中文关键词土壤重金属空间分布LUR模型金坛区 英文关键词soilheavy metalsspatial distributionland use regression (LUR) modelJintan District

作者	单位	E-mail
曾菁菁	南京大学地理与海洋科学学院, 南京 210023 国土资源部海岸带开发与保护重点实验室, 南京 210008	zjjclassic@163.com
沈春竹	江苏省土地勘测规划院, 南京 210008 国土资源部海岸带开发与保护重点实验室, 南京 210008
周生路	南京大学地理与海洋科学学院, 南京 210023 国土资源部海岸带开发与保护重点实验室, 南京 210008	zhousl@nju.edu.cn
陆春锋	南京大学地理与海洋科学学院, 南京 210023 南京南源土地开发利用咨询有限公司, 南京 210008
金志丰	江苏省土地勘测规划院, 南京 210008 国土资源部海岸带开发与保护重点实验室, 南京 210008
朱雁	南京南源土地开发利用咨询有限公司, 南京 210008

中文摘要 以江苏省常州市金坛区为例，借鉴传统LUR模型思路，考虑土壤重金属的源汇关系，加入土壤属性因子，构建LUR-S模型模拟预测了研究区土壤重金属含量空间分布，并与传统LUR模型及普通克里格插值模型结果进行对比，结果表明：①研究区土壤重金属含量受到以土地利用为主的源因子及反映重金属在土壤中赋存环境的汇因子的共同影响.就源影响因子而言，土壤Cu、Zn含量分别与2000 m缓冲区内交通用地面积、2000 m缓冲区内城市建设用地面积极显著相关（P<0.01）；就汇影响因子而言，土壤Cr、Cu、Zn含量与OM、C_org、TC、TN极显著相关（P<0.01）.②研究区土壤重金属Pb、Cr、Cu、Zn空间分布预测的LUR-S模型方程R²较传统LUR模型分别提高了0.041、0.406、0.102、0.501，精度检验R²较普通克里格插值模型分别提高了0.1477、0.0116、0.2310、0.081，RMSE较普通克里格插值分别减少了2.413、0.631、1.112、2.138，表明考虑了源汇关系的LUR-S模型预测精度高于传统LUR模型和普通克里格插值模型；③LUR-S模型对污染较低、变异较小重金属空间分布预测的适用性较好，而对污染较高、变异较大重金属则较差. 英文摘要 Using the Jintan District of Changzhou City, Jiangsu Province as an example, the LUR model was used to study the spatial distribution of heavy metals and to simulate the spatial distribution of heavy metals in the study area. Compared with the traditional LUR model and the ordinary Kriging interpolation model, the following conclusions were obtained. ① The soil heavy metal content in the study area was highly and significantly correlated with land factors, with the main factor of land use and influencing factors of heavy metals in the soil environment (P<0.01). In terms of influencing factors, the soil Cu and Zn contents were significantly correlated with the area related to traffic in a 2000 m buffer area and 2000 m buffer zone, respectively. The soil Cr, Cu, and Zn contents were significantly correlated with OM, C_org, TC, and TN (P<0.01). ② The R² of the LUR-S models of the spatial distribution of the heavy metals, Pb, Cr, Cu, and Zn, in the study area were improved by 0.041, 0.406, 0.102, and 0.501, respectively, compared with the traditional LUR model. The accuracy test R² values were improved by 0.1477, 0.0116, 0.2310, and 0.081, respectively; and the RMSE was reduced by 2.413, 0.631, 1.112, and 2.138, respectively. It was shown that the LUR-S model, which considered the source-sink relationship, had a higher accuracy than the traditional LUR model and ordinary Kriging interpolation model. ③ The LUR-S model was more suitable for the prediction of the spatial distribution of heavy metals with lower pollution and smaller variations, while results for the prediction of the heavy metals with higher pollution and larger variations were worse.

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