Identification of Tobacco-Specific Nitrosamines as Disinfection Byproducts in Chloraminated Water
Bibliographic record
Abstract
Tobacco-specific nitrosamines (TSNAs) exist in environmental waters; however, it is unknown whether TSNAs can be produced during water disinfection. Here we report on the investigation and evidence of TSNAs as a new class of disinfection byproducts (DBPs). Using five common TSNAs, including (methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) as the targets, we first developed a solid phase extraction (SPE) and liquid chromatography-tandem mass spectrometry (LC-MS/MS) method capable of rapidly determining these TSNAs at levels as low as 0.02 ng/L in treated water. Using this highly sensitive method, we investigated the occurrence and formation potential (FP) (precursor test conducted in the presence of chloramines) of TSNAs in treated water from two wastewater treatment plants (WWTPs) and seven drinking water treatment plants (DWTPs). NNAL was detected in the FP samples, but not in the samples before the FP test, confirming NNAL as a DBP. NNK was detected in the treated wastewater before the FP test, but its concentration increased significantly after chloramination in two of three tests. Thus, NNK could be a DBP and/or a contaminant in wastewater. Moreover, these TSNAs were detected in FP tests of wastewater-impacted DWTP plant influents in 9 of 11 samples. However, TSNAs were not detected at full-scale DWTPs, except for at one DWTP with high ammonia where breakpoint chlorination was not achieved. The concentration of the sum of five TSNAs (0.3 ng/L) was 100-fold lower than NDMA, suggesting that TSNAs have a minor contribution to total nitrosamines in water. We examined several factors in the treatment process and found that chlorine or ozone may destroy TSNA precursors and granular activated carbon (GAC) treatment may remove the precursors. Further research is warranted into the efficiency of these processes at different DWTPs using sources of varying water quality.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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 teacher head, 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".