Städte üben einen hohen Nutzungsdruck auf die Ressourcen Fläche, Wasser, Stoffe und Energie aus. Der entwickelte RessourcenPlan verankert die effiziente Nutzung von Ressourcen in kommunale Planungs- und Entscheidungsprozesse zum Neubau oder der Sanierung von Stadtquartieren. Aus wasserwirtschaftlicher Sicht werden praxisrelevante Zielgrößen hergeleitet, die bereits in sehr frühen Planungsphasen entscheidungsrelevant sein können. Eine wasserwirtschaftliche Flächenkategorisierung und -bewertung verhilft zur Identifikation von Defiziten und Synergieoptionen. Der Verschnitt mit diesen Ressourcen ermöglicht eine ortsspezifische Effizienzbetrachtung sowie ganzheitlich ressourcenoptimierte Planungsentscheidungen.

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.

Im Rahmen des Forschungsvorhabens „Phorwärts“ wurde auf Basis aktuell erhobener Daten die konventionelle Phosphatdüngemittelherstellung (vom Abbau des Phosphaterzes in der Mine bis zur Anwendung auf dem Feld) mit ausgewählten Verfahren der P-Rückgewinnung aus dem Abwasserpfad ökobilanziell verglichen. Die verschiedenen Düngemittel wurden hinsichtlich ihrer Kontaminationen wie den Schwermetallen, den organischen Schadstoffen und den Pharmaka-Rückständen zusätzlich in einer vergleichenden Risikobewertung der Düngemittelanwendung für die Wirkungspfade Bodenorganismen, Grundwasser und im Hinblick auf die menschliche Gesundheit untersucht. Eine Kostenschätzung der verschiedenen Produktionswege komplettiert den Vergleich der konventionellen Phosphatdüngemittelproduktion mit der Produktion von Recyclingdüngern aus der Kläranlage. Die Ergebnisse der Studie zeigen, dass eine technische Phosphatrückgewinnung aus dem Abwasserpfad unter bestimmten Bedingungen ökologisch und wirtschaftlich sinnvoll ist. Neben dem eigentlichen Phosphatrückgewinnungsverfahren sind in hohem Maße die lokalen Randbedingungen bezüglich der Ergebnisse der vergleichenden Bewertung entscheidend. Unter Berücksichtigung der kommenden gesetzlichen Randbedingungen der Dünge- und der Klärschlammverordnung wird in Zukunft voraussichtlich die Monoverbrennung als primäre Option für die Klärschlammentsorgung dienen und die Phosphatrückgewinnung vorwiegend aus der Klärschlammasche erfolgen. Da bei der Rückgewinnung aus der Klärschlammasche hohe Rückgewinnungsraten, die den Vorgaben der Klärschlammverordnung genügen, erzielt werden können, ist ab dem Kalenderjahr 2029 mit etwa 30.000 bis 40.000 Tonnen Phosphor pro Jahr in Form von Phosphatrezyklaten zu rechnen. Inwieweit und zu welchen Preisen diese Rezyklate durch den Markt angenommen werden, kann aus heutiger Sicht noch nicht abgeschätzt werden.

This paper presents the assessment of a planned scheme of indirect potable reuse (IPR) in the Vende´e region of France in its potential risks for human health and ecosystems, and also in its overall environmental impacts. Methods of risk assessment (quantitative microbial and chemical risk assessment) and life cycle assessment (LCA) are used to characterize the risk associated with the use of reclaimed water for IPR, but also the environmental benefits compared with other options for additional drinking water supply. The LCA results show that IPR is competitive with other options of water supply in its energy demand and greenhouse gas emissions. Pathogens as the main health hazard are controlled effectively by existing and planned preventive measures. For chemicals the number of potentially relevant substances could be reduced substantially by the assessment.

Fecal indicator organisms such as Escherichia coli, enterococci, and coliphages are important to assess, monitor, and predict microbial water quality in natural freshwater ecosystems. To improve predictive modelling of fecal indicators in surface waters, it is vital to assess the influence of autochthonous and allochthonous environmental factors on microbial water quality in riverine systems. To better understand how environmental conditions influence the fate of fecal indicators under varying weather conditions, the interdependencies of environmental parameters and concentrations of E. coli, intestinal enterococci, and somatic coliphages were studied at two rivers (Rhine and Moselle in Rhineland-Palatinate, Germany) over a period of 2 years that exhibited contrasting hydrological conditions. Both riverine sampling sites were subject to similar meteorological conditions based on spatial proximity, but differed in hydrodynamics and hydrochemistry, thus providing further insight into the role of river-specific determinants on fecal indicator concentrations. Furthermore, a Bayesian multiple linear regression approach that complies with the European Bathing Water Directive was applied to both rivers’ datasets to test model transferability and the validity of microbial water quality predictions in riverine systems under varying flow regimes. According to multivariate statistical analyses, rainfall events and high water discharge favored the input and dissemination of fecal indicators in both rivers. As expected, concentrations declined with rising global solar irradiance, water temperature, and pH. While variations in coliphage concentrations were predominantly driven by hydro-meteorological factors, bacterial indicator concentrations were strongly influenced by autochthonous biotic factors related to primary production. This was more pronounced under low flow conditions accompanied by strong phytoplankton blooms. Strong seasonal variations pointed towards bacterial indicator losses due to grazing activities. The Bayesian linear regression approach provided appropriate water quality predictions at the Rhine sampling site based on discharge, global solar irradiance, and rainfall as fecal indicator distributions were predominantly driven by hydro-meteorological factors. Assessment of microbial water quality predictions implied that rivers characterized by strong hydrodynamics qualify for multiple linear regression models using readily measurable hydro-meteorological parameters. In rivers where trophic interactions exceed hydrodynamic influences, such as the Moselle, viral indicators may pose a more reliable response variable in statistical models.

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.