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The ecotoxicological condition of soils around mining areas is most often unsatisfactory, which affects entire ecosystems and human health. This research sought to analyze the morphological, agrochemical properties, and content of heavy metals (Cd, Cu, Zn) and metalloids (As) of soils located in a floodplain. The study was conducted within the city of Sibay (Republic of Bashkortostan, Russia). The soil samples were collected from the floodplains of the rivers Karagayly and Khudolaz. According to morphological studies, the soil cover was represented by the Lithic Leptosols, Stagnic Phaeozems, and Fluvisols. The results showed that the soils were characterized by high values of organic matter, potassium, and low levels of phosphorus. Soils that were located away from the city in the Karagayly River were not contaminated. However, the floodplain areas pertaining to the urban district and located near the quarries were characterized by severe anthropogenic soil pollution, disrupted integrity of the soil cover, decreased vegetation, and accumulating labile forms of heavy metals and metalloids. The highest degree of pollution was observed in the floodplain soil of the river Khudolaz where all elements exceeded the maximum permissible concentration (MPC) level. Soils in the floodplain of the Karagayly river were marked by an increased degree of contamination of Zn: exceeding MPC by 1.6 times. With the trend toward an arid climate, the ecotoxicological condition of floodplain soils is an important challenge. [ABSTRACT FROM AUTHOR]

Topographic features of territory have a significant impact on the spatial distribution of soil properties. This research is focused on digital soil mapping (DSM) of main agrochemical soil properties—values of soil organic carbon (SOC), nitrogen, potassium, calcium, magnesium, sodium, phosphorus, pH, and thickness of the humus-accumulative (AB) horizon of arable lands in the Trans-Ural steppe zone (Republic of Bashkortostan, Russia). The methods of multiple linear regression (MLR) and support vector machine (SVM) were used for the prediction of soil nutrients spatial distribution and variation. We used 17 topographic indices calculated using the SRTM (Shuttle Radar Topography Mission) digital elevation model. Results showed that SVM is the best method in predicting the spatial variation of all soil agrochemical properties with comparison to MLR. According to the coefficient of determination R2, the best predictive models were obtained for content of nitrogen (R2 = 0.74), SOC (R2 = 0.66), and potassium (R2 = 0.62). In our study, elevation, slope, and MMRTF (multiresolution ridge top flatness) index are the most important variables. The developed methodology can be used to study the spatial distribution of soil nutrients and large-scale mapping in similar landscapes. [ABSTRACT FROM AUTHOR]