Bayesian spatiotemporal modeling for estimating short‐term exposure to air pollution in Santiago de Chile

Spatial prediction of exposure to air pollution in a large city such as Santiago de Chile is a challenging problem because of the lack of a dense air‐quality monitoring network. Statistical spatiotemporal models exploit the space–time correlation in the pollution data and other relevant meteorological and land‐use information to generate accurate predictions in both space and time. In this paper, we develop a Bayesian modeling method to accurately predict hourly PM2.5 concentrations in a 1‐km high‐resolution grid covering the city. The modeling method combines a spatiotemporal land‐use regression model for PM2.5 and a Bayesian calibration model for the input meteorological variables used in the land‐use regression model. Using a 3‐month winter‐time pollution data set, the output of sample validation results obtained in this paper shows a substantial increase in accuracy due to the incorporation of the linear calibration model. The proposed Bayesian modeling method is then used to provide short‐term spatiotemporal predictions of PM2.5 concentrations on a fine (1 km2) spatial grid covering the city. Along with the paper, we publish the R code used and the output of sample predictions for future scientific use.