Skip to main content
SpringerLink
Log in

Evaluating Hydrochemical Anomaly Parameters with the Use of Mining Software

  • HYDROCHEMISTRY, HYDROBIOLOGY: ENVIRONMENTAL ASPECTS
  • Published:
Water Resources Aims and scope Submit manuscript

Abstract

The volumes of groundwater pollution in the zone of technogenic impact were calculated. A hydrochemical anomaly formed because of the penetration of industrial liquid wastes into the environment. The infiltrates reach groundwater table, mix with this water, and propagate with its flow. The anomaly shows a relatively high total water TDS. In the mixing zone of technogenic and natural waters, the concentrations of salts decrease because of dilution. Software packages ArcGIS and Micromine were used to construct a three-dimensional model of the technogenic anomaly. The database for model construction contains some monitoring data on wells in the location area of waste settling basins. Long-term observations of groundwater level for each well were used in the study. The lithological features of the area were also taken into account. The model can be used to estimate the total pollution volume and the changes in the volume over time and to determine the contours and the least polluted areas.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1.
Fig. 2.

Similar content being viewed by others

REFERENCES

  1. Basargin, A.A., Procedure for constructing three-dimensional geological models of deposits with the use of Micromine Geoinformation System, Sb. Materialov mezhdunar. nauch. konf. “Interekspo Geo-Sibir” 2015 (Proc. Intern. Sci. Conf. “Interexpo Geo-Sibir” 2015), vol. 1, no. 1, pp. 15–20.

  2. Grevtsev, N.V., Aleksandrov, B.M., and Egoshina, O.S., Integrated evaluation of peat deposit reserves with the use of GIS-technologies, in Izv. vuzov. Gornyi zhurn. (Bull. Instit. Higher Educ., Mining J.), Yekaterinburg: Ur. Gos. Gorn. Univ., 2016, vol. 2, pp. 93–99.

  3. Evdokimova, N.K., Kharitonova, L.Ya., and Alekseeva, A.K., Oil and gas perspectives of the southern Laptev Sea shelf by the results of modern processing of geophysical data, Sb. Statei “Geologo-geofizicheskie kharakteristiki litosfery Arkticheskogo regiona” (Coll. Pap. “Geological-Geophysical Characteristic of Arctic Region Lithosphere”), 2006, vol. 210, no. 6, pp. 49–56.

  4. Metodicheskie rekomendatsii po organizatsii i vedeniyu monitoringa podzemnykh vod na melkikh gruppovykh vodozaborakh i odinochnykh ekspluatatsionnykh skvazhinakh (Methodological Recommendations for the Organization and Performance of Groundwater Monitoring in Shallow Group Water Intake Systems and Single Operation Wells), Moscow: Minprirody Rossii, 2000.

  5. Boguslavskiy, A.E., Gaskova, O.L., and Shemelina, O.V., Uranium migration in the ground water of the region of sludge dumps of the Angarsk Electrolysis Chemical Combine, Chem. Sustainable Dev., 2012, vol. 20, no. 5, pp. 515–529.

    Google Scholar 

  6. Kuznetsov, Yu.N., Stadnik, D.A., Stadnik, N.M., and Kurtsev, B.V., Automatic recognition of the geostructures in the sheet deposits, Gorn. Zh., 2016, vol. 2016, no. 2, pp. 86–91.

    Article  Google Scholar 

  7. Li, Y.-S., Qin, D.-X., Cai, Y., Chen, A.-B., Yu, Y.-X., Pu, C.-J., and Lin, X.-P., Three-dimension mathematical model of the Dahongshan iron ore deposit in Yunnan Province, Bull. Mineral., Petrol. Geochem., 2004, vol. 23, no. 4, pp. 332–335.

    Google Scholar 

  8. Qiu, S., He, B.B., Bai, X.J., Li, X., Liao, Z.M., and Yin, C.M., A mineral resources quantitative assessment and 3D visualization system, IEEE IGARSS. Milan, 2015, pp. 4554–4557.

    Book  Google Scholar 

  9. Vasilieva, M.A. and Katkov, S.M., Prediction of possible tectonic disturbance zones using rock mass strength characteristics in Micromine, Gorn. Zh., 2017, vol. 7, pp. 88–91.

    Article  Google Scholar 

  10. Zaychenko, A.P., Domarenko, V.A., and Peregudina, E.V., Internal architecture of ore-bearing structure of complex gold-uranium deposit Severnoe (Elkon uranium ore region), Bull. Tomsk Polytechnic Univ. Geo Assets Eng., 2015, vol. 326, no. 9, pp. 78–86.

  11. Zhang, L.-S., Zhang, Z., Wang, Y., and Chen, J.-P., Establishment of 3D solid model for Meilinggou coalfield in Chengde area, Heibei, China, Geol. Bull. China, 2009, vol. 28, no. 1, pp. 146–149.

    Google Scholar 

Download references

Funding

The study was carried out under the Governmental Order to IGM SB RAS.

Author information

Authors and Affiliations

  1. Sobolev Institute of Geology and Mineralogy, Siberian Branch, Russian Academy of Sciences, 630090, Novosibirsk, Russia

    V. A. Lyamina & O. V. Shemelina

Authors
  1. V. A. Lyamina
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. O. V. Shemelina
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding authors

Correspondence to V. A. Lyamina or O. V. Shemelina.

Additional information

Translated by G. Krichevets

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Lyamina, V.A., Shemelina, O.V. Evaluating Hydrochemical Anomaly Parameters with the Use of Mining Software . Water Resour 48, 609–613 (2021). https://doi.org/10.1134/S0097807821040138

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1134/S0097807821040138

Keywords:

Search

Navigation