Isotopic Characterization of Atmospheric Gaseous Elemental Mercury by Passive Air Sampling
Bibliographic record
Abstract
Tracing emission sources and transformations of atmospheric mercury with Hg stable isotopes depends on the ability to collect amounts sufficient for reliable quantification. Commonly employed active sampling methods require power and long pumping times, which limits the ability to deploy in remote locations and at high spatial resolution and can lead to compromised traps. In order to overcome these limitations, we conducted field and laboratory experiments to assess the preservation of isotopic composition during sampling of gaseous elemental mercury (GEM) with a passive air sampler (PAS) that uses a sulfur-impregnated carbon sorbent and a diffusive barrier. Whereas no mass independent fractionation (MIF) was observed during sampling, the mass dependent fractionation (MDF, δ 202 Hg) of GEM taken up by the PAS was lower than that of actively pumped samples by 1.14 ± 0.24‰ (2SD). Because the MDF offset was consistent across field studies and laboratory experiments conducted at 5, 20, and 30 °C, the PAS can be used for reliable isotopic characterization of GEM (±0.3‰ for MDF, ±0.05‰ for MIF, 2SD). The MDF offset occurred more during the sorption of GEM rather than during diffusion. PAS field deployments confirm the ability to record differences in the isotopic composition of GEM (i) with distance from point sources and (ii) sampled at different background locations globally.
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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 | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".