Halobenzoquinones in Swimming Pool Waters and Their Formation from Personal Care Products
Why this work is in the frame
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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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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 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it