Factorial kriging of a geochemical dataset for heavy-metal spatial-variability characterization : The Wallonian region

• Published in: Environmental earth sciences Vol. 71; no. 7; pp. 3161 - 3170
• Main Authors: Benamghar, Ahcène, Gómez-Hernández, J. Jaime
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer-Verlag 01.04.2014; Springer Berlin Heidelberg; Springer; Springer Nature B.V
• Subjects: Biogeosciences; data collection; Earth and Environmental Science; Earth Sciences; Earth, ocean, space; Engineering and environment geology. Geothermics; Environmental Science and Engineering; Exact sciences and technology; Factorial experiments; Geochemistry; Geology; Geostatistics; Heavy metal content; heavy metals; Hydrology/Water Resources; kriging; Marine and continental quaternary; Multivariate analysis; Original Article; Pollution, environment geology; remediation; Surficial geology; Terrestrial Pollution; variance; Belgium; Europe; Western Europe; Wallonia geochemical data set; Belgium; Factorial kriging analysis; Geostatistics; Coregionalization; Heavy metal contamination; geostatistics; maps; heavy metals; kriging; lithology; concentration; covariance; pollution; anomalies; contamination; remediation; mineralization
• ISSN: 1866-6280; 1866-6299
• DOI: 10.1007/s12665-013-2704-5

Characterizing the spatial patterns of variability is a fundamental aspect when investigating what could be the causes behind the spatial spreading of a set of variables. In this paper, a large multivariate dataset from the southeast of Belgium has been analyzed using factorial kriging. The purpose of the study is to explore and retrieve possible scales of spatial variability of heavy metals. This is achieved by decomposing the variance-covariance matrix of the multivariate sample into coregionalization matrices, which are, in turn, decomposed into transformation matrices, which serve to decompose each regionalized variable as a sum of independent factors. Then, factorial cokriging is used to produce maps of the factors explaining most of the variance, which can be compared with maps of the underlying lithology. For the dataset analyzes, this comparison identifies a few point scale concentrations that may reflect anthropogenic contamination, and it also identifies local and regional scale anomalies clearly correlated to the underlying geology and to known mineralizations. The results from this analysis could serve to guide the authorities in identifying those areas which need remediation.