Integrated Environmental Impact Assessment And Comparative Evaluation Of Coal And Metal Mining Activities Using Gis And Multivariate Statistical Techniques

Abstract

Mining operations particularly coal extraction and metal processing generate multifaceted environmental 
disturbances affecting soil quality, water resources, vegetation cover, and atmospheric conditions. This study 
integrates Geographic Information Systems (GIS) with multivariate statistical analyses to quantify and compare 
environmental degradation across coal and metal mining sites in central India. Using remote sensing data 
spanning 2015–2023, we assessed 24 mining regions covering 34,500 hectares, measuring parameters including 
land use change, soil contamination indices, vegetation loss (NDVI), water quality metrics, and air quality 
indicators. Principal Component Analysis (PCA) reduced 18 environmental variables into three primary 
components accounting for 74.3% cumulative variance. Comparative evaluation revealed coal mining operations 
exhibited 62% greater land surface temperature elevation and 48% more substantial vegetation loss, whereas 
metal mining demonstrated 3.4-fold higher soil heavy metal accumulation. K-means clustering identified four 
distinct impact zones: severe degradation (14.2%), moderate impact (31.8%), minimal disturbance (38.5%), and 
recovery areas (15.5%). Multivariate analysis of variance (MANOVA) confirmed statistically significant 
differences between mining types (Wilks' λ = 0.187, p < 0.001). Spatial interpolation using kriging produced 
impact gradient maps revealing contamination hotspots concentrated within 2 km of active extraction zones. 
Findings indicate site-specific mitigation strategies substantially outperform uniform approaches, with 
restoration success correlating strongly with post-mining land-use interventions (r = 0.78). Results provide 
quantitative baseline data for policy formulation and environmental remediation planning.