An evaluation of european air pollution regulations for particulate matter monitored from a heterogeneous network

Statistical methods are needed for evaluating many aspects of air pollution regulations increasingly adopted by many different governments in the European Union. The atmospheric particulate matter (PM) is an important air pollutant for which regulations have been issued recently. A challenging task here is to evaluate the regulations based on data monitored on a heterogeneous network where PM has been observed at a number of sites and a surrogate has been observed at some other sites. This paper develops a hierarchical Bayesian joint space-time model for the PM measurements and its surrogate between which the exact relationship is unknown, and applies the methods to analyse spatio-temporal data obtained from a number of sites in Northern Italy. The model is implemented using MCMC techniques and methods are developed to meet the regulatory demands. These enable full inference with regard to process unknowns, calibration, validation, predictions in time and space and evaluation of regulatory standards.