Passive air sampling of gaseous elemental mercury: a critical review
Bibliographic record
Abstract
Because gaseous elemental mercury (GEM) is distributed globally through the atmosphere, reliable means of measuring its concentrations in air are important. Passive air samplers (PASs), designed to be cheap, simple to operate, and to work without electricity, could provide an alternative to established active sampling techniques in applications such as (1) long-term monitoring of atmospheric GEM levels in remote regions and in developing countries, (2) atmospheric mercury source identification and characterization through finely resolved spatial mapping, and (3) the recording of personal exposure to GEM. An effective GEM PAS requires a tightly constrained sampling rate, a large and stable uptake capacity, and a sensitive analytical technique. None of the GEM PASs developed to date achieve levels of accuracy and precision sufficient for the reliable determination of background concentrations over extended deployments. This is due to (1) sampling rates that vary due to meteorological factors and manufacturing inconsistencies, and/or (2) an often low, irreproducible and/or unstable uptake capacity of the employed sorbents. While we identify shortcomings of existing GEM PAS, we also reveal potential routes to overcome those difficulties. Activated carbon and nanostructured metal surfaces hold promise as effective sorbents. Sampler designs incorporating diffusive barriers should be able to notably reduce the influence of wind on sampling rates.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".