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dc.contributor.authorAkita, Yasuyuki
dc.contributor.authorBaldasano Recio, José María
dc.contributor.authorBeelen, Rob M. J.
dc.contributor.authorCirach, Marta
dc.contributor.authorDe Hoogh, Kees
dc.contributor.authorHoek, Gerard
dc.contributor.authorNieuwenhuijsen, Mark J.
dc.contributor.authorSerre, Marc L.
dc.contributor.authorDe Nazelle, Audrey
dc.contributor.otherUniversitat Politècnica de Catalunya. Departament de Projectes d'Enginyeria
dc.date.accessioned2014-05-19T16:09:46Z
dc.date.available2014-05-19T16:09:46Z
dc.date.created2014-04-15
dc.date.issued2014-04-15
dc.identifier.citationAkita, Y. [et al.]. Large scale air pollution estimation method combining land use regression and chemical transport modeling in a geostatistical framework. "Environmental science and technology", 15 Abril 2014, vol. 48, núm. 8, p. 4452-4459.
dc.identifier.issn0013-936X
dc.identifier.urihttp://hdl.handle.net/2117/23014
dc.description.abstractIn recognition that intraurban exposure gradients may be as large as between-city variations, recent air pollution epidemiologic studies have become increasingly interested in capturing within-city exposure gradients. In addition, because of the rapidly accumulating health data, recent studies also need to handle large study populations distributed over large geographic domains. Even though several modeling approaches have been introduced, a consistent modeling framework capturing within-city exposure variability and applicable to large geographic domains is still missing. To address these needs, we proposed a modeling framework based on the Bayesian Maximum Entropy method that integrates monitoring data and outputs from existing air quality models based on Land Use Regression (LUR) and Chemical Transport Models (CTM). The framework was applied to estimate the yearly average NO2 concentrations over the region of Catalunya in Spain. By jointly accounting for the global scale variability in the concentration from the output of CTM and the intraurban scale variability through LUR model output, the proposed framework outperformed more conventional approaches.
dc.format.extent8 p.
dc.language.isoeng
dc.rightsAttribution-NonCommercial-NoDerivs 3.0 Spain
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/3.0/es/
dc.subjectÀrees temàtiques de la UPC::Desenvolupament humà i sostenible::Degradació ambiental::Contaminació atmosfèrica
dc.subject.lcshAir quality -- Measurement -- Mathematical models
dc.subject.otherAir pollution
dc.subject.otherAir quality
dc.subject.otherCatalunya
dc.subject.otherModeling
dc.subject.otherLand Use Regression
dc.subject.otherChemical Transport Models
dc.subject.otherGeostatistical
dc.titleLarge scale air pollution estimation method combining land use regression and chemical transport modeling in a geostatistical framework
dc.typeArticle
dc.subject.lemacAire -- Qualitat -- Mesurament -- Models matemàtics
dc.contributor.groupUniversitat Politècnica de Catalunya. MTA - Modelització i Tecnologia Ambiental
dc.identifier.doi10.1021/es405390e
dc.relation.publisherversionhttp://pubs.acs.org/doi/full/10.1021/es405390e
dc.rights.accessOpen Access
local.identifier.drac14581118
dc.description.versionPostprint (published version)
dc.relation.projectidinfo:eu-repo/grantAgreement/EC/FP7/211250/EU/European Study of Cohorts for Air Pollution Effects/ESCAPE
local.citation.authorAkita, Y.; Baldasano, J.; Beelen, R.; Cirach, M.; De Hoogh, K.; Hoek, G.; Nieuwenhuijsen, M.; Serre, M.; De Nazelle, A.
local.citation.publicationNameEnvironmental science and technology
local.citation.volume48
local.citation.number8
local.citation.startingPage4452
local.citation.endingPage4459


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Attribution-NonCommercial-NoDerivs 3.0 Spain
Except where otherwise noted, content on this work is licensed under a Creative Commons license : Attribution-NonCommercial-NoDerivs 3.0 Spain