Effect of UV/Chlorine Oxidation on Disinfection Byproduct Formation from Diverse Model Compounds
Bibliographic record
Abstract
Disinfection byproduct (DBP) formation is a potential concern for the UV/chlorine advanced oxidation process (AOP) in water treatment. In this study, 11 model compounds were selected as natural organic matter (NOM) surrogates, including seven active DBP precursors and four poor precursors. The effect of UV/chlorine on their DBP formation in the UV reactor and during 24 h postchlorination was investigated in comparison to dark chlorination. DBPs evaluated included adsorbable organic halides (AOX), trihalomethanes, haloacetic acids (HAAs), haloacetaldehydes (HALs), trichloronitromethane (TCNM), and dichloroacetonitrile (DCAN). UV/chlorine AOP was conducted at a typical UV dose adopted in drinking water treatment and at both pH 6.0 and 7.8 with postchlorination pH kept at pH 7.8. For most of the active DBP precursors, UV/chlorine either decreased their AOX formation potential (FP) and DBPFP by <25% or showed an insignificant impact. Three poor DBP precursors were activated by UV/chlorine oxidation, especially at pH 6.0, with a significant increase of AOXFP and DBPFP percentage-wise, but the absolute increase was low (less than 186 μg-Cl/mg-C and 50 μg DBP/mg-C). UV/chlorine converted some chloroform precursors to HAA or HAL precusors. For N-containing precursors, UV/chlorine increased TCNM formation and decreased DCAN formation, especially at pH 6.
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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".