Long-term exposure models for traffic related NO2 across geographically diverse areas over separate years

dc.contributor.authorSally Liu, L.-J.
dc.contributor.authorTsai, Ming-Yi
dc.contributor.authorKeidel, Dirk
dc.contributor.authorGemperli, Armin
dc.contributor.authorIneichen, Alex
dc.contributor.authorHazenkamp-von Arx, Marianne
dc.contributor.authorBayer-Oglesby, Lucy
dc.contributor.authorRochat, Thierry
dc.contributor.authorKünzli, Nino
dc.contributor.authorAckermann-Liebrich, Ursula
dc.contributor.authorStraehl, Peter
dc.contributor.authorSchwartz, Joel
dc.contributor.authorSchindler, Christian
dc.date.accessioned2024-04-29T12:53:21Z
dc.date.available2024-04-29T12:53:21Z
dc.date.issued2012
dc.description.abstractAlthough recent air pollution epidemiologic studies have embraced land-use regression models for estimating outdoor traffic exposure, few have examined the spatio-temporal variability of traffic related pollution over a long term period and the optimal methods to take these factors into account for exposure estimates. We used home outdoor NO2 measurements taken from eight geographically diverse areas to examine spatio-temporal variations, construct, and evaluate models that could best predict the within-city contrasts in observations. Passive NO2 measurements were taken outside of up to 100 residences per area over three seasons in 1993 and 2003 as part of the Swiss cohort study on air pollution and lung and heart disease in adults (SAPALDIA). The spatio-temporal variation of NO2 differed by area and year. Regression models constructed using the annual NO2 means from central monitoring stations and geographic parameters predicted home outdoor NO2 levels better than a dispersion model. However, both the regression and dispersion models underestimated the within-city contrasts of NO2 levels. Our results indicated that the best models should be constructed for individual areas and years, and would use the dispersion estimates as the urban background, geographic information system (GIS) parameters to enhance local characteristics, and temporal and meteorological variables to capture changing local dynamics. Such models would be powerful tools for assessing health effects from long-term exposure to air pollution in a large cohort
dc.identifier.doihttps://doi.org/10.1016/j.atmosenv.2011.09.021
dc.identifier.issn1352-2310
dc.identifier.issn0004-6981
dc.identifier.urihttps://irf.fhnw.ch/handle/11654/45621
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofAtmospheric Environment
dc.spatialAmsterdam
dc.subjectAir pollution
dc.subjectGeographic Information Systems (GIS)
dc.subjectLand Use Regression (LUR)
dc.subjectNO2
dc.subjectExposure assessment
dc.subjectMeteorology
dc.subject.ddc300 - Sozialwissenschaften, Soziologie, Anthropologie
dc.subject.ddc610 - Medizin und Gesundheit
dc.titleLong-term exposure models for traffic related NO2 across geographically diverse areas over separate years
dc.type01A - Beitrag in wissenschaftlicher Zeitschrift
dc.volume46
dspace.entity.typePublication
fhnw.InventedHereNo
fhnw.ReviewTypeAnonymous ex ante peer review of a complete publication
fhnw.affiliation.hochschuleHochschule für Soziale Arbeitde_CH
fhnw.affiliation.institutInstitut Soziale Arbeit und Gesundheitde_CH
fhnw.openAccessCategoryClosed
fhnw.pagination460-471
fhnw.publicationStatePublished
relation.isAuthorOfPublication017c0337-409d-4019-9982-c988f4fdea67
relation.isAuthorOfPublication.latestForDiscovery017c0337-409d-4019-9982-c988f4fdea67
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