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Epidemiologic studies have established associations between various airpollutants and adverse health outcomes for adults and children. Due to highcosts of monitoring air pollutant concentrations for subjects enrolled in astudy, statisticians predict exposure concentrations from spatial models thatare developed using concentrations monitored at a few sites. In the absence ofdetailed information on when and where subjects move during the study window,researchers typically assume that the subjects spend their entire day at home,school or work. This assumption can potentially lead to large exposureassignment bias. In this study, we aim to determine the distribution of theexposure assignment bias for an air pollutant (ozone) when subjects are assumedto be static as compared to accounting for individual mobility. To achieve thisgoal, we use cell-phone mobility data on approximately 400,000 users in thestate of Connecticut during a week in July, 2016, in conjunction with an ozonepollution model, and compare individual ozone exposure assuming static versusmobile scenarios. Our results show that exposure models not taking mobilityinto account often provide poor estimates of individuals commuting into and outof urban areas: the average 8-hour maximum difference between these estimatescan exceed 80 parts per billion (ppb). However, for most of the population, thedifference in exposure assignment between the two models is small, therebyvalidating many current epidemiologic studies focusing on exposure to ozone.

Distributions of Human Exposure to Ozone During Commuting Hours in Connecticut using the Cellular Device Network