Publikasjoner

Zürich II Statement on Per- and Polyfluoroalkyl Substances (PFASs): Scientific and Regulatory Needs

DeWitt, Jamie C.; Glüge, Juliane; Cousins, Ian T.; Goldenman, Gretta; Herzke, Dorte; Lohmann, Rainer; Miller, Mark; Ng, Carla A.; Patton, Sharyle; Trier, Xenia; Vierke, Lena; Wang, Zhanyun; Adu-Kumi, Sam; Balan, Simona; Buser, Andreas M.; Fletcher, Tony; Haug, Line Småstuen; Heggelund, Audun; Huang, Jun; Kaserzon, Sarit; Leonel, Juliana; Sheriff, Ishmail; Shi, Ya-Li; Valsecchi, Sara; Scheringer, Martin

Per- and polyfluoroalkyl substances (PFASs) are a class of synthetic organic chemicals of global concern. A group of 36 scientists and regulators from 18 countries held a hybrid workshop in 2022 in Zürich, Switzerland. The workshop, a sequel to a previous Zürich workshop held in 2017, deliberated on progress in the last five years and discussed further needs for cooperative scientific research and regulatory action on PFASs. This review reflects discussion and insights gained during and after this workshop and summarizes key signs of progress in science and policy, ongoing critical issues to be addressed, and possible ways forward. Some key take home messages include: 1) understanding of human health effects continues to develop dramatically, 2) regulatory guidelines continue to drop, 3) better understanding of emissions and contamination levels is needed in more parts of the world, 4) analytical methods, while improving, still only cover around 50 PFASs, and 5) discussions of how to group PFASs for regulation (including subgroupings) have gathered momentum with several jurisdictions proposing restricting a large proportion of PFAS uses. It was concluded that more multi-group exchanges are needed in the future and that there should be a greater diversity of participants at future workshops.

American Chemical Society (ACS)

Two-Stage Feature Engineering to Predict Air Pollutants in Urban Areas

Naz, Fareena; Fahim, Muhammad; Cheema, Adnan Ahmad; Nguyen, Trung Viet; Cao, Tuan-Vu; Hunter, Ruth; Duong, Trung Q.

Air pollution is a global challenge to human health and the ecological environment. Identifying the relationship among pollutants, their fundamental sources and detrimental effects on health and mental well-being is critical in order to implement appropriate countermeasures. The way forward to address this issue and assess air quality is through accurate air pollution prediction. Such prediction can subsequently assist governing bodies in making prompt, evidence-based decisions and prevent further harm to our urban environment, public health, and climate, all of which co-benefit our economy. In this study, the main objective is to explore the strength of features and proposed a two stage feature engineering approach, which fuses the advantage of influential factors along with the decomposition approach and generates an optimum feature combination for five major pollutants including Nitrogen Dioxide (NO 2 ), Ozone (O 3 ), Sulphur Dioxide (SO 2 ), and Particulate Matter (PM2.5, and PM10). The experiments are conducted using a dataset from 2015 to 2020 which is publicly available and is collected from Belfast-based air quality monitoring stations in Northern Ireland, UK. In stage-1, using the dataset new features such as trigonometric and statistical features are created to capture their dependency on the target pollutant and generated correlation-inspired best feature combinations to improve forecasting model performance. This is further enhanced in stage-2 by an optimum feature combination which is an integration of stage-1 and Variational Mode Decomposition (VMD) based features. This study employed a simplified Long Short Term Memory (LSTM) neural network and proposed a single-step forecasting model to predict multivariate time series data. Three performance indicators are used to evaluate the effectiveness of forecasting model: (a) root mean square error (RMSE), (b) mean absolute error (MAE), and (c) R-squared (R 2 ). The results demonstrate the effectiveness of proposed approach with 13% improvement in performance (in terms of R 2 ) and the lowest error scores for both RMSE and MAE.

IEEE (Institute of Electrical and Electronics Engineers)

Unexpected anthropogenic emission decreases explain recent atmospheric mercury concentration declines

Feinberg, Aryeh; Selin, Noelle E.; Braban, Christine F.; Chang, Kai-Lan; Custódio, Danilo; Jaffe, Daniel A.; Kyllönen, Katriina; Landis, Matthew S.; Leeson, Sarah R.; Luke, Winston; Molepo, Koketso M.; Murovec, Marijana; Nerentorp Mastromonaco, Michelle G.; Pfaffhuber, Katrine Aspmo; Rüdiger, Julian; Sheu, Guey-Rong; St Louis, Vincent L.

Måling av gasser i Statsarkivets lokaler i Trondheim. Fase 2 - 2024

Berglen, Tore Flatlandsmo; Håland, Alexander; Grøntoft, Terje

Denne rapporten viser resultater fra fase 2 i måleprosjektet NILU har utført ved Statsarkivet i Trondheim. Det er gjort prøvetaking og analyse i en periode på sju dager fra 23. til 30. mai ved to lokaliteter, én innendørs og én utendørs. Totalkonsentrasjonen av VOC’er (TVOC) ble målt til 135 µg/m3 gitt som toluen-ekvivalenter ved lokaliteten inne (MAG A, Reol 097) og 33 µg/m3 ved lokaliteten ute. Resultatene synliggjør effekten av innendørs ventilasjonssystemer og begge studiene vil brukes av Statsarkivet i sitt videre arbeid med innendørs luftkvalitet.

NILU

Assessing climatic, hydrogeological, and anthropogenic factors shaping sustainable soil management: A case study in northern Germany

Gollo, Vahid Sobhi; Hassani, Amirhossein; Fröhle, Peter; Shokri, Nima

Estimation of spatio-temporal source of microplastics using Bayesian Neural networks

Brožová, Antonie; Šmídl, Václav; Tichý, Ondřej; Evangeliou, Nikolaos

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

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

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

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)

Task Offloading Optimization for UAV-Aided NOMA Networks With Coexistence of Near-Field and Far-Field Communications

Bui, Tinh Thanh; Do, Thinh Quang; Huynh, Dang Van; Do-Duy, Tan; Nguyen, Long D.; Cao, Tuan-Vu; Sharma, Vishal; Duong, Trung Q.

IEEE (Institute of Electrical and Electronics Engineers)

Stress management with HRV following AI, semantic ontology, genetic algorithm and tree explainer

Chatterjee, Ayan; Riegler, Michael Alexander; Ganesh, K.; Halvorsen, Pål

Heart Rate Variability (HRV) serves as a vital marker of stress levels, with lower HRV indicating higher stress. It measures the variation in the time between heartbeats and offers insights into health. Artificial intelligence (AI) research aims to use HRV data for accurate stress level classification, aiding early detection and well-being approaches. This study’s objective is to create a semantic model of HRV features in a knowledge graph and develop an accurate, reliable, explainable, and ethical AI model for predictive HRV analysis. The SWELL-KW dataset, containing labeled HRV data for stress conditions, is examined. Various techniques like feature selection and dimensionality reduction are explored to improve classification accuracy while minimizing bias. Different machine learning (ML) algorithms, including traditional and ensemble methods, are employed for analyzing both imbalanced and balanced HRV datasets. To address imbalances, various data formats and oversampling techniques such as SMOTE and ADASYN are experimented with. Additionally, a Tree-Explainer, specifically SHAP, is used to interpret and explain the models’ classifications. The combination of genetic algorithm-based feature selection and classification using a Random Forest Classifier yields effective results for both imbalanced and balanced datasets, especially in analyzing non-linear HRV features. These optimized features play a crucial role in developing a stress management system within a Semantic framework. Introducing domain ontology enhances data representation and knowledge acquisition. The consistency and reliability of the Ontology model are assessed using Hermit reasoners, with reasoning time as a performance measure. HRV serves as a significant indicator of stress, offering insights into its correlation with mental well-being. While HRV is non-invasive, its interpretation must integrate other stress assessments for a holistic understanding of an individual’s stress response. Monitoring HRV can help evaluate stress management strategies and interventions, aiding individuals in maintaining well-being.

Nature Portfolio