Advances in membrane bioreactor for landfill leachate treatment: A review of characterization, challenges, and novel configurations
Bibliographic record
Abstract
Landfill leachate (LFL) is a complex wastewater that poses a serious environmental threat for the public health, owing to the toxic and recalcitrant nature of its components. Hence, an effective treatment is imperative before being discharged into the environment. To ensure an appropriate treatment, a thorough comprehension of LFL physico-chemical properties is essential. In addition to conventional contaminants such as chemical oxygen demand, biochemical oxygen demand, solids, ammonia, metals, recent studies have reported the presence of dissolved organic matter (DOM) and emerging contaminants such as bisphenols, PFAS, xenobiotics in trace concentrations. While conventional detection techniques are chemical and time consuming and reveal limited information regarding DOM, spectroscopic techniques such as UV–visible spectroscopy, Fourier-transform ion cyclotron resonance mass spectrometry, excitation emission matrix fluorescence spectroscopy are comparably more efficient, and effective. Furthermore, the conventional MBR has shown lower efficiency for treating old LFL and removal of heavy metals, phosphorus, micropollutants and recalcitrant. However, novel configurations in MBR such as high-retention MBRs (nanofiltration-MBR, osmotic MBR, and membrane distillation bioreactor), and electrochemical MBR are more effective alternatives with excellent removal efficiencies of micropollutants, and pharmaceuticals. One of the major limitations in MBR is membrane fouling which reduces the lifetime of membrane and in turn increases the operational cost of MBRs. Novel strategies such as electrically or mechanically assisted scouring, chemical cleaning, enzymatic treatment and the development of novel nanomaterial-based membranes have been proposed to mitigate membrane fouling in MBRs. Further, it is essential to decipher the microbial dynamics in MBR which facilitates contaminant removal by using genome sequencing tools and understand the economic and environmental aspects of MBR. • A state-of-the art review of MBR for treating LFL is provided. • UV–VIS, 3D-EEM, PARAFAC, and FT-ICR-MS are novel methods for LFL characterization. • High-retention MBRs and electro-MBR show excellent removal of emerging contaminants. • Membrane fouling mitigation strategies are discussed. • Application of genomic sequencing to optimize MBR is extensively detailed.
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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".