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Application of VNIR and machine learning technologies to predict heavy metals in soil and pollution indices in mining areas

  • Soils, Sec 5 • Soil and Landscape Ecology • Research Article
  • Published:
Journal of Soils and Sediments Aims and scope Submit manuscript

Abstract

Purpose

Soil pollution indices are an effective tool in the computation of metal contamination in soil. They monitor soil quality and ensure future sustainability in agricultural systems. However, calculating a soil pollution index requires laboratory measurements of multiple soil heavy metals, which increases the cost and complexity of evaluating soil heavy metal pollution. Visible and near-infrared spectroscopy (VNIR, 350–2500 nm) has been widely used in predicting soil properties due to its advantages of a rapid analysis, non-destructiveness, and a low cost.

Methods

In this study, we evaluated the ability of the VNIR to predict soil heavy metals (As, Cu, Pb, Zn, and Cr) and two commonly used soil pollution indices (Nemerow integrated pollution index, NIPI; potential ecological risk index, RI). Three nonlinear machine learning techniques, including cubist regression tree (Cubist), Gaussian process regression (GPR), and support vector machine (SVM), were compared with partial least squares regression (PLSR) to determine the most suitable model for predicting the soil heavy metals and pollution indices.

Results

The results showed that the nonlinear machine learning models performed significantly better than the PLSR model in most cases. Overall, the SVM model showed a higher prediction accuracy and a stronger generalization for Zn (R2V = 0.95, RMSEV = 6.75 mg kg−1), Cu (R2V = 0.95, RMSEV = 8.04 mg kg−1), Cr (R2V = 0.90, RMSEV = 6.57 mg kg−1), Pb (R2V = 0.86, RMSEV = 4.14 mg kg−1), NIPI (R2V = 0.93, RMSEV = 0.31), and RI (R2V = 0.90, RMSEV 3.88). In addition, the research results proved that the high prediction accuracy of the three heavy metal elements Cu, Pb, and Zn and their significant positive correlations with the soil pollution indices were the reason for the accurate prediction of NIPI and RI.

Conclusion

Using VNIR to obtain soil pollution indices quickly and accurately is of great significance for the comprehensive evaluation, prevention, and control of soil heavy metal pollution.

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Acknowledgements

We would like to thank Dr. Xianzhang Pan and Dr. Changkun Wang of ISSAS for their help in VNIR spectroscopy analysis. Many thanks also go to the anonymous reviewers whose constructive comments helped improve this paper.

Funding

This work was supported by the Strategic Priority Research Program of the Chinese Academy of Sciences (XDA28100500) and the National Natural Science Foundation of China (41771253) .

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Authors and Affiliations

  1. State Key Laboratory of Soil and Sustainable Agriculture, Institute of Soil Science, Chinese Academy of Sciences, 71 East Beijing Road, Nanjing, 210008, China

    Yingyi Wang, Yongcun Zhao & Shengxiang Xu

  2. University of Chinese Academy of Sciences, Beijing, 100049, China

    Yingyi Wang, Yongcun Zhao & Shengxiang Xu

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  1. Yingyi Wang
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  2. Yongcun Zhao
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Correspondence to Shengxiang Xu.

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Wang, Y., Zhao, Y. & Xu, S. Application of VNIR and machine learning technologies to predict heavy metals in soil and pollution indices in mining areas. J Soils Sediments 22, 2777–2791 (2022). https://doi.org/10.1007/s11368-022-03263-3

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  • DOI: https://doi.org/10.1007/s11368-022-03263-3

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