Geospatial Modeling of Environmental Risk Factors in Mining-Industrial Areas of Eastern Kazakhstan
DOI:
https://doi.org/10.31489/3106-9649/2026-3-4.GSD/55-61Keywords:
geospatial modelling, environmental risk , mining, Eastern Kazakhstan , remote sensing, GIS, heavy metals, Sentinel-2, soil contamination, tailingsAbstract
This paper synthesises empirical findings from geospatial and remote sensing studies conducted in mining-industrial zones of Eastern Kazakhstan between 2021 and 2025. Sixty surface soil samples from the Ust-Kamenogorsk (Oskemen) industrial area (1,000 ha) were analysed for Pb, Cu, and Zn via atomic absorption spectroscopy and correlated with Sentinel-2 multispectral bands using stepwise regression in ArcGIS. Pearson correlation coefficients reached R = 0.80 (Cu), R = 0.76 (Pb), and R = 0.72 (Zn), with root-mean-square errors of 1.8, 2.5, and 1.60 mg/kg, respectively. A Pollution Load Index (PLI) analysis of the Northern Industrial Zone (NIZ) of Pavlodar classified 93.757% of the studied area as moderately contaminated (PLI: 1.05–3.38) and 0.702% as significantly contaminated (Zc up to 51.34). An AI-assisted geostatistical framework applied to a tailings deposit in Eastern Kazakhstan improved spatial coherence from Moran's I = 0.29 to 0.52 (+79%) and Silhouette Index from 0.41 to 0.59 (+44%) versus conventional borehole-only clustering. Remote sensing and GIS structural analysis of the Kalba-Narym rare-metal zone confirmed a dominant NW-SE pegmatite orientation and clustered spatial distribution of ore bodies. Cadmium was consistently identified as the highest-risk element across all study sites despite its low absolute concentrations.
Keywords: geospatial modelling, environmental risk, mining, Eastern Kazakhstan, remote sensing, GIS, heavy metals, Sentinel-2, soil contamination, tailings.
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