  We present a simple framework to easily pre-select the most essential data
for accurately forecasting the concentration of the pollutant PM$_{10}$, based
on pollutants observations for the years 2002 until 2006 in the metropolitan
region of Lisbon, Portugal. Starting from a broad panoply of different data
sets collected at several meteorological stations, we apply a forward stepwise
regression procedure that enables us not only to identify the most important
variables for forecasting the pollutant but also to rank them in order of
importance. We argue the importance of this variable ranking, showing that the
ranking is very sensitive to the urban spot where measurements are taken.
Having this pre-selection, we then present the potential of linear and
non-linear neural network models when applied to the concentration of pollutant
PM$_{10}$. Similarly to previous studies for other pollutants, our validation
results show that non-linear models in average perform as well or worse as
linear models for PM$_{10}$. Finally, we also address the influence of
Circulation Weather Types, characterizing synoptic scale circulation patterns
and the concentration of pollutants.
