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Assessment of Near-Source Air Pollution at a Fine Spatial Scale Utilizing a Mobile Monitoring Approach
Office of Research and DevelopmentNational Risk Management Research Laboratory, Air Pollution Prevention and Control Division June 23, 2015
Jonathan SteffensOak Ridge National Laboratory, Oak Ridge TN, 37830Sue Kimbrough, Gayle Hagler U.S. EPA, National Risk Management Research Laboratory, Research Triangle Park, NC 27711Timothy Barzyk, Vlad IsakovU.S. EPA, National Exposure Research Laboratory, Research Triangle Park, NC 27711Ryan Brown, Alan PowellU.S. EPA, Region 4, Atlanta, GA 30303
A&WMA 2015 Conference – Raleigh, NC
Background• Ports play a critical role in the United States and global economies.1
• The Panama Canal is undergoing an expansion which will double its capacity and allow for larger vessels to pass through.2
• While this is expected to provide a positive economic impact, the environmental impact is uncertain.
• Port facilities service traffic from ocean going vessels (OGV), on-terminal equipment, heavy trucks, and rail.
• Research on roadways and railways has shown they contribute to elevated levels of harmful pollutants nearby.3-6
3/14/20161 U.S. Environmental Protection Agency
Project Overview• Early effort in investigating the effect of ports on the local-scale air quality (within a few hundred meters from the terminals).–Mobile monitoring campaign conducted around the
Port of Charleston–Measurement data supplemented with modeling
results from AERMOD and RLINE• Use data to isolate the port contribution from other source contributions (e.g. roadways) and control for confounding variables (e.g. meteorological conditions)
3/14/20162 U.S. Environmental Protection Agency
Study Overview• Mobile monitoring campaign
– February and March, 2014 – Port of Charleston area in South Carolina – Measurement conducted using EPA’s Geospatial
Monitoring of Air Pollution (GMAP) vehicle• GMAP vehicle
– all-electric– measures real-time (1 Hz) concentrations of BC,
NO2, UFP, PM2.5, PM10, CO, and CO2
– on-board GPS records geospatial coordinates– 3 to 4 hour range– Repeated laps at various times of day and week
near different port terminals• Meteorological conditions recorded with nearby
stationary sampling3/14/20163 U.S. Environmental Protection Agency
Wando Welch TerminalSource:
http://www.scspa.com/
GMAP Vehicle
Port of Charleston
3/14/20164 U.S. Environmental Protection Agency
Wando Welch
Terminal
Columbus Street
Terminal
Veteran’s Terminal
Bennett Rail Yard
Driving Routes• Sampling at each occurred
over 3-4 hour periods on multiple days
• Measurement start times were selected to cover a wide range of port operational times– Normal port operational hours are
7 AM to 7 PM
• Driving routes shown in green.
• Port terminals outlined in red.
3/14/20165 U.S. Environmental Protection Agency
Wando Welch Terminal (10 sampling days)
Columbus Street Terminal (6 sampling days)
Veteran’s Terminal (4 sampling days)
Bennett Rail Yard (4 sampling days)
GMAP Vehicle Instrumentation
3/14/20166 U.S. Environmental Protection Agency
Measurement SamplingRate Instrument Stationary/
Mobile
NO2 1sVisible (450 nm) absorption Cavity Attenuated Phase Shift Spectroscopy (CAPS, Aerodyne Research, Inc., Billerica, MA, USA)
Mobile
Carbon monoxide (CO) 1 s Quantum cascade laser (QCL, Aerodyne Research, Inc., Billerica, MA, USA) Mobile
Carbon dioxide (CO2) 1 s Li-COR 820 non-dispersive infrared (NDIR), (LI-COR, Lincoln, Nebraska USA) Mobile
Particle number concentration (size range 5.6-560 nm, 32 channels)
1 s Engine Exhaust Particle Sizer (EEPS, Model 3090, TSI, Inc., Shoreview, MN, USA) Mobile
Particle number concentration (size range 0.5-20 µm, 52 channels)
1 s Aerodynamic Particle Sizer (APS, Model 3321, TSI, Inc., Shoreview, MN, USA) Mobile
Black carbon 1-5 s Single-channel Aethalometer (Magee Scientific, AE-42, Berkeley, CA, USA) Mobile
Longitude and latitude 1 s Global positioning system (Crescent R100, Hemisphere GPS, Scottsdale, AZ, USA) Mobile
3D wind speed and direction 1 s Ultrasonic anemometer (RM Young, Model,
Traverse City, MI, USA ) Stationary
SO2 1 s Ecotech 9850 (Ecotech, Knoxfield Victoria, 3180, Australia) Stationary
Spatially Averaged Concentration• Each point represents average of all black carbon concentrations (µm/m3)
measured within 20 m radius.• High concentrations observed along major roadways (significant non-port impact)• Analysis will focus on measurements within neighborhood zones (outlined in black)
3/14/20167 U.S. Environmental Protection Agency
Wando Welch Terminal Columbus Street Terminal
Spatially Averaged Concentration• Spatially averaged Black Carbon concentrations (µm/m3) at Veteran’s Terminal and
Bennett Rail Yard
3/14/20168 U.S. Environmental Protection Agency
Bennett Rail YardVeteran’s Terminal
Time of Day
3/14/20169 U.S. Environmental Protection Agency
• Distributions of concentration show high variability in measurement
• Higher concentrations observed in the morning and afternoon (likely traffic related spikes)
• Generally higher concentrations observed during port operational hours (7 AM to 7 PM)
• Significant non-port effect observed – high concentrations outside of port hours
• Limited hours of measurement at Veteran’s Terminal and Rail Yard
10 11 12 14 15 16 17 180
500
1000
1500
2000
2500
3000
3500
4000
4500
HourB
C ( µ
g/m
3 )
5 6 7 9 10 11 12 13 14 15 16 17 20 21 22 230
1000
2000
3000
4000
5000
6000
HourB
C ( µ
g/m
3 )5 6 7 8 10 11 12 13 14 15 16 17 20 21 22 23
0
500
1000
1500
2000
2500
3000
3500
4000
Hour
BC
( µg/
m3 )
10 11 14 15 16 170
500
1000
1500
2000
2500
3000
3500
4000
4500
Hour
BC
( µg/
m3 )
Wando Welch Columbus
Veteran’s Rail Yard
Wando Welch Terminal
3/14/201610 U.S. Environmental Protection Agency
• Local background concentration is taken by selecting periods where wind is from a direction away from the port (from the South at Wando for lower neighborhoods)
• This is compared to periods where wind is blowing from over the port
• Comparison was confined to periods during normal port operating hours (7 am to 7 pm) during similar atmospheric conditions to make a more fair comparison
• A significant effect from the port is observed in all measured pollutants (only BC and CO shown)
Port Background0
200
400
600
800
1000
1200
1400
BC
( µg/
m3 )
MedianMean25%-75%10%-90%
Port Background100
120
140
160
180
200
220
240
CO
(ppb
)
Columbus Street Terminal
3/14/201611 U.S. Environmental Protection Agency
• Only small (if any) port influence is observed at the Columbus Street terminal
• Many confounding sources in the vicinity make it difficult to isolate port effect
Port Background0
500
1000
1500
BC ( µ
g/m
3 )
Port Background150
200
250
300
350
400
450
CO (p
pb)
Veteran’s Terminal
3/14/201612 U.S. Environmental Protection Agency
• “Background” is observed to be higher near Veteran’s Terminal
• Port is further away from neighborhood reducing its impact
• Major highway immediately on the far end of the neighborhood causing much higher concentration when wind is blowing from that direction
Port Background180
200
220
240
260
280
300
320
340
CO
(ppb
)
Port Background0
500
1000
1500
2000
2500
3000
BC
( µg/
m3 )
Bennett Rail Yard
3/14/201613 U.S. Environmental Protection Agency
• Little difference observed between rail and background
• Very strong influence from major roadways in all directions
Rail Background220
240
260
280
300
320
340
360
380
CO
(ppb
)
Rail Background0
500
1000
1500
2000
2500
BC
( µg/
m3 )
Modeling Analysis
3/14/201614 U.S. Environmental Protection Agency
• Model port-related emissions of PM2.5using AERMOD and RLINE• AERMOD models port on-terminal
sources such as heavy equipment and docked vessels as area source using emissions inventory data
• RLINE models roadway and railways as line sources using AADT counts
• Receptor grids Uniformly spaced at 270m resolution (8,100 receptors)
Modeling Analysis
3/14/201615 U.S. Environmental Protection Agency
• Differences in sampling times/days, met conditions and distance from source to sampling locations makes it difficult to accurately compare each site to each other
• However, comparison between measurement and model in the neighborhood regions along the four measurement routs for PM2.5 shows good qualitative agreement at Wando, Veteran’s and the Rail Yard
• Model results for Columbus Street terminal are much lower than measurement, suggesting the model may be missing some major emission source near this location
Wando Columbus Veteran Rail Yard0
2
4
6
8
10
12
PM
2.5 ( µ
g/m
3 )
Wando Columbus Veteran Rail Yard0
2
4
6
8
10
12
PM
2.5 ( µ
g/m
3 )
MedianMean25%-75%10%-90%outliers
Modeling Analysis
3/14/201616 U.S. Environmental Protection Agency
• Isolating percent contribution from the three source types shows that roadway sources dominate port and rail source everywhere except Wando Welch terminal
• Measurement route near Veteran’s terminal is further away than other terminal routes, explaining minor port impact
• Port contribution only relates to on-terminal activity. Part of road and rail contribution would also be attributable to port activity
Wando Columbus Veteran Rail Yard0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1Model Source Percent Contribution
Port Contribution Rail Contribution Road Contribution
Summary and Future Work
3/14/201617 U.S. Environmental Protection Agency
• Mobile monitoring campaign conducted around the Port of Charleston, South Carolina, using GMAP vehicle.
• Very large amount of data collected – over 55 hours of real-time sampling of multiple pollutants and meteorological conditions.
• Ports are shown to have a potentially significant impact on local air quality (Wando Welch) which quickly diminishes away from the port (Veteran’s). This effect can be difficult to isolate as the impact of roadways is generally much higher.
• This work represents an early effort in mapping near-port air quality. More port-related mobile monitoring campaigns may be conducted to facilitate a more comprehensive analysis.
• This data will be used to develop and refine a community tool, C-PORT, for near-source air quality assessment.
ACKNOWLEDGMENTS
3/14/201618 U.S. Environmental Protection Agency
This project was supported in part by an appointment to the Research Participation Program at the Office of Research and Development, U.S. Environmental Protection Agency, administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and EPA.
The US Environmental Protection Agency through its Office of Research and Development funded and conducted the research described here through Contract EP-C-09-027 with ARCADIS, Geraghty & Miller. Mention of trade names or commercial products does not constitute endorsement or recommendations for use. Although this work was reviewed by EPA and approved for publication, it may not necessarily reflect official Agency policy.
References
3/14/201619 U.S. Environmental Protection Agency
1. AAPA U.S. Public Port Facts. http://www.aapa-ports.org/Industry/content.cfm?ItemNumber=1032 (May 10, 2015).
2. Panama Canal Authority. 2006. Proposal for the Expansion of the Panama Canal: Third Set of Locks Project.
3. Zhou, Y.; Levy, J. 2007. Factors influencing the spatial extent of mobile source air pollution impacts: a meta-analysis. BMC Public Health. 7(1), 89.
4. Salam, M.T.; Islam, T.; Gilliand, F.D. 2008. Recent evidence for adverse effects of residential proximity to traffic sources on asthma. Current Opinion in Pulmonary Medicine 14(1), 3-8.
5. HEI. 2010. Traffic-related air pollution: A critical review of the literature on emissions, exposure, and health effects. Health Effects Institute: Boston, MA.
6. Analysis of diesel particulate matter health risk disparities in selected US harbor areas. Am. J. Public Health. 101(S1), S217−S223.