Estimation of Potentially Toxic Elements Contamination in Anthropogenic Soils on a Brown Coal Mining Dumpsite by Reflectance Spectroscopy: A Case Study

Gholizadeh, Asa; Borůvka, Luboš; Vašát, Radim; Saberioon, Mohammadmehdi; Klement, Aleš; Kratina, Josef; Tejnecký, Václav; Drábek, Ondřej

doi:10.1371/journal.pone.0117457

Cited by 72 publications

(44 citation statements)

References 41 publications

(76 reference statements)

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“…This can be the reason why the CEC prediction was worse (RPD = 1.48, R 2 test = 0.62). For heavy metals, the results are comparable or even better than those published by [10] when employing SVM modelling together with the 1st derivative as a pretreatment technique or by Bray et al (2009) [62] who used an ordinal logistic regression technique to predict Zn, Cu, Pb and Cd.…”

Section: Discussionmentioning

confidence: 56%

“…Spectroscopy, covering the optical (reflected solar radiation) and thermal infrared (earth tsurface emitted radiation) regions across the 0.4-15 µm spectral range, can be used to determine a wide range of soil properties [1] such as organic carbon (OC) [2], texture [3], cationic exchange capacity (CEC) [4], total phosphorus (P) [5], exchangeable potassium (K) [6], electrical conductivity (EC) [7][8][9], total concentration of potential pollutant metals/metalloids [10] and mineral content [11,12]. Aside from the fundamental interaction of electromagnetic radiation with matter, indirect interaction can be found and provide additional quantitative information of the soil in question [13].…”

Section: Introductionmentioning

confidence: 99%

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Modelling Diverse Soil Attributes with Visible to Longwave Infrared Spectroscopy Using PLSR Employed by an Automatic Modelling Engine

et al. 2017

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Abstract:The study tested a data mining engine (PARACUDA ® ) to predict various soil attributes (BC, CEC, BS, pH, C org , Pb, Hg, As, Zn and Cu) using reflectance data acquired for both optical and thermal infrared regions. The engine was designed to utilize large data in parallel and automatic processing to build and process hundreds of diverse models in a unified manner while avoiding bias and deviations caused by the operator(s). The system is able to systematically assess the effect of diverse preprocessing techniques; additionally, it analyses other parameters, such as different spectral resolutions and spectral coverages that affect soil properties. Accordingly, the system was used to extract models across both optical and thermal infrared spectral regions, which holds significant chromophores. In total, 2880 models were evaluated where each model was generated with a different preprocessing scheme of the input spectral data. The models were assessed using statistical parameters such as coefficient of determination (R 2 ), square error of prediction (SEP), relative percentage difference (RPD) and by physical explanation (spectral assignments). It was found that the smoothing procedure is the most beneficial preprocessing stage, especially when combined with spectral derivation (1st or 2nd derivatives). Automatically and without the need of an operator, the data mining engine enabled the best prediction models to be found from all the combinations tested. Furthermore, the data mining approach used in this study and its processing scheme proved to be efficient tools for getting a better understanding of the geochemical properties of the samples studied (e.g., mineral associations).

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Section: Discussionmentioning

confidence: 56%

Section: Introductionmentioning

confidence: 99%

Modelling Diverse Soil Attributes with Visible to Longwave Infrared Spectroscopy Using PLSR Employed by an Automatic Modelling Engine

et al. 2017

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“…Spectra preprocessing algorithms entailed a range of mathematical techniques for refining light scattering in spectral reflectance measurements and data improvement before the data were used in calibration models. The first derivative transformation, which was utilized in this study, is very efficient for eliminating baseline offset and, according to some researchers, gives the best results and uppermost accuracy among other algorithms [28,41,53]. In this study, before all further spectra treatments, the noisy parts of the spectra, ranges 350-399 nm and 2450-2500 nm, were removed, and the spectra were subjected to Savitzky-Golay smoothing with a second-order polynomial fit and 11 smoothing points [18,54] for eliminating the artificial noise caused by the spectroradiometer device.…”

Section: Spectra Preprocessingmentioning

confidence: 99%

A Memory-Based Learning Approach as Compared to Other Data Mining Algorithms for the Prediction of Soil Texture Using Diffuse Reflectance Spectra

et al. 2016

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Successful determination of soil texture using reflectance spectroscopy across Visible and Near-Infrared (VNIR, 400-1200 nm) and Short-Wave-Infrared (SWIR, 1200-2500 nm) ranges depends largely on the selection of a suitable data mining algorithm. The objective of this research was to explore whether the new Memory-Based Learning (MBL) method performs better than the other methods, namely: Partial Least Squares Regression (PLSR), Support Vector Machine Regression (SVMR) and Boosted Regression Trees (BRT). For this purpose, we chose soil texture (contents of clay, silt and sand) as testing attributes. A selected set of soil samples, classified as Technosols, were collected from brown coal mining dumpsites in the Czech Republic (a total of 264 samples). Spectral readings were taken in the laboratory with a fiber optic ASD FieldSpec III Pro FR spectroradiometer. Leave-one-out cross-validation was used to optimize and validate the models. Comparisons were made in terms of the coefficient of determination (R 2 cv ) and the Root Mean Square Error of Prediction of Cross-Validation (RMSEP cv ). Predictions of the three soil properties by MBL outperformed the accuracy of the remaining algorithms. We found that the MBL performs better than the other three methods by about 10% (largest R 2 cv and smallest RMSEP cv ), followed by the SVMR. It should be pointed out that the other methods (PLSR and BRT) still provided reliable results. The study concluded that in this examined dataset, reflectance spectroscopy combined with the MBL algorithm is rapid and accurate, offers major efficiency and cost-saving possibilities in other datasets and can lead to better targeting of management interventions.

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“…According to some researchers, using SVMR can overcome the problems of other calibration methods, as the above-mentioned calibration methods require the creation of robust and generalized models due to their potential tendency to over-fit the data (Vapnik 1995;Gholizadeh et al 2013). Based on work by Vapnik (1995) and Gholizadeh et al (2015), SVMR is a supervised non-parametric statistical learning technique; thus, it represents a different method class compared with the previous techniques.…”

mentioning

confidence: 99%

Comparing different data preprocessing methods for monitoring soil heavy metals based on soil spectral features

Gholizadeh¹,

Borůvka²,

Saberioon³

et al. 2015

Soil Water Res.

142

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Gholizadeh A., Borůvka L., Saberioon M.M., Kozák J., Vašát R., Němeček K. (2015): Comparing different data preprocessing methods for monitoring soil heavy metals based on soil spectral features. Soil & Water Res., 10: 218-227.The lands near mining industries in the Czech Republic are subjected to soil pollution with heavy metals. Excessive heavy metal concentrations in soils not only dramatically impact the soil quality, but also due to their persistent nature and indefinite biological half-lives, potentially toxic metals can accumulate in the food chain and can eventually endanger human health. Monitoring and spatial information of these elements require a large number of samples and cumbersome and time-consuming laboratory measurements. A faster method has been developed based on a multivariate calibration procedure using support vector machine regression (SVMR) with cross-validation, to establish a relationship between reflectance spectra in the visible-near infrared (Vis-NIR) region and concentration of Mn, Cu, Cd, Zn, and Pb in soil. Spectral preprocessing methods, first and second derivatives (FD and SD), standard normal variate (SNV), multiplicative scatter correction (MSC), and continuum removal (CR) were employed after smoothing with Savitzky-Golay to improve the robustness and performance of the calibration models. According to the criteria of maximal coefficient of determination (R 2 cv ) and minimal root mean square error of prediction in cross-validation (RMSEP cv ), the SVMR algorithm with FD preprocessing was determined as the best method for predicting Cu, Mn, Pb, and Zn concentration, whereas the SVMR model with CR preprocessing was chosen as the final method for predicting Cd. Overall, this study indicated that the Vis-NIR reflectance spectroscopy technique combined with a continuously enriched soil spectral library as well as a suitable preprocessing method could be a nondestructive alternative for monitoring of the soil environment. The future possibilities of multivariate calibration and preprocessing with real-time remote sensing data have to be explored.

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Estimation of Potentially Toxic Elements Contamination in Anthropogenic Soils on a Brown Coal Mining Dumpsite by Reflectance Spectroscopy: A Case Study

Cited by 72 publications

References 41 publications

Modelling Diverse Soil Attributes with Visible to Longwave Infrared Spectroscopy Using PLSR Employed by an Automatic Modelling Engine

Modelling Diverse Soil Attributes with Visible to Longwave Infrared Spectroscopy Using PLSR Employed by an Automatic Modelling Engine

A Memory-Based Learning Approach as Compared to Other Data Mining Algorithms for the Prediction of Soil Texture Using Diffuse Reflectance Spectra

Comparing different data preprocessing methods for monitoring soil heavy metals based on soil spectral features

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