Fant 798 publikasjoner. Viser side 1 av 34:
Wood building materials can be a source of volatile organic compounds (VOCs) in the indoor environment and increasing focus is put on classification and regulation of the use of wood building materials in Europe. The main wood related VOCs such as monoterpenes rarely pose adverse health effects for humans, but as analytical procedures become more sensitive new hazardous VOCs are detected in low concentration. There is a need for comprehensive identification of VOCs emitting from different wood building materials for indoor use. This study performed a first semi-quantitative non-target and suspect screening of VOC emissions from three important wood-based building materials in Europe. Air samples collected from emission chambers were analyzed using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry and resulting mass spectra were classified into confidence groups. A total of 84, 133 and 197 compounds were found to emit from cross-laminated timber, untreated spruce panel and untreated pine panel, respectively. Pine panel was found to emit a higher number of VOCs as well as higher concentrations of most VOCs compared to the spruce building materials. Several new VOCs were detected in the emission profile of pine and spruce. However, they were mostly structurally similar to previously reported wood VOCs. Two compounds of concern emitting from all three wood building materials were furfural and (E)-2-octenal, as these have been classified as group 2 carcinogen and potent eye irritant, respectively.
Elsevier
2025
Little is known about the exposure of aquatic biota to tire and road wear particles (TRWP) washed away from roads. Mussels were exposed for 7 days to model TRWP (m-TRWP), produced by milling tire tread particles with pure sand, and analyzed for 21 tire-related compounds by liquid chromatography-high resolution-mass spectrometry (LC-HRMS). Upon exposure to 0.5 g/L of m-TRWP, 15 compounds were determined from 944 μg/kg wet weight (diphenylguanidine, DPG) over 18 μg/kg for an oxidation product of N-(1,3-dimethylbutyl)-N′-phenyl-p-phenylenediamine (6-PPDQ) to 0.6 μg/kg (4-hydroxydiphenyl amine). Transfer into mussels was highest for PTPD, DTPD and 6-PPDQ and orders of magnitude lower for 6-PPD. During 7 days depuration the concentration of all determined chemicals decreased to remaining concentrations between ~50 % (PTPD, DTPD) and 6 % (6-PPD). Suspect and non-target screening found 37 additional transformation products (TPs) of tire additives, many of which did not decrease in concentration during depuration, among them ten likely TPs of DPG, two of 6-PPD and PTPD and two of 1,2-dihydro-2,2,4-trimethylquinoline. A wide variety of chemicals is taken up by mussels upon exposure to m-TRWP and a wide range of TPs is formed, enabling the differentiation of biomarkers of exposure to TRWP and biomarkers of exposure to tire-associated chemicals.
Elsevier
2025
Stochastic and deterministic processes in Asymmetric Tsetlin Machine
This 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.
Frontiers Media S.A.
2025
Marine plastic litter is subject to different abiotic and biotic forces that lead to its degradation, the main driver being UV-induced photodegradation. Since UV-exposure leads to both physical and chemical degradation of plastic, leading to a release of micro- and nanoplastics as well as leaching of chemicals and degradation products – it is expected to have radical impacts on plastics fate and effects in the marine environment. The number of laboratory studies investigating the mechanisms of plastic UV-degradation in seawater has increased significantly in the past 10 years, but are the exposures designed in a manner that allow observations to be extrapolated to environmental fate? Most studies to date focus on quantifying plastic fragmentation and surface changes, but is this relevant for impact assessments? Here, we provide a review of the current scientific literature on UV-degradation of plastic under marine conditions. Plastic fragmentation processes and surface changes as well as implications of UV-degradation of plastics on additive leaching and the toxicity of UV-weathered versus non-weathered plastics are highlighted. Furthermore, experimental set-ups are critically inspected and recommendations for future studies are issued.
Elsevier
2025
Anthropogenic compounds in the northernmost Atlantic puffin population
Contamination by organic pollutants, even in remote regions, poses a growing threat to wildlife, including seabirds. However, for many seabirds breeding at high latitudes, both the extent and nature of contaminant exposure remain largely unknown. This study aimed to identify the persistent organic pollutants (POPs) present in the Svalbard Atlantic puffin Fratercula arctica at the northern limit of its range. We also compare contaminant concentrations with those found in other species breeding on Svalbard and in puffin colonies further south. The Svalbard puffins were found to be contaminated by organochlorine pesticides (OCPs), polychlorinated biphenyls (PCBs), and per- and polyfluoroalkyl substances (PFAS). No significant sex difference was found. OCPs, PCBs and/or PFASs concentrations in Svalbard puffins were comparable to those of Brünnich's guillemots Uria lomvia, black guillemots Cepphus grylle, and/or little auks Alle alle, but lower than in glaucous gulls Larus hyperboreus. PFAS concentrations were also lower than in black-legged kittiwakes Rissa tridactyla. OCP and PCB concentrations were lower on Svalbard than in puffin colonies further south. This study is the first to document PFAS concentrations in puffins, therefore it remains unknown whether PFAS levels were also lower on Svalbard than further south. These comparisons should be interpreted with caution, as data for different species or colonies were collected in different years, and contaminant levels vary over time. The current contaminant concentrations indicate that Svalbard puffins are still at low risk for biological effects, but continued monitoring is needed to assess potential future changes.
Elsevier
2025
A Nano Risk Governance Portal supporting risk governance of nanomaterials and nano-enabled products
isk governance (RG) of nanomaterials (NMs) has been at the focus of the Horizon 2020 Programme of the European Union, through the funding of three research projects (Gov4Nano, NANORIGO, RISKGONE). The extensive collaboration of the three projects, in various scientific topics, aimed to enhance RG of NMs and provide a solid scientific basis for effective collaboration of the various types of stakeholders involved. In this paper the development of a digital Nano Risk Governance Portal (NRGP) and associated information technology (IT) infrastructure supporting the risk governance of (engineered) nanomaterials and nano-enabled products, is presented, alongside considerations for future work and enhancement within the domain of Advanced Materials (AdMa). This paper describes several elements of this digital portal, which serves as a single-entry point for all stakeholders in need of, or interested in, nano-risk governance aspects. In its simplest form, the NRGP allows users to be efficiently guided towards tailored information about nanomaterials, risk governance concepts, guidance documents, harmonized methods for risk assessment, publicly accessible data, information and knowledge, as well as a directory of tools, to assess the exposure and hazard of nanomaterials and perform Safe-and-Sustainable-by-Design (SSbD) assessment in the context of nano-risk governance. This paper presents the technical implementation and the content of the first version of the NRGP alongside the vision for the future and further plans for development, implementation, hosting and maintenance of the NRGP aimed at ensuring its sustainability. This includes a procedure to link to, or include, currently available and future (nano)material-related (cloud) platforms, decision support systems, tools, guidance, and databases in line with good governance objectives.
Elsevier
2025
Regulatory practices on the genotoxicity testing of nanomaterials and outlook for the future
Elsevier
2025
This 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.
Elsevier
2025
Sources of ultrafine particles at a rural midland site in Switzerland
Ultrafine particles (UFPs; i.e., atmospheric aerosol particles smaller than 100 nm in diameter) are known to be responsible for a series of adverse health effects as they can deposit in humans' bodies. So far, most field campaigns studying the sources of UFPs have focused on urban environments. This study investigates the outdoor sources of UFPs at the atmospheric monitoring station in Payerne, which represents a typical rural location in Switzerland. We aim to quantify the primary and secondary fractions of UFPs based on specific measurements between July 2020 and July 2021 complementing a series of operational meteorological, trace gas and in situ aerosol observations. To distinguish between primary and secondary contributions, we use a method that relies on measuring the fraction of non-volatile particles as a proxy for primary particles. We further compare our measurement results to previously established methods. We find that primary particles resulting from traffic and residential wood burning (direct emissions – mostly non-volatile BC-rich) contribute less than 40 % to the total number of UFPs, mostly in the Aitken mode. On the other hand, we observe local new particle formation (NPF) events (observed from ∼ 1 nm) evident from the increase in cluster ions (1.5–3 nm) and nucleation-mode particle (2.5–25 nm) concentrations, especially in spring and summer. These events, mediated by sulfuric acid, contribute to increasing the UFP number concentration, especially in the nucleation mode. Besides NPF, the chemical processing of particles emitted from multiple sources (including traffic and residential wood burning) contributes substantially to the nucleation-mode particle concentration. Under the present conditions investigated here, we find that secondary processes mediate the increase in UFP concentration to levels equivalent to those in urban locations, affecting both air quality and human health.
2025
Marine plastic litter is subject to different abiotic and biotic forces that lead to its degradation, the main driver being UV-induced photodegradation. Since UV-exposure leads to both physical and chemical degradation of plastic, leading to a release of micro- and nanoplastics as well as leaching of chemicals and degradation products – it is expected to have radical impacts on plastics fate and effects in the marine environment. The number of laboratory studies investigating the mechanisms of plastic UV-degradation in seawater has increased significantly in the past 10 years, but are the exposures designed in a manner that allow observations to be extrapolated to environmental fate? Most studies to date focus on quantifying plastic fragmentation and surface changes, but is this relevant for impact assessments? Here, we provide a review of the current scientific literature on UV-degradation of plastic under marine conditions. Plastic fragmentation processes and surface changes as well as implications of UV-degradation of plastics on additive leaching and the toxicity of UV-weathered versus non-weathered plastics are highlighted. Furthermore, experimental set-ups are critically inspected and recommendations for future studies are issued.
Elsevier
2025
Air Quality and Healthy Ageing: Predictive Modelling of Pollutants using CNN Quantum-LSTM
The concept of healthy ageing is emerging and becoming a norm to achieve a high quality of life, reducing healthcare costs and promoting longevity. Rapid growth in global population and urbanisation requires substantial efforts to ensure healthy and supportive environments to improve the quality of life, closely aligned with the principles of healthy ageing. Access to fundamental resources which include quality healthcare services, clean air, green and blue spaces plays a pivotal role in achieving this goal. Air quality, in particular, is a critical factor in achieving healthy ageing targets. However, it necessitates a global effort to develop and implement policies aimed at reducing air pollution, which has severe implications for human health including cognitive impairment and neurodegenerative diseases, while promoting healthier environments such as high quality green and blue spaces for all age groups. Such actions inevitably depend on the current status of air pollution and better predictive models to mitigate the harmful impact of emissions on planetary health and public health. In this work, we proposed a hybrid model referred as AirVCQnet, which combines the variational mode decomposition (VMD) method with a convolutional neural network (CNN) and a quantum long short-term memory (QLSTM) network for the prediction of air pollutants. The performance of the proposed model is analysed on five key pollutants including fine Particulate Matter PM2.5, Nitrogen Dioxide (NO2), Ozone (O3), PM10, and Sulphur Dioxide (SO2), sourced from air quality monitoring station in Northern Ireland, UK. The effectiveness of the proposed model is evaluated by comparing its performance with its equivalent classical counterpart using root mean square error (RMSE), mean absolute error (MAE), and R-squared (R2). The results demonstrate the superiority of the proposed model, achieving a performance gain of up to 14% and validating its robustness, efficiency and reliability by leveraging t.
IEEE (Institute of Electrical and Electronics Engineers)
2025
HTAP3 Fires: towards a multi-model, multi-pollutant study of fire impacts
Open biomass burning has major impacts globally and regionally on atmospheric composition. Fire emissions include particulate matter, tropospheric ozone precursors, and greenhouse gases, as well as persistent organic pollutants, mercury, and other metals. Fire frequency, intensity, duration, and location are changing as the climate warms, and modelling these fires and their impacts is becoming more and more critical to inform climate adaptation and mitigation, as well as land management. Indeed, the air pollution from fires can reverse the progress made by emission controls on industry and transportation. At the same time, nearly all aspects of fire modelling – such as emissions, plume injection height, long-range transport, and plume chemistry – are highly uncertain. This paper outlines a multi-model, multi-pollutant, multi-regional study to improve the understanding of the uncertainties and variability in fire atmospheric science, models, and fires' impacts, in addition to providing quantitative estimates of the air pollution and radiative impacts of biomass burning. Coordinated under the auspices of the Task Force on Hemispheric Transport of Air Pollution, the international atmospheric modelling and fire science communities are working towards the common goal of improving global fire modelling and using this multi-model experiment to provide estimates of fire pollution for impact studies. This paper outlines the research needs, opportunities, and options for the fire-focused multi-model experiments and provides guidance for these modelling experiments, outputs, and analyses that are to be pursued over the next 3 to 5 years. The paper proposes a plan for delivering specific products at key points over this period to meet important milestones relevant to science and policy audiences.
2025
American Geophysical Union (AGU)
2025
2025
Global greenhouse gas reconciliation 2022
n 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).
2025
Poor 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.
Elsevier
2025
Road 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.
Elsevier
2025
Potato plant disease detection: leveraging hybrid deep learning models
Agriculture, a crucial sector for global economic development and sustainable food production, faces significant challenges in detecting and managing crop diseases. These diseases can greatly impact yield and productivity, making early and accurate detection vital, especially in staple crops like potatoes. Traditional manual methods, as well as some existing machine learning and deep learning techniques, often lack accuracy and generalizability due to factors such as variability in real-world conditions. This study proposes a novel approach to improve potato plant disease detection and identification using a hybrid deep-learning model, EfficientNetV2B3+ViT. This model combines the strengths of a Convolutional Neural Network - EfficientNetV2B3 and a Vision Transformer (ViT). It has been trained on a diverse potato leaf image dataset, the “Potato Leaf Disease Dataset”, which reflects real-world agricultural conditions. The proposed model achieved an accuracy of 85.06, representing an 11.43 improvement over the results of the previous study. These results highlight the effectiveness of the hybrid model in complex agricultural settings and its potential to improve potato plant disease detection and identification.
BioMed Central (BMC)
2025
Introduction
Per- and polyfluoroalkyl substances (PFAS) have been linked to effects on human lipid profiles, with several epidemiological studies reporting associations between specific PFAS and blood lipid concentrations. However, these associations have been inconsistent, and most studies have focused on cross-sectional analyses with limited repeated measurements.
Objective
In this study, we investigated associations between serum PFAS concentrations and major blood lipid classes over a 30-year period (1986–2016) and up to five time points. Lipids analyzed included total cholesterol (TC), low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG).
Methods
This study included 145 participants from The Tromsø Study, Norway, who donated plasma samples three to five times over the study period. Linear mixed-effects (LME) models assessed longitudinal associations between PFAS and lipid classes, while multiple linear regression (MLR) models were used for cross-sectional associations.
Results
LME models demonstrated positive longitudinal associations between perfluorooctanoic acid (PFOA), perfluorononanoic acid (PFNA), perfluorodecanoic acid (PFDA), perfluoroundecanoic acid (PFUnDA), perfluorododecanoic acid (PFDoDA), and perfluorotridecanoic acid (PFTrDA) with TC. Additionally, PFOA, PFDA, PFUnDA, PFDoDA, and PFTrDA were associated with LDL-C, and PFUnDA and summed perfluorooctane sulfonate isomers (∑PFOS) with HDL-C. Cross-sectional analyses corroborated positive associations between the six PFAS compounds and TC at least three times, but the LDL-C and HDL-C associations were not confirmed. Summed perfluorooctane sulfonamide isomers (∑PFOSA) showed a negative association with LDL-C longitudinally, but this was not confirmed cross-sectionally. No associations were observed between PFAS and TG, longitudinally or cross-sectionally.
Conclusion
Concentrations of multiple PFAS were positively associated with blood lipids in longitudinal analyses, with the most consistent associations observed between six PFCA compounds and TC. These findings highlight the need for further investigation into these complex associations.
Elsevier
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.
IEEE (Institute of Electrical and Electronics Engineers)
2025
2025
Australia has significant sources of atmospheric methane (CH₄), driven by extensive coal and natural gas production, livestock, and large-scale fires. Accurate quantification and characterization of CH₄ emissions are critical for effective climate mitigation strategies in Australia. In this study, we employed an inverse analysis of atmospheric CH₄ observations from the GOSAT satellite and surface measurements from 2016 to 2021 to assess CH₄ emissions in Australia. The inversion process integrates anthropogenic and natural emissions as prior estimates, optimizing them with the NIES-TM-FLEXPART-variational model (NTFVAR) at a resolution of up to 0.1° × 0.1°. We validated the performance of our inverse model using data obtained from the United Nations Environment Program Methane Science (UNEP), Airborne Research Australia 2018 aircraft-based atmospheric CH₄ measurement campaigns. Compared to prior emission estimates, optimized emissions dramatically enhanced the accuracy of modeled concentrations, aligning them much better with observations. Our results indicate that the estimated inland CH4 emissions in Australia amount to 6.84 ± 0.51 Tg CH4 yr−1 and anthropogenic emissions amount to 4.20 ± 0.08 Tg CH4 yr−1, both slightly lower than the values reported in existing inventories. Moreover, our results unveil noteworthy spatiotemporal characteristics, such as upward corrections during the warm season, particularly in Southeastern Australia. During the three most severe months of the 2019–2020 bushfire season, emissions from biomass burning surged by 0.68 Tg, constituting over 71% of the total emission increase. These results highlight the importance of continuous observation and analysis of sectoral emissions, particularly near major sources, to guide targeted emission reduction strategies. The spatiotemporal characteristics identified in this study underscore the need for adaptive and region-specific approaches to CH₄ emission management in Australia.
2025
Sb-PiPLU: A Novel Parametric Activation Function for Deep Learning
The choice of activation function—particularly non-linear ones—plays a vital role in enhancing the classification performance of deep neural networks. In recent years, a variety of non-linear activation functions have been proposed. However, many of these suffer from drawbacks that limit the effectiveness of deep learning models. Common issues include the dying neuron problem, bias shift, gradient explosion, and vanishing gradients. To address these challenges, we introduce a new activation function: Softsign-based Piecewise Parametric Linear Unit (Sb-PiPLU). This function offers improved non-linear approximation capabilities for neural networks. Its piecewise, parametric design allows for greater adaptability and flexibility, which in turn enhances overall model performance. We evaluated Sb-PiPLU through a series of image classification experiments across various Convolutional Neural Network (CNN) architectures. Additionally, we assessed its memory usage and computational cost, demonstrating that Sb-PiPLU is both stable and efficient in practical applications. Our experimental results show that Sb-PiPLU consistently outperforms conventional activation functions in both classification accuracy and computational efficiency. It achieved higher accuracy on multiple benchmark datasets, including CIFAR-10, CINIC-10, MWD, Brain Tumor, and SVHN, surpassing widely-used functions such as ReLU and Tanh. Due to its flexibility and robustness, Sb-PiPLU is particularly well-suited for complex image classification tasks.
IEEE (Institute of Electrical and Electronics Engineers)
2025
More than 70 years of industrial production of per- and polyfluoroalkyl substances (PFAS) have resulted in their ubiquitous presence in the environment on a global scale, although differences in sources, transport and fate lead to variability of occurrence in the environment. Gull eggs are excellent bioindicators of environmental pollution, especially for persistent organic pollutants such as PFAS, known to bioaccumulate in organisms and to be deposited in bird eggs by maternal transfer. Using yellow-legged gull (Larus michahellis) eggs, we investigated the occurrence of more than 30 PFAS, including the most common chemicals (i.e., legacy PFAS) as well as their alternatives (i.e., emerging PFAS) in the Bay of Marseille, the second largest city in France. Compared to eggs from other colonies along the Mediterranean coast, those from Marseille had PFAS concentrations ranging from slightly higher to up to four times lower, suggesting that this area cannot be specifically identified as a hotspot for these compounds. We also found several emerging PFAS including 8:2 and 10:2 FTS, 7:3 FTCA or PFECHS in all collected eggs. Although the scarcity in toxicity thresholds for seabirds, especially during embryogenesis, does not enable any precise statement about the risks faced by this population, this study contributes to the effort in documenting legacy PFAS contamination on Mediterranean coasts while providing valuable novel inputs on PFAS of emerging concern. Identifying exposure in free-ranging species also participate to determine the main target for toxicity testing in wildlife.
Elsevier
2025