Adsorption of <i>N</i> -Nitrosodimethylamine onto Polyvinyl Chloride and Polyethylene Terephthalate Microplastics in Drinking Water
Bibliographic record
Abstract
N -Nitrosodimethylamine (NDMA) poses significant public health risks as a potent carcinogen found in drinking water, while microplastics have raised concerns due to their ubiquity and potential to act as contaminant carriers. This study investigates the adsorption behavior of NDMA onto virgin and weathered polyvinyl chloride (PVC) and polyethylene terephthalate (PET) microplastics and evaluates the influence of water quality parameters on their adsorptive capacities. Adsorption isotherm experiments were conducted across diverse water matrices (ultrapure water, artificial freshwater, lake, river, and groundwater). The results demonstrated low NDMA adsorption on virgin or weathered PVC (generally <1 ng/g), and while virgin PET also experienced minimal adsorption, weathered PET displayed up to an order of magnitude higher adsorption than other materials, ranging from 0.5 to 7.5 ng/g. The adsorption capacity was influenced by ionic strength, natural organic matter, and polymer surface properties. Higher adsorption occurred in matrices with lower natural organic matter (NOM), and the NOM fractions of humic substances, biopolymers, and low-molecular-weight neutrals competitively reduced NDMA adsorption. These findings highlight the need for further research on microplastics as vectors for toxic contaminants and the regulation of microplastic pollution to mitigate associated risks in water 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) | 0.000 | 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) | 0.000 | 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".