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Comparison of Highly Resolved Model-Based Exposure Metrics for Traffic-Related Air Pollutants to Support Environmental Health Studies

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  • Shih Ying Chang

    (Institute for the Environment, University of North Carolina at Chapel Hill, 100 Europa Drive, Suite 490, Chapel Hill, NC 27517, USA
    Department of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA)

  • William Vizuete

    (Department of Environmental Sciences and Engineering, University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA)

  • Michael Breen

    (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)

  • Saravanan Arunachalam

    (Institute for the Environment, University of North Carolina at Chapel Hill, 100 Europa Drive, Suite 490, Chapel Hill, NC 27517, USA)

Abstract

Human exposure to air pollution in many studies is represented by ambient concentrations from space-time kriging of observed values. Space-time kriging techniques based on a limited number of ambient monitors may fail to capture the concentration from local sources. Further, because people spend more time indoors, using ambient concentration to represent exposure may cause error. To quantify the associated exposure error, we computed a series of six different hourly-based exposure metrics at 16,095 Census blocks of three Counties in North Carolina for CO, NO x , PM 2.5 , and elemental carbon (EC) during 2012. These metrics include ambient background concentration from space-time ordinary kriging (STOK), ambient on-road concentration from the Research LINE source dispersion model (R-LINE), a hybrid concentration combining STOK and R-LINE, and their associated indoor concentrations from an indoor infiltration mass balance model. Using a hybrid-based indoor concentration as the standard, the comparison showed that outdoor STOK metrics yielded large error at both population (67% to 93%) and individual level (average bias between −10% to 95%). For pollutants with significant contribution from on-road emission (EC and NO x ), the on-road based indoor metric performs the best at the population level (error less than 52%). At the individual level, however, the STOK-based indoor concentration performs the best (average bias below 30%). For PM 2.5 , due to the relatively low contribution from on-road emission (7%), STOK-based indoor metric performs the best at both population (error below 40%) and individual level (error below 25%). The results of the study will help future epidemiology studies to select appropriate exposure metric and reduce potential bias in exposure characterization.

Suggested Citation

  • Shih Ying Chang & William Vizuete & Michael Breen & Vlad Isakov & Saravanan Arunachalam, 2015. "Comparison of Highly Resolved Model-Based Exposure Metrics for Traffic-Related Air Pollutants to Support Environmental Health Studies," IJERPH, MDPI, vol. 12(12), pages 1-21, December.
    • Handle: RePEc:gam:jijerp:v:12:y:2015:i:12:p:15007-15625:d:60249
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    References listed on IDEAS

    as
    1. Michael S. Breen & Janet M. Burke & Stuart A. Batterman & Alan F. Vette & Christopher Godwin & Carry W. Croghan & Bradley D. Schultz & Thomas C. Long, 2014. "Modeling Spatial and Temporal Variability of Residential Air Exchange Rates for the Near-Road Exposures and Effects of Urban Air Pollutants Study (NEXUS)," IJERPH, MDPI, vol. 11(11), pages 1-24, November.
    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. 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.
    4. 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.
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