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Automated classification of time-activity-location patterns for improved estimation of personal exposure to air pollution

BACKGROUND: Air pollution epidemiology has primarily relied on measurements from fixed outdoor air quality monitoring stations to derive population-scale exposure. Characterisation of individual time-activity-location patterns is critical for accurate estimations of personal exposure and dose becaus...

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Autores principales: Chatzidiakou, Lia, Krause, Anika, Kellaway, Mike, Han, Yiqun, Li, Yilin, Martin, Elizabeth, Kelly, Frank J., Zhu, Tong, Barratt, Benjamin, Jones, Roderic L.
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9733291/
https://www.ncbi.nlm.nih.gov/pubmed/36482402
http://dx.doi.org/10.1186/s12940-022-00939-8
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author Chatzidiakou, Lia
Krause, Anika
Kellaway, Mike
Han, Yiqun
Li, Yilin
Martin, Elizabeth
Kelly, Frank J.
Zhu, Tong
Barratt, Benjamin
Jones, Roderic L.
author_facet Chatzidiakou, Lia
Krause, Anika
Kellaway, Mike
Han, Yiqun
Li, Yilin
Martin, Elizabeth
Kelly, Frank J.
Zhu, Tong
Barratt, Benjamin
Jones, Roderic L.
author_sort Chatzidiakou, Lia
collection PubMed
description BACKGROUND: Air pollution epidemiology has primarily relied on measurements from fixed outdoor air quality monitoring stations to derive population-scale exposure. Characterisation of individual time-activity-location patterns is critical for accurate estimations of personal exposure and dose because pollutant concentrations and inhalation rates vary significantly by location and activity. METHODS: We developed and evaluated an automated model to classify major exposure-related microenvironments (home, work, other static, in-transit) and separated them into indoor and outdoor locations, sleeping activity and five modes of transport (walking, cycling, car, bus, metro/train) with multidisciplinary methods from the fields of movement ecology and artificial intelligence. As input parameters, we used GPS coordinates, accelerometry, and noise, collected at 1 min intervals with a validated Personal Air quality Monitor (PAM) carried by 35 volunteers for one week each. The model classifications were then evaluated against manual time-activity logs kept by participants. RESULTS: Overall, the model performed reliably in classifying home, work, and other indoor microenvironments (F1-score>0.70) but only moderately well for sleeping and visits to outdoor microenvironments (F1-score=0.57 and 0.3 respectively). Random forest approaches performed very well in classifying modes of transport (F1-score>0.91). We found that the performance of the automated methods significantly surpassed those of manual logs. CONCLUSIONS: Automated models for time-activity classification can markedly improve exposure metrics. Such models can be developed in many programming languages, and if well formulated can have general applicability in large-scale health studies, providing a comprehensive picture of environmental health risks during daily life with readily gathered parameters from smartphone technologies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12940-022-00939-8.
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spelling pubmed-97332912022-12-10 Automated classification of time-activity-location patterns for improved estimation of personal exposure to air pollution Chatzidiakou, Lia Krause, Anika Kellaway, Mike Han, Yiqun Li, Yilin Martin, Elizabeth Kelly, Frank J. Zhu, Tong Barratt, Benjamin Jones, Roderic L. Environ Health Research BACKGROUND: Air pollution epidemiology has primarily relied on measurements from fixed outdoor air quality monitoring stations to derive population-scale exposure. Characterisation of individual time-activity-location patterns is critical for accurate estimations of personal exposure and dose because pollutant concentrations and inhalation rates vary significantly by location and activity. METHODS: We developed and evaluated an automated model to classify major exposure-related microenvironments (home, work, other static, in-transit) and separated them into indoor and outdoor locations, sleeping activity and five modes of transport (walking, cycling, car, bus, metro/train) with multidisciplinary methods from the fields of movement ecology and artificial intelligence. As input parameters, we used GPS coordinates, accelerometry, and noise, collected at 1 min intervals with a validated Personal Air quality Monitor (PAM) carried by 35 volunteers for one week each. The model classifications were then evaluated against manual time-activity logs kept by participants. RESULTS: Overall, the model performed reliably in classifying home, work, and other indoor microenvironments (F1-score>0.70) but only moderately well for sleeping and visits to outdoor microenvironments (F1-score=0.57 and 0.3 respectively). Random forest approaches performed very well in classifying modes of transport (F1-score>0.91). We found that the performance of the automated methods significantly surpassed those of manual logs. CONCLUSIONS: Automated models for time-activity classification can markedly improve exposure metrics. Such models can be developed in many programming languages, and if well formulated can have general applicability in large-scale health studies, providing a comprehensive picture of environmental health risks during daily life with readily gathered parameters from smartphone technologies. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s12940-022-00939-8. BioMed Central 2022-12-09 /pmc/articles/PMC9733291/ /pubmed/36482402 http://dx.doi.org/10.1186/s12940-022-00939-8 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Chatzidiakou, Lia
Krause, Anika
Kellaway, Mike
Han, Yiqun
Li, Yilin
Martin, Elizabeth
Kelly, Frank J.
Zhu, Tong
Barratt, Benjamin
Jones, Roderic L.
Automated classification of time-activity-location patterns for improved estimation of personal exposure to air pollution
title Automated classification of time-activity-location patterns for improved estimation of personal exposure to air pollution
title_full Automated classification of time-activity-location patterns for improved estimation of personal exposure to air pollution
title_fullStr Automated classification of time-activity-location patterns for improved estimation of personal exposure to air pollution
title_full_unstemmed Automated classification of time-activity-location patterns for improved estimation of personal exposure to air pollution
title_short Automated classification of time-activity-location patterns for improved estimation of personal exposure to air pollution
title_sort automated classification of time-activity-location patterns for improved estimation of personal exposure to air pollution
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9733291/
https://www.ncbi.nlm.nih.gov/pubmed/36482402
http://dx.doi.org/10.1186/s12940-022-00939-8
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