Hierarchical space-time modelling of PM_(10) pollution

摘要

In this paper, we propose a hierarchical spatio-temporal model for daily mean concentrations of PM_(10) pollution. The main aims of the proposed model are the identification of the sources of variability characterising the PM_(10) process and the estimation of pollution levels at unmonitored spatial locations. We adopt a fully Bayesian approach, using Monte Carlo Markov Chain algorithms. We apply the model on PM_(10) data measured at 11 monitoring sites located in the major towns and cities of Italy's Emilia-Romagna Region. The model is designed for areas with PM_(10) measurements available; the case of PM_(10) level estimation from emissions data is not handled. The model has been carefully checked using Bayesian p-values and graphical posterior predictive checks. Results show that the temporal random effect is the most important when explaining PM_(10) levels.