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dc.creatorGómez Losada, Álvaroes
dc.creatorSantos, Francisca M.es
dc.creatorGibert, Karinaes
dc.creatorPires, José Carlos M.es
dc.date.accessioned2022-10-20T08:16:57Z
dc.date.available2022-10-20T08:16:57Z
dc.date.issued2019
dc.identifier.citationGómez Losada, Á., Santos, F.M., Gibert, K. y Pires, .C.M. (2019). A data science approach for spatiotemporal modelling of low and resident air pollution in Madrid (Spain): Implications for epidemiological studies. Computers, Environment and Urban Systems, 75, 1-11. https://doi.org/10.1016/j.compenvurbsys.2018.12.005.
dc.identifier.issn0198-9715es
dc.identifier.issn1873-7587es
dc.identifier.urihttps://hdl.handle.net/11441/138157
dc.description.abstractModel developments to assess different air pollution exposures within cities are still a key challenge in environmental epidemiology. Background air pollution is a long-term resident and low-level concentration pollution difficult to quantify, and to which population is chronically exposed. In this study, hourly time series of four key air pollutants were analysed using Hidden Markov Models to estimate the exposure to background pollution in Madrid, from 2001 to 2017. Using these estimates, its spatial distribution was later analysed after combining the interpolation results of ordinary kriging and inverse distance weighting. The ratio of ambient to background pollution differs according to the pollutant studied but is estimated to be on average about six to one. This methodology is proposed not only to describe the temporal and spatial variability of this complex exposure, but also to be used as input in new modelling approaches of air pollution in urban areas.es
dc.formatapplication/pdfes
dc.format.extent11 p.es
dc.language.isoenges
dc.publisherElsevieres
dc.relation.ispartofComputers, Environment and Urban Systems, 75, 1-11.
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectAir pollution exposurees
dc.subjectBackground pollution levelses
dc.subjectHidden Markov Modelses
dc.subjectInverse distance weightinges
dc.subjectOrdinary kriginges
dc.titleA data science approach for spatiotemporal modelling of low and resident air pollution in Madrid (Spain): Implications for epidemiological studieses
dc.typeinfo:eu-repo/semantics/articlees
dcterms.identifierhttps://ror.org/03yxnpp24
dc.type.versioninfo:eu-repo/semantics/publishedVersiones
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses
dc.contributor.affiliationUniversidad de Sevilla. Departamento de Estadística e Investigación Operativaes
dc.relation.publisherversionhttps://doi.org/10.1016/j.compenvurbsys.2018.12.005es
dc.identifier.doi10.1016/j.compenvurbsys.2018.12.005es
dc.journaltitleComputers, Environment and Urban Systemses
dc.publication.volumen75es
dc.publication.initialPage1es
dc.publication.endPage11es

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Except where otherwise noted, this item's license is described as: Attribution-NonCommercial-NoDerivatives 4.0 Internacional