Smart sewers in practice: a systematic review of instrumentation, applications and pathways to adoption for sustainable urban systems.

Sewer systems are critical to public health and urban economies, yet failures cause overflows, pollution, and costly disruption. Smart sewer systems combine sensors, communications, and data analytics to monitor conditions, support operational control, and guide maintenance, enabling optimised management and reducing health and environmental impacts. This review systematically analysed real-world case studies of smart sewer systems with permanent monitoring and examined instrumentation and applications across five domains: hydraulic and sediment dynamics, hydraulic blockage detection, pollutant monitoring and forecasting, real-time operational control and predictive operational optimisation. Instrumentation included level and depth sensing, flow and water-quality monitoring, gas and odour sensing, temperature profiling, and actuators. High-frequency monitoring provided the temporal resolution to quantify flow, infiltration and pollutant loads beyond routine inspection. Applications were tailored to local dynamics and differed between combined and separate systems, informing real-time control and optimisation. Smart sewer systems improve understanding of system behaviour and enable more proactive operations and targeted responses to failures. Uptake depends on governance and evaluation practice, with drivers such as operational value and partnerships, and barriers including sensor drift, maintenance burden, data gaps, limited model generality, and opaque analytics. This review links field evidence linking instrumentation choices to operational applications and reported evaluation practice, highlighting where evidence is sufficient for adoption and where benchmarking is constrained by reporting gaps. Key challenges remain in developing practical implementation and operational frameworks and establishing transferrable evaluation practices. Future work should integrate sensing with interpretable, uncertainty-aware analytics, expand hybrid sensing, and report long-duration multi-site evaluations that include costs, benefits, and uncertainty.