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Urban areas experience elevated air pollution levels which pose significant health risks. Reducing exposure to poor air quality and mitigating the associated negative health impacts requires informed policy measures. This study advances urban air quality modelling by developing an air quality model (baseline model) and further integrating measurements from a network of low-cost sensors and regulatory monitors into the model output (data fusion model). The resulting data fusion model provides accurate air quality data in high spatiotemporal resolution. The data fusion model showed higher PM2.5 concentrations during evening hours and winter months, with a population-weighted exposure to PM2.5 almost twice as high as predicted by the baseline model during these months. The models exhibited different spatial patterns, with the data fusion model showing a shift in peak concentrations from the city centre to residential areas, where levels were up to 10 µg/m3 higher than the baseline model. These differences are likely attributable to an underestimation of residential emissions in the baseline model. While both models were FAIRMODE compliant, the data fusion model showed a reduced bias for most monitoring stations compared to the baseline model. The data fusion model enabled a more accurate assessment of existing policies, specifically those aimed at reducing urban air pollution from solid fuel burning. Moreover, by identifying locations and sectors which contribute significantly to high levels of PM2.5, the data fusion model supports the formation of targeted air quality policies. This enables cities to maximise reductions in air pollution and exposures, thereby safeguarding public health.
2026
Nonlinear Atmospheric Inversion with Interpretable Bias Correction via Gaussian Process Prior
Accurate quantification of atmospheric pollutant emissions is essential for evaluating the consequences of environmental incidents. Inverse modelling of such releases commonly employs a linear framework based on a source–receptor sensitivity (SRS) matrix; however, this matrix can be substantially biased or may even fail to represent the true scale of the release. We introduce a method in which the SRS matrix is corrected jointly with the inversion, resulting in a nonlinear inverse problem. The SRS discrepancies are interpreted as small shifts of observation points, leading to a deformation of the sensitivity field. The shifts are regularized through a Gaussian process prior, which imposes smoothness and sparsity while allowing inference at unobserved locations. The resulting posterior predictions of the shift field offer a practical tool for hyperparameter selection: the inferred shifts can be visualized geographically and evaluated by domain experts. This leads to a Bayesian framework that integrates inversion, SRS correction, and a tuning strategy based on L-curve-type diagnostics combined with maps of the predicted shifts. It will be demonstrated on a selected real continental-scale scenario of an atmospheric release. This research has been supported by the Czech Science Foundation (grant no. GA24-10400S). FLEXPART model simulations are cross-atmospheric research infrastructure services provided by ATMO-ACCESS (EU grant agreement No 101008004). Nikolaos Evangeliou was funded by the same EU grant. The computations were performed on resources provided by Sigma2 - the National Infrastructure for High Performance Computing and Data Storage in Norway.
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
Polluted air is a major global health risk factor, yet the chemical composition and toxicity of airborne gases and particles remain underexplored due to their complexity and difficulties in sampling. We recently introduced how polydimethylsiloxane (PDMS) foam─or silicone foam─can be synthesized for passive air sampling, enabling simple and cost-effective nontarget chemical profiling of indoor air. Here, we demonstrate expanded applications, indoors and outdoors, with commercial PDMS-foam, including for: (i) wide-scope target analysis of >220 priority substances by quantitative liquid- and gas chromatography-high-resolution mass spectrometry, (ii) microscopic characterization and nontarget profiling of accumulated fine particles, and (iii) effect-guided discovery of harmful substances, combining toxicological data with nontarget analysis in silico. Median method quantification limits were 0.12 ng/mL, 90% of target analytes had absolute recoveries between 70 and 130%, and hazardous substances were discovered, including ethylene glycols, insecticides, and UV filters. Microscopy revealed the accumulation of abundant fine particles, and the automated characterization of the fluorescent fraction revealed that most were <4 μm. Extracts from outdoor samples reduced human lung cell viability, and multivariate modeling flagged families of potentially toxic substances in a virtual effect-directed analysis. PDMS-foam disks require field calibration to determine their linear sampling rate(s), but current results and applications establish PDMS-foam as a multimodal passive sampler, enabling integrated chemical quantitation, toxicological analysis, and molecular discovery in air.
2026
Abstract Methane is a powerful greenhouse gas with a shorter lifetime than carbon dioxide (CO 2 ), making it an important target for near‐term climate action. The Global Methane Pledge (GMP) aims to cut anthropogenic methane emissions by 30% from 2020 levels by 2030. Using an Earth system model with interactive CH 4 sources and sinks, we assess the Pledge's impact through 2050. Results show that current GMP commitments deliver only a 10% cut by 2030—well below the target. Only the maximum technically feasible reduction (MTFR) pathway can achieve the 30% goal. By 2050, current GMP commitments lowers methane concentrations by 3% relative to 2025, while MTFR achieves 8%. Both pathways slow warming slightly, avoiding about 0.1°C of global temperature rise, with the Arctic seeing the greatest benefits (up to 2°C less warming). Without wider participation, the GMP with current signatories will fall short of its targets and Paris Agreement goals.
2026
Accumulation patterns of polychlorinated alkanes in an Arctic marine food web
Polychlorinated alkanes (PCAs), otherwise known as chlorinated paraffins, are contaminants of emerging Arctic concern where our understanding of their occurrence and trophic transfer in Arctic food webs remains limited. To investigate biomagnification potential of PCAs, we analyzed short-chain PCAs: C10-C13 and medium-chain PCAs-C14-17 in three Arctic species: polar cod (Boreogadus saida), ringed seal (Pusa hispida), and polar bear (Ursus maritimus) and Subarctic capelin (Mallotus villosus) samples collected from the northern Barents Sea in 2017 and 2021. PCAs-C10-13 concentrations were low, but detectable in all species, while PCAs-C14-17 concentrations were mainly below detection limits in the mammals. PCAs did not biomagnify, as the lowest concentrations were found in polar bear (0.7 ng g−1 lw) and the highest in capelin (56.9 ng g−1 lw). The PCA homologue profiles were similar among Arctic species, with PCAs-C10-13 dominating in polar cod and marine mammals, which may suggest a contribution from long-range atmospheric transport.
In contrast, PCAs-C14-17 were most abundant in the Subarctic capelin, likely reflecting a different exposure. Despite differing PCAs-C14-17 concentrations among the two fish species, their PCAs-C14-17 homologue profile was similar, indicating uniform global production trends. Subarctic capelin is increasingly being preyed upon by Arctic predators and may facilitate the biological transport of PCAs-C14-17 into Arctic ecosystems.
These findings suggest that climate-driven shifts in species distribution may have the potential to alter contaminant exposure pathways in Arctic marine food webs.
2026
Aerosol-Cloud Interactions: Overcoming a Barrier to Projecting Near-Term Climate Evolution and Risk
Aerosol-cloud interactions (ACI) are a major source of uncertainty in climate science, critically affecting our ability to project near-term climate evolution and assess societal risks. These interactions influence effective radiative forcing, cloud dynamics, and precipitation patterns, yet remain insufficiently constrained due to limitations in observations, modeling, and process understanding. This uncertainty hampers robust policy advice across multiple domains—from estimating remaining carbon budgets and climate sensitivity, to anticipating regional extreme events and evaluating climate interventions such as solar radiation modification. In many cases, the influence of ACI is either underappreciated or excluded from decision-making frameworks due to its complexity and lack of quantification. This perspective outlines a path forward to overcome these barriers by leveraging emerging opportunities in satellite remote sensing, ground-based and airborne observations, high-resolution climate modeling, and machine learning. We identify key areas where rapid progress is feasible, including improved retrievals of cloud microphysical properties, better representation of natural aerosols in a warming world, and enhanced integration of observational and modeling communities. Even as anthropogenic aerosol and its impacts on clouds is reducing owing to emissions controls, addressing ACI uncertainties remains essential for refining climate projections, supporting effective mitigation and adaptation strategies, and delivering actionable science to policymakers in a rapidly changing climate system.
2026
2026
Global black carbon emissions from 2015-2022 constrained by observations and transport modelling
Black carbon (BC) is a product of incomplete combustion, is climate relevant and has negative impacts on human health. It absorbs radiation as an aerosol in the atmosphere, but also changes the albedo of snow covered surfaces and leads to earlier melting. The origins are either anthropogenic or natural, with different annual cycles. While anthropogenic sources like domestic burning peak in the winter, natural sources like wild fires and agricultural burning peak during spring and summer. Due to its short lifetime are the global atmospheric concentrations highly variable.We use ground based observational data from different global networks, and the atmospheric transport model FLEXPART driven by ERA5 meteorological analysis, emission inventories and the inversion framework FLEXINVERT. By minimizing the mismatch modelled and observed BC concentration we improve the emission inventories for natural emissions (GFAS) and anthropogenic emissions (LRTAP). For every year up to 50 stations are used and each observation is matched with a 50 day FLEXPART backward calculation.We discuss the distribution and sources of global BC aerosols over the period 2015 to 2022 and compare existing emission inventories with the improved constraints of the global BC emissions derived with the FLEXINVERT inversion framework.
2026
2026
2026
2026
The Fire Modeling Intercomparison Project (FireMIP) for CMIP7
Fire is a global phenomenon and a key Earth system process. Extreme fire events have increased in recent years, and fire frequency and intensity are projected to rise across most regions and biomes, posing substantial challenges for ecosystems, the carbon cycle, and society. The Fire Model Intercomparison Project (FireMIP), launched in 2014, has advanced global fire modeling in Dynamic Global Vegetation Models (DGVMs) and improved understanding of fire's local and direct drivers and its local impacts on vegetation and land carbon budgets through land offline simulations (i.e., uncoupled from the atmosphere). We now bring FireMIP into Coupled Model Intercomparison Project Phase 7 (CMIP7) to: (1) evaluate fire simulations in state-of-the-art fully coupled Earth system models (ESMs); (2) assess fire regime changes in the past, present, and future, and identify their primary natural and anthropogenic forcings and causal pathways within the Earth system, including the associated uncertainties; and (3) quantify the impacts of fires and fire changes on climate, ecosystems, and society across Earth system components, regions, and timescales, and elucidate the underlying mechanisms. FireMIP in CMIP7 will advance the fire and fire-related modeling in fully coupled ESMs, and provide a quantitative, comprehensive, and process-based understanding of fire's role in the Earth system by using models that incorporate critical climate feedbacks and CMIP7 multi-model, multi-initial-condition, and multi-scenario ensemble. This protocol paper presents the motivation, scientific questions, experimental design and rationale, model inputs and outputs, and recommended analysis framework for FireMIP in CMIP7, providing guidance to Earth system modeling teams conducting simulations and informing communities studying fire, climate change, and climate solutions.
2026
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
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.
2026
2026
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