Characterization and Determination of Chloro- and Bromo-Benzoquinones as New Chlorination Disinfection Byproducts in Drinking Water
Bibliographic record
Abstract
We report the characterization and determination of 2,6-dichloro-1,4-benzoquinone and three new disinfection byproducts (DBPs): 2,6-dichloro-3-methyl-1,4-benzoquinone, 2,3,6-trichloro-1,4-benzoquinone, and 2,6-dibromo-1,4-benzoquinone. These haloquinones are suspected bladder carcinogens and are likely produced during drinking water disinfection treatment. However, detection of these haloquinones is challenging, and consequently, they have not been characterized as DBPs until recently. We have developed an electrospray ionization tandem mass spectrometry technique based on our observation of unique ionization processes. These chloro- and bromo-quinones were ionized through a reduction step to form [M + H](-) under negative electrospray ionization. Tandem mass spectra and accurate mass measurements of these compounds showed major product ions, [M + H - HX](-), [M + H - HX - CO](-), [M + H - CO](-), and/or X(-) (where X represents Cl or Br). The addition of 0.25% formic acid to water samples was found to effectively stabilize the haloquinones in water and to improve the ionization for analysis. These improvements were rationalized from the estimates of pK(a) values (5.8-6.3) of these haloquinones. The method of tandem mass spectrometry detection, combined with sample preservation, solid phase extraction, and liquid chromatography separation, enabled the detection of haloquinones in chlorinated water samples collected from a drinking water treatment plant. The four haloquinones were detected only in drinking water after chlorination treatment, with concentrations ranging from 0.5 to 165 ng/L, but were not detectable in the untreated water. This method will be useful for future studies of occurrence, formation pathways, toxicity, and control of these new halogenated 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 | 0.000 | 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 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".