Non targeted screening of nitrogen containing disinfection by-products in formation potential tests of river water and subsequent monitoring in tap water samples
Bibliographic record
Abstract
The generation of disinfection by-products during water chlorination is a major concern in water treatment, given the potential health risks that these substances may pose. In particular, nitrogen-containing DBPs are believed to have greater toxicological significance than carbon-based DBPs. Hence, high performance liquid chromatography coupled to high-resolution mass spectrometry (HPLC-HRMS) in positive mode was employed to identify new non-volatile nitrogen containing disinfection by-products (DBPs) and to assess their presence in potable water. Nine water samples were taken in the Llobregat river, in the context of a water reuse trial, near the catchment of a drinking water treatment plant (DWTP) in 2019. River samples were disinfected with chlorine under controlled formation potential tests conditions and analysed with a non-target approach. The peak lists of raw and chlorinated samples were compared exhaustively, resulting in an extensive list of 495 DBPs that include bromine and/or chlorine atoms. 172 of these species were found frequently, in three or more chlorinated samples. The empirical formulae of these DBPs were unambiguously annotated on the basis of accurate m/z measurements, isotopic patterns and common heuristic rules. Most of the annotated species (310) contained bromide, which is consistent with the relatively high bromide content of the Llobregat basin (>0.3 mg/l). Drinking water samples were taken at the outlet of the DWTP during the same sampling period. According to their analysis, a large portion of the DBPs detected after the formation potential tests do not reach real-life drinking water, which suggests that the treatment train successfully removes a significant fraction of DBP precursors. However, 131 DBPs could still be detected in the final product water. A larger sampling was carried in the Barcelona water distribution network, during six consecutive weeks, and it revealed the presence of 78 halogenated DBPs in end-consumer water, most of which were nitrogen-containing. MS/MS fragmentation and retention times were employed to tentatively suggest molecular structure for these recalcitrant DBPs.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".