Found 10458 publications. Showing page 1 of 419:
2026
Environmental Contaminants in an Urban Fjord, 2025
This report presents data from the last year of a 5-year period of the Urban Fjord programme. The programme started in 2013 and has since been altered/advanced. In 2025 the programme covers sampling and analyses of sediment, polychaetes, krill, shrimps, blue mussels, herring, cod, eider, and herring gull from the Inner Oslofjord. A total of 230 single compounds/isomers were analysed, and frequent detection was found of specific PFAS compounds (e.g. PFOS) in most matrices, certain QACs in several matrices (ATAC-C22 in all
matrices), specific benzothiazoles in eider blood, LCCPs in certain matrices, certain siloxanes in most matrices, metals in all matrices and PCBs in all matrices. Biomagnification was observed for 24 PCB congeners, LCCPs and D4 (siloxane; lipid wt. basis). Furthermore, biomagnification was observed for PFOSA, ATAC-C20 and ATAC-C22, as well as for the metals As and Hg (wet wt. basis).
Norsk institutt for vannforskning (NIVA)
2026
Plastic pollution is an ever-growing concern around the globe, with current research portraying the presence of contaminants even in previously pristine regions. Especially small particles, micro- and nanoplastics, can be dispersed over long stretches by atmospheric transport, reaching even the most remote areas of the planet. This study aims to portray current levels of nanoplastic contamination (< 1 μm) by analyzing snow samples from high-altitude glaciers in the European Alps (2300–3800 m) via TD-PTR-MS. We were able to detect six common plastic pollutants, with PE and PP contributing over 60% of the total mass concentration measured across all sites. On average, nanoplastic concentrations of 85 ng mL − 1 (range: 3.6 ± 12 ng mL − 1 – 470 ± 76 ng mL − 1 ) were detected at sampled glacier surfaces. Access to these remote regions was enabled through a citizen-science initiative, involving trained mountaineers for sample collection. The results were applied to atmospheric modelling, highlighting possible point sources of contamination. While agriculture and local plastic factories were revealed as potential sources of nanoplastics, spatial and temporal limitations complicated comparison inside the sample cluster.
2026
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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2026
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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