Particulate contaminants and treatment decision-making: maximizing the value of raw water pathogen monitoring for drinking water safety
Bibliographic record
Abstract
ABSTRACT Periodic evaluation of particulate contaminants in raw/untreated water is integral to assessing risk, establishing treatment requirements, and ensuring drinking water safety. However, pathogenic microorganisms and other discrete particles (e.g., microplastics) are not typically monitored with any regularity. When monitoring is required, recommended, or proactively used to evaluate the adequacy of treatment or assess treatment needs, there is a need for guidance on how to collect data and use them to maximize return on investment. The potentially increasing variability in source water quality associated with climate change emphasizes the importance of knowing contaminant concentrations to effectively manage risks. This work presents a framework to guide the development of monitoring protocols for particulate contaminants in water and the integration of monitoring data and quantitative microbial risk assessment into treatment decisions. The protozoa monitoring and risk-based compliance approach of a drinking water utility in Canada is presented along with 7 years of data. Guidance for determining sampling frequencies and locations is provided. It is shown that Cryptosporidium monitoring may be insufficient to inform treatment needs when Giardia cysts are more abundant in source water. This work underscores the importance of revisiting and enhancing monitoring practices for effective treatment and public health protection.
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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 | 0.012 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".