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
Abstract
. Geostatistical mapping highlighted coal mines and adjacent agricultural areas as persistent contamination hotspots. Ecological risk assessment indicated Cd and Hg as the principal contributors to high ecological risks, particularly in agricultural and roadside soils. Probabilistic health risk assessment revealed unacceptable risks for the local population, with children being the most vulnerable. Cr was identified as the primary driver of carcinogenic risk, contributing ∼81% in children, while Co-dominated non-carcinogenic risks resulting in hazard indices for children approaching unacceptable thresholds across all land-uses. Our findings provide a precise and scientific framework for source-specific risk assessment to target soil remediation and environmental management in mining-impacted landscapes worldwide.
Fields of Study & Subjects
Heavy metals in environment
Geochemistry and Geologic Mapping
Soil Geostatistics and Mapping
Publication details
- Journal / source
- RSC Advances
- Publisher
- Royal Society of Chemistry (RSC)
- Volume
- 16
- Issue
- 11
- Pages
- 10158–10178
- Publication date
- 1 January 2026
- Language
- EN