Updated trends for atmospheric mercury in the Arctic: 1995–2018
Bibliographic record
Abstract
The Arctic region forms a unique environment with specific physical, chemical, and biological processes affecting mercury (Hg) cycles and limited anthropogenic Hg sources. However, historic global emissions and long range atmospheric transport has led to elevated Hg in Arctic wildlife and waterways. Continuous atmospheric Hg measurements, spanning 20 years, and increased monitoring sites has allowed a more comprehensive understanding of how Arctic atmospheric mercury is changing over time. Time-series trend analysis of TGM (Total Gaseous Mercury) in air was performed from 10 circumpolar air monitoring stations, comprising of high-Arctic, and sub-Arctic sites. GOM (gaseous oxidised mercury) and PHg (particulate bound mercury) measurements were also available at 2 high-Arctic sites. Seasonal mean TGM for sub-Arctic sites were lowest during fall ranging from 1.1 ng m −3 Hyytiälä to 1.3 ng m −3 , Little Fox Lake. Mean TGM concentrations at high-Arctic sites showed the greatest variability, with highest daily means in spring ranging between 4.2 ng m −3 at Amderma and 2.4 ng m −3 at Zeppelin, largely driven by local chemistry. Annual TGM trend analysis was negative for 8 of the 10 sites. High-Arctic seasonal TGM trends saw smallest decline during summer. Fall trends ranged from −0.8% to −2.6% yr −1 . Across the sub-Arctic sites spring showed the largest significant decreases, ranging between −7.7% to −0.36% yr −1 , while fall generally had no significant trends. High-Arctic speciation of GOM and PHg at Alert and Zeppelin showed that the timing and composition of atmospheric mercury deposition events are shifting. Alert GOM trends are increasing throughout the year, while PHg trends decreased or not significant. Zeppelin saw the opposite, moving towards increasing PHg and decreasing GOM. Atmospheric mercury trends over the last 20 years indicate that Hg concentrations are decreasing across the Arctic, though not uniformly. This is potentially driven by environmental change, such as plant productivity and sea ice dynamics.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".