Spatial prediction of air pollution levels using a hierarchical Bayesian spatiotemporal model in Catalonia, Spain

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 of the integrated nested Laplace approximations approximation. 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 long- term 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. Highlights We show a hierarchical Bayesian spatiotemporal model. Our model provides predictions of both long-term and short-term exposure. The computational cost is low. The model only needs a minimum number of stations being distributed throughout the territory. The other requirement of our model is that the spatial and temporal dimensions are either independent or separable. Graphical abstract Software and Data availability We used open data with free access using these sources. Air pollutants Departament de Territori i Sostenibilitat, Generalitat de Catalunya [Available at: https://analisi.transparenciacatalunya.cat/en/Medi-Ambient/Qualitat-de-l-aire-als-punts-de-mesurament-autom-t/tasf-thgu , last accessed on March 14, 2021]. Meteorological variables METEOCAT, Generalitat de Catalunya. Meteorological data from XEMA [Available at: https://analisi.transparenciacatalunya.cat/en/Medi-Ambient/Dades-meteorol-giques-de-la-XEMA/nzvn-apee , last accessed on March 14, 2021]. AEMET. AEMET Open Data [in Spanish] [Available at: http://www.aemet.es/es/datos_abiertos/AEMET_OpenData , last accessed on March 14, 2021]. Digitized cartography of the ABS Departament de Salut. Cartography [Available at: https://salutweb.gencat.cat/ca/el_departament/estadistiques_sanitaries/cartografia/ , accessed on March 14, 2021]. Code will be available at www.researchprojects.es