European Commission

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.

Soil biodiversity and functioning are the foundation of ecosystem sustainability, but we do not know whether current nature conservation areas are efficiently protecting soils. Here, we investigated the contribution of terrestrial nature conservation areas to protect soil biodiversity and functioning across scales. We found no general positive effect on diversity of bacteria, fungi, protists, invertebrates, and nematodes, captured as richness, Shannon diversity and dissimilarity, and 5 soil functions (pathogen control, nutrient provision, soil carbon, soil organic matter decomposition, and soil aggregate stability). Our findings suggest that nature conservation areas show an inconsistent role in protecting soil biodiversity and functioning, as we found mostly non-significant and otherwise mixed effects across scales. Despite few positive and negative effects of protection status, our work highlights the urgent need to directly target soils in nature conservation.

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.

Quantifying floating macrolitter in rivers and seas contributes to designing effective mitigation strategies and evaluating environmental policies. This field has undergone a significant transformation over the last years, transitioning from fragmented local studies to large scale harmonized monitoring. This evolution is rooted in the first systematic effort to quantify riverine litter inputs, which established visual observation and the use of a mobile App as a robust and accessible method (González-Fernández & Hanke, 2017).Building upon these foundations, the monitoring landscape has been further refined through scientific publications and the development of European and international guidelines. Such guidelines provide the scientific and methodological basis to ensure that data collected across different regions and basins are comparable and representative. Here we review data available in the literature and the use of the existing guidelines at global level.In 2025, the European Commission has launched the new JRC Floating Litter Monitoring App. This digital tool integrates the official Joint List of Litter Categories and allows for real-time, geo-referenced data acquisition in both riverine and marine environments. Beyond its technical capabilities, the app is designed to be an effective tool for facilitating standardized reporting and data management. By bridging the gap between field observations and global databases, it enables a consistent evaluation of pollution levels at a global scale. This presentation will highlight how the integration of harmonized protocols and user-friendly technology can empower a global network of observers, providing the reliable data needed to support international environmental regulations and the fight against plastic pollution.

In Europe soil health is facing emerging challenges that require innovative solutions and policy interventions. We acknowledge that 62% of the European Union (EU) soils are not in healthy condition, while we face serious challenges such as climate change, food security, biodiversity loss, and socio-economic pressures. The severity of these issues is evident in the fact that soil erosion is unsustainable for around ¼ of the EU territory, carbon stocks in soils are declining, nutrients are depleting, and emerging contaminants can pose a serious threat to soil and human health. The costs of soil degradation in the EU may reach up to €70 billion per year, highlighting the urgent need for action. To address these challenges, the EU has put in place many policies for agro-environmental protection since 2000, including soil protection. The European Green Deal, launched in 2020, has set an ambitious roadmap to make the EU the first carbon-neutral continent with a modern, competitive, and resource-efficient economy. As part of the Green Deal, the European Commission (EC) has put soil protection in a high position on the EU policy agenda, recognizing that healthy soils are essential to achieve climate neutrality, zero pollution, sustainable food provision, and a resilient environment. This increased focus on soil health has led to the development of new policies and initiatives, such as the Soil Monitoring and Resilience Directive, which aims to establish a common framework for monitoring and assessing soil health in the EU. The Soil Monitoring and Resilience Directive, in place since December 2025, lays down measures for monitoring and assessing soil health, managing soils sustainably, and restoring contaminated sites. Furthermore, the Mission Soil, which aims to set up 100 Living Labs to promote sustainable land and soil management in urban and rural areas, will play a crucial role in achieving the objective of healthy soils by 2050. With an estimated investment of nearly €800 million until 2028, funded research projects under the Mission Soil are expected to reverse soil degradation through action on the ground, underpinned by the development and monitoring of a set of indicators. In addition to these initiatives, the Carbon Removals and Carbon Farming (CRCF) regulation is the first EU volunteer framework for certifying carbon removals and carbon farming. This regulation will monitor, report, and verify carbon removals, soil emission reduction, and biodiversity benefits, providing a new opportunity for farmers and other stakeholders to contribute to climate change mitigation. The carbon farming framework can also serve as an interesting business model for additional income to farmers, while involving diverse actors such as certification bodies, auditors, tech industry, and creating new jobs. By promoting sustainable land use practices, the CRCF regulation can help sequester carbon, reduce greenhouse gas emissions, and improve soil health. This presentation will discuss these EU soil policies in detail, with a specific focus on the role and activities of the EU Soil Observatory (EUSO). Overall, the presentation will show how the EU soil policies and the EUSO are advancing the data, knowledge and tools on soils and leading the transition towards healthy soils in the EU.

Air pollution poses a critical risk to both human health and the environment, particularly in the Arctic and Northern Europe, where pollutants are primarily transported from mid-latitudes by atmospheric circulation. Also, local sources further contribute to pollution levels in Arctic communities. Accurate short-term forecasts of atmospheric pollutant concentrations are vital for enabling adaptive measures and protecting public health during pollution episodes.The Copernicus Atmospheric Monitoring Service (CAMS) provides 96-hour forecasts for key pollutants across Europe using 11 state-of-the-art models and an ensemble approach. However, these forecasts exhibit significant errors in Northern Europe and the Arctic. To address this, we investigate the applicability of deep learning (Transformer-based) models for 48-hour PM10 concentration forecasting at monitoring stations in Northern Europe. Our approach integrates in situ PM10 observations with CAMS model outputs and forecasted meteorological parameters as input features. We evaluated four time-series specialized models—Informer, Autoformer, FEDformer, and Crossformer—to identify the most effective architecture for this task. The Crossformer model demonstrated superior performance, outperforming CAMS by 30% in Mean Squared Error (MSE) and 23% in Mean Absolute Error (MAE). It also surpassed the newly introduced CAMS Model Output Statistics (MOS), reducing MSE by 12% and MAE by 14%.With its low computational complexity, fast execution time, and minimal resource requirements, the Crossformer presents a viable alternative to traditional numerical models for local-scale predictions. Future work will extend the forecasting window to 72 hours and incorporate additional pollutants, such as PM2.5, NO2, and O3, to enhance predictive capabilities for Arctic and Northern European communities.

Forest soils represent the largest carbon reservoir in terrestrial ecosystems, yet decadal scale changes in soil organic carbon (SOC) across Europe remain insufficiently quantified. Here, we analyze harmonized topsoil data from 0 to 20 cm in the LUCAS Soil Surveys conducted between 2009 and 2018 to investigate spatial patterns, environmental controls, and national level changes in forest SOC across the European Union and the United Kingdom. Using spatiotemporal machine learning models, we estimate a net SOC increase of approximately 1.31 Pg C over the decade, accompanied by pronounced regional heterogeneity. Climatic factors, particularly the aridity index, mean annual temperature, and available water capacity, emerged as the dominant drivers of SOC distribution, while forest structure, topography, and land cover change provided additional contributions. France, Sweden, and Germany accounted for the largest shares of total SOC gains and associated CO₂ equivalent reductions. These results highlight forest soils as dynamic and spatially heterogeneous carbon sinks and underscore their importance for national carbon accounting and climate mitigation strategies.