Bioremediation of Microplastics in Wastewater Treatment Plants: A Sustainable Approach
Bibliographic record
Abstract
Microplastics (MPs) are emerging contaminants of growing concern in wastewater treatment plants (WWTPs), which serve as both critical sinks and inadvertent sources of these persistent pollutants. This chapter explores the potential of bioremediation as a sustainable strategy for MP removal, critically examining microbial and enzymatic degradation pathways across different stages of WWTPs. It synthesizes current knowledge on bacteria, fungi, algae, and higher eukaryotes capable of MP degradation while emphasizing their relevance within the operational context of biological treatment, tertiary polishing, and sludge stabilization units. Key strategies such as bioaugmentation, biostimulation, and enzyme-assisted treatment are evaluated with case studies and conceptual models, highlighting integration challenges related to retention time, biodegradation efficiency, and ecological compatibility. Advanced configurations like enzymatic membrane reactors and hyperthermophilic composting are presented as promising yet underexplored solutions. The chapter concludes with a critical reflection on the limitations of current bioremediation efforts, advocating for pilot-scale testing, microbial consortia engineering, and techno-economic assessments to enable scalable application in WWTPs. This comprehensive synthesis provides both foundational insights and forward-looking perspectives on deploying biotechnology for MP mitigation in engineered wastewater 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.010 |
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".