D5.3 Intermediate testing report in case studies

This deliverable, D5.3, presents the outcomes of intermediate testing and implementation activities carried out under Tasks T5.1 to T5.5 between M30 and M35 of the D4RUNOFF project. Building upon the preparatory work documented in D5.1, this report provides an updated overview of how the tools and methodologies developed in WP1–WP4 have performed during real-world deployment across the three case studies: Odense (Denmark), Santander (Spain), and Pontedera (Italy).
The deliverable covers several key areas: The application of advanced analytical methods for pollutant detection (WP1), initial field validation of the risk assessment and mapping module (WP4), deployment of the online monitoring prototype (WP2), implementation and early-stage testing of the AI-assisted decision support platform (WP4), and refinement of the multi-criteria decision analysis (MCDA) framework for selecting Nature-Based Solutions (NBS) (WP3).
Field testing revealed both technical progress and operational challenges. In WP1, the detection methods (LC-HRMS, HILIC-HRMS, ddPCR) are now actively being used to analyse runoff samples at the case study sites, though weather-related delays have impacted sampling schedules. In WP2, the online sensor system has been successfully installed in Santander but encountered hardware and software issues during initial operation—many of which have now been resolved through iterative troubleshooting. WP3's MCDA framework has been tailored through close engagement with local stakeholders to ensure it reflects city-specific constraints and priorities. WP4’s AI-assisted platform has begun receiving and processing data streams from sensors and laboratory results, enabling preliminary modelling and visualisation of pollution risks.
Feedback across all tasks indicates a strong need for continued harmonisation of data structures, improved interconnection between modules, and enhanced support for end-user needs. The importance of local stakeholder involvement is underscored throughout this phase, with the establishment of dedicated local working groups in each city providing essential insights and co-creating feasible implementation pathways. Looking ahead, the next phase will focus on completing remaining sampling campaigns, further refining the AI-based modelling tools, validating the risk assessment outputs, and supporting full integration of all technical components. A key milestone will be the final testing and evaluation activities leading up to Deliverable D5.4, which will consolidate the project’s impact and provide guidance for broader replication. Overall, this report reflects a shift from technical development to applied integration, demonstrating how scientific methods can be operationalised to support resilient and data-driven urban runoff management through hybrid nature-based solutions.