Joint Research Centre

PEGylated liposomes are well-known for their properties as long-circulating nanocarriers in drug delivery systems, where the structure and organization of the polyethylene glycol (PEG) coating critically influence their stability, circulation lifetime, and biological interactions. Given the crucial role of PEG coating attributes in these structured nanoparticles, it becomes essential to explore advanced technologies capable of specifically addressing the surface properties of liposomes. A detailed surface characterization, combined with nanoparticle composition analysis, enables deep insights into critical quality attributes of nanomedicines, which are essential for ensuring both quality and safety. In this study, small-angle X-ray scattering (SAXS) was employed to examine the structural organization of PEGylated liposomes, alongside their composition analysis using High-Performance Liquid Chromatography coupled to a Charged Aerosol Detector (HPLC-CAD). The liposomes were formulated through commonly used formulation strategies, such as pre- and post-insertion of various molecular mass, PEG2000 (PEG2K) and PEG5000 (PEG5K)-modified lipids. SAXS profiles revealed systematic variations in scattering features with increasing PEG content, indicating progressive changes in corona density and interfacial organization. In addition, core-multishell modeling confirmed that the liposomes prepared using both strategies were unilamellar with stable bilayers. Pre-insertion PEG2K liposomes showed pronounced reorganization of the outer interface, with higher PEG levels producing thicker, denser coronas consistent with a mushroom-like configuration. In post-insertion formulations, both PEG2K and PEG5K systems were well described by four-shell models. PEG2K post-insertion liposomes displayed thinner but denser coronas, whereas PEG5K yielded thicker, more hydrated layers that compacted with increasing PEG concentration. Despite these interfacial variations, the bilayer core remained largely unaffected, though pre-insertion induced marked PEG-lipid leaflet asymmetry. Overall, these findings demonstrate that PEG chain length, surface density, and incorporation strategy each play a critical role in shaping the structure and density of the PEG corona. By quantifying these factors, this work provides detailed insight into PEG organization at the liposome interface and supports the rational design and quality control of sterically stabilized liposomes.

Air pollution interpolation is crucial for civil management: it is used to transform limited sensor data into comprehensive pollution maps. Various methods, including deterministic, geostatistical, and Machine Learning (ML)-based techniques have been utilized for this purpose. Deterministic methods rely on mathematical rules for estimation, whereas geostatistical techniques are based on spatial correlations. ML leverages historical data for predictions. Each method has limitations: deterministic methods do not adequately model environmental complexity, geostatistical methods struggle with small-scale areas, and ML depends heavily on data availability. On the other hand, air pollution simulators based on physicochemical dispersion models capture intricate pollution dispersion patterns, yet running them at high resolution for continuous interpolation is often computationally demanding. Recent advancements in ML offer a complementary pathway by leveraging simulated data to enable rapid, real-time interpolation. This study introduces an air pollution interpolation approach combining simulated air-dispersion patterns through an educated machine that includes machine learning and environmental modeling that considers boundaries, pollution sources, obstacles, wind dynamics, and topography. Specifically, we present a combined ML-based linear regression model designed to infer dense concentration maps from a sensor array using state-of-the-art simulation methods. We dub this the Ridiculously Simple data-driven air pollution Interpolation Method (RSIM). The RSIM method was evaluated on both synthetic and real-life-based simulation models. The synthetic scenarios included an industrial area with point pollution sources and an urban road surrounded by buildings simulating traffic-related pollution. The real-world environment consisted of sensor data and simulations from Antwerp, Belgium. The results indicate that this method outperforms standard techniques for reconstructing dense pollution maps from sparse sensing, and demonstrates significant promise for other real-world applications. • We propose a novel method for interpolating air pollution using machine learning. • The approach is validated on both synthetic data and real data from Antwerp, Belgium. • Our method outperforms classical techniques in generating dense pollution maps. • The workflow integrates simulated dispersion fields with real-world sensor readings. • The methodology supports scalable, high-resolution mapping for urban air quality.

This paper explores how recycling and the integration of secondary materials contribute to the reduction of the carbon footprint (CF) of different lithium-ion batteries under the current EU regulatory framework. It quantifies the net benefit of the default recycling process, and the possible benefit of (i) alternative, more advanced recycling processes and of (ii) increasing the use of secondary (recycled) materials for manufacturing. Under the current CF calculation method, we find the use of secondary material for battery cell production to have a very limited impact on the CF (decrease by only 0.4% with 30% secondary material), while the use of advanced recycling processes can reduce the CF by up to 16% compared to the default. As such, the findings highlight the need for a combined approach to advance the circularity of batteries via the CF, mandatory recycled content and recycling targets.

Abstract. HTAP3-OPNS is a multi-model exercise designed to support the revision of the Gothenburg Protocol under the UNECE Convention on Long-Range Transboundary Air Pollution (CLRTAP). Using an ensemble of Chemical Transport Models (CTMs) and Chemistry-Climate Models (CCMs), this study investigates the long-range transport and impacts of ground-level ozone, particulate matter (PM), and nitrogen and sulphur deposition across different global regions. The project aims to assess the contributions of regional versus extra-regional emission sources, evaluate the suitability of current models, and project changes in air pollution under future emission scenarios and climate conditions. A series of perturbation simulations will enable the development of an ensemble emulator to explore and evaluate potential mitigation strategies efficiently. This paper outlines the scientific and policy questions motivating the study, describes the experimental design, including input datasets, model configurations, and required outputs, and discusses methodologies for data handling and analysis. The results will provide crucial insights for policy decisions aiming to improve air quality, protect human health, and protect ecosystems worldwide.

Abstract Background Analytical data for ²²⁵Ac-labeled radiopharmaceuticals are currently obtained using thin-layer chromatography or high-performance liquid chromatography followed by offline fraction collection and delayed γ-counting after secular equilibrium with γ-emitting daughters has been reached. These procedures are slow and do not depict the distribution of daughter radionuclides. An inline HPLC detection setup combining liquid scintillation detection and LaBr₃-based γ-spectroscopy is presented to enable faster analysis and to establish reproducible quality control parameters for ²²⁵Ac radiopharmaceuticals. Results Four chromatographic signals were identified corresponding to uncomplexed ²²¹Fr and ²¹³Bi, [²¹³Bi]Bi-PSMA I&T, [²²⁵Ac]Ac-PSMA I&T, and [²⁰⁹Pb]Pb-PSMA I&T. The retention times corresponded to those of the non-radioactive analogs determined by UV detection. Assignments were confirmed by fraction collection followed by γ-spectroscopy and β liquid scintillation counting. Conclusions The combined detection setup enables rapid observation of isotope distribution and daughter behavior during chromatographic analysis and avoids fraction handling by operating as a closed system, thereby reducing contamination risk and radiation exposure. This approach may contribute to improved quality control procedures for ²²⁵Ac radiopharmaceuticals and other α-emitting radionuclides.

Soil organic carbon (SOC) plays a central role in regulating soil fertility, water retention, and quantifying risks of soil degradation (Afshar et al., 2025). While climate variability is increasingly recognized as a major pressure on SOC, large-scale assessments of drought impacts on forest soils remain limited. Recent studies emphasize that drought effects on SOC are highly context dependent, shaped by soil carbon status, climate regime, and interacting environmental controls, calling for flexible modeling frameworks that can capture nonlinear responses (Hassani et al., 2024; Shokri et al., 2025).In this study, we analyze SOC change between 2009 and 2018 across European forest soils using generalized additive models (GAMs) applied to harmonized LUCAS topsoil observations. SOC change is modelled as a nonlinear function of initial SOC, drought characteristics derived from the Standardized Precipitation Evapotranspiration Index (SPEI), climate, and soil properties.GAM results show that drought severity exerts a significant, nonlinear impact on SOC change (p < 0.001), strongly modulated by initial SOC and climatic parameters. On average, under severe drought conditions, SOC declines by ~32% relative to mild drought conditions. Overall, the results demonstrate that drought impacts on forest SOC are state-dependent and spatially heterogeneous, governed by the combined influence of drought severity, initial carbon stocks, and regional climate conditions.

Droughts are prolonged periods of water shortages driven by meteorological conditions or human behaviour, affecting social, economic, financial, and eco-systems. These hydroclimatic phenomena can lead to crop failures, diminished water supplies, and negatively impact the environment, posing challenges to food security and economic stability. Meteorological drivers of drought (e.g. precipitation, temperature, radiation patterns) fluctuate due to natural climate variability, but can also be impacted by long-term climatic changes. We exploit the concept of climate model analogs to single out specific observed drought events based on the standardized precipitation index, in order to attribute and explain the dynamic drivers that lead to these events months before they occur. In particular the sea surface temperature drivers which remotely modulate hydroclimatic variability over continents in seasonal to annual timescales via teleconnections. This is done by subsampling the most similar events in a large multi-model ensemble to the observed target event and extracting their common signals of prior sea surface temperature conditions and their temporal evolution. In a complementary manner, and as a counterfactual experiment, we predict the standardized precipitation index based on analogs of observed sea surface temperature conditions months before the target drought event and evaluate its skill by comparing predicted values against observed SPI data. We have extended the use of an established method of selecting climate model analogs to attribute drought events to internal climate variability. The method can still be further extended to quantify the impact of externally forced climate change versus internal climate variability.

Copernicus Ground-Based Observations for Validation (GBOV) is a project funded by the European Commission and is part of the Copernicus Land Monitoring Service (CLMS). The project has two main purposes: to support yearly validation efforts of core CLMS products (BRF, Albedo, FAPAR, LAI, FCOVER, LST, Soil Moisture, GPP, NPP, LSP and Evapotranspiration), five of which are listed among GCOS Essential Climate Variables (ECV); and to maintain a data portal making these validation products available. GBOV has reached a broad user community, with about 1700 users, including ESA optical MPC.A large number of ground-based measurement networks disseminate publicly available data such as NEON, ICOS, TERN, BSRN and ISMN. Within GBOV, long-term datasets are needed in order to maximise the number of matchups between ground-based measurements and satellite data.GBOV produces both ground-based, point-scale observations (the so-called “Reference Measurements”), and Analysis Ready Validation Data (ARVD) which are spatially upscaled to match the resolution of CLMS products (the so-called “Land Products”). Those GBOV products cover more than 150 sites and are made available to the community via a data portal freely accessible on https://gbov.land.copernicus.eu/. Available Land Products variables include Surface Bi-directional Reflectance Factors (BRFs), Surface Albedo, Leaf Area Index (LAI), Fraction of Absorbed Photosynthetically Available Radiation (FAPAR), Fraction of Vegetation Cover (FCover), Land Surface Temperature (LST), Surface Soil Moisture (SSM). Gross Primary Production (GPP), Net Primary Production (NPP), Land Surface Phenology (LSP) and Evapotranspiration are new products and are not yet available, their release is planned for the end of 2026.The networks providing GBOV initial input data are unfortunately not evenly distributed. In an attempt to reduce the thematic and geographical gaps, GBOV is developing its own network as part of a collaboration with existing networks. In GBOV phase 1, six ground stations have been upgraded with additional instrumentation. In GBOV phase 2, two ground stations monitoring vegetation variables have been deployed in 2023, one at the Fuji Hokuroku research station in Japan as part of a collaboration with NIES (National Institute of Environmental Studies) and one in 2024 at the Fontainebleau research station (France) as part of a GBOV/ICOS collaboration. In addition, two LST stations were deployed in Fuji Hokuroku and Litchfield (TERN network Australia) in 2024.Over the past year, several updates have been implemented in the GBOV database to better respond to CLMS and general user requirements. This includes new procedures for specific land cover types in vegetation products, improved uncertainty estimates for soil moisture, and enhanced processing procedures for LST products. Procedures are being developed for the new products: Gross Primary Production, Net Primary Production, Land Surface Phenology and Evapotranspiration.This presentation will focus on the current status of GBOV products and highlight recent developments and evolutions.

Afforestation and reforestation (A&R) are central components of current climate mitigation strategies and hold substantial potential for supplying biomass. However, large uncertainties remain regarding their actual mitigation potential, with considerable variation across global estimates, partly due to methodological differences. This study refines existing estimates of A&R potential by integrating temporal dynamics, climate change impacts, and land-use competition within a deliberately conservative framework.We combine climate-sensitive forest biome projections with current land-use cover data to assess global A&R potentials under three representative concentration pathways (RCP 2.6, 4.5, and 8.5) up to 2080 at a spatial resolution of 1 × 1 km. To account for land-use competition, A&R is restricted to non-managed pastureland. Potential biomass production and associated carbon sequestration are estimated using region-specific growth data in accordance with IPCC guidelines.Across all RCPs, we identify 731 million hectares globally as suitable for A&R through 2080. Relative to the historical baseline, climate change scenarios lead to a a net reduction of up to 24 million hectares of potential A&R area. While global potentials decline, regional patterns diverge markedly: boreal regions experience an increase in suitable area (+34 million hectares), whereas tropical and temperate regions exhibit substantial reductions (–33 and -18 million hectares, respectively). The A&R potentials presented here are intentionally conservative with respect to climate uncertainty, land-use competition, and long-term viability. Integrating these estimates with complementary scientific assessments is essential to underpin the feasibility of current climate policy targets and to support robust projections of biomass availability for scaling up the bioeconomy.If implemented in accordance with local ecological conditions, the identified A&R potentials can inform policy responses to climate-related risks and may contribute to climate mitigation while supporting a biomass supply as a substitute for fossil-based products. However, successful implementation requires careful consideration of resource constraints (e.g., water availability) and future abiotic and biotic risks.