Impact of water characteristics on UV disinfection of unfiltered water
Bibliographic record
Abstract
Abstract The objective of this study was to examine the impact of unfiltered water conditions on UV disinfection. UV biodosimetry tests were conducted over a year using water samples from two treatment plants that apply UV without filtration. The influence of turbidity, absorbance, and zeta potential on UV dose–response curves was analyzed to evaluate relationships between unfiltered water quality and log-inactivation of surrogate organisms. It was observed that diminishing inactivation with increasing UV dose (tailing effect) was governed principally by the surface charge of particulate matter. The increased tailing level observed in raw waters was postulated to be due to having more neutral surface charges, resulting in elevated electrostatic attraction between particles and microorganisms that increased UV resistance. Inactivation at a dose of 35 mJ/cm2 in water samples with low turbidity levels (0.38 NTU) and relatively negative surface charge resulted in 3.0 log-removal in comparison with 2.2 and 2.0 log-removal for samples with turbidity levels of 1.57 and 0.61 NTU, respectively. The results of this study highlight the risks of UV disinfection of unfiltered supplies with respect to the effects of water quality characteristics on UV effectiveness and could be employed to optimize the estimation of UV disinfection potential.
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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 | 0.001 | 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".