Fant 10344 publikasjoner. Viser side 408 av 414:
2025
2025
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
Geoengineering skulle kunna skapa ett mer orättvist klimat
Världens klimatsystem hänger ihop, så geoengineering för bättre klimat i en region kan påverka andra regioner negativt. Men vem styr och tar beslut om ...
2025
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
This study examines how southern wintering areas may contribute to organochlorine (OCs) loads in arctic seabirds during breeding. Light-sensitive geolocators (GLS loggers) were deployed on Arctic skuas (Stercorarius parasiticus) in one high arctic and two subarctic colonies. Hexcahlorobenzene (HCB), chlordanes, mirex, p,p′-dichlorodiphenyldichloro-ethylene (p,p′-DDE), and polychlorinated biphenyls (PCBs) were measured in the blood of breeding adults at the nest (58 individuals, a total of 128 samples) in northern Norway and Svalbard between 2009 and 2015. We compared OC concentrations and OC profiles among nesting skuas wintering in five Atlantic regions, determined by the GLS loggers: the coast of Argentina, the Caribbean, off West Africa, off the coast of southern Africa, and the Mediterranean Sea. As predicted, HCB, which is semi-volatile and has high long-range transport potential, showed high prevalence in birds wintering in all regions except the Mediterranean. Mirex showed the highest prevalence in birds wintering off the coasts of Argentina and southern Africa, in accordance with high background levels previously documented in the Southern Ocean. Chlordanes were particularly prevalent in skuas wintering off southern Africa, whereas p,p′-DDE seemed relatively evenly distributed among wintering areas. As predicted, the prevalence of PCBs was much higher in birds wintering in the Mediterranean Sea than in birds from other regions. This study thus suggests that the Mediterranean Sea and the mid- and southern Atlantic are essential sources of different OCs in the blood of Arctic skuas breeding in the European Arctic.
2025
This study critically examines the workflow for untargeted analysis of volatile organic compounds (VOCs) in ambient air, from sampling strategies to data interpretation by using GC-HRMS. While untargeted approaches are well-established in liquid chromatography (LC) due to advanced-deconvolution tools and extensive metabolomic libraries, their application in gas chromatography (GC) remains less developed, particularly for VOCs. The high structural isomerism of VOCs and the relative novelty of GC-based untargeted methodologies present unique challenges, including limited software tools and reference libraries. Air samples from suburban and rural sites in central Italy were analyzed to explore chemical diversity and address methodological gaps. This study evaluates critical decisions, such as sampling strategies, extraction techniques, and data-processing workflows, highlighting the limitations of automated deconvolution tools and the need for manual validation. Results revealed distinct source contributions, with suburban areas showing higher levels of anthropogenic compounds and rural areas dominated by biogenic emissions. This work underscores the potential of GC-HRMS untargeted analysis to advance environmental chemistry, while addressing key pitfalls and providing practical recommendations for reliable application. By bridging methodological gaps, it offers a roadmap for future studies aiming to integrate untargeted and targeted approaches in air quality research.
2025
This document provides technical details and support for the implementation of air quality monitoring under the Directive (EU) 2024/2881 of the European Parliament and of the Council of 23 October 2024 on ambient air quality and cleaner air for Europe (recast) (AAQD, Directive (EU) 2024/2881). It presents an overview of current knowledge and best practices, signposting to existing technical guidance on air quality monitoring and to sources of ongoing technical guidance development. This document does not formulate any legal provisions and as such, it does not have a legally binding value.
Publications Office of the European Union/European Commission. Directorate-General for Environment
2025
This report presents data from the fourth year of a five-year period of the MILFERSK program. In 2024, the monitoring program focused on the sampling and analysis of the benthic food chain in Lake Mjøsa, encompassing the following sample types: Chironomids, Ruffe, Perch, Pike and the stomach contents of ruffe. Additionally, brown trout from the pelagic zone in Lake Mjøsa were collected and analyzed, with the contaminant levels compared to samples of brown trout from the reference lake, Femunden. The concentrations of 175 individual compounds/isomers were determined, with frequent detections of specific per- and polyfluoroalkyl substances (PFAS), polybrominated diphenyl ethers (PBDEs), mercury (Hg), and siloxanes exhibiting biomagnifying properties throughout the food chain. Certain contaminants, such as quaternary ammonium compounds, were found in higher concentrations in sediment and lower trophic levels. Concentrations of chlorinated paraffins (CPs), particularly medium-chain chlorinated paraffins (MCCPs) were higher in chironomids, ruffe, and the livers of perch and pike, compared to levels observed in 2021 and 2022, with an increase up the food chain in 2024. A slight downward trend in perfluorooctane sulfonate (PFOS) concentrations was observed in Lake Mjøsa from 2014 – 2024. Additionally, a lower length-adjusted mercury concentration was noted for brown trout in Lake Mjøsa during the period from 2015 to 2024, compared to the preceding nine years (2006 – 2014).
Norsk institutt for vannforskning (NIVA)
2025
2025
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
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
2025
Transboundary particulate matter, photo-oxidants, acidifying and eutrophying components
Norwegian Meteorological Institute
2025