Rapid Determination of Low Heavy Metal Concentrations in Grassland Soils around Mining Using Vis-NIR Spectroscopy: A Case Study of Inner Mongolia, China.

Aru Han,Jiquan Zhang,Yongbin Bao,Song Qing,Xiaoling Lu,Yuhai Bao,Xingpeng Liu,Qing Ma

doi:10.3390/s21093220

Abstract

Proximal sensing offers a novel means for determination of the heavy metal concentration in soil, facilitating low cost and rapid analysis over large areas. In this respect, spectral data and model variables play an important role. Thus far, no attempts have been made to estimate soil heavy metal content using continuum-removal (CR), different preprocessing and statistical methods, and different modeling variables. Considering the adsorption and retention of heavy metals in spectrally active constituents in soil, this study proposes a method for determining low heavy metal concentrations in soil using spectral bands associated with soil organic matter (SOM) and visible–near-infrared (Vis–NIR). To rapidly determine the concentration of heavy metals using hyperspectral data, partial least squares regression (PLSR), principal component regression (PCR), and support vector machine regression (SVMR) statistical methods and 16 preprocessing combinations were developed and explored to determine an optimal combination. The results showed that the multiplicative scatter correction and standard normal variate preprocessing methods evaluated with the second derivative spectral transformation method could accurately determine soil Cr and Ni concentrations. The root-mean-square error (RMSE) values of Vis–NIR model combinations with PLSR, PCR, and SVMR were 0.34, 3.42, and 2.15 for Cr, and 0.07, 1.78, and 1.14 for Ni, respectively. Soil Cr and Ni showed strong spectral responses to the Vis–NIR spectral band. The R2 value of the Vis–NIR-based PLSR model was higher than 0.99, and the RMSE value was 0.07–0.34, suggesting higher stability and accuracy. The results were more accurate for Ni than Cr, and PLSR showed the best performance, followed by SVMR and PCR. This perspective has critical implications for guiding quantitative biogeochemical analysis using proximal sensing data.

Highlights

Coal mining promotes local economies, it causes serious environmental pollution [1,2,3]
In order to explore the most suitable model combination for determination, 201 absorption spectral bands associated with soil organic matter (SOM) and 2150 Vis–NIR spectral bands were extracted as independent variables to establish the estimation model, considering partial least squares regression (PLSR), principal component regression (PCR), and support vector machine regression (SVMR) for soil Cr and Ni concentrations
This study evaluated three preprocessing methods (NOR, multiplicative scatter correction (MSC), and standard normal variate (SNV)), three spectral transformations (FD, Second Derivative (SD), and LOG), and three statistical methods (PLSR, PCR, and SVMR)

Summary

Introduction

Coal mining promotes local economies, it causes serious environmental pollution [1,2,3]. Heavy metals in coal and coal spoil can enter soil through various routes, leading to the contamination of soil around mining areas [4,5]. Soil heavy metal contamination increases food safety risks, and directly threatens human health [6]. Heavy metals in the human body can undergo a latent accumulation process, and when their content exceeds the maximum capacity of the human body, various diseases may arise. Heavy metal poisoning increases the likelihood of liver, kidney, stomach, and nerve tissue damage, leading to teratogenesis, carcinogenesis, and mutagenesis, in serious cases. With increasing focus on environmental issues and ecological conservation, the real-time monitoring of soil around mining areas has become an urgent requirement

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Conclusion