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Development of TracMyAir Smartphone Application for Modeling Exposures to Ambient PM 2.5 and Ozone

Author

Listed:
  • Michael Breen

    (Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA)

  • Catherine Seppanen

    (Institute for the Environment, University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA)

  • Vlad Isakov

    (Office of Research and Development, U.S. Environmental Protection Agency, Research Triangle Park, NC 27711, USA)

  • Saravanan Arunachalam

    (Institute for the Environment, University of North Carolina at Chapel Hill, Chapel Hill, NC 27517, USA)

  • Miyuki Breen

    (Office of Research and Development, ORISE/U.S. Environmental Protection Agency, Chapel Hill, NC 27514, USA)

  • James Samet

    (Office of Research and Development, U.S. Environmental Protection Agency, Chapel Hill, NC 27514, USA)

  • Haiyan Tong

    (Office of Research and Development, U.S. Environmental Protection Agency, Chapel Hill, NC 27514, USA)

Abstract

Air pollution epidemiology studies of ambient fine particulate matter (PM 2.5 ) and ozone (O 3 ) often use outdoor concentrations as exposure surrogates. Failure to account for the variability of the indoor infiltration of ambient PM 2.5 and O 3 , and time indoors, can induce exposure errors. We developed an exposure model called TracMyAir, which is an iPhone application (“app”) that determines seven tiers of individual-level exposure metrics in real-time for ambient PM 2.5 and O 3 using outdoor concentrations, weather, home building characteristics, time-locations, and time-activities. We linked a mechanistic air exchange rate (AER) model, a mass-balance PM 2.5 and O 3 building infiltration model, and an inhaled ventilation model to determine outdoor concentrations (Tier 1), residential AER (Tier 2), infiltration factors (Tier 3), indoor concentrations (Tier 4), personal exposure factors (Tier 5), personal exposures (Tier 6), and inhaled doses (Tier 7). Using the application in central North Carolina, we demonstrated its ability to automatically obtain real-time input data from the nearest air monitors and weather stations, and predict the exposure metrics. A sensitivity analysis showed that the modeled exposure metrics can vary substantially with changes in seasonal indoor-outdoor temperature differences, daily home operating conditions (i.e., opening windows and operating air cleaners), and time spent outdoors. The capability of TracMyAir could help reduce uncertainty of ambient PM 2.5 and O 3 exposure metrics used in epidemiology studies.

Suggested Citation

  • Michael Breen & Catherine Seppanen & Vlad Isakov & Saravanan Arunachalam & Miyuki Breen & James Samet & Haiyan Tong, 2019. "Development of TracMyAir Smartphone Application for Modeling Exposures to Ambient PM 2.5 and Ozone," IJERPH, MDPI, vol. 16(18), pages 1-17, September.
    • Handle: RePEc:gam:jijerp:v:16:y:2019:i:18:p:3468-:d:268223
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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.
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    Cited by:

    1. Jian Hou & Yifang An & Hongfeng Song & Jiancheng Chen, 2019. "The Impact of Haze Pollution on Regional Eco-Economic Treatment Efficiency in China: An Environmental Regulation Perspective," IJERPH, MDPI, vol. 16(21), pages 1-18, October.

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