Oceanic evasion fuels Arctic summertime rebound of atmospheric mercury and drives transport to Arctic terrestrial ecosystems
Bibliographic record
Abstract
Mercury (Hg) contamination poses a persistent threat to the remote Arctic ecosystem, yet the mechanisms driving the pronounced summer rebound of atmospheric gaseous elemental Hg (Hg0) and its subsequent fate remain unclear due to limitations in large-scale seasonal studies. Here, we use an integrated atmosphere–land–sea-ice–ocean model to simulate Hg cycling in the Arctic comprehensively. Our results indicate that oceanic evasion is the dominant source (~80%) of the summer Hg0 rebound, particularly driven by seawater Hg0 release facilitated by seasonal ice melt (~42%), with further contributions from anthropogenic deposition and terrestrial re-emissions. Enhanced Hg0 dry deposition across the Arctic coastal regions, especially in the Arctic tundra, during the summer rebound highlights the potential transport of Hg from the pristine Arctic Ocean to Arctic terrestrial ecosystems. Arctic warming, with a transition from multi-year to first-year ice and tundra greening, is expected to amplify oceanic Hg evasion and intensify Hg0 uptake by the Arctic tundra due to increased vegetation growth, underlining the urgent need for continued research to evaluate Hg mitigation strategies effectively in the context of a changing Arctic. The Arctic summer mercury (Hg) rebound has been debated for decades. This study uses a process-based model to show sea ice melt drives Hg emissions and increases Hg deposition in Arctic tundra, highlighting its key role in Arctic Hg cycling
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".