Discovering oxidative potential (OP) drivers of atmospheric PM10, PM2.5, and PM1 simultaneously in North-Eastern Spain

Ambient particulate matter (PM) is a major contributor to air pollution, leading to adverse health effects on the human population. It has been suggested that the oxidative potential (OP, as a tracer of oxidative stress) of PM is a possible determinant of its health impact. In this study, samples of...

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Autores: In 't Veld, Marten, Pandolfi, Marco, Amato, Fulvio, Pérez, Noemí, Reche, Cristina, Dominutti, P., Jaffrezo, J., Alastuey, Andrés, Querol, Xavier, Uzu, G.
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2022
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/282461
Acceso en línea:http://hdl.handle.net/10261/282461
https://api.elsevier.com/content/abstract/scopus_id/85140287397
Access Level:acceso abierto
Palabra clave:Source apportionment
Oxidative potential
PM(1)
PM(10)
PM(2.5)
Positive matrix factorization
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spelling Discovering oxidative potential (OP) drivers of atmospheric PM10, PM2.5, and PM1 simultaneously in North-Eastern SpainIn 't Veld, MartenPandolfi, MarcoAmato, FulvioPérez, NoemíReche, CristinaDominutti, P.Jaffrezo, J.Alastuey, AndrésQuerol, XavierUzu, G.Source apportionmentOxidative potentialPM(1)PM(10)PM(2.5)Positive matrix factorizationAmbient particulate matter (PM) is a major contributor to air pollution, leading to adverse health effects on the human population. It has been suggested that the oxidative potential (OP, as a tracer of oxidative stress) of PM is a possible determinant of its health impact. In this study, samples of PM10, PM2.5, and PM1 were collected roughly every four days from January 2018 until March 2019 at a Barcelona urban background site and Montseny rural background site in northeastern Spain. We determined the chemical composition of samples, allowing us to perform source apportionment using positive matrix factorization. The OP of PM was determined by measuring reactive oxygen species using dithiothreitol and ascorbic acid assays. Finally, to link the sources with the measured OP, both a Pearson's correlation and a multiple linear regression model were applied to the dataset. The results showed that in Barcelona, the OP of PM10 was much higher than those of PM2.5 and PM1, whereas in Montseny results for all PM sizes were in the same range, but significantly lower than in Barcelona. In Barcelona, several anthropogenic sources were the main drivers of OP in PM10 (Combustion + Road Dust + Heavy Oil + OC-rich) and PM2.5 (Road Dust + Combustion). In contrast, PM1 -associated OP was driven by Industry, with a much lower contribution to PM10 and PM2.5 mass. Meanwhile, Montseny exhibited no clear drivers for OP evolution, likely explaining the lack of a significant difference in OP between PM10, PM2.5, and PM1. Overall, this study indicates that size fraction matters for OP, as a function of the environment typology. In an urban context, OP is driven by the PM10 and PM1 size fractions, whereas only the PM1 fraction is involved in rural environments.The present work was supported by the European Union's Horizon 2020 research and innovation program under grant agreement 101036245 (RI-URBANS); the “Agencia Estatal de Investigación” from the Spanish Ministry of Science and Innovation, and FEDER funds under the projects CAIAC (PID2019-108990RB-I00); and the Generalitat de Catalunya (AGAUR 2017 SGR41) and the Direcció General de Territori.Peer reviewedElsevierEuropean CommissionConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202220222022info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_6501Publisher's versioninfo:eu-repo/semantics/publishedVersionhttp://hdl.handle.net/10261/282461https://api.elsevier.com/content/abstract/scopus_id/85140287397reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Inglés#PLACEHOLDER_PARENT_METADATA_VALUE#info:eu-repo/grantAgreement/EC/H2020/101036245The Science of the total environmenthttps://doi.org/10.1016/j.scitotenv.2022.159386Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/2824612026-05-22T06:33:51Z
dc.title.none.fl_str_mv Discovering oxidative potential (OP) drivers of atmospheric PM10, PM2.5, and PM1 simultaneously in North-Eastern Spain
title Discovering oxidative potential (OP) drivers of atmospheric PM10, PM2.5, and PM1 simultaneously in North-Eastern Spain
spellingShingle Discovering oxidative potential (OP) drivers of atmospheric PM10, PM2.5, and PM1 simultaneously in North-Eastern Spain
In 't Veld, Marten
Source apportionment
Oxidative potential
PM(1)
PM(10)
PM(2.5)
Positive matrix factorization
title_short Discovering oxidative potential (OP) drivers of atmospheric PM10, PM2.5, and PM1 simultaneously in North-Eastern Spain
title_full Discovering oxidative potential (OP) drivers of atmospheric PM10, PM2.5, and PM1 simultaneously in North-Eastern Spain
title_fullStr Discovering oxidative potential (OP) drivers of atmospheric PM10, PM2.5, and PM1 simultaneously in North-Eastern Spain
title_full_unstemmed Discovering oxidative potential (OP) drivers of atmospheric PM10, PM2.5, and PM1 simultaneously in North-Eastern Spain
title_sort Discovering oxidative potential (OP) drivers of atmospheric PM10, PM2.5, and PM1 simultaneously in North-Eastern Spain
dc.creator.none.fl_str_mv In 't Veld, Marten
Pandolfi, Marco
Amato, Fulvio
Pérez, Noemí
Reche, Cristina
Dominutti, P.
Jaffrezo, J.
Alastuey, Andrés
Querol, Xavier
Uzu, G.
author In 't Veld, Marten
author_facet In 't Veld, Marten
Pandolfi, Marco
Amato, Fulvio
Pérez, Noemí
Reche, Cristina
Dominutti, P.
Jaffrezo, J.
Alastuey, Andrés
Querol, Xavier
Uzu, G.
author_role author
author2 Pandolfi, Marco
Amato, Fulvio
Pérez, Noemí
Reche, Cristina
Dominutti, P.
Jaffrezo, J.
Alastuey, Andrés
Querol, Xavier
Uzu, G.
author2_role author
author
author
author
author
author
author
author
author
dc.contributor.none.fl_str_mv European Commission
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Source apportionment
Oxidative potential
PM(1)
PM(10)
PM(2.5)
Positive matrix factorization
topic Source apportionment
Oxidative potential
PM(1)
PM(10)
PM(2.5)
Positive matrix factorization
description Ambient particulate matter (PM) is a major contributor to air pollution, leading to adverse health effects on the human population. It has been suggested that the oxidative potential (OP, as a tracer of oxidative stress) of PM is a possible determinant of its health impact. In this study, samples of PM10, PM2.5, and PM1 were collected roughly every four days from January 2018 until March 2019 at a Barcelona urban background site and Montseny rural background site in northeastern Spain. We determined the chemical composition of samples, allowing us to perform source apportionment using positive matrix factorization. The OP of PM was determined by measuring reactive oxygen species using dithiothreitol and ascorbic acid assays. Finally, to link the sources with the measured OP, both a Pearson's correlation and a multiple linear regression model were applied to the dataset. The results showed that in Barcelona, the OP of PM10 was much higher than those of PM2.5 and PM1, whereas in Montseny results for all PM sizes were in the same range, but significantly lower than in Barcelona. In Barcelona, several anthropogenic sources were the main drivers of OP in PM10 (Combustion + Road Dust + Heavy Oil + OC-rich) and PM2.5 (Road Dust + Combustion). In contrast, PM1 -associated OP was driven by Industry, with a much lower contribution to PM10 and PM2.5 mass. Meanwhile, Montseny exhibited no clear drivers for OP evolution, likely explaining the lack of a significant difference in OP between PM10, PM2.5, and PM1. Overall, this study indicates that size fraction matters for OP, as a function of the environment typology. In an urban context, OP is driven by the PM10 and PM1 size fractions, whereas only the PM1 fraction is involved in rural environments.
publishDate 2022
dc.date.none.fl_str_mv 2022
2022
2022
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_6501
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/282461
https://api.elsevier.com/content/abstract/scopus_id/85140287397
url http://hdl.handle.net/10261/282461
https://api.elsevier.com/content/abstract/scopus_id/85140287397
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv #PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/EC/H2020/101036245
The Science of the total environment
https://doi.org/10.1016/j.scitotenv.2022.159386
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.publisher.none.fl_str_mv Elsevier
publisher.none.fl_str_mv Elsevier
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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