Water residence time in a distribution system and its impact on disinfectant residuals and trihalomethanes
Bibliographic record
Abstract
The most critical water quality conditions are generally found at a water distribution network's extremities, given their high residence times.As chlorine injected at the water treatment plant (WTP) or at rechlorination sites has more time to react, residual chlorine concentrations at the extremities may not be enough to prevent microbial regrowth.This study focuses on the relationships between residence times, and residual chlorine and trihalomethane (THM) concentrations in a sector supplied with water coming directly from the WTP and from a large reservoir within the network.A hydraulic model was calibrated based on residence times obtained from a tracer study and a water quality characterization campaign at 47 sampling sites.Results showed that chlorine decay in water from the reservoir is faster than for water directly from the WTP.THM concentrations differ, with those in water from the reservoir being far higher than those from the WTP.A slight increase in THM concentrations is seen with residence time in both cases.By using a hydraulic model, we can evaluateat any time and in any placethe impact of a hydraulic change on the network in terms of hydraulics, as well as in terms of the vulnerability associated with low residual chlorine and high THM concentrations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".