The strategic development of selenium-enriched soil cultivation is essential for effective agricultural land management. This research explores the sustainable utilization of selenium-rich soils in Qiongzhong County, Hainan Island. An extensive evaluation was conducted on 7266 surface soil samples, assessing the selenium concentrations, nutrient levels, heavy metals, pH values, and soil organic matter (SOM). In addition, analyses of 70 samples for organochlorine compounds and PAHs were performed. The results indicated average selenium content of 0.46 mg/kg, with 55.2% of the samples surpassing the selenium enrichment threshold of 0.4 mg/kg. According to the GB15618-2018 standards, 127 samples (1.75%) showed medium or high chromium-associated risks. No contamination from organochlorine compounds or PAHs was found, including the 16 priority-controlled PAHs with an average concentration of 30.3 µg/kg, confirming the soil’s high quality. The correlation and factor analysis identified surface enrichment as the main factor influencing selenium accumulation, presenting minimal environmental risks. Consequently, three categories of selenium-rich soil were defined: selenium-rich, pollution-free, and high-nutrient selenium-rich soil. Recommendations based on the soil characteristics and existing agricultural practices were made for the cultivation of selenium-rich vegetables, rubber, and nuts. This study lays a foundation for the sustainable management of selenium-rich soils, providing insights for further research and decision-making to optimize these resources, thus promoting environmental protection and agricultural sustainability.

Soil C:N:P stoichiometry reflects biological inputs, land management, and mineral weathering, but how these controls vary with depth remains unclear in tropical basaltic landscapes. We analyzed 500 soil profiles sampled at 0–20, 80–100, and 180–200 cm in northern Chengmai County, Hainan Island, to test directional predictions about weathering–carbon associations, surface enrichment, land-use contrasts, and nutrient covariation. Mean Chemical Index of Alteration (CIA) was high at all depths (92.8–93.7), while molar SiO₂/Al₂O₃ decreased from 14.0 to 10.5. Surface organic carbon (Corg) was positively associated with CIA, Al₂O₃, and TFe₂O₃. From surface to deep soil, Corg, total nitrogen (TN), and total phosphorus (TP) decreased by 68.4%, 64.4%, and 36.3%, respectively. Within-profile enrichment factors were greater for Corg and TN than TP (Holm-adjusted p < 0.001), accompanying larger declines in C:P and N:P than C:N. Fruit orchards, dryland, and paddy fields had higher mean surface TP and lower C:P and N:P than grassland and forest land. Corg differences among land uses disappeared at 180–200 cm. Corg–TP and TN–TP associations weakened with depth, whereas TP remained positively associated with Fe- and Al-rich components. These results support unequal biological and mineral influences on nutrient depth patterns.

Thermomagnetic-fraction geochemistry has shown promise beneath transported cover, but area-scale applications rarely combine explicit element-association analysis, statistical robustness tests and transparent target prioritization. We evaluate this workflow in the largely covered Jing’erquan Ni-Cu metallogenic area using 985 soil samples collected over approximately 140 km² at an average spacing of about 300 m (7.0 samples km⁻²). After removal of pre-existing strongly magnetic grains, 100 g aliquots were heated under oxygen-limited conditions at 650 °C for 45 min, and the newly generated magnetic fraction was separated at an instrument-current setting of 1 A. Twelve indicators were analyzed using descriptive statistics, correlation analysis, hierarchical clustering, principal component analysis (PCA), spatial clustering and a three-term composite score. Four rotated factors explain 75.1% of the variance; Cr and Ni load at 0.95 on F2, while TFe₂O₃, Zn, Co and Cu covary on F1. Pearson–Spearman matrix agreement (r = 0.990), winsorized-versus-original Pearson agreement (r = 0.999), and PCA factor-congruence coefficients (>0.998) show that the principal associations are not controlled by the largest Cu, Ni or Cr values. Alternative composite weights retain all eight named follow-up targets, although internal ranking changes when Cu is emphasized. Among them, JQ-1–JQ-4 are assigned high follow-up priority by the expert-informed score. The coincidence of JQ-1 with the known Jing’erquan Ni-Cu mining area provides an internal plausibility check for the workflow, whereas JQ-2–JQ-8 remain unverified follow-up targets. These results show that the thermomagnetic-fraction dataset delineates multielement surface geochemical anomalies in covered terrain and can serve as a complementary screening tool for subsequent Ni-Cu exploration.

Stream-sediment anomalies around the Sawayardun gold deposit integrate mineralized detritus with variable lithological and hydraulic backgrounds. This study evaluates whether multielement coherence and spatial continuity can distinguish Au-related signals from that mixed background in 1080 primary samples and 35 field duplicates. Au is strongly right-skewed (median 1.62 ng/g, P95 6.55 ng/g, maximum 754.3 ng/g). Correlation and hierarchical clustering identify Au–As–Sb–Mo as the most persistent mineralization-related association, with a secondary Bi contribution; Ag and Cu provide important local support, whereas Co–Cr–Ni–Sn–W mainly record broader lithological variation. Three rotated factors explain 75.35% of total variance. F1 explains the largest share (39.78%) and is most effective for characterizing lithological background, whereas F3 (17.28%) is most effective for gold targeting because Au, As, Sb, and Mo load together and its high scores form spatially coherent clusters. A robust composite index and P95 spatial grouping delineate eight targets. T1–T3 substantially reproduce conventional anomalies HS4, HS2, and HS1; T4 partly overlaps HS3; and T5–T6 are new multielement targets. Maximum Au values in HS1–HS4 are 14.9, 14.4, 49.8, and 754.3 ng/g, respectively, and each anomaly is supported by a different pathfinder suite. Removing the maximum Au value leaves the F3 loading vector nearly unchanged (r = 0.997). The strongest sedimentary Au responses are most plausibly related to upstream mineralized shear zones, weathering release of Au-bearing sulfides, and hydraulic concentration, but catchment mixing and a possible nugget effect preclude direct inference of orebody boundaries. The results provide a transparent basis for staged field verification rather than confirmation of mineralization.