Identification of Precursors and Mechanisms of Tobacco-Specific Nitrosamine Formation in Water during Chloramination
Bibliographic record
Abstract
We report here that tobacco-specific nitrosamines (TSNAs) are produced from specific tobacco alkaloids during water chloramination. To identify the specific precursors for the formation of specific TSNAs in drinking water, we have developed a solid-phase extraction-liquid chromatography-tandem mass spectrometry (SPE-LC-MS/MS) method for simultaneous determination of five TSNAs and three tobacco alkaloids. Using this method, we detected nicotine (NIC) at 15.1 ng/L in a source water. Chloramination of this source water resulted in the formation of 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanone (NNK) (0.05 ng/L) and 4-(methylnitrosamino)-1-(3-pyridyl)-1-butanol (NNAL) (0.2 ng/L) along with the reduction of NIC to 1.1 ng/L, suggesting that NNK and NNAL were formed from NIC. To confirm that tobacco alkaloids are the precursors of TSNAs, we chloraminated water-leaching samples of tobacco from three brands of cigarettes and found that the formation of TSNAs coincides with the reduction of the alkaloids. Chloramination of individual alkaloids confirms that NNK and NNAL are produced from NIC, N-nitrosonornicotine (NNN) from nornicotine (NOR), and N-nitrosoanabasine (NAB) from anabasine (ANA). Furthermore, we have identified specific intermediates of these reactions and proposed potential pathways of formation of TSNAs from specific alkaloids. These results confirm that NNK and NNAL are the disinfection byproducts (DBPs) resulting from NIC in raw water.
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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 | 0.001 |
| 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".