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Operational Air Quality and Source Contribution Forecasting in Georgia

Operational Air Quality and Source Contribution Forecasting in Georgia. 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.

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Operational Air Quality and Source Contribution Forecasting in Georgia

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  1. Operational Air Quality and Source Contribution Forecasting in Georgia Yongtao Hu1, M. Talat Odman1, Michael E. Chang2 and Armistead G. Russell1 1School of Civil & Environmental Engineering, 2Brook Byers Institute of Sustainable Systems Georgia Institute of Technology 10th Annual CMAS Conference, October 24th, 2010 Georgia Institute of Technology

  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 O3 and PM2.5 performance for metro Atlanta • New SOA module and its impact on PM2.5 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 O3 guidance, Statistical model results, Weather forecasts • Hi-Res O3 and PM2.5 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 PM2.5 at a 4-km resolution for metro areas in Georgia Georgia Institute of Technology

  5. Hi-Res Forecast Products Snapshots from Hi-Res homepage: http://forecast.ce.gatech.edu • “Single Value” Report: tomorrow’s AQI, ozone and PM2.5 by metro area in Georgia • Air Quality Forecasts: AQI, ozone and PM2.5, 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 Georgia Institute of Technology

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

  8. Ambient Monitoring Sites for Performance Evaluation in Atlanta Metro

  9. Met Performance

  10. Overall 2006-2011 Performance (Ozone Season): Atlanta Metro Humidity Temperature Georgia Institute of Technology

  11. Forecastvs. Observed Temperature Georgia Institute of Technology

  12. Forecastvs. Observed Humidity Georgia Institute of Technology

  13. Air Quality Performance Metrics NAAQS Forecast NAAQS Observation

  14. Overall 2006-2011 Performance (Ozone Season): Atlanta Metro PM2.5 Ozone Georgia Institute of Technology

  15. Ozone Performance

  16. Forecastvs. Observed O3 2006 2007 2009 2008 2011 2010 Georgia Institute of Technology

  17. 2009 O3 Performance: Hi-Res vs. GA EPD’s Our 4-km Forecast EPD Ensemble Forecast

  18. 2010 O3 Performance: Hi-Res vs. GA EPD’s Our 4-km Forecast EPD Ensemble Forecast

  19. 2011 O3 Performance: Hi-Res vs. GA EPD’s Our 4-km Forecast EPD Ensemble Forecast

  20. PM2.5 Performance

  21. Summer

  22. Forecastvs. Observed PM2.5 2006 2007 2009 2008 2010 2011 Georgia Institute of Technology

  23. 2009 PM2.5 Performance: 4-km vs. GA EPD’s Our 4-km Forecast EPD Ensemble Forecast

  24. 2010 PM2.5 Performance: 4-km vs. GA EPD’s Our 4-km Forecast EPD Ensemble Forecast

  25. 2011 PM2.5 Performance: 4-km vs. GA EPD’s Our 4-km Forecast EPD Ensemble Forecast

  26. Winter

  27. Forecastedvs. Observed PM2.5 2007 2008 2009 2010

  28. +OH,+O3 Aerosol LSVOC +OH,+O3 Multigenerational Oxidation : HSVOC A New SOA Module (Baek, J., Georgia Tech, 2009) SOA species in CMAQ: Included processes: • 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 • AORGAJ and AORGAI • AORGBJ and AORGBI • AORGBISJ and AORGBISI • AORGBSQJ and AORGBSQI • AORGAGJ and AORGAGI

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

  30. Source Contribution Forecasting in Pilot Operation • Source contribution forecasts for ozone and PM2.5. • Traffic, Power plants and others such as prescribed fires (needs additional efforts). • Extra information on top of ozone and PM2.5 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 PM2.5 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. 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 PM2.5 forecasts are not very accurate. • May-September bias is -17% and error is 32% • The new SOA module helped much better 2009-2011 PM2.5 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. Georgia Institute of Technology

  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.

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