The article analyzes the features of the radioecological situation in the zone of possible influence of the State Scientific Center «Scientific Research Institute of Atomic Rectors» (SSC NIIAR, Dimitrovgrad, Ulyanovsk region). Field studies were conducted according to the methods recommended for conducting radioecological monitoring. Measurements of the specific activity of natural and man-made radionuclides were carried out on a scintillation gamma-ray spectrometer «Multirad» (Russia) with a scintillation detector NaI(Tl) 63×63 mm in 3-fold repetition. It is shown that at present the radioecological situation in the forests in the territory under consideration is satisfactory: the power of the equivalent absorbed dose of gamma radiation, the density of soil pollution and the levels of specific activity of 137Cs in biota do not exceed the normative indicators, with the exception of fungi, the specific activity of which is close to the maximum normative —2500 Bq∙kg-1. In the Cheremshansky Bay of Volga River the volume activity of 137Cs in the surface and bottom waters does not exceed the standard (11 Bq∙l-1), however, significant amounts of technogenic radionuclides such as 137Cs, 134Cs, 60Co were found in the bottom sediments. In forest and aquatic ecosystems, «critical components» can be distinguished, which are characterized by an increased storage capacity in relation to man-made radionuclides. In forest ecosystems, this is the litter of gray typical forest soil and the dark humus horizon of the urban quasi-ecosystem, and the components of the biota are higher fungi. In aquatic ecosystems — there are bottom sediments.

The soils of two pairs of catena were studied under conditions of petroleum hydrocarbon (PH) contamination and lack of exposure within the Middle Taiga subzone (Tyumen region). 1st pair of catena: Gleyic Albic Podzol, Dystric Gleyic Histic Fluvisols, Dystric Gleyic Fluvisols; 2nd pair of catena:Albic Gleyic Retisols, Albic Gleyic Histic Retisols, Dystric Histic Gleysols . The effects of PH contamination on the biological characteristics of soils (respiration and catalase activity) and the possibility of their use for rationing the content of PH in soils were evaluated. The lateral migration of PH in soils prevailed over the radial one. The highest concentrations of PH were adsorbed in the upper peat layer (from 83 to 399 g∙kg-1). For soils of the superaqual position, a radial monotonously decreasing distribution of PH along the profile was revealed (from 40–50 to 3–4 g∙kg-1). PH contamination of soils led to a violation of the barrier and transformational hydrosphere functions of soils. The pH reaction of soils significantly changed towards alkalinization (0,5 to 2,0 pH units). The accumulation of chlorides (up to 0,7 g∙kg-1) with a maximum in the soils of the superaqual position was recorded; a reliable correlation was established between the pairs: the content of PH – pH, PH – Cl- and Cl- – pH, which indicated the income of crude oil into the soil as a result of emergency pipeline holes. PH underwent changes over time: heavy high-boiling hydrocarbons (99.9%) were present in the soils after 3 years of pollution. A significant correlation was revealed between PH and basal respiration (BR): direct for humic and reverse for organic soil horizons. It was found the microbial coefficient of qCO2 in organic soils of Western Siberia under oil contamination decreased with an increase in the concentration of PH. A negative correlation was found between PH concentrations and catalase activity in soils (R = –0,80 – (–0,90), p<0,05). Th e levels of PH content at which stable soil functioning occurs are justifi ed: no more than 6,8 g∙kg-1 for soils with 1-year contamination and 2,3 g∙kg-1 for soils with 3-year contamination and high concentrations of salts.

In the territories of urban specially protected natural areas in intact ecosystems, the predominance of nemoral species (larch, spruce, linden), meadow species (birch forest), and weed-ruderal species (pine forest) in the living ground cover was revealed. This species composition is not typical for natural zonal phytocenoses. Boreal species are found in small numbers in all plantings, only in larch their share reaches 35%. At the same time, in terms of typology, reserves and spatial distribution of litter of deciduous and coniferous plantations, protected areas correspond to similar characteristics of natural plantations, with the exception of pine plantations. Among the tree species studied, spruce exhibits the most stable influence on the conditions of formation of terrestrial detritus as an edificator. The intensity of the biological cycle in deciduous plantings is higher than in coniferous ones, as evidenced by data on the typology and stocks of litter. Litters, as a component of the ecosystem and as an important link in the biological cycle, are very stable in urban environments and can retain the properties of natural analogues in the absence of significant anthropogenic impact, unlike living ground cover. To expressly indicate the impact of a megacity on forest ecosystems, it is proposed to use an integrated approach based on the analysis of a number of informative and most accessible indicators of grass cover and litter. It has been established that litter of pine plantations is more sensitive to the influence of the metropolis.

The paper characterizes the profiles of virgin chestnut soils and solonetz in the north of the Caspian lowland adjacent to the Djanibek reserve. The objective of the study was to analyze the morphology, physical properties and chemical and mineralogical composition of the so-called xerometamorphic horizons of chestnut soils in the research area. The initial hypothesis was the general genetic similarity of xerometamorphic horizons with natric horizons in the corresponding conjugate soils. With the general similarity of morphological features, mineralogical and chemical composition of these horizons, there was a significant difference in the content of exchangeable sodium and in the presence of signs of clay illuviation in the natric horizons. It is suggested that xerometamorphic horizons reflect the initial stages of formation of alkaline soils, further diluted by divergent pedogenesis. The proposal of a number of specialists on the allocation of the BMK horizon in soils used in agriculture and, accordingly, the type of agro-soils is supported.

Remote sensing data is an important source of information for monitoring and mapping vegetation cover. Machine learning methods are a modern and powerful tool for data processing. However, machine learning methods combined with remote sensing data were practically not used for soil salinity assessment and mapping in the southern steppe zone of Russia. This paper examines the possibility of applying various spectral characteristics to soil salinity mapping in solonetzic complexes in the southern steppe zone of Russia (Republic of Kalmykia) using machine learning methods. A number of predictors were considered, including reflectance coefficients in blue, green, red, infrared spectral zones, vegetation indices (NDVI, NDVIt, TVI, SAVI, MSAVI, EVI1-EVI4), salinity indices (SI1-SI6), intensity indices (Int1, Int2), brightness index (BI), and an index proposed by the authors. High-resolution images from QuickBird (2.4 m) and SuperView-1 (2 m) satellites were used. Soil salinity was assessed using two indicators: specific electrical conductivity in water suspension (EC1:5) and sodium activity (aNa1:5). Two different machine learning models were applied in the study: linear regression and neural networks. According to the results obtained, the linear regression model for EC1:5 in 0–30, 0–50, 0–100 cm layers has coefficients of determination (R2) of 0.53, 0.59, 0.79 on the training sample; the test sample managed to obtain coefficients of determination of 0.49, 0.58, 0.70, respectively. The neural network model has significantly higher coefficients of determination: R2 for EC1:5 in layers 0–30, 0–50, 0–100 cm on the training sample is equal to 0.68, 0.91, 0.97; on the test sample is 0.87, 0.86, 0.88, respectively. This indicates a greater potential of this model for cartographic modeling of soil salinity. The best predictors were the following indices: NDVIt, TVI, EVI1, Int1. The study showed the potential of using the neural network model and spectral indices obtained with SuperView-1 images for soil salinity mapping of solonetzic complexes in the south of the steppe zone of Russia.