Biomass burning is significant to emission estimates because: (1) it is a major contributor of particulate matter and other pollutants; (2) it is one of the most poorly documented of all sources; (3) it can adversely affect human health; and (4) it has been identified as a significant contributor to climate change through feedbacks with the radiation budget. Additionally, biomass burning can be a significant contributor to a regions inability to achieve the National Ambient Air Quality Standards for PM 2.5 and ozone, particularly on the top 20% worst air quality days. The United States does not have a standard methodology to track fire occurrence or area burned, which are essential components to estimating fire emissions. Satellite imagery is available almost instantaneously and has great potential to enhance emission estimates and their timeliness. This investigation compares satellite-derived fire data to ground-based data to assign statistical error and helps provide confidence in these data. The largest fires are identified by all satellites and their spatial domain is accurately sensed. MODIS provides enhanced spatial and temporal information, and GOES ABBA data are able to capture more small agricultural fires. A methodology is presented that combines these satellite data in Near-Real-Time to produce a product that captures 81 to 92% of the total area burned by wildfire, prescribed, agricultural and rangeland burning. Each satellite possesses distinct temporal and spatial capabilities that permit the detection of unique fires that could be omitted if using data from only one satellite.
Various methods to generate satellite-based biomass burning emission estimates
have recently been developed for their use in air quality models. Each method has different
assumptions, data sources, and algorithms. This paper compares three different satellitebased
biomass burning emission estimates against a control case of no biomass burning and
ground-based biomass estimate in an air quality model. We have chosen August 2002 for
comparison, since all data sets were readily available. In addition, there was significant
wildfire activity during this month. Our results suggest that there is large uncertainty in the
emission estimates which results in both under-prediction and over-prediction of PM2.5
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.