Effect of Metal Ions on the Formation of Trichloronitromethane during Chlorination of Catechol and Nitrite
Bibliographic record
Abstract
Catechol, nitrite, and dissolved metals are ubiquitous in source drinking water. Catechol and nitrite have been identified as precursors for halonitromethanes (HNMs), but the effect of metal ions on HNM formation during chlorination remains unclear. The main objective of this study was to investigate the effect of metal ions (Fe3+, Ti4+, Al3+) on the formation of trichloronitromethane (TCNM) (the most representative HNM species in disinfected water) on chlorinating catechol and nitrite. Trichloronitromethane was extracted by methyl tert‐butyl ether and detected by gas chromatography. The results show that metal ions promoted the formation of TCNM and that the enhancement efficiency followed the order of Fe3+ > Ti4+ > Al3+. Trichloronitromethane formation increased greatly within 2 h, and a basic condition (pH 8–9) favored TCNM formation more than acidic or neutral conditions. The conjoint effect of the metal–ion mixtures was shown to be similar to that of the single metal ion having the highest promoting effect on TCNM formation. Our results strongly suggest that metal ions play a significant role in enhancing TCNM formation. Core Ideas Metal ions increased TCNM formation during chlorination of catechol and nitrite. Type and concentration of metal ions affected TCNM yield. Effect of metal ions on the formation of TCNM varied with pH. Conjoint effect of metal ions on TCNM formation was not obvious.
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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) | 0.000 | 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".