Geochemical and mineralogical investigations of niobium mine tailings in Québec, Canada
Bibliographic record
Abstract
Niobium (Nb) has been identified as a critical metal important in the transition to a low-carbon economy. As the global demand for Nb mining increases, it is important to ensure that Nb mine tailings management facilities are designed to limit leaching of potentially toxic elements. In this study, we combine geochemical and mineralogical analyses to identify elements of concern in the tailings from an active Nb mine in Québec, Canada. The major components of the ten tailings samples characterized include CaO (<37.2 wt.%), Fe 2 O 3(total) (<13.7 wt.%), MgO (<12.7 wt.%), P 2 O 5 (<17.1 wt.%), and inorganic carbon (<35.6 wt.%). The minor components include Al 2 O 3 , K 2 O, SrO, Nb 2 O 5 , BaO, and Na 2 O, in decreasing order of abundance. Mineralogical observations indicate dolomite (>40 vol.%), calcite, apatite, magnetite, and ankerite to be the major (>5 vol.%) phases. Nb is primarily hosted by columbite and pyrochlore. Phosphorus (P) is almost entirely hosted by apatite. Lanthanum (La), cerium (Ce), and neodymium (Nd) are also hosted primarily by apatite and to a lesser extent by parisite. The sequential extraction data are generally consistent with their expected mineral-hosts determined by element deportment calculations. Niobium remains in the residual fraction and is considered environmentally immobile. Apatite-hosted elements (mainly P, and lesser amounts of Ce, La, and Nd) are mobilized by the modified aqua regia leach step, reflecting dissolution of apatite under acidic conditions. Of note is a small proportion of Ce (<1%) associated with weak acid soluble/carbonate fraction. Additionally, aluminum (Al) is an important element of the tailings (up to 0.9 wt.%). Acidification and alkalinization of the tailings, along with the presence of complexing ligands, such as F and dissolved organic carbon, could increase Al mobility, leading to its potential toxicity to aquatic organisms. Therefore, we recommend that the design of Nb tailings management facilities is guided by an inventory of Al, P, Ce, La and Nd and their release rates, with these data iteratively applied in risk assessment models. The goal is to achieve predicted impacts consistent with water quality guidelines for the protection of aquatic life, or, if not possible, to reach concentrations that are as low as reasonably achievable. • Elements of interest (Al, P, Ce, La, Nd) in niobium mine waste are identified. • Apatite, pyrochlore, and parisite host some of the rare earth elements (Ce, La, Nd). • Apatite-hosted elements could leach under acidic conditions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".