Evidence map›Paper›PMID 36764437›Full record

ArticleEnvironmental research2023

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

Sun-Young Kim, Magali N Blanco, Jianzhao Bi, Timothy V Larson, Lianne Sheppard

Abstract readScoping Review
In one paragraph

Article in Environmental research, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
16citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

16 citing papers in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
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  7. When air gets heavy: joint PMFrontiers in public health · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Sun-Young KimDepartment 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 BlancoDepartment of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA.
Jianzhao BiDepartment of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA.
Timothy V LarsonDepartment 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 SheppardDepartment of Environmental and Occupational Health Sciences, University of Washington, Seattle, WA, USA; Department of Biostatistics, University of Washington, Seattle, WA, USA.

Funding

Air Pollution, the Aging Brain and Alzheimer's DiseaseR01ES026187 · NIEHS · UNIVERSITY OF WASHINGTON · PI LI, GAIL, SHEPPARD, ELIZABETH A LIANNE · 2016 to 2020
$3.8M
NIEHS NIH HHS R01 ES026187
6 · The paper itself

Abstract

backgroundBoth 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.

objectivesWe 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.

methodsWe 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.

resultsMost 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.

Indexed as

Air PollutantsAir PollutionEnvironmental MonitoringHumansParticulate MatterResidence CharacteristicsAir PollutantsParticulate MatterCohortLow-cost sensorMobile monitoringMonitoring designNew technologyUltrafine particles

Identifiers

PMID36764437
PMCPMC9992293

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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.