The implementation of the sustainable management of the interaction between agriculture and the environment requires an increasingly deep understanding and numerical description of the soil genesis and properties of soils. One of the areas of application of relevant knowledge is digital irrigated agriculture. During the development of such technologies, the traditional methods of soil research can be quite expensive and time consuming. Proximal soil sensing in combination with predictive soil mapping can significantly reduce the complexity of the work. In this study, we used topographic variables and data from the Electromagnetic Induction Meter (EM38-mk) in combination with soil surface hydrological variables to produce cartographic models of the gravimetric soil moisture for a number of depth intervals. For this purpose, in dry steppe zone conditions, a test site was organized. It was located at the border of the parcel containing the irrigated soybean crop, where 50 soil samples were taken at different points alongside electrical conductivity data (ECa) measured in situ in the field. The modeling of the gravimetric soil moisture was carried out with the stepwise inclusion of independent variables, using methods of ensemble machine learning and spatial cross-validation. The obtained cartographic models showed satisfactory results with the best performance R2cv 0.59–0.64. The best combination of predictors that provided the best results of the model characteristics for predicting gravimetric soil moisture were geographical variables (buffer zone distances) in combination with the initial variables converted into the principal components. The cartographic models of the gravimetric soil moisture variability obtained this way can be used to solve the problems of managed irrigated agriculture, applying fertilizers at variable rates, thereby optimizing the use of resources by crop producers, which can ultimately contribute to the sustainable management of natural resources.

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The analysis of the dust content (pm10) and NO2 in the atmosphere over Russia for January - May 2020 in comparison with previous years was carried out. Copernicus Atmosphere Monitoring Service data archives are used as a source of information. It was found that the imposition of socio-economic restrictions due to the COVID-19 pandemic affected the content of dust and nitrogen oxide in the atmosphere unevenly for different regions of the country. The state of atmospheric dust and NO2 content has improved due to the restrictions imposed in a number of regions of the Far East (apparently, also due to restrictions on the territory of neighboring China) and, to a lesser degree, in the center of the European part of Russia. The information obtained can be used to predict the development of the social and economic situation in the coming years and to plan preventive measures to overcome the economic and social consequences of the COVID-19 pandemic, as well as to develop proposals to overcome negative consequences for the environment, including measures to optimize territorial development, nature protection and consideration of ecosystem functions.

<p>Phosphogypsum (PG) is a by-product of phosphoric acid production, a valuable raw material for reclamation of acidic soils, for remediation of soils contaminated with oil products, a source of rare-earth elements (REE). The use of PG has a positive effect on the development of plants, on the value and quality of yield. Most of the PG produced at the present time is stored in phosphogypsum dumps (PGD), which are a source of pollution of the environment, since the dust particles from dumps can be transported over significant distances. To assess the impact of PGD on the environment and agricultural production it is necessary to identify zones of priority distribution of dust particles and their accumulation in the soils of the surrounding areas. In recent years, geoinformation modeling (GM) have been used to analyze dusting of different types of dumps. There are very few studies on the possibility of using such technologies for modeling the dusting of PGD.</p><p>We carried out GM of dust emissions in the impact area of phosphate fertilizer production factory in Balakovo (Russian Federation).</p><p>The chemical composition of PG samples was determined for whole samples and fractions most susceptible to dusting – <100 µm. The determination of the total REE composition was carried out by ICP-OES method. REEs content in samples of PG is 30-60 times higher than the Clark values for soils. The predominant indicator elements are La, Ce and Nd, the content of which reaches 500-3000 µg/g. The distribution of microparticles in the fine fractions was analyzed using a laser particle size analyzer from ultrasound-stabilized suspensions. In the aqueous suspension PG aggregates disperse to particles <1 µm, forming in turn several size groups. Local maximum contents form particles with sizes 0.03, 0.14 and 0.67 µm.</p><p>The data allowed using the GM to allocate zones of priority distribution of dust particles and their accumulation in the soils surrounding the PGD area. Dusting simulations were performed for particle sizes 8-1, 1-0.1, 0.1-0.05, 0.05-0.03, 0.03-0.01 and <0.01 mm. The results of spatial modeling of the weighted sum of the relative concentration of dust particles indicate that particles up to 0.1 mm predominantly move in northeast, north and southwest directions, particles 0.1-1 mm predominantly fall in northeast direction, particles 1-8 mm - in north direction.</p><p>Correlation analysis showed that the results of dusting modeling are in good agreement with the spatial distribution of REE. The greatest correlation between the weighted sum of the relative concentration of particles of the analyzed size is noted for the content of La and Ce (correlation coefficients 0.74 and 0.68 respectively). Validation of the model was carried out in a field. Joint analysis of the constructed maps and field data showed that the map of the weighted sum of the relative concentrations of analyzed particles well reflects the spatial variability in the soil content of La and Ce.</p><p>The results of modeling can be used to assess the impact of PGD on the surrounding area and its soil cover.</p><p>The reported study was funded by RFBR, project number 19-05-50016.</p>