Rapid identification of soil cadmium pollution risk at regional scale based on visible and near-infrared spectroscopy

Chen, Tao; Chang, Qingrui; Clevers, J.G.P.W.; Kooistra, Lammert

doi:10.1016/j.envpol.2015.07.009

Cited by 115 publications

(45 citation statements)

References 54 publications

(62 reference statements)

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“…Various data mining techniques, such as principal component regression (PCR) [13], partial least squares regression (PLSR) [14,15,16], artificial neural network (ANN) [4], multivariate adaptive regression splines (MARS) [17] and support vector machine (SVM) [18,19,20] were employed to train models from spectral data for estimating soil properties, including heavy metals. The ‘training model’ process is synonymously described as ‘machine-learning’, which can be defined as the process of discovering the relationships between predictor and response variables using computer-based statistical methods [21].…”

Section: Introductionmentioning

confidence: 99%

Spectroscopic Diagnosis of Arsenic Contamination in Agricultural Soils

Shi¹,

Liu

Chen

et al. 2017

Sensors

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This study investigated the abilities of pre-processing, feature selection and machine-learning methods for the spectroscopic diagnosis of soil arsenic contamination. The spectral data were pre-processed by using Savitzky-Golay smoothing, first and second derivatives, multiplicative scatter correction, standard normal variate, and mean centering. Principle component analysis (PCA) and the RELIEF algorithm were used to extract spectral features. Machine-learning methods, including random forests (RF), artificial neural network (ANN), radial basis function- and linear function- based support vector machine (RBF- and LF-SVM) were employed for establishing diagnosis models. The model accuracies were evaluated and compared by using overall accuracies (OAs). The statistical significance of the difference between models was evaluated by using McNemar’s test (Z value). The results showed that the OAs varied with the different combinations of pre-processing, feature selection, and classification methods. Feature selection methods could improve the modeling efficiencies and diagnosis accuracies, and RELIEF often outperformed PCA. The optimal models established by RF (OA = 86%), ANN (OA = 89%), RBF- (OA = 89%) and LF-SVM (OA = 87%) had no statistical difference in diagnosis accuracies (Z < 1.96, p < 0.05). These results indicated that it was feasible to diagnose soil arsenic contamination using reflectance spectroscopy. The appropriate combination of multivariate methods was important to improve diagnosis accuracies.

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Section: Introductionmentioning

confidence: 99%

Spectroscopic Diagnosis of Arsenic Contamination in Agricultural Soils

Shi¹,

Liu

Chen

et al. 2017

Sensors

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“…The traditional method of measuring and monitoring heavy metal contamination in soil is field sampling combined with laboratory analysis. Although highly accurate, this method is costly, time‐consuming, and laborious if applied on a large scale (Chen et al, 2015). In contrast, hyperspectral remote sensing technology is quicker, has high resolution and accuracy, and suits monitoring on a large spatial scale (Kemper and Sommer, 2002; Shi et al, 2014; Dong et al, 2016).…”

Section: List Of Previous Studiesmentioning

confidence: 99%

Hyperspectral‐based Inversion of Heavy Metal Content in the Soil of Coal Mining Areas

Hou

2019

J of Env Quality

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Coal mining has environmental impacts on surrounding areas, including heavy metal contamination of soil. This study explores the feasibility of using hyperspectral remote sensing to determine the heavy metal (Cr, Ni, Cu, Zn, Cd, Pb) content of soils in a coal‐mining area in the city of Zoucheng, Shandong Province, China. We used a plasma mass spectrometer to measure the heavy metal contents of soils and an ASD Field Spec4 spectrometer to measure soil hyperspectral data. Savitzky–Golay (SG) convolution smoothing and multiplicative scatter correction (MSC) were applied to the data, along with multiple mathematical transformations. Finally, a regression model for estimating heavy metal content of soils was developed using partial least squares regression (PLSR) analysis. Results show that the average heavy metal content of study soils was lower than the national standard value of soil environmental quality. The model's predictive accuracy is extremely high for Ni (R2 = 0.923 and RMSE = 0.831 by modeling; R2 = 0.879 and RMSE = 1.292 by testing); ideal for Cr, Cu, Zn, and Pb; and insufficient for Cd. Preprocessing the reflectance spectra with SG convolution smoothing in combination with MSC and reciprocal logarithm transformation yields the highest model accuracy. Hyperspectral PLSR modeling can effectively predict heavy metal content of soils in coal‐mining areas, and preprocessing spectral data is crucial for achieving high prediction accuracy. Core Ideas Quantitative inversion can provide technical support for monitoring of soil heavy metals. Quantitative inversion can provide theoretical information for further environmental recovery in mining areas. Hyperspectral remote sensing can quickly evaluate the status of heavy metal contamination in soil.

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“…Gholizadeh et al [11] assessed the usefulness of the VIS-NIR method in the monitoring of toxic elements in soil samples from reclaimed dumpsites. Chen et al [12] applied visible and near infrared for fast analysis of cadmium pollution of soils. Niazi et al [13] used the DRIFT mode in the MIR range combined with PLS to estimate the contamination of soil with arsenic.…”

Section: Introductionmentioning

confidence: 99%

Simultaneous Analysis of Heavy Metal Concentration in Soil Samples

2019

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The application of Fourier transform infrared spectroscopy to the simultaneous analysis of heavy metal concentration in soil samples was demonstrated in this paper. Two spectral techniques, namely, attenuated total reflectance (ATR) and diffuse reflectance (DRIFT), were applied and the whole infrared spectral region, i.e., far IR, mid IR, and near IR were considered in this work. Spectral data with reference to the results of laboratory analysis enabled the development of calibration partial least squares (PLS) models. The PLS models for the ATR near IR were characterized by a good fit and good prediction abilities. According to the results obtained, the most accurate description and prediction were realized in the case of mid/far and near IR for the mercury and nickel concentration in soil. Application of far IR slightly improved the prediction possibilities of the model. The construction of PLS models based on the Fourier-transform infrared (FT-IR) spectra enables the perception of FT-IR spectroscopy as a supplementary method that is useful in the estimation and monitoring of the contamination level in soils.

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Rapid identification of soil cadmium pollution risk at regional scale based on visible and near-infrared spectroscopy

Cited by 115 publications

References 54 publications

Spectroscopic Diagnosis of Arsenic Contamination in Agricultural Soils

Spectroscopic Diagnosis of Arsenic Contamination in Agricultural Soils

Hyperspectral‐based Inversion of Heavy Metal Content in the Soil of Coal Mining Areas

Simultaneous Analysis of Heavy Metal Concentration in Soil Samples

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