Bibliographic record
Abstract
Background and objectives: Natural plant-assisted filtration is a convenient method in addition to having a low cost, easy manipulation, low technology and less energy consumption compared to other purification methods.One of specific plants for wastewater treatment is the vetiver plant.Nowadays, subsurface wetland is not only used for major wastewater and sewage pollutants but also for such special compounds as linear alkyl benzene sulfonates (LAS).This study aimed to determination of vetiver plants efficiency in the artificial wetland for removal leanr alkylbenzene sulfonate(LAS) from hospital laundry wastewater.Materials and methods: In this qualitative-analytical study, various parameters such as LAS, COD, BOD, phosphate and nitrate in the hospital wastewater and LAS content of laundry unit sewage (LAS) were measured before and after treatment with vetiver plant.All conditions for sampling and performing other tests were based on the Standard Method handbook guidelines.After different tests, the results were analyzed by SPSS and Excel software.Results: Results of BOD, COD, LAS, phosphate and nitrate concentrations in the clarifier effluent of hospital WWTP, effluent of vetiver pilot outflow, and control pilot showed that the highest post-treatment efficiencies of vetiver plant for the removal of LAS BOD, COD, phosphate and nitrate of the WWTP were 70, 73, 84, 33, and 44%, respectively.However, removal efficiencies of 40, 30, 22, 23 and 16%, respectively, were obtained for the same parameters in the control pilot without the plant.Discussion and conclusion: Overall, our study showed that vetiver plant's root in reducing the LAS and pollutants in effluent of the hospital WWTP was much more efficient than filtration and overland treatment.Also, the effluent of vetiver plant pilot had an appropriate filtration and certified for environmental wastewater discharge.
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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) | 0.993 | 0.996 |
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; both teacher heads agree on what is shown here.
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".