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2007
2011
2013
Concentrations of organohalogenated contaminants (OHCs) can show significant temporal and spatial variation in the environment and wildlife. Most of the variation is due to changes in use and production, but environmental and biological factors may also contribute to the variation. Nestlings of top predators are exposed to maternally transferred OHCs in the egg and through their dietary intake after hatching. The present study investigated spatial and temporal variation of OHCs and the role of age and diet on these variations in plasma of Norwegian white-tailed eagle (Haliaeetus albicilla) nestlings. The nestlings were sampled at two locations, Smøla and Steigen, in 2015 and 2016. The age of the nestlings was recorded (range: 44 - 87 days old) and stable carbon and nitrogen isotopes (δ13C and δ15N) were applied as dietary proxies for carbon source and trophic position, respectively. In total, 14 polychlorinated biphenyls (PCBs, range: 0.82 - 59.05 ng/mL), 7 organochlorinated pesticides (OCPs, range: 0.89 - 52.19 ng/mL), 5 polybrominated diphenyl ethers (PBDEs, range: 0.03 - 2.64 ng/mL) and 8 perfluoroalkyl substances (PFASs, range: 4.58 - 52.94 ng/mL) were quantified in plasma samples from each location and year. The OHC concentrations, age and dietary proxies displayed temporal and spatial variations. The age of the nestlings was indicated as the most important predictor for OHC variation as the models displayed significantly decreasing plasma concentrations of PCBs, OCPs, and PBDEs with increasing age, while concentrations of PFASs were significantly increasing with age. Together with age, the variations in PCB, OCP and PBDE concentrations were also explained by δ13C and indicated decreasing concentrations with a more marine diet. Our findings emphasise age and diet as important factors to consider when investigating variations in plasma OHC concentrations in nestlings.
2018
2013
2010
2013
2014
Plan hopes to capture carbon by tapping the ocean's power - Earth.com
A look at how ocean carbon removal fits into climate plans and why experts say strong safeguards are needed before it can scale.
2025
2017
2010
Pioneering an effect-based early warning system for hazardous chemicals in the environment
Existing regulatory frameworks often prove inadequate in promptly identifying contaminants of emerging concern (CECs) and determining their impacts on biological systems at an early stage. The establishment of Early Warning Systems (EWSs) for CECs is becoming increasingly relevant for policy-making, aiming to proactively detect chemical hazards and implement effective mitigation measures. Effect-based methodologies, including bioassays and effect-directed analysis (EDA), offer valuable input to EWSs by pinpointing the relevant toxicity drivers and prioritizing the associated risks. This review evaluates the analytical techniques currently available to assess biological effects, and provides a structured plan for their systematic integration into an EWS for hazardous chemicals in the environment. Key scientific advancements in effect-based approaches and EDA are discussed, underscoring their potential for early detection and management of chemical hazards. Additionally, critical challenges such as data integration and regulatory alignment are addressed, emphasizing the need for continuous improvement of the EWS and the incorporation of analytical advancements to safeguard environmental and public health from emerging chemical threats.
2024
PikMe: a flexible prioritization tool for chemicals of emerging concern
Identifying new contaminants of emerging concern remains a complex task due to the sheer number of chemical substances potentially released into the environment, the scattered sources of information, and often the lack of adequate data. Environmental screening and monitoring programs are designed to map the presence, sources, and potential environmental impacts of contaminants, yet prioritizing which chemicals to include in such efforts remains resource-intensive and technically challenging. PikMe is a modular, open-access prioritization tool that integrates information from major data bases and evaluates the concern and reliability of the data for more than one million substances. PikMe is built in a modular way so that prioritization can be done based on specific chemical properties relevant to a given scenario (i.e., drinking water contaminants or bioaccumulation in biota) rather than assigning only a global risk score. PikMe scores substances based on persistence, bioaccumulation, mobility, environmental toxicity, and human toxicity, assigning individual score per property. Additionally, PikMe is designed for flexibility by allowing the integration of external lists of chemicals and supporting optional add-ons. Different scenarios of use are described in this article, including the selection of chemicals for environmental monitoring and screening in Norway and the assessment of the implications of the new classifications according to the regulation for classification, labelling and packaging of substances and mixtures on persistent chemicals.
2025
2015
2012
2011
2025
Physics-Informed Deep Learning for Wind Downscaling over Oslo
Running a numerical weather model such as WRF at kilometre or sub-kilometre grid spacing over a regional domain is computationally expensive. We present physics-informed deeplearning models that ingest a single 9km WRF wind field and simultaneously predict two finer-scale wind fields at 3 km and 1 km resolution via dual decoder heads. Four representative architectures are benchmarked-Deep Residual U-Net (DeepRU), DEVINE, a bespoke 3-D Transformer, and a Fourier Neural Operator (FNO)-each trained with divergence-free, vorticity, and Navier-Stokes residual constraints plus Charbonnier and gradient perceptual losses. We train and validate our models on the city of Oslo for the year 2018. DeepRU achieves R2=0.94 (RMSE =0.050) at 3km and R2=0.89(RMSE=0.065) at 1 km. DEVINE, Transformer 3-D, and FNO yield 3 km scores of 0.91−0.93, with 1km scores lower by 0.02−0.08, illustrating the increased difficulty of finer-scale reconstruction. Physicsinformed losses improve all models compared to MSE-only baselines, and the residual architecture (DeepRU) remains most effective for this dual-scale task.
2025
2006
2006
1999
2003