Long-term exposure models for traffic related NO2 across geographically diverse areas over separate years
Vorschaubild nicht verfügbar
Autor:innen
Sally Liu, L.-J.
Tsai, Ming-Yi
Keidel, Dirk
Gemperli, Armin
Ineichen, Alex
Hazenkamp-von Arx, Marianne
Rochat, Thierry
Künzli, Nino
Ackermann-Liebrich, Ursula
Autor:in (Körperschaft)
Publikationsdatum
2012
Typ der Arbeit
Studiengang
Typ
01A - Beitrag in wissenschaftlicher Zeitschrift
Herausgeber:innen
Herausgeber:in (Körperschaft)
Betreuer:in
Übergeordnetes Werk
Atmospheric Environment
Themenheft
DOI der Originalpublikation
Link
Reihe / Serie
Reihennummer
Jahrgang / Band
46
Ausgabe / Nummer
Seiten / Dauer
460-471
Patentnummer
Verlag / Herausgebende Institution
Elsevier
Verlagsort / Veranstaltungsort
Amsterdam
Auflage
Version
Programmiersprache
Abtretungsempfänger:in
Praxispartner:in/Auftraggeber:in
Zusammenfassung
Although 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
Schlagwörter
Air pollution, Geographic Information Systems (GIS), Land Use Regression (LUR), NO2, Exposure assessment, Meteorology
Fachgebiet (DDC)
300 - Sozialwissenschaften, Soziologie, Anthropologie
610 - Medizin und Gesundheit
610 - Medizin und Gesundheit
Veranstaltung
Startdatum der Ausstellung
Enddatum der Ausstellung
Startdatum der Konferenz
Enddatum der Konferenz
Datum der letzten Prüfung
ISBN
ISSN
1352-2310
0004-6981
0004-6981
Sprache
Englisch
Während FHNW Zugehörigkeit erstellt
Nein
Zukunftsfelder FHNW
Publikationsstatus
Veröffentlicht
Begutachtung
Peer-Review der ganzen Publikation
Open Access-Status
Closed
Lizenz
Zitation
SALLY LIU, L.-J., Ming-Yi TSAI, Dirk KEIDEL, Armin GEMPERLI, Alex INEICHEN, Marianne HAZENKAMP-VON ARX, Lucy BAYER-OGLESBY, Thierry ROCHAT, Nino KÜNZLI, Ursula ACKERMANN-LIEBRICH, Peter STRAEHL, Joel SCHWARTZ und Christian SCHINDLER, 2012. Long-term exposure models for traffic related NO2 across geographically diverse areas over separate years. Atmospheric Environment. 2012. Bd. 46, S. 460–471. DOI 10.1016/j.atmosenv.2011.09.021. Verfügbar unter: https://irf.fhnw.ch/handle/11654/45621