Optimizing spatial interpolation method and sampling number for predicting cadmium distribution in the largest shallow lake of North China.

Cadmium (Cd) pollution has been widely recognized in lake ecosystems. Although the accurate prediction of the spatial distributions of Cd in lakes is important for controlling Cd pollution, the traditional monitoring methods of setting discrete and limited sampling points cannot actually reflect the continuous spatial distribution characteristics of Cd. In this study, we set up 93 sampling points in Baiyangdian Lake (BYDL), and collected surface water, overlying water and sediment samples from each sampling point. Cd contents were measured to predict their spatial distributions in different environmental components by three interpolation methods, inverse distance weighted (IDW), radial basis function (RBF) and ordinary kriging (OK), and the effects of different sampling numbers on the interpolation accuracy were also assessed to optimize the interpolation method and sampling number. The results showed that the interpolation accuracy of IDW decreased with increasing power values. The best basis function for RBF was IMQ, and the best semivariogram models for OK were the spherical model and stable model. The best interpolation method for the waters and sediments was RBF-IMQ compared with OK and IDW. Within the sampling number range of 50-93, the interpolation accuracy for Cd in surface water increased with the increase in sampling number. Comparatively, the interpolation accuracy was the highest for overlying water and sediments when the sampling number was 60. The findings of this work provide a combined sampling and spatial interpolation method for monitoring the spatial distribution and pollution levels of Cd in the waters and sediments of shallow lakes.