Characterization of source-specific air pollution exposure for a large population-based Swiss cohort (SAPALDIA)

dc.contributor.authorLiu, L.-J. Sally
dc.contributor.authorCurjuric, Ivan
dc.contributor.authorKeidel, Dirk
dc.contributor.authorHeldstab, Jürg
dc.contributor.authorKünzli, Nino
dc.contributor.authorBayer-Oglesby, Lucy
dc.contributor.authorAckermann-Liebrich, Ursula
dc.contributor.authorSchindler, Christian
dc.date.accessioned2024-04-29T06:27:06Z
dc.date.available2024-04-29T06:27:06Z
dc.date.issued2007
dc.description.abstractBackground: Although the dispersion model approach has been used in some epidemiologic studies to examine health effects of traffic-specific air pollution, no study has evaluated the model predictions vigorously. Methods: We evaluated total and traffic-specific particulate matter < 10 and < 2.5 microm in aero-dynamic diameter (PM(10), PM(2.5)), nitrogren dioxide, and nitrogen oxide concentrations predicted by Gaussian dispersion models against fixed-site measurements at different locations, including traffic-impacted, urban-background, and alpine settings between and across cities. The model predictions were then used to estimate individual subjects' historical and cumulative exposures with a temporal trend model. Results: Modeled PM(10) and NO(2) predicted at least 55% and 72% of the variability of the measured PM(10) and NO(2), respectively. Traffic-specific pollution estimates correlated with the NO(x) measurements (R(2) >or=0.77) for background sites but not for traffic sites. Regional background PM(10) accounted for most PM(10) mass in all cities. Whereas traffic PM(10) accounted for < 20% of the total PM(10), it varied significantly within cities. The modeling error for PM(10) was similar within and between cities. Traffic NO(x) accounted for the majority of NO(x) mass in urban areas, whereas background NO(x) accounted for the majority of NO(x) in rural areas. The within-city NO(2) modeling error was larger than that between cities. Conclusions: The dispersion model predicted well the total PM(10), NO(x), and NO(2) and traffic-specific pollution at background sites. However, the model underpredicted traffic NO(x) and NO(2) at traffic sites and needs refinement to reflect local conditions. The dispersion model predictions for PM(10) are suitable for examining individual exposures and health effects within and between cities.
dc.identifier.doihttps://doi.org/10.1289/ehp.10177
dc.identifier.issn0091-6765
dc.identifier.issn1552-9924
dc.identifier.urihttps://irf.fhnw.ch/handle/11654/45635
dc.identifier.urihttps://doi.org/10.26041/fhnw-8910
dc.issue11
dc.language.isoen
dc.publisherNational Institute of Environmental Health Sciences
dc.relation.ispartofEnvironmental Health Perspectives
dc.rights.urihttps://creativecommons.org/publicdomain/zero/1.0/
dc.spatialDurham
dc.subjectcohort study
dc.subjectcumulative exposure
dc.subjectdispersion model
dc.subjectexposure assessment
dc.subjectlong-term exposure
dc.subject.ddc300 - Sozialwissenschaften, Soziologie, Anthropologie
dc.subject.ddc600 - Technik
dc.subject.ddc610 - Medizin und Gesundheit
dc.titleCharacterization of source-specific air pollution exposure for a large population-based Swiss cohort (SAPALDIA)
dc.type01A - Beitrag in wissenschaftlicher Zeitschrift
dc.volume115
dspace.entity.typePublication
fhnw.InventedHereNo
fhnw.ReviewTypeAnonymous ex ante peer review of a complete publication
fhnw.affiliation.hochschuleHochschule für Soziale Arbeit FHNWde_CH
fhnw.affiliation.institutInstitut Soziale Arbeit und Gesundheitde_CH
fhnw.openAccessCategoryDiamond
fhnw.pagination1638-1645
fhnw.publicationStatePublished
relation.isAuthorOfPublication017c0337-409d-4019-9982-c988f4fdea67
relation.isAuthorOfPublication.latestForDiscovery017c0337-409d-4019-9982-c988f4fdea67
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