Mining and e-waste recycling influence the spatial distribution of technology-critical elements, but not rare earth elements, in boreal lakes
Bibliographic record
Abstract
Mining and more recent e-waste recycling have contributed trace elements (TEs) to the environment. However, the occurrence of emerging technology-critical elements (TCEs), including rare earth elements (REEs), remains poorly reported. Our study aims to i) investigate the spatial distribution of TEs, including TCEs, across different environmental matrices; ii) compare measured concentrations in water and sediment against environmental quality guidelines; and iii) assess potential risks to human health from fish consumption. In this study, we sampled water, sediment, and fish tissues (muscle and liver) across six boreal lakes near the historically mining region of Rouyn-Noranda, home to North America's largest copper smelting and recycling facility (Horne Smelter). Concentrations of TEs (e.g., Cu, Se) were higher in lakes closest to the smelter. Similarly, some TCEs (i.e., Ti, Co, Tl) followed this same spatial distribution pattern, suggesting that their release may be linked to historical and current mining activities. Conversely, REEs displayed distinct spatial patterns, likely influenced by geological sources rather than pollution. Several TEs (e.g., Zn, Cd, Pb) exceeded Canadian water and sediment quality guidelines in lakes closer to the mining area. Muscle tissue from walleye or yellow perch showed Zn, Cd, or Pb concentrations above safety limits in at least one lake. This study highlights the importance of including emerging TCEs (e.g., Sr, Tl, Co) in biomonitoring programs. Our findings provide critical insights into the environmental distribution of TEs across multiple matrices of boreal lake ecosystems, contributing to global efforts in risk assessment and sustainable freshwater management in the context of growing electronic waste recycling. • First report of various TCEs and REEs in 3 matrices of boreal lakes • The spatial distribution of several newly reported TCEs is similar to historical TEs • REE concentrations are associated with geology rather than mining sources • Some TE concentrations in water and sediment are above guidelines in closest lakes • All lakes exceeded safety limits for at least one element (Zn, Cd, or Pb) in the muscle tissue
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| 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".