Our objective in this work was to present a hierarchical Bayesian spatiotemporal model that allowed us to make spatial predictions of air pollution levels in an effective way and with very few computational costs. 

We specified a hierarchical spatiotemporal model, using the Stochastic Partial Differential Equations (SPDE) of the integrated nested Laplace approximations approximation (INLA). This approach allowed us to spatially predict, in the territory of Catalonia (Spain), the levels of the four pollutants for which there is the most evidence of an adverse health effect.

Our model allowed us to make fairly accurate spatial predictions of both longterm and short-term exposure to air pollutants, with a low computational cost. The only requirements of the method we propose are the minimum number of stations distributed throughout the territory where the predictions are to be made, and that the spatial and temporal dimensions are either independent or separable.