Fant 918 publikasjoner. Viser side 3 av 39:
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.
2026
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
Surveys in Norwegian schools showed that some students experienced health problems, such as headaches or concentration issues which have been linked to indoor environment quality (IEQ). This research investigates the relationship between measured IEQ and students’ perceived IEQ as user-feedback in one lower secondary school. This study explores the factors contributing to the connection with certain parameters such as carbon dioxide (CO2), volatile organic compounds (VOC), and temperature levels with perceived IEQ. Despite achieving good IEQ levels according to standards, there is a notable discrepancy between measured IEQ and how students perceive the air quality. Two classrooms served by a demand-controlled ventilation system were monitored with IEQ measurement sensors and online questionnaires were given individually to students in each classroom. This enables to provide real-time students’ perception of indoor air and room temperature quality. Measurement results showed IEQ are of good quality, but students’ responses on perceived IEQ vary and showed over 25% are dissatisfied, indicating mixed feelings and dissatisfaction about perceived IEQ. Future research should focus on refining ventilation systems to bridge the gap between measured and perceived IEQ.
2025
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
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
Circulating MicroRNAs in Cord Blood to Predict Attention-Deficit/Hyperactivity Disorder Diagnosis
Background
There are large knowledge gaps in the etiology of attention-deficit/hyperactivity disorder (ADHD), and although it is a prevalent and highly heritable neurodevelopmental disorder, diagnosis can be challenging. We aimed to assess the association of circulating blood plasma microRNAs (miRNAs) at birth with ADHD for use as biomarker candidates and build an miRNA-based prediction model.
Methods
Our study population consisted of 206 children with ADHD (33.0% female), 207 control children (33.8% female), and their parents from the MoBa (Norwegian Mother, Father, and Child Cohort Study). Expression levels of 51 selected miRNAs in plasma from children’s cord blood at birth and from both parents during early pregnancy were quantified by quantitative polymerase chain reaction and tested for association with children’s ADHD diagnosis and ADHD symptom scores based on ratings by parents.
Results
Seven miRNAs were differentially expressed at birth in children with ADHD and control children (false discovery rate < .05), and 31 had a statistically significant linear relationship with parent-rated ADHD symptom score at 8 years. A 19-miRNA ADHD prediction model achieved good discrimination in the test population (area under the receiver operating curve = 0.959, accuracy = 0.893). Functional analysis for the 19-miRNA prediction set revealed involvement in several highly relevant pathways, e.g., dopaminergic synapse, circadian rhythm, and axon guidance. We also found that parental miRNA expression levels significantly associated with children’s ADHD diagnoses and/or ADHD symptoms scores.
Conclusions
We showed that expression levels of circulating miRNAs at birth may be used to predict increased risk of ADHD diagnosis, and our 19-miRNA set should be included in future efforts to develop a biomarker panel.
2025
Building-related symptoms in school environment: Predictability using machine learning approach
Building-related symptoms (BRS) are commonly experienced by students in schools and are potentially affecting academic performance and health. Even though indoor environment quality (IEQ) measurements indicated fair conditions, students still perceived discomfort that led to symptoms, highlighting the necessity of collecting user-feedback about IEQ-complaints. This study aimed to predict and understand the prevalence of BRS (headache, tiredness, cough, dry eyes-hands) experienced by students in classrooms using machine-learning (ML) approach based on measurement data, building factors, and prevalence of IEQ-complaints. We collected measurement data (from indoor and outdoor climate), building factors, and user-feedback by students via online-platform across three sampled classrooms each campaign during three consecutive school semesters. Significant input variables for ML were pre-selected using statistical tests. ML models were evaluated based on accuracy metrics and SHAP analysis for input interpretation. Models using measurement data alone performed poorly (testing R² <50 %) to predict prevalence of BRS, whereas adding building factors and prevalence of IEQ-complaints increased accuracy (R² up to 95 %) of prediction of BRS with lower RMSE. In addition, interpretation from SHAP analysis showed IEQ-complaints especially related with indoor air quality (e.g., heavy air, dust & dirt, and dry air) as significant contributors for predicting prevalence of BRS. We conclude that the framework of combining objective measurements with occupant-reported complaints can be reliable, interpretable predictions of symptom prevalence. This study is limited by single-school setting, health confounders, and symptoms verification. Future research may contribute to exploring wider set of input variables, applicability, and variation of complaints preference.
2025
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.
2026
Worldwide, edible fish are well studied for plastic occurrence. Microplastic (MP) occurrence in edible tissues raises concern for the organism’s health, but also on food safety. In the Arctic region, MP occurrence in other tissues than the digestive tract of fish has not been published yet. Plastic-related chemicals such as UV stabilisers (UVS) were also scarcely studied in Arctic biota. Our objectives were to (1) provide data on MP occurrence in Atlantic cod (Gadus morhua) fillets, (2) quantify UVS in the same fillets, and (3) provide estimations of both MP and UVS intake by Norwegian and European consumers through cod consumption. Twenty individuals were collected in the Barents Sea, south-west of Svalbard. They were dissected onboard and a piece of fillet was used for the extraction of MPs in the lab. Particles’ identification was performed by µRaman spectroscopy. MPs were found in 45 % of the fillets, with an average of 0.25 MP/g ww. Only one UV stabiliser (UV-326) was detected, in four fillets. Based on consumption data of cod, an average Norwegian man and woman would ingest 20.8 and 33.5 MPs weekly, respectively. Considering a European diet, a weekly intake of 8 MPs and a yearly intake of 403 MPs through cod consumption was calculated. The impacts of MP exposure to humans are unknown. Through this study, rather than to raise potential risks of consuming fish, we aimed to trigger further research on microplastics occurrence in seafood, i.e. in the edible tissues of aquatic animals.
2025
Spatial and temporal assessment of soil degradation risk in Europe
Soil degradation threatens agricultural productivity and ecosystem resilience across Europe, yet spatially consistent assessments of its intensity and drivers remain limited. In this study, we used Soil Degradation Proxy (SDP), that integrates four key indicators of soil degradation, including erosion rate, soil pH, electrical conductivity, and organic carbon content, to quantify soil degradation risk. Using over 38,000 LUCAS topsoil observations and a machine learning model trained on climate, land cover, topographic, soil parent material properties, and spectral variables, we map annual SDP values between years 2000 to 2022 across Europe. Results show soil degradation risk is highest in southern Europe, especially in intensively managed and sparsely vegetated landscapes. Over the past two decades, approximately 7.1% of land area across the EU and the UK has experienced increasing degradation risk (most notably across Eastern Europe), with rainfed croplands emerging as the most affected land cover type. Land cover is the most influential driver, modulating effects of climatic variables such as precipitation and temperature on SDP. This data-driven framework provides a consistent and scalable approach for monitoring soil degradation risk and offers actionable insights to support targeted conservation and EU-wide policy implementation.
2025
2026
2026
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
Construction of an enterprise-level global supply chain database
Data tracing global supply chains, commonly captured in input–output models, is a foundational resource across economic, regulatory, investment, defense, and environmental applications. Such models provide insight into interdependency and environmental burden-shifting, forming part of the empirical basis for policies such as Scope 3 embodied emissions targets, supply chain transparency, life cycle assessments, and product declarations. Current approaches, based on national statistics, remain constrained by sector-level resolution, limiting their precision and utility in certain applications. Here, we document the construction of an enterprise-level multi-regional input–output (EMRIO) table. This database merges official national input–output tables with publicly available firm-level production and transaction data, creating a globally consistent account of purchases and sales across 9,466 companies, 86,305 subsegments, and 121 countries. The finer resolution allows supply chain transactions to be represented in greater detail, providing an additional resource for analyses and policy tools requiring more disaggregated supply chain information.
2025
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
2025
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
Impact of leakage during HFC-125 production on the increase in HCFC-123 and HCFC-124 emissions
Hydrochlorofluorocarbons (HCFCs) are ozone-depleting substances whose production and consumption have been phased out under the Montreal Protocol in non-Article 5 (mainly developed) countries and are currently being phased out in the rest of the world. Here, we focus on two HCFCs, HCFC-123 and HCFC-124, whose emissions are not decreasing globally in line with their phase-out. We present the first measurement-derived estimates of global HCFC-123 emissions (1993–2023) and updated HCFC-124 emissions for 1978–2023. Around 5 Gg yr−1 of HCFC-123 and 3 Gg yr−1 of HCFC-124 were emitted in 2023. Both HCFC-123 and HCFC-124 are intermediates in the production of HFC-125, a non-ozone-depleting hydrofluorocarbon (HFC) that has replaced ozone-depleting substances in many applications. We show that it is possible that the observed global increase in HCFC-124 emissions could be entirely due to leakage from the production of HFC-125, provided that its leakage rate is around 1 % by mass of HFC-125 production. Global emissions of HCFC-123 have not decreased despite its phase-out for production under the Montreal Protocol, and its use in HFC-125 production may be a contributing factor to this. Emissions of HCFC-124 from western Europe, the USA and East Asia have either fallen or not increased since 2015 and together cannot explain the entire increase in the derived global emissions of HCFC-124. These findings add to the growing evidence that emissions of some ozone-depleting substances are increasing due to leakage and improper destruction during fluorochemical production.
2025
Machine learning for mapping glacier surface facies in Svalbard
Glaciers are dynamic and highly sensitive indicators of climate change, necessitating frequent and precise monitoring. As Earth observation technology evolves with advanced sensors and mapping methods, the need for accurate and efficient approaches to monitor glacier changes becomes increasingly important. Glacier Surface Facies (GSF), formed through snow accumulation and ablation, serve as valuable indicators of glacial health. Mapping GSF provides insights into a glacier's annual adaptations. However, satellite-based GSF mapping presents significant challenges in terms of data preprocessing and algorithm selection for accurate feature extraction. This study presents an experiment using very high-resolution (VHR) WorldView-3 satellite data to map GSF on the Midtre Lovénbreen glacier in Svalbard. We applied three machine learning (ML) algorithms—Random Forest (RF), Artificial Neural Network (ANN), and Support Vector Machine (SVM)—to explore the impact of different image preprocessing techniques, including atmospheric corrections, pan sharpening methods, and spectral band combinations. Our results demonstrate that RF outperformed both ANN and SVM, achieving an overall accuracy of 85.02 %. However, nuanced variations were found for specific processing conditions and can be explored for specific applications. This study represents the first clear delineation of ML algorithm performance for GSF mapping under varying preprocessing conditions. The data and findings from this experiment will inform future ML-based studies aimed at understanding glaciological adaptations in a rapidly changing cryosphere, with potential applications in long-term spatiotemporal monitoring of glacier health.
2025
Temporal changes in per and polyfluoroalkyl substances and their associations with type 2 diabetes
We assessed temporal changes of PFAS and associations with T2DM over a period of 30 years in a nested case–control study with repeated measurements. Logistic regression was used to assess associations between 11 PFAS and T2DM at five time-points in 116 cases and 139 controls (3 pre- and 2 post-diagnostic time-points in cases). Mixed linear models were applied to assess if changes in PFAS were related to T2DM status. In the pre-diagnostic time-point T3 (2001), future cases had higher concentrations of PFHpA, PFNA, PFHxS and PFHpS compared to controls. In the post-diagnostic time point T5 (2015/16), PFNA and PFOS were higher in prevalent cases. PFHxS and PFHpS were positively associated with future T2DM at the pre-diagnostic time-point T3, whereas PFTrDA were inversely associated with future T2DM at T1 (1986/87) and prevalent T2DM at T4 (2007/8). Temporal changes in PFAS across the study period showed that cases experienced a greater increase in pre-diagnostic concentrations of PFHpA, PFTrDA, PFHxS and PFOSA, as well as a larger post-diagnostic decrease in PFOSA, compared to controls. This study is the first to show that temporal changes in PFAS are associated with T2DM status for certain PFAS, and associations between PFAS and T2DM vary according to sample year.
2025
Killer whales (Orcinus orca) accumulate high levels of persistent organic pollutants (POPs), which have been linked to immunomodulation. Over the past decades, large-scale mortality events associated with cetacean morbillivirus (CeMV) have affected cetacean populations, and concerns have been raised about the role of contaminants in exacerbating these outbreaks. However, establishing cause-effect relationships in free-roaming cetaceans remains a significant challenge. In vitro approaches present unique potential for furthering our understanding of the effects of multiple environmental stressors in marine mammal health. In this study, we used primary fibroblasts cultured from wild Norwegian killer whale skin biopsies (n = 6) to assess how exposure to POP mixtures affects cell viability and CeMV replication. Our findings demonstrate that CeMV successfully replicates in killer whale fibroblasts, with the viral replication significantly increasing over the duration of the experiment. POP exposure led to a concentration-dependent decrease in cell viability and a significant increase in viral replication. These results validate killer whale primary fibroblasts as a valuable in vitro tool for the study of co-exposure of POPs and morbillivirus on toothed cetaceans. Moreover, these findings support the need for further research to confirm the role of contaminants in intensifying the severity of CeMV infections in the wild.
2025
Measurements from the Advanced Global Atmospheric Gases Experiment (AGAGE) combined with a global 12-box model of the atmosphere have long been used to estimate global emissions and surface mean mole fraction trends of atmospheric trace gases. Here, we present annually updated estimates of these global emissions and mole fraction trends for 42 compounds through 2023 measured by the AGAGE network, including chlorofluorocarbons, hydrochlorofluorocarbons, hydrofluorocarbons, perfluorocarbons, sulfur hexafluoride, nitrogen trifluoride, methane, nitrous oxide, and selected other compounds. The data sets are available at https://doi.org/10.5281/zenodo.15372480 (Western et al., 2025). We describe the methodology to derive global mole fraction and emissions trends, which includes the calculation of semihemispheric monthly mean mole fractions, the mechanics of the 12-box model and the inverse method that is used to estimate emissions from the observations and model. Finally, we present examples of the emissions and mole fraction data sets for the 42 compounds.
2025