Refining fire emissions for air quality modeling with remotely sensed fire counts: A wildfire case study |
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| Institution: | 1. Atmospheric Modeling Division, National Exposure Research Laboratory, US Environmental, Protection Agency, Research Triangle Park, NC 27711, USA;2. Atmospheric Sciences Modeling Division, NOAA Air Resources Laboratory, in partnership, with the United States Environmental Protection Agency, Research Triangle Park, NC 27711, USA;1. Centre for Remote Imaging, Sensing and Processing, National University of Singapore, Block S17, Level 2, 10 Lower Kent Ridge Road, Singapore 119076, Singapore;2. Naval Research Laboratory, Marine Meteorology Division, 7 Grace Hopper Avenue Stop 2, Monterey, CA 93943-5502, USA;3. Micro-Pulse Lidar Network, Code 612, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA;4. Code 618, NASA Goddard Space Flight Center, Greenbelt, MD 20771, USA;1. Centro de Investigación Forestal – Lourizán, PO Box 127, 36080 Pontevedra, Spain;2. Departamento de Ingeniería Agroforestal, Escuela Politécnica Superior, Universidad de Santiago de Compostela, Campus Universitario s/n, 27002 Lugo, Spain;3. Centro de Investigación Forestal, Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA) Ctra La Coruña km 7.5, 28040 Madrid, Spain;1. DST-Mahamana Centre of Excellence in Climate Change Research, Banaras Hindu University, Varanasi, India;2. Institute of Environment and Sustainable Development, Banaras Hindu University, Varanasi, India;3. NASA Ames Research Center, Moffett Field, CA, USA;4. Civil and Environmental Engineering, Technion, Hafia, Israel;5. Center for Earth Sciences and Environment, Faculty of Science and Technology, Universiti Kebangsaan Malaysia, Bangi, Malaysia |
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| Abstract: | This paper examines the use of Moderate Resolution Imaging Spectroradiometer (MODIS) observed active fire data (pixel counts) to refine the National Emissions Inventory (NEI) fire emission estimates for major wildfire events. This study was motivated by the extremely limited information available for many years of the United States Environmental Protection Agency (US EPA) NEI about the specific location and timing of major fire events. The MODIS fire data provide twice-daily snapshots of the locations and breadth of fires, which can be helpful for identifying major wildfires that typically persist for a minimum of several days. A major wildfire in Mallory Swamp, FL, is used here as a case study to test a reallocation approach for temporally and spatially distributing the state-level fire emissions based on the MODIS fire data. Community Multiscale Air Quality (CMAQ) model simulations using these reallocated emissions are then compared with another simulation based on the original NEI fire emissions. We compare total carbon (TC) predictions from these CMAQ simulations against observations from the Inter-agency Monitoring of Protected Visual Environments (IMPROVE) surface network. Comparisons at three IMPROVE sites demonstrate substantial improvements in the temporal variability and overall correlation for TC predictions when the MODIS fire data is used to refine the fire emission estimates. These results suggest that if limited information is available about the spatial and temporal extent of a major wildfire fire, remotely sensed fire data can be a useful surrogate for developing the fire emissions estimates for air quality modeling purposes. |
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