The Legacy of Mercury Contamination from Colonial Nonferrous Mining in the Southern Hemisphere
Bibliographic record
Abstract
High Resolution Image Download MS PowerPoint Slide The Mount Lyell copper (Cu) mine in Tasmania, Australia, underwent historical operational changes that influenced mercury (Hg) emissions from ore processing and smelting. This study presents the first record of Hg concentrations (Hg C ) and accumulation rates (Hg AR ) using sediment cores from four lakes around Mount Lyell. Hg C and Hg AR increased from the 1890s (onset of smelting), peaked from the 1920s (introduction of the flotation processing method), and declined after 1969 (smelter closure). Mercury isotopic signatures confirmed its anthropogenic source. Modeling of Hg deposition vs distance over the period 1922–1969 showed that it followed a power-law function. The Mount Lyell emissions may have affected an area up to ∼270,000 km 2, beyond which deposition was indistinguishable from the natural background. Estimated total Hg loadings ranged from 23 to 43 t, compared to an estimated ∼150 t Hg contained in the ore floated. Isotopic data showed Δ 199 Hg trending toward zero near the smelter, indicating that the smelter was the main source of Hg. Our findings highlight that pyrometallurgical smelting methods contributed more significantly to Hg emissions than production volume. Studying legacy mines in the Southern Hemisphere, responsible for 29.1% of global Cu production during the preregulatory era (1880–1950), is critical to understanding historical Hg dispersion in this understudied region.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".