Found 10461 publications. Showing page 1 of 419:
The dietary footprints and transition priorities of over 12 000 urban centers
Urban consumption plays a central role in driving global carbon emissions and is associated with a wide range of environmental pressures, including deforestation, water use, and biodiversity loss. As cities and networks of cities work to define and achieve quantitative sustainability goals, the lack of insight into their unique food footprints, shaped by each city’s specific dietary patterns and demographic composition, remains a significant obstacle. Here we combine demographic, group-specific diet, and supply-chain environmental footprint data to estimate the dietary footprints in 12 228 urban centers across 159 countries. We show that diet-related greenhouse gas emissions (GHGE) are highly concentrated in a small number of cities: The 200 urban centers with the largest footprints account for 39.8% of global urban population and 45.6% of total global urban dietary emissions. Overall, adoption of the EAT-Lancet 2.0 diet across all studied centers delivers a modest net reduction in total footprints (≈5%–16%). However, this aggregate outcome masks substantial variability: high-income centers achieve considerable reductions through lower meat and dairy consumption, while lower-income centers presently consuming below EAT-Lancet recommended levels experience footprint increases, partially offsetting global gains. Within transnational city networks (C40 Cities, Eurocities, and Milan Urban Food Policy Pact members), where member centers are predominantly high-income, EAT-Lancet 2.0 adoption delivers substantially larger reductions of 22%–50% in GHGE, underscoring the policy relevance of targeted urban-level dietary interventions. Complementary supply-side interventions, including decarbonization of agricultural production, processing, and distribution, offer additional reductions, indicating that dietary and supply-side levers must be deployed jointly. Our results highlight the need for context-specific urban dietary strategies. The resulting database, MATILDA-City, provides a globally harmonized evidence base to benchmark, rank, and monitor urban-level dietary footprints, supporting municipalities to coordinate actions toward sustainable food systems.
2026
A machine learning method for estimating atmospheric trace gas concentration baselines
Estimates of trace gas baseline mole fractions in high-frequency atmospheric measurement records are crucial for analysing long-term changes in atmospheric composition. Baseline mole fractions are those that would be observed far from emission sources (and hence are representative of background conditions). Previous methods for inferring baseline mole fractions have used statistical or meteorological approaches, or, if available, co-measured tracer species thought only to be emitted from non-baseline wind sectors. Combinations of these techniques have also been employed in some applications. Statistical methods typically fit a baseline to the observations themselves, while meteorological methods use atmospheric models of varying complexity to categorise air mass origins. In this paper, we present a novel machine learning method for estimating trace gas baseline mole fractions, which benefits from the physical basis of model-based filtering without the need for running an expensive simulator. Our approach offers the accessibility and computational cost-effectiveness of statistical models, without the associated smoothing or difficulty in identifying rapid baseline variations. By training on historical Lagrangian particle dispersion model outputs, our model learns to predict baseline mole fractions directly from meteorological fields. This advancement opens new avenues for low-latency trace gas time series data analysis, reconstruction of historical baseline trends, and improved utilisation of tracer measurement air mass classification methods.
2026
On the reliability of seasonal snow forecasts
Reliable information on seasonal snow conditions is important for long-range weather forecasting and climate modeling. The reliability of winter-mean hindcasts of snow water equivalent (SWE) produced by the ECMWF for the period 1993–2022 within the CopERnIcus climate change Service Evolution (CERISE) project is evaluated in this study. In probabilistic forecasting, reliability is defined as the consistency between forecast probabilities and observed frequencies for a binary event. Here, reliability is assessed using two independent SWE datasets (ERA5-Land and ESA Snow-CCI v4) across eight land regions in the Northern Hemisphere non-mountainous regions. The reliability assessment is performed for two tercile-based binary events representing low- and high snow accumulation winters. Reliability is quantified using a weighted linear regression applied to reliability diagrams and is grouped into five categories from perfect to dangerous. The results show that the ECMWF seasonal snow hindcasts consistently yield marginally useful to perfect reliability categories for both low- and high-snow conditions independently to the chosen benchmark. The assessment shows sensitivity to the choice of verification dataset, with ERA5-Land yielding higher reliability categories than ESA Snow-CCI, typically 1 to 2 categories higher. It is found that differences in hindcasts reliability between regions and between verification datasets may be linked to snow variability, model representation, and observational uncertainty.
2026
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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.
2026
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