Fant 9806 publikasjoner. Viser side 387 av 393:
2024
2024
Zürich II Statement on Per- and Polyfluoroalkyl Substances (PFASs): Scientific and Regulatory Needs
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)
2024
The Aerosol, Clouds and Trace Gases Research Infrastructure (ACTRIS) officially became the 33rd European Research Infrastructure Consortium (ERIC) on April 25, 2023 with the support of 17 founding member and observer countries. As a pan-European legal organization, ACTRIS ERIC will coordinate the provision of data and data products on short-lived atmospheric constituents and clouds relevant to climate and air pollution over the next 15-20 years. ACTRIS was designed more than a decade ago, and its development was funded at national and European levels. It was included in the European Strategy Forum on Research Infrastructures (ESFRI) Roadmap in 2016 and subsequently, in the national infrastructure roadmaps of European countries. It became a landmark of the ESFRI roadmap in 2021. The purpose of this paper is to describe the mission of ACTRIS, its added value to the community of atmospheric scientists, providing services to academia as well as the public and private sectors, and to summarize its main achievements. The present publication serves as a reference document for ACTRIS, its users and the scientific community as a whole. It provides the reader with relevant information and an overview on ACTRIS governance and services, as well as a summary of the main scientific achievements of the last 20 years. The paper concludes with an outlook on the upcoming challenges for ACTRIS and the strategy for its future evolution.
American Meteorological Society
2024
Two-Stage Feature Engineering to Predict Air Pollutants in Urban Areas
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)
2024
2024
2024
2024
Måling av gasser i Statsarkivets lokaler i Trondheim. Fase 2 - 2024
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
2024
2024
2024
2024
2025
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
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