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

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Authors
Sally Liu, L.-J.
Tsai, Ming-Yi
Keidel, Dirk
Gemperli, Armin
Ineichen, Alex
Hazenkamp-von Arx, Marianne
Rochat, Thierry
Künzli, Nino
Ackermann-Liebrich, Ursula
Author (Corporation)
Publication date
2012
Typ of student thesis
Course of study
Type
01A - Journal article
Editors
Editor (Corporation)
Supervisor
Parent work
Atmospheric Environment
Special issue
DOI of the original publication
Link
Series
Series number
Volume
46
Issue / Number
Pages / Duration
460-471
Patent number
Publisher / Publishing institution
Elsevier
Place of publication / Event location
Amsterdam
Edition
Version
Programming language
Assignee
Practice partner / Client
Abstract
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
Keywords
Air pollution, Geographic Information Systems (GIS), Land Use Regression (LUR), NO2, Exposure assessment, Meteorology
Subject (DDC)
300 - Sozialwissenschaften, Soziologie, Anthropologie
610 - Medizin und Gesundheit
Project
Event
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ISBN
ISSN
1352-2310
0004-6981
Language
English
Created during FHNW affiliation
No
Strategic action fields FHNW
Publication status
Published
Review
Peer review of the complete publication
Open access category
Closed
License
Citation
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