Citation

Pratt GC, Parson K, Shinoda N, Lindgren P, Dunlap S, Yawn B, Wollan P, Johnson J. J. Expo. Sci. Environ. Epidemiol. 2014; 24(3): 290-296.

Affiliation

Minnesota Pollution Control Agency, St. Paul, Minnesota, USA.

Copyright

(Copyright © 2014, Nature Publishing Group)

DOI

10.1038/jes.2013.51

PMID

24045427

Abstract

Living near traffic adversely affects health outcomes. Traffic exposure metrics include distance to high-traffic roads, traffic volume on nearby roads, traffic within buffer distances, measured pollutant concentrations, land-use regression estimates of pollution concentrations, and others. We used Geographic Information System software to explore a new approach using traffic count data and a kernel density calculation to generate a traffic density surface with a resolution of 50 m. The density value in each cell reflects all the traffic on all the roads within the distance specified in the kernel density algorithm. The effect of a given roadway on the raster cell value depends on the amount of traffic on the road segment, its distance from the raster cell, and the form of the algorithm. We used a Gaussian algorithm in which traffic influence became insignificant beyond 300 m. This metric integrates the deleterious effects of traffic rather than focusing on one pollutant. The density surface can be used to impute exposure at any point, and it can be used to quantify integrated exposure along a global positioning system route. The traffic density calculation compares favorably with other metrics for assessing traffic exposure and can be used in a variety of applications.Journal of Exposure Science and Environmental Epidemiology advance online publication, 18 September 2013; doi:10.1038/jes.2013.51.

Language: en