Efficiency of Water Hyacinth (Eichhornia crassipes) in the Phytoremediation of Copper-Contaminated Waters of Lake Tempe, South Sulawesi Indonesia
Bibliographic record
Abstract
Lake Tempe, situated in Wajo Regency, South Sulawesi, Indonesia, is currently experiencing increased levels of toxicity due to heavy metal contamination stemming from industrial operations and human activities in the area.The presence of copper (Cu), a heavy metal, in water has been reported to raise concerns regarding the potential negative effects on the local ecosystem.This indicates that there is a need to develop a phytoremediation technique to efficiently reduce the levels of contamination.Therefore, this study aimed to assess the effectiveness of water hyacinth phytoremediation in reducing Cu contamination.Metal-contaminated water medium from Lake Tempe was used to cultivate water hyacinth, and the 30-day trial was carried out in a natural setting.Control was carried out as a comparison by measuring the decrease in Cu levels in the water.Measurements of the physicochemical characteristics of water were carried out both before and after phytoremediation process.The results showed that the levels of pH, total suspended solid (TSS), dissolved oxygen (DO), and Cu decreased after the procedure.Furthermore, there was an increase in the values of BOD5, total dissolved solid (TDS), total nitrogen content, and total phosphate (P).Water hyacinth capacity to absorb metals was determined by measuring the bio-concentration factor (BCF).The results showed a decrease in Cu levels with a range of 7.1692 mg/kg to 14.0202 mg/kg for 30 days.The BCF value obtained was 94.2217, indicating that there is a relationship between the BCF value and the phytoremediation time.The higher the value obtained, the longer the phytoremediation.The infrared data indicated that Cu was attached to the test plant by engaging the C=S, C=N, and O-H functional groups.Based on the results, water hyacinth could be used as a phytoremediation agent to reduce the levels of Cu in water.
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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 | 0.001 | 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".