A Survey of Mercury in Air and Precipitation across Canada: Patterns and Trends
Bibliographic record
Abstract
Atmospheric mercury (Hg) measurements from across Canada were compiled and analysed as part of a national Hg science assessment. Here we update long-term trends of Hg in air and precipitation, and present more extensive measurements on patterns and trends in speciated Hg species (gaseous elemental mercury—GEM, reactive gaseous mercury—RGM, and total particulate mercury on particles <2.5 μm—TPM2.5) at several sites. A spatial analysis across Canada revealed higher air concentrations and wet deposition of Hg in the vicinity of local and regional emission sources, and lower air concentrations of Hg at mid-latitude maritime sites compared to continental sites. Diel and seasonal patterns in atmospheric GEM, RGM and TPM2.5 concentrations reflected differences in patterns of anthropogenic emissions, photo-induced surface emissions, chemistry, deposition and mixing. Concentrations of GEM decreased at rates ranging from −0.9% to −3.3% per year at all sites where measurements began in the 1990s. Concentrations of total Hg in precipitation declined up to 3.7% yr−1. Trends in RGM and TPM2.5 were less clear due to shorter measurement periods and low concentrations, however, in spring at the high Arctic site (Alert) when RGM and TPM2.5 concentrations were high, concentrations of both increased by 7%–10% per year.
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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 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".