Conference paper
A Data Integration Approach to Estimating Personal Exposures to Air Pollution
University of Exeter1
Royal Holloway University of London2
University of Manchester3
Climate Economics and Risk Management, Sustainability, Society and Economics, Department of Technology, Management and Economics, Technical University of Denmark4
Department of Technology, Management and Economics, Technical University of Denmark5
Newcastle University6
Alan Turing Institute7
University of British Columbia8
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/m3 (new WHO guidelines) lead to an estimated (mean) reduction in personal exposures between 2.7 and 3.1 μg/m3 across population (gender-age) groups.
| Language: | English |
|---|---|
| Publisher: | IEEE |
| Year: | 2022 |
| Pages: | 4551-4559 |
| Proceedings: | 2022 IEEE International Conference on Big Data, Big Data |
| ISBN: | 1665480459 and 9781665480451 |
| Types: | Conference paper |
| DOI: | 10.1109/BigData55660.2022.10020701 |
| ORCIDs: | Morrissey, Karyn |
Air pollution Data Integration Health effects Micro-simulation SDG 3 - Good Health and Well-being
Atmospheric modeling Big Data DIMEX Data integration Data models Pollution measurement Sociology UK WHO air pollution air pollution control daily travel patterns data integration data integration model environmental factors environmental risk government policies greater manchester health and safety mitigation strategies public health risk analysis
