Integrated machine learning and positive matrix factorization for the source-specific contamination and predictive risk assessment of potentially toxic elements in multi-land-use soils around an active coal mine.

Investigating the distribution, sources, and risks of potentially toxic elements (PTEs) in mining-impacted soils is critical for effective environmental monitoring and human health protection. However, traditional assessments often fail to integrate spatial, source-oriented, and predictive approaches limiting a comprehensive understanding. In this study, 120 soil samples were collected from five land-use types surrounding an active opencast coal mine in the Godavari Valley coalfields, India. Pollution indices revealed severe multi-metal contamination, with Co and Cd emerging as the most consistently enriched elements across land uses, while Zn showed pronounced but spatially restricted enrichment, particularly in coal mine soils. An integrated framework combining positive matrix factorization (PMF), machine learning, and geospatial analysis was developed to identify source-specific contamination patterns. A robust four-factor PMF solution identified mixed industrial-mining activities as the dominant source (∼49%) of contamination. A random forest (RF) model integrating soil properties, spatial variables, and PMF-derived source contributions demonstrated strong to moderate predictive performance (average