Microbial Fermentation of Organic Carbon Substrates Drives Rapid pH Neutralization and Element Removal in Bauxite Residue Leachate
Bibliographic record
Abstract
Globally, mineral processing activities produce an estimated 680 GL/yr of alkaline wastewater. Neutralizing pH and removing dissolved elements are the main goals of wastewater treatment prior to discharge. Here, we present the first study to explicitly evaluate the role of microbial communities in driving pH neutralization and element removal in alkaline wastewaters by fermentation of organic carbon, using bauxite residue leachate as a model system, and evaluate the effects of organic carbon complexity and microbial inoculum addition rates on the performance of these treatment systems at laboratory scale. Rates and extents of pH neutralization were higher in bioreactors fed with simpler organic carbon substrates (glucose and banana: 6 days to reach pH ≤ 8) than those fed with more complex organic carbon substrates (eucalyptus mulch: 15 days to reach pH ≤ 8; woodchips: equilibrium pH around 9). Concentrations of dissolved Al, As, B, Mo, Na, S, and V all significantly decreased after bioremediation. Increasing soil inoculant addition rate accelerated rates and extent of pH neutralization and element removal up to 0.1 wt %; further increases had little effect. Overall, glucose added at 1.8 wt % and soil inoculum added at 0.1 wt % provided the most effective minimal combination of carbon substrate and inoculum to drive pH neutralization and element removal.
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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 | 0.001 |
| 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".