Beijing Key Laboratory of Remediation of Industrial Pollution Sites, Environmental Protection Research Institute of Light Industry, Beijing, 100089, China.
Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing, 100101, China.
Environ Geochem Health. 2021 Jan;43(1):23-36. doi: 10.1007/s10653-020-00673-5. Epub 2020 Jul 22.
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.
工业场地土壤污染是中国乃至世界大多数地区的一个重要问题。准确预测污染工业场地污染物的空间分布是大多数土壤修复策略制定的要求，通常采用空间插值方法。然而，污染物空间分布的显著和突然变化会降低预测精度。在本研究中，应用分区插值方法对污染场地四个土层中的苯并芘进行了研究，以确定其与非分区方法相比提高预测精度的能力。所研究的分区插值方法包括反距离加权（IDW）、径向基函数（RBF）和普通克里金（OK）。比较了三种分区插值方法的预测结果，确定了分区插值的适用性。与非分区插值相比，分区插值方法的预测误差降低了 70%。基于 IDW 的分区插值的预测精度高于基于 RBF 和 OK 的分区插值技术，适用于识别高污染区。分区插值也适用于可检测的 12 种受美国环保署控制的其他 PAHs，预测效果也可以验证这种插值选择。此外，结果还表明，最大浓度与“规范”偏差越大，插值模型的平滑效应引起的预测误差越大。这些结果表明，基于 IDW 方法的分区插值可以有效地用于获得相对准确的空间污染物分布信息，并识别高污染区。
