Spatial Interpolation of Soil Contamination Data Using Geostatistical Methods in Mining-Affected Regions

Abstract

Soil contamination in mining-affected regions poses significant environmental and public health concerns due to the accumulation and spatial dispersion of toxic metals and hazardous substances. Accurate assessment of contamination distribution is essential for effective environmental monitoring, risk evaluation, and remediation planning. The present study investigates the spatial interpolation of soil contamination data using geostatistical methods in mining-affected regions under varying environmental conditions. The research focuses on evaluating the effectiveness of geostatistical interpolation techniques for predicting the spatial variability and distribution patterns of soil contaminants, including heavy metals commonly associated with mining activities. Experimental analysis was conducted using field-sampled contamination datasets to examine the influence of sampling density, spatial correlation structure, semivariogram modeling, and interpolation parameters on prediction accuracy and mapping reliability. Results demonstrate that geostatistical methods effectively capture spatial dependence and heterogeneity of contaminated soils, enabling the generation of detailed contamination distribution maps for environmental assessment. The study further reveals that kriging-based interpolation techniques provide improved estimation accuracy and uncertainty evaluation compared with conventional deterministic interpolation approaches. Optimized semivariogram models significantly enhanced prediction performance and supported accurate delineation of contamination hotspots within mining-impacted landscapes. Comparative assessment with traditional mapping methods confirms the advantages of geostatistical analysis in identifying pollution trends, guiding sampling strategies, and supporting environmental remediation decision-making. In addition, the findings highlight the importance of integrating spatial statistical tools with environmental monitoring programs for sustainable land management and ecological risk reduction.