  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 air
quality monitors greatly restricts the spatio-temporal coverage of PM$_{2.5}$
measurements, limiting the accuracy of PM$_{2.5}$-related health studies. We
develop a method to combine estimates for PM$_{2.5}$ 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 PM$_{2.5}$ in the Southeastern US, the ensemble approach
outperforms statistical downscalers that use either AOD or CMAQ in
cross-validation analyses. In addition to PM$_{2.5}$, our approach is also
highly applicable for estimating other environmental risks that utilize
information from both satellite imagery and numerical model simulation.
