Found 9759 publications. Showing page 279 of 391:
2018
2018
NILU og Urbanet Analyse har på oppdrag fra Miljødirektoratet utviklet modellen NERVE («Norwegian Emissions from Road
Vehicle Exhaust») for klimagassutslipp fra veitrafikken i norske kommuner. NERVE beregner klimagassutslipp fra
veitrafikken totalt innenfor hver kommune geografisk og for kommunens innbyggere, både som totalt utslipp og som en
utslippsfaktor (g/km). NERVE en en «bottom-up» modell som bygger på fire detaljerte datasett; 1) Veinettet ved alle
offentlige veier fra Nasjonal vegdatabank (NVDB), 2) trafikk på vei fra Regional Transport Model (RTM), 3)
kjørelengdestatistikken for norskregistrerte kjøretøy fra Statistisk Sentralbyrå Norge (SSB) og 4) utslippsfaktorer fra HBEFA(Hand Book of Emission FActors for Road Transport.
NILU
2018
2018
Simulating CH4 and CO2 over South and East Asia using the zoomed chemistry transport model LMDz-INCA
The increasing availability of atmospheric measurements of greenhouse gases (GHGs) from surface stations can improve the retrieval of their fluxes at higher spatial and temporal resolutions by inversions, provided that transport models are able to properly represent the variability of concentrations observed at different stations. South and East Asia (SEA; the study area in this paper including the regions of South Asia and East Asia) is a region with large and very uncertain emissions of carbon dioxide (CO2) and methane (CH4), the most potent anthropogenic GHGs. Monitoring networks have expanded greatly during the past decade in this region, which should contribute to reducing uncertainties in estimates of regional GHG budgets. In this study, we simulate concentrations of CH4 and CO2 using zoomed versions (abbreviated as "ZAs") of the global chemistry transport model LMDz-INCA, which have fine horizontal resolutions of ∼ 0.66° in longitude and ∼ 0.51° in latitude over SEA and coarser resolutions elsewhere. The concentrations of CH4 and CO2 simulated from ZAs are compared to those from the same model but with standard model grids of 2.50° in longitude and 1.27° in latitude (abbreviated as "STs"), both prescribed with the same natural and anthropogenic fluxes. Model performance is evaluated for each model version at multi-annual, seasonal, synoptic and diurnal scales, against a unique observation dataset including 39 global and regional stations over SEA and around the world. Results show that ZAs improve the overall representation of CH4 annual gradients between stations in SEA, with reduction of RMSE by 16–20% compared to STs. The model improvement mainly results from reduction in representation error at finer horizontal resolutions and thus better characterization of the CH4 concentration gradients related to scattered distributed emission sources. However, the performance of ZAs at a specific station as compared to STs is more sensitive to errors in meteorological forcings and surface fluxes, especially when short-term variabilities or stations close to source regions are examined. This highlights the importance of accurate a priori CH4 surface fluxes in high-resolution transport modeling and inverse studies, particularly regarding locations and magnitudes of emission hotspots. Model performance for CO2 suggests that the CO2 surface fluxes have not been prescribed with sufficient accuracy and resolution, especially the spatiotemporally varying carbon exchange between land surface and atmosphere. In addition, the representation of the CH4 and CO2 short-term variabilities is also limited by model's ability to simulate boundary layer mixing and mesoscale transport in complex terrains, emphasizing the need to improve sub-grid physical parameterizations in addition to refinement of model resolutions.
2018
Modification of local urban aerosol properties by long-range transport of biomass burning aerosol
During August 2016, a quasi-stationary high-pressure system spreading over Central and North-Eastern Europe, caused weather conditions that allowed for 24/7 observations of aerosol optical properties by using a complex multi-wavelength PollyXT lidar system with Raman, polarization and water vapour capabilities, based at the European Aerosol Research Lidar Network (EARLINET network) urban site in Warsaw, Poland. During 24–30 August 2016, the lidar-derived products (boundary layer height, aerosol optical depth, Ångström exponent, lidar ratio, depolarization ratio) were analysed in terms of air mass transport (HYSPLIT model), aerosol load (CAMS data) and type (NAAPS model) and confronted with active and passive remote sensing at the ground level (PolandAOD, AERONET, WIOS-AQ networks) and aboard satellites (SEVIRI, MODIS, CATS sensors). Optical properties for less than a day-old fresh biomass burning aerosol, advected into Warsaw’s boundary layer from over Ukraine, were compared with the properties of long-range transported 3–5 day-old aged biomass burning aerosol detected in the free troposphere over Warsaw. Analyses of temporal changes of aerosol properties within the boundary layer, revealed an increase of aerosol optical depth and Ångström exponent accompanied by an increase of surface PM10 and PM2.5. Intrusions of advected biomass burning particles into the urban boundary layer seem to affect not only the optical properties observed but also the top height of the boundary layer, by moderating its increase.
MDPI
2018
2018
2018
The comet assay applied to cells of the eye
Oxford University Press
2018
2018
Monitoring of long-range transported air pollutants in Norway, Annual Report 2017
This report presents results from the monitoring of atmospheric composition and deposition of air pollution in 2017, and focuses on main components in air and precipitation, particulate and gaseous phase of inorganic constituents, particulate carbonaceous matter, ground level ozone and particulate matter. The concentration levels were generally low in 2017 compared to previous years.
NILU
2018
Polarized response of East Asian winter temperature extremes in the era of Arctic warming
American Meteorological Society
2018
An aerosol particle containing enriched uranium encountered in the remote upper troposphere
Elsevier
2018
The safety of high quality drinking water supply relies on the quantities to be delivered, on the complexity of the water supply systems, and on the widespread phenomena of the contamination of water bodies. These parameters indicate the need for the development of an application that will allow the quick acquisition of data on strategic management. This is requires both the analysis of factors related to the hydraulic operation of the plants and the characteristics of water quality. The present paper aims to evaluate the use of models that predict data for water quality in a distribution system. The assessment is made in order to consider the use of the model as a support tool for the management system of a supply network and to optimize the quality of the provided service. The improvement of the control system related to the operations of disinfection, in particular, in the case of long pipelines, is absolutely mandatory in order to ensure the safety of public health and respect for the environment at high levels.
MDPI
2018
Curating scientific information in knowledge infrastructures
Interpreting observational data is a fundamental task in the sciences, specifically in earth and environmental science where observational data are increasingly acquired, curated, and published systematically by environmental research infrastructures. Typically subject to substantial processing, observational data are used by research communities, their research groups and individual scientists, who interpret such primary data for their meaning in the context of research investigations. The result of interpretation is information—meaningful secondary or derived data—about the observed environment. Research infrastructures and research communities are thus essential to evolving uninterpreted observational data to information. In digital form, the classical bearer of information are the commonly known “(elaborated) data products,” for instance maps. In such form, meaning is generally implicit e.g., in map colour coding, and thus largely inaccessible to machines. The systematic acquisition, curation, possible publishing and further processing of information gained in observational data interpretation—as machine readable data and their machine readable meaning—is not common practice among environmental research infrastructures. For a use case in aerosol science, we elucidate these problems and present a Jupyter based prototype infrastructure that exploits a machine learning approach to interpretation and could support a research community in interpreting observational data and, more importantly, in curating and further using resulting information about a studied natural phenomenon.
Ubiquity Press
2018
2018