Cyperus laevigatus L. Enhances Diesel Oil Remediation in Synergism with Bacterial Inoculation in Floating Treatment Wetlands
Bibliographic record
Abstract
Diesel oil is considered a very hazardous fuel due to its adverse effect on the aquatic ecosystem, so its remediation has become the focus of much attention. Taking this into consideration, the current study was conducted to explore the synergistic applications of both plant and bacteria for cleaning up of diesel oil contaminated water. We examined that the application of floating treatment wetlands (FTWs) is an economical and superlative choice for the treatment of diesel oil contaminated water. In this study, a pilot scale floating treatment wetlands system having diesel oil contaminated water (1% w/v), was adopted using Cyperus laevigatus L and a mixture of hydrocarbons degrading bacterial strains; viz., Acinetobacter sp.61KJ620863, Bacillus megaterium 65 KF478214, and Acinetobacter sp.82 KF478231. It was observed that consortium of hydrocarbons degrading bacteria improved the remediation of diesel oil in combination with Cyperus laevigatus L. Moreover, the performance of the FTWs was enhanced by colonization of bacterial strains in the root and shoot of Cyperus laevigatus L. Independently, the bacterial consortium and Cyperus laevigatus L exhibited 37.46% and 56.57% reduction in diesel oil, respectively, while 73.48% reduction in hydrocarbons was exhibited by the joint application of both plant and bacteria in FTWs. Furthermore, microbial inoculation improved the fresh biomass (11.62%), dry biomass (33.33%), and height (18.05%) of plants. Fish toxicity assay evaluated the effectiveness of FTWs by showing the extent of improvement in the water quality to a level that became safe for living organisms. The study therefore concluded that Cyperus laevigatus L augmented with hydrocarbons degrading bacterial consortium exhibited a remarkable ability to decontaminate the diesel oil from water and could enhance the FTWs performance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 teacher head, 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".