Achieving Environmental Sustainability in Wastewater Treatment by Phytoremediation with Water Hyacinth (Eichhornia Crassipes)
Bibliographic record
Abstract
Small and medium scale industries in Nigeria play a major role in polluting water bodies, and key among these pollutants are suspended solids, biological oxygen demand and heavy metals contamination. Conventional methods of treatment, such as chemical precipitation, do not provide sustainable solutions as the pollutants are merely transferred from the waste water to a sludge residue which is disposed of by land-filling. The pollutants eventually find their way to freshwater supplies thereby contaminating it.Water hyacinth is a noxious weed that has a rapid growth rate and easily congests the water ways in Lagos, a coastal city in Nigeria, thereby creating serious problems in navigation, and irrigation. This can be harvested, and in line with the golden rules of sustainable development, used for the sustainable treatment of some industrial wastewaters. This work investigates the effectiveness of water hyacinth in wastewater treatment. After a 5-week simple experiment, in which water hyacinths were planted in wastewater samples obtained from three different industries, the average removal of pollutants were found to be 53.03%, 64.41%, 65.4%, 47.22%, 94.67% and 30.30% for Total Suspended Solids (TSS), Biochemical Oxygen Demand (BOD), Dissolved Oxygen (DO), nitrate-nitrogen, cadmium and iron respectively. Average Biocentration Factors (BCF) obtained for cadmium, copper and iron were 583.83, 734.41 and 2982.95 respectively.
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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 | 0.000 | 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) | 0.000 | 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".