Multivariate geostatistical analyses of heavy metals in soils: Spatial multi-scale variations in Wulian, Eastern China

• Factorial kriging analysis was applied to examine the spatial variations of six heavy metals.
• Human influences were merely observed on Cd, Cu, Pb and Zn variability at local scale.
• Spatial variations of Cr and Ni had natural geochemical sources at both scales.
• Spatial variations of Cd, Cu, Pb and Zn were dominated by parent material at regional scale.

The objective of this study was to examine spatial multi-scale variability of six heavy metals (Cd, Cr, Cu, Ni, Pb and Zn) in relation to environmental factors in Wulian, Eastern China. Factorial kriging analysis (FKA) was applied to a data set consisting of 432 topsoils. We found that most of the heavy metal contents in soils did not exceed the guideline values of Environmental Quality Standard for Soils (EQSS) in China. Through linear model of coregionalization (LMC) fitting, spatial variation in six heavy metals could be grouped into one nugget effect, and two sphere structures with ranges of 6 km (local scale) and 14 km (regional scale). Spatial correlations among six heavy metals depended on local or regional scales. The high correlations between Cr, Ni and among Cd, Cu, Pb, Zn were found regardless of the spatial scale, while correlations of Cr and Ni with other four metals decreased with increasing spatial scale. Spatial variation of Cr and Ni was related to parent material at both local and regional scales, and was derived from natural sources. Mining activity was observed to affect the spatial variation of Cd, Cu, Pb and Zn at local scale, while parent material dominated spatial variation of those metals at regional scale. However, agricultural practices and human activity in urban area did not alter spatial variation of heavy metals in soils. It could be concluded that human influence on heavy metals variation was noted on local scale, and parent material had greater influence on spatial variation of heavy metals at both local and regional scales.