Evidence map›Paper›PMID 32788611›Full record

ArticleJournal of exposure science & environmental epidemiology2020

Using low-cost sensor technologies and advanced computational methods to improve dose estimations in health panel studies: results of the AIRLESS project.

Lia Chatzidiakou, Anika Krause, Yiqun Han, Wu Chen, Li Yan, Olalekan A M Popoola, Mike Kellaway, Yangfeng Wu, Jing Liu, Min Hu and 5 more

Registry-linked trialAbstract read
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In one paragraph

Article in Journal of exposure science & environmental epidemiology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT06447974 (The Barts Charity Children's Environmental Health Clinic), which is not on this map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing 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.

NCT06447974 narecruitingnot on this mapstarted 2025, after this paper: background citation

The Barts Charity Children's Environmental Health Clinic

TypeinterventionalSponsorQueen Mary University of LondonRan2025 to 2027Enrolled200ConditionsEnvironmental Exposure, Health Behavior, Asthma in Children, Environmental IllnessArmsExposure mitigations
3 · Its place in the literature

Who cites it

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

  1. Pooled it
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  3. Article
  4. Feasibility Study on the Use of NOSensors (Basel, Switzerland) · 2024
    Article
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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

15 authors.

Lia Chatzidiakou *Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK. ec571@cam.ac.uk.ORCID http://orcid.org/0000-0002-8753-1386
Anika Krause *Department of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK.
Yiqun HanMRC Centre for Environment & Health, Imperial College London and King's College London, London, UK.
Wu ChenCollege of Environmental Sciences and Engineering, Peking University, 100871, Beijing, China.
Li YanMRC Centre for Environment & Health, Imperial College London and King's College London, London, UK.
Olalekan A M PopoolaDepartment of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK.
Mike KellawayAtmospheric Sensors Ltd, Bedfordshire, SG19 3SH, UK.
Yangfeng WuPeking University Clinical Research Institute, 100191, Beijing, China.
Jing LiuInstitute of Heart, Lung and Blood Vessel Diseases, Beijing Anzhen Hospital, Capital Medical University, 100029, Beijing, China.
Min HuCollege of Environmental Sciences and Engineering, Peking University, 100871, Beijing, China.
AIRLESS team
Ben BarrattMRC Centre for Environment & Health, Imperial College London and King's College London, London, UK.
Frank J KellyMRC Centre for Environment & Health, Imperial College London and King's College London, London, UK.
Tong ZhuCollege of Environmental Sciences and Engineering, Peking University, 100871, Beijing, China.
Roderic L JonesDepartment of Chemistry, University of Cambridge, Cambridge, CB2 1EW, UK.

Funding

Medical Research Council MR/L01341X/1Medical Research Council MR/L019744/1Medical Research Council MR/S019669/1Medical Research Council MR/S020810/1Medical Research Council MR/S020810/2
6 · The paper itself

Abstract

backgroundAir pollution epidemiology has primarily relied on fixed outdoor air quality monitoring networks and static populations.

methodsTaking advantage of recent advancements in sensor technologies and computational techniques, this paper presents a novel methodological approach that improves dose estimations of multiple air pollutants in large-scale health studies. We show the results of an intensive field campaign that measured personal exposures to gaseous pollutants and particulate matter of a health panel of 251 participants residing in urban and peri-urban Beijing with 60 personal air quality monitors (PAMs). Outdoor air pollution measurements were collected in monitoring stations close to the participants' residential addresses. Based on parameters collected with the PAMs, we developed an advanced computational model that automatically classified time-activity-location patterns of each individual during daily life at high spatial and temporal resolution.

resultsApplying this methodological approach in two established cohorts, we found substantial differences between doses estimated from outdoor and personal air quality measurements. The PAM measurements also significantly reduced the correlation between pollutant species often observed in static outdoor measurements, reducing confounding effects.

conclusionsFuture work will utilise these improved dose estimations to investigate the underlying mechanisms of air pollution on cardio-pulmonary health outcomes using detailed medical biomarkers in a way that has not been possible before.

Indexed as

Air PollutantsAir PollutionBeijingEnvironmental MonitoringHumansParticulate MatterAir PollutantsParticulate MatterDose estimationExposure misclassificationGaseous pollutantsHealth outcomesNovel sensor technologiesParticulate matterTime-activity-location patterns

Identifiers

PMID32788611

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Registered trials

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.