1. Simulating prescribed fire impacts for air quality management Georgia Institute of Technology M. Talat Odman, Yongtao Hu, Fernando Garcia-Menendez, Aika Yano, and Armistead G. Russell School of Civil & Environmental Engineering, Georgia Institute of Technology AQAST Meeting, June 12 th, 2012 Improving Operational Regional Air Quality Forecasting Performance through Emissions Correction Using NASA Satellite Retrievals and Surface Measurements PI: Armistead G. Russell 1, Co-Is: Yongtao Hu 1, M. Talat Odman 1, Lorraine Remer 2 1 Georgia Institute of Technology, 2 NASA Goddard Space Flight Center Primary Stakeholder Clients: Georgia EPD; Georgia Forestry Commission

2. Georgia Institute of Technology Activities  Overview of first year AQAST research  Expanded Hi-Res operational forecasting system  Forecasting efforts supporting field studies  Discover AQ & Fort Jackson Prescribed burn  Simulating biomass burning air quality impacts  Simulating biomass burning using satellite-derived fire emissions  Discover AQ and ARCTAS Campaign  Evaluation with ground-based and satellite data  Simulating biomass burning  Williams, CA prescribed fire  Uncertainty and evaluation  Related: Bayesian CMAQ-satellite data assimilation  Exposure estimation for epidemiologic studies

3. Georgia Institute of Technology Hi-Res: forecasting ozone and PM 2.5 48 hr forecast @ 4-km resolution for Georgia and 12-km for most states of eastern US Hi-Res forecasting products are in use by Georgia EPD assisting their local AQI forecasts for multiple metro areas Hi-Res forecasting products are potentially useful for other states

4. Georgia Institute of Technology AQAST Modeling Domains Bottom-up estimates of fire emissions used for the Williams Burn and GA-FL wildfire simulations. GOES biomass burning emissions GBBEP used for the ARCTAS and DISCOVER-AQ modeling.

5. Provided 48 hour pollutant forecasts during Discover –AQ (with Emory) –Providing spatially more detailed AQ fields for comparison with observations ( Yang Liu’s poster) Forecasting for Prescribed Burn Study on October 30, 2011 at Fort Jackson, SC –Concerned with impacting Columbia Forecasting in Support of Field Studies Forecasting with Assimilated PM Fields Using satellite-data-assimilated PM fields as IC/BC in forecasting system (with NOAA ARL, Pius Lee’s presentation) –Testing using Discover-AQ campaign period. Fort Jackson, SC

6. Georgia Institute of Technology CMAQ simulation: DISCOVER-AQ Campaign 12-km4-km1-km O3(40ppb)MNB MNEMNBMNE 16.725.316.823.716.823.5 24hr PM 2.5 FB FEFBFE 9.135.010.330.112.626.3 Performance (Surface networks) Peak hour surface ozone Surface 24-hr PM 2.5

7. Georgia Institute of Technology DISCOVER-AQ Campaign: Comparison with Satellite-derived AOD Fields CMAQ AOD at 16Z 07022011 CMAQ AOD at 18Z 07022011 MODIS AOD Terra (L2) 16Z 07022011 MODIS AOD Aqua (L2) 18Z 07022011 Simulated AOD is 25% lower in general

9. Georgia Institute of Technology ARCTAS: Northern California Wildfires June 27, 2008 July 8, 2008 12-km4-km O3 (40ppb)MNBMNEMNBMNE 7.421.33.620.0 24h PM 2.5 FBFEFBFE -28.951.3-32.348.6 Performance (Surface networks) Underestimation of surface PM 2.5

10. ARCTAS: CMAQ–Satellite Comparison CMAQ AOD at 21Z 06272008 CMAQ surface 24-hr PM 2.5 06272008 MODIS AOD Aqua (L2) at 21Z 06272008 Simulated AOD is factor of 10 lower in general, though the maximum is 1.2 versus 4.4 (sim vs. obs) Simplified treatment of biomass fire plumes may cause issues. There may be missing fires from the GBBEP products.

11. Estimation of Emissions Fuel load is estimated using photo-series, if available, or satellites 3 years Fuel Load (tons per acre) Fuel consumption is calculated by CONSUME 3.0. –Fuel moisture is a key fire parameter. Emission Factors (EF) are available from field and/or laboratory studies. –Fire Sciences Lab in Missoula, MT 

12. Fire Progression Model: Rabbit Rules (A cellular automata/free agent model) Fuel Density Map (Satellite –derived) Fire Induced Winds

13. Parameters provided by Rabbit Rules No. of updraft cores Vertical velocities Core diameters Emissions as f(t)

14. Dispersion and Transport Models Daysmoke is a dynamic-stochastic Lagrangian particle model specifically designed for prescribed burn plumes. AG-CMAQ is the adaptive grid regional air quality model. Daysmoke has been coupled with AG-CMAQ as an inert, subgrid-scale plume model through a process called “handover”.

15. Williams fire: A chaparral burn in CA A suite of gases and aerosols and meteorological parameters were measured aboard an aircraft in the plume of Williams fire on 17 November 2009 ( Akagi et al., ACP, 2012). Burn observed by satellites Fuels/burn information is limited.

16. Modeled plume in PBL and Aircraft Track Georgia Institute of Technology Unpaired Peaks Observed = 676  g/m 3 Modeled = 508  g/m 3

17. Potential Sources of Uncertainty Georgia Institute of Technology PM 2.5 Emissions Under-predicted by 15% Field Study at Eglin AFB, FL Sensitivity to PBL Height Sensitivity to Wind Speed

18. Uncertainty in Satellite Data? Georgia Institute of Technology Modeled PM 2.5 and Aircraft Track MODIS Aqua AOD (regridded from L2 products 10-km resolution at nadir)

19. Next Steps  Evaluate using airborne measurements and high resolution, level-3 AOD Injection heights: MISR multi-angle products Column information from satellites can provide information on plume aloft  Integrate satellite observations in forecast system Data assimilation, potentially using direct sensitivity analysis Extend 12-km domain  Knowledge learned will be applied to inverse modeling Improve burn emissions (mass and injection height) Better predict impacts from prescribed burns Georgia Institute of Technology

20. Acknowledgements NASA Georgia EPD Georgia Forestry Commission US Forest Service – Scott Goodrick, Yongqiang Liu, Gary Achtemeier Strategic Environmental Research and Development Program Joint Fire Science Program (JFSP) Environmental Protection Agency (EPA)