Overcoming carbamazepine (CBZ) recalcitrance in wastewater: A critical review of membrane bioreactor (MBR) performance, limitations, and optimization strategies
Bibliographic record
Abstract
Carbamazepine (CBZ), a widely prescribed antiepileptic drug, is among the most persistent and frequently detected pharmaceutical contaminants in wastewater treatment plant (WWTP) effluents and surface waters worldwide. Due to its high chemical stability, low sorption affinity, and resistance to biodegradation, CBZ often passes through conventional treatment systems, posing ecological and human health risks. As a result, developing effective treatment strategies capable of removing CBZ from wastewater has become a critical priority, particularly through biological methods. Membrane bioreactors (MBRs) have gained significant attention as a promising method to remove recalcitrant compounds such as CBZ. This review critically examines the current state of MBR technology for CBZ removal, highlighting the influence of microbial communities, operational parameters, and membrane fouling dynamics on the treatment efficiency. Furthermore, integrated MBR systems, combining MBRs with advanced oxidation processes (AOPs), adsorption techniques, or biofilms, are evaluated for their potential to overcome the limitations of standalone MBR systems. Although these integrated approaches significantly improve CBZ removal and mitigate fouling, they face operational, economic, and scalability challenges. This review highlights the need for biologically optimized MBR configurations and the strategic enrichment of specialized microbial communities, including bacteria and fungi, capable of CBZ biotransformation. The findings offer a comprehensive perspective on advancing MBR-based technologies toward more efficient, resilient, and sustainable wastewater treatment systems.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".