融合自然-人为因子改进回归克里格对土壤镉空间分布预测

掌握土壤重金属的空间分布对于科学制定土壤污染风险管控策略具有重要支撑作用.针对目前重金属空间模拟较少考虑影响因素且平行变量间存在多重共线性，导致预测精度较低问题，选取自然-人为的23个影响因素，采用OK（普通克里格法）、NRK（仅基于自然因子的回归克里格法）和NARK（基于自然-人为因子的回归克里格法）对土壤镉空间分布进行模拟，评估预测精度，以冶炼厂周边区域实证研究.结果表明：该区土壤镉点位超标率达85.93%，对土壤镉空间异质性的影响表现为冶炼厂大气排放 > 钢铁厂大气排放 > pH > 有机质 > 与道路的欧氏距离 > 与河流的欧氏距离.NARK对土壤镉预测结果的均方根误差和平均绝对误差较OK法分别降低26.86%和30.56%，模型决定系数R²由0.78提升到0.88；较NRK分别降低24.15%和24.23%，R²由0.81提升到0.88.融合自然和人为因素的回归克里格模型明显提高了土壤镉空间分布模拟精度，增加人为因素作为辅助变量对模型精度的提升贡献很大，尤其是大气点源污染排放.

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

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.