Determination of physical properties of undisturbed soil samples according to V. Novák.

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 Rejsek (1999), Valla et al. (2011), Pospísilová 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.

Prediction of the concentration of cadmium in agricultural soil in the Czech Republic using legacy data, preferential sampling, Sentinel-2, Landsat-8, and ensemble models.

The current study assesses and predicts cadmium (Cd) concentration in agricultural soil using two Cd datasets, namely legacy data (LD) and preferential sampling-legacy data (PS-LD), along with four streams of auxiliary datasets extracted from Sentinel-2 (S2) and Landsat-8 (L8) bands. The study was divided into two contexts: Cd prediction in agricultural soil using LD, ensemble models, 10 and 20 m spatial resolution of S2 and L8 (context 1), and Cd prediction in agricultural soil using PS-LD, ensemble models and 10 and 20 m spatial resolution of S2 and L8 (context 2). In context 1, ensemble 1, L8 with PS-LD was the cumulative optimal approach that predicted Cd in agricultural soil with a higher R2 value of 0.76, root mean square error (RMSE) of 0.66, mean absolute error (MAE) of 0.35, and median absolute error (MdAE) of 0.13. However, with R2 = 0.78, RMSE = 0.63, MAE = 0.34, and MdAE = 0.15, ensemble 1, S2 of PS-LD was the best prediction approach in predicting Cd concentration in agricultural soil in context 2. Overall, the predictions from both contexts indicated that ensemble 1 of S2 combined with PS-LD was the most appropriate and best model for Cd prediction in agricultural soil. The modeling approaches' uncertainty in both contexts was assessed using ensemble-sequential gaussian simulation (EnSGS), which revealed that the degree of uncertainty propagated in the study area was within 5% in both contexts. The combination of the PS dataset and the LD along with ensemble models and the remote sensing dataset, produced promising results. Nonetheless, the results demonstrated that the 20 m spatial resolution band dataset used in the prediction of Cd in agricultural soil outperformed the 10 m spatial resolution. When PS is combined with LD, an appropriate modeling approach, and a well-correlated remote sensing dataset are used, good results are obtained.

Prediction of the concentration of antimony in agricultural soil using data fusion, terrain attributes combined with regression kriging.

Potentially toxic elements in agricultural soils are primarily derived from anthropogenic and geogenic sources. This study aims to predict and map antimony (Sb) concentration in soil using multiple regression kriging in two distinct modeling approaches, namely Sb prediction using data fusion coupled with regression kriging (scenario 1) and Sb prediction using data fusion, terrain attributes, and regression kriging (scenario 2). Cubist regression kriging (cubist_RK), conditional inference forest regression kriging (CIF_RK), extreme gradient boosting regression kriging (EGB_RK) and random forest regression kriging (RF_RK) were the modeling techniques used in the estimation of Sb concentration in agricultural soil. The validation results suggested that in scenario 1, EGB_RK was the optimal modeling approach for Sb prediction in agricultural soil with root mean square error (RMSE) = 1.31 and mean absolute error (MAE) = 0.61, bias = 0.37, and high coefficient of determination R2 = 0.81. Similarly, the EGB_RK was also the optimal modeling approach in scenario 2, with the highest R2 = 0.76, RMSE = 0.90, bias = 0.06, and MAE = 0.48 values than the other regression kriging modeling approaches. The cumulative assessment suggested that the EGB_RK in scenario 2 yielded optimal results compared to the respective modeling approach in scenario 1. The uncertainty propagated by the modeling approaches in both scenarios indicated that the degree of uncertainty during the modeling process was distributed across the study area from a low to a moderate uncertainty level. However, cubist_RK in scenario 2 exhibited some elevated spots of uncertainty levels. As a result, the combination of data fusion, terrain attributes, and regression kriging modeling approaches produces optimal results with a high R2 value, minimal errors as well as bias. Furthermore, combining terrain attributes with data fusion is promising for reducing model error, bias and yielding high-accuracy predictions.

Optimal zinc level and uncertainty quantification in agricultural soils via visible near-infrared reflectance and soil chemical properties.

Zinc (Zn) is a vital element required by all living creatures for optimal health and ecosystem functioning. Therefore, several researchers have modeled and mapped its occurrence and distribution in soils. Nonetheless, leveraging model predictive performances while coupling information derived from visible near-infrared (Vis-NIR) and soils (i.e. chemical properties) to estimate potential toxic elements (PTEs) like Zn in agricultural soils is largely untapped. This study applies two methods to rapidly monitor Zn concentration in agricultural soil. Firstly, employing Vis-NIR and machine learning algorithms (MLAs) (Context 1) and secondly, applying Vis-NIR, soil chemical properties (SCP), and MLAs (Context 2). For the Vis-NIR information, single and combined pretreatment methods were applied. The following MLAs were used: conditional inference forest (CIF), partial least squares regression (PLSR), M5 tree model (M5), extreme gradient boosting (EGB), and support vector machine regression (SVMR) respectively. For context 1, the results indicated that M5-MSC (M5 tree model-multiplicative scatter correction) with coefficient of determination (R2) = 0.72, root mean square error (RMSE) = 21.08 (mg/kg), median absolute error (MdAE) = 13.69 and ratio of performance to interquartile range (RPIQ) = 1.63 was promising. Regarding context 2, CIF with spectral pretreatment and soil properties [CIF-DWTLOGMSC + SCP (conditional inference forest-discrete wavelet transformation-logarithmic transformation-multiplicative scatter correction-soil chemical properties)] yielded the best performance of R2 = 0.86, RMSE = 14.52 (mg/kg), MdAE = 6.25 and RPIQ = 1.78. Altogether, for contexts 1 and 2, the CIF-DWTLOGMSC + SCP approach (context 2) was the best Zn model outcome for the agricultural soil. The uncertainty map revealed a low to high error distribution in context 1, and a low to moderate distribution in context 2 for all models except CIF, which had some patches with high uncertainty. We conclude that a multiple optimization approach for modeling Zn levels in agricultural soils is invaluable and may provide fast and reliable information needed for area-specific decision-making.

Combination of enrichment factor and positive matrix factorization in the estimation of potentially toxic element source distribution in agricultural soil.

The study intended to assess the level of pollution of potential toxic elements (PTEs) at different soil depths and to evaluate the source contribution in agricultural soil. One hundred and two soil samples were collected for both topsoil (51), and the subsoil (51) and the content of PTEs (Cr, Cu, Cd, Mn, Ni, Pb, As and Zn) were determined using inductively coupled plasma-optical emission spectroscopy (ICP-OES). The concentrations of Zn and Cd in both soil horizons indicated that the current study levels were higher than the upper continental crust (UCC), world average value (WAV), and European average values (EAV). Nonetheless, the concentration values of PTEs such as Mn and Cu for EAV, As, Cu, Mn, and Pb for UCC, and Pb for WAV were lower than the average values of the corresponding PTEs in this study. The single pollution index, enrichment factor, and ecological risk revealed that the pollution level ranged from low to high. The pollution load index, Nemerow pollution index, and risk index all revealed that pollution levels ranged from low to high. The spatial distribution confirmed that pollution levels varied between the horizons; that is, the subsoil was considered slightly more enriched than the topsoil. Principal component analysis identified the PTE source as geogenic (i.e. for Mn, Cu, Ni, Cr) and anthropogenic (i.e. for Pb, Zn, Cd, and As). PTEs were attributed to various sources using enrichment factor-positive matrix factorization (EF-PMF) and positive matrix factorization (PMF), including geogenic (e.g. rock weathering), fertilizer application, steel industry, industrial sewage irrigation, agrochemicals, and metal works. Both receptor models allotted consistent sources for the PTEs. Multiple linear regression analysis was applied to the receptor models (EF-PMF and PMF), and their efficiency was tested and assessed using root-mean-square error (RMSE), mean absolute error (MAE), and R2 accuracy indicators. The validation and accuracy assessment of the receptor models revealed that the EF-PMF receptor model output significantly reduces errors compared with the parent model PMF. Based on the marginal error levels in RMSE and MAE, 7 of the 8 PTEs (As, Cd, Cr, Cu, Ni, Mn, Pb, and Zn) analysed performed better under the EF-PMF receptor model. The EF-PMF receptor model optimizes the efficiency level in source apportionment, reducing errors in determining the proportion contribution of PTEs in each factor. The purpose of building a model is to maximize efficiency while minimizing inaccuracy. The marginal error limitation encountered in the parent model PMF was circumvented by EF-PMF. As a result, EF-PMF is feasible and useful for apparently polluted environments, whether farmland, urban land, or peri-urban land.

Assessment of potential exposure to As, Cd, Pb and Zn in vegetable garden soils and vegetables in a mining region.

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.

Can in situ spectral measurements under disturbance-reduced environmental conditions help improve soil organic carbon estimation?

In situ visible and near-infrared (Vis-NIR) spectroscopy has proven to be a reliable tool for determining soil organic carbon (SOC) content with a small loss of precision as compared to laboratory measurements. The loss of precision is a result of disturbing external environmental factors that disrupt spectral measurements. For example, roughness, changes in weather conditions, humidity, temperature, human factors, spectral noise and especially soil water. It has been assumed that, in situ predictive capability could be improved if some of these factors are either minimized or eliminated during the in situ measurement. For this study, the prediction of SOC was carried out under two different in situ measurement conditions; less favourable environmental conditions (with disturbances) and more favourable site-specific conditions (disturbance-reduced conditions). The primary goal is to determine whether the estimate of SOC can be improved under more favourable site-specific conditions, as well as the impact of pre-treatment algorithms on both less and more favourable disturbed conditions. The study employed a large range of pretreatment algorithms and their combinations. Three separate multivariate models were used to predict SOC, namely Cubist, support vector machine regression (SVMR), and partial least squares regression (PLSR). The result clearly shows that reduced disturbing factors (i.e., drier and unploughed soil as well as noise reduction) result in an improvement of SOC prediction with in situ Vis-NIR spectroscopy. The best overall result was achieved with SVMR (R2CV = 0.72, RMSEPcv = 0.21, RPIQ = 2.34). Although the combination of pre-treatment algorithms resulted in an improvement, overall, these pre-treatment algorithms could not compensate for the factors affecting the measured spectra with disturbance. Though the obtained result is promising, further study is still needed to disentangle the impacts and interactions of various disturbing factors for different soil types.

Prediction of nickel concentration in peri-urban and urban soils using hybridized empirical bayesian kriging and support vector machine regression.

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.

An in-depth human health risk assessment of potentially toxic elements in highly polluted riverine soils, Príbram (Czech Republic).

Environmental pollution by potentially toxic element (PTE) and the associated health risks in humans are increasingly becoming a global challenge. The current study is an in-depth assessment of PTEs including the often studied lead (Pb), manganese (Mn), zinc (Zn), arsenic (As) and the less-studied titanium (Ti), rubidium (Rb), strontium (Sr), zirconium (Zr), barium (Ba) and thorium (Th) in highly polluted floodplain topsoil samples from the Litavka River, Czech Republic. Soil chemical properties including carbon (Cox) and reaction (pH_H2O) together with iron (Fe) were assessed in the same soils. A portable X-ray fluorescence spectrometer (p-XRFS) (Delta Premium) was used to measure the PTEs and Fe contents of the soils. Soil organic carbon and reaction pH were determined following routine laboratory procedures. The concentration level of each PTE was compared against world average and crustal values, with the majority of elements exceeding the aforementioned geochemical background levels. Distributions of the PTEs were mapped. Two pollution assessment indices including enrichment factor (EF) and pollution index (PI) levels were calculated and their means for Zn (43.36, 55.54), As (33.23, 43.59) and Pb (81.08, 103.21) show that these elements were enriched. Zn, As and Pb accounted for the high pollution load index (PLI) levels observed in the study. The EF and PI distribution maps corresponded with the concentration distribution maps for each PTE. On health risk assessment, hazard quotients (HQ) in different human groups varied. Children had the highest HQs for all PTEs than adults (women and men). PTEs with high HQ levels in distinct human groups were As, Zr and Pb. Zirconium is a less likely element to pose a health risk in humans. Nonetheless, it should be kept in check despite its low pollution occurrence.

Multi-geochemical background comparison and the identification of the best normalizer for the estimation of PTE contamination in agricultural soil.

Identifying a suitable geochemical background level (GBL) and an appropriate normalizer is imperative for ensuring soil quality, health, and security. The objective of this study was to identify the appropriate normalizer and suitable GBL for determining PTE enrichment levels in agricultural soils and investigate if there are any statistical differences due to the GBL [World Average Value (WAV) European Average Value (EAV)] used. Forty-nine topsoil samples were obtained from seven agricultural communities in the Frdek-Mstek District (Czech Republic). Portable X-ray fluorescence was used to determine the total PTEs (Cr, Ni, Cu, Y, Ba, Th, As, Pb, and Zn) concentration levels in the soil. Correlation matrix analysis was used to determine the metallic relationship between the PTEs and the normalizers (Al, Fe, Ti, Zr, Sr and Rb). Pollution indices such as contamination factor (CF), geoaccumulation index (Igeo) and enrichment factor (EF) analysis were used to determine the most suitable GBL. Al, Fe, Sr, Ti and Rb strongly correlated with the CF, Igeo and EF, whereas WAV performed better than the other geochemical background (EAV). The results indicated that Rb was the suitable normalizer and WAV was the appropriate GBL for agricultural soil and provided a foundation for evaluating and surveilling soil quality and health in agricultural soil.

Using an ensemble model coupled with portable X-ray fluorescence and visible near-infrared spectroscopy to explore the viability of mapping and estimating arsenic in an agricultural soil.

Increasing concentrations of potentially toxic elements (PTE) in agricultural soils remain a major source of public concern. Monitoring PTEs in an agricultural field with no history of contaminants necessitate adequate analysis utilizing a robust model to accurately uncover hidden PTEs. Detecting and mapping the distribution of soil properties using portable X-ray fluorescence (pXRF) and proximal sensing techniques is not only rapid, but also relatively inexpensive. In this study, an ensemble model, consisting of partial least square regression (PLSR), support vector machine (SVM), random forest (RF) and cubist, was used for the prediction and mapping of soil As content in an agricultural field with no history of pollution. The datasets were collected using pXRF and field spectroscopy techniques. The main goal was to compare the ensemble model to each of the calibration techniques in terms of prediction accuracy of As content in such a field. Other components [e.g., soil organic carbon (SOC), Mn, S, soil pH, Fe] that are known to influence As levels in the soil were also retrieved to assess their correlation with soil As. The models were evaluated using the root mean squared error (RMSECV), the coefficient of determination (R2CV) and the ratio of performance to interquartile range (RPIQ). In terms of prediction accuracy, the ensemble model outperformed each of the individual techniques (R2CV = 0.80/0.75) and obtained the least error margin (RMSECV = 1.91/2.16). Overall, all the predictive techniques were able to detect both low and high estimated values of soil As within the study field, but with the ensemble model resembling the measurements better. The ensemble model, a promising tool as demonstrated by the current study, is highly recommended to be included in future studies for more accurate estimation of As and other PTEs in other agricultural fields.

A geostatistical approach to estimating source apportionment in urban and peri-urban soils using the Czech Republic as an example.

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.

Modeling and Assessing the Spatial and Vertical Distributions of Potentially Toxic Elements in Soil and How the Concentrations Differ.

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.

vis-NIR and XRF Data Fusion and Feature Selection to Estimate Potentially Toxic Elements in Soil.

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.

Trend analysis of global usage of digital soil mapping models in the prediction of potentially toxic elements in soil/sediments: a bibliometric review.

The rising and continuous pollution of the soil from anthropogenic activities is of great concern. Owing to this concern, the advent of digital soil mapping (DSM) has been a tool that soil scientists use in this era to predict the potentially toxic element (PTE) content in the soil. The purpose of this paper was to conduct a review of articles, summarize and analyse the spatial prediction of potentially toxic elements, determine and compare the models' usage as well as their performance over time. Through Scopus, the Web of Science and Google Scholar, we collected papers between the year 2001 and the first quarter of 2019, which were tailored towards the spatial PTE prediction using DSM approaches. The results indicated that soil pollution emanates from diverse sources. However, it provided reasons why the authors investigate a piece of land or area, highlighting the uncertainties in mapping, number of publications per journal and continental efforts to research as well as published on trending issues regarding DSM. This paper reveals the complementary role machine learning algorithms and the geostatistical models play in DSM. Nevertheless, geostatistical approaches remain the most preferred model compared to machine learning algorithms.

Source apportionment, contamination levels, and spatial prediction of potentially toxic elements in selected soils of the Czech Republic.

The sustenance of humans and livestock depends on the protection of the soil. Consequently, the pollution of the soil with potentially toxic elements (PTEs) is of great concern to humanity. The objective of this study is to investigate the source apportionment, concentration levels and spatial distribution of PTEs in selected soils in Frýdek-Místek District of the Czech Republic. The total number of soil samples was 70 (topsoil 49 and 21 subsoils) and was analysed using a portable XRF machine. Contamination factor and the pollution index load were used for the assessment and interpreting the pollution and distribution of PTEs in the soils. The inverse distance weighting was used for the spatial evaluation of the PTEs. The results of the analysis showed that the area is composed of low-to-high pollution site. PTEs displayed spatial variation patterns. The average PTE concentration decreases in this Fe > Ti > Ba > Zr > Rb > Sr > Cr > Y>Cu > Ni > Th order for the topsoil and also decreases in this Fe > Ti > Zr > Ba > Rb > Sr > Cr > Y > Cu > Ni > and Th order for the subsoil. These PTEs Cr, Ni, Cu, Rb, Y, Zr, Ba, Th, and Fe were far above the baseline European average value and the World average value level, respectively. The source apportionment showed the dominance of Cr, Ni, Rb, Ti, Th, Zr, Cu, Fe in the topsoil, while the subsoil was dominated by all the PTEs (factor 1 to 6) except Ba. The study concludes that indiscriminate human activities have an enormous effect on soil pollution.

Modelling potentially toxic elements in forest soils with vis-NIR spectra and learning algorithms.

The surface organic horizons in forest soils have been affected by air and soil pollutants, including potentially toxic elements (PTEs). Monitoring of PTEs requires a large number of samples and adequate analysis. Visible-near infrared (vis-NIR: 350-2500 nm) spectroscopy provides an alternative method to conventional laboratory measurements, which are time-consuming and expensive. However, vis-NIR spectroscopy relies on an empirical calibration of the target attribute to the spectra. This study examined the capability of vis-NIR spectra coupled with machine learning (ML) techniques (partial least squares regression (PLSR), support vector machine regression (SVMR), and random forest (RF)) and a deep learning (DL) approach called fully connected neural network (FNN) to assess selected PTEs (Cr, Cu, Pb, Zn, and Al) in forest organic horizons. The dataset consists of 2160 samples from 1080 sites in the forests over all the Czech Republic. At each site, we collected two samples from the fragmented (F) and humus (H) organic layers. The content of all PTEs was higher in horizon H compared to F horizon. Our results indicate that the reflectance of samples tended to decrease with increased PTEs concentration. Cr was the most accurately predicted element, regardless of the algorithm used. SVMR provided the best results for assessing the H horizon (R2 = 0.88 and RMSE = 3.01 mg/kg for Cr). FNN produced the best predictions of Cr in the combined F + H layers (R2 = 0.89 and RMSE = 2.95 mg/kg) possibly due to the larger number of samples. In the F horizon, the PTEs were not predicted adequately. The study shows that PTEs in forest soils of the Czech Republic can be accurately estimated with vis-NIR spectra and ML approaches. Results hint in availability of a large sample size, FNN provides better results.

The influence of land-use on tropical soil chemical characteristics with emphasis on aluminium.

Composition of soil vegetation cover and land management directly influences the cycling of chemical elements and is a key factor for soil biogeochemistry and also Al behaviour. Moreover, Al is an important factor limiting the growth of cultural plants. Our results are based on long-term observations of soils translocated from selected small areas of eight 1 ha plots of different land-use gradient, with identical geological, climatic and geographical conditions, located in the North of Congo Basin (near Mbalmayo, Cameroon). The plots are established in primary and secondary forests, cocoa agroforestry systems and a maize field (two plots per habitat). All soil plots were exchanged between each other in two layers; A. 0-5 cm, and B. 5-20 cm of depths. The soil was sampled at the times 0, +3, +6 months, and soil chemical parameters were determined. The most important differences between the particular habitats comprise of vegetation cover as a consequence of the land management. Particular plots differed mainly in their pH, organic C, exchangeable Al and contents of base cations. The most marked trends comprise of significant decrease of pH, increase of Al and decrease of the Ca/Al ratio in A layer after translocation to the agricultural plots. All translocations resulted into rapid loss of organic C and release of Al, which was more obvious when the forest-to-agriculture translocation took place.

Magnetic mapping of distribution of wood ash used for fertilization of forest soil.

The effect of wood-ash fertilization on forest soils has been assessed mainly through geochemical methods (e.g., content of soil organic matter or nutrients). However, a simple and fast method of determining the distribution of the ash and the extent of affected soil is missing. In this study we present the use of magnetic susceptibility, which is controlled by Fe-oxides, in comparing the fertilized soil in the forest plantation of pine and oak with intact forest soil. Spatial and vertical distribution of magnetic susceptibility was measured in an oak and pine plantation next to stems of young plants, where wood ash was applied as fertilizer. Pattern of the susceptibility distribution was compared with that in non-fertilized part of the plantation as well as with a spot of intact natural forest soil nearby. Our results show that the wood-ash samples contain significant amount of ferrimagnetic magnetite with susceptibility higher than that of typical forest soil. Clear differences were observed between magnetic susceptibility of furrows and ridges. Moreover, the dispersed ash remains practically on the surface, does not penetrate to deeper layers. Finally, our data suggest significant differences in surface values between the pine and oak plants. Based on this study we may conclude that magnetic susceptibility may represent a simple and approximate method of assessing the extent of soil affected by wood-ash.

Behaviour of aluminium in forest soils with different lithology and herb vegetation cover.

The aim of this study was to determine the content, distribution and behaviour of Al in soils under beech forest with different parent rock, and to assess the role of herbaceous vegetation on soil Al behaviour. We hypothesize that the contents of elements in the soil sorption complex (Al etc.) are strongly influenced by vegetation cover. Also, low molecular mass organic acids (LMMOA) can be considered as an indicator of soil organic matter (SOM) decomposition and vegetation litter turnover. Speciation of LMMOA, nutrition content (PO43-, Ca2+, K+) and element composition in aqueous extracts were determined by means of ion chromatography and inductively coupled plasma - optical emission spectrometry (ICP-OES) respectively. Active and exchangeable pH, sorption characteristics and exchangeable Al (Alex) were determined in BaCl2 extracts by ICP-OES. Elemental composition of parent rocks was assessed by means of X-ray fluorescence spectroscopy. Herb-poor localities showed lower pH, less nutrients (PO43-, Ca2+, K+), less LMMOA, a larger stock of SOM and greater cation exchange capacity. There was also lower mobilisation of Al in organic horizons, which explains the larger pools of Al. Generally, we can conclude that LMMOA, and thus soil vegetation cover, play an important role in the Al soil cycle.