2018 37.68805 -122.40570 1.02 0.12 SM-BRI-02-C 5/29/2018 37.68809 -122.40442 0.04 0.07 SM-BRI-02-D 5/29/2018 37.68975 -122.41143 0.01 0.04 SM-BRI-02-G 5/29/2018 37.68803 -122.40585 0.01 0.06 SM-BRI-02-H 5/29/2018 37.68933 -122.40681 0.01 0.05 SM-BRI-02-I 5/29/2018 37.68765 -122.40319 0.04 0.23 SM-BRI-02-J 5/14/2019 37.68805 -122.40571 0.03 0.06 SM-BRI-02-L 5/14/2019 37.68826 -122.40579 0.56 0.14 SM-BRI-02-M 5/14/2019 37.68930 -122.41998 0.01 0.09 SM-BRI-02-N 5/14/2019 37.69007 -122.40282 0.15 0.05

Burlingame

1006

SMC015 9/6/2001 37.59387 -122.36823 0.06 0.12 SMC017 9/6/2001 37.59229 -122.36591 0.14 0.35 SM-BUR-02-A 2/11/2015 37.59448 -122.36737 0.10 0.30 SM-BUR-04-A 2/11/2015 37.59425 -122.37052 0.10 0.39 SM-BUR-04-B 2/12/2015 37.59425 -122.36840 0.01 0.06 SM-BUR-03-D 5/23/2018 37.59043 -122.36304 0.03 0.12 SM-BUR-03-E 5/23/2018 37.59030 -122.36303 0.03 0.15

138 SM-BUR-06-B 5/13/2019 37.58840 -122.33720 0.18 0.16

142

SM-BUR-03-A 2/11/2015 37.58994 -122.36429 0.15 0.33 SM-BUR-03-B 2/12/2015 37.59181 -122.36623 0.06 0.09 SM-BUR-03-C 5/23/2018 37.59087 -122.36455 0.01 0.07 SM-BUR-03-F 5/23/2018 37.59119 -122.36517 0.02 0.05 SM-BUR-03-G 5/23/2018 37.59098 -122.36502 0.03 0.06 SM-BUR-03-H 5/23/2018 37.59134 -122.36547 0.01 0.06 SM-BUR-03-I 5/23/2018 37.59049 -122.36408 0.03 0.08

16 SM-BUR-06-A 2/11/2015 37.59107 -122.33662 0.05 0.14

2


Mercury (mg/kg)

164

SMC016 9/6/2001 37.59790 -122.37708 0.08 0.10 SM-BUR-05-A 2/11/2015 37.59820 -122.38085 0.05 0.31 SM-BUR-05-B 2/11/2015 37.59761 -122.37918 0.09 0.83 SM-BUR-05-C 2/11/2015 37.59523 -122.37808 0.04 0.10

85 SM-BUR-01-A 2/12/2015 37.60248 -122.37588 0.03 0.16 SM-BUR-01-B 2/11/2015 37.59990 -122.37191 0.03 0.17

Colma Other - COL

SMC024 9/6/2001 37.67407 -122.45691 16.81 1.31 SMC024 10/16/2003 37.67407 -122.45691 0.00 0.02 SMC048 10/16/2003 37.67407 -122.45728 0.00 0.02 SMC049 10/16/2003 37.67352 -122.45770 0.05 0.24

Daly City 1004 SM-DCY-01-A 5/29/2018 37.70427 -122.41417 0.01 0.06

East Palo Alto

1015 SM-EPA-01-C 1/19/2015 37.47474 -122.12710 0.02 0.08 SM-EPA-01-D 1/19/2015 37.47558 -122.13191 0.06 0.10

67 SM-EPA-01-A 1/19/2015 37.47722 -122.13418 0.21 0.22 SM-EPA-01-B 1/19/2015 37.47208 -122.13429 0.02 0.12

70

SM-EPA-02-A 1/19/2015 37.47084 -122.13069 0.05 0.26 SM-EPA-02-D 1/19/2015 37.47033 -122.13036 0.34 0.45 SM-EPA-02-G 3/27/2017 37.47029 -122.13244 0.03 0.05 SM-EPA-02-H 3/27/2017 37.47194 -122.13406 0.01 0.05

72 SM-EPA-02-C 1/19/2015 37.47443 -122.12743 0.02 0.33 SM-EPA-02-F 3/27/2017 37.47300 -122.13143 0.02 0.08

Other - EPA SMC019 9/20/2001 37.46112 -122.12421 0.07 0.13 Foster City 1010 SM-FCY-01-A 5/13/2019 37.56762 -122.27260 0.00 0.09

Menlo Park

1012 SM-MPK-05-A 3/27/2017 37.48209 -122.16096 0.06 0.10

1014 SM-MPK-03-A 1/22/2015 37.48678 -122.18090 0.02 0.04 SM-MPK-02-E 3/27/2017 37.48525 -122.18228 0.03 0.04

238A SM-MPK-04-A 1/20/2015 37.48307 -122.17529 0.03 0.21 SM-MPK-04-C 1/20/2015 37.48270 -122.17420 0.01 0.12 SM-MPK-04-D 1/19/2015 37.48342 -122.17178 0.25 0.03

238B SM-MPK-04-E 1/19/2015 37.48281 -122.16719 0.29 0.10

239 SM-MPK-02-B 1/20/2015 37.48610 -122.18564 0.57 0.13 SM-MPK-02-D 3/27/2017 37.48592 -122.18493 0.01 0.06

332 SM-MPK-02-A 1/20/2015 37.48664 -122.18868 0.03 0.04 66 SM-MPK-06-A 1/19/2015 37.47566 -122.14726 0.06 0.12 71 SM-MPK-05-B 3/27/2017 37.47939 -122.15569 0.01 0.13

Other - MPK SM-MPK-01-A 1/20/2015 37.45565 -122.18395 0.02 0.07 Millbrae 401 SM-MIL-01-A 5/13/2019 37.60764 -122.39189 0.00 0.03

Redwood City 1000

SM-RCY-04-D 1/22/2015 37.49742 -122.21299 0.02 0.07 SM-RCY-05-A 1/22/2015 37.50961 -122.20813 0.57 0.96 SM-RCY-05-C 4/5/2017 37.51096 -122.20742 0.75 0.35

1014 SM-RCY-10-E 3/27/2017 37.48510 -122.18221 0.01 0.05

3


Mercury (mg/kg)

239 SM-RCY-10-A 1/20/2015 37.48636 -122.18757 0.04 0.06 SM-RCY-10-C 3/27/2017 37.48581 -122.18504 0.16 0.05 SM-RCY-10-D 3/27/2017 37.48571 -122.18474 0.02 0.04

253 SM-RCY-09-A 1/22/2015 37.48606 -122.19643 0.05 0.06 254 SM-RCY-06-A 1/22/2015 37.48850 -122.20902 0.09 0.07 267 SM-RCY-04-B 1/22/2015 37.49303 -122.21726 0.01 0.10 269 SM-RCY-05-D 5/13/2019 37.51154 -122.20694 0.02 0.01

327 SMC-033 10/4/2001 37.48907 -122.23151 0.00 -- SMC-034 10/4/2001 37.48889 -122.22821 0.08 -- SM-RCY-15-A 2/10/2015 37.48952 -122.23632 0.05 0.08

333 SM-RCY-04-A 1/22/2015 37.49547 -122.21968 0.02 0.07 336 SM-RCY-03-B 5/13/2019 37.49198 -122.22804 0.01 0.03

337

SMC004 10/24/2000 37.49731 -122.23700 0.08 0.11 SM-RCY-01-A 2/10/2015 37.49504 -122.23654 0.03 0.33 SM-RCY-01-B 2/10/2015 37.49607 -122.23841 0.05 0.09 SM-RCY-03-A 2/10/2015 37.49366 -122.23425 0.02 0.13

379

SMC002 10/24/2000 37.48730 -122.21368 0.12 -- SMC-035 10/4/2001 37.48651 -122.21399 0.08 -- SMC-036 10/4/2001 37.48810 -122.21338 0.07 -- SMC-037 10/4/2001 37.48309 -122.21759 0.01 -- SMC-038 10/4/2001 37.48413 -122.21667 0.09 -- SMC001 10/24/2000 37.48730 -122.20648 0.07 0.17 SM-RCY-07-A 1/21/2015 37.48669 -122.21235 0.10 0.08 SM-RCY-07-B 1/21/2015 37.48650 -122.20665 0.35 0.21 SM-RCY-07-C 1/21/2015 37.48650 -122.20681 0.13 0.08 SM-RCY-11-A 1/22/2015 37.48006 -122.22206 0.03 0.16 SM-RCY-07-D 3/28/2017 37.48532 -122.21334 1.97 0.14 SM-RCY-12-A 3/28/2017 37.48444 -122.21848 0.02 0.07 SM-RCY-12-B 3/28/2017 37.48430 -122.21787 0.08 0.09 SM-RCY-12-C 3/30/2017 37.48438 -122.21774 0.00 0.01 SM-RCY-12-E 3/28/2017 37.48471 -122.21958 0.01 0.05 SM-RCY-12-F 3/28/2017 37.48551 -122.21624 0.01 0.08 SM-RCY-07-E 5/29/2018 37.48604 -122.21158 0.04 0.07 SM-RCY-07-F 5/29/2018 37.48554 -122.21191 0.04 0.06 SM-RCY-12-G 5/22/2018 37.48419 -122.21715 0.01 0.10 RCA-201409241050 9/24/2014 37.48538 -122.21345 2.37 --

RCB-201409241015 9/24/2014 37.48528 -122.21358 1.25 --

RCC-201409291115 9/29/2014 37.48550 -122.21441 0.57 --

RCD-201409241200 9/24/2014 37.48418 -122.21685 6.93 --

4


Mercury (mg/kg)

RCE-201409291030 9/29/2014 37.48573 -122.21774 0.04 --

RCF-201409291230 9/29/2014 37.48721 -122.21461 0.02 --

RCG-201409240945 9/24/2014 37.48726 -122.21372 0.07 --

407 SM-RCY-04-C 1/22/2015 37.49129 -122.21345 0.01 0.23 SM-RCY-04-E 5/13/2019 37.49309 -122.21312 0.00 0.12

Other - RCY

SMC011 10/24/2000 37.48889 -122.22699 0.34 -- SMC-032 10/4/2001 37.48828 -122.22699 0.02 -- SMC030 10/4/2001 37.48090 -122.23450 0.01 0.66 SMC031 10/4/2001 37.48053 -122.22693 0.14 0.18 SM-RCY-13-A 1/22/2015 37.48136 -122.22602 0.01 0.10

San Bruno 292

SBO01 7/12/2007 37.63690 -122.41241 0.03 0.36 SBO02 7/12/2007 37.63708 -122.41162 0.18 0.27 SSO05 7/12/2007 37.63690 -122.41229 0.00 0.47 SBO03 7/12/2007 37.63489 -122.41150 0.01 0.15 SBO04 7/12/2007 37.63647 -122.41241 0.00 0.07 SBO05 7/12/2007 37.63611 -122.41150 0.16 0.11 SBO06 7/12/2007 37.63892 -122.41248 0.00 0.23 SBO07 7/12/2007 37.63928 -122.41241 0.11 0.30 SBO08 7/12/2007 37.63928 -122.41272 0.00 0.20 SBO09 7/12/2007 37.63892 -122.41162 0.15 0.21 SBO10 7/12/2007 37.63831 -122.41162 0.00 0.06 SBO11 7/12/2007 37.63971 -122.41162 0.12 0.22 SBO13 7/12/2007 37.63831 -122.41339 0.00 0.13

362 SM-SBO-05-D 5/14/2019 37.63538 -122.40616 0.07 0.06

San Carlos 1011

S-1 7/10/2015 37.51538 -122.25843 0.02 -- S-10 7/10/2015 37.51589 -122.25769 0.03 -- S-11 7/10/2015 37.51560 -122.25717 0.05 -- S-12 7/10/2015 37.51551 -122.25644 0.08 -- S-13 7/10/2015 37.51549 -122.25581 0.10 -- S-14 7/10/2015 37.51579 -122.25521 0.02 -- S-15 7/10/2015 37.51632 -122.25485 0.01 -- S-16 7/10/2015 37.51681 -122.25468 0.01 -- S-17 7/10/2015 37.51711 -122.25429 0.01 -- S-2 7/10/2015 37.51519 -122.25826 0.01 -- S-3 7/10/2015 37.51435 -122.25789 0.02 -- S-4 7/10/2015 37.51377 -122.25783 0.05 -- S-5 7/10/2015 37.51328 -122.25760 0.04 -- S-6 7/10/2015 37.51286 -122.25743 0.07 -- S-7 7/10/2015 37.51232 -122.25783 0.01 -- S-8 7/10/2015 37.52043 -122.26604 0.02 --

5


Mercury (mg/kg)

S-9 7/10/2015 37.52019 -122.26633 0.01 -- SMC028 9/20/2001 37.52051 -122.26599 0.00 0.05 SMC029 9/20/2001 37.51251 -122.25879 0.42 0.63 BG-1 10/17/2014 37.51785 -122.26117 0.72 0.09 S-1 10/17/2014 37.51775 -122.26106 0.37 0.09 SCA37 8/24/2007 37.50909 -122.25781 0.00 0.06 SCA38 8/24/2007 37.50970 -122.25708 0.00 0.07 SCA39 9/21/2007 37.51050 -122.25598 0.00 0.13

1016

PUL27 5/14/2013 37.50470 -122.24899 0.96 0.15 SMC023 9/25/2001 37.50472 -122.24899 2.26 0.32 SCA11 8/23/2007 37.50189 -122.25281 0.00 0.28 SMC-023 9/25/2001 37.50472 -122.24895 6.19 -- SMC-045 10/3/2002 37.50171 -122.25238 0.00 --

210

PUL12 9/25/2012 37.49697 -122.24599 0.84 0.07 PUL13 9/25/2012 37.49748 -122.24727 0.02 0.36 PUL14 9/25/2012 37.49804 -122.24707 0.11 0.18 PUL18 5/14/2013 37.50006 -122.24399 0.22 0.10 PUL19 5/14/2013 37.49980 -122.24349 0.09 0.21 PUL20 5/14/2013 37.49959 -122.24349 0.55 0.10 PUL21 5/14/2013 37.49897 -122.24209 0.02 0.05 PUL22 5/14/2013 37.50027 -122.24356 192.91 0.07 PUL23 5/14/2013 37.49852 -122.24898 0.11 0.06 PUL24 5/14/2013 37.49770 -122.24746 0.07 0.12 PUL25 5/14/2013 37.49620 -122.24625 0.02 0.07 PUL28 5/14/2013 37.49824 -122.24547 1.19 0.14 PUL4 9/25/2012 37.50014 -122.24373 2.45 0.13 PUL7 9/24/2012 37.50029 -122.24783 0.40 0.13 PUL8 9/25/2012 37.49979 -122.24445 0.05 0.22 PUL9 9/25/2012 37.49940 -122.24394 0.05 1.10 SMC021 9/20/2001 37.49876 -122.24596 1.22 0.92 SCA01 8/23/2007 37.49811 -122.24268 0.13 0.17 SCA02 8/23/2007 37.49609 -122.24530 0.00 0.13 SCA03 8/23/2007 37.49670 -122.24628 0.41 0.30 SCA04 8/23/2007 37.49817 -122.24532 2.22 0.24 SCA05 8/23/2007 37.49872 -122.24609 0.07 0.27 SCA06 8/23/2007 37.49829 -122.24658 0.00 0.13 SCA07 8/23/2007 37.49811 -122.24701 0.10 0.19 SCA08 8/23/2007 37.49768 -122.24750 0.00 0.09 SCA09 8/23/2007 37.49824 -122.24880 0.00 0.11 SCA10 8/23/2007 37.50067 -122.25153 0.00 0.12 SCA16 8/23/2007 37.50371 -122.24857 0.04 0.10

6


Mercury (mg/kg)

SCA17 8/23/2007 37.50067 -122.24481 0.10 0.18 SCA18 8/23/2007 37.50049 -122.24469 0.06 0.29 SCA19 8/23/2007 37.49918 -122.24656 0.13 0.24 SCA20 8/23/2007 37.49926 -122.24664 0.17 0.15 SCA21 8/23/2007 37.50035 -122.24769 0.10 0.16 SCA22 8/23/2007 37.50005 -122.24397 0.12 0.11 SCA25 8/23/2007 37.49887 -122.24225 0.01 0.07 SCA36 8/24/2007 37.49969 -122.24463 0.30 0.77 SMC-021 9/20/2001 37.49875 -122.24597 1.82 -- SMC-046 10/3/2002 37.50269 -122.24719 0.18 -- SMC-047 10/3/2002 37.50012 -122.24371 11.52 -- SM-SCS-06-A 3/30/2017 37.49628 -122.24492 0.01 0.17 SM-SCS-06-B 3/30/2017 37.49690 -122.24589 0.03 0.08 SM-SCS-06-C 3/30/2017 37.49746 -122.24638 5.64 0.04 SM-SCS-06-D 3/30/2017 37.49733 -122.24555 1.84 3.93 SM-SCS-06-E 3/30/2017 37.49614 -122.24537 0.00 0.02 SM-SCS-06-F 3/30/2017 37.49768 -122.24626 3.73 0.12 SM-SCS-06-G 3/30/2017 37.49776 -122.24615 1.29 0.07 SM-SCS-06-H 3/30/2017 37.49942 -122.24278 0.07 0.06 SM-SCS-06-I 3/30/2017 37.50158 -122.24354 0.03 0.27 SM-SCS-06-L 4/5/2017 37.50021 -122.24113 0.06 0.13 SM-SCS-06-M 5/22/2018 37.49727 -122.24686 0.25 0.10 SM-SCS-06-N 5/22/2018 37.49731 -122.24662 0.06 0.05

31

PUL1 9/24/2012 37.50623 -122.25353 1.61 -- PUL10 9/24/2012 37.50583 -122.25432 0.34 -- PUL15 9/25/2012 37.50661 -122.25300 1.44 0.23 PUL2 9/24/2012 37.50510 -122.25538 0.05 -- PUL26 5/14/2013 37.50653 -122.25444 0.14 0.07 PUL5 9/24/2012 37.50484 -122.25542 0.02 -- SMC022 9/20/2001 37.50653 -122.25330 0.29 0.07 SCA12 8/23/2007 37.50372 -122.25403 0.00 0.13 SCA13 8/23/2007 37.50378 -122.25417 0.01 0.21 SCA14 8/23/2007 37.50452 -122.25311 0.30 0.35 SCA15 8/23/2007 37.50606 -122.25071 0.00 0.05 SCA26 8/23/2007 37.50484 -122.25572 0.00 0.09 SCA27 8/23/2007 37.50639 -122.25329 1.09 0.06 SCA28 8/24/2007 37.50633 -122.25355 0.19 0.04 SCA29 8/24/2007 37.50751 -122.25194 0.09 0.08 SCA30 8/24/2007 37.50737 -122.25185 0.21 0.15 SCA31 8/24/2007 37.50838 -122.25279 0.87 0.12 SCA32 8/24/2007 37.50732 -122.25439 0.00 0.08

7


Mercury (mg/kg)

SCA33 8/24/2007 37.50700 -122.25572 0.27 0.29 SCA34 8/24/2007 37.50787 -122.25421 0.01 0.13 SCA35 8/24/2007 37.50873 -122.25330 0.05 0.27 SMC-042 10/3/2002 37.50738 -122.25189 0.31 -- SMC-043 10/3/2002 37.50761 -122.25178 0.32 -- SMC-044 10/3/2002 37.50525 -122.24961 0.03 -- SM-SCS-05-A 4/3/2017 37.50645 -122.25071 0.12 0.06 SM-SCS-05-B 4/3/2017 37.50686 -122.25492 0.14 0.07

59 SM-SCS-01-L 3/30/2017 37.51528 -122.26202 0.18 0.17 SM-SCS-01-M 3/30/2017 37.51397 -122.26382 0.04 2.36 SM-SCS-01-O 5/22/2018 37.51538 -122.26179 0.31 0.16

75

SMC020 9/20/2001 37.51770 -122.26379 20.29 1.84 SM-SCS-01-A 2/10/2015 37.51798 -122.26640 0.10 0.05 SM-SCS-01-B 2/10/2015 37.51915 -122.26483 0.09 0.05 SM-SCS-01-C 2/10/2015 37.51631 -122.26494 0.04 0.17 SM-SCS-01-D 2/10/2015 37.51778 -122.26358 0.02 0.08 SM-SCS-01-E 2/10/2015 37.51548 -122.26660 0.03 0.09 SM-SCS-01-G 3/30/2017 37.51664 -122.26351 1.20 0.11 SM-SCS-01-H 4/3/2017 37.51623 -122.26485 0.06 0.14 SM-SCS-01-I 4/3/2017 37.51798 -122.26386 0.02 0.05 SM-SCS-01-J 4/3/2017 37.51818 -122.26392 0.09 0.09 SM-SCS-01-N 3/30/2017 37.51686 -122.26358 49.40 0.80 SM-SCS-01-P 5/22/2018 37.51643 -122.26308 0.76 0.06

80 SM-SCS-07-A 5/13/2019 37.49684 -122.24727 0.14 0.17

San Mateo

1007 SMC012 10/25/2000 37.57013 -122.31860 0.01 0.05

1009 SM-SMO-07-B 2/12/2015 37.55247 -122.30973 0.04 0.04 SM-SMO-08-A 2/12/2015 37.54986 -122.30739 0.03 0.04

101 SM-SMO-11-A 2/18/2015 37.53200 -122.28861 0.08 0.13

111 SM-SMO-04-A 2/18/2015 37.56774 -122.32320 0.06 0.11 SM-SMO-05-A 2/12/2015 37.56514 -122.31933 0.05 0.07

114 SM-SMO-06-A 2/18/2015 37.56134 -122.31515 0.23 0.25

149 SMC005 10/25/2000 37.58691 -122.33191 0.19 0.20 SM-SMO-14-A 2/12/2015 37.58631 -122.33303 0.07 0.63

156 SM-SMO-07-C 4/5/2017 37.55516 -122.30717 0.01 0.05 25 SM-SMO-02-A 2/11/2015 37.57746 -122.32173 0.03 0.13

403 SM-SMO-15-A 2/12/2015 37.56700 -122.31035 0.02 0.08

408

SM-SMO-07-D 5/23/2018 37.55756 -122.30338 0.01 0.11 SM-SMO-07-E 5/23/2018 37.55402 -122.30207 0.00 0.04 SM-SMO-07-F 5/23/2018 37.55515 -122.30259 0.00 0.06 SM-SMO-07-G 5/23/2018 37.55513 -122.30234 0.00 0.04 SM-SMO-07-H 5/23/2018 37.55674 -122.30272 0.02 0.10

8


Mercury (mg/kg)

SM-SMO-07-I 5/23/2018 37.55757 -122.30439 0.01 0.13 SM-SMO-07-J 5/23/2018 37.55840 -122.30395 0.01 0.13

89 SM-SMO-08-B 2/12/2015 37.54552 -122.30445 0.01 0.07 92 SM-SMO-08-C 5/13/2019 37.54847 -122.29967 0.00 0.02

Other - SMO SMC013 10/25/2000 37.58087 -122.32343 0.09 0.11 SM-SMO-09-A 5/23/2018 37.54157 -122.30636 0.04 0.07

South San Francisco

1001

SM-SSF-09-D 2/13/2015 37.65025 -122.41140 0.04 0.07 SM-SSF-09-A 2/17/2015 37.65047 -122.41284 0.02 0.18 SM-SSF-09-C 2/17/2015 37.65147 -122.41703 0.02 0.16 SM-SSF-10-A 2/17/2015 37.65328 -122.42609 0.01 0.05 SM-SSF-03-E 5/24/2018 37.64792 -122.40022 0.09 0.07 SM-SSF-04-G 5/29/2018 37.64229 -122.40323 0.01 0.11

1001B

SM-SSF-05-A 2/17/2015 37.63734 -122.40605 0.46 0.05 SM-SSF-05-C 5/24/2018 37.64013 -122.40653 0.06 0.06 SM-SSF-05-D 5/24/2018 37.63774 -122.40618 0.01 0.07 SM-SSF-05-E 5/24/2018 37.64090 -122.40648 0.02 0.10 SM-SSF-05-F 5/24/2018 37.64025 -122.40633 0.35 0.06 SM-SSF-05-G 5/24/2018 37.64072 -122.40652 0.01 0.18

1001D

SMC003 10/25/2000 37.65033 -122.41388 0.23 0.17 SSO10 7/12/2007 37.64807 -122.41248 0.43 0.34 SSO19 7/12/2007 37.64709 -122.41290 0.04 0.12 SSO24 7/12/2007 37.64893 -122.41461 0.02 0.10 SM-SSF-08-B 2/13/2015 37.65035 -122.41412 0.04 0.06 SM-SSF-08-C 2/13/2015 37.64932 -122.41211 0.01 0.04 SM-SSF-08-D 2/13/2015 37.64706 -122.41390 0.04 0.17

1002 SMC026 9/6/2001 37.65088 -122.38373 0.12 0.35 SM-SSF-02-C 4/5/2017 37.66440 -122.39508 0.02 0.05 SM-SSF-02-D 4/5/2017 37.66303 -122.39861 0.08 0.15

291

SMC009 10/25/2000 37.64429 -122.41669 0.48 -- SMC-039 10/2/2001 37.64508 -122.41632 0.07 -- SMC-040 10/2/2001 37.64429 -122.41718 2.72 -- SMC-041 10/2/2001 37.64410 -122.41650 0.04 -- SSO16 7/12/2007 37.64252 -122.41119 0.00 0.03 SSO18 7/12/2007 37.64209 -122.41241 0.00 0.01 SSO20 7/12/2007 37.64752 -122.41638 0.00 0.05 SSO21 7/12/2007 37.64771 -122.41663 0.00 0.08 SSO22 7/12/2007 37.64728 -122.41803 0.13 0.09 SSO25 7/5/2007 37.64313 -122.41742 0.03 0.12 SM-SSF-06-A 2/16/2015 37.64411 -122.41159 0.02 0.06 SM-SSF-06-B 2/17/2015 37.64219 -122.41329 0.48 0.07 SM-SSF-06-C 2/13/2015 37.64612 -122.41585 0.05 0.05

9


Mercury (mg/kg)

SM-SSF-06-F 4/5/2017 37.64299 -122.41425 0.04 0.08 SM-SSF-06-H 4/5/2017 37.64240 -122.41370 0.44 0.08 SM-SSF-06-I 4/5/2017 37.64212 -122.41325 0.04 0.24 SM-SSF-07-C 5/24/2018 37.64534 -122.42094 0.21 0.06

292

SBO12 7/12/2007 37.64111 -122.41150 0.00 0.10 SSO15 7/12/2007 37.64093 -122.41241 0.00 0.17 SMC027 9/6/2001 37.64130 -122.40961 0.03 0.04 SM-SSF-05-B 2/17/2015 37.64109 -122.41145 0.02 0.09 SM-SSF-06-D 2/17/2015 37.64128 -122.40868 0.14 3.40 SM-SSF-06-G 4/5/2017 37.64079 -122.41729 0.15 0.06

293 SM-SSF-02-A 2/16/2015 37.65172 -122.40318 0.07 0.37 SM-SSF-02-B 2/16/2015 37.65591 -122.40464 0.01 0.07

294 SM-SSF-03-A 2/16/2015 37.64910 -122.40172 0.07 0.28 SM-SSF-03-C 2/16/2015 37.65181 -122.40008 0.19 0.18 SM-SSF-03-D 4/5/2017 37.65253 -122.40021 0.28 0.47

295 SSO01 7/5/2007 37.63971 -122.40381 0.33 0.18 SSO02 7/5/2007 37.64130 -122.40363 0.00 0.06 SM-SSF-04-B 2/16/2015 37.63974 -122.40212 0.30 0.09

296 SM-SSF-07-B 5/24/2018 37.64722 -122.41981 0.02 0.83 313 SM-SSF-02-F 4/5/2017 37.66189 -122.39608 0.01 0.05

314

SM-SSF-01-B 2/16/2015 37.66032 -122.38511 0.12 0.07 SM-SSF-01-E 4/3/2017 37.65864 -122.39130 0.15 0.19 SM-SSF-01-G 4/3/2017 37.66241 -122.38908 0.05 0.03 SM-SSF-01-R 5/14/2019 37.65858 -122.39122 0.02 0.16

315

SM-SSF-01-L 5/14/2019 37.65693 -122.39556 0.27 0.27 SM-SSF-01-M 5/14/2019 37.66021 -122.38526 0.02 0.26 SM-SSF-01-N 5/14/2019 37.65977 -122.38571 0.03 0.50 SM-SSF-01-O 5/14/2019 37.65871 -122.38623 0.43 0.14 SM-SSF-01-P 5/14/2019 37.65504 -122.39049 0.01 0.06 SM-SSF-01-Q 5/14/2019 37.65647 -122.39420 0.07 0.56

316 SSO03 7/12/2007 37.65192 -122.39429 0.00 1.24 SM-SSF-01-D 2/16/2015 37.65031 -122.39213 0.02 0.14 SM-SSF-01-J 5/24/2018 37.65270 -122.39367 0.03 0.05

318 SM-SSF-01-C 2/16/2015 37.64896 -122.38728 0.01 0.24 319 SM-SSF-01-I 4/3/2017 37.65870 -122.38012 0.06 0.22 354 SM-SSF-08-A 2/13/2015 37.65088 -122.41622 0.02 0.23

356 SSO17 7/12/2007 37.64587 -122.40991 0.00 0.08 SM-SSF-06-E 2/13/2015 37.64883 -122.40961 0.03 3.59

357 SM-SSF-03-B 2/16/2015 37.64918 -122.40410 0.09 0.15

358 SM-SSF-04-A 2/16/2015 37.64606 -122.40160 1.46 0.15 SM-SSF-04-C 4/3/2017 37.64613 -122.40198 0.01 0.08

10


Mercury (mg/kg)

SM-SSF-04-D 4/3/2017 37.64450 -122.40173 0.09 0.11 SM-SSF-04-E 4/3/2017 37.64608 -122.40147 0.05 0.07 SM-SSF-04-H 5/14/2019 37.64551 -122.40344 0.03 0.09

359

SM-SSF-03-F 5/24/2018 37.64449 -122.39690 0.05 0.07 SM-SSF-03-G 5/24/2018 37.64458 -122.39694 0.01 0.08 SM-SSF-03-H 5/24/2018 37.64463 -122.39747 0.02 0.09 SM-SSF-03-J 5/14/2019 37.64438 -122.39728 0.13 0.44

362 SM-SSF-05-H 5/24/2018 37.63642 -122.40572 0.01 0.08 SM-SSF-05-J 5/14/2019 37.63666 -122.40587 0.00 0.12

Other - SSF SMC010 10/25/2000 37.65332 -122.42548 0.19 0.06

Unincorporated

1005 SM-SMC-09-A 2/17/2015 37.63283 -122.40533 0.01 0.05 1011 SM-SMC-08-A 2/10/2015 37.51758 -122.27088 0.02 0.10 247 SM-SMC-01-A 3/27/2017 37.41451 -122.19379 0.00 0.04

379

SM-SMC-04-A 1/21/2015 37.47622 -122.20808 0.09 0.11 SM-SMC-04-C 1/21/2015 37.47851 -122.21224 0.06 0.13 SM-SMC-05-A 1/21/2015 37.47476 -122.21126 0.03 0.10 SM-SMC-06-A 1/21/2015 37.48194 -122.20616 0.02 0.05 SM-SMC-06-B 1/21/2015 37.48307 -122.20310 0.02 0.06 SM-SMC-06-C 1/21/2015 37.48426 -122.20777 0.93 0.39 SM-SMC-07-A 1/21/2015 37.48484 -122.21082 0.06 0.20 SM-SMC-07-B 1/21/2015 37.48516 -122.21341 0.07 0.14 SM-SMC-06-D 3/28/2017 37.48389 -122.20673 0.05 0.06 SM-SMC-06-E 3/28/2017 37.48384 -122.20653 0.01 0.07 SM-SMC-06-F 3/28/2017 37.48291 -122.20734 0.02 0.07 SM-SMC-06-G 3/28/2017 37.48285 -122.20546 0.05 0.30 SM-SMC-06-H 3/28/2017 37.48278 -122.20531 0.03 0.07 SM-SMC-06-I 3/28/2017 37.48415 -122.20792 0.14 3.15 SM-SMC-06-J 3/28/2017 37.48349 -122.20874 0.08 0.09 SM-SMC-06-K 3/28/2017 37.48396 -122.20634 0.02 0.04 SM-SMC-06-L 3/28/2017 37.48256 -122.20875 0.03 0.10

Other - RCY SMC006 10/24/2000 37.47528 -122.28278 0.01 0.04 Other - SMC SM-SMC-03-A 1/21/2015 37.47682 -122.19520 0.00 0.03 Other - SMC SM-SMC-10-A 1/20/2015 37.43302 -122.20285 0.04 0.06 Other - WDE SMC007 10/25/2000 37.44452 -122.29108 0.00 0.03

Woodside Other - WDE SMC008 10/24/2000 37.41632 -122.26910 0.00 0.04

11

Permittee WMA Sample ID Sample Date Latitude Longitude Total PCBs

(mg/kg) Mercury (mg/kg)

Belmont 60

SM-BEL-60-A 5/22/2018 37.52699 -122.27609 0.00 0.21 SM-BEL-60-B 5/22/2018 37.52667 -122.27568 0.00 0.02 SM-BEL-60-C 5/22/2018 37.52297 -122.27790 0.01 0.17 SM-BEL-60-D 5/22/2018 37.52281 -122.27776 0.02 0.23 SM-BEL-60-E 5/22/2018 37.52200 -122.27684 0.02 0.09 SM-BEL-60-F 5/22/2018 37.52295 -122.27849 0.02 0.12 SM-BEL-60-G 5/22/2018 37.52701 -122.27293 0.01 0.08 SM-BEL-60-J 5/13/2019 37.52585 -122.27464 0.00 0.01

77 SM-BEL-01-A 5/13/2019 37.52513 -122.26635 0.01 0.24

Brisbane

1004

SMC025 9/20/2001 37.70673 -122.39801 0.14 1.73 SM-BRI-01-A 2/18/2015 37.70150 -122.40867 0.04 0.17 SM-BRI-01-B 2/18/2015 37.70102 -122.40810 0.01 0.04 SM-BRI-01-C 2/18/2015 37.69897 -122.40682 0.04 0.06 SM-BRI-01-D 2/18/2015 37.70024 -122.40736 0.01 0.04

17

SM-BRI-02-A 2/18/2015 37.68805 -122.40444 1.22 0.07 SM-BRI-02-B 5/29/2018 37.68805 -122.40570 1.02 0.12 SM-BRI-02-C 5/29/2018 37.68809 -122.40442 0.04 0.07 SM-BRI-02-D 5/29/2018 37.68975 -122.41143 0.01 0.04 SM-BRI-02-G 5/29/2018 37.68803 -122.40585 0.01 0.06 SM-BRI-02-H 5/29/2018 37.68933 -122.40681 0.01 0.05 SM-BRI-02-I 5/29/2018 37.68765 -122.40319 0.04 0.23 SM-BRI-02-J 5/14/2019 37.68805 -122.40571 0.03 0.06 SM-BRI-02-L 5/14/2019 37.68826 -122.40579 0.56 0.14 SM-BRI-02-M 5/14/2019 37.68930 -122.41998 0.01 0.09 SM-BRI-02-N 5/14/2019 37.69007 -122.40282 0.15 0.05

Burlingame

1006

SMC015 9/6/2001 37.59387 -122.36823 0.06 0.12 SMC017 9/6/2001 37.59229 -122.36591 0.14 0.35

SM-BUR-02-A 2/11/2015 37.59448 -122.36737 0.10 0.30 SM-BUR-04-A 2/11/2015 37.59425 -122.37052 0.10 0.39 SM-BUR-04-B 2/12/2015 37.59425 -122.36840 0.01 0.06 SM-BUR-03-D 5/23/2018 37.59043 -122.36304 0.03 0.12 SM-BUR-03-E 5/23/2018 37.59030 -122.36303 0.03 0.15

138 SM-BUR-06-B 5/13/2019 37.58840 -122.33720 0.18 0.16

142

SM-BUR-03-A 2/11/2015 37.58994 -122.36429 0.15 0.33 SM-BUR-03-B 2/12/2015 37.59181 -122.36623 0.06 0.09 SM-BUR-03-C 5/23/2018 37.59087 -122.36455 0.01 0.07 SM-BUR-03-F 5/23/2018 37.59119 -122.36517 0.02 0.05 SM-BUR-03-G 5/23/2018 37.59098 -122.36502 0.03 0.06 SM-BUR-03-H 5/23/2018 37.59134 -122.36547 0.01 0.06 SM-BUR-03-I 5/23/2018 37.59049 -122.36408 0.03 0.08

16 SM-BUR-06-A 2/11/2015 37.59107 -122.33662 0.05 0.14

164

SMC016 9/6/2001 37.59790 -122.37708 0.08 0.10 SM-BUR-05-A 2/11/2015 37.59820 -122.38085 0.05 0.31 SM-BUR-05-B 2/11/2015 37.59761 -122.37918 0.09 0.83 SM-BUR-05-C 2/11/2015 37.59523 -122.37808 0.04 0.10

85 SM-BUR-01-A 2/12/2015 37.60248 -122.37588 0.03 0.16 SM-BUR-01-B 2/11/2015 37.59990 -122.37191 0.03 0.17

Colma Other - COL

SMC024 9/6/2001 37.67407 -122.45691 16.81 1.31 SMC024 10/16/2003 37.67407 -122.45691 0.00 0.02 SMC048 10/16/2003 37.67407 -122.45728 0.00 0.02 SMC049 10/16/2003 37.67352 -122.45770 0.05 0.24

12



Daly City 350 SM-DCY-01-A 5/29/2018 37.70427 -122.41417 0.01 0.06

East Palo Alto

1015 SM-EPA-01-C 1/19/2015 37.47474 -122.12710 0.02 0.08 SM-EPA-01-D 1/19/2015 37.47558 -122.13191 0.06 0.10

67 SM-EPA-01-A 1/19/2015 37.47722 -122.13418 0.21 0.22 SM-EPA-01-B 1/19/2015 37.47208 -122.13429 0.02 0.12

70

SM-EPA-02-A 1/19/2015 37.47084 -122.13069 0.05 0.26 SM-EPA-02-D 1/19/2015 37.47033 -122.13036 0.34 0.45 SM-EPA-02-G 3/27/2017 37.47029 -122.13244 0.03 0.05 SM-EPA-02-H 3/27/2017 37.47194 -122.13406 0.01 0.05

72 SM-EPA-02-C 1/19/2015 37.47443 -122.12743 0.02 0.33 SM-EPA-02-F 3/27/2017 37.47300 -122.13143 0.02 0.08

Other - EPA SMC019 9/20/2001 37.46112 -122.12421 0.07 0.13

Foster City 1010 SM-FCY-01-A 5/13/2019 37.56762 -122.27260 0.00 0.09

Menlo Park

1012 SM-MPK-05-A 3/27/2017 37.48209 -122.16096 0.06 0.10

1014 SM-MPK-03-A 1/22/2015 37.48678 -122.18090 0.02 0.04 SM-MPK-02-E 3/27/2017 37.48525 -122.18228 0.03 0.04

238A SM-MPK-04-A 1/20/2015 37.48307 -122.17529 0.03 0.21 SM-MPK-04-C 1/20/2015 37.48270 -122.17420 0.01 0.12 SM-MPK-04-D 1/19/2015 37.48342 -122.17178 0.25 0.03

238B SM-MPK-04-E 1/19/2015 37.48281 -122.16719 0.29 0.10

239 SM-MPK-02-B 1/20/2015 37.48610 -122.18564 0.57 0.13 SM-MPK-02-D 3/27/2017 37.48592 -122.18493 0.01 0.06

332 SM-MPK-02-A 1/20/2015 37.48664 -122.18868 0.03 0.04 66 SM-MPK-06-A 1/19/2015 37.47566 -122.14726 0.06 0.12 71 SM-MPK-05-B 3/27/2017 37.47939 -122.15569 0.01 0.13

Other - MPK SM-MPK-01-A 1/20/2015 37.45565 -122.18395 0.02 0.07

Millbrae 401 SM-MIL-01-A 5/13/2019 37.60764 -122.39189 0.00 0.03

Redwood City

1000 SM-RCY-04-D 1/22/2015 37.49742 -122.21299 0.02 0.07 SM-RCY-05-A 1/22/2015 37.50961 -122.20813 0.57 0.96 SM-RCY-05-C 4/5/2017 37.51096 -122.20742 0.75 0.35

1014 SM-RCY-10-E 3/27/2017 37.48510 -122.18221 0.01 0.05

239 SM-RCY-10-A 1/20/2015 37.48636 -122.18757 0.04 0.06 SM-RCY-10-C 3/27/2017 37.48581 -122.18504 0.16 0.05 SM-RCY-10-D 3/27/2017 37.48571 -122.18474 0.02 0.04

253 SM-RCY-09-A 1/22/2015 37.48606 -122.19643 0.05 0.06 254 SM-RCY-06-A 1/22/2015 37.48850 -122.20902 0.09 0.07 267 SM-RCY-04-B 1/22/2015 37.49303 -122.21726 0.01 0.10 269 SM-RCY-05-D 5/13/2019 37.51154 -122.20694 0.02 0.01

327 SMC-033 10/4/2001 37.48907 -122.23151 0.00 -- SMC-034 10/4/2001 37.48889 -122.22821 0.08 --

SM-RCY-15-A 2/10/2015 37.48952 -122.23632 0.05 0.08 333 SM-RCY-04-A 1/22/2015 37.49547 -122.21968 0.02 0.07 336 SM-RCY-03-B 5/13/2019 37.49198 -122.22804 0.01 0.03

337

SMC004 10/24/2000 37.49731 -122.23700 0.08 0.11 SM-RCY-01-A 2/10/2015 37.49504 -122.23654 0.03 0.33 SM-RCY-01-B 2/10/2015 37.49607 -122.23841 0.05 0.09 SM-RCY-03-A 2/10/2015 37.49366 -122.23425 0.02 0.13

379 SMC002 10/24/2000 37.48730 -122.21368 0.12 -- SMC-035 10/4/2001 37.48651 -122.21399 0.08 -- SMC-036 10/4/2001 37.48810 -122.21338 0.07 --

13



SMC-037 10/4/2001 37.48309 -122.21759 0.01 -- SMC-038 10/4/2001 37.48413 -122.21667 0.09 -- SMC001 10/24/2000 37.48730 -122.20648 0.07 0.17

SM-RCY-07-A 1/21/2015 37.48669 -122.21235 0.10 0.08 SM-RCY-07-B 1/21/2015 37.48650 -122.20665 0.35 0.21 SM-RCY-07-C 1/21/2015 37.48650 -122.20681 0.13 0.08 SM-RCY-11-A 1/22/2015 37.48006 -122.22206 0.03 0.16 SM-RCY-07-D 3/28/2017 37.48532 -122.21334 1.97 0.14 SM-RCY-12-A 3/28/2017 37.48444 -122.21848 0.02 0.07 SM-RCY-12-B 3/28/2017 37.48430 -122.21787 0.08 0.09 SM-RCY-12-C 3/30/2017 37.48438 -122.21774 0.00 0.01 SM-RCY-12-E 3/28/2017 37.48471 -122.21958 0.01 0.05 SM-RCY-12-F 3/28/2017 37.48551 -122.21624 0.01 0.08 SM-RCY-07-E 5/29/2018 37.48604 -122.21158 0.04 0.07 SM-RCY-07-F 5/29/2018 37.48554 -122.21191 0.04 0.06 SM-RCY-12-G 5/22/2018 37.48419 -122.21715 0.01 0.10

RCA-201409241050 9/24/2014 37.48538 -122.21345 2.37 -- RCB-201409241015 9/24/2014 37.48528 -122.21358 1.25 -- RCC-201409291115 9/29/2014 37.48550 -122.21441 0.57 -- RCD-201409241200 9/24/2014 37.48418 -122.21685 6.93 -- RCE-201409291030 9/29/2014 37.48573 -122.21774 0.04 -- RCF-201409291230 9/29/2014 37.48721 -122.21461 0.02 -- RCG-201409240945 9/24/2014 37.48726 -122.21372 0.07 --

407 SM-RCY-04-C 1/22/2015 37.49129 -122.21345 0.01 0.23 SM-RCY-04-E 5/13/2019 37.49309 -122.21312 0.00 0.12

Other - RCY

SMC011 10/24/2000 37.48889 -122.22699 0.34 -- SMC-032 10/4/2001 37.48828 -122.22699 0.02 -- SMC030 10/4/2001 37.48090 -122.23450 0.01 0.66 SMC031 10/4/2001 37.48053 -122.22693 0.14 0.18

SM-RCY-13-A 1/22/2015 37.48136 -122.22602 0.01 0.10

San Bruno 292

SBO01 7/12/2007 37.63690 -122.41241 0.03 0.36 SBO02 7/12/2007 37.63708 -122.41162 0.18 0.27 SSO05 7/12/2007 37.63690 -122.41229 0.00 0.47 SBO03 7/12/2007 37.63489 -122.41150 0.01 0.15 SBO04 7/12/2007 37.63647 -122.41241 0.00 0.07 SBO05 7/12/2007 37.63611 -122.41150 0.16 0.11 SBO06 7/12/2007 37.63892 -122.41248 0.00 0.23 SBO07 7/12/2007 37.63928 -122.41241 0.11 0.30 SBO08 7/12/2007 37.63928 -122.41272 0.00 0.20 SBO09 7/12/2007 37.63892 -122.41162 0.15 0.21 SBO10 7/12/2007 37.63831 -122.41162 0.00 0.06 SBO11 7/12/2007 37.63971 -122.41162 0.12 0.22 SBO13 7/12/2007 37.63831 -122.41339 0.00 0.13

362 SM-SBO-05-D 5/14/2019 37.63538 -122.40616 0.07 0.06

Note: Total PCBs = sum of the 40 PCBs congeners analyzed by the RMP for Bay samples.

Attachment 5 Summary of PCBs and Mercury Monitoring Results in San Mateo County WMAs

1

WMA ID Permittee Area

(acres)

Area High Interest Parcels (acres)

Percent High

Interest Parcels

Sediment Samples Stormwater Runoff Samples

n PCBs

Median (mg/kg)

PCBs Range

(mg/kg) n

PCBs Particle

Ratio Median (mg/kg)

PCBs Particle

Ratio Range (mg/kg)

210 San Carlos 141 33 23.2% 47 0.11 0 - 192.91 33 1.78 0.20 - 37 17 Brisbane 1,639 55 3.4% 7 0.04 0.01 - 1.22 1 -- 0.11

142 Burlingame 20 9 44.3% 9 0.03 0.01 - 0.15 1 -- 0.67 359 South San Francisco 23 12 51.2% 3 0.02 0.01 - 0.06 1 -- 0.79 408 San Mateo 43 7 16.3% 7 0.01 0 - 0.02 1 -- 1.90 60 Belmont 298 6 1.9% 7 0.01 0 - 0.02 2 0.60 0.18 - 1.02

379 Redwood City 802 110 13.7% 44 0.06 0 - 6.93 2 0.14 0.11 - 0.18 291 South San Francisco 194 64 33.1% 19 0.05 0 - 2.72 1 -- 0.74

1000 Redwood City 148 108 73.0% 3 0.57 0.02 - 0.75 0 -- -- 75 San Carlos 66 38 58.3% 12 0.09 0.02 - 49.4 1 -- 6.14 31 San Carlos 99 27 27.2% 26 0.19 0 - 1.61 4 1.12 0.41 - 2.15

1016 San Carlos 142 27 19.0% 8 0.54 0 - 6.19 0 -- -- 239 Menlo Park / EPA 36 11 29.1% 5 0.04 0.01 - 0.57 0 -- -- 358 South San Francisco 32 7 21.8% 4 0.07 0.01 - 1.46 0 -- -- 70 East Palo Alto 490 16 3.3% 4 0.04 0.01 - 0.34 1 -- 0.11

314 South San Francisco 66 4 5.4% 2 0.10 0.05 - 0.15 2 0.91 0.86 - 0.95 294 South San Francisco 67 21 31.2% 3 0.19 0.07 - 0.28 1 -- 0.37

1001 South San Francisco 413 107 26.0% 17 0.04 0.01 - 0.43 2 1.03 0.35 - 1.71 407 Redwood City 18 10 52.9% 1 0.01 0.01 - 0.01 0 -- -- 85 Burlingame 121 13 10.4% 2 0.03 0.03 - 0.03 1 -- 0.24

164 Burlingame 241 79 32.6% 4 0.07 0.04 - 0.09 1 -- 0.45 336 Redwood City 66 4 6.6% 0 -- -- 0 -- --

1011 Redwood City 507 63 12.3% 25 0.03 0 - 0.72 4 0.19 0.17 - 0.23 25 San Mateo 219 6 2.9% 1 -- 0.03 1 -- 0.15

149 Burlingame 480 5 1.1% 2 0.13 0.07 - 0.19 1 -- 0.11

2


(acres)


Percent High

Interest Parcels


n PCBs

Median (mg/kg)

PCBs Range

(mg/kg) n

PCBs Particle


PCBs Particle

Ratio Range (mg/kg)

266 Redwood City 91 4 4.1% 0 -- -- 1 -- 0.00 77 Belmont 86 4 4.7% 0 -- -- 0 -- -- 59 San Carlos 28 9 32.1% 3 0.18 0.04 - 0.31 0 -- --

356 South San Francisco 10 2 18.0% 2 0.02 0 - 0.03 1 -- 0.09 333 Redwood City 15 4 29.4% 1 -- 0.02 1 -- 0.02 111 San Mateo 95 5 4.8% 2 0.06 0.05 - 0.06 0 -- --

1008 San Mateo 111 1 0.5% 0 -- -- 0 -- -- 139 Burlingame 63 2 3.0% 0 -- -- 0 -- -- 181 Daly City 75 12 15.6% 0 -- -- 0 -- -- 298 South San Francisco 122 3 2.7% 0 -- -- 0 -- -- 307 Daly City 1,277 5 0.4% 0 -- -- 0 -- -- 401 Millbrae 52 7 12.6% 0 -- -- 0 -- -- 238 Menlo Park 345 84 24.2% 4 0.14 0.01 - 0.29 2 0.08 0.04 - 0.12 67 East Palo Alto 95 11 12.0% 2 0.12 0.02 - 0.21 0 -- --

114 San Mateo 85 8 9.3% 1 -- 0.23 0 -- -- 295 South San Francisco 25 3 11.7% 4 0.155 0 - 0.33 0 -- -- 362 South San Francisco 18 9 51.6% 2 0.234 0.01 - 0.46 0 -- -- 350 Daly City 317 15 4.8% 1 0.009 0.01 0 -- -- 32 Belmont 67 2 3.3% 0 -- -- 1 -- 0.17

317 South San Francisco 32 9 27.1% 0 -- -- 1 -- 0.45 66 Menlo Park 64 19 29.8% 1 0.06 0.06 1 -- 0.39

1006 Burlingame 306 49 15.9% 5 0.10 0.01 - 0.14 1 -- 0.36 319 South San Francisco 99 31 31.2% 1 -- 0.06 1 -- 0.08 318 South San Francisco 70 32 45.4% 1 -- 0.01 1 -- 0.27

1004 Brisbane 804 507 63.0% 4 0.02 0.01 - 0.04 1 -- 0.11

3


(acres)


Percent High

Interest Parcels


n PCBs

Median (mg/kg)

PCBs Range

(mg/kg) n

PCBs Particle


PCBs Particle

Ratio Range (mg/kg)

156 San Mateo 40 7 17.0% 1 -- 0.01 1 -- 0.20 323 Redwood City 185 2 0.9% 0 -- -- 1 -- 0.19 306 South San Francisco 37 7 18.4% 0 -- -- 2 0.18 0.17 - 0.18 315 South San Francisco 108 34 31.8% 1 -- 0.12 2 0.60 0.17 - 1.02 324 Redwood City 44 1 2.0% 0 -- -- 1 -- 0.17 141 Burlingame 62 4 6.9% 0 -- -- 1 -- 0.17 89 San Mateo 98 10 10.3% 2 0.02 0.01 - 0.04 1 -- 0.14

327 Redwood City 126 7 5.1% 3 0.05 0 - 0.08 1 -- 0.13 337 Redwood City 138 16 11.5% 4 0.04 0.02 - 0.08 1 -- 0.12 293 South San Francisco 654 58 8.9% 2 0.04 0.01 - 0.07 1 -- 0.12 254 Redwood City 39 4 9.9% 1 -- 0.09 1 -- 0.11 316 South San Francisco 117 26 21.9% 3 0.02 0 - 0.03 1 -- 0.10 72 East Palo Alto 26 12 44.4% 2 0.02 0.02 - 0.02 1 -- 0.08

267 Redwood City 75 16 20.9% 1 -- 0.01 1 -- 0.06 388 Redwood City 42 1 1.4% 0 -- -- 1 -- 0.05 71 Menlo Park 1,394 22 1.6% 1 -- 0.01 1 -- 0.04

296 South San Francisco 1,272 7 0.6% 0 -- -- 1 -- 0.03 292 San Bruno 220 37 16.9% 19 0.12 0 - 0.18 1 -- 0.01 313 South San Francisco 77 11 14.3% 1 -- 0.01 0 -- --

1005 Millbrae 791 59 7.4% 1 -- 0.01 0 -- -- 1007 San Mateo 87 7 8.4% 1 -- 0.01 0 -- -- 1014 Menlo Park 176 18 10.3% 3 0.02 0.01 - 0.03 0 -- -- 354 South San Francisco 10 4 44.7% 1 -- 0.02 0 -- -- 403 San Mateo 48 1 1.4% 1 -- 0.02 0 -- -- 332 Menlo Park 17 1 5.1% 1 -- 0.03 0 -- --

4


(acres)


Percent High

Interest Parcels


n PCBs

Median (mg/kg)

PCBs Range

(mg/kg) n

PCBs Particle


PCBs Particle

Ratio Range (mg/kg)

1009 San Mateo 175 43 24.3% 2 0.03 0.03 - 0.04 0 -- -- 1015 East Palo Alto 52 48 92.7% 2 0.04 0.02 - 0.06 0 -- -- 253 Redwood City 280 16 5.8% 1 -- 0.05 0 -- -- 16 Burlingame 24 8 31.4% 1 -- 0.05 0 -- --

1012 Menlo Park 54 42 79.4% 1 -- 0.06 0 -- -- 101 San Mateo 221 10 4.3% 1 -- 0.08 0 -- --

1002 South San Francisco 316 66 20.9% 3 0.08 0.02 - 0.12 0 -- -- 357 South San Francisco 17 3 18.5% 1 -- 0.09 0 -- --

1010 Foster City 273 8 3.1% 0 -- -- 0 -- -- 1013 Redwood City 40 4 8.9% 0 -- -- 0 -- -- 1017 San Mateo 19 4 21.1% 0 -- -- 0 -- -- 120 San Mateo 10 1 4.9% 0 -- -- 0 -- -- 138 Burlingame 15 5 29.9% 0 -- -- 0 -- -- 207 San Carlos 82 7 8.2% 0 -- -- 0 -- -- 247 Menlo Park 239 20 8.5% 0 -- -- 0 -- -- 252 Menlo Park 108 5 4.9% 0 -- -- 0 -- -- 261 Atherton 1,679 3 0.2% 0 -- -- 0 -- -- 269 Redwood City 45 4 9.2% 0 -- -- 0 -- -- 290 San Bruno 2,017 9 0.4% 0 -- -- 0 -- -- 297 South San Francisco 30 2 6.7% 0 -- -- 0 -- -- 311 South San Francisco 111 3 2.8% 0 -- -- 0 -- -- 325 Redwood City 21 1 4.8% 0 -- -- 0 -- -- 329 Colma 806 4 0.5% 0 -- -- 0 -- -- 334 Redwood City 19 4 18.3% 0 -- -- 0 -- -- 335 Redwood City 24 0 0.0% 0 -- -- 0 -- --

5


(acres)


Percent High

Interest Parcels


n PCBs

Median (mg/kg)

PCBs Range

(mg/kg) n

PCBs Particle


PCBs Particle

Ratio Range (mg/kg)

352 South San Francisco 40 7 16.7% 0 -- -- 0 -- -- 378 Menlo Park 138 4 2.9% 0 -- -- 0 -- -- 395 Millbrae 480 8 1.6% 0 -- -- 0 -- -- 399 San Mateo 32 1 4.6% 0 -- -- 0 -- -- 405 Redwood City 22 22 100.0% 0 -- -- 0 -- -- 57 San Carlos 63 4 5.6% 0 -- -- 0 -- -- 68 East Palo Alto 317 0.5 0.2% 0 -- -- 0 -- -- 80 San Carlos 21 1 4.7% 0 -- -- 0 -- -- 90 San Mateo 21 0.3 1.4% 0 -- -- 0 -- -- 92 San Mateo 136 4 2.7% 0 -- -- 0 -- --

Other - Unincorporated 10,917 343 3.1% 3 0.00 0 - 0.04 0 -- -- Other - Woodside 7,286 5 0.1% 1 -- 0 0 -- -- Other - Menlo Park 2,487 25 1.0% 1 -- 0.02 0 -- -- Other - Colma 1,139 5 0.4% 4 0.03 0 - 16.81 0 -- -- Other - San Carlos 2,517 2 0.1% 1 -- 0.06 0 -- -- Other - East Palo Alto 274 4 1.4% 1 -- 0.07 0 -- -- Other - Redwood City 6,030 6 0.1% 6 0.07 0.01 - 0.34 0 -- -- Other - San Mateo 5,800 55 0.9% 1 -- 0.09 0 -- -- Other - South San Francisco 1,554 3 0.2% 1 -- 0.19 0 -- -- Other - Atherton 2,315 1 0.0% 0 -- -- 0 -- -- Other - Belmont 2,511 5 0.2% 0 -- -- 0 -- -- Other - Brisbane 245 0.4 0.2% 0 -- -- 0 -- -- Other - Burlingame 1,827 9 0.5% 0 -- -- 0 -- -- Other - Daly City 1,131 11 1.0% 0 -- -- 0 -- -- Other - Foster City 2,065 0 0.0% 0 -- -- 0 -- --

6


(acres)


Percent High

Interest Parcels


n PCBs

Median (mg/kg)

PCBs Range

(mg/kg) n

PCBs Particle


PCBs Particle

Ratio Range (mg/kg)

Other - Hillsborough 3,974 3 0.1% 0 -- -- 0 -- -- Other - Millbrae 1,309 3 0.2% 0 -- -- 0 -- -- Other - Portola Valley 5,790 0 0.0% 0 -- -- 0 -- -- Other - San Bruno 542 0 0.0% 0 -- -- 0 -- --

Notes: Total PCBs = sum of the 40 PCBs congeners analyzed by the RMP for Bay samples.

March 31, 2020



Provision C.8.h.v

PART E: WATER QUALITY MONITORING COST SUMMARY FOR

SAN MATEO COUNTY Water Years 2014 – 2019

CREDITS


Town of Atherton City of Foster City City of San Bruno City of Belmont City of Half Moon Bay City of San Carlos City of Brisbane Town of Hillsborough City of San Mateo City of Burlingame City of Menlo Park City of South San Francisco Town of Colma City of Millbrae Town of Woodside City of Daly City City of Pacifica County of San Mateo City of East Palo Alto Town of Portola Valley SM County Flood Control District City of Redwood City



Prepared by:


IMR Part E: Water Quality Monitoring Cost Summary for San Mateo County, WYs 2014 – 2019

i

TABLE OF CONTENTS TABLE OF CONTENTS ...................................................................................................................................... i List of Tables .................................................................................................................................................. i 1.0 INTRODUCTION ................................................................................................................................... 1 2.0 WATER QUALITY MONITORING COST SUMMARY .............................................................................. 1 3.0 COSTS AND BENEFITS OF MRP-REQUIRED WATER QUALITY MONITORING ....................................... 3 4.0 REFERENCES ........................................................................................................................................ 6

List of Tables Table 1. Water quality monitoring cost summary for implementing MRP Provision C.8 during Water Years 2014 – 2019. ........................................................................................................................................ 3 Table 2. Qualitative cost-benefit evaluation of MRP 2.0 Provision C.8 water quality monitoring. .............. 4


1

1.0 INTRODUCTION This Part E: Water Quality Monitoring Cost Summary for San Mateo County, Water Years1 (WYs) 2014 through 2019, was prepared by the San Mateo Countywide Water Pollution Prevention Program (SMCWPPP), as part of SMCWPPP’s WY 2014 – 2019 Integrated Monitoring Report (IMR). SMCWPPP is a program of the San Mateo County City/County Association of Governments (C/CAG). SMCWPPP prepared this report on behalf of San Mateo County local municipal agencies subject to the regional stormwater National Pollutant Discharge Elimination System (NPDES) permit for Bay Area municipalities issued by the San Francisco Regional Water Quality Control Board (Regional Water Board). The stormwater permit is usually referred to as the Municipal Regional Permit (MRP). The version reissued on November 19, 2015 is referred to as MRP 2.0 (Regional Water Board 2015). This report fulfills the requirements of Provision C.8.h.v(4) of MRP 2.0, which requires that the IMR includes a “budget summary for each monitoring requirement”. Water quality monitoring in compliance with MRP 2.0 Provision C.8 is conducted by SMCWPPP on behalf of San Mateo County MRP Permittees. This report summarizes the approximate budget expended by SMCWPPP for its water quality monitoring conducted from WY 2014 through WY 2019, a six-year period. The previous SMCWPPP IMR (SMCWPPP 2014) was submitted to the Regional Water Board in March 2014 and summarized approximate costs for water quality monitoring conducted by SMCWPPP during WY 2012 and WY 2013 in compliance with MRP 1.0 (Regional Water Board 2009). Water quality monitoring required by Provision C.8 of MRP 2.0 is intended to assess the condition of water quality in the Bay area receiving waters (creeks and the Bay); identify and prioritize stormwater associated impacts, stressors, sources, and loads; identify appropriate management actions; and detect trends in water quality over time and the effects of stormwater control measure implementation. SMCWPPP conducts creek water quality monitoring and monitoring projects in San Mateo County in collaboration with the Regional Monitoring Coalition (RMC), and actively participates in the San Francisco Bay Regional Monitoring Program (RMP), which focuses on assessing Bay water quality and associated impacts. This report provides a summary of monitoring costs expended by SMCWPPP to comply with MRP 2.0 and also provides qualitative estimates of the water quality benefits realized. This report is included as an appendix to SMCWPPP’s WY 2014 – 2019 IMR. 2.0 WATER QUALITY MONITORING COST SUMMARY Table 1 presents approximate costs expended by SMCWPPP to comply with Provision C.8 of MRP 2.0 during Water Years (WYs) 2014 – 2019.2 Costs presented include all aspects of implementing Provision C.8, including:

• Monitoring program and project planning,

• Monitoring program coordination and management,

• Fieldwork to collect data,

• Laboratory analysis,

1 Most water quality monitoring is conducted on a Water Year basis. A Water Year begins on October 1 and ends on September 30 of the named year. For example, Water Year 2019 (WY 2019) began on October 1, 2018 and concluded on September 30, 2019. 2 Costs presented do not include costs incurred by Permittees to implement other water quality monitoring activities and programs required by other NPDES permits issued to Permittees (e.g., POTW monitoring, aquatic pesticide application monitoring, stream maintenance program monitoring).


2

• Quality assurance / quality control (QA/QC),

• Data evaluation, analysis, and interpretation,

• Data management, and

• Data and reporting. Direct financial contributions to the RMP by SMCWPPP on behalf of San Mateo County Permittees and the NPDES permit fee surcharges that were paid by Permittees during that time frame (and used by the State and/or Regional Water Board to fund its Surface Water Ambient Monitoring Program (SWAMP)) are also included in the reported costs. The costs listed in Table 1 show the considerable resources (~$3.7 million) that SMCWPPP expended over the course of WYs 2014 – 2019 towards complying with water quality monitoring requirements described in MRP 2.0 Provision C.8. Average annual costs to San Mateo County Permittees during this six-year timeframe were roughly $620,000. The costs are associated with the following monitoring activities:

• San Francisco Bay Estuary Receiving Water Monitoring (RMP) – Permittee monetary contributions and SMCWPPP and Permittee staff time spent actively participating in the RMP, including participation in several workgroups and strategy teams, in compliance with MRP 2.0 Provision C.8.c.

• Creek Status Monitoring – Preparation, coordination, management and implementation of the SMCWPPP’s Creek Status Monitoring Program, which is implemented in compliance with MRP 2.0 Provision C.8.d.

• Stressor/Source Identification (SSID) Projects – Preparation, coordination, management and implementation of SSID projects that were implemented in compliance with MRP 2.0 Provision C.8.e.

• Pollutants of Concern Monitoring – Preparation, coordination, management and implementation of the SMCWPPP Pollutants of Concern (POC) Monitoring Program that was implemented in compliance with MRP 2.0 Provision C.8.f., including investigations conducted to attempt to find properties that are sources of PCBs to the storm drain system.

• Pesticides and Toxicity Monitoring – Preparation, coordination, management and implementation of the SMCWPPP Pesticides and Toxicity Monitoring Program that was implemented in compliance with MRP 2.0 Provision C.8.g.

• Data Management & QA/QC – Coordination and implementation of the SMCWPPP Water Quality Monitoring Data Management and Quality Assurance Program, which implements all aspect of data management and quality assurance procedures required by MRP 2.0 Provision C.8.b, and consistent with approved Standard Operating Procedures (SOPs) and Quality Assurance Project Plans (QAPPs).

• Reporting – Analysis, interpretation and reporting of all data collected via the SMCWPPP’s Creek Status Monitoring, SSID projects, POC Monitoring, and Pesticides and Toxicity Monitoring Programs, consistent with MRP 2.0 Provision C.8.h.

• NPDES Surcharge: SWAMP – Monetary contributions provided by Permittees to the State of California as part of the SWAMP surcharge issued to Permittee as part of their annual NDPES fee.


3

Table 1. Water quality monitoring cost summary for implementing MRP Provision C.8 during Water Years 2014 – 2019.


Approximate Total Costs

WYs 2014 - 2019 (6 years)

Approximate Average Costs

per Water Year

Percent of Total Costs

C.8.b Data Management & Quality Assurance/Quality Control (QA/QC) $200,000 $33,333 5%

C.8.c San Francisco Bay Estuary Receiving Water Monitoring (RMP) $600,000 $100,000 16%

C.8.d Creek Status Monitoring $1,300,000 $216,667 35%

C.8.e Stressor/Source Identification (SSID) Projects $220,000 $36,667 6%

C.8.f Pollutants of Concern (POC) Monitoring $800,000 $133,333 22%

C.8.g Pesticides and Toxicity Monitoring $200,000 $33,333 5%

C.8.h Reporting $250,000 $41,667 7%

NA NPDES Surcharge - Surface Water Ambient Monitoring Program (SWAMP) $150,000 $25,000 4%

Total $3,720,000 $620,000 100% Note: see above bullets for the activities that are included in each monitoring line item.

3.0 COSTS AND BENEFITS OF MRP-REQUIRED WATER QUALITY MONITORING

SMCWPPP’s water quality monitoring program generates data designed to answer core management questions outlined in MRP 2.0. In many instances, these management questions are further delineated into scientific monitoring questions, which assist in developing and implementing appropriate monitoring designs. This section provides a qualitative cost-benefit evaluation of the water quality monitoring data collection programs implemented by SMCWPPP to comply with MRP Provision C.8. The cost-benefit evaluation was conducted based on the ability of SMCWPPP to answer core management and scientific monitoring questions using the water quality monitoring data collected. Table 2 presents the results of the evaluation, which informed SMCWPPP’s recommendations for water quality monitoring under MRP 3.0, the next version of the MRP. The recommendations for MRP 3.0 monitoring are described in IMR Parts A, B, C, and D, and summarized in the IMR Executive Summary.


4

Table 2. Qualitative cost-benefit evaluation of MRP 2.0 Provision C.8 water quality monitoring.


Relative Cost of




( - )

Evaluation Summary

C.8.c

San Francisco Bay Estuary Receiving Water Monitoring (RMP)

$$$$

Contributions to the RMP provided useful information on the status and trends of water quality in the Bay and provided supplemental information to help SMCWPPP identify PCBs and mercury source areas for management actions. Attempts to focus RMP-led monitoring on high priority issues remains an on-going challenge due to competing interests and information needs. Overall, the RMP provides useful information to track water quality conditions in the Bay and help inform broad-scale management and policy directions based on science, but at a relatively high cost.

C.8.d Creek Status Monitoring $$$$

Creek status monitoring continued to provide useful information on the status of water quality in urban creeks that receive stormwater discharges, and the biological condition of those creeks. Many parameters were monitored, however, the utility of the data that the MRP requires to be collected is variable among parameters. Some parameters have provided valuable, baseline data or helped identify concerns that should be addressed. Other parameters were less useful and did not directly assist stormwater managers in validating, refining, or adjusting current practices. The high relative costs and the variability in the usefulness of data collected via this provision suggest that refinements are needed to improve the cost-effectiveness of Creek Status Monitoring during MRP 3.0.

C.8.e Stressor/Source Identification (SSID) Projects

$$

SSID studies have provided useful information that is needed to help better define potential water quality concerns and identify sources of pollutants or environmental stress occurring in San Mateo County streams. SSID projects have been challenging due to the lack of methods available to differentiate the causes of stress and sources of pollutants/stress, due to the complex and overlapping watershed/runoff processes observed in streams. The relatively moderate costs and moderate/high benefits of data collected via this provision suggest that SSID projects are cost-effective. However, refinements are needed to the study methods and endpoint expectations to improve the utility of the data collected via Provision C.8.e during MRP 3.0.


5


Relative Cost of




( - )

Evaluation Summary

C.8.f Pollutants of Concern (POC) Monitoring

$$$$

Monitoring conducted under Provision C.8.f provided valuable data on potential sources of PCBs in Watershed Management Areas (WMAs) and helped prioritize land areas for further source property evaluations. Additionally, the data collected under this provision helped further understand the geographical distribution of POCs in the urban portion of San Mateo County that drains to the Bay. Although the costs associated with POC monitoring are relatively high, the PCBs data collected during MRP 2.0 have helped to characterize the urban landscape and identify some source areas. However, recent monitoring data suggest that the PCBs monitoring program in the public ROW in San Mateo County may be approaching diminishing returns in terms of finding PCBs and potentially identifying new source areas. Thus PCBs monitoring would show a lower benefit towards answering core management questions (one or two stars) if evaluated solely on potential future benefit. In addition, nutrient and copper monitoring data collected during MRP 2.0 were not particularly useful in answering monitoring questions associated with these pollutants and would show a lower benefit towards answering core management questions (one or two stars) if evaluated individually.

C.8.g Pesticides and Toxicity Monitoring $$

SMCWPPP expended a relatively low level of budget for Pesticides and Toxicity Monitoring during MRP 2.0. Data collected via the statewide SPoT program provided important information on trends in pesticides and toxicity in stream sediments over time. Low costs and low/moderate benefits suggest that refinements are needed to improve the cost-benefits of the data collected via Provision C.8.g during MRP 3.0. Currently a statewide effort to develop an Urban Pesticide Coordinated Monitoring Program is underway, and SMCWPPP is actively participating in this process. For SMCWPPP, the goal is to stabilize costs for pesticide/toxicity monitoring, while improving and enhancing coordination of data collection efforts on a statewide basis with the California Department of Pesticide Regulation (DRP) to fill important information gaps that will improve the regulation of pesticides that effect stormwater quality.

NA

NPDES Surcharge - Surface Water Ambient Monitoring Program (SWAMP)

$

The costs to SMCWPPP for this program were relatively low, but benefits to local stormwater programs and managers were not readily apparent.

3 Qualitative cost categories were based on the relative percentage of total costs for each major monitoring component shown above, with data management, QA/QC and reporting costs incorporated into the appropriate component costs. Cost categories were defined as: $ = <5%, $$ = 5 - 10%; $$$ = 10 - 15%; $$$$ = >15%.


6

4.0 REFERENCES San Francisco Regional Water Quality Control Board (SFRWQCB). 2009. Municipal Regional Stormwater NPDES Permit. Order R2-2009-0074, NPDES Permit No. CAS612008. 125 pp plus appendices. Adopted October 14, 2009 Revised November 28, 2011. San Francisco Regional Water Quality Control Board (SFRWQCB). 2015. Municipal Regional Stormwater NPDES Permit. Order R2-2015-0049, NPDES Permit No. CAS612008. 152 pp plus appendices. November 2015. SMCWPPP, 2014. Integrated Monitoring Report. Prepared for San Mateo Countywide Water Pollution Prevention Program (SMCWPPP) by EOA, Inc., Oakland, California. March 2014.

Documents

Electronically submitted to SF Bay Regional Water Board ... · • Data Management & QA/QC – Coordination and implementation of the SMCWPPP Water Quality Monitoring Data Management