Factors affecting <i>N</i> -nitrosodimethylamine formation from poly(diallyldimethyl-ammonium chloride) degradation during chloramination
Bibliographic record
Abstract
Poly(diallyldimethylammonium chloride) (polyDADMAC) has been shown to be an important precursor of the probable human carcinogen N -nitrosodimethylamine (NDMA) when in contact with chloramine. In this study, we conducted an orthogonal experiment design to evaluate the effects of pH values, ammonia, bromide, natural organic matter (NOM) and monochloramine dosages on the formation of NDMA from polyDADMAC during chloramination. Meanwhile, single-factor experiments of pH, bromide and NOM prove the results of orthogonal experiment. The results supported that pH was the most critical factor affecting NDMA formation from polyDADMAC during chloramination, and the highest NDMA formation from polyDADMAC occurred at pH near 7 due to released DMA from polyDADMAC degradation and the critical importance of low concentrations of dichloramine in water. In the presence of excess bromide, the NDMA formation was enhanced significantly at all different pH values owing to bromochloramine, which has higher electronegativity of the brominated nitrogen atom than monochloramine or dichloramine. The NDMA formation from polyDADMAC in the presence of NOM was 41.7% lower than NDMA formation in the absence of NOM. The overwhelming majority of NDMA formation from polyDADMAC under simulated conditions was lower than the current advisory levels (i.e. 9 ng l −1 in Ontario, 10 ng l −1 in California).
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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 | 0.000 | 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".