Impact of Closing Canada’s Largest Point-Source of Mercury Emissions on Local Atmospheric Mercury Concentrations
Bibliographic record
Abstract
The Flin Flon, Manitoba copper smelter was Canada's largest point source of mercury emissions until its closure in 2010 after ~80 years of operation. The objective of this study was to understand the variables controlling the local ground-level air mercury concentrations before and after this major point source reduction. Total gaseous mercury (TGM) in air, mercury in precipitation, and other ancillary meteorological and air quality parameters were measured pre- and postsmelter closure, and mercury speciation measurements in air were collected postclosure. The results showed that TGM was significantly elevated during the time period when the smelter operated (4.1 ± 3.7 ng m(-3)), decreased only 20% during the year following its closure, and remained ~2-fold above background levels. Similar trends were observed for mercury concentrations in precipitation. Several lines of evidence indicated that while smelter stack emissions would occasionally mix down to the surface resulting in large spikes in TGM concentrations (up to 61 ng m(-3)), the largest contributor to elevated TGM concentrations before and after smelter closure was from surface-air fluxes from mercury-enriched soils and/or tailings. These findings highlight the ability of legacy mercury, deposited to local landscapes over decades from industrial activities, to significantly affect local air concentrations via emissions/re-emissions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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".