The Khosrow Abad Fe-skarn deposit lies within the Sonqor igneous–sedimentary series of the northern Sanandaj–Sirjan Zone (N-SaSZ), western Iran, where Late Jurassic quartz monzonite–quartz monzodiorite intrusions cut meta-basaltic andesite and dolomitic limestone. Field relationships and petrography are integrated with LA–ICP–MS zircon UPb geochronology, whole-rock geochemistry, and mineral chemistry to constrain magmatism, skarn evolution, and magnetite deposition. To our knowledge, the zircon ages provide the first deposit-specific UPb constraints for the skarn-related intrusions. Weighted mean ages of 148.50 ± 0.48 Ma, 147.91 ± 0.72 Ma, and 146.97 ± 2.49 Ma overlap within analytical uncertainty and define an intrusive episode at ca. 148–149 Ma; they date crystallization rather than mineralization directly. The intrusions are predominantly calc-alkaline and I-type, although hydrothermal alteration locally modified alkali-element distributions. Contact metamorphism and restricted garnet-bearing prograde skarn were followed by widespread hydrous retrograde alteration. The principal magnetite stage accompanied albite–actinolite and epidote–chlorite alteration and was subsequently overprinted by sulfides and oxidation. Ore localization reflects fluid–rock interaction, reactive lithological contacts, and structurally enhanced permeability. Mineral chemistry is evaluated with the observed assemblages and ore textures rather than used as an independent genetic test. The resulting deposit-scale model identifies the wider retrograde alteration footprint and permeable contacts within carbonate–metavolcanic successions as complementary exploration criteria for comparable intrusion-related calcic Fe-skarn systems.
Coal combustion byproducts are increasingly examined as secondary sources of rare earth elements (REEs) and critical elements (CEs), but resource appraisal based only on total concentration can overstate practical recovery potential and overlook environmental liabilities. This study integrated geochemical enrichment, acid extractability, contamination, and ecological risk to evaluate feed coal, fly ash, bottom ash, and mixed coal combustion residue. Thirty-six composite samples were characterized for elemental composition and mineralogy using standard laboratory techniques. Recovery performance was assessed through standardized hydrochloric-acid leaching, target-element leaching efficiency, matrix dissolution, selectivity, standardized HCl dosage, and a recovery-potential score. Environmental performance was evaluated using contamination factors, the pollution load index, geoaccumulation index, and potential ecological risk index. Fly ash contained the highest mean ΣREY concentration (1205.44 ± 182.95 mg kg − 1), enrichment factor (3.65 ± 0.36), target-element leaching efficiency (66.45 ± 8.20%), and recovery-potential score (0.77 ± 0.06). Mixed combustion residues retained substantial resource value but also had the highest mean pollution load and ecological risk. CRITIC–TOPSIS weighting shifted the strongest priorities toward fly ash and selected bottom-ash samples while reducing the priority of the most environmentally burdened residues. The integrated framework distinguished high-value recoverable streams, pre-treatment-dependent targets, environmental-management priorities, and low-priority materials, providing a defensible basis for screening coal-derived materials before detailed process development.
Trace-element patterns in pyrite (Py) record successive stages of fluid evolution and metal transport and are widely used to reconstruct ore-forming processes. In this study of the Zawar PbZn deposit, Rajasthan, India, an unsupervised machine learning workflow is applied to trace element data by integrating centred log-ratio transformation, Hierarchical Density-Based Spatial Clustering of Applications with Noise, i.e. HDBSCAN clustering, and non-linear dimensionality reduction techniques (t-SNE and UMAP), to discriminate Py generations based on multivariate geochemical signature. In-situ LA-ICP-MS analyses, encompassing framboidal, euhedral and coarse crystalline textures, are conducted on 96 analytical spots, quantifying the concentrations of 22 trace elements. This framework identifies three geochemically distinct pyrite populations Py-1, Py-2, Py-3, without prior labels, consistent with petrographic observations while preserving transitional compositions indicative of fluid mixing. Geochemical ratios (As/Sb, Co/Ni, Cu/Zn, Pb/Zn, Au/Ag) show systematic compositional differences among the machine-learning derived clusters. Weighted modular correlation network analysis (WMCNA) further shows a progressive reorganisation of trace-element coupling from dense and coherent early-stage associations to progressively more fragmented late-stage associations. Elemental enrichments and characteristic inter-element linkages revealed by WMCNA further emphasize systematic geochemical differences corresponding to the discrete stages of Py growth. Py-1 exhibits a distinct multielement geochemical signature characterized by a densely connected trace-element correlation network. Py-2 shows elevated Zn concentrations and relatively higher As/Sb ratios and is petrographically associated with the principal sulphide mineralization stage. Py-3 exhibits weaker and more heterogeneous trace-element associations, reflecting compositional differences among successive pyrite generations. This integrated approach complements petrography and enhances the reconstruction of pyrite paragenesis and ore-forming processes.
