Characterization of Tailings Dust from Two Abandoned Mine Sites: Effects on Nearby Surface Waters and Evaluation of Dust Sampling Methods
Bibliographic record
Abstract
Canada’s climate is warming and as a result the hydrological cycle is expected to become more intense, with rainfall concentrated in extreme events with longer dry spells. Consequently, dry tailings may be more likely to blow into nearby surface waters. This research is an investigation into the geochemical effects of tailings dust on nearby surface waters and evaluates dust sampling methods used to investigate seasonal trends. During this study, two abandoned mine sites in Nova Scotia, Canada were investigated: Stirling Zn-Pb and Goldenville Au mine. At Stirling, tailings were sieve to <63μm as a proxy for dust and analyzed to determine metal concentrations and identify primary and secondary metal-bearing phases. Metal-hosting phases identified include sphalerite, smithsonite, aurichalcite, hydrohetaerolite, goethite. tennantite, galena, cerussite, Pb-Mn phases, and chalcopyrite. Shake flask tests were conducted to investigate dust solubility in simulated stream waters (pH=7). Results indicate that dust particles are partially water soluble in the shake flask test and that sphalerite, cerussite and chalcopyrite are likely the main sources of Zn, Pb and Cu in the shake flask leachate, based on calculated saturated indices and mass balance. Analyses of stream water indicate similar conditions (pH, Eh, etc.) and saturation indices compared to shake flask tests, and therefore provide reasonable insight for processes occurring the field. Dust was also sampled using a variety of different methods to identify the most suitable method for seasonal sampling. These included passive dry deposition collectors (PAS-DDs), high volume total suspended particle (TSP) sampler (HVAS), dust deposition gauges (DDGs), and lichen. Results indicate that Pas-DDs with a glass fiber filters (GFF) and dust deposition gauges likely underestimate dust deposition. In comparison, Pas-DDs with a polyurethane foam disk (PUF) efficiently accumulate dust. However, PUFs had additional challenges including metal(loid)s within the filters themselves, difficulty obtaining stable weights, and potential dust collection from the sides and bottom. Despite difference between dust sampling methods, it was observed that dust deposition was highest in the winter months due to higher wind speeds. Future dust generation remains difficult to predict due to the unknown combined effect of changes in temperature, precipitation, evaporation, and wind speed.
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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 | 0.001 | 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) | 0.000 | 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 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".