Person: Ries, Ludwig
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Veröffentlichung Variability of black carbon mass concentrations, sub-micrometer particle number concentrations and size distributions: results of the German Ultrafine Aerosol Network ranging from city street to high alpine locations(2019) Sun, Junying; Birmili, Wolfram; Gerwig, Holger; Hermann, Markus; Ries, Ludwig; Schwerin, Andreas; Sohmer, Ralf; Meinhardt, Frank; Wirtz, KlausThis work reports the first statistical analysis of multi-annual data on tropospheric aerosols from the German Ultrafine Aerosol Network (GUAN). Compared to other networks worldwide, GUAN with 17 measurement locations has the most sites equipped with particle number size distribution (PNSD) and equivalent black carbon (eBC) instruments and the most site categories in Germany ranging from city street/roadside to High Alpine. As we know, the variations of eBC and particle number concentration (PNC) are influenced by several factors such as source, transformation, transport and deposition. The dominant controlling factor for different pollutant parameters might be varied, leading to the different spatio-temporal variations among the measured parameters. Currently, a study of spatio-temporal variations of PNSD and eBC considering the influences of both site categories and spatial scale is still missing. Based on the multi-site dataset of GUAN, the goal of this study is to investigate how pollutant parameters may interfere with spatial characteristics and site categories. © 2019 The AuthorsVeröffentlichung On the diurnal, weekly, and seasonal cycles and annual trends in atmospheric CO2 at Mount Zugspitze, Germany, during 1981-2016(2019) Yuan, Ye; Couret, Cédric; Petermeier, Hannes; Ries, Ludwig; Sohmer, Ralf; Meinhardt, FrankA continuous, 36-year measurement composite of atmospheric carbon dioxide (CO2) at three measurement locations on Mount Zugspitze, Germany, was studied. For a comprehensive site characterization of Mount Zugspitze, analyses of CO2 weekly periodicity and diurnal cycle were performed to provide evidence for local sources and sinks, showing clear weekday to weekend differences, with dominantly higher CO2 levels during the daytime on weekdays. A case study of atmospheric trace gases (CO and NO) and the passenger numbers to the summit indicate that CO2 sources close by did not result from tourist activities but instead obviously from anthropogenic pollution in the near vicinity. Such analysis of local effects is an indispensable requirement for selecting representative data at orographic complex measurement sites. The CO2 trend and seasonality were then analyzed by background data selection and decomposition of the long-term time series into trend and seasonal components. The mean CO2 annual growth rate over the 36-year period at Zugspitze is 1:8+/-0:4 ppm yr-1, which is in good agreement with Mauna Loa station and global means. The peak-to-trough amplitude of the mean CO2 seasonal cycle is 12:4+/-0:6 ppm at Mount Zugspitze (after data selection: 10:5+/-0:5 ppm), which is much lower than at nearby measurement sites at Mount Wank (15:9+/-1:5 ppm) and Schauinsland (15:9+/-1:0 ppm), but following a similar seasonal pattern. © Author(s) 2019.Veröffentlichung Long-term observations of tropospheric particle number size distributions and equivalent black carbon mass concentrations in the German Ultrafine Aerosol Network (GUAN)(2016) Weinhold, Kay; Bath, Olaf; Rasch, Fabian; Birmili, Wolfram; Sonntag, Andre; Sun, Jia; Merkel, Maik; Wiedensohler, Alfred; Gerwig, Holger; Bastian, Susanne; Schladitz, Alexander; Löschau, Gunter; Cyrys, Josef; Pitz, Mike; Gu, Jianwei; Kusch, Thomas; Flentje, Harald; Quass, Ulrich; Kaminski, Heinz; Kuhlbusch, Thomas A.J.; Ries, Ludwig; Meinhardt, Frank; Schwerin, Andreas; Fiebig, Markus; Wirtz, KlausThe German Ultrafine Aerosol Network (GUAN) is a cooperative atmospheric observation network, which aims at improving the scientific understanding of aerosol-related effects in the troposphere. The network addresses research questions dedicated to both climate- and health-related effects. GUAN's core activity has been the continuous collection of tropospheric particle number size distributions and black carbon mass concentrations at 17 observation sites in Germany. These sites cover various environmental settings including urban traffic, urban background, rural background, and Alpine mountains. In association with partner projects, GUAN has implemented a high degree of harmonisation of instrumentation, operating procedures, and data evaluation procedures. The quality of the measurement data is assured by laboratory intercomparisons as well as on-site comparisons with reference instruments. This paper describes the measurement sites, instrumentation, quality assurance, and data evaluation procedures in the network as well as the EBAS repository, where the data sets can be obtained. Quelle: http://www.earth-syst-sci-data.netVeröffentlichung Adaptive selection of diurnal minimum variation: a statistical strategy to obtain representative atmospheric CO2 data and its application to European elevated mountain stations(2018) Yuan, Ye; Couret, Cédric; Petermeier, Hannes; Ries, Ludwig; Meinhardt, FrankCritical data selection is essential for determining representative baseline levels of atmospheric trace gases even at remote measurement sites. Different data selection techniques have been used around the world, which could potentially lead to reduced compatibility when comparing data from different stations. This paper presents a novel statistical data selection method named adaptive diurnal minimum variation selection (ADVS) based on CO2 diurnal patterns typically occurring at elevated mountain stations. Its capability and applicability were studied on records of atmospheric CO2 observations at six Global Atmosphere Watch stations in Europe, namely, Zugspitze-Schneefernerhaus (Germany), Sonnblick (Austria), Jungfraujoch (Switzerland), Izanã (Spain), Schauinsland (Germany), and Hohenpeissenberg (Germany). Three other frequently applied statistical data selection methods were included for comparison. Among the studied methods, our ADVS method resulted in a lower fraction of data selected as a baseline with lower maxima during winter and higher minima during summer in the selected data. The measured time series were analyzed for long-term trends and seasonality by a seasonal-trend decomposition technique. In contrast to unselected data, mean annual growth rates of all selected datasets were not significantly different among the sites, except for the data recorded at Schauinsland. However, clear differences were found in the annual amplitudes as well as the seasonal time structure. Based on a pairwise analysis of correlations between stations on the seasonal-trend decomposed components by statistical data selection, we conclude that the baseline identified by the ADVS method is a better representation of lower free tropospheric (LFT) conditions than baselines identified by the other methods. © Author(s) 2018.