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We present a simple framework to easily pre-select the most essential datafor accurately forecasting the concentration of the pollutant PM$_{10}$, basedon pollutants observations for the years 2002 until 2006 in the metropolitanregion of Lisbon, Portugal. Starting from a broad panoply of different datasets collected at several meteorological stations, we apply a forward stepwiseregression procedure that enables us not only to identify the most importantvariables for forecasting the pollutant but also to rank them in order ofimportance. We argue the importance of this variable ranking, showing that theranking is very sensitive to the urban spot where measurements are taken.Having this pre-selection, we then present the potential of linear andnon-linear neural network models when applied to the concentration of pollutantPM$_{10}$. Similarly to previous studies for other pollutants, our validationresults show that non-linear models in average perform as well or worse aslinear models for PM$_{10}$. Finally, we also address the influence ofCirculation Weather Types, characterizing synoptic scale circulation patternsand the concentration of pollutants.

Daily pollution forecast using optimal meteorological data at synoptic and local scales