Luboš Borůvka

Visible, Near-Infrared, and Mid-Infrared Spectroscopy Applications for Soil Assessment with Emphasis on Soil Organic Matter Content and Quality: State-of-the-Art and Key Issues

Visible near-infrared (Vis-NIR) reflection spectroscopy and mid-infrared (mid-IR) reflection spectroscopy are cost- and time-effective and environmentally friendly techniques that could be alternatives to conventional soil analysis methods. Successful determination of spectrally active soil components, including soil organic matter (SOM), depends on the selection of suitable pretreatment and multivariate calibration techniques. The objective of the present review is to critically examine the suitability of Vis-NIR (350–2500 nm) and mid-IR (4000–400 cm−1) spectroscopy as a tool for SOM quantity and quality determination. Particular attention is paid to different pretreatment and calibration procedures and methods, and their ability to predict SOM content from Vis-NIR and mid-IR data is discussed. We then review the most recent research using spectroscopy in different calibration scales (local, regional, or global). Finally, accuracy and robustness, as well as uncertainty in Vis-NIR and mid-IR spectroscopy, are considered. We conclude that spectroscopy, especially the mid-IR technique in association with Savitzky–Golay smoothing and derivatization and the least squares support vector machine (LS-SVM) algorithm, can be useful in determining SOM quantity and quality. Future research conducted for the standardization of protocols and soil conditions will allow more accurate and reliable results on a global and international scale.

Combined Chemical and Mineralogical Evidence for Heavy Metal Binding in Mining-and Smelting-Affected Alluvial Soils

The binding of metallic contaminants (Pb, Cd, and Zn) and As on soil constituents was studied on four highly contaminated alluvial soil profiles from the mining/smelting district of Přibram (Czech Republic) using a combination of mineralogical and chemical methods. Sequential extraction analysis (SEA) was supplemented by mineralogical investigation of both bulk samples and heavy mineral fractions using X-ray diffraction analysis (XRD) and scanning electron microscopy with an energy dispersive X-ray spectrometer (SEM/EDS). The mineralogy of Fe and Mn oxides was studied by voltammetry of microparticles (VMP) and diffuse reflectance spectrometry (DRS). Zinc and Pb were predominantly bound in the reducible fraction attributed to Fe oxides and Mn oxides (mainly birnessite, Na4Mn14O27•9H2O), which were detected in soils by XRD and SEM/EDS. In contrast, Cd was the most mobile contaminant and was predominantly present in the exchangeable fraction. Arsenic was bound to the residual and reducible fractions (corresponding to Fe oxides or to unidentified Fe-Pb arsenates). SEM/EDS observations indicate the predominant affinity of Pb for Mn oxides, and to a lesser extent, for Fe oxides. Thus, a more suitable SEA procedure should be used for these mining-affected soils to distinguish between the contaminant fraction bound to Mn oxides and Fe oxides.

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

In order to monitor Potentially Toxic Elements (PTEs) in anthropogenic soils on brown coal mining dumpsites, a large number of samples and cumbersome, time-consuming laboratory measurements are required. Due to its rapidity, convenience and accuracy, reflectance spectroscopy within the Visible-Near Infrared (Vis-NIR) region has been used to predict soil constituents. This study evaluated the suitability of Vis-NIR (350–2500 nm) reflectance spectroscopy for predicting PTEs concentration, using samples collected on large brown coal mining dumpsites in the Czech Republic. Partial Least Square Regression (PLSR) and Support Vector Machine Regression (SVMR) with cross-validation were used to relate PTEs data to the reflectance spectral data by applying different preprocessing strategies. According to the criteria of minimal Root Mean Square Error of Prediction of Cross Validation (RMSEPcv) and maximal coefficient of determination (R2 cv) and Residual Prediction Deviation (RPD), the SVMR models with the first derivative pretreatment provided the most accurate prediction for As (R2 cv) = 0.89, RMSEPcv = 1.89, RPD = 2.63). Less accurate, but acceptable prediction for screening purposes for Cd and Cu (0.66 ˂ R2 cv) ˂ 0.81, RMSEPcv = 0.0.8 and 4.08 respectively, 2.0 ˂ RPD ˂ 2.5) were obtained. The PLSR model for predicting Mn (R2 cv) = 0.44, RMSEPcv = 116.43, RPD = 1.45) presented an inadequate model. Overall, SVMR models for the Vis-NIR spectra could be used indirectly for an accurate assessment of PTEs’ concentrations.

Comparing Different Data Preprocessing Methods for Monitoring Soil Heavy Metals Based on Soil Spectral Features

Gholizadeh A., Borůvka L., Saberioon M.M., Kozak J., Vasat R., Němecek K. (2015): Comparing different data pre processing 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. Ex cessive 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.

Some Approaches to the Research of Forest Soils Affected by Acidification in the Czech Republic

Soil acidification in mountainous regions of the Czech Republic presents a serious problem. This paper summarizes several projects dealing with this problem exploiting different methods and having different objectives: 1) Long-term soil and forest development in the Krkonoše Mountains. 2) Soil and forest development along an elevation transect in the Šumava Mountains. 3) Long-term effects of liming. 4) Comparison of soil acidification between three mountainous regions, with emphasis on labile Al content. 5) Spatial distribution and factors of soil acidification and Al release in the Jizera Mountains. The results of these projects showed that acidification in forest soils in the mountainous areas is caused by a combination of different factors, especially by the type of vegetation, atmospheric deposition, parent rock, altitude, and others. A slight improvement of soil properties is apparent after the decrease of immissions in the 1990s.

Profile distribution and temporal changes of sulphate and nitrate contents and related soil properties under beech and spruce forests

The behaviour of principal inorganic anions in forest soils, originating mainly from acid deposition, strongly influences the forest ecosystem response on acidification. The aim of this study was to describe seasonal and temporal changes of sulphate and nitrate contents and related soil properties under beech and spruce forests in a region heavily impacted by acidification. The Jizera Mountains area (Czech Republic) was chosen as such a representative mountainous soil ecosystem. Soil samples were collected at monthly intervals from April to October during the years 2008-2010 under both beech and spruce stands. Soil samples were collected from surface fermentation (F) and humified (H) organic horizons, humic (A) organo-mineral horizons and subsurface mineral (B) horizons (cambic or spodic). A deionised water extract was applied to unsieved fresh samples and the content of anions in these extracts was determined by ion chromatography (IC). In the studied soil profiles, the lowest amount of SO(4)(2-) was found in the organo-mineral A horizons under both types of vegetation. Under spruce the highest amount of SO(4)(2-) was determined in mineral spodic (B) horizons, where a strong sorption influence of Fe and Al oxy-hydroxides is expected. Under beech the highest amount was observed in the surface organic F horizons (forest floor). The amount of NO(3)(-) is highest in the F horizons and decreases with increasing soil profile depth under both types of vegetation. A significantly higher amount of NO(3)(-) was determined in soils under the beech stand compared to spruce. For both soil environments - under beech and also spruce stands - we have determined a general increase of water-extractable SO(4)(2-) and NO(3)(-) during the whole monitoring period. The behaviour of SO(4)(2-) and NO(3)(-) in the soils is strongly related to the dynamics of soil organic matter and particularly to the DOC.

Comparison of Al speciation and other soil characteristics between meadow, young forest and old forest stands

The aim of this paper is to describe the influence of spruce (Picea abies) afforestation on soil chemical properties, especially on soil acidity and aluminium (Al) mobilization and speciation in soil. For our study we used a unique set of three adjacent plots, including a meadow and two spruce forest stands of different age, in otherwise comparable conditions. The plots were located in the region of Giant Mountains, north-eastern Czech Republic. In general, pH values decreased and Al concentrations increased significantly after afforestation. Speciation of KCl-extractable and water-soluble Al in soil samples was done by means of HPLC/IC method. The concentrations of Al(X)(1+) and Al(Y)(2+) forms (in both extracts) are higher in humic and organically enriched (Bhs) horizons. The highest concentration of Al(3+) in both extracts is in the B horizons of old forest. Generally, in all studied stands majority of Al in aqueous extract is in the Al(X)(1+) form, which indicates that a large amount of mobile Al is bound in organic complexes. It suggests that actual toxicity is rather low. On the other hand, we have proved that majority of KCl-extractable Al exists in Al(3+) form. Thus we can conclude that disturbance of existing equilibrium may cause massive release of highly toxic Al(3+) from soil sorption complex to the soil solution, and consequently it can endanger the whole ecosystem. Moreover, continuous soil acidification accelerated by anthropogenic factors leading to Al mobilization represents a chemical time bomb.

Assessment of soil aluminium pools along three mountainous elevation gradients

Anthropogenic soil acidification in mountain forests and consequent Al release still present a significant problem in many regions. The effect of deposition may differ according to stand conditions, including altitude. This contribution is focused on three elevation transects, two in the Jizera Mountains strongly influenced by acid deposition, one in the less affected Novohradske Mountains. Quantification of pools of different Al forms and related soil characteristics (organic carbon, exchangeable hydrogen cations, sorption characteristics, etc.) is evaluated. In the Novohradske Mountains, the pool of both organically bound and water-soluble Al increases with increasing altitudes. In the Jizera Mountains, the distribution is more complicated; it is strongly affected by different forest type (beech vs. spruce), deforestation, and other local differences. Higher amounts of Al are bound in the mineral horizons compared to the surface organic horizons, even in the case of organically bound Al pools. Further differences between different altitudes and between soil horizons in Al distribution were revealed by detailed Al speciation using HPLC/IC method.

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

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 (R2cv) and the Root Mean Square Error of Prediction of Cross-Validation (RMSEPcv). 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 R2cv and smallest RMSEPcv), 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.

Different low-molecular-mass organic acids specifically control leaching of arsenic and lead from contaminated soil.

Low-molecular-mass organic acids (LMMOA) are of key importance for mobilisation and fate of metals in soil, by functioning as ligands that increase the amount of dissolved metal in solution or by dissociation of metal binding minerals. Column leaching experiments were performed on soil polluted with As and Pb, in order to determine the specificity of LMMOA related release for individual elements, at varying organic acid concentrations. Acetic, citric and oxalic acids were applied in 12h leaching experiments over a concentration range (0.5-25 mM) to soil samples that represent organic and mineral horizons. The leaching of As followed the order: oxalic>citric>acetic acid in both soils. Arsenic leaching was attributed primarily to ligand-enhanced dissolution of mineral oxides followed by As released into solution, as shown by significant correlation between oxalic and citric acids and content of Al and Fe in leaching solutions. Results suggest that subsurface mineral soil layers are more vulnerable to As toxicity. Leaching of Pb from both soils followed the order: citric>oxalic>acetic acid. Mineral soil samples were shown to be more susceptible to leaching of Pb than samples characterised by a high content of organic matter. The leaching efficiency of citric acid was attributed to formation of stable complexes with Pb ions, which other acids are not capable of. Results obtained in the study are evidence that the extent of As and Pb leaching in contaminated surface and subsurface soil depends significantly on the types of carboxylic acid involved. The implications of the type of acid and the specific element that can be mobilised become increasingly significant where LMMOA concentrations are highest, such as in rhizosphere soil.