Found 10224 publications. Showing page 1 of 409:
This study assesses trends in the total ozone column (TOC) and the atmospheric factors influencing ozone variability at three Antarctic stations (Marambio, Troll/Trollhaugen, and Concordia) from 2007 to 2023. Ground-based TOC measurements were used, supplemented by satellite observations from the Ozone Monitoring Instrument on NASA's Aura satellite. TOC trends were derived using a multiple linear regression model provided by the Long-term Ozone Trends and Uncertainties in the Stratosphere (LOTUS) project. The selected LOTUS model was able to explain 94 %–97 % of the TOC variability at all three stations. The regression analysis showed that ozone variability at these stations is mainly driven by the lower stratospheric temperature, eddy heat flux, and the Quasi-Biennial Oscillation. A statistically significant increasing trend was found at the Marambio station (3.43 ± 3.22 DU per decade), while statistically insignificant trends were detected at the other two stations. Using MERRA-2 reanalyses, the LOTUS model was applied to each grid point in the 40–90° S region, which effectively illustrates the spatial distribution of the impacts of individual predictors. It was found that warmer conditions in the Antarctic stratosphere in September 2019 caused TOC to be up to 100 DU higher than normal, especially over East Antarctica. The results improve understanding of regional TOC trends and how the Antarctic ozone layer responds to changes in ozone-depleting substances.
2026
Exceptional high AOD over Svalbard in summer 2019: a multi-instrumental approach
In the summer of 2019, the Arctic region registered exceptionally high aerosol optical depth (AOD) values over Svalbard, linked to intense biomass burning (BB) and volcanic activity across the Northern Hemisphere. This study presents a comprehensive, multi-instrumental analysis of the aerosol conditions in and around Ny-Ålesund (Spitsbergen, Norway), combining data from ground-based sun-photometry, in-situ observations, active remote sensing (ground-based and on satellite), and atmospheric dispersion modelling (FLEXPART). Despite high AOD was observed during all the period, three different aerosol events are identified in the atmospheric column (6–10 July, 25–28 July, and 6–17 August). In contrast, in-situ surface stations only recorded significant aerosol load during 5–9 July, 30 August, and 12 September, suggesting that most of the aerosol particles remained above the boundary layer. Lidar and photometric observations revealed the presence of spherical, weakly absorbing Accumulation-mode particles (with effective radii between 0.1 and 0.2 µm) in both the troposphere and stratosphere, with persistent layers extending above 10 km. Simulations carried out with FLEXPART correlate well with the measurements, attributing the observed aerosol events to multiple sources, including Siberian and North American wildfires, the Raikoke (Russia) volcanic eruption, and anthropogenic pollution. While the simulations show a contribution from volcanic aerosols, the contribution from biomass-burning aerosols in the upper troposphere and lower stratosphere were likely more significant under the atmospheric conditions of summer 2019. Overall, the aerosol radiative impact during this long-lasting event was substantial, with a mean reduction in direct solar radiation of approximately −74 W m−2 during July and August. This work shows how the use of dispersion modelling together with multiple observation sources allows to achieve a more complete description of the atmospheric aerosol events and contributes to a better understanding of the overall picture.
2026
A multi-year analysis of aerosol optical depth (AOD, τ) and Ångström exponent (α) was conducted using ground-based photometer data from 15 Arctic and 11 Antarctic sites. Extending the dataset of (Tomasi et al., 2015) through December 2024, the study incorporates stellar and lunar photometric observations to fill data gaps during the polar night. Daily mean values of τ at 0.500 µm and α (0.440–0.870 µm) were used to derive monthly means and seasonal histograms. In the Arctic, persistent haze events in winter and early spring lead to peak τ values. A decreasing trend in Arctic τ suggests the impact of European emission regulations, while biomass-burning aerosols are becoming more significant. In Antarctica, τ increases from the plateau to the coast. Fine-mode aerosols dominate in summer-autumn, while coarse-mode particles are more prevalent in winter-spring. Shipborne photometer data align well with ground-based measurements, confirming the reliability of mobile observations. Trend analyses using the Mann-Kendall test and Theil-Sen regression indicate a significant negative trend in τ at Andenes (−2.43 % per year), likely driven by reduced anthropogenic emissions. Antarctic stations such as Syowa and South Pole show positive trends (+3.84 % and +3.54 % per year), though these are subject to uncertainties from data limitations and instrument changes. This work contributes to the Polar-AOD network (https://polaraod.net/, last access: 15 May 2025), enhancing the understanding of aerosol variability and long-term trends in polar regions while promoting open data access for the scientific community.
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Abstract Potato plants are highly vulnerable to numerous diseases that can substantially affect both yield and quality. Conventional approaches for detecting these diseases are often labor-intensive, slow, and prone to inaccuracies, particularly under variable environmental conditions. This study presents a hybrid deep learning architecture, termed potato leaf diseases DenseNet (PLDNet) , which integrates a DenseNet-based convolutional neural network with a Transformer-based attention module to accurately classify potato leaf diseases. Furthermore, an adaptive parametric activation function, referred to as Adaptive Flatten p-Mish (AFpM) , is proposed to enhance the model’s learning flexibility and representational capacity. When evaluated on the PlantVillage and Mendeley datasets, PLDNet attains classification accuracies of 99.54% and 87.50%, respectively, surpassing contemporary state-of-the-art models and activation techniques. The proposed framework exhibits strong generalization performance and offers a scalable, efficient approach for automated plant disease identification. To highlight the novelty, the proposed AFpM activation function introduces a learnable parameter enabling adaptive nonlinearity, improving over Mish, Swish, and PFpM activation functions through dynamic gradient control. AFpM improves accuracy by 2.52% on Mendeley dataset, and 1.93% on PlantVillage dataset compared to PFpM, and by more than 3% compared to Swish and Mish.
2026
City-produced and transported black carbon: Synergy of in-situ optical measurements and modeling
The implementation of air pollution mitigation strategies requires not only high-quality continuous measurements of pollutants but also proper definitions of ways to differentiate between transported and locally produced contributions, as only the latter can be effectively reduced by authorities. To address this issue, we propose a new approach for partitioning monitored black carbon (BC) concentrations into city-produced (urban) and transported fractions using a combination of measured and modeled data. Two simultaneous measurement campaigns (warm season 2022 and cold season 2022/23) were conducted in two urban environments: Vilnius (Lithuania) and Warsaw (Poland). In the cold season in Warsaw, BC mass concentration was 90% higher than in the warm season, while in Vilnius, an increase of 44% was observed, as compared to the warm season. Aerosol optical properties showed more complex aerosol mixtures of dust, BC and brown carbon (BrC) during the cold season, forming larger particles. Single scattering albedo (SSA) anti-correlated with BCFF, proving that fossil fuel (FF) combustion contributes to the warming effect in both cities. A positive correlation between the population density of the emission areas of transported BC and the BC mass concentrations in Vilnius and Warsaw was found. The impact of transported BC on the local BC levels in the cities was of % and % in the cold season and of % and % in the warm season for Warsaw and Vilnius, respectively. Thus, the approach of BC partitioning showed that in the cold season, the two cities suffered from worse air quality, in part due to more transported BC.
2026
Boreal forests at risk: Absence of climate perspectives in current management policies
Boreal forests influence climate both biogeochemically through carbon uptake and biogeophysically through evapotranspiration, turbulent fluxes and albedo, and are in turn impacted by climate through biotic and abiotic damages. This systematic literature review and qualitative narrative policy review and analysis aims to get a better insight into the discrepancy between policy and science on forestry action to mitigate climate warming in high latitude jurisdictions. We identify climate effects on and from forests with corresponding management options in a systematic review of scientific literature following PRISMA guidelines. These results were combined with a qualitative policy review and analysis to identify the climate and forestry policies from all boreal-to-Arctic jurisdictions and determine how (many of) these climate effects ended up in forest and climate policy. There is mounting evidence that in boreal regions, albedo-driven warming can partially offset, and in some contexts be comparable to, carbon-driven cooling; the balance varies by season, forest type and disturbance history. However, although all analysed jurisdictions (Alaska, Canada, European Union, Sweden, Finland, Iceland, Norway and Russia) recognise the forests' role in carbon uptake, none recognise the albedo effect, and none translate these climate effect into binding regulatory measures. Nor do most of the jurisdictions take into account possible risk of climate-related damages. This might lead to ineffective and even adverse forest and climate measures. Our study emphasises a need for more evidence-based and comprehensive climate and forestry policies and regulations, along with a proactive approach to adopting these measures swiftly.
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A Global Compendium of Nature-based Solutions in Small-Medium Islands
Small and medium-sized islands (SMI) combine high ecological value with limited resources and vulnerability to climatic and environmental risks. Nature-based solutions (NbS) can contribute to addressing some of these challenges, but studies on the uptake and effectiveness of NbS in SMI remain scattered, with few systematic syntheses. Here, we introduce the SMI-NbS compendium, a comprehensive and open-access dataset compiling 280 NbS case studies implemented across SMI worldwide, developed through a systematic review of published and grey literature. Each SMI-NbS case study includes information on the location, NbS category, ecosystem types, societal challenges addressed, associated co-benefits, and links to the United Nations’ Sustainable Development Goals (SDGs). The SMI-NbS compendium provides practical information on NbS implementation and identifies current research trends and gaps, such as the dominance of ecological and climate-focused NbS, with limited integration of other socio-economic challenges, thereby supporting further research and enabling knowledge exchange across the science-policy-practice interface to inform sustainable development pathways in SMI.
2026
2026
Exposures in Indoor Air Affecting Health
Indoor air quality (IAQ) is influenced by a wide range of chemical, biological and physical agents that can negatively impact physical, immunological and mental health. Adverse health effects depend on the type and concentration of pollutants, duration of exposure, and individual susceptibility. The availability of data on IAQ is limited, as are standardized approaches for evaluating its health impact. This expert review aims to describe the most important indoor air determinants affecting health, and present the IDEAL cluster, which comprises seven EU‐funded scientific projects on the topic of IAQ and human health. Across the IDEAL projects, knowledge is generated on exposure to a wide range of indoor air pollutants, including well‐known hazards and more explorative chemical and microbiological determinants. The projects will also contribute to the implementation of low‐cost and/or real‐time sensors on IAQ, as well as advanced chemical and microbiological analyses, and evaluate various interventions to improve IAQ. Several of them focus on particularly vulnerable groups. Raising public awareness and implementing measures to reduce pollutant levels are essential for safeguarding health, particularly in urban areas with elevated pollution levels.
2026
An inter-comparison of inverse models for estimating European CH4 emissions
Atmospheric inversions are widely used to evaluate and improve inventories of methane (CH4) emissions across scales from global to local, combining observations with atmospheric transport models. This study uses the dense network of in situ stations of the Integrated Carbon Observation System (ICOS) to explore how well in situ data can constrain European CH4 emissions. Following the concept of inter-comparison studies of the atmospheric tracer transport model inter-comparison Project (TransCom), a CH4 inverse inter-comparison modeling study has been performed, focusing on Europe for the period 2006–2018. The aim is to investigate the capability of inverse models to deliver consistent flux estimates at the national scale and evaluate trends in emission inventories, using a detailed dataset of CH4 emissions described and presented here for first time.
Study participants were asked to perform inverse modelling computations using a common database of a priori CH4 emissions and in-situ observations as specified in a protocol. The participants submitted their best estimates of CH4 emissions for the 27 European Union (EU-27) member states, the United Kingdom (UK), Switzerland, and Norway. Results were collected from 9 different inverse modelling systems, using 7 different global and regional transport models. The range of outcomes allows us to assess posterior emission uncertainty, accounting for transport model uncertainty and inversion design decisions, including a priori emission and model-data mismatch uncertainty.
This paper presents inversion results covering 15 years, that are used to investigate the seasonality and trends of CH4 emissions. The different inversion systems show a range of a posteriori emission adjustments, pointing to factors that should receive further attention in the design of inversions such as optimising background mole fractions. Most inverse models increase the seasonal cycle amplitude, by up to 400 Gg month−1, with the largest adjustments to the a priori emissions in Western and Eastern Europe. This might be due to underestimation of emissions from wetlands during summer or the importance of seasonality in other microbial sources, such as landfills and waste water treatment plants. In Northern Europe, absolute flux adjustments are comparatively small, which could imply that the emission magnitude is relatively well captured by the a priori, though the lower station density could contribute also.
Across Europe, the inverse models yield a similar decreasing trend in CH4 emissions compared to the a priori emissions (−12.3 % instead of −9.1 %) from 2006 to 2018. While both the a priori and the a posteriori trend for the EU-27 are statistically significant from zero, their difference is not. On a subregional scale, the differences between a posteriori and a priori trends are more statistically significant over regions with more in-situ measurement sites, such as over Western and Southern Europe.
Uncertainties in the a priori anthropogenic emissions, such as in the agriculture sector (cows, manure), or waste sector (microbial CH4 emissions), but also in the a priori natural emissions, e.g. wetlands, might be responsible for the discrepancies between the a priori and a posteriori emission shift in the trends in Western, Eastern and Southern Europe.
Our results highlight the importance of improving the inversion setup, such as the treatment of lateral boundary conditions and the model representation of measurement sites, to narrow the uncertainty ranges further. The referenced dataset related to the analysis and figures are available at the ICOS portal: https://doi.org/10.18160/KZ63-2NDJ (Ioannidis et al., 2025).
2026
Abstract Hierarchical agglomerative clustering is a useful analysis technique which allows for a level of stability, interpretability and flexibility not available in other similar techniques such as K‐means, density‐based clustering or positive matrix factorization. Previous studies using hierarchical clustering on atmospheric model output have been limited to small domain sizes (roughly 100 × 100 grid cells) by the computational expense and memory requirements of the algorithm. Here we present a scalable hierarchical clustering implementation that we apply to two year‐long, hourly atmospheric data sets: model concentration and deposition timeseries at 290,520 locations over Alberta and Saskatchewan (538 540 grid); and 366,427 multi‐pollutant observations from 51 national air pollution surveillance stations located across Canada. When combined with other information such as emissions source locations, orography, and prevailing meteorological conditions, the method yields coherent, interpretable structures. In the case of model time series, the clustering provides regions of similar air quality (airsheds) which can be used to inform air quality monitoring network placement, or regions of similar deposition which can inform critical load assessment as well as monitoring site locations. In the case of the multi‐pollutant observations, we show that a single low‐primary pollutant cluster appears the most frequently at all but one of 51 stations across Canada, accounting for 62% of all station‐hours, while elevated SO 2 appears in factor profiles at certain monitoring locations near industrial and shipping activity. Together, these results demonstrate that hierarchical clustering can efficiently summarize patterns relevant to airshed mapping and source apportionment at previously unreachable scales.
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Microplastic and other anthropogenic particles in surface waters of the Isfjorden system (Svalbard)
Knowledge of sources and transport mechanisms of anthropogenic particles (APs) such as microplastics (MPs) and related plastic chemicals, in the Arctic marine environment is limited. This study investigates the surface waters of the Isfjordensystem, where Svalbard's largest settlement, Longyearbyen, is located, for the presence of APs. The wastewater from Longyearbyen is released untreated into Adventfjorden, which is a branch of Isfjorden. Samples from the inflowing current of Isfjorden into Adventfjorden, and its outflowing current were sampled and analyzed for APs (>50 μm). APs were classified regarding size, shape, and polymer type via μFTIR spectroscopy. Each location showed an AP burden (Isfjorden: 26 APs/L, Adventfjorden: 20 APs/L). Highest amounts of APs were found in the Isfjorden current (37 APs/L), before entering Adventfjorden. 14 APs/L were indicated near the wastewater effluent in Adventfjorden, and 15 APs/L in the outflowing current in Isfjorden. Plastic related chemicals, polypropylene and other polyolefins had high frequencies, but silk and rayon material dominated each location except the inflowing current from Isfjorden. Local sources like wastewater and other anthropogenic activities, as well as northwards long-range transport from the south into the Arctic, are considered. Oceanographic dynamics, and the time of sampling seems to affect the distribution of APs in the surface waters, besides its characteristics itself (e.g., polymer type and size).
2026
Evaluating the role of low-cost sensors in machine learning based European PM2.5 monitoring
We evaluate the added value of integrating validated Low-Cost Sensor (LCS) data into a Machine Learning (ML) framework for providing surface PM2.5 estimates over Central Europe at 1 km spatial resolution. The synergistic ML-based S-MESH (Satellite and ML-based Estimation of Surface air quality at High resolution) approach is extended, to incorporate LCS data through two strategies: using validated LCS data as a target variable (LCST) and as an input feature via an inverse distance weighted spatial convolution layer (LCSI). Both strategies are implemented within a stacked XGBoost model that ingests satellite-derived aerosol optical depth, meteorological variables, and CAMS (Copernicus Atmospheric Monitoring Service) regional forecasts. Model performance for 2021–2022 is evaluated against a baseline trained on air quality monitoring stations without any form of LCS integration. Our results indicate that the LCSI approach consistently outperforms both the baseline and LCST models, particularly in urban areas, with RMSE reductions of up to 15–20 %. It also exhibits higher accuracy than the CAMS regional interim reanalysis with a lower annual mean absolute error (MAE) of 2.68 μg/m3 compared to 3.32 μg/m3. SHapley Additive exPlanations based analysis indicates that LCSI information improves both spatial and temporal representativeness, with the LCSI strategy better capturing localized pollution dynamics. However, the LCSI's dependency on the spatial LCS layer limits its ability to capture inter-urban pollution transport in regions with sparse or no LCS data. These findings highlight the value of large-scale sensor networks in addressing spatial coverage gaps in official air quality monitoring stations and advancing high-resolution air quality modeling.
2026
Efficacy of individual and combined terrestrial and marine carbon dioxide removal
Abstract Limiting global temperature rise below 2°C requires significant reduction in greenhouse gas emissions and likely large-scale carbon dioxide removal (CDR). This study assesses the CO2 sequestration and efficacy of two CDR approaches, Bioenergy with Carbon Capture and Storage (BECCS) and Ocean Alkalinity Enhancement (OAE), applied individually and in combination. Using the Norwegian Earth System Model (NorESM2-LM), simulations were designed to ramp up deployment of BECCS and OAE, to an additional area of 5.2 million km² by 2100 for bioenergy feedstock for BECCS, and a CaO deployment rate of approximately 2.7 Gt/year for OAE within the exclusive economic zones of Europe, the United States and China. The combined land-ocean CDR simulation revealed a largely additive carbon removal effect. Over 2030-2100, OAE sequestered 7 ppm of CO 22 with an accumulated 82.3 Gt CaO, achieving a CDR effectiveness of 0.08 ppm (~ 0.17 PgC) per Gt CaO, while BECCS reduced 16 ppm of CO2, with CDR effectiveness of 3.1 ppm per million km² of bioenergy crops. Together, the carbon removal achieved by BECCS and OAE corresponds to anthropogenic CO₂ emissions of 5.4 Gt CO₂/year by 2100, slightly more than 60% of current global transport sector emissions. Notably, the efficiency of BECCS and OAE alone was unaffected by their concurrent deployment. Nevertheless, simulations revealed distinct non- linear interactions, such as declines in land and soil carbon sinks in the combined scenario. Furthermore, all simulations show negligible effects on the global annual mean temperature. These results highlight near-additive CDR responses even under net-negative emissions, but feedback on land and ocean carbon sinks must be considered when designing CDR portfolios. This study provides new insights into CDR portfolio design and Earth system feedback under an overshoot scenario, highlighting both their potential and the need for continued emissions cuts and supportive policies.
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
Nitrogen dioxide (NO2) is a well-known air pollutant, mostly elevated by car traffic in cities. To date, small, reliable, cost-efficient multipollutant sensors with sufficient power and accuracy for community-based atmospheric studies are still lacking. The HAPADS (highly accurate and autonomous programmable platforms for providing air pollution data services) platforms, developed and tested in real conditions, can be a possible approach to solving this issue. The developed HAPADS platforms are equipped with three different NO2 sensors (7E4-NO2–5, SGX-7NO2, MICS-2711 MOS) and a combined ambient air temperature, humidity, and pressure sensor (BME280). The platforms were tested during the driving test, which was conducted across various roads, including highways, expressways, and national and regional routes, as well as major cities and the countryside, to analyse the environmental conditions as much as possible (Poland, 2024). The correlation coefficient r was more than 0.8, and RMSE (root mean squared error) was in the 3.3–4.3 μg/m3 range during the calibration process. The results obtained during the driving tests showed R2 of 0.9–1.0, which proves the ability of HAPADS platforms to work in the hard environmental conditions (including high rain and snow, as well as sun and a wide range of temperatures and humidity).
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