Phytoremediation: A Promising and Sustainable Approach to Eradicate Heavy Metals from Heavy Metals-Polluted Soils and Revegetating them
Bibliographic record
Abstract
Rapid industrialization, increased waste production and surge in agricultural activities, mining, contaminated irrigation water and industrial effluents contribute to the contamination of water resources due to heavy metal accumulation. Phytoremediation is a realistic and promising strategy for heavy metal removal from polluted areas, based on the employment of hyper-accumulator plant species that are extremely tolerant to heavy metals present in the environment/soil. Green plants are used to remove, decompose, or detoxify hazardous metals in this technique. Five types of phytoremediation methods viz. Phytostabilization, rhizofiltration, phytoextraction and phytovolatilization are used for decontamination of the soil. Traditional phytoremediation methods have significant limits in terms of large-scale application, thus biotechnological efforts to modify plants for heavy metal phytoremediation ways are being explored to improve the efficacy of plants as heavy metal decontamination candidates. It is relatively economic, efficient and environment friendly. New metal hyperaccumulators with high efficiency are being explored and employed for their use in phytoremediation and Phyto mining. This review comprehensively discusses different strategies and biotechnological approaches for the removal of various heavy metal containments from the environment, with emphasis on the advancements and implications of phytoremediation, along with their applications in cleaning up various toxic pollutants.
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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 | 0.001 | 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) | 0.002 | 0.001 |
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".