Fant 9758 publikasjoner. Viser side 67 av 391:
2021
2021
Rapporten presenterer en oversikt over dataassimilasjonsmetoder som kan anvendes for luftkvalitetsmodellering. Innledningsvis beskrives kort historisk bakgrunn for bruk av dataassimilasjon i numerisk værvarsling, der vi legger vekt på forskjellene mellom anvendelse av assimilasjon i meteorologiske varslingsmodeller og i spredningsmodeller for luftkvalitet. Basert på disse forskjellene beskrives så ønskede egenskaper til assimilasjonsmetoder for luftkvalitetsmodellering. Deretter gis en oversikt over tilgjengelige assimilasjonsmetoder, der vi søker å identifisere de mest aktuelle for vårt bruk som grunnlag for videre anvendelser i prosjektet.
NILU
2021
American Geophysical Union (AGU)
2021
Royal Society of Chemistry (RSC)
2021
Norsk institutt for naturforskning (NINA)
2021
Method for high resolution emission estimations from construction sites. Phase I: Mapping input data
Denne rapporten presenterer resultatene fra analyse av tilgjengelige inngangsdata for å utvikle en modell for estimering av luftforurensende stoffer og klimagassutslipp basert på en «bottom-up»-tilnærming, inkludert både eksos- og ikke-eksosutslipp. Tilgjengeligheten av pålitelige inngangsdata er den begrensende faktoren og den mest kritiske delen av utformingen av en slik «bottom-up»-tilnærming. I denne studien har vi fokusert på å vurdere inngangsdata som gjør det mulig å definere; i) den nøyaktige plasseringen og området som påvirkes under bygging og konstruksjon, ii) start- og sluttdatoer; iii) typen byggeaktivitet; iv) aktiviteter med ikke-veigående mobile maskiner (NRMM) innen bygg og anlegg; v) veier i nærheten av byggeplasser.
NILU
2021
2021
2021
2021
2021
2021
Editorial for the Special Issue From Nanoinformatics to Nanomaterials Risk Assessment and Governance
MDPI
2021
Ren luft for alle. ExtraStiftelsen project 2019/HE1-263918.
In 2019, in the framework of Oslo being European Green Capital, NILU invited students from elementary schools to
measure air pollution in their neighbourhood, using simple and affordable measuring methods based on paper and
Vaseline. The students prepared the measuring devices and selected the places where they wanted to monitor. After one
week, they retrieved the devices and used a scale to compare the amount of dust fastened to the Vaseline. All of the data
gathered by the students was uploaded by the teachers to a website (https://luftaforalle.nilu.no/), where a map showed all the results from the participating schools. The school campaign has helped researchers to get data on particulate matter from many places where data was not available, and has increased awareness among the children about the sustainability challenges cities are facing.
NILU
2021
2021
Academic Press
2021
Within the framework of the AeroCom (Aerosol Comparisons between Observations and Models) initiative, the state-of-the-art modelling of aerosol optical properties is assessed from 14 global models participating in the phase III control experiment (AP3). The models are similar to CMIP6/AerChemMIP Earth System Models (ESMs) and provide a robust multi-model ensemble. Inter-model spread of aerosol species lifetimes and emissions appears to be similar to that of mass extinction coefficients (MECs), suggesting that aerosol optical depth (AOD) uncertainties are associated with a broad spectrum of parameterised aerosol processes.
Total AOD is approximately the same as in AeroCom phase I (AP1) simulations. However, we find a 50 % decrease in the optical depth (OD) of black carbon (BC), attributable to a combination of decreased emissions and lifetimes. Relative contributions from sea salt (SS) and dust (DU) have shifted from being approximately equal in AP1 to SS contributing about 2∕3 of the natural AOD in AP3. This shift is linked with a decrease in DU mass burden, a lower DU MEC, and a slight decrease in DU lifetime, suggesting coarser DU particle sizes in AP3 compared to AP1.
Relative to observations, the AP3 ensemble median and most of the participating models underestimate all aerosol optical properties investigated, that is, total AOD as well as fine and coarse AOD (AODf, AODc), Ångström exponent (AE), dry surface scattering (SCdry), and absorption (ACdry) coefficients. Compared to AERONET, the models underestimate total AOD by ca. 21 % ± 20 % (as inferred from the ensemble median and interquartile range). Against satellite data, the ensemble AOD biases range from −37 % (MODIS-Terra) to −16 % (MERGED-FMI, a multi-satellite AOD product), which we explain by differences between individual satellites and AERONET measurements themselves. Correlation coefficients (R) between model and observation AOD records are generally high (R>0.75), suggesting that the models are capable of capturing spatio-temporal variations in AOD. We find a much larger underestimate in coarse AODc (∼ −45 % ± 25 %) than in fine AODf (∼ −15 % ± 25 %) with slightly increased inter-model spread compared to total AOD. These results indicate problems in the modelling of DU and SS. The AODc bias is likely due to missing DU over continental land masses (particularly over the United States, SE Asia, and S. America), while marine AERONET sites and the AATSR SU satellite data suggest more moderate oceanic biases in AODc.
Column AEs are underestimated by about 10 % ± 16 %. For situations in which measurements show AE > 2, models underestimate AERONET AE by ca. 35 %. In contrast, all models (but one) exhibit large overestimates in AE when coarse aerosol dominates (bias ca. +140 % if observed AE < 0.5). Simulated AE does not span the observed AE variability. These results indicate that models overestimate particle size (or underestimate the fine-mode fraction) for fine-dominated aerosol and underestimate size (or overestimate the fine-mode fraction) for coarse-dominated aerosol. This must have implications for lifetime, water uptake, scattering enhancement, and the aerosol radiative effect, which we can not quantify at this moment.
Comparison against Global Atmosphere Watch (GAW) in situ data results in mean bias and inter-model variations of −35 % ± 25 % and −20 % ± 18 % for SCdry and ACdry, respectively. The larger underestimate of SCdry than ACdry suggests the models will simulate an aerosol single scattering albedo that is too low. The larger underestimate of SCdry than ambient air AOD is consistent with recent findings that models overestimate scattering enhancement due to hygroscopic growth. The broadly consistent negative bias in AOD and surface scattering suggests an underestimate of aerosol radiative effects in current global aerosol models.
Considerable ...
2021
2021