An Interhemispheric Difference in Atmospheric Gaseous Elemental Mercury Isotopes Reveals a New Insight in Oceanic Mercury Emissions
Bibliographic record
Abstract
Abstract Oceanic emission of gaseous elemental mercury (Hg0 or GEM) is an important source for atmospheric mercury (Hg), but existing estimates of global gross oceanic Hg0 emissions are highly variable (800–7,220 Mg yr−1). This study measured atmospheric GEM concentrations and isotopic compositions at two coastal sites in Terengganu, Malaysia, a region that receives air masses from both hemispheres, during 2019–2021 to diagnose the amount of oceanic Hg0 emissions. Significantly lower mean (±1sd) concentration (1.28 ± 0.20 ng m−3), Δ199Hg (−0.23 ± 0.03‰), and Δ200Hg (−0.066 ± 0.018‰) and significantly higher δ202Hg (0.43 ± 0.12‰) were observed during the wet season when air masses were predominantly from the Southern Hemisphere, compared with those (mean concentration, Δ199Hg, Δ200Hg, and δ202Hg of 1.77 ± 0.09 ng m−3, −0.17 ± 0.03‰, −0.045 ± 0.023‰, and 0.25 ± 0.11‰, respectively) during the dry season when air masses were predominantly from the Northern Hemisphere, suggesting interhemispheric difference in GEM concentrations and its isotopic compositions. Using a Δ200Hg mass balance model, we estimated that the oceanic Hg0 emissions from HgII reduction should be below 2,250 ± 891 Mg yr−1 (±1sd), which is at the low‐end range of the literature reported values. We then used the constrained value as emission input to a three‐dimensional atmospheric Hg isotope model and reproduced well the global distributions and interhemispheric gradient of atmospheric GEM Δ200Hg. The findings from the present study will help to better understand Hg0 emissions from global oceans and their roles in global atmospheric Hg cycling.
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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) | 0.000 | 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".