Estimation of Heavy Metals in Tailings and Soils Using Hyperspectral Technology: A Case Study in a Tin-Polymetallic Mining Area

Bian, Zijin; Sun, Lina; Tian, Kang; Liu, Benle; Zhang, Xiao-Hui; Mao, Zhiqiang; Huang, Biao; Wu, Longhua

doi:10.1007/s00128-021-03311-7

Cited by 18 publications

(12 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…For the inversion of Cu in soils, the indirect inversion using Fe concentrations based on the backpropagation neural network (BPNN) model performed higher predictive accuracy than direct inversion using spectral characteristic bands of Cu [ 38 ]. Bian et al showed that the BPNN model was better than the PLSR model in retrieving Cu, Zn and Pb concentrations, while the optimal spectral transformation methods for these elements are different [ 3 ]. Normally, the predictive accuracy of inversion models could be reflected by the comparison between measured and predicted concentrations (and the derived spatial distribution maps) [ 19 , 38 ].…”

Section: Discussionmentioning

confidence: 99%

“…With the increasing human demand for mineral resources, the accumulation of mining waste and slag has not only degraded land resources but has also degraded the soil ecology around mining areas via heavy metal pollution [ 2 ]. Hyperspectral remote sensing technology provides an efficient approach to the in situ monitoring of soil heavy metal contents as it provides information with good timeliness, spectral resolution, and measurement range [ 3 , 4 , 5 ].…”

Section: Introductionmentioning

confidence: 99%

“…Strong correlations between heavy metals and soil components may lead to better measurement [ 31 , 32 , 33 , 34 ]. Due to the complex composition of clay and carbonate minerals, most studies have constructed models for estimating indirect inversion based on the concentrations of Fe elements or organic matter and their characteristic bands [ 3 , 9 , 35 , 36 ]. Xia et al analysed the correlation between eight types of heavy metal elements and organic matter concentrations, compared the locations of sensitive bands for different heavy metal elements and organic matter, and developed a partial least squares regression model for the inversion of heavy metal concentrations [ 37 ].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Estimating the Heavy Metal Contents in Entisols from a Mining Area Based on Improved Spectral Indices and Catboost

Fu,

Zhang,

Yuan

et al. 2024

Sensors

View full text Add to dashboard Cite

In the study of the inversion of soil multi-species heavy metal element concentrations using hyperspectral techniques, the selection of feature bands is very important. However, interactions among soil elements can lead to redundancy and instability of spectral features. In this study, heavy metal elements (Pb, Zn, Mn, and As) in entisols around a mining area in Harbin, Heilongjiang Province, China, were studied. To optimise the combination of spectral indices and their weights, radar plots of characteristic-band Pearson coefficients (RCBP) were used to screen three-band spectral index combinations of Pb, Zn, Mn, and As elements, while the Catboost algorithm was used to invert the concentrations of each element. The correlations of Fe with the four heavy metals were analysed from both concentration and characteristic band perspectives, while the effect of spectral inversion was further evaluated via spatial analysis. It was found that the regression model for the inversion of the Zn elemental concentration based on the optimised spectral index combinations had the best fit, with R2 = 0.8786 for the test set, followed by Mn (R2 = 0.8576), As (R2 = 0.7916), and Pb (R2 = 0.6022). As far as the characteristic bands are concerned, the best correlations of Fe with the Pb, Zn, Mn and As elements were 0.837, 0.711, 0.542 and 0.303, respectively. The spatial distribution and correlation of the spectral inversion concentrations of the As and Mn elements with the measured concentrations were consistent, and there were some differences in the results for Zn and Pb. Therefore, hyperspectral techniques and analysis of Fe elements have potential applications in the inversion of entisols heavy metal concentrations and can improve the quality monitoring efficiency of these soils.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Estimating the Heavy Metal Contents in Entisols from a Mining Area Based on Improved Spectral Indices and Catboost

Fu,

Zhang,

Yuan

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…In order to give more precise monitoring, hyperspectral data processing is introduced based on multispectral data processing technology. However, at present, most use the traditional methods such as partial least squares regression (PLSR) or back propagation neural network (BPNN) assessment using hyperspectral data of sampling points to establish prediction models (Bian et al, 2021; Entezari et al, 2013; Sanchez et al, 2020). In addition, Riaza et al (2012) have only used the Hyperion hyperspectral data and Endmember Spectrum Library to establish spectral angle mapper for mineral mapping of tailings ponds.…”

Section: Introductionmentioning

confidence: 99%

Satellite‐air‐ground integrated multi‐source earth observation and machine learning processing brain for tailings reservoir monitoring and rapid emergency response

Wan

et al. 2023

Land Degrad Dev

View full text Add to dashboard Cite

Tailings reservoirs are an inevitable part of the production process of metal mines and are typical of post‐mining and post‐industrial lands. Tailings reservoirs are often located in mountainous areas so earth observation and remote sensing is a necessary monitoring means. However, existing observation are generally based on satellite, air or ground platforms, Combined satellite‐air‐ground observation is rare, and the coordination ability of processing methods to cope with it is insufficient. Therefore, the response to sudden major environmental and disaster events is slow and lagging. In this study, inspired by the human brain, a satellite‐air‐ground integrated multi‐source earth observation and machine learning processing system was built up for tailings reservoir stereoscopic monitoring and rapid emergency response (SAGTR). Firstly, the brain‐inspired human visual multimodal data acquisition by satellite‐air‐ground integration platform has been established to break through the traditional single‐source and single‐mode data approach and involves satellite remote sensing images, UAV hyperspectral images, and ground parameters. Moreover, it is unique among current tailings reservoir monitoring systems. In general, our proposed SAGTR framework has promise for improving the comprehensive monitoring and emergency response to tailings reservoir failures.

show abstract

“…For example, Zhao et al [34] combined BPNN and spectral data to predict the contents of Cd, Hg, As, Pb, Cu, and Zn in soil around Tai Lake in China and found that the estimation accuracy of the BPNN model is higher than that of the partial least square method. Bian et al [35] used BPNN and spectral data to predict the content of Cu, Sn, Zn and Pb in different types of soils. The results showed that BPNN had a good prediction effect and generalization ability in predicting the content of heavy metals.…”

Section: Introductionmentioning

confidence: 99%

Estimation of Heavy Metal Content in Soil Based on Machine Learning Models

Shi

Hou

et al. 2022

Land

View full text Add to dashboard Cite

Heavy metal pollution in soil is threatening the ecological environment and human health. However, field measurement of heavy metal content in soil entails significant costs. Therefore, this study explores the estimation method of soil heavy metals based on remote sensing images and machine learning. To accurately estimate the heavy metal content, we propose a hybrid artificial intelligence model integrating least absolute shrinkage and selection operator (LASSO), genetic algorithm (GA) and error back propagation neural network (BPNN), namely the LASSO-GA-BPNN model. Meanwhile, this study compares the accuracy of the LASSO-GA-BPNN model, SVR (Support Vector Regression), RF (Random Forest) and spatial interpolation methods with Huanghua city as an example. Furthermore, the study uses the LASSO-GA-BPNN model to estimate the content of eight heavy metals (including Ni, Pb, Cr, Hg, Cd, As, Cu, and Zn) in Huanghua and visualize the results in high resolution. In addition, we calculate the Nemerow index based on the estimation results. The results denote that, the simultaneous optimization of BPNN by LASSO and GA can greatly improve the estimation accuracy and generalization ability. The LASSO-GA-BPNN model is a more accurate model for the estimate heavy metal content in soil compared to SVR, RF and spatial interpolation. Moreover, the comprehensive pollution level in Huanghua is mainly low pollution. The overall spatial distribution law of each heavy metal content is very similar, and the local spatial distribution of each heavy metal is different. The results are of great significance for soil pollution estimation.

show abstract

Estimation of Heavy Metals in Tailings and Soils Using Hyperspectral Technology: A Case Study in a Tin-Polymetallic Mining Area

Cited by 18 publications

References 32 publications

Estimating the Heavy Metal Contents in Entisols from a Mining Area Based on Improved Spectral Indices and Catboost

Estimating the Heavy Metal Contents in Entisols from a Mining Area Based on Improved Spectral Indices and Catboost

Satellite‐air‐ground integrated multi‐source earth observation and machine learning processing brain for tailings reservoir monitoring and rapid emergency response

Estimation of Heavy Metal Content in Soil Based on Machine Learning Models

Contact Info

Product

Resources

About