A critical review on bioremediation technologies for Cr(VI)-contaminated soils and wastewater
Bibliographic record
Abstract
Chromium (Cr) is a potentially toxic metal originating from natural processes and anthropogenic activities such as the iron-steel, electroplating, and leather industries, which is carcinogen to living organisms and has an ecological risk. Hence, research into the remediation of Cr pollution has attracted widespread attention. Bioremediation techniques have advantages of causing little disturbance to soil and water, low cost, simple and convenient operation, and less secondary pollution. In this review, we briefly describe the chemical properties of Cr, sources of Cr pollution, environmental quality, toxicological/health effects of Cr, and analytical methods. We also discuss the factors that govern methods for the bioremediation of Cr and compare their advantages and disadvantages. In particular, we focus on efforts to establish Cr bioremediation processes and their mechanisms. The main mechanisms include biosorption, bioaccumulation, complexation, electrostatic attraction, Cr(VI) reduction to Cr(III), and ion exchange, which decrease the Cr(VI) concentrations and convert Cr(VI) into Cr(III) lowering its toxicity and making it environmentally benign. However, bioremediation is still a challenging technique and most studies remain at the laboratory stage. Therefore we suggest areas for future research and provide theoretical guidance and a scientific basis for the application of biosorbents for Cr(VI) bioremediation in soils and wastewater.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.003 |
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".