Abstract

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 km(2)) 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.