DRAFT STOCHASTIC EMPIRICAL LOADING AND DILUTION …i-80will.com/images/FinalSubmittal/1-SELDM... · DRAFT STOCHASTIC EMPIRICAL LOADING AND DILUTION MODEL (SELDM) MODELING RESULTS

DRAFT STOCHASTIC EMPIRICAL LOADING AND DILUTION MODEL (SELDM) MODELING RESULTS

Interstate 80 Improvements Ridge Road to US Route 30 Grundy, Kendall, and Will County, Illinois

March 1, 2018 File No. 81.0220163.00

PREPARED FOR: HBP Illinois Partners 1 South Wacker Drive, Suite 900 Chicago, IL 60606

Huff & Huff, A Subsidiary of GZA 915 Harger Road, Suite 330 │ Oak Brook, IL 60523 630-684-9100 GZA has 28 Offices Nationwide www.huffnhuff.com www.gza.com

H:\Client\HNTB\I80-Grundy Line to US 30\Water\Pollutant Loading Analysis\Report\I-80 Joliet SELDM Report.docx

March 1, 2018 Mr. Don Wittmer, P.E. HBP Illinois Partners 1 South Wacker Drive, Suite 900 Chicago, Illinois 60606 Re: Stochastic Empirical Loading and Dilution Model (SELDM) Modeling Results Interstate 80 from Ridge Road to US Route 30 Kendall, Grundy, and Will Counties, Illinois Dear Mr. Wittmer:

Huff & Huff, Inc., a subsidiary of GZA GeoEnvironmental, Inc. (H&H) is pleased to submit this draft Stochastic Empirical Loading and Dilution Model (SELDM) Modeling Report for the Project Corridor along the Interstate 80 from Ridge Road to US Route 30 in Kendall, Grundy, and Will Counties, Illinois.

Operational impacts due to pollutant loading from highway runoff to receiving waters were analyzed using the SELDM version 1.02 for receiving waters with drainage basins greater than one square mile in area. This assessment included highway storm water runoff to drain to the following receiving waters (from west to east):

• Unnamed Tributary (UNT) #005 to DuPage River

• UNT #007 to DuPage River

• DuPage River

• Rock Run

• Des Plaines River

• Hickory Creek at IL-53

• Hickory Creek at US-30

The results from the SELDM analysis are summarized in Table 9a through Table 9g. None of the predicted downstream 3-year concentrations for proposed conditions was above the applicable water quality standard for the contaminants of concern.

If you have any questions or comments, please don’t hesitate to contact us at 630-684-9100.

GZA GEOENVIRONMENTAL, INC.

Margaret A. Panatera, P.E. Richard Ray Associate Principal Consultant/Reviewer Cory Wilson Engineer I Attachments: SELDM Results Report

March 1, 2018 SELDM Results: I-80 from Ridge Road to US 30

Kendall, Grundy, and Will Counties, Illinois Table of Contents | ii


TABLE OF CONTENTS

GLOSSARY OF ACRONYMS AND ABBREVIATIONS ...................................................................................................... III

1.0 INTRODUCTION .............................................................................................................................................1

PROJECT CORRIDOR SUMMARY................................................................................................................................. 1

2.0 FUNDAMENTAL CONCEPTS OF SELDM ............................................................................................................1

3.0 DATA INPUT MODULES OF SELDM ..................................................................................................................2

3.1 SELDM PRECIPITATION MODULE .................................................................................................................. 3 3.2 HIGHWAY DRAINAGE BASIN – RUNOFF QUANTITY ...................................................................................... 3 3.3 HIGHWAY RUNOFF QUALITY ......................................................................................................................... 4 3.4 HIGHWAY DRAINAGE BASIN BMPS ............................................................................................................... 5 3.5 RECEIVING WATER UPSTREAM DRAINAGE BASIN – STORM WATER QUANTITY AND STREAM FLOW ........ 6 3.6 RECEIVING WATER UPSTREAM QUALITY ...................................................................................................... 7

4.0 SELDM RESULTS .............................................................................................................................................8

4.1 PREDICTED DOWNSTREAM CONCENTRATIONS AND POLLUTANT LOADING ............................................... 8

FIGURES Figure 1 Project Location Map TABLES Table 1 Storm Event Statistics for the SELDM Analysis Table 2 Highway Drainage Basin Characteristics for SELDM Analysis Table 3 Summary of Highway Contaminant Runoff Concentrations used in the SELDM Analysis Table 4 Comparison of Metal Concentrations in Highway Runoff to Illinois General Use Water Quality Standards Table 5 Fraction of Highway Runoff Routed to Each Type of BMP Table 6 Receiving Water Drainage Basin and Stream Flow Characteristics for SELDM Analysis Table 7a Summary of Upstream Water-Quality Statistics for SELDM: DuPage River Table 7b Summary of Upstream Water-Quality Statistics for SELDM: Hickory Creek Table 7c Summary of Upstream Water-Quality Statistics for SELDM: Aux Sable Creek (UNTs to DuPage River) Table 7d Summary of Upstream Water-Quality Statistics for SELDM: Des Plaines River Table 8 Water Quality Standards for Contaminants of Concern based on Critical Hardness Table 9a Summary of Water Quality Results from SELDM Analysis: DuPage River Table 9b Summary of Water Quality Results from SELDM Analysis: UNT 005 to DuPage River Table 9c Summary of Water Quality Results from SELDM Analysis: UNT 007 to DuPage River Table 9d Summary of Water Quality Results from SELDM Analysis: Hickory Creek Table 9e Summary of Water Quality Results from SELDM Analysis: Hickory Creek at US-30 Table 9f Summary of Water Quality Results from SELDM Analysis: Rock Run Table 9g Summary of Water Quality Results from SELDM Analysis: Des Plaines River


Kendall, Grundy, and Will Counties, Illinois Table of Contents | iii


GLOSSARY OF ACRONYMS AND ABBREVIATIONS

ADT Average Daily Traffic BMP Best Management Practice BNSF Burlington-Northern Santa Fe cfsm cubic feet per square mile CN Canadian National CSSC Chicago Sanitary and Ship Canal DOT Department of Transportation EMC Event Mean Concentration EPA Environmental Protection Agency FHWA Federal Highway Administration GIS Geographic Information System GZA GZA GeoEnvironmental, Inc. HRDB Highway Runoff Database H&H Huff & Huff, Inc. IDOT Illinois Department of Transportation IEPA Illinois Environmental Protection Agency NOAA National Oceanic and Atmospheric Administration NWIS National Water Information System ROS Regression on Order Statistics ROW Right-of-Way SELDM Stochastic Empirical Loading and Dilution Model STORET STOrage and RETrieval TSS Total Suspended Solids UCL Upper Confidence Limit UNT Unnamed Tributary UP Union Pacific USGS United States Geological Survey WQP Water Quality Portal

March 8, 2018 SELDM Results: I‐80 from Ridge Road to US 30

Kendall, Grundy, and Will Counties, Illinois Page | 1

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1.0 INTRODUCTION

The Illinois Department of Transportation (IDOT), in cooperation with the Federal Highway Administration (FHWA) is undertaking a preliminary engineering and environmental (Phase I) study of the transportation needs within the Interstate 80 (I‐80) corridor between Ridge Road (Kendall County Highway 11 and Grundy County Highway 5) and US Route 30 (Lincoln Highway and Maple Street), a length of approximately 16 miles within Kendall, Grundy, and Will Counties. The project traverses the City of Joliet and the Villages of Minooka, Channahon, Shorewood, Rockdale, and New Lenox. Adjacent land use consists of agricultural, industrial, commercial, residential, Canadian National (CN) Railway right‐of‐way (ROW), Union Pacific (UP) Railroad ROW, Burlington‐Northern Santa Fe (BNSF) Railway ROW, and Forest Preserve District of Will County Lower Rock Run Preserve.

To fully address the long‐term transportation needs within the corridor, the Phase I study will consider reconstruction of I‐80 within the project limits. Based on existing and projected travel demand, the proposed improvements would include provisions for additional mainline auxiliary lanes. Other geometric and/or interchange improvements may also be included as part of the project. The Stochastic Empirical Loading and Dilution Model (SELDM) version 1.02 was used to analyze pollutant loading to receiving waters with drainage basins greater than one square mile.

PROJECT CORRIDOR SUMMARY

The project corridor extends along existing I‐80 from Illinois State Route 11 (Ridge Road) at Minooka, Illinois eastward to US‐30 east of Joliet, Illinois. Highway storm water runoff drains to the following receiving waters which have drainage basin areas greater than one square mile, from west to east in the project corridor (see Figure 1):

Unnamed Tributary (UNT) #005 to DuPage River

UNT #007 to DuPage River

DuPage River

Rock Run

Des Plaines River

Hickory Creek at IL‐53

Hickory Creek at US‐30

2.0 FUNDAMENTAL CONCEPTS OF SELDM

Gregory E. Granato of the United States Geological Survey (USGS) in cooperation with the Federal Highway Administration (FHWA) developed SELDM1, which was released in 2013. The FHWA has since adopted SELDM as the replacement for the 1990 FHWA highway‐runoff‐quality model (Driscoll Model) developed by Driscoll, et. al. The FHWA describes the design, purpose, and applicability of SELDM as follows:

SELDM is designed as a tool that can be used to transform disparate and complex scientific data into meaningful information about the risk for adverse effects of runoff on receiving waters, the potential need for mitigation measures, and the potential effectiveness of such management measures for reducing these risks. SELDM is designed to help develop planning‐level estimates of event mean concentrations, flows and

1 Gregory E. Granato, “Stochastic Empirical Loading and Dilution Model (SELDM) Version 1.0.0,” Techniques and Methods (U.S. Gelogical Survey

(USGS), 2013), http://pubs.usgs.gov/tm/04/c03/.




loads from a highway site and an upstream or lake basin. The model has been tested by the FHWA, many State [departments of transportation] DOTs, the [Environmental Protection Agency] EPA, and several other regulatory agencies. This resulted in a robust model that will be acceptable to DOTs, regulators, and resource-management agencies.

The operation of SELDM can best be understood by describing each aspect of the components that make up the acronym “SELDM”. The “LDM” portion of SELDM stands for Loading and Dilution Model. Fundamentally, the SELDM calculates the downstream or lake basin pollutant concentration based on the pollutant load in highway runoff being diluted in the receiving water. Mathematically, the loading and dilution calculation for a receiving stream is expressed by the following pair of mass balance equations:

Qupstream x Cupstream + Qhighway x Chighway = Qdownstream x Cdownstream

Qupstream + Qhighway = Qdownstream

where Q is the stream flow, C is the pollutant concentration, and the subscripts denote receiving water upstream, receiving water downstream, and highway runoff.

SELDM is designed with a module to account for best management practices (BMPs) applied to the highway runoff. The application of BMPs can affect the runoff quantity or quality.

A Monte Carlo analysis constitutes the Stochastic portion of SELDM, in which a random number generator is used to determine storm water quality and quantity-related parameters for hundreds to thousands of simulated storms. Statistical properties of the distribution – average, standard deviation, and skewness – for each of the water quality-related parameters are provided as input to SELDM, and the random number generator determines what value from the distribution is used in each simulated storm. The output of SELDM is the resulting water quality concentration estimates for all the simulated storms.

The Empirical aspect of SELDM is introduced through the parameters which are used to determine the stream flows, runoff flows, and pollutant concentrations, which are based on measured values, not on theoretical calculations. For example, the concentration of pollutants in highway runoff and in upstream receiving waters are based on directly measured values.

SELDM was used to assess the potential impacts to receiving water quality due to total suspended solids (TSS), copper, and zinc in highway runoff.

3.0 DATA INPUT MODULES OF SELDM

SELDM is programmed as a customized Microsoft Access database. The user interface for the model consists of a series of forms that allow data viewing, selection of data provided with SELDM, and entry of user-specified data. For user-specified data other software and analytical tools, such as ProUCL, a spreadsheet program, and the Highway Runoff Database (HRDB), are necessary to determine the statistical properties of the data sets or to access the data. The procedures used to gather and prepare data for the SELDM analysis are discussed herein.

The data required to complete an analysis with SELDM can be compartmentalized into the following modules:

• Precipitation

• Highway Drainage Basin – Runoff Quantity




• Highway Runoff Quality

• Highway Drainage Basin Best Management Practices (BMPs)

• Receiving Water Upstream Drainage Basin – Storm Water Quantity and Stream Flow

• Receiving Water Upstream Quality

The level of specificity for empirical data used in a SELDM analysis will depend on the quality and quantity of the available data for a specific highway site and receiving water. Often, limited data from a specific receiving water may be available, so data from nearby similar waters and drainage basins may be relied on to infer the properties of the receiving water under evaluation.

To help the user select representative data, the databases within SELDM rely on the concept of ecoregions, a system of classification developed and used by the US EPA. Ecoregions are areas where ecosystems (and the type, quality, and quantity of environmental resources) are generally similar. SELDM used US EPA Level III Ecoregions for organization of its ecoregion-level data. The project corridor is located entirely within Ecoregion number 54 “Central Corn Belt Plains”. In addition to ecoregion level grouping, the user may select data from specific sites and studies for many modules in SELDM or define customized, site-specific values for the inputs.

3.1 SELDM PRECIPITATION MODULE

The SELDM Precipitation Module contains a data set (named “FHWA 2010”) from many surface weather stations operated by the National Oceanic and Atmospheric Administration (NOAA) and programmed routines to calculate the statistical values for parameters necessary for the analysis, which include the following:

• Storm Event Volume (inches)

• Storm Event Duration (hours)

• Time Between Storm Events (hours)

• Number of Storms per Year

• Total Annual Precipitation (inches)

The user may select a specific station or set of stations from the FHWA 2010 data set, select all stations within the ecoregion, or select all stations within the rain zone from the SELDM options. For this analysis, the precipitation statistics were derived from the median values at the stations in Crete and Kankakee. These stations were deemed most representative of the project corridor. The precipitation data used in the SELDM analysis is summarized in Table 1.

3.2 HIGHWAY DRAINAGE BASIN – RUNOFF QUANTITY

For each highway drainage basin modeled in SELDM, the following parameters are required to define the hydraulic properties of the basin:

• Drainage Area (acres)

• Drainage Length (feet)

• Mean Basin Slope (feet per mile)

• Impervious Fraction (0 to 1)

• Basin Development Factor (0 to 12 or -1 to use impervious fraction)

The highway site hydraulic parameters are used with the precipitation data to calculate the highway storm water runoff hydrograph for each modeled storm event. This information is derived from the physical properties of the highway site as




documented on the highway drainage plans. For a highway widening project, the impervious fraction and/or drainage area are the most commonly affected parameters that change from existing to proposed conditions.

Rather than determine the basin development factor which requires a subjective analysis, the value was set to -1 instructing SELDM to use the total impervious area for calculating the basin lag time of the highway runoff hydrograph.

Each SELDM analysis is limited to a single highway basin and receiving water. For receiving waters where multiple highway sub-basins discharge to a receiving stream or its tributaries at multiple points, the tributaries were analyzed with individual runs in SELDM using the parameters of the highway sub-basin discharging to those tributaries, and the highway basin properties for downstream receiving waters were an aggregate of the sub-basins that discharge upstream and to tributaries. For the aggregate basin, the drainage area was the sum from the sub-basins, and the impervious fraction was determined over the entire aggregated area. The drainage length and mean basin slope for the aggregated basin were the weighted average of the sub-basins, with the weighting based on impervious area of the sub-basins. The Des Plaines River basin used in SELDM was an aggregate of Des Plaines River at the confluence of the Chicago Sanitary and Ship Canal (CSSC) in Joilet, the CSSC, and Hickory Creek. For the Des Plaines River, mean basin slope for the aggregated basin was the weighted average of the sub-basins; however, the drainage length was based on the Des Plaines River at the confluence of the CSSC, as the length best represented the basin characteristics.

Hickory Creek crosses the project corridor twice: at the eastern project limits near the US-30 and I-80 interchange and at the central portion of the project between IL-53/US-52 and New Richards Street. Each crossing of Hickory Creek was modeled separately to give a more accurate portrayal of the project’s potential effects at each location.

Table 2 summarizes the highway drainage basin characteristics for the SELDM analysis.

3.3 HIGHWAY RUNOFF QUALITY

SELDM provides two methods for defining the pollutant concentrations in highway runoff – “Highway Random” and “Highway Dependent”. For the Highway Random method, the statistical properties (average, standard deviation, and skewness) of the highway runoff concentrations for the pollutant being modeled are provided, and the concentration used in each simulated storm event is determined by the random number generator in the Monte Carlo analysis. For the Highway Dependent method, which is typically the primary method used in SELDM, a regression equation correlating the concentration of the modeled pollutant (dependent variable) and the concentration of another pollutant (independent variable) is defined. For example, the concentration of a metal may be correlated to and determined by the suspended solids concentration. The Highway Random method was used for all pollutants of potential concern in this analysis.

The HRDB was developed as a complementary program for SELDM2. The HRDB contains several data sets of highway runoff event mean concentration data. The user can elect to include the data from one or more records of highway sites contained in the HRDB. The HRDB then calculates the statistical properties of the data set of chosen highway sites which are required for the SELDM Highway Random method of analysis.

Although dissolved metals are of primary concern for evaluating the potential effects of highway runoff on receiving streams, existing data for dissolved metals is not as reliable as data for total metals3. Concentrations of total copper and

2 Gregory E. Granato and Patricia A. Cazenas, “Highway-Runoff Database (HRDB Version 1.0): A Data Warehouse and Preprocessor for the

Stochastic Empirical Loading and Dilution Model” (U.S. Gelogical Survey (USGS), June 2009). 3 Gregory E. Granato and Susan C. Jones, “Estimating Risks for Water-Quality Exceedances of Total-Copper from Highway and Urban Runoff under

Predevelopment and Current Conditions with the Stochastic Empirical Loading and Dilution Model (SELDM),” in Proceedings of the 2017 World




total zinc in highway runoff were used for the analysis of metals in this assessment. The most recent data sets within the HRDB – MA 2009 and MA 2010 – were selected for this assessment of metals. These data sets include event mean concentration (EMC) results for well controlled studies completed in Massachusetts between 2005 and 20084. Sites with average daily traffic (ADT) of at least 39,000 vehicles per day were selected for the analysis. Table 3 provides a summary of the HRDB query results. The arithmetic (non-transformed) statistical values are shown for reference. The log-10-transformed statistics were used in the SELDM analysis.

The highway runoff concentrations of total lead, total nickel, total cadmium, and total chromium were also considered for analysis with SELDM. In Table 4 the concentration of these metals in highway runoff is compared to Illinois water quality standards based on a conservatively low estimate of 200 mg/L critical hardness. The comparison demonstrates that highway runoff concentrations of lead, nickel, cadmium, and chromium do not threaten the water quality standards, so further analysis with SELDM is not warranted. Lead was considered a contaminant of concern with the 1990 FHWA model; however, as shown in Table 4, more recent highway runoff concentrations of lead are much lower than those at the time the 1990 FHWA model was developed, likely due to the change to unleaded gasoline and general reduction in the use of lead in modern materials.

SELDM contains statistical properties for several highway runoff data sets, some of which were used in this analysis. The “Ultra Urban” sites from the 1990 FHWA data set contained within SELDM were used for the analysis of total suspended solids (TSS), and the MA 2009-2010 total hardness values were used.

3.4 HIGHWAY DRAINAGE BASIN BMPS

The BMP module in SELDM accounts for the effects BMPs can have on highway storm water runoff quantity and quality before mixing with the receiving water. The BMP module considers only BMPs which treat the highway runoff directly and not BMPs which may be applied elsewhere within the receiving water basin. The effects of BMPs are considered in these three manners:

• Effect on the total volume of runoff discharged, measured as the ratio of outflow from the BMP to inflow to the

BMP;

• Effect on the timing of runoff discharge, measured as the amount of time by which the tail of the highway runoff

hydrograph is extended; and

• Effect on the quality of highway runoff, measured as the ratio of a pollutant concentration in BMP effluent to the

pollutant concentration in BMP influent.

The performance of BMPs in each of these instances is defined stochastically within SELDM and varied with the random number generator in the Monte Carlo analysis. Granato evaluated performance data for several types of BMP in the International BMP database and derived the statistical values needed for input to the SELDM BMP module5.

SELDM allows only a single definition of BMP performance in each model run. For example, the user cannot specify within SELDM that half the highway runoff is controlled by a BMP with defined performance statistics while the other half is not

Environmental & Water Resources Congress (2017 World Environmental & Water Resources Congress, Sacramento, CA: Amaerican Society of Civil Engineers, 2017), 16. 4 Kirk P. Smith and Gregory E. Granato, “Quality of Stormwater Runoff Discharged from Massachusetts Highways, 2005–07,” Scientific

Investigations Report (U.S. Gelogical Survey (USGS), 2010), http://pubs.usgs.gov/sir/2009/5269/. 5 Gregory E. Granato, “Statistics for Stochastic Modeling of Volume Reduction, Hydrograph Extension, and Water-Quality Treatment by Structural

Stormwater Runoff Best Management Practices (BMPs),” Scientific Investigations Report (U.S. Gelogical Survey (USGS), 2014), http://pubs.usgs.gov/sir/2014/5037/.




controlled by any BMP. In most cases, individual BMPs or multiple types of BMPs may control different portions of the highway runoff within a highway drainage basin. Huff & Huff, Inc. (H&H) developed a spreadsheet utility to account for application of BMPs to a portion of the highway runoff rather than the full volume of highway runoff.

The fraction of highway runoff flow from the basin treated by each type of BMP or BMP train (sequence of individual BMPs) is the input to the H&H BMP spreadsheet. The fraction of impervious area within the basin which drains to a type of BMP is used as an estimate for the fraction of highway runoff draining to the BMP. The spreadsheet uses the fractional flow information input by the user and the statistical values for individual BMPs from Granato to calculate the flow-weighted BMP performance statistics for input to the SELDM BMP module.

The fraction of highway runoff routed through BMPs is summarized in Table 5.

3.5 RECEIVING WATER UPSTREAM DRAINAGE BASIN – STORM WATER QUANTITY AND STREAM FLOW

The quantity and timing of flow in receiving waters is determined in SELDM based on the inputs provided on the forms “Upstream Basin: Identify Basin Characteristics” (upstream basin form) and “Streamflow Statistics: Select Statistics” (streamflow form) along with the precipitation. The following receiving water drainage basin characteristics are used to calculate the storm water runoff:

• Drainage Area (square miles)

• Drainage Length (feet)

• Mean Basin Slope (feet per mile)

• Impervious Fraction (0 to 1)

• Basin Development Factor (0 to 12, or -1 to use impervious area)

H&H obtained the values for these parameters, except for the basin development factor, from StreamStats (https://water.usgs.gov/osw/streamstats/), the web-based application from the United States Geological Survey (USGS) which provides a variety of drainage basin properties delineated using geographic information system (GIS) software and analytical tools. Rather than determine the basin development factor, the value was set to -1 instructing SELDM to use the total impervious area for calculating the basin lag time of the receiving stream hydrograph.

The pre-storm streamflow from the upstream receiving stream basin is also determined as a stochastic variable within SELDM. For calculating pre-storm streamflow statistics, SELDM includes a set of streamflow data from 2,783 stream gage sites in the US. The user can select from one or more of these sites available within SELDM to develop pre-storm streamflow values representative of the drainage basin being modeled. The fundamental parameter to describe pre-storm streamflow in SELDM is the streamflow normalized by basin drainage area, measured in cubic feet per square mile (cfsm). After the stream gage sites to be included in the analysis are selected, the normalized streamflow statistics consisting of the average, median, standard deviation, and skewness for both non-transformed (arithmetic) values for all streamflow values and log-10-transformed values for non-zero streamflow values are calculated by SELDM.

Table 6 provides a summary of the receiving stream basin properties and pre-storm flow statistics derived from SELDM for each of the receiving streams.

Des Plaines River

The nearest upstream flow gage on the Des Plaines River is located at the confluence of the Des Plaines River and the CSSC. To achieve a representative flow rate of the Des Plaines River at the crossing with I-80, daily average flow data were

https://water.usgs.gov/osw/streamstats/




obtained from two flow gages: USGS 05537980 on the Des Plaines River at IL-53 and USGS 05539000 on Hickory Creek at Joliet. The daily average flow data were normalized by the drainage area and used to compute a flow duration curve that describes the range of flows observed at these points along the Des Plaines River. The normalized SELDM flow rate results are shown on the plot below, and the statistics are presented in Table 6.

3.6 RECEIVING WATER UPSTREAM QUALITY

The upstream water quality of receiving waters is typically modeled as a stochastic variable within SELDM using the “Upstream Random” definitions within SELDM. The user is required to provide the average, standard deviation, and skewness of upstream concentrations of the contaminants being analyzed and to specify whether the data set for which those statistics were derived was log-transformed or non-transformed.

The receiving water quality data sets were obtained from monitoring results publicly available through the waterqualitydata.us online Water Quality Portal (WQP) database which provides access to the EPA’s “STOrage and RETrieval” (STORET) system and the USGS National Water Information System (NWIS). The data obtained from the query

0.0

5.0

10.0

15.0

20.0

25.0

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Are

a-N

orm

aliz

ed F

low

Rat

e (c

fs/s

q-m

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Area-Normalized Stream Flow Distribution

Des Plaines River Des Plaines River with Hickory Creek




of waterquality.us requires analysis by the user and preparation for use in SELDM. Typically, a considerable fraction of the copper and zinc are recorded as non-detect concentrations. The EPA statistical software ProUCL was used to properly account for the non-detect concentrations when calculating the statistics required by SELDM. The following procedure was followed when preparing the upstream water quality data for SELDM:

1. Query the WQP to obtain monitoring data records for contaminants of concern at all monitoring stations in the

region under evaluation.

2. Add any independently collected water quality data to the data set.

3. Identify which stations are most representative of upstream water quality for the SELDM assessment. Station

selection preference is as follows, ordered from most preferred to least preferred:

a. Upstream stations near the highway crossing on the receiving water

b. Downstream stations near the highway crossing on the receiving water

c. Stations from nearby streams which have similar characteristics to the receiving water

4. Filter the data obtained from waterquality.us to obtain the most representative data available for each receiving

water and each contaminant of concern.

5. Evaluate the filtered data sets by looking at the concentrations reported over time by sorting the data by sample

date and viewing on a chart. A small number of outliers or obviously aberrant data which should be removed from

the data set are frequently identified. Also, older results in the database were often reported as non-detects with

higher detection limits than more recent data. If sufficient recent data (at least 10 samples) are available, the older

data with relatively higher detection limits may be discarded.

6. Evaluate the data sets in ProUCL. First, use ProUCL’s Regression on Order Statistics (ROS) method to substitute

imputed data for non-detect concentrations. The log-transformed ROS (LnROS) method typically results in the

most plausible imputed non-detect values. Then, calculate the statistics (average, standard deviation, median,

skewness) for the non-transformed (raw) and the log-transformed data sets with imputed non-detect values.

Table 7a through Table 7d summarizes the concentration statistics representative of upstream water quality for the project used in SELDM.

No water quality monitoring stations for the UNTs to DuPage River were present in the WQP database. Monitoring results from nearby Aux Sable Creek were used to represent the UNTs to DuPage River in SELDM.

Limited water quality monitoring data were available for Rock Run (three samples of TSS and one sample for chlorides). Monitoring results from nearby DuPage River were used to define the upstream contaminant concentrations for Rock Run.

4.0 SELDM RESULTS

4.1 PREDICTED DOWNSTREAM CONCENTRATIONS AND POLLUTANT LOADING

The SELDM output consists of a series of text files providing information about the calculations for each storm event modeled in the Monte-Carlo analysis for streams and rivers. Separate output files are provided for each of the following key components of the SELDM assessment:

• Downstream contaminant concentrations




• Highway runoff contaminant concentrations

• Upstream contaminant concentrations

• Precipitation event details for each modeled storm event

• Pre-storm upstream stream flow (base flow) details

• Storm water flows for highway and upstream basins

• Downstream dilution factor (dilution of highway runoff into receiving water)

SELDM also provides an output file which summarizes the pollutant and runoff volume loads for each year of storm events modeled. The primary output files evaluated to assess the highway project are the downstream contaminant concentrations and the annual summary file. The other files are useful in situations where existing water quality may be close to the water quality standards and advanced analysis is required to determine if the project is predicted to contribute to an exceedance of the water quality standard.

The water quality standards for the receiving waters for the contaminants of concern are summarized in Table 8.

To assess the SELDM results, the output files were imported into a spreadsheet. The predicted downstream concentrations are ranked by SELDM with a Weibull plotting position. In the SELDM documentation, Granato provided a method for calculating the return period of the predicted concentration for each modeled storm event as a frequency of the Weibull plotting position, the total number of storm events modeled, and the average number of storm events per year. This calculation was completed, and the predicted 3-year downstream concentration was found for each contaminant of concern.

The results from the SELDM analysis are summarized in Table 9a through Table 9g. None of the predicted downstream 3-year concentrations for proposed conditions was above the applicable water quality standard for the contaminants of concern.


FIGURES

SOURCE: U.S. DEPARTMENT OF THE INTERIOR

U.S. GEOLOGICAL SURVEYCHANNAHON, ELWOOD, JOLIET, MANHATTAN, MINOOKAMOKENA, PLAINFIELD AND YORKVILLE SE QUADRANGLE

FIGURE 1PROJECT LOCATION MAPHNTB I-80WILL COUNTY, ILLINOIS

CADFILE: HNTB\I80\TREES\FIGURES

6,000

Feet

I

§̈¦80

§̈¦55

COUNTY BOUNDARY

EXISTING ROW / PROJECT BOUNDARY

HILL RD

DUPAGERIVER

ROCKRUN

DES PLAINESRIVER

HICKORYCREEK AT

IL-53

HICKORYCREEK AT

US-30

UNT 005 TODUPAGE

RIVER

UNT 007 TODUPAGE

RIVER


TABLES

Table 1. Storm Event Statistics for the SELDM Analysis

Storm Event Parameters

Storm Event Volume, inches

Storm Event Duration, hours

Time Between Storm Events, hours

Minimum Total Storm Volume, inches

Minimum Interevent Time, hours

Annual Precipitation Parameters

Number of Storm Events per YearTotal Annual Precipitation

Summary of Precipitation Measurement Stations Used to Represent Site Conditions

Average

Annual

Precipitation

Average

Storm

Volume

Average

Storm

Duration

Average Hours

Between Storm

Midpointsinches inches hours hours

112011 CRETE IL -87.633 41.45 46 28.1 0.61 6.81 171114603 KANKAKEE WASTEWATER IL -87.883 41.133 47 29.2 0.61 6.49 175

Notes:

The station(s) within the SELDM database nearest the Site were used to determine the values of the precipitation statistics.

Precipitation Parameter

0.270.27

4628.65

Coefficient of

Variation

0.94

0.89

1.23

--

--

Average

0.61

6.65

173

0.1

6

Storms

per YearLatitudeLongitudeStateStation NameStation Number

Table 2. Highway Drainage Basin Characteristics for SELDM Analysis

Existing Proposed Existing Proposed Existing Proposed Existing Proposed

Drainage Area (acres) 18.25 17.91 10.73 11.02 59.91 60.22 69.73 68.93

Drainage Length (ft) 2,265 2,265 2,450 2,450 4,120 4,145 5,780 5,780

Mean Basin Slope (ft per mile) 8.1 18.9 53.2 63.75 30.75 37.75 38.25 42.6

Impervious Fraction (0-1) 0.45 0.68 0.39 0.52 0.27 0.33 0.30 0.45

Existing Proposed Existing Proposed Existing Proposed

Drainage Area (acres) 75.93 76.76 10.34 10.66 59.81 61.84

Drainage Length (ft) 3,080 3,080 1,805 2,040 2050 2075

Mean Basin Slope (ft per mile) 114 114 43.7 60 81.9 82.5

Impervious Fraction (0-1) 0.39 0.51 0.33 0.51 0.36 0.47

Des Plaines RiverHighway Basin Characteristic

Hickory Creek Hickory Creek

Highway Basin Characteristic

UNT 005 to

DuPage River

UNT 007 to

DuPage RiverDuPage River Rock Run

H:\Client\HNTB\I80-Grundy Line to US 30\Water\Pollutant Loading Analysis\I-80 Joliet SELDM Input Tracking - Bloom Data.xlsx

Table 3. Summary of Highway Contaminant Runoff Concentrations used in the SELDM Analysis

TSS (mg/L)

Total

Copper

(ug/L)

Total Zinc

(ug/L)

Arithmetic

Average 231 57 289Standard Deviation 313 70 320Coefficient of Variation 1.35 1.23 1.11

Skewness -- 4.02 2.38

Log-10 Transformed

Average 2.14 1.56 2.25

Standard Deviation 0.443 0.411 0.424

Coefficient of Variation 0.21 0.26 0.19

Skewness -0.139 0.23 0.21

Reference A B B

References:

A - SELDM - FHWA 1990 data set - Urban TSS

B - HRDB - Massachusettes 2009-10 data set, ADT >30,000

H:\Client\HNTB\I80-Grundy Line to US 30\Water\Pollutant Loading Analysis\HRDB-SELDM Highway Runoff COC

Summary.xlsx

Table 4

Comparison of Metal Concentrations in Highway Runoff to Illinois General Use Water Quality Standards

Receiving Water

Use Designation

ReferenceDuration Acute Average Median Average Median St. Dev.

Copper, dissolved (ug/L) 32.7 -- -- -- -- --

Copper, total (ug/L) 34.1 -- 54 57 34 70

Lead, dissolved (ug/L) 160 -- -- -- -- --

Lead, total (ug/L) 231 -- 400 25 11 43

Zinc, dissolved (ug/L) 215 -- -- -- -- --

Zinc, total (ug/L) 220 -- 329 289 178 320

Cadmium, dissolved (ug/L) 20 -- -- -- -- --

Cadmium, total (ug/L) 21 -- -- 0.586 0.2 0.906

Nickel, dissolved (ug/L) 148 -- -- -- -- --

Nickel, total (ug/L) 148 -- -- 9.17 4.47 11

Chromium, hexavalent (ug/L) 16 -- -- -- -- --

Chromium, trivalent, dissolved (ug/L) 968 -- -- -- -- --

Chromium, total (ug/L) 3,064 -- -- 20 13 20

Critical Hardness (mg/L as CaCO3):

Notes:1) Driscoll, et. al., 1990. "Pollutant Loadings and Impacts from Highway Stormwater Runoff", FHWA-RD-88-006

1) Highway Runoff Database (HRDB) v. 1.0.0a, Massachusetts 2009-10 data sets. ADT > 39,000.

3) Used HRDB calculations of Regression on Order Statistics (ROS) when censored data existed.

Concentration in Highway Runoff

FHWA 1990

(Driscoll)1

HRDB 2009-10 MA2,3

Hypothetical Illinois

Stream

General Use

35 IAC 302.208

200

H:\Client\HNTB\I80-Grundy Line to US 30\Water\Pollutant Loading Analysis\Illinois WQS.xlsx

Table 5. Fraction of Highway Runoff Routed to Each Type of BMP

None 0.03 0.26 0.07 0.05 0.02 0.13 0.02 0.01 0.05 0.15 0.09 0.64 0.05 0.10

Biofilter (swale) 0.97 0.6 0.93 0.01 0.15 0.05 0.47 0.06 0.95 0.85 0.91 0.36 0.95 0.75

Biofilter (swale) & Wetland

Channel Series0 0 0 0 0.83 0.45 0.51 0.2 0 0 0 0 0 0

Biofilter (swale), Retention

Pond & Wetland Channel

Series

0 0 0 0 0 0.31 0 0.22 0 0 0 0 0 0

Biofilter (swale) & Retention

Pond Series0 0.14 0 0.94 0 0.06 0 0.51 0 0 0 0 0 0.15

Hickory

Creek at US-

30 - Existing

Hickory

Creek at US-

30 -

Proposed

Des Plaines

River -

Existing

Des Plaines

River -

Proposed

Hickory

Creek -

Proposed

DuPage

River -

Proposed

Type of BMPRock Run -

Existing

Rock Run -

Proposed

Hickory

Creek -

Existing

UNT 005 to

DuPage

River -

Existing

UNT 005 to

DuPage

River -

Proposed

UNT 007 to

DuPage

River -

Existing

UNT 007 to

DuPage

River -

Proposed

DuPage

River -

Existing

H:\Client\HNTB\I80-Grundy Line to US 30\Water\Pollutant Loading Analysis\I-80 Joliet SELDM BMP Calculation Sheet.xlsx

Table 6. Receiving Water Drainage Basin and Stream Flow Characteristics for SELDM Analysis

Drainage Area (sq. mi.) 0.98 1.79 333.47 13.72 109.9 78.97 1502

Drainage Length (ft) 11,141 12,038 229,522 42,398 121,968 95,146 466,541

Mean Basin Slope (ft per mile) 28.17 30.869 4.142 7.366 8.153 7.269 1.795

Impervious Fraction (0-1) 0.099 0.033 0.316 0.344 0.248 0.269 0.286

Average daily flow (cfsm) 1.091 1.091 1.0396 0.9798 0.8732 0.8732 2.4930

Median daily flow (cfsm) 0.6931 0.6931 0.6296 0.2791 0.2897 0.2897 1.9640

Standard deviation (cfsm) 1.5273 1.5273 1.3697 2.3764 2.1077 2.1077 1.8190

Skew 7.455 7.455 5.9933 8.6044 10.368 10.368 3.1190

Notes:

Drainage basin characteristics obtained from USGS StreamStats program

Stream flow statistics derived from SELDM database using nearest streams with similar drainage basin characteristics

Des Plaines RiverHickory Creek

at US-30Characteristic

UNT 005 to

DuPage River

UNT 007 to

DuPage RiverDuPage River Rock Run Hickory Creek

H:\Client\HNTB\I80-Grundy Line to US 30\Water\Pollutant Loading Analysis\I-80 Joliet SELDM Input Tracking - Bloom Data.xlsx

Table 7a. Summary of Upstream Water-Quality Statistics for SELDM: DuPage River

Total

Copper

Dissolved

Copper

Total

Zinc

Dissolved

Zinc Chloride TSSug/L ug/L ug/L ug/L mg/L mg/L

Arithmetic

Average 5.886 1.664 33.350 10.950 200.4 27.2

Median 3.177 0.664 17.950 9.065 176.5 14.0

Standard Deviation 7.810 3.211 60.530 6.777 132.6 36.2

Skewness 3.013 5.270 7.300 1.070 6.06 2.67

COV 1.327 1.930 1.815 0.619 0.662 1.331

Ln-Transformed

Average 1.165 -0.409 2.915 2.207 5.181 2.726

Median 1.156 -0.409 2.887 2.204 5.173 2.639


Skewness 0.0588 0.0465 0.35 -0.0541 0.0596 0.563

COV 0.954 -3.337 0.347 0.283 0.091 0.375

Number of Samples

Metal Translator Ratio

Notes:

Water Quality Sampling Stations Included in Analysis:Monitoring Location ID Latitude Longitude

USGS-05540500 41.52225 -88.19256 NWIS

IL_EPA-GB-11 41.52157 -88.19483 STORET

IL_EPA_WQX-GB-11 41.52157 -88.19483 STORET

USGS-05540480 41.53614 -88.18173 NWISIL_EPA_WQX-GB-18 41.53611 -88.18139 STORET

c) The metal translator is the ratio of dissolved-to-total metal for a specific metal, characteristic of the water

body, as documented in The Metals Translator: Guidance for Calculating a Total Recoverable Permit

Limit from a Dissolved Criterion (EPA 1996)

Database / Program

0.283 0.328

a) Non-detect values imputed using Regression on Order Statistics (ROS) method with ProUCL.

b) Data set includes samples from stations located downstream of highway runoff discharge due to limited

data from upstream sites. Use of the downstream sites in the SELDM analysis provides a conservative

estimate of downstream water quality with highway runoff loads.

J:\81.0220577.00 Knight I-55 @ I-80 SELDM Modeling\Calcs\WQ Monitoring Data\waterquality.us\I-80 Joliet Region - Receiving Water Quality SELDM

Summary Table.xlsx

Table 7b. Summary of Upstream Water-Quality Statistics for SELDM: Hickory Creek

Total

Copper

Dissolved

Copper

Total

Zinc

Dissolved


Arithmetic

Average 3.830 3.030 18.450 8.606 197.3 35.0

Median 1.959 2.611 13.080 5.251 180.0 13.0


Skewness 3.870 1.620 3.534 5.737 2.46 6.40

COV 1.496 0.577 0.996 1.414 0.606 2.035

Ln-Transformed

Average 0.675 0.963 2.605 1.684 5.132 2.751

Median 0.673 0.96 2.571 1.658 5.193 2.565


Skewness 0.0701 0.0518 0.165 0.171 -0.348 0.326

COV 1.716 0.561 0.297 0.560 0.110 0.435

Number of Samples


Notes:

Water Quality Sampling Stations Included in Analysis:

Monitoring Location ID Latitude Longitude

USGS-05538410 41.53086 -88.02172

IL_EPA_WQX-GG-02 41.52434 -88.06364

USGS-05539000 41.51503 -88.07339




Database / Program

NWIS

STORETNWIS

c) The metal translator is the ratio of dissolved-to-total metal for a specific metal, characteristic of the

water body, as documented in The Metals Translator: Guidance for Calculating a Total Recoverable

Permit Limit from a Dissolved Criterion (EPA 1996)

0.791 0.466



Summary Table.xlsx

Table 7c. Summary of Upstream Water-Quality Statistics for SELDM: Aux Sable Creek

(UNTs to DuPage River)

Total

Copper

Dissolved

Copper

Total

Zinc

Dissolved


Arithmetic

Average 2.234 2.686 9.259 3.103 40.1 32.0

Median 1.513 2.539 5.700 1.788 35.0 18.0


Skewness 3.229 0.909 2.843 3.213 7.78 5.47

COV 1.040 0.334 1.119 1.259 0.888 1.625

Ln-Transformed

Average 0.424 0.935 1.777 0.606 3.562 2.884

Median 0.414 0.932 1.741 0.581 3.554 2.89


Skewness 0.0653 0.022 0.116 0.128 1.147 -0.0332

COV 2.052 0.350 0.534 1.688 0.124 0.366

Number of Samples


Notes:


IL_EPA_WQX-DW-07 41.51222 -88.31083

IL_EPA_WQX-DW-03 41.48329 -88.3004

USGS-05541710 41.41725 -88.34757

IL_EPA-DW-01 41.41704 -88.34768

IL_EPA_WQX-DW-01 41.41704 -88.34768

IL_EPA_WQX-DW-99 41.39652 -88.33047 STORETSTORET

1.000 0.335





c) The metal translator is the ratio of dissolved-to-total metal for a specific metal, characteristic of the water

body, as documented in The Metals Translator: Guidance for Calculating a Total Recoverable Permit

Limit from a Dissolved Criterion (EPA 1996)

Database / Program

STORET

STORET

NWIS

STORET


Summary Table.xlsx

Table 7d. Summary of Upstream Water-Quality Statistics for SELDM: Des Plaines River

Total Copper

Dissolved

Copper

Total

Zinc

Dissolved


Arithmetic

Average 6.651 4.929 35.580 16.230 171.1 22.4

Median 5.950 2.801 29.700 13.460 131.5 17.0


Skewness 3.821 10.040 4.007 3.238 2.289 4.182

COV 0.699 3.535 0.803 0.832 0.615 1.029

Ln-Transformed

Average 1.73 1.052 3.402 2.509 5.011 2.830

Median 1.783 1.03 3.391 2.599 4.879 2.833


Skewness -0.173 1.672 0.915 -0.496 0.88 0.331

COV 0.332 0.680 0.157 0.313 0.097 0.249

Number of Samples 96 104 103 102 36 103


Notes:


IL_EPA_WQX-G-23 41.536467 -88.083002

IL_EPA-G-23 41.536467 -88.083002

USGS-05537980 41.536389 -88.0825 STORET

Database / Program

STORET

STORET

b) Data set includes samples from stations located downstream of highway runoff discharge due to limited data

from upstream sites. Use of the downstream sites in the SELDM analysis provides a conservative estimate of

downstream water quality with highway runoff loads.

0.741 0.456


Table 8. Water Quality Standards for Contaminants of Concern based on Critical Hardness

Receiving Water

Use Designation

Reference

Duration Acute Chronic Acute Chronic Acute Chronic Acute Chronic

Copper, dissolved (ug/L) 46.4 28.2 49.0 29.6 49.0 29.6 34.4 21.5

Zinc, dissolved (ug/L) 295 77 310 81 310 81 225 59

Critical Hardness (mg/L as CaCO3):

Notes:

Critical hardness values provided by the IEPA (Scott Twait) via email.

290 307.5 307.5

Des Plaines River

General Use

35 IAC 302.208

211

35 IAC 302.208 35 IAC 302.208 35 IAC 302.208

General Use General Use General Use

DuPage River Hickory Creek Rock Run

H:\Client\HNTB\I80-Grundy Line to US 30\Water\Pollutant Loading Analysis\Illinois WQS.xlsx

Table 9a. Summary of Water Quality Results from SELDM Analysis: DuPage River

Total

Net

Change

Percent

Change

Copper, Dissolved ug/L pounds pounds pounds ug/L pounds

Existing 14.3 0.00% 0.6 -- -- 1.664 27,237.2

Proposed 14.6 0.00% 1.0 0.4 65% 1.664 27,237.5

Zinc, Dissolved ug/L pounds pounds pounds ug/L pounds

Existing 117.4 0.00% 2.8 -- -- 10.95 179,234.9

Proposed 119.7 0.00% 4.7 1.9 68% 10.95 179,236.8

TSS mg/L pounds pounds pounds mg/L pounds

Existing 205 -- 9,540 -- -- 27.2 445,222,846

Proposed 205 -- 13,722 4,182 44% 27.2 445,227,028

Notes:

Basin characteristics used to calculate annual loading:

Receiving Stream Basin Size (mi2) 333.47

Receiving Stream Average Normalized Flow (cfs/mi2) 1.0396

Receiving Stream Average Flow (cfs) 346.7

Receiving Stream Average Flow (gal/min) 155,588

d) Average receiving stream concentration for proposed conditions was calculated by adding the average annual runoff load modeled by SELDM to the existing annual

stream load and dividing the total by the annual average stream flow volume.

No WQS

b) The fraction of storms predicted to exceed the acute WQS was obtained by using SELDM to estimate the hardness for each storm event modeled based on the EcoRegion-

level transport curve within SELDM and comparing to the hardness-dependent WQS calculated for each storm.

a) The fraction of storms corresponding to a 3-year return period is 0.67%. If the fraction of storms with predicted downstream concentrations is not greater than this

threshold, then compliance with the acute water quality standard (WQS) based on a 3-year return period is predicted in the SELDM analysis.

c) Includes application of best management practices (BMPs) in SELDM analysis that directly mitigate highway runoff from the subject site. The average annual loads are

calculated as the average (arithmetic) of the SELDM predicted annual discharge loads. The SELDM-generated storm event outputs are used for generating a random

(stochastic) sample of annual flows and loads from a highway site, which will account for instances in which the proposed annual load is less than or equal to the existing

annual load.

46.4

295

Predicted Once in

3-Year

Downstream

Concentration

Fraction of Storms with

Predicted Downstream

Concentration Above

Acute WQS(a,b)

Average Annual Load

from Highway Runoff(c) Average

Receiving Stream

Concentration(d)

Average

Receiving

Stream Annual

Load

Acute WQS

Based on

Critical

Hardness

H:\Client\HNTB\I80-Grundy Line to US 30\Water\Pollutant Loading Analysis\SELDM Results\SELDM Results - DuPage River.xlsx

Table 9b. Summary of Water Quality Results from SELDM Analysis: UNT 005 to DuPage River

TotalNet

ChangePercentChange

Copper, Dissolved ug/L pounds pounds pounds ug/L poundsExisting 4.9 0.00% 0.2 -- -- 2.686 135.6Proposed 7.0 0.00% 0.5 0.3 118% 2.692 135.9

Zinc, Dissolved ug/L pounds pounds pounds ug/L poundsExisting 28.2 0.00% 1.2 -- -- 3.103 156.6Proposed 48.9 0.00% 2.8 1.5 126% 3.134 158.2

TSS mg/L pounds pounds pounds mg/L poundsExisting 340 -- 3,030 -- -- 32.0 1,615,423Proposed 340 -- 7,195 4,165 137% 32.1 1,619,588

Notes:



Receiving Stream Average Normalized Flow (cfs/mi2) 1.091Receiving Stream Average Flow (cfs) 1.1Receiving Stream Average Flow (gal/min) 480



No WQS

b) The fraction of storms predicted to exceed the acute WQS was obtained by using SELDM to estimate the hardness for each storm event modeled based on the

EcoRegion-level transport curve within SELDM and comparing to the hardness-dependent WQS calculated for each storm.






annual load.

32.7

215

Predicted Once in3-Year

DownstreamConcentration

Fraction of Storms withPredicted DownstreamConcentration Above

Acute WQS(a,b)

Average Annual Load

from Highway Runoff (c) AverageReceiving Stream

Concentration(d)

AverageReceiving

Stream AnnualLoad

Acute WQSBased onCritical

Hardness

H:\Client\HNTB\I80-Grundy Line to US 30\Water\Pollutant Loading Analysis\SELDM Results\SELDM Results - DuPage River UNT 005.xlsx

Table 9c. Summary of Water Quality Results from SELDM Analysis: UNT 007 to DuPage River

Total

Net

Change

Percent

Change


Existing 15.2 0.00% 0.48 -- -- 2.686 247.7

Proposed 14.2 0.00% 0.50 0.02 5% 2.686 247.7


Existing 21.8 0.00% 0.68 -- -- 3.103 286.1

Proposed 21.1 0.00% 0.66 -0.01 -2% 3.103 286.1


Existing 372 -- 1,940 -- -- 32.0 2,950,620

Proposed 205 -- 1,754 -186 -10% 32.0 2,950,434

Notes:





Receiving Stream Average Flow (gal/min) 876



No WQS








annual load.

46.4

295

Predicted Once in

3-Year

Downstream

Concentration



Concentration Above

Acute WQS (a,b)

Average Annual Load

from Highway Runoff (c) Average

Receiving Stream

Concentration (d)

Average

Receiving

Stream Annual

Load

Acute WQS

Based on

Critical

Hardness

Table 9d. Summary of Water Quality Results from SELDM Analysis: Hickory Creek

TotalNet

ChangePercentChange

Copper, Dissolved ug/L pounds pounds pounds ug/L poundsExisting 25.2 0.00% 2.3 -- -- 3.03 13,729.0Proposed 25.2 0.00% 3.4 1.1 45% 3.03 13,730.1

Zinc, Dissolved ug/L pounds pounds pounds ug/L poundsExisting 54.1 0.00% 6.7 -- -- 8.61 38,994.0Proposed 55.5 0.00% 10.1 3.4 51% 8.61 38,997.4


Notes:



Receiving Stream Average Normalized Flow (cfs/mi2) 0.8732Receiving Stream Average Flow (cfs) 96.0Receiving Stream Average Flow (gal/min) 43,069



No WQS








annual load.

46.4

295




Acute WQS(a,b)

Average Annual Load


Concentration(d)

AverageReceiving

Stream AnnualLoad


Hardness

H:\Client\HNTB\I80-Grundy Line to US 30\Water\Pollutant Loading Analysis\SELDM Results\SELDM Results - Hickory Creek.xlsx

Table 9e. Summary of Water Quality Results from SELDM Analysis: Hickory Creek at US-30

TotalNet

ChangePercentChange




Notes:



Receiving Stream Average Normalized Flow (cfs/mi2) 0.8732Receiving Stream Average Flow (cfs) 69.0Receiving Stream Average Flow (gal/min) 30,948



No WQS








annual load.

32.7

215




Acute WQS(a,b)

Average Annual Load


Concentration(d)

AverageReceiving

Stream AnnualLoad


Hardness

H:\Client\HNTB\I80-Grundy Line to US 30\Water\Pollutant Loading Analysis\SELDM Results\SELDM Results - Hickory Creek US-30.xlsx

Table 9f. Summary of Water Quality Results from SELDM Analysis: Rock Run (DuPage River used for Upstream WQ Data)(e)

TotalNet

ChangePercentChange




Existing 181 -- 11,757 -- -- 27.2 17,264,180Proposed 182 -- 15,289 3,532 30% 27.2 17,267,711

Notes:





Receiving Stream Average Flow (gal/min) 6,033

e) Due to limited water quality (WQ) monitoring data available for Rock Run (3 samples of TSS and 1 sample for chloride), monitoring results for nearby DuPage River

were used to define the upstream contaminant concentrations for Rock Run.



No WQS








annual load.

49.0

310

Predicted Once in

3-YearDownstream

Concentration


Acute WQS (a,b)

Average Annual Load


Concentration (d)

Average

ReceivingStream Annual

Load

Acute WQS

Based onCritical

Hardness

H:\Client\HNTB\I80-Grundy Line to US 30\Water\Pollutant Loading Analysis\SELDM Results\SELDM Results - Rock Run.xlsx

Table 9g. Summary of Water Quality Results from SELDM Analysis: Des Plaines River

Total

Net

Change

Percent

Change


Existing 31.3 0.20% 2.4 -- -- 1.664 294,192.1

Proposed 31.3 0.19% 3.4 1.0 43% 1.664 294,193.1


Existing 182.0 0.07% 10.8 -- -- 10.95 1,935,939.7

Proposed 184.0 0.13% 14.0 3.2 30% 10.95 1,935,942.9


Existing 119 -- 10,823 -- -- 27.2 4,808,909,592

Proposed 119 -- 14,272 3,448 32% 27.2 4,808,913,040

Notes:


Receiving Stream Basin Size (mi2) 1502



Receiving Stream Average Flow (gal/min) 1,680,525



No WQS








annual load.

34.4

225

Predicted Once in

3-Year

Downstream

Concentration



Concentration Above

Acute WQS(a,b)

Average Annual Load

from Highway Runoff(c) Average

Receiving Stream

Concentration(d)

Average

Receiving

Stream Annual

Load

Acute WQS

Based on

Critical

Hardness

H:\Client\HNTB\I80-Grundy Line to US 30\Water\Pollutant Loading Analysis\SELDM Results\SELDM Results - Des Plaines.xlsx

Huff & Huff, a Subsidiary of GZA

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