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Abstract

Summary

This study develops and tests a GIS-based methodology for modeling the distribution of microelements in surface waters using the Triangulated Irregular Network (TIN) interpolation method. The approach was applied to historical hydro-lithochemical survey data from the Poltava Region (Ukraine) to spatially interpolate barium (Ba) concentrations and identify zones exceeding established critical thresholds. The methodology integrates stages of data preparation, geocoding, interpolation, classification, and spatial analysis, followed by quantitative assessment of contaminated areas. Barium was selected as the primary indicator due to its high sensitivity to anthropogenic impacts and the completeness of available datasets for two survey periods (1985–1988 and 1991–1993). Statistical analysis revealed a lognormal distribution of Ba concentrations, justifying the choice of the TIN method for preserving local spatial variability. Interpolated surfaces were clipped to administrative boundaries, and exceedance zones were extracted as vector layers for area calculations using geodetic parameters of the WGS84 ellipsoid. The results demonstrated a significant increase in the exceedance area from 2,408.62 km2 (4.24% of the region) in 1985–1988 to 21,354.60 km2 (37.55%) in 1991–1993. These findings indicate a substantial expansion of contamination zones over time, highlighting the influence of anthropogenic activities and environmental changes. The developed methodology is adaptable to various regions and chemical components, providing a reliable framework for environmental monitoring and risk assessment. It can be applied to contemporary datasets for rapid evaluation and long-term observation of surface water quality, supporting decision-making in environmental management and conservation planning.

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2025-10-06
2026-01-23
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References

  1. Adedapo, S. M., & Zurqani, H. A. (2024). Evaluating the performance of various interpolation techniques on digital elevation models in highly dense forest vegetation environment. Ecological Informatics, 81, 102646. https://doi.org/10.1016/j.ecoinf.2024.102646
    [Google Scholar]
  2. Aydöner, C. (2024). Development and application of a GIS tool in the design of surface water quality monitoring networks: A micro-watershed–based approach. Environmental Monitoring and Assessment, 196, 985. https://doi.org/10.1007/s10661-024-13193-x
    [Google Scholar]
  3. Biedunkova, O., & Kuznietsov, P. (2024). Dataset on heavy metal pollution assessment in freshwater ecosystems. Scientific Data, 11, 1241. https://doi.org/10.1038/s41597-024-04116-z
    [Google Scholar]
  4. Dzhumelia, E., Ruda, M., Shybanova, A., & Salamon, I. (2024). Hydrochemical indicators dynamic in surface water of Ukraine-Border areas with Poland and Slovakia case study. Ecological Engineering & Environmental Technology, 25(12), 305–314. https://doi.org/10.12912/27197050/194986
    [Google Scholar]
  5. Jha, D., Das, A., Saravanane, N., Abdul Nazar, A. K., & Kirubagaran, R. (2010). Sensitivity of GIS-based interpolation techniques in assessing water quality parameters of Port Blair Bay, Andaman. Journal of the Marine Biological Association of India, 52, 55–61. https://www.researchgate.net/publication/258383565
    [Google Scholar]
  6. Klypa, A. V. (2024a). Prospects for the application of GIS-technologies in environmental monitoring amidst military impact on urbanized areas. Spat. Dev, 9, 238–249. https://doi.org/10.32347/2786-7269.2024.9.238-249
    [Google Scholar]
  7. Klypa, A. V. (2024b). The impact of military actions on natural ecosystems: consequences, rehabilitation, and an integrated approach. Spat. Dev, 10, 471–481. https://doi.org/10.32347/2786-7269.2024.10.471-481
    [Google Scholar]
  8. Pal, D., Saha, S., Mukherjee, A., Sarkar, P., Banerjee, S., & Mukherjee, A. (2025). GIS-based modeling for water resource monitoring and management: A critical review. In GIS and Remote Sensing Applications (pp 537–561). Springer. https://doi.org/10.1007/978-3-031-62376-9_24
    [Google Scholar]
  9. Semenchuk, M. (2022). Triangulated irregular network interpolation method in spatial analysis. Connectivity, 156. https://doi.org/10.31673/2412-9070.2022.026269
    [Google Scholar]
  10. Shukla, B. K., Gupta, L., Parashar, B., Sharma, P., Sihag, S., & Shukla, A. (2025). Integrative assessment of surface water contamination using GIS, WQI, and machine learning in urban–industrial confluence zones surrounding the National Capital Territory of the Republic of India. Water, 17, 1076. https://doi.org/10.3390/w17071076
    [Google Scholar]
  11. Sivasubramani, R., Balamurugan, G., & Rajagopal, B. (2011). Triangulated irregular network (TIN) model for water resource management for the sustainable development of Kottakarai Aru watershed, Tamilnadu, India. International Journal of Geomatics and Geosciences, 2(3), 837–845. https://www.researchgate.net/publication/221657448
    [Google Scholar]
  12. World Health Organization. (2022). Guidelines for drinking-water quality: Fourth edition incorporating the first and second addenda. Geneva: World Health Organization. https://www.who.int/publications/i/item/9789240045064
    [Google Scholar]
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