Synergistic Benefits Between Ultraviolt Light and Chlorine-Based Disinfectants for the Inactivation of Escherichia coli
Bibliographic record
Abstract
Abstract Ultraviolet light is increasing in popularity as a primary disinfectant in drinking water treatment because of its effectiveness against chlorine-resistant pathogens and lack of disinfection by-product (DBP) formation. Previous bench-scale studies have shown that there are possibly synergistic benefits in reducing heterotrophic bacteria when ultraviolet (UV) light is coupled with chlorine (Cl2) or monochloramine (NH2Cl). Additional experiments have demonstrated that synergy exists between various disinfectants in controlling numerous bacteria, viruses, and protozoan. Few studies, to date, have specifically investigated synergy with UV in combination with chlorine-based drinking water disinfectants including chlorine dioxide (ClO2), Cl2, and NH2Cl. This preliminary study looked at the effectiveness of seven disinfection strategies (UV, Cl2, ClO2, NH2Cl, UV/Cl2, UV/ClO2, and UV/NH2Cl) against Escherichia coli in a single-species system at various combinations of disinfectant dose and contact time. Spiked solutions containing E. coli were treated by UV alone, chemical disinfectant alone, or UV coupled with chemical disinfectant. It was found that the combined disinfection strategies achieved the highest removal. Data were additionally analyzed for synergistic benefits, and each combination had a positive result. Results suggest that drinking water utilities may see enhanced removal of bacteria and potentially other pathogens due to synergistic benefits when UV was used in combination with any chlorine-based disinfectant. However, more data are required to conclusively determine synergistic relationship(s) between UV light and chlorine-based disinfectants.
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How this classification was reachedexpand
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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".