Pathogen performance testing of a natural swimming pool using a cocktail of microbiological surrogates and QMRA-derived management goals

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 U.S. EPA 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 [bakers' 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 maintain 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.

Differences in laboratory versus field treatment performance of point-of-use drinking water treatment methods: research gaps and ways forward

“…It is now accepted that compliance is essential in QMRA modelling to estimate health gains, which are then used to make public health recommendations 11,13,14,72 . We propose that site-specific microbiological field performance data, gathered via field challenge testing by use of food-safe surrogates (i.e., probiotic bacteria 73 and/or baker's yeast [32][33][34][35][36] ) could and should be incorporated into QMRA models the same way, although a research gap exists regarding food-safe viral surrogates. QMRA models are currently essential in understanding risk and facilitating important public health decisions; adding site-specific challenge test data in combination with compliance data would be highly instructive 74 .…”

“…An appropriate bacterial surrogate (a probiotic health supplement containing non-pathogenic E. coli) has been identified and was subject to preliminary validation through previous work 37 via an established surrogate selection framework 62 . Baker's yeast (Saccharomyces cerevisiae) has been identified as a possible non-pathogenic surrogate for protozoans 32,33 , has been applied as a challenge organism to evaluate in situ microbiological performance in non-potable water applications 34,36 and has been recommended for further use for other in situ evaluations 35 . A suitable viral surrogate has not yet been proposed in the published literature; this is a research gap that would be valuable to address, completing the "suite" of food-safe microbiological surrogates.…”

“…We will show in this Perspective that such QMRA analyses are utilizing performance data (LRVs) of POUWT methods that have been overestimated using laboratory-based studies; consequently, the conclusions reached by such QMRA analyses could be inaccurate. There is a research gap to gather more representative data (i.e., field-based assessments); such techniques do exist [32][33][34][35][36][37] but are not directly applicable to low-resource contexts.…”