Pathogen performance testing of a natural swimming pool using a cocktail of microbiological surrogates and QMRA-derived management goals
Bibliographic record
Abstract
In recent decades, natural swimming pools (NSPs) have gained popularity in Europe, especially in Germany and Austria. NSPs differ from swimming pools in that they utilize biological treatment processes based on wetland processes with no disinfection residual. However, data are missing on the specific log-reduction performance of NSPs to address enteric virus, bacteria, and parasitic protozoa removal considered necessary to meet the North American risk-based benchmark (<35 illnesses per 1,000 swimming events) set by the USEPA for voluntary swimming. In this study, we examined Canada's first NSP at Borden Park, Edmonton, Canada, to address the following three questions: (1) Given normal faecal shedding rates by bathers, what is the total log reduction (TLR) theoretically needed to meet the EPA benchmark? (2) what is the in-situ performance of the NSP based on spiking suitable microbial surrogates (MS2 coliphage, Enterococcus faecalis, and Saccharomyces cerevisiae [Baker's yeast])? and (3) how much time is required to reach acceptable bather risk levels under different representative volume-turnover rates? A reverse-quantitative microbial risk assessment (QMRA) revealed that of the four reference pathogens selected (Norovirus, Campylobacter, Cryptosporidium, and Giardia), only Norovirus was estimated to exceed the risk benchmark at the 50th, 75th, and 95th percentiles, while Campylobacter was the only other reference pathogen to exceed at the 95th percentile. Log-reduction values (LRVs) were similar to previous reports for bacterial indicators, and novel LRVs were estimated for the other two surrogates. A key finding was that more than 24 h treatment time would be necessary to provide acceptable bather protection following heavy bather use (378 bathers/day for main pool and 26 bathers/day for children's pool), due to the mixing dynamics of the treated water diluting out possible residual pool faecal contamination. The theoretical maximum number of people in the pool per day to be below USEPA's 35 gastro cases in 1,000 swimming events was 113, 47, and 8, at the 50th, 75th, and 95th percentiles. Further, the use of ultra-violet disinfection to the pool return flow had little effect on reducing the treatment time required.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".