Bayesian spatiotemporal modelling for estimating short-term exposure to air pollution in Santiago de Chile

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 spatio-temporal 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 modelling method to accurately predict hourly PM2.5 concentration in a one kilometer high resolution grid covering the city. The modelling 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 show a substantial increase in accuracy due to the incorporation of the linear calibration model. The proposed Bayesian modelling method is then used to provide short-term spatio-temporal predictions of PM2.5 concentrations on a fine (one kilometer square) 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.

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Identifiers

ORCID for Sujit K. Sahu: orcid.org/0000-0003-2315-3598

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