Found 9584 publications. Showing page 3 of 384:
2024
Atmospheric Supply of Nitrogen, Cadmium, Mercury and B(a)P to the Baltic Sea in 2022
Norwegian Meteorological Institute
2024
2024
2024
2024
Screening of compounds in tire wear road run off
Tire related additive chemicals can leach out and enter the environment. Road run-off and recipient waters are particularly prone to contamination by these chemicals, though data from large screening studies is lacking. Here, we present data from water (road run-off & recipients, atmospheric deposition (rain), snow), sediment (marine, snow dumping sites) and biota (blue mussels) samples collected in the Nordic countries. The aim of this study was to provide a first assessment of the presence of tire related chemicals in road run-off and associated samples in the Nordic countries. Tire related additive chemicals were detected in 85 out of 87 samples, with varying concentrations depending on the sample type and location.
Nordic Council of Ministers
2024
2024
2024
Investigating snow deposition of cyclic siloxanes in an Arctic environment
cVMS are high production volume chemicals that are used for a wide range of industrial and domestic applications. Given the high volatility of cVMS, emissions occur mainly to the atmosphere, and cVMS are present in the Arctic atmosphere, e.g. at the Zeppelin Observatory near Ny Ålesund, Svalbard, suggesting potential for long-range atmospheric transport. A study to investigate whether cVMS have the potential to deposit to surface media, and thereby represent a potential risk to the terrestrial or marine environment in polar and Arctic regions was carried out. Overall, cVMS levels in samples of vegetation, soil, sediment and marine biota were low. D4 was detected in most samples at concentrations above LOD, but below LOQ, while D5 and D6 were generally not detected. The low cVMS concentrations in soil, vegetation, sediments, and fish are in line with most current research on cVMS in remote regions, which together suggest that input of cVMS from atmospheric deposition and snow melt is likely not a major contributing source.
NILU
2024
NILU og Transportøkonomisk institutt (TØI) har på oppdrag fra Miljødirektoratet videreutviklet modellen NERVE («Norwegian Emissions from Road Vehicle Exhaust») for beregning av klimagassutslipp fra veitrafikken i norske kommuner. NERVE-modellen anvender de mest detaljerte datasettene for bilpark, utslippsfaktorer, trafikk og veier for spesifikke lokale forhold. Datasettene er kombinert i en datastruktur som gjør at resultat kan aggregeres på et lite eller et stort geografisk område. NERVE kan således betegnes som en «bottom-up»-utslippsmodell, fordi den er bygget opp «nedenfra» fra detaljerte datakilder. Denne rapporten presenterer metodikken og antagelsene bak beregningene med NERVE, og sammenligner resultat aggregert på nasjonalt nivå med annen tilgjengelig nasjonal statistikk.
NILU
2024
2024
2024
2024
Transboundary particulate matter, photo-oxidants, acidifying and eutrophying components
Norwegian Meteorological Institute
2024
The report provides the annual update of the European air quality concentration maps and population and vegetation exposure estimates for human health related indicators of pollutants PM10 (annual average, 90.4 percentile of daily means), PM2.5 (annual average), ozone (93.2 percentile of maximum daily 8-hour means, peak season average of maximum daily 8-hour means, SOMO35, SOMO10), NO2 (annual average) and benzo(a)pyrene (annual average), and vegetation related ozone indicators (AOT40 for vegetation and for forests) for the year 2022. The report contains also maps of Phytotoxic ozone dose (PODY) for selected crops (wheat, potato and tomato) and trees (spruce and beech) and NOx annual average map for the same year 2022. The ozone map of peak season average of maximum daily 8-hour means is presented for the first time. The trends in exposure estimates in the period 2005–2022 are summarized. The analysis for 2022 is based on the interpolation of the annual statistics of the 2022 observational data reported by the EEA member and cooperating countries and other voluntary reporting countries and stored in the Air Quality e-reporting database, complemented, when needed, with measurements from additional sources. The mapping method is the Regression – Interpolation – Merging Mapping (RIMM). It combines monitoring data, chemical transport model results and other supplementary data using linear regression model followed by kriging of its residuals (residual kriging). The paper presents the mapping results and gives an uncertainty analysis of the interpolated maps. It also presents concentration change in 2022 in comparison to the five-year average 2017-2021 using the difference maps and exposure estimates.
ETC/HE
2024
Monitoring air quality in ports and nearby cities is crucial to understanding the role of emissions from shipping and other port activities. This report analyzes air quality in 23 European ports, revealing limited observations in and around port areas. Only 5 of the 23 ports had at least one air quality sampling point for NO2 and PM10 inside the port area. Concentrations in nearby cities can be up to double (NO2) and 74% higher (PM10) when the wind comes from the port. EEA air quality maps showed higher annual mean NO2 concentrations in port areas compared to surrounding regions, with some ports exceeding the 2030 limit value of 20 µg/m³. Annual mean PM10 concentrations were also higher in port areas, with nine ports exceeding the new limit value. The limited number of sampling points makes it challenging to assess trends in NO2 and PM10 concentrations. International shipping emissions significantly contribute to NO2 levels in port cities, as shown by pollution episodes in Antwerpen and Barcelona.
ETC/HE
2024
In the framework of the Forum for Air Quality Modelling in Europe (FAIRMODE), a modelling intercomparison exercise for computing NO2 long-term average concentrations in urban districts with a very high spatial resolution was carried out. This exercise was undertaken for a district of Antwerp (Belgium). Air quality data includes data recorded in air quality monitoring stations and 73 passive samplers deployed during one-month period in 2016. The modelling domain was 800 × 800 m2. Nine modelling teams participated in this exercise providing results from fifteen different modelling applications based on different kinds of model approaches (CFD – Computational Fluid Dynamics-, Lagrangian, Gaussian, and Artificial Intelligence). Some approaches consisted of models running the complete one-month period on an hourly basis, but most others used a scenario approach, which relies on simulations of scenarios representative of wind conditions combined with post-processing to retrieve a one-month average of NO2 concentrations.
The objective of this study is to evaluate what type of modelling system is better suited to get a good estimate of long-term averages in complex urban districts. This is very important for air quality assessment under the European ambient air quality directives. The time evolution of NO2 hourly concentrations during a day of relative high pollution was rather well estimated by all models. Relative to high resolution spatial distribution of one-month NO2 averaged concentrations, Gaussian models were not able to give detailed information, unless they include building data and street-canyon parameterizations. The models that account for complex urban geometries (i.e. CFD, Lagrangian, and AI models) appear to provide better estimates of the spatial distribution of one-month NO2 averages concentrations in the urban canopy. Approaches based on steady CFD-RANS (Reynolds Averaged Navier Stokes) model simulations of meteorological scenarios seem to provide good results with similar quality to those obtained with an unsteady one-month period CFD-RANS simulations.
Elsevier
2024
2024