Using Mercury Stable Isotopes to Quantify Bidirectional Water–Atmosphere Hg(0) Exchange Fluxes and Explore Controlling Factors
Bibliographic record
Abstract
In this study, exchange fluxes and Hg isotope fractionation during water–atmosphere Hg(0) exchange were investigated at three lakes in China. Water–atmosphere exchange was overall characterized by net Hg(0) emissions, with lake-specific mean exchange fluxes ranging from 0.9 to 1.8 ng m –2 h –1, which produced negative δ 202 Hg (mean: −1.61 to −0.03‰) and Δ 199 Hg (−0.34 to −0.16‰) values. Emission-controlled experiments conducted using Hg–free air over the water surface at Hongfeng lake (HFL) showed negative δ 202 Hg and Δ 199 Hg in Hg(0) emitted from water, and similar values were observed between daytime (mean δ 202 Hg: −0.95‰, Δ 199 Hg: −0.25‰) and nighttime (δ 202 Hg: −1.00‰, Δ 199 Hg: −0.26‰). Results of the Hg isotope suggest that Hg(0) emission from water is mainly controlled by photochemical Hg(0) production in water. Deposition-controlled experiments at HFL showed that heavier Hg(0) isotopes (mean ε 202 Hg: −0.38‰) preferentially deposited to water, likely indicating an important role of aqueous Hg(0) oxidation played during the deposition process. A Δ 200 Hg mixing model showed that lake-specific mean emission fluxes from water surfaces were 2.1–4.1 ng m –2 h –1 and deposition fluxes to water surfaces were 1.2–2.3 ng m –2 h –1 at the three lakes. Results from the this study indicate that atmospheric Hg(0) deposition to water surfaces indeed plays an important role in Hg cycling between atmosphere and water bodies.
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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) | 0.001 | 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".