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Using Rank-Order Geostatistics for Spatial Interpolation of Highly Skewed Data in a Heavy-Metal Contaminated Site

Kai-Wei Juanga, Dar-Yuan Leea and Timothy R. Ellsworthb

a Graduate Institute of Agricultural Chemistry, National Taiwan Univ., Taipei, 106 Taiwan
b Dep. of Natural Resources and Environmental Sciences, Univ. of Illinois, Urbana–Champaign, IL 61801



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Fig. 1. The study site and the sampling points. The coordinate system is 2°UTM for Taiwan.

 


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Fig. 2. A hypothetical empirical distribution function (EDF) for a sample data set with great variation and high skewness used to illustrate a deviation, d, in the standardized-rank space back-transformed into greatly contrasting uncertainty intervals {delta} or {Delta} in the concentration space.

 


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Fig. 3. Sampling location map and soil Cd concentrations.

 


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Fig. 4. Histograms of (a) soil Cd concentrations and (b) logarithmic-transformed values.

 


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Fig. 5. Semivariograms of (a) soil Cd concentration values and (b) normal-scored values.

 


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Fig. 6. Experimental indicator semivariograms for the (a) 5th, (b) 25th, (c) 50th, (d) 75th, and (e) 95th percentiles of the sample distribution. The solid lines depict the indicator semivariograms for the multi-Gaussian distribution.

 


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Fig. 7. Empirical distribution function for the soil Cd concentrations.

 


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Fig. 8. Semivariogram of standardized ranks for Cd concentrations.

 


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Fig. 9. Contour map of estimated Cd concentrations.

 


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Fig. 10. Contour maps of the (a) lower and (b) upper bounds of the concentration interval for soil Cd concentrations.

 


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Fig. 11. Contour maps of (a) the interval range (the deviation between the upper and lower bounds) for soil Cd concentrations and (b) the kriging standard deviation for standardized ranks.

 


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Fig. 12. Contour map of the probability of soil Cd concentration z(x) being higher than 10 mg/kg.

 





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