This chapter reviews methods for selecting sampling locations in contaminated soils for three situations. In the first situation a global estimate of the soil contamination in an area is required. The result of the surey is a number or a series of numbers per contaminant, e.g. the estimated mean concentration, median, 90th percentile, or the cumulative frequency distribution for the area as a whole. In the second case we want more spatial detail, and interest is in the mean or median concentration for several delineated blocks. Finally, in the third case the aim is to construct a high resolution map of the concentrations, for instance by geostatistical interpolation. For the first aim, design-based sampling methods, in which locations are selected by probability sampling, are most appropriate. Several basic sampling designs are described. Laboratory costs can be saved by bulking soil samples. The precision of estimates can be increased by exploiting ancillary information on variables correlated with the contaminants. For mapping purposes, model-based sampling methods, in which locations typically are selected by purposive sampling, are the best option. Examples are sampling on a centred grid, spatial coverage sampling, and geostatistical sampling. A simple method, based on the k-means clustering algorithm, is described for computing spatial coverage samples. For geostatistical interpolation a variogram is required. Variogram estimation is enhanced by adding several tens of locations within short distance of the locations of a grid or spatial coverage sample. A separate section describes sampling methods for detecting and for delineating hot spots.
|Title of host publication||Dealing with Contaminated Sites. From Theory towards Practical Application|
|Number of pages||1114|
|Publication status||Published - 2011|
Brus, D. J. (2011). Statistical sampling strategies for survey of soil contamination. In F. A. Swartjes (Ed.), Dealing with Contaminated Sites. From Theory towards Practical Application (pp. 165-206). Springer. http://link.springer.com/chapter/10.1007%2F978-90-481-9757-6_4