The Impact of Fire Emission Inputs on Smoke Plume Dispersion Modeling Results

Fire smoke significantly affects human health and air quality. The HYSPLIT dispersion model estimates the area impacted by smoke downwind, but the results are sensitive to input data. This study investigates the impact of different fire emission inputs on dispersion modeling results, focusing on three versions of the Wildland Fire Emissions Inventory System (WFEIS) used to initialize HYSPLIT. The three input datasets include MODIS (FEI_BASE), a combination of MODIS and MTBS (FEI_COMBO), and a version incorporating a cloud cover regression (FEI_COMBO+CC). Dispersion modeling results are compared across the western U.S. for 2013, 2016, and 2018, showing a variation of up to 200% in results depending on the emissions input. Model results are evaluated with ground-based PM2.5 data and visible satellite imagery. The cloud cover regression improves the identification of fire days missed by FEI_BASE potentially impacting health effect studies. Correlations between modeled PM2.5 and EPA data improve with FEI_COMBO+CC, particularly in 2013 and 2016, making it a stronger candidate for use in research on health effects. Despite some variability in RMSE, the higher correlation observed with FEI_COMBO+CC supports its use as a more accurate representation of fire-related PM2.5 transport.