Aquatic Macrophytes in Constructed Wetlands: A Fight against Water Pollution
Bibliographic record
Abstract
There is growing concern among health institutions worldwide to supply clean water to their populations, especially to more vulnerable communities. Although sewage treatment systems can remove most contaminants, they are not efficient at removing certain substances that can be detected in significant quantities even after standard treatments. Considering the necessity of perfecting techniques that can remove waterborne contaminants, constructed wetland systems have emerged as an effective bioremediation solution for degrading and removing contaminants. In spite of their environmentally friendly appearance and efficiency in treating residual waters, one of the limiting factors to structure efficient artificial wetlands is the choice of plant species that can both tolerate and remove contaminants. For sometimes, the chosen plants composing a system were not shown to increase wetland performance and became a problem since the biomass produced must have appropriated destination. We provide here an overview of the use and role of aquatic macrophytes in constructed wetland systems. The ability of plants to remove metals, pharmaceutical products, pesticides, cyanotoxins and nanoparticles in constructed wetlands were compared with the removal efficiency of non-planted systems, aiming to evaluate the capacity of plants to increase the removal efficiency of the systems. Moreover, this review also focuses on the management and destination of the biomass produced through natural processes of water filtration. The use of macrophytes in constructed wetlands represents a promising technology, mainly due to their efficiency of removal and the cost advantages of their implantation. However, the choice of plant species composing constructed wetlands should not be only based on the plant removal capacity since the introduction of invasive species can become an ecological problem.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".