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Tytuł artykułu

Geospatial Assessment of Regression Analysis Between the Hydrocarbon Content in Surface Waters and Snow Cover on the Example of the Territories of the Far North of Russia

Treść / Zawartość
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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The article presents the generalized results obtained from the analysis of oil pollution of surface waters in the fields of the Far North. The research considered the administrative territorial division of the Russian Federation, the territory of the Khanty-Mansi Autonomous Okrug – Yugra (KhMAO). The results of the study performed on the basis of field data on sampling for the year were presented. The influence of the hydrocarbon content in surface waters and snow cover was assessed. The aim of the work was to consider the snow cover as a natural source of pollutants, affecting the accumulation in surface waters and snow cover. The results obtained can be used for subsequent observations of snow cover and surface waters. The data obtained can serve as a basis for planning further research and developing the solutions for environmental protection in the Far North. The analysis of the dependencies between the indicators of hydrocarbon pollution in surface waters and snow cover was carried out using the methods of correlation and parametric multivariate regression analysis. The methods of geoinformation analysis and GIS technologies were also used in the work. It was revealed that the problem of the state of snow cover and its role as an indicator of atmospheric and soil pollution require further research. On the one hand, the snow cover detains metals, and polluted soil areas are formed locally, on the other hand, after the snow melts, the pollutants remaining on the surface with surface runoff enter rivers and are carried by the wind for quite long distances.
Rocznik
Strony
74--83
Opis fizyczny
Bibliogr. 13 poz., rys., tab.
Twórcy
  • Industrial University of Tyumen, 38 Volodarskogo St. Tyumen, 652000 Russia
  • Industrial University of Tyumen, 38 Volodarskogo St. Tyumen, 652000 Russia
  • Industrial University of Tyumen, 38 Volodarskogo St. Tyumen, 652000 Russia
Bibliografia
  • 1. Bogdanov O., Shuvaev A., Abraeva T., Sabirianova R. 2020. Petroleum potential of the north-western part of the Khanty-mansiysk autonomous region KhmAO on the basis of petroleum system development history reconstruction. Paper presented at the Society of Petroleum Engineers - SPE Russian Petroleum Technology Conference 2019.
  • 2. Boori M.S., Choudhary K., Paringer R., Kupriyanov A. 2022. Using RS/GIS for spatiotemporalecological vulnerability analysis based on DPSIR framework in the republic of Tatarstan, Russia. Ecological Informatics, 67. DOI: 10.1016/j.ecoinf.2021.101490
  • 3. Budarova V.A., Martynova N.G., Medvedeva Y.D., Budarov V.P. 2017. Geoinformation support at the facilities of the KHMAO - YUGRA oil and gas complex. In the collection: Oil and gas of Western Siberia. materials of the International Scientific and Technical Conference, 223–226.
  • 4. Chabuk A., Al-Zubaidi H.A.M., Abdalkadhum A.J., Al-Ansari N., Ali Abed S., Al-Maliki A., Ewaid S. 2022. Application ArcGIS on modified-WQI method to evaluate water quality of the Euphrates river, Iraq, using physicochemical parameters. DOI: 10.1007/978-981-16-2380-6_58
  • 5. Ebdon D. 1985. Statistics in Geography. Blackwell.
  • 6. Fischer M., Getis A. 2009. Handbook of Applied Spatial Analysis: Software Tools, Methods and Applications. DOI: 10.1007/978-3-642-03647-7.
  • 7. Khodzhaeva G.K. 2019. Crude oil lines accident rate analysis in Nizhnevartovsk district KhMAO-ugra for years 2014–2018. Paper presented at the IOP Conference Series: Earth and Environmental Science, 381(1) DOI: 10.1088/1755-1315/381/1/012040
  • 8. Klemmer K., Neill D.B. 2021.Auxiliary-task learning for geographic data with autoregressive embeddings. Paper presented at the GIS: Proceedings of the ACM International Symposium on Advances in Geographic Information Systems, 141–144. DOI: 10.1145/3474717.3483922
  • 9. Kurakova A.A., Chalov R.S. 2020. Channel morphology and bank erosion in the lower reaches of the Ob River (within the Khmao-yugra autonomous district). Vestnik Moskovskogo Universiteta, Seriya 5: Geografiya, 2020(6), 41–50.
  • 10. Mitchell A. 2005. The ESRI Guide to GIS Analysis. ESRI Press, 2.
  • 11. Nath H., Rafizul I.M. 2022. Spatial variability of metal elements in soils of a waste disposal site in khulna: A geostatistical study. DOI: 10.1007/978-981-16-5547-0_3
  • 12. Schabenberger O., Gotway C.A. 2017. Statistical methods for spatial data analysis. Statistical methods for spatial data analysis , 1–488. DOI: 10.1201/9781315275086
  • 13. Zhelonkina E.E., Pafnutova E.G., Andreev A.A., Pafnutova I.D., Andreev K.A. 2021. Ecological assessment of wastewater on the environment by the example of khanty-mansiysk (KhMAO-Yugra). Paper presented at the IOP Conference Series: Earth and Environmental Science, 723(4). DOI: 10.1088/1755-1315/723/4/042031.
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-ff085b07-bce9-45f2-bcda-0eb8bcdf39f5
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