Grain size proxies of aeolian dust deposits have widely been applied in environmental
and sedimentary studies. However, large body of research papers are not taking into
consideration that a complex grain size distribution curve cannot be an indicator
of a single one environmental factor (e.g. wind speed/strength, transportation distance,
aridity).
The aim of the present paper is to discuss the main differences of frequently used
statistical methods and to provide possible interpretations of the results by applying
these various approaches on the high-resolution loess-paleosol profile of Dunaszekcso,
South Hungary (Central Europe). Beside single statistical descriptors (mean, median,
mode) of grain size and simple indices of size-fraction ratios (U-ratio, Grain Size
Index), some more complex algorithms were also used in our paper. The applied parametric
curve-fitting, end-member modelling and hierarchical cluster analysis techniques are
using the whole spectrum of the measured grain size distributions and provide a more
reliable and more representative results even in case of small scale variations.
According to our findings, approaches which provide direct linkage among simple statistical
descriptors and single atmospheric or other environmental elements are rather oversimplified
as properties aeolian dust deposits are influenced by the integrated effects of several
concurrent processes. Differences of more complex decomposition methods arise from
the different approach and scope. End-members are determined from the unmixing based
on the covariance structure of the whole grain size data-series of the section, while
the parametric curve-fitting is based on the one-by-one deconvolution of the grain
size distribution curves. End-members of loess-paleosol samples are regarded as representation
of the average dust grain size distribution of various temporal sediment clusters
of seasonal or other short-term intervals, while (sub) populations by parametric curve-fitting
are proposed to illustrate process-related elements of background and dust storm depositional
components for each sample. Results of cluster analysis represent similar grouping
conditions as end-member modelling with a reduced sedimentary and genetically meaning.
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