学术文献库

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.