Dataset Information

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

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 ?g/m3 using the automated method compared to 6.8 ?g/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.

SUBMITTER: Nethery E

PROVIDER: S-EPMC4046178 | biostudies-literature | 2014 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

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

Nethery Elizabeth E Mallach Gary G Rainham Daniel D Goldberg Mark S MS Wheeler Amanda J AJ

Environmental health : a global access science source 20140508 1

<h4>Background</h4>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 h ...[more]

PMID: 24885722

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Dataset Information

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

Background

Methods

Results

Conclusions

Publications

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

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