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Air pollution mixture complexity and its effect on PM2.5-related mortality: A multicountry time-series study in 264 cities

dc.contributor.authorMasselot, Pierre
dc.contributor.authorKan, Haidong
dc.contributor.authorKharol, Shailesh K
dc.contributor.authorBell, Michelle L.
dc.contributor.authorSera, Francesco
dc.contributor.authorLavigne, Eric
dc.contributor.authorBreitner, Susanne
dc.contributor.authordas Neves Pereira da Silva, Susana
dc.contributor.authorBurnett, Richard T.
dc.contributor.authorGasparrini, Antonio
dc.contributor.authorBrook, Jeffrey R.
dc.contributor.authorMCC Collaborative Research Network
dc.date.accessioned2025-04-08T10:15:38Z
dc.date.available2025-04-08T10:15:38Z
dc.date.issued2024-10-30
dc.descriptionMCC Collaborative Research Network - INSA: Susana das Neves Pereira da Silva (Department of Epidemiology, Instituto Nacional de Saúde Dr. Ricardo Jorge, Lisbon, Portugal); Joana Madureira (Department of Environmental Health, Instituto Nacional de Saúde Dr. Ricardo Jorge, Porto, Portugal)
dc.description.abstractBackground: Fine particulate matter (PM2.5) occurs within a mixture of other pollutant gases that interact and impact its composition and toxicity. To characterize the local toxicity of PM2.5, it is useful to have an index that accounts for the whole pollutant mix, including gaseous pollutants. We consider a recently proposed pollutant mixture complexity index (PMCI) to evaluate to which extent it relates to PM2.5 toxicity. Methods: The PMCI is constructed as an index spanning seven different pollutants, relative to the PM2.5 levels. We consider a standard two-stage analysis using data from 264 cities in the Northern Hemisphere. The first stage estimates the city-specific relative risks between daily PM2.5 and all-cause mortality, which are then pooled into a second-stage meta-regression model with which we estimate the effect modification from the PMCI. Results: We estimate a relative excess risk of 1.0042 (95% confidence interval: 1.0023, 1.0061) for an interquartile range increase (from 1.09 to 1.95) of the PMCI. The PMCI predicts a substantial part of within-country relative risk heterogeneity with much less between-country heterogeneity explained. The Akaike information criterion and Bayesian information criterion of the main model are lower than those of alternative meta-regression models considering the oxidative capacity of PM2.5 or its composition. Conclusions: The PMCI represents an efficient and simple predictor of local PM2.5-related mortality, providing evidence that PM2.5 toxicity depends on the surrounding gaseous pollutant mix. With the advent of remote sensing for pollutants, the PMCI can provide a useful index to track air quality.eng
dc.description.abstractWhat this study adds: This study assesses to which extent the complexity of the air pollutant mix, including several gaseous pollutants, can explain differential mortality risks of PM2.5. It shows that this index can represent an efficient summary of the toxicity of PM2.5, especially when comparing cities within the same country.por
dc.description.sponsorshipSupported by Environments and Health Signature Initiative of the Canadian Institutes for Health Research.
dc.identifier.citationEnviron Epidemiol . 2024 Oct 30;8(6):e342. doi: 10.1097/EE9.0000000000000342. eCollection 2024 Dec.
dc.identifier.doi10.1097/EE9.0000000000000342
dc.identifier.eissn2474-7882
dc.identifier.pmid39483640
dc.identifier.urihttp://hdl.handle.net/10400.18/10491
dc.language.isoeng
dc.peerreviewedyes
dc.publisherWolters Kluwer
dc.relation.hasversionhttps://journals.lww.com/environepidem/fulltext/2024/12000/air_pollution_mixture_complexity_and_its_effect_on.9.aspx
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectAir Pollution
dc.subjectFine Particulate Matter
dc.subjectPollutant Mixture
dc.subjectMortality
dc.subjectTime Series
dc.subjectToxicity
dc.subjectMCC
dc.subjectDeterminantes da Saúde e da Doença
dc.subjectToxicologia
dc.titleAir pollution mixture complexity and its effect on PM2.5-related mortality: A multicountry time-series study in 264 citiespor
dc.typeresearch article
dspace.entity.typePublication
oaire.citation.issue6
oaire.citation.startPagee342
oaire.citation.titleEnvironmental Epidemiology
oaire.citation.volume8
oaire.versionhttp://purl.org/coar/version/c_970fb48d4fbd8a85
person.familyNamedas Neves Pereira da Silva
person.givenNameSusana
person.identifier.ciencia-idB718-8EF6-EBD8
person.identifier.orcid0000-0003-2524-0548
relation.isAuthorOfPublication49b180bc-3e03-4347-9e32-5725fd7142f8
relation.isAuthorOfPublication.latestForDiscovery49b180bc-3e03-4347-9e32-5725fd7142f8

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