Microenvironment Tracker (MicroTrac) model to estimate time-location of individuals for air pollution exposure assessments: model evaluation using smartphone data.
Air pollution
GPS
Microenvironment
Smartphone
exposure assessment
Journal
Journal of exposure science & environmental epidemiology
ISSN: 1559-064X
Titre abrégé: J Expo Sci Environ Epidemiol
Pays: United States
ID NLM: 101262796
Informations de publication
Date de publication:
05 2023
05 2023
Historique:
received:
31
05
2022
accepted:
05
12
2022
revised:
02
12
2022
medline:
5
6
2023
pubmed:
17
12
2022
entrez:
16
12
2022
Statut:
ppublish
Résumé
A critical aspect of air pollution exposure assessments is determining the time spent in various microenvironments (ME), which can have substantially different pollutant concentrations. We previously developed and evaluated a ME classification model, called Microenvironment Tracker (MicroTrac), to estimate time of day and duration spent in eight MEs (indoors and outdoors at home, work, school; inside vehicles; other locations) based on input data from global positioning system (GPS) loggers. In this study, we extended MicroTrac and evaluated the ability of using geolocation data from smartphones to determine the time spent in the MEs. We performed a panel study, and the MicroTrac estimates based on data from smartphones and GPS loggers were compared to 37 days of diary data across five participants. The MEs were correctly classified for 98.1% and 98.3% of the time spent by the participants using smartphones and GPS loggers, respectively. Our study demonstrates the extended capability of using ubiquitous smartphone data with MicroTrac to help reduce time-location uncertainty in air pollution exposure models for epidemiologic and exposure field studies.
Sections du résumé
BACKGROUND
A critical aspect of air pollution exposure assessments is determining the time spent in various microenvironments (ME), which can have substantially different pollutant concentrations. We previously developed and evaluated a ME classification model, called Microenvironment Tracker (MicroTrac), to estimate time of day and duration spent in eight MEs (indoors and outdoors at home, work, school; inside vehicles; other locations) based on input data from global positioning system (GPS) loggers.
OBJECTIVE
In this study, we extended MicroTrac and evaluated the ability of using geolocation data from smartphones to determine the time spent in the MEs.
METHOD
We performed a panel study, and the MicroTrac estimates based on data from smartphones and GPS loggers were compared to 37 days of diary data across five participants.
RESULTS
The MEs were correctly classified for 98.1% and 98.3% of the time spent by the participants using smartphones and GPS loggers, respectively.
SIGNIFICANCE
Our study demonstrates the extended capability of using ubiquitous smartphone data with MicroTrac to help reduce time-location uncertainty in air pollution exposure models for epidemiologic and exposure field studies.
Identifiants
pubmed: 36526873
doi: 10.1038/s41370-022-00514-w
pii: 10.1038/s41370-022-00514-w
doi:
Substances chimiques
Air Pollutants
0
Environmental Pollutants
0
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
407-415Informations de copyright
© 2022. This is a U.S. Government work and not under copyright protection in the US; foreign copyright protection may apply.
Références
US Environmental Protection Agency. Integrated science assessment for particulate matter. In EPA600/R-08/139F; Environmental Protection Agency: Washington, DC, USA, 2009.
US Environmental Protection Agency. Integrated science assessment for ozone and related photochemical oxidants. In EPA 600/R-10/076F; Environmental Protection Agency: Washington, DC, USA, 2013.
Zeger SL, Thomas D, Dominici F, Sarnet JM, Schwartz J, Dockery D, et al. Exposure measurement error in time-series studies of air pollution: Concepts and consequences. Environ Health Perspect. 2000;108:419–26.
doi: 10.1289/ehp.00108419
pubmed: 10811568
pmcid: 1638034
Sheppard L, Burnett RT, Szpiro AA, Kim SY, Jerrett M, Pope CA III, et al. Confounding and exposure measurement error in air pollution epidemiology. Air Qual Atmos Health. 2012;5:203–16.
doi: 10.1007/s11869-011-0140-9
pubmed: 22662023
National Research Council. Exposure Science in the 21st Century: A Vision and a Strategy; The National Academies Press: Washington, DC, USA, 2012.
National Research Council. Research Priorities for Airborne Particulate Matter: I. Immediate Priorities and a Long-Range Research Portfolio; The National Academies Press: Washington, DC, USA, 2004.
National Academies of Sciences, Engineering, and Medicine. Health Risks of Indoor Exposure to Particulate Matter: Workshop Summary; The National Academies Press: Washington, DC, USA, 2016.
National Academies of Sciences, Engineering, and Medicine. Using 21st Century Science to Improve Risk-Related Evaluations; The National Academies Press: Washington, DC, USA, 2017.
Breen MS, Seppanen C, Isakov V, Arunachalam S, Breen M, Samet J, et al. Development of TracMyAir smartphone application for modeling exposures to ambient PM
doi: 10.3390/ijerph16183468
pubmed: 31540404
pmcid: 6766031
Peltier RE, Buckley TJ. Sensor technology: a critical cutting edge of exposure science. J Exp Sci Environ Epidemiol. 2020;30:901–2.
doi: 10.1038/s41370-020-00268-3
Breen MS, Long T, Schultz B, Crooks J, Breen M, Langstaff J, et al. GPS-based microenvironment tracker (MicroTrac) model to estimate time-location of individuals for air pollution exposure assessments: model evaluation in central North Carolina. J Exp Sci Environ Epidemiol. 2014;24:412–20.
doi: 10.1038/jes.2014.13
Donaire-Gonzalez D, Valentín A, de Nazelle A, Ambros A, Carrasco-Turigas G, Seto E, et al. Benefits of mobile phone technology for personal environmental monitoring. JMIR Mhealth Uhealth. 2016;4:e126 https://doi.org/10.2196/mhealth.5771 . PMID: 27833069; PMCID: PMC5122720
doi: 10.2196/mhealth.5771
pubmed: 27833069
pmcid: 5122720
Asimina S, Chapizanis D, Karakitsios S, Kontoroupis P, Asimakopoulos DN, Maggos T, et al. Assessing and enhancing the utility of low-cost activity and location sensors for exposure studies. Environ Monit Assess. 2018;190:155 https://doi.org/10.1007/s10661-018-6537-2 . PMID: 29464404
doi: 10.1007/s10661-018-6537-2
pubmed: 29464404
National Academy of Engineering 2022. Indoor Exposure to Fine Particulate Matter and Practical Mitigation Approaches: Proceedings of a Workshop. Washington, DC: The National Academies Press. https://doi.org/10.17226/26331 .
Quinn C, Anderson GB, Magzamen S, Henry CS, Volckens J. Dynamic classification of personal microenvironments using a suite of wearable, low-cost sensors. J Expo Sci Environ Epidemiol. 2020;30:962–70. https://doi.org/10.1038/s41370-019-0198-2 . Epub 2020 Jan 14. Erratum in: J Expo Sci Environ Epidemiol. 2020 Mar 5;: PMID: 31937850; PMCID: PMC7358126
doi: 10.1038/s41370-019-0198-2
pubmed: 31937850
pmcid: 7358126
Adams C, Riggs P, Volckens J. Development of a method for personal, spatiotemporal exposure assessment. J Environ Monit. 2009;11:1331–9.
doi: 10.1039/b903841h
pubmed: 20449221
Tandon P, Saelens B, Zhou C, Kerr J, Christakis D. Indoor versus outdoor time in preschoolers at child care. Am J Prev Med. 2013;1:85–88.
doi: 10.1016/j.amepre.2012.09.052
McCurdy T, Glen G, Smith L, Lakkadi Y. The national exposure research laboratory’s Consolidated Human Activity Database. J Expo Anal Environ Epidemiol. 2000;10:566–78.
doi: 10.1038/sj.jea.7500114
pubmed: 11140440