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Using Measurements and Modeling to Understand Local and Regional Influences on PM 2.5 in Vicinity of the PRGS.

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Presentation on theme: "Using Measurements and Modeling to Understand Local and Regional Influences on PM 2.5 in Vicinity of the PRGS."— Presentation transcript:

1 Using Measurements and Modeling to Understand Local and Regional Influences on PM 2.5 in Vicinity of the PRGS

2 2 Patrick Campbell & David Shea; 01/30/2008; 11:20 a.m. Background –Need to determine best way to quantify PM 2.5 impacts near PRGS –Performed at VA DEQ’s request using VA DEQ-approved methodology –Utilize monitoring and modeling

3 3 Patrick Campbell & David Shea; 01/30/2008; 11:20 a.m. Overview –PM 2.5 = Particulate aerodynamic diameter < 2.5 microns –Multi-faceted data set from 2006 – 2007 –ENSR PM 2.5, SO 2, and meteorological data local to the Mirant Potomac River Generating Station (PRGS) –Regional FRM PM 2.5 data (<130 km) obtained from the states of Virginia, Maryland, and The District Department of the Environment* (Washington, DC; <12km) –AERMOD dispersion modeling based on PM 2.5 and SO 2 emissions from PRGS *2007 DDOE data have not yet been fully validated and certified using the standard procedures to guarantee their quality and are subject to change.

4 4 Patrick Campbell & David Shea; 01/30/2008; 11:20 a.m. Objectives –Analyze local versus regional PM 2.5 for yearly, seasonal, localized-urban, and meteorological trends –Establish source-specific (PRGS) PM 2.5 impact by: Comparing near-field to regional PM 2.5 measurements Correlating near-field SO 2 and PM 2.5 measurements Modeling PM 2.5 emissions –Develop recommendations for localized PM 2.5 modeling

5 5 Patrick Campbell & David Shea; 01/30/2008; 11:20 a.m. Monitoring Methodology: Local Monitors & Regional PM 2.5 Monitors Local MonitorsRegional Monitors

6 6 Patrick Campbell & David Shea; 01/30/2008; 11:20 a.m. Local vs. Regional PM 2.5 : Trend Comparison

7 7 Patrick Campbell & David Shea; 01/30/2008; 11:20 a.m. EBAM vs. Regional Average and PRGS Monitors

8 8 Patrick Campbell & David Shea; 01/30/2008; 11:20 a.m. Local vs. Regional PM 2.5 : Yearly (2007) Comparison

9 9 Patrick Campbell & David Shea; 01/30/2008; 11:20 a.m. Local Continuous PM 2.5 (SE-TEOM): Meteorological Analysis PRGS Location

10 10 Patrick Campbell & David Shea; 01/30/2008; 11:20 a.m. Conclusions: Regional vs. Local PM 2.5 Monitoring –Local PM 2.5 (PRGS) agrees with regional PM 2.5 –Local PM 2.5 measurements do not predict a “hot spot” –Met analyses of continuous PM 2.5 (SE-TEOM) confirms regional PM 2.5 phenomena Questions formulated for quantitative impact analysis and AERMOD dispersion modeling –How much PM 2.5 does the PRGS contribute to local monitors? –How much PRGS contribution is from filterable or condensable particulates?

11 11 Patrick Campbell & David Shea; 01/30/2008; 11:20 a.m. Quantitative PRGS PM 2.5 Impact Using Monitor Data –Estimated PRGS SO 2 impact = Marina Towers SO 2 monitor conc minus average of all other PRGS SO 2 monitors (4 total = background) –Ratio of PM 2.5 emissions to SO 2 emissions PM 2.5 lb/MMBtu rates from average of December 2006 stack tests  Filterable + condensable= 0.013 lb/MMBtu  Filterable only = 0.0008 lb/MMBtu SO 2 lb/MMBtu rates: actual operations data November 1, 2006 through October 31, 2007, 24-hour average lb/MMBtu –Estimated PRGS PM 2.5 impact = Ratio of emissions x PRGS SO 2 impact

12 12 Patrick Campbell & David Shea; 01/30/2008; 11:20 a.m. Quantitative PRGS PM 2.5 Impact Using Monitor Data (Cont.)

13 13 Patrick Campbell & David Shea; 01/30/2008; 11:20 a.m. Quantitative PRGS PM 2.5 Impact Using AERMOD –Meteorological Data from Reagan International Airport for November 1, 2006 through October 31, 2007 –Equivalent Building Dimensions (EBDs) used to account for building downwash of stacks –PM 2.5 from five stacks + fugitive ground level sources –AERMOD used to predict concentrations on roof of Marina Towers residential complex, where PRGS FRM PM 2.5 monitor is located

14 14 Patrick Campbell & David Shea; 01/30/2008; 11:20 a.m. Source and Receptor Locations Used in AERMOD

15 15 Patrick Campbell & David Shea; 01/30/2008; 11:20 a.m. Stack Parameters and PM 2.5 Emissions Input to AERMOD –Stack parameters = actual operations data from Nov. 1, 2006 through Oct. 31, 2007 –Hourly PM 2.5 emissions input to AERMOD (lb/hr) = Ratio of PM 2.5 lb/MMBtu to actual hourly SO 2 lb/MMBtu x actual hourly SO 2 emissions (lb/hr) –Fugitive PM 2.5 emissions data developed from U.S. EPA’s AP-42

16 16 Patrick Campbell & David Shea; 01/30/2008; 11:20 a.m. PM 2.5 Concentrations on High SO 2 Marina Towers Measurement Days –Local PM 2.5 is both > and < regional PM 2.5 during high SO 2 events –No indication of a relationship between high SO 2 days and PM2.5 –On high SO 2 days, local PM2.5 concentrations are reflective of regional conditions

17 17 Patrick Campbell & David Shea; 01/30/2008; 11:20 a.m. AERMOD Marina Towers Modeling Results: High SO 2 Impact Days

18 18 Patrick Campbell & David Shea; 01/30/2008; 11:20 a.m. AERMOD Marina Towers Modeling Results: High SO 2 Impact Days (Cont.)

19 19 Patrick Campbell & David Shea; 01/30/2008; 11:20 a.m. PM 2.5 Concentrations on High Southeast SO 2 Measurement Days

20 20 Patrick Campbell & David Shea; 01/30/2008; 11:20 a.m. AERMOD Ground Level Modeling Results: High SO 2 Impact Days

21 21 Patrick Campbell & David Shea; 01/30/2008; 11:20 a.m. AERMOD Ground Level Modeling Results: High SO 2 Impact Days (Cont.)

22 22 Patrick Campbell & David Shea; 01/30/2008; 11:20 a.m. PM 2.5 NAAQS Compliance Modeling –Typical Analysis 98 th percentile modeled impact (filterable + condensable stack PM 2.5 + fugitives) Add in background conc (98 th percentile, EPA monitor) Example: 20 µg/m 3 (modeled) + 32 µg/m 3 (background) = 52 µg/m 3 –ENSR Recommended Analysis 98 th percentile modeled impact (filterable stack PM 2.5 + realistic fugitives) Add in realistic background conc (on days with high plant impact) Example: 2 µg/m 3 (modeled) + 20 µg/m 3 (background) = 22 µg/m 3

23 23 Patrick Campbell & David Shea; 01/30/2008; 11:20 a.m. Conclusions: Qualitative Comparison, Quantitative Analysis, and AERMOD Modeling –PRGS contributes very little to local monitor; low impact –PRGS contribution is likely from filterable particulate –AERMOD PM 2.5 over-prediction on MT likely due to: Under-estimation of plume rise from merging multiple plumes Inclusion of condensable particulate –AERMOD PM 2.5 prediction at SE Fenceline in agreement with measured data: likely due to fugitives –PM 2.5 NAAQS Compliance: Accurate fugitive emission calculations imperative Use realistic background concentrations on high plant impact days

24 24 Patrick Campbell & David Shea; 01/30/2008; 11:20 a.m.

25 25 Patrick Campbell & David Shea; 01/30/2008; 11:20 a.m.

26 26 Patrick Campbell & David Shea; 01/30/2008; 11:20 a.m.

27 27 Patrick Campbell & David Shea; 01/30/2008; 11:20 a.m.

28 28 Patrick Campbell & David Shea; 01/30/2008; 11:20 a.m.

29 29 Patrick Campbell & David Shea; 01/30/2008; 11:20 a.m.

30 30 Patrick Campbell & David Shea; 01/30/2008; 11:20 a.m.

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