Photoreactivation following combined peracetic acid-UV disinfection of a physicochemical effluent
Bibliographic record
Abstract
Photoreactivation of microorganisms following UV inactivation is a well-known but complex phenomenon. It is believed to be affected by several factors, including UV fluence (the effect may be diminished at higher fluences), wavelength, light intensity, and exposure time to photoreactivating light. The effect on photoreactivation of a combined peracetic acid (PAA)/UV process has not been investigated. Accordingly, the primary objective of the current study was to assess the degree of photoreactivation, under both sunlight and artificial lights, following UV and combined PAA/UV inactivation of fecal coliforms (FC). Results of the study indicate that average photoreactivation for UV-treated wastewater samples is 1.2 logs, compared to 0.1 logs for the combined PAA/UV treatment. Hence, the use of PAA in combination with UV can significantly reduce the potential for photoreactivation. With respect to photoreactivating light intensity, wastewater samples exposed for 3 hours to both low and high-intensity artificial lights as well as sunlight all resulted in similar photoreactivation levels. Additional experiments determined a threshold value of approximately 700 lux for photoreactivating light intensity, which allowed for a simple model relating photoreactivation with water depth to be developed from Lambert's Law. In addition, experimental results indicate that with respect to photoreactivation, there is no difference between UV fluences of 20 and 40 mJ/cm2. Finally, in an attempt to recreate the unique photoreactivation conditions of the Montreal Wastewater Treatment Plant effluent (due to a 4 Km outfall tunnel), UV-treated samples were kept in the dark for 3 hours prior to photoreactivating light exposure. Interestingly, the experimental results indicate that after the 3 hour dark time, photoreactivation levels are close to zero.
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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 | 0.000 | 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) | 0.001 | 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 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".