Geostatistical analysis of heavy metals in a one-hectare plot under natural vegetation in a serpentine area

• Published in: Canadian journal of soil science Vol. 81; no. 4; pp. 469 - 479
• Main Authors: Paz-Gonzalez, A, Taboada Castro, M.T, Vieira, S.R
• Format: Journal Article
• Language: English
• Published: 01-08-2001
• Subjects: forest soils; heavy metals; kriging; semivariograms; serpentine soils; soil chemistry; spatial variation; spatial variations; statistical analysis; topsoil; Spain
• ISSN: 0008-4271; 1918-1841
• DOI: 10.4141/S00-095

The objective of this study was to examine the spatial variability of selected heavy metals in a soil developed over serpentine. Both total and EDTA-extractable Fe, Mn, Cr, Ni, Cu, Zn and Co were determined in 53 samples, collected from the topsoil of a 1-ha forested plot. Naturally occurring soil Cr and Ni concentrations were much higher than critical limits for safety. Experimental semivariograms were computed and modelled by a nugget component plus a structure with autocorrelation ranges varying between 25 and 90 m. EDTA-extractable heavy metal contents exhibit a different spatial variation pattern from that of total contents, although Ni and Cu semivariograms present some similarities. The joint spatial variation for pairs of variables with significant correlation was also investigated. The nugget variances in the cross-semivariograms were not very different from those of individual semivariograms, suggesting heterogeneity within the shortest sampling interval. Semivariograms provided a clear description of the spatial structure of heavy metals and some insight into possible processes affecting their distribution. Kriging maps allowed the identification of small regions with distinct metal concentrations and confirmed the suitability of geostatistics for investigating processes controlling heavy metal variation. Isotopic cokriging performed better than kriging, but the gain for mapping purposes was limited. Key words: Serpentine, heavy metals, geostatistics, scaling, spatial variability