Evaluation of MANE-VU CMAQ Annual Modeling on PM2.5 Species

1. Evaluation of MANE-VU CMAQ Annual Modeling on PM2.5 Species Shan He, John Graham, Jung-Hun Woo, Emily Savelli, and Gary Kleiman NESCAUM Review of Application and Assessment of CMAQ in OTC Albany, NY November 16, 2005

2. Acknowledgement Cooperative CMAQ modeling team members: • NYDEC ! • NJDEP & Rutgers ! • UMD ! • VADEQ ! • and NESCAUM !

3. Outline • Background - CMAQ modeling overview (meteorology, emission, CMAQ configuration) - Observations used in evaluation (STN, IMPROVE, CASTNET) • Evaluation and Analysis - Performance on PM2.5 species (SO4, NO3, NH4, OC, EC, Fine Soil, PM2.5) - CMAQ performance from other RPOs - Visibility parameters (Extinction Coefficient, and Haze Index) - PM2.5 composition on 20% best visibility days and 20% worst visibility days at Class I areas in Northeastern U.S. • Summary

4. CMAQ Modeling • CMAQ v4.4 (2004 Release) running on Linux Clusters • CB-IV gas-phase chemistry, EBI solver, AERO3 and AERO_DEPV2 module for aerosol dynamics and aerosol deposition, RADM for cloud chemistry • Eastern U.S. domain from 66oW~94oW in longitude and 29oN~50oN in latitudewith 172X172 grid at 12km resolution and 22 vertical layers with the first level at 10m and a radiative upper-boundary condition at 50hPa • Dynamic boundary condition generated from annual CMAQ modeling on 36km US domain which using boundary condition derived from global chemistry model GEOS-CHEM • Meteorology generated by UMD running NCAR/PSU MM5 V3.6.1 using the Blackadar high-resolution planetary boundary layer parameterization scheme, Lambert Conformal map projection, and 29 vertical layers.MM5 meteorology processed with MCIP for CMAQ input • Emission processed by SMOKE with UMD MM5 12km meteorological field using RPO 2002 NEI, and CEM data (except MANE-VU); using Mobile 6.2 for on-road EI processing and BEIS 3.12 for biogenic emissions estimate

5. 36km U.S. Domain 12km Eastern U.S. Domain CMAQ Modeling Domain

6. Observation Networks • STN (urban) – Daily SO4, NO3, NH4, EC, OC and PM2.5 • IMPROVE (rural) – Daily SO4, NO3, EC, OC, Fine Soil, PM2.5, and Bext • CASTNet (sub-urban and rural) – Weekly SO4, NO3, and NH4

7. Comparison of Sulfate

8. Comparison of Sulfate (II)

9. Comparison of Nitrate

10. Comparison of Nitrate (II)

11. Comparison of Ammonium

12. Comparison of Ammonium (II)

13. Comparison of Organic Carbon

14. Comparison of Elemental Carbon

15. Comparison of Fine Soil

16. Comparison of PM2.5

17. Model Performance from other RPOs “Model Performance Evaluation” Michael Ku Denver, June 2005 Sulfate Nitrate OC EC Soil

18. Comparison of Extinction Coefficient

19. Visibility (deciview) in Acadia, ME IMPROVE CMAQ

20. Visibility (deciview) in Brigantine, NJ IMPROVE CMAQ

21. Visibility (deciview) in Lye Brook, VT IMPROVE CMAQ

22. Visibility (deciview) in Shenandoah, VA IMPROVE CMAQ

23. Comparison of Haze Index at 4 Class I areas

24. PM2.5 and Visibility

25. PM2.5 Composition and Visibility

26. Summary • MANE-VU CMAQ modeling for 2002 base year shows reasonably well performance on PM2.5 species, and visibility. It’s consistent with other RPOs’ modeling performance • Model captures general trend of PM2.5 species throughout year with bias. PM2.5 underestimated in Summer due to OC and Soil peak, overestimated in Fall due to Sulfate, and overestimated at beginning and end of the year due to Nitrate • HI of 20% (best or worst visibility) modeling days agrees with HI of 20% (best or worst visibility) measured days • Linear relationship observed between average PM2.5 concentration at 20% worst visibility days and average PM2.5 concentration at 20% best visibility days • Sulfate is the dominant species making observed “clean” days to “dirty” days; while Sulfate and Nitrate both are major contributors making modeling “clean” days to “dirty” days