UV or PAA for Wastewater Disinfection: a Comparison of the Impact on Virulence Genes
Bibliographic record
Abstract
Ultraviolet (UV) and peracetic acid (PAA) disinfection have been used in municipal wastewater treatment plants (WWTPs) for their biocidal effect on microorganisms. The current study used PCR-Bioplex and DNA microarray genotyping techniques to investigate the impact of UV and PAA on the change in frequency of virulence genes in E. coli isolates and on the prevalence of uropathogenic E. coli (UPEC) isolates in wastewater effluents. Effluents from four WWTPs (activated sludge [AS], biofiltration [BF] and physicochemical [PC1 and PC2]) located in Québec, Canada, were sampled before disinfection and exposed to UV or PAA doses in the lab to reach a target count of approximately 200 CFU/100 mL. To achieve this, the required UV fluences ranged between 7 – 30 mJ/cm2, depending on the plant, while the PAA dose varied between 0.9 – 2.0 mg/L. E. coli isolates totaling 1,766 were extracted from the samples pre- and post- disinfection, then screened by PCR/Bioplex to detect those likely to be UPECs, using three virulence genes (hlyA, papC and cnf1). The UPEC pathotypes of the positively screened isolates were confirmed by microarrays. The proportion of UPEC isolates decreased in all samples after disinfection, with that due to UV varying between 22% – 80%, and the reduction due to PAA ranging between 11% – 100%. The average reductions by UV (55%) and PAA (52%) were statistically significant (P<0.05). Gene frequency analysis revealed that the decline in the population of UPEC pathotypes by UV or PAA was not linked to specific virulence factors as most virulence genes were lost, suggesting that entire pathogenicity islands (PAIs), carrying clusters of virulence genes, were lost through disinfection. Thus, this study showed that both UV and PAA disinfection appear to significantly reduce the proportions of UPECs in the surviving E. coli populations in wastewater effluents.
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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) | 0.000 | 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".