IMPROVED MONITORING OF URBAN RUNOFF USING REAL-TIME CONTROL SYSTEMS AND PROXY SENSING: LESSONS LEARNED FROM THREE CASE STUDIES

Urban runoff monitoring can be significantly enhanced by making use of real-time control and advanced data analytics. This study presents models and tools developed within the EU financed project ‘StopUP’. These were applied to three case studies (CSs) and aimed at improving the detection and management of pollution events. In CS Bologna, low-cost IoT sensors were deployed at the wastewater treatment plant (WWTP) and used to collect hydraulic and water quality data. The collected data were analyzed with Detrended Cross-Correlation Analysis (DCCA) and Autoregressive Distributed Lag (ARDL) models and show relations between hydraulic quantities and water quality parameters, demonstrating the feasibility of low-cost proxy sensing. In the CS Wetteren, an innovative auto-sampling triggering system that integrates real-time observations, short-term rainfall predictions, and near-real-time urban runoff modeling is tested. The system demonstrates the potential of combining different data sources to refine sampling control and collect less, but more informative water quality samples. In the CS Birsfelden, pollution loads and urban runoff were approximated using online measurements of water flow and quality. These data served as a basis for operational decision-making and proactive system control to mitigate impact of pollution. The integration of predictive tools and real-time data across all three CSs supports improved identification and tracking of pollution sources. These findings suggest a promising application of real-time & proxy sensing combined with proactive control strategies for managing urban water systems under pressure.