Passive Air Sampling for Gaseous Mercury in Workplace Atmospheres and for Personal Inhalation Exposure Monitoring
Bibliographic record
Abstract
Inhalation exposure to gaseous elemental mercury (GEM) continues to be a concern in a number of workplaces. Examples are facilities handling mercury-containing electronic waste, such as compact fluorescent light bulbs, or so-called “gold-shops” processing the amalgams from artisanal and small-scale gold mining operations. GEM is also often present near historically mercury-contaminated sites or in dental facilities, creating the conditions for low-level, chronic inhalation exposure. Considering that inhaled GEM is readily absorbed and transferred across the blood-brain barrier, there is a need to be able to measure its air concentrations reliably and continuously. We are therefore exploring the feasibility of using a passive air sampler (PAS) recently developed for recording long term average GEM concentrations in the ambient atmosphere in the monitoring of personal GEM inhalation exposure and GEM concentrations in workplace atmospheres. This PAS is small, light, inexpensive, easy-to-operate, and requires no power. By tightly controlling sampling rates through (i) the use of a radial porous diffusive barrier, and (ii) a wind shelter, the sampler achieves high precision and accuracy. Eliminating the PAS’s wind shelter allows for its use as a wearable personal sampler, while only slightly increasing sampling rate variability. Average GEM concentrations need to be in excess of ~20 ng/m3 for the PAS to take up sufficient GEM during an 8-hour workday to achieve reliable quantification. For a 40-hour sampling period, corresponding to a traditional workweek, the threshold drops to ~5 ng/m3. At higher GEM concentrations, (i) even shorter sampling times are achievable, and (ii) a thicker diffusive barrier can be used, which increases precision by better controlling sampling rates. Side-by-side comparisons of the PAS in a number of occupational settings with personal pumped samplers support the validity of using occupational exposure estimates acquired with a PAS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".