Differential impacts of conventional and biodegradable microplastics on treatment performance and bacterial community in sequencing batch reactors
Bibliographic record
Abstract
Microplastics (MPs) are emerging contaminants in wastewater treatment plants (WWTPs), yet their polymer-specific impacts on biological treatment processes remain poorly understood. This study systematically compared the effects of conventional (Polyethylene (PE), Polypropylene (PP), Polystyrene (PS), Polyethylene terephthalate (PET)) and biodegradable (Polylactic acid (PLA)) MPs under identical sequencing batch reactor (SBR) conditions (100 particles/g VSS, 30-day exposure). Conventional MPs induced moderate microbial stress, evidenced by increased reactive oxygen species (~2-fold with PS), but had minimal inhibitory effects on COD removal (83–86 %) and ammonia oxidation (86–92 %). In contrast, PLA significantly affected system performance by reducing the removal of COD and ammonia by 4.3 % and 5.9 %, respectively. PLA also caused suppression (~0.7–0.8-fold as compared to the control experiment) in the production of extracellular polymeric substance (EPS), and abundance in Proteobacteria by ~14.6 %. PLA also led to reduced microbial richness (the Chao1 index) and diversity (the Simpson index), likely due to selective biofilm formation, with potential enrichment of pathogenic taxa. These findings raise concern over the generalized perception that biodegradable MPs are inherently safer and emphasize the need for polymer-specific risk assessments for different MPs found in WWTPs. • Conventional and biodegradable microplastics had distinct impacts on treatment. • Polylactic acid inhibited COD removal (4.3 %) and ammonia oxidation (5.9 %). • Polystyrene microplastics significantly inhibited denitrification (16.7 %). • Conventional microplastics enhanced extracellular polymeric substance secretion. • Polylactic acid and polystyrene induced elevated oxidative stress in sludge.
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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 | 0.001 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".