  Epidemiologic studies have established associations between various air
pollutants and adverse health outcomes for adults and children. Due to high
costs of monitoring air pollutant concentrations for subjects enrolled in a
study, statisticians predict exposure concentrations from spatial models that
are developed using concentrations monitored at a few sites. In the absence of
detailed 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 exposure
assignment bias. In this study, we aim to determine the distribution of the
exposure assignment bias for an air pollutant (ozone) when subjects are assumed
to be static as compared to accounting for individual mobility. To achieve this
goal, we use cell-phone mobility data on approximately 400,000 users in the
state of Connecticut during a week in July, 2016, in conjunction with an ozone
pollution model, and compare individual ozone exposure assuming static versus
mobile scenarios. Our results show that exposure models not taking mobility
into account often provide poor estimates of individuals commuting into and out
of urban areas: the average 8-hour maximum difference between these estimates
can exceed 80 parts per billion (ppb). However, for most of the population, the
difference in exposure assignment between the two models is small, thereby
validating many current epidemiologic studies focusing on exposure to ozone.
