Abstract

An accurate estimation of human exposure to ambient air pollution is crucial for air pollution health studies. Time-activity patterns may introduce substantial uncertainties in exposure estimation. As smartphones are becoming increasingly popular and their ownership is becoming ubiquitous in the US. Virtually all smartphones can collect location data, and such data is continuously somewhere. Therefore, it is clear that such stored location data has the potential to be used for characterizing an individual's time-activity patterns for air pollution health studies. However, studies on the accuracy and feasibility of using a smartphone's location data in air pollution exposure estimation are still limited. Here, a pilot study was conducted to evaluate the accuracy of the iPhone's Significant Location (iSL) data, in capturing an individual's time-activity patterns. Specifically, iSL data collected from a single individual were compared with reference GPS data to evaluate the ability of iSL in capturing: 1) all microenvironments the subject visited during the study period; 2) the duration and frequency the subject spent in each microenvironment, if the location is labelled as significant and captured by iSL; and 3) the impact of neglecting time-activity pattern on the subject's air pollution exposure estimates. The results showed a favorable performance of the iSL data, which accurately captured the time the subject spent in 16 microenvironments encompassing 93% of all time during the study period. To further understand the availability of iSL data, an online survey was conducted among 349 participants. Among the surveyed users, 72% have iSL data available which highlighted the potential substantial coverage of iSL data. With the popularity of iPhone, detailed significant location data could be available for a considerable portion of the population, and such iSL data may have great potentials for improving retrospective air pollution exposure estimation.

Notes

If this is your thesis or dissertation, and want to learn how to access it or for more information about readership statistics, contact us at STARS@ucf.edu.

Graduation Date

2021

Semester

Fall

Advisor

Yu, Haofei

Degree

Master of Science (M.S.)

College

College of Engineering and Computer Science

Department

Civil, Environmental and Construction Engineering

Degree Program

Environmental Engineering; Environmental Engineering Sciences

Format

application/pdf

Identifier

CFE0008828; DP0026107

Language

English

Release Date

December 2021

Length of Campus-only Access

None

Access Status

Masters Thesis (Open Access)

Share

COinS