Found 10488 publications. Showing page 416 of 420:
2026
Source Attribution of High-Latitude Aerosols Based on Multi-Wavelength Optical Properties
Atmospheric aerosols represent one of the largest sources of uncertainty in estimates of future climate predictions. A key challenge arises from the large variety of aerosol types differing in physical properties, e.g. size and shape, and chemical composition as well as concentration. Coastal regions are particularly complex environments, where natural and anthropogenic aerosols co-exist, mix and interact, often fundamentally altering their original properties. At the same time, coastal areas are densely populated, hosting approximately 40 % of the global population. Consequently, improved knowledge of aerosol properties in coastal regions is essential not only for climate studies but also because of their relevance to human health.The aerosols’ optical properties, defined by their interactions with sunlight through scattering and absorption, provide valuable insight into both their physical and chemical properties. The wavelength-dependent light scattering signal can be predominantly related to the particles size, while the wavelength-dependent absorption signal rather more reflects the aerosol particles’ chemical composition. By combining these types of information within a so-called Ångström matrix, the aerosol sources and types can be assessed.In this work, aerosol optical properties were measured at three different coastal sites representing contrasting environments to identify dominant aerosol sources and types. Measurement campaigns were conducted in an urban environment at Aarhus Bay, Denmark, a rural environment at Askö, Sweden and a pristine Arctic environment at Villum Research Station, Northwest Greenland. Wavelength-dependent scattering coefficients were measured using a nephelometer (AURORA 3000, Ecotech) and wavelength-dependent absorption coefficients were obtained by an aethalometer (AE33 or AE36s, MAGEE). In addition, aerosol number size distributions were measured and air-mass back-trajectory analysis was performed. One intense measurement campaign of approximately five weeks was carried out at each site between spring 2023 and spring 2025. The resulting datasets were analysed regarding dominant aerosol sources, determining the importance of natural vs. anthropogenic emissions and locally emitted vs. long-range transported aerosols.
2026
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
New Approach Methodologies (NAMs) are gaining significant momentum globally to reduce animal testing and enhance the efficiency and human relevance of chemical safety assessment. Even with substantial EU commitment from regulatory agencies and the academic community, the full regulatory adoption of NAMs remains a distant prospect. This challenge is further complicated by the fact that the academic world, oriented toward NAMs development, and regulatory agencies, focused on practical application, frequently operate in separate spheres. Addressing this disconnect, the present paper, developed within the European Partnership for the Assessment of Risks from Chemicals (PARC), provides a clear overview of both the available non-animal tests and current evaluation practices for genotoxic and carcinogenic hazard assessment, while simultaneously highlighting existing regulatory needs, gaps, and challenges toward greater human health protection and the replacement of animal testing through NAMs adoption.
The analysis reveals a complex landscape: while the EU is deeply committed to developing and adopting NAMs, as outlined in its Chemical Strategy for Sustainability and supported by initiatives like PARC, prescriptive regulations such as Classification, Labelling and Packaging (CLP) and Registration, Evaluation, Authorisation and Restriction of Chemicals (REACH) still heavily mandate in vivo animal data for hazard classification, particularly for germ cell mutagenicity and carcinogenicity. This reliance creates a “too-short-blanket-problem,” where efforts to reduce animal testing may impact human health protection because of the current in vivo-based classification criteria. In contrast, sectors such as cosmetics and certain European Food Safety Authority (EFSA)-regulated products demonstrate greater flexibility toward progressive integration of NAMs. While the deep mechanistic understanding of genotoxicity and carcinogenicity has significantly advanced the integration of alternatives to animal tests into regulatory chemical hazard assessment, their broader and full implementation faces considerable challenges due to both scientific complexities (i.e., the development and validation of fit-for-purpose NAMs) and existing legislative provisions.
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
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
Airborne microplastics and nanoplastics (MNPs) are emerging contaminants of global concern due to their persistence, widespread distribution, and potential health impacts. With sizes comparable to PM2.5, they are inhalable and may penetrate deep into the respiratory tract. Their detection and quantification remain challenging because of low environmental concentrations, complex matrices, and particle sizes often below the detection limits of current micro spectroscopic techniques. Pyrolysis–gas chromatography–mass spectrometry (Py-GC-MS) offers a powerful mass-based approach, enabling identification and quantification of polymers regardless of particle size. This review critically examines current methodologies for airborne MNP analysis using Py-GC-MS, including sampling strategies, pretreatment protocols, instrumental parameters, and data modeling approaches. A technological readiness level assessment highlights limited methodological harmonization and insufficient validation, particularly for nanoplastics. Progress toward routine atmospheric monitoring requires standardized workflows, agreed polymer-specific markers, interlaboratory validation, and improved reporting practices to establish Py-GC-MS as a cornerstone technique for airborne MNP assessment.
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
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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