Per- and polyfluoroalkyl substances (PFAS) and industrial persistent, mobile, and toxic (iPMT) compounds pose growing risks to water quality. However, quantitative tools to prioritize compounds for remediation and evaluate exposure under varying environmental scenarios remain limited. This paper presents the first application of a Bayesian probabilistic early warning exposure assessment (EWEX) model to evaluate PFAS and iPMTs in indirect potable water reuse (IPR) systems. By integrating literature-derived and site-specific data, the model explicitly accounts for uncertainty and provides a flexible framework for scenario analysis. The model’s performance was demonstrated for PFOS, trifluoromethanesulfonic acid (TFMSA), and carbamazepine (CBZ), revealing sensitivity to data quality and emphasizing the importance of robust monitoring and transferable data sets. In addition to removal via treatment processes, dilution in the environment substantially reduces contaminant concentrations, highlighting the influence of the hydrological setting and the associated climate-related environmental factors on exposure outcomes. The EWEX framework serves as a decision-support tool for utilities and regulators, facilitating risk-informed management of emerging contaminants in water reuse systems. Future work should expand the data set for poorly studied compounds, refine treatment process representations, especially for short-chain PFAS, and validate model outputs through real-world monitoring.

R interface and control unit for the RAINDROP hydrodynamic planning tool: long-term simulation of blue-green infrastructure (swale-trench elements) driven by real climate series, plus two cost optimisers that find the cost-minimal design per allowed overflow frequency — a deterministic bisection-based coordinate descent whose search order is derived from the construction cost rates, and an assumption-free simultaneous search (penalised Nelder-Mead, differential evolution and Halton sampling) as cross-check.

The proprietary calculation engine ("Regenwasserbewirtschaftung", © Tandler.com GmbH) is not part of this archive; it is downloaded on demand from the companion repository kwb.raindrop.binaries.

Berlins Trinkwasser stammt aus Grundwasser, gefördert von über 600 Brunnen der Berliner Wasserbetriebe. Mit der Zeit lässt die Leistungsfähigkeit dieser Brunnen jedoch nach – die Brunnen altern. Die Ursache für den Leistungsverlust sind überwiegend chemische und biologische Prozesse im Brunnenfilter und in der Filterschüttung, die zur Bildung von Ablagerungen führen und den Wassereintritt aus den wasserführenden Schichten in den Brunnen behindern. Durch rechtzeitige Brunnenregenerierungen lassen sich diese Ablagerungen teilweise entfernen und die Brunnen länger effektiv nutzen.

Die Leistungsfähigkeit eines Brunnens wird durch die spezifische Ergiebigkeit (Förderrate [m3/s] je Meter Absenkung) bemessen. Diese wird üblicherweise durch Pumpversuche direkt nach Brunnenneubau sowie vor bzw. nach einer Regenerierungsmaßnahme bestimmt. Zwischen den Regenerierungen vergehen oft mehrere Jahre und es besteht eine gewisse Unsicherheit über die Entwicklung des Brunnenzustands in der Zwischenzeit. Damit fehlt eine wichtige Grundlage, um den Brunnenzustand zu bewerten und z.B. den optimalen Zeitpunkt für eine Regenerierung oder die Planung von Ersatzbrunnen zu bestimmen. Genau diese Wissenslücke möchte WELL-KI schließen. Das KWB entwickelt mit den Berliner Wasserbetrieben Verfahren zur Bestimmung der spezifischen Brunnenergiebigkeit im laufenden Betrieb. Im Projekt werden zwei Lösungsansätze entwickelt:

- Analytische Modelle: In einer Brunnengalerie wird die an einem Brunnen gemessene Absenkung oft nicht allein durch dessen eigene Förderung verursacht, sondern durch die benachbarten Brunnen überlagert (Interferenz). Um den unbeeinflussten Alterungsverlauf zu isolieren, wird der von den Nachbarbrunnen verursachte Absenkungsanteil durch analytische Modelle bestimmt und von der gemessenen Gesamtabsenkung abgezogen (Superposition). Es werden verschiedene analytische Ansätze auf Grundlage der erfassten Betriebsdaten getestet.

- Maschinelles Lernen: Erprobt werden mehrere Verfahren, darunter neuronale Netze, Random Forest und Gradient Boosting. Kann ein KI-Modell diese Einflüsse aus realen und synthetischen Betriebsdaten lernen und die Ergiebigkeit kontinuierlich bestimmen?

Ziel ist die Entwicklung eines robusten Prototyps zur Bestimmung der Qs -Wert -Entwicklung im Lebenszyklus eines Brunnens vom Neubau bis zum Ist-Zustand, welches als weitgehend autonomes Analyseverfahren in die Monitoringsysteme der BWB integriert werden kann.

Parallel wertet eine Literaturstudie (siehe Seitenleiste) den aktuellen Forschungsstand aus: Wie wirken sich Brunnenbetriebsdauer, Förderraten und Schaltintervalle auf die Brunnenergiebigkeit aus?

In zwei Workshops zum Thema Brunnenalterung werden wir Fachleute aus Praxis und Forschung zusammenbringen und Erfahrungen sowie Kenntnisse austauschen.

This deliverable describes in detail the five pilot use cases (PUC) (1) Lviv, (2) Turin, (3) Paris, (4) London and (5) Limassol. These PUC, further specified in use case scenarios, will be implemented during the project in order to showcase the AQUAMON system and its components.

Based on an iterative, co-creation process with the consortium, the deliverable further elaborates the user, legal and ethical requirements that will serve as a foundation for all Research and Development (R&D) work within the project.

After a short introduction, the deliverable summarises the applied methods to map stakeholder and define user requirements as well as legal and ethical requirements. This is followed by a detailed description of the PUC themselves, linking each of the defined stakeholders and user requirements directly to the PUC. Finally, the deliverable describes the use case scenarios for each PUC that will eventually be implemented within the project.

In the core of this deliverable the user requirements that will serve as the basis of all further work in AQUAMON have been categorized and described. They are differentiated between functional user requirements or non-functional user requirements and prioritized using the MoSCoW method.

Finally, the document contains a short analysis of the ethical and legal framework from the users' perspective with a focus on privacy and copyright protection. The deliverable ends with a brief summary and conclusion that also contains a short outline of the next steps that will be done in WP2.

Persistent and mobile (PM) substances are suspected to pass drinking water treatment; however, the extent of the treatment gap remains uncertain. This study aimed to determine the elimination of 115 potentially persistent and mobile substances (PM candidates) for four established (biological filtration, adsorption onto activated carbon, ozonation, reverse osmosis (RO)) and one developing treatment method (reductive treatment represented by catalytic hydrogenation) for drinking water supply individually and in combinations. In systematic lab-scale experiments, less than half of all PM candidates were eliminated above 80% by adsorption or ozonation at typical doses (10 mg/L with 48 h contact time, 0.67 mg O3/mg DOC), whereby adsorption indicated slightly higher elimination. Overall, RO achieved the highest elimination, exceeding 80% removal for 96% of all PM candidates. In three common combined treatment sequences, 16 PM candidates remained below 50% elimination and were therefore identified as critical for drinking water treatment, among them six halomethanesulfonates and seven components of ionic liquids. For adsorption onto activated carbon, polarizability, logD and solubility showed strong correlations with PM candidate eliminations, whereas for RO, strong correlations were observed between PM candidate eliminations, and molar mass and polarizability. These correlations provide suitable and readily accessible molecular properties to feed predictive models for the assessment of PM candidate elimination.

The SmartWater project is developing a digital urban planning tool – the Blue-Green Infrastructure Planner (BGI Planner) – to integrate decentralised stormwater management measures into planning processes at an early stage. The aim is to strengthen the climate resilience of urban areas, mitigate flood risks, reduce urban heat and lessen the pollution of Berlin's waterways. The BGI Planner is based on a modular structure and links geodata, checklists and effect simulations. In two pilot areas in Berlin, the effects of blue-green infrastructure are being analysed using models – in terms of flooding, water pollution, urban climate and water balance. Established modelling tools such as InfoWorks ICM, GERRIS/HYDRAX/QSim, ENVI-met and ABIMO are being used. Initial simulation results confirm the effectiveness of selected measures and form the basis for simplified evaluation models in the tool. In future, the BGI Planner will be used as a digital tool in Berlin's administration to support sustainable and climate-adapted urban development.

SmartWater is a Berlin-wide initiative that integrates blue and green infrastructure measures into urban planning through three digital tools. The tools are being tested in two contrasting districts and comprise of a planning tool, a digital game and a flood information portal. These prototypes demonstrate the potential of blue-green infrastructure to reduce flood risk, mitigate heat stress and enhance urban climate resilience. The development process has been meticulously structured to facilitate a collaborative co-design process involving administrations, utilities, experts and residents. This approach has been instrumental in elucidating the governance needs, data requirements and communication pathways. The findings illustrate the efficacy of coordinated modelling and shared digital infrastructures in facilitating decision-making processes, enhancing public comprehension, and providing a transferable framework for urban areas aspiring to enhance climate adaptation through integrated and pragmatic approaches.