Chromite is a strategic mineral resource for many countries, and chromite deposit occurrences are widespread in the ultramafic rocks of the Yarlung Zangbo ophiolite belt, particularly in the harzburgite unit of the mantle section. Conducting field surveys in complex and poorly accessible terrain is challenging, expensive, and time-consuming. Remote sensing is an advanced method of achieving modern geological work and is a powerful technical means of geological research and mineral exploration. In order to delineate outcrops of chromite-bearing mantle peridotite, the present research study integrates seven image-enhancement techniques, including optimal band combination, decorrelation stretching, band ratio, independent component analysis, principal component analysis, minimum noise fraction, and false color composite, for the interpretation of Landsat8 OLI and WorldView-2 satellite data. This integrated approach allows the effective discrimination of chromite-containing peridotite outcrops in the Luobusa area, Tibet. The interpretation results derived from these integrated image-processing techniques were systematically verified in the field and formed the basis of the feature selection process of different lithologies supported by the support vector machine algorithm. Furthermore, the distribution range of the ferric contamination anomaly is detected through the de-interference abnormal principal component thresholding technique, which shows a high spatial matching relationship with mantle peridotite. This is the first study to utilize Landsat8 OLI and WorldView-2 remote sensing satellite data to explore the largest chromite deposit in China, which enriches the research methods for the chromite deposits in the Luobusa area. Accordingly, the results of this investigation indicate that the integration of information extracted from image-processing algorithms using remote sensing data could be a broadly applicable tool for prospecting chromite ore deposits associated with ophiolitic complexes in mountainous and inaccessible regions such as Tibet’s ophiolitic zones.

The Aksu River Basin oasis, a typical arid ecological environment, faces considerable ecological and public health risks from fluoride accumulation in soil and groundwater. However, systematic investigations integrating soil–groundwater co-enrichment mechanisms with multi-pathway health risk assessments under environmentally relevant conditions remain scarce. We examined spatial fluoride distribution in the soil–groundwater system, associated health risks, and key driving mechanisms. Based on 2009 soil and 264 groundwater samples, we applied radial basis function (RBF) interpolation, Getis-Ord G_i* hotspot analysis, the geo-accumulation index (I_geo), the ecological risk index (ER), and the U.S. EPA health risk assessment model to evaluate pollution levels, ecological risks, and health impacts on adults and children. Spearman’s correlation analysis revealed relationships with 12 environmental factors, including topography, climate, soil properties, and vegetation. Key results are as follows: (1) High-fluoride soils (>700 mg·kg⁻¹) clustered in the eastern basin, while groundwater fluoride increased along a west–east gradient, with RBF interpolation yielding the highest accuracy; (2) soil fluoride was generally “unpolluted–moderate risk” (mean I_geo = −0.14, ER = 1.40), whereas groundwater posed the primary health risk, with a mean hazard quotient of 1.83 for children via drinking water, indicating non-carcinogenic risk; (3) soil enrichment was driven by evaporation–concentration–alkaline activation, while groundwater enrichment followed a convergence–concentration–evaporation mechanism, being negatively correlated with elevation. Groundwater fluoride presents a clear health risk, particularly to children, arising from high geological background levels and intense evaporation. Managing fluoride pollution and safeguarding drinking water quality in arid oasis regions is consequential. These findings provide a scientific basis for sustainable groundwater management and public health protection in arid oases.

In arid regions, potentially toxic elements (PTEs) can accumulate in oasis farmland soils, posing risks to both ecosystems and human health and threatening the long-term sustainability of agricultural production. However, we still lack a clear understanding of how multiple elements co-accumulate and what non-linear processes drive their buildup. Here, we investigated typical agricultural soils in the Aksu area of Xinjiang. We measured 12 elements (As, Cr, Cu, Ni, Zn, Co, V, Se, F, Ba, Sn, Mn) in 28 surface samples. To assess the pollution levels, we used three indices: the single-factor index (P_i), the geo-accumulation index (I_geo), and the Nemerow composite index. Source apportionment was performed with positive matrix factorization (PMF). We then built an XGBoost model to predict the Nemerow index, and applied SHAP (Shapley additive explanations) to quantify the marginal contribution and non-linear response of each element. Our results show that the average concentrations of Se, F, and As are 1.47, 1.27, and 1.35 times the national background values, respectively. The exceedance rate (P_i > 1) for these elements ranges from 78.6% to 92.9%. Nevertheless, the overall pollution is mild: only one out of 28 sampling sites (3.6%) falls into the moderately polluted category. PMF resolved three major sources: (1) parent material plus evaporation enrichment (F, Mn, and Ba, ~45% of the total contribution); (2) agricultural and anthropogenic activities (As, Cr, V, Zn, ~40%); and (3) local industrial or waste inputs (Sn and Ba, ~15%). The XGBoost model shows good predictive performance on the test set (R² = 0.864, RMSE = 0.104). SHAP analysis reveals that Se, F, and As are the main drivers of the composite pollution index. Se has a clear threshold: once its concentration goes above 0.3 mg·kg⁻¹, its positive contribution jumps sharply. Overall, the farmland soils in Aksu show mild enrichment of several elements, with Se and F as the main indicators. Evaporation enrichment and farming practices are the dominant processes behind this enrichment. The integrated framework—pollution indices, PMF source apportionment, XGBoost prediction, and SHAP interpretation—provides a scientifically sound way to manage soil environments in arid regions.