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dc.contributor.authorGupta, Shivam
dc.contributor.authorPebesma, Edzer
dc.contributor.authorMateu, Jorge
dc.contributor.authorDegbelo, Auriol
dc.date.accessioned2018-10-31T18:59:44Z
dc.date.available2018-10-31T18:59:44Z
dc.date.issued2018
dc.identifier.citationGUPTA, Shivam, et al. Air Quality Monitoring Network Design Optimisation for Robust Land Use Regression Models. Sustainability (2071-1050), 2018, 10.5ca_CA
dc.identifier.issn2071-1050
dc.identifier.urihttp://hdl.handle.net/10234/177163
dc.description.abstractA very common curb of epidemiological studies for understanding the impact of air pollution on health is the quality of exposure data available. Many epidemiological studies rely on empirical modelling techniques, such as land use regression (LUR), to evaluate ambient air exposure. Previous studies have located monitoring stations in an ad hoc fashion, favouring their placement in traffic “hot spots”, or in areas deemed subjectively to be of interest to land use and population. However, ad-hoc placement of monitoring stations may lead to uninformed decisions for long-term exposure analysis. This paper introduces a systematic approach for identifying the location of air quality monitoring stations. It combines the flexibility of LUR with the ability to put weights on priority areas such as highly-populated regions, to minimise the spatial mean predictor error. Testing the approach over the study area has shown that it leads to a significant drop of the mean prediction error (99.87% without spatial weights; 99.94% with spatial weights in the study area). The results of this work can guide the selection of sites while expanding or creating air quality monitoring networks for robust LUR estimations with minimal prediction errors.ca_CA
dc.format.extent27 p.ca_CA
dc.format.mimetypeapplication/pdfca_CA
dc.language.isoengca_CA
dc.publisherMDPIca_CA
dc.relation.isPartOfSustainability, 2018, vol. 10, núm. 5ca_CA
dc.rights© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).ca_CA
dc.rightsAtribución 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/*
dc.subjectair quality monitoringca_CA
dc.subjectland use regressionca_CA
dc.subjectmonitoring location optimisationca_CA
dc.subjectsimulated annealingca_CA
dc.subjectspatial mean prediction errorca_CA
dc.titleAir Quality Monitoring Network Design Optimisation for Robust Land Use Regression Modelsca_CA
dc.typeinfo:eu-repo/semantics/articleca_CA
dc.identifier.doihttps://doi.org/10.3390/su10051442
dc.relation.projectIDThe authors gratefully acknowledge funding from the European Commission through the GEO-C project (H2020-MSCA-ITN-2014, Grant Agreement Number 642332, http://www.geo-c.eu/)ca_CA
dc.rights.accessRightsinfo:eu-repo/semantics/openAccessca_CA
dc.relation.publisherVersionhttps://www.mdpi.com/2071-1050/10/5/1442ca_CA
dc.type.versioninfo:eu-repo/semantics/publishedVersionca_CA


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© 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/).
Except where otherwise noted, this item's license is described as © 2018 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).