Found 9758 publications. Showing page 261 of 391:
2014
2010
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2001
Populations of killer whale (Orcinus orca) contain some of the most polluted animals on Earth. Yet, the knowledge on effects of chemical pollutants is limited in this species. Cell cultures and in vitro exposure experiments are pertinent tools to study effects of pollutants in free-ranging marine mammals. To investigate transcriptional responses to pollutants in killer whale cells, we collected skin biopsies of killer whales from the Northern Norwegian fjords and successfully established primary fibroblast cell cultures from the dermis of 4 out of 5 of them. Cells from the individual with the highest cell yield were exposed to three different concentrations of a mixture of persistent organic pollutants (POPs) that reflects the composition of the 10 most abundant POPs found in Norwegian killer whales (p,p’-DDE, trans-nonachlor, PCB52, 99, 101, 118, 138, 153, 180, 187). Transcriptional responses of 13 selected target genes were studied using digital droplet PCR, and whole transcriptome responses were investigated utilizing RNA sequencing. Among the target genes analysed, CYP1A1 was significantly downregulated in the cells exposed to medium (11.6 µM) and high (116 µM) concentrations of the pollutant mixture, while seven genes involved in endocrine functions showed a non-significant tendency to be upregulated at the highest exposure concentration. Bioinformatic analyses of RNA-seq data indicated that 13 and 43 genes were differentially expressed in the cells exposed to low and high concentrations of the mixture, respectively, in comparison to solvent control. Subsequent pathway and functional analyses of the differentially expressed genes indicated that the enriched pathways were mainly related to lipid metabolism, myogenesis and glucocorticoid receptor regulation. The current study results support previous correlative studies and provide cause-effect relationships, which is highly relevant for chemical and environmental management.
Elsevier
2023
2004
Establishment of Decadal-scale UV climatologies for high-latitude ecosystems studies. AMAP Report, 2004:4
2004
2024
2020
2006
Esso Slagen refinery - Monitoring program 2006-2011. NILU OR
NILU - Norwegian Institute for Air Research has been conducting a monitoring pogram for Esso (ExxonMobile) at Slagen refinery from 2006-2011. The program included monitoring of meteorology, SO2, PM10, BTEX and rain water chemistry. Results show that air quality standards for SO2 were exceeded in 2009, both concerning hourly mean values (29 values above 350 µg/m3), daily mean values (five values above 125 µg/m3) and annual mean (20,3 µg/m3 vs 20 µg/m3). For the other components there were no exceedence of air quality standards, except for benzene in 2009 (2,6 µg/m3 vs national target value of 2 µg/m3).
2013
Prepared by Earth Observation Data Centre for Water Resources Monitoring (EODC) GmbH in cooperation with TU Wien, GeoVille, ETH Zürich, TRANSMISSIVITY, AWST, FMI, UCC and NILU
The ESA Climate Change Initiative Phase 2 Soil Moisture Project
2018
2017
2002
Instead of a flag valid/non-valid usually proposed in the quality control (QC) processes of air quality (AQ), we proposed a method that predicts the p-value of each observation as a value between 0 and 1. We based our error predictions on three approaches: the one proposed by the Working Group on Guidance for the Demonstration of Equivalence (European Commission (2010)), the one proposed by Wager (Journal of Machine Learning Research, 15, 1625–1651 (2014)) and the one proposed by Lu (Journal of Machine Learning Research, 22, 1–41 (2021)). Total Error framework enables to differentiate the different errors: input, output, structural modeling and remnant. We thus theoretically described a one-site AQ prediction based on a multi-site network using Random Forest for regression in a Total Error framework. We demonstrated the methodology with a dataset of hourly nitrogen dioxide measured by a network of monitoring stations located in Oslo, Norway and implemented the error predictions for the three approaches. The results indicate that a simple one-site AQ prediction based on a multi-site network using Random Forest for regression provides moderate metrics for fixed stations. According to the diagnostic based on predictive qq-plot and among the three approaches used in this study, the approach proposed by Lu provides better error predictions. Furthermore, ensuring a high precision of the error prediction requires efforts on getting accurate input, output and prediction model and limiting our lack of knowledge about the “true” AQ phenomena. We put effort in quantifying each type of error involved in the error prediction to assess the error prediction model and further improving it in terms of performance and precision.
MDPI
2021
2022
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2012