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/Conference proceeding › Chapter › 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 - 2022 IEEE International Conference on Big Data, Big Data 2022
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
Publisher	IEEE
Pages	4551-4559
Number of pages	9
ISBN (Electronic)	9781665480451
ISBN (Print)	9781665480451
DOIs	https://doi.org/10.1109/BigData55660.2022.10020701
Publication status	Published - 26 Jan 2023
Event	2022 IEEE International Conference on Big Data, Big Data 2022 - Osaka, Japan Duration: 17 Dec 2022 → 20 Dec 2022

Publication series

Name	Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

Conference

Conference	2022 IEEE International Conference on Big Data, Big Data 2022
Country/Territory	Japan
City	Osaka
Period	17/12/22 → 20/12/22

Keywords

Air pollution
Data Integration
Health effects
Micro-simulation

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, Big Data 2022 (pp. 4551-4559). (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022). 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, Big Data 2022. 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. IEEE, 2023. pp. 4551-4559 (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022).

@inbook{a4b1afd84e65422ea0c3004270b68fd4,

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.",

keywords = "Air pollution, Data Integration, Health effects, Micro-simulation",

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.}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Big Data, Big Data 2022 ; Conference date: 17-12-2022 Through 20-12-2022",

year = "2023",

month = jan,

day = "26",

doi = "10.1109/BigData55660.2022.10020701",

language = "English",

isbn = "9781665480451",

series = "Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022",

publisher = "IEEE",

pages = "4551--4559",

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",

booktitle = "Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022",

address = "United States",

}

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, Big Data 2022. Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022, IEEE, pp. 4551-4559, 2022 IEEE International Conference on Big Data, Big Data 2022, Osaka, Japan, 17/12/22. 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, Big Data 2022. 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. IEEE, 2023. p. 4551-4559 (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022).

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

TY - CHAP

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 - 2023/1/26

Y1 - 2023/1/26

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 - Chapter

AN - SCOPUS:85147954289

SN - 9781665480451

T3 - Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

SP - 4551

EP - 4559

BT - Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022

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

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

Y2 - 17 December 2022 through 20 December 2022

ER -

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, Big Data 2022. IEEE. 2023. p. 4551-4559. (Proceedings - 2022 IEEE International Conference on Big Data, Big Data 2022). doi: 10.1109/BigData55660.2022.10020701

A Data Integration Approach to Estimating Personal Exposures to Air Pollution

Abstract

Publication series

Conference

Keywords

UN SDGs

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