Integration of High-Resolution Wildfire Smoke Simulation and Satellite Observations and Its Application in Health Impact Assessment

Large wildfires are an increasing threat to human society in the western U.S. in recent years. The 2017 fire season revealed the extensive impact of severe wildfires across the Pacific Northwest region caused by uncharacteristically hot and dry weather during the summer. Here we conducted numerical simulation for an extreme fire event in early September of 2017 using a coupled WRF-CMAQ model system and integrated modeling results with the high-resolution (1km) Multi-Angle Implementation of Atmospheric Correction (MAIAC) satellite aerosol optical depth (AOD) product. We employed multiple statistical algorithms including a generalized boosted model (GBM) and a two-stage Bayesian ensemble method to optimize the estimation of surface PM_2.5 concentrations with high spatio-temporal coverage. Evaluation of the smoke concentration data with the US EPA ground monitoring PM_2.5 concentrations shows improved posterior PM_2.5 concentration estimation with increased spatio-temporal correlations and decreased root-mean-squared errors in cross-validation. Lastly, we applied the high-resolution PM_2.5 exposure estimation with short-term exposure-response functions to assess fire smoke health impacts of the extreme event. Such application demonstrates the need for a high-performance fire smoke forecasting and reanalysis system and its practical utility to reduce public exposure risks of smoke hazards.