Found 10000 publications. Showing page 203 of 400:
Low-cost sensor (LCS) networks can complement sparse regulatory monitoring, but their value depends on robust integration strategies that preserve data quality while exploiting dense spatial sampling. Here we assess the added value of incorporating validated LCS PM2.5 observations into the S-MESH (Satellite and ML-based Estimation of Surface air quality at High resolution) machine learning framework (Shetty et al., 2024, 2025) to generate continental-scale, 1 km resolution surface PM2.5 fields across Central Europe. Two integration strategies are evaluated for 2021–2022 within a stacked XGBoost architecture driven by satellite aerosol optical depth, meteorological predictors, and CAMS regional forecasts: a) using LCS data as an additional training target (LCST), and b) using LCS information as a model input feature (LCSI) via an inverse-distance-weighted spatial convolution layer that encodes local sensor influence. Relative to a baseline trained only on official monitoring stations, LCSI yields consistent performance gains, with RMSE reductions of ~15–20% in urban areas, whereas LCST provides less consistent improvement. The resulting high-resolution mapping product achieves skill comparable to the CAMS regional reanalysis, often considered as a modelling “gold standard” for European air-quality assessment, and in some evaluations surpasses it, with lower annual mean absolute error (2.68 vs 3.32 µg m⁻³) (Shetty et al., 2026). This demonstrates that a data-fusion ML approach including LCS information can deliver reanalysis-level performance at 1 km resolution while requiring only modest computational resources compared with running full chemical transport model reanalyses, enabling rapid updates and scalable deployment. SHAP-based attribution further suggests that LCSI improves the model’s ability to capture localized pollution variability, while performance degrades where sensor density is low, limiting representation of inter-urban transport.Although demonstrated in Europe, the underlying methodology, namely combining globally available satellite products and meteorology with quality-controlled LCS networks in a computationally efficient ML framework, has potential to strengthen air-quality assessment also in resource-limited settings where regulatory infrastructure is scarce. A requirement for this is that appropriate sensor calibration/validation workflows are in place and equitable partnerships support sustainable sensor deployment and data stewardship. Shetty, S., Schneider, P., Stebel, K., Hamer, P. D., Kylling, A., and Koren Berntsen, T.: Estimating surface NO2 concentrations over Europe using Sentinel-5P TROPOMI observations and Machine Learning, Remote Sens. Environ., 312, 114321, https://doi.org/10.1016/j.rse.2024.114321, 2024.Shetty, S., Hamer, P. D., Stebel, K., Kylling, A., Hassani, A., Berntsen, T. K., and Schneider, P.: Daily high-resolution surface PM2.5 estimation over Europe by ML-based downscaling of the CAMS regional forecast, Environ. Res., 264, 120363, https://doi.org/10.1016/j.envres.2024.120363, 2025.Shetty, S., Hassani, A., Hamer, P. D., Stebel, K., Salamalikis, V., Berntsen, T. K., Castell, N., and Schneider, P.: Evaluating the role of low-cost sensors in machine learning based European PM2.5 monitoring, Environ. Res., 291, 123558, https://doi.org/10.1016/j.envres.2025.123558, 2026.
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Anthropogenic activities are introducing multiple chemical contaminants into ecosystems that act as stressors for wildlife. Perfluoroalkyl substances (PFAS) and mercury (Hg) are two relevant contaminants that may cause detrimental effects on the fitness of many aquatic organisms. However, there is a lack of information on their impact on the expression of secondary sexual signals that animals use for mate choice. We have explored the correlations between integument carotenoid-based colourations, blood levels of carotenoids, and blood levels of seven PFAS and of total Hg (THg) in 50 adult male black-legged kittiwakes (Rissa tridactyla) from the Norwegian Arctic during the pre-laying period, while controlling for other colouration influencing variables such as testosterone and body condition. Kittiwakes with elevated blood concentrations of PFAS (PFOSlin, PFNA, PFDcA, PFUnA, or PFDoA) had less chromatic but brighter bills, and brighter gape and tongue; PFOSlin was the pollutant with the strongest association with bill colourations. Conversely, plasma testosterone was the only significant correlate of hue and chroma of both gape and tongue, and of hue of the bill. Kittiwakes with higher concentrations of any PFAS, but not of THg, tended to have significantly higher plasma concentrations of the carotenoids astaxanthin, zeaxanthin, lutein, and cryptoxanthin. Our work provides the first correlative evidence that PFAS exposure might interfere with the carotenoid metabolism and the expression of integument carotenoid-based colourations in a free-living bird species. This outcome may be a direct effect of PFAS exposure or be indirectly caused by components of diet that also correlate with elevated PFAS concentrations (e.g., proteins). It also suggests that there might be no additive effect of THg co-exposure with PFAS on the expression of colourations. These results call for further work on the possible interference of PFAS with the expression of colourations used in mate choice.
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Intensive forest monitoring in 2006. Results from ICP Forest Level 2 plots in Norway. Forskning fra Skog og landskap, 4/07
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Intensive forest monitoring in 2007. Results from ICP Forest Level 2 plots in Norway. Forskning fra Skog og landskap, 5/08
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Intensive forest monitoring in 2008. Results from ICP Forests Level 2 plots in Norway. Forskning fra skog og landskap, 3/09
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Interactions between the atmosphere, cryosphere, and ecosystems at northern high latitudes
The Nordic Centre of Excellence CRAICC (Cryosphere–Atmosphere Interactions in a Changing Arctic Climate), funded by NordForsk in the years 2011–2016, is the largest joint Nordic research and innovation initiative to date, aiming to strengthen research and innovation regarding climate change issues in the Nordic region. CRAICC gathered more than 100 scientists from all Nordic countries in a virtual centre with the objectives of identifying and quantifying the major processes controlling Arctic warming and related feedback mechanisms, outlining strategies to mitigate Arctic warming, and developing Nordic Earth system modelling with a focus on short-lived climate forcers (SLCFs), including natural and anthropogenic aerosols.
The outcome of CRAICC is reflected in more than 150 peer-reviewed scientific publications, most of which are in the CRAICC special issue of the journal Atmospheric Chemistry and Physics. This paper presents an overview of the main scientific topics investigated in the centre and provides the reader with a state-of-the-art comprehensive summary of what has been achieved in CRAICC with links to the particular publications for further detail. Faced with a vast amount of scientific discovery, we do not claim to completely summarize the results from CRAICC within this paper, but rather concentrate here on the main results which are related to feedback loops in climate change–cryosphere interactions that affect Arctic amplification.
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