Halobenzoquinones in Swimming Pool Waters and Their Formation from Personal Care Products
Bibliographic record
Abstract
Halobenzoquinones (HBQs) are a class of disinfection byproducts (DBPs) of health relevance. In this study, we aimed to uncover which HBQs are present in swimming pools. To achieve this goal, we developed a new method capable of determining eight HBQs while overcoming matrix effects to achieve reliable quantification. The method provided reproducible and quantitative recovery (67-102%) and detection limits of 0.03-1.2 ng/L for all eight HBQs. Using this new method, we investigated water samples from 10 swimming pools and found 2,6-dichloro-1,4-benzoquinone (2,6-DCBQ) in all the pools at concentrations of 19-299 ng/L, which was as much as 100 times higher than its concentration in the input tap water (1-6 ng/L). We also identified 2,3,6-trichloro-(1,4)benzoquinone (TriCBQ), 2,3-dibromo-5,6-dimethyl-(1,4)benzoquinone (DMDBBQ), and 2,6-dibromo-(1,4)benzoquinone (2,6-DBBQ) in some swimming pools at concentrations of <0.1-11.3, <0.05-0.7, and <0.05-3.9 ng/L, respectively, but not in the input tap water. We examined several factors to determine why HBQ concentrations in pools were much higher than in the input tap water. Higher dissolved organic carbon (DOC), higher doses of chlorine and higher temperatures enhanced the formation of HBQs in the pools. In addition, we conducted laboratory disinfection experiments and discovered that personal care products (PCPs) such as lotions and sunscreens can serve as precursors to form additional HBQs, such as TriCBQ, 2,6-dichloro-3-methyl-(1,4)benzoquinone (DCMBQ), and 2,3,5,6-tetrabromo-(1,4)benzoquinone (TetraB-1,4-BQ). These results explained why some HBQs existed in swimming pools but not in the input water. This study presents the first set of occurrence data, identification of new HBQ DBPs, and the factors for their enhanced formation in the swimming pools.
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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) | 0.001 | 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".