  Air quality has become one of the main issues in public health and urban
planning management, due to the proven adverse effects of high pollutant
concentrations. Considering the mitigation measures that cities all over the
world are taking in order to face frequent low air quality episodes, the
capability of foreseeing future pollutant concentrations is of great
importance. Through this paper, we present SOCAIRE, an operational tool based
on a Bayesian and spatiotemporal ensemble of neural and statistical nested
models. SOCAIRE integrates endogenous and exogenous information in order to
predict and monitor future distributions of the concentration for several
pollutants in the city of Madrid. It focuses on modeling each and every
available component which might play a role in air quality: past concentrations
of pollutants, human activity, numerical pollution estimation, and numerical
weather predictions. This tool is currently in operation in Madrid, producing
daily air quality predictions for the next 48 hours and anticipating the
probability of the activation of the measures included in the city's official
air quality \no protocols through probabilistic inferences about compound
events.
