Found 10000 publications. Showing page 213 of 400:
Intensive forest monitoring in 2006. Results from ICP Forest Level 2 plots in Norway. Forskning fra Skog og landskap, 4/07
2007
2014
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
2006
2012
2010
2009
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
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
2023
Integrating Solar Energy and Nature-Based Solutions for Climate-Neutral Urban Environments
This study focuses on achieving climate neutrality in European cities by integrating solar energy technologies and nature-based solutions. Through an examination of current practices, emerging trends, and case examples, the study explores the benefits, challenges, and prospects associated with this integration in urban contexts. A pioneering approach is presented to assess the urban heat and climate change mitigation benefits of combining building-integrated photovoltaics and nature-based solutions within the European context. The results highlight the synergistic relationship between nature-based components and solar conversion technology, identifying effective combinations for different climatic zones. In Southern Europe, strategies such as rooftop photovoltaics on cool roofs, photovoltaic shadings, green walls, and urban trees have demonstrated effectiveness in warmer regions. Conversely, mid- and high-latitude European cities have seen positive impacts through the integration of rooftop photovoltaics and photovoltaic facades with green roofs and green spaces. As solar cell conversion efficiency improves, the environmental impact of photovoltaics is expected to decrease, facilitating their integration into urban environments. The study emphasizes the importance of incorporating water bodies, cool pavements, spaces with high sky-view factors, and effective planning in urban design to maximize resilience benefits. Additionally, the study highlights the significance of prioritizing mitigation actions in low-income regions and engaging citizens in the development of social photovoltaics-positive energy houses, resilient neighbourhoods, and green spaces. By adopting these recommendations, European cities can create climate-neutral urban environments that prioritize clean energy, nature-based solutions, and the overall wellbeing of residents. The findings underscore the need for a multidisciplinary approach combining technological innovation, urban planning strategies, and policy frameworks to effectively achieve climate neutrality.
2023
2024
Urban areas experience elevated air pollution levels which pose significant health risks. Reducing exposure to poor air quality and mitigating the associated negative health impacts requires informed policy measures. This study advances urban air quality modelling by developing an air quality model (baseline model) and further integrating measurements from a network of low-cost sensors and regulatory monitors into the model output (data fusion model). The resulting data fusion model provides accurate air quality data in high spatiotemporal resolution. The data fusion model showed higher PM2.5 concentrations during evening hours and winter months, with a population-weighted exposure to PM2.5 almost twice as high as predicted by the baseline model during these months. The models exhibited different spatial patterns, with the data fusion model showing a shift in peak concentrations from the city centre to residential areas, where levels were up to 10 µg/m3 higher than the baseline model. These differences are likely attributable to an underestimation of residential emissions in the baseline model. While both models were FAIRMODE compliant, the data fusion model showed a reduced bias for most monitoring stations compared to the baseline model. The data fusion model enabled a more accurate assessment of existing policies, specifically those aimed at reducing urban air pollution from solid fuel burning. Moreover, by identifying locations and sectors which contribute significantly to high levels of PM2.5, the data fusion model supports the formation of targeted air quality policies. This enables cities to maximise reductions in air pollution and exposures, thereby safeguarding public health.
2026
Integrating Low-cost Sensor Systems and Networks to Enhance Air Quality Applications
Low-cost air quality sensor systems (LCS) are emerging technologies for policy-relevant air quality analysis, including pollution levels, source identification, and forecasting. This report discusses LCS use in networks and alongside other data sources for comprehensive air quality applications, complementing other WMO publications on LCS operating principles, calibration, performance assessment, and data communication.
The LCS’s utility lies in their ability to provide new insights into air quality that existing data sources may not offer. While LCS data must be verified, their integration with other data sources can enhance understanding and management of air quality. In areas without reference-grade monitors, LCS can identify factors affecting local air quality and guide future monitoring efforts. Combining LCS data with satellite and other air quality systems can improve data reliability and establish corroborating evidence for observed trends. LCS can extend the spatial coverage of existing monitoring networks, offering localized insights and supporting effective air quality management policies. Co-locating LCS with reference-grade monitors helps quantify measurement uncertainties and apply LCS data appropriately for forecasting, source impact analysis, and community engagement.
World Meteorological Organization
2024
2018
Integrated water vapor during rain and rain-free conditions above the Swiss Plateau
Water vapor column density, or vertically-integrated water vapor (IWV), is monitored by ground-based microwave radiometers (MWR) and ground-based receivers of the Global Navigation Satellite System (GNSS). For rain periods, the retrieval of IWV from GNSS Zenith Wet Delay (ZWD) neglects the atmospheric propagation delay of the GNSS signal by rain droplets. Similarly, it is difficult for ground-based dual-frequency single-polarisation microwave radiometers to separate the microwave emission of water vapor and cloud droplets from the rather strong microwave emission of rain. For ground-based microwave radiometry at Bern (Switzerland), we take the approach that IWV during rain is derived from linearly interpolated opacities before and after the rain period. The intermittent rain periods often appear as spikes in the time series of integrated liquid water (ILW) and are indicated by ILW ≥ 0.4 mm. In the present study, we assume that IWV measurements from radiosondes are not affected by rain. We intercompare the climatologies of IWV(rain), IWV(no rain), and IWV(all) obtained by radiosonde, ground-based GNSS atmosphere sounding, ground-based MWR, and ECMWF reanalysis (ERA5) at Payerne and Bern in Switzerland. In all seasons, IWV(rain) is 3.75 to 5.94 mm greater than IWV(no rain). The mean IWV differences between GNSS and radiosonde at Payerne are less than 0.26 mm. The datasets at Payerne show a better agreement than the datasets at Bern. However, the MWR at Bern agrees with the radiosonde at Payerne within 0.41 mm for IWV(rain) and 0.02 mm for IWV(no rain). Using the GNSS and rain gauge measurements at Payerne, we find that IWV(rain) increases with increase of the precipitation rate during summer as well as during winter. IWV(rain) above the Swiss Plateau is quite well estimated by GNSS and MWR though the standard retrievals are limited or hampered during rain periods.
2021
2005
2007