Impact of raw water turbidity fluctuations on drinking water quality in a distribution system
Bibliographic record
Abstract
Turbidity is a widely used parameter around the world for describing drinking water quality. Sometimes, turbidity at water treatment plant outlets may reach high values during short periods of time, and this is acceptable according to some current drinking water regulations. In this study, the quantity and nature (chemical and microbiological) of suspended matter, which may travel throughout a distribution system (DS) during turbid events affecting both raw water and water treatment were evaluated. Treated water included filtration with no coagulant addition. During turbid events, the concentration of suspended particles increased in treated water, and a similar increase (quantity and nature) was observed throughout the DS. Bacterial indicators of contamination (total and fecal coliforms, enteroccocci, spores of Clostridium perfringens) were not found in either treated water nor in the DS during turbid events. Nevertheless, a higher bacterial aerobic spore concentration was associated with turbid events for raw, treated, and distributed water, therefore suggesting the potential passage of pathogens, if present in raw waters. Cultivable bacteria concentrations remained low in treated and distributed water regardless of the turbidity. These results emphasize the need to carefully monitor raw and treated water quality for utilities using "high quality" water resources with limited treatment barriers, especially when such water resources are affected by even slight turbidity variations. Key words: aerobic spore-forming bacteria, distribution system, drinking water, filtration, turbidity, suspended particles, water quality.
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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 | 0.001 |
| 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 | 0.000 |
| 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".