Machine learning-driven geochemical fingerprinting and risk characterization of mineral dust across different operational settings in El-Gedida Iron Mine, Egypt
Mouataz T. Mostafa, Ahmed Abdelaal, Madiha S M Osman, Hassan I. Farhat, Mariam Y. Zakaria, Reham Y. Abu Elwafa, Sahar M. Abd El-Bakey
TL;DR
This study uses machine learning and geochemical analysis to assess mineral dust risks and identify unique elemental patterns in an Egyptian iron mine.
Contribution
The novel use of supervised machine learning to extract geochemical fingerprints of dust from distinct mining zones.
Findings
Cu showed extreme variability and localized enrichment in the mine dust samples.
Cr and Ni posed unacceptable cancer risks to children, while Cr had the highest non-carcinogenic risk.
Machine learning models accurately identified geochemical signatures, such as Cu–Pb in cabins and Fe–Mn in ore-handling zones.
Abstract
Investigating mineral dust emitted from mining activities enables the assessment of environmental risks posed by potentially toxic elements (PTEs) and the discrimination of geochemical fingerprints characteristic of distinct operational settings. Accordingly, this study employed site-specific dust sampling, geochemical analysis of PTEs using ICP-AES, supervised machine learning (e.g., Support Vector Machine and Multinomial Logistic Regression), multivariate statistics (e.g., Principal Component Analysis), pollution and ecological indices (e.g., Pollution Load Index), and health risk modeling to delineate PTE contamination patterns, determine high-risk microenvironments, and identify geochemical fingerprints (e.g., ore-handling zones vs. confined cabins) within El-Gedida Iron Mine (Western Desert, Egypt), thereby establishing dust-borne elemental profiles as tracers for evidence-based…
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Taxonomy
TopicsForensic Fingerprint Detection Methods · Geochemistry and Geologic Mapping · Heavy metals in environment