Evidence map›Paper›PMID 42209715›Full record

ArticleJournal of exposure science & environmental epidemiology2026

Facility-wide assessment of occupational exposure to dust, noise, and formaldehyde.

Emilio Molina Rueda, Ellison Carter, Christian L'Orange, Meghan R Cosgrove, Elizabeth A Williams, Ashley A Anderson, Marie J Andales, Jessica Tryner, Marilee Long, Ander Wilson and 1 more

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Article in Journal of exposure science & environmental epidemiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0cells of the map it votes in
0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

11 authors.

Emilio Molina RuedaDepartment of Mechanical Engineering, Colorado State University, Fort Collins, CO, USA.
Ellison CarterDepartment of Civil and Environmental Engineering, Colorado State University, Fort Collins, CO, USA. ellison.carter@colostate.edu.
Christian L'OrangeDepartment of Mechanical Engineering, Colorado State University, Fort Collins, CO, USA.
Meghan R CosgroveDepartment of Communication Studies, Colorado State University, Fort Collins, CO, USA.
Elizabeth A WilliamsDepartment of Communication Studies, Colorado State University, Fort Collins, CO, USA.
Ashley A AndersonDepartment of Journalism and Media Communication, Colorado State University, Fort Collins, CO, USA.
Marie J AndalesDepartment of Mechanical Engineering, Colorado State University, Fort Collins, CO, USA.
Jessica TrynerAccess Sensor Technologies, Fort Collins, CO, USA.
Marilee LongDepartment of Journalism and Media Communication, Colorado State University, Fort Collins, CO, USA.
Ander WilsonDepartment of Statistics, Colorado State University, Fort Collins, CO, USA.
John VolckensDepartment of Mechanical Engineering, Colorado State University, Fort Collins, CO, USA.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundExposure to particulate matter, hazardous gases, and noise among workers causes millions of deaths, injuries, and disabilities annually. These exposures are not measured frequently, and most workers are never monitored because resources for monitoring are limited globally. Even in high-income countries, just two or three samples per workplace inspection are collected on average. Considerably larger sample sizes are required to estimate measures of central tendency and upper percentiles of common exposure distributions. Therefore, interventions and epidemiological studies often rely on poor estimates of those parameters.

objectiveThe objective of this study was to demonstrate that a small team can cost-effectively measure all workers' exposures to multiple hazards within a facility (~100 workers) in a single day while minimizing participant burden and loss of productivity.

methodsWe deployed novel, compact personal monitors-called AirPens-to measure exposures to total particulates, formaldehyde, and A-weighted noise among workers at a furniture manufacturing facility during one full work shift. AirPens were preloaded with sampling media and preprogrammed to minimize field deployment time. On-board sensor data were used to identify sampling anomalies, confirm that AirPens were worn, and validate sampling results. Valid samples were then used to estimate exposure distribution parameters.

resultsA team of five people deployed 83 AirPens at the facility. After quality assurance screening, 67 PM, 67 noise, and 22 formaldehyde samples remained valid (or 72, 67, and 31, respectively if samples below the limit of detection are counted as valid). PM and noise exposure distribution parameters (e.g., arithmetic and geometric mean, 95th percentile) were estimated with high certainty. Uncertainty (i.e., confidence intervals) grew several fold when smaller sample sizes were analyzed. SIGNIFICANCE: Use of streamlined multi-hazard monitoring technology enables dramatically higher sample throughput for workplace exposure assessment. This approach reduces the uncertainty of workplace risk assessment and facilitates more in-depth analyses of personal exposure. IMPACT STATEMENT: Comprehensive monitoring of personal exposure to air pollution is lacking due to technological and logistical limitations inherent to established monitoring methods. This lack of monitoring limits occupational health practice and epidemiologic research, which rely on sufficient sample sizes to support expert judgments or statistical inferences. This work demonstrates a new wearable sampler for particle, gas, and noise hazards, designed to overcome typical barriers to personal exposure assessment (e.g., cost, time, participant burden). Results show that this new sampling approach can produce measurements of similar quality, but on a much larger scale, compared to established technology.

Indexed as

exposure assessmentindustrial hygieneparticulate matterPersonal exposure

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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.