Soil pollution at industrial sites is an important issue in China and in most other regions of the world. The accurate prediction of the spatial distribution of pollutants at contaminated industrial sites is a requirement for the development of most soil remediation strategies, and is commonly performed using spatial interpolation methods. However, significant and abrupt variations in the spatial distribution of pollutants decrease prediction accuracy. During this study, the use of partition interpolation methods was applied to benzo fluoranthene in four soil layers at a contaminated site to determine their ability to improve prediction accuracy in comparison to unpartitioned methods. The examined methods for partitioned interpolation included inverse distance weighting (IDW), radial basis function (RBF), and ordinary kriging (OK). The prediction results of the three methods for partitioned interpolation were compared, and the applicability of partition interpolation was determined. The prediction error associated with the partitioned interpolation methods decreased by 70% compared to unpartitioned interpolation. The prediction accuracy of IDW-based partition interpolation was higher than that of RBF- and OK-based partition interpolation techniques, and it was suitable for identification of highly polluted areas. Partition interpolation is also applicable to 12 other PAHs controlled by USEPA that can be detected, and the prediction effects could also verify this interpolation choice. In addition, the results also demonstrated that the more the maximum concentration deviated from the "norm", the greater the prediction error was caused by the smoothing effects of the interpolation models. These results suggest that the partition interpolation with IDW method can be effectively used to obtain relatively accurate spatial contaminant distribution information, and to identify highly polluted areas.
Keywords: Accuracy; Data partitioning; Effectiveness; Improvement degree; Industrial sites; Soil pollution.