Exposure assessment for air pollution epidemiology: A scoping review of emerging monitoring platforms and designs.


Journal

Environmental research
ISSN: 1096-0953
Titre abrégé: Environ Res
Pays: Netherlands
ID NLM: 0147621

Informations de publication

Date de publication:
15 04 2023
Historique:
received: 31 08 2022
revised: 10 01 2023
accepted: 07 02 2023
pmc-release: 15 04 2024
pubmed: 11 2 2023
medline: 3 3 2023
entrez: 10 2 2023
Statut: ppublish

Résumé

Both exposure monitoring and exposure prediction have played key roles in assessing individual-level long-term exposure to air pollutants and their associations with human health. While there have been notable advances in exposure prediction methods, improvements in monitoring designs are also necessary, particularly given new monitoring paradigms leveraging low-cost sensors and mobile platforms. We aim to provide a conceptual summary of novel monitoring designs for air pollution cohort studies that leverage new paradigms and technologies, to investigate their characteristics in real-world examples, and to offer practical guidance to future studies. We propose a conceptual summary that focuses on two overarching types of monitoring designs, mobile and non-mobile, as well as their subtypes. We define mobile designs as monitoring from a moving platform, and non-mobile designs as stationary monitoring from permanent or temporary locations. We only consider non-mobile studies with cost-effective sampling devices. Then we discuss similarities and differences across previous studies with respect to spatial and temporal representation, data comparability between design classes, and the data leveraged for model development. Finally, we provide specific suggestions for future monitoring designs. Most mobile and non-mobile monitoring studies selected monitoring sites based on land use instead of residential locations, and deployed monitors over limited time periods. Some studies applied multiple design and/or sub-design classes to the same area, time period, or instrumentation, to allow comparison. Even fewer studies leveraged monitoring data from different designs to improve exposure assessment by capitalizing on different strengths. In order to maximize the benefit of new monitoring technologies, future studies should adopt monitoring designs that prioritize residence-based site selection with comprehensive temporal coverage and leverage data from different designs for model development in the presence of good data compatibility. Our conceptual overview provides practical guidance on novel exposure assessment monitoring for epidemiological applications.

Sections du résumé

BACKGROUND
Both exposure monitoring and exposure prediction have played key roles in assessing individual-level long-term exposure to air pollutants and their associations with human health. While there have been notable advances in exposure prediction methods, improvements in monitoring designs are also necessary, particularly given new monitoring paradigms leveraging low-cost sensors and mobile platforms.
OBJECTIVES
We aim to provide a conceptual summary of novel monitoring designs for air pollution cohort studies that leverage new paradigms and technologies, to investigate their characteristics in real-world examples, and to offer practical guidance to future studies.
METHODS
We propose a conceptual summary that focuses on two overarching types of monitoring designs, mobile and non-mobile, as well as their subtypes. We define mobile designs as monitoring from a moving platform, and non-mobile designs as stationary monitoring from permanent or temporary locations. We only consider non-mobile studies with cost-effective sampling devices. Then we discuss similarities and differences across previous studies with respect to spatial and temporal representation, data comparability between design classes, and the data leveraged for model development. Finally, we provide specific suggestions for future monitoring designs.
RESULTS
Most mobile and non-mobile monitoring studies selected monitoring sites based on land use instead of residential locations, and deployed monitors over limited time periods. Some studies applied multiple design and/or sub-design classes to the same area, time period, or instrumentation, to allow comparison. Even fewer studies leveraged monitoring data from different designs to improve exposure assessment by capitalizing on different strengths. In order to maximize the benefit of new monitoring technologies, future studies should adopt monitoring designs that prioritize residence-based site selection with comprehensive temporal coverage and leverage data from different designs for model development in the presence of good data compatibility.
DISCUSSION
Our conceptual overview provides practical guidance on novel exposure assessment monitoring for epidemiological applications.

Identifiants

pubmed: 36764437
pii: S0013-9351(23)00243-8
doi: 10.1016/j.envres.2023.115451
pmc: PMC9992293
mid: NIHMS1875320
pii:
doi:

Substances chimiques

Particulate Matter 0
Air Pollutants 0

Types de publication

Journal Article Review Research Support, N.I.H., Extramural Research Support, Non-U.S. Gov't

Langues

eng

Sous-ensembles de citation

IM

Pagination

115451

Subventions

Organisme : NIEHS NIH HHS
ID : R01 ES026187
Pays : United States

Informations de copyright

Copyright © 2023 Elsevier Inc. All rights reserved.

Déclaration de conflit d'intérêts

Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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Auteurs

Sun-Young Kim (SY)

Department of Cancer AI and Digital Health, Graduate School of Cancer Science and Policy, National Cancer Center, Goyang-si, Gyeonggi-do, Republic of Korea; Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA. Electronic address: sykim@ncc.re.kr.

Magali N Blanco (MN)

Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA.

Jianzhao Bi (J)

Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA.

Timothy V Larson (TV)

Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA; Department of Civil and Environmental Engineering, University of Washington, Seattle, WA, USA.

Lianne Sheppard (L)

Department of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA; Department of Biostatistics, University of Washington, Seattle, WA, USA.

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