Methylated Mercury Species in Canadian High Arctic Marine Surface Waters and Snowpacks
Bibliographic record
Abstract
We sampled seawater and snowpacks in the Canadian high Arctic for methylated species of mercury (Hg). We discovered that, although seawater sampled under the sea ice had very low concentrations of total Hg (THg, all forms of Hg in a sample; on average 0.14-0.24 ng L(-1)), 30-45% of the THg was in the monomethyl Hg (MMHg) form (on average 0.057-0.095 ng L(-1)), making seawater itself a direct source of MMHg for biomagnification through marine food webs. Seawater under the ice also contained high concentrations of gaseous elemental Hg (GEM; 129 +/- 36 pg L(-1)), suggesting that open water regions such as polynyas and ice leads were a net source of approximately 130 +/- 30 ng Hg m(-2) day(-1) to the atmosphere. We also found 11.1 +/- 4.1 pg L(-1) of dimethyl Hg (DMHg) in seawater and calculated that there could be a significant flux of DMHg to the atmosphere from open water regions. This flux could then resultin MMHg deposition into nearby snowpacks via oxidation of DMHg to MMHg in the atmosphere. In fact, we found high concentrations of MMHg in a few snowpacks near regions of open water. Interestingly, we discovered a significant log-log relationship between Cl- concentrations in snowpacks and concentrations of THg. We hypothesize that as Cl- concentrations in snowpacks increase, inorganic Hg(II) occurs principally as less reducible chloro complexes and, hence, remains in an oxidized state. As a result, snowpacks that receive both marine aerosol deposition of Cl- and deposition of Hg(II) via springtime atmospheric Hg depletion events, for example, may contain significant loads of Hg(II). Overall, though, the median wet/dry loads of Hg in the snowpacks we sampled in the high Arctic (5.2 mg THg ha(-1) and 0.03 mg MMHg ha(-1)) were far below wet-only annual THg loadings throughout southern Canada and most of the U.S. (22-200 mg ha(-1)). Therefore, most Arctic snowpacks contribute
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".