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Found 9883 publications. Showing page 395 of 396:

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Hazard characterization of the mycotoxins enniatins and beauvericin to identify data gaps and improve risk assessment for human health

Behr, Anne-Cathrin; Fæste, Christiane Kruse; Azqueta, Amaya; Tavares, Ana M.; Spyropoulou, Anastasia; Solhaug, Anita; Olsen, Ann-Karin Hardie; Vettorazzi, Ariane; Mertens, Birgit; Zegura, Bojana; Streel, Camille; Ndiaye, Dieynaba; Spilioti, Eliana; Dubreil, Estelle; Buratti, Franca Maria; Crudo, Francesco; Eriksen, Gunnar Sundstøl; Snapkov, Igor; Teixeira, João Paulo; Rasinger, Josef; Sanders, Julie; Machera, Kyriaki; Ivanova, Lada; Gaté, Laurent; Le Hegarat, Ludovic; Novak, Matjaz; Smith, Nicola Margareta; Tait, Sabrina; Fraga, Sónia; Hager, Sonja; Marko, Doris; Braeuning, Albert; Louro, Henriqueta; Silva, Maria João; Dirven, Hubert; Dietrich, Jessica

Enniatins (ENNs) and beauvericin (BEA) are cyclic hexadepsipeptide fungal metabolites which have demonstrated antibiotic, antimycotic, and insecticidal activities. The substantial toxic potentials of these mycotoxins are associated with their ionophoric molecular properties and relatively high lipophilicities. ENNs occur extensively in grain and grain-derived products and are considered a food safety issue by the European Food Safety Authority (EFSA). The tolerable daily intake and maximum levels for ENNs in humans and animals remain unestablished due to key toxicological and toxicokinetic data gaps, preventing full risk assessment. Aiming to find critical data gaps impeding hazard characterization and risk evaluation, this review presents a comprehensive summary of the existing information from in vitro and in vivo studies on toxicokinetic characteristics and cytotoxic, genotoxic, immunotoxic, endocrine, reproductive and developmental effects of the most prevalent ENN analogues (ENN A, A1, B, B1) and BEA. The missing information identified showed that additional studies on ENNs and BEA have to be performed before sufficient data for an in-depth hazard characterisation of these mycotoxins become available.

Springer

2025

Evaluation of fire emissions for HTAP3 with CAMS GFAS and IFS-COMPO

Kaiser, Johannes; Huijnen, Vincent; Remy, Samuel; Ytre-Eide, Martin Album; de Jong, Marc C.; Zheng, Bo; Wiedinmyer, Christine

2025

Gravity Wave-Induced Perturbations in Lidar Backscatter Profiles above La Réunion (21°S, 55°E)

Ming, Fabrice Chane; Tremoulu, Samuel; Gantois, Dominique; Payen, Guillaume; Sicard, Michael; Khaykin, Sergey; Hauchecorne, Alain; Keckhut, Philippe; Duflot, Valentin

2025

Air Quality and Healthy Ageing: Predictive Modelling of Pollutants using CNN Quantum-LSTM

Naz, Fareena; Fahim, Muhammad; Cheema, Adnan Ahmad; McNiven, Bradley D. E.; Cao, Tuan-Vu; Hunter, Ruth; Duong, Trung Q.

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

Methane emissions from the Nord Stream subsea pipeline leaks

Harris, Stephen; Schwietzke, Stefan; France, James L.; Salinas, Nataly Velandia; Fernandez, Tania Meixus; Randles, Cynthia; Guanter, Luis; Irakulis-Loitxate, Itziar; Calcan, Andreea; Aben, Ilse; Abrahamsson, Katarina; Balcombe, Paul; Berchet, Antoine; Biddle, Louise C.; Bittig, Henry C.; Böttcher, Christian; Bouvard, Timo; Broström, Göran; Bruch, Valentin; Cassiani, Massimo; Chipperfield, Martyn P.; Ciais, Philippe; Damm, Ellen; Dammers, Enrico; van der Gon, Hugo Denier; Dogniaux, Matthieu; O'Dowd, Emily; Dupouy, François; Eckhardt, Sabine; Evangeliou, Nikolaos; Feng, Wuhu; Jia, Mengwei; Jiang, Fei; Kaiser-weiss, Andrea; Kamoun, Ines; Kerridge, Brian J.; Lampert, Astrid; Lana, José; Li, Fei; Maasakkers, Joannes D.; Maclean, Jean-Philippe W.; Mamtimin, Buhalqem; Marshall, Julia; Mauger, Gédéon; Mekkas, Anouar; Mielke, Christian; Mohrmann, Martin; Moore, David P.; Nanni, Ricardo; Pätzold, Falk; Pison, Isabelle; Pisso, Ignacio; Platt, Stephen Matthew; Préa, Raphaël; Queste, Bastien Y.; Ramonet, Michel; Rehder, Gregor; Remedios, John J; Reum, Friedemann; Roiger, Anke; Schmidbauer, Norbert; Siddans, Richard; Sunkisala, Anusha; Thompson, Rona Louise; Varon, Daniel J.; Ventres, Lucy J.; Chris, Wilson; Zhang, Yuzhong

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

Environmental sustainability of urban expansion: Implications for transport emissions, air pollution, and city growth

Lopez-Aparicio, Susana; Grythe, Henrik; Drabicki, Arkadiusz; Chwastek, Konrad; Tobola, Kamila; Górska-Niemas, Lidia; Kierpiec, Urszula; Markelj, Miha; Strużewska, Joanna; Kud, Bartosz; Sousa Santos, Gabriela

This study examines the environmental impacts of urban growth in Warsaw since 2006 and models the implications of future urban development for traffic pollutant emissions and pollution levels. Our findings demonstrate that, over the past two decades, urban sprawl has resulted in decreases in accessibility to public transport, social services, and natural areas. We analyse CO2 traffic emissions, NO2 concentrations, and population exposure across urban areas in future scenarios of further sprawling or alternative compacting land-use development. Results indicate that a compact future scenario reduces transport CO2 emissions and urban NO2 levels, though increases in population density raise exposure to air pollution. A sprawl future scenario increases CO2 and NOx emissions due to longer commutes and congestion, and NO2 levels increase up to 25% in parts of the city. Several traffic abatement strategies were simulated, and in all simulations a compact city consistently yields the largest reductions in CO2 emissions and NO2 levels, implying that the best abatement strategy for combating negative consequences of sprawl is to reduce sprawling. In both city layouts, network-wide improvements of public transport travel times gave significantly reduced emissions. Combined, our findings highlight the importance of co-beneficial urban planning strategies to balance CO2 emissions reduction, and air pollution exposure in expanding cities.

Elsevier

2025

Utslipp til luft ved Miljø Norge AS. Målinger av PFAS og støv

Halvorsen, Helene Lunder; Celentano, Samuel; Hanssen, Linda; Hartz, William Frederik; Berglen, Tore Flatlandsmo

NILU

2025

Det svarte fotballparadokset

Herzke, Dorte (interview subject); Larsen, Christiane Jordheim (journalist)

2025

Leaching of Organic Compounds from Tire Particles Under Conditions Simulating the Deep Sea

Schmidt, Natascha; Foscari, Aurelio Giovanni; Garel, Marc; Tamburini, Christian; Seiwert, Bettina; Herzke, Dorte; Reemtsma, Thorsten; Sempere, Richard

2025

Non-target and suspect screening of volatile organic compounds from Scots pine and Norway spruce building materials

Bakke, Ingrid Marie; Kallenborn, Roland; Nyrud, Anders Q.; Håland, Alexander

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

Årsrapport 2024. Nasjonalt referanselaboratorium for luftkvalitetsmålinger

Marsteen, Leif; Johnsrud, Mona; Hak, Claudia; Dauge, Franck Rene; Tørnkvist, Kjersti Karlsen; Vo, Dam Thanh; Amundsen, Filip

Denne rapporten oppsummerer oppgavene til Nasjonalt referanselaboratorium for luftkvalitetsmålinger (NRL), delkontrakt 1b, for året 2024.

NILU

2025

Predicting the student's perceptions of multi-domain environmental factors in a Norwegian school building: Machine learning approach

Alam, Azimil Gani; Bartonova, Alena; Høiskar, Britt Ann Kåstad; Fredriksen, Mirjam; Sharma, Jivitesh; Mathisen, Hans Martin; Yang, Zhirong; Gustavsen, Kai; Hart, Kent; Fredriksen, Tore; Cao, Guangyu

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

Let’s Investigate Methane for Climate Action

Houweling, Sander; Petrescu, Roxana; Zaidi, Mekky; Roeckmann, Thomas; Paris, Jean-Daniel; Sachs, Torsten; Aalto, Tuula; Gloor, Manuel; Boesch, Hartmut; Stohl, Andreas; van der Gon, Hugo Denier; Saunois, Marielle; Thompson, Rona Louise; Gromov, Sergey; Hoglund-Isaksson, Lena; Koffi, Ernest

2025

Er glassflasker tryggere for helsa?

Skaar, Jøran Solnes (interview subject); Kjørstad, Elise (journalist)

2025

The pollution fast-track to the Arctic: how southern wintering areas contribute to organochlorine loads in a migrant seabird breeding in the Arctic

Bustnes, Jan Ove; Bårdsen, Bård-Jørgen; Moe, Børge; Herzke, Dorte; van Bemmelen, Rob S.A.; Tulp, Ingrid; Schekkerman, Hans; Hanssen, Sveinn Are

Pergamon Press

2025

Critical Insights into Untargeted GC-HRMS Analysis: Exploring Volatile Organic Compounds in Italian Ambient Air

Cerasa, Marina; Balducci, Catia; Moneta, Benedetta Giannelli; Santoro, Serena; Perilli, Mattia; Nikiforov, Vladimir

This study critically examines the workflow for untargeted analysis of volatile organic compounds (VOCs) in ambient air, from sampling strategies to data interpretation by using GC-HRMS. While untargeted approaches are well-established in liquid chromatography (LC) due to advanced-deconvolution tools and extensive metabolomic libraries, their application in gas chromatography (GC) remains less developed, particularly for VOCs. The high structural isomerism of VOCs and the relative novelty of GC-based untargeted methodologies present unique challenges, including limited software tools and reference libraries. Air samples from suburban and rural sites in central Italy were analyzed to explore chemical diversity and address methodological gaps. This study evaluates critical decisions, such as sampling strategies, extraction techniques, and data-processing workflows, highlighting the limitations of automated deconvolution tools and the need for manual validation. Results revealed distinct source contributions, with suburban areas showing higher levels of anthropogenic compounds and rural areas dominated by biogenic emissions. This work underscores the potential of GC-HRMS untargeted analysis to advance environmental chemistry, while addressing key pitfalls and providing practical recommendations for reliable application. By bridging methodological gaps, it offers a roadmap for future studies aiming to integrate untargeted and targeted approaches in air quality research.

MDPI

2025

Investigating climate change impacts on PCB-153 exposure in Arctic food webs using the Nested Exposure Model

Krogseth, Ingjerd Sunde; Routti, Heli; Breivik, Knut; Eckhardt, Sabine; Eulaers, Igor; Dietze, Jörn Lukas Franz; Decristoforo, Gregor; Harju, Mikael; Wania, Frank

2025

UV-degradation is a key driver of the fate and impacts of marine plastics. How can laboratory experiments be designed to effectively inform risk assessment?

Hernandez, Laura M.; Howarth-Forster, Lucy; Sørensen, Lisbet; Booth, Andy; Vidal, Alice; Tufenkji, Nathalie; Sempéré, Richard; Schmidt, Natascha

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

Langt nede i isen finnes det luft som er flere hundre tusen år gammel

Eckhardt, Sabine; Steen-Larsen, Hans Christian (interview subjects); Aas, Vilde Aardahl (journalist)

2025

Potato plant disease detection: leveraging hybrid deep learning models

Sinamenye, Jackson Herbert; Chatterjee, Ayan; Shrestha, Raju

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

Non-Target Screening of Chemicals of Emerging Concern in Marine Mammals in the Nordic Environment

Zhu, Linyan; Rehnstam, Svante; Ahrens, Lutz; Harju, Mikael; Rostkowski, Pawel; Søndergaard, Jens; Vorkamp, Katrin

2025

Generalized Convergence Analysis of Tsetlin Automaton Based Algorithms: A Probabilistic Approach to Concept Learning

Belaid, Mohamed-Bachir; Sharma, Jivitesh; Lei, Jiao; Granmo, Ole-Christoffer; Andersen, Per-Arne; Yazidi, Anis

2025

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