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Study of Process Contributions to PM2.5 Formation during the 2004 ICARTT Period using the Eta-CMAQ Forecast Model over t

This study evaluates the contributions of different processes to the formation of PM2.5 during the 2004 ICARTT period using the Eta-CMAQ forecast model over the eastern U.S. The results highlight the inadequacies in representing biomass burning effects and provide insights into the composition and vertical profiles of PM2.5.

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Study of Process Contributions to PM2.5 Formation during the 2004 ICARTT Period using the Eta-CMAQ Forecast Model over t

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  1. A study of process contributions to PM2.5 formation during the 2004 ICARTT period using the Eta-CMAQ forecast model over the eastern U.S. Shaocai Yu$,Rohit Mathur+, Kenneth Schere+, Daiwen Kang$, Jonathan Pleim+, Jeffrey Young+,and Daniel Tong$ Atmospheric Sciences Modeling Division NERL, U.S. EPA, RTP, NC 27711. $On assignment from Science and Technology Corporation + On assignment from Air Resources Laboratory, NOAA

  2. Introduction

  3. CMAQ Community Multiscale Air QualityModel • Community Model • Multiscale • consistent model structures for interaction of urban through Continental scales • Multi-pollutant • ozone, speciated particulate matter, visibility, acid deposition • and air toxics

  4. P-3: Northeast; • DC-8: Eastern US P-3 DC-8 Tracks of (a) P-3, (b) DC-8

  5. Results: Operational evaluation at AIRNOW sites • Significant underprediction (7/16-7/24) • due to inadequate representation of biomass burning effects from outside the domain (Alaskan fire)

  6. Results (PM2.5 forecast) • July 16-22, 2004: Evidence of effects of long range transport (Alaskan fire) (1) MODIS (satellite) observations for AOD (2) TOMS (satellite) observations for absorbing aerosol index 7/19/04 7/17/04 7/18/04 • Significant underpredictions of PM2.5 by the model during July 16 to 26 are mainly due to inadequate representation of biomass burning (carbonaceous aerosol) effects from outside the domain (Alaskan fire)

  7. Results (PM2.5 Composition at IMPROVE, STN, CASTNET) PM2.5 SO42- • PM2.5 under prediction • Overpredicted SO42- • Scatter for NO3- • The model under predicted OC by more than a factor of 2 • Cause underprediction of PM2.5 Model NH4+ NO3- OC, TC EC Observation

  8. Results (PM2.5 composition) • The model overpredicted SO42- by 20% • The model under predicted OC by more than a factor of 2 • Cause under prediction of PM2.5 IMPROVE STN Obs Model Obs Model

  9. Results (PM2.5): vertical profiles • Over predicted SO42- aloft • under predicted NH4+ and NO3-

  10. Results (Vertical profiles for SO2 and H2O2) Daily Layer Means • SO2: • Close to obs at high altitude • Higher than obs at low altitude relative to P3 obs (2) DC-8 (1) P-3 • The model overpredicted SO42- both at the surface and aloft, • in part, possibly reflecting the too much SO2 cloud oxidation because of overpredictions of both SO2 and H2O2 in the model. (3) DC-8

  11. Results: HNO3, and O3 Vertical profiles (7/1-8/15) Daily Layer Means • HNO3: • good at high altitude (1) P-3 (2) DC-8 O3: • good at low altitude • Overprediction • at high altitudes (4) DC-8 (3) P-3

  12. Preliminary results:PA along theback trajectories SAHS John • PM2.5>38 mg m-3: • two sites in PA (8/17) • South Allegheny High School • John • two sites in GA (8/18) • South Dekalb • Newnan • Primary PM2.5 and SO2 sources: Washington, DC/NY/Boston urban corridor, Ohio River valley, Chicago Newnan SD

  13. PA Results for PM2.5 • Column mean: layers 1-14 (typical daytime boundary layer) • At SAHS: CLD and AERO productions as airmass travels over Ohio valley contribute to higher PM2.5 on 8/17 24-hr back trajectories ending at 11 UTC 8/17

  14. PA Results for PM2.5 • Column mean: layers 1-14 • At John site: CLD and AERO productions as airmass travels over Ohio valley contribute to higher PM2.5 on 8/17

  15. PA Results for PM2.5 • Column mean: layers 1-14 • At NN site: AERO and EMIS production as airmass travels over AL, MS, LA contributes to high PM2.5 24 hr back trajectories ending at 11 UTC 8/19

  16. PA Results for PM2.5 • Column mean: layers 1-14 • AT SD site: AERO and EMIS contribute to high PM2.5 as airmass travels over AL and MS, while HADV is the dominant sink

  17. Contacts: Brian K. Eder email: eder@hpcc.epa.gov www.arl.noaa.gov/ www.epa.gov/asmdnerl

  18. Results Layer conc. Of PM2.5 at NN, SD (GA)

  19. Results: SO2 and HNO3 Vertical profiles (1) P-3 (SO2) • SO2: • Close to obs at high altitude • Higher than obs at low altitude most of time. (2) DC-8 (SO2) (3) P-3 (HNO3) • HNO3: • Good performance • Except • P3: 7/9, 8/11 • DC-8: 7/18 (4) DC-8 (HNO3)

  20. Results: O3 Vertical profiles • Model reproduced • obs at low altitude • and more uniform • Except: DC-8: 7/28, 8/11 • P-3: 7/9, 7/15, 7/20-22, 7/28, 8/14 (1) DC-8 (2) P-3

  21. Results: O3 Vertical profiles • Results: O3 Vertical profiles Obs • Lidar: Model reproduced obs magnitude at low altitude but smoother distribution • Ozonesonde: Over predictions above 6 km: • Impact from GFS derived LBC and coarse model resolution in FT Model (1) Lidar on Ship 6km (2) July-August Median Profiles (Ozonesonde)

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