THESIS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY Modelling of Substance Flows in Urban Drainage Systems

Stormwater is recognised as a large contributor of toxic substances to receiving waters. Different measures to manage stormwater quality have been proposed, including structural and non-structural best management practices (BMPs). Computer models have become useful tools for the analysis, evaluation and design of these BMPs. The main objective of this study was to develop a modelling framework that enables an analysis of the pollutant sources in urban drainage systems. In the modelling framework the sources of pollutants from different activities in the urban area were separated according to their origin, e.g. material corrosion, brake wear and tyre wear. The model was named SEWSYS and was developed in MATLAB/Simulink. SEWSYS simulates substance flows in urban drainage systems. At present the model contains 20 different substances, including nutrients, heavy metals and organic pollutants. The model can simulate both stormwater and domestic wastewater flows, in either combined or separate sewer systems. In the stormwater quality module, the pollutants from sources such as atmospheric deposition, traffic and construction materials are generated and accumulated on impervious surfaces during dry weather until they are washed away during rainfall. Validation studies of the SEWSYS model were carried out using measurements of stormwater flow and quality. Calibration and validation were performed using a splitsample technique, i.e. with independent data sets for calibration and validation. The hydrological part of the model performed well in the validation but the quality part produced less reliable results. Uncertainty analysis of the stormwater module in SEWSYS was carried out for the model outputs runoff volume, pollutant concentrations (EMCs and SMC), and pollutant load of heavy metals for an uncalibrated and calibrated model. Uncertainty assessment methods included Monte Carlo simulations, multi-linear regression and a Markov-Chain Monte Carlo method for parameter calibration. The results of the uncertainty analysis showed that predictions made with an uncalibrated model were associated with a considerable amount of uncertainty. It was also shown that by means of calibration this uncertainty could be reduced to an acceptable level. The application of the SEWSYS model in different types of scenario studies has been an important part of the model development. The results from the application studies demonstrate that the model is a useful tool for simulating and evaluating pollutant source control measures.