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Sykliske siloksaner i terrestrisk og akvatisk miljø i Arktis
No full textCyclic volatile methyl siloxanes (cVMS) are widely used chemicals with high emissions to the atmosphere due to their volatility. They are found in the Arctic atmosphere, indicating potential for long-range transport. This study examined the potential for deposition of cVMS (D4, D5, D6) to surface media via snow in Arctic regions. Results showed low cVMS levels in vegetation, soil, sediment, and marine biota. D4 was detected above detection limits but generally below quantification limits, while D5 and D6 were generally not detected. This aligns with current research, suggesting negligible cVMS input from atmospheric deposition via snow and snow melt.Sykliske metylsiloksaner (cVMS) er mye brukte kjemikalier med høye utslipp til atmosfæren på grunn av deres flyktighet. De finnes i den arktiske atmosfæren, noe som indikerer potensial for langtransport. Denne studien undersøkte potensialet for avsetning av cVMS (D4, D5, D6) til overflatemedia via snø i arktiske områder. Resultatene viste lave cVMS-nivåer i vegetasjon, jord, sediment og marine organismer. D4 ble påvist over deteksjonsgrensene, men generelt under kvantifiseringsgrensene, mens D5 og D6 generelt ikke ble påvist. Dette stemmer overens med nåværende forskning, som antyder ubetydelig cVMS-tilførsel fra atmosfærisk avsetning via snø og snøsmelting.publishedVersio
A framework for advancing independent air quality sensor measurements via transparent data generating process classification
No full textWe propose operational definitions and a classification framework for air quality sensor-derived data, thereby aiding users in interpreting and selecting suitable data products for their applications. We focus on differentiating independent sensor measurements (ISM) from other data products, emphasizing transparency and traceability. Recommendations are provided for manufacturers, academia, and standardization bodies to adopt these definitions, fostering data product differentiation and incentivizing the development of more robust, reliable sensor hardware.publishedVersio
Estimating the air quality standard exceedance areas and the spatial representativeness of urban air quality stations applying microscale modelling
Full text linkThis study builds upon the findings of a FAIRMODE intercomparison exercise conducted in a district of Antwerp, Belgium, where a comprehensive dataset of air pollutant measurements (air quality stations and passive samplers) was available. Long-term average NO2 concentrations at very high spatial resolution were estimated by several dispersion modelling systems (Martín et al., 2024) to investigate the ability of these to capture the detailed spatial distribution of NO2 concentrations at the microscale in urban environments. In this follow-up research, we extend the analysis by evaluating the capability of these modelling systems to predict the NO2 annual limit value exceedance areas (LVEAs) and spatial representativeness areas (SRAs) for NO₂ at two reference air quality stations. The different modelling approaches used are based on CFD, Lagrangian, Gaussian, and AI-driven models. The different modelling approaches are generally good at predicting the LVEA and SRAs of urban air quality stations, although a small SRA (corresponding to low concentration tolerances or the traffic station) is more difficult to predict correctly. However, there are notable differences in performance among the modelling systems. Those based on CFD models seem to provide more consistent results predicting LVEAs and SRAs. Then, lower accuracy is obtained with AI-based systems, Lagrangian models, and Gaussian models with street canyon parameterizations. The Gaussian models with street-canyon parametrizations show significantly better results than models using simply a Gaussian dispersion parametrization. Furthermore, little differences are observed in most of the statistical indicators corresponding to the LVEA and SRA estimates obtained from the unsteady full month CFD simulations compared to those from the scenario-based CFD simulation methodologies, but there are some noticeable differences in the LVEA or SRA (traffic station, 10 % tolerance) sizes. The number of scenarios does not seem to be relevant to the results. Different bias correction methodologies are explored.publishedVersio
Predicting the student’s perceptions of multi-domain environmental factors in a Norwegian school building: Machine learning approach
Full text linkPoor Indoor Environmental Quality (IEQ) in schools significantly impacts students’ well-being, learning capabilities, and health. Perceived dissatisfaction rates (PD%) among students often remain high, even when indoor environmental variables appear well-controlled. This study aims to predict perceived dissatisfaction rates (PD%) across multi-domain environmental factors—thermal, acoustic, visual, and indoor air quality (IAQ)—using machine learning (ML) models. The research integrates sensor-based environmental measurements, outdoor weather data, building parameters, and 1437 student survey responses collected from three classrooms in a Norwegian school across multiple seasons. Statistical tests were used to pre-select relevant input variables, followed by the development and evaluation of multiple ML algorithms. Among the tested ML models, Random Forest (RF) demonstrated the highest predictive accuracy for PD%, outperforming multi-linear regression (MLR) and decision trees (DT), with R² values up to 0.91 for overall IEQ dissatisfaction (PDIEQ%). SHAP analysis revealed key predictors: CO₂ levels, VOCs, humidity, temperature, solar radiation, and room window orientation. IAQ, thermal comfort, and acoustic environment were the most influential factors affecting students' perceived well-being. Despite limitations as implementation in building level scale, the study demonstrates the feasibility of deploying predictive ML models under real-world constraints for improving IEQ monitoring system. The findings support practical strategies for adaptive indoor environmental management, particularly in educational settings, and provide a replicable framework for future research. Future research can expand to other climates, buildings, measurements, occupant levels, and ML training optimization.Predicting the student’s perceptions of multi-domain environmental factors in a Norwegian school building: Machine learning approachpublishedVersio
New Approach Methods (NAMs) for genotoxicity assessment of nano- and advanced materials; Advantages and challenges
Full text linkGenotoxicity assessment is essential for ensuring chemical safety and mitigating risks to human health and the environment. Traditional methods, reliant on animal models, are time-consuming, costly, and raise ethical concerns. New Approach Methods (NAMs) offer innovative, cost-effective, and ethical alternatives, playing a pivotal role in both traditional and next-generation risk assessment (NGRA) by minimizing the need for animal testing, particularly in genotoxicity evaluations. However, the development of NAMs often overlooks the particular physicochemical properties of nanomaterials (NMs), which significantly influence their toxicological behaviour and can interfere with genotoxicity evaluation. This underscores an urgent need for the standardization and adaptation of NAMs to address nano- and advanced material-specific genotoxicity challenges. In this review, we summarize the challenges associated with genotoxicity testing of NMs and highlight the suitability of existing in vitro and in silico NAMs for NMs and advanced materials, enabling genotoxicity testing across various exposure routes and organ systems. Despite considerable progress, regulatory validation remains constrained by the absence of approved test guidelines and standardized protocols. To achieve regulatory acceptance, it is crucial to adapt NAMs to NM-specific exposure scenarios, refine test systems to better mimic human biology, develop tailored in vitro protocols, and ensure thorough characterisation of NMs both in pristine form and dispersed in culture medium. Collaborative efforts among scientists, regulators, industry, and advocacy groups are vital to improving the reliability and regulatory acceptance of NAMs. By addressing these challenges, NAMs have the potential to revolutionize genotoxicity risk assessment, advancing it towards a more sustainable, efficient and ethical framework.publishedVersio
Stochastic and deterministic processes in Asymmetric Tsetlin Machine
Full text linkThis paper introduces a new approach to enhance the decision-making capabilities of the Tsetlin Machine (TM) through the Stochastic Point Location (SPL) algorithm and the Asymmetric Steps technique. We incorporate stochasticity and asymmetry into the TM's process, along with a decaying normal distribution function that improves adaptability as it converges toward zero over time. We present two methods: the Asymmetric Probabilistic Tsetlin (APT) Machine, influenced by random events, and the Asymmetric Tsetlin (AT) Machine, which transitions from probabilistic to deterministic states. We evaluate these methods against traditional machine learning algorithms and classical Tsetlin (CT) machines across various benchmark datasets. Both AT and APT demonstrate competitive performance, with the AT model notably excelling, especially in complex datasets.publishedVersio
Modelling the influence of suburban sprawl vs. compact city development upon road network performance and traffic emissions
Full text linkRoad traffic externalities are an important consequence of land-use and transport interactions and may be especially induced by their inefficient combinations. In this study, we integrate land-use, transport and emission modelling tools (the LUTEm framework) to assess how suburban expansion vs. inward densification scenarios influence journey parameters, road network performance and traffic emissions. Case-study simulations for Warsaw (Poland) underscore the negative consequences of suburban sprawl development, which are hardly mitigated by additional land-use or transport interventions, such as rebalancing of population-workplace distribution or road capacity reductions. On the other side, compact city development lowers global traffic congestion and emissions, but can also raise the risks of traffic externalities in central city area unless complemented with further interventions such as improved public transport attractiveness. This study aims to enrich the understanding of how integrating the land-use development and transport interventions can ultimately influence travel parameters and reduce urban road traffic externalities.publishedVersio
Global greenhouse gas reconciliation 2022
Full text linkn this study, we provide an update on the methodology and data used by Deng et al. (2022) to compare the national greenhouse gas inventories (NGHGIs) and atmospheric inversion model ensembles contributed by international research teams coordinated by the Global Carbon Project. The comparison framework uses transparent processing of the net ecosystem exchange fluxes of carbon dioxide (CO2) from inversions to provide estimates of terrestrial carbon stock changes over managed land that can be used to evaluate NGHGIs. For methane (CH4), and nitrous oxide (N2O), we separate anthropogenic emissions from natural sources based directly on the inversion results to make them compatible with NGHGIs. Our global harmonized NGHGI database was updated with inventory data until February 2023 by compiling data from periodical United Nations Framework Convention on Climate Change (UNFCCC) inventories by Annex I countries and sporadic and less detailed emissions reports by non-Annex I countries given by national communications and biennial update reports. For the inversion data, we used an ensemble of 22 global inversions produced for the most recent assessments of the global budgets of CO2, CH4, and N2O coordinated by the Global Carbon Project with ancillary data. The CO2 inversion ensemble in this study goes through 2021, building on our previous report from 1990 to 2019, and includes three new satellite inversions compared to the previous study and an improved managed-land mask. As a result, although significant differences exist between the CO2 inversion estimates, both satellite and in situ inversions over managed lands indicate that Russia and Canada had a larger land carbon sink in recent years than reported in their NGHGIs, while the NGHGIs reported a significant upward trend of carbon sink in Russia but a downward trend in Canada. For CH4 and N2O, the results of the new inversion ensembles are extended to 2020. Rapid increases in anthropogenic CH4 emissions were observed in developing countries, with varying levels of agreement between NGHGIs and inversion results, while developed countries showed a slowly declining or stable trend in emissions. Much denser sampling of atmospheric CO2 and CH4 concentrations by different satellites, coordinated into a global constellation, is expected in the coming years. The methodology proposed here to compare inversion results with NGHGIs can be applied regularly for monitoring the effectiveness of mitigation policy and progress by countries to meet the objectives of their pledges. The dataset constructed for this study is publicly available at https://doi.org/10.5281/zenodo.13887128 (Deng et al., 2024).publishedVersio
Sovereignty in Automated Stroke Prediction and Recommendation System with Explanations and Semantic Reasoning
Full text linkPersonalized approaches are required for stroke management due to the variability in symptoms, triggers, and patient characteristics. An innovative stroke recommendation system that integrates automatic predictive analysis with semantic knowledge to provide personalized recommendations for stroke management is proposed by this paper. Stroke exacerbation are predicted and the recommendations are enhanced by the system, which leverages automatic Tree-based Pipeline Optimization Tool (TPOT) and semantic knowledge represented in an OWL Ontology (StrokeOnto). Digital sovereignty is addressed by ensuring the secure and autonomous control over patient data, supporting data sovereignty and compliance with jurisdictional data privacy laws. Furthermore, classifications are explained with Local Interpretable Model-Agnostic Explanations (LIME) to identify feature importance. Tailored interventions based on individual patient profiles are provided by this conceptual model, aiming to improve stroke management. The proposed model has been verified using public stroke dataset, and the same dataset has been utilized to support ontology development and verification. In TPOT, the best Variance Threshold + DecisionTree Classifier pipeline has outperformed other supervised machine learning models with an accuracy of 95.2%, for the used datasets. The Variance Threshold method reduces feature dimensionality with variance below a specified threshold of 0.1 to enhance predictive accuracy. To implement and evaluate the proposed model in clinical settings, further development and validation with more diverse and robust datasets are required.publishedVersio
Unchanged PM2.5 levels over Europe during COVID-19 were buffered by ammonia
Full text linkThe coronavirus outbreak in 2020 had a devastating impact on human life, albeit a positive effect on the environment, reducing emissions of primary aerosols and trace gases and improving air quality. In this paper, we present inverse modelling estimates of ammonia emissions during the European lockdowns of 2020 based on satellite observations. Ammonia has a strong seasonal cycle and mainly originates from agriculture. We further show how changes in ammonia levels over Europe, in conjunction with decreases in traffic-related atmospheric constituents, modulated PM2.5. The key result of this study is a −9.8 % decrease in ammonia emissions in the period of 15 March–30 April 2020 (lockdown period) compared to the same period in 2016–2019, attributed to restrictions related to the global pandemic. We further calculate the delay in the evolution of the ammonia emissions in 2020 before, during, and after lockdowns, using a sophisticated comparison of the evolution of ammonia emissions during the same time periods for the reference years (2016–2019). Our analysis demonstrates a clear delay in the evolution of ammonia emissions of −77 kt, which was mainly observed in the countries that imposed the strictest travel, social, and working measures. Despite the general drop in emissions during the first half of 2020 and the delay in the evolution of the emissions during the lockdown period, satellite and ground-based observations showed that the European levels of ammonia increased. On one hand, this was due to the reductions in SO2 and NOx (precursors of the atmospheric acids with which ammonia reacts) that caused less binding and thus less chemical removal of ammonia (smaller loss – higher lifetime). On the other hand, the majority of the emissions persisted because ammonia mainly originates from agriculture, a primary production sector that was influenced very little by the lockdown restrictions. Despite the projected drop in various atmospheric aerosols and trace gases, PM2.5 levels stayed unchanged or even increased in Europe due to a number of reasons that were attributed to the complicated system. Higher water vapour during the European lockdowns favoured more sulfate production from SO2 and OH (gas phase) or O3 (aqueous phase). Ammonia first reacted with sulfuric acid, also producing sulfate. Then, the continuously accumulating free ammonia reacted with nitric acid, shifting the equilibrium reaction towards particulate nitrate. In high-free-ammonia atmospheric conditions such as those in Europe during the 2020 lockdowns, a small reduction in NOx levels drives faster oxidation toward nitrate and slower deposition of total inorganic nitrate, causing high secondary PM2.5 levels.publishedVersio