Found 10066 publications. Showing page 346 of 403:
2022
Denne rapporten sammenstiller kunnskap om forurensningstilstanden i forvaltningsplanområdene Barentshavet, Norskehavet og Nordsjøen/Skagerrak, med hovedvekt på miljøgifter og radioaktiv forurensning. Rapporten oppsummerer resultater fra overvåkning av Overvåkingsgruppens indikatorsett, som publiseres på miljostatus.no, det vil si 43 indikatorer for forurensning og fire indikatorer for potensielt forurensende menneskelig aktivitet i havområdene. Rapporten inneholder også annen relevant kunnskap. Det er også gjort en evaluering av indikatorsettet og de ulike indikatorenes egnethet til å vurdere tilstand, utvikling og effekter på mattrygghet og miljøkvalitet.
Havforskningsinstituttet
2022
2022
2022
2022
Epidemiological studies have increasingly shown that ambient air pollution is not only associated with mortality but also with the occurrence of a number of long and short-term diseases. Further, the Global Burden of Disease study clearly indicated, that e. g. particulate matter pollution is also associated with a considerable burden of disease related to morbidity effects.
In addition to the most recent EEA’s health risk assessments, this report estimates the morbidity related health burden associated with exposure to the same three key air pollutants: fine particulate matter (PM2.5), nitrogen dioxide (NO2) and ozone (O3). Years lived with disability (YLDs) or attributable hospitalisation cases are assessed for the year 2019 for numerous European countries, depending on the respective data availability. Besides, the methodological approach as well as reviews on evidence-based health outcomes, health data and concentration-response functions are provided.
For the ten considered risk-outcome pairs, the results showed the highest morbidity related burden of disease in Europe for PM2.5 associated with chronic obstructive pulmonary disease (COPD) with 51.6 YLDs per 100 000 inhabitants ≥25 years. For NO2 the highest morbidity burden resulted from diabetes mellitus (DM) with 54.6 YLDs per 100 000 inhabitants ≥35 years. For short-term O3 exposure hospital admissions due to respiratory diseases were estimated at 18 attributable cases per 100 000 inhabitants ≥65 years.
In addition to the estimates, the report contains suggestions for further sensitivity analyses. These would allow a better assessment of the effects resulting from different input data on the results.
The estimations presented in this report are the first of its kind that are carried out for a wide range of morbidity health outcomes associated with different outdoor air pollutants in Europe, using a consistent methodology and data from European health databases.
ETC/HE
2022
2022
The influence of photochemistry on outdoor to indoor NO2 in some European museums
This paper reports 1 year of monthly average NO2 indoor to outdoor (I/O) concentrations measured in 10 European museums, and a simple steady-state box model that explains the annual variation. The measurements were performed in the EU FP5 project Master (EVK-CT-2002-00093). The work provides extensive documentation of the annual variation of NO2 I/O concentration ratios, with ratios above unity in the summer, in situations with no indoor emissions of NO2. The modelling included the most relevant production and removal processes of NO2 and showed that the outdoor photolysis was the probable main explanation of the annual trends in the NO2 I/O concentration ratios.
2022
2022
2022
The aim of this project was to collect, integrate and analyse observations of climate-relevant aerosol parameters (aerosol optical depth (AOD), Ångstrøm exponent (AE), black carbon (BC)) in and around Svalbard. These observations have been performed at different places and with different instrument types, the analysis procedures of which follow different protocols. Annual merged datasets of AOD, AE and BC have been provided to the SIOS Data Management System and are now available for network-wide use in, e.g., Arctic climate and pollution studies. The analysis of the 2002-2020 data have confirmed earlier results showing a good correlation between measurements in Ny-Ålesund and Hornsund, but not a high degree of short-term agreement due to aerosol variability arising from geographical locations and local conditions. There is also a clear link between the columnar AOD/AE-measurements and in-situ aerosol measurements at Gruvebadet Observatory, while a comparison of in-situ measurements at Gruvebadet and Zeppelin Observatory shows deviations varying with season.
NILU
2022
2022
Lack of mutagenicity of TiO2 nanoparticles in vitro despite cellular and nuclear uptake
The potential genotoxicity of titanium dioxide (TiO2) nanoparticles (NPs) is a conflictive topic because both positive and negative findings have been reported. To add clarity, we have carried out a study with two cell lines (V79–4 and A549) to evaluate the effects of TiO2 NPs (NM-101), with a diameter ranging from 15 to 60 nm, at concentrations 1–75 μg/cm2. Using two different dispersion procedures, cell uptake was determined by Transmission Electron Microscopy (TEM). Mutagenicity was evaluated using the Hprt gene mutation test, while genotoxicity was determined with the comet assay, detecting both DNA breaks and oxidized DNA bases (with formamidopyrimidine glycosylase - Fpg). Cell internalization, as determined by TEM, shows TiO2 NM-101 in cytoplasmic vesicles, as well as close to and inside the nucleus. Such internalization did not depend on the state of agglomeration, nor the dispersion used. In spite of such internalization, no cytotoxicity was detected in V79–4 cells (relative growth activity and plating efficiency assays) or in A549 cells (AlamarBlue assay) after exposure lasting for 24 h. However, a significant decrease in the relative growth activity was detected at longer exposure times (48 and 72 h) and at the highest concentration 75 µg/cm2. When the modified enzyme-linked alkaline comet assay was performed on A549 cells, although no significant induction of DNA damage was detected, a positive concentration-effects relationship was observed (Spearman’s correlation = 0.9, p 0.0001). Furthermore, no significant increase of DNA oxidized purine bases was observed. When the frequency of Hprt gene mutants was determined in V79–4 cells, no increase was observed in the exposed cells, relative to the unexposed cultures. Our general conclusion is that, under our experimental conditions, TiO2 NM-101 exposure does not exert mutagenic effects despite the evidence of NP uptake by V79–4 cells.
2022
2022
2022
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
2022
Machine Learning-Based Digital Twin for Predictive Modeling in Wind Turbines
Wind turbines are one of the primary sources of renewable energy, which leads to a sustainable and efficient energy solution. It does not release any carbon emissions to pollute our planet. The wind farms monitoring and power generation prediction is a complex problem due to the unpredictability of wind speed. Consequently, it limits the decision power of the management team to plan the energy consumption in an effective way. Our proposed model solves this challenge by utilizing a 5G-Next Generation-Radio Access Network (5G-NG-RAN) assisted cloud-based digital twins’ framework to virtually monitor wind turbines and form a predictive model to forecast wind speed and predict the generated power. The developed model is based on Microsoft Azure digital twins infrastructure as a 5-dimensional digital twins platform. The predictive modeling is based on a deep learning approach, temporal convolution network (TCN) followed by a non-parametric k-nearest neighbor (kNN) regression. Predictive modeling has two components. First, it processes the univariate time series data of wind to predict its speed. Secondly, it estimates the power generation for each quarter of the year ranges from one week to a whole month (i.e., medium-term prediction) To evaluate the framework the experiments are performed on onshore wind turbines publicly available datasets. The obtained results confirm the applicability of the proposed framework. Furthermore, the comparative analysis with the existing classical prediction models shows that our designed approach obtained better results. The model can assist the management team to monitor the wind farms remotely as well as estimate the power generation in advance.
2022