Analytical and Toxicity Characterization of Halo-hydroxyl-benzoquinones as Stable Halobenzoquinone Disinfection Byproducts in Treated Water
Bibliographic record
Abstract
Exposure to chlorination disinfection byproducts (DBPs) is potentially associated with an increased risk of bladder cancer. Four halobenzoquinones (HBQs) have been detected in treated drinking water and have shown potency in producing reactive oxygen species and inducing damage to cellular DNA and proteins. These HBQs are unstable in drinking water. The fate and behavior of these HBQs in drinking water distribution systems is unclear. Here we report the high-resolution mass spectrometry identification of the transformation products of HBQs as halo-hydroxyl-benzoquinones (OH-HBQs) in water under realistic conditions. To further examine the kinetics of transformation, we developed a solid-phase extraction with ultrahigh-performance liquid chromatography tandem mass spectrometry (SPE-UHPLC-MS/MS) method to determine both the HBQs and OH-HBQs. The method provides reproducible retention times (SD < 0.05 min), limits of detection (LODs) at subnanogram per liter levels, and recoveries of 68%-96%. Using this method, we confirmed that decrease of HBQs correlated with increase of OH-HBQs in both the laboratory experiments and several distribution systems, supporting that OH-HBQs were more stable forms of HBQ DBPs. To understand the toxicological relevance of the OH-HBQs, we studied the in vitro toxicity with CHO-K1 cells and determined the IC50 of HBQs and OH-HBQs ranging from 15.9 to 72.9 μM. While HBQs are 2-fold more toxic than OH-HBQs, both HBQs and OH-HBQs are substantially more toxic than the regulated 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".