Found 10478 publications. Showing page 4 of 420:
2026
Unidentified Halon-2402 emissions in East Asia are driving the global trend
Halon-2402 (1,2‑dibromotetrafluoroethane, H-2402) is a potent ozone‑depleting substance and greenhouse gas whose global production has been banned under the Montreal Protocol since 2010, while the use of recovered or recycled stocks remains permitted for essential uses. Although these controls led to nearly two decades of declining atmospheric abundances, recent observations indicate renewed emissions. Here, we present the first observation-based regional emission estimates of H-2402 in East Asia for 2008–2023, derived using high-frequency measurements at Gosan, South Korea, and a Bayesian inversion framework. While most AGAGE stations measure background mole fractions or intermittent low-level increases, Gosan exhibits increasingly frequent and intense pollution events, revealing growing regional emissions. We find that East Asia accounted for most global H-2402 emissions in recent years, with particularly sharp increases in Japan and the Vladivostok region of Russia. Since 2015, regional emissions from East Asia have effectively driven the global emission trend, reversing the long-term decline. These emissions are spatially linked to petrochemical infrastructure, ship-repair activity, and military decommissioning sites, suggesting releases from legacy halon banks rather than new production. Cumulative emissions from East Asia between 2008 and 2023 reached ~52 Gg CFC-11-equivalent emissions. These findings imply a tangible delay in ozone layer recovery and underscore the urgent need for strengthened monitoring, transparent reporting, and verifiable management of remaining H-2402 stocks under the Montreal Protocol.
2026
Long-term trend of elemental carbon in the high Arctic and its potential drivers
The Arctic is warming at more than twice the global average rate, a phenomenon known as Arctic amplification (Rantanen et al., 2022). In addition to greenhouse gases, short-lived climate forcers play a critical role in modulating Arctic climate through their impacts on radiation, cloud properties, and the surface energy balance (e.g. AMAP, 2015, 2021). Among these forcers, elemental carbon (EC) is of particular importance due to its strong light-absorbing properties and its ability to reduce surface albedo when deposited on snow and ice. Furthermore, aged EC particles transported to the Arctic can act as cloud condensation nuclei, influencing cloud microphysical processes and thereby modifying Arctic radiative forcing and climate feedbacks.In this study, we investigate long-term trends in EC concentrations and their potential drivers in the high Arctic using 16 years of continuous EC measurements from the Villum Research Station in northeast Greenland. We combine in situ observations with Lagrangian transport modelling and back-trajectory analyses to assess the relative contributions of changes in source-region emissions, transport pathway variability, and wet scavenging processes to the observed EC trends. Robust non-parametric statistical methods are applied to assess monotonic trends over the full observational period and before 2020, enabling a systematic comparison between the declining and stagnating phases. This integrated observational–modelling framework provides new constraints on the processes controlling EC variability in the Arctic and advances our understanding of how anthropogenic emission reductions are reflected in Arctic atmospheric composition under a rapidly evolving climate.
2026
Climate change intensifies multi‑hazard risks that affect ecosystems, societies, and economies. Addressing these interconnected risks requires integrated systems, harmonized data, and cross‑sectoral collaboration. Research infrastructures (RIs) that observe climate‑ and nature‑related processes generate essential data and services for understanding climate risk determinants: hazard, exposure, and vulnerability, yet their potential remains underutilised by financial, banking, and insurance sectors that increasingly face nature‑dependent risks.IRISCC (Integrated Research Infrastructure Services for Climate Change Risks; www.iriscc.eu) unites leading European Research Infrastructures (Ris) to provide open, standardized climate‑risk data, tools, and services through transnational and virtual access. With nearly 80 partners across natural and social sciences, IRISCC strengthens the scientific foundations for integrated climate‑risk assessment and supports the translation of RI data and tools into risk‑management landscapeWe conducted a stakeholder analysis to map the current and emerging climate‑risk service landscape and to assess how IRISCC services connect with academic, industry and decision making sectors. Survey data from IRISCC partners combined with a preliminary mapping of climate‑risk service providers, show that while strong links exist with EU‑level organizations, direct engagement with financial, banking, and insurance sectors is still very limited. This gap is critical: recent assessments by the European Central Bank indicate that around 72% of European companies depend heavily on at least one ecosystem service, underscoring the financial sector’s exposure to nature degradation (Elderson F.2023, Network for Greening the Financial System NGFS, 2022)Our findings highlight significant opportunities to embed scientific communities more efficiently, to enhance RI usage, harmonized datasets, and analytical tools into multi‑hazard climate‑risk services. Strengthening these connections can support more robust risk detection, prevention, and early‑warning capabilities, particularly for nature‑dependent industries.This presentation outlines the key findings from stakeholder analysis, identifies gaps in the current service landscape related to climate risks, and open the potential of IRISCC’s services to contribute to the needs of financial and insurance sectors. By fostering new collaborations and co‑created solutions, IRISCC aims to advance a more holistic, interoperable, and science‑based climate‑risk ecosystem in Europe.IRISCC is funded by the European Union (project number 101131261). Views and opinions expressed are however those of the author(s) only and do not necessarily reflect those of the European Union. Neither the European Union nor the granting authority can be held responsible for them. Elderson F. The economy and banks need nature to survive. European Central Bank. Published June 8, 2023. Accessed January 15, 2026. https://www.ecb.europa.eu/press/blog/date/2023/html/ecb.blog230608~5cffb7c349.en.htmlNetwork for Greening the Financial System (NGFS). Nature‑related risks. Published 2022. Accessed January 15, 2026. https://www.ngfs.net/en/what-we-do/nature-related-risks
2026
Dust in the Arctic: feedbacks and interactions between climate change, aeolian dust and ecosystems
Dust in the Arctic is an emerging topic related to climate and environmental impacts. The United Nations (UN) General Assembles and the UN Coalition to Combat Desertification (UNCCD) have reiterated that the global frequency, intensity, and duration of Sand and Dust Storms (SDS) have increased in the last decade and that SDS have natural and human causes that can be exacerbated by desertification, land degradation, drought, biodiversity loss, and climate change. UNCCD and FAO have also highlighted that emerging SDS source areas have been associated with the warming of the Arctic and high latitude regions, the seasonal or permanent drying of inland waters and river deltas, or are following large-scale deforestation and wildfires, or even the ploughing of a single field. Loss of snow cover, retreat of glaciers, and increase in drought intensity due to climate change can lead to surface conditions that increase the likelihood of creation, continuation and expansion of SDS source areas.Climatic feedback mechanisms and ecosystem impacts related to dust in the Arctic include direct radiative forcing (absorption and scattering), indirect radiative forcing (via clouds and cryosphere), semi-direct effects of dust on meteorological parameters, effects on atmospheric chemistry, as well as impacts on terrestrial, marine, freshwater, and cryosphere ecosystems. Here we give an overview of our recent understanding on dust emissions and their long-range transport routes, deposition, and ecosystem effects in the Arctic as presented in Meinander et al. (2025), part of the series of review papers of the Arctic Council Working Group AMAP (Arctic Monitoring and Assessment Program) and CAFF (Conservation of Arctic Flora and Fauna), where the target audience is the scientific community focusing on the Arctic. Additional audiences include policy advisers and other staff in environmental-related ministries.We conclude that the multiple mechanisms related to dust emissions, transport and deposition both cool and warm the climate system, with an uncertain net effect. Dust plays a significant role in terrestrial and aquatic ecosystems, e.g., by providing nutrients, and with impacts on the availability of light and water. Due to Arctic warming, HLD dust emissions can be expected to increase. The contributions of LLD and HLD complicates the interpretation of how much different sources contribute to the dust loadings and corresponding temporal and spatial deposition patterns. Another challenge is that low latitude dust source emissions of road and agricultural dust is barely characterized.Reference:Meinander O, Uppstu A, Dagsson-Waldhauserova P, Groot Zwaaftink C, Juncher Jørgensen C, Baklanov A, Kristensson A, Massling A and Sofiev M (2025). Dust in the arctic: a brief review of feedbacks and interactions between climate change, aeolian dust and ecosystems. Front. Environ. Sci. Sec. Interdisciplinary Climate Studies, Volume 13 – 2025. doi: 10.3389/fenvs.2025.1536395. CAFF-special issue.
2026
The Arctic is warming up to 4 times faster than the global average, leading to rapid ice melting and consequently, a drastic change of the sources and processing of aerosols and their impact on clouds. Monitoring of these changes over the Arctic is extremely sparse, especially in the most remote regions where harsh conditions make it difficult to carry out even simple measurements. To address these knowledge gaps and develop better and new methods of remote sensing of aerosols and clouds, the CleanCloud project carried out the field campaign CLeancloud Arctic VIllum ExpeRiment (CLAVIER) at Villum Research Station (VRS) in northeast Greenland to study aerosol-cloud interaction (ACI) using in-situ surface and remote sensing as well as airborne measurements.CLAVIER covered two phase; spring (April) and summer (July/August) 2024, each lasting for one month. We have employed the existing in-situ surface aerosol monitoring at VRS, which includes a Scanning Mobility Particle Sizer (SMPS), a Cloud Condensation Nuclei Counter (CCNC), a High-Volume Sampler (HVS), a Nephelometer, an Aethalometer, a Neutral cluster and Air Ion Spectrometer (NAIS), a wind lidar and a ceilometer. During CLAVIER, the site was additionally equipped with a AeRosol aerosol-cloud lIdar System (ARIS lidar) and a Wideband Integrated Bioaerosol Sensor (WIBS-5/NEO) to provide realtime measurement of aerosols and fluorescent particles to infer the presence of bioaerosols and their potential contribution to Ice Nucelating Particles (INP). In addition, a W-band Cloud Doppler Radar (WProf) and a tethered balloon (Helikite) was operated during the spring phase. The helikite was equipped with aerosol and cloud instrumentation, including a Portable Optical Particle Spectrometer (POPS), a Miniaturized Scanning Electrical Mobility Sizer (mSEMS), a Single-channel tricolor absorption photometer (STAP) and a miniaturized Cloud Droplet Analyzer (miniCDA), and a filter sampler with the new nano-electromechanical membrane FTIR (NEMS-FTIR) technique. A second tethered balloon was also employed for meteorological and flux measurements. In the summer phase, a Proton-Transfer-Reaction Mass Spectrometry (PTR-MS) was used to measure VOCs online and cartridge sampling was performed for offline sampling of VOCs, as well as a WELAS (white-light aerosol spectrometer) for size distribution of larger sizes and the newest aethalometer AE36s. Finally, summertime measurements were also coordinated with the NASA ARCSIX aircraft mission for clousure experiments. In order to get a better understanding of the processes related to aerosol-cloud interactions, several modelling activities were and are being carried out for the CLAVIER period. These include the Flexible Particle Dispersion Model (FLEXPART), the WRF-SIP model to study in detail the secondary ice production in clouds, OpenIFSv48 global model to simulate the aerosol composition and forcing during the campaign, and finally, the FLEXPART-SOSAA framework and the ADCHEM model to study in detail the aerosol chemistry and impacts on CCN.This presentation will provide an overview of these activities and some preliminary results.
2026
Quantifying European SF6 emissions (2005-2021) using a large ensemble of atmospheric inversions
Sulfur hexafluoride (SF₆) is an extremely potent (GWP100 = 24,300) and long-lived greenhouse gas whose atmospheric concentrations continue to rise due to anthropogenic emissions. Europe represents a particularly relevant test case for investigating SF₆ emissions, as successive EU F-gas regulations over the past two decades have aimed to substantially reduce emissions. A key question is whether these regulatory measures are reflected in observed emission trends and whether reported national inventories are consistent with observation-based estimates. In this study, we quantify European SF₆ emissions for the period 2005–2021 using a large ensemble of atmospheric inversions with a strong focus on uncertainty characterization. Uncertainties are assessed using an extensive set of sensitivity tests in which key inversion parameters are systematically varied, while final uncertainties are quantified via a Monte Carlo ensemble that randomly samples combinations of these parameters. This allows us to identify the main sources of uncertainty and to evaluate the robustness of inferred emission trends.Our analysis focuses on countries with relatively dense observational coverage - the United Kingdom, Germany, France, and Italy - while also examining aggregated emissions for the EU-27. The inversion results reveal declining SF₆ emissions in all studied regions except Italy, broadly consistent with the timing of EU F-gas regulations (842/2006, 517/2014). In several countries, inferred emissions exceed reported national inventories, although the agreement generally improves in more recent years. At the EU-27 scale, emissions exhibit a pronounced decline between 2017 and 2018, coinciding with a marked reduction in emissions from southwestern Germany, suggesting regional actions were taken as the 2014 regulation took effect.Our sensitivity tests highlight the crucial role of dense and sustained atmospheric monitoring networks for robust inversion-based emission estimates. In particular, expansions of the UK observing system in 2012 and 2014 lead to significant reductions in emission uncertainties, demonstrating the importance of comprehensive observational networks in refining emission estimates.
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
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
Uncertainties associated with Lagrangian transport in greenhouse gas flux estimates
Atmospheric Lagrangian particle dispersion models (LPDMs) are commonly combined with Bayesian inversion/optimization methods to infer emission fluxes across spatial scales from local to global. These tools are central to monitoring greenhouse gases, especially CO₂, CH₄, and N₂O. However, uncertainties in flux estimates arise from multiple sources: prior flux information, representation of the background atmospheric composition, statistical model choices (including hyperparameters and error covariance assumptions), and errors in atmospheric transport. In this presentation, we describe current uncertainty quantification activities linked to ongoing projects (e.g. EYE-CLIMA). We will discuss the use of meteorological ensemble simulations to assess transport related uncertainty and explore connections with dynamical systems tools and common assumptions such as Gaussian errors. Emphasis will be placed on high-resolution transport modelling applications.
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
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.
2026
Monitoring of long range transported air pollutants in Norway. Annual Report 2025
This report presents results from the monitoring of atmospheric composition and deposition of air pollution in 2025, and focuses on main components in air and precipitation, particulate and gaseous phase of inorganic constituents, particulate carbonaceous matter, ground level ozone and particulate matter.
NILU
2026
Global methane emission estimates from a dual-isotope inversion: new constraints from δD-CH4
Methane (CH4) is a potent greenhouse gas; however, the causes of its growth since 2006 are a subject of debate. While measurements of CH4 mole fraction and carbon isotopic composition (δ13C-CH4) have been extensively used to investigate the global CH4 budget, the hydrogen isotopic composition (δD-CH4) remains underutilised despite its unique sensitivity to source types and oxidation processes. Here, we assimilate a newly harmonised 35-year dataset of dual isotope measurements from high-latitude monitoring stations in both hemispheres within a two-box Bayesian inversion to quantify global CH4 sources and sinks. The model integrates prior emissions from five source categories based on global bottom-up inventories. Methane removal processes are represented by sink-specific kinetic isotope effects as tropospheric and stratospheric loss, and soil uptake. We find that the inclusion of δD-CH4 improves the model's ability to constrain emission apportionment between biogenic and thermogenic sources, particularly for fossil fuel emissions during the late 1990s and early 2000s, which affects CH4 lifetime estimate. CH4 increase post-2006 is driven mainly by rising wetland emissions, while fossil-fuel growth is modest, biomass burning declines, and agriculture and waste make smaller, regionalised contributions. The optimised inversion results favour a strong 13C kinetic isotope effect in total tropospheric CH4 removal and a net shortening of the NH lifetime of CH4 by 0.2 years. This study demonstrates the added value of incorporating δD-CH4 into inverse modelling frameworks and underscores the importance of long-term δD-CH4 measurements for advancing our understanding of CH4 biogeochemistry and its role in the global carbon cycle.
2026
Benzotriazole UV stabilizers (BUVs) are widely used plastic additives and are increasingly recognized as contaminants of emerging concern due to their persistence and potential for long-range environmental transport. In this study, bird feathers collected from polar regions and China were analyzed to investigate the occurrence and profiles of BUVs. BUVs were detected in all studied regions, with detection frequencies varying among compounds (up to 89.2%) and concentration levels ranging from 9.64 to 52.68 ng/g ww for ΣBUVs. Among individual compounds, UV-329 exhibited the highest median concentration (up to 26.0 ng/g ww), followed by UV-326 and UV-327. BUVs were identified in Antarctic bird feathers, whereas previous biomonitoring studies based on other biological matrices reported non-detectable levels in Antarctic samples. This work also represents the first application of bird feathers to investigate BUV contamination across the Arctic, the Antarctic, and China. Differences in BUV profiles were observed across regions and species, but these patterns should be interpreted cautiously because sampling year, species ecology, and feather-specific processes may all influence feather concentrations. The results also support the use of bird feathers as a non-destructive biomonitoring matrix for monitoring BUV contamination in both remote and populated regions. These findings highlight the need for further investigation into the sources, transport pathways, and ecological risks of BUVs.
2026
This study presents results from an Intensive Measurement Period (IMP2022) conducted during the European heatwave of July 2022, focusing on ozone, volatile organic compounds (VOCs), and carbonaceous aerosols at 31 sites across Europe. The episode featured persistent high-pressure systems, record-breaking temperatures, widespread ozone exceedances and concurrent atmospheric new particle formation and growth events. Coordinated measurements and chemistry transport modelling were used to examine the spatial variability of ozone, VOC composition, and secondary organic aerosol (SOA) formation under extreme meteorological conditions. Oxygenated VOCs (O-VOCs) constituted the largest fraction of total measured VOC mixing ratios, followed by non-methane hydrocarbons (NMHCs) and aromatics, with contributions from both anthropogenic and biogenic sources. Sensitivity simulations indicate that ozone formation was predominantly NOx-limited across most regions during IMP2022. However, the highest ozone peaks occurred under conditions of elevated NOx in combination with enhanced BVOC emissions. In contrast, SOA formation was slightly enhanced under low-NOx conditions and reduced in elevated NOx. Isoprene, aliphatic NMHCs, and O-VOCs dominated the ozone formation potential, while aromatics and monoterpenes were major contributors to SOA potential. Model simulations indicated that higher NOx concentrations can reduce SOA formation by about 10 %. The campaign also highlighted observational gaps underscoring the need for broader and higher-resolution VOC monitoring across Europe. Overall, further reductions in NOx emissions, alongside targeted control of key anthropogenic VOCs, would benefit air quality under future climate extremes.
2026
An interlaboratory comparison (ILC) was conducted for levoglucosan, mannosan, and galactosan, as widely used organic tracers for assessing biomass burning aerosol in ambient air. Organized as part of the European research infrastructure ACTRIS (Aerosol, Clouds and Trace Gases Research Infrastructure) activities the OrGanic Tracers and Aerosol Constituents - Calibration Centre (OGTAC-CC) distributed aliquots from three ambient PM2.5 filter samples and two prepared aqueous standard solutions to ten research laboratories across Europe, each using its own analytical protocol. Overall agreement was good for the ambient filter samples, with relative standard deviations relative to the general mean of 14% for levoglucosan, 22% for mannosan, and 33% for galactosan. Individual measurement accuracy, expressed as mean percentage error, ranged from −33% to 13% for levoglucosan, −51% to 15% for mannosan, and −54% to 42% for galactosan. Laboratory performance was also assessed using z-scores, showing that despite methodological diversity, nearly all results were classified as acceptable. This ILC provides a timely snapshot of current European laboratory capability for key biomass burning tracers. The joint intercomparison study demonstrates the readiness of European laboratories to provide harmonized levoglucosan measurements at a continental scale, meeting the comparability needs arising from the inclusion of levoglucosan in the revised EU Ambient Air Quality Directive (AAQD), and supporting requirements across European (Co-operative Programme for Monitoring and Evaluation of the Long-range Transmission of Air Pollutants in Europe (EMEP), ACTRIS) and national monitoring networks.
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
Inverse modelling is employed to reconcile greenhouse gas (GHG) emission inventories, based on bottom-up methods, with the observed atmospheric GHG concentrations. The Community Inversion Framework (CIF) was created to unify inverse-model developments and simplify the generation of inversions. It makes atmospheric transport models and inversion algorithms easily interchangeable and facilitates the comparison of inversion results obtained using such diverse components.After several years of development and the coupling of CIF with a wide range of transport models used by the inversion community, we present the first intercomparison study conducted with CIF. This exercise focuses on Europe and aims to refine CO₂ natural emissions for the year 2019, following a strict protocol. It involves five transport models (CHIMERE, ICON-ART, LMDz, STILT, and WRF-CHEM) and two inversion algorithms (variational and ensemble-based). Two additional transport models, TM5 and FLEXPART, will be incorporated in the near future.The results show a good agreement, both across transport models, and inversion algorithms. It paves the way towards using CIF as an operational tool for intercomparison studies. It also highlights its strong potential to support the systematic derivation of GHG budgets with multiple transport models, enable a proper and easy quantification of the modelling uncertainty, and improve the robustness of emission estimates, for any relevant atmospheric species, at any scale.
2026
Large aerosol particles within the coarse mode affect the environment, climate, and human health in ways that strongly depend on particle type. Although this size range is dominated by mineral dust and sea spray aerosol (SSA), less abundant biological particles can exert disproportionate effects, such as triggering ice formation at comparatively warm temperatures. Accurate, type-resolved characterization of coarse-mode aerosols is therefore critical for understanding their environmental and climatic roles. Here, we present a new laboratory-based reference dataset for common coarse-mode aerosol sources, including pollen, dust, bacteria, and microplastics, based on laboratory measurements of single-particle ultraviolet light-induced fluorescence (UV-LIF) spectroscopy and particle morphology. Comparison with existing datasets reveals source-specific fluorescence signatures, but also demonstrates substantial overlap between biological and non-biological particles, which can lead to misclassification when fluorescence information is used alone.Building on this dataset, we introduce a new machine-learning classification framework that combines fluorescence and morphological features. The algorithm is trained using laboratory data and evaluated with field observations from Zeppelin Observatory, Svalbard. To improve discrimination of combustion-related particles and to better separate dust from SSA, we apply domain adaptation using in situ measurements. The updated classifier successfully reproduces the previously reported annual bioaerosol cycle, yields higher bioaerosol concentrations than a fluorescence-only method, and maintains similar correlations with established biological and combustion tracers. Our open-source code enables more robust quantification of bioaerosols across a range of environments, allows reassessment of prior observations, and can be further improved as new particle characterization data become available.
2026
Ozone responses to the geomagnetic storms in 2024 and 2025
Solar Cycle 25 has approached its maximum phase, bringing an elevated frequency of solar eruptive events and associated geomagnetic disturbances. During 2024 and 2025, several intense geomagnetic storms have provided rare opportunities to examine the short-term coupling between space‐weather forcing and the middle atmosphere. Previous studies have shown that energetic particle precipitation (EPP) during geomagnetic storms can substantially modify the chemical composition of the mesosphere and lower thermosphere (MLT), particularly through the production of odd nitrogen (NOx) and odd hydrogen (HOx), which catalytically destroy ozone. In this presentation, we investigate the MLT ozone responses to several large geomagnetic storms occurring in 2024–2025 using MLS satellite observation. We will also estimate the particle forcing associated with these events using the observed ozone chemical responses. This analysis provides a testbed for climate model inputs.
2026