From Micropollutant to DBP Driver: The Unexpected Reactivity of 1-Chlorobenzotriazole in Water Disinfection
Bibliographic record
Abstract
The increasing adoption of potable wastewater reuse is challenged by the persistence of micropollutants—such as benzotriazole (BTR)—which are poorly removed by conventional water/wastewater treatment and may produce toxic disinfection by-products (DBPs) with potential environmental and human health risks. This study investigated the chlorination of BTR and identified 1-chlorobenzotriazole (1-Cl BTR) as the primary chlorination product, formed preferentially under near-neutral to acidic pH and excess chlorine—conditions typically found in water disinfection. The DBP formation potential of 1-Cl BTR was evaluated using Suwannee River humic acid (SRHA), natural organic matter (SRNOM), and secondary wastewater effluents (SWE), and compared against hypochlorous acid (HOCl) and monochloramine (NH2Cl). Surprisingly, 1-Cl BTR formed DBP levels comparable or greater than those from HOCl, including trihalomethanes, haloaldehydes, haloketones, haloacetonitriles and halonitromethanes. This unexpected reactivity is attributed to 1-Cl BTR’s unique ability to function as a free chlorine reservoir, sustaining chlorination reactions over time and promoting elevated DBP formation—unlike typical N-halamines. DBP speciation trends with 1-Cl BTR were strongly pH-dependent and consistent with those observed for HOCl, further supporting its role as a free chlorine reservoir. Additionally, 1-Cl BTR exhibited precursor- and matrix-dependent reactivity, especially with complex matrices like SWE, where it acted both as a chlorine source and direct DBP precursor. This work presents the first detailed evaluation of 1-Cl BTR’s DBP formation potential, revealing an overlooked pathway for halogenated DBP production in water disinfection. These findings emphasize the importance of considering BTR transformation products in water treatment and highlight the need for improved strategies to mitigate DBP risks in advanced reuse systems.
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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".