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Soil contamination by potentially toxic elements (PTEs) is intensifying under increasing industrialization. Thus, the ability to efficiently delineate contaminated sites is crucial. Visible–near infrared (vis–NIR: 350–2500 nm) and X-ray fluorescence (XRF: 0.02–41.08 keV) spectroscopic techniques have attracted tremendous attention for the assessment of PTEs. Recently, the application of fused vis–NIR and XRF spectroscopy, which is based on the complementary effect of data fusion, is also increasing. Moreover, different data manipulation methods, including feature selection approaches, affect the prediction performance. This study investigated the feasibility of using single and fused vis–NIR and XRF spectra while exploring feature selection algorithms for the assessment of key soil PTEs. The soil samples were collected from one of the most heavily polluted areas of the Czech Republic and scanned using laboratory vis–NIR and XRF spectrometers. Univariate filter (UF) and genetic algorithm (GA) were used to select the bands of greater importance for the PTE prediction. Support vector machine (SVM) was then used to train the models using the full-range and feature-selected spectra of single sensors and their fusion. It was found that XRF spectra alone (primarily GA-selected) performed better than single vis–NIR and fused spectral data for predictions of PTEs. Moreover, the prediction models that were derived from the fused data set (particularly the GA-selected) enhanced the models’ accuracies as compared with the single vis–NIR spectra. In general, the results suggest that the GA-selected spectra obtained from the single XRF spectrometer (for As and Pb) and from the fusion of vis–NIR and XRF (for Pb) are promising for accurate quantitative estimation detection of the mentioned PTEs.

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 (R2cv) and Residual Prediction Deviation (RPD), the SVMR models with the first derivative pretreatment provided the most accurate prediction for As (R2cv) = 0.89, RMSEPcv = 1.89, RPD = 2.63). Less accurate, but acceptable prediction for screening purposes for Cd and Cu (0.66 ˂ R2cv) ˂ 0.81, RMSEPcv = 0.0.8 and 4.08 respectively, 2.0 ˂ RPD ˂ 2.5) were obtained. The PLSR model for predicting Mn (R2cv) = 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.

The monitoring and quantification of soil carbon provide a better understanding of soil and atmosphere dynamics. Visible-near-infrared-short-wave infrared (VIS-NIR-SWIR) reflectance spectroscopy can quantitatively estimate soil carbon content more rapidly and cost-effectively compared to traditional laboratory analysis. However, effective estimation of soil carbon using reflectance spectroscopy to a great extent depends on the selection of a suitable preprocessing sequence and data-mining algorithm. Many efforts have been dedicated to the comparison of conventional chemometric techniques and their optimization for soil properties prediction. Instead, the current study focuses on the potential of the new data-mining engine PARACUDA-II®, recently developed at Tel-Aviv University (TAU), by comparing its performance in predicting soil oxidizable carbon (Cox) against common data-mining algorithms including partial least squares regression (PLSR), random forests (RF), boosted regression trees (BRT), support vector machine regression (SVMR), and memory based learning (MBL). To this end, 103 soil samples from the Pokrok dumpsite in the Czech Republic were scanned with an ASD FieldSpec III Pro FR spectroradiometer in the laboratory under a strict protocol. Spectra preprocessing for conventional data-mining techniques was conducted using Savitzky-Golay smoothing and the first derivative method. PARACUDA-II®, on the other hand, operates based on the all possibilities approach (APA) concept, a conditional Latin hypercube sampling (cLHs) algorithm and parallel programming, to evaluate all of the potential combinations of eight different spectral preprocessing techniques against the original reflectance and chemical data prior to the model development. The comparison of results was made in terms of the coefficient of determination (R2) and root-mean-square error of prediction (RMSEp). Results showed that the PARACUDA-II® engine performed better than the other selected regular schemes with R2 value of 0.80 and RMSEp of 0.12; the PLSR was less predictive compared to other techniques with R2 = 0.63 and RMSEp = 0.29. This can be attributed to its capability to assess all the available options in an automatic way, which enables the hidden models to rise up and yield the best available model.

Soil organic carbon (SOC) is an important soil characteristic as well as a way how to mitigate climate change. Information on its content and spatial distribution is thus crucial. Digital soil mapping (DSM) is a suitable way to evaluate spatial distribution of soil properties thanks to its ability to obtain accurate information about soil. This research aims to apply machine learning algorithms using various environmental covariates to generate digital SOC maps for mineral topsoils in the Liberec and Domažlice districts, located in the Czech Republic. The soil class, land cover, and geology maps as well as terrain covariates extracted from the digital elevation model and remote sensing data were used as covariates in modelling. The spatial distribution of SOC was predicted based on its relationships with covariates using random forest (RF), cubist, and quantile random forest (QRF) models. Results of the RF model showed that land cover (vegetation) and elevation were the most important environmental variables in the SOC prediction in both districts. The RF had better efficiency and accuracy than the cubist and QRF to predict SOC in both districts. The greatest R2 value (0.63) was observed in the Domažlice district using the RF model. However, cubist and QRF showed appropriate performance in both districts, too.

This study introduces an innovative standard and protocol approach for accurate assessment and harmonization of undisturbed soil surface spectra under field conditions, achieving laboratory-grade precision, while minimizing systematic discrepancies. The SoilPRO® assembly was employed for its efficacy in precisely measuring seven different soil samples under similar field and laboratory conditions in six different laboratories. While some discrepancies were noted in and between field and laboratory measurements, aligning the laboratory measurements with a reference sample, Lucky Bay (LB) internal soil standard (ISS), significantly reduced variations across different laboratory settings. A similar approach was employed to align field spectra using a field ISS (FISS), which could be practically applied in the field and adjusted to the laboratory reference LB before utilization. Correcting to the FISS facilitated the alignment of field reflectance among the six laboratories and closely matched with laboratory ISS measurements adjusted to the LB standard. This alignment of field reflectance with ISS-corrected laboratory measurements represents a ground-breaking achievement in field soil spectroscopy, which suffers from instabilities. It not only ensures preservation of the soil surface condition in the field, but also enables objective comparisons with all soil spectral libraries (SSLs) measurements and robust harmonization of field spectral data from different sources. The suggested method masks out technical aspects of spectral measurements in the field and prepares the signals to be analyzed for the physicochemical characteristics of the soil surface. The current study marks the first use of a controlled method for soil-surface spectroscopy measurements, opening a path for the construction of in-situ field SSLs. This advancement represents a significant step toward obtaining more accurate and standardized soil analyses of diverse surface properties under field conditions.

This review focuses on the behaviour of four psychoactive compounds (carbamazepine, tramadol, sertraline and citalopram) in the environment. The review presents how they may directly affect freshwater systems, soils and living organisms and to which extent. The transformation of these very stable compounds in soils were controlled by oxidation, demethylation, decarboxylation and hydroxylation. Sorption to organic matter and clay particles controlled their mobility. Despite their expected sorption in soils, sediments and sludge, all compounds can be taken up by plants and by fish. In plants, all compounds and several of their metabolites were found in the leaves, indicating the mobility and transformation of the compounds within the plant systems. Factors that control the compounds mobility in plants were found to be the pH of soils and the xylem flow. As for fish, many of the compounds were found in the brain and muscles of fish, some of which, depending on the species, affected the behaviour of the fish. The implications of these compounds so widely present in the environment indicate the need for certain measures to be put into place to prevent these compounds from continuously entering plant and animal systems.

Soil organic carbon (SOC) and clay, as indicators of soil fertility, are mainly used to determine the ability of soil to retain water and store the nutrients that are necessary for plant growth. However, the distribution of SOC and clay is influenced by topography and land-use. In the present study, the relationships between SOC, clay, altitude, and slope in the topsoil of two different districts in the Czech Republic including the Liberec (71 samples) and Domažlice (67 samples) districts were investigated. To analyse the relationships between slope and SOC, linear regression was used. Results showed that SOC content increased when slope, clay, or altitude increased; however, there were no significant correlations between SOC and clay in both districts. Clay increased with decreasing slope, but clay and altitude were not correlated well in both areas. Then, study areas were divided into three land-use types including arable land, forest, and complex system of agriculture, parcels, and forests. Consequently, the correlations between SOC and slope and clay and slope were generally improved, indicating the importance of land-use on SOC and clay content. Additionally, using multiple regression with several topographic factors can provide a better prediction of SOC and clay content in each land-use for both districts, indicating the complex effects of topography on SOC and clay.

Base cations have declined within European forests due to leaching, accelerated by atmospheric acid deposition. This study aims at predicting the spatial distribution of pseudototal content of Ca, Mg, and K for coniferous, broadleaved and mixed forest stands. A harmonised database of about 7000 samples from the top mineral layer of 0–30 cm from the entire forest areas of the Czech Republic was used. A regression kriging model was used for spatial prediction of the content of the elements. The influence of the covariates used for the prediction was assessed using generalized additive models for location scale and shape (GAMLSS). The variance explained by the model was best for Ca with the R2 of 0.32, the R2 for Mg was 0.30, and the R2 for K was 0.26. Model fitting assessed by the ratio of performance to inter-quartile distance (RPIQ) showed K as the best fit with a value of 1.12, followed by Mg with the value 0.87, and Ca with 0.25. Ca exhibited the best prediction fit for the GAMLSS, compared with K and Mg, based on their AIC matrix values. The predicted spatial distribution in this study provides information for policy and will provide information for the sustainable management of forests.

Forestry reclamation practices have been very popular in the second half of the last century, and many spoil heaps have been converted into forests since. In our experiment, three forest reclamations of different ages (~90, ~50, and ~30 years) and three soil vegetation covers (I—maple and cherry, II—maple, and III—alder) from Sokolov, Czech Republic, were investigated. In each of the three stands, two soil profiles have been dug, and both disturbed and undisturbed soil samples were taken from all recognized horizons. Samples were tested for bulk and particle density, porosity, water retention capacity, pH (H2O, KCl), cation exchange capacity, oxidizable carbon content, organic matter quality, plant available nutrients, and risk elements. A comparison of these properties throughout the profile, as well as between the stands, was presented. A significant role of stand age in soil profile development and soil quality was observed, as well as the tendency of the anthropogenic mine Technosol to evolve into a forest Cambisol in this climate region and parent material. Influence of forest vegetation cover was observed to ameliorate soil properties by accumulating organic matter, thus reducing compaction and increasing CEC and nutrient availability.

The SoilPRO® (SP) is an assembly designed to acquire soil reflectance information in the field without disturbing the soil surface, and regardless of atmospheric and solar radiation conditions. This paper summarizes five case studies in which the SP assembly was used for different applications. The case studies consisted of: (1) generating surface spectral measurements under any atmospheric condition; (2) comparing the performance of the SP to the traditional bare fiber method for vicarious calibration of hyperspectral satellite sensors; (3) assessing water repellency of a soil surface governed by organic matter hydrophobicity; (4) spatial prediction of the rate of water infiltration into the soil profile as governed by the soil surface seal; and (5) using the SP apparatus to measure soil surface reflectance in South Shetland Island, Antartica under severe weather conditions. The case studies included calculation of spectral quality, prediction accuracy and measurement stability. The paper discusses each of the cases in detail and concludes that the SP (or similar assembly) is the best way to measure the reflectance of the original soil surface in the field. In the first case study, the spectrum collected by the SP under daily changing illumination was shown to be stable relative to the traditional measurement methods of contact probe or bare fiber. The second case study indicated that use of the SP for vicarious calibration is much more efficient (in terms of time and stability) than ground-truth practice over a large area, and in the third case study, the SP was able to assess a soil surface property governed by organic matter hydrophobicity better than the contact probe, which destroys the soil surface organic seal. A similar achievement was gained in the fourth case study, providing a better assessment of the water-infiltration rate into the soil. In the fifth case study, the SP demonstrated impressive high-quality acquisition of soil surface reflectance with a very low sun angle over the South Pole. Based on these case studies and the high quality of the data generated by the SP in the field, we suggest building, in parallel to the classical soil spectral libraries generated in the laboratory, field soil spectral libraries that will preserve the soil surface properties scanned in the field. We anticipate the development of more applications for the SP assembly based on the capabilities shown in this paper.

Abstract Mining and smelting activities can contaminate soils and affect farming due to high emissions and input of potentially toxic elements (PTE) into the environment. Soils (sampled from two depths) and market vegetables from vegetable gardens located within the vicinity of unconfined slag deposits from decades of mining and smelting activities in Kutná Hora, Czechia were assessed to determine to what extent they pose a health hazard to communities that use these gardens. Pseudo-total As concentrations in the soils exceeded background levels (4.5 mg kg−1) 1.9–93 times, with higher concentrations in the deeper layer. The pseudo-total concentrations of PTE in soils ranked in the order As > Zn > Cd > Pb. Phyto-available concentrations of PTE in soils were relatively low, compared to pseudo-total concentrations. Concentration of As, Cd, Pb and Zn in the vegetables exceeded guideline values, with the highest concentrations found in the fruits of cucumber, peppers, and zucchini. Despite low phyto-available PTE concentrations in soils, all the PTE concentrations in the vegetables surpassed the guidelines set by the Czech Ministry of Health and EU directive, indicating a health hazard to consumers.

Forest soils have a high potential to store carbon and thus mitigate climate change. The information on spatial distribution of soil organic carbon (SOC) stocks is thus very important. This study aims to analyse the importance of environmental predictors for forest SOC stock prediction at the regional and national scale in the Czech Republic. A big database of forest soil data for more than 7 000 sites was compiled from several surveys. SOC stocks were calculated from SOC content and bulk density for the topsoil mineral layer 0-30 cm. Spatial prediction models were developed separately for individual natural forest areas and for four subsets with different altitude range, using random forest method. The importance of environmental predictors in the models strongly differs between regions and altitudes. At lower altitudes, forest edaphic series and soil classes are strong predictors, while at higher altitudes the predictors related to topography become more important. The importance of soil classes depends on the pedodiversity level and on the difference in SOC stock between the classes. The contribution of forest types as predictors is limited when one (mostly coniferous) type dominates. Better prediction results can be obtained in smaller, but consistent regions, like some natural forest areas.

This study aims to compare the chromium (Cr) mobility in two soils with different Cr sources. The first one is an anthropogenically contaminated soil from an area of a piston ring production factory, and the second one is a serpentine soil with a naturally increased Cr level from the National Nature Reserve Mohelno serpentine steppe in the Czech Republic. To assess the influence of naturally occurring environmental factors on the Cr mobility, two sets of column leaching experiments were constructed, namely an acidified water treatment (AW) and a simulated root exudate treatment (SRE). It was found that the anthropogenically contaminated soil leached out many times more Cr for both (the AW and SRE) treatments in comparison with the serpentine soil. Generally, all the samples showed enhanced leaching from the bottom layer, respectively the horizon, for both treatments. Only the anthropogenic soil showed some differences in the bottom layer, which was more susceptible to the AW treatment than to the SRE one. These findings show the high dependence of the leachability of Cr on the Cr content origin. It has been proven that naturally enriched sites where elevated levels of Cr originate from the bedrock are more resistant to Cr leaching than anthropogenically contaminated soils. Feasible causes of these differences are discussed in this study.

Abstract Soil pollution is a big issue caused by anthropogenic activities. The spatial distribution of potentially toxic elements (PTEs) varies in most urban and peri-urban areas. As a result, spatially predicting the PTEs content in such soil is difficult. A total number of 115 samples were obtained from Frydek Mistek in the Czech Republic. Calcium (Ca), magnesium (Mg), potassium (K), and nickel (Ni) concentrations were determined using Inductively Coupled Plasma Optical Emission Spectroscopy. The response variable was Ni, while the predictors were Ca, Mg, and K. The correlation matrix between the response variable and the predictors revealed a satisfactory correlation between the elements. The prediction results indicated that support vector machine regression (SVMR) performed well, although its estimated root mean square error (RMSE) (235.974 mg/kg) and mean absolute error (MAE) (166.946 mg/kg) were higher when compared with the other methods applied. The hybridized model of empirical bayesian kriging-multiple linear regression (EBK-MLR) performed poorly, as evidenced by a coefficient of determination value of less than 0.1. The empirical bayesian kriging-support vector machine regression (EBK-SVMR) model was the optimal model, with low RMSE (95.479 mg/kg) and MAE (77.368 mg/kg) values and a high coefficient of determination (R2 = 0.637). EBK-SVMR modelling technique output was visualized using a self-organizing map. The clustered neurons of the hybridized model CakMg-EBK-SVMR component plane showed a diverse colour pattern predicting the concentration of Ni in the urban and peri-urban soil. The results proved that combining EBK and SVMR is an effective technique for predicting Ni concentrations in urban and peri-urban soil.

The methodology described here presents the procedures for determining physical soil properties of undisturbed soil samples. Besides describing the methods for determining bulk and particle density, moisture content and porosity of the soil in detail, it also offers a way of determining soil's water holding properties when there is no pressure membrane apparatus available. This method is based on a capillary water saturation experiment and gravimetric measurements performed in different time intervals after the saturation (30 minutes, 2 hours, and 24 hours). With a few, simple to follow steps, and not using complicated and space-consuming equipment, it can be replicated in almost any laboratory, and the results are easily interpreted. The method was, and still is, widely used in the Czech Republic, and some parts of it are used as standard soil testing methods. To a lesser or greater detail, this method is described in Rejšek (1999), Valla et al. (2011), Pospíšilová et al. (2016) and ÚKZÚZ (2016), and this methodology is compiled from those publications, mainly focusing (and using the same abbreviations) on the procedures described by Valla et al. (2011). The methodology described does not essentially differ from the original, but the steps here have been described to a greater detail, based on the practical experiences obtained over the years, in order to make some common mistakes less likely to happen. The methodology is further complemented with graphical illustrations for each step described in the process, making it clearer, more easily understood, and easier to replicate. Since this methodology has not been available in English so far, this guide offers a great opportunity of its replication on an international level. • Simple, cost-effective and environmentally friendly method for determining physical soil properties • Easy replication and results interpretation • Results can be obtained even in non-highly specialized soil laboratories

Abstract Unhealthy soils in peri-urban and urban areas expose individuals to potentially toxic elements (PTEs), which have a significant influence on the health of children and adults. Hundred and fifteen (n = 115) soil samples were collected from the district of Frydek Mistek at a depth of 0–20 cm and measured for PTEs content using Inductively coupled plasma—optical emission spectroscopy. The Pearson correlation matrix of the eleven relevant cross-correlations suggested that the interaction between the metal(loids) ranged from moderate (0.541) correlation to high correlation (0.91). PTEs sources were calculated using parent receptor model positive matrix factorization (PMF) and hybridized geostatistical based receptor model such as ordinary kriging-positive matrix factorization (OK-PMF) and empirical Bayesian kriging-positive matrix factorization (EBK-PMF). Based on the source apportionment, geogenic, vehicular traffic, phosphate fertilizer, steel industry, atmospheric deposits, metal works, and waste disposal are the primary sources that contribute to soil pollution in peri-urban and urban areas. The receptor models employed in the study complemented each other. Comparatively, OK-PMF identified more PTEs in the factor loadings than EBK-PMF and PMF. The receptor models performance via support vector machine regression (SVMR) and multiple linear regression (MLR) using root mean square error (RMSE), R square (R2) and mean square error (MAE) suggested that EBK-PMF was optimal. The hybridized receptor model increased prediction efficiency and reduced error significantly. EBK-PMF is a robust receptor model that can assess environmental risks and controls to mitigate ecological performance.

Open-cast coal mining presents a big global issue because of the large areas the mines occupy, which get entirely changed. Their ecosystems lose most of their functions, and a huge amount of fertile soil gets utterly destroyed. Reclamation is a process of returning the functions of the soil after the excavation is finished, most commonly achieved by establishing vegetation, which can sometimes be very difficult. This happens due to the physical, chemical and biological changes that occur on these sites, which are described in this paper. Also, some directions for mitigating these problems are given. Once the vegetation is successfully introduced, natural cycles that were compromised by the mining are established once again, and the process of soil formation begins. Some trends and problems related to pedogenesis research on reclaimed mine sites are presented and discussed, along with presumptions of how the process of soil formation evolves on afforested clayey Technosols of central Europe. The potential future research which would confirm these presumptions is discussed, with the emphasis on the need of research performed on older reclamation sites, as well as sites with similar ecological conditions and different tree species cover.

Open-cast coal mining is one of the most often-debated industries in the world. Due to the significant environmental and health issues it causes, many of these sites have been reclaimed over the years, and many scientific publications and research has followed. In this paper, we have tried to assess the trends in recent research performed on reclaimed coal mining sites (RMS) by analyzing the publications visible on Web of Science (WoS) between 2015 and 2020 and dividing the research into six categories. The results show that there is a trend of rapid increase in research that deals with carbon and its pooling, nutrients, vegetation, and microbiology, and a significant decline in research on RMS soil physical properties, whereas other categories have shown an increasing but relatively steady trend. The application of modern technologies is also discussed. China, the USA, and India are the countries that quantitatively take the lead in coal RMS research, with India slowly overtaking the US in more recent years.

The study aimed to estimate and characterise soil organic matter under different land uses (cropland, grassland, and forest) and soil depths. The soil organic matter composition of the soil was assessed by diffuse reflectance infrared spectroscopy (DRIFT). Humic and fulvic acids (HAs, FAs) were extracted from soils and their compositions were evaluated by DRIFT. Low molecular mass organic acids content was also measured. Our result revealed that the largest differences of the spectra in the composition of organic matter were observed in the upper parts of the soil profile. The forest soil spectra had more intense aliphatic bands, carboxylic, and CH bands than spectra of grassland and cropland soils. The difference of HAs spectra was at 3 010 to 2 800/cm where the most intensive aliphatic bands were in forest soil HAs, followed by grassland and cropland soil HAs. The grassland topsoil FAs spectrum differs most from the other land uses. It has lower peaks around 1 660-1 600/cm and 1 200/cm than cropland and forest. The concentration of low molecular mass organic acid (LMMOA) was the highest in the forest soil and the most abundant acid was citrate.

The aim of the study was to evaluate the effect of clay as an amendment to decrease the mobility and plant availability of potentially toxic elements (PTEs). Kaolinite and vermiculite were added at a 9% application rate to a contaminated garden soil from Kutná Hora. Half of the original soil was acidified by the addition of HNO3, to observe the effect of the pH on the adsorption of the PTEs. The samples were analysed four times over an eight-week period to evaluate the effect of the clay additions on the As, Cd, Pb and Zn plant availability and different fractions of the BCR sequential extraction. The results indicate that the PTEs were mainly bound by ion/cation exchange on the edges of the clay minerals and onto the Fe/Mn oxides and organic fractions in the soils. The significance (P < 0.05) of the pH was observed for the plant availability of the PTE, especially on the As and Cd. A comparison of the vermiculite in the form of chunks and as powder displayed the most statistically significant results, where the vermiculite powder significantly decreased the plant availability of the As, Cd and Pb, while the opposite effect was observed for the Zn.

The concentration and distribution of organochlorine pesticides (OCPs) were studied in sierozem-meadow soils around a buried obsolete chemicals stock in Mirzaabad district of Syrdarya region of Uzbekistan. 23 soil samples were collected from the topsoil (0-30 cm of soil depth) and 15 samples were collected from three soil profiles (down to 125 cm of soil depth) located in nearby vicinity of the stock. They were extracted using an automated Soxhlet extractor and analyses were done by gas chromatography-mass spectrometry. The detected OCPs were DDTs, HCHs, aldrin, dieldrin, endrin, chlorpyrifos, and endosulfans. The concentration of HCH was detected only as α-HCH in the range of 0-3.159 μg/kg. Only two isomers of DDT: p,p'-DDE and p,p'-DDD were found to be dominant, with the range of their values of 0.068-4.941 and 0-13.63 μg/kg, respectively. The concentrations of aldrin, dieldrin, endrin and chlorpyrifos in the samples collected around the source were in the range of 0-0.504, 0-1.117, 2.414-20.87 μg/kg and 0-3.819 μg/kg. β-endosulfan varied from a non-detectable amount to 74.56 μg/kg. According to results, the sum of DDTs was observed to be higher in the upper horizons, especially at depths of 0-10 and 10-50 cm, with the values 543.15 and 212.22 μg/kg, respectively, which can be a cause of concern. Due to their ability to migrate in the environment, accumulate in the food chain and be harmful to human health, monitoring the level of residues of organochlorine pesticides in soil is of vital importance.

A healthy soil is a healthy ecosystem because humans, animals, plants, and water highly depend upon it. Soil pollution by potentially toxic elements (PTEs) is a serious concern for humankind. The study is aimed at (i) assessing the concentrations of PTEs in soils under a long-term heavily industrialized region for coal and textiles, (ii) modeling and mapping the spatial and vertical distributions of PTEs using a GIS-based ordinary kriging technique, and (iii) identifying the possible sources of these PTEs in the Jizerské Mountains (Jizera Mts.) using a positive matrix factorization (PMF) model. Four hundred and forty-two (442) soil samples were analyzed by applying the aqua regia method. To assess the PTE contents, the level of pollution, and the distribution pattern in soil, the contamination factor (CF) and the pollution load index load (PLI) were applied. ArcGIS-based ordinary kriging interpolation was used for the spatial analysis of PTEs. The results of the analysis revealed that the variation in the coefficient (CV) of PTEs in the organic soil was highest in Cr (96.36%), followed by Cu (54.94%) and Pb (49.40%). On the other hand, the mineral soil had Cu (96.88%), Cr (66.70%), and Pb (64.48%) as the highest in CV. The PTEs in both the organic soil and the mineral soil revealed a high heterogeneous variability. Though the study area lies within the “Black Triangle”, which is a historic industrial site in Central Europe, this result did not show a substantial influence of the contamination of PTEs in the area. In spite of the rate of pollution in this area being very low based on the findings, there may be a need for intermittent assessment of the soil. This helps to curtail any excessive accumulation and escalation in future. The results may serve as baseline information for pollution assessment. It might support policy-developers in sustainable farming and forestry for the health of an ecosystem towards food security, forest safety, as well as animal and human welfare.

Pendimethalin [N-(1-ethylpropyl)-3,4-dimethyl-2, 6-dinitrobenzenamine] is a herbicide used worldwide to control most annual grasses and common weeds in cereals, fruit, and vegetables. Its degradation in Haplic Chernozem under controlled greenhouse conditions was studied in this paper. The effect of recommended and doubled pendimethalin doses, as well as the effect of the biopreparate EM-EKO ProBio Plus on pendimethalin degradation in soil and on soil microorganisms was investigated. Pendimethalin half-life ranged from 24.4 to 34.4 days and the double dose did not increase the pendimethalin half-life. Thirty-eight days after pendimethalin application there was no statistical difference between the pendimethalin concentration in soil when applied at the recommended and doubled dose. No effect of pendimethalin on the amount or the activity of soil microorganisms was observed. The effect of EM-EKO ProBio Plus was apparent only on the first sampling of double-dose pendimethalin, however, this bio-preparate had no significant effect on the half-life of pendimethalin, as observed at the end of the experiment.