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SOCAIRE: Forecasting and monitoring urban air quality in Madrid

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dc.contributor.authorMedrano, Rodrigo de
dc.contributor.authorBuen Remiro, Víctor de
dc.contributor.authorAznarte Mellado, José Luis
dc.contributor.orcidhttps://orcid.org/0000-0002-4428-7053
dc.date.accessioned2024-10-28T10:25:38Z
dc.date.available2024-10-28T10:25:38Z
dc.date.issued2021
dc.descriptionThe registered version of this article, first published in “Environmental Modelling & Software, vol. 143", is available online at the publisher's website: Elsevier, https://doi.org/10.1016/j.envsoft.2021.105084 La versión registrada de este artículo, publicado por primera vez en “Environmental Modelling & Software, vol. 143", está disponible en línea en el sitio web del editor: Elsevier, https://doi.org/10.1016/j.envsoft.2021.105084
dc.description.abstractAir 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.en
dc.description.versionversión original
dc.identifier.citationRodrigo de Medrano, Víctor de Buen Remiro, José L. Aznarte, SOCAIRE: Forecasting and monitoring urban air quality in Madrid, Environmental Modelling & Software, Volume 143, 2021, 105084, ISSN 1364-8152, https://doi.org/10.1016/j.envsoft.2021.105084
dc.identifier.doihttps://doi.org/10.1016/j.envsoft.2021.105084
dc.identifier.issn1364-8152
dc.identifier.urihttps://hdl.handle.net/20.500.14468/24136
dc.journal.issue105084
dc.journal.titleEnvironmental Modelling & Software
dc.journal.volume143
dc.language.isoen
dc.publisherELSEVIER
dc.relation.centerE.T.S. de Ingeniería Informática
dc.relation.departmentInteligencia Artificial
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/deed.es
dc.subject33 Ciencias Tecnológicas
dc.subject.keywordsAir qualityen
dc.subject.keywordsSpatio-temporal seriesen
dc.subject.keywordsStatistical modelingen
dc.subject.keywordsNeural networksen
dc.titleSOCAIRE: Forecasting and monitoring urban air quality in Madriden
dc.typeartículoes
dc.typejournal articleen
dspace.entity.typePublication
relation.isAuthorOfPublication055e077d-5311-4756-a605-78fcee32b633
relation.isAuthorOfPublication.latestForDiscovery055e077d-5311-4756-a605-78fcee32b633
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