This session brings together researchers, technologists, and stakeholders to showcase advances illustrating (1) the role of emerging technologies and (2) service-oriented approaches in shaping the future of environmental monitoring.
Emerging technologies include advanced instrumentation, miniaturized and autonomous sensors for atmospheric, hydrological, soil, and marine processes, as well as unmanned aerial systems, drones, satellite constellations, and IoT networks that link in-situ with remote sensing. Artificial intelligence (AI) is transforming how environmental data are processed, harmonized, and applied in predictive modelling.
The ocean, a key climate regulator, remains critically under-observed for carbon fluxes, particularly beyond shipping routes. Addressing this gap, the GEORGE project—a collaboration between EMSO ERIC, EURO-ARGO ERIC, ICOS ERIC, research institutions, universities, and industry—develops novel tools and methods to measure carbonate chemistry (e.g., pH, alkalinity, dissolved inorganic carbon, pCO₂) across diverse marine environments.
Services are equally vital. Trans-National Access (TNA) schemes offered by ENVRIs provide opportunities for researchers to use state-of-the-art facilities, advanced instrumentation, and high-quality data services beyond national systems. These services foster collaboration, accelerate innovation, and support co-created solutions to pressing challenges. The convergence of cloud-based infrastructures, FAIR data principles, interoperability frameworks, and user-centered service design ensures that resources are not only technically robust but also widely accessible and impactful for science, policy, and society.
Orals: Thu, 7 May, 16:15–18:00 | Room -2.62
Environmental challenges require Research Infrastructures (RIs) that combine long-term observations with innovation in technologies and services. The Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS) addresses this need within the atmospheric domain by integrating advanced observational platforms with user-oriented, interoperable services that enhance scientific and societal impact.
ACTRIS supports technological innovation through state-of-the-art in situ and remote sensing instrumentation, mobile platforms and atmospheric simulation chambers. These Exploratory and Observational Platforms enable process-oriented studies, instrument and methodological developments, while FAIR, long-term and high-quality datasets contribute to international frameworks, ensuring scientific robustness and continuity. ACTRIS observations are also key in the development, evaluation and validation of climate and atmospheric composition models, such as those used by the Copernicus Atmosphere Monitoring Service (CAMS), as well as in the calibration and validation of satellite missions, including EarthCARE.
In parallel, ACTRIS offers virtual, physical and hybrid Trans-National Access (TNA) to advanced facilities, data and expertise, fostering collaboration, experimentation and co-creation across Europe. Engagement within the ENVRI community and the ERIC Forum further supports shared innovation pathways and user-oriented approaches.
How to cite: Saponaro, G. and the ACTRIS RI Committee Members and ACTRIS Experts: ACTRIS in the Earth system landscape: interoperable observations from research to services, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-16946, https://doi.org/10.5194/egusphere-egu26-16946, 2026.
Addressing environmental challenges related to climate change and air quality requires high-quality observations and data services. The Obs4Clim project is a joint initiative of the three French components of European Research Infrastructures (RIs) in the atmospheric domain: ACTRIS, IAGOS, and ICOS. OBS4CLIM aims at developing innovative services to meet the evolving needs of research communities and stakeholders. The objectives and outcomes of the Obs4Clim project include the development of advanced data services, expansion of spatial and temporal coverage of atmospheric observations, and establishment of a mature access framework for users.
Obs4Clim provides atmospheric RIs with adequate investment to keep serving the users at the highest level of quality over the next 15 years and to engage in developments to further respond to emerging needs, e.g. enhancing the networks in their four dimensions (longer and uninterrupted time-series, synergies with space-based observations, expanding global, denser network in specific areas, smart specializations). The 8-year investment plan has three main objectives: fostering attractiveness of atmospheric facilities, enhancing the capacity of atmospheric RIs to provide state-of-the-art data services, and expanding spatial and temporal coverage.
Significant progress has been made in the investment phase of the project, with a substantial portion of equipment expenditures already realized. Adjustments to technical choices and budget reallocations have been made to accommodate specific operations and facilitate co-financing opportunities. Implementation of acquired instruments has advanced significantly, with innovative developments in new observation variables. For example, the use of fluorescence on Lidars now provides new information on aerosol characteristics. High-performance instruments have been developed to better quantify greenhouse gases. ICOS and ACTRIS observation platforms have been equipped with new observation capabilities to measure variables of interest, such as bioaerosols and ammonia. The IAGOS equipment project has shifted towards a new type of aircraft, the Airbus Beluga, to enhance geographic and temporal coverage of vertical profiles. The onboard instruments are currently undergoing certification.
The Obs4Clim project is developing unique services to remain a hub for innovation in research and technology. It is integrated into a mature framework for access, recognized at both national and international levels, which includes physical and remote access to atmospheric facilities as an integral part of the RI service portfolios. By strengthening the capacity to translate the wealth of climate and atmospheric data into actionable insights, Obs4Clim supports decision-makers in finding ways to achieve a clean-air, climate-resilient, and low-carbon society.
How to cite: Galane, P., Stéphane, S., Ariane, D., Matilde, O., Sabine, P., Thouret, V., and Ramonet, M.: Obs4Clim: A Collaborative Innovation Project for an Integrated Atmospheric Observing System in France, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20594, https://doi.org/10.5194/egusphere-egu26-20594, 2026.
EU Member states are allowed to subtract the PM10 contribution from natural sources (such as desert dust or sea salts) from the observations when verifying compliance with air quality standards. However, they must do so with pertinent data, which can be sometimes challenging. The recent EU Air Quality Directive enforces a drastic reduction of PM10 annual limit values (from 40 to 20µg/m3) and daily limit values (from 35 times above 50µg/m3 to 18 times above 45µg/m3) by 2030. These constraints will increase the need to apportion carefully natural and anthropogenic PM sources in the coarse fraction, with particular attention to traffic sites. In fact, the latter exhibit high PM concentrations and are exposed to various local (road traffic resuspension) and regional (long-range transported) dust sources.
AERŌTAPE®, a novel cost-effective instrument developed by Oberon Sciences (France), enables real-time (down to a few seconds), in-situ characterization of supermicron aerosols by integrating impaction-based aerosol sampling, onboard microscopy, and AI-driven image analysis. AERŌTAPE® produces high-resolution pictures with detailed single particle-resolved data, including number, size, shape, and color, enabling accurate information of supermicron aerosols (with no hypotheses on their shape or optical properties) and allowing to capture the dynamic of the various coarse PM sources. Compared to Optical Particle Counters (OPCs), AERŌTAPE® provides added value through (i) camera-based real-time counting, (ii) acquisition of geometric shape information, and (iii) color capture via RGB arrays. This enhances differentiation between particle types such as dust, pollen, and combustion ash, thus enabling a more accurate assessment of natural contributions to PM levels.
Field measurements at urban background sites in Cyprus (Eastern Mediterranean) allowed to demonstrate the instrument’s robustness (1-year continuous outdoor deployment), and its high precision and reproducibility against regulatory PM reference instruments (TEOM-FDMS and FIDAS), while providing useful additional high-time resolution information on aerosol properties. These results highlight the potential of AERŌTAPE® to deliver unattended stable and reliable measurements of coarse PM (PM2.5-10) together with a comprehensive single particle characterization, thereby supporting regulatory compliance, air quality management, and potentially improved source apportionment in response to increasingly stringent air quality standards.
Further field campaigns in Athens, Cairo, Beirut, Paris, and Abu Dhabi will provide region-specific samples for training and validating particle classification methods. These data will support the development of a robust PM dust database and enhance characterization of dust sources, including quantification of local versus regional contributions.
Funding:
This research is supported by the AERODUST project, funded by the Agence Nationale pour la Recherche (grant agreement ANR 24 CE04 0814 01).
This research is supported by the Dust-DN project, funded by the European Union under the Marie Skłodowska-Curie Actions (grant agreement 101168425), and by the corresponding national agencies of the United Kingdom (UKRI) and Switzerland (SERI). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union and Marie Skłodowska-Curie Actions (MSCA). Neither the European Union nor MSCA can be held responsible for them.
How to cite: Kolintziki, E. and the ANR-AERODUST / DUST-DN team: AERŌTAPE®: a novel technology for real-time quantification and characterization of dust and its sources, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22424, https://doi.org/10.5194/egusphere-egu26-22424, 2026.
Atmospheric ice-nucleating particles (INP) play an important role for primary ice formation in clouds, and by that often initiate the formation of precipitation, influence the phase of clouds, and also impact their albedo and lifetime. A lack of data on the spatial and temporal variation of INPs around the globe limits our predictive capacity and understanding of clouds containing ice. Automated instrumentation that can robustly and accurately measure INP concentrations across the full range of tropospheric temperatures is needed to address this knowledge gap.
The Portable Ice Nucleation Experiment PINE was developed to close this gap. It became available in 2019, and an increasing number of instruments is producing a quickly growing database of INP number concentrations around the world (see https://zenodo.org/records/16745515). The measurements of immersion freezing INP cover a temperature range from about -15°C to -33°C and deliver longer term continuous data records for months or years with a time resolution of up to 5 minutes.
Of particular interest are INP measurements in the free troposphere which are ice-active at temperatures below -40°C and contribute to the formation of ice crystals in cirrus clouds. This led to the development of the two new PINE versions called PINEair and PINEtri, which are optimized for measuring INPs at controlled cirrus formation temperatures between -40°C and -65 °C and at controlled ice supersaturations. PINEair was successfully tested and operated onboard the German HALO research aircraft during the HALO-South campaign, the first versions of PINEtri are currently built. PINEtri can be operated like PINEair but is developed for laboratory or ground-based measurements e.g. at high-altitude observatories for measurements in the free troposphere.
The latest innovation is the development of another PINE version called PINEmon. This instrument version will especially be optimized and suitable for longer-term and continuous monitoring of immersion freezing INP at global atmospheric observatories, e.g. as part of the ACTRIS Research Infrastructure or the Global Atmospheric Watch program.
This contribution will explain the working principle of the PINE instruments and shows highlights of previous and ongoing measurements and applications.
How to cite: Möhler, O., Murray, B. J., Gehring, M., Curtius, J., Bogert, P., Böhmländer, A., Büttner, N., Daily, M., Hobl, A., Lacher, L., Macklin, J., Robinson, J., Ullrich, R., and Vatagin, A.: The Portable Ice Nucleation Experiment PINE: Current activities and new developments, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20972, https://doi.org/10.5194/egusphere-egu26-20972, 2026.
Methane emission measurements are crucial in emerging reporting frameworks such as UNEP’s Oil and Gas Methane Partnership (OGMP) 2.0 Standards and the European Union Methane Regulation. Whilst aerial platforms increasingly provide site-level quantification for upstream operations, advanced mobile leak detection (AMLD) remains the dominant methodology for municipal natural gas distribution networks. A growing number of service providers commercialize this methodology, but open-source academic models remain essential to promote transparency and harmonize quantification across regions. Colorado State University introduced an algorithm that correlates leak rates with the methane mole fraction peak maxima measured when driving downwind methane plumes; Utrecht University improved this method by focusing on the peak-integrated area to reduce instrument-specific bias. However, the area quantification is sensitive to the errors in the detection of the peak bases and currently requires substantial human-based (HB) quality control; thus, limiting scalability of this algorithm and opening up to bias introduction by the individual operator’s HB actions.
This study refines the original algorithm by revising detection logic to reduce the need for HB intervention. Unlike the previous single-step approach, the revised version leverages the benefits of signal smoothing to improve peak detection while mitigating the delays introduced by the high-frequency component filtering. Performances have been evaluated on two replication datasets from the original study (November 2022 and June 2024), observing recall ranging from 93.0% - 95.7%, enabling a clear one-to-one matching of algorithm-detected and HB-validated peaks. For 83.6% of the peaks, the algorithm-integrated area was within 20% from the HB-validated counterpart, with precision losses being attributed to the faulted detection of the peak bases at small peaks close to the validation threshold of the method. Finally, the revised algorithm is used on public AMLD data collected in several municipalities across Europe to benchmark similarities and differences across regions and assess usability potential and challenges of integrating AMLD data to support robust methane emission reporting within city networks.
Our findings suggest that the revised algorithm can evolve into a practical proxy for HB area quantification, reducing HB effort by focusing only on peaks characterized by target features such as anomalous duration. This would preserve the overall transparency and reproducibility of the algorithm across different data sources, enabling scalability and benchmarking across different operators and regions, and promote harmonization in city network methane emission reporting initiatives.
How to cite: Paglini, R. and Röckmann, T.: Advancing Automated Methane Peak Detection for Mobile Surveys: Accuracy, Robustness and Implications for Scalable Deployment, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12568, https://doi.org/10.5194/egusphere-egu26-12568, 2026.
Agriculture today faces a complex set of challenges, with agricultural lands threatened in two aspects: the impact of climate change and the environmental and social consequences of current agricultural practices. To address this urgent need for more sustainable and resilient food production systems, AgroServ was established as a collaborative project designed to support transdisciplinary solutions. AgroServ brings together researchers, cutting-edge research facilities, industry stakeholders, policymakers, and the farming community into a collaborative ecosystem to accelerate agroecological research and innovation, and knowledge exchange.
With 73 partners across more than 20 countries, AgroServ provides 143 research services distributed within 12 RIs that address areas ranging from molecular processes to ecosystems and social sciences, designed to advance sustainable agriculture practices. Funded by the European Union under the Horizon Europe program (grant agreement No. 101058020), AgroServ has been operating from 2022 to 2027, creating a unique ecosystem that enables cross-sector collaboration through the excellence-based selection of transdisciplinary research projects combining several research services. These services are open to a global agroecology community, including researchers, industry representatives, advisors, innovators, and farmers’ organisations, both within and outside Europe.
To date, AgroServ has successfully launched and completed four Transnational and Virtual Access (TA/VA) calls, with two more calls planned in 2026. Initial findings indicate a balanced mix of early-career and established researchers (57.9% and 42.1%, respectively) among the principal applicants. In addition, data from applications across the 49 selected projects from the first three calls reflected a diverse range of institutions, including universities (64%), applied research centres (26%), industry (9%) and government (2%). The geographical reach of the proposals was also broad, with submissions from across the EU, associated countries, and some non-EU participants, including several low- and mid-income countries.
Beyond its operational period, AgroServ aims to leave a lasting legacy for the global agroecology community. The networks, research services, and insights developed during the project are designed to continue supporting sustainable agriculture. By connecting researchers, policymakers, industry, and farmers, AgroServ envisions a future where knowledge flows seamlessly across borders, accelerating the adoption of resilient, environmentally sound food systems and empowering agricultural communities for years to come.
How to cite: Florent, P., Arsic, J., Avila, J. M., Baldo, D., Baumont, R., Boer, M., Cavoski, I., Drame, S., Heil, K. F., Ibrahim, H., Pieruschka, R., Pommier, C., Soto, I., Tota, T., and Zoani, C.: AgroServ: an integrated multi-RI platform supporting agroecological transition, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-5747, https://doi.org/10.5194/egusphere-egu26-5747, 2026.
Accurate assessment of forest structure, biomass, and carbon stocks is critical for understanding terrestrial ecosystem dynamics and supporting climate change mitigation strategies. Recent advances in remote sensing technologies and artificial intelligence offer opportunities to improve the spatial detail, temporal frequency, and predictive capacity of forest monitoring systems. This study presents an integrated, AI-driven framework that combines multi-source remote sensing data to generate detailed forest inventories and support biomass and carbon stock estimation. LiDAR-derived structural parameters enable the characterization of individual trees, including height, crown dimensions, and canopy density. Elevation and terrain variables are further considered to derive site-specific environmental parameters influencing forest growth and productivity. Deep learning models are employed to harmonize heterogeneous data streams, automate tree-level parameter extraction, and predict forest biomass and carbon stocks across spatial and temporal scales. The approach supports continuous monitoring, uncertainty reduction, and growth prediction, enabling improved detection of changes due to management practices, disturbance events, and climate variability. By linking advanced sensing technologies with AI-based methods and service-oriented data processing pipelines, this work demonstrates how emerging technologies can enhance the operation and value of environmental observation systems. The proposed framework aligns with ENVRI objectives by contributing scalable, reproducible, and FAIR-compatible solutions that bridge in-situ and remote sensing data, supporting science-driven policy development and long-term ecosystem monitoring.
How to cite: Zenonos, A., Sciare, J., and Ciais, P.: An AI-driven multi-source remote sensing framework for forest structure, biomass, and carbon monitoring, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14143, https://doi.org/10.5194/egusphere-egu26-14143, 2026.
Research Infrastructures (RIs) and Observatories (Obs) are essential to the advancement of environmental and water sciences as they offer facilities, services, and data that foster innovative and high-quality research. However, their effective application beyond institutional or national borders is frequently prevented by fragmentation, low visibility, and complicated access mechanisms. In order to promote multidisciplinary research, maximize the benefits of current research infrastructures, and support evidence-based decision-making, these issues must be resolved.
In this context, as part of the European Partnership WATER4ALL, a comprehensive repository of Research Infrastructures and Observatories (RIs/Obs) is being developed to enhance the connections, use, and accessibility of water-related RIs throughout Europe and beyond. With a focus on their services, data provisioning methods, and their ability to provide remote access to their data and services to users outside of the hosting institution itself, the repository offers an organized and effectively categorized overview of a large number of water-related research infrastructures and observatories across Europe and beyond, that is being continually updated.
The WATER4ALL RIs/Obs repository's added value lies in its ability to include as many as possible freshwater-related RIs & Obs in a fully detailed catalogue, enhancing their connectivity and visibility and acting as a major catalyst of the needs and gaps of the European water sector. The repository serves as a link between data producers, academics, researchers and stakeholders by providing data and metadata, encouraging interoperability, and connecting research with policy and innovation. It improves the effective reuse of current investments in research infrastructures, fosters capacity growth, and makes cross-domain research easier. Additionally, it supports coordinated European and worldwide initiatives, including contributions to global water-related policy processes, and helps to strengthen collaboration across RIs.
This paper presets how the WATER4ALL RIs/Obs repository supports research, innovation, collaboration, and excellence in environmental and water sciences by outlining its design principles, implementation status, and expected impact aligned with European water policy directives, SDGs, and key water priorities.
How to cite: Villa, I. and Mimikou, M.: Enhancing Access, Interoperability and Innovation in Water Research through the WATER4ALL Research Infrastructures and Observatories Repository , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-6695, https://doi.org/10.5194/egusphere-egu26-6695, 2026.
Critical environmental changes challenge aquatic ecosystems worldwide. Therefore, coordinating research efforts is increasingly urgent. Mesocosm experiments offer controlled yet realistic settings, and are crucial for understanding the impact of various, often combined stressors on complex aquatic ecosystems and to test mitigation efforts. The AQUACOSM-RI (Research Infrastructure) consortium, comprising over 60 state-of-the-art mesocosm facilities at 28 host institutions across Europe, has been instrumental in advancing aquatic research across climate zones including marine, brackish and freshwater ecosystems.
We will introduce a new tool that enables a highly tailored exploration of existing mesocosm research knowledge to individual search parameters, thereby allowing more collaboration and efficient use of research efforts and resources. Within the EU OSCARS funded AQUANAVI project (Navigating Grand Challenges and their Mitigation using Aquatic Experimental Ris), we created an interactive atlas of aquatic mesocosm-based experimental research information including the data, publications, reports and further information on mesocosm facilities and research generated by the AQUACOSM consortium and other mesocosm facilities worldwide. Expert knowledge is integrated into a single, accessible platform incorporating Open Knowledge Maps' AI-driven visual discovery tools. AQUANAVI will foster international collaborations, facilitate coordinated mesocosm experiments, knowledge exchange and efficient use of aquatic RIs globally to accelerate the development of environmental mitigation strategies.
How to cite: Kraker, P., Berger, S. A., Nejstgaard, J. C., Makower, K., Heger, T., Jeschke, J. M., Kittel, C., Mietchen, D., Schramm, M., and Tyszka, S.: AQUANAVI: A New Navigation Tool for Aquatic Mesocosm-Based Research To Address Grand Challenges and Their Mitigation, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-14079, https://doi.org/10.5194/egusphere-egu26-14079, 2026.
The ocean plays a central role in regulating Earth’s climate system, driving the global carbon cycle, and sustaining marine ecosystems. However, substantial data gaps persist in deep and remote ocean regions due to extreme operating conditions, limited underwater acoustic communication capabilities, and the high cost of long-term deployment and maintenance.With the ENVRI community’s growing demand for long-term, distributed, and autonomous observations, current networking architectures—typically centralized, strictly synchronized, and statically configured—are increasingly inadequate to support next-generation marine observatory research infrastructures.
We propose an intelligent underwater communication and collaborative observation networking framework to support autonomous operation of marine environmental research infrastructures , with a focus on unmanned underwater cluster observation scenarios. The framework elevates the communication network from a passive data-transfer layer to an intrinsic infrastructure capability, enabling distributed underwater observing units to self-organize and operate collaboratively under long propagation delays and limited local information.
From a system-design perspective, the framework introduces a multi-segment, multi-orthogonal resource-block time–frequency structure, and formulates underwater link scheduling as a conflict-constrained Maximum Weighted Independent Set (MWIS) problem. Link weights jointly capture mission load, information freshness, historical resource utilization, and node-level credibility, thereby reflecting fairness and stability requirements under long-term operation. In contrast to conventional multi-round contention-based or centralized scheduling schemes, we develop a distributed, asynchronous, and consensus-oriented scheduling mechanism: lightweight contention is performed only at transmitters, while receivers act as local consensus anchors to enable conflict-free selection. This design supports concurrent scheduling of multiple links across multiple resource blocks within a single control cycle.
To improve nodes’ awareness of local conflict structures and traffic dynamics, we incorporate graph neural networks (GNNs) as cognitive components to compute link priority scores on locally constructed conflict subgraphs. This enables an approximation of global scheduling relevance without requiring global topology knowledge or centralized control.
Simulation studies and underwater acoustic sensor-network experiments conducted in realistic marine environments show that the proposed framework outperforms conventional approaches in clustered underwater communication scenarios. It effectively prevents individual observation nodes from monopolizing communication resources, enables conflict-free data exchange among unmanned underwater clusters, and improves fairness and operational stability under long-term deployment conditions. Overall, the framework provides a scalable, autonomous, and service-oriented communication and collaborative observation capability for marine environmental research infrastructures (RIs). It can operate in conjunction with advanced sensors, autonomous observation platforms, and cloud-based data services, supporting long-term observations of marine carbon cycling, ecological change, and climate-driven processes.
How to cite: Ji, X., Zhou, F., and Liu, Z.: A Service-Oriented Intelligent Underwater Networking Framework for Autonomous Marine Research Infrastructures, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-9027, https://doi.org/10.5194/egusphere-egu26-9027, 2026.
Understanding and predicting how deltas change under accelerating climate change requires research infrastructures that can capture complex processes across spatial scales, environmental compartments, and disciplinary boundaries. Delta systems are distributed systems, spanning rivers, estuaries, coasts, and dunes, and they emerge from interactions between hydrodynamics, sediment transport, ecological processes, and human interventions. To address this complexity, Delta-ENIGMA is a new, fully distributed research infrastructure in which field instruments, experimental laboratories, knowledge interaction facilities, and data services are spatially and institutionally dispersed, yet functionally integrated within a coherent framework. Delta-ENIGMA is embedded within the pan-European Danubius-RI.
Delta-ENIGMA is a 10+ year research infrastructure (2023-2032) of state-of-art instruments placed across river, estuary, and coastal environments in the Dutch delta. Instead of focusing on one location, the network uses a distributed design with fixed monitoring transects and mobile systems that can be deployed quickly. Advanced tools such as current profilers, seabed mapping systems, laser scanners, wave and turbidity sensors, vegetation cameras and drone observations are used at multiple sites to measure changes along the river-sea continuum. This approach will track both gradual morphological change and short-lived extreme events, which are important for understanding how deltas evolve. Along with the field network, are our experimental laboratory facilities that are hosted at multiple partner institutions. These laboratories include advanced flume systems, wind tunnels, mesocosm setups, and bio-morphodynamic experimental environments that enable controlled investigation of processes that cannot be isolated or sufficiently resolved in the field. By distributing laboratory facilities rather than centralizing them, Delta-ENIGMA leverages existing expertise and infrastructure while ensuring methodological diversity and flexibility. Experimental results can be linked to field observations, enabling systematic cross-scale comparison and model development.
Delta-ENIGMA’s distributed infrastructure also includes a Productive Knowledge Interaction (PROD) facility that extends research beyond measurement and experiments. The PROD facility is a network of thematic labs, such as design labs, serious gaming labs, and interactive decision-support environments. The PROD facility facilitates structured collaboration among researchers, policymakers, practitioners, and other stakeholders. By integrating these facilities within the broader infrastructure, Delta-ENIGMA ensures that scientific insights are translated into usable knowledge and that societal questions actively guide the research directions.
The distributed nature of Delta-ENIGMA is unified through a centralized, open data platform that functions as the digital backbone to the infrastructure. Sensor data from field instruments and laboratories are standardized, documented with metadata, and integrated into a federated data environment based on iRODS and the Yoda repository. This platform supports long-term data storage, interoperability, and open access, enabling researchers to combine datasets across sites, disciplines, and time scales.
Together the distributed set-up of instruments, laboratories, interaction facilities, and data services establish Delta-ENIGMA as a coherent large-scale research infrastructure open to an international community of researchers, practitioners, stakeholders and policy makers. The infrastructure provides a robust foundation for advancing biogeomorphological science, improving predictive capacity, and supporting adaptive delta management in a changing world.
How to cite: Dutta, S., Middelkoop, H., and Ruessink, G.: Delta-ENIGMA: an integrated large-scale research infrastructure for delta dynamics, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-13825, https://doi.org/10.5194/egusphere-egu26-13825, 2026.
European environmental research infrastructures (ENVRIs) such as ACTRIS, ICOS and eLTER provide long-term, high-quality observations that underpin our understanding of atmospheric composition, greenhouse gas budgets and ecosystem processes. While these infrastructures deliver indispensable reference data, their observing systems are primarily based on fixed stations and plots, which limits the ability to resolve fine-scale spatial variability, short-term dynamics and vertical gradients in the atmospheric boundary layer and across ecosystem canopies. Addressing these gaps is increasingly critical in the context of climate change, air quality, land–atmosphere interactions and anthropogenic emission monitoring.
Unmanned Aerial Systems (UAS) have rapidly matured as scientific platforms capable of carrying lightweight atmospheric and environmental sensors with high spatial and temporal flexibility. Drones enable targeted measurements above and within ecosystems, around existing observation sites, and in heterogeneous or rapidly changing environments that are difficult to capture using traditional infrastructure alone. At the same time, UAS operations offer a relatively low environmental footprint and can complement fixed infrastructures without compromising long-term measurement continuity.
Despite their growing use in individual research projects, the integration of drone-based measurements into ENVRIs remains fragmented. Challenges include sensor integration, data interoperability, regulatory constraints, operational standardisation, and alignment with existing RI data quality and governance frameworks. As a result, the potential of drones to systematically enhance ENVRI observing capabilities has not yet been fully realised.
This contribution outlines the scientific and infrastructural motivation for a coordinated approach to drone-based environmental observations within European ENVRIs. We discuss how UAS can complement atmospheric, greenhouse gas and ecosystem measurements by bridging spatial scales, supporting process-level studies, and improving the interpretation of long-term observations. The presentation highlights key requirements for successful integration, including sensor traceability, interoperability with RI data systems, and operational concepts compatible with routine RI use. By bringing together perspectives from atmospheric, carbon cycle and ecosystem research communities, this work aims to stimulate discussion and engagement around the role of drones as enabling platforms for the next generation of environmental research infrastructures in Europe.
How to cite: sciare, J.: Unmanned Aerial Systems (UAS) as Enabling Platforms for Next-Generation Environmental Research Infrastructures, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-22327, https://doi.org/10.5194/egusphere-egu26-22327, 2026.
Environmental research infrastructures increasingly rely on in-situ Earth observations to address complex challenges such as climate change, biodiversity loss, water scarcity, air pollution, etc. While the availability of observing systems and data services continue to increase, the effective use of in-situ geospatial data remains fragmented due to the lack of common data management practices and limited interoperable mechanisms to align user demands with data provisions.
This contribution presents the G-REQS (Geospatial in-situ Requirements), a database and methodology developed within the Group on Earth Observations (GEO) to systematically capture, manage, and analyse user needs and requirements for in-situ observations. G-REQS enables the identification of technical barriers to data access and use, as well as gaps in spatial and temporal coverages, and supports a structured matchmaking process between data users, data providers and data intermediary actors or networks that supply or could supply that data. Through this process, opportunities for improved data access can be identified, while recurring requirements can reveal systemic gaps that can be escalated within GEO to inform coordinated actions and future data production.
Building on the G-REQS experience, the Geospatial Observation Needs and Requirements (GONAR) Standards Working Group has been established within the Open Geospatial Consortium (OGC). GONAR aims to standardize the capture of user needs and requirements for geospatial observations through a common data model and a proposed “OGC API – Requirements”, enabling exploitation, interoperability, and reuse of requirements across systems. By establishing open, interoperable, and machine-actionable representations of observational requirements, this approach sets the foundation for more automated, user-cantered, and fit-for-purpose environmental data services.
This work is funded by the European Environment Agency (EEA) under the EEA-RTD SLA on "Enhancing the access to in situ Earth observation data in support of climate change adaptation policies and activities" know as GEO-IDEA project (Framework Contract No EEA/DIS/R0/24/007), as a continuation of the EEA-RTD SLA on "Mainstreaming GEOSS Data Sharing and Management Principles in support of Europe's environment" known as InCASE project (Framework Contract No EEA/DIS/R0/21/016).
How to cite: Brobia, A. and Masó, J.: Aligning user requirements and in-situ Earth observations: from G-REQS to interoperable standards in OGC GONAR, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-21471, https://doi.org/10.5194/egusphere-egu26-21471, 2026.
The Svalbard Integrated Arctic Earth Observing System (SIOS) is a regional observing system for long-term measurements in and around Svalbard, Norway, addressing Earth System Science (ESS) questions related to Global Change. The observing system builds on the extensive and diverse world class research infrastructure already established in Svalbard by institutions from many nations. This includes a substantial capability for utilising remote sensing resources to complement ground-based observations. SIOS currently has 29 members from 10 countries who collaborate to develop the observing system and share infrastructure, data and knowledge.
SIOS has established an innovation award programme for initiatives that develop an innovative technology or method to improve observation capability or decrease the environmental footprint of research and monitoring in the field of Earth System Science in Svalbard.
Up to now, four projects have received the award, whereof one project has been implemented and three are currently being developed. This talk will present the concept of the innovation award and the winning projects.
Hodson, A et al. "A Terrestrial methane seepage observatory" - the project implemented real-time, continuous methane emission monitoring from a representative coastal hotspot for methane emission: the Lagoon Pingo near Longyearbyen.
Santarelli M et al. "Develop an Automatic Climate Station prototype for remote sites observations in the Arctic" - the project aims to increase the observational capacity of standard automated weather stations used for monitoring atmospheric variables. It will develop and test an integrated solution with a hydrogen-based energy storage system for storing available power from renewable sources (photovoltaics and wind energy). The solution will demonstrate advantages of the hydrogen-based storage system as compared to traditional battery storage in terms of compactness, energy storage efficiency, environmental sustainability, and long-term storage under intermittent energy sources.
Jonassen M et al. "Mobile Atmospheric Observations in Svalbard" - the projects aims to develop a prototype atmospheric boundary layer observing system to increase the coverage of in-situ observations in the Arctic. The idea is to mount meteorological sensors on snowmobiles and belt wagons that are regularly used during field operations. These mobile platforms represent a great untapped potential for filling data gaps in the operational network of weather stations.
Teruzzi L et al. “Snow Physical properties and Assessment of Radiative transfer in the snowpacK” (SPARK) - the project will design and validate a custom optical probe for measurement of light propagation, snow stratigraphy and grain size directly in the field. This is a completely new experimental approach which will help scientists to understand the complex interplay between light, ice, photochemical and biological activity: critical knowledge for predicting Arctic climate feedbacks, ecosystem responses, and broader Earth-system dynamics.
How to cite: Hübner, C., Hodson, A., Santarelli, M., Jonassen, M., and Teruzzi, L.: Advancing Earth system science through innovation – the SIOS innovation award programme , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-20352, https://doi.org/10.5194/egusphere-egu26-20352, 2026.
Open science extends beyond open access to journal publications and datasets, and into the realm of services, instrumentation, and facilities. Of particular interest to the European research infrastructure landscape is transnational access (TNA), where users obtain free-of-charge physical or remote access to infrastructure, facilities, or equipment. The European Commission has recognised the vital nature of TNA in stimulating research and collaboration within Europe, by funding projects through dedicated EC Horizon calls, and harmonising access policies and regulations.
EXCITE and EXCITE2 are examples of successful EC-funded TNA projects, which provide free-of-charge access to advanced electron microscopy, X-ray tomography, and complimentary imaging and data processing systems, enabling research into Earth and Environmental materials at 22 European partners institutes. To manage the combined total of 7500 days of access for 1500 projects to 40 installations, we have developed the Facility Access SysTem (FAST - https://fast.geo.uu.nl/) as our dedicated access management application.
FAST streamlines the call-for-proposals access process and includes call setup and advertisement, proposal submission, technical feasibility check, scientific review, and reporting. FAST has a database component in which facility and equipment information is stored alongside GDPR-compliant metadata about users, facility managers, reviewers, coordinators, and database managers. The FAST stack consists of an HTML/JS front-end (Tailwind), and a Slim, Laravel/Eloquent and Postgres back-end, while the webserver infrastructure is hosted at Utrecht University. The FAST database can be queried by REST/JSON API, which is used by EPOS ERIC and EPOS MSL to extract facility information that is subsequently displayed in their data portals. FAST integrates ROR-identifiers for facilities and institutions and ORCID for natural persons. This enables linking datasets (DOI) to the facilities and researchers who created them, thereby contributing to the FAIR open science landscape.
Based on user feedback and project requirements FAST is continuously developed further under EXCITE2. Our ambition is to make this robust and user-friendly access system available to the broader ESFRI Environmental community by aligning with ongoing efforts to consolidate the European transnational access research infrastructure landscape. We actively engage with, and are open to, other ERICs/ESFRI landmarks to strengthen collaborations and coordinate shared access policies, technical interoperability, and other synergies. As such, we aim to make FAST the central access system for the Earth and Environmental sciences in Europe.
How to cite: Wessels, R., de Vries, R., and ter Maat, G.: FAST – coordinating access to world-class imaging facilities in Europe and beyond, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-19449, https://doi.org/10.5194/egusphere-egu26-19449, 2026.
The modern world uses predictive computer models for many important purposes, including weather predictions, epidemic management, flood forecasting and warnings, and economic policymaking. We need to know how much we can trust the projections of these models, not only to achieve more accurate projections for systems, but also to undertake scientific learning about systems by incrementally testing hypotheses using models. But we routinely fail to adequately benchmark the performance of our complicated models of systems due to the cost and complexity of the task and owing to social and institutional barriers. Decades of lessons learned from Model Intercomparison Projects (MIPs) and similar community modeling efforts have yielded understanding of both the challenge and the opportunity facing 21st century model benchmarking efforts. To implement this understanding at scale, we call for the establishment of a major national research facility for scientific computer model benchmarking- a new class of "environmental research infrastructure". Such a research infrastructure will institutionalize and properly resource the technically challenging and laborious work of computer model benchmarking, thereby establishing a firm foundation for 21st century science and prediction. This facility would advance basic science, overcome many of the social barriers to benchmarking, and improve projections and decisions.
How to cite: Ruddell, B.: Calling for a National Model Benchmarking Facility, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8306, https://doi.org/10.5194/egusphere-egu26-8306, 2026.
The FAAM Airborne Laboratory is a national capability research facility dedicated to the advancement of atmospheric science, funded by the United Kingdom Research and Innovation agency. The facility employs 25 full time staff, composed of a multi-disciplinary team of instrumentation and data scientists.
The FAAM Airborne Laboratory and its partners from the university sector offers its users – academic and commercial – a complete package of support and access to state-of-the art measurement technology.
The FAAM aircraft is a specially adapted BAe-146-301 Atmospheric Research Aircraft designed to support atmospheric measurements for various applications, thanks to its configurable scientific payload.
We present FAAM's measurements capability for meteorology, greenhouse and reactive gases, aerosols, cloud physics, radiation and remote sensing. FAAM data scientists also support its users community by providing digital tools to guide missions, visualise online data, analyse and interpret observations.
Recent results from deployments of our airborne laboratory to study methane emissions from off- and on-shore oil and gas facilities, sulphur emissions from shipping, and aircraft emissions (air corridors NOx), including the first UK chase flight of a sustainable aviation fuelled aircraft, are summarised in this presentation. The calibration and evaluation of the EarthCARE satellite retrieval products performed by in-situ sampling in various cloud conditions was funded by ESA.
We finally present the concept of a digital twin to improve the operational flights of the FAAM aircraft, and the first results of an In-Situ Observations Simulator toolkit developed in collaboration with University partners to assimilate airborne observations in geophysical models.
For the past five years, the FAAM Airborne Laboratory has been undergoing a significant upgrade programme of its airframe, scientific infrastructure and services, to safeguard the UK’s research capability, provide frontier science capability and reduce our environmental impact. Some of the upgrades are presented.
How to cite: Lakomiec, P., Bauguitte, S., Kozhura, O., Sproson, D., and Woolley, A.: The FAAM Airborne Laboratory - The UK National Capability Research Infrastructure for Airborne Atmospheric Measurements, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-12369, https://doi.org/10.5194/egusphere-egu26-12369, 2026.
During February-March of 2020 the majority of the world experienced an accelerating pandemic outbreak, driving the authorities to employ social distancing measures (lockdown) in order to slow the SARS-CoV2 spreading. While the pandemic restriction measures were implemented for health reasons, environmental implications became evident, as the social distancing restrictions escalated. A new quantitative index was developed as a ratio assigned to represent the severity of restriction measures on population mobility with respect to non-pandemic “business as usual” in the two greater-cities of Milan (Italy) and Tel-Aviv (Israel). Our index which we named as COVID19 Restrictions Index (C.R.I), was found to be following fairly well the trends and intensity of the apparent transportation-related NOx changes due to authorities’ measures. Although the C.R.I was developed based on the pandemic “first wave”, a further evaluation of the C.R.I. conducted with data from a later moderated pandemic-measures period (late 2020) and with post-lockdowns data (2021), confirmed the consistency of the C.R.I. as an indicator for air-pollution changes related to public mobility indicators.
The new index is unique by its independence of population or monitoring databases. Therefore, it may be used to represent the potential impacts of restriction measures implemented upon populated areas, either for environmental assessments or planning, or for epidemiological models, air-pollution models or multi-factor analysis, in a broad scenario and not only for pandemic situation (an occurrence of a natural disaster, for example).
Haikin et al 2025 Environ. Res. Commun. https://doi.org/10.1088/2515-7620/ae0875
How to cite: Alpert, P., Haikin, N., and Trini-Castelli, S.: From Pandemic to Other Emergencies: A New Index Reflects Reduction of Air-Pollution Due to Changes in Mobility Pattern , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-3727, https://doi.org/10.5194/egusphere-egu26-3727, 2026.
Environmental science increasingly relies on large, heterogeneous, and rapidly growing data collections that must be accessed, subsetted, and harmonised efficiently for use in models, digital twins, AI pipelines, and Virtual Research Environments (VREs). The open-source (AGPLv3) Beacon software developed by MARIS addresses this challenge by enabling cloud-native, high-performance data lakes that are easy and fast to access (user) and set-up (provider).
Beacon is designed for very fast real-time access to data subsets from large collections, returning one harmonised file on-the-fly. The software can read datasets stored in a wide variety of file formats (NetCDF, Parquet, Zarr, and Beacon Binary Format) stored locally or stored on S3 compatible Object Stores. Subsetting by users can be done using SQL or JSON queries on individual datasets, multiple datasets at the same time, or entire collections of datasets.
It is written in Rust and C, chosen for their low-level control and superior performance compared to Python-based or traditional database systems. It runs on any platform via Docker containers and consists of a REST API for data querying and index management, combined with core libraries that enable fast data indexing and search. Next to this, Beacon supports making your data collection more interoperable, by including mappings and allowing for harmonisation with other sources on the fly.
From a provider perspective it is very simple to set-up a Beacon instance containing your data collection. The easiest and fastest way to get a Beacon Instance up and running is through using the Beacon docker compose file. To enable Beacon to connect to an existing S3 bucket requires only 2 additional environment variables to be set. The “AWS_ENDPOINT” which tells Beacon what the URL to the S3 provider is, and the “BEACON_S3_BUCKET” which tells Beacon which Bucket to use as data collection to enable subsetting on. This means it can be set up in less than a minute.
After setting up your Beacon instance, it is immediately accessible via various entries, such as Jupyter Notebooks or a newly developed User Interface called Beacon Studio. Beacon Studio enables users to easily query, explore, download, and visualise data from a Beacon instance through a User Interface, without requiring programming skills. It allows users to build and execute queries against a Beacon instance using simplified menus that describe the contents of the collection. After running a query, users can download the resulting dataset in multiple formats or display the data directly on an interactive map.
This presentation will highlight Beacon’s technological innovations, cloud-ready deployment pathways, successful implementations in BlueCloud2026 context, and practical and simple applications from a user’s perspective. With its domain-agnostic and scalable architecture, Beacon is now being adopted in national and European initiatives, showcasing its value for a wide variety of different use cases.
How to cite: Kooyman, R., Thijsse, P., Schaap, D., Krijger, T., and Weerheim, P.: Beacon: A FAIR high-performance, ARCO data lake technology supporting interoperable environmental research, EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-10638, https://doi.org/10.5194/egusphere-egu26-10638, 2026.
Mangroves play a critical role in coastal protection, carbon sequestration, and biodiversity support, yet they are increasingly threatened by anthropogenic activities and climate-induced changes. Long-term environmental monitoring can help to understand the spatial and temporal dynamics of these fragile systems. However, field instrumentation in mangrove environments faces severe operational challenges, including high humidity, salinity, heat, and the absence of reliable power and/or communications infrastructure.
In this work, we present the implementation of a LoRa-based Internet of Things (IoT) network designed to support continuous, autonomous monitoring in five mangrove sites located in southern Brazil—one of the most endangered coastal regions in South America. The system integrates low-power sensors and multi-hop communication nodes capable of maintaining connectivity through harsh and dynamic conditions. To ensure efficient deployment, a radio propagation model specific to mangrove vegetation and canopy density was developed, allowing optimization of transmitter locations and link performance. The network employs a custom communication protocol designed to enhance data resilience and self-diagnose node failures, minimizing maintenance requirements.
All field data are synchronized to a web-based platform enabling real-time visualization, analysis, and integration with other geospatial datasets. This study demonstrates the potential of LoRa IoT networks as a cost-effective tool for continuous monitoring of coastal ecosystems, supporting geoscientific research and conservation efforts in remote and data-scarce environments.
How to cite: Teixeira, M., Miranda, V., Kougem, E., and Santos-Junior, J.: From Field to Cloud: a LoRa IoT System for Mangrove Environmental Monitoring , EGU General Assembly 2026, Vienna, Austria, 3–8 May 2026, EGU26-8300, https://doi.org/10.5194/egusphere-egu26-8300, 2026.
