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.

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.

Bank filtration schemes for the production of drinking water are increasingly affected by constituents such as sulphate and organic micropollutants (OMP) in the source water. Within the European project AquaNES, the combination of bank filtration followed by capillary nanofiltration (capNF) is being demonstrated as a potential solution for these challenges at pilot scale. As the bank filtration process reliably reduces total organic carbon and dissolved organic carbon (DOC), biopolymers, algae and particles, membrane fouling is reduced resulting in long term operational stability of capNF systems. Iron and manganese fouling could be reduced with the possibility of anoxic operation of capNF. With the newly developed membrane module HF-TNF a good retention of sulphate (67–71%), selected micropollutants (e.g., EDTA: 84–92%) and hardness (41–55%) was achieved together with further removal of DOC (82–87%). Fouling and scaling could be handled with a good cleaning concept with acid and caustic. With the combination of bank filtration and capNF a possibility for treatment of anoxic well water without further pre-treatment was demonstrated and retention of selected current water pollutants was shown.

Im BMBF-Forschungsprojekt KURAS wurde eine Methode vorgeschlagen, mit der Maßnahmen der Regenwasserbewirtschaftung für konkrete Stadtquartiere ausgewählt und platziert werden können. Hinsichtlich der möglichen Ziele geht die Methode über die wasserwirtschaftliche Wirkung hinaus und betrachtet zusätzlich Effekte auf Umwelt (Grundwasser und Oberflächengewässer, Biodiversität) und Bewohner (Stadtklima, Freiraumqualität, Gebäudeebene) sowie den Aufwand an Kosten und Ressourcen.

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.

Im Forschungsprojekt KUBAS (Konzepte für urbane Regenwasserbewirtschaftung und Abwassersysteme) wurde eine Methode vorgeschlagen, mit der Maßnahmen der Regenwasserbewirtschaftung für konkrete Stadtquartiere ausgewählt und platziert werden können. Ende 2016 wurde die "KURAS-Methode" als Ausgangspunkt für die zukünftige dezentrale Regenwasserbewirtschaftung in der Koalitionsvereinbarung der neuen Regierung des Landes Berlin zur Umsetzung in die Praxis und zur Weiterentwicklung festgeschrieben. Dadurch werden aktuell in verschiedenen Neubau- und Sanierungsvorhaben in Berlin Elemente der Methode eingesetzt; insbesondere der Ansatz, dass die Maßnahmenauswahl erst nach einer Festlegung nicht-monetärer Ziele erfolgt, wird dabei berücksichtigt. Die Anwendung in der Praxis erfordert aber auch eine Vereinfachung (z. B. Reduktion der Ziele) und Weiterentwicklung der Methode. Diese Anpassung wird durch das Forschungsprojekt netWORKS 4 unterstützt, welches wichtige sozio-kulturelle Ziele berücksichtigt und konkrete Planungsworkshops in Berlin begleitet.

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.

Closed Circuit Television Inspection is used since decades as industry standard for sewer system inspection and structural performance evaluation. In current practice, inspection data are helpful to support asset management decisions. However, the quality and uncertainty of sewer condition assessment is rarely questioned. This article presents a methodology to determine the probability to underestimate, overestimate or accurately estimate the real condition of a pipe using visual inspection. The approach is based on the analysis of double inspections of the same sewer pipes and has been tested using the extensive data-set of the city of Braunschweig in Germany. Results indicate that the probability to inspect correctly a pipe in poor condition is close to 80%. The probability to overestimate the condition of a pipe in bad condition (false negative) is 20% whereas the probability to underestimate the condition of a pipe in good condition (false positive) is 15%. Finally, sewer condition evaluation can be used to assess the general condition of the network with an excellent accuracy probably because the respective effects of false positive and false negative are buffered. © 2017 Informa UK Limited, trading as Taylor & Francis Group.