Wang,L. and Wu,J.(2008): Spatial variability of heavy metals in soils across a valley plain in Southeastern China. Environ.Geol., 55, 1207-1217.


 An investigation was carried out to survey the magnitude and spatial distribution of heavy metals, as well as their relation with soil series, in a valley plain in Southeastern China. Soil was sampled at 159 sites by combining a squared grid and nested sampling strategies along the transect perpendicular to the Qujiang River in Zhejiang Province, China. Total concentrations of six metals, namely Cu, Fe, Mn, Ni, Pb and Zn, were measured. Classical statistics and geostatistics were used to quantify their spatial characteristics. There was a considerable variation in many of these parameters. The total concentrations ranged from 6.8 to 29.3 mg kg-1 for Cu, 6,784 to 18,678 mg kg-1 for Fe, 94 to 385 mg kg-1 for Mn, 6.1 to 20.3 mg kg-1 for Ni, 25.0 to 49.5 mg kg-1 for Pb, and 12 to 160 mg kg-1 for Zn. Pearson correlation coefficients among total metal concentrations and selected soil properties showed a number of strong associations. By virtue of analysis of variance, a predominant influence of soil series on the spatial variability of metal concentrations was observed. All metals were spatially correlated. The semi-variograms of Cu, Fe, Mn, Ni and Zn were dominated by short range correlation (600 or 700 m), and that of Pb by long range (1200 m). Block kriging maps of total metal concentrations and soil properties showed strip distributions, perpendicular to the river, in the manner similar to the soil series. Principal component analysis was run to identify common distribution patterns of heavy metals and soil properties. These results illustrate that soil series information of valley plain may be useful for developing management zones for site-specific agriculture.

Keywords: Valley plain; Heavy metals; Spatial variability; Soil series』

Materials and methods
 Site description
 Soil sampling and analyses
 Data processing
 Exploratory data analysis
 Correlation analysis
 ANOVA analysis
 Spatial analysis
 Principal component analysis