The Impact of Fire Emissions 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 emissions 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 up to 200% variation in results depending on the input. Model results are validated with ground-based PM_2.5 data and visible satellite imagery. The cloud cover regression improves the identification of fire days missed by the base dataset, potentially impacting health effects studies. Correlations between modeled PM_2.5 and EPA data improve with FEI_COMBO+CC, particularly in 2013 and 2016, making it a stronger candidate for use in health effects research. Despite some variability in RMSE, the higher correlation observed with FEI_COMBO+CC supports its use as a more accurate representation of fire-relatedPM_2.5 transport.