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Integrating CMAQ Model into Smartphone App (TracMyAir) for Modeling Exposures to Ambient PM2.5 and Ozone
Citation:
Breen, M., C. Seppanen, V. Isakov, S. Arunachalam, M. Breen, J. Samet, H. Tong, AND W. Cascio. Integrating CMAQ Model into Smartphone App (TracMyAir) for Modeling Exposures to Ambient PM2.5 and Ozone. 19th Annual Community Modeling and Analysis System (CMAS) Conference, NA, NC, October 26 - 30, 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, time spent indoors, and personal inhalation rates, can introduce exposure errors. To address these limitations, we developed a smartphone-based exposure model called TracMyAir, which automatically determines individual-level exposure metrics in real-time for ambient PM2.5 and O3. TracMyAir can improve exposure assessments for epidemiological investigations, in support of improving health risk assessments. Also, the forecasting capability of TracMyAir could be used for public health strategies to help at risk individuals reduce their exposures to ambient air pollutants.
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, time spent indoors, and personal inhalation rates, 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 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, (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 previously linked a building infiltration model, a geolocation-based microenvironment model, and PAL-based inhalation rate model to determine personal exposures and inhaled doses. In this study, we integrated the CMAQ air quality model into the TracMyAir smartphone app to account for the spatio-temporal variability of outdoor PM2.5 and O3 concentrations, and enable future forecasting capabilities. The extended TracMyAir app is being evaluated in a pilot study in central North Carolina. Using the air quality model to account for the variability of outdoor air pollutants can improve exposure assessments for epidemiological investigations, in support of improving health risk assessments. Also, the forecasting capability of TracMyAir could be used for public health strategies to help at risk individuals reduce their exposures to ambient air pollutants, such as wildfire smoke.