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基于改进LUR模型的区域土壤重金属空间分布预测
摘要点击 480  全文点击 158  投稿时间:2017-04-04  修订日期:2017-07-07
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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  soil  heavy metals  spatial distribution  land use regression (LUR) model  Jintan 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、Corg、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, Corg, 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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