In the last two decades, increasing attention has been given to air pollution around the world, mainly because of its impact on human health and on the environment. In the Po valley (northern Italy), one of the most troublesome pollutant is PM10 (particulate matter with an aerodynamic diameter of less than 10 micron). In order to assess PM10 concentration over an entire region, environmental agencies need models to predict PM10 at unmonitored sites. To choose among possible predictive models and then meet the agencies’ request, we focus on the class of Bayesian hierarchical models as they provide a flexible framework for incorporating relevant covariates as well as spatio-temporal interactions. We consider six alternative models for PM10 concentration in Piemonte region (north-western Po Valley), during the winter season October 2005–March 2006. Our aim is to choose a model that is satisfactory in terms of goodness of fit, interpretability, parsimony, prediction capability and computational costs. In order to support this choice, we propose a comparison approach based on a set of criteria summarized in a table that can be easily communicated to non-statisticians. The comparison findings allow to provide Piemonte environmental agencies with an effective statistical model for building reliable PM10 concentration maps, equipped with the corresponding uncertainty measure.
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