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

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 NO 2 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 NO 2 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 NO 2 differed by area and year. Regression models constructed using the annual NO 2 means from central monitoring stations and geographic parameters predicted home outdoor NO 2 levels better than a dispersion model. However, both the regression and dispersion models underestimated the within-city contrasts of NO 2 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. ► Long-term exposure models developed for 8 different areas in Switzerland. ► Unique comparison of LUR models using seasonal NO 2 measurements made a decade apart. ► Best LUR models used land-use, dispersion, meteorological, and temporal predictors. ► Individual local area models by year best captured NO 2 spatial distribution. ► Implications on the generalizability of LUR models for air pollution health studies.