Geostatistical simulated realization maps can represent the spatial heterogeneity of the studied spatial variable more realistically than the kriged optimal map because they overcome the smoothing effect of interpolation. The difference among realizations indicates spatial uncertainty. These realizations may serve as input data to transfer functions to further evaluate the resulting uncertainty in impacted dependent variables. In this study, sequential Gaussian simulation was used to simulate the spatial distribution of soil nickel (Ni) in the top soils of a 31 km2 area within the urban-rural transition zone of Wuhan, China. Simulated realizations were then imported into transfer functions to calculate the health risk costs caused by Ni polluted areas ignored in remediation due to underestimation of the Ni contents and the remediation risk costs caused by unnecessary remediation of unpolluted areas due to overestimation of the Ni contents. The uncertainty about the input Ni content values thus propagated through these transfer functions, leading to uncertain responses in health risk costs and remediation risk costs. The spatial uncertainty of the two forms of risk costs were assessed based on the response realizations. Because the risk of exposure of soil Ni to humans and animals is generally greater in contaminated arable lands than in industrial and residential areas, the effect of land use types was also taken into account in risk cost estimation. Results showed that high health costs mainly appear in the southwest part of the study area, while high remediation costs mainly occur in the east, middle and northwest of the study area, and that most of the south part of the study area was delineated as contaminated according to the minimum expected cost standard. This study shows that sequential Gaussian simulation and transfer functions are valuable tools for assessing risk costs of soil contamination delineation and associated spatial uncertainty.