Study on the Method of Soil Sample Collection for Heavy Metal Content Analysis Considering the Influence of Human Factors —Taking Longkou as an Example
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摘要: 合理布置土壤采样点是全面掌握重金属空间变异特征及变化趋势的重要环节。目前在土壤重金属相关研究中还是以网格化均匀采样为主的传统采样。然而，这些方法未能充分考虑采样的成本和代表性，无法满足高精度空间分析方法要求。本文基于人为影响因素进行重金属采样布局，通过筛选区域范围内影响土壤重金属的人为影响因素，利用核密度方法对影响因素进行指标量化，以半变异方差为目标函数，通过变程分析采样点的采样方案。本文以龙口北部平原区为典型研究区，选择了工矿企业密度（DI）和道路密度（DR）两种重要的人为因素指标，寻求土壤Hg元素的最佳采样点采集方案，最后通过基于不同间距的采样点分析来验证本文提出的采样方案的有效性。结果表明（1）DI与Hg在研究区南部和东北部区域具有相似的空间分布趋势，DR与Hg在研究区南部和东南部区域的空间分布趋势相似程度较大；（2）DI和DR的有效变程分别为5819 m、6079 m，与土壤数据Hg的变程（6000 m）也较为相近；（3）为了验证人为因素指标所求变程可以作为土壤Hg采样距离参考值，设置了不同采样间距的Hg实际采样方案（3000 m、4000 m、5000 m、6000 m和7000 m），利用克里金插值精度（MSE、RMSDE）获取不同采样方案的重金属估测误差。对比不同采样间距发现，当采样间距 ≤ 6000 m时，不同采样间距之间的误差相差无几，而以7000 m作为采样间距时，误差却大幅增加。所以6000 m作为采样间距时，既能充分考虑采样的成本和代表性，也能满足高精度空间分析方法要求，而且这个数值与DI、DR所求变程（6000 m）相同，进一步证明了人为因素指标DI、DR所求变程具有Hg样本采集方案布局的参考价值。Abstract: Reasonable arrangement of soil sampling points is important to fully grasp the spatial variation characteristics and trends of heavy metals. At present, meshed uniform sampling is still the main method in soil heavy metal researches. However, these methods fail to fully consider the cost and representativeness of sampling and cannot meet the requirements of high-precision spatial analysis methods. In this paper, the heavy metal sampling layout was carried out based on human influencing factors. By screening the human influencing factors affecting soil heavy metals in the region, the spatial generalization of influencing factors was carried out by using the nuclear density method. With the optimal semi-variational variance as the objective function, the sampling scheme of sampling points was analyzed through the range of variation. The north plain area of Longkou was taken as a typical research area, and two important factors, namely industrial and mining enterprise density (DI) and road traffic density (DR), were selected as the prior knowledge to seek the optimal sampling point collection scheme for soil Hg element. Finally, the effectiveness of the proposed layout scheme was verified by sampling based on different intervals. The results showed that (1) DI and Hg showed similar spatial distribution trends in the southern and northeastern regions of the study area, while DR and Hg showed relatively similar spatial distribution trends in the southern and southeastern regions of the study area. (2) The effective range length of DI and DR was 5819 m and 6079 m, respectively, which was also similar to the range lengths of the verification data Hg (6000 m). (3) In order to verify the variation range of human factor index can be used as the reference value of soil Hg sampling distance, the actual Hg sampling scheme with different sampling spacing (3000 m, 4000 m, 5000 m, 6000 m and 7000 m) was set, and the Kriging interpolation accuracy (MSE, RMSDE) was used to obtain the estimation error of heavy metals under different sampling schemes. By comparing the errors of different sampling intervals, it is found that when the sampling interval is less than 6000 m, the errors of different sampling intervals are almost the same, but when the sampling interval is 7000 m, the errors increase significantly. Therefore, when 6000 m is used as the sampling interval, the cost and representativity of sampling can be fully considered and the requirements of high-precision spatial analysis method can be met. Moreover, this value is the same as the range (6000 m) calculated by DI and DR, which further proves that the range calculated by human factor indicators DI and DR has reference value for the layout of Hg sample collection scheme.
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Key words:
- Human factor /
- Heavy metal in soil /
- Nuclear density analysis /
- Semivariogram
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表 1 Hg的描述性统计特征
Table 1. Descriptive statistical characteristics of Hg
样本个数
Number of sample最大值（mg kg−1）
Maximum最小值（mg kg−1）
Minimum平均值（mg kg−1）
Mean标准差
Standard deviation偏度
Skewness峰度
Kurtosis变异系数（%）
Coefficient of variation125 0.0842 0.0115 0.0274 0.0121 2.29 10.31 44.16 
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表 2 不同尺度下DI和DR的半变异函数拟合参数值
Table 2. Parameter values of the theoretical variogram model of DI and DR in different scales
尺度（km）
Dimension人为因素
Humanfactor模型类型
Model type块金方差
Nugget variance基台值
Partial sill变程（m）
aRange残差
Residual决定系数
Determinationcoefficient比值
Ratio0.2 × 0.2 DI Gaussian 0.0333 0.1302 6252 0.0013 0.934 0.256 DR Gaussian 0.166 0.595 6131 0.0313 0.920 0.279 0.5 × 0.5 DI Gaussian 0.0326 0.1298 6114 0.0012 0.940 0.251 DR Gaussian 0.156 0.590 5958 0.0256 0.936 0.264 1.0 × 1.0 DI Gaussian 0.0337 0.1262 5819 0.0010 0.944 0.267 DR Gaussian 0.154 0.605 6079 0.0317 0.928 0.254 1.5 × 1.5 DI Gaussian 0.0311 0.1288 5924 0.0012 0.876 0.241 DR Gaussian 0.169 0.608 6166 0.0325 0.854 0.278 2.0 × 2.0 DI Gaussian 0.0317 0.135 6547 0.0032 0.954 0.235 DR Gaussian 0.155 0.526 5040 0.0532 0.912 0.295
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表 3 DI、DR、Hg半变异函数拟合模型的参数值
Table 3. Parameters of DI，DR and Hg semi-variance function models
模型类型
Model type块金方差
Nugget variance基台值
Partial sill变程（m）
aRange残差
Residual决定系数
Determinationcoefficient比值
RatioDI Gaussian 0.034 0.126 5819 0.0010 0.944 0.267 DR Gaussian 0.154 0.605 6079 0.0317 0.928 0.254 Hg Gaussian 0.016 0.032 6062 0.0002 0.619 0.498
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表 4 不同采样间距的克里金插值精度结果的指标比较
Table 4. Comparison of Kriging Interpolation Accuracy Results with Different Sample Spacing
间距（m） 标准化平均误差
MSE标准化均方根误差
RMSDE3000 0.1044 0.9750 4000 0.1185 0.9327 5000 0.1136 0.9036 6000 0.1154 0.9156 7000 0.3142 0.7337
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