2018/02/08 by Nancy L. Murray, Howard H. Chang, Murray, Nancy +5
Earth and Planetary Sciences · Environmental Science · #Air Quality Monitoring and Forecasting #Air Quality and Health Impacts #Applications (stat.AP) #Atmospheric chemistry and aerosols #FOS: Computer and information sciences
paper · pdf · doi:10.48550/arxiv.1802.03077
openalex publication_date 2018/02/08 · openalex created_date 2018/02/23 · openalex updated_date 2026/07/28
Ambient fine particulate matter less than 2.5 μm in aerodynamic diameter (PM2.5) has been linked to various adverse health outcomes and has, therefore, gained interest in public health. However, the sparsity of air quality monitors greatly restricts the spatio-temporal coverage of PM2.5 measurements, limiting the accuracy of PM2.5-related health studies. We develop a method to combine estimates for PM2.5 using satellite-retrieved aerosol optical depth (AOD) and simulations from the Community Multiscale Air Quality (CMAQ) modeling system. While most previous methods utilize AOD or CMAQ separately, we aim to leverage advantages offered by both methods in terms of resolution and coverage by using Bayesian model averaging. In an application of estimating daily PM2.5 in the Southeastern US, the ensemble approach outperforms statistical downscalers that use either AOD or CMAQ in cross-validation analyses. In addition to PM2.5, our approach is also highly applicable for estimating other environmental risks that utilize information from both satellite imagery and numerical model simulation.