In 2020, the European Union published ordinance EU 2020/741, establishing minimum requirements for water reuse in agriculture. The ordinance differentiates between several water quality classes. For the highest water quality class (Class A), the ordinance mandates analytical validation of the treatment performance of new water reuse treatment plants (WRTP) related to the removal of microbial indicators for viral, bacterial, and parasitic pathogens. While the ordinance clearly defines the numeric target values for the required log₁₀-reduction values (LRV), it provides limited to no guidance on the necessary sample sizes and statistical evaluation approaches. The main requirement is that at least 90 % of the validation samples should meet the requirements. However, the interpretation of this 90 % validation target can significantly impact the required sample size, efforts necessary, and the risk of misclassifying WRTPs in practice. The present study compares different statistical evaluation approaches that might be considered applicable for LRV validation monitoring. Special emphasis is placed on the use of tolerance intervals, which combine percentile estimations with sample size-based uncertainty and confidence regions. Tolerance interval-based approaches are compared with alternative methods, including a) a binomial evaluation and b) the calculation of empirical percentiles. The latter are already used in existing European and U.S. regulations for bathing water and irrigation water quality. Our study demonstrates that using tolerance intervals allows for the reliable validation of WRTPs that achieve high LRVs relative to regulatory targets with comparatively smaller sample sizes compared to the other two approaches, while reducing the risk of misclassification. Additionally, we show that simplified approaches, such as a “9 out of 10” approach, pose a substantial risk of misclassification and should not be applied. We illustrate the behavior of these different approaches through simulation experiments and application to real data collected in 2022 and 2023 at a large WRTP in Germany.

Norovirus infections are among the major causes of acute gastroenteritis worldwide. In Germany, norovirus infections are the most frequently reported cause of gastroenteritis, although only laboratory confirmed cases are officially counted. The high infectivity and environmental persistence of norovirus, makes the virus a relevant pathogen for water related infections. In the 2017 guidelines for potable water reuse, the World Health Organization proposes Norovirus as a reference pathogen for viral pathogens for quantitative microbial risk assessment (QMRA). A challenge for QMRA is, that norovirus data are rarely available over long monitoring periods to assess inter-annual variability of the associated health risk, raising the question about the relevance of this source of variability regarding potential risk management alternatives. Moreover, norovirus infections show high prevalence during winter and early spring and lower incidence during summer. Therefore, our objective is to derive risk scenarios for assessing the potential relevance of the within and between year variability of norovirus concentrations in municipal wastewater for the assessment of health risks of fieldworkers, if treated wastewater is used for irrigation in agriculture. To this end, we use the correlation between norovirus influent concentration and reported epidemiological incidence (R²=0.93), found at a large city in Germany. Risk scenarios are subsequently derived from long-term reported epidemiological data, by applying a Bayesian regression approach. For assessing the practical relevance for wastewater reuse we apply the risk scenarios to different irrigation patterns under various treatment options, namely “status-quo” and “irrigation on demand”. While status-quo refers to an almost all-year irrigation, the latter assumes that irrigation only takes place during the vegetation period from May - September. Our results indicate that the log-difference of infection risks between scenarios may vary between 0.8 and 1.7 log given the same level of pre-treatment. They also indicate that under the same exposure scenario the between-year variability of norovirus infection risk may be > 1log, which makes it a relevant factor to consider in future QMRA studies and studies which aim at evaluating safe water reuse applications. The predictive power and wider use of epidemiological data as a suitable predictor variable should be further validated with paired multi-year data.