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Background

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

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  1. Using Measurements and Modeling to Understand Local and Regional Influences on PM2.5 in Vicinity of the PRGS

  2. Background • Need to determine best way to quantify PM2.5 impacts near PRGS • Performed at VA DEQ’s request using VA DEQ-approved methodology • Utilize monitoring and modeling

  3. Overview • PM2.5 = Particulate aerodynamic diameter < 2.5 microns • Multi-faceted data set from 2006 – 2007 • ENSR PM2.5, SO2, and meteorological data local to the Mirant Potomac River Generating Station (PRGS) • Regional FRM PM2.5 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 PM2.5 and SO2 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. Objectives • Analyze local versus regional PM2.5 for yearly, seasonal, localized-urban, and meteorological trends • Establish source-specific (PRGS) PM2.5 impact by: • Comparing near-field to regional PM2.5 measurements • Correlating near-field SO2 and PM2.5 measurements • Modeling PM2.5 emissions • Develop recommendations for localized PM2.5 modeling

  5. Monitoring Methodology: Local Monitors & Regional PM2.5 Monitors Local Monitors Regional Monitors

  6. Local vs. Regional PM2.5: Trend Comparison

  7. EBAM vs. Regional Average and PRGS Monitors

  8. Local vs. Regional PM2.5: Yearly (2007) Comparison

  9. Local Continuous PM2.5 (SE-TEOM): Meteorological Analysis PRGS Location

  10. Conclusions: Regional vs. Local PM2.5 Monitoring • Local PM2.5 (PRGS) agrees with regional PM2.5 • Local PM2.5 measurements do not predict a “hot spot” • Met analyses of continuous PM2.5 (SE-TEOM) confirms regional PM2.5 phenomena Questions formulated for quantitative impact analysis and AERMOD dispersion modeling • How much PM2.5 does the PRGS contribute to local monitors? • How much PRGS contribution is from filterable or condensable particulates?

  11. Quantitative PRGS PM2.5 Impact Using Monitor Data • Estimated PRGS SO2 impact = Marina Towers SO2 monitor conc minus average of all other PRGS SO2 monitors (4 total = background) • Ratio of PM2.5 emissions to SO2 emissions • PM2.5 lb/MMBtu rates from average of December 2006 stack tests • Filterable + condensable = 0.013 lb/MMBtu • Filterable only = 0.0008 lb/MMBtu • SO2 lb/MMBtu rates: actual operations data November 1, 2006 through October 31, 2007, 24-hour average lb/MMBtu • Estimated PRGS PM2.5 impact = Ratio of emissions x PRGS SO2 impact

  12. Quantitative PRGS PM2.5 Impact Using Monitor Data (Cont.)

  13. Quantitative PRGS PM2.5 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 • PM2.5 from five stacks + fugitive ground level sources • AERMOD used to predict concentrations on roof of Marina Towers residential complex, where PRGS FRM PM2.5 monitor is located

  14. Source and Receptor Locations Used in AERMOD

  15. Stack Parameters and PM2.5 Emissions Input to AERMOD • Stack parameters = actual operations data from Nov. 1, 2006 through Oct. 31, 2007 • Hourly PM2.5 emissions input to AERMOD (lb/hr) = Ratio of PM2.5 lb/MMBtu to actual hourly SO2 lb/MMBtu x actual hourly SO2 emissions (lb/hr) • Fugitive PM2.5 emissions data developed from U.S. EPA’s AP-42

  16. PM2.5 Concentrations on High SO2 Marina Towers Measurement Days • Local PM2.5 is both > and < regional PM2.5 during high SO2 events • No indication of a relationship between high SO2 days and PM2.5 • On high SO2 days, local PM2.5 concentrations are reflective of regional conditions

  17. AERMOD Marina Towers Modeling Results: High SO2 Impact Days

  18. AERMOD Marina Towers Modeling Results: High SO2 Impact Days (Cont.)

  19. PM2.5 Concentrations on High Southeast SO2 Measurement Days

  20. AERMOD Ground Level Modeling Results: High SO2 Impact Days

  21. AERMOD Ground Level Modeling Results: High SO2 Impact Days (Cont.)

  22. PM2.5 NAAQS Compliance Modeling • Typical Analysis • 98th percentile modeled impact (filterable + condensable stack PM2.5 + fugitives) • Add in background conc (98th percentile, EPA monitor) Example: 20 µg/m3 (modeled) + 32 µg/m3 (background) = 52 µg/m3 • ENSR Recommended Analysis • 98th percentile modeled impact (filterable stack PM2.5 + realistic fugitives) • Add in realistic background conc (on days with high plant impact) Example: 2 µg/m3 (modeled) + 20 µg/m3 (background) = 22 µg/m3

  23. 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 PM2.5 over-prediction on MT likely due to: • Under-estimation of plume rise from merging multiple plumes • Inclusion of condensable particulate • AERMOD PM2.5 prediction at SE Fenceline in agreement with measured data: likely due to fugitives • PM2.5 NAAQS Compliance: • Accurate fugitive emission calculations imperative • Use realistic background concentrations on high plant impact days

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