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As climate change impacts intensify across Europe and globally, societies are confronted with increasingly frequent and severe hazards that challenge public health, urban livability, and environmental sustainability. While adaptation measures are urgently needed to cope with current and near-term climate risks, it is becoming increasingly evident that mitigation efforts are essential to ensure a resilient and sustainable future. Too often, however, adaptation and mitigation strategies are planned and implemented in isolation, within sectoral silos, overlooking their potential interdependencies, synergies, and co-benefits. This contribution draws on the on-going experience and perspectives of the EU-funded healthRiskADAPT project, which addresses climate-related health risks by explicitly linking adaptation and mitigation pathways across multiple hazards.The project adopts a broad and integrated perspective that combines existing technical solutions, nature-based interventions, and engagement strategies, with a strong emphasis on co-benefits for health and well-being in the face of climate hazards namely heatwaves, air pollution including wildfire emission, and pollen. Central to this framework is the use of cost–benefit and co-benefit analyses to support decision-makers in identifying, prioritizing, and implementing measures that maximize societal resilience while delivering climate resilience solutions, considering natural based solutions (e.g., greening) as well as technical solutions (e.g., smart-buildings, do-it-yourself air purifier devices, evaporative cooling, high efficiency filtering). Beyond technical assessments, the healthRiskADAPT project recognizes that increasing resilience requires engagement beyond institutional actors. Social solutions such as education, awareness-raising, and capacity building at the stakeholder level are considered essential components of effective climate strategies. The contribution therefore also explores participatory formats and stakeholder engagement approaches designed to enhance understanding of climate-related health risks and support the co-design of locally relevant policies and interventions.By presenting the project’s methodological pathways, tools, and engagement strategies, this contribution illustrates how integrated adaptation–mitigation planning can be operationalized in practice. It highlights the value of moving beyond sector-specific solutions toward systemic approaches that acknowledge complex interdependencies between climate, environment, health, and society. Ultimately, the contribution aims to demonstrate how such integrated frameworks can support cities and regions in developing more coherent, evidence-based, and socially inclusive climate policies, strengthening resilience in the face of a changing climate.
2026
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.
2026
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2006
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.
2022
2014
Intensiv skogovervåking i 2006. Resultater fra ICP Forest Level 2 flater i Norge. Forskning fra Skog og landskap, 4/07
2007
Intensiv skogovervåking i 2007. Resultater fra ICP Forest Level 2 flater i Norge. Forskning fra Skog og landskap, 5/08
2008
Intensiv skogovervåking i 2008. Resultater fra ICP Forests Level 2 flater i Norge. Forskning fra skog og landskap, 3/09
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2007
Intensive skogovervåkingsflater. Resultater fra 1999. Aktuelt fra Skogforskningen, 5/00
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Intensive skogovervåkingsflater. Resultater fra 2001. Aktuelt fra Skogforskningen, 4/02
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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.
2019
2015