Fant 10267 publikasjoner. Viser side 408 av 411:
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
Characterization of German SF6 Emissions
Sulfur hexafluoride (SF6) is a highly potent greenhouse gas with a Global Warming Potential (GWP) of 24,700 over 100 years and is globally mainly used as an electrical insulator in switchgear. Several measurement networks have tracked SF6 for many years and their European data reveal significant emissions in southern Germany. This study focuses on German SF6 emissions (2020–2023), using atmospheric measurements from 22 European sites, offering high spatial and temporal resolution for robust emission assessments. While German UNFCCC inventory bottom-up emission estimates report a major source of SF6 through the disposal of soundproof windows, the spatial distribution of German SF6 emissions derived on top-down inversion techniques (InTEM and Flexinvert+) reveals a different picture: The continuous pattern of high emissions from a particular region is responsible for one-third of total SF6 emissions in Germany. Despite this, total German SF6 emissions have decreased from 112 ± 26 t in 2020 to 89 ± 15 t in 2023 (InTEM), with estimates from all methods (both bottom-up and top-down) showing similar trends. Our findings suggest that the emissions from soundproof windows are overestimated, while industrial sources - particularly from SF6 production and recycling in the focus region - are likely underestimated.
2025
Per- and polyfluoroalkyl substances (PFAS) have gained significant global attention due to their extensive industrial use and harmful effects on various organisms. Among these, perfluoroalkyl acids (PFAAs) are well-studied, but their diverse precursors remain challenging to monitor. The Total Oxidizable Precursor (TOP) assay offers a powerful approach to converting these precursors into detectable PFAAs. In this study, the TOP assay was applied to samples from the East Asian-Australian Flyway, a critical migratory route for millions of shorebirds. Samples included shellfish from China's coastal mudflats, key stopover sites for these birds, and blood and liver samples from shorebirds overwintering in Australia. The results showed a substantial increase in perfluorocarboxylic acids (PFCAs) across all sample types following the TOP assay, with the most significant increases in shorebird livers (Sum PFCAs increased by 18,156 %). Intriguingly, the assay also revealed unexpected increases in perfluorosulfonic acids (PFSAs), suggesting the presence of unidentified precursors. These findings highlight the need for further research into these unknown precursors, their sources, and their ecological impacts on shorebirds, other wildlife, and potential human exposure. This study also provides crucial insights into the TOP assay’s strengths and limitations in studying PFAS precursor dynamics in biological matrices.
Elsevier
2025
From streets to seas: New greener ways to analyse urban snow pollution
Arctic cities experience long winters with heavy snowfalls. Every year, tonnes of urban snow contaminated with microplastics from tire wear and other traffic-related environmental pollutants are dumped into the sea.
2025
VKM skal lage oversikt over hvilke krav som bør stilles til konsekvensutredninger ved planlegging av nye vindkraftprosjekter. Det er laget en protokoll som beskriver hvordan VKM vil gå frem for å løse oppdraget.
Bakgrunn for oppdraget
Et vindkraftverk kan forurense omgivelsene både under etablering, drift og avvikling. Dersom området ligger innenfor et vanntilsigsområde for drikkevann, kan det utgjøre en forurensningsfare for drikkevannet.
Mattilsynet er høringsinstans når vindkraftverk skal etableres, og de ønsker en oversikt over hvilke krav som bør stilles til konsekvensutredningene.
Dette er en bestilling fra Mattilsynet, som fører tilsyn med drikkevann.
Om protokollen
VKM har utarbeidet en protokoll for hvordan vi skal løse oppdraget som går på å utarbeide krav til informasjon om, og risikovurdering av farene ved søknad om etablering av vindkraftverk. Protokollen favner bruk av kjemiske stoffer og annen aktuell forurensing som kan utgjøre en risiko for drikkevann gjennom hele vindkraftverkets livsløpssyklus (anlegg, drift, vedlikehold og avvikling)
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
Methane emissions from the Nord Stream subsea pipeline leaks
The amount of methane released to the atmosphere from the Nord Stream subsea pipeline leaks remains uncertain, as reflected in a wide range of estimates1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18. A lack of information regarding the temporal variation in atmospheric emissions has made it challenging to reconcile pipeline volumetric (bottom-up) estimates1,2,3,4,5,6,7,8 with measurement-based (top-down) estimates8,9,10,11,12,13,14,15,16,17,18. Here we simulate pipeline rupture emission rates and integrate these with methane dissolution and sea-surface outgassing estimates9,10 to model the evolution of atmospheric emissions from the leaks. We verify our modelled atmospheric emissions by comparing them with top-down point-in-time emission-rate estimates and cumulative emission estimates derived from airborne11, satellite8,12,13,14 and tall tower data. We obtain consistency between our modelled atmospheric emissions and top-down estimates and find that 465 ± 20 thousand metric tons of methane were emitted to the atmosphere. Although, to our knowledge, this represents the largest recorded amount of methane released from a single transient event, it is equivalent to 0.1% of anthropogenic methane emissions for 2022. The impact of the leaks on the global atmospheric methane budget brings into focus the numerous other anthropogenic methane sources that require mitigation globally. Our analysis demonstrates that diverse, complementary measurement approaches are needed to quantify methane emissions in support of the Global Methane Pledge19.
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
A pooled analysis of host factors that affect nucleotide excision repair in humans
Nucleotide excision repair (NER) is crucial for repairing bulky lesions and crosslinks in DNA caused by exogenous and endogenous genotoxins. The number of studies that have considered DNA repair as a biomarker is limited, and therefore one of the primary objectives of the European COST Action hCOMET (CA15132) was to assemble and analyse a pooled database of studies with data on NER activity. The database comprised 738 individuals, gathered from 5 laboratories that ran population studies using the comet-based in vitro DNA repair assay. NER activity data in peripheral blood mononuclear cells were normalized and correlated with various host-related factors, including sex, age, body mass index (BMI), and smoking habits. This multifaceted analysis uncovered significantly higher NER activity in female participants compared to males (1.08 ± 0.74 vs. 0.92 ± 0.71; P = .002). Higher NER activity was seen in older subjects (>30 years), and the effect of age was most pronounced in the oldest females, particularly those over 70 years (P = .001). Females with a normal BMI (<25 kg/m2) exhibited the highest levels of NER, whereas the lowest NER was observed in overweight males (BMI ≥ 25 kg/m2). No independent effect of smoking was found. After stratification by sex and BMI, higher NER was observed in smoking males (P = .017). The biological implication of higher or lower repair capacity remains unclear; the inclusion of DNA repair as a biomarker in molecular epidemiological trials should elucidate the link between health and disease status.
2025