Formation of <i>N</i>-Nitrosamines from Eleven Disinfection Treatments of Seven Different Surface Waters
Bibliographic record
Abstract
Formation of nine N-nitrosamines has been investigated when seven different source waters representing various qualities were each treated with eleven bench-scale disinfection processes, without addition of nitrosamine precursors. These disinfection treatments included chlorine (OCl-), chloramine (NH2Cl), chlorine dioxide (ClO2), ozone (O3), ultraviolet (UV), advanced oxidation processes (AOP), and combinations. The total organic carbon (TOC) of the seven source waters ranged from 2 to 24 mg x L(-1). The disinfected water samples and the untreated source waters were analyzed for nine nitrosamines using a solid phase extraction and liquid chromatography-tandem mass spectrometry method. Prior to any treatment, N-nitrosodimethylamine (NDMA) was detected ranging from 0 to 53 ng x L(-1) in six of the seven source waters, and its concentrations increased in the disinfected water samples (0-118 ng x L(-1)). N-nitrosodiethylamine (NDEA), N-nitrosomorpholine (NMor), and N-nitrosodiphenylamine (NDPhA) were also identified in some of the disinfected water samples. NDPhA (0.2-0.6 ng x L(-1)) was formed after disinfection with OCl-, NH2Cl, O3, and MPUV/OCl-. NMEA was produced with OCl- and MPUV/OCl-, and NMor formation was associated with O3. In addition, UVtreatment alone degraded NDMA; however, UV/ OCl- and AOP/OCl- treatments produced higher amounts of NDMA compared to UV and AOP alone, respectively. These results suggest that UV degradation or AOP oxidation treatment may provide a source of NDMA precursors. This study demonstrates that environmental concentrations and mixtures of unknown nitrosamine precursors in source waters can form NDMA and other nitrosamines.
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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) | 0.001 | 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".