Combining raw and compositional data to determine the spatial patterns of potentially toxic elements in soils

article

Boente, C.
Albuquerque, M.T.D.
Fernández-Braña, A.
Gerassis, S.
Sierra, C.
Gallego, J.R.

Elsevier

BOENTE, C. [et al.] (2018) - Combining raw and compositional data to determine the spatial patterns of potentially toxic elements in soils. Science of The Total Environment. ISSN 0048-9697. Vol. 631–632, p. 1117-1126

0048-9697

10.1016/j.scitotenv.2018.03.048

Combining raw and compositional data to determine the spatial patterns of potentially toxic elements in soils

Soil pollution
PTEs
Compositional data
Ordinary kriging
Local G-clustering
Relative enrichment

567 SFRH/BSAB/127907/2016

2018-03-27T15:31:39Z
2020-08-31T00:30:10Z
2018

When considering complex scenarios involving several attributes, such as in environmental characterization, a clearer picture of reality can be achieved through the dimensional reduction of data. In this context, maps facilitate the visualization of spatial patterns of contaminant distribution and the identification of enriched areas. A set, of 15 Potentially Toxic Elements (PTEs) – (As, Ba, Cd, Co, Cr, Cu, Hg,Mo, Ni, Pb, Sb, Se, Tl, V, and Zn), was measured in soil, collected in Langreo's municipality (80 km2), Spain. Relative enrichment (RE) is introduced here to refer to the proportion of elements present in a given context. Indeed, a novel approach is provided for research into PTE fate. This method involves studying the variability of PTE proportions throughout the study area, thereby allowing the identification of dissemination trends. Traditional geostatistical approaches commonly use raw data (concentrations) accepting that the elements analyzedmake up the entirety of the soil. However, in geochemical studies the analyzed elements are just a fraction of the total soil composition. Therefore, considering compositional data is pivotal. The spatial characterization of PTEs considering raw and compositional data together allowed a broad discussion about, not only the PTEs concentration's distribution but also to reckon possible trends of relative enrichment (RE). Transformations to open closed data are widely used for this purpose. Spatial patterns have an indubitable interest. In this study, the Centered Log-ratio transformation (clr) was used, followed by its back-transformation, to build a set of compositional data that, combined with raw data, allowed to establish the sources of the PTEs and trends of spatial dissemination.
info:eu-repo/semantics/publishedVersion

openAccess

eng