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A Comparison of Exposure Metrics for Traffic-Related Air Pollutants: Application to Epidemiology Studies in Detroit, Michigan

Author

Listed:
  • Stuart Batterman

    (Department of Environmental Health Sciences, School of Public Health, University of Michigan, 1420 Washington Heights, Ann Arbor, MI 48109, USA)

  • Janet Burke

    (National Exposure Research Laboratory, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA)

  • Vlad Isakov

    (National Exposure Research Laboratory, U.S. Environmental Protection Agency, 109 T.W. Alexander Drive, Research Triangle Park, NC 27711, USA)

  • Toby Lewis

    (Department of Pediatric Pulmonary, Medical School, University of Michigan, 1500 East Medical Center Drive, Ann Arbor, MI 48109, USA)

  • Bhramar Mukherjee

    (Department of Biostatistics, School of Public Health, University of Michigan, 1420 Washington Heights, Ann Arbor, MI 48109, USA)

  • Thomas Robins

    (Department of Environmental Health Sciences, School of Public Health, University of Michigan, 1420 Washington Heights, Ann Arbor, MI 48109, USA)

Abstract

Vehicles are major sources of air pollutant emissions, and individuals living near large roads endure high exposures and health risks associated with traffic-related air pollutants. Air pollution epidemiology, health risk, environmental justice, and transportation planning studies would all benefit from an improved understanding of the key information and metrics needed to assess exposures, as well as the strengths and limitations of alternate exposure metrics. This study develops and evaluates several metrics for characterizing exposure to traffic-related air pollutants for the 218 residential locations of participants in the NEXUS epidemiology study conducted in Detroit (MI, USA). Exposure metrics included proximity to major roads, traffic volume, vehicle mix, traffic density, vehicle exhaust emissions density, and pollutant concentrations predicted by dispersion models. Results presented for each metric include comparisons of exposure distributions, spatial variability, intraclass correlation, concordance and discordance rates, and overall strengths and limitations. While showing some agreement, the simple categorical and proximity classifications (e.g., high diesel/low diesel traffic roads and distance from major roads) do not reflect the range and overlap of exposures seen in the other metrics. Information provided by the traffic density metric, defined as the number of kilometers traveled (VKT) per day within a 300 m buffer around each home, was reasonably consistent with the more sophisticated metrics. Dispersion modeling provided spatially- and temporally-resolved concentrations, along with apportionments that separated concentrations due to traffic emissions and other sources. While several of the exposure metrics showed broad agreement, including traffic density, emissions density and modeled concentrations, these alternatives still produced exposure classifications that differed for a substantial fraction of study participants, e.g., from 20% to 50% of homes, depending on the metric, would be incorrectly classified into “low”, “medium” or “high” traffic exposure classes. These and other results suggest the potential for exposure misclassification and the need for refined and validated exposure metrics. While data and computational demands for dispersion modeling of traffic emissions are non-trivial concerns, once established, dispersion modeling systems can provide exposure information for both on- and near-road environments that would benefit future traffic-related assessments.

Suggested Citation

  • Stuart Batterman & Janet Burke & Vlad Isakov & Toby Lewis & Bhramar Mukherjee & Thomas Robins, 2014. "A Comparison of Exposure Metrics for Traffic-Related Air Pollutants: Application to Epidemiology Studies in Detroit, Michigan," IJERPH, MDPI, vol. 11(9), pages 1-25, September.
  • Handle: RePEc:gam:jijerp:v:11:y:2014:i:9:p:9553-9577:d:40250
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    References listed on IDEAS

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    1. Vlad Isakov & Saravanan Arunachalam & Stuart Batterman & Sarah Bereznicki & Janet Burke & Kathie Dionisio & Val Garcia & David Heist & Steve Perry & Michelle Snyder & Alan Vette, 2014. "Air Quality Modeling in Support of the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS)," IJERPH, MDPI, vol. 11(9), pages 1-17, August.
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    Cited by:

    1. Wenqi Wu & James Stamey & David Kahle, 2015. "A Bayesian Approach to Account for Misclassification and Overdispersion in Count Data," IJERPH, MDPI, vol. 12(9), pages 1-14, August.
    2. Michelle Snyder & Saravanan Arunachalam & Vlad Isakov & Kevin Talgo & Brian Naess & Alejandro Valencia & Mohammad Omary & Neil Davis & Rich Cook & Adel Hanna, 2014. "Creating Locally-Resolved Mobile-Source Emissions Inputs for Air Quality Modeling in Support of an Exposure Study in Detroit, Michigan, USA," IJERPH, MDPI, vol. 11(12), pages 1-28, December.
    3. Sheena E. Martenies & Chad W. Milando & Guy O. Williams & Stuart A. Batterman, 2017. "Disease and Health Inequalities Attributable to Air Pollutant Exposure in Detroit, Michigan," IJERPH, MDPI, vol. 14(10), pages 1-24, October.
    4. Stuart Batterman & Rajiv Ganguly & Paul Harbin, 2015. "High Resolution Spatial and Temporal Mapping of Traffic-Related Air Pollutants," IJERPH, MDPI, vol. 12(4), pages 1-21, April.
    5. Daniela Dias & Oxana Tchepel, 2018. "Spatial and Temporal Dynamics in Air Pollution Exposure Assessment," IJERPH, MDPI, vol. 15(3), pages 1-23, March.
    6. Saravanan Arunachalam & Alejandro Valencia & Yasuyuki Akita & Marc L. Serre & Mohammad Omary & Valerie Garcia & Vlad Isakov, 2014. "A Method for Estimating Urban Background Concentrations in Support of Hybrid Air Pollution Modeling for Environmental Health Studies," IJERPH, MDPI, vol. 11(10), pages 1-19, October.
    7. Vasilis Kazakos & Zhiwen Luo & Ian Ewart, 2020. "Quantifying the Health Burden Misclassification from the Use of Different PM 2.5 Exposure Tier Models: A Case Study of London," IJERPH, MDPI, vol. 17(3), pages 1-21, February.

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