Phytoremediation of contaminated soils using ornamental plants
Bibliographic record
Abstract
Phytoremediation has attracted increasing attention and is a promising technology for addressing soil contamination problems. Unlike other kinds of remediation plants, ornamental plants grown for decorative purposes in gardens and landscape design projects have been an important source of remediation plants in recent years. In addition to beautifying the environment, some ornamental plants can not only grow well but also accumulate or degrade contaminants when growing in soil contaminated with heavy metals or organic pollutants. Especially in contaminated urban areas, it is comparatively rare and commendable when remediation plants with ornamental value are applied. In this review, we summarized the current research on the phytoremediation of contaminated soils using ornamental plants, evaluated the phytoremediation capacity of ornamental plants in heavy-metal and organic pollutant-contaminated soils, and highlighted specific ornamental plants with a strong accumulation ability and tolerance to pollutants. The findings related to the main mechanisms of the phytoremediation of contaminated soils were explained. Enhancement measures aimed at promoting the bioavailability of contaminants and the tolerance of ornamental plants were also reviewed in this article. It is hoped that this study will draw attention to a new path for phytoremediation technology.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".