A floating aquatic system employing water hyacinth for municipal landfill leachate treatment: effect of leachate characteristics on the plant growth
Bibliographic record
Abstract
The current research demonstrates the effects of certain parameters, usually present in municipal landfill leachate, on the growth of water hyacinth (Eichhornia crassipes), a floating aquatic plant, when used for treatment of leachate. Experiments were carried out to investigate the ability of water hyacinth to grow in leachate with different salinity ions concentrations, nutrients, pH, and heavy metals concentrations. The ability of water hyacinth to remove some parameters such as nitrogen, potassium, phosphorus, total solids, and chloride were also investigated. All experiments were conducted in batch reactors in a greenhouse environment. The leachate samples were collected from Essex–Windsor Regional Landfill, Windsor, Ontario. It was found that this treatment system required the presence of sufficient amount of nutrients (N, P, and K) to ensure plant growth. Optimum growth took place when the initial chloride and sodium concentrations were 560 mg L–1 and 330 mg L–1, respectively. The leachate pH for optimum water hyacinth growth was found to be in the range of 5.8 to 6.0. Total heavy metal concentrations below 0.10 mequiv L–1 supported the plant growth; concentrations above 0.91 mequiv L–1 inhibited the plant growth. Removal efficiencies of nitrogen, potassium, phosphorus, chloride, and total solids from leachate were found to be affected by the growth of water hyacinth. Higher removals were obtained with higher plant growth. Key words: water hyacinth, municipal landfill leachate, chloride, sodium, heavy metals, water hyacinth growth, leachate treatment, bioconcentration factor.
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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 | 0.000 | 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".