Ulrich Dittmer

Organic micropollutants discharged by combined sewer overflows - Characterisation of pollutant sources and stormwater-related processes.

To characterise emissions from combined sewer overflows (CSOs) regarding organic micropollutants, a monitoring study was undertaken in an urban catchment in southwest Stuttgart, Germany. The occurrence of 69 organic micropollutants was assessed at one CSO outfall during seven rain events as well as in the sewage network at the influent of the wastewater treatment plant (WWTP) and in the receiving water. Several pollutant groups like pharmaceuticals and personal care products (PPCPs), urban biocides and pesticides, industrial chemicals, organophosphorus flame retardants, plasticisers and polycyclic aromatic hydrocarbons (PAHs) were chosen for analysis. Out of the 69 monitored substances, 60 were detected in CSO discharges. The results of this study show that CSOs represent an important pathway for a wide range of organic micropollutants from wastewater systems to urban receiving waters. For most compounds detected in CSO samples, event mean concentrations varied between the different events in about one order of magnitude range. When comparing CSO concentrations with median wastewater concentrations during dry weather, two main patterns could be observed depending on the source of the pollutant: (i) wastewater is diluted by stormwater; (ii) stormwater is the most important source of a pollutant. Both wastewater and stormwater only play an important role in pollutant concentration for a few compounds. The proportion of stormwater calculated with the conductivity is a suitable indicator for the evaluation of emitted loads of dissolved wastewater pollutants, but not for all compounds. In fact, this study demonstrates that remobilisation of in-sewer deposits contributed from 10% to 65% to emissions of carbamazepine in CSO events. The contribution of stormwater to CSO emitted loads was higher than 90% for all herbicides as well as for PAHs. Regarding the priority substance di(2-ethylhexyl)phthalate (DEHP), this contribution varied between 39% and 85%. The PAH concentrations found along the river indicate environmental risk, especially during rainfall events.

Modeling climate change impacts on combined sewer overflow using synthetic precipitation time series

In the presented study climate change impacts on combined sewer overflows (CSOs) in Baden-Wuerttemberg, Southern Germany, were assessed based on continuous long-term rainfall-runoff simulations. As input data, synthetic rainfall time series were used. The applied precipitation generator NiedSim-Klima accounts for climate change effects on precipitation patterns. Time series for the past (1961-1990) and future (2041-2050) were generated for various locations. Comparing the simulated CSO activity of both periods we observe significantly higher overflow frequencies for the future. Changes in overflow volume and overflow duration depend on the type of overflow structure. Both values will increase at simple CSO structures that merely divide the flow, whereas they will decrease when the CSO structure is combined with a storage tank. However, there is a wide variation between the results of different precipitation time series (representative for different locations).

Schritte zu einem anpassungsfähigen Management des urbanen Wasserhaushalts

Die städtische Wasserinfrastruktur wird zukünftig einem Wandel der Randbedingungen ausgesetzt sein. Globale Trends wie der Klimawandel und demografische Veränderungen werden von stadtspezifischen Entwicklungen überlagert. Beispiele dafür sind wachsende oder schrumpfende Städte, die Nutzung von Flüssen und Seen als Badegewässer oder der Umgang mit überflutungsgefährdeten Wohngebieten. In der Siedlungsentwässerung wiegen solche Entwicklungen besonders schwer, da sie derzeit auf sehr unflexible Systeme treffen. Im Verbundprojekt SAMUWA werden Wege aufgezeigt, die bisher statischen Ansätze in Planung und Betrieb von Entwässerungssystemen zu einem anpassungsfähigen dynamischen Management zu führen.

A Graph-Theory Based Algorithm to Generate Decentralized Urban Drainage Layouts

Recently, the implementation of centralized urban drainage networks, has increasingly been questioned. The main reasons are the lack of capital especially in developing countries, environmental-ecological concerns, the risk of failure, rapid change and high uncertainty in a developing country context. Latest investigations suggest a transition from centralized urban water management to decentralized or hybrid schemes. Therefore, there is a need for methodologies to evaluate the difference in performance of all feasible systems, from purely centralized to purely decentralized. For this purpose, the current study introduces the hanging gardens algorithm based on graph theory to generate all possible layouts for optimization purposes. The focus of this study is to find the optimum configuration of the pipe networks (grey infrastructures) considering different number and location of outlets. The algorithm starts with generating a centralized layout using an arbitrarily selected outlet, than another randomly nominated outlet among all candidates is added to the current layout. In the next step, the only path between new and old outlet is systematically found. Employing a randomly generated variable a pipe in the path is chosen to cut and decentralize the layout. A real case study, the stormwater collection network of a part of Ahvaz city in Iran is designed using the proposed approach. The number and location of the outlets, the layout configuration of each part and the size of the pipes are used as optimization variables. A single-objective optimization for the cost is done and obtained results are discussed.

Combined Sewer Flow Prediction Using Hybrid Wavelet Artificial Neural Network Model

This study presents a hybrid model for the prediction of dry and wet-weather flows in a combined sewer system. For this aim, a model based on wavelet transformation and artificial neural network (WANN) is developed. A high-resolution data set of rainfall and combined sewer flow from a catchment in Germany is used in the hybrid model. To simulate the combined sewer flow, the dry-weather flow is firstly modelled employing Artificial Neural Network (ANN). Subsequently, another ANN is applied and fed with rainfall time series, dry-weather flow simulated in the previous step and lagged combined sewer flow time series as the main input variables to simulate the combined sewer flow. In modeling both the dry-weather flow and the combined sewer flow, the wavelet transformation is firstly applied to extract the temporal and the spectral features of the measured sewer flow time series before using them in the ANN. To improve the WANN hybrid model result, different mother wavelet functions and decomposition levels, various lagged values for input variables, several training functions and network structures are implemented in the model and their influence on the hybrid model is investigated. According to this study, the proposed hybrid model can identify the complicated and dynamic nature of the combined sewer systems and thus provide accurate results.

Using Precipitation and Combined Sewer Overflow Data for Predicting Hygienic Contaminations in Bathing Waters – A Data Analysis

Rivers and lakes in urbanised areas are increasingly appreciated for their high ecological and recreational value. The demand for bathing sites is growing. However, in many German rivers as the Ruhr River, bathing has been prohibited for decades. At Lake Baldeney, an impounded lake of the Ruhr River, located in a densely populated area in North-Rhine-Westphalia, the first bathing place was opened in 2017. Receiving water from combined sewer overflows during heavy rain events, the hygienic quality of the water body could be affected. Due to that an extensive monitoring programme of quality parameters along the Ruhr River has been carried out during the last years and an early warning system has been established to assure a safe bathing environment at all times in a previous research project. In this article highly resolved precipitation data from six rain gauges, overflow data from four combined sewer overflow (CSO) tanks and one storage sewer as well as flow data from the River Ruhr was analysed together with daily measurements of Escherichia coli (E. coli) and intestinal enterococci (Int. E.) to assess the prediction capacity of hygienic contaminations at the bathing site during three swimming seasons. Data evaluation showed a good predictive capability of precipitation data. Overflow data showed less predictive performance when using overflow information from one tank only, indicating that single overflow events not necessarily lead to hygienic contaminations above critical thresholds.