Found 10000 publications. Showing page 65 of 400:
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2025
2017
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2021
2017
Blood lead concentrations in the population of Tbilisi, Georgia. Results of model estimates. NILU OR
2000
2012
2013
Boreal forests at risk: Absence of climate perspectives in current management policies
Boreal forests influence climate both biogeochemically through carbon uptake and biogeophysically through evapotranspiration, turbulent fluxes and albedo, and are in turn impacted by climate through biotic and abiotic damages. This systematic literature review and qualitative narrative policy review and analysis aims to get a better insight into the discrepancy between policy and science on forestry action to mitigate climate warming in high latitude jurisdictions. We identify climate effects on and from forests with corresponding management options in a systematic review of scientific literature following PRISMA guidelines. These results were combined with a qualitative policy review and analysis to identify the climate and forestry policies from all boreal-to-Arctic jurisdictions and determine how (many of) these climate effects ended up in forest and climate policy. There is mounting evidence that in boreal regions, albedo-driven warming can partially offset, and in some contexts be comparable to, carbon-driven cooling; the balance varies by season, forest type and disturbance history. However, although all analysed jurisdictions (Alaska, Canada, European Union, Sweden, Finland, Iceland, Norway and Russia) recognise the forests' role in carbon uptake, none recognise the albedo effect, and none translate these climate effect into binding regulatory measures. Nor do most of the jurisdictions take into account possible risk of climate-related damages. This might lead to ineffective and even adverse forest and climate measures. Our study emphasises a need for more evidence-based and comprehensive climate and forestry policies and regulations, along with a proactive approach to adopting these measures swiftly.
2026
2019
2017
2008
Bridge to Copernicus. Final project report. NILU OR
NILU has a mandate to monitor air quality and particularly its changes over time, both nationally through Miljødirektoratet (MD) and internationally through the European Monitoring and Evaluation Programme (EMEP). Satellite data related to atmospheric composition are increasingly used for monitoring as they provide long time series of spatially continuous observations. It is therefore essential for NILU to begin preparing for the upcoming Copernicus missions. Here, we evaluate methane products from AIRS, TES, TANSO-FTS and SCIAMACHY as added value for GHG monitoring in Norway and Svalbard. As expected, due to the low sensitivity of the sensors to ground-level Artic large deviations are seen when comparing to in situ data from Birkenes and Ny-Ålesund. Higher level products (L4), combining satellite and ground-based information, seem more appropriate for future reporting purposes. Further, we investigated the usability of the current set of long-term operational ground-based MAX-DOAS stations worldwide for inter-comparing their NO2 observations to those of satellite-based instruments, in particular OMI and GOME-2A. The two data sources agree very well for sites located in rural, non-polluted regions. For sites located in polluted areas we found strong systematic biases, large random errors, or slightly shifting systematic biases. The systematic biases can be explained primarily by the strong spatial gradients in NO2 levels in urban areas in conjunction with the large differences in the spatial representativity of the measurements. We evaluated the possibility to use the now relatively long time series of MAX-DOAS observations to fit a statistical trend model and to directly compare the resulting trends to those obtained for the satellite-based time series for the same area and time period. It was found that the sites with approximately 50 months of valid data for both data sources showed quite similar long-term trends and that sites with fewer than 30 months of valid data exhibited significant discrepancies in the resulting trends.
2014
2016