Bioaugmentation: An Emerging Strategy of Industrial Wastewater Treatment for Reuse and Discharge
Bibliographic record
Abstract
A promising long-term and sustainable solution to the growing scarcity of water worldwide is to recycle and reuse wastewater. In wastewater treatment plants, the biodegradation of contaminants or pollutants by harnessing microorganisms present in activated sludge is one of the most important strategies to remove organic contaminants from wastewater. However, this approach has limitations because many pollutants are not efficiently eliminated. To counterbalance the limitations, bioaugmentation has been developed and consists of adding specific and efficient pollutant-biodegrading microorganisms into a microbial community in an effort to enhance the ability of this microbial community to biodegrade contaminants. This approach has been tested for wastewater cleaning with encouraging results, but failure has also been reported, especially during scale-up. In this review, work on the bioaugmentation in the context of removal of important pollutants from industrial wastewater is summarized, with an emphasis on recalcitrant compounds, and strategies that can be used to improve the efficiency of bioaugmentation are also discussed. This review also initiates a discussion regarding new research areas, such as nanotechnology and quorum sensing, that should be investigated to improve the efficiency of wastewater bioaugmentation.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".