Minimization of NDMA Formation during Chlorine Disinfection of Municipal Wastewater by Application of Pre-Formed Chloramines
Bibliographic record
Abstract
To evaluate the potential for minimizing the formation of the disinfection byproduct N-nitrosodimethylamine (NDMA) by controlling the conditions under which chlorine is added, a pilot-scale study was conducted under conditions that simulated a microfiltration system at an advanced wastewater treatment facility. NDMA formation was detected consistently during direct addition of sodium hypochlorite to the ammonia-containing wastewater and when chloramines were formed in a dosing tank by the addition of hypochlorite after ammonium chloride in tap water preadjusted to pH 7.0. No NDMA formation was observed when chloramines formed at higher pH or by addition of ammonium chloride prior to sodium hypochlorite. The conditions under which NDMA was formed were accompanied by breakpoint chlorination and enhanced formation of dichloramine in the dosing tank. The formation of NDMA appears to be related to the enhanced reactivity of dichloramine with NDMA precursors. Although direct addition of sodium hypochlorite to ammonia-containing wastewater also has the potential to enhance the formation of dichloramine, this effect was counterbalanced by the reaction of hypochlorous acid with organic-nitrogen containing NDMA precursors, which decreases their reactivity with inorganic chloramines. Although this offset may reduce some of the benefits of using preformed chloramines for disinfection, the application of preformed chloramines may still be a useful way to reduce NDMA formation, provided that the chloramines are produced under the appropriate conditions.
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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 | 0.001 |
| 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".