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.

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.

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).