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. 2014 May 8;13(1):33.
doi: 10.1186/1476-069X-13-33.

Using Global Positioning Systems (GPS) and temperature data to generate time-activity classifications for estimating personal exposure in air monitoring studies: an automated method

Elizabeth Nethery  1 , Gary Mallach, Daniel Rainham, Mark S Goldberg, Amanda J Wheeler
Affiliations

Using Global Positioning Systems (GPS) and temperature data to generate time-activity classifications for estimating personal exposure in air monitoring studies: an automated method

Elizabeth Nethery et al. Environ Health. .

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.

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Figures

Figure 1
Figure 1
Steps of the automated GPS-based classification method.
Figure 2
Figure 2
Example of the impact of the automated classification system on personal exposure assessment. A) The temporal component (temperature, GPS speed, and concentrations of PM2.5). B) The spatial component (classification of spatial locations).
Figure 3
Figure 3
Boxplots comparing diary to automated method A) Average daily percentage of time spent in locations (30 & 1 minute average). B) Average daily PM2.5 concentration (μg/m3), not time-weighted C) Percent contribution (time-weighted) of average PM2.5 concentration to total daily average.
See this image and copyright information in PMC

References

    1. Henderson SB, Beckerman B, Jerrett M, Brauer M. Application of land Use regression to estimate long-term concentrations of traffic-related nitrogen oxides and fine particulate matter. Environ Sci Technol. 2007;41:2422–2428. doi: 10.1021/es0606780. - DOI - PubMed
    1. Allen RW, Wallace L, Larson T, Sheppard L, Liu LJS. Estimated hourly personal exposures to ambient and nonambient particulate matter among sensitive populations in Seattle, Washington. J Air Waste Manage Assoc. 2004;54:1197–1211. doi: 10.1080/10473289.2004.10470988. - DOI - PubMed
    1. Howard-Reed C, Rea AW, Zufall MJ, Burke JM, Williams RW, Suggs JC, Sheldon LS, Walsh D, Kwok R. Use of a continuous nephelometer to measure personal exposure to particles during the U.S. Environmental protection agency Baltimore and Fresno panel studies. J Air Waste Manage Assoc. 2000;50:1125–1132. doi: 10.1080/10473289.2000.10464150. - DOI - PubMed
    1. Wu C-F, Delfino RJ, Floro JN, Quintana PJE, Samimi BS, Kleinman MT, Allen RW, Sally Liu L-J. Exposure assessment and modeling of particulate matter for asthmatic children using personal nephelometers. Atmos Environ. 2005;39:3457–3469. doi: 10.1016/j.atmosenv.2005.01.061. - DOI
    1. Williams R, Suggs J, Rea AW, Sheldon L, Rodes C, Thornburg J. The Research Triangle Park particulate matter panel study: modeling ambient source contribution to personal and residential PM mass concentrations. Atmos Environ. 2003;37:5365–5378. doi: 10.1016/j.atmosenv.2003.09.010. - DOI

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