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

Journal: Environmental health : a global access science source
Published Date: Dec 9, 2022

Abstract

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.

Authors

  • Lia Chatzidiakou
    Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Rd, CB2 1EW, Cambridge, UK. ec571@cam.ac.uk.
  • Anika Krause
    Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Rd, CB2 1EW, Cambridge, UK.
  • Mike Kellaway
    Atmospheric Sensors Ltd, SG19 3SH, Bedfordshire, UK.
  • Yiqun Han
    Environmental Research Group, MRC Centre for Environment and Health, Imperial College London, W12 0BZ, London, UK.
  • Yilin Li
    Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Gastrointestinal Oncology, Peking University Cancer Hospital & Institute, Beijing 100142, China.
  • Elizabeth Martin
    Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Rd, CB2 1EW, Cambridge, UK.
  • Frank J Kelly
    Environmental Research Group, MRC Centre for Environment and Health, Imperial College London, W12 0BZ, London, UK.
  • Tong Zhu
  • Benjamin Barratt
    Environmental Research Group, MRC Centre for Environment and Health, Imperial College London, W12 0BZ, London, UK.
  • Roderic L Jones
    Yusuf Hamied Department of Chemistry, University of Cambridge, Lensfield Rd, CB2 1EW, Cambridge, UK.

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