Collected data in soil heavy metal investigations may containsignificant levels of uncertainty, including complex and evenunexplainable spatial variations at a small investigation site.Therefore, this study identifies the spatial structure of soil zincin the northern part of Changhua County in Taiwan to understand thespatial variation and uncertainty of soil zinc. The spatial maps ofthis heavy metal are simulated by using the geostatisticalsimulation, and estimated by using ordinary kriging and natural logkriging. The estimation and simulation results indicate thatSequential Gaussian Simulations can reproduce the spatial structurefor investigated data. Furthermore, displaying a low spatialvariability, the ordinary kriging and natural log kriging estimatescan not fit the spatial structure and small-scale variation for thesoil zinc investigated data. The maps of kriging estimates are muchsmoother than those of simulations. Sequential Gaussian Simulationwith multiple realizing has significant advantages at a site withhigh variation investigated data over ordinary kriging, even naturallog kriging techniques. Geographic information systems display thesesimulation and estimation results.
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