Securing bathing water quality after extreme storms in Barcelona, Spain

Key Learnings

• Early Warning Systems: Predictive modelling enhances decision-making processes, supporting authorities to act before contamination events pose significant health risks as heavy rainfall can lead to pollution in recreational areas. Untreated wastewater can flow into bathing waters and pose a potential health risk to swimmers.
• Integration of Quantitative Microbiological Risk Assessment modelling with real-time environmental data: This provides a valuable tool for proactive water quality management as it enables predicting microbiological risks for bathers in different situations after a sewage overflow and determining the areas which present the greatest risk.
• Transferability potential: The methodology and workflow are transferable to other locations facing similar challenges, allowing for adapting the Quantitative Microbiological Risk Assessment model to different water basin environments, when multiple sewage systems overflow during extreme weather conditions.

Summary

Integrating real-time data and modelling tools enables local authorities to make faster, more informed decisions to protect public health. By continuously monitoring sea and weather conditions and predicting pollution spread after heavy rainfall, the system helps anticipate risks and supports timely actions, such as issuing health warnings or temporarily closing beaches. These forecasts are a key climate change adaptation measure, especially as storms causing sewage overflow become more frequent and intense. The solution reduces health risks while avoiding unnecessary beach closures, helping to sustain tourism, an important economic sector in coastal areas. It supports both public health and local businesses and is adaptable to other coastal areas facing similar climate-related challenges. Real-time data from actual “Combined Sewer Overflow” events allows for modelling pathogen spread and potential health risks. Incorporating artificial intelligence could further enhance the modelling system by learning from past events to better predict future overflow severity. While challenges remain, such as limited data availability and varying local regulations, the approach is a valuable tool for early warning systems, enabling authorities to respond proactively and protect both people and the environment.

Contact

Mireia Mesas Suárez
Eurecat, Centre Tecnològic de Catalunya
E-mail: mireia [dot] mesaseurecat [dot] org (mireia[dot]mesas[at]eurecat[dot]org)

Carmen Torres Costa
Eurecat, Centre Tecnològic de Catalunya
E-mail: carmen [dot] torreseurecat [dot] org (carmen[dot]torres[at]eurecat[dot]org)