A dependent Bayesian Dirichlet process model for source apportionment of particle number size distribution

Autores
Baerenbold, Oliver; Meis, Melanie; Martínez Hernández, Israel; Euán, Carolina; Burr, Wesley S.; Tremper, Anja; Fuller, Gary; Pirani, Monica; Blangiardo, Marta
Año de publicación
2022
Idioma
inglés
Tipo de recurso
artículo
Estado
versión publicada
Descripción
The relationship between particle exposure and health risks has been well established in recent years. Particulate matter (PM) is made up of different components coming from several sources, which might have different level of toxicity. Hence, identifying these sources is an important task in order to implement effective policies to improve air quality and population health. The problem of identifying sources of particulate pollution has already been studied in the literature. However, current methods require an a priori specification of the number of sources and do not include information on covariates in the source allocations. Here, we propose a novel Bayesian nonparametric approach to overcome these limitations. In particular, we model source contribution using a Dirichlet process as a prior for source profiles, which allows us to estimate the number of components that contribute to particle concentration rather than fixing this number beforehand. To better characterize them we also include meteorological variables (wind speed and direction) as covariates within the allocation process via a flexible Gaussian kernel. We apply the model to apportion particle number size distribution measured near London Gatwick Airport (UK) in 2019. When analyzing this data, we are able to identify the most common PM sources, as well as new sources that have not been identified with the commonly used methods.
Fil: Baerenbold, Oliver. Imperial College London; Reino Unido
Fil: Meis, Melanie. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina
Fil: Martínez Hernández, Israel. Lancaster University; Reino Unido
Fil: Euán, Carolina. Lancaster University; Reino Unido
Fil: Burr, Wesley S.. Trent University (trent University);
Fil: Tremper, Anja. Imperial College London; Reino Unido
Fil: Fuller, Gary. Imperial College London; Reino Unido
Fil: Pirani, Monica. Imperial College London; Reino Unido
Fil: Blangiardo, Marta. Imperial College London; Reino Unido
Materia
BAYESIAN MODELING
DEPENDENT DIRICHLET PROCESS
PARTICLE CONCENTRATIONS
SOURCE APPORTIONMENT
Nivel de accesibilidad
acceso abierto
Condiciones de uso
https://creativecommons.org/licenses/by/2.5/ar/
Repositorio
CONICET Digital (CONICET)
Institución
Consejo Nacional de Investigaciones Científicas y Técnicas
OAI Identificador
oai:ri.conicet.gov.ar:11336/213061
Acceder
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oai_identifier_str oai:ri.conicet.gov.ar:11336/213061
network_acronym_str CONICETDig
repository_id_str 3498
network_name_str CONICET Digital (CONICET)
spelling A dependent Bayesian Dirichlet process model for source apportionment of particle number size distributionBaerenbold, OliverMeis, MelanieMartínez Hernández, IsraelEuán, CarolinaBurr, Wesley S.Tremper, AnjaFuller, GaryPirani, MonicaBlangiardo, MartaBAYESIAN MODELINGDEPENDENT DIRICHLET PROCESSPARTICLE CONCENTRATIONSSOURCE APPORTIONMENThttps://purl.org/becyt/ford/1.5https://purl.org/becyt/ford/1The relationship between particle exposure and health risks has been well established in recent years. Particulate matter (PM) is made up of different components coming from several sources, which might have different level of toxicity. Hence, identifying these sources is an important task in order to implement effective policies to improve air quality and population health. The problem of identifying sources of particulate pollution has already been studied in the literature. However, current methods require an a priori specification of the number of sources and do not include information on covariates in the source allocations. Here, we propose a novel Bayesian nonparametric approach to overcome these limitations. In particular, we model source contribution using a Dirichlet process as a prior for source profiles, which allows us to estimate the number of components that contribute to particle concentration rather than fixing this number beforehand. To better characterize them we also include meteorological variables (wind speed and direction) as covariates within the allocation process via a flexible Gaussian kernel. We apply the model to apportion particle number size distribution measured near London Gatwick Airport (UK) in 2019. When analyzing this data, we are able to identify the most common PM sources, as well as new sources that have not been identified with the commonly used methods.Fil: Baerenbold, Oliver. Imperial College London; Reino UnidoFil: Meis, Melanie. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; ArgentinaFil: Martínez Hernández, Israel. Lancaster University; Reino UnidoFil: Euán, Carolina. Lancaster University; Reino UnidoFil: Burr, Wesley S.. Trent University (trent University);Fil: Tremper, Anja. Imperial College London; Reino UnidoFil: Fuller, Gary. Imperial College London; Reino UnidoFil: Pirani, Monica. Imperial College London; Reino UnidoFil: Blangiardo, Marta. Imperial College London; Reino UnidoJohn Wiley & Sons Ltd2022-09info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/resource_type/c_6501info:ar-repo/semantics/articuloapplication/pdfapplication/pdfapplication/pdfhttp://hdl.handle.net/11336/213061Baerenbold, Oliver; Meis, Melanie; Martínez Hernández, Israel; Euán, Carolina; Burr, Wesley S.; et al.; A dependent Bayesian Dirichlet process model for source apportionment of particle number size distribution; John Wiley & Sons Ltd; Environmetrics; 34; 1; 9-2022; 1-191180-4009CONICET DigitalCONICETenginfo:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/10.1002/env.2763info:eu-repo/semantics/altIdentifier/doi/10.1002/env.2763info:eu-repo/semantics/openAccesshttps://creativecommons.org/licenses/by/2.5/ar/reponame:CONICET Digital (CONICET)instname:Consejo Nacional de Investigaciones Científicas y Técnicas2025-09-10T13:16:25Zoai:ri.conicet.gov.ar:11336/213061instacron:CONICETInstitucionalhttp://ri.conicet.gov.ar/Organismo científico-tecnológicoNo correspondehttp://ri.conicet.gov.ar/oai/requestdasensio@conicet.gov.ar; lcarlino@conicet.gov.arArgentinaNo correspondeNo correspondeNo correspondeopendoar:34982025-09-10 13:16:25.454CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicasfalse
dc.title.none.fl_str_mv A dependent Bayesian Dirichlet process model for source apportionment of particle number size distribution
title A dependent Bayesian Dirichlet process model for source apportionment of particle number size distribution
spellingShingle A dependent Bayesian Dirichlet process model for source apportionment of particle number size distribution
Baerenbold, Oliver
BAYESIAN MODELING
DEPENDENT DIRICHLET PROCESS
PARTICLE CONCENTRATIONS
SOURCE APPORTIONMENT
title_short A dependent Bayesian Dirichlet process model for source apportionment of particle number size distribution
title_full A dependent Bayesian Dirichlet process model for source apportionment of particle number size distribution
title_fullStr A dependent Bayesian Dirichlet process model for source apportionment of particle number size distribution
title_full_unstemmed A dependent Bayesian Dirichlet process model for source apportionment of particle number size distribution
title_sort A dependent Bayesian Dirichlet process model for source apportionment of particle number size distribution
dc.creator.none.fl_str_mv Baerenbold, Oliver
Meis, Melanie
Martínez Hernández, Israel
Euán, Carolina
Burr, Wesley S.
Tremper, Anja
Fuller, Gary
Pirani, Monica
Blangiardo, Marta
author Baerenbold, Oliver
author_facet Baerenbold, Oliver
Meis, Melanie
Martínez Hernández, Israel
Euán, Carolina
Burr, Wesley S.
Tremper, Anja
Fuller, Gary
Pirani, Monica
Blangiardo, Marta
author_role author
author2 Meis, Melanie
Martínez Hernández, Israel
Euán, Carolina
Burr, Wesley S.
Tremper, Anja
Fuller, Gary
Pirani, Monica
Blangiardo, Marta
author2_role author
author
author
author
author
author
author
author
dc.subject.none.fl_str_mv BAYESIAN MODELING
DEPENDENT DIRICHLET PROCESS
PARTICLE CONCENTRATIONS
SOURCE APPORTIONMENT
topic BAYESIAN MODELING
DEPENDENT DIRICHLET PROCESS
PARTICLE CONCENTRATIONS
SOURCE APPORTIONMENT
purl_subject.fl_str_mv https://purl.org/becyt/ford/1.5
https://purl.org/becyt/ford/1
dc.description.none.fl_txt_mv The relationship between particle exposure and health risks has been well established in recent years. Particulate matter (PM) is made up of different components coming from several sources, which might have different level of toxicity. Hence, identifying these sources is an important task in order to implement effective policies to improve air quality and population health. The problem of identifying sources of particulate pollution has already been studied in the literature. However, current methods require an a priori specification of the number of sources and do not include information on covariates in the source allocations. Here, we propose a novel Bayesian nonparametric approach to overcome these limitations. In particular, we model source contribution using a Dirichlet process as a prior for source profiles, which allows us to estimate the number of components that contribute to particle concentration rather than fixing this number beforehand. To better characterize them we also include meteorological variables (wind speed and direction) as covariates within the allocation process via a flexible Gaussian kernel. We apply the model to apportion particle number size distribution measured near London Gatwick Airport (UK) in 2019. When analyzing this data, we are able to identify the most common PM sources, as well as new sources that have not been identified with the commonly used methods.
Fil: Baerenbold, Oliver. Imperial College London; Reino Unido
Fil: Meis, Melanie. Consejo Nacional de Investigaciones Científicas y Técnicas. Oficina de Coordinación Administrativa Ciudad Universitaria. Centro de Investigaciones del Mar y la Atmósfera. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales. Centro de Investigaciones del Mar y la Atmósfera; Argentina
Fil: Martínez Hernández, Israel. Lancaster University; Reino Unido
Fil: Euán, Carolina. Lancaster University; Reino Unido
Fil: Burr, Wesley S.. Trent University (trent University);
Fil: Tremper, Anja. Imperial College London; Reino Unido
Fil: Fuller, Gary. Imperial College London; Reino Unido
Fil: Pirani, Monica. Imperial College London; Reino Unido
Fil: Blangiardo, Marta. Imperial College London; Reino Unido
description The relationship between particle exposure and health risks has been well established in recent years. Particulate matter (PM) is made up of different components coming from several sources, which might have different level of toxicity. Hence, identifying these sources is an important task in order to implement effective policies to improve air quality and population health. The problem of identifying sources of particulate pollution has already been studied in the literature. However, current methods require an a priori specification of the number of sources and do not include information on covariates in the source allocations. Here, we propose a novel Bayesian nonparametric approach to overcome these limitations. In particular, we model source contribution using a Dirichlet process as a prior for source profiles, which allows us to estimate the number of components that contribute to particle concentration rather than fixing this number beforehand. To better characterize them we also include meteorological variables (wind speed and direction) as covariates within the allocation process via a flexible Gaussian kernel. We apply the model to apportion particle number size distribution measured near London Gatwick Airport (UK) in 2019. When analyzing this data, we are able to identify the most common PM sources, as well as new sources that have not been identified with the commonly used methods.
publishDate 2022
dc.date.none.fl_str_mv 2022-09
dc.type.none.fl_str_mv info:eu-repo/semantics/article
info:eu-repo/semantics/publishedVersion
http://purl.org/coar/resource_type/c_6501
info:ar-repo/semantics/articulo
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/11336/213061
Baerenbold, Oliver; Meis, Melanie; Martínez Hernández, Israel; Euán, Carolina; Burr, Wesley S.; et al.; A dependent Bayesian Dirichlet process model for source apportionment of particle number size distribution; John Wiley & Sons Ltd; Environmetrics; 34; 1; 9-2022; 1-19
1180-4009
CONICET Digital
CONICET
url http://hdl.handle.net/11336/213061
identifier_str_mv Baerenbold, Oliver; Meis, Melanie; Martínez Hernández, Israel; Euán, Carolina; Burr, Wesley S.; et al.; A dependent Bayesian Dirichlet process model for source apportionment of particle number size distribution; John Wiley & Sons Ltd; Environmetrics; 34; 1; 9-2022; 1-19
1180-4009
CONICET Digital
CONICET
dc.language.none.fl_str_mv eng
language eng
dc.relation.none.fl_str_mv info:eu-repo/semantics/altIdentifier/url/https://onlinelibrary.wiley.com/doi/10.1002/env.2763
info:eu-repo/semantics/altIdentifier/doi/10.1002/env.2763
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
https://creativecommons.org/licenses/by/2.5/ar/
eu_rights_str_mv openAccess
rights_invalid_str_mv https://creativecommons.org/licenses/by/2.5/ar/
dc.format.none.fl_str_mv application/pdf
application/pdf
application/pdf
dc.publisher.none.fl_str_mv John Wiley & Sons Ltd
publisher.none.fl_str_mv John Wiley & Sons Ltd
dc.source.none.fl_str_mv reponame:CONICET Digital (CONICET)
instname:Consejo Nacional de Investigaciones Científicas y Técnicas
reponame_str CONICET Digital (CONICET)
collection CONICET Digital (CONICET)
instname_str Consejo Nacional de Investigaciones Científicas y Técnicas
repository.name.fl_str_mv CONICET Digital (CONICET) - Consejo Nacional de Investigaciones Científicas y Técnicas
repository.mail.fl_str_mv dasensio@conicet.gov.ar; lcarlino@conicet.gov.ar
_version_ 1842980893411508224
score 12.993085

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