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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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.

Original language	English
Title of host publication	Proceedings 2022 IEEE international conference on big data
Subtitle of host publication	December 17 - December 20, 2022 - Osaka, Japan
Editors	Shusaku 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 Publication	Piscataway, NJ
Publisher	IEEE
Pages	4551-4559
Number of pages	9
ISBN (Electronic)	9781665480451
ISBN (Print)	9781665480468
DOIs	https://doi.org/10.1109/BigData55660.2022.10020701
Publication status	Published - 23 Jan 2023

Keywords

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

UN SDGs

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

Access to Document

10.1109/BigData55660.2022.10020701

Cite this

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. (2023). A data integration approach to estimating personal exposures to air pollution. In S. Tsumoto, Y. Ohsawa, L. Chen, D. Van den Poel, X. Hu, Y. Motomura, T. Takagi, L. Wu, Y. Xie, A. Abe, & V. Raghavan (Eds.), Proceedings 2022 IEEE international conference on big data: December 17 - December 20, 2022 - Osaka, Japan (pp. 4551-4559). IEEE. https://doi.org/10.1109/BigData55660.2022.10020701

Thomas, Matthew L. ; Shaddick, Gavin ; Topping, David et al. / A data integration approach to estimating personal exposures to air pollution. Proceedings 2022 IEEE international conference on big data: December 17 - December 20, 2022 - Osaka, Japan. editor / Shusaku Tsumoto ; Yukio Ohsawa ; Lei Chen ; Dirk Van den Poel ; Xiaohua Hu ; Yoichi Motomura ; Takuya Takagi ; Lingfei Wu ; Ying Xie ; Akihiro Abe ; Vijay Raghavan. Piscataway, NJ : IEEE, 2023. pp. 4551-4559

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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/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.",

keywords = "Air pollution, Health effects, Micro-simulation, Data integration, Big data, Data models",

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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doi = "10.1109/BigData55660.2022.10020701",

language = "English",

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editor = "Shusaku Tsumoto and Yukio Ohsawa and Lei Chen and {Van den Poel}, Dirk and Xiaohua Hu and Yoichi Motomura and Takuya Takagi and Lingfei Wu and Ying Xie and Akihiro Abe and Vijay Raghavan",

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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 2023, A data integration approach to estimating personal exposures to air pollution. in S Tsumoto, Y Ohsawa, L Chen, D Van den Poel, X Hu, Y Motomura, T Takagi, L Wu, Y Xie, A Abe & V Raghavan (eds), Proceedings 2022 IEEE international conference on big data: December 17 - December 20, 2022 - Osaka, Japan. IEEE, Piscataway, NJ, pp. 4551-4559. https://doi.org/10.1109/BigData55660.2022.10020701

A data integration approach to estimating personal exposures to air pollution. / Thomas, Matthew L.; Shaddick, Gavin ; Topping, David et al.
Proceedings 2022 IEEE international conference on big data: December 17 - December 20, 2022 - Osaka, Japan. ed. / Shusaku Tsumoto; Yukio Ohsawa; Lei Chen; Dirk Van den Poel; Xiaohua Hu; Yoichi Motomura; Takuya Takagi; Lingfei Wu; Ying Xie; Akihiro Abe; Vijay Raghavan. Piscataway, NJ: IEEE, 2023. p. 4551-4559.

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

TY - GEN

T1 - A data integration approach to estimating personal exposures to air pollution

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 - 2023/1/23

Y1 - 2023/1/23

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/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.

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/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.

KW - Air pollution

KW - Health effects

KW - Micro-simulation

KW - Data integration

KW - Big data

KW - Data models

UR - https://ieeexplore.ieee.org/xpl/conhome/10020192/proceeding

U2 - 10.1109/BigData55660.2022.10020701

DO - 10.1109/BigData55660.2022.10020701

M3 - Conference contribution

SN - 9781665480468

SP - 4551

EP - 4559

BT - Proceedings 2022 IEEE international conference on big data

A2 - Tsumoto, Shusaku

A2 - Ohsawa, Yukio

A2 - Chen, Lei

A2 - Van den Poel, Dirk

A2 - Hu, Xiaohua

A2 - Motomura, Yoichi

A2 - Takagi, Takuya

A2 - Wu, Lingfei

A2 - Xie, Ying

A2 - Abe, Akihiro

A2 - Raghavan, Vijay

PB - IEEE

CY - Piscataway, NJ

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Thomas ML, Shaddick G, Topping D, Morrissey K, Brannan TJ, Diessner M et al. A data integration approach to estimating personal exposures to air pollution. In Tsumoto S, Ohsawa Y, Chen L, Van den Poel D, Hu X, Motomura Y, Takagi T, Wu L, Xie Y, Abe A, Raghavan V, editors, Proceedings 2022 IEEE international conference on big data: December 17 - December 20, 2022 - Osaka, Japan. Piscataway, NJ: IEEE. 2023. p. 4551-4559 doi: 10.1109/BigData55660.2022.10020701

A data integration approach to estimating personal exposures to air pollution

Abstract

Keywords

UN SDGs

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