The present study aims to explore the relationship between rainfall variables and water quality/quantity characteristics of combined sewer overflows (CSO), by the use of multivariate statistical methods and online measurements at a principal CSO outlet in Berlin (Germany). Canonical correlation results showed that the maximum and average rainfall intensities are the most influential variables to describe CSO water quantity and pollutant loads whereas the duration of the rainfall event and the rain depth seem to be the most influential variables to describe CSO pollutant concentrations. The analysis of Partial Least Squares (PLS) regression models confirms the findings of the canonical correlation and highlights three main influences of rainfall on CSO characteristics: (i) CSO water quantity characteristics are mainly influenced by the maximal rainfall intensities, (ii) CSO pollutants concentrations were found to be mostly associated with duration of the rainfall and (iii) pollutants loads seemed to be principally influenced by dry weather duration before the rainfall event. The prediction quality of PLS models is rather low (R² < 0.6) but results can be useful to explore qualitatively the influence of rainfall on CSO characteristics.

The presented work studies the influence of the sampling strategy on the quality of locally calibrated UV-VIS probe measurements in combined sewer overflows (CSO) and the receiving river. Results indicate that UV-VIS spectrometers are not able to provide reliable measurements of water quality in urban stormwater without being calibrated to local conditions with laboratory analyses of water samples. The use of the global calibration (supplied by the manufacturer) led to errors of at least 30% and 45% for CSO load and river concentration of chemical oxygen demand (COD), respectively. Even with reliable local calibration, COD loads contained significant uncertainties close to 20%. Uncertainties in COD load and concentration decrease below 30% if more than 15-20 samples (i.e. 3-4 stormwater events) are sampled for local calibration. The effort and associated sampling costs to gain more than 15-20 samples are much less effective, since load and concentration uncertainties remain relatively stable with an increasing number of samples used for the calibration. The presented analysis aims at supporting practitioners in the planning, operation and calibration of UV-VIS spectrometer probes.

The present study aims at demonstrating the possibilities of on-line sensors for describing CSO emissions and river impacts. A continuous integrated monitoring, using state-of-the-art on-line sensors, was started in Berlin in 2010. It combines (i) continuous measurements of water quality and flow rates of combined sewer overflows (CSO) at one main CSO outlet and (ii) continuous measurements of water quality parameters at four sites within the urban stretch of the receiving river. UV-VIS probes provide continuous measurements of parameters such as chemical oxygen demand (COD) with relatively low uncertainties (10-30%). However, experience shows that on-line UV-VIS probes are not able to provide accurate measurements of water quality without being calibrated to local conditions. Several methodologies to analyze on-line CSO and river measurements are presented and illustrated with an exemplary event. Results show that reliable information such as the CSO load, the proportion of wastewater in CSO, the contribution of wastewater to CSO load, the first flush effect and the intensity of river impacts can be gained at high precision and temporal resolution. Given the broad range of high quality information from CSO impacts in the river to the characterization of CSO emissions, the study suggests the use of continuous integrated monitoring programs to support decisions on CSO management.

Asset management is an increasing concern for wastewater utilities and municipalities. Sewer deterioration models have been developed by research and municipalities to support the definition of cost-effective inspection and rehabilitation strategies. However, the acceptance of deterioration models among sewer operators and decision makers still raise considerable challenges. This article presents the state of the art of condition classification and sewer deterioration modeling and discusses key issues for the future development of deterioration models. Research is needed (i) to identify the most appropriate approaches for condition classification and deterioration modeling and (ii) to conclude clearly about their quality of prediction. Due to the high costs associated with CCTV inspection and data collection, the influence of input data on modeling quality and the optimal input data requirement are still to be evaluated. The ongoing project SEMA aims precisely to assess the suitability of models to simulate sewer deterioration. Objectives and strategy are shortly presented at the end of the article.

The adoption of decision support tools for the definition of cost-effective strategies is seen to gain more importance in the coming years. This development is due for one part to the general degradation of the existing systems and for the other part to changes into the regulations and demands for more transparency in decision-making (Ana and Bauwens, 2007). A key element of decision support systems is the ability to assess and predict the remaining life of the assets (Marlow et al., 2009). For this purpose, deterioration models have been developed to understand and describe the sewer aging based on available CCTV inspections and a list of factors that influence the deterioration. This report first describes the potential sewer deterioration factors and analyzes a panel of literature case studies regarding the relevance of each factor on sewer deterioration. Results are hardly directly comparable, because of the different construction practices, historical backgrounds and environmental conditions of the networks investigated. However, some trends regarding the most significant factors may be identified. In most studies, the construction year and the material seem to be the most relevant factor to explain sewer aging. Pipe size, depth, location and sewer function show generally a medium significance on sewer deterioration. Pipe slope was found to have a low significance for the structural deterioration, but a high relevance on the hydraulic deterioration. The effect of other factors as pipe shape, pipe length, soil type, sewer bedding, presence of trees, installation method, standard of workmanship, joint type, and ground water level have been highlighted but rarely or never investigated. On a second step, this report presents three main approaches for sewer deterioration modeling: deterministic, statistical and artificial intelligence based models. The models can be further categorized into pipe group and pipe level models (Ana and Bauwens, 2010). Pipe group models (e.g. Cohort survival or Markov) can be used to predict the condition of a group of sewers or cohorts and are useful to support strategic asset management, i.e. the definition of long term strategies and budget requirements. These models enable to evaluate the efficiency of several scenarios at the network scale. Pipe level models (e.g. regression, discriminant analysis, neural networks) can be used to simulate the condition of each single pipe. They may be useful to set priorities and justify asset management operations. Pipe level models are tools that can support the utilities in the short and mid-term planning and determine at a finer resolution how, when, and where to rehabilitate sewers. Literature results indicate that cohort survival and Markov models are two useful approaches for modeling the degradation of pipe groups. However, the quality of prediction of these models depends highly on the availability of a large amount of inspection data. Extensive datasets are required to create representative sewer groups (cohorts) with sufficient inspected sewers in each condition state. Regression and Discriminant Analysis were tested on several case studies but showed pretty low prediction performances. Three main reasons could be (i) the non-validity of model assumptions, (ii) the biased distribution of the datasets in terms of number of samples for each condition state and (iii) the lack of data for important deterioration factors. Neural networks have proven to be successful tools for the prediction of the deterioration of individual pipes. However, they require (i) relatively complex and time-consuming training processes and (ii) extensive datasets of CCTV inspection and deterioration factors. Only very few case studies intended to evaluate the quality of prediction of these deterioration models. Furthermore, validation results are often contradictory and hardly comparable since (i) the data available for model calibration differ (percentage of CCTV available, type of deterioration factors available) and (ii) the metrics of the methodologies used to assess the quality of prediction differ. Thus, there is still no clear conclusion about the best modeling approach depending on the modeling purpose (pipe group or pipe level). There is also no clear conclusion regarding the quality of prediction that can be reached since in most case studies only a few percentages of CCTV data were available and many data regarding potential deterioration factors were missing. Further research work is needed in order to (i) identify the most appropriate modeling approach depending on the modeling purpose, (ii) understand the influence of CCTV data availability on the modeling results, (iii) analyze the influence of input data uncertainty (CCTV and deterioration factors) on the modeling processes and (iv) find out the optimum input data requirement (availability of CCTV data and deterioration factors) for model calibration.

A continuous monitoring, using UV-VIS spectrometers, was carried out in Berlin from 2010 to 2012. It combined (i) continuous measurements of the quality and flow rates of combined sewer overflows (CSO) at one main CSO outlet downstream of the overflow structure and (ii) continuous measurements of water quality parameters at five sites within the urban stretch of the receiving River Spree. Locally, the collection of data aims at (i) characterizing CSO emissions, (ii) assessing the local dynamics and intensity of CSO impacts on the river and (iii) calibrating sewer and river water quality models being part of a planning tool for future CSO management in Berlin (Riechel et al., 2011). UV-VIS spectrometers are in-situ probes, which measure absorbance spectra ranging from UV to visual wavelengths. Concentrations, such as chemical oxygen demand (COD), are calculated from these spectra. Due to the varying composition of waste and river water a local calibration is required to enhance the measurement quality. According to Gamerith et al. (2011), manufacturer global calibration can lead to systematic error up to 50% for COD measurements.

Das vorgestellte modellbasierte Werkzeug bildet Mischwasserüberläufe aus dem Berliner Mischkanalsystem und deren kurzfriste Auswirkungen im Gewässer ab. Es soll für die Maßnahmenplanung und die Berechnung von Zukunftsszenarien verwendet werden. Das Werkzeug zeigt eine gute Übereinstimmung mit Messungen bezüglich des Verlaufes der Sauerstoffkonzentration im Gewässer und des Auftretens kritischer Bedingungen für die Fischfauna. Eine Szenarienuntersuchung für ein Extremjahr zeigt, dass durch die bis zum Jahr 2020 geplante Stauraumvergrößerung die Häufigkeit fischkritischer Bedingungen im Gewässer bereits um ein Drittel reduziert werden kann. Eine Reduktion um ein zusätzliches Drittel wäre durch weitergehende Maßnahmen im Bereich der Entsiegelung möglich. Die verbleibenden fischkritischen Bedingungen sind das Ergebnis von sehr starken Regenereignissen und können kaum verhindert werden. Eine durch Klimaveränderung erhöhte oder reduzierte Regenintensität im Sommer hätte starken Einfluss auf das Auftreten fischkritischer Bedingungen; die erwartete Temperaturerhöhung würde hingegen hauptsächlich die Sauerstoffsituation bei Trockenwetter verschlechtern.

Das vorgestellte modellbasierte Werkzeug bildet Mischwasserüberläufe aus dem Berliner Mischkanalsystem und deren kurzfristige Auswirkungen im Gewässer ab. Es soll für die Massnahmenplanung und die Berechnung von Zukunftsszenarien verwendet werden. Das Werkzeug zeigt eine gute Übereinstimmung mit Messungen bezüglich des Verlaufes der Sauerstoffkonzentration im Gewässer und des Auftretens kritischer Bedingungen für die Fischfauna.