Aleš Klement

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.

An analysis of the dissipation of pharmaceuticals under thirteen different soil conditions.

The presence of human and veterinary pharmaceuticals in the environment is recognized as a potential threat. Pharmaceuticals have the potential to contaminate soils and consequently surface and groundwater. Knowledge of contaminant behavior (e.g., sorption onto soil particles and degradation) is essential when assessing contaminant migration in the soil and groundwater environment. We evaluated the dissipation half-lives of 7 pharmaceuticals in 13 soils. The data were evaluated relative to the soil properties and the Freundlich sorption coefficients reported in our previous study. Of the tested pharmaceuticals, carbamazepine had the greatest persistence (which was mostly stable), followed by clarithromycin, trimethoprim, metoprolol, clindamycin, sulfamethoxazole and atenolol. Pharmaceutical persistence in soils was mostly dependent on the soil-type conditions. In general, lower average dissipation half-lives and variability (i.e., trimethoprim, sulfamethoxazole, clindamycin, metoprolol and atenolol) were found in soils of better quality (well-developed structure, high nutrition content etc.), and thus, probably better microbial conditions (i.e., Chernozems), than in lower quality soil (Cambisols). The impact of the compound sorption affinity onto soil particles on their dissipation rate was mostly negligible. Although there was a positive correlation between compound dissipation half-life and Freundlich sorption coefficient for clindamycin (R=0.604, p<0.05) and sulfamethoxazole (R=0.822, p<0.01), the half-life of sulfamethoxazole also decreased under better soil-type conditions. Based on the calculated dissipation and sorption data, carbamazepine would be expected to have the greatest potential to migrate in the soil water environment, followed by sulfamethoxazole, trimethoprim and metoprolol. The transport of clindamycin, clarithromycin and atenolol through the vadose zone seems less probable.

Transformation of atenolol, metoprolol, and carbamazepine in soils: The identification, quantification, and stability of the transformation products and further implications for the environment.

Pharmaceuticals are a large group of substances that have been recognized as environmental contaminants in recent years. Research on the pharmaceutical fate in soils is currently limited or missing. In this study, three pharmaceuticals (atenolol (ATE), carbamazepine (CAR), and metoprolol (MET)) were introduced to soils and exposed for 61 day under aerobic conditions. Thirteen different soils were used in the study to increase the understanding of pharmaceutical behaviour in the soil matrix. Ten metabolites were detected and tentatively identified. Some of them, such as atenolol acid (AAC), carbamazepine 10,11-epoxide (EPC), 10,11-dihydrocarbamazepine (DHC), trans-10,11-Dihydro-10,11-dihydroxy carbamazepine (RTC), and metoprolol acid (MAC), were consequently confirmed using commercial reference standards. It was concluded that the aerobic conditions of the experiment determined the pharmaceutical degradation pathway of studied compounds in the soils. The different amounts/rates and degradation of the transformation products can be attributed to differences in the soil properties. ATE degraded relatively quickly compared with CAR, whereas MET degradation in the soils was unclear. The persistence of CAR and its metabolites, in combination with low CAR sorption, enable the transportation of CAR and its metabolites within soils and into the ground water. Thus, CAR may cause adverse effects on the environment and humans.

Predicting oxidizable carbon content via visible- and near-infrared diffuse reflectance spectroscopy in soils heavily affected by water erosion.

Soil spectroscopy represents a low-cost alternative to routine time-consuming and expensive laboratory analyses. Its ability to measure a wide range of different chemical and physical soil properties was shown previously in many studies. Particularly, for organic carbon content, a reliable prediction accuracy is usually achieved. This is due to strong spectral signature of soil organic carbon and other distinct spectral implications of soil characteristics strongly tied to it, e.g. soil colour. All the known studies, however, deal with situation where the study area is fully covered (either in the manner of design- or model-based sampling approach) with calibration points. But in many cases the sampling strategy was initially designed for other purposes, falling outside requirements of spectroscopy for proper model calibration. Hence, here we attempt to test the ability of soil spectroscopy in the situation when only a minor isolated part (the steepest one) of the study area was sampled for calibration points, and predictions were made for its several time larger surroundings. For model training we used Partial Least Squares Regression (PLSR) technique and four different spectra pre-treatment methods (Savitzky-Golay smoothing, first and second derivative, and baseline normalization via continuum removal). Results show high potential (R 2 ≈ 0.70-0.80) of the method for rough terrain landscapes strongly affected by water erosion, even if the distance from calibration to prediction points is large.

Simultaneous sorption of four ionizable pharmaceuticals in different horizons of three soil types

Soils may be contaminated by human or veterinary pharmaceuticals. Their behaviour in soil environment is largely controlled by sorption of different compounds in a soil solution onto soil constituents. Here we studied the sorption affinities of 4 pharmaceuticals (atenolol, trimethoprim, carbamazepine and sulfamethoxazole) applied in solute mixtures to soils taken from different horizons of 3 soil types (Greyic Phaeozem on loess, Haplic Luvisol on loess and Haplic Cambisol on gneiss). In the case of the carbamazepine (neutral form) and sulfamethoxazole (partly negatively charged and neutral), sorption affinity of compounds decreased with soil depth, i.e. decreased with soil organic matter content. On the other hand, in the case of atenolol (positively charged) and trimethoprim (partly positively charged and neutral) compound sorption affinity was not depth dependent. Compound sorption affinities in the four-solute systems were compared with those experimentally assessed in topsoils, and were estimated using the pedotransfer rules proposed in our previous study for single-solute systems. While sorption affinities of trimethoprim and carbamazepine in topsoils decreased slightly, sorption affinity of sulfamethoxazole increased. Decreases in sorption of the two compounds could be attributed to their competition between each other and competition with atenolol. Differences between carbamazepine and atenolol behaviour in the one- and four-solute systems could also be explained by the slightly different soil properties in this and our previous study. A great increase of sulfamethoxazole sorption in the Greyic Phaeozem and Haplic Luvisol was observed, which was attributed to elimination of repulsion between negatively charged molecules and particle surfaces due to cation sorption (atenolol and trimethoprim) on soil particles. Thus, our results proved not only an antagonistic but also a synergic affect of differently charged organic molecules on their sorption to soil constituents.

Agricultural Soil Spectral Response and Properties Assessment: Effects of Measurement Protocol and Data Mining Technique

Soil spectroscopy has shown to be a fast, cost-effective, environmentally friendly, non-destructive, reproducible and repeatable analytical technique. Soil components, as well as types of instruments, protocols, sampling methods, sample preparation, spectral acquisition techniques and analytical algorithms have a combined influence on the final performance. Therefore, it is important to characterize these differences and to introduce an effective approach in order to minimize the technical factors that alter reflectance spectra and consequent prediction. To quantify this alteration, a joint project between Czech University of Life Sciences Prague (CULS) and Tel-Aviv University (TAU) was conducted to estimate Cox, pH-H2O, pH-KCl and selected forms of Fe and Mn. Two different soil spectral measurement protocols and two data mining techniques were used to examine seventy-eight soil samples from five agricultural areas in different parts of the Czech Republic. Spectral measurements at both laboratories were made using different ASD spectroradiometers. The CULS protocol was based on employing a contact probe (CP) spectral measurement scheme, while the TAU protocol was carried out using a CP measurement method, accompanied with the internal soil standard (ISS) procedure. Two spectral datasets, acquired from different protocols, were both analyzed using partial least square regression (PLSR) technique as well as the PARACUDA II®, a new data mining engine for optimizing PLSR models. The results showed that spectra based on the CULS setup (non-ISS) demonstrated significantly higher albedo intensity and reflectance values relative to the TAU setup with ISS. However, the majority of statistics using the TAU protocol was not noticeably better than the CULS spectra. The paper also highlighted that under both measurement protocols, the PARACUDA II® engine proved to be a powerful tool for providing better results than PLSR. Such initiative is not only a way to unlock current limitations of soil spectroscopy, but also offers considerable efficiency and cost- and time-saving possibilities, which lead to further improvements in prediction performance of spectral models.

Absorption Features in Soil Spectra Assessment.

From a wide range of techniques appropriate to relate spectra measurements with soil properties, partial least squares (PLS) regression and support vector machines (SVM) are most commonly used. This is due to their predictive power and the availability of software tools. Both represent exclusively statistically based approaches and, as such, benefit from multiple responses of soil material in the spectrum. However, physical-based approaches that focus only on a single spectral feature, such as simple linear regression using selected continuum-removed spectra values as a predictor variable, often provide accurate estimates. Furthermore, if this approach extends to multiple cases by taking into account three basic absorption feature parameters (area, width, and depth) of all occurring features as predictors and subjecting them to best subset selection, one can achieve even higher prediction accuracy compared with PLS regression. Here, we attempt to further extend this approach by adding two additional absorption feature parameters (left and right side area), as they can be important diagnostic markers, too. As a result, we achieved higher prediction accuracy compared with PLS regression and SVM for exchangeable soil pH, slightly higher or comparable for dithionite-citrate and ammonium oxalate extractable Fe and Mn forms, but slightly worse for oxidizable carbon content. Therefore, we suggest incorporating the multiple linear regression approach based on absorption feature parameters into existing working practices.

Using magnetic susceptibility mapping for assessing soil degradation due to water erosion.

Jaksik O., Kodesova R., Kapicka A., Klement A., Fer M., Nikodem A. (2016): Using magnetic susceptibility mapping for assessing soil degradation due to water erosion. Soil & Water Res., 11: 105–113. This study focused on developing a method for estimating topsoil organic carbon content from measured massspecific magnetic susceptibility in Chernozems heavily affected by water erosion. The study was performed on a 100 ha area, whereby 202 soil samples were taken. A set of soil samples was divided into 3 subsets: A (32 samples), B (67 samples), and C (103 samples). The mass-specific magnetic susceptibility using low ( χ lf ) and high ( χ hf ) frequency, and organic carbon content were measured at all soil samples. The contents of iron and manganese, extracted with a dithionite-citrate solution (Fe d , Mn d ) and ammonium oxalate (Fe o , Mn o ), were quantified in A and B samples. Models for predicting organic carbon content from magnetic susceptibilities were designed as follows: (1) subset A was used as the training set for calibration, and subsets B and C were used as the test sets for model validation, either separately (subset B only), or together (merged subsets B and C); (2) merged subsets A and B were used as the training set and subset C was used as the test set. Results showed very close correlations between organic carbon content and all measured soil properties. Obtained models relating organic carbon content to mass-specific magnetic susceptibility successfully predicted soil organic carbon contents.

Influence of soil-water content on CO2 efflux within the elevation transect heavily impacted by erosion: Influence of Soil-Water Content on CO2 Efflux

Faculty of Agrobiology, Food and Natural, Resources, Dept. of Soil Science and Soil Protection, Czech University of Life Sciences Prague, Kamýcká 129, CZ‐16500 Prague 6, Czech Republic Correspondence Miroslav Fér, Faculty of Agrobiology, Food and Natural, Resources, Dept. of Soil Science and Soil Protection, Czech University of Life Sciences Prague, Kamýcká 129, CZ‐16500 Prague 6, Czech Republic. Email: mfer@af.czu.cz Funding information Czech Science Foundation, Grant/Award Number: 17‐08937S; Ministry of Agriculture of the Czech Republic, Grant/Award Number: QJ1230319

Sorption of citalopram, irbesartan and fexofenadine in soils: Estimation of sorption coefficients from soil properties

The sorption of 3 pharmaceuticals, which may exist in 4 different forms depending on the solution pH (irbesartan in cationic, neutral and anionic, fexofenadine in cationic, zwitter-ionic and anionic, and citalopram cationic and neutral), in seven different soils was studied. The measured sorption isotherms were described by Freundlich equations, and the sorption coefficients, KF (for the fixed n exponent for each compound), were related to the soil properties to derive relationships for estimating the sorption coefficients from the soil properties (i.e., pedotransfer rules). The largest sorption was obtained for citalopram (average KF value for n = 1 was 1838 cm3 g-1) followed by fexofenadine (KF = 35.1 cm3/n μg1-1/n g-1, n = 1.19) and irbesartan (KF = 3.96 cm3/n μg1-1/n g-1, n = 1.10). The behavior of citalopram (CIT) in soils was different than the behaviors of irbesartan (IRB) and fexofenadine (FEX). Different trends were documented according to the correlation coefficients between the KF values for different compounds (RIRB,FEX = 0.895, p-value<0.01; RIRB,CIT = -0.835, p-value<0.05; RFEX,CIT = -0.759, p-value<0.05) and by the reverse relationships between the KF values and soil properties in the pedotransfer functions. While the KF value for citalopram was positively related to base cation saturation (BCS) or sorption complex saturation (SCS) and negatively correlated to the organic carbon content (Cox), the KF values of irbesartan and fexofenadine were negatively related to BCS, SCS or the clay content and positively related to Cox. The best estimates were obtained by combining BCS and Cox for citalopram (R2 = 93.4), SCS and Cox for irbesartan (R2 = 96.3), and clay content and Cox for fexofenadine (R2 = 82.9).