Probabilistic quantitative microbial risk assessment (QMRA) studies define model inputs as random variables and use Monte-Carlo simulation to generate distributions of potential risk outcomes. If local information on important QMRA model inputs is missing, it is widely accepted to justify assumptions about these model inputs by using external literature information. A question, which remains unexplored, is the extent to which previously published external information should influence local estimates in cases of nonexistent, scarce, and moderate local data. This question can be addressed by employing Bayesian hierarchical modeling (BHM). Thus, we study the effects and potential benefits of BHM on risk and performance target calculations at three wastewater treatment plants (WWTP) in comparison to alternative statistical modeling approaches (separate modeling, no-pooling, complete pooling). The treated wastewater from the WWTPs is used for restricted irrigation, potable reuse, or influences recreational waters, respectively. We quantify the extent to which external data affects local risk estimations in each case depending on the statistical modeling approach applied. Modeling approaches are compared by calculating the pointwise expected log-predictive density for each model. As reference pathogens and example data, we use locally collected Norovirus genogroup II data with varying sample sizes (n = 4, n = 7, n = 27), and complement local information with external information from 44 other WWTPs (n = 307). Results indicate that BHM shows the highest predictive accuracy and improves estimates by reducing parameter uncertainty when data are scarce. In such situations, it may affect risk and performance target calculations by orders of magnitude in comparison to using local data alone. Furthermore, it allows making generalizable inferences about new WWTPs, while providing the necessary flexibility to adjust for different levels of information contained in the local data. Applying this flexible technique more widely may contribute to improving methods and the evidence base for decision-making in future QMRA studies.

For ensuring microbial safety, the current European bathing water directive (BWD) (76/160/EEC 2006) demands the implementation of reliable early warning systems for bathing waters, which are known to be subject to short-term pollution. However, the BWD does not provide clearly defined threshold levels above which an early warning system should start warning or informing the population. Statistical regression modelling is a commonly used method for predicting concentrations of fecal indicator bacteria. The present study proposes a methodology for implementing early warning systems based on multivariate regression modelling, which takes into account the probabilistic character of European bathing water legislation for both alert levels and model validation criteria. Our study derives the methodology, demonstrates its implementation based on information and data collected at a river bathing site in Berlin, Germany, and evaluates health impacts as well as methodological aspects in comparison to the current way of long-term classification as outlined in the BWD.

Combined sewer systems are one of the major sources of microbiological contamination in urban water bodies. However, identification of hotspots for pathogen emissions is not straightforward, especially in large and complex drainage systems. To determine the relevance of different CSO outlets for bathing water quality a simple tracer approach which uses wastewater volume as a proxy for pathogen emissions has been developed and tested for the city of Berlin, Germany. The approach reveals that the average wastewater ratio in CSO varies largely between different river outlets (0 to 15%). Hence, the outlets with the largest CSO volumes are not automatically the greatest wastewater emitters and assumed hotspots for pathogen contamination do not coincide with hydraulic hotspots. This is verified with own measurements that show enormous differences in pathogen concentrations between waste and stormwater of 4 orders of magnitude. As a result, wastewater which represents only 5% of the CSO volume contributes > 99% of the pathogen loadings to the river. The study highlights the relevance of wastewater volumes for the identification of point sources for the hygienic impairment of water bodies.

Swimming in urban surface waters is still an exception in European cities. At the same time there are numerous initiatives trying to achieve a quality of urban surface waters that allows recreational activities including swimming. In order to manage bathing waters properly the EU Bathing Water Directive (2006/7/EC) demands the elaboration of bathing water profiles in which sources of pollution have to be assessed. In order to investigate the relevance of stormwater as a source of microbial contamination as well as the influence of catchment characteristics on the faecal loading, E.Coli, intestinal Enterococci and colony counts have been measured in event related stormwater samples of three different catchment areas in Berlin. The catchment areas were chosen to be as homogeneous as possible representing catchments of old housing buildings (OLD), new housing buildings (NEW), and commercial areas (COM). N-Formylaminoantipyrine (FAA) was measured as a tracer for raw wastewater. Results showed elevated concentrations (1-2 log units) of faecal indicator organisms (FIO) in catchment OLD (104-105 in comparison to 103 cfu/100mL) suggesting illicit connections of wastewater discharges to rainwater drains, which is supported by elevated concentrations of FAA in the same catchment type. This underlines the relevance of these illicit connections as a source of hygienic contamination, which has to be considered when planning urban bathing water activities.