Počvovedenie

For an area of 8.6 km² in the central part of the Smolenskoye Poozerye National Park, soil maps (1 : 25 000) were made using conventional and digital mapping (DSM) methods based on relief, vegetation, time and antropogenic factors of soil formation with subsequent comparative analysis of the results. Both maps show that the study area is dominated by gray humus soils (Umbric Cambisols (Arenic, Aric)), which are related to agricultural land-use in the past and self-restoration of abandoned plowed soils. Smaller territories are occupied by alluvial peat (Histic Fluvisols) and postagrogenic texture-differentiated soils (Albic Glossic Retisols (Aric, Loamic)). The overall accuracy of the map made by the DSM method was 57%. The Topographical Wetness Index (TWI) and the forest taxation map were leading soil-forming factors, represented in both expert and digital models. The distribution of alluvial peat (Histic Fluvisols) and agrozems (Arenosols (Aric)) is best predicted by DSM method (with an accuracy of 100 and 86%, respectively) and associated with peat deposits and modern arable lands. The location of peat soils (Fibric Histosols) and peat-podzols (Histic Albic Podzols (Arenic)) is predicted with the least accuracy (0%). The accuracy of predicting the location of Al-Fe humus (Podzols), texture-differentiated (Retisols), organo-accumulative (Umbric Cambisols), gley (Histic Gleysols), and alluvial humus gley soils (Gleyic Fluvisols) varied within 50–63%. The DSM method reproduced the result of conventional mapping by 63%.

The results of digital mapping of the soil cover structure of Volgograd are presented. A soil map-scheme scale 1 : 50 000 reflecting the peculiarities of spatial distribution of separate types (subtypes) of soils and technogenic surface formations (TSF) has been created. The mapping is based on the results of space imagery interpretation with subsequent refinement in the framework of field studies (155 transects). Geoinformation processing was carried out in QGIS software using remote sensing data of space images of natural colours from QuickBird satellite. On the basis of soil and TSF areal content and character of their distribution 6 types of soil urbanised space structures were identified. It is established that the soil cover of the city reflects the structure and character of land use. Thus, anthropogenic soils are predominantly located in the eastern part of the city, which is explained by the confinement of the main objects of industrial, residential and transport infrastructure to the bank of the Volga River and corresponds to the historical direction of development. Natural and agrogenic soils are observed in the western suburbs and Sarpinskiy Island, annexed in 2014. The obtained results are of practical value and can be demanded for solving the tasks of territorial planning in the implementation of landscaping and other types of improvement.

The iron compound composition of permafrost-affected soils in tundra and taiga landscapes of the Russian permafrost was examined to determine soil and geochemical properties. Total iron concentrations were evaluated using the X-ray fluorescence method. The physical and chemical properties of soils and concentrations of silicate, non-silicate, crystallized, and organic matter-associated iron compounds were studied using standard methods. These soils were classified as cryometamorphic (Haplic Cryosols), cryozems (Turbic Cryosols), peat-cryozems (Histic Turbic Cryosols), gleezems (Reductic Gleysols), peat-gleezems (Histic Reductaquic Cryosols), podzolic soils (Albic Retisols), podburs and podzols (Entic and Albic Podzols). The permafrost active layer’s thickness in the soils ranged from 0.3 to 1.7 m, and deeper. On average, the total iron content ranged between 1.2 and 4.5, with the maximum reaching 10.2%. Silicate compounds presented up to 85% of the total iron due to the poor pedogenic transformation of the soil’s mass. The non-silicate iron compound content did not exceed 1.2%. Their increased contents occurred morphologically in the upper mineral soil horizons as well as in the soils of the slope landscapes with ginger-red spots and interlayers. Increased lateral difference ratios from 1.1 to 2.3 demonstrated that supra-permafrost horizons at depths lower than 1 m have shown non-silicate iron compound accumulation, the concentrations of crystallized and organic matter-associated compounds were 0.6–0.8 and 0.2–0.4%, respectively. It was probably connected to the accumulation of organic matter, sorption by the clay particles, and reductive conditions above the permafrost horizon.

A model experiment on PAH biodegradation in the upper horizons of urban soils with different contents of organic matter was carried out. Airborn dust depositions containing polycyclic aromatic hydrocarbons were added to the soil samples, then the soils were incubated at constant humidity and temperature. Total and potentially bioavailable fractions of phenanthrene, pyrene and benz(a)pyrene were determined in the soils after 1, 51, 102, 190 and 365 days of incubation. The total PAH content determined by exhaustive extraction and the amount of their potentially bioavailable fraction extracted with n-butanol decreased exponentially during 365 days of the experiment both in control samples and in mixtures with atmospheric dust. The biodegradation rate of PAHs was proportional to the absolute content of their bioavailable fraction in soils, and for soil with high organic matter content correlated inversely with the hydrophobicity of the three PAHs examined. The relative content of the bioavailable fraction for phenanthrene and pyrene decreased during the experiment, but remained almost constant for benz(a)pyrene. Based on the obtained results, a scheme for transformation of PAHs from airborne dust depositions in soils is proposed, in which, when assessing the bioavailability of PAHs, not only the molecular parameters of polyarenes are taken into account, but also the phase composition of the polluting source material containing PAH. It has been shown that the procedure for determining the potentially bioavailable fraction of polyarenes in soil by directly measuring their concentration in n-butanol together with measuring the total PAH content can be used as a method for environmental assessment of the state of PAH in urban soils when predicting the rate of accumulation and transformation of hydrophobic pollutants.

The capabilities of the pilot model classifier trained to recognize microbial activity traces on solid surfaces for studying soils and soil-like bodies have been preliminarily assessed. A database of 500 samples described by the authors and in open sources from 1988 to the present was collected for machine learning; among them, 59 samples represented soil horizons, 146 parent rocks and soil-like bodies, as well as rock-forming minerals, accompanying components of soil formation, xenobiotics common in technogenically transformed landscapes of the world. The samples were envolved in the database as options of dispersion, coverage with biofilms and films of other nature, chemical and physical treatment. The array of sample features significant for machine learning included quantiles of the wetting contact angle distribution and generalizing categorical indicators of surface geometry, mineral composition, and state of organic matter. The classification target function was the presence of microbial activity stable traces on a solid surface. Missing data were reconstructed using Monte Carlo procedure and bootstrapping. As a result of numerical experiments on optimizing the machine learning a balanced training dataset containing 1233 pseudo-sample elements was obtained. Six classifier models with parameter variations were trained and evaluated. The most productive classifier, a five-layer neural network with randomly dropout neurons, demonstrated a prediction accuracy of 0.74 and an ROC AUC of 0.80 on the test sample, which is higher than that of simpler and faster classifiers (accuracy and ROC AUC of 0.70). Based on the disagreement between the classifications of a human expert and trained algorithm common feature of samples that are difficult for machine classification were established: with traces of life activity, carbonate, dispersed, which allows one to determine the direction of collecting information to improve the performance of the classifier. The development of an algorithm for recognizing traces of microbial activity is useful for clarifying the mechanisms of biogeochemical and biogeotechnological processes in soils of various origins, including soil formation and terraforming.

A new quantitative assessment of the factors of soil erosion and its intensity from storm and melt runoff was carried out in most of the European part of Russia for 2014–2019. Assessment is based on the universal soil loss equation USLE/RUSLE with spatial resolution 250 m. The results are generalized and cartographically presented in the geosystems of small river basins. A new approach has been developed for modeling the rainfall erosivity (R-factor) using intra-daily precipitation data. A rainfall erosivity model was developed using the GAM method and explained 87% of the data variability. A new methodology has been developed for detecting the cover management factor (C-factor) based on Earth remote sensing data. New results on the C-factor were obtained based on multi-temporal satellite data on vegetation density, spectral vegetation indices and phenological metrics. Snow Water Equivalent data from the Copernicus program was used as current data on water reserves in snow to determine the intensity of soil erosion from melt runoff. The annual intensity of soil erosion (from rain and melt runoff) throughout the entire territory is insignificant: on average 0.6 t/ha per year, median 0.02 t/ha per year. On the plowed lands of the basins, these values are higher: 2.4 t/ha per year and 1.6 t/ha per year, respectively.

The development of rare metal ore deposits in the Murmansk region over the past 70 years has been accompanied by the storage of fine-grained enrichment waste, which led to the formation of two tailings fields. The field, which was decommissioned 35 years ago, is undergoing natural overgrowth processes. Studies of the mineral and chemical composition, quantitative and qualitative characteristics of the microbiota of technogenic surface formations (TSF) and soils, formed on waste from the enrichment of loparite ores, have been carried out. With increasing age of TSF, the destruction of weakly stable alkaline minerals was observed simultaneously with an increase in carbon content from 0 to 4.5% in the upper soil horizon. Differential thermal analysis has shown that organic matter of the coarse humus horizon of the conditionally background soil had a more complex composition in comparison with the organic matter formed on the material of the tailings of rare metal ores. An increase in the number and biomass of bacteria and microscopic fungi, the species diversity of micromycetes, and a leveling of the functional profile of microorganisms during the transition from the initial tailings material to areas with vegetation were noted. The results obtained can form the basis for the development of a nature-based technology for initializing the soil-forming process using indigenous strains of microorganisms that are resistant to the unfavorable conditions of rare metal tailings.