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

doi:10.1109/BigData55660.2022.10020701

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

^*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceeding › Article in proceedings › Research › peer-review

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/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.

Original language	English
Title of host publication	Proceedings of 2022 IEEE International Conference on Big Data (Big Data)
Number of pages	9
Publication date	2022
Pages	4551-4559
DOIs	https://doi.org/10.1109/BigData55660.2022.10020701
Publication status	Published - 2022
Event	2022 IEEE International Conference on Big Data, Big Data - Osaka, Japan Duration: 17 Dec 2022 → 20 Dec 2022

Conference

Conference	2022 IEEE International Conference on Big Data, Big Data
Country/Territory	Japan
City	Osaka
Period	17/12/2022 → 20/12/2022
Sponsor	Ankura Consulting Group, LLC, Hitachi Zosen Corporation, KPMG Consulting Co., Ltd., NTT Data Intellilink Corporation, Think in Data Initiative, Association Inc

Keywords

Air pollution
Data Integration
Health effects
Micro-simulation

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1109/BigData55660.2022.10020701

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Thomas, M. L., Shaddick, G., Topping, D., Morrissey, K., Brannan, T. J., Diessner, M., Bowyer, R. C. E., Siegert, S., Coe, H., Evans, J., Benitez-Paez, F., & Zidek, J. V. (2022). A Data Integration Approach to Estimating Personal Exposures to Air Pollution. In Proceedings of 2022 IEEE International Conference on Big Data (Big Data) (pp. 4551-4559) https://doi.org/10.1109/BigData55660.2022.10020701

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title = "A Data Integration Approach to Estimating Personal Exposures to Air Pollution",

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/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.",

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

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year = "2022",

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Thomas, ML, Shaddick, G, Topping, D, Morrissey, K, Brannan, TJ, Diessner, M, Bowyer, RCE, Siegert, S, Coe, H, Evans, J, Benitez-Paez, F & Zidek, JV 2022, A Data Integration Approach to Estimating Personal Exposures to Air Pollution. in Proceedings of 2022 IEEE International Conference on Big Data (Big Data). pp. 4551-4559, 2022 IEEE International Conference on Big Data, Big Data, Osaka, Japan, 17/12/2022. https://doi.org/10.1109/BigData55660.2022.10020701

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AU - Thomas, Matthew L.

AU - Shaddick, Gavin

AU - Topping, David

AU - Morrissey, Karyn

AU - Brannan, Thomas J.

AU - Diessner, Mike

AU - Bowyer, Ruth C.E.

AU - Siegert, Stefan

AU - Coe, Hugh

AU - Evans, James

AU - Benitez-Paez, Fernando

AU - Zidek, James V.

PY - 2022

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N2 - 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.

AB - 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.

KW - Air pollution

KW - Data Integration

KW - Health effects

KW - Micro-simulation

U2 - 10.1109/BigData55660.2022.10020701

DO - 10.1109/BigData55660.2022.10020701

M3 - Article in proceedings

AN - SCOPUS:85147954289

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EP - 4559

BT - Proceedings of 2022 IEEE International Conference on Big Data (Big Data)

T2 - 2022 IEEE International Conference on Big Data, Big Data

Y2 - 17 December 2022 through 20 December 2022

ER -

A Data Integration Approach to Estimating Personal Exposures to Air Pollution

Abstract

Conference

Keywords

UN SDGs

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