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dc.contributor.authorNethery, E
dc.contributor.authorMallach, G
dc.contributor.authorRainham, D
dc.contributor.authorGoldberg, MS
dc.contributor.authorWheeler, AJ
dc.date.accessioned2020-12-21T01:17:37Z
dc.date.available2020-12-21T01:17:37Z
dc.date.issued2014-05-08
dc.identifierpii: 1476-069X-13-33
dc.identifier.citationNethery, 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), https://doi.org/10.1186/1476-069X-13-33.
dc.identifier.issn1476-069X
dc.identifier.urihttp://hdl.handle.net/11343/256448
dc.description.abstractBACKGROUND: 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.
dc.languageEnglish
dc.publisherBMC
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titleUsing Global Positioning Systems (GPS) and temperature data to generate time-activity classifications for estimating personal exposure in air monitoring studies: an automated method
dc.typeJournal Article
dc.identifier.doi10.1186/1476-069X-13-33
melbourne.affiliation.departmentMelbourne School of Population and Global Health
melbourne.source.titleEnvironmental Health: A Global Access Science Source
melbourne.source.volume13
melbourne.source.issue1
dc.rights.licenseCC BY
melbourne.elementsid1222558
melbourne.contributor.authorWheeler, Amanda
dc.identifier.eissn1476-069X
melbourne.accessrightsOpen Access


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