The Impact of Blue‐Green Infrastructure and Urban Area Densification on the Performance of Real‐Time Control of Sewer Networks

Abstract

Urban drainage systems (UDS) are designed to convey wastewater and urban runoff away from populated areas to treat the water and increasingly to recover valuable resources from it. To prevent flooding during rainfall events, combined sewer overflows (CSOs) are installed to discharge the excess of combined wastewater and urban runoff (i.e., stormwater) into a receiving water body. These CSO events can negatively impact the receiving water quality (Owolabi et al., 2022) and can be highly persistent and long-lasting in nature, for example, as in the case of micro-pollutants from CSO events impacting water resources (Mutzner et al., 2022). These effects are projected to be exacerbated by more intense rainfall events resulting from climate change (Gogien et al., 2022) and the expansion and densification of the population in existing urban areas.To minimize the adverse effects of CSOs, blue-green infrastructure in the form of sustainable urban drainage systems (SUDS) is increasingly implemented to revert to the pre-urbanized hydrological conditions by allowing more infiltration in the urban environment (Carter & Fowler, 2008). Allowing significant parts of the urban runoff to infiltrate or to be temporarily stored, alleviates pressure on the drainage system and can therefore significantly reduce the CSO volumes (e.g., Chen et al., 2019). Using these types of nature based solutions are increasingly mandated by legislative bodies. The new EU Urban Wastewater Treatment Directive, for example, explicitly states a preference for green developments and states that gray infrastructure should only be envisaged where absolutely necessary. In future (re)developments of the urban environment, different runoff patterns compared to the current situation are therefore highly likely.Another increasingly popular option to reduce CSOs is the use of Real-Time Control (RTC) procedures. RTC relies on real-time data obtained from the UDS to optimally control the system actuators (pumps and moveable gates) which has the potential to improve the UDS performance by activating redundant storage capacity in the system (Schütze et al., 2004). Various methods have been developed to achieve this, and can broadly be defined as heuristic and real-time optimization control methods (Garcia et al., 2015). Heuristic control methods are usually in the form of rule-based control, where a set of (optimized) rules are defined offline and implemented online.