Found 10488 publications. Showing page 417 of 420:
Promoting healthy lifestyle behaviors such as physical activity, sleep, diet quality, stress management, hydration, and healthy habits requires adaptive systems capable of responding dynamically to changing behavioral and environmental conditions. However, the development and evaluation of personalized recommendation systems are challenged by fragmented observational data, privacy constraints, delayed feedback, and ethical limitations associated with long-term human experimentation. To address these challenges, this study proposes a digital twin-driven reinforcement learning framework for generating personalized behavioral recommendations in a fully simulated and statistically validated environment. The proposed framework formulates personalized behavioral recommendation as a stochastic Markov Decision Process (MDP) incorporating adherence uncertainty, behavioral drift, environmental modulation, and engagement dynamics. Synthetic longitudinal behavioral trajectories are generated through a digital twin simulator that models demographic heterogeneity, lifestyle behaviors, contextual variables, and variability in policy adherence over time. The optimization objective is defined through an effective reward formulation that balances behavioral compliance gains against penalties associated with health and environmental constraint violations. This study implements several reinforcement learning (RL) paradigms under simulated conditions, such as multi-armed bandits, table-based Q-learning, State-Action-Reward-State-Action (SARSA), function approximation-based temporal difference (TD) learning, and deep Q-learning network (DQN). The results demonstrate that richer state representations and context-dependent action dynamics are necessary for higher-capacity reinforcement learning models to consistently outperform simpler baselines. Furthermore, this study provides a reproducible method for comparing learning dynamics, performance, and computational cost in digital twin-based recommender systems. The framework additionally supports privacy-preserving experimentation through the exclusive use of synthetic behavioral data and locally controlled simulation environments.
2026
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
Forbedring av klimagassregnskapet for veitrafikk. Casestudie Oslo kommune
Arbeidet i denne rapporten omfatter en gjennomgang av tilgjengelige datakilder for å forbedre kjøretøysammensetningen i Oslo spesielt og andre kommuner generelt, med mål om å gi en mer presis beregning av klimagassutslippet med modellen NERVE. Analyser viser at bompasseringsdata kan definere en representativ lokal kjøretøypark, og det er utviklet og implementert metoder for å forbedre kjøretøysammetningen i NERVE i henhold til dette.
NILU
2026
Soil degradation in Europe is projected to accelerate under changing land use and climate
Soil degradation threatens food security and environmental sustainability, yet future projections of it are rare. Using projections from 18 global climate models under two Shared Socioeconomic Pathways (SSP2-4.5 and SSP5-8.5) and land-use projections from the Land Use and Climate Across Scales Land Use Change (LUCAS LUC) dataset, we assess future soil vulnerability to degradation by linking a Soil Degradation Proxy (SDP) to climate, land-use, soil characteristics, and socio-economic factors at 7433 observation sites across Europe. We project that by 2071–2100, ~59% of sites may become more vulnerable under the high-emission scenario. Cold forest regions in northern Europe are projected to face increased degradation pressure by ~+0.04SDP. However, some European croplands may improve locally through conversion to secondary lands, reduced human pressures, and natural recovery processes. These regionally specific trends highlight that, while soil degradation remains a major threat, proactive land management can mitigate soil vulnerability under future climate trajectories.
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Hidden Resources: The Quantity and Recovery Potential of Rare-Earth Elements in Norwegian Households
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
The comet assay is one of the most popular tests for genotoxicity in cell cultures, non-animal species, animals and humans. It has high sensitivity to detect low levels of DNA damage, can be applied to non-proliferating cells, requires relatively few cells, is technically simple, and is low cost. The Organisation for Economic Co-operation and Development (OECD) adopted in 2016 the in vivo comet assay for measurement of DNA strand breaks in animal tissues. There is a desire to expand the comet assay to genotoxicity testing in cell cultures, including the detection of oxidatively damaged DNA by incubation of gel-embedded nucleoids with DNA repair enzymes, especially formamidopyrimidine DNA glycosylase (Fpg) which converts oxidised purines to DNA breaks. Based on available information in the literature, this review provides a retrospective evaluation of the validation status of this assay, focusing on accuracy and reliability in genotoxicity testing in vitro. Information on accuracy is scarce, although limited evidence suggests levels of Fpg-sensitive sites are similar to those obtained by Fpg-linked alkaline unwinding and alkaline elution assays. Several ring studies have shown that estimated background levels of DNA breaks vary within and between laboratories. However, ring studies indicate good intra- and inter-laboratory reproducibility of the standard assay on ionizing radiation-exposed and the Fpg-linked assay on potassium bromate exposed cells. Further studies are needed to assess the reproducibility in multiple laboratories using coded samples of non-genotoxins and genotoxins. Nevertheless, the available results indicate the comet assay is a reliable in vitro genotoxicity test.
2026
Precise estimation of atmospheric pollutant releases is crucial for assessing the impact of environmental accidents. Atmospheric inversion typically relies on a linear model with a source–receptor sensitivity (SRS) matrix, which may contain significant errors or even completely fail to capture the real magnitude of the event. We propose a correction of the SRS matrix formulated as slight shifts in the observation locations, effectively warping the sensitivity field. To constrain these shifts and ensure data-driven corrections, we model them using a Gaussian process prior. This prior not only enforces smoothness and sparsity, but also enables posterior prediction of shifts at previously unseen locations. This key feature provides a mechanism for hyper-parameter tuning: the predicted shift field can be visualized on a map and assessed by an expert. We present a user-friendly framework that combines a Bayesian inversion model with correction and a tuning algorithm based on L-curve-like plots and the maps of predicted shifts. The proposed method is demonstrated on three case studies: the ETEX-I experiment, the 137Cs emissions during the 2020 Chernobyl wildfires, and the 106Ru release in 2017.
2026
Understanding new particle formation (NPF) and the fate of nanoparticles is crucial because of their close links to air quality, cloud formation, and climate. These effects vary spatially and temporally owing to diverse aerosol sources and their relatively short atmospheric lifetime. Here, we present a comprehensive analysis of long-term trends in NPF-associated nucleation-mode particles and cloud condensation nuclei (CCN) concentrations across diverse observation environments using quality-controlled particle number size distribution (PNSD) and CCN data from 37 sites, primarily from Global Atmosphere Watch (GAW) stations. We identify declining decadal trends in both NPF occurrences and nucleated particle concentrations across most site types, with the strongest declines in urban areas. We observe simultaneous reductions in both CCN concentrations and nucleation-mode particles, suggesting that newly formed particles are a potential source of CCN. This, in turn, suggests that cloud microphysical properties and radiative effects can be indirectly influenced through aerosol–cloud interactions that modify cloud droplet formation. These findings indicate that decreasing anthropogenic emissions could influence the climate forcing potential of aerosol–cloud interactions, with important implications for future climate projections.
2026
ML-based data fusion of model, satellite, and ground observations for 1-km PM2.5 mapping over Europe
2026
Arctic Amplification is not well understood. It is the result of a complicated interplay between remote and local forcing and feedback processes. Therefore, it is crucial to enhance our understanding of the transport of energy and moisture from lower latitudes. The amount of aerosol in the Arctic is also an important quantity as their role in Arctic Amplification, via direct radiative forcing and aerosol-cloud interactions, remain poorly quantified.In this work, we aim to better quantify how aerosols, energy, and moisture are transported to and distributed within the Arctic. We investigate observations at Arctic stations, including, Villum and Zeppelin, and perform backward-in-time simulations with the Lagrangian atmospheric transport model FLEXPART (Pisso et al., 2019; Bakels et al., 2024) to derive so-called emission sensitivities and use these sensitivities to better quantify source regions of aerosols, energy, and moisture.In general, we aim to better describe the spatial and temporal atmospheric transport characteristics into the Arctic and how these characteristics have changed in recent years. We focus on the transport during warm-air intrusions, since almost 30% of the total poleward transport of moisture (during winter) occurs during such events (Woods et al., 2013). Warm-air intrusions are often associated with large-scale atmospheric blocking patterns forcing a change in transport direction from east to more poleward, bringing warm, moist, and cloudy air into the Arctic. Warm-air intrusions can also be favourable for an enhanced transport of aerosols (e.g., Dada et al., 2022).Since climate models show large biases in moisture flux during these events (Woods et al., 2017), there is clearly a need to better quantify the transport of moisture, energy, and aerosols during these events. This will also help to provide better forcing for climate simulations.Bakels et al. (2024): 10.5194/gmd-17-7595-2024; Dada et al. (2022): 10.1038/s41467-022-32872-2; Lapere et al. (2024): 10.1029/2023JD039606; Pisso et al. (2019): 10.5194/gmd-12-4955-2019; Woods et al. (2013): 10.1002/grl.50912; Woods et al. (2017): 10.1175/JCLI-D-16-0710.
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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.
2026