Unregulated Disinfection By‐Products: Spatiotemporal Variation in Water Distribution Networks
Bibliographic record
Abstract
Abstract Residual disinfectants react with various natural organic matter in distribution networks (DNs) to form disinfection by‐products (DBPs), which are a public health concern. The regulated DBPs in various jurisdictions are trihalomethanes, haloacetic acids, bromate, and chlorate, which are mostly associated with chlorine. Owing to this, several alternative disinfectants, such as chloramines, ozone, and chlorine dioxide are also used. Although the use of alternate disinfectants has reduced the formation of regulated DBPs, these disinfectants have gathered much attention by introducing another cluster of DBPs known as unregulated DBPs (U‐DBPs). Many U‐DBPs are potentially toxic, mutagenic, and/or carcinogenic. This study reviews the models for predicting the spatiotemporal variation of U‐DBPs and analyzes their variability in DNs in the provinces of Quebec, and Newfoundland and Labrador, Canada. The findings show that limited predictive models are available for U‐DBPs compared to regulated DBPs. The concentrations of haloacetonitriles, chloropicrin, and haloketones significantly differed along the DNs based on the distance traveled and/or residence time. Similarly, temporal variability was significant in different seasons and in various weeks in summer but not significant by days from Monday to Friday. The formation of U‐DBPs can be controlled by source water protection, use of advanced treatment, alternative disinfectants, etc.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".