Methodological approach to the study of the sedimentation stability of finely-dispersed mineral processing waste by satellite data on lakes pollution

DOI: https://doi.org/10.30686/1609-9192-2022-6-104-110
Читать на русскоя языкеS.P. Ostapenko, S.P. Mesyats
Mining Institute Kola Science Centre of the Russian Academy of Sciences, Apatity, Russian Federation
Russian Mining Industry №6 / 2022 р. 104-110

Abstract: The paper presents the results of the study of sedimentation stability of suspended finely-dispersed mineral particles based on satellite observations of their aggregation and sedimentation. The authors studied satellite data on self-purification of subarctic lakes polluted by apatite-nepheline, iron, and copper-nickel ore processing wastes. The multispectral images of lakes taken by Sentinel-2 spacecraft have allowed determining the average size of suspended finely-dispersed mineral processing wastes and the density of particle size distribution. To account for aggregation, the authors have designed a computer model of the dynamics of suspended particles and have set parameters of the forces of electrostatic and dispersion interactions of mineral particles from the Kola deposits. It is shown that the balance of repulsive electrostatic and dispersion attractive forces appears in the generation of aggregates of finely-dispersed mineral particles with characteristic fractal dimension, using Nepheline, Hematite, Quartz, and Pyrite as examples. The authors have developed an algorithm for matching the computer simulation results of the dynamics of suspended particles with the results of satellite images processing of lakes. The developed methodological approach makes it possible to determine the sedimentation stability of lakes’ pollution by finedispersed mineral processing wastes without surface observations. The research results allow calculating the sedimentation velocity of mineral particles and their aggregates of a given size. The good correspondence between the calculated parameters of particles aggregation and sedimentation and satellite observations data can be used to monitor the pollution of water bodies when adapting mineral processing technologies to modern production ecologization requirements, as well as to assess the waterenvironmental potential of the territory for rational management of natural resources.

Keywords: mineral raw materials, processing, finely-dispersed particles, lakes, aggregation, sedimentation, man-induced pollution, self-purification, satellite data, computer modeling

For citation: Ostapenko S.P., Mesyats S.P. Methodological approach to the study of the sedimentation stability of finelydispersed mineral processing waste by satellite data on lakes pollution. Russian Mining Industry. 2022;(6):104–110. https://doi.org/10.30686/1609-9192-2022-6-104-110

Article info

Received: 19.11.2022

Revised: 02.12.2022

Accepted: 02.12.2022

Information about the authors

Sergey P. Ostapenko – Cand. Sci. (Eng.), Leading Researcher, Mining Institute of the Kola Science Centre of the Russian Academy of Science; Apatity, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

Svetlana P. Mesyats – Leading Researcher, Head of Laboratory, Mining Institute of the Kola Science Centre of the Russian Academy of Science; Apatity, Russian Federation; e-mail: This email address is being protected from spambots. You need JavaScript enabled to view it.

References

1. Kostadinov T.S., Siegel D.A., Maritorena S. Retrieval of the particle size distribution from satellite ocean color observations. Journal of Geophysical Research. 2009;114(С4):C09015. https://doi.org/10.1029/2009JC005303

2. Labutina I.A., Tarasov M.K. Study of the selenga river sediment runoff distribution using satellite images. Geografia i Prirodnye Resursy. 2018;(1):66–72. (In Russ.)

3. Yakutseni S.P. Water: resources, reserves, markets. Russian Mining Industry. 2022;(4):120–128. (In Russ.) https://doi.org/10.30686/1609-9192-2022-4-120-128

4. Sherjah P.Y., Sajikumara N., Nowshaja P.T. Semi-analytical model for TSI estimation of inland water bodies from Sentinel 2 imagery. Journal of Hydroinformatics. 2022;24(2):444–463. https://doi.org/10.2166/hydro.2022.151

5. Turetta L., Lattuada M. The role of hydrodynamic interactions on the aggregation kinetics of sedimenting colloidal particles. Soft Matter. 2022;(8):1715–1730. https://doi.org/10.1039/D1SM01637G

6. Ostapenko S.P., Mesyats S.P. Methodological approach to characterizing pollution of natural water bodies using satellite data with account of aggregation of finely dispersed mineral processing waste. Russian Mining Industry. 2021;(6):110–116. (In Russ.) https://doi.org/10.30686/1609-9192-2021-6-110-116

7. Moiseenko T.I., Dauvalter V.A., Lukin A.A., Kudryavtseva L.P., Ilyashchuk B.P., Ilyashchuk L.I. et al. The anthropogenic modifications of the ecosystem of the lake Imandra. Moscow: Nauka; 2002. 402 p. (In Russ.)

8. Kashulin N.A. (ed.) Annotated ecological catalogue of lakes in the Murmansk region: the central and southwest areas of the Murmansk region (basins of the Barents Sea, the White Sea and the Bothnia gulf of the Baltic Sea). Apatity: Mining Institute Kola Science Centre of the Russian Academy of Sciences; 2013. Part. 1. 298 p. (In Russ.)

9. Kashulin N.A. (ed.) Annotated ecological catalogue of lakes in the Murmansk region: the central and southwest areas of the Murmansk region (basins of the Barents Sea, the White Sea and the Bothnia gulf of the Baltic Sea). Apatity: Mining Institute Kola Science Centre of the Russian Academy of Sciences; 2013. Part. 2. 253 p. (In Russ.)

10. Nasiha H.J., Shanmugam P., Sundaravadivelu R. Estimation of sediment settling velocity in estuarine and coastal waters using optical remote sensing data. Advances in Space Research. 2019;63(11):3473–3478. https://doi.org/10.1016/j.asr.2019.02.023

11. Winterwerp J.C. A simple model for turbulence induced flocculation of cohesive sediment. Journal of Hydraulic Research. 1998;36(3):309–326. https://doi.org/10.1080/00221689809498621

12. Lee B.J., Fettweis M., Toorman E., Molz F.J. Multimodality of a particle size distribution of cohesive suspended particulate matters in a coastal zone. Journal of Geophysical Research. 2012;117(C3):C03014. https://doi.org/10.1029/2011JC007552

13. Lazzari S., Nicoud L., Jaquet B., Lattuada M., Morbidelli M. Fractal-like structures in colloid science. Advances in Colloid and Interface Science. 2016;235:1–13. https://doi.org/10.1016/j.cis.2016.05.002

14. Jungblut S., Joswig J.-O., Eychmüller A. Diffusion- and reaction-limited cluster aggregation revisited. Physical Chemistry Chemical Physics. 2019;21(10):5723–5729. https://doi.org/10.1039/c9cp00549h

15. Pashminehazar R., Kharaghani A., Tsotsas E. Determination of fractal dimension and prefactor of agglomerates with irregular structure. Powder Technology. 2019;343:765–774. https://doi.org/10.1016/j.powtec.2018.10.046

16. Shi W., Wang M. Characterization of suspended particle size distribution in global highly turbid waters from VIIRS measurements. Journal of Geophysical Research: Oceans. 2019;124(6):3796–3817. https://doi.org/10.1029/2018JC014793