Bioaugmentation-Enhanced Remediation of Crude Oil Polluted Water in Pilot-Scale Floating Treatment Wetlands
Bibliographic record
Abstract
Floating treatment wetlands (FTWs) are cost-effective systems for the remediation of polluted water. In FTWs, the metabolic activity of microorganisms associated with plants is fundamental to treatment efficiency. Bioaugmentation, the addition of microorganisms with pollutant-degrading capabilities, appears to be a promising means to enhance the treatment efficiency of FTWs. Here, we quantified the effect of bioaugmentation with a four-membered bacterial consortium on the remediation of water contaminated with crude oil in pilot-scale FTWs planted with Phragmites australis or Typha domingensis. The bacteria had been isolated from the endosphere and rhizosphere of various plants and carry the alkane hydroxylase gene, alkB, involved in aerobic hydrocarbon degradation. During a treatment period of 36 days, FTWs planted with P. australis achieved a reduction in hydrocarbon concentration from 300 mg/L to 16 mg/L with and 56 mg/L without bioaugmentation. In the FTWs planted with T. domingensis, respective hydrocarbon concentrations were 46 mg/L and 84 mg/L. The inoculated bacteria proliferated in the rhizoplane and in the plant interior. Copy numbers of the alkB gene and its mRNA increased over time in plant-associated samples, suggesting increased bacterial hydrocarbon degradation. The results show that bioaugmentation improved the treatment of oil-contaminated water in FTWs by at least a factor of two, indicating that the performance of full-scale systems can be improved at only small costs.
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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".