A data integration approach to estimating personal exposures to air pollution

Matthew L. Thomas, Gavin Shaddick, David Topping, Karyn Morrissey, Thomas J Brannan, Mike Diessner, Ruth C. E. Bowyer, Stefan Siegert, Hugh Coe, James Evans, Fernando Benitez-Paez, James V. Zidek

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Globally, air pollution is the largest environmental risk to public health. In order to inform policy and target mitigation strategies there is a need to increase our understanding of the (personal) exposures experienced by different population groups. The Data Integration Model for Exposures (DIMEX) integrates data on daily travel patterns and activities with measurements and models of air pollution using agent-based modelling to simulate the daily exposures of different population groups. Here we present the results of a case study using DIMEX to model personal exposures to PM2.5 in Greater Manchester, UK, and demonstrate its ability to explore differences in time activities and exposures for different population groups. DIMEX can also be used to assess the effects of reductions in ambient air pollution and when run with concentrations reduced to 5 µg/m 3 (new WHO guidelines) lead to an estimated (mean) reduction in personal exposures between 2.7 and 3.1 µg/m 3 across population (gender-age) groups.
Original languageEnglish
Title of host publicationProceedings 2022 IEEE international conference on big data
Subtitle of host publicationDecember 17 - December 20, 2022 - Osaka, Japan
EditorsShusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan
Place of PublicationPiscataway, NJ
Number of pages9
ISBN (Electronic)9781665480451
ISBN (Print)9781665480468
Publication statusPublished - 23 Jan 2023


  • Air pollution
  • Health effects
  • Micro-simulation
  • Data integration
  • Big data
  • Data models


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