Fant 2600 publikasjoner. Viser side 1 av 260:
Biomass burning emission analysis based on MODIS
We assessed the biomass burning (BB) smoke aerosol optical depth (AOD) simulations of 11 global models that participated in the AeroCom phase III BB emission experiment. By comparing multi-model simulations and satellite observations in the vicinity of fires over 13 regions globally, we (1) assess model-simulated BB AOD performance as an indication of smoke source–strength, (2) identify regions where the common emission dataset used by the models might underestimate or overestimate smoke sources, and (3) assess model diversity and identify underlying causes as much as possible. Using satellite-derived AOD snapshots to constrain source strength works best where BB smoke from active sources dominates background non-BB aerosol, such as in boreal forest regions and over South America and southern hemispheric Africa. The comparison is inconclusive where the total AOD is low, as in many agricultural burning areas, and where the background is high, such as parts of India and China. Many inter-model BB AOD differences can be traced to differences in values for the mass ratio of organic aerosol to organic carbon, the BB aerosol mass extinction efficiency, and the aerosol loss rate from each model. The results point to a need for increased numbers of available BB cases for study in some regions and especially to a need for more extensive regional-to-global-scale measurements of aerosol loss rates and of detailed particle microphysical and optical properties; this would both better constrain models and help distinguish BB from other aerosol types in satellite retrievals. More generally, there is the need for additional efforts at constraining aerosol source strength and other model attributes with multi-platform observations.
2025
Fungus-farming termites cultivate a Termitomyces fungus monoculture in enclosed gardens (combs) free of other fungi, except during colony declines, where Pseudoxylaria spp. stowaway fungi appear and take over combs. Here, we determined Volatile Organic Compounds (VOCs) of healthy Macrotermes bellicosus nests in nature and VOC changes associated with comb decay during Pseudoxylaria takeover. We identified 443 VOCs and unique volatilomes across samples and nest volatilomes that were mainly composed of fungus comb VOCs with termite contributions. Few comb VOCs were linked to chemical changes during decay, but longipinocarvone and longiverbenone were only emitted during comb decay. These terpenes may be involved in Termitomyces defence against antagonistic fungi or in fungus-termite signalling of comb state. Both comb and Pseudoxylaria biomass volatilomes contained many VOCs with antimicrobial activity that may serve in maintaining healthy Termitomyces monocultures or aid in the antagonistic takeover by Pseudoxylaria during colony decline. We further observed a series of oxylipins with known functions in the regulation of fungus germination, growth, and secondary metabolite production. Our volatilome map of the fungus-farming termite symbiosis provides new insights into the chemistry regulating complex interactions and serves as a valuable guide for future work on the roles of VOCs in symbioses.
2025
Previous Common Era (i.e., the past 2000 years) climate reconstructions from Fennoscandia have focused on northern and central areas, with scarce data from the southern areas. Using varved sediments from Lake Sagtjernet in southeastern Norway, we developed a hydrogen isotope record from sedimentary leaf waxes (n-alkanes) as a proxy for hydrogen isotopes in precipitation, which we interpret as an indicator of temperature variability over the past 2000 years. The climate reconstruction provides high, decadal resolution for the period 360–770 CE, allowing critical evaluation during the Dark Ages Cold Period (around 300–800 CE) and a cooling during the 6th century, previously suggested as the coldest period of the Common Era. Our results reveal that the most rapid drop in temperature occurred from 536 to 545 CE (+74/-90 years), corresponding in time to the 536 and 540 CE volcanic eruptions. We also document an inferred cold interval that persisted from around 650 to 710 CE (+72/-90 years). While past studies have suggested prolonged cooling during the Dark Ages Cold Period, our findings show that, on average, the climate during 360–770 CE was similar to the Common Era average in the Lake Sagtjernet record. To explore socio-environmental interactions throughout the past 2000 years, we present a pollen-based environmental reconstruction and integrate it with archaeological evidence from around Lake Sagtjernet. These analyses reveal significant societal activities such as land clearing, cereal cultivation, and large-scale iron production, which drastically altered the landscape in the Viking Age (around 800–1050 CE) and the first half of the Norwegian Middle Ages (around 1050–1350 CE). Modern cultivation practices following the Black Death (1349–1350 CE) were first established around 1470 CE and increased continuously until around 1940 CE. Intensification of societal activities through the past millennium, including iron production and modern cultivation, occurred during both warmer (Medieval Climate Anomaly; 950–1250 CE) and colder (Little Ice Age; 1450–1850 CE) periods.
2025
Personalized 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.
2025
Intrusion Detection Systems (IDS) are critical in safeguarding network infrastructures against malicious attacks. Traditional IDSs often struggle with knowledge representation, real-time detection, and accuracy, especially when dealing with high-throughput data. This paper proposes a novel IDS framework that leverages machine learning models, streaming data, and semantic knowledge representation to enhance intrusion detection accuracy and scalability. Additionally, the study incorporates the concept of Digital Sovereignty, ensuring that data control, security, and privacy are maintained according to national and regional regulations. The proposed system integrates Apache Kafka for real-time data processing, an automatic machine learning pipeline (e.g., Tree-based Pipeline Optimization Tool (TPOT)) for classifying network traffic, and OWL-based semantic reasoning for advanced threat detection. The proposed system, evaluated on NSL-KDD and CIC-IDS-2017 datasets, demonstrated qualitative outcomes such as local compliance, reduced data storage needs due to real-time processing, and improved adaptability to local data laws. Experimental results reveal significant improvements in detection accuracy, processing efficiency, and Sovereignty alignment.
2025
CompSafeNano project: NanoInformatics approaches for safe-by-design nanomaterials
The CompSafeNano project, a Research and Innovation Staff Exchange (RISE) project funded under the European Union's Horizon 2020 program, aims to advance the safety and innovation potential of nanomaterials (NMs) by integrating cutting-edge nanoinformatics, computational modelling, and predictive toxicology to enable design of safer NMs at the earliest stage of materials development. The project leverages Safe-by-Design (SbD) principles to ensure the development of inherently safer NMs, enhancing both regulatory compliance and international collaboration. By building on established nanoinformatics frameworks, such as those developed in the H2020-funded projects NanoSolveIT and NanoCommons, CompSafeNano addresses critical challenges in nanosafety through development and integration of innovative methodologies, including advanced in vitro models, in silico approaches including machine learning (ML) and artificial intelligence (AI)-driven predictive models and 1st-principles computational modelling of NMs properties, interactions and effects on living systems. Significant progress has been made in generating atomistic and quantum-mechanical descriptors for various NMs, evaluating their interactions with biological systems (from small molecules or metabolites, to proteins, cells, organisms, animals, humans and ecosystems), and in developing predictive models for NMs risk assessment. The CompSafeNano project has also focused on implementing and further standardising data reporting templates and enhancing data management practices, ensuring adherence to the FAIR (Findable, Accessible, Interoperable, Reusable) data principles. Despite challenges, such as limited regulatory acceptance of New Approach Methodologies (NAMs) currently, which has implications for predictive nanosafety assessment, CompSafeNano has successfully developed tools and models that are integral to the safety evaluation of NMs, and that enable the extensive datasets on NMs safety to be utilised for the re-design of NMs that are inherently safer, including through prediction of the acquired biomolecule coronas which provide the biological or environmental identities to NMs, promoting their sustainable use in diverse applications. Future efforts will concentrate on further refining these models, expanding the NanoPharos Database, and working with regulatory stakeholders thereby fostering the widespread adoption of SbD practices across the nanotechnology sector. CompSafeNano's integrative approach, multidisciplinary collaboration and extensive stakeholder engagement, position the project as a critical driver of innovation in NMs SbD methodologies and in the development and implementation of computational nanosafety.
2025
Methane emissions from the Nord Stream subsea pipeline leaks
The amount of methane released to the atmosphere from the Nord Stream subsea pipeline leaks remains uncertain, as reflected in a wide range of estimates1,2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18. A lack of information regarding the temporal variation in atmospheric emissions has made it challenging to reconcile pipeline volumetric (bottom-up) estimates1,2,3,4,5,6,7,8 with measurement-based (top-down) estimates8,9,10,11,12,13,14,15,16,17,18. Here we simulate pipeline rupture emission rates and integrate these with methane dissolution and sea-surface outgassing estimates9,10 to model the evolution of atmospheric emissions from the leaks. We verify our modelled atmospheric emissions by comparing them with top-down point-in-time emission-rate estimates and cumulative emission estimates derived from airborne11, satellite8,12,13,14 and tall tower data. We obtain consistency between our modelled atmospheric emissions and top-down estimates and find that 465 ± 20 thousand metric tons of methane were emitted to the atmosphere. Although, to our knowledge, this represents the largest recorded amount of methane released from a single transient event, it is equivalent to 0.1% of anthropogenic methane emissions for 2022. The impact of the leaks on the global atmospheric methane budget brings into focus the numerous other anthropogenic methane sources that require mitigation globally. Our analysis demonstrates that diverse, complementary measurement approaches are needed to quantify methane emissions in support of the Global Methane Pledge19.
2025
This study examines how southern wintering areas may contribute to organochlorine (OCs) loads in arctic seabirds during breeding. Light-sensitive geolocators (GLS loggers) were deployed on Arctic skuas (Stercorarius parasiticus) in one high arctic and two subarctic colonies. Hexcahlorobenzene (HCB), chlordanes, mirex, p,p′-dichlorodiphenyldichloro-ethylene (p,p′-DDE), and polychlorinated biphenyls (PCBs) were measured in the blood of breeding adults at the nest (58 individuals, a total of 128 samples) in northern Norway and Svalbard between 2009 and 2015. We compared OC concentrations and OC profiles among nesting skuas wintering in five Atlantic regions, determined by the GLS loggers: the coast of Argentina, the Caribbean, off West Africa, off the coast of southern Africa, and the Mediterranean Sea. As predicted, HCB, which is semi-volatile and has high long-range transport potential, showed high prevalence in birds wintering in all regions except the Mediterranean. Mirex showed the highest prevalence in birds wintering off the coasts of Argentina and southern Africa, in accordance with high background levels previously documented in the Southern Ocean. Chlordanes were particularly prevalent in skuas wintering off southern Africa, whereas p,p′-DDE seemed relatively evenly distributed among wintering areas. As predicted, the prevalence of PCBs was much higher in birds wintering in the Mediterranean Sea than in birds from other regions. This study thus suggests that the Mediterranean Sea and the mid- and southern Atlantic are essential sources of different OCs in the blood of Arctic skuas breeding in the European Arctic.
2025
Aerosol hygroscopicity influenced by seasonal chemical composition variations in the Arctic region
In this study, we quantified aerosol hygroscopicity parameter using aerosol microphysical observation data (κphy), analyzing monthly and seasonal trends in κphy by correlating it with aerosol chemical composition over 6 years from April 2007 to March 2013 at the Zeppelin Observatory in Svalbard, Arctic region. The monthly mean κphy value exhibited distinct seasonal variations, remaining high from winter to spring, reaching its minimum in summer, followed by an increase in fall, and maintaining elevated levels in winter. To verify the reliability of κphy, we employed the hygroscopicity parameter calculated from chemical composition data (κchem). The chemical composition and PM2.5 mass concentration required to calculate κchem was obtained through Modern-Era Retrospective Analysis for Research and Applications, version 2 (MERRA-2) reanalysis data and the calculation of κchem assumed that Arctic aerosols comprise only five species: black carbon (BC), organic matter (OM), ammonium sulfate (AS), sea salt aerosol less than a diameter of 2.5 μm (SSA2.5), and dust aerosol less than a diameter of 2.5 μm (Dust2.5). The κchem had no distinct correlation but had a similar seasonal trend compared to κphy. The κchem value followed a trend of SSA2.5 and was much higher by a factor of 1.6 ± 0.3 than κphy on average, due to a large proportion of SSA2.5 mass concentration in MERRA-2 reanalysis data. This may be due to the overestimation of sea salt aerosols in MERRA-2 reanalysis. The relationship between monthly mean κphy and the chemical composition used to calculate κchem was also analyzed. The elevated κphy from October to February resulted from the dominant influence of SSA2.5, while the maximum κphy in March was concurrently influenced by increasing AS and Dust2.5 associated with long-range transport from mid-latitude regions during Arctic haze periods and by SSA mass concentration obtained from in-situ sampling, which remained high from the preceding winter. The relatively low κphy from April to September can be attributed to low SSA2.5 and the dominance of organic compounds in the Arctic summer. Either natural sources such as those of marine and terrestrial biogenic origin or long-range-transported aerosols may contribute to the increase in organic aerosols in summer, potentially influencing the reduction in κphy of atmospheric aerosols. To our knowledge, this is the first study to analyze the monthly and seasonal variation of aerosol hygroscopicity calculated using long-term microphysical data, and this result provides evidence that changes in monthly and seasonal hygroscopicity variation occur depending on chemical composition.
2025
Nitrous oxide (N2O) is the most important stratospheric ozone-depleting agent based on current emissions and the third largest contributor to increased net radiative forcing. Increases in atmospheric N2O have been attributed primarily to enhanced soil N2O emissions. Critically, contributions from soils in the Northern High Latitudes (NHL, >50°N) remain poorly quantified despite their exposure to rapid rates of regional warming and changing hydrology due to climate change. In this study, we used an ensemble of six process-based terrestrial biosphere models (TBMs) from the Global Nitrogen/Nitrous Oxide Model Intercomparison Project (NMIP) to quantify soil N2O emissions across the NHL during 1861–2016. Factorial simulations were conducted to disentangle the contributions of key driving factors, including climate change, nitrogen inputs, land use change, and rising atmospheric CO2 concentration, to the trends in emissions. The NMIP models suggests NHL soil N2O emissions doubled from 1861 to 2016, increasing on average by 2.0 ± 1.0 Gg N/yr (p
2025