Short-term fecal pollution events are a major challenge for managing microbial safety at recreational waters. Long turn-over times of current laboratory methods for analyzing fecal indicator bacteria (FIB) delay water quality assessments. Data-driven models have been shown to be valuable approaches to enable fast water quality assessments. However, a major barrier towards the wider use of such models is the prevalent data scarcity at existing bathing waters, which questions the representativeness and thus usefulness of such datasets for model training. The present study explores the ability of five data-driven modelling approaches to predict short-term fecal pollution episodes at recreational bathing locations under data scarce situations and imbalanced datasets. The study explicitly focuses on the potential benefits of adopting an innovative modeling and risk-based assessment approach, based on state/cluster-based Bayesian updating of FIB distributions in relation to different hydrological states. The models are benchmarked against commonly applied supervised learning approaches, particularly linear regression, and random forests, as well as to a zero-model which closely resembles the current way of classifying bathing water quality in the European Union. For model-based clustering we apply a non-parametric Bayesian approach based on a Dirichlet Process Mixture Model. The study tests and demonstrates the proposed approaches at three river bathing locations in Germany, known to be influenced by short-term pollution events. At each river two modelling experiments (“longest dry period”, “sequential model training”) are performed to explore how the different modelling approaches react and adapt to scarce and uninformative training data, i.e., datasets that do not include event pollution information in terms of elevated FIB concentrations. We demonstrate that it is especially the proposed Bayesian approaches that are able to raise correct warnings in such situations (> 90 % true positive rate). The zero-model and random forest are shown to be unable to predict contamination episodes if pollution episodes are not present in the training data. Our research shows that the investigated Bayesian approaches reduce the risk of missed pollution events, thereby improving bathing water safety management. Additionally, the approaches provide a transparent solution for setting minimum data quality requirements under various conditions. The proposed approaches open the way for developing data-driven models for bathing water quality prediction against the reality that data scarcity is common problem at existing and prospective bathing waters.

This deliverable summarises progress at month 18 of the AD4GD project on three pilot studies on air quality, water and biodiversity, and identifies the key next steps for all partners to support the implementation. The pilot studies are designed to demonstrate the feasibility of re-using, developing, extending and integrating a range of tools, semantics and standards to facilitate data-driven decision making on Green Deal priority topics. The progress described includes:

• engagement with stakeholders;

• requirements gathering;

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

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

• progress on development and integration of the identified components.

The purpose of Deliverable 6.1 is to review the context and lessons learnt in the first 6 months of the pilot work package, and to identify and plan priority actions for the next 18 months to ensure robust integration of accessible, re-usable tools and work flows by the end of the project. Where deliverables already exist from the project that document underpinning technologies and services, these will be sign posted. Evaluation of performance and scaling potential is beyond the scope of the current deliverable, and will be addressed in its second iteration (D6.2). The current deliverable focuses primarily on the integration of existing and bespoke tools to support the work flows necessary to consume, use and produce data and metadata for the three identified pilot case studies.

We describe a human-centred co-design approach employed by FIT in eliciting high-level requirements for interfaces and user experience in the Green Deal Data Space, both during the project and in a dedicated workshop in September 2023. This work has required us to work closely with sister projects and existing GEO initiatives to ensure efficiency and interoperability.

For each pilot, we describe the initial rationale, indicators to be computed and stakeholders, before delineating the relative contribution (and potential future contribution) of EO, citizen science, socio-economic and IoT data. Next, we present the value proposition and design for an e d-user tool (to be developed by FIT) which will allow GDDS users to easily access the application or work flow , with a high-level view of the underlying data and processing services. Finally, for each pilot study, we describe the technical components that have been identified as necessary to support such interfaces from end to end, including 12 bespoke tools and components being developed by project partners to ease the integration of existing solutions.

Progress on these 12 technical components are explained, including whether each is being re-used, extended or specifically developed within tasks and work packages of the project. In each case, URLs are given for supporting demonstrations, instances or code repositories. We have aligned their development and iteratively integrated them at two face-to-face project hackathons in October 2023 and February 2024. We then revisit each pilot study to assess the progress of integration and development, and identify priorities for the next 3, 6, 9 and 12 months, aiming towards an integration that can be documented and evaluated within the final 6 months of the project.

Die Wiederverwendung von gereinigtem Abwasser zur landwirtschaftlichen Bewässerung ist eine vielversprechende Lösung, um die landwirtschaftliche Produktivität in Zeiten des Klimawandels zu erhalten. Allerdings sind mit dieser Praxis auch Umweltrisiken verbunden, da gereinigtes Abwasser, Rückstände von Krankheitserregern und Schadstoffen, darunter vor allem Spurenstoffen enthalten kann. Eine weitergehende Abwasserreinigung ist daher entscheidend, um potentielle Risiken zu minimieren. Forschungsprojekte wie FlexTreat arbeiten an technischen Lösungen zur sicheren Wasserwiederverwendung. Diese Szenarienanalyse zeigt, dass die Bewässerungsmenge und die Art der Abwasserbehandlung wesentliche Faktoren für den Eintrag von Spurenstoffen ins Grundwasser sind. Eine bedarfsgerechte Bewässerung während der Vegetationsperiode kann die benötigte Wassermenge reduzieren und den Spurenstoffeintrag in das Grundwasser verringern. Zusätzlich kann eine höhere Bewässerungsfrequenz den Spurenstoffeintrag weiter reduzieren, wobei sich die Summe der verwendeten Bewässerungsmenge nicht erhöht. Diese Erkenntnisse unterstreichen die Bedeutung von Managementansätzen, die sowohl technische Lösungen als auch angepasste Bewässerungspraktiken umfassen, um eine sichere Wasserwiederverwendung zu gewährleisten und Grundwasserbelastungen zu minimieren.

During the last decades, municipalities have increasingly invested in new approaches for rehabilitating sewerage networks. With the increasing number of rehabilitation techniques, objectives and constraints, the number of rehabilitation scenarios rises exponentially. This article proposes an asset management approach to create long-term rehabilitation plans where different budget allocations for rehabilitation techniques are considered every year depending on performance and cost indicators. It builds long-term strategies through multiobjective black-box optimization where the impact of the budget allocations over the network life cycle is part of the decision process. It employs a pipe deterioration model based on Markov chains whose transition matrices are estimated by survival curves for different pipe cohorts. The proposed approach seeks to determine the appropriate investment (CAPEX) and operational expenses (OPEX) levels in the coming decades. It was tested with real-world data from a sewerage network in Sofia, Bulgaria, and the results show that it provides efficient long-term rehabilitation plans.

This report presents the findings from task 2.1 of the SafeCREW project, which aimed to monitor seasonal microbial quality changes in source waters of near-natural treatment systems, such as managed aquifer recharge (MAR). Two case study locations, Hamburg and Berlin, were examined to understand microbial dynamics over time. Microbial cell counts in source waters were monitored using flow cytometry (FCM), which enables the analysis of bacteria, protozoa, and viruses. In addition, organic matter in source waters and during near-natural treatment was analyzed using techniques such as Liquid Chromatography-Organic Carbon Detection (LC-OCD), fluorescence spectroscopy, and absorption measurements. These methods provided detailed insights into the type and quantity of organic substances, which influence microbial growth. Notably, biopolymers—organic substances produced during microbial degradation—were identified as indicators of microbial activity and surface water influence. By combining microbiological and organic analyses, a comprehensive monitoring system can be developed that provides extensive information not only on seasonal changes in microbial quality, but also on the underlying causes and influencing factors. This enables targeted and effective control of water treatment processes and helps to ensure high water quality.

Smart water management is acknowledged as a key component of the solutions to address climate change impact and secure water resources availabilities in the context of Sustainable Development Goals. Over the last decades, digital solutions have become an essential part of water management. Numerous initiatives have been developed to explore hybrid and new AI modeling with concrete approaches such as digital twins. The ambition is to provide water managers with tailored IT solutions that can be implemented in their current management system. These developments raise a wide range of questions in terms of sensors’ approach, interoperable open data models, reference architecture, and cybersecurity that are presented in this chapter. Additionally, IT innovation, as groundbreaking as it may be, requires additional dimensions such as governance, capacity building, and economics to ensure its adoption by water managers. These aspects are also presented in the latest sections of this chapter.