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Development of TracMyAir Smart Phone App for Predicting Exposures to Ambient PM2.5 and Ozone
Breen, M., Y. Xu, C. Seppanen, S. Arunachalam, T. Barzyk, J. Burke, D. Diaz-Sanchez, P. Egeghy, S. Graham, V. Ilacqua, V. Isakov, Vasu Kilaru, L. Kolb, J. Langstaff, T. Long, S. Prince, J. Richmond-Bryant, AND R. Williams. Development of TracMyAir Smart Phone App for Predicting Exposures to Ambient PM2.5 and Ozone. 2018 CMAS Conference, Chapel Hill, NC, October 22 - 24, 2018.
To better understand people’s contact with air pollutants, and their potential for adverse health effects, it is important to estimate time spent in different locations and the air pollutant concentrations in those locations. Using personal air monitors to collect this information has several limitations, including burden on participants, cost, and technical expertise required. Alternatively, exposure models must be used by specially-trained researchers, and near real-time predictions are not possible since large and diverse input data (e.g., high temporally resolved air pollution concentrations, weather, time-locations) must be collected, organized, and processed by specialized exposure models. To address these limitations, this project will develop TracMyAir, an iPhone application that predicts near real-time exposures to ambient air pollution (PM2.5, ozone). TracMyAir has benefits for ORD researchers, EPA’s Clean Air Act, and US public health which include: (1) more efficient, cost-effective method for exposure assessments in health studies, which provide scientific basis for air pollution regulations, (2) broaden range of health studies feasible, which are often limited by availability of exposures, (3) improve exposure assessment for health studies, which often use central-site monitor as exposure surrogate, (4) provide public health strategies to help susceptible people, like those with asthma, reduce their exposure to air pollution (e.g., smart phone notifications), (5) future integration with wearable health monitors
To better understand human exposure to air pollutants and their potential for adverse health effects, it is important to account for time spent in different indoor and outdoor locations, and the air pollutant concentrations in those locations. Currently available exposure models have several limitations, including need for substantial technical exposure modeling expertise. Near real-time predictions are not possible since large and diverse input data must be collected, organized, and processed by complex exposure models. To address these limitations, we are developing TracMyAir, an iPhone application that automatically estimates exposures to ambient PM2.5 and ozone based on several sources of input data available from iPhones, including: near real-time ambient air pollution measurements from local monitors, local weather, user’s locations, and building characteristics of user’s home. TracMyAir uses an exposure model that accounts for time spent in different indoor and outdoor locations, and building-specific attenuation of ambient PM2.5 and ozone when indoors. The app will be an efficient and cost-effective tool for researchers to apply exposure predictions for epidemiology studies and other situations where direct air quality monitoring cannot be performed. TracMyAir can also be used for developing public health strategies to help susceptible people (e.g., those with asthma) reduce their exposure to air pollution. Future versions of TracMyAir can be integrated with air quality models when nearby monitoring data is not available.
Record Details:Record Type: DOCUMENT (PRESENTATION/SLIDE)
Organization:U.S. ENVIRONMENTAL PROTECTION AGENCY
OFFICE OF RESEARCH AND DEVELOPMENT
NATIONAL EXPOSURE RESEARCH LABORATORY
COMPUTATIONAL EXPOSURE DIVISION
HUMAN EXPOSURE & DOSE MODELING BRANCH