The role of sulphate-reducing bacteria (SRB) in bioremediation of sulphate-rich wastewater: Focus on the source of electron donors
Bibliographic record
Abstract
The industrial activity has increased with the world’s population in urban and non-urban environments. Sulphate (SO42-) is commonly found in aquatic ecosystems and is generally non-toxic to aquatic life, unless present in very high concentrations in an environment affected by human activities. Nowadays, there has been growing interest in the bioremediation of SO42- wastewater as a sustainable and a viable method with the advantages of employing microorganism. This paper provides an overview of the sulfur biogeochemical cycle, microbiology of sulphate-reducing bacteria (SRB), their application in treating SO42- laden wastewater, the crucial factors influencing SO42- removal efficiency, and more importantly, explores the potential for sulfur and metal recovery from mining and industrial waste, along with the source of electron donors. Previous studies in this field have shown that the removal of SO42- and other pollutants is affected by the wastewater matrix chemistry, such as temperature, pH, SO42- and sulfide concentration, ionic strength, nutrients concentration, moisture, redox condition, and the bioavailability of toxic metal ions. Apart from these results, more than 90% of potentially toxic elements (PTEs) and SO42- can be removed by the desired bacteria. Although bioremediation has disadvantages, it remains a viable method for removing PTEs and anions such as SO42- from SO42--rich wastewater. In the future, the application of SRB in combination with other organic compounds as electron donors and adsorbents is suggested as an effective solution for SO42- bioremediation, and sulfur and metals recovery.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".