Development of a biomass burning emissions inventory by combining satellite and ground-based information

George A. Pouliot; Thomas G. Pace; Roy Biswadev; Tom Pierce; David Mobley

doi:10.1117/1.2939551

1 May 2008 Development of a biomass burning emissions inventory by combining satellite and ground-based information

George A. Pouliot, Thomas G. Pace, Roy Biswadev, Tom Pierce, David Mobley

Author Affiliations +

Journal of Applied Remote Sensing, Vol. 2, Issue 1, 021501 (May 2008). https://doi.org/10.1117/1.2939551

Abstract

A 2005 biomass burning (wildfire, prescribed, and agricultural) emission inventory has been developed for the contiguous United States using a newly developed simplified method of combining information from multiple sources for use in the US EPA's National Emission Inventory (NEI). Our method blends the temporal and spatial resolution of the remote sensing information with the ground based fire size estimate. This method is faster and considerably less expensive than the method used for the 2002 National Emissions Inventory and is more accurate than methods used for 2001 and prior years. In addition, the 2005 fire inventory is the first EPA inventory utilizing remote sensing information. A comparison with the 2002 inventory for wildfire, prescribed, and agricultural fires indicates a large year-to-year variability in wildfire emissions and less variation for prescribed and agricultural fires. Total PM2.5 emissions from wildfires, prescribed burning, and agricultural burning for the contiguous United States were estimated to be 109,000 short tons, 209,000 short tons, and 232,000 short tons, respectively, for 2005. Our total emission estimate for 2005 is 550,000 short tons. Our analysis shows that year-to-year spatial variability accounts for the substantial difference in the wildfire emission estimates.

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George A. Pouliot, Thomas G. Pace, Roy Biswadev, Tom Pierce, and David Mobley "Development of a biomass burning emissions inventory by combining satellite and ground-based information," Journal of Applied Remote Sensing 2(1), 021501 (1 May 2008). https://doi.org/10.1117/1.2939551

Published: 1 May 2008

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