A comparison of sodium sulfite, ammonium chloride, and ascorbic acid for quenching chlorine prior to disinfection byproduct analysis
Bibliographic record
Abstract
Abstract This study compared 3 commonly used quenching agents for dechlorinating samples prior to disinfection byproduct (DBP) analysis under typical drinking water sampling conditions for a representative suite of chlorination byproducts. Ascorbic acid and sodium sulfite quenched the residual free chlorine to below detection within 5 seconds. Ammonium chloride did not quench the chlorine to below detection with up to a 70% molar excess, which agrees with published ammonium chloride-chlorine chemistry. With respect to the DBPs, ascorbic acid worked well for the trihalomethanes and haloacetic acids, except for dibromoiodomethane, which exhibited 2.6–28% error when using ascorbic acid compared to non-quenched control samples. Sodium sulfite also worked well for the trihalomethanes (and performed similarly to ascorbic acid for dibromoiodomethane) and was the best performing quenching agent for MX and the inorganic DBPs, but contributed to the decay of several emerging DBPs, including several halonitromethanes and haloacetamides. Ammonium chloride led to considerable errors for many DBPs, including 27–31% errors in chloroform concentrations after 24 hours of storage. This work shows that ascorbic acid is suitable for many of the organic DBPs analyzed by gas chromatography-electron capture detection and that sodium sulfite may be used for simultaneous chlorite, chlorate, and bromate analysis.
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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 | 0.001 | 0.001 |
| 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".