Microplastics and nitrogenous disinfection byproducts in drinking water: complex interactions beyond adsorption
Bibliographic record
Abstract
Abstract Microplastics (MPs) in drinking water are an emerging concern due to their potential health risks, environmental impacts, and ability to adsorb organic micropollutants. Nitrogenous disinfection byproducts (DBPs), which are generally more toxic than the regulated DBPs, may interact with MPs via hydrophobic or electrostatic mechanisms. Such interactions raise concern that MPs in treated water could concentrate toxic DBPs during distribution or storage, increasing potential human exposure beyond the risks posed by either MPs or DBPs alone. This study investigates the adsorption behavior of select DBPs, including nitrogenous DBPs like N-nitrosodimethylamine (NDMA) and halonitromethanes (HNMs), as well as trihalomethanes (THMs), which are included for comparison as the most commonly regulated DBPs, onto virgin and weathered MPs. The polymers studied include polyethylene (PE), polypropylene (PP), polyvinyl chloride (PVC), polyamide (PA), polyacrylonitrile (PAN), and polyethylene terephthalate (PET). The results indicate that hydrophobic DBPs such as THMs adsorb onto both virgin and weathered PVC at levels of roughly 10–20 µg/g. Hydrophilic DBPs like NDMA show negligible adsorption on hydrophobic microplastics but greater interaction with hydrophilic polymers such as PET, at roughly 10 ng/g. In addition, trichloronitromethane degrades completely in the presence of PA, and weathered PA accelerates this process, with full degradation and conversion to DCNM observed within 14 days. Given that these DBPs and MPs represent a small fraction of the total to be considered, it is clear that there is much work to be done to fully evaluate the possible interactions and potential for human health effects.
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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".