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Abstract Significant trends of ultraviolet radiation (UVR) have been reported at limited European sites due to changing atmospheric conditions, such as cloudiness. Whether these findings are applicable to larger areas or even the entire continent remains unclear. In a unique comparison, we analyzed measurements of erythemally weighted daily ambient radiant exposure from 40 European locations, covering the period from 2013 to 2022. At 26 locations annual means increases statistically significantly with a median of + 1.2%/yr. At 38 sites at least monthly means increases with medians between + 2%/yr and + 4%/yr. Increases were seen in satellite products of UVR, global solar radiation, and sunshine duration, too. Analysis at six stations, with data extending back to the mid-1990s, indicates that the period of statistically significant increases generally began between 2010 and 2013. Comparison of UVR data from 40 stations with a high-resolution global solar radiation trend map reveals complementary patterns, highlighting widespread but not uniform UVR changes across Europe. This combination enables figuring out the regional extent of trends. Thus, ground-based UVR measurements remain essential for quantifying changes, highlighting the need for continued monitoring with sufficiently dense measurement networks. This is also essential for understanding and mitigating the impacts of changing UVR on human health, particularly by assessing its potential for increased UVR-related skin cancer incidence and development of preventive measures. Moreover, detailed regional UVR data is essential for achieving the UN’s Sustainable Development Goals, specifically in human health, sustainable cities, climate action, and environmental conservation.
2026
Requirements and scientific needs for coordinated organic pollutant monitoring in both polar regions
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This study investigates atmospheric microplastic (MP) exchange between marine and terrestrial compartments and associated deposition patterns at Bushehr Port, Persian Gulf. We combined field sampling of the sea-surface microlayer (SML), bulk seawater, sea foam, deposited particles, and suspended airborne particles with FLEXPART Lagrangian dispersion modelling and exploratory Elastic Net regression to evaluate MP sources, transport pathways, and meteorological controls. The simulations indicate a pronounced seasonal contrast in atmospheric MP transport and suggest that land-based sources collectively represented the largest modelled contribution to the atmospheric MP burden. Within the FLEXPART inventory, textile-related microfibres were the largest modelled source category for suspended MPs (∼61%); for deposited MPs, the estimated microfibre contribution (∼32%) was comparable to bare-soil resuspension (∼31%), while sea spray contributed ∼11% to both fractions. Elastic Net regression repeatedly retained air pressure as a positive predictor and the Lifted Index as a negative predictor; however, these associations are interpreted as exploratory because of the limited number of independent sampling intervals. Sea foam and SML samples were enriched in MPs relative to bulk seawater, although the enrichment pattern varied with wave period and tidal-current conditions. The large difference between field-derived net deposition velocities (Vd) and theoretical terminal velocities (Vt) indicates that turbulence, resuspension, environmental mixing, and particle-shape assumptions substantially affect the apparent removal of atmospheric MPs. Overall, the results suggest that MP cycling at this semi-enclosed coastal margin is influenced by coupled land-based emissions, marine surface processes, and atmospheric dynamics, highlighting the need for mitigation strategies that consider both local terrestrial inputs and air-sea exchange.
2026
Climate change impacts on designing the power system of Kenya in 2050
Africa’s economic growth in the coming decades will hinge on the strategic planning of national power systems to meet growing demand and ensure rural energy access. Renewable energy sources, particularly wind and solar, will be pivotal in the expansion and transition to a low-carbon energy system. However, the vulnerability of these renewable sources to climate change could introduce imponderability in the design of the energy mix. In this regard, this paper explores a climate-informed energy system pathway by integrating the future climate projections directly into the capacity expansion of Kenya’s power system within three scenarios towards 2050, to formulate future energy profiles under climate change. The PyPSA-Earth model is applied to calculate capacity expansion decisions of the Kenyan power system. The EC-Earth3-Veg Earth System Model projects future climate variables such as wind speed. The cooling demands with global warming are estimated based on Cooling Degree Days (CDDs) projected by the Multi-Climate Model ensemble. The capacity expansion optimization results reveal that temperature increase leads to a rise in cooling demand, resulting in capacity expansion by 12% to 52% larger than the cooling demand without global warming. The projected change in wind speed is complex, with the onshore wind speed declining and the offshore wind speed rising, which causes a jump in the share of offshore wind power, taking 9% to 19% of the total capacity. Life cycle assessment indicates that large-scale deployment of wind and solar has less 5% climate change mitigation potential, but has 35% more total system cost compared to the baseline. In Kenya, geothermal is expected to continue playing a critical role in the energy strategy. Besides solar power, offshore wind is forecasted to become an important renewable energy source for Kenya. However, climate models’ resolutions and accuracy need to be further improved for effective integration into energy system modelling.
2026
Promoting healthy lifestyle behaviors, including physical activity, sleep, diet, stress management, and healthy habits, requires adaptive systems capable of responding to dynamic changes in human behavior. Sustained behavioral change improves individual wellbeing, reduces disease risk, and contributes to healthier societies. However, developing personalized behavioral intervention systems is challenged by demographic heterogeneity, limited and fragmented datasets, reporting inconsistencies, and scarce high-quality labeled data. Ethical, privacy, and cost constraints further restrict the collection of large-scale longitudinal behavioral data. Consequently, there is a need for robust simulation and synthetic data generation frameworks that enable the development and evaluation of adaptive decision-making systems capable of optimizing personalized behavioral interventions over time. This study presents a digital twin framework integrated with tabular Q-learning for personalized behavioral recommendation under World Health Organization (WHO) lifestyle constraints. The framework combines synthetic behavioral data generation, reinforcement learning, and a TSP-inspired planning mechanism to investigate long-term behavioral adaptation in privacy-preserving simulated environments. The digital twin environment models user adherence variability, misreporting, dropout, and behavioral drift, enabling the evaluation of intervention strategies under realistic conditions. Experimental evaluation on synthetic populations demonstrates that Q-learning achieves competitive reward performance while maintaining favorable computational efficiency and stability compared with heuristic and reinforcement learning baselines. Statistical analysis indicates that reward differences among the evaluated methods are not significant; however, the proposed framework provides a flexible platform for adaptive behavioral recommendation and simulation-based experimentation. Furthermore, a real-time recommendation interface illustrates how simulation knowledge can be translated into actionable behavioral guidance. The proposed framework offers a scalable foundation for future digital health systems, particularly in scenarios where data scarcity, privacy constraints, and personalization requirements limit the use of real-world datasets.
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Climate change is expected to intensify the frequency and severity of droughts across Europe,
posing significant risks to agricultural production and the food systems that depend on it. As drought
severity can range widely within one country, estimates of supply chains with higher spatial
resolution are needed to characterize drought risk exposure. This study develops an inter-regional
(NUTS-2) version of the Food and Agriculture Biomass Input Output (FABIO)[1] model to quantify
the exposure of European food trade to drought risk under current and future climate conditions.
Drought exposure is quantified by linking regional agricultural trade flows to Copernicus
satellite-based soil moisture anomaly data.
Input output (IO) analysis has been widely used to study supply-chain dependencies, resource
use, and environmental pressures embedded in production and trade. The development of
multi-regional input output (MRIO) databases, which combine national IO tables into a single
balanced global framework, has enabled such analyses at the global scale. Prominent examples
include EXIOBASE[2], Eora[3], and FIGARO[4], which have been applied to assess environmental
impacts embodied in trade. However, while these databases provide global coverage, their
national-level resolution limits their ability to capture spatially heterogeneous climate risks such as
droughts, which often vary substantially within countries. Subnational or interregional IO tables are
rarely published due to data and resource constraints, creating a critical gap for climate risk
assessments that require finer spatial detail.
This study extends FABIO[1], an established physical MRIO framework for food, feed, and biomass
flows, into an interregional European model. The interregional FABIO disaggregates national food
trade into NUTS2-level trade using regional crop and livestock production, regional demand, and
reported trade flows. The FABIO database is regionalized following two main steps, similarly to
Ouyang et al[5]. First, international trade is estimated using reported subnational trade data where
available, subject to the constraints that regional exports and imports do not exceed supply and
demand, and that national totals remain consistent with the original FABIO data. Second, domestic
interregional flows are estimated using the CHARM approach in combination with a gravity model[6].
The model distinguishes 67 sectors and 332 NUTS 2 regions.
Using this framework, drought exposure is assessed by linking regional agricultural trade with
satellite-based drought intensity metrics, considering both historical events and projected future
conditions. The results show that accounting for trade significantly increases estimated drought
exposure of food consumption in Northern Europe compared with production-based assessments.
Exposure varies strongly across food categories; non-essential commodities are generally more
exposed than staple foods. Trade can both amplify and buffer drought impacts, depending on the
geographic extent of the drought and the availability of alternative sourcing regions.
Overall, this study demonstrates the value of combining interregional input output modeling with
satellite-based climate indicators to assess the vulnerability of European food trade to droughts. The
results reveal the exposure to drought risk in regions and support the design of diversification and
resilience strategies in agricultural supply chains. The annual resolution of the model, consistent
with input output tables and available trade data, cannot capture dynamics such as monthly stock
variations that can be critical for drought impacts, and merits further research. Beyond drought risk
assessment, the interregional FABIO framework can be applied to other spatially explicit
environmental questions, including consumption-based analyses of nitrogen emissions, land use,
and other local pressures.
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