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Ambient fine particulate matter less than 2.5 $\mu$m in aerodynamic diameter(PM$_{2.5}$) has been linked to various adverse health outcomes and has,therefore, gained interest in public health. However, the sparsity of airquality monitors greatly restricts the spatio-temporal coverage of PM$_{2.5}$measurements, limiting the accuracy of PM$_{2.5}$-related health studies. Wedevelop a method to combine estimates for PM$_{2.5}$ using satellite-retrievedaerosol optical depth (AOD) and simulations from the Community Multiscale AirQuality (CMAQ) modeling system. While most previous methods utilize AOD or CMAQseparately, we aim to leverage advantages offered by both methods in terms ofresolution and coverage by using Bayesian model averaging. In an application ofestimating daily PM$_{2.5}$ in the Southeastern US, the ensemble approachoutperforms statistical downscalers that use either AOD or CMAQ incross-validation analyses. In addition to PM$_{2.5}$, our approach is alsohighly applicable for estimating other environmental risks that utilizeinformation from both satellite imagery and numerical model simulation.

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