Combining Satellite Imagery and Numerical Model Simulation to Estimate Ambient Air Pollution: An Ensemble Averaging Approach

• Activity Number: 259 - SPEED: Environmetrics: Spatio-Temporal and Other Models
• Type: Contributed
• Date/Time: Monday, July 30, 2018 : 3:05 PM to 3:50 PM
• Sponsor: Section on Statistics and the Environment
• Author(s): Nancy Murray* and Howard Chang and Heather Holmes and Yang Liu
• Companies: Emory University and Emory University and University of Nevada, Reno and Emory University
• Keywords: Air pollution; Exposure assessment ; Health impact; Spatial modeling

Abstract

Ambient fine particulate matter less than 2.5 micrometers 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.