This deliverable presents the implementation progress and results of the innovative technologies
demonstrated across the IMPETUS demo sites during the period M1-M49 of the project (01/10/2021–
30/09/2025). It covers the full set of activities carried out under WP4 Tasks 4.5.1 to 4.12, which together
form Bundle 2: Innovative Technologies Implementation.
Bundle 2 aims to demonstrate a suite of advanced technical solutions that increase climate resilience
across diverse geographic, hydrological, and socio-economic contexts. The bundle includes
decentralised water reuse systems, digital modelling tools, pathogen monitoring technologies, sediment
transport modelling, multi-agent water balance models, decision-support systems for heat and flood risk,
and early-warning technologies for geological hazards. These solutions collectively reinforce the
broader WP4 objective of testing and validating multi-benefit adaptation innovations that can be scaled
across Europe.
The 15 tasks reported in this deliverable demonstrate substantial progress toward climate-resilient water
management, environmental protection, and risk reduction. Task 4.5.1 deployed a hybrid decentralised
fit-for-use water reclamation system in the Coastal demo site (Catalonia), producing high-quality
reclaimed water for irrigation and cleaning within a touristic complex and validating decentralised reuse
under highly variable seasonal demand. Task 4.5.2 implemented a Sewer Mining unit in East Attica
(Mediterranean demo site), integrating real-time data, energy-autonomous operation, and co-created
adaptation services. Task 4.5.3 developed a water-energy simulation and optimisation model, enabling
the operator of the East Attica system to explore climate-proof operation strategies and circulareconomy pathways.
Across several additional tasks, advanced modelling and monitoring capabilities were demonstrated.
Tasks 4.6 and 4.7.1 developed computational tools for sediment transport and regional water balance
simulation, supporting adaptation measures under hydrological and demographic pressures. Tasks
4.7.2 and 4.10.1 - 4.10.3 delivered decision-support systems that integrate multi-layer data for WEFEnexus planning, heat stress management, and flood risk visualisation, many of which are connected to
digital twin environments. Tasks 4.8.1 and 4.8.2 tackled climate-exacerbated water quality risks by
improving bathing water management during storm events and assessing drinking water resilience to
pathogens. Finally, Tasks 4.11 and 4.12 implemented technologies for urban climate proofing in coastal
settings facing sea-level rise and for geological and avalanche early-warning systems in the Arctic and
mountainous demo sites.
Together, the technologies demonstrated under Bundle 2 provide actionable, scalable, and evidencebased adaptation options. The solutions directly support regional water resilience, enable cross-sectoral
decision-making, and reduce exposure to climate-related risks. Their integration into the Resilience
Knowledge Boosters, digital twins, and participatory processes strengthens the IMPETUS vision of
empowering local stakeholders and decision-makers with robust, technology-driven adaptation
pathways.

The deliverable D3.2 “Scalability and edge computing optimization” presents the updates of the heterogeneous IoT data sources encountered in the three pilots of the AD4GD project. The first pilot concerns the water quantity and quality in the lakes located in Berlin, Germany. The second pilot studies the biodiversity in the region of Catalonia. Finally, the third pilot is dedicated to the air quality. All the pilots are using IoT data and different components and building blocks were developed during the AD4GD project. The updates of these components are described in this deliverable. Furthermore, the SIMPL middleware initiative is also discussed in the deliverable D3.2. The edge computing is an important part of the Internet of Things in the context of the AD4GD project: this topic is presented in a dedicated chapter where different Key Performance Indicators (KPIs) related to edge computing are specified. Finally, some actions to improve these KPIs are proposed, followed by several recommendations.

This document presents the final form of the work done in WP 4 and previously partially presented in D4.1 and D4.2, the Dataspace architecture, the data catalogue and metadata system and the data trustworthiness framework. Like in D4.2, the text follows the components’ architecture defined by D6.1, focussing primarily on new work done since D4.2:

Component 2 – Evaluation of Connector Solutions and Deployments
Component 9 – Data catalogue and Metadata
Component 11 – Data Trustworthiness Framework

In terms of tasks, this deliverable predominantly discusses work in WP4 “Satellite and Green Deal Data Space Integration”, including tasks 4.2 “Green Deal Data Space Implementation”, 4.3 “Green Data Space integration with third-party services” and 4.4 “Ground truthing and data trustworthiness framework”. It also has overlap with and incorporates work from the three pilot projects within WP6 and the machine learning work being done in WP5.

This deliverable builds upon D5.1 and outlines progress in applying Artificial Intelligence and High-Performance Computing within the AD4GD project. It details the use of AI models in pilot studies, such as water level prediction in Berlin lakes and connectivity mapping in Catalonia, highlighting the AI models ability to process complex environmental data efficiently. The document also presents the development of user-friendly interfaces that make these advanced tools accessible to non-expert stakeholders, promoting informed decision-making. Additionally, it reports on the integration of HPC resources to support AI model training and execution, enhancing performance and scalability. The deliverable concludes with reflections on the benefits, limitations, and future directions of these technologies in AD4GD pilots.

This document is the Deliverable D6.2 for the AD4GD project. It presents the final results achieved in the context of Tasks T6.1, T6.2, T6.3, and T6.4. The document is a follow-up version of the Deliverable 6.1 “Pilot Technical Implementation Planning, Implementation and Assessment” that reported on pilot establishment, design of workflow and requirements analysis.

The purpose of Deliverable D6.2 is to review and report on the integration of accessible, re-usable tools and workflows, including re-use and extension of existing tools, semantics and standards as well as bespoke development of 12 new interoperable components and approaches within the project. Where component reports have already been published within other deliverables that document underpinning technologies and services, these will be signposted to avoid redundancy and duplication.

This collection of Green Deal Data Space components is presented in the form of tested FAIR workflows that consume, use and produce data and metadata for the three identified pilot case studies, to facilitate data-driven decision making on Green Deal priority topics.

The progress described includes:

re-use and extension of existing re-usable components, data and services which can support the pilots and, more broadly, the Green Deal Data Space;

identification of remaining gaps, and of components required to fill those gaps;

development and integration of the identified components;

evaluation of workflow and interface performance, and of output quality and consistency.

Our human-centred co-design approach has enabled us to work closely with sister projects and existing GEO initiatives to ensure efficiency and interoperability.
For each pilot, the reader may refer to D6.1 for in-depth descriptions of the initial rationale, indicators and stakeholders, and evaluation of the relative contribution of EO, citizen science, socio-economic and IoT data. In D6.2 we show how the workflows developed to support some areas of the Green Deal decision-making have been developed, and illustrate how a range of data and services can be transparently and reproducibly integrated within the Green Deal Data Space to generate scientifically defensible outputs which can be easily discovered, re-used and visualised by stakeholders. The corresponding assessment of scalability, performance, and technology convergence can be found in D6.3.

A risk-based human health exposure assessment (HHEA) model was developed to evaluate the exposure for humans in 4 circular economy (CE) routes investigated in 6 of the 7 case studies in the project PROMISCES. The HHEA is a probabilistic tool evaluating the risk posed to human health. The HHEA was applied to the following routes: 1) semi-closed drinking water cycle; 2) groundwater remediation; 3) water reuse for agricultural irrigation; and 4) nutrient recovery. Each of these exposure routes results in a product – drinking water or lettuce – which can be consumed by humans. For some routes, the exposure is purely theoretical, while for others, the entire process chain is investigated in the PROMISCES case study.

The HHEA is built on Bayesian principles and includes Bayesian updating, which enables assessment of risk under conditions of low data availability and high uncertainty. This is particularly useful for evaluation of substances such as PFAS and other industrial persistent, mobile and potentially toxic (iPMT) substances, the removal of which in treatment processes is not yet well studied in literature. The deliverable explains the different treatments, environmental matrices, and substances which were the focus of the initial assessment. It describes the construction of the HHEA model, with explanations of how different data types – literature data, site specific data, and modelled data – are used to update the prior probability of the removal factor for substances in a process. It also describes how non-technical processes, such as mixing or evaporation, have been included into the treatment trains evaluated. Finally, individual reference quotients for the substances are established, which are used to assess the relative risk of the final concentrations in the products which could be consumed by humans.

This Layman's report is part of Deliverable D6.6 showcasing H2020 PROMISCES project outcomes and results.