Optimizing sampling location for water quality degradation monitoring in distribution systems: Assessing global representativeness and potential health risk
Bibliographic record
Abstract
Selecting the optimal monitoring points in a water distribution network is challenging due to the complex spatiotemporal variability of water quality degradation. The lack of a standardized methodology for monitoring point selection forces operators to rely on general recommendations, historical data and professional experience, which can mask water quality problems and increase the risk to consumers. This study proposes a new methodology to optimize the selection of monitoring points in distribution networks. The method considers the spatiotemporal degradation of water quality, the definition of representative zones and two selection criteria: global representativeness and potential health risk. Representative zones were determined for each node of the network based on hydraulic paths and their water quality spatial variability. Part of the distribution network in Quebec City, Canada was used as the case study, in which four water quality parameters were investigated: free chlorine residual (FRC), heterotrophic plate counts (HPC), trihalomethanes (THMs) and haloacetic acids (HAAs). Seasonal variabilities (summer and winter) were also analyzed. The results obtained for the two criteria and for both seasons were compared, and methodological and practical recommendations were established for dynamic monitoring programs that respond to the needs of operators. • Understanding the spatiotemporal variability of drinking water quality is crucial for monitoring purposes. • Representative zones in the distribution system allows to identify optimal monitoring locations for water quality monitoring. • Seasonal variability strongly impacts the number and location of optimal monitoring points. • Free residual chlorine is the most important indicator affecting the number and location of required monitoring points.
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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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".