D4RUNOFF: Data-Driven Implementation of Hybrid Nature-Based Solutions for Urban Water Pollution Management

The D4RUNOFF project aims to address the pressing challenges of urban water pollution exacerbated by urbanization and climate change. It seeks to enhance the detection and management of diffuse pollutants in urban runoff through innovative technologies and nature-based solutions. The initiative also focuses on developing decision-making tools to support stakeholders, including policymakers, water professionals, and citizens, in mitigating pollution impacts. By integrating data-driven approaches with hybrid solutions, the project intends to improve ecosystem health and biodiversity in urban and receiving water bodies across Europe.

Keywords: Urban runoff pollution, Nature-based solutions, Climate change adaptation, Data-driven decision-making, Hybrid pollution management, AI-assisted urban water platforms, Chemical detection methods, Stakeholder engagement, GIS and risk assessment, Blue-green infrastructure;

The Intersection of Urbanization, Climate Change, and Pollution Gaps

Urbanization and climate change are intensifying the challenges associated with urban runoff, leading to increased diffusion of pollutants into water systems. While significant investments are being made across Europe for climate adaptation, the focus on urban pollutants—particularly their sources, pathways, and discharge into water environments—remains insufficient. Known pollutants in stormwater, such as those from industrial, residential, traffic, and building material sources, have not been adequately studied or managed, contributing to declining ecosystems and biodiversity. Additionally, the lack of comprehensive knowledge about emerging pollutants and their behavior in urban environments hinders effective mitigation strategies. The project highlights the need for a systematic approach to bridge these gaps, combining advanced detection methods with scalable solutions to prevent long-term environmental degradation.

A data-driven, hybrid approach to urban runoff pollution management

This project employs a multi-step, technology-integrated methodology to detect, analyze, and mitigate diffused pollution from urban water runoff. The approach combines advanced detection, digital tools, and stakeholder engagement to create a scalable framework.

• Develop novel detection methods using non-target and suspect screening techniques to identify unknown and emerging pollutants in urban runoff samples.
• Design and prototype chemical sensors tailored to specific pollutants, enabling real-time or near-real-time monitoring of water quality in urban environments.
• Collect and analyze field data through systematic water sampling in case study sites, integrating hands-on measurements with laboratory-based chemical analysis.
• Build a GIS-based risk assessment system to map pollutant sources, pathways, and discharge points, supporting spatial decision-making for urban planners and water managers.
• Create an AI-assisted urban runoff management platform that consolidates data, risk maps, policy tools, and social engagement features into a single digital interface for stakeholders.
• Implement and test nature-based solutions (NBS) across diverse urban contexts, including blue-green infrastructure, artificial wetlands, and hybrid systems for combined and separate sewer networks.
• Validate technologies and methods in three European case studies (Denmark, Italy, Spain), each representing distinct urban water management challenges and NBS applications.
• Engage stakeholders through co-creation workshops to refine tools, gather local insights, and ensure the platform’s usability for citizens, professionals, and policymakers.
• Disseminate results via digital platforms (website, social media) and interactive sessions, ensuring knowledge transfer to cities across Europe for broader adoption.

Data-driven tools and validated solutions for cleaner urban water management

The project will deliver an AI-assisted urban runoff management platform that integrates real-time data, risk mapping, and decision-making tools for stakeholders, including citizens, water professionals, and policymakers. This platform will enable dynamic monitoring of pollutants from diverse urban sources such as industrial sites, traffic, hospitals, and building materials, providing actionable insights for mitigation strategies. By developing and deploying advanced chemical detection methods, including non-target suspect screening and prototype sensors, the initiative will expand the ability to identify and track both known and emerging pollutants in stormwater. The gathered data will be synthesized into GIS-based risk assessment models, allowing cities to prioritize interventions and optimize the placement of nature-based solutions. Validation through three European case studies—Denmark, Italy, and Spain—will ensure the scalability and adaptability of these tools across different urban and climatic contexts, ultimately supporting evidence-based policy and infrastructure investments.

The project will also generate a comprehensive knowledge base on the performance of nature-based solutions in reducing pollutant loads from combined and separate sewer networks, informing future urban planning. Co-creation workshops and stakeholder engagement activities will foster collaborative problem-solving, ensuring that the solutions are tailored to local needs and socially accepted. Digital accessibility through the platform and public dissemination via social media and reports will democratize information, empowering communities to participate in water quality management. Long-term outcomes include reduced ecosystem degradation, improved biodiversity in receiving water bodies, and more resilient urban water systems capable of withstanding climate change impacts. By bridging gaps in pollutant detection, decision-support tools, and public engagement, the project aims to set a new standard for sustainable urban runoff management across Europe.

Unique Selling Proposition

This project uniquely combines AI-driven analytics, cutting-edge sensor technology, and nature-based solutions into a single, scalable platform for real-time urban pollutant management. Unlike traditional approaches, it integrates stakeholder co-creation and validated case studies to ensure practical, adaptable, and community-informed solutions for cities worldwide.

This article was generated with the support of artificial intelligence. While it has been reviewed and edited for clarity and accuracy, the primary content was generated by an AI tool.

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