The aim of this thesis is to investigate the effectiveness and economic feasibility of installing a biogas treatment plant and power-to-gas (PtG) technology at a wastewater treatment plant (WWTP) in Berlin. After extensive literary research, suitable technologies for the biogas treatment as well as the PtG technology were selected. The next step was to develop an energy tool to determine the best technological solution for the available biogas at the WWTP in question. Several scenarios were selected to be tested by the energy tool. In addition, the selected scenarios were analysed and evaluated from both economic and ecological standpoints. The results show that the use of a combined heat and power (CHP) plant along with a wind turbine or a biogas treatment plant is the best option for the selected WWTP. A biogas upgrading plant does not currently offer any environmental and economic benefits. However, the results of economic analysis also reveal that a biogas treatment plant is very cost-effective for digester gas. Compared to the current situation regarding the reference WWTP, the gas treatment technology requires approximately 75% less investment and approximately 85% lower operating costs. In addition, a biogas treatment can compete with a CHP plant if the 2017 CHP Act is considered and CHP subsidy is no longer granted. The results show that PtG technology is not an economically viable investment, since this technology is associated with very high investment costs and has no support scheme.

The last 60 years has seen unprecedented groundwater extraction and overdraft as well as development ofnew technologies for water treatment that together drive the advance in intentional groundwater replenishment known as managed aquifer recharge (MAR). This paper is the first known attempt to quantify the volume ofMAR at global scale, and to illustrate the advancement of all the major types ofMAR and relate these to research and regulatory advancements. Faced with changing climate and rising intensity ofclimate extremes, MAR is an increasingly important water management strategy, alongside demand management, to maintain, enhance and secure stressed groundwater systems and to protect and improve water quality. During this time, scientific research—on hydraulic design offacilities, tracer studies, managing clogging, recovery efficiency and water quality changes in aquifers—has underpinned practical improvements in MAR and has had broader benefits in hydrogeology. Recharge wells have greatly accelerated recharge, particularly in urban areas and for mine water management. In recent years, research into governance, operating practices, reliability, economics, risk assessment and public acceptance ofMAR has been undertaken. Since the 1960s, implementation of MAR has accelerated at a rate of 5%/year, but is not keeping pace with increasing groundwater extraction. Currently, MAR has reached an estimated 10 km3/year, ~2.4% of groundwater extraction in countries reporting MAR (or ~1.0% of global groundwater extraction). MAR is likely to exceed 10% of global extraction, based on experience where MAR is more advanced, to sustain quantity, reliability and quality ofwater supplies.

Die Erhöhung der Resilienz urbaner Wasserinfrastrukturen wird oft als wichtiges Ziel genannt. Eine Literaturstudie zeigt, dass dafür konkretisiert werden muss, um welche Infrastruktur es sich handelt, gegenüber welcher Störung sie resilient sein soll und an welcher Leistung sich die Resilienz zeigen soll. Hier wird darauf aufbauend ein quantitativer Ansatz der Resilienzmessung vorgeschlagen, der die Schwere des Leistungsausfalls gegenüber einem Grenzwert über die Zeit integriert und dieses Integral über das Zeitintervall und den gewählten Grenzwert normiert. Eine beispielhafte Anwendung für Stadtentwässerungsstrategien bei Starkregenereignissen zeigt, dass der vorgeschlagene Ansatz den Vorteil hat, dass Dauer und Ausmaß eines Leistungsausfalls in einem Resilienzwert berücksichtigt werden können. Zudem erlaubt der Ansatz eine Evaluation unterschiedlicher Störungen, beispielsweise durch Systemausfälle. Durch die Normierung wird ein Vergleich unterschiedlicher Leistungen von Wasserinfrastruktur ermöglicht. Allerdings ist die normierte Resilienz stark von der Wahl des Zeitintervalls und des festgelegten Grenzwertes abhängig und damit nicht ohne weiteres auf andere Systeme übertragbar.

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.

In the aftermath of the adoption of the Sustainable Development Goals (SDGs) and the Paris Agreement (COP21) by virtually all United Nations, producing more with less is imperative. In this context, phosphorus processing, despite its high efficiency compared to other steps in the value chain, needs to be revisited by science and industry. During processing, phosphorus is lost to phosphogypsum, disposed of in stacks globally piling up to 3–4 billion tons and growing by about 200 million tons per year, or directly discharged to the sea. Eutrophication, acidification, and long-term pollution are the environmental impacts of both practices. Economic and regulatory framework conditions determine whether the industry continues wasting phosphorus, pursues efficiency improvements or stops operations altogether. While reviewing current industrial practice and potentials for increasing processing efficiency with lower impact, the article addresses potentially conflicting goals of low energy and material use as well as Life Cycle Assessment (LCA) as a tool for evaluating the relative impacts of improvement strategies. Finally, options by which corporations could pro-actively and credibly demonstrate phosphorus stewardship as well as options by which policy makers could enforce improvement without impairing business locations are discussed.

Kanalalterungsmodelle, mit denen sich der Zustand von Abwasserkanälen simulieren lässt, können wertvolle Werkzeuge für die Sanierungsplanung sein. Dennoch werden sie in Deutschland bisher nur von wenigen Kanalnetzbetreibern eingesetzt. Im Rahmen des Forschungsvorhabens SEMA-Berlin wurden verschiedene Modellansätze getestet und hinsichtlich ihrer Prognosequalität bewertet. Für den Modellaufbau wurden die Ergebnisse von mehr als 100 000 TV-Inspektionen sowie Daten zu den individuellen Kanaleigenschaften und Umgebungsfaktoren der Stadt Berlin verwendet. Die Untersuchungen zeigen, dass das statistische Modell GompitZ die Zustandsverteilung des Kanalnetzes mit einer Genauigkeit von 99 % wiedergeben kann. Mit Random Forest, einem Modell des maschinellen Lernens, kann mit einer Trefferquote von 67 % vorhergesagt werden, welcher Kanal sich im schlechten Zustand befindet. Die Ergebnisse können dafür genutzt werden, prioritäre Haltungen für Kanalinspektionen zu identifizieren und Investitionen so zu steuern, dass der Zustand der Kanalisation langfristig erhalten oder sogar verbessert wird.

Deterioration models can be successfully deployed only if decision-makers trust the modelling outcomes and are aware of model uncertainties. Our study aims to address this issue by developing a set of clearly understandable metrics to assess the performance of sewer deterioration models from an end-user perspective. The developed metrics are used to benchmark the performance of a statistical model, namely, GompitZ based on survival analysis and Markov-chains, and a machine learning model, namely, Random Forest, an ensemble learning method based on decision trees. The models have been trained with the extensive CCTV dataset of the sewer network of Berlin, Germany (115,258 inspections). At network level, both models give satisfactory outcomes with deviations between predicted and inspected condition distributions below 5%. At pipe level, the statistical model does not perform better than a simple random model, which attributes randomly a condition class to each inspected pipe, whereas the machine learning model provides satisfying performance. 66.7% of the pipes inspected in bad condition have been predicted correctly. The machine learning approach shows a strong potential for supporting operators in the identification of pipes in critical condition for inspection programs whereas the statistical approach is more adapted to support strategic rehabilitation planning.