Fant 8714 publikasjoner.
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Vurdering av utslipp til luft fra nasjonalhavn i Tromsø.
Norsk institutt for luftforskning (NILU) har beregnet konsentrasjoner i luft av NO2 og PM10 som følge av en framtidig nasjonalhavn ved Tromsø. Beregningene viser at for NO2 vil det trolig bli overskridelser av EUs grenseverdier og SFTs luftkvalitetskriterium i området omkring havna. For PM10 blir det trolig ingen overskridelser.
Vurdering av utslipp til luft fra Wistingfeltet i Barentshavet. Underlag for konsekvensutredning.
NILU har vurdert miljøkonsekvensene av utslipp til luft fra fremtidig utbygging og drift av Wisting-feltet i Barentshavet. Utslipp av CO2, CH4, N2O og NMVOC er vurdert utfra bidrag til strålingspådriv/global oppvarming. Kraftforsyning fra land med sjøkabel vil sterkt redusere utslippene av CO2. Klimaeffekten av utslipp til luft fra produksjonen vil bli liten. Bidraget fra Wisting til eutrofiering og forsuring gjennom avsetning av NOx og SOx forventes å være lite og knapt målbart. Likeledes vil bidraget fra Wisting til ozonproduksjon være minimalt og knapt målbart. Klimaeffekten av BC-utslipp (Black Carbon) fra installasjonene på Wisting vil bli liten. Samtidig gir utslipp av BC i Arktis større effekt pr. utslippsenhet enn utslipp lenger sør. Det bør derfor være et mål å optimalisere faklingen fra Wisting slik at utslipp av BC blir redusert til et absolutt minimum.
Warm Arctic–cold Siberia: comparing the recent and the early 20th century Arctic warmings
The Warm Arctic–cold Siberia surface temperature pattern during recent boreal winter is suggested to be triggered by the ongoing decrease of Arctic autumn sea ice concentration and has been observed together with an increase in mid-latitude extreme events and a meridionalization of tropospheric circulation. However, the exact mechanism behind this dipole temperature pattern is still under debate, since model experiments with reduced sea ice show conflicting results. We use the early twentieth-century Arctic warming (ETCAW) as a case study to investigate the link between September sea ice in the Barents–Kara Sea (BKS) and the Siberian temperature evolution. Analyzing a variety of long-term climate reanalyses, we find that the overall winter temperature and heat flux trend occurs with the reduction of September BKS sea ice. Tropospheric conditions show a strengthened atmospheric blocking over the BKS, strengthening the advection of cold air from the Arctic to central Siberia on its eastern flank, together with a reduction of warm air advection by the westerlies. This setup is valid for both the ETCAW and the current Arctic warming period.
Water column distribution of mercury species in permanently stratified aqueous environments
Biogeochemical structures of three permanently stratified waterbodies were studied: a sea water basin (the Black Sea), an estuary (Hunnbunn fjord), and a freshwater lake (Nordbytjernet), with focus on the distributions of methylmercury (MeHg) and total mercury (THg). THg concentrations were similar in the sea water basin (0.2–1.8 ng/L) and the freshwater lake (0.8–1.2 ng/L), but significantly higher in the estuary (0.6–9.4 ng/L). An increase in the MeHg concentration and MeHg/THg ratio were found in the redox zone in all three basins, indicating bacterial production of MeHg in the aqueous phase. In the lake and estuary, the maximum MeHg concentration and MeHg/THg ratio were found in samples located closest to the bottom sediments, likely due to the formation of MeHg in surface sediments and subsequent diffusion to the overlying waters.
Webcrawling and machine learning as a new approach for the spatial distribution of atmospheric emissions
In this study we apply two methods for data collection that are relatively new in the field of atmospheric science. The two developed methods are designed to collect essential geo-localized information to be used as input data for a high resolution emission inventory for residential wood combustion (RWC). The first method is a webcrawler that extracts openly online available real estate data in a systematic way, and thereafter structures them for analysis. The webcrawler reads online Norwegian real estate advertisements and it collects the geo-position of the dwellings. Dwellings are classified according to the type (e.g., apartment, detached house) they belong to and the heating systems they are equipped with. The second method is a model trained for image recognition and classification based on machine learning techniques. The images from the real estate advertisements are collected and processed to identify wood burning installations, which are automatically classified according to the three classes used in official statistics, i.e., open fireplaces, stoves produced before 1998 and stoves produced after 1998. The model recognizes and classifies the wood appliances with a precision of 81%, 85% and 91% for open fireplaces, old stoves and new stoves, respectively. Emission factors are heavily dependent on technology and this information is therefore essential for determining accurate emissions. The collected data are compared with existing information from the statistical register at county and national level in Norway. The comparison shows good agreement for the proportion of residential heating systems between the webcrawled data and the official statistics. The high resolution and level of detail of the extracted data show the value of open data to improve emission inventories. With the increased amount and availability of data, the techniques presented here add significant value to emission accuracy and potential applications should also be considered across all emission sectors.