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Smartphone App (TracMyAir) to Model Individual Exposures to Ambient Air Pollutants
Citation:
Breen, M., C. Seppanen, V. Isakov, M. Breen, S. Arunachalam, J. Samet, R. Devlin, W. Cascio, D. Diaz-Sanchez, AND H. Tong. Smartphone App (TracMyAir) to Model Individual Exposures to Ambient Air Pollutants. The International Society of Exposure Science (ISES) 2020 Annual Meeting, NA, NC, September 20 - 24, 2020.
Impact/Purpose:
Epidemiological studies of ambient fine particulate matter (PM2.5) and ozone (O3) often use outdoor concentrations from central-site monitors as exposure surrogates. Failure to account for variability of indoor infiltration of ambient PM2.5 and O3, and time spent indoors can introduce exposure errors. We developed a smartphone-based exposure model called TracMyAir, which automatically determines five tiers of individual-level exposure metrics in real-time for ambient PM2.5 and O3. Our study demonstrates the ability to apply a smartphone-based exposure model to determine multiple tiers of exposure metrics for epidemiological investigations, in support of improving health risk assessments. TracMyAir could also be used for public health strategies to help at risk individuals reduce their exposures to ambient air pollutants, such as wildfire smoke.
Description:
Epidemiological studies of ambient fine particulate matter (PM2.5) and ozone (O3) often use outdoor concentrations from central-site monitors as exposure surrogates. Failure to account for variability of indoor infiltration of ambient PM2.5 and O3, and time spent indoors can introduce exposure errors. Personal air pollution measurements are often not feasible, and currently available exposure models require substantial technical expertise and near real-time exposure estimates are not possible since they require collection, organization and processing of large and diverse input data. To address these limitations, we developed a smartphone-based exposure model called TracMyAir, which automatically determines five tiers of individual-level exposure metrics in real-time for ambient PM2.5 and O3. The input data for TracMyAir includes: (1) outdoor concentrations from the nearest air pollution monitors including official US monitors from AirNow, low-cost sensors from PurpleAir, and international monitors from OpenAQ; (2) outdoor temperatures and wind speeds from the nearest weather stations; (3) the user’s geolocation and physical activity level (PAL) from smartphone sensors; and (4) the user’s home building characteristics and operating conditions. We linked a residential air exchange rate model, building infiltration model, a geolocation-based microenvironment model, and PAL-based ventilation rate model to determine residential infiltration factors (Finf_home, Tier 1), residential indoor concentrations (Cin_home, Tier 2), personal exposure factors (Fpex, Tier 3), exposures (E, Tier 4), and inhaled doses (D, Tier 5). We applied TracMyAir to determine daily 24 h average PM2.5 and O3 exposure metrics (Tiers 1-5) for PISCES, a panel study conducted in central North Carolina examining the role of dietary omega-3 fatty acids modulating the health effects of air pollution. Daily modeled exposure metrics showed considerable temporal and home-to-home variability of Finf_home and Cin_home (Tiers 1-2) and person-to-person variability of Fpex, E, and D (Tiers 3-5). Our study demonstrates the ability to apply a smartphone-based exposure model to determine multiple tiers of exposure metrics for epidemiological investigations, in support of improving health risk assessments. TracMyAir could also be used for public health strategies to help at risk individuals reduce their exposures to ambient air pollutants, such as wildfire smoke.