Evaluation of Peracetic Acid Treatment for Reducing Disinfection By-product Formation in Drinking Water
Bibliographic record
Abstract
Prechlorination is a widely adopted mussel control strategy by water treatment utilities. Although prechlorination is effective and inexpensive, it leads to disinfection by-product (DBP) formation. Peracetic acid (PAA), which exists in equilibrium with hydrogen peroxide (H₂O₂), is a promising alternative to prechlorination for mussel control. At the same time, PAA may also destroy DBP precursors, thereby serving two concurrent roles in drinking water treatment. This research explored the ability of PAA pre-oxidation to reduce subsequent chlorination DBPs (THMs, HAAs, and AOX). The results showed that a 5 mg/L PAA dose (containing about 10% H₂O₂ by mass) decreased DBP formation potential by up to 40% in raw water (pH:7.8-8.2, temperature:22-24 °C) collected from three Ontario utilities. The findings also indicate that H₂O₂ is more effective than PAA for reducing DBPs on a per mass basis. However, the poorer reactivity of PAA is compensated by its higher concentration in the PAA/H₂O₂ mixture.
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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".