农用地土壤重金属污染调查最优网格尺度及布点优化方法

Optimal grid scale and sampling design optimization method for heavy metal pollution investigation in farmland soil

  • 摘要: 为探索土壤重金属污染调查最优网格尺度和布点优化方法,在中南地区某老工业园区附近农田进行了土壤采样分析,通过构建70 m×70 m、100 m×100 m、160 m×160 m和200 m×200 m 4种网格,采用Matheron矩估计法试验和地统计学方法,对土壤中镉(Cd)的污染空间位置估计精度进行分析验证。结果表明,70 m×70 m和100 m×100 m网格尺度下污染区域空间位置的估计精度相似,且高于160 m×160 m和200 m×200 m。对100 m×100 m网格下调查结果不确定区进行加密布点,污染区域空间位置估计精度由78.89%提高至86.25%,相对于70 m×70 m网格,调查点位数量减少了35%,有效提高了污染区域空间位置估计精度,大幅降低了采样及分析成本。

     

    Abstract: In order to explore the optimal grid scale and sampling design optimization method for soil heavy metal pollution investigation, soil samples were collected and analyzed in farmland near an old industrial park in central and southern China. Four meshes of different scales, including 70 m×70 m, 100 m×100 m, 160 m×160 m and 200 m×200 m, were constructed, and the estimation accuracy of Cd polluted spatial location in soil was analyzed and verified by estimation test of Matheron moment estimation and geostatistical method. The results indicated that the estimation accuracy of polluted spatial location was similar for 70 m×70 m and 100 m×100 m grid, and higher than 160 m×160 m and 200 m×200 m grid. By encrypting the sampling points on the uncertain region of 100 m×100 m grid, the estimation accuracy of polluted spatial location increased from 78.89% to 86.25%, and the number of investigation samples decreased by 35% for 70 m×70 m grid, not only reducing the cost of sampling and testing, but also improving estimation accuracy of polluted spatial location effectively.

     

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