ArticleJournal of occupational and environmental hygiene2018
Laboratory evaluation of a low-cost, real-time, aerosol multi-sensor.
Article in Journal of occupational and environmental hygiene, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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.
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.
Who cites it
11 citing papers in PubMed, 26 citations in OpenAlex.
- Biomass-fuelled improved cookstove intervention to prevent household air pollution in Northwest Ethiopia: a cluster randomized controlled trial.Environmental health and preventive medicine · 2021Trial
- Calibration of Low-Cost Particulate Matter Sensors for Exposure Assessment: A Critical Review.Current environmental health reports · 2026Review
- Assessing the efficacy of a low-cost air pollution monitoring device for environmental and occupational exposure assessments.Environmental monitoring and assessment · 2025Article
- A wearable real-time particulate monitor demonstrates that soaking hay reduces dust exposure.Equine veterinary journal · 2025Article
- Aerosol Measurement Degradation in Low-Cost Particle Sensors Using Laboratory Calibration and Field Validation.Toxics · 2023Article
- Using dynamic time warping self-organizing maps to characterize diurnal patterns in environmental exposures.Scientific reports · 2021Article
- W-TSS: A Wavelet-Based Algorithm for Discovering Time Series Shapelets.Sensors (Basel, Switzerland) · 2021Article
- Indoor Air Quality Issues for Rocky Mountain West Tribes.Frontiers in public health · 2021Article
- A Review of Low-Cost Particulate Matter Sensors from the Developers' Perspectives.Sensors (Basel, Switzerland) · 2020Review
- Development of a calibration chamber to evaluate the performance of low-cost particulate matter sensors.Environmental pollution (Barking, Essex : 1987) · 2019Article
- Investigating Measurement Variation of Modified Low-Cost Particle Sensors.Journal of aerosol science · 2019Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors at 1 institution in 1 country.
Funding
Abstract
Exposure to occupational aerosols are a known hazard in many industry sectors and can be a risk factor for several respiratory diseases. In this study, a laboratory evaluation of low-cost aerosol sensors, the Dylos DC1700 and a modified Dylos known as the Utah Modified Dylos Sensor (UMDS), was performed to assess the sensors' efficiency in sampling respirable and inhalable dust at high concentrations, which are most common in occupational settings. Dust concentrations were measured in a low-speed wind tunnel with 3 UMDSs, collocated with an aerosol spectrometer (Grimm 1.109) and gravimetric respirable and inhalable samplers. A total of 10 tests consisting of 5 different concentrations and 2 test aerosols, Arizona road dust and aluminum oxide, were conducted. For the Arizona road dust, total particle count was strongly related between the spectrometer and the UMDS with a coefficient of determination (R
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Identifiers
What OpenQuestion holds
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.