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.

Per- und polyfluorierte Alkylsubstanzen (PFAS) stellen auf-grund ihrer Persistenz und Toxizität ein wachsendes Risiko für Wasser-ressourcen dar. In einer achtmonatigen Messkampagne wurde Regen-wasserabfluss eines Berliner Industriegebiets auf 26 PFAS und andere Industriechemikalien untersucht. Zusätzlich wurde ein urbaner See beprobt, der ausschließlich durch Regenwasserabfluss und Grundwasser gespeist wird. PFAS-Konzentrationen im Regenwasserabfluss lagen zwischen 5 und 35 ng/L, PFOA und PFHxA waren am häufigsten nachweisbar. Die Konzen-trationen lagen im Bereich vorgeschlagener Umweltqualitätsnormen für Oberflächengewässer mit Maximalwerten deutlich darüber. Im See wurden deutlich höhere Konzentrationen (bis 99 ng/L) gemessen, die vermutlich durch Altlasten des benachbarten Flughafens und nicht primär durch Regenwasserabfluss verursacht werden. Im Vergleich zu Kläranlagenab-läufen waren die gemessenen PFAS-4-Konzentrationen im Regenwasser-abfluss in dieser Studie um den Faktor 3-10 niedriger. Für Gewässer sind Kläranlagenabläufe auch durch die größeren Volumina als Eintragspfad von PFAS wahrscheinlich von größerer Relevanz als Regenwasserabflüsse. Dennoch ist Regenwasserabfluss insbesondere in Schwammstadt-konzepten mit Versickerungssystemen als potentiell relevanter Eintrags-pfad für PFAS zu betrachten. Die Ergebnisse zeigen die Notwendigkeit eines besseren Verständnisses urbaner PFAS-Quellen für ein effektives Wasserschutzmanagement.

Sewer rehabilitation is a costly challenge for cities like Berlin, with annual investments exceeding €100 million, compounded by aging infrastructure and low replacement rates. Traditional CCTV inspections, used since the 1980s, face limitations in data completeness, accuracy and automation. To address this, Kompetenzzentrum Wasser Berlin and Berliner Wasserbetriebe developed SEMAplus, a suite of data-driven tools modernizing sewer asset management. The system uses machine learning to prioritize inspections and forecast long-term network conditions. This abstract highlights advancements in deterioration modeling and innovation pathways for the digitalization of sewer management. As workforce shortages, budget constraints, and sustainability goals intensify, these innovations are crucial for optimizing investments and strengthening sewer system resilience.

A measurement campaign of wastewater temperatures was carried out in a section of the Berlin wastewater network. These results were used to carry out a temperature simulation using the EPA SWMM-Fork SWMM-HEAT. It was shown that a good agreement between measurements and simulations is possible for predominantly residential areas, even if the network was only moderately thermally calibrated (MAE ≤ 1 °C).

Leaks and bursts in water supply networks can cause significant infrastructure damage and pose contamination risks. Even utilities with robust rehabilitation strategies are not immune to the costly consequences of major bursts. A key question is whether such events can be prevented by detecting and localizing them while they are still small (i.e., leakage flows below 3 L/s). The model-based algorithm Dual Model has demonstrated both simplicity and precision, securing first place among 18 algorithms in the Battle of the Leakage Detection and Isolations Methods. However, mismatches of around 10% between the hydraulic model and the real network can hinder its performance, particularly in detecting and locating small leaks. In this work, we enhance the Dual Model by incorporating source inflows, allowing discrepancies between the real and simulated networks to be expressed as residual virtual flows. These residuals are integrated into the model as demand patterns, enabling the detection of leaks as small as 2–3 L/s even under perturbations of roughness and base demand exceeding 35%. Additionally, this approach calibrates nodal pressures without requiring manual adjustments to roughness or demand values.