Found 10001 publications. Showing page 114 of 401:
2018
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2018
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.
2018
Seasonal and interannual variability in surface water partial pressure of CO2 (pCO2) and air‐sea CO2 fluxes from a West Spitsbergen fjord (IsA Station, Adventfjorden) are presented, and the associated driving forces are evaluated. Marine CO2 system data together with temperature, salinity, and nutrients, were collected at the IsA Station between March 2015 and June 2017. The surface waters were undersaturated in pCO2 with respect to atmospheric pCO2 all year round. The effects of biological activity (primary production/respiration) followed by thermal forcing on pCO2 were the most important drivers on a seasonal scale. The ocean was a sink for atmospheric CO2 with annual air‐sea CO2 fluxes of −36 ± 2 and −31 ± 2 g C·m−2·year−1 for 2015–2016 and 2016–2017, respectively, as estimated from the month of April. Waters of an Arctic origin dominated in 2015 and were replaced in 2016 by waters of a transformed Atlantic source. The CO2 uptake rates over the period of Arctic origin waters were significantly higher (2 mmol C·m−2·day−1) than the rates of the Atlantic origin waters of the following year.
2018
Monitoring of the atmospheric ozone layer and natural ultraviolet radiation: Annual report 2017.
This report summarizes the results from the Norwegian monitoring programme on stratospheric ozone and UV radiation measurements. The ozone layer has been measured at three locations since 1979: in Oslo, Tromsø/Andøya and Ny-Ålesund. The UV measurements started in 1995. The results show that there was a significant decrease in stratospheric ozone above Norway between 1979 and 1997. After that the ozone layer stabilized at a level ~2% below pre-1980 level. There are large inter-annual variations and in 2017 there were relatively low values at all the three Norwegian stations during the winter. However, the ozone situation normalized towards the end of spring.
NILU
2018
2018
2018
In 2005, the European Commission funded the NORMAN project to promote a permanent network of reference laboratories and research centers, including academia, industry, standardization bodies, and NGOs. Since then, NORMAN has (i) facilitated a more rapid and wide-scope exchange of data on the occurrence and effects of contaminants of emerging concern (CECs), (ii) improved data quality and comparability via validation and harmonization of common sampling and measurement methods (chemical and biological), (iii) provided more transparent information and monitoring data on CECs, and (iv) established an independent and competent forum for the technical/scientific debate on issues related to emerging substances. NORMAN plays a significant role as an independent organization at the interface between science and policy, with the advantage of speaking to the European Commission and other public institutions with the “bigger voice” of more than 70 members from 20 countries. This article provides a summary of the first 10 years of the NORMAN network. It takes stock of the work done so far and outlines NORMAN’s vision for a Europe-wide collaboration on CECs and sustainable links from research to policy-making. It contains an overview of the state of play in prioritizing and monitoring emerging substances with reference to several innovative technologies and monitoring approaches. It provides the point of view of the NORMAN network on a burning issue—the regulation of CECs—and presents the positions of various stakeholders in the field (DG ENV, EEA, ECHA, and national agencies) who participated in the NORMAN workshop in October 2016. The main messages and conclusions from the round table discussions are briefly presented.
2018
2018
Screening Programme 2017 – AMAP Assessment Compounds
This report summarizes the findings of a screening study on the occurrence of emerging substances selected by AMAP and other related substances measured earlier. The study includes selected solvents, siloxanes, flame retardants, UV compounds, pesticides, bisphenols and other PBT compounds in effluent, ambient air, biota, and marine plastic.
NILU
2018
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