Long range transport of mercury to the Arctic and across Canada
Bibliographic record
Abstract
Abstract. This study is the most extensive study to date on the transport of mercury to the Arctic. Moreover, it is the first such study to use a fully-coupled, online chemical transport model, Environment Canada's Global/Regional Atmospheric Heavy Metals model (GRAHM), where the meteorology and mercury processes are fully integrated. It is also the only study to date on the transport of mercury across Canada. We estimated source attribution from Asia, North America, Russia and Europe at six arctic verification stations, as well as three subarctic and eight midlatitude Canadian stations. We have found that Asia, despite having transport efficiencies that were almost always lower than those of North America and often lower than those of Russia, was the dominant source of gaseous atmospheric mercury at all verification stations: it contributed the most mercury (29–37% at all stations, seasons and levels considered), its concentrations frequently explained nearly 100% of the variability in the concentrations produced by the simulation performed with full global emissions, particularly in the absence of local sources, and it generated the most long range transport (LRT) events, causing 43%, 67% and 75% of the events at the arctic, subarctic and midlatitude stations, respectively. For the Arctic, Russian transport efficiencies tended to be the strongest, as expected, while European and Asian efficiencies were lower and higher, respectively, than those found in the literature. This disagreement is likely produced by mercury's long lifetime relative to that of other pollutants. The accepted springtime preference for the trans-Pacific transport of Asian pollution was evident only in the midlatitude group of stations, being masked in the arctic and subarctic groups by the occurrence of atmospheric mercury depletion events. Some neighbouring arctic stations recorded dissimilar numbers of LRT events; despite their proximity, the behaviour of mercury at these stations was governed by different dynamics and transport pathways. The column burden of GEM in the lowest 5 km of the Northern Hemisphere was largest in summer from Asia, North America and Russia, but in winter from Europe. In the vertical, transport of mercury from all source regions occurred principally in the mid-troposphere.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".