Inorganic Thiolated Arsenic in Mine Wastewater: Sources, Stability, and Potential Environmental Impacts
Bibliographic record
Abstract
Aqueous inorganic thiolated As species (thio-As) can be essential for the biogeochemical cycling of As in both natural and engineered environments. Mine waste systems such as tailings ponds and waste rock piles may have the sub-oxic and pH conditions that allow for the formation and mobilization of thio-As, but there remains a paucity of quantitative data on the presence and kinetic stability of these forms of As. This thesis investigated the presence, behaviour, and stability of thio-As in mine waste systems across various environmental conditions and evaluated geochemical controls on their formation and degradation (oxidation kinetics). Laboratory column experiments were conducted with mine waste rocks from the Antamina mine, Peru, and processed tailings from Montague, Nova Scotia, to assess the production of thio-As. Drainage from column experiments contained thio-As concentrations up to 13 µg/L, primarily as monothioarsenate. Thio-As abundances were notably higher (<5% of total dissolved As) in drainages from enargite-rich materials compared to those of arsenopyrite-bearing materials (<0.5%). Investigation of legacy tailings in Ontario and Nova Scotia revealed significant thio-As in porewaters (up to 5 mg/L; 17% of total dissolved As), primarily as monothioarsenate, as well as lesser amounts of di- and tri-thioarsenates and methylated thioarsenates. Thio-As was most abundant in sub-oxic porewaters and tailings across the studied sites, and strongly related to the prevailing redox conditions and porewater hydrochemistry, less to the As-bearing mineralogy. The oxidation kinetics of thio-As at varying pH, dissolved Fe, and (thio-)As concentration were also examined. Thio-As oxidation rates increased at lower pH, reaching several μmoles/L/d at pH 3. Trithioarsenate oxidation was about two orders-of-magnitude faster than di- and mono-thioarsenate. Experimental data was used to parameterize rate equations and calibrate a kinetic model for rate constants, offering insights into thio-As mobility and its potential environmental impacts. This thesis demonstrates that thio-As can exist in mine waste systems and potentially influence As mobility in these and receiving downstream environments. This highlights the need for including thio-As in mine waste management, environmental assessment, and remediation strategies, particularly at high-As sites.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 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".