460 research outputs found
Retrieval of total and tropospheric ozone from observations by the Global Ozone Monitoring Experiment
Zur Glaukomtherapie bei Hund und Katze unter besonderer Berücksichtigung experimenteller Studien aus den Jahren 1990-2008
In der vorliegenden Arbeit wurden 115 Veröffentlichungen der letzten 18 Jahre eines der wichtigsten Themengebiete der Ophthalmologie ausgewertet: Das Glaukom von Hund und Katze. Ziel war es, auf Basis klinischer Studien, neue und alte Medikamente der Glaukomtherapie, die in der Humanmedizin eingesetzt werden, hinsichtlich ihrer Wirksamkeit für die Tiermedizin zu prüfen. Auf diese Weise sollen neue Tendenzen und Möglichkeiten der medikamentösen Glaukombehandlung dargestellt werden
Combining Satellite and Ground Based Remote Sensing Measurements with In-situ Sensor Network to Derive Spatial Distribution of Pollutants
Trends of tropical tropospheric ozone from 20 years of European satellite measurements and perspectives for the Sentinel-5 Precursor
In preparation of the TROPOMI/S5P launch in early 2017, a tropospheric ozone retrieval based on the convective cloud differential method was developed. For intensive tests we applied the algorithm to the total ozone columns and cloud data of the satellite instruments GOME, SCIAMACHY, OMI, GOME-2A and GOME-2B. Thereby a time series of 20 years (1995–2015) of tropospheric column ozone was generated. To have a consistent total ozone data set for all sensors, one common retrieval algorithm, namely GODFITv3, was applied and the L1 reflectances were also soft calibrated. The total ozone columns and the cloud data were input into the tropospheric ozone retrieval. However, the tropical tropospheric column ozone (TCO) for the individual instruments still showed small differences and, therefore, we harmonised the data set. For this purpose, a multilinear function was fitted to the averaged difference between SCIAMACHY's TCO and those from the other sensors. The original TCO was corrected by the fitted offset. GOME-2B data were corrected relative to the harmonised data from OMI and GOME-2A. The harmonisation leads to a better agreement between the different instruments. Also, a direct comparison of the TCO in the overlapping periods proves that GOME-2A agrees much better with SCIAMACHY after the harmonisation. The improvements for OMI were small.
Based on the harmonised observations, we created a merged data product, containing the TCO from July 1995 to December 2015. A first application of this 20-year record is a trend analysis. The tropical trend is 0.7 ± 0.12 DU decade−1. Regionally the trends reach up to 1.8 DU decade−1 like on the African Atlantic coast, while over the western Pacific the tropospheric ozone declined over the last 20 years with up to 0.8 DU decade−1. The tropical tropospheric data record will be extended in the future with the TROPOMI/S5P data, where the TCO is part of the operational products
Satellite-based detection of volcanic sulphur dioxide from recent eruptions in Central and South America
Volcanic eruptions can emit large amounts of rock fragments and fine particles (ash) into the atmosphere, as well as several gases, including sulphur dioxide (SO<sub>2</sub>). These ejecta and emissions are a major natural hazard, not only to the local population, but also to the infrastructure in the vicinity of volcanoes and to aviation. Here, we describe a methodology to retrieve quantitative information about volcanic SO<sub>2</sub> plumes from satellite-borne measurements in the UV/Visible spectral range. The combination of a satellite-based SO<sub>2</sub> detection scheme and a state-of-the-art 3D trajectory model enables us to confirm the volcanic origin of trace gas signals and to estimate the plume height and the effective emission height. This is demonstrated by case-studies for four selected volcanic eruptions in South and Central America, using the GOME, SCIAMACHY and GOME-2 instruments
Nitrogen dioxide decline and rebound observed by GOME-2 and TROPOMI during COVID-19 pandemic
Since its first confirmed case in December 2019, coronavirus disease 2019 (COVID-19) has become a worldwide pandemic with more than 90 million confirmed cases by January 2021. Countries around the world have enforced lockdown measures to prevent the spread of the virus, introducing a temporal change of air pollutants such as nitrogen dioxide (NO2) that are strongly related to transportation, industry, and energy. In this study, NO2 variations over regions with strong responses to COVID-19 are analysed using datasets from the Global Ozone Monitoring Experiment-2 (GOME-2) sensor aboard the EUMETSAT Metop satellites and TROPOspheric Monitoring Instrument (TROPOMI) aboard the EU/ESA Sentinel-5 Precursor satellite. The global GOME-2 and TROPOMI NO2 datasets are generated at the German Aerospace Center (DLR) using harmonized retrieval algorithms; potential influences of the long-term trend and seasonal cycle, as well as the short-term meteorological variation, are taken into account statistically. We present the application of the GOME-2 data to analyze the lockdown-related NO2 variations for morning conditions. Consistent NO2 variations are observed for the GOME-2 measurements and the early afternoon TROPOMI data: regions with strong social responses to COVID-19 in Asia, Europe, North America, and South America show strong NO2 reductions of 30–50% on average due to restriction of social and economic activities, followed by a gradual rebound with lifted restriction measures
The 1997 El Niño impact on clouds, water vapour, aerosols and reactive trace gases in the troposphere, as measured by the Global Ozone Monitoring Experiment
The El Niño event of 1997/1998 caused dry conditions over the Indonesian area that were followed by large scale forest and savannah fires over Kalimantan, Sumatra, Java, and parts of Irian Jaya. Biomass burning was most intense between August and October 1997, and large amounts of ozone precursors, such as nitrogen oxides, carbon monoxide and hydrocarbons were emitted into the atmosphere. In this work, we use satellite measurements from the Global Ozone Monitoring Experiment (GOME) sensor to study the teleconnections between the El Niño event of 1997 and the Indonesian fires, clouds, water vapour, aerosols and reactive trace gases (nitrogen dioxide, formaldehyde and ozone) in the troposphere
Smart campus tools: Een verkenning bij Nederlandse universiteiten en lessen uit andere sectoren
Sinds we onderzoek doen naar het managen van universiteitscampussen is meer grip krijgen op het werkelijk ruimtegebruik – naast de (theoretische) bezetting en benutting die uit de roosters blijkt – een belangrijk thema. De match tussen vraag en aanbod van ruimte en faciliteiten kan nog veel scherper, vinden velen op de campus. Temeer omdat de vraag steeds dynamischer wordt, de gebruiker steeds veeleisender en het aanbod steeds duurzamer en kostenbewuster gemanaged moet worden. Met de technologische vooruitgang is het steeds beter mogelijk om te meten en te weten hoe gebruikers van de campus zich over de campus bewegen.
Dit was voor de Nederlandse universiteiten en hun facilitair/vastgoeddirecteuren (verenigd in het DFB netwerk) aanleiding om eind 2015 een onderzoeksopdracht te verstrekken, met als thema “Smart campus tools†voor het meten en verbeteren van ruimtegebruik. Doel van de opdracht was tweeledig: om te onderzoeken of/hoe de Nederlandse universiteiten anno 2016 “smart tools†inzetten en om te verkennen wat de nieuwste (technologische) ontwikkelingen zijn. Dit conceptrapport presenteert de resultaten van dit onderzoek.
Via deze weg willen we het DFB en alle personen die via interviews of enquêtes hun medewerking verleenden, bedanken voor hun inspiratie, informatie en tijd. Met dit rapport hopen we de universiteiten verder te helpen bij het (nog) beter managen van hun campus en faciliteiten.
 
Fertility parameters of dairy cows with cystic ovarian disease after treatment with gonadotrophin-releasing hormone
Fertility data were collected every four weeks for 10 years from 40 herds of Holstein-Friesian dairy cattle. The data collected during 925 lactations from cows with cystic ovarian disease which were treated with 500 μg gonadorelin were compared with data from a control group of 13,869 normal lactations. The intervals between parturition and first insemination and between parturition and conception were significantly shorter (P<0.001) in the normal cows, but the interval between first insemination and conception was not. The overall conception rate tended to be higher (P<0.10) in the normal cows, but the conception rate after the first insemination was significantly higher (P<0.001) and the number of services per conception was significantly lower (P=0.008) in the normal cows
Retrieval and validation of ozone columns derived from measurements of SCIAMACHY on Envisat
International audienceThis paper describes a new ozone column retrieval algorithm and its application to SCIAMACHY measurements. The TOSOMI algorithm is based on the Differential Optical Absorption Spectroscopy (DOAS) technique and implements several improvements over older algorithms. These improvements include aspects like (i) the explicit treatment of rotational Raman scattering, (ii) an improved air-mass factor formulation which is based on a simulation of the reflectivity spectrum and a subsequent DOAS fit of this simulated spectrum, (iii) the use of an improved ozone climatology and a column dependent air-mass factor, (iv) the use of daily varying ECMWF temperature profile analyses. The results of three validation exercises are reported. The TOSOMI columns are compared with an extensive set of ground-based observations (Brewer, Dobson) for the years 2003 and 2004. Secondly, a direct comparison for January?June 2003 with two new GOME retrievals, GDP Version 4 and TOGOMI, is presented. Third, data assimilation is used to study the dependence of the TOSOMI columns with retrieval parameters such as the viewing angle, cloud fraction and geographical location. These comparisons show a good consistency on the percent level between the GOME and SCIAMACHY algorithms. The present TOSOMI implementation (v0.32) shows an offset of about ?1.5% with respect to ground-based observations and the GOME retrievals
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