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.

• Klíčová slova
• Agricultural soil, Conditional inference forest, Spectral reflectance, Uncertainty assessment, Zinc,
• MeSH
• ekosystém * MeSH
• nejistota MeSH
• půda * MeSH
• zemědělství MeSH
• zinek MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• půda * MeSH
• zinek MeSH

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.

• Klíčová slova
• Agricultural soil, Data fusion, Regression kriging, Terrain attributes, Uncertainty,
• MeSH
• antimon * MeSH
• prostorová analýza MeSH
• půda * MeSH
• zemědělství MeSH
• Publikační typ
• časopisecké články MeSH
• Názvy látek
• antimon * MeSH
• půda * MeSH

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.

• Klíčová slova
• Algorithms, Digital soil mapping, Geostatistics, Machine learning, Potentially toxic elements, Soil pollution, Spatial prediction,
• MeSH
• algoritmy MeSH
• bibliometrie MeSH
• geologické sedimenty analýza   MeSH
• látky znečišťující půdu analýza   MeSH
• monitorování životního prostředí metody   MeSH
• půda * MeSH
• strojové učení MeSH
• znečištění životního prostředí analýza   MeSH
• Publikační typ
• časopisecké články MeSH
• přehledy MeSH
• Názvy látek
• látky znečišťující půdu MeSH
• půda * MeSH

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.

• Klíčová slova
• Contamination degree, European average value, Potentially toxic elements, Soil pollution, Source apportionment, Spatial distribution,
• MeSH
• hodnocení rizik MeSH
• látky znečišťující půdu analýza   toxicita   MeSH
• lidé MeSH
• monitorování životního prostředí MeSH
• prostorová analýza MeSH
• půda chemie   MeSH
• těžké kovy analýza   toxicita   MeSH
• znečištění životního prostředí analýza   statistika a číselné údaje   MeSH
• Check Tag
• lidé MeSH
• Publikační typ
• časopisecké články MeSH
• Geografické názvy
• Česká republika MeSH
• Názvy látek
• látky znečišťující půdu MeSH
• půda MeSH
• těžké kovy MeSH