Nationwide assessment of nitrosamine occurrence and trends
Bibliographic record
Abstract
Nitrosamine data reported from the first rounds of samples collected under the second Unregulated Contaminants Monitoring Rule (UCMR2) and the Ontario Drinking Water Surveillance Program were reviewed to assess the frequency and magnitude of occurrence and the effect of disinfectant type and other treatment factors on reported nitrosamine concentrations. Initial monitoring data reveal that N‐nitrosodimethylamine (NDMA) was detected in drinking water at concentrations higher than the UCMR2 minimum reporting level (MRL) of 2 ng/L in 1 of every 10 samples. Other nitrosamines (e.g., N‐nitrosodiethylamine, N‐nitroso‐di‐n‐butylamine, N‐nitrosopyrrolidine, and N‐nitroso‐methylethylamine) were rarely detected at levels above their MRLs. NDMA was primarily detected in systems using chloramines, with more than two thirds of all chloraminated water systems detecting NDMA in at least one sample. Follow‐up survey results from 45 water systems participating in UCMR2 and 6 water systems from Ontario, Canada, generally followed expected trends based on the literature. NDMA occurrence was more frequent and concentrations were higher in water systems having long contact times with chloramines. A comparison of maximum‐residence‐time distribution system samples with entry point samples indicates that NDMA concentrations may increase in a chloraminated distribution system if precursors have not fully reacted at the entry point.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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".