A data integration approach to estimating personal exposures to air pollution

Thomas, Matthew L., Shaddick, Gavin ORCID: https://orcid.org/0000-0002-4117-4264, Topping, David, Morrissey, Karyn, Brannan, Thomas J., Diessner, Mike, Bowyer, Ruth C.E., Siegert, Stefan, Coe, Hugh, Evans, James, Benitez-Paez, Fernando and Zidek, James V. 2023. A data integration approach to estimating personal exposures to air pollution. Presented at: IEEE International Conference on Big Data (Big Data), Osaka, Japan, 17-20 December 2022. Proceedings 2022 IEEE International Conference on Big Data (Big Data). IEEE, pp. 4551-4559. 10.1109/BigData55660.2022.10020701

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Abstract

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.

Item Type:	Conference or Workshop Item (Paper)
Date Type:	Published Online
Status:	Published
Schools:	?? VCO ??
Publisher:	IEEE
ISBN:	9781665480451
Last Modified:	23 Jul 2024 15:30
URI:	https://orca.cardiff.ac.uk/id/eprint/170715

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