The effects of sampling design on spatial structure analysis of contaminated soil

Spatial structure analysis has been identified to be a useful tool in illustrating the spatial patterns of variables, and a necessary basis for a number of other spatial analysis procedures, such as kriging analysis. The design of a feasible spatial soil sampling plan at a contaminated site is extremely important for undertaking such analysis and is of considerable economic interest. In this paper, simulated data sets were applied in studying the effects of sampling pattern and density on spatial structure features of pollutants in contaminated soils. Three sampling patterns (regular grid, random and cellular stratified) and three sample sizes were applied in the analysis. Results show that both sample size and sample pattern have significant influences on the availability of experimental variograms. With the decrease of sampling densities, a systematic deviation of regular grid sampling and significant noise effects of cellular stratified and random sampling were observed. Given a certain sampling density, the regular grid pattern illustrated better estimation than the other two patterns. Results also show that, with appropriate tolerances, the estimation results obtained from low sampling densities could be improved significantly.