This solution is based on deployment of a network of innovative low-cost temperature sensors to estimate emissions from combined sewer overflows (CSO) across a large number of points in a sewer system. The sensors are installed at the overflow crest and measure air temperature during dry-weather conditions and water temperature when the overflow crest is submerged in case of a discharge. A CSO event and its duration can be detected by a shift in temperature, thanks to the temperature difference between air phase and stormwater discharge.
Combined sewer systems comprise an underground sewage collection system composed of a network of pipes and tunnels designed to collect both wastewater and rainwater surface runoff. During heavy rainfalls, the drainage capacity of the sewer pipes and the pumping station is not able to transfer all the volume of wastewater and rainwater to the treatment plants or retention tanks. In these cases, the excess of combined sewerage is discharged directly to the receiving water body in an event called a combined sewer overflow (CSO). CSOs are a major source of contaminants for receiving water bodies, including suspended solids, organic matter, nutrients, heavy metals, organic compounds and pathogenic microorganisms. CSO events can have various detrimental effects such as oxygen depletion, ammonia toxicity and hydraulic stress on aquatic organisms. Compliance with the Water Framework Directive (WFD) requires the implementation of CSO control measures and the continuous upgrade of sewer networks to avoid environmental contamination.
Limitations of current practices
Traditionally there has been a lack of reliable data on the occurrence of CSOs. Recent EU regulations have correctly identified CSOs as an important source of contamination, and so promote appropriate monitoring of all CSO structures in order to control and avoid the detrimental effects described above, with the aim of achieving and maintaining high ecological quality of the receiving water. Two of the main limitations with regards to CSOs to date have been: i) the high number of CSO structures per municipality or catchment, and ii) the high cost of the flow-monitoring equipment available on the market to measure CSO events. These two factors have delayed the implementation of extensive monitoring of CSOs. Overflows are generally measured using standard flow meters (combination of velocity and water level sensors); however these are costly (over 2000 € CAPEX / CSO + operational costs) and not always reliable due to the harsh conditions in sewers. As a result, simultaneous monitoring of several CSO structures within the same sewer system is very expensive. Moreover, there are technical constraints to accessing and installing monitoring equipment in some CSO structures. As a result, utilities lack information about the behaviour of the network and potential impacts on the local water bodies.
This solution provides a simple and robust method for CSO detection. It reduces CAPEX and OPEX for CSO monitoring and allows utilities to monitor their network extensively. The implementation of artificial intelligence (AI) algorithms improves measurement accuracy and provides tailor-made optimal operational strategies for each case-catchment. The solution can be deployed offline or online. The offline version can quantify the CSO occurrence and duration, and roughly estimate the volume discharged in a CSO event. In addition, online sensors can remotely provide real-time overflow information through Lorawan/2G communication protocols. The technology can be especially useful to improve the accuracy of hydrodynamic sewer modelling by providing high spatial and temporal distribution of reliable data.
The solution is being deployed in Sofia to improve knowledge on CSO emissions and get indications of overflows in dry weather due to blockages. It includes the installation of 50 sensors (40 online and 10 offline) to collect data and fully understand the behaviour of a catchment. Improving the quality and reducing the reaction time of the operational teams is also an expected result.
The solution is also tested in Berlin with a focus on identifying the right balance between type, number and location of sensors. Data produced by the distributed network of low-cost temperature sensors (> 20 sensors) is used to improve the accuracy of hydrodynamic sewer modelling for resilience analysis, flood forecasting and efficient investment in stormwater management measures.
ICRA – Oriol Gutierrez
IOTSENS – Ignacio Llopis
IOTSENS – Jose Luis Martinez