Document Type

Journal Article

School

Natural Science

Comments

This article was originally published as: Nethery, E., Mallach, G., Rainham, D., Goldberg, M. S., & Wheeler, A. J. (2014). Using Global Positioning Systems (GPS) and temperature data to generate time-activity classifications for estimating personal exposure in air monitoring studies: an automated method. Environmental Health, 13(1), 33. Original article available here


The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.


Abstract

Background: Personal exposure studies of air pollution generally use self-reported diaries to capture individuals’ time-activity data. Enhancements in the accuracy, size, memory and battery life of personal Global Positioning Systems (GPS) units have allowed for higher resolution tracking of study participants’ locations. Improved time activity classifications combined with personal continuous air pollution sampling can improve assessments of location-related air pollution exposures for health studies. Methods: Data was collected using a GPS and personal temperature from 54 children with asthma living in Montreal, Canada, who participated in a 10-day personal air pollution exposure study. A method was developed that incorporated personal temperature data and then matched a participant’s position against available spatial data (i.e., road networks) to generate time-activity categories. The diary-based and GPS-generated time-activity categories were compared and combined with continuous personal PM2.5 data to assess the impact of exposure misclassification when using diary based methods. Results: There was good agreement between the automated method and the diary method; however, the automated method (means: outdoors = 5.1%, indoors other =9.8%) estimated less time spent in some locations compared to the diary method (outdoors = 6.7%, indoors other = 14.4%). Agreement statistics (AC1 = 0.778) suggest ‘good’ agreement between methods over all location categories. However, location categories (Outdoors and Transit) where less time is spent show greater disagreement: e.g., mean time “Indoors Other” using the time-activity diary was 14.4% compared to 9.8% using the automated method. While mean daily time “In Transit” was relatively consistent between the methods, the mean daily exposure to PM2.5 while “In Transit” was 15.9 μg/m3 using the automated method compared to 6.8 μg/m3 using the daily diary. Conclusions: Mean times spent in different locations as categorized by a GPS-based method were comparable to those from a time-activity diary, but there were differences in estimates of exposure to PM2.5 from the two methods. An automated GPS-based time-activity method will reduce participant burden, potentially providing more accurate and unbiased assessments of location. Combined with continuous air measurements, the higher resolution GPS data could present a different and more accurate picture of personal exposures to air pollution.

DOI

10.1186/1476-069X-13-33

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution 2.0 License.

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