Articles

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.

The physical, chemical and physico-chemical properties of soils and man-made surface formations of the territories of coal dumps in the southeastern part of the Donetsk ridge within the Grushevsko-Nesvetaevskaya syncline (Rostov region), differing in age, lithological properties of the overburden and host rocks composing them, and pyrogenic dynamics, have been studied. The main characteristics of the soil properties of the studied territories have been established to assess the geoecological situation, patterns of the spread of potentially toxic substances and the development of projects for the restoration of territories. The unequal influence of soil formation factors was revealed, manifested in high variability and contrast of soil properties. Electron microscopic studies of technogenic samples have shown that the coarse microstructure was formed as a result of the impact of modern compaction, cementation and sintering processes on the rock.

Microbial production and CO2 emission in Albic Retisol (Loamic) and Albic Retisol (Aric, Loamic) of Lomonosov Moscow State University carbon polygon «Chashnikovo»≫ in the Moscow region were studied during the peak of vegetation activity. The object represents monitoring sites in two natural ecosystems (secondary spruce forest and mixed-grass meadow) and in two agroecosystems (perennial grasses and bare fallow). The soils differed in their stocks
of organic matter carbon (Corg), microbial carbon (Cmic), and potentially mineralizable carbon (Cpm). The highest values of microbial indicators and carbon content were observed in the upper 30 cm with a maximum in the upper 10 cm. Corg stocks in the 0–30 cm layer were 1.1–1.3 times higher in agricultural soils (72.7–75.6 t ha-1) compared to natural ones (59.4–65.0 tha-1). The contribution of Cmic stocks to Corg stocks in meadow soils and agricultural soils under perennial grasses (1.8 and 1.4%) was higher than in forest soils and soils under bare fallow. Сpm stocks in natural soils (1.34 and 0.97 tha-1 for a layer of 0–10 cm) were 1.3–2.3 times greater than in agricultural soils, but the intensity of mineralization of organic matter in agricultural soils was 1.4–2.9 times lower than in soils of natural ecosystems. For ecosystems with similar vegetation, the influence of Сpm stocks on the amount of microbial CO2 production was shown; its proportional increase was noted. Maximum potential microbial CO2 production (4.8 gCm-2day-1 for the 0–10 cm layer and 10.5 gCm-2day-1 for the 0–30 cm layer), Cmic stocks (0.50 tha-1), as well as CO2 emission (11.09±0.29 gCm-2day-1) were characteristic of the soils of a dry meadow. In other ecosystems, CO2 production by microorganisms was lower by approximately 2 times. Since CO2 emission, in addition to microbial respiration, is also caused by the respiration of plant roots, its minimum values were observed in agricultural soils under bare fallow (5.01±1.43 gCm-2・day-1). CO2 emission from forest soils and agricultural soils under perennial grasses was statistically not significantly lower compared to the meadow.

The FAO methodology within the Global Soil Nutrient and Nutrient Budget Maps (GSNmap) project was tested for the first time for mapping humus content with a spatial resolution of 250 meters per pixel in soils of the Russian Federation at the regional scale, using the Bryansk Region as an example. The map was created in the R soft ware environment using data from Agrochemical Service and remote sensing, global databases and soil maps. The centroids of the sites from which the composite samples were taken by Agrochemical Service were selected as sampling points. The set of predictors available under the FAO project was expanded by additional data, including soil maps and maps of soil-forming rocks. The importance of the predictors was assessed using the Boruta algorithm, which is usually used as an initial stage for a random forest. The model was created using the caret package with the quantile regression forest method. The modeling efficiency coefficient (MEC) was 55%, the coefficient of determination (R2) was 0.57. The map reflects current information that can be used to monitor the dynamics of organic matter content in the soil and assess the state of the arable soils in the Bryansk region.

Soils are the largest terrestrial reservoir of organic carbon, so even small changes in soil carbon stocks can have significant eff ects on the atmosphere and climate. To select effective strategies to mitigate climate change, predictions of how soils will respond to future changes in climate and land use are needed. Achieving meaningful predictions requires a deep understanding of the highly complex, open, multicomponent soil organic matter system. One of the most effective methods for predicting the dynamics of soil organic matter is mathematical modeling. Process-oriented (physically based) models make it possible to present the basic concepts about the mechanisms that determine the behavior of this system in a mathematically formalized form and conduct a quantitative analysis. The uncertainty of the forecasts depends on the level of development of the theory explaining the dynamics of soil organic matter, the models representing it and their experimental support. This review examines the achievements of the last decade in modeling the role of microorganisms in the stabilization of soil organic matter, the concept of soil saturation with organic carbon, temperature control, as well as the development of reactive transport models describing the dynamics of organic carbon in the soil profile, and the representation of the dynamics of soil organic matter in global climate models. Unsolved problems associated with the high variability in the structure of new generation soil organic matter dynamics models are discussed.

Winter temperature regime was studied for soils of thufur field formed on gazha sediments within the first terrace above the floodplain, and for the dark humus soil formed under the conditions which were closer to zonal ones. The temperature of atmospheric air, that of soil surface under snow and soil temperatures at the depths of 5–120 cm were measured with autonomous temperature recorders. The surface temperature of soil under snow depended on the snow depth. In the thufur center the sum of surface temperatures for the period from December 10 to March 30 was equal to –387°C, and in the microdepression this sum was smaller by absolute value and equal to –170°C. Soil temperature in thufur microelevation for different depths was lower than soil temperature in microdepression between the thufurs; soil temperature in zonal conditions was intermediate. The zero-isotherm penetration depth was 55 cm within the thufur, 25 cm in microdepression and 45 cm in the zonal soil. The microtopography of the thufur field is a factor of snow redistribution and formation of heterogeneity of snow depth, thus forming regular differences in the zero-isotherm penetration depth in different areas and spatial heterogeneities in the temperature field of the studied soils in winter.

The thermal diffusivity of the upper plow horizon of the urban soil from the territory of MSU Experimental soil station, lowland packed peat, and their mixtures was studied. The soil from Ap horizon was sieved through a 1 mm sieve and mixed with peat in various proportions; the content of peat in mixtures ranged from 1 to 80% by dry weight. Metal cylinders 10 cm high and 3.8 cm in diameter were filled with soil, peat, and soil-peat mixtures. The thermal diffusivity was measured using the unsteady-state method with a working temperature range of 20–26°C. The heating rate of the packed samples was measured after the samples were placed in a liquid thermostat with a constant water temperature. For each sample, a series of measurements was carried out with a step-by-step change in soil water content from the maximum one after capillary saturation of the sample to the minimum one, corresponding to the air-dry state. Within the studied range of water contents, the thermal diffusivity of different samples changed by a factor of 1.3–3.5; the greatest changes with water content were typical for mineral soil. The thermal diffusivity vs. water content dependence was almost linear for peat, and for soil it was an S-shaped curve with a weakly expressed maximum. The lowest thermal diffusivity was obtained for peat and mixtures with high peat contents; the highest one — for soil without peat additions. The non-linear character of the thermal diffusivity vs. peat content dependence was revealed. Small additions of peat to mineral soil resulted in a noticeable decrease in the thermal diffusivity of the mixture; small additions of soil to peat had practically no effect on thermal diffusivity. The thermal diffusivity of the studied substrates increased with increasing sample bulk density and decreased with increasing organic matter content. It was concluded that the patterns typical for soil-peat mixtures are similar to those earlier obtained for peat-sand mixtures, but in the case of soil-peat mixtures these patterns are less clearly expressed.

The main purpose of the work was to verify the applicability of the approach to assessing the ecological state of soils and the theoretical model of the response of a living being in the form of a vegetation index to the impact of a complex of heavy metals in the zone of impact of coal mining and processing facilities in the conditions of the Far Eastern taiga-forest soil-bioclimatic region. The research was carried out in conjunction with a geochemical assessment of natural environments, which made it possible to consider the size of the impact zones of coal mining and processing facilities on the territory of the emission quota experiment conducted by the Ministry of Natural Resources of the Russian Federation. Th e inventory of emission sources allowed us to identify several priority areas: the main industrial site, an industrial boiler house, an integrated industrial site, two coal mines, etc. According to the results of the summary calculations of the dispersion of pollutants, the following priority pollutants were identified: carbon (soot), inorganic and coal dust. The determination of soil and snow pollution levels made it possible to identify the localization of technogenic anomalies of heavy metals, as well as to determine the dust load on the studied area. The soil cover is characterized by a low level of pollution in terms of the total pollution index, however, the accumulation of trace elements in the surface layer of soils is typical for most of the studied metals and metalloids, as evidenced by concentration coefficients > 1. Centers of snow cover pollution have been identified, confined to the residential area. Areas with very high levels of snow pollution are located near municipal boiler houses. Based on a theoretical model of the response of a living in the form of a vegetation index to the effect of a complex of heavy metals of a certain set contained in the soils of the vicinity of a coal mining and processing enterprise, maximum permissible levels of their content have been established. The boundaries of the zone of influence of the main structural divisions of the coal mining enterprise, beyond which the territories of the natural and anthropogenic background are distributed, are determined. The soils in the vicinity of coal mining and processing facilities are characterized mainly by a background (undisturbed) condition or are poorly degraded, thus no harm has been done to the soils.