Atmospheric mercury in the Canadian Arctic. Part II: Insight from modeling
Bibliographic record
Abstract
A review of mercury in the Canadian Arctic with a focus on field measurements is presented in part I (see Steffen et al., this issue). Here we provide insights into the dynamics of mercury in the Canadian Arctic from new and published mercury modeling studies using Environment Canada's mercury model. The model simulations presented in this study use global anthropogenic emissions of mercury for the period 1995-2005. The most recent modeling estimate of the net gain of mercury from the atmosphere to the Arctic Ocean is 75 Mg year(-1) and the net gain to the terrestrial ecosystems north of 66.5° is 42 Mg year(-1). Model based annual export of riverine mercury from North American, Russian and all Arctic watersheds to the Arctic Ocean are in the range of 2.8-5.6, 12.7-25.4 and 15.5-31.0 Mg year(-1), respectively. Analysis of long-range transport events of Hg at Alert and Little Fox Lake monitoring sites indicates that Asia contributes the most ambient Hg to the Canadian Arctic followed by contributions from North America, Russia, and Europe. The largest anthropogenic Hg deposition to the Canadian Arctic is from East Asia followed by Europe (and Russia), North America, and South Asia. An examination of temporal trends of Hg using the model suggests that changes in meteorology and changes in anthropogenic emissions equally contribute to the decrease in surface air elemental mercury concentrations in the Canadian Arctic with an overall decline of ~12% from 1990 to 2005. A slow increase in net deposition of Hg is found in the Canadian Arctic in response to changes in meteorology. Changes in snowpack and sea-ice characteristics and increase in precipitation in the Arctic related with climate change are found to be primary causes for the meteorology-related changes in air concentrations and deposition of Hg in the region. The model estimates that under the emissions reduction scenario of worldwide implementation of the best emission control technologies by 2020, mercury deposition could potentially be reduced by 18-20% in the Canadian Arctic.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 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".