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DDC: 550 × Subject: principal component analysis ×

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    On the abundance and source contributions of dicarboxylic acids in size-resolved aerosol particles at continental sites in central Europe
    (München : European Geopyhsical Union, 2014) van Pinxteren, D.; Neusüß, C.; Herrmann, H.
    Dicarboxylic acids (DCAs) are among the most abundant organic compounds observed in atmospheric aerosol particles and have been extensively studied at many places around the world. The importance of the various primary sources and secondary formation pathways discussed in the literature is often difficult to assess from field studies, though. In the present study, a large data set of size-resolved DCA concentrations from several inland sites in Germany is combined with results from a recently developed approach of statistical back-trajectory analysis and additional data. Principal component analysis is then used to reveal the most important factors governing the abundance of DCAs in different particle size ranges. The two most important sources revealed are (i) photochemical formation during intense radiation days in polluted air masses, likely occurring in the gas phase on short timescales (gasSOA), and (ii) secondary reactions in anthropogenically influenced air masses, likely occurring in the aqueous phase on longer timescales (aqSOA). While the first source strongly impacts DCA concentrations mainly in small and large particles, the second one enhances accumulation mode DCAs and is responsible for the bulk of the observed concentrations. Primary sources were found to be minor (sea salt, soil resuspension) or non-existent (biomass burning, traffic). The results can be regarded as representative for typical central European continental conditions.
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    Regional and inter-regional effects in evolving climate networks
    (Göttingen : Copernicus GmbH, 2014) Hlinka, J.; Hartman, D.; Jajcay, N.; Vejmelka, M.; Donner, R.; Marwan, N.; Kurths, J.; Paluš, M.
    Complicated systems composed of many interacting subsystems are frequently studied as complex networks. In the simplest approach, a given real-world system is represented by an undirected graph composed of nodes standing for the subsystems and non-oriented unweighted edges for interactions present among the nodes; the characteristic properties of the graph are subsequently studied and related to the system's behaviour. More detailed graph models may include edge weights, orientations or multiple types of links; potential time-dependency of edges is conveniently captured in so-called evolving networks. Recently, it has been shown that an evolving climate network can be used to disentangle different types of El Niño episodes described in the literature. The time evolution of several graph characteristics has been compared with the intervals of El Niño and La Niña episodes. In this study we identify the sources of the evolving network characteristics by considering a reduced-dimensionality description of the climate system using network nodes given by rotated principal component analysis. The time evolution of structures in local intra-component networks is studied and compared to evolving inter-component connectivity.