Recent infrastructure studies underline the general deterioration of sewer systems and the risk reversing public health, environment and increasing costs (ASCE, 2009). Aging pipes have not been inspected, replaced or rehabilitated rapidly enough to prevent sewer deterioration and increasing system failures (Tuccillo et al., 2010). According to a need survey conducted by EPA (2008), total funding needs for replacement, rehabilitation and expansion of existing collection systems for a 20 year period in the USA is 82.7 billions $, i.e. 28% of the total need of public agencies for wastewater treatment and collection. In the last 30 years, most municipalities have invested in sewer system expansion and treatment plant upgrade but a relatively small component has been allocated to the improvement of sewer system condition.

Data play an important role in water-related research. In the field of limnology, monitoring data are needed to assess the ecological status of water bodies and understand the bio-geochemical processes that affect this status. In wastewater management, measured or simulated data are the basis for planning and control of sewer networks. Given the importance of data in water-related research makes them a valuable resource, which should be handled in an adequate way. Based on experiences in data collection and data processing in water-related research this paper proposes – both from a computer scientist’s and an environmental engineer’s point of view – a set of rules for data handling: Rule 1: Protect raw data. Rule 2: Save metadata. Rule 3: Use databases. Rule 4: Separate data from processing. Rule 5: Use programming. Rule 6: Avoid redundancy. Rule 7: Be transparent. Rule 8: Use standards and naming conventions. Applying these rules (i) increases the quality of data and results, (ii) allows to prepare data for long-term usage and make data accessible to different people, (iii) makes data processing transparent and results reproducible, and (iv) saves – at least in the long run – time and effort. With this contribution the authors would like to start a discussion about best data handling practices and present a first checklist of data handling and data processing for practitioners and researchers working in the water sector.

Recent infrastructure studies underline the general deterioration of sewer system and the risk reversing public health, environment and increasing costs (ASCE, 2009). Since the origin of sewer systems in the 19th century, sewers have been installed at different periods using available standards and technologies. Sewer assets have limited service life and it is crucial to assess their condition throughout their life cycles to avoid potential catastrophic failure and expensive emergency rehabilitation due to their deterioration (Hao et al., 2011). This report first presents the wide panel of inspection technologies available to obtain information about sewer defects and condition. Visual inspection (e.g. Closed-circuit television CCTV, zoom camera) appears to be the industry standard for sewer inspection. It provides visual data (images and/or videos) of the internal surface of the pipe. Defects are usually coded manually by the inspection staff according to standard coding methods. In Europe, the current codification system is the normative EN 13508-2 for visual inspection (EN 13508-2, 2011) used by the CEN-Members (European Committee for Standardization). In addition, physical techniques are available that can give further information and details about pipe defects. These techniques do not replace the CCTV inspection but can give deeper insights on the type and severity of defects. Sonar and Lasers enables to analyze pipe geometry and can identify defects such as deflections, cracks, sediments or corrosion. Ultrasonic testing and magnetic flux leakage (MFL) are applied directly on the pipe wall. They enable to measure wall thickness and detect pipe defects such as corrosion, deflections and cracks. Ground Penetrating Radar (GPR) and Infrared Thermography are used from above ground and are useful to locate pipes and identify bedding conditions, voids and leaks. Finally, network wide inspection technologies like smoke testing or Distributed Temperature Sensing (DTS) can locate cross-connections and/or sewer infiltration. The purpose, inspection procedure and limitations of these methodologies are briefly presented. On a second step, this report presents the available classification methodologies developed to interpret automatically visual CCTV inspection reports and evaluate sewer condition. These methodologies enable to transfer the extensive amount of visual inspection data from CCTV inspection into a more easily manageable number, useful to support asset management practices. Most approaches have a similar goal: they aim to rank rehabilitation priorities and support municipalities in the definition of rehabilitation programs. They do not pretend to replace the knowledge and analysis skills of a local expert but can help him to identify rehabilitation priorities. All methodologies provide an overall condition score for each sewer segment or sub-scores for different requirements (e.g. structural and operational condition) or dysfunctions. From the review of available methodologies, two main approaches can be distinguished: priority based and substance based methodologies. For priority based methodologies, the calculation of sewer condition grades is based on the most severe defects, the density of defects and/or the defects length. Condition grades express the priority of rehabilitation, i.e. the emergency of action regarding the probability of failure or collapse. For substance based methodologies, the final score is calculated based on the length of sewer that will be affected by rehabilitation actions. Substance based methodologies do not aim to assess the condition of sewers but rather to rank sewer pipes considering the amount and type of rehabilitation needs: replacement, renovation and repair. Each methodology aggregates and combines sewer defects in a very different way making very hazardous the benchmarking of final scores from different methods. Therefore, municipalities using different evaluation system are not able to benchmark the condition of theirs networks. Finally, the accuracy of the classification results remains a key issue, crucial for the further use of inspection data to support asset management strategies.

Theoretically the Berlin River Spree could be under pressure from depressions in dissolved oxygen (DO) and high concentration of fish toxic ammonia following overflows of the combined sewer system. However, monitoring results indicate that the Spree is only under pressure from depressions in dissolved oxygen (DO). Consequently, a sewer model, a river water quality model and an impact assessment tool were calibrated and validated for representation of DO depressions. The three elements are joined in a planning tool, which will be used to test the effect of CSO management approaches for the current situation and with altered boundary conditions to account for expected climate change.

Im Rahmen eines Forschungsprojektes wurden die Auswirkungen von Mischwasserentlastungen auf die Berliner Stadtspree untersucht und ein Planungsinstrument zur Reduzierung der Auswirkungen von Mischwasserüberläufen entwickelt.

In the city of Berlin combined sewer overflows (CSO) can lead to severe depressions in dissolved oxygen (DO) of receiving urban rivers and hence to acute stress for the local fish fauna. To quantify CSO impacts and optimize sewer management strategies, a model-based planning instrument has been developed. It couples the urban drainage model InfoWorks CS which simulates hydraulics and pollutant transport in the sewer with the river water quality model QSim which simulates hydraulics, mass transport and various biogeochemical processes in the receiving water body. To identify simulated CSO impacts, concentration-durationfrequency-thresholds for DO are applied to river model results via an impact assessment tool. Two kinds of impacts are distinguished: i) suboptimal conditions and ii) critical conditions for which acute fish kills are possible. In the case of Berlin, suboptimal conditions are observed on up to 92 days per year, predominantly during periods of low discharge and high temperatures whereas critical conditions only occur after CSO. For model calibration and validation, continuous measurements in both river and sewer are used. First simulations show good accordance between simulated and measured DO concentration in the river with Nash-Sutcliffe efficiencies between 0.70 and 0.79 for an eight-month time period at three different river monitoring points. However, to assure satisfactory model performance for adverse DO conditions in particular, impact assessment results for measured and simulated data are compared. Regarding suboptimal DO conditions simulated and measured data show good agreement. Nevertheless model representation for critical conditions is poor for some river sections and requires further improvement for CSO conditions. The results underline the importance of combining different validation approaches when dealing with complex systems.

This report presents practical field aspects gained during two years of monitoring with state-of-the-art spectrometers and ion-selective sensors, combining (i) continuous measurements of the quality and flow rates of combined sewer overflows (CSO) with (ii) continuous measurements of water quality parameters within the urban stretch of the River Spree. It describes the set-up and the implementation of the monitoring and evaluates the outcomes and experiences towards “lessons learnt”. The challenge of CSO monitoring is their event-based and highly dynamic nature during rain events. Applied online sensors allow dynamic measurements of CSO and water quality impacts for a wide range of parameters. However, the success of online monitoring campaigns depends highly on three main considerations. Firstly, the representativity of the measurement station. The location of the probe must be representative of the concentration over the entire cross section of the sewer or the river. Further criteria have to be considered for the selection of the monitoring sites (e.g. easy access to the probes for maintenance) (chapter 2). Secondly, the quality of the raw measurements. External conditions can influence the quality of measurements and lead to wrong values or outliers. – To avoid drifts, probes need to be cleaned and checked regularly. We found that monitoring stations must be visited at least once a week for functional check-ups. During the two years of monitoring, the maintenance methodology have been continously improved to ensure the best measurement conditions (chapter 3). – But even under state-of-the-art operation of the probes, some values can be affected by errors and lead to misinterpretation. Thus, a validation step is required to detect wrong values and separate them from valid values. Given the large amount of data, an Access-based tool has been developed to support semi-automatic validation of monitoring data (chapter 4). Lastly, the calibration of raw measuments and the determination of uncertainties is critical. Online probes were not able to provide accurate measurements without being calibrated to local conditions with parallel laboratory measurements (online probe refers in this document to spectrometer and ISE-Probe). A Monte-Carlo method was adapted to perform regressions between raw measurement and lab values, which allows considering both uncertainties of sensor and lab chain. For instance, total uncertainty of the UV/VIS probe was between 15 and 30% for chemical oxygen demand (COD), accounting for errors from sensor, laboratory and field (representativity of site). The uncertainties in concentration and flow measurements lead to an uncertainty in CSO COD load between 20 and 70%, depending on the average concentration and flow of the event (chapter 5). In order to gain grab samples and provide high quality calibration, an automatic sampler has been installed at the sewer monitoring. However, for operational purposes, a sewer operator will expect to gain quality online data without the effort and costs of sampling each CSO. In order to estimate the optimal sampling effort, we investigated how many events (or how many lab measurements) are necessary for calibration depending on aimed at uncertainty. From a set of 12 sampled CSO events, we simulate all possible random combinations of events and calculated each time the resulting measurement uncertainty (chapter 5.5). Results shown in Figure A indicate that at least 7 random events need to be sampled to calibrate the probe reducing uncertainties of COD measurement under 30%. It has to be noted that the concentration range of the grab samples has a high influence on the quality of the calibration. A similar analysis considering only events with high lab variations (range > 500 mg/l) showed that then only 4 events must be sampled to reduce uncertainty under 30%. Considering these results, we recommend parallel short sampling campaigns with autosamplers (grab sampling) for application of spectrometers for CSO monitoring. If the lab measurements cover the entire range of water quality variations, a minimum of 3-4 rain events should be sampled to build an accurate calibration function with acceptable uncertainty. If sampled concentration range is exceeded by later measurements, new sampling campaigns should be planned. Since both sensor and autosampling results were available, CSO COD loads have been calculated using both spectrometer and lab values (chapter 6). Results indicate that load calculated with lab samples are within the error range of the loads calculated with spectrometer values. However, the frequency of grab sampling should be less than 10 minutes, to match concentration peaks and quick quality variations in our case. For the purpose of CSO load calculation, autosampler-based monitoring remains a cost-effective alternative to online probes. For a dynamic description of CSO (pollutant sources, mass/flow balance, etc.), autosampler-based data are limited by the minimal sample frequency and the sampling capacity. Investment and effort of online monitoring can overcome these limitations. For river monitoring, online probes enable measuring water quality variations with an acceptable uncertainty, if the probes are properly calibrated. Here, autosamplers are clearly limited by their sampling capacity as the impacts are spread on several days in the case of the River Spree. Since no autosampler was available during the two monitoring years no clear correlation could be established for the spectrometer parameters (TSS, COD, BOD). As the manual approach often fails to catch CSO impacts, an autosampler has been purchased for the last monitoring year in 2012. For NH4 + measurement, the ISE probe has been successfully calibrated performing monthly NH4 measurements in a bucket of river water spiked with ammonium standard solution to reach values in the range expected during CSO (1-2 mg/l).

The urban stretch of the River Spree is a regulated lowland-river, which is affected by a number of anthropogenic pressures, most notably impacts from combined sewer overflows (CSO) of the Berlin sewer system. Collected data show that occurrence of CSO can be detected in the river through a combination of continuous monitoring data, such as specific conductivity, ammonium (NH4), chemical oxygen demand and dissolved oxygen (DO). Comparison with stormwater guidelines indicates that drops in DO from CSO lead to regular problematic conditions for the fish fauna. In contrast, observed NH4 peaks never reach fish-toxic levels. Mitigation measures are currently implemented to reduce these negative impacts during storm events. The effect of past and potential future CSO measures can be studied with a model tool, which has been tested and is currently calibrated based on the above monitoring data.