In decentralised storm water management green roofs play a vital role. Nevertheless questions remain concerning the runoff quality for nutrients and herbicides used against root penetration. In this study monitoring is conducted on two 18 year old green and gravel roofs comparing runoff quality based on concentrations and substance loads. The results indicate that runoff concentrations do not differ for total suspended solids (TSS) and total phosphorus (TP). Nitrate (NO3N) and total nitrogen (TN) concentrations are clearly reduced by the green roof (TN green roof: 1.14 mg/L, gravel roof: 2.99 mg/L, n=7), given plant uptake of atmospheric nitrogen. In contrast, organic indicators chemical oxygen demand (COD green roof: 28.1 mg/L, gravel roof: 16.1 mg/L, n=11) and total organic nitrogen (TON) are higher in green roof runoff, possibly from soil leaching. However, total substance loads for 11 sampled storm events are lower by a factor of 0.8 to 0.2 (TSS, COD, TP, TN, TON) for of the green roof compared to the gravel roof, given their different hydraulic behaviours. Regarding herbicides, Mecoprop is still found in relevant concentrations from 0.08 to 6.59 µg/L in the green roof runoff, exceeding the EU threshold for pesticides in surface water bodies of 0.1 µg/L.

Several infrastructure studies highlight the ongoing deterioration of critical assets in water and wastewater systems (WERF, 2007). A recent survey among 397 water and wastewater industry participants in the U.S.A. and Canada highlights that aging infrastructure and the management of capital and operational costs are the two main industry issues (Black and Veatch, 2013). From the participants, more than 70% of municipalities and utilities have already implemented condition assessment and inspection programs to assess the condition state of their systems. However, less than 10% are currently using simulation tools to support their asset management strategies. These results underline the strong opportunity for municipalities and utilities to increase the efficiency of their asset management programs by extracting the value of their (already) available data. Several modeling approaches are now available but not commonly used by sewer operators to support strategies (Caradot et al., 2013). Indeed, most of these models still fail to show that they can adequately forecast future conditions (Ana and Bauwens, 2010; Scheidegger et al., 2011). This article presents an assessment of the ability of sewer deterioration models to simulate the condition distribution of sewer networks. The analysis has been done using the extensive CCTV dataset of a German city, Braunschweig.

This article presents an assessment of the quality of prediction of a Markov-based statistical sewer deterioration model using the extensive CCTV dataset of a German city, Braunschweig. Additionally, a sensitivity analysis has been performed in order to assess the influence of input data availability on model performance. Results indicate that models are able to simulate quite accurately the condition distribution of the network with deviations smaller than 1%. Results also indicate that the performance of deterioration models is quite independent of the amount of CCTV data available to calibrate the model. Even when using very few data (˜3%, i.e. 1000 inspections) to calibrate the model, very good model performance can be obtained.This article presents an assessment of the quality of prediction of a Markov-based statistical sewer deterioration model using the extensive CCTV dataset of a German city, Braunschweig. Additionally, a sensitivity analysis has been performed in order to assess the influence of input data availability on model performance. Results indicate that models are able to simulate quite accurately the condition distribution of the network with deviations smaller than 1%. Results also indicate that the performance of deterioration models is quite independent of the amount of CCTV data available to calibrate the model. Even when using very few data (˜3%, i.e. 1000 inspections) to calibrate the model, very good model performance can be obtained.

Im Zusammenhang mit der Wasserqualität von Niederschlagsabflüssen wird seit einigen Jahren vermehrt die Rolle organischer Mikroverunreinigungen aus Baumaterialien diskutiert. Einer der bekanntesten Vertreter ist das Biozid Mecoprop, welches als Durchwurzelungsschutz in bitumenhaltigen Dachabdichtungen eingesetzt wird und die Qualität von Gewässern und Böden beeinträchtigt. Vor diesem Hintergrund wird im Rahmen einer einjährigen Messkampagne das Auswaschverhalten eines 18 Jahre alten Gründachs sowie zweier neuer, unbegrünter Versuchsdächer untersucht. Darüber hinaus wird der potenzielle Rückhalt von Mecoprop in einem Retentionsbodenfilter quantifiziert. Die bisherigen Ergebnisse zeigen, dass Mecoprop auch nach vielen Jahren noch in relevanten Konzentrationen vom Gründach ausgewaschen wird (Mittelwert: 1,3 µg L-1). Im Regenabfluss von neuen, unbegrünten Bitumenbahnen wurden sogar 100fach höhere Konzentrationen festgestellt. Der Retentionsbodenfilter kann zwar mit einer Reinigungsleistung von 59% zu einer Reduktion der Frachten ins Gewässer beitragen. Eine wesentliche Verbesserung der Wasserqualität ließe sich aber vor allem durch den Verzicht auf mecoprophaltige Dachabdichtungen erreichen.

This paper reports about experiences gathered from five on-line monitoring campaigns in the sewer systems of Berlin (Germany), Graz (Austria), Lyon (France) and Bogota (Colombia) using UV-VIS spectrometers and turbidimeters. The influence of local calibration on the quality of on-line COD measurements of wet weather discharges has been assessed. Results underline the need to establish local calibration functions for both UV-VIS spectrometers and turbidimeters. It is suggested to practitioners to calibrate locally their probes using at least 15-20 samples. However, these samples should be collected over several events and cover most of the natural variability of the measured concentration. For this reason, the use of automatic peristaltic samplers in parallel to on-line monitoring is recommended with short representative sampling campaigns during wet weather discharges. Using reliable calibration functions, COD loads of CSO and storm events can be estimated with a relative uncertainty of approximately 20 %. If no local calibration is established, concentrations and loads are estimated with strong errors questioning the reliability and meaning of the on-line measurement. Similar results have been obtained for TSS measurements.

In recent years, organic micropollutants have been detected in urban storm runoff in several European studies. As rain water runoff in Berlin and other German and European cities is often discharged untreated in separated sewer systems, urban stormwater is a large potential source of micropollutants affecting receiving surface waters. As a consequence, it is important to know the local extent of the issue to be able to evaluate potential measures. In this study, a one year monitoring programme is conducted in the city of Berlin to estimate yearly loads of micropollutants from urban stormwater entering Berlin surface waters. Five different catchment types typical for Berlin were determined after analysis of GIS data (old building areas <1930, newer building areas >1950, single houses with gardens, roads and commercial areas) and monitoring points were selected fulfilling a number of criteria (including representativeness of catchment type, accessibility, sufficient flow, manhole size). Samples are taken using automatic samplers and a sampling strategy was developed to obtain best possible representative composite samples representing the average concentration of the sampled storm event. Results will then be used with measured flow data to calculate micropollutant loads of individual catchment types. A runoff model for Berlin applied to the individual catchment types and coupled with pollutant concentration relationships will be used to extrapolate results to city scale.