This study investigates the concentrations of potentially toxic elements (PTEs) in sediments from the Sundarbans mangrove region, India. It represents the unique first coast-wide comprehensive assessment of intertidal sediment-associated PTEs across the Indian Sundarbans, integrating GIS-based spatial analysis, Positive Matrix Factorization (PMF) based source apportionment, multiple pollution indices, and human health risk assessment. A total of 50 samples (n = 3 replicates per site) were collected from different intertidal locations along the coast and analyzed for Al, Ba, Cd, Cr, Co, Cu, Fe, Pb, Mn, V, and Zn using Inductively Coupled Plasma- Optical Emission Spectroscopy (ICP-OES). Sediment contamination was evaluated using multiple pollution indices such as contamination factor (CF), enrichment factor (EF), geoaccumulation index (Igeo), ecological risk index (ERI), pollution load index (PLI), and improved Nemerow index (INI). Unlike conventional multivariate approaches, PMF quantitatively resolved the major pollution sources and estimated their relative contributions, providing a more robust basis for source identification and pollution management. The mean concentrations of PTEs followed the descending order: Fe > Al > Mn > Cr > Ba > V > Zn > Cu > Co > Pb > Cd. Among these, Fe (65,545.72 mg/kg), Al (3975.73 mg/kg), Mn (2980.39 mg/kg), and Cr (658.30 mg/kg) were the most abundant elements. The Igeo values (−6.21 to 5.91) and EF values (0.01 to 37.50) suggest significant enrichment of Cd, Cr, and Pb in the sediments. The overall PLI was 1.26, with Cd and Pb identified as the primary contributors to sediment contamination. Health risk assessment indicated that the hazard index (HI) for Cr (2.72) in children exceeds the acceptable safety threshold, suggesting potential non-carcinogenic health risks. The total carcinogenic risk (TCR) for children ranged from 1.15 × 10−5 to 3.67 × 10–3, and for adults, 6.88 × 10−6 to 5.50 × 10−4. Overall, this study, provides a scientific framework for identifying contamination hotspots, pollution sources, and supporting evidence-based environmental management and policy interventions.
The Maoping PbZn (–Ag–Ge) deposit in northeastern Yunnan is a giant, second largest PbZn deposit in the Sichuan–Yunnan–Guizhou (SYG) metallogenic province, SW China, yet the sources of ore-forming materials and fluids remain controversial, hindering genetic understanding and exploration. This study integrates detailed geological characterization with multiple isotope analyses (S, Pb, HeAr, Sr) of ore-stage sulfides and Emeishan basalts. Hydrothermal mineralization (Stage II) is subdivided into massive sulfide‑carbonate (II-A) and veined sulfide‑carbonate (II-B) substages. Sulfur isotopes of sulfides (δ34S = +9.8‰ to +21.8‰) are distinctly higher than those of Emeishan basalts (+0.9‰ to +2.5‰) and match evaporite-hosted sulfates in Devonian–Carboniferous carbonates. The δ34S order (pyrite > sphalerite > galena) and calculated equilibrium temperatures (159–232 °C) indicate that reduced sulfur was produced by thermochemical sulfate reduction (TSR). Homogeneous Pb isotope ratios (206Pb/204Pb = 18.589–18.821; 207Pb/204Pb = 15.725–15.885; 208Pb/204Pb = 39.009–39.742) overlap with those of Devonian–Carboniferous sedimentary rocks, demonstrating a dominant crustal Pb source from the host strata. Helium isotopes (3He/4He = 0.01–0.02 Ra) and 40Ar/36Ar ratios (620–1040) reveal a crustal-dominated fluid with minor meteoric water input, without mantle contribution. Initial Sr ratios (87Sr/86Sr)₂₀₂.₅Ma of sphalerite (0.71041–0.71709) indicate mixed Sr sources from Devonian–Carboniferous carbonates and minor the Early Cambrian black shales/basement rocks. The ore-forming fluids of the early substage (II-A) likely extracted slightly more Pb and Sr from the deep Early Cambrian black shales and/or basement rocks than those of the late substage (II-B). Collectively, the ore-forming materials (S, Pb, Sr) were mainly derived from evaporite-rich Devonian–Lower Permian carbonate rocks, and the ore fluids were crustal basinal brines mixed with meteoric water. The main mineralization at Maoping occurred during the Late Triassic to Early Jurassic, linked to the closure of the Paleo-Tethys Ocean and the Indosinian orogeny. The Maoping deposit is classified as a SYG-type PbZn deposit, distinct from typical MVT and SEDEX deposits. A preliminary metallogenic model of “tectonic- and gravity-driven large-scale migration of dual-basin brines followed by injection and filling” is proposed.
Significant Cu (Mo) exploration potential exists within the Duobaoshan ore field, concealed beneath extensive cover sequences in Heilongjiang Province, NE China. This study evaluates the depth-dependent detection capability of soil-gas geochemical prospecting using a Portable multi-component gas rapid analyzer (PMGRA), integrated with vertical component magnetic anomalies (ΔZ), polarizability (ηs), and soil Cu geochemical surveys across three profiles at the Tongshan and Duobaoshan porphyry Cu (Mo) deposits. The anomaly intensities of soil-gas H₂S (0.003–0.9 and 0–0.8 ppmv) and SO₂ (0–0.5 and 0–0.4 ppmv), together with those of the newly introduced gases H₂ (0.6–189 and 0.6–169 ppmv) and CS₂ (0.1–100 and 0–12 ppmv), decrease exponentially with increasing ore-body depth (Profiles 1108–1 and 1108–2: ~500–1000 m and ~ 1100–1700 m, respectively) in the Tongshan deposits. In contrast, in the Duobaoshan deposits (profile-110: < 200 m), the overall anomaly intensity is reduced due to the inclined, cone-shaped geometry of the ore bodies and the presence of low-permeability tuffaceous cover, which together suppress gas migration from their deeper portions. CO₂ (667–50,000 ppmv and 1152-24691ppmv, respectively) anomalies are also influenced by overburden thickness and predominantly reflect subsurface fault and fracture systems that act as primary conduits for gas migration in Tongshan deposits. Integration with geophysical anomalies reveals that ΔZ reflects alteration zonation (enrichment coefficients (ECs): 0.76–1.35, indicating weak to moderate anomalies), ηs effectively delineates disseminated sulfide mineralization (ECs: 0.89–1.16, near-background levels), and soil Cu provides (ECs: 0.95–6.37, ranging from background levels to pronounced anomalies) complementary but cover-sensitive geochemical information. Certain segments of the Tongshan deposits retain substantial prospecting potential, suggesting the presence of additional concealed ore bodies or previously unrecognized mineralized zones that require detailed follow-up investigation. Distinct multi-parameter anomaly signatures along the three profiles provide high-confidence drill targets, demonstrating that PMGRA-based soil-gas surveys, when integrated with ΔZ, ηs, and soil Cu, constitute a robust, cost-effective strategy for exploring concealed porphyry Cu (Mo) systems in variable-depth and thick-cover terrains.
The paper focuses on the use of unsupervised clustering methods to identify and characterize mineralized catchment basins in the Jebal Barez region in southeast Iran, known for its copper deposits. The study utilized a dataset of 658 stream sediment samples that were collected and analyzed for 20 elements. Different clustering approaches – K-means, fuzzy C-means, hierarchical, and spectral clustering – were employed to evaluate, compare and contrast their efficiency in distinguishing and characterizing mineralized catchment basins. To evaluate the performance of the clustering techniques, different clustering validation criteria such as the Caliński–Harabasz Index, Silhouette Index, and within-cluster sum of squares were utilized to determine the optimal number of clusters and to evaluate the outcomes of different clustering approaches. The study found that K-means clustering was the superior method for accurately distinguishing different clusters. Of the 16 known mineral occurrences in the study area, 11 were recognized by two K-means clusters. It was also found that K-means clusters 2 and 3 describe the most promising zones for copper mineralization. Based on these findings, the study demonstrated the potential of unsupervised clustering methods for effective exploration targeting in the region.
Artificial intelligence algorithms have been widely applied in mineral prospectivity mapping (MPM) due to their strong nonlinear learning capabilities. However, these purely data-driven approaches remain inherently opaque and fail to explicitly characterize the spatial coupling and matching relationships among ore-controlling factors within a mineral system. To address this limitation, a locally weighted coupling coordination degree (LW_D) approach is proposed, integrating the traditional coupling coordination degree model (CCD) with random forest and Shapley additive explanations (SHAP). Applied to WCu polymetallic mineralization in the Taqian–Zhuxi area of South China, this study first evaluates the synergistic state of ore-controlling factors—including preferred strata, geochemical anomalies, ore-forming intrusions, NE-trending faults, and magnetic anomalies—using three composite indicators: coupling degree (C), coordination degree (T), and coupling coordination degree (D). The analysis demonstrates that D substantially outperforms both C and T individually, delineating nearly all known deposits within only 8% of the study area. Subsequently, SHAP values are derived from 50 independent random forest models. The global SHAP-based interpretation reveals that preferred strata contribute most significantly to predictions, followed by geochemical anomalies; both factors exhibit pronounced threshold effects on mineralization. To address the inherent limitation of globally fixed weights in the traditional coupling coordination degree model, local weights for each ore-controlling factor are derived from the SHAP values. The resulting locally weighted coupling coordination degree model (LW_D) achieves a balanced accuracy of 0.9039, an ROC–AUC of 0.9876, and a normalized density of 21.22 on the prediction–area (P-A) plot, indicating predictive performance comparable to that of random forest. More importantly, LW_D corrects mispredictions generated by the traditional coupling coordination degree in regions where geochemical anomalies alone exert undue influence, thereby producing prospectivity maps that are geologically coherent. These findings suggest that evaluating the synergistic state of ore-controlling factors offers a new perspective for mineral prospectivity mapping, shifting the focus from purely probabilistic prediction toward a systemic assessment of the coupling and coordination of ore-controlling factors within mineral systems. Nevertheless, the general applicability of this approach requires further substantiation through extensive mineral exploration.
