Exposure to air pollution is associated with a variety of adverse health outcomes. Measuring human exposures to ambient air pollutants is challenging, particularly in large epidemiologic studies in which direct monitoring is not feasible. Thus, several exposure estimation methods, including land use regression and Kriging, have been developed to estimate individual exposures within urban areas. A major limitation of these methods is their use of residential address to estimate exposures. Because of the variation in air pollutant concentrations within an urban area, a residential exposure may differ substantially from exposures experienced while away from home. We propose an innovative, feasible, and cost-effective method to measure time-activity data, i.e. human movement over time, and incorporate these data into current residence-based methods of air pollutant exposure estimation. We will use cell phones equipped with global positioning system (GPS) to measure the daily movements of 40 cell phone-using volunteers in western New York for a period of three months. The cell phones will measure and record the person's location, measured as geocoordinates, several times a day throughout the study period. We will also design two models to estimate fine particulate matter (PM2.5) concentrations in this region. These models, a land use regression model and a Kriging model, will both be constructed from PM2.5 concentrations measured at local monitoring sites. We will apply the geocoordinates measured using the cell phones to each of the models to obtain a cell phone-based PM2.5 exposure estimate for each participant. We will develop techniques to improve the efficiency of this procedure so that it is feasible for use in epidemiologic studies. By incorporating time-activity data into air pollutant exposure estimation models, we will improve the accuracy with which we can measure the associations between air pollution and health outcomes.

Public Health Relevance

This project will be the development of a method to improve our ability to estimate human exposures to air pollutants. This method will improve public health by allowing researchers to more accurately measure human air pollutant exposures and relate these exposures to health outcomes.

National Institute of Health (NIH)
National Institute of Environmental Health Sciences (NIEHS)
Exploratory/Developmental Grants (R21)
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Biostatistical Methods and Research Design Study Section (BMRD)
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Gray, Kimberly A
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State University of New York at Buffalo
Public Health & Prev Medicine
Schools of Allied Health Profes
United States
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Glasgow, Mark L; Rudra, Carole B; Yoo, Eun-Hye et al. (2016) Using smartphones to collect time-activity data for long-term personal-level air pollution exposure assessment. J Expo Sci Environ Epidemiol 26:356-64
Mu, Lina; Liu, Li; Niu, Rungui et al. (2013) Indoor air pollution and risk of lung cancer among Chinese female non-smokers. Cancer Causes Control 24:439-50
Li, Yanli; Rittenhouse-Olson, Kate; Scheider, William L et al. (2012) Effect of particulate matter air pollution on C-reactive protein: a review of epidemiologic studies. Rev Environ Health 27:133-49