Found 10066 publications. Showing page 17 of 403:
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Statusrapport 2024. Nasjonalt referanselaboratorium for luftkvalitetsmålinger
Denne rapporten oppsummerer oppgavene til Nasjonalt referanselaboratorium for luftkvalitetsmålinger (NRL), delkontrakt 1b, for første halvår 2024.
NILU
2024
Emission ensemble approach to improve the development of multi-scale emission inventories
Many studies have shown that emission inventories are one of the inputs with the most critical influences on the results of air quality modelling. Comparing emission inventories among themselves is, therefore, essential to build confidence in emission estimates. In this work, we extend the approach of Thunis et al. (2022) to compare emission inventories by building a benchmark that serves as a reference for comparisons. This benchmark is an ensemble that is based on three state-of-the-art EU-wide inventories: CAMS-REG, EMEP and EDGAR. The ensemble-based methodology screens differences between inventories and the ensemble. It excludes differences that are not relevant and identifies among the remaining ones those that need special attention. We applied the ensemble-based screening to both an EU-wide and a local (Poland) inventory.
The EU-wide analysis highlighted a large number of inconsistencies. While the origin of some differences between EDGAR and the ensemble can be identified, their magnitude remains to be explained. These differences mostly occur for SO2 (sulfur oxides), PM (particulate matter) and NMVOC (non-methane volatile organic carbon) for the industrial and residential sectors and reach a factor of 10 in some instances. Spatial inconsistencies mostly occur for the industry and other sectors.
At the local scale, inconsistencies relate mostly to differences in country sectorial shares that result from different sectors/activities being accounted for in the two types of inventories. This is explained by the fact that some emission sources are omitted in the local inventory due to a lack of appropriate geographically allocated activity data. We identified sectors and pollutants for which discussion between local and EU-wide emission compilers would be needed in order to reduce the magnitude of the observed differences (e.g. in the residential and industrial sectors).
The ensemble-based screening proved to be a useful approach to spot inconsistencies by reducing the number of necessary inventory comparisons. With the progressive resolution of inconsistencies and associated inventory improvements, the ensemble will improve. In this sense, we see the ensemble as a useful tool to motivate the community around a single common benchmark and monitor progress towards the improvement of regionally and locally developed emission inventories.
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Vitenskapskomiteen for mat og miljø (VKM) har oppdatert et metodedokument for helse og miljørisikovurderinger av plantevernmidler.
Målet med oppdateringen er å gjenspeile gjeldende regelverk og praksis, og sikre kvaliteten på fremtidige risikovurderinger utført av faggruppen for plantevernmidler i VKM. Det forrige metodedokumentet er fra 2012, og oppdateringen var nødvendig for å tilpasse metodene til nytt EU-regelverk for plantevernmidler, og for å innarbeide nye datakrav og retningslinjer for plantevernmidler og biocider. Ved å oppdatere metodedokumentet, ønsket faggruppen å sikre at risikovurderingene de leverer er i tråd med gjeldende regelverket og vitenskapelig kunnskap.
Viktige endringer
Dokumentet er oppdatert med henvisninger til nye forskrifter og veiledninger, om for eksempel biocider, nye typer plantevernmidler, og forenklet godkjenning/risikovurdering for mikrobielle stoffer. Det nye dokumentet inneholder også veiledning om fareidentifikasjon av stoffer med hormonforstyrrende egenskaper, alternative metoder for å redusere toksikologisk testing hos dyr, og vurdering av ikke-kostholdeksponering av plantevernmidler.
Dokumentet inneholder oppdatert informasjon om metodikk knyttet til vurdering av plantevernmidlers egenskaper og skjebne i miljøet, inkludert norske jord- og klimaforhold, renseanlegg og drikkevannsrenseprosesser. Veiledning om risikovurdering for bier og andre insekter, akvatiske organismer, fugler, pattedyr og andre vertebrater, samt meitemark og andre jordlevende organismer, er også oppdatert. Innen flere av feltene er eller vil det bli etablert spesifikke beskyttelsesmål og trinnvise risikovurderinger.
Samlet sett fungerer det oppdaterte metodedokumentet som en referanse for VKMs risikovurderingsarbeid for plantevernmidler, og sikrer at fremtidige vurderinger gjennomføres i samsvar med gjeldende regelverk og vitenskapelig kunnskap.
Metode
VKM har benyttet en semi-systematisk tilnærming, ved å utarbeide et arbeidsdokument for innhenting og sammenstilling av nødvendig informasjon om nye datakrav fra gjeldende regelverk for plantevernmidler og biocider i EU.
Dokumentet er godkjent av VKMs faggruppe for plantevernmidler.
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Accurate modeling of ash clouds from volcanic eruptions requires knowledge about the eruption source parameters including eruption onset, duration, mass eruption rates, particle size distribution, and vertical-emission profiles. However, most of these parameters are unknown and must be estimated somehow. Some are estimated based on observed correlations and known volcano parameters. However, a more accurate estimate is often needed to bring the model into closer agreement with observations.
This paper describes the inversion procedure implemented at the Norwegian Meteorological Institute for estimating ash emission rates from retrieved satellite ash column amounts and a priori knowledge. The overall procedure consists of five stages: (1) generate a priori emission estimates, (2) run forward simulations with a set of unit emission profiles, (3) collocate/match observations with emission simulations, (4) build system of linear equations, and (5) solve overdetermined systems. We go through the mathematical foundations for the inversion procedure, performance for synthetic cases, and performance for real-world cases. The novelties of this paper include a memory efficient formulation of the inversion problem, a detailed description and illustrations of the mathematical formulations, evaluation of the inversion method using synthetic known-truth data as well as real data, and inclusion of observations of ash cloud-top height. The source code used in this work is freely available under an open-source license and is able to be used for other similar applications.
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Fine particulate matter (PM2.5) is a key air quality indicator due to its adverse health impacts. Accurate PM2.5 assessment requires high-resolution (e.g., atleast 1 km) daily data, yet current methods face challenges in balancing accuracy, coverage, and resolution. Chemical transport models such as those from the Copernicus Atmosphere Monitoring Service (CAMS) offer continuous data but their relatively coarse resolution can introduce uncertainties. Here we present a synergistic Machine Learning (ML)-based approach called S-MESH (Satellite and ML-based Estimation of Surface air quality at High resolution) for estimating daily surface PM2.5 over Europe at 1 km spatial resolution and demonstrate its performance for the years 2021 and 2022. The approach enhances and downscales the CAMS regional ensemble 24 h PM2.5 forecast by training a stacked XGBoost model against station observations, effectively integrating satellite-derived data and modeled meteorological variables. Overall, against station observations, S-MESH (mean absolute error (MAE) of 3.54 μg/m3) shows higher accuracy than the CAMS forecast (MAE of 4.18 μg/m3) and is approaching the accuracy of the CAMS regional interim reanalysis (MAE of 3.21 μg/m3), while exhibiting a significantly reduced mean bias (MB of −0.3 μg/m3 vs. −1.5 μg/m3 for the reanalysis). At the same time, S-MESH requires substantially less computational resources and processing time. At concentrations >20 μg/m3, S-MESH outperforms the reanalysis (MB of −7.3 μg/m3 and -10.3 μg/m3 respectively), and reliably captures high pollution events in both space and time. In the eastern study area, where the reanalysis often underestimates, S-MESH better captures high levels of PM2.5 mostly from residential heating. S-MESH effectively tracks day-to-day variability, with a temporal relative absolute error of 5% (reanalysis 10%). Exhibiting good performance at high pollution events coupled with its high spatial resolution and rapid estimation speed, S-MESH can be highly relevant for air quality assessments where both resolution and timeliness are critical.
2024