Toxicity and metal speciation in acid mine drainage treated by passive bioreactors
Bibliographic record
Abstract
Sulfate-reducing passive bioreactors treat acid mine drainage (AMD) by increasing its pH and alkalinity and by removing metals as metal sulfide precipitates. In addition to discharge limits based on physicochemical parameters, however, treated effluent is required to be nontoxic. Acute and sublethal toxicity was assessed for effluent from 3.5-L column bioreactors filled with mixtures of natural organic carbon sources and operated at different hydraulic retention times (HRTs) for the treatment of a highly contaminated AMD. Effluent was first tested for acute (Daphnia magna and Oncorhynchus mykiss) and sublethal (Pseudokirchneriella subcapitata, Ceriodaphnia dubia, and Lemna minor) toxicity. Acute toxicity was observed for D. magna, and a toxicity identification evaluation (TIE) procedure was then performed to identify potential toxicants. Finally, metal speciation in the effluent was determined using ultrafiltration and geochemical modeling for the interpretation of the toxicity results. The 10-d HRT effluent was nonacutely lethal for O. mykiss but acutely lethal for D. magna. The toxicity to D. magna, however, was removed by 2 h of aeration, and the TIE procedure suggested iron as a cause of toxicity. Sublethal toxicity of the 10-d HRT effluent was observed for all test species, but it was reduced compared to the raw AMD and to a 7.3-d HRT effluent. Data regarding metal speciation indicated instability of both effluents during aeration and were consistent with the toxicity being caused by iron. Column bioreactors in operation for more than nine months efficiently improved the physicochemical quality of highly contaminated AMD at different HRTs. The present study, however, indicated that design of passive treatment should include sufficient HRT and posttreatment aeration to meet acute toxicity requirements.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 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".