Bacterial Augmented Floating Treatment Wetlands for Efficient Treatment of Synthetic Textile Dye Wastewater
Bibliographic record
Abstract
Floating treatment wetland (FTW) is an innovative, cost effective and environmentally friendly option for wastewater treatment. The dyes in textile wastewater degrade water quality and pose harmful effects to living organisms. In this study, FTWs, vegetated with Phragmites australis and augmented with specific bacteria, were used to treat dye-enriched synthetic effluent. Three different types of textile wastewater were synthesized by adding three different dyes in tap water separately. The FTWs were augmented with three pollutants degrading and plant growth promoting bacterial strains (i.e., Acinetobacter junii strain NT-15, Rhodococcus sp. strain NT-39, and Pseudomonas indoloxydans strain NT-38). The water samples were analyzed for pH, electrical conductivity (EC), total dissolved solid (TDS), total suspended solids (TSS), chemical oxygen demand (COD), biological oxygen demand (BOD), color, bacterial survival and heavy metals (Cr, Ni, Mn, Zn, Pb and Fe). The results indicated that the FTWs removed pollutants and color from the treated water; however, the inoculated bacteria in combination with plants further enhanced the remediation potential of floating wetlands. In FTWs with P. australis and augmented with bacterial inoculum, pH, EC, TDS, TSS, COD, BOD and color of dyes were significantly reduced as compared to only vegetated and non-vegetated floating treatment wetlands without bacterial inoculation. Similarly, the FTWs application successfully removed the heavy metal from the treated dye-enriched wastewater, predominately by FTWs inoculated with bacterial strains. The bacterial augmented vegetated FTWs, in the case of dye 1, reduced the concentration of Cu, Ni, Zn, Fe, Mn and Pb by 75%, 73.3%, 86.9%, 75%, 70% and 76.7%, respectively. Similarly, the bacterial inoculation to plants in the case of dye 2 achieved 77.5% (Cu), 73.3% (Ni), 83.3% (Zn), 77.5% (Fe), 66.7% (Mn) and 73.3% (Pb) removal rates. Likewise in the case of dye 3, which was treated with plants and inoculated bacteria, the metals removal rates were 77.5%, 73.3%, 89.7%, 81.0%, 70% and 65.5% for Cu, Ni, Zn, Fe, Mn and Pb, respectively. The inoculated bacteria showed persistence in water, in roots and in shoots of the inoculated plants. The bacteria also reduced the dye-induced toxicity and promoted plant growth for all three dyes. The overall results suggested that FTW could be a promising technology for the treatment of dye-enriched textile effluent. Further research is needed in this regard before making it commercially applicable.
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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) | 0.001 | 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 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".