Scalable Bayesian Geostatistical Regression Model for Bias-Correcting Large-Scale Daily Satellite-Retrieved Aerosol...

Ambient fine particulate matter (PM_2.5) is linked to numerous adverse health outcomes. Accurate large-scale PM_2.5 prediction and uncertainty quantification at fine spatial resolution are crucial for health analyses. We introduce a scalable Bayesian spatial-temporal geostatistical regression model (GRM) that performs bias-correction of gridded Chemical Transport Model (CTM) outputs or satellite-retrieved Aerosol Optical Depth (AOD) and additional spatiotemporal covariates to improve PM_2.5 predictions. This model employs nearest neighbor Gaussian process (NNGP) spatial random effects to efficiently exploit spatial correlation when analyzing large numbers of PM_2.5 monitors (7.64 times faster than a regular Gaussian process), and when predicting over large spatial grids (43.7 times faster than a regular Gaussian process). We apply the model to two case studies: an analysis using 12-km CTM outputs over the contiguous United States, and an analysis using 1-km AOD data over the state of California. For the U.S. 12-km case study, we demonstrate the spatial predictive performance and accurate uncertainty quantification of the model with several cross-validation experiments. For the California 1-km case study, in addition to the above, we also assess Random Forest (RF) and GRM-RF hybrid models which include a RF mean term in the GRM. Under the California 1-km case study the GRM-RF provides higher predictive accuracy (RMSE 7.325 μg/m3) than both the stand-alone RF (RMSE 7.732 μg/m3) and GRM (RMSE 8.557 μg/m3) while maintaining accurate predictive uncertainty (95% credible interval coverage 0.957). Finally, we provide open-source R software for researchers to implement the models.