Through the use of cellphones to measure daily movements of study volunteers, this project aims to improve the accuracy with which to measure the associations between air pollution and health outcomes.
Title: Use of Cellphone-based Time-activity Data for Air Pollutant Exposure Estimation
Principal Investigator: Lina Mu, MD, PhD
Funding Agency: National Institute of Environmental Health Sciences
Period: 09/20/10 - 01/31/14
Abstract: 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 are developing 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 cellphones equipped with global positioning system (GPS) to measure the daily movements of 40 cellphone-using volunteers in western New York for a period of three months.
The cellphones will measure and record the person’s location, measured as geocoordinates, several times a day throughout the study period. We will also design land use regression-based and Kriging-based models to estimate fine particulate matter (PM_2.5 ) concentrations in this region. We will apply the geocoordinates measured using the cellphones to each of the models to obtain a cellphone-based PM_2.5 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.