Microplastic-associated biofilms in wastewater treatment plants: Mechanisms and impacts
Bibliographic record
Abstract
Wastewater treatment plants (WWTPs) represent critical interfaces controlling microplastic (MP) flux between urban and natural environments, with removal efficiencies ranging from 70 to 99.9 %. Within these engineered systems, MPs undergo biological transformations through biofilm formation, creating unique ‘engineered plastisphere’ that fundamentally alter their environmental fate and impact. This review comprehensively analyzes MP-biofilm formation in WWTPs, examining the complex interplay between MP properties, operational parameters, and environmental conditions governing these interactions. Our synthesis reveals that MP-associated biofilms create a paradoxical scenario: enhancing MP removal through improved settling (5-time increase in settling velocities) while simultaneously serving as reservoirs for pathogens and antimicrobial resistance , with substantially higher ARB abundance within the biofilm than surrounding wastewater. Advanced analytical techniques have unveiled distinct microbial succession patterns and community structures unique to MP surfaces in different treatment stages. This analysis identifies critical research needs: standardization of MP-biofilm characterization methods, understanding of biofilm-mediated MP transformation mechanisms, and quantification of treatment operational impacts, providing insights for optimizing MP removal while minimizing associated microbial risks.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".