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Quantifying traffic exposure

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.

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Acknowledgements

We thank Megan Forbes of the Minnesota Department of Transportation for her help in obtaining and understanding traffic count data and Shawn Nelson of the Minnesota Pollution Control Agency for assistance with geoprocessing. We also thank Julian Marshall and Matthew Bechle of the Civil Engineering Department at the University of Minnesota for allowing us to use their land-use regression data. This work was supported in part by grant #R833627010 (‘Measuring the Impacts of Particulate Matter Reductions by Environmental Health Outcome Indicators’) from the US Environmental Protection Agency. This study was reviewed and approved by the Olmsted Medical Center IRB for use of REP data.

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Correspondence to Gregory C Pratt.

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Pratt, G., Parson, K., Shinoda, N. et al. Quantifying traffic exposure. J Expo Sci Environ Epidemiol 24, 290–296 (2014). https://doi.org/10.1038/jes.2013.51

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  • DOI: https://doi.org/10.1038/jes.2013.51

Keywords

  • traffic
  • density
  • land-use regression
  • geographic information system
  • global positioning system

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