A Simplified Anaerobic Bioreactor for the Treatment of Selenium-Laden Discharges from Non-acidic, End-Pit Lakes
Bibliographic record
Abstract
Selenium (Se) contamination of aquatic resources and its mitigation is of global concern. Anaerobic bioreactors are the most promising method for treating Se-laden water in end-pit lakes resulting from intensive coal mining and waste rock leachate in the Canadian Rockies. This study assessed the suitability of a bioreactor system to treat non-acidic, coal mine effluent containing 85 μg/L of Se, near Grande Cache, AB, Canada, while making the system as cheap as possible using locally available materials. We successfully used a sediment inoculum from the same end-pit lake as the effluent source to obtain sulfur/Se-reducing bacteria and mixed the inoculum with mulch, manure, gravel, limestone, and bone meal to comprise the ‘active substrate’ for the bioreactors. The anaerobic bioreactors reduced >95 % of the total Se in the inflow water with a flow rate of ≈0.2 m 3 /h. Se removal was not related to water temperature, which declined from 17 to ≈2 °C in November, suggesting water can be treated regardless of temperature. The use of manure as a bacterial carbon/nitrogen source introduced Escherichia coli into the downstream environment, but after a short elevated concentration, the abundance of E. coli dropped below water quality guidelines. We were able to show that successful Se reduction can be achieved using an anaerobic bioreactor design and locally available material. This design kept the building and maintenance price lower than previous reactors, making the approach promising for larger scale applications and making bioreactors a more cost accessible remediation technology for non-acidic end-pit lakes.
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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) | 0.001 | 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 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".