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TracMyAir App: Using Smartphones to Predict Near Real-time Air Pollution Exposures
Citation:
Breen, M. TracMyAir App: Using Smartphones to Predict Near Real-time Air Pollution Exposures. A-E BOSC Subcommittee Meeting, Hillsborough, NC, February 17 - 19, 2021.
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. 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 multiple tiers of individual-level exposure metrics in real-time for ambient PM2.5 and O3.
Description:
Air pollution epidemiology studies of ambient fine particulate matter (PM2.5) and ozone (O3) often use outdoor concentrations as exposure surrogates. Failure to account for variability of indoor infiltration of ambient PM2.5 and O3, 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 PM2.5 and O3 using outdoor concentrations, weather, home building characteristics, time-locations, and time-activities. We linked a mechanistic air exchange rate (AER) model, mass-balance PM2.5 and O3 building infiltration model, and 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 App 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, operating air cleaners), and time spent outdoors. The capability of TracMyAir could help reduce uncertainty of ambient PM2.5 and O3 exposure metrics used in epidemiology studies.