Comparing and optimizing spatial interpolation methods for predicting polychlorinated biphenyls (PCBs) distribution in soil&#160;
Authors: Sun Wen , Liu Ao , Qu Chengkai 
Published: March 2024

Polychlorinated biphenyls (PCBs) are a group of chemically synthesized persistent organic pollutant, which consist of 209 individual chlorinated congeners depending on the chlorine substitution on the biphenyl rings (Kraft et al., 2017). Because of the hydrophobicity of PCBs, they are strongly adsorbed in soil organic matter (Melnyk et al., 2015), and thus soil is a primary reservoir of PCBs, and it serves as the emitter to re-release these pollutants to the atmosphere (Xu et al., 2019). Spatial interpolation is the process of using existing measurements to estimate the required values of other unknown locations (Comber and Zeng, 2019). So far, many studies are focused primarily on the comparison of several different interpolation methods for predicting potentially toxic elements (Ding et al., 2018), heavy metals (Wen et al., 2022; Xie et al., 2011), and soil organic matter and nutrients (e.g. nitrogen, phosphorus and potassium) (Shen et al., 2019). However, there is limited literature regarding the spatial interpolation of organic pollutants, especially PCBs. The study presented the comparison of some classic interpolation methods including inverse distance weighting (IDW) and, radical basis function (RBF), global polynomial interpolation (GPI), local polynomial interpolation (LPI), and many different types of kriging methods such as ordinary kriging (OK), simple kriging (SK), universal kriging (UK), disjunctive kriging (DK) and empirical Bayesian kriging (EBK) using a high-resolution soil monitoring database. The results showed that EBK has the highest accuracy for predicting the total PCB concentration, while root mean squared error (RMSE) in IDW is among the highest in these interpolation methods. The logarithmic transformation of non-normally distributed data contributed to enhance considerably the semivariogram for modeling in kriging interpolation. The increasing of search neighborhood reduced IDW&#8217;s RMSE, but slightly affected in OK, while both of them resulted in over smooth of prediction map. The existence of outliers made the difference between two points increase sharply, and thereby weakening spatial autocorrelation and decreasing the accuracy. As predicted error increased continuously, the prediction accuracy of different interpolation methods reached unanimity gradually. The attempt of the assisted interpolation algorithm did not significantly improve the prediction accuracy of the IDW method. This study constructed a standardized workflow for interpolation, which could reduce human error to reach higher interpolation accuracy for mapping soil-borne PCBs.
References
Comber, A., Zeng, W., 2019. Geogr. Compass 13, e12465.
Ding, Q., Wang, Y., Zhuang, D., 2018. J. Environ. Manage. 212, 23-31.
Kraft, M., Rauchfuss, K., Sievering, S., W&#246;ckner, M., Neugebauer, F., Fromme, H., 2017.&#160; Int. J. Hyg. Environ. Health. 220, 201-208.
Melnyk, A., Dettlaff, A., Kukli&#324;ska, K., Namie&#347;nik, J., Wolska, L., 2015.&#160; Sci. Total Environ. 530-531, 18-27.
Shen, Q., Wang, Y., Wang, X., Liu, X., Zhang, X., Zhang, S., 2019. Catena 174, 59-72.
Wen, L., Zhang, L., Bai, J., Wang, Y., Wei, Z., Liu, H., 2022. Chemosphere 309, 136789.
Xie, Y., Chen, T.B., Lei, M., Yang, J., Guo, Q.J., Song, B., Zhou, X.Y., 2011. Chemosphere 82, 468-476.
Xu, C., Niu, L., Zou, D., Zhu, S., Liu, W., 2019. Sci. Total Environ. 684, 486-495.