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

Owais Gilani (), Simon Urbanek and Michael J. Kane
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Owais Gilani: Bucknell University
Simon Urbanek: AT&T Labs Research
Michael J. Kane: Yale University

Journal of Agricultural, Biological and Environmental Statistics, 2020, vol. 25, issue 1, No 4, 54-73

Abstract: Abstract 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, USA 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-h 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. Supplementary materials accompanying this paper appear online.

Keywords: Environmental epidemiology; Exposure assignment bias; Human mobility; Pollutant modeling (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s13253-019-00378-y

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