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dc.contributor.authorHeimann, Inesen
dc.contributor.authorBright, VBen
dc.contributor.authorMcLeod, MWen
dc.contributor.authorMead, MIen
dc.contributor.authorPopoola, Olalekanen
dc.contributor.authorStewart, GBen
dc.contributor.authorJones, Rodericen
dc.date.accessioned2015-06-03T08:48:10Z
dc.date.available2015-06-03T08:48:10Z
dc.date.issued2015-04-25en
dc.identifier.citationAtmospheric Environment Volume 113, July 2015, Pages 10–19. DOI: 10.1016/j.atmosenv.2015.04.057en
dc.identifier.issn1352-2310
dc.identifier.urihttps://www.repository.cam.ac.uk/handle/1810/248187
dc.description.abstractTo carry out detailed source attribution for air quality assessment it is necessary to distinguish pollutant contributions that arise from local emissions from those attributable to non-local or regional emission sources. Frequently this requires the use of complex models and inversion methods, prior knowledge or assumptions regarding the pollution environment. In this paper we demonstrate how high spatial density and fast response measurements from low-cost sensor networks may facilitate this separation. A purely measurement-based approach to extract underlying pollution levels (baselines) from the measurements is presented exploiting the different relative frequencies of local and background pollution variations. This paper shows that if high spatial and temporal coverage of air quality measurements are available, the different contributions to the total pollution levels, namely the regional signal as well as near and far field local sources, can be quantified. The advantage of using high spatial resolution observations, as can be provided by low-cost sensor networks, lies in the fact that no prior assumptions about pollution levels at individual deployment sites are required. The methodology we present here, utilising measurements of carbon monoxide (CO), has wide applicability, including additional gas phase species and measurements obtained using reference networks. While similar studies have been performed, this is the first study using networks at this density, or using low cost sensor networks.
dc.description.sponsorshipThe authors thank EPSRC (EP/E001912/1) for funding for the Message project. IH thanks the German National Academic Foundation for funding of MPhil degree.
dc.languageEnglishen
dc.language.isoenen
dc.publisherElsevier
dc.rightsAttribution 2.0 UK: England & Wales*
dc.rights.urihttp://creativecommons.org/licenses/by/2.0/uk/*
dc.subjectAir qualityen
dc.subjectBaseline extractionen
dc.subjectEmission scalesen
dc.subjectSource attributionen
dc.subjectElectrochemical sensorsen
dc.subjectSensor networksen
dc.titleSource attribution of air pollution by spatial scale separation using high spatial density networks of low cost air quality sensorsen
dc.typeArticle
dc.description.versionThis is the final published version. It first appeared at http://www.sciencedirect.com/science/article/pii/S1352231015300583#.en
prism.endingPage19
prism.publicationDate2015en
prism.publicationNameAtmospheric Environmenten
prism.startingPage10
prism.volume113en
dc.rioxxterms.funderEPSRC
dc.rioxxterms.projectidEP/E001912/1
rioxxterms.versionofrecord10.1016/j.atmosenv.2015.04.057en
rioxxterms.licenseref.urihttp://www.rioxx.net/licenses/all-rights-reserveden
rioxxterms.licenseref.startdate2015-04-25en
dc.contributor.orcidPopoola, Olalekan [0000-0003-2390-8436]
dc.contributor.orcidJones, Roderic [0000-0002-6761-3966]
dc.identifier.eissn1873-2844
rioxxterms.typeJournal Article/Reviewen
pubs.funder-project-idNERC (NE/I007490/1)


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Attribution 2.0 UK: England & Wales
Except where otherwise noted, this item's licence is described as Attribution 2.0 UK: England & Wales