Precursors of Halobenzoquinones and Their Removal During Drinking Water Treatment Processes
Bibliographic record
Abstract
Halobenzoquinones (HBQs) widely occur in drinking water treatment plant (DWTP) effluents; however, HBQ precursors and their removal by treatments remain unclear. Thus, we have investigated HBQ precursors in plant influents and their removal by each treatment before chlorination in nine DWTPs. The levels of HBQ precursors were determined using formation potential (FP) tests for 2,6-dichloro-1,4-benzoquinone (DCBQ), 2,3,6-trichloro-1,4-benzoquinone (TCBQ), 2,6-dichloro-3-methyl-1,4-benzoquinone (DCMBQ), and 2,6-dibromo-1,4-benzoquinone (DBBQ). HBQ precursors were present in all plant influents. DCBQ precursors were the most abundant (DCBQ FP up to 205 ng/L). Coagulation removed dissolved organic carbon (DOC) (up to 56%) and HBQ precursors (up to 39% for DCBQ). The level of removal of DOC was significantly greater than the level of removal of HBQ FP, suggesting that organic matter removed by coagulation had a high proportion of non-HBQ-precursor material. Granular activated carbon (GAC) decreased the level of HBQ FPs by 10-20%, where DOC removal was only 0.2-4.7%, suggesting that the GAC was not in the adsorption mode and biodegradation of HBQ precursors may have been occurring. Ozonation destroyed/transformed HBQ FPs by 10-30%, whereas anthracite/sand filtration and UV irradiation appeared to have no impact. The results demonstrated that the combined treatments did not substantially reduce HBQ precursor levels in water.
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How this classification was reachedexpand
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 | 0.002 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".