Satellite-Based Daily PM _2.5 Estimates During Fire Seasons in Colorado

Abstract

The western United States has experienced increasing wildfire activities, which have negative effects on human health. Epidemiological studies on fine particulate matter (PM _2.5) from wildfires are limited by the lack of accurate high-resolution PM _2.5 exposure data over fire days. Satellite-based aerosol optical depth (AOD) data can provide additional information in ground PM _2.5 concentrations and has been widely used in previous studies. However, the low background concentration, complex terrain, and large wildfire sources add to the challenge of estimating PM _2.5 concentrations in the western United States. In this study, we applied a Bayesian ensemble model that combined information from the 1 km resolution AOD products derived from the Multi-angle Implementation of Atmospheric Correction (MAIAC) algorithm, Community Multiscale Air Quality (CMAQ) model simulations, and ground measurements to predict daily PM _2.5 concentrations over fire seasons (April to September) in Colorado for 2011–2014. Our model had a 10-fold cross-validated R ² of 0.66 and root-mean-squared error of 2.00 μg/m ³, outperformed the multistage model, especially on the fire days. Elevated PM _2.5 concentrations over large fire events were successfully captured. The modeling technique demonstrated in this study could support future short-term and long-term epidemiological studies of wildfire PM _2.5.