SOCAIRE: Forecasting and monitoring urban air quality in Madrid. (September 2021)

• Record Type: Journal Article
• Authors: de Medrano, Rodrigo; de Buen Remiro, Víctor; Aznarte, José L.
• Is Part Of: Environmental modelling & software. Volume 143(2021)
• Publication Date: 2021-09
• Subjects: Air quality -- Spatio-temporal series -- Statistical modeling -- Neural networks; Environmental monitoring -- Computer programs -- Periodicals; Ecology -- Computer simulation -- Periodicals; Digital computer simulation -- Periodicals; Computer software -- Periodicals; Environmental Monitoring -- Periodicals; Computer Simulation -- Periodicals; Environnement -- Surveillance -- Logiciels -- Périodiques; Écologie -- Simulation, Méthodes de -- Périodiques; Simulation par ordinateur -- Périodiques; Logiciels -- Périodiques; Computer software; Digital computer simulation; Ecology -- Computer simulation; Environmental monitoring -- Computer programs; Periodicals; Electronic journals; 363.70015118
• DOI: 10.1016/j.envsoft.2021.105084
• Languages: English
• ISSNs: 1364-8152

Abstract

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. Graphical abstract: Image 1 Highlights: A new operational tool for air quality forecasting in Madrid is presented. Produces probabilistic forecasts via a pipeline of statistical and neural models. Can foresee the probability distribution of NO2, O3, PM2.5 and PM10 48h in advance. Bayesian aggregation of compound events allow for the prediction of NO2 episodes.