Characterization of organic matter by HRMS in surface waters: Effects of chlorination on molecular fingerprints and correlation with DBP formation potential
Bibliographic record
Abstract
In order to understand and minimize the formation of halogenated disinfection by-products (DBPs), it is important to investigate how dissolved organic matter (DOM) contributes to their generation. In the present study, we analysed the DOM profile of water samples from the Barcelona catchment area by high resolution mass spectrometry (HRMS) and we studied the changes after chlorination. Chlorination produced significant changes in the DOM, decreased the average m/z and Kendrick mass defect (KMD) of their spectra and decreased the number and abundance of lignin-like features. The Van Krevelen (VK) fingerprint exhibited several noticeable changes, including the appearance of highly oxidized peaks in the tannin-like region (average O/C, 0.78 ± 0.08), the appearance of features with low H/C and the disappearance of more than half of the lipids-like features. Up to 657 halogenated peaks were generated during sample chlorination, most of which in the condensed hydrocarbons-like and the lignin-like region of the VK diagram. Around 200 features were found to be strongly correlated (ρ ≥ 0.795) to the formation potential of trihalomethanes (THMs) and 5 were correlated with the formation potential of haloacetonitrile (HANs). They all were plotted in the lignin fraction of the VK diagram, but both groups of features exhibited different nitrogen content: those features related to HANs FP had at least one nitrogen atoms in their structures, whilst those related to THMs did not.
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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 | 0.000 |
| 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 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".