Abstract
Air quality has become a central issue in public health and urban planning management, due to the proven adverse effects of airborne pollutants. Considering temporary mobility restriction measures used to face low air quality episodes, the capability of foreseeing pollutant concentrations is crucial. We thus present SOCAIRE (Spanish acronim for “operational forecast system for air quality”), 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 the main pollutants. It focuses on modeling available components which affect air quality: past concentrations of pollutants, human activity, and numerical pollution and weather predictions. This tool is currently in operation in Madrid, producing daily air quality predictions for the next 48 h and anticipating the probability of the activation of the measures included in the city's official air quality NO2 protocols through probabilistic inferences about compound events.
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