Characterization of Source-Specific Air Pollution Exposure for a Large Population-Based Swiss Cohort (SAPALDIA)

• Published in: Environmental health perspectives Vol. 115; no. 11; pp. 1638 - 1645
• Main Authors: L. -J. Sally Liu, Ivan Curjuric, Dirk Keidel, Jürg Heldstab, Künzli, Nino, Bayer-Oglesby, Lucy, Ackermann-Liebrich, Ursula, Schindler, Christian, SAPALDIA Team
• Format: Journal Article
• Language: English
• Published: United States National Institute of Environmental Health Sciences. National Institutes of Health. Department of Health, Education and Welfare 01-11-2007; National Institute of Environmental Health Sciences
• Subjects: Air Pollutants - analysis; Air pollution; Air Pollution - analysis; Air quality management; Cities; Cohort Studies; Environmental Exposure - statistics & numerical data; Health aspects; Humans; Laws, regulations and rules; Lung diseases; Modeling; Models, Theoretical; Mortality; Motor vehicle traffic; Nitrogen Dioxide - analysis; Nitrogen Oxides - analysis; Normal Distribution; Particulate Matter; Pollutant emissions; Risk factors; Roads; Switzerland; Traffic; Traffic estimation; Urban Health; Vehicle Emissions - analysis; Switzerland; exposure assessment; cohort study; dispersion model; cumulative exposure; long-term exposure
• ISSN: 0091-6765; 1552-9924
• DOI: 10.1289/ehp.10177

Background: 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 $< 2.5 \mum$ in aerodynamic diameter (PM10, PM2.5), 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 PM10and NO2predicted at least 55% and 72% of the variability of the measured PM10and NO2, respectively. Traffic-specific pollution estimates correlated with the NOxmeasurements ($R^2 \geq 0.77$) for background sites but not for traffic sites. Regional background PM10) accounted for most PM10mass in all cities. Whereas traffic PM10accounted for < 20% of the total PM10, it varied significantly within cities. The modeling error for PM10was similar within and between cities. Traffic NOxaccounted for the majority of NOxmass in urban areas, whereas background NOxaccounted for the majority of NOxin rural areas. The within-city NO2modeling error was larger than that between cities. Conclusions: The dispersion model predicted well the total PM10, NOx, and NO2and traffic-specific pollution at background sites. However, the model underpredicted traffic NOxand NO2at traffic sites and needs refinement to reflect local conditions. The dispersion model predictions for PM10are suitable for examining individual exposures and health effects within and between cities.