Geochemical controls on critical Metals (REEs, Sc, Ga) in Red Mud from Porto Vesme (Sardinia, Italy): a data-driven constraints

Geochemical controls on critical Metals (REEs, Sc, Ga) in Red Mud from Porto Vesme (Sardinia, Italy): a data-driven constraints | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Geochemical controls on critical Metals (REEs, Sc, Ga) in Red Mud from Porto Vesme (Sardinia, Italy): a data-driven constraints Abdelhamid Ouladmansour, Paola Mameli, Rabah Kechiched, Maria Lacalamita, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8888690/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 13 You are reading this latest preprint version Abstract Red mud, the alkaline residue generated during the Bayer process for alumina production, represents a significant secondary resource of critical raw materials (CRMs), including rare earth elements (REEs), scandium (Sc), and gallium (Ga). However, the complex mineralogical and geochemical heterogeneity of red muds complicates the identification of the controls governing CRM distribution and hosting. In this study, we investigate the behaviour of REEs, Sc, and Ga in red muds from the Porto Vesme disposal site (Sardinia, Italy) by integrating geochemical data analysis, multivariate statistics, machine learning, and empirical modelling. A dataset comprising geochemical information was analysed using centred log-ratio transformation, correlation analysis, and principal component analysis (PCA) to identify the dominant compositional controls on CRM distribution. XGBoost models based on major oxides (Fe₂O₃, TiO₂, P₂O₅, Al₂O₃, Na₂O, and CaO) were subsequently developed to predict Sc, Ga, and grouped REE concentrations. The results indicate that Sc and heavy REEs (HREEs) are strongly associated with Fe-Ti-P-bearing phases, yielding robust predictive performance, whereas light REEs (LREEs) and Ga exhibit more heterogeneous behaviour and lower predictability. Gallium displays weak correlations with individual major oxides, reflecting its diffuse distribution in poorly crystalline aluminosilicate and oxide domains. An empirical, nonlinear gallium prediction formula incorporating logarithmic, ratio-based, and interaction terms was proposed and externally validated, demonstrating reproducible predictive capability on independent samples. The results indicate that the integrated statistical and machine-learning framework captures the geochemical controls governing CRM behaviour in red mud and provides a data-driven basis for interpreting complex secondary CRMs . Physical sciences/Chemistry Earth and environmental sciences/Environmental sciences Earth and environmental sciences/Solid earth sciences Critical raw materials Red mud Gallium Scandium Rare earth elements Machine learning PCA Full Text Additional Declarations No competing interests reported. Supplementary Files Appendix.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 07 May, 2026 Reviewers agreed at journal 07 May, 2026 Reviews received at journal 07 May, 2026 Reviewers agreed at journal 05 May, 2026 Reviews received at journal 25 Apr, 2026 Reviewers agreed at journal 23 Apr, 2026 Reviews received at journal 04 Mar, 2026 Reviewers agreed at journal 26 Feb, 2026 Reviewers invited by journal 25 Feb, 2026 Editor assigned by journal 25 Feb, 2026 Editor invited by journal 25 Feb, 2026 Submission checks completed at journal 23 Feb, 2026 First submitted to journal 23 Feb, 2026 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8888690","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":605696011,"identity":"5eb07667-8035-49bc-97f3-5c18fd21655f","order_by":0,"name":"Abdelhamid 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