Uncertainty Analyses of Arsenic Element Assessments in Cultivated Soils at Different Sampling Densities in High-Altitude Regions

Monitoring and assessing the spatial heterogeneity of soil elements within cultivated land presents core challenges in precision agriculture. Uncertainty analysis methods and optimal sampling densities for arsenic (As) pollution risk assessments in typical high-altitude regions of the southeastern Qinghai–Tibet Plateau remain insufficiently studied. This study investigates arsenic contamination in cultivated soils of a representative high-altitude region. By combining multi-level grid deployment with random sampling, seven datasets with varying sampling densities (62, 98, 139, 221, 289, 394, and 570 samples) were collected from 612 monitoring sites. The results indicate significant arsenic enrichment in the study area, with concentrations reaching 3.6 times the national soil background value and 2.4 times the plateau soil background value. Compared to arithmetic mean and median analyses, the geometric mean evaluation demonstrates lower uncertainty across different sampling densities in ecological environment assessments, averaging 4.3%, thereby providing more accurate results. Significant directional anisotropy exhibits a pronounced quadratic trend. The strongest spatial correlation occurs along the northwest–southeast direction, with the spatial autocorrelation distance in the vertical direction being 2.39 times greater than in other directions. Increasing sampling density is a macro-level requirement for accurately assessing the environmental risk characteristics of arsenic in plateau ecosystems; however, it is not the only factor influencing the spatial variability of arsenic concentrations. By comprehensively considering model fitting parameters, spatial distribution patterns, and cost–benefit analysis, a moderate sampling density of 20 points per square kilometer was determined to be optimal. This density provides a basis for exploring the distribution patterns and dynamic monitoring of arsenic as a risk element in plateau environments, while also provide monitoring guidance for developing countries with limited and fragmented agricultural land areas.