1. Operational Air Quality and Source Contribution Forecasting in Georgia Georgia Institute of Technology Yongtao Hu 1, M. Talat Odman 1, Michael E. Chang 2 and Armistead G. Russell 1 1 School of Civil & Environmental Engineering, 2 Brook Byers Institute of Sustainable Systems Georgia Institute of Technology 10 th Annual CMAS Conference, October 24 th, 2010

2. Outline  Local Air Quality Forecasting in Georgia  The Hi-Res air quality forecasting system  Evolution of Hi-Res during 2006-2011  Met performance and O 3 and PM 2.5 performance for metro Atlanta  New SOA module and its impact on PM 2.5 performance  Pilot Source Contribution Forecasting Georgia Institute of Technology

3. Local Forecasting: How it works in Georgia  A forecasting panel with 5-7 local expert members air quality researchers, modelers, meteorologists, etc.  “Voting” through a decision making online system the consensus of the panel will be the “ensemble” forecasts  Base their “opinion” on multiple available resources NOAA O 3 guidance, Statistical model results, Weather forecasts Hi-Res O 3 and PM 2.5 forecasts Experiences  Broadcast to the public Website: http://www.gaepd.org/air/smogforecast/ and AirNOW Smog alert through highway traffic information system Georgia Institute of Technology

4. Hi-Res: forecasting ozone and PM 2.5 at a 4-km resolution for metro areas in Georgia

5. Georgia Institute of Technology Hi-Res Forecast Products  “Single Value” Report: tomorrow’s AQI, ozone and PM 2.5 by metro area in Georgia  Air Quality Forecasts: AQI, ozone and PM 2.5, 48-hrs spatial plots and station profiles  Meteorological Forecasts: precipitation, temperature and winds, 48-hrs spatial plots and station profiles  Performance Evaluation: time series comparison and scatter plots for the previous day Snapshots from Hi-Res homepage: http://forecast.ce.gatech.edu

6. Evolution of Hi-Res during 2006-2011  Updated to latest release of WRF each year before the ozone season. WRF 2.1, 2.2, 3.0, 3.1, 3.2 and 3.3  CMAQ is typically one version behind. CMAQ 4.6 with Georgia Tech extensions  Projected NEI to current year in the very beginning of each year.  Updated forecast products website each year before ozone season.  Switched from single-cycle forecasting to two-cycles in 2008.  Enlarged 4-km domain to cover the entire state of Georgia in 2009.  Introduced Georgia Tech’s new SOA module in 2009.  Enlarged 36-km domain to cover the CONUS and enlarged 12-km domain to cover the eastern US in 2011.  In 2011 switched landuse data from old USGS data to new MODIS data in WRF, to reflect recent changes in land cover.

7. 2006-2010 Current Enlarged Modeling Domains Georgia Institute of Technology

8. Ambient Monitoring Sites for Performance Evaluation in Atlanta Metro

9. Met Performance

10. Georgia Institute of Technology Overall 2006-2011 Performance (Ozone Season): Atlanta Metro Temperature Humidity MB-0.39K ME1.55K MB-0.68g/kg ME1.19g/kg

11. Georgia Institute of Technology Forecast vs. Observed Temperature MB-1.16K ME1.90K MB-0.80K ME1.94K MB0.37K ME1.47K MB-0.19K ME1.15K MB-0.05K ME1.29K MB0.50K ME1.57K

12. Georgia Institute of Technology Forecast vs. Observed Humidity MB-0.59g/kg ME1.22g/kg MB-0.89g/kg ME1.33g/kg MB-0.36g/kg ME0.96g/kg MB-0.73g/kg ME1.03g/kg MB-0.74g/kg ME1.39g/kg MB-0.73g/kg ME1.22g/kg

13. Air Quality Performance Metrics False AlarmsHits Correct Nonevents Missed Exceedences Forecast Observation NAAQS

14. Georgia Institute of Technology Overall 2006-2011 Performance (Ozone Season): Atlanta Metro Ozone PM 2.5 MNB17% MNE23% MNB-17% MNE32%

15. Ozone Performance

16. Georgia Institute of Technology Forecast vs. Observed O 3 2007 2010 2009 2008 MNB14% MNE18% MNB8.5% MNE19% MNB17% MNE23% MNB28% MNE30% 2006 MNB11% MNE29% 2011 MNB18% MNE21%

17. 2009 O 3 Performance: Hi-Res vs. GA EPD’s Our 4-km Forecast EPD Ensemble Forecast MNB28% MNE30% MNB13% MNE21%

18. 2010 O 3 Performance: Hi-Res vs. GA EPD’s Our 4-km Forecast EPD Ensemble Forecast MNB14% MNE18% MNB9% MNE17%

19. 2011 O 3 Performance: Hi-Res vs. GA EPD’s Our 4-km Forecast EPD Ensemble Forecast MNB18% MNE21% MNB8% MNE16%

20. PM 2.5 Performance

21. Summer

22. Georgia Institute of Technology Forecast vs. Observed PM 2.5 2007 2008 2009 2010 MNB-37% MNE44% MNB-38% MNE42% MNB8% MNE25% MNB4% MNE21% 2011 MNB-2% MNE25% 2006 MNB-38% MNE43%

23. 2009 PM 2.5 Performance: 4-km vs. GA EPD’s Our 4-km ForecastEPD Ensemble Forecast MNB8% MNE25% MNB11% MNE24%

24. 2010 PM 2.5 Performance: 4-km vs. GA EPD’s Our 4-km ForecastEPD Ensemble Forecast MNB4% MNE21% MNB14% MNE30%

25. 2011 PM 2.5 Performance: 4-km vs. GA EPD’s Our 4-km ForecastEPD Ensemble Forecast MNB-2% MNE25% MNB7% MNE24%

26. Winter

27. Forecasted vs. Observed PM 2.5 2007 2009 2008 2010

28. A New SOA Module (Baek, J., Georgia Tech, 2009) SOA partitioned from anthropogenic VOCs’ oxidations (8 SVOCs) From monoterpenes (2 SVOCs) From isoprene (2 SVOCs added) From sesquiterpenes (1 SVOC added, gas phase oxidation reactions added for α-caryphyllene, β-humulene, and other sesquiterpenes) Multigenerational oxidation of all semi-volatile organic carbons (SVOCs) added Included processes: SOA species in CMAQ: AORGAJ and AORGAI AORGBJ and AORGBI AORGBISJ and AORGBISI AORGBSQJ and AORGBSQI AORGAGJ and AORGAGI HSVOC LSVOC Aerosol Multigenerational Oxidation : +OH,+O3

29. Forecast vs. Observed OC at South DeKalb Ozone Season May-September 2009 2010

30. Source Contribution Forecasting in Pilot Operation  Source contribution forecasts for ozone and PM 2.5. Traffic, Power plants and others such as prescribed fires (needs additional efforts).  Extra information on top of ozone and PM 2.5 concentration forecasts. Providing quantitative information on specific source contributions Alerting on specific source impacts To help public targeting actions to prevent pollution events  Using forward sensitivity tool DDM3D in CMAQ to calculate first order sensitivity coefficients Interpret such sensitivities of ozone and PM 2.5 concentrations to total emissions from specific sources as their contributions  Challenges in operational forecasting Computationally expensive, but doable Instability in calculation

31. Preliminary Source Contribution Forecasts: Traffic and Power Plants Impacts 2009 2010

32. Georgia Institute of Technology Summary 2006-2011 Temperature and humidity performance in May-September are good. –Daily high temperature bias is -0.39K and error is 1.55K –Daily average humidity bias is and error is -0.68g/kg and 1.19g/kg 2006-2011 Ozone forecasts are good. –Overall bias is +17% and error is 23% 2006-2011 PM 2.5 forecasts are not very accurate. –May-September bias is -17% and error is 32% The new SOA module helped much better 2009-2011 PM 2.5 performances in May-September –Bias is +8% and error is 25% for 2009 –Bias is +4% and error is 21% for 2010 –Bias is -2% and error is 25% for 2011 Preliminary source contribution forecasting products.

33. Acknowledgements We thank Georgia EPD for funding the Hi-Res forecasts, Our former group member Dr. Jaemeen Baek for the new SOA module, and Dr. Carlos Cardelino of Georgia Tech for team forecasts.