Transformation of microplastics during UV-LED based water disinfection: Mechanistic insights and environmental implications
Bibliographic record
Abstract
Microplastic (MPs) pollution poses an urgent environmental challenge. UV-based disinfection processes generate oxidative radicals (e.g., hydroxyl (HO • ) and chloride radicals (Cl • , Cl 2 • ¯)), which may alter MPs’ polymer structures during water treatment. However, their impacts on MPs’ characteristics and environmental behaviors remain insufficiently understood. This study evaluated UV/H 2 O 2 , UV/chlorine, and UV/peracetic acid treatments on polystyrene, polyethylene, and polyvinyl chloride MPs. Spectroscopic and microscopic analysis revealed significant morphological changes, including surface cracks and pits. Chemically, oxygen-containing functional groups (e.g., carboxyl, hydroxyl) formed, while water contact angle tests showed decreased hydrophobicity. LC-MS identified various low- and high-molecular-weight degradation products. Acute toxicity assessments (using ECOSAR software) indicated that small-molecule products from polystyrene and polyvinyl chloride MPs showed high toxicity, while medium-molecule products from polyethylene MPs also exhibited notable toxicity. These findings highlight that the formation or potentially hazardous byproducts during UV-based disinfection. We further assessed the natural decomposition of aged MPs across different water matrices and their sorption behavior toward hydrophobic and hydrophilic micropollutants in mixed wastewater. This research aims to provide critical insights into MPs’ transformations during UV-based treatments, informing strategies for mitigating MPs pollution while minimizing associated environmental 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 | 0.000 | 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) | 0.001 | 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".