Phytoremediation of pesticide-contaminated freshwaters by aquatic plants: a meta-analysis
Bibliographic record
Abstract
In the last decade, water contamination by pesticides has become a global concern, and phytoremediation has gained increasing attention. This approach is cost-effective and ecologically beneficial, revealing the abilities of plants to remove, detoxify, or immobilize environmental contaminants. Despite the growing number of publications, some questions remain: (i) How effectively do aquatic plants reduce pesticides in water? (ii) How is the effectiveness of water phytoremediation influenced by plant characteristics, pesticide properties, and environmental/experimental conditions? To answer those questions, we conducted a meta-analysis with 405 extracted pairs of data points from 56 studies to systematically analyze and explore the efficiency of pesticide removal by aquatic plants. We found that, compared to the control without plants, aquatic vegetation increases pesticide removal from water by 38.86 % (95 % CI = 31.50–46.21 %). We conducted subgroup and meta-regression analyses to identify factors influencing the global effect size. The taxonomy (order) and the life form of the plants did not significantly influence the degree of pesticide removal. The removal efficiency was influenced by pesticides’ type/mode of action, with insecticides being less efficiently removed than other pesticides. Additionally, we observed higher phytoremediation efficiency with increasing log K OW (lipophilicity), molecular mass, and in experiments conducted with contaminant mixtures. Environmental conditions also influenced removal efficiency, with higher temperatures and light intensity enhancing phytoremediation. Our results provide insights into the key factors determining the success of phytoremediation in aquatic environments contaminated by pesticides, thereby guiding decision-making on using this technology and directing new research toward developing strategies to enhance its effectiveness.
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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 | 0.014 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.054 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".