Spatio-temporal air pollution modelling using a compositional approach

Air pollutant data are compositional in character because they describe quantitatively the parts of a whole (atmospheric composition). However, it is common to use air pollutant concentrations in statistical models without considering this characteristic of the data and, therefore, without control o...

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Detalles Bibliográficos
Autores: Sánchez Balseca, Joseph|||0000-0002-1741-3229, Pérez Foguet, Agustí|||0000-0002-2737-4710
Tipo de recurso: artículo
Fecha de publicación:2020
País:España
Institución:Universitat Politècnica de Catalunya (UPC)
Repositorio:UPCommons. Portal del coneixement obert de la UPC
Idioma:inglés
OAI Identifier:oai:upcommons.upc.edu:2117/333780
Acceso en línea:https://hdl.handle.net/2117/333780
https://dx.doi.org/10.1016/j.heliyon.2020.e04794
Access Level:acceso abierto
Palabra clave:Air--Pollution--Mathematical models
Statistics
Engineering
Atmospheric science
Environmental analysis
Environmental chemical engineering
Environmental impact assessment
Compositional data
CoDa
Air quality
Environmental statistics
Modelling
Aire -- Contaminació -- Models matemàtics
Àrees temàtiques de la UPC::Desenvolupament humà i sostenible::Degradació ambiental::Contaminació atmosfèrica
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spelling Spatio-temporal air pollution modelling using a compositional approachSánchez Balseca, Joseph|||0000-0002-1741-3229Pérez Foguet, Agustí|||0000-0002-2737-4710Air--Pollution--Mathematical modelsStatisticsEngineeringAtmospheric scienceEnvironmental analysisEnvironmental chemical engineeringEnvironmental impact assessmentCompositional dataCoDaAir qualityEnvironmental statisticsModellingAire -- Contaminació -- Models matemàticsÀrees temàtiques de la UPC::Desenvolupament humà i sostenible::Degradació ambiental::Contaminació atmosfèricaAir pollutant data are compositional in character because they describe quantitatively the parts of a whole (atmospheric composition). However, it is common to use air pollutant concentrations in statistical models without considering this characteristic of the data and, therefore, without control of common statistical problems, such as spurious correlations and subcompositional incoherence. This paper now proposes a daily multivariate spatio-temporal model with a compositional approach. The air pollution spatio-temporal model is based on a dynamic linear modelling framework with Bayesian inference. The novel modelling methodology was applied in an urban area for carbon monoxide (CO, mg·m-3), sulfur dioxide (SO2, µg·m-3), ozone (O3, µg·m-3), nitrogen dioxide (NO2, µg·m-3), and particulate matter less than 2.5 µm in aerodynamic diameter (PM2.5, µg·m-3). The proposal complemented and improved the conventional approach in air pollution modelling. The main improvements come from a fast multivariate data description, high spatial-correlation, and adequate modelling of air pollutants with high variability.Joseph Sánchez Balseca is the recipient of a full scholarship from the Secretaria de Educación Superior, Ciencia, Técnología e Innovación (SENESCYT), Ecuador. The authors want to thank the CoDa knowledge management to the Ministry of Science, Innovation and Universities of Spain (Ref: RTI2018-095518-B-C22) and the Agència de Gestió d'Ajuts Universitaris i de Recerca de la Generalitat de Catalunya (Ref. 2017 SGR 1496).Peer ReviewedElsevier Ltd20202020-09-0120202020-12-02journal articlehttp://purl.org/coar/resource_type/c_6501VoRhttp://purl.org/coar/version/c_970fb48d4fbd8a85info:eu-repo/semantics/articleapplication/pdfhttps://hdl.handle.net/2117/333780https://dx.doi.org/10.1016/j.heliyon.2020.e04794reponame:UPCommons. Portal del coneixement obert de la UPCinstname:Universitat Politècnica de Catalunya (UPC)InglésengAgencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 RTI2018-095518-B-C22 TRANSFERENCIA Y DESARROLLO METODOLOGICO DE TECNICAS COMPOSICIONALES PARA LAS CIENCIAS APLICADAS Y LA INGENIERIAopen accesshttp://purl.org/coar/access_right/c_abf2Attribution-NonCommercial-NoDerivatives 4.0 Internationalhttps://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccessoai:upcommons.upc.edu:2117/3337802026-05-27T15:37:01Z
dc.title.none.fl_str_mv Spatio-temporal air pollution modelling using a compositional approach
title Spatio-temporal air pollution modelling using a compositional approach
spellingShingle Spatio-temporal air pollution modelling using a compositional approach
Sánchez Balseca, Joseph|||0000-0002-1741-3229
Air--Pollution--Mathematical models
Statistics
Engineering
Atmospheric science
Environmental analysis
Environmental chemical engineering
Environmental impact assessment
Compositional data
CoDa
Air quality
Environmental statistics
Modelling
Aire -- Contaminació -- Models matemàtics
Àrees temàtiques de la UPC::Desenvolupament humà i sostenible::Degradació ambiental::Contaminació atmosfèrica
title_short Spatio-temporal air pollution modelling using a compositional approach
title_full Spatio-temporal air pollution modelling using a compositional approach
title_fullStr Spatio-temporal air pollution modelling using a compositional approach
title_full_unstemmed Spatio-temporal air pollution modelling using a compositional approach
title_sort Spatio-temporal air pollution modelling using a compositional approach
dc.creator.none.fl_str_mv Sánchez Balseca, Joseph|||0000-0002-1741-3229
Pérez Foguet, Agustí|||0000-0002-2737-4710
author Sánchez Balseca, Joseph|||0000-0002-1741-3229
author_facet Sánchez Balseca, Joseph|||0000-0002-1741-3229
Pérez Foguet, Agustí|||0000-0002-2737-4710
author_role author
author2 Pérez Foguet, Agustí|||0000-0002-2737-4710
author2_role author
dc.subject.none.fl_str_mv Air--Pollution--Mathematical models
Statistics
Engineering
Atmospheric science
Environmental analysis
Environmental chemical engineering
Environmental impact assessment
Compositional data
CoDa
Air quality
Environmental statistics
Modelling
Aire -- Contaminació -- Models matemàtics
Àrees temàtiques de la UPC::Desenvolupament humà i sostenible::Degradació ambiental::Contaminació atmosfèrica
topic Air--Pollution--Mathematical models
Statistics
Engineering
Atmospheric science
Environmental analysis
Environmental chemical engineering
Environmental impact assessment
Compositional data
CoDa
Air quality
Environmental statistics
Modelling
Aire -- Contaminació -- Models matemàtics
Àrees temàtiques de la UPC::Desenvolupament humà i sostenible::Degradació ambiental::Contaminació atmosfèrica
description Air pollutant data are compositional in character because they describe quantitatively the parts of a whole (atmospheric composition). However, it is common to use air pollutant concentrations in statistical models without considering this characteristic of the data and, therefore, without control of common statistical problems, such as spurious correlations and subcompositional incoherence. This paper now proposes a daily multivariate spatio-temporal model with a compositional approach. The air pollution spatio-temporal model is based on a dynamic linear modelling framework with Bayesian inference. The novel modelling methodology was applied in an urban area for carbon monoxide (CO, mg·m-3), sulfur dioxide (SO2, µg·m-3), ozone (O3, µg·m-3), nitrogen dioxide (NO2, µg·m-3), and particulate matter less than 2.5 µm in aerodynamic diameter (PM2.5, µg·m-3). The proposal complemented and improved the conventional approach in air pollution modelling. The main improvements come from a fast multivariate data description, high spatial-correlation, and adequate modelling of air pollutants with high variability.
publishDate 2020
dc.date.none.fl_str_mv 2020
2020-09-01
2020
2020-12-02
dc.type.none.fl_str_mv journal article
http://purl.org/coar/resource_type/c_6501
VoR
http://purl.org/coar/version/c_970fb48d4fbd8a85
dc.type.openaire.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv https://hdl.handle.net/2117/333780
https://dx.doi.org/10.1016/j.heliyon.2020.e04794
url https://hdl.handle.net/2117/333780
https://dx.doi.org/10.1016/j.heliyon.2020.e04794
dc.language.none.fl_str_mv Inglés
eng
language_invalid_str_mv Inglés
language eng
dc.relation.none.fl_str_mv Agencia Estatal de Investigación http://doi.org/10.13039/501100011033 Plan Estatal de Investigación Científica y Técnica y de Innovación 2017-2020 RTI2018-095518-B-C22 TRANSFERENCIA Y DESARROLLO METODOLOGICO DE TECNICAS COMPOSICIONALES PARA LAS CIENCIAS APLICADAS Y LA INGENIERIA
dc.rights.none.fl_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
dc.rights.openaire.fl_str_mv info:eu-repo/semantics/openAccess
rights_invalid_str_mv open access
http://purl.org/coar/access_right/c_abf2
Attribution-NonCommercial-NoDerivatives 4.0 International
https://creativecommons.org/licenses/by-nc-nd/4.0/
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Elsevier Ltd
publisher.none.fl_str_mv Elsevier Ltd
dc.source.none.fl_str_mv reponame:UPCommons. Portal del coneixement obert de la UPC
instname:Universitat Politècnica de Catalunya (UPC)
instname_str Universitat Politècnica de Catalunya (UPC)
reponame_str UPCommons. Portal del coneixement obert de la UPC
collection UPCommons. Portal del coneixement obert de la UPC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15,301603

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