Water resource contamination substantially threatens the environment. Rapid identification of chemicals and their emission sources in watersheds is crucial for sustainable water resources management. Despite studies on the measurement of micropollutants in the water resources around Europe, efficient utilization of the data in decision-making to protect water resources from detrimental chemical pollution is currently not available. Novel Internet of Things (IoT) technologies, coupled with advanced Artificial Intelligence (AI) strategies, may provide faster and more efficient responses to these challenges in real-time reactions as well as long-term planning. The proposed solution aims at providing: better understanding and near real-time response to pollution incidents; better prediction of pollution spread and improved response for mitigation of effects in the long run; data-driven AI life-long learning and evolution of the algorithm. The primary outcome would be an integrated decision support system utilizing micropollutant measurements along with real-time collected hydrodynamic and meteorological data of a watershed for sustainable water quality management. Since micropollutants are related to emission sources and are resistant to degradation, they are good indicators of pollution and fingerprints of the pollution sources. Our approach is based on introducing and combining novel technologies in improving water pollution management in several critical phases. First of all, we rely on an advanced, scalable IoT technology that adapts to the considered problem's specific needs through a novel mechanism called viscoelasticity. Therefore, we obtain desirable data from the locations and at the time that is optimized for further data analysis. Then, we introduce a novel methodology for creating a more accurate hybrid model integrating the expert-based physical environment model and data-driven, evidence-based techniques. To that end, we introduce a novel graph-based functional representation of data that captures affinities and dependencies among data streams more efficiently.

ATeNaS aims to contribute to the evidence of nature-based solutions in cities for increasing urban climate and water resilience, and to building a critical mass of human and social capital for nature stewardship that allows NBS. ATeNaS partnership is to capitalize on achievements of RDI actions financed by European Union and applied worldwide. It will review the experiences coming from NBS implementations, confront them with local physical, economic, and social realms of living labs in Poland, Finland and France, make them an attraction point for decision makers, business and citizens, and empower co-creation process in integrative NBS implementation. To give space to NBS in urban planning, ATeNaS has ambition to: To create overview, select, and suggest re-design of NBS able to address two aspects of water gap – quantity and quality - in the context of city gradients (reflecting density of population and infrastructure); To develop scenarios for implementation of NBS in partner demonstration sites, based on running pilot projects, and modelling their effect based on upscaling, diversification and replication; To secure the continuation of the approach beyond ATeNaS lifetime by identifying, supporting and increasing the capacity of local leaders in mutual learning process.