Fant 862 publikasjoner. Viser side 1 av 36:
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
Abstract Hierarchical agglomerative clustering is a useful analysis technique which allows for a level of stability, interpretability and flexibility not available in other similar techniques such as K‐means, density‐based clustering or positive matrix factorization. Previous studies using hierarchical clustering on atmospheric model output have been limited to small domain sizes (roughly 100 × 100 grid cells) by the computational expense and memory requirements of the algorithm. Here we present a scalable hierarchical clustering implementation that we apply to two year‐long, hourly atmospheric data sets: model concentration and deposition timeseries at 290,520 locations over Alberta and Saskatchewan (538 540 grid); and 366,427 multi‐pollutant observations from 51 national air pollution surveillance stations located across Canada. When combined with other information such as emissions source locations, orography, and prevailing meteorological conditions, the method yields coherent, interpretable structures. In the case of model time series, the clustering provides regions of similar air quality (airsheds) which can be used to inform air quality monitoring network placement, or regions of similar deposition which can inform critical load assessment as well as monitoring site locations. In the case of the multi‐pollutant observations, we show that a single low‐primary pollutant cluster appears the most frequently at all but one of 51 stations across Canada, accounting for 62% of all station‐hours, while elevated SO 2 appears in factor profiles at certain monitoring locations near industrial and shipping activity. Together, these results demonstrate that hierarchical clustering can efficiently summarize patterns relevant to airshed mapping and source apportionment at previously unreachable scales.
2026
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
2026
2026
This paper reviews results published by the International Co-operative Programme on Effects on Materials including Historic and Cultural Monuments (ICP Materials) with emphasis on those obtained after the turn of the century. Data from ICP Materials come from two main sources. The first is through exposures of materials and collection of environmental data in a network of atmospheric exposure test sites mainly distributed across Europe. Corrosion of carbon steel has continued to decrease during the period 2000–2020 but corrosion of zinc only up until 2014, and the trend in zinc corrosion is only visible when examining four-year data. Surface recession of limestone as well as soiling of modern glass show no decreasing trend during 2000–2020. The second is through case studies performed at heritage sites across Europe. Risk analysis of corrosion and soiling for twenty-six sites indicate that currently soiling is a more significant maintenance trigger than corrosion. Costs for maintaining heritage sites are substantial and costs attributable to air pollution is estimated from 40% to as much as 80% of the total cost. Future directions of the program are work on effects of particulate matter, improving the scientific basis for the work, and making the monitoring data publicly available.
2025
Microplastic and other anthropogenic particles in surface waters of the Isfjorden system (Svalbard)
Knowledge of sources and transport mechanisms of anthropogenic particles (APs) such as microplastics (MPs) and related plastic chemicals, in the Arctic marine environment is limited. This study investigates the surface waters of the Isfjordensystem, where Svalbard's largest settlement, Longyearbyen, is located, for the presence of APs. The wastewater from Longyearbyen is released untreated into Adventfjorden, which is a branch of Isfjorden. Samples from the inflowing current of Isfjorden into Adventfjorden, and its outflowing current were sampled and analyzed for APs (>50 μm). APs were classified regarding size, shape, and polymer type via μFTIR spectroscopy. Each location showed an AP burden (Isfjorden: 26 APs/L, Adventfjorden: 20 APs/L). Highest amounts of APs were found in the Isfjorden current (37 APs/L), before entering Adventfjorden. 14 APs/L were indicated near the wastewater effluent in Adventfjorden, and 15 APs/L in the outflowing current in Isfjorden. Plastic related chemicals, polypropylene and other polyolefins had high frequencies, but silk and rayon material dominated each location except the inflowing current from Isfjorden. Local sources like wastewater and other anthropogenic activities, as well as northwards long-range transport from the south into the Arctic, are considered. Oceanographic dynamics, and the time of sampling seems to affect the distribution of APs in the surface waters, besides its characteristics itself (e.g., polymer type and size).
2025
Construction of an enterprise-level global supply chain database
Data tracing global supply chains, commonly captured in input–output models, is a foundational resource across economic, regulatory, investment, defense, and environmental applications. Such models provide insight into interdependency and environmental burden-shifting, forming part of the empirical basis for policies such as Scope 3 embodied emissions targets, supply chain transparency, life cycle assessments, and product declarations. Current approaches, based on national statistics, remain constrained by sector-level resolution, limiting their precision and utility in certain applications. Here, we document the construction of an enterprise-level multi-regional input–output (EMRIO) table. This database merges official national input–output tables with publicly available firm-level production and transaction data, creating a globally consistent account of purchases and sales across 9,466 companies, 86,305 subsegments, and 121 countries. The finer resolution allows supply chain transactions to be represented in greater detail, providing an additional resource for analyses and policy tools requiring more disaggregated supply chain information.
2025
Evaluating the role of low-cost sensors in machine learning based European PM2.5 monitoring
We evaluate the added value of integrating validated Low-Cost Sensor (LCS) data into a Machine Learning (ML) framework for providing surface PM2.5 estimates over Central Europe at 1 km spatial resolution. The synergistic ML-based S-MESH (Satellite and ML-based Estimation of Surface air quality at High resolution) approach is extended, to incorporate LCS data through two strategies: using validated LCS data as a target variable (LCST) and as an input feature via an inverse distance weighted spatial convolution layer (LCSI). Both strategies are implemented within a stacked XGBoost model that ingests satellite-derived aerosol optical depth, meteorological variables, and CAMS (Copernicus Atmospheric Monitoring Service) regional forecasts. Model performance for 2021–2022 is evaluated against a baseline trained on air quality monitoring stations without any form of LCS integration. Our results indicate that the LCSI approach consistently outperforms both the baseline and LCST models, particularly in urban areas, with RMSE reductions of up to 15–20 %. It also exhibits higher accuracy than the CAMS regional interim reanalysis with a lower annual mean absolute error (MAE) of 2.68 μg/m3 compared to 3.32 μg/m3. SHapley Additive exPlanations based analysis indicates that LCSI information improves both spatial and temporal representativeness, with the LCSI strategy better capturing localized pollution dynamics. However, the LCSI's dependency on the spatial LCS layer limits its ability to capture inter-urban pollution transport in regions with sparse or no LCS data. These findings highlight the value of large-scale sensor networks in addressing spatial coverage gaps in official air quality monitoring stations and advancing high-resolution air quality modeling.
2026
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
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
Etablering av vindkraftverk på land kan medføre en risiko for drikkevann når installasjonene ligger i eller nær vanntilsigsområder til drikkevannskilder. Denne rapporten, utarbeidet av VKM på oppdrag fra Mattilsynet, gir Mattilsynet et kunnskapsbasert grunnlag for å stille krav til konsekvensutredninger og detaljplan for å beskytte drikkevannet.
Rapporten identifiserer potensielle farer for kjemisk og fysisk forurensning av drikkevann gjennom hele livsløpet til et vindkraftverk – fra planlegging og anleggsfase, til drift og avvikling. Den beskriver relevante lover og forskrifter, sentrale aktører og deres roller, og legger vekt på når og hvordan Mattilsynet kan involveres og komme med innspill i den kommunale planprosessen etter plan- og bygningsloven og i konsesjonsprosessen etter energiloven som forvaltes av NVE. Det er av stor betydning at Mattilsynet varsles og involveres tidlig i prosessen. Tiltakshaver må sørge for at risiko for forurensning av drikkevann og vanntilsigsområde utredes på en etterprøvbar måte, slik at Mattilsynet kan gi tydelige innspill til utredningen for å sikre at drikkevannshensyn er ivaretatt.
2025
Abstract. Airborne microplastics are a recently identified atmospheric aerosol species with potential air quality and climate impacts, yet they are not currently represented in global climate models. Here, we describe the addition of microplastics to the aerosol scheme of the UK Earth System Model (UKESM1.1): the Global Model of Aerosol Processes (GLOMAP). Microplastics are included as both fragments and fibres across a range of aerosol size modes, enabling interaction with existing aerosol processes such as ageing and wet and dry deposition. Simulated microplastics have higher concentrations over land, but can be transported into remote regions including Antarctica despite no assumed emissions from these regions. Lifetimes range between ∼17 d to ∼1 h, with smaller, hydrophilic microplastics having longer lifetimes. Microplastics are present throughout the troposphere, and the smallest particles are simulated to reach the lower stratosphere in small numbers. Dry deposition is the dominant microplastic removal pathway, but greater wet deposition occurs for smaller hydrophilic microplastic, due to interactions with clouds. Although microplastics currently contribute a minor fraction of the total aerosol burden, their concentration is expected to increase in future if plastic production continues to increase, and as existing plastic waste in the environment degrades to form new microplastic. Incorporating microplastics into UKESM1.1 is a key step toward quantifying their current atmospheric impact and offers a framework for simulating future emission scenarios for an assessment of their long term impacts on air quality and climate.
2025
Airborne microplastics on the move: Urban Europe as a source to remote regions
This study presents a comprehensive assessment of unique parallel measurements of surface airborne and deposited microplastics (AMPs) across urban and remote sites in Norway, employing pyrolysis-GC/MS for polymer-specific analysis. MPs were detected in nearly all samples, with significantly higher concentrations and fluxes observed in urban areas like Oslo, where tire wear particles (TWP) dominated (>90 % of AMP mass). Seasonal peaks in TWP coincided with the transition to winter tires, while remote sites showed consistent but lower AMP levels, indicating long-range transport (LRT) from European source regions. Parallel measurements of suspended and deposited AMPs revealed consistent polymer signatures, highlighting common sources and transport pathways. Although urban TWP contributions to PM2.5 were generally low, episodic events reached up to 30 %, raising concerns about human exposure. The dual dataset enabled a robust cross-validation of atmospheric loading estimates and facilitated integration into advanced transport models for remote sites. Our findings confirm AMPs as significant components of urban air pollution and subsequent carriers of chemical and biological contaminants to remote regions, emphasizing the need for targeted monitoring and mitigation strategies.
2025
The semi-annual oscillation (SAO) dominates seasonal variability in the equatorial stratosphere and mesosphere. However, the seasonally dependent modulation of the SAO in the stratosphere (SSAO) and mesosphere (MSAO) by sudden stratospheric warmings (SSWs) in the Arctic has not been investigated in detail. In this study, we examine the seasonal evolution of the SAO during 16 major SSW events spanning 2004 to 2024 using the Japanese Atmospheric General Circulation Model for Upper Atmosphere Research Data Assimilation System Whole Neutral Atmosphere Re-analysis (JAWARA). Basic features of the SAO are well captured by JAWARA, as evidenced by the SSAO and MSAO appearing at around 50 km and 85 km, respectively. The different responses of the SAO to early and late winter SSWs are particularly strong during the Northern Hemisphere winter of 2023/24. Early winter SSWs tend to significantly intensify the westward SSAO, while late winter SSWs tend to weaken the eastward SSAO. Similarly, the eastward MSAO is amplified during early winter SSWs, whereas the westward MSAO is slightly weakened during late winter SSWs. The weak MSAO response is probably due to its smaller climatological magnitude. Modulation of the SAO by SSWs is related to meridional temperature changes during SSWs through the thermal wind balance. Our findings contribute to the understanding of coupling between the tropics and high latitudes, as well as interhemispheric coupling.
2025