Wu,C., Wu,J., Luo,Y., Zhang,H. and Teng,Y.(2008): Statistical and geostatistical characterization of heavy metal concentrations in a contaminated area taking into account soil map units. Geoderma, 144, 171-179.

『土壌図単位を考慮した汚染地域での重金属濃度の統計学的かつ地球統計学的特徴づけ』

『Abstract
　Soil map units taken into account in geostatistical prediction of heavy metals concentrations in a contaminated site of Fuyang Valley, Zhejiang Province, China. To compare the spatial variabilities of Cu, Zn, Pb and Cd concentrations in different soil map unit or combination of soil map units, a total of 94 samples distributed in three map units (Yellowish-red soil, Gravelly-yellowish-red soil and Gritty-yellowish-red soil) were collected in this area. The values of Cu, Zn, Pb and Cd concentrations were natural logarithm transformed to fit normal distribution. The variations of four heavy metal concentrations in Gravelly-yellowish-red soil was similar to that in Gritty-yellowish-red soil, but they were different to that in Yellowish-red soil. Then, the 94 soil samples were grouped into two classes (samples of Yellowish-red soil, and samples of Gravelly-yellowish-red soil or Gritty-yellowish-red soil). Semivariogram analysis revealed that all the four heavy metal concentrations in the study area showed moderate to strong spatial dependency, and local spatial variability (i.e. the variance between soil map units) played an important role in their spatial prediction. The spatial distribution maps of the four heavy metals were drawn by using only the measured data and by using the measured data plus taking into account soil map units, respectively. The results showed that spatial prediction by taking into account soil map units could reveal the huge spatial variability of the heavy metals, much better than the prediction without using soil map information in this contaminated site.

Keywords: Contamination; Heavy metals; Semivariogram; Soil map unit; Spatial variability』

1. Introduction
2. Materials and methods
　2.1. Descriptions of study area
　2.2. Sample collection and analyses
　2.3. Data transformation and methods
　2.4. Coefficient of variation
　2.5. One-way analysis of variance
　2.6. Semivariance analysis and kriging interpolation
3. Results and discussion
　3.1. Pollution status of the four heavy metals
　3.2. Comparison of heavy metal concentration in different map units
　3.3. Semivariograms and spatial prediction comparison
4. Summary
Acknowledgements
References

ホームへ