RUDN Journal of Agronomy and Animal IndustriesRUDN Journal of Agronomy and Animal Industries2312-797X2312-7988Peoples’ Friendship University of Russia named after Patrice Lumumba1966410.22363/2312-797X-2021-16-2-146-153Research ArticleModel of monitoring of oil soil pollution and its terminationGermanovaSvetlana Evgenievna<p>Senior Lecturer, Department of Technospheric Security, Agrarian and Technological Institute</p>germanova-se@rudn.ruhttps://orcid.org/0000-0003-2601-6740MagdeevaTatiana Valeryevna<p>Senior Lecturer, Department of Technospheric Security, Agrarian and Technological Institute</p>dremova-tv@rudn.ruhttps://orcid.org/0000-0002-5584-5321PliushchikovVadim Gennadievich<p>Doctor of Agricultural sciences, Professor, Director of Department of Technospheric Security, Agrarian and Technological Institute</p>pliushchikov-vg@rudn.ruhttps://orcid.org/0000-0003-2057-4602Рeoples’ Friendship University of Russia0707202116214615307072021Copyright © 2021, Germanova S.E., Magdeeva T.V., Pliushchikov V.G.2021<p style="text-align: justify;">The assessment of impact of oil production economic activities on land pollution in Russia contributes to evolutionary management decision making. Oil industrial pollution affects negatively flora and fauna. Thus, its important to identify the level of its exposure and danger, the site of contamination. A system approach is needed. When studying the environment, its necessary to consider the presence of risk situations and stochastic irreversible changes. Its essential to identify the nature and type of soil contamination with petroleum products using high-tech tools, intellectual procedures. The work considers modeling of such situation, forecasting and identification of oil contaminants. The submodel of optimal termination of monitoring is also considered. Ending monitoring of environmental optimization will result in lower monitoring costs, since monitoring oilcontaminated environments is an expensive and complex technological mechanism, often requiring satellite data. The proposed algorithm for modeling and system analysis is based on situational modeling. Evolutionary modeling allows to adapt the procedure (methodology) of forecasting and assessment to environmental risk factors. It increases the accuracy (formalization and evidence) and completeness of conclusions, the efficiency of situation analysis, which affects manageability of risk both for the oil complex and for individual enterprise in the industry. The results of the research may be used for development of software tools, in particular expert and predictive systems. Situational models are needed when oil companies are solving multi-criteria and multifactor problems.</p>pollutionoilwater surfaceevolutionary modelingmonitoringtermination of monitoringзагрязнениенефтьводная поверхностьэволюционное моделированиемониторингпрекращение мониторинга[Trofimov SY, Ammosova YM, Orlov DS. Influence of oil on soil cover and the problem of developing a regulatory framework for the influence of oil pollution on soils. Moscow University Soil Science Bulletin. 2000; (2):30—34. (In Russ).][Kulikov OV. Technogenic oil pollution of soil and water. Burenie i neft’. 2002; (12):24—27. (In Russ).][Deryabin AN, Unguryanu TN, Buzinov RV. Population health risk caused by exposure to chemicals in soils. Health Risk Analysis. 2019; (3):18—25. (In Russ). doi: 10.21668/health.risk/2019.3.02][Germanova SE, Ryzhova TA, Kocheva MV, Fedorova TA, Petukhov NV. Situational modelling of oil pollution risks monitored by distributed monitoring. Amazonia Investiga. 2020;9(25):44—48. (In Russ).][Vasiliev AV, Bykov DE, Pimenov AA. Ecological monitoring of pollution of the soils by oily waste. Izvestia of Samara Scientific Center of the Russian Academy of Sciences. 2015. 17(4):269—272. (In Russ).][Kalitsev DM. The pollution model of the «responsibility» zone of the production infrastructure of an oil and gas industry. Sovremennye nauchnye issledovaniya i razrabotki. 2018; 2(11):290—292. (In Russ).][Gluhova LV, Kaziev VM, Kazieva BV. System rules of financial control and management of innovative business processes of the enterprise. Vestnik Volzhskogo universiteta im. V.N. Tatishcheva. 2018; 2(1):125—133. (In Russ).][Timofeev YM, Berezin IA, Virolainen JA., Makarova MV, Nikitenko AA. Analysis of mesoscale variability of carbon dioxide in the vicinity of Moscow megacity based on satellite data. Current problems in remote sensing of the Earth from space. 2019; 16(4):263—272. (In Russ). doi: 10.21046/2070-7401-2019-16-4-263-270][Chen SH, Yu T. Big data in computational social sciences and humanities: an introduction. In: Chen SH. (ed.) Big Data in Computational Social Science and Humanities. Cham: Springer; 2018. p.1—25. doi: 10.1007/9783-319-95465-3_1][Miheeva TI. Data Mining in geo-information technologies. Vestnik of Samara State Technical University. Technical Sciences Series. 2006; (41):96—99. (In Russ).][Abramov NS, Makarov DA, Talalaev AA, Fralenko VP. Modern methods for intelligent processing of Earth remote sensing data. Program Systems: Theory and Applications. 2018; 9(4):417—442. (In Russ). doi: 10.25209/2079-3316-2018-9-4-417-442][Fedotov DV, Belov ML, Matrosova OA, Gorodnichev VA, Kozintsev VI. Method of detecting oil contamination on water surface based on registration of fluorescent radiation in two narrow spectral ranges. Herald of the Bauman Moscow State Technical University. Series Instrument Engineering. 2010; (2):39—47. (In Russ).][Belov ML, Shteingart AD, Matrosova OA, Gorodnichev VA. Laser fluorescent method for monitoring leaks from petrol pipes based on the neural network algorithm. Science and Education. 2014; (1):5—69. (In Russ). doi: 10.7463/0114.0676410][Fedotov YV, Matrosova OA, Belov ML, Gorodnichev VA. Method of detection of oil pollution on the Earth’s surface based on fluorescence radiation recording within three narrow spectral bands. Atmospheric and oceanic optics. 2013; 26(3):208—212. (In Russ).][Krapivin VF, Mkrtchyan FA. Effectiveness of monitoring systems of detection. Ecological systems and devices. 2002; (6):3—5. (In Russ).]