Compositional data analysis in geochemistry: Are we sure to see what really occurs during natural processes?

Buccianti, Antonella; Grunsky, Eric

doi:10.1016/j.gexplo.2014.03.022

Cited by 121 publications

(53 citation statements)

References 15 publications

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“…The geochemical data obtained in this work were statistically processed (Buccianti & Grunsky, 2014). The standard deviation was calculated by the Thompson-Howarth method with replicate measurements (Stanley, 2003) and shown as the error bars in appropriate figures.…”

Section: Data Processing and Statistical Approachmentioning

confidence: 99%

Effect of organic matter on arsenic(V) and antimony(V) adsorption in soils

Doušová

Bůzek

Herzogová

et al. 2014

European J Soil Science

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The interaction of toxic oxyanions with organic matter can affect their stability and binding mechanisms in soils. The adsorption/desorption properties of arsenic (As V )/antimony (Sb V ) oxyanions were tested with two types of soil with a variable content of natural organic matter (NOM). Sample JEZ (I) represented a locality near a brown-coal power plant, and therefore formerly exposed to large amounts of atmospheric pollution, while sample LIZ (II) originated in an uncontaminated region without anthropogenic impact. Soil samples were investigated from the organic O and mineral B horizons. Different As/Sb fixations were indicated by their different adsorption affinity to O and B horizons. Arsenic as As V was strongly adsorbed in B horizons, while antimony as Sb V was predominantly bound in O horizons. The sorption followed the Langmuir model with maximum adsorption capacities of 1.4 × 10 −2 mmol g −1 for As and 1.0 × 10 −2 mmol g −1 for Sb adsorptions. The release of As and Sb to distilled water and 0.1 m KCl illustrated the significantly larger stability of As and Sb oxyanions in ionic solutions, where available ions prevented the aggregation of organic matter, thus maintaining the balanced distribution of surface charge. Distilled water with a pH of about 6 supported a negative surface charge density and enhanced the release of As/Sb oxyanions.

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Section: Data Processing and Statistical Approachmentioning

confidence: 99%

Effect of organic matter on arsenic(V) and antimony(V) adsorption in soils

Doušová

Bůzek

Herzogová

et al. 2014

European J Soil Science

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“…By virtue of the unit-sum constraint, CoDa can be naturally displayed only in the restricted sample space (compositional space) known as simplex and consisting of D parts or components (geochemical variables). A set of D-part composition (S D ) occupies a restricted part (from zero to, say, 100%) of a D-dimensional real space (R D ), forming a subset of its vectors [12,13,46]. The principles of the simplex as the natural sample space for compositional data are conveyed through the following expression [12,47]:…”

Section: Compositional Data Analysismentioning

confidence: 99%

Discrimination of topsoil environments in a karst landscape: an outcome of a geochemical mapping campaign

et al. 2020

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The study presented in this work emerged as a result of a multiyear regional geochemical survey based on lowdensity topsoil sampling and the ensuing geochemical atlas of Croatia. This study focuses on the Dinaric part of Croatia to expound the underlying mechanisms controlling the mobilities and variations in distribution of potentially harmful elements as observed from different environmental angles. Although serious environmental degradation of the vulnerable karst soil landscapes was expected to occur chiefly through the accumulation of various heavy metals, the most acute threat materialized through the soil acidification (Al-toxicity) affecting the entire Dinaric karst area. This picture surfaced from the analysis of all three investigated discriminant function models employing the abovementioned environmental criteria selected autonomously with respect to the evaluated soil geochemistry, namely, geologic setting, regional placement and land use. These models are presented by not only the characteristic discriminant-function diagrams but also a set of appropriate mathematically derived geochemical maps disclosing the allocations of potential threats to the karst soil landscapes posed by soil acidity.

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“…Calculating centered log ratios allows avoiding the restrictions inherent to compositional data; that is, the data are nonnegative, asymmetric, constrained, and only carry relative information (Aitchison, 1986, 10.1029/2018GC008154 Geochemistry, Geophysics, Geosystems 1990; Buccianti & Grunsky, 2014;Weltje, 2002;Weltje & Tjallingii, 2008). After log-transformation, the data can be analyzed by statistical methods such as PCA (Aitchison, 1986;Schuenemeyer & Drew, 2011) and PLS (Bloemsma et al, 2012;Weltje et al, 2015).…”

Section: Log-ratio Transformationmentioning

confidence: 99%

An Empirical Method to Predict Sediment Grain Size From Inorganic Geochemical Measurements

Liu

Bertrand

Weltje

2019

Geochem Geophys Geosyst

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Grain size is one of the most fundamental properties of sediments. It is frequently used in paleoclimate, paleoceanographic, and paleoenvironmental research as a proxy for river discharge, current and wind strength, and to identify mass flow deposits. Measuring grain size is, however, time‐consuming and destructive. Given the strong influence of grain size on sediment inorganic geochemistry, single‐element variations measured by, for example, X‐ray fluorescence core scanning are increasingly used to estimate grain‐size variations at high resolution in sediment cores. This approach is however limited to a narrow grain‐size range since individual elements only monotonically relate to grain size over a narrow size range. Here, we present a simple, code‐free, multielement method based on partial least squares regression to predict sediment mean grain size from inorganic geochemical data over the range of sizes commonly encountered in sedimentary basins (clay to sand). The method was first tested using river sediment samples separated in 11 grain‐size fractions, and it was later successfully applied to two sediment cores from the Chilean fjords. Our method only requires measuring grain size on a limited number (around 10) of selected training samples, and it allows to predict mean grain size at X‐ray fluorescence core scanner resolution. This method has the potential to be applied to any lake or marine sediment core, provided sediment provenance, weathering, and diagenesis remain relatively stable through time, and we anticipate that it will result in a significant increase in the resolution of sediment proxy records of climate and environmental change.

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Compositional data analysis in geochemistry: Are we sure to see what really occurs during natural processes?

Cited by 121 publications

References 15 publications

Effect of organic matter on arsenic(V) and antimony(V) adsorption in soils

Effect of organic matter on arsenic(V) and antimony(V) adsorption in soils

Discrimination of topsoil environments in a karst landscape: an outcome of a geochemical mapping campaign

An Empirical Method to Predict Sediment Grain Size From Inorganic Geochemical Measurements

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