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As climate change impacts intensify across Europe and globally, societies are confronted with increasingly frequent and severe hazards that challenge public health, urban livability, and environmental sustainability. While adaptation measures are urgently needed to cope with current and near-term climate risks, it is becoming increasingly evident that mitigation efforts are essential to ensure a resilient and sustainable future. Too often, however, adaptation and mitigation strategies are planned and implemented in isolation, within sectoral silos, overlooking their potential interdependencies, synergies, and co-benefits. This contribution draws on the on-going experience and perspectives of the EU-funded healthRiskADAPT project, which addresses climate-related health risks by explicitly linking adaptation and mitigation pathways across multiple hazards.The project adopts a broad and integrated perspective that combines existing technical solutions, nature-based interventions, and engagement strategies, with a strong emphasis on co-benefits for health and well-being in the face of climate hazards namely heatwaves, air pollution including wildfire emission, and pollen. Central to this framework is the use of cost–benefit and co-benefit analyses to support decision-makers in identifying, prioritizing, and implementing measures that maximize societal resilience while delivering climate resilience solutions, considering natural based solutions (e.g., greening) as well as technical solutions (e.g., smart-buildings, do-it-yourself air purifier devices, evaporative cooling, high efficiency filtering). Beyond technical assessments, the healthRiskADAPT project recognizes that increasing resilience requires engagement beyond institutional actors. Social solutions such as education, awareness-raising, and capacity building at the stakeholder level are considered essential components of effective climate strategies. The contribution therefore also explores participatory formats and stakeholder engagement approaches designed to enhance understanding of climate-related health risks and support the co-design of locally relevant policies and interventions.By presenting the project’s methodological pathways, tools, and engagement strategies, this contribution illustrates how integrated adaptation–mitigation planning can be operationalized in practice. It highlights the value of moving beyond sector-specific solutions toward systemic approaches that acknowledge complex interdependencies between climate, environment, health, and society. Ultimately, the contribution aims to demonstrate how such integrated frameworks can support cities and regions in developing more coherent, evidence-based, and socially inclusive climate policies, strengthening resilience in the face of a changing climate.
2026
2026
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
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
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
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
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