Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy
Estimate of heavy metal contamination in soils after a mining accident using reflectance spectroscopy
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DOI：
10.1021/es015747j
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发表时间：
2002-06-15
影响因子：
11.4
通讯作者：
Sommer, S
中科院分区：
文献类型：
--
作者：
Kemper, T;Sommer, S
探讨了采用化学计量学方法定量估算矿难污染土壤中重金属的可能性。1998年4月，西班牙阿兹纳尔科尔一个尾矿库的大坝坍塌，4000多公顷的土地被高浓度重金属污染的黄铁矿污泥淹没。在第一次修复运动结束六个月后，收集了土壤样本，用于化学分析和测量可见光到近红外的反射率(0.35-2.4微米)。砷、镉、铜、铁、汞、铅、S、锑和锌的浓度远高于背景值。通过逐步多元线性回归分析(MLR)和人工神经网络(ANN)方法实现了对重金属的预测。它有可能以高精度预测九种元素中的六种。预测浓度与化学分析浓度之间的最佳R2分别为：As，0.84；Fe，0.72；Hg，0.96；Pb0.95；S，0.87；Sb，0.93。镉(0.51)、铜(0.43)和锌(0.24)的测定结果差异不显著。MLR和ANN都取得了类似的结果。相关分析表明，对预测最重要的波长可以归因于铁和铁氧化物的吸收特征。这些结果表明，利用快速、经济的反射光谱技术预测被采矿废渣污染的土壤中的重金属是可行的。
The possibility to adapt chemometrics approaches for the quantitative estimation of heavy metals in soils polluted by a mining accident was explored. In April 1998, the dam of a mine tailings pond in Aznalcollar (Spain) collapsed and flooded an area of more than 4000 ha with pyritic sludge contaminated with high concentrations of heavy metals. Six months after the end of the first remediation campaign, soil samples were collected for chemical analysis and measurement of visible to near-infrared reflectance (0.35-2.4 mum). Concentrations for As, Cd, Cu, Fe, Hg, Pb, S, Sb, and Zn were well above background values. Prediction of heavy metals was achieved by stepwise multiple linear regression analysis (MLR) and an artificial neural network (ANN) approach. It was possible to predict six out of nine elements with high accuracy. Best R 2 between predicted and chemically analyzed concentrations were As, 0.84; Fe, 0.72; Hg, 0.96; Pb, 0.95; S, 0.87; and Sb, 0.93. Results for Cd (0.51), Cu (0.43), and Zn (0.24) were not significant. MLR and ANN both achieved similar results. Correlation analysis revealed that most wavelengths important for prediction could be attributed to absorptions features of iron and iron oxides. These results indicate that it is feasible to predict heavy metals in soils contaminated by mining residuals using the rapid and cost-effective reflectance spectroscopy.