Using Cell Phone Location To Assess Misclassification Errors In Air Pollution Exposure Estimation

Keywords

Air pollution; Call detail record; Exposure estimation; Exposure misclassification; Health assessment

Abstract

Air pollution epidemiologic and health impact studies often rely on home addresses to estimate individual subject's pollution exposure. In this study, we used detailed cell phone location data, the call detail record (CDR), to account for the impact of spatiotemporal subject mobility on estimates of ambient air pollutant exposure. This approach was applied on a sample with 9886 unique simcard IDs in Shenzhen, China, on one mid-week day in October 2013. Hourly ambient concentrations of six chosen pollutants were simulated by the Community Multi-scale Air Quality model fused with observational data, and matched with detailed location data for these IDs. The results were compared with exposure estimates using home addresses to assess potential exposure misclassification errors. We found the misclassifications errors are likely to be substantial when home location alone is applied. The CDR based approach indicates that the home based approach tends to over-estimate exposures for subjects with higher exposure levels and under-estimate exposures for those with lower exposure levels. Our results show that the cell phone location based approach can be used to assess exposure misclassification error and has the potential for improving exposure estimates in air pollution epidemiology studies. Cell phone location-based exposure estimation has the potential for improving exposure estimates vs. home address-based approaches that are likely to have increased misclassification errors because it does not account for individual mobility.

Publication Date

2-1-2018

Publication Title

Environmental Pollution

Volume

233

Number of Pages

261-266

Document Type

Article

Personal Identifier

scopus

DOI Link

https://doi.org/10.1016/j.envpol.2017.10.077

Socpus ID

85032302053 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/85032302053

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