Found 9758 publications. Showing page 248 of 391:
Metal pollution is a global environmental issue with adverse biological effects on wildlife. Long-term studies that span declines in metal emissions due to regulation, resulting in varying levels of environmental contamination, are therefore well-suited to investigate effects of toxic metals, while also facilitating robust analysis by incorporating fluctuating environmental conditions and food availability. Here, we examined a resident population of tawny owls in Norway between 1986 and 2019. Tail feathers from females were collected annually, resulting in over 1000 feathers. Each feather served as an archive of local environmental conditions during molt, including the presence of metals, and their dietary ecology, proxied by stable isotopes of nitrogen (δ15N) and carbon (δ13C), as well as corticosterone levels (CORTf), the primary avian glucocorticoid and a measure of physiological stress. We analyzed feathers to examine how exposure to toxic metal(loid)s (Al, As, Cd, Hg, and Pb) and variability in dietary proxies modulate CORTf. Using structural equation modelling, we found that increased Al concentrations and δ15N values, linked directly to increased CORTf. In opposite, we found that increased Hg concentrations and δ13C related to decreased CORTf concentrations. δ15N was indirectly linked to CORTf through Al and Hg, while δ13C was indirectly linked to CORTf through Hg. This supports our hypothesis that metal exposure and dietary ecology may individually or jointly influence physiological stress. Notably, our results suggest that dietary ecology has the potential to mediate the impact of metals on CORTf, highlighting the importance of considering multiple variables, direct and indirect effects, when assessing stress in wildlife. In conclusion, feathers represent an excellent non-destructive biomonitoring strategy in avian wildlife, providing valuable insights not easily accessible using other methods. Further research is warranted to fully comprehend implications of alterations in CORTf on the tawny owl's health and fitness.
Elsevier
2024
Feasibility study for asphalt rubber pavements in Norway. ‘Rubber Road’ feasibility study.
RubberRoad foreslår å bruke gummi fra brukte bildekk i asfaltproduksjon til bil- og sykkelveier. Denne resirkuleringsmetoden er ikke særlig kjent i Norge, til tross for påviste fordeler som blant annet støyreduksjon, økt holdbarhet, støtdemping ved ulykker samt redusert klima- og miljøpåvirkning. Livssyklusanalysen, som ble utført i løpet av dette prosjektets gjennomførbarhetsstudie, har vist en rekke miljøfordeler ved bruk av gummi i asfaltproduksjon. Den har også bidratt til å identifisere kunnskapshull relatert spesielt til bruken av gummiasfalten og dens innvirkning på støy, luft og mikro-plastforurensning. Bedre forståelse av disse effektene vil trolig føre til enda større miljøfordeler av gummiasfalt i forhold til standard asfaltproduksjon. Imidlertid, mens dekkgjenvinningsindustrien generelt er positiv til bruk av brukte dekk i asfaltproduksjon, må det legges til rette for ytterligere incitamenter for at norske asfaltprodusenter skal vurdere å bidra aktivt til denne utviklingen.
NILU
2020
Fatty alcohols. Anthropogenic and natural occurence in the environment. 2nd edition.
Royal Society of Chemistry (RSC)
2019
2006
Fate of Springtime Atmospheric Reactive Mercury: Concentrations and Deposition at Zeppelin, Svalbard
American Chemical Society (ACS)
2021
2023
Fate and distribution of perflourinated alkylated substances (PFAS) in the Nordic environment. NILU PP
2004
2001
Fast simulation tool for UV radiation and diagnosis of measured UV spectra. Proceedings of SPIE, vol. 5156
2003
2005
2018
2018
FAIRMODE Guidance Document on Modelling Quality Objectives and Benchmarking. Version 3.3.
The development of the procedure for air quality model benchmarking in the context of the Air Quality Directive 2008/50/EC (AQD) has been an on-going activity in the context of the FAIRMODE community, chaired by the JRC. A central part of the studies was the definition of proper modelling quality indicators and criteria to be fulfilled in order to allow sufficient level of quality for a given model application under the AQD. The focus initially on applications related to air quality assessment has gradually been expanded to other applications, such as forecasting and planning. The main purpose of this Guidance Document is to explain and summarise the current concepts of the modelling quality objective methodology, elaborated in various papers and documents in the FAIRMODE community, addressing model applications for air quality assessment and forecast. Other goals of the Document are linked to presentation and explanation of templates for harmonised reporting of modelling results. Giving an overview of still open issues in the implementation of the presented methodology, the document aims at triggering further research and discussions. A core set of statistical indicators is defined using pairs of measurement-modelled data. The core set is the basis for the definition of a modelling quality indicator (MQI) and additional modelling performance indicators (MPI), which take into account the measurement uncertainty. The MQI describes the discrepancy between measurements and modelling results (linked to RMSE), normalised by measurement uncertainty and a scaling factor. The modelling quality objective (MQO) requires MQI to be less than or equal to 1. With an arbitrary selection of the scaling factor of 2, the fulfilment of the MQO means that the allowed deviation between modelled and measured concentrations is twice the measurement uncertainty. Expressions for the MQI calculation based on time series and yearly data are introduced. MPI refer to aspects of correlation, bias and standard deviation, applied to both the spatial and temporal dimensions. Similarly to the MQO for the MQI, modelling performance criteria (MPC) are defined for the MPI; they are necessary, but not sufficient criteria to determine whether the MQO is fulfilled. The MQO is required to be fulfilled at 90% of the stations, a criterion which is implicitly taken into account in the derivation of the MQI. The associated modelling uncertainty is formulated, showing that in case of MQO fulfilment the modelling uncertainty must not exceed 1.75 times the measurement one (with the scaling factor fixed to 2). A reporting template is presented and explained for hourly and yearly average data. In both cases there is a diagram and a table with summary statistics. In a separate section open issues are discussed and an overview of related publications and tools is provided. Finally, a chapter on modelling quality objectives for forecast models is introduced. In Annex 1, we discuss the measurement uncertainty which is expressed in terms of concentration and its associated uncertainty. The methodology for estimating the measurement uncertainty is overviewed and the parameters for its calculation for PM, NO2 and O3 are provided. An expression for the associated modelling uncertainty is also given. This aim of this document is to support modelling groups, local, regional and national authorities in their modelling application, in the context of air quality policy.
Publications Office for the European Union
2022
2009