Fant 10464 publikasjoner. Viser side 409 av 419:
2025
Tire particles can enter the marine environment e.g. through direct discharge of road runoff, sewage systems or riverine inputs. Their fate in marine waters remains largely unknown, though the deep sea could be a final sink as for other marine litter. To simulate these conditions, we investigated in laboratory-controlled conditions the effects of high-hydrostatic pressure [20 MPa] vs atmospheric pressure [0.1 MPa] on the leaching of 17 organic compounds from cryo-milled tire tread particles (μm sized) and crumb rubber particles (mm sized) into natural seawater. We monitored the abundance of heterotrophic prokaryotes in the leachates over the 14 day exposure period under biotic conditions. Abiotic controls were employed to delineate the influence of prokaryotes on the fate of leached chemicals. Our results showed leaching of dissolved organic carbon and target chemicals under all experimental conditions, with higher concentrations of certain target chemicals under high-hydrostatic pressure conditions (e.g., 1,3-diphenylguanidine [DPG]: max. 703 (20 MPa) vs 119 μg/L (0.1 MPa) from cryo-milled tire tread particles under biotic conditions). Under abiotic conditions leaching was weaker for DPG and other chemicals, with contrasting trends for chemicals prone to biotransformation. In crumb rubber leachates chemical concentrations increased with time, but showed no significant differences between biotic/abiotic or high-hydrostatic/atmospheric pressure conditions. Prokaryotic abundance increased in all samples containing tire particles compared to seawater controls, indicating the use of the rubber and/or leached chemicals as an energy source.
2025
NO2-måling i omgivelsene til Eramet Sauda
På oppdrag fra Eramet Sauda AS har NILU utført målinger av NO2 i omgivelsene til smelteverket i Sauda. Målingene ble utført med NOx-monitor ved Birkelandsvegen nordøst for bedriften. I tillegg ble NO2 målt med passive prøvetakere ved 3 steder i Sauda. Måleperioden varte fra 30. august 2024 til 10. mars 2025. Norske grenseverdier for luftkvalitet (NO2) og luftkvalitetskriterier ble overholdt ved Birkelandsvegen for alle midlingsperioder. Formålet med prosjektet var å vurdere effekten av det nye energigjenvinningsanlegget (bestående av 7 gassmotorer) på NO2 konsentrasjonen. Det ble ikke funnet noen sammenheng mellom vindretning fra sør-sørvest (fra bedriften mot målestasjonen), motordrift og NO2 konsentrasjonene målt ved måleboden.
NILU
2025
Kartlegging av utslipp fra aktiviteter i Oslo Havn
Stiftelsen NILU har utarbeidet en utslippsberegning for aktiviteter på land ved Oslo Havn. Arbeidet omfatter innhenting av aktivitetsdata og utslippsfaktorer fra relevante kilder. Utslippet er beregnet for Dagens situasjon 2023 og framskrevet til 2030 og 2040. For 2040 er det også regnet på effekten av å bytte til bio-basert brensel i fabrikkene.
NILU
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
2025
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
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
Modelling Arctic lower-tropospheric ozone: processes controlling seasonal variations
Abstract. Previous assessments on modelling Arctic tropospheric ozone (O3) have shown that most atmospheric models continue to experience difficulties in simulating tropospheric O3 in the Arctic, particularly in capturing the seasonal variations at coastal sites, primarily attributed to the lack of representation of surface bromine chemistry in the Arctic. In this study, two independent chemical transport models (CTMs), DEHM (Danish Eulerian Hemispheric Model) and GEM-MACH (Global Environmental Multi-scale – Modelling Air quality and Chemistry), were used to simulate Arctic lower-tropospheric O3 for the year 2015 at considerably higher horizontal resolutions (25 and 15 km, respectively) than the large-scale models in the previous assessments. Both models include bromine chemistry but with different mechanistic representations of bromine sources from snow- and ice-covered polar regions: a blowing-snow bromine source mechanism in DEHM and a snowpack bromine source mechanism in GEM-MACH. Model results were compared with a suite of observations in the Arctic, including hourly observations from surface sites and mobile platforms (buoys and ships) and ozonesonde profiles, to evaluate models' ability to simulate Arctic lower-tropospheric O3, particularly in capturing the seasonal variations and the key processes controlling these variations. Both models are found to behave quite similarly outside the spring period and are able to capture the observed overall surface O3 seasonal cycle and synoptic-scale variabilities, as well as the O3 vertical profiles in the Arctic. GEM-MACH (with the snowpack bromine source mechanism) was able to simulate most of the observed springtime ozone depletion events (ODEs) at the coastal and buoy sites well, while DEHM (with the blowing-snow bromine source mechanism) simulated much fewer ODEs. The present study demonstrates that the springtime O3 depletion process plays a central role in driving the surface O3 seasonal cycle in central Arctic, and that the bromine-mediated ODEs, while occurring most notably within the lowest few hundred metres of air above the Arctic Ocean, can induce a 5 %–7 % of loss in the total pan-Arctic tropospheric O3 burden during springtime. The model simulations also showed an overall enhancement in the pan-Arctic O3 concentration due to northern boreal wildfire emissions in summer 2015; the enhancement is more significant at higher altitudes. Higher O3 excess ratios (ΔO3/ΔCO) found aloft compared to near the surface indicate greater photochemical O3 production efficiency at higher altitudes in fire-impacted air masses. The model simulations further indicated an enhancement in NOy in the Arctic due to wildfires; a large portion of NOy produced from the wildfire emissions is found in the form of PAN that is transported to the Arctic, particularly at higher altitudes, potentially contributing to O3 production there.
2025
2025
2025
2025
2025
Congener group profiles of MCCPs in environmental samples from Norwegian monitoring programmes, 2025
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.
European Topic Centre on Human Health and the Environment (ETC HE)
2025