  There has been growing interest in extending the coverage of ground PM2.5
monitoring networks based on satellite remote sensing data. With broad spatial
and temporal coverage, satellite based monitoring network has a strong
potential to complement the ground monitor system in terms of the
spatial-temporal availability of the air quality data. However, most existing
calibration models focused on a relatively small spatial domain and cannot be
generalized to national-wise study. In this paper, we proposed a statistically
reliable and interpretable national modeling framework based on Bayesian
downscaling methods with the application to the calibration of the daily ground
PM2.5 concentrations across the Continental U.S. using satellite-retrieved
aerosol optical depth (AOD) and other ancillary predictors in 2011. Our
approach flexibly models the PM2.5 versus AOD and the potential related
geographical factors varying across the climate regions and yields spatial and
temporal specific parameters to enhance the model interpretability. Moreover,
our model accurately predicted the national PM2.5 with a R2 at 70% and
generates reliable annual and seasonal PM2.5 concentration maps with its SD.
Overall, this modeling framework can be applied to the national scale PM2.5
exposure assessments and also quantify the prediction errors.
