Fant 10084 publikasjoner. Viser side 391 av 404:
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
Supervised Anomaly Detection in Univariate Time-Series Using 1D Convolutional Siamese Networks
In time-series data analysis, identifying anomalies is crucial for maintaining data integrity and ensuring accurate analyses and decision-making. Anomalies can compromise data quality and operational efficiency. The complexity of time-series data, with its temporal dependencies and potential non-stationarity, makes anomaly detection challenging but essential. Our research introduces ADSiamNet, a 1D Convolutional Neural Network-based Siamese network model for anomaly detection and rectification. ADSiamNet effectively identifies localized patterns in time-series data and smooths detected anomalies using a quantile-based technique. In tests with physical activity data from Actigraph watches and MOX2-5 sensors, ADSiamNet achieved accuracies of 98.65% and 85.0%, respectively, outperforming other supervised anomaly detection methods. The model uses a contrastive loss function to compare input sequences and adjusts network weights iteratively during training to recognize intricate patterns. Additionally, we evaluated various univariate time-series forecasting algorithms on datasets with and without anomalies. Results show that anomaly-smoothed data reduces forecasting errors, highlighting our approach’s effectiveness in enhancing time-series data analysis’s integrity and reliability. Future research will focus on multivariate time-series datasets.
2025
A worldwide aerosol phenomenology: Elemental and organic carbon in PM2.5 and PM10
Elemental carbon (EC), organic carbon (OC), and particulate matter (PM) concentrations in the inhalable (PM10) and fine (PM2.5) size fractions are measured worldwide, albeit with different analytical methods. These measurements from many researchers were collected and analyzed for Africa, America, Asia, and Europe for 2012–2019. EC/PM, OC/PM, and OC/EC ratios were examined based on region, site type, and season to infer potential sources and impacts. These analyses demonstrate that carbonaceous materials are important PM constituents throughout the world. Mean EC/PM ratios were lowest in PM10 in Sahelian Africa and Europe (∼0.01), highest (>0.07) in PM2.5 at urban sites in North America, South America, and Japan. Mean OC/PM ratios were lowest in PM10 in the Sahel (∼0.06) and in PM2.5 in China and Thailand (0.10), and highest in central and eastern Europe (∼0.3) and North America (∼0.4). OC/EC ratios were elevated in western and northern Europe, and at regional background sites in North America. EC/PM increased with PM10 in Thailand, while OC/PM increased with higher PM mass in Thailand, India, and North America, highlighting the specific contribution of carbonaceous aerosols to PM pollution in these regions. At European and North American background sites, OC/EC ratios increased with PM mass. Higher OC/EC ratios in dry periods indicate influence of wildfires, prescribed burns, and secondary aerosol formation. Elevated wintertime EC/PM ratios coincide with residential heating in temperate climate zones.
2025
Monitoring of the atmospheric ozone layer and natural ultraviolet radiation. Annual report 2024
This report summarizes the results from the Norwegian monitoring programme on stratospheric ozone and UV radiation measurements. The ozone layer has been measured at three locations since 1979: In Oslo/Kjeller, Tromsø/Andøya and Ny-Ålesund. The UV measurements started in 1995. The results show that there was a significant decrease in stratospheric ozone above Norway between 1979 and 1997. After that, the ozone layer stabilized at a level ~2% below pre-1980 level. The year 2024 was characterized by high total ozone values most of the year, especially in the Arctic stations in March. For Ny-Ålesund, 2024 showed the highest annual average total ozone value since systematic ground-based ozone measurements started in 1997.
NILU
2025
2025
Stress management with HRV following AI, semantic ontology, genetic algorithm and tree explainer
Heart Rate Variability (HRV) serves as a vital marker of stress levels, with lower HRV indicating higher stress. It measures the variation in the time between heartbeats and offers insights into health. Artificial intelligence (AI) research aims to use HRV data for accurate stress level classification, aiding early detection and well-being approaches. This study’s objective is to create a semantic model of HRV features in a knowledge graph and develop an accurate, reliable, explainable, and ethical AI model for predictive HRV analysis. The SWELL-KW dataset, containing labeled HRV data for stress conditions, is examined. Various techniques like feature selection and dimensionality reduction are explored to improve classification accuracy while minimizing bias. Different machine learning (ML) algorithms, including traditional and ensemble methods, are employed for analyzing both imbalanced and balanced HRV datasets. To address imbalances, various data formats and oversampling techniques such as SMOTE and ADASYN are experimented with. Additionally, a Tree-Explainer, specifically SHAP, is used to interpret and explain the models’ classifications. The combination of genetic algorithm-based feature selection and classification using a Random Forest Classifier yields effective results for both imbalanced and balanced datasets, especially in analyzing non-linear HRV features. These optimized features play a crucial role in developing a stress management system within a Semantic framework. Introducing domain ontology enhances data representation and knowledge acquisition. The consistency and reliability of the Ontology model are assessed using Hermit reasoners, with reasoning time as a performance measure. HRV serves as a significant indicator of stress, offering insights into its correlation with mental well-being. While HRV is non-invasive, its interpretation must integrate other stress assessments for a holistic understanding of an individual’s stress response. Monitoring HRV can help evaluate stress management strategies and interventions, aiding individuals in maintaining well-being.
2025
The apportionment of equivalent black carbon (eBC) to combustion sources from liquid fuels (mainly fossil; eBCLF) and solid fuels (mainly non-fossil; eBCSF) is commonly performed using data from Aethalometer instruments (AE approach). This study evaluates the feasibility of using AE data to determine the absorption Ångström exponents (AAEs) for liquid fuels (AAELF) and solid fuels (AAESF), which are fundamental parameters in the AE approach. AAEs were derived from Aethalometer data as the fit in a logarithmic space of the six absorption coefficients (470–950 nm) versus the corresponding wavelengths. The findings indicate that AAELF can be robustly determined as the 1st percentile (PC1) of AAE values from fits with R2 > 0.99. This R2-filtering was necessary to remove extremely low and noisy-driven AAE values commonly observed under clean atmospheric conditions (i.e., low absorption coefficients). Conversely, AAESF can be obtained from the 99th percentile (PC99) of unfiltered AAE values. To optimize the signal from solid fuel sources, winter data should be used to calculate PC99, whereas summer data should be employed for calculating PC1 to maximize the signal from liquid fuel sources. The derived PC1 (AAELF) and PC99 (AAESF) values ranged from 0.79 to 1.08, and 1.45 to 1.84, respectively. The AAESF values were further compared with those constrained using the signal at mass-to-charge 60 (m/z 60), a tracer for fresh biomass combustion, measured using aerosol chemical speciation monitor (ACSM) and aerosol mass spectrometry (AMS) instruments deployed at 16 sites. Overall, the AAESF values obtained from the two methods showed strong agreement, with a coefficient of determination (R2) of 0.78. However, uncertainties in both approaches may vary due to site-specific sources, and in certain environments, such as traffic-dominated sites, neither approach may be fully applicable.
2025
Status report of air quality in Europe for year 2023, using validated data
This report presents summarised information on the status of air quality in Europe in 2023, based on validated air quality monitoring data officially reported by the member and cooperating countries of the EEA. It aims at informing on the status of ambient air quality in Europe in 2023 and on the progress towards meeting the European air quality standards for the protection of health, as well as the WHO air quality guidelines. The report also compares the air quality status in 2023 with the previous years. The pollutants covered in this report are particulate matter (PM10 and PM2.5), tropospheric ozone (O3), nitrogen dioxide (NO2), benzo(a)pyrene (BaP), sulphur dioxide (SO2), carbon monoxide (CO), benzene (C6H6) and toxic metals (As, Cd, Ni, Pb). Measured concentrations above the European air quality standards for PM10, PM2.5, O3, and NO2 were reported by 18, 6, 20, and 9 reporting countries for 2022, respectively. Exceedances of the air quality standards for BaP, SO2, CO, and benzene were measured in, respectively, 9, 2, 2, and 0 reporting countries in 2023. Exceedances of European standards for toxic metals were reported by 5 stations for As, none for Cd, 1 for Pb and 2 for Ni.
ETC/HE
2025
2025
The influence of aerosols on the Arctic system remains associated with significant uncertainties, particularly concerning black carbon (BC). The polar aerosol station “Island Bely” (IBS), located in the Western Siberian Arctic, was established to enhance aerosol monitoring. Continuous measurements from 2019 to 2022 revealed the long-term effects of light-absorbing carbon. During the cold period, the annual average light-absorption coefficient was 0.7 ± 0.7 Mm−1, decreasing by 2–3 times during the warm period. The interannual mean showed a peak in February (0.9 ± 0.8 Mm−1) then 10 times the lower minimum in June and exhibited high variability in August (0.7 ± 2.2 Mm−1). An increase of up to 1.5 at shorter wavelengths from April to September suggests contribution from brown carbon (BrC). The annual mean equivalent black carbon (eBC) demonstrated considerable interannual variability, with the lowest in 2020 (24 ± 29 ng m−3). Significant difference was observed between Arctic haze and Siberian wildfire periods, with record-high pollution levels in February 2022 (110 ± 70 ng m−3) and August 2021 (83 ± 249 ng m−3). Anthropogenic BC contributed 83 % to the total for the entire study period, and gas flaring, domestic combustion, transportation, and industrial emissions dominated. During the cold season, > 90 % of surface BC was attributed to anthropogenic sources, mainly gas flaring. In contrast, during the warm period, Siberian wildfires contributed to BC concentrations by 48 %. In August 2021, intense smoke from Yakutian wildfires was transported at high altitudes during the region's worst fire season in 40 years.
2025
Reliable quantification of polychlorinated alkanes (PCAs) remains a major challenge, hindering environmental research across diverse matrices. Each sample can contain over 500 homologue groups, collectively producing >1000 m/z ratios that require interference checks. High-resolution mass spectrometry methods vary in ionization signals and data formats and require specialized algorithms for quantification. CPxplorer streamlines data processing through the integration of three modules: (1) CPions generates target ion sets and isotopic thresholds for compound identification into the next module; (2) Skyline performs instrument-independent data integration, interference evaluation, and homologue profiling; and (3) CPquant deconvolves homologues and reports concentrations using reference standards and homologue profiles from Skyline. Evaluation of the workflow with NIST-SRM-2585 dust and ERM-CE100 fish tissue material yielded comparable results across raw data formats from different instruments. Further applications of CPxplorer across diverse matrices, including indoor dust, organic films, silicone wrist bands, and food samples, demonstrated the usefulness in biological and environmental monitoring. Compared to existing tools limited to qualitative detection, CPxplorer enables quantitative outputs, reduces processing time, and expands functionality to PCA-like substances (e.g., BCAs) and PCA degradation products (e.g., OH-PCAs). CPxplorer reduces learning barriers, empowers users to quantify PCAs across various analytical instruments, and contributes to generating comparable results in the field.
2025