Combining numerical model output and particulate data using Bayesian space–time modeling

Over the past few years, Bayesian models for combining output from numerical models and air monitoring data have been applied to environmental data sets to improve spatial prediction. This paper develops a new hierarchical Bayesian model (HBM) for fine particulate matter (PM 2.5 ) that combines U. S. EPA Federal Reference Method (FRM) PM 2.5 monitoring data and Community Multi‐scale Air Quality (CMAQ) numerical model output. The model is specified in a Bayesian framework and fitted using Markov Chain Monte Carlo (MCMC) techniques. We find that the statistical model combining monitoring data and CMAQ output provides reliable information about the true underlying PM 2.5 process over time and space. We base these conclusions on results of a validation exercise in which independent monitoring data were compared with predicted values from the HBM and predictions from a standard kriging model based solely on the monitoring data.