Development, characterization, and testing of a personal passive sampler for measuring inhalation exposure to gaseous elemental mercury
Bibliographic record
Abstract
Inhalation of gaseous elemental mercury (GEM) is an occupational exposure concern for workers handling elemental mercury or mercury-containing waste. GEM is also often present near historically mercury-contaminated sites, potentially resulting in low-level, chronic exposure of the wider population. Here we introduce a passive sampler for personal GEM monitoring which combines a radial porous diffusive barrier with an activated carbon sorbent. A total mercury analyzer is used to quantify GEM sorbed to the carbon by thermal decomposition, amalgamation, and atomic absorption spectroscopy. A sampling rate of 0.070 m3/day was determined by calibrating the sampler at low and high concentrations. Deployments lasting 8 h result in limits of quantification well below 200 ng/m3. The sampler has a measurement range of at least four orders of magnitude. Derived air concentrations were not statistically significantly different from those obtained by active air sampling but were more precise than those obtained using a personal pump. If properly stored, the sampler maintains low blank levels in high GEM environments. Affordability, sturdiness, simplicity, and the wide availability of total mercury analyzers make this sampler highly suited for monitoring GEM inhalation exposure, including in developing countries.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".