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Forecasting the Impacts of Wildland Fires

Forecasting the Impacts of Wildland Fires. Yongtao Hu 1 , William Jackson 2 , M. Talat Odman 1 and Armistead G. Russell 1 1 School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia 2 USDA Forest Service, Asheville, North Carolina.

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Forecasting the Impacts of Wildland Fires

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  1. Forecasting the Impacts of Wildland Fires Yongtao Hu1, William Jackson2, M. Talat Odman1 and Armistead G. Russell1 1School of Civil and Environmental Engineering, Georgia Institute of Technology, Atlanta, Georgia 2USDA Forest Service, Asheville, North Carolina Presented at the 6th Annual CMAS Conference,October 2nd, 2007 Georgia Institute of Technology

  2. Wild land fire (wild and prescribed fires) burned ~9 million acres nationwide in US each year during the past three years. • Burning of wild land vegetation increases emissions of PM2.5, CO, VOC, NOx …, which impact air quality, visibility and potentially public health. • A severe wild land fire could cause rapid increases of both PM2.5 and O3 to extremely high levels at populated area and cause exposures to unhealthy air for several hours or even days. Such as those hit Atlanta metro area with thick smoke clouds this year: • To what degree can we forecast wild-land-fire-impacts on air quality by adopting current operational air quality forecasting system? • The prescribed fires on February 28, 2007 in Jasper County Georgia. • The Georgia-Florida wildfires lasting from April through May. Wild land Fires and Air Quality Georgia Institute of Technology

  3. Emission Inventory NAM 84-hr Forecast Emissions Meteorology CMAQ SMOKE WRF Forecast Product Air Quality Hi-Res Air Quality Forecasting SystemServing Metro-Atlanta Area since 2006 Georgia Institute of Technology

  4. Hi-Res Modeling Domains 4-km 12-km 36-km Georgia Institute of Technology

  5. What could be done? 00Z 45Z 77Z 12- & 4-km forecast 36- & 12-km ramp up simulation Forecast Day 0am 8pm 0am 0am 0am Start Job Finish Job Release Product Hi-Res Cycle • Hi-Res cycle allows sufficient time for extra efforts. • For prescribed burning, the air quality forecast ahead of the actual igniting would help plan and conduct burns. • For existing/ongoing wildfires, the air quality forecast would warn people to avoid unhealthy air exposures at the following days. Georgia Institute of Technology

  6. Estimate Emissions of Potential Fires • Using models: the Fire Emission Production Simulator (FEPS) and the Consume 3.0 (http://www.fs.fed.us/pnw/fera/research/smoke/consume/index.shtml) • Prescribed fire: collect pre-burning information from the burning plans prepared in advance. acreage of planned burning area, approximate locations, fuel load descriptions, igniting method and operation schedules … • Existing/ongoing wild fire: determine the most likely fire locations on the following days according to the analysis of forecast meteorological conditions combined with the information on previous days’ burning locations. Then collect and estimate other fire information: approximate acreage of burning area, fuel consumption and expected fire temperatures … • Allocate estimated potential fire emissions to the corresponding Hi-Res grid cells according to the geographical information. Georgia Institute of Technology

  7. Wild-Land-Impacts on Air Quality • One way to calculate the air quality impacts of a fire is to run two simulations: Run (1): “typical” emissions default in Hi-Res Run (2): estimated potential fire emissions added in and to take their difference: Impact = Air Quality (2) – Air Quality (1). • A more efficient way is to estimate the contribution of the fires by calculating emission sensitivities with the Decoupled Direct Method (DDM) provided by the Hi-Res system. Requires a single model run with potential fire emissions added in. Georgia Institute of Technology

  8. Application to prescribed fire: forecast and hindcast the February 28th, 2007 episode • Forecast: to test the predictive capability of this system. • Forecast meteorology • Emissions estimated from pre-burning information • Hindcast: to identify key weaknesses in the system. • Re-analysis data (through FDDA) to predict the meteorology • Post-burning information to estimate emissions Georgia Institute of Technology

  9. Atlanta Smoke Detected by Geostationary Satellite(1:15-1:45 pm EST on February 28th, 2007 ) Georgia Institute of Technology

  10. Ambient Monitoring and Prescribed Burning Sites Georgia Institute of Technology

  11. Hourly PM2.5 Mass Georgia Institute of Technology

  12. Hourly Ozone Concentrations Georgia Institute of Technology

  13. PM Impact of the Oconee NF and Piedmont NWR Fires Georgia Institute of Technology

  14. PM2.5 O3 Forecast Observed Hindcast Contribution from fire Observed and Predicted Max. Concentration and Predicted Max. Impact from the Fires within Atlanta Urban Area • Sensitivity analysis has shown that observed ozone peaks can only be reached at 5 times typical biogenic VOC emissions from burning areas. Bursts of fire-induced isoprenoid (isoprene and monoterpenes) emissions are reported in the literature. Georgia Institute of Technology

  15. Observed Forecast Hindcast Smoke hit Atlanta at hour 17 Organic Matter to PM2.5 Ratios • Increased VOC emissions also make up part of missing secondary organic aerosol (SOA). Evaporation and re-condensation of leaf surface wax may be another source of SOA as suggested by GC/MS analysis. Also background primary OM from other fires missing in the “typical” inventory. Georgia Institute of Technology

  16. Hindcast 35 ug m-3 Forecast 65 ug m-3 1-hr Exposures to Ambient PM2.5 Georgia Institute of Technology

  17. Forecast, Hindcast and Observed Plumes Georgia Institute of Technology

  18. Data obtained from http://www.air.dnr.state.ga.us/amp/ http://idea.ssec.wisc.edu/ Fig 6 MODIS aerosol optical depth (AOD) on May 21st (L) and 22nd (R), 2007 Fig 6 MODIS aerosol optical depth (AOD) on May 21st (L) and 22nd (R), 2007 Fig 6 MODIS aerosol optical depth (AOD) on May 21st (L) and 22nd (R), 2007 http://idea.ssec.wisc.edu/ http://idea.ssec.wisc.edu/ http://idea.ssec.wisc.edu/ Fig 7 Observed Hourly PM2.5 during May 21-22, 2007 Fig 7 Observed Hourly PM2.5 during May 21-22, 2007 Fig 7 Observed Hourly PM2.5 during May 21-22, 2007 Data obtained from http://www.air.dnr.state.ga.us/amp/ Data obtained from http://www.air.dnr.state.ga.us/amp/ Data obtained from http://www.air.dnr.state.ga.us/amp/ Application to wild fires: the May 18th-23th, 2007 episode of Georgia-Florida wildfires MODIS aerosol optical depth (AOD) on May 21st (L) and 22nd (R), 2007 Observed Hourly PM2.5 G-F fires plume reached Atlanta after long-range transport through Alabama under easterly winds on 21st that turned to westerly on 22nd. Georgia Institute of Technology

  19. Missed Atlanta Wildfire Impacts on Hourly PM25 Simulated period: May 18 – 23, 2007 Preliminary Results on May 22 • Possible reasons could be the absence of the surface thermal changes induced by the fire from the meteorological model and the coarse vertical resolution in CMAQ above 1-km from the ground. Georgia Institute of Technology

  20. Summary • We have developed and tested a modeling system to forecast wildland-fire-impacts on air quality in Atlanta, Georgia. • The application to forecast the prescribed fires on February 28, 2007 was successful and indicates that the fires could be reasonably well estimated using the system. The “forecast” predictions are in good agreement with observations, though the hindcast improves significantly on timing and location. • More efforts are needed to improve the capability of the system to simulate wildfires. Georgia Institute of Technology

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