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Effect of geocoding errors on traffic-related air pollutant exposure and concentration estimates

Journal of Exposure Science & Environmental Epidemiology volume 25, pages 490–498 (2015)Cite this article

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Abstract

Exposure to traffic-related air pollutants is highest very near roads, and thus exposure estimates are sensitive to positional errors. This study evaluates positional and PM2.5 concentration errors that result from the use of automated geocoding methods and from linearized approximations of roads in link-based emission inventories. Two automated geocoders (Bing Map and ArcGIS) along with handheld GPS instruments were used to geocode 160 home locations of children enrolled in an air pollution study investigating effects of traffic-related pollutants in Detroit, Michigan. The average and maximum positional errors using the automated geocoders were 35 and 196 m, respectively. Comparing road edge and road centerline, differences in house-to-highway distances averaged 23 m and reached 82 m. These differences were attributable to road curvature, road width and the presence of ramps, factors that should be considered in proximity measures used either directly as an exposure metric or as inputs to dispersion or other models. Effects of positional errors for the 160 homes on PM2.5 concentrations resulting from traffic-related emissions were predicted using a detailed road network and the RLINE dispersion model. Concentration errors averaged only 9%, but maximum errors reached 54% for annual averages and 87% for maximum 24-h averages. Whereas most geocoding errors appear modest in magnitude, 5% to 20% of residences are expected to have positional errors exceeding 100 m. Such errors can substantially alter exposure estimates near roads because of the dramatic spatial gradients of traffic-related pollutant concentrations. To ensure the accuracy of exposure estimates for traffic-related air pollutants, especially near roads, confirmation of geocoordinates is recommended.

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Acknowledgements

The NEXUS study involves a community-based participatory research partnership, and we thank Community Allies Against Asthma (CAAA) and the following organizations: Arab Community Center for Economic and Social Services, Community Health and Social Services Center, Detroit Hispanic Development Corporation, Detroiters Working for Environmental Justice, Friends of Parkside, Detroit Department of Health and Wellness Promotion, Latino Family Services, Southwest Detroit Environmental Vision, Warren/Conner Development Corporation, the University of Michigan School of Medicine, the University of Michigan School of Public Health and an independent community activist. CAAA is an affiliated project of the Detroit Community-Academic Urban Research Center. We also thank Janet Burke, Steve Perry and Dave Heist at the US EPA, Laprisha Berry Vaughn, Sonya Grant, Graciela Menz and other staff at the University of Michigan, and the NEXUS participants and their families who assisted us with the collection of these data. The US Environmental Protection Agency through its Office of Research and Development partially funded the research described here under cooperative agreement R834117 (University of Michigan). It has been subjected to Agency review and approved for publication. The study was conducted as part of NIEHS grants 5-R01-ESO14677-02 and R01 ES016769-01.

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Authors and Affiliations

  1. Department of Civil Engineering, Jaypee University of Information Technology, Solan, India

    Rajiv Ganguly

  2. Environmental Health Sciences, University of Michigan, Ann Arbor, Michigan, USA

    Stuart Batterman

  3. NERL, US EPA, Research Triangle Park, North Carolina, USA

    Vlad Isakov & Michael Breen

  4. University of North Carolina, Chapel Hill, North Carolina, USA

    Michelle Snyder

  5. Community Action Against Asthma (CAAA), Detroit, Michigan, USA

    Wilma Brakefield-Caldwell

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  1. Rajiv Ganguly
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Correspondence to Stuart Batterman.

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Ganguly, R., Batterman, S., Isakov, V. et al. Effect of geocoding errors on traffic-related air pollutant exposure and concentration estimates. J Expo Sci Environ Epidemiol 25, 490–498 (2015). https://doi.org/10.1038/jes.2015.1

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