A detection method of trace metal elements in black soil based on hyperspectral technology: Geological implications
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摘要: 在土壤中重金属含量较低的情况下，重金属的高光谱特征响应非常微弱，不易构建精确的高光谱直接反演模型。为了解决上述问题，依据土壤化学变量间的理化性质，将重金属富集特征转移到与之相关的化学主量元素上，使重金属微弱的信息得以间接定量反演。文中以海伦市黑土土壤为研究对象，通过主成分分析、聚类分析确定了主量元素氧化铁（Fe2O3）与微量重金属As、Zn、Cd之间存在明显吸附赋存关系。选用偏最小二乘法构建了研究区氧化铁含量的最佳反演模型（决定系数为0.704，均方根误差为0.148，F检验为12.732），并利用氧化铁与As、Zn、Cd之间的赋存关系，通过神经网络构建了氧化铁预测值与重金属真实值间的非线性拟合模型，得出As含量的拟合程度最高，Zn的拟合程度较好，Cd的拟合效果较理想，总体相关性分别为0.796、0.732、0.530。研究结果表明，基于氧化铁含量的间接预测模型能对微量重金属As、Zn、Cd进行较好的定量预测，为微量重金属含量的定量分析提供了新的方法参考，为高光谱遥感技术预测土壤重金属含量提供了依据，增强了土壤微量重金属反演可行性，对细化自然资源质量监测、深化开展地学系统综合分析与评价有重要意义。Abstract: In the case of low content of heavy metals in soil, the hyperspectral characteristic response of heavy metals is very weak, so it is difficult to construct an accurate direct hyperspectral inversion model. In order to solve the above problems, according to the physical and chemical properties of soil chemical variables, the enrichment characteristics of heavy metals are transferred to the related major chemical elements, so that the weak information of heavy metals can be indirectly quantitatively inverted. In this paper, the black soil in Hailun was taken as the research object. Through principal component analysis and cluster analysis, it was confirmed that there was an obvious adsorption occurrence relationship between the major element iron oxide (Fe2O3) and trace heavy metals As, Zn, Cd. The best inversion model of iron oxide content in the study area was established by partial least square method (the determination coefficient is 0.704, the root mean square difference is 0.148, and the F-test is 12.732). Based on the occurrence relationship between iron oxide and As, Zn, CD, a nonlinear fitting model between the predicted value of iron oxide and the real value of heavy metals was constructed by neural network. The fitting results show that the fitting degree of As, Zn and Cd is As>Zn>Cd. The overall correlations are 0.796, 0.732, 0.530 respectively. The study results show that the indirect prediction model based on iron oxide content can better quantitatively predict As, Zn and Cd, which provides a new method for the quantitative analysis of trace heavy metal content. This model provides a basis for hyperspectral remote sensing technology to predict soil heavy metal content, enhances the feasibility of soil trace heavy metal inversion, and is helpful to refine the quality monitoring of natural resource. It is of great significance to deepen the comprehensive analysis and evaluation of geoscience system.
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Key words:
- heavy metal /
- soil /
- occurrence relationship /
- hyperspectral /
- geochemistry /
- nonlinear fitting model /
- indirect prediction /
- neural networks
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图 2 土壤化学变量聚类分析树状图
Figure 2. Cluster analysis tree diagram of the selected chemical variables in soil
图 3 土壤原始反射波谱与粘土矿物波谱特征对比图
Figure 3. Comparison of original reflection spectrum of soil and spectrum of clay minerals
图 4 基于PLSR模型的氧化铁预测值与实测值散点图
Figure 4. Scatter plot of predicted and measured values of iron oxide based on PLSR model
图 5 重金属As、Zn、Cd含量拟合曲线模型及预测结果精度评价
Figure 5. Fitting curve model for heavy metal contents of As, Zn and Cd and accuracy evaluation of the predicted results
表 1 土壤重金属含量的统计特征
Table 1. Statistical characteristics of heavy metal contents in soil
重金属
Heavy Metal最小值
Min/(mg/kg)最大值
Max/(mg/kg)均值
Mean/(mg/kg)标准差
Standard Deviation(SD)/(mg/kg)变异系数
Coefficient of Variation(CV)/%单项污染指数
Average of Pollution index (Pi)背景值
(张慧等，2018)
Background Values (BV)/(mg/kg)GB 15618-2018
风险筛选值/(mg/kg)Cd镉 0.065 0.123 0.089 0.007 0.079 0.297 0.078 0.3 As砷 5.136 10.703 8.164 0.764 0.094 0.204 9.282 40 Hg汞 0.020 0.089 0.029 0.010 0.345 0.016 0.016 1.8 Cr铬 52.549 124.378 72.930 13.025 0.179 0.486 50.583 150 Cu铜 13.006 71.859 17.794 6.083 0.342 0.356 18.683 50 Pb铅 16.100 68.741 20.225 8.513 0.421 0.225 22.652 90 Ni镍 20.171 89.038 29.238 9.781 0.335 0.418 24.037 70 Zn锌 46.469 80.848 58.668 6.579 0.112 0.293 57.112 200 注：1.样本数目Number=111；2.CV=SD/Mean；3.平均污染指数Pi=Mean/评价标准值，“评价标准值”(GB15618－1995)；4.风险筛选值选取(pH：5.5＜pH≤7.5)为评价标准值 
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表 2 土壤化学变量的Pearson相关系数矩阵
Table 2. Pearson correlation coefficient matrix for the selected chemical variables in soil
土壤化学变量
(Soilchemicalvariable)Cd As Hg Cr Cu Pb Ni Zn pH SOM Fe2O3 Cd 1 As 0.398** 1 Hg 0.046 0.043 1 Cr 0.210* 0.302** 0.427** 1 Cu 0.029 0.203* 0.153 0.336** 1 Pb -0.014 -0.006 0.201* 0.355** 0.450** 1 Ni 0.085 0.134 0.583** 0.737** 0.253* 0.178 1 Zn 0.263** 0.473** 0.128 0.351** 0.337** 0.064 0.245** 1 pH 0.183 -0.012 -0.060 -0.061 -0.068 -0.037 -0.087 0.083 1 SOM 0.071 0.019 0.070 -0.015 0.103 0.014 0.087 0.298** 0.296** 1 Fe2O3 0.417** 0.762** 0.158 0.376** 0.345** 0.112 0.195* 0.647** 0.004 0.157 1 注：**—在0.01水平上显著相关；*—在0.05水平上显著相关
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表 3 土壤化学变量的主成分分析结果
Table 3. Principal component analysis of the selected chemical variables in soil
土壤化学变量
Soil chemical variable旋转前矩阵Component matrix 旋转后矩阵Rotated component matrix PC1 PC2 PC3 PC4 PC1 PC2 PC3 PC4 Cd 0.386 0.480 0.068 -0.276 0.079 0.628 -0.199 0.141 As 0.589 0.555 -0.297 -0.208 0.060 0.875 0.029 -0.126 Hg 0.743 -0.555 0.217 -0.251 0.979 0.067 0.080 0.011 Cr 0.814 -0.335 0.007 -0.092 0.792 0.285 0.269 -0.057 Cu 0.537 -0.059 -0.234 0.614 0.151 0.223 0.807 0.015 Pb 0.343 -0.277 -0.206 0.680 0.136 -0.076 0.822 -0.017 Ni 0.743 -0.555 0.217 -0.251 0.979 0.067 0.080 0.011 Zn 0.661 0.420 0.062 0.095 0.184 0.684 0.236 0.261 Fe2O3 0.712 0.548 -0.214 -0.019 0.108 0.891 0.215 0.017 pH -0.018 0.326 0.702 0.165 -0.085 0.039 -0.123 0.777 SOM 0.205 0.244 0.685 0.328 0.061 0.090 0.118 0.807 特征值 3.655 1.988 1.296 1.238 2.652 2.579 1.583 1.363 方差贡献率/% 33.226 18.072 11.783 11.254 24.109 23.442 14.391 12.392 累计方法贡献率/% 33.226 51.298 63.081 74.334 24.109 47.551 61.942 74.334
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表 4 土壤化学变量相关性分类表
Table 4. Correlation table of chemical variables in soil
组别
Group土壤化学变量
Soil chemical variable个数
Number第一类 Cd、As、Zn、氧化铁 4 第二类 Hg、Cr、Ni 4 第三类 Cu、Pb 2 第四类 pH、SOM 2
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表 5 土壤氧化铁含量高光谱反演模型对比分析
Table 5. Comparative analysis of hyperspectral inversion models for iron oxide content in soil
模型
Model波谱变换
Spectral index决定系数
R2均方根误差
RMSEMLSR CR 0.443 3.311 FDR 0.569 1.980 BP CR 0.314 1.769 FDR 0.632 2.750 PLSR CR 0.704 0.148 FDR 0.223 0.240
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表 6 基于偏最小二乘法(PLSR)的土壤化学变量高光谱反演模型精度评价表
Table 6. Accuracy evaluation table of the hyperspectral inversion model of chemical variables in soil based on PLSR
土壤化学变量
Soil chemical variable光谱变换
Spectral index自变量所在波段
Independent variable band预测样本集Training：89 验证样本集Validation：22 RMSE R2 F RMSE R2 RPD Cd CR 2246、1678、356、1680 0.008 0.184 5.680 0.007 0.012 0.939 FDR 2204、1438、2490、1767、366、356、1376、2495 0.006 0.421 6.390 0.009 0.001 1.317 As CR 2484、371、1679 0.709 0.237 8.811 0.543 0.110 1.152 FDR 407、1642、382、393 0.672 0.316 9.695 0.629 0.031 1.216 Zn CR 2331、2356 6.622 0.112 5.394 5.245 0.140 1.070 FDR 377、363、366 7.075 0.073 4.702 5.285 0.140 0.999 Fe2O3 CR 371、2475、2331、2362、2398、2438、2447、2417、2414、2382、2452、2207、2192、2418 0.148 0.704 12.723 0.110 0.657 1.700 FDR 377、1642、1087、447 0.240 0.223 6.014 0.343 0.016 1.141
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