Sources and Transformation Mechanisms of Atmospheric Particulate Bound Mercury Revealed by Mercury Stable Isotopes
Bibliographic record
Abstract
This study examined the isotopic composition of particulate bound mercury (PBM) in 10 Chinese megacities and explored the associated sources and transformation mechanisms. PBM in these cities was characterized by negative δ 202 Hg (mean: −2.00 to −0.78‰), slightly negative to highly positive Δ 199 Hg (mean: −0.04 to 0.47‰), and slightly positive Δ 200 Hg (mean: 0.02 to 0.06‰) values. The positive PBM Δ 199 Hg signatures were likely caused by physiochemical reactions in aerosols. The Δ 199 Hg/Δ 201 Hg ratio varied from 0.94 to 1.39 in the cities and increased with the increase in the corresponding mean Δ 199 Hg PBM value. We speculate that, in addition to the photoreduction of oxidized Hg, other transformation mechanisms in aerosols (e.g., isotope exchange, complexation, and oxidation, which express nuclear volume effects) also shape the Δ 199 Hg PBM signatures in the present study. These processes are likely enhanced in the presence of strong gas-particle partitioning of gaseous oxidized Hg (GOM) and elevated levels of redox active metals (e.g., Fe), halides, and elemental carbon. Based on Δ 200 Hg PBM data presented in this and previous studies, we estimate that large proportions (∼47 ± 22%) of PBM were sourced from the oxidation of gaseous elemental Hg followed by the partitioning of GOM onto aerosols globally, indicating the transformation of Hg(0) to PBM as an important sink of atmospheric Hg(0).
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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 | 0.001 | 0.001 |
| 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) | 0.000 | 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".