Modeling spatial uncertainty of heavy metal content in soil by conditional Latin hypercube sampling and geostatistical simulation

This study proposes the method of simulating spatial patterns and quantifying the uncertainty in multivariate distribution of heavy metals (Cr, Cu, Ni, and Zn) by sequential indicator simulation (SIS) combined with conditional Latin hypercube sampling (cLHS) in Changhua County, Taiwan. The cLHS is used for a sampling then for SIS mapping and assessing uncertainties of heavy metal concentrations. The indicator variogram results indicate that the 700 cLHS samples replicate statistical multivariate distribution and spatial structure of the 1,082 samples. Moreover, the SIS realizations based on 700 cLHS samples are more conservative and reliable than those based on 1,082 samples for delineating soil contamination by all heavy metals with the exception of Zn. Given adequate sampling, soil contamination simulation provides sufficient information for delineating contaminated areas and planning environmental management.