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.

This study exemplifies the use of Life Cycle Assessment (LCA) as a tool to quantify the environmental impacts of processes for wastewater treatment. In a case study, the sludge treatment line of a large wastewater treatment plant (WWTP) is analysed in terms of cumulative energy demand and the emission of greenhouse gases (carbon footprint). Sludge treatment consists of anaerobic digestion, dewatering, drying, and disposal of stabilized sludge in mono- or co-incineration in power plants or cement kilns. All relevant forms of energy demand (electricity, heat, chemicals, fossil fuels, transport) and greenhouse gas emissions (fossil CO2,CH4,N2O) are accounted in the assessment, including the treatment of return liquor from dewatering in the WWTP. Results show that the existing process is positive in energy balance (–162 MJ/PECOD * a) and carbon footprint (–11.6 kg CO2-eq/PECOD *a) by supplying secondary products such as electricity from biogas production or mono-incineration and substituting fossil fuels in co-incineration. However, disposal routes for stabilized sludge differ considerably in their energy and greenhouse gas profiles. In total, LCA proves to be a suitable tool to support future investment decisions with information of environmental relevance on the impact of wastewater treatment, but also urban water systems in general.

Vier verschieden-konfigurierte Multigas-Sensorsysteme (Elektronische Nasen) sind Gegenstand von Versuchen an einer Kanalforschungsanlage der Berliner Wasserbetriebe. Die Systeme werden 6 Monate verschiedenen realitätsnahen Prozessbedingungen ausgesetzt, um im Anschluss eine Aussage zur Einsetzbarkeit der Systeme auf derzeitigem Stand der Technik im Geruchsmanagement von Abwasserkanalisationen machen zu können. Momentan ist kein Standard zum Test und zur Bewertung von solchen technischen Messsystemen unter Praxisbedingungen verfügbar. Daher wurde eine Methode entwickelt, die eine anwendungs- und innovationsorientierte Bewertung zulässt. Bewertungskriterien werden aufgestellt, orientiert an Verfahrenskenngrößen laut DIN EN ISO 9169 [3]. Die Kriterien werden an das Messkonzept der Elektronischen Nasen, sowie an die Versuchsbedingungen angepasst und erweitert. Das Versuchsprogramm ist so konzipiert, dass verschiedene Zielanwendungsfälle (wie z. B. die Planung einer Dosierstrategie mit geruchsreduzierenden Additiven) abgedeckt sind. Das Vorhaben wird zusammen mit den Berliner Wasserbetrieben und Veolia Wasser sowie in Kooperation mit evado-engineering durchgeführt.

This study presents the use of Life Cycle Assessment as a tool to quantify the environmental impacts of processes for wastewater treatment. In a case study, the sludge treatment line of a large sewage treatment plant is analysed in energy demand and the emission of greenhouse gases. Results show that the existing process is positive in energy balance (+166 MJ/PECODa) and GHG emissions (+19 kg CO2-eq/PECODa) by supplying secondary products such as electricity from biogas production and substituting fossil fuels in incineration. However, disposal routes for stabilised sludge differ considerably in their environmental impacts. In total, LCA proves to be a suitable tool to support future investment decisions with information of environmental relevance on the impact of WWTPs, but also larger urban water systems.

The Berliner Wasserbetriebe are the largest water supply and wastewater disposal company in Germany. They are challenged to tackle various kinds of odour problems emerging from the sewer network. The continuous extension of sewer networks and a decrease in water consumption (in Berlin: ~ 20 % in the last 16 years, according to the statistical office BB, 2009) have led to elevated odour emissions arising from sewer systems. Together with growing public concern over odours from water treatment works, this has led to increasing numbers of odour complaints in urban catchments (Stuetz and Frechen, 2001; ATV-DVWK-M 154, 2003; Barjenbruch, 2003). Different odour abatement technologies are widely-used but often response only after consumer complaints and do not consider adequate identification of odour problems beforehand. An operational, together with a scientific approach is necessary in order to apply effective measures or combinations thereof. In Berlin annually almost 3 Mio € are spent by BWB to reduce odour emissions from the Berlin sewer system (BWB, 2006). Applied measures vary from dosing of nitrate or iron hydroxide sludge, flushing, or compensation by means of bio-filters or masking. The quantification of odour by means of continuous odour monitoring solutions such as electronic noses can contribute to minder economic and operational risks in odour management. The paper presents the research project ODOCO-ARTNOSE, dealing with the evaluation of electronic noses for the online application in sewer systems. The KompetenzZentrum Wasser Berlin (research centre) in cooperation with the Berlin water utilities Berliner Wasserbetriebe and Veolia Water will carry out bench tests with selected, commercially available chemosensor arrays to identify advanced applications of electronic noses in odour management in sewer networks. Objectives of the project are to analytically assess the online-ability of electronic noses by means of a multi-criteria methodology and to specify future odour control services based on the application of e-noses in sewer networks. The potential of electronic noses will be evaluated as tool to fulfil certain needs, namely (i) support for planning/designing of odour preventive measures and abatement strategies, (ii) support for real-time odour control and (iii) data acquisition tool to supervise and document (industrial) dischargers, document the effect of abatement measures and document legal compliance. Tests are possible to be carried out in the frame of a sewer research plant or within the sewer system of Berlin. A large-scale research plant was developed by Berliner Wasserbetriebe for investigating different odour and corrosion strategies. The plant consists of 2 independent gravity lines and is fed by combined wastewater from Berlin, pumped directly from the sewer. Various milieu conditions can be generated. The paper places current challenges within the city of Berlin into perspective and displays examples of odour abatement strategies of Berliner Wasserbetriebe. Expected outcomes and correlated benefits of the project will be presented. The methodological approach relies on a transparent selection of chemosensor array systems, on bench tests following a sophisticated measurement program and the evaluation of the electronic noses by clear defined criteria.