Publikasjoner

Validation of the EarthCARE ATLID and MSI total column aerosol products and evaluation of their associated uncertainties

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

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

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.

Estimate Rooftop Heights from Orthophotos: a Machine Learning Approach

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Platt, Stephen Matthew (intervjuobjekt); Ursin, Lars (journalist)

Atmospheric microplastics emissions estimation and uncertainty quantification using Gibbs sampler

Oceans pull carbon from air; world not ready to scale up technologies - India Today

Some emissions, especially from aviation, shipping and heavy industry, are nearly impossible to eliminate entirely.

Preliminary in vitro investigation of the biological effects of key tire chemicals

Exploring the Chemical Complexity and Sources of Airborne Fine Particulate Matter in East Asia by Nontarget Analysis and Multivariate Modeling

The complex and dynamic nature of airborne fine particulate matter (PM2.5) has hindered understanding of its chemical composition, sources, and toxic effects. In the first steps of a larger study, here, we aimed to elucidate relationships between source regions, ambient conditions, and the chemical composition in water extracts of PM2.5 samples (n = 85) collected over 16 months at an observatory in the Yellow Sea. In each extract, we quantified elements and major ions and profiled the complex mixtures of organic compounds by nontarget mass spectrometry. More than 50,000 nontarget features were detected, and by consensus of in silico tools, we assigned a molecular formula to 13,907 features. Oxygenated compounds were most prominent, followed by mixed nitrogenated/oxygenated compounds, organic sulfates, and sulfonates. Spectral matching enabled identification or structural annotation of 43 substances, and a workflow involving SIRIUS and MS-DIAL software enabled annotation of 74 unknown per- and polyfluoroalkyl substances with primary source regions in China and the Korean Peninsula. Multivariate modeling revealed seasonal variations in chemistry, attributable to the combination of warmer temperatures and maritime source regions in summer and to cooler temperatures and source regions of China in winter.

Air quality monitoring for air quality policy. Technical support document on the use of reference and non-reference methods, and on the quality assurance process to meet relevant data quality objectives for regulated air pollutants

This document provides technical details and support for the implementation of air quality monitoring under the Directive (EU) 2024/2881 of the European Parliament and of the Council of 23 October 2024 on ambient air quality and cleaner air for Europe (recast) (AAQD, Directive (EU) 2024/2881). It presents an overview of current knowledge and best practices, signposting to existing technical guidance on air quality monitoring and to sources of ongoing technical guidance development. This document does not formulate any legal provisions and as such, it does not have a legally binding value.

Publications Office of the European Union/European Commission. Directorate-General for Environment

Monitoring of environmental contaminants in freshwater food webs (MILFERSK), 2024 Overvåkning av miljøgifter i ferskvann (MILFERSK), 2024

This report presents data from the fourth year of a five-year period of the MILFERSK program. In 2024, the monitoring program focused on the sampling and analysis of the benthic food chain in Lake Mjøsa, encompassing the following sample types: Chironomids, Ruffe, Perch, Pike and the stomach contents of ruffe. Additionally, brown trout from the pelagic zone in Lake Mjøsa were collected and analyzed, with the contaminant levels compared to samples of brown trout from the reference lake, Femunden. The concentrations of 175 individual compounds/isomers were determined, with frequent detections of specific per- and polyfluoroalkyl substances (PFAS), polybrominated diphenyl ethers (PBDEs), mercury (Hg), and siloxanes exhibiting biomagnifying properties throughout the food chain. Certain contaminants, such as quaternary ammonium compounds, were found in higher concentrations in sediment and lower trophic levels. Concentrations of chlorinated paraffins (CPs), particularly medium-chain chlorinated paraffins (MCCPs) were higher in chironomids, ruffe, and the livers of perch and pike, compared to levels observed in 2021 and 2022, with an increase up the food chain in 2024. A slight downward trend in perfluorooctane sulfonate (PFOS) concentrations was observed in Lake Mjøsa from 2014 – 2024. Additionally, a lower length-adjusted mercury concentration was noted for brown trout in Lake Mjøsa during the period from 2015 to 2024, compared to the preceding nine years (2006 – 2014).

Norsk institutt for vannforskning (NIVA)

Activities of the Aerosol Clouds and Trace gases European Research Infrastructure (ACTRIS) Expert Group on Satellite Cal/Val