Impact of raw water turbidity fluctuations on drinking water quality in a distribution system
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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 it