Sampling Survey of Heavy Metal in Soil Using SSSI

Much attention has been given to sampling design, and the sampling method chosen directly affects the sampling accuracy. The development of spatial sampling theory has lead to the recognition of the importance of taking spatial dependency into account when sampling. This text uses the new Sandwich Spatial Sampling and Inference (SSSI) software as a tool to compare the relative error, coefficient of variation (CV), and design effect with five sampling models – simple random sampling, stratified sampling, spatial random sampling, spatial stratified sampling, and sandwich spatial sampling. The five models are simulated 1000 times each with a range of sample sizes from 10 to 80. SSSI includes six models in all, but systematic sampling is not used here because the sample positions are fixed. The dataset consists of 84 points measuring soil heavy metal content in Shanxi Province, China. The whole area is stratified into four layers by soil type、hierarchical cluster and geochronology, and three layers by geological surface.The research shows that the accuracy of spatial simple random sampling and spatial stratified sampling is better than simple random sampling and stratified sampling because the soil content is spatially continuous, and stratified models are more efficient than non-stratified models. Stratification by soil type yields higher accuracy than by geochronology in the case of smaller sample sizes, but lower accuracy in larger sample sizes. Based on spatial stratified sampling, sandwich sampling develops a report layer composed of the user’s final report units, allowing the user to obtain the mean and variance of each report unit with high accuracy. In the case of soil sampling, SSSI was a useful tool for evaluating the accuracy of different sampling techniques.