The technology is based on the deployment of a network of innovative low-cost temperature sensors to estimate emissions from combined sewer overflows (CSO) in a large number of points in a sewer system. The sensors are installed at the overflow crest. They 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 consist of an underground sewage collection system composed by 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. Then the excess of combined sewerage is discharged directly to the receiving water body in an event called combined sewer overflow (CSO). CSO’s are an important source of contaminants for receiving water bodies, including suspended solids, organic matter, nutrients, heavy metals, organic compounds and pathogenic microorganisms. CSO events can cause several detrimental effects such as oxygen depletion, ammonia toxicity and hydraulic stress on aquatic organisms. The 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-information about the occurrence of CSO’s. Recent EU regulations have correctly identified CSO’s as an important source of contamination and promote the appropriate monitoring of all CSO structures in order to control and avoid the detrimental effects described above to achieve and maintain a good ecological status of the receiving water. Two of the main limitations with regards to CSO to date were: i) the high number of CSO structures per municipality or catchment and ii) the high cost of the flow-monitoring equipment’s available in the market to measure CSO’s. These 2 factors have delayed the implementation of extensive monitoring of CSO’s. CSO are generally measured using standard flow meters (combination of velocity and water level sensors). Flow meters are costly (i.e. >2000 € CAPEX / CSO + operational costs) and not always reliable due to the harsh conditions in sewers. Hence, it makes the simultaneous monitoring of several CSO structures within the same sewer system 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 behavior 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 extensively their network. The implementation of AI algorithms improves the 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, duration and roughly estimate the volume discharged in a CSO event. In addition to the later features, 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 deployed in Sofia to improve the knowledge on CSO emissions, and also to 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 behavior 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 sensor type, number and location. Data produced by the distributed network of low-cost temperature sensors (> 20 sensors) are used to improve the accuracy of hydrodynamic sewer modelling for resilience analysis, flood forecast and efficient investment in stormwater management measures.
ICRA – Oriol Gutierrez
IOTSENS – Ignacio Llopis
IOTSENS – Jose Luis Martinez