Bioremediation of Organic Pollutants in Soil–Water System: A Review
Bibliographic record
Abstract
Soil-water pollution is of serious concern worldwide. There is a public outcry against the continually rising problems of pollution to ensure the safest and healthiest subsurface environment for living beings. A variety of organic pollutants causes serious soil-water pollution, toxicity and, therefore, the removal of a wide range of organic pollutants from contaminated matrix through the biological process rather than physico-chemical methods is an urgent need to protect the environment and public health. Being an ecofriendly technology, bioremediation can solve the problems of soil-water pollution due to hydrocarbons as it is a low-cost and self-driven process that utilises microorganisms and plants or their enzymes to degrade and detoxify pollutants and thus, promote sustainable development. This paper describes the updates on the bioremediation and phytoremediation techniques which have been recently developed and demonstrated at the plot-scale. Further, this paper provides details of wetland-based treatment of BTEX contaminated soils and water. The knowledge acquired in our study contributes extensively towards understanding the impact of dynamic subsurface conditions on engineered bioremediation techniques.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.002 |
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".