Found 10001 publications. Showing page 123 of 401:
Equinors miljøovervåkingsprogram for Snøhvit. Overvåking av vegetasjon og jord – reanalyser i 2018
Petroleumsanlegget på Melkøya utenfor Hammerfest ble startet opp i 2007 og slipper ut karbon-dioksid (CO2), nitrogenoksider (NOx), metan (CH4), flyktige organiske forbindelser utenom metan (nmVOC), svoveldioksid (SO2) og hydrogensulfid (H2S) fra energiproduksjon og prosessanlegg. Utslipp av nitrogen og svovelholdige gasser kan generelt påvirke terrestriske økosystemer ved forsuring og gjødsling av jordsmonn og vegetasjon. Petroleumsanlegget på Melkøya tar imot naturgass fra feltene Snøhvit og Albatross i Barentshavet. Her prosesseres og nedkjøles natur-gassen til flytende gass (LNG) for videre distribuering. Utslippene fra LNG-anlegget er beregnet til å ligge under gjeldene kritiske tålegrenseverdier for terrestriske naturtyper, men tålegrense-verdiene i arktisk/alpine naturtyper er imidlertid usikre. For å kunne dokumentere eventuelle ef-fekter av utslipp til luft, ble det i 2006 (før utslipp) etablert et overvåkingsprogram for vegetasjon og jord i influensområdet fra LNG-anlegget på Melkøya. Grunnlagsundersøkelsen ble utført samme år, og det ble utført analyser i 2008, 2013 og 2018 etter samme metodikk som i 2006.
To overvåkingsområder ble opprettet i 2006, ett med estimert relativt høy avsetning av nitrogen, nordøst på Kvaløya ved Forsøl og ett område med relativt lav avsetning sør på Kvaløya ved Stangnes. Områdene er samkjørt med Norsk institutt for luftforskning (NILU) sine overvåkings-stasjoner for luft- og nedbørskvalitet. Innen hvert område utføres det en integrert overvåking av vegetasjonens artssammensetning og kjemisk innhold av planter og jord i to atskilte naturtyper (næringsfattig kreklinghei og litt kalkfattig og svakt intermediær jordvannsmyr).
Vegetasjonen overvåkes i permanent oppmerkede ruter (1m × 1m i arktisk hei og 0,5m × 0,5m på myr). I hver rute registreres mengde av karplanter, moser og lav, samt vegetasjonssjiktenes høyde og dekning. Lys reinlav/fjellreinlav (reinlav) og rusttorvmose analyseres for kjemisk inn-hold, Kjeldahl-nitrogen, tungmetallene bly (Pb), nikkel (Ni) og sink (Zn) og polyaromatiske hydro-karboner (PAH). Jordprøver fra hver av naturtypene analyseres for pH, Kjeldahl-nitrogen, ekstraherbare kationer, utbyttingskapasitet, basemetning, Pb, Ni, Zn og PAH. De kjemiske analysene av planter og jord utføres av Norsk institutt for bioøkonomi og NILU.
Analysene av vegetasjonens artssammensetning viste få endringer i mengdeforhold mellom artene fra 2006 til 2018. De små endringene vi fant skyldes trolig årlige variasjoner. Det ble funnet noen få endringer av arter som normalt responderer positivt på nitrogengjødsling, slik som gress. Lav har gått noe tilbake mest sannsynlig pga. økt beitepress fra rein. Det er således ingen indikasjon på at en eventuell forurensing fra LNG-anlegget på Melkøya har påvirket vegetasjonens artssammensetning og mengdeforholdet mellom arter.
NØKKELORD : Hammerfest, Melkøya, Kvaløya, LNG-anlegg, forurensing, forsuring, gjødsling, nitrogen, arktisk/ alpin vegetasjon, kreklinghei, myr, plantekjemi, jordkjemi, polyaromatiske hydrokarboner,
KEY WORDS : Hammerfest, Melkøya, Kvaløya, LNG plant, pollution, acidification, fertilization, nitrogen, arctic/ alpine vegetation, mire, plant chemistry, soil chemistry, polynuclear aromatic hydrocarbons
Norsk institutt for naturforskning
2018
2018
2018
Discounting the effect of meteorology on trends in surface ozone: Development of statistical tools
This report presents the results using a statistical method to single out the influence of interannual meteorological variability on surface ozone. The reason for using such a tool is two-fold: Firstly, to explain the ozone levels in one specific year in terms of weather anomalies and secondly, to estimate the part of long-term ozone trends that is due to the meteorology alone. The method is a so-called GAM (generalized additive model), which could be regarded an advanced multiple regression method relating daily ozone levels to certain meteorological variables. The performance of the method was evaluated by comparing observed ozone data with those predicted by the GAM. This revealed a good to very good agreement in central Europe and Germany in particular. For southern Europe the performance was poorer. The method indicated that meteorology contributed to the downward trend in ozone seen at most sites for both 1990-2000 and 2000-2010.
ETC/ACM
2018
Interim Annual Assessment Report for 2017. European air quality in 2017.
Copernicus Atmosphere Monitoring Service
2018
Grenseområdene Norge-Russland. Luft- og nedbørkvalitet, årsrapport 2017.
Smelteverkene i NV-Russland slipper ut store mengder svoveldioksid (SO2) og tungmetaller. Utslippene påvirker luft- og nedbørkvalitet i grenseområdene. Miljøovervåkingen viser at grenseverdier for SO2 er overholdt i kalenderåret
2017, samt sesongmiddel vinter 2016/17. Målsettingsverdier for Ni og As er overholdt.
NILU
2018
In the frame of the Chemistry-Aerosol Mediterranean Experiment (ChArMEx), we analyse the budget of primary aerosols and secondary inorganic aerosols over the Mediterranean Basin during the years 2012 and 2013. To do this, we use two year-long numerical simulations with the chemistry-transport model MOCAGE validated against satellite- and ground-based measurements. The budget is presented on an annual and a monthly basis on a domain covering 29 to 47° N latitude and 10° W to 38° E longitude.
The years 2012 and 2013 show similar seasonal variations. The desert dust is the main contributor to the annual aerosol burden in the Mediterranean region with a peak in spring, and sea salt being the second most important contributor. The secondary inorganic aerosols, taken as a whole, contribute a similar level to sea salt. The results show that all of the considered aerosol types, except for sea salt aerosols, experience net export out of our Mediterranean Basin model domain, and thus this area should be considered as a source region for aerosols globally. Our study showed that 11 % of the desert dust, 22.8 to 39.5 % of the carbonaceous aerosols, 35 % of the sulfate and 9 % of the ammonium emitted or produced into the study domain are exported. The main sources of variability for aerosols between 2012 and 2013 are weather-related variations, acting on emissions processes, and the episodic import of aerosols from North American fires.
In order to assess the importance of the anthropogenic emissions of the marine and the coastal areas which are central for the economy of the Mediterranean Basin, we made a sensitivity test simulation. This simulation is similar to the reference simulation but with the removal of the international shipping emissions and the anthropogenic emissions over a 50 km wide band inland along the coast. We showed that around 30 % of the emissions of carbonaceous aerosols and 35 to 60 % of the exported carbonaceous aerosols originates from the marine and coastal areas. The formation of 23, 27 and 27 %, respectively of, ammonium, nitrate and sulfate aerosols is due to the emissions within the marine and coastal area.
2018
2018
Comparison of dust-layer heights from active and passive satellite sensors
Aerosol-layer height is essential for understanding the impact of aerosols on the climate system. As part of the European Space Agency Aerosol_cci project, aerosol-layer height as derived from passive thermal and solar satellite sensors measurements have been compared with aerosol-layer heights estimated from CALIOP measurements. The Aerosol_cci project targeted dust-type aerosol for this study. This ensures relatively unambiguous aerosol identification by the CALIOP processing chain. Dust-layer height was estimated from thermal IASI measurements using four different algorithms (from BIRA-IASB, DLR, LMD, LISA) and from solar GOME-2 (KNMI) and SCIAMACHY (IUP) measurements. Due to differences in overpass time of the various satellites, a trajectory model was used to move the CALIOP-derived dust heights in space and time to the IASI, GOME-2 and SCIAMACHY dust height pixels. It is not possible to construct a unique dust-layer height from the CALIOP data. Thus two CALIOP-derived layer heights were used: the cumulative extinction height defined as the height where the CALIOP extinction column is half of the total extinction column, and the geometric mean height, which is defined as the geometrical mean of the top and bottom heights of the dust layer. In statistical average over all IASI data there is a general tendency to a positive bias of 0.5–0.8 km against CALIOP extinction-weighted height for three of the four algorithms assessed, while the fourth algorithm has almost no bias. When comparing geometric mean height there is a shift of −0.5 km for all algorithms (getting close to zero for the three algorithms and turning negative for the fourth). The standard deviation of all algorithms is quite similar and ranges between 1.0 and 1.3 km. When looking at different conditions (day, night, land, ocean), there is more detail in variabilities (e.g. all algorithms overestimate more at night than during the day). For the solar sensors it is found that on average SCIAMACHY data are lower by −1.097 km (−0.961 km) compared to the CALIOP geometric mean (cumulative extinction) height, and GOME-2 data are lower by −1.393 km (−0.818 km).
2018
A multi-model comparison of meteorological drivers of surface ozone over Europe
The implementation of European emission abatement strategies has led to a significant reduction in the emissions of ozone precursors during the last decade. Ground-level ozone is also influenced by meteorological factors such as temperature, which exhibit interannual variability and are expected to change in the future. The impacts of climate change on air quality are usually investigated through air-quality models that simulate interactions between emissions, meteorology and chemistry. Within a multi-model assessment, this study aims to better understand how air-quality models represent the relationship between meteorological variables and surface ozone concentrations over Europe. A multiple linear regression (MLR) approach is applied to observed and modelled time series across 10 European regions in springtime and summertime for the period of 2000–2010 for both models and observations. Overall, the air-quality models are in better agreement with observations in summertime than in springtime and particularly in certain regions, such as France, central Europe or eastern Europe, where local meteorological variables show a strong influence on surface ozone concentrations. Larger discrepancies are found for the southern regions, such as the Balkans, the Iberian Peninsula and the Mediterranean basin, especially in springtime. We show that the air-quality models do not properly reproduce the sensitivity of surface ozone to some of the main meteorological drivers, such as maximum temperature, relative humidity and surface solar radiation. Specifically, all air-quality models show more limitations in capturing the strength of the ozone–relative-humidity relationship detected in the observed time series in most of the regions, for both seasons. Here, we speculate that dry-deposition schemes in the air-quality models might play an essential role in capturing this relationship. We further quantify the relationship between ozone and maximum temperature (mo3 − T, climate penalty) in observations and air-quality models. In summertime, most of the air-quality models are able to reproduce the observed climate penalty reasonably well in certain regions such as France, central Europe and northern Italy. However, larger discrepancies are found in springtime, where air-quality models tend to overestimate the magnitude of the observed climate penalty.
2018
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.
2018
2018