Improved Regression Kriging Prediction of the Spatial Distribution of the Soil Cadmium by Integrating Natural and Human Factors

Abstract

Mastering the spatial distribution of heavy metals in the soil plays an important supporting role in the scientific formulation of soil pollution risk management and control strategies. Few factors were considered and multiple collinearity between parallel variables existed,resulting in low prediction accuracy. OK (common Kriging method), NRK (regressive Kriging method based on natural factors only), and NARK (regressive Kriging based on natural-human factors)were used to simulate the spatial distribution of soil Cd by selecting 23 natural-artificial influencing factors. The prediction accuracy was evaluated based on an empirical study of the area around Shaoguan Qujiang smelter. The results showed that the above-standard rate of soil cadmium in this area reached 85.93%, and the effect on the spatial heterogeneity of soil cadmium was shown as air emissions from smelters > air emissions from steel plants > pH > organic matter > Euclidean distance to road > Euclidean distance to river. The root-mean-square error and average absolute error of NARK's prediction results for soil cadmium were 26.86% and 30.56% lower than that of the OK method, respectively. The model determination coefficient R2 increased from 0.78 to 0.88. Compared with that of NRK, it was reduced by 24.15% and 24.23% and R2 increased from 0.81 to 0.88. The NRK combining natural and human factors significantly improved the simulation accuracy of the spatial distribution of soil cadmium, and the addition of human factors as auxiliary variables, especially atmospheric point source pollution emissions, greatly contributed to the improvement of the model accuracy.