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Using low-cost sensor technologies and advanced computational methods to improve dose estimations in health panel studies: results of the AIRLESS project.

Publication ,  Journal Article
Chatzidiakou, L; Krause, A; Han, Y; Chen, W; Yan, L; Popoola, OAM; Kellaway, M; Wu, Y; Liu, J; Hu, M; AIRLESS team, ; Barratt, B; Kelly, FJ ...
Published in: Journal of exposure science & environmental epidemiology
November 2020

Air pollution epidemiology has primarily relied on fixed outdoor air quality monitoring networks and static populations.Taking advantage of recent advancements in sensor technologies and computational techniques, this paper presents a novel methodological approach that improves dose estimations of multiple air pollutants in large-scale health studies. We show the results of an intensive field campaign that measured personal exposures to gaseous pollutants and particulate matter of a health panel of 251 participants residing in urban and peri-urban Beijing with 60 personal air quality monitors (PAMs). Outdoor air pollution measurements were collected in monitoring stations close to the participants' residential addresses. Based on parameters collected with the PAMs, we developed an advanced computational model that automatically classified time-activity-location patterns of each individual during daily life at high spatial and temporal resolution.Applying this methodological approach in two established cohorts, we found substantial differences between doses estimated from outdoor and personal air quality measurements. The PAM measurements also significantly reduced the correlation between pollutant species often observed in static outdoor measurements, reducing confounding effects.Future work will utilise these improved dose estimations to investigate the underlying mechanisms of air pollution on cardio-pulmonary health outcomes using detailed medical biomarkers in a way that has not been possible before.

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Published In

Journal of exposure science & environmental epidemiology

DOI

EISSN

1559-064X

ISSN

1559-0631

Publication Date

November 2020

Volume

30

Issue

6

Start / End Page

981 / 989

Related Subject Headings

  • Particulate Matter
  • Humans
  • Epidemiology
  • Environmental Monitoring
  • Beijing
  • Air Pollution
  • Air Pollutants
  • 4206 Public health
  • 4202 Epidemiology
  • 11 Medical and Health Sciences
 

Citation

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Chicago
ICMJE
MLA
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Chatzidiakou, L., Krause, A., Han, Y., Chen, W., Yan, L., Popoola, O. A. M., … Jones, R. L. (2020). Using low-cost sensor technologies and advanced computational methods to improve dose estimations in health panel studies: results of the AIRLESS project. Journal of Exposure Science & Environmental Epidemiology, 30(6), 981–989. https://doi.org/10.1038/s41370-020-0259-6
Chatzidiakou, Lia, Anika Krause, Yiqun Han, Wu Chen, Li Yan, Olalekan A. M. Popoola, Mike Kellaway, et al. “Using low-cost sensor technologies and advanced computational methods to improve dose estimations in health panel studies: results of the AIRLESS project.Journal of Exposure Science & Environmental Epidemiology 30, no. 6 (November 2020): 981–89. https://doi.org/10.1038/s41370-020-0259-6.
Chatzidiakou L, Krause A, Han Y, Chen W, Yan L, Popoola OAM, et al. Using low-cost sensor technologies and advanced computational methods to improve dose estimations in health panel studies: results of the AIRLESS project. Journal of exposure science & environmental epidemiology. 2020 Nov;30(6):981–9.
Chatzidiakou, Lia, et al. “Using low-cost sensor technologies and advanced computational methods to improve dose estimations in health panel studies: results of the AIRLESS project.Journal of Exposure Science & Environmental Epidemiology, vol. 30, no. 6, Nov. 2020, pp. 981–89. Epmc, doi:10.1038/s41370-020-0259-6.
Chatzidiakou L, Krause A, Han Y, Chen W, Yan L, Popoola OAM, Kellaway M, Wu Y, Liu J, Hu M, AIRLESS team, Barratt B, Kelly FJ, Zhu T, Jones RL. Using low-cost sensor technologies and advanced computational methods to improve dose estimations in health panel studies: results of the AIRLESS project. Journal of exposure science & environmental epidemiology. 2020 Nov;30(6):981–989.

Published In

Journal of exposure science & environmental epidemiology

DOI

EISSN

1559-064X

ISSN

1559-0631

Publication Date

November 2020

Volume

30

Issue

6

Start / End Page

981 / 989

Related Subject Headings

  • Particulate Matter
  • Humans
  • Epidemiology
  • Environmental Monitoring
  • Beijing
  • Air Pollution
  • Air Pollutants
  • 4206 Public health
  • 4202 Epidemiology
  • 11 Medical and Health Sciences