Found 2707 publications. Showing page 2 of 271:
2025
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
Rethinking Global Soil Degradation: Drivers, Impacts, and Solutions
Abstract The increasing threat of soil degradation presents significant challenges to soil health, especially within agroecosystems that are vital for food security, climate regulation, and economic stability. This growing concern arises from intricate interactions between land use practices and climatic conditions, which, if not addressed, could jeopardize sustainable development and environmental resilience. This review offers a comprehensive examination of soil degradation, including its definitions, global prevalence, underlying mechanisms, and methods of measurement. It underscores the connections between soil degradation and land use, with a focus on socio‐economic consequences. Current assessment methods frequently depend on insufficient data, concentrate on singular factors, and utilize arbitrary thresholds, potentially resulting in misclassification and misguided decisions. We analyze these shortcomings and investigate emerging methodologies that provide scalable and objective evaluations, offering a more accurate representation of soil vulnerability. Additionally, the review assesses both physical and biological indicators, as well as the potential of technologies such as remote sensing, artificial intelligence, and big data analytics for enhanced monitoring and forecasting. Key factors driving soil degradation, including unsustainable agricultural practices, deforestation, industrial activities, and extreme climate events, are thoroughly examined. The review emphasizes the importance of healthy soils in achieving the United Nations Sustainable Development Goals, particularly concerning food and water security, ecosystem health, poverty alleviation, and climate action. It suggests future research directions that prioritize standardized metrics, interdisciplinary collaboration, and predictive modeling to facilitate more integrated and effective management of soil degradation in the context of global environmental changes.
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
2025
PikMe: a flexible prioritization tool for chemicals of emerging concern
Abstract Identifying new contaminants of emerging concern remains a complex task due to the sheer number of chemical substances potentially released into the environment, the scattered sources of information, and often the lack of adequate data. Environmental screening and monitoring programs are designed to map the presence, sources, and potential environmental impacts of contaminants, yet prioritizing which chemicals to include in such efforts remains resource-intensive and technically challenging. PikMe is a modular, open-access prioritization tool that integrates information from major data bases and evaluates the concern and reliability of the data for more than one million substances. PikMe is built in a modular way so that prioritization can be done based on specific chemical properties relevant to a given scenario (i.e., drinking water contaminants or bioaccumulation in biota) rather than assigning only a global risk score. PikMe scores substances based on persistence, bioaccumulation, mobility, environmental toxicity, and human toxicity, assigning individual score per property. Additionally, PikMe is designed for flexibility by allowing the integration of external lists of chemicals and supporting optional add-ons. Different scenarios of use are described in this article, including the selection of chemicals for environmental monitoring and screening in Norway and the assessment of the implications of the new classifications according to the regulation for classification, labelling and packaging of substances and mixtures on persistent chemicals.
2025
Abstract. Establishing interlaboratory compatibility among measurements of stable isotope ratios of atmospheric methane (δ13C-CH4 and δD-CH4) is challenging. Significant offsets are common because laboratories have different ties to the VPDB or SMOW-SLAP scales. Umezawa et al. (2018) surveyed numerous comparison efforts for CH4 isotope measurements conducted from 2003 to 2017 and found scale offsets of up to 0.5 ‰ for δ13C-CH4 and 13 ‰ for δD-CH4 between laboratories. This exceeds the World Meteorological Organisation Global Atmospheric Watch (WMO-GAW) network compatibility targets of 0.02 ‰ and 1 ‰ considerably. We employ a method to establish scale offsets between laboratories using their reported CH4 isotope measurements on atmospheric samples. Our study includes data from eight laboratories with experience in high-precision isotope ratio mass spectrometry (IRMS) measurements for atmospheric CH4. The analysis relies exclusively on routine atmospheric measurements conducted by these laboratories at high-latitude stations in the Northern and Southern Hemispheres, where we assume each measurement represents sufficiently well-mixed air at the latitude for direct comparison. We use two methodologies for interlaboratory comparisons: (I) assessing differences between time-adjacent observation data and (II) smoothing the observed data using polynomial and harmonic functions before comparison. The results of both methods are consistent, and with a few exceptions, the overall average offsets between laboratories align well with those reported by Umezawa et al. (2018). This indicates that interlaboratory offsets remain robust over multi-year periods. The evaluation of routine measurements allows us to calculate the interlaboratory offsets from hundreds, in some cases thousands of measurements. Therefore, the uncertainty in the mean interlaboratory offset is not limited by the analytical error of a single analysis but by real atmospheric variability between the sampling dates and stations. Using the same method, we assess this uncertainty by investigating measurements from four high-latitude sites analysed by the INSTAAR laboratory. After applying the derived interlaboratory offsets, we present a harmonised time series for δ13C-CH4 and δD-CH4 at high northern and southern latitudes, covering the period from 1988 to 2023.
2025