Integrating Time-Activity and Air Quality Sensors and Models into Smartphone-based PM2.5 and Ozone Exposure Model (TracMyAir)
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Epidemiologic studies of ambient fine particulate matter (PM2.5) and ozone (O3) often use outdoor concentrations as exposure surrogates, which can induce measurement error. We developed an exposure model called TracMyAir, which is an iPhone application that determines eight tiers of individual-level exposure metrics in real-time for ambient PM2.5 and O3 using outdoor concentrations, home building characteristics, weather, time-locations, and time-activities. In this study, we extended TracMyAir by including (1) outdoor concentrations from an air quality model (CMAQ) and air monitoring network (OpenAQ), (2) indoor and outdoor PM2.5 concentrations from low-cost air sensors (PurpleAir), (3) a microenvironment (ME) model (MicroTrac) based on time-resolved smartphone geolocations, and (4) inhaled ventilation models based on physical activity data from smartphone and smartwatch accelerometers and heart rate sensors. The eight tiers of exposure metrics with increasing information needs and model complexity include: residential air exchange rates (AER, Tier 1), infiltration factors (Finf_home, Tier 2), indoor concentrations (Cin_home, Tier 3); and personal outdoor concentrations (Cout, Tier 4), time spent in ME (TME, Tier 5), exposure factors (Fpex, Tier 6), exposures (E, Tier 7), and inhaled doses (D, Tier 8). We applied TracMyAir to determine hourly PM2.5 and O3 exposure metrics for two panel studies with nine participants living in central North Carolina: one study with 216 participant¿hours during November 2019, and another study with 648 participant-hours during September-October 2020. The TracMyAir predictions showed considerable temporal and house-to-house variability of AER, Finf_home, and Cin_home (Tiers 1-3), and person-to-person variability of Cout, TME, Fpex, E, and D (Tiers 4-8). Our study demonstrates the capability of extending TracMyAir with air quality and time-activity sensors and models to estimate individual-level ambient PM2.5 and O3 exposure metrics, in support of epidemiologic studies and public health strategies to help individuals reduce their exposures to ambient air pollutants.