Photoreduction of mercuric bromides in polar ice
Bibliographic record
Abstract
In the polar regions, which are vulnerable receptors of mercury pollution, atmospheric mercury depletion events (AMDEs) efficiently convert elemental mercury (Hg(0)) into oxidized mercury (Hg(II)) via bromine oxidation. Hg(II) subsequently deposits onto snow and sea ice. While field observations have shown that a large percentage of deposited mercury is re-emitted from the ice to the atmosphere by a photoinduced process, the fundamental photochemistry that drives the re-emission process remains unknown. Here, using multiconfigurational quantum chemistry, we find that the photoreduction of HgBr 2 , HgBr 3 − , and HgBr 4 2− in ice is more efficient than in the gas phase. This results from the influence of water molecules on the molecular geometry and electronic structure of mercuric bromides in ice, which enhances the absorption intensities at wavelengths relevant in the troposphere (λ > 290 nm), as compared to gas phase. Kinetic modeling shows that ~30 to 60% of deposited mercury in AMDEs can be reemitted due to the photoreduction of mercuric bromides in ice, in agreement with field observations. Our results reveal a photoreduction mechanism of sunlight-induced excited state chemistry of mercuric bromides on ice. These findings strongly suggest that this chemistry should be incorporated into atmospheric models to account for ice-atmosphere mercury cycling in the polar environments, currently not considered.
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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".