The environmental capacity of rare heavy metal calculation in the Qinghai‒Tibet Plateau region via multifractal analysis.

Accurate assessments of the soil environmental capacity are important for evaluating heavy metal pollution levels, facilitating effective prevention and control measures against such pollution. However, due to the lack of risk screening values for certain key elements, such as Rb, Sn, and Th, the assessment of the soil environmental capacity is not comprehensive. Therefore, in this study, the Menyuan-Huangzhong area of Qinghai Province was selected as the research area, and local background and risk values were established via multifractal analysis, thereby systematically examining the environmental capacity. The findings indicated that within the study area, the static environmental capacity values of 15 elements could be ranked as follows: Ba, Cu, Zn, Cr, Rb, Ni, La, Pb, Th, As, U, Sn, Tl, Cd, and Hg. In general, the residual capacity distribution of the various elements varied across the study area, with lower values primarily found in the northern and central regions and higher values obtained in the northwestern and southwestern regions. Between 2018 and 2068, there was a notable and rapid decline in the dynamic environmental capacity of Hg, Cu, and Cd in the study area. In the Menyuan-Huangzhong area of Qinghai, the average comprehensive soil environmental capacity index reached 0.91, indicating a moderate environmental capacity and slight associated health risks. The findings of this study could serve as a valuable reference for soil heavy metal pollution assessment, early warning, and management in this area; enhance the study of soil environmental capacity methods; and provide a theoretical foundation for subsequent research.

Prediction of the fluoride contents of different crop species via the random forest algorithm.

Fluoride (F) is a trace element that is essential to the human body and occurs naturally in the environment. However, a deficiency or excess of F in the environment can potentially lead to human health issues. The pseudototal amount of F in soil often does not correlate directly with the F content in plants. Instead, the F content within plants tends to have a greater correlation with the bioavailable F in soils. In large-scale soil surveys, only the pseudototal elemental content of soils is typically measured, which may not be highly reliable for developing agricultural zoning plans. There are significant variations in the ability of different plants to accumulate F from soil. Additionally, due to variations in soil elemental absorption mechanisms among different plant species, when multiple crops are grown in an area, it is typically necessary to study the elemental absorption mechanisms of each crop. To address these issues, in this study, we examined the factors influencing F bioaccumulation coefficients in different crops based on 1:50,000 soil geochemical survey data. Using the random forest algorithm, four indicators-bioavailable P, bioavailable Zn, leachable Pb, and Sr-were selected from among 29 parameters to predict the F content within crops to replace bioavailable F in the soil. Compared with the multivariate linear regression (MLR) model, the random forest (RF) model provided more accurate and reliable predictions of the fluoride content in crops, with the RF model's prediction accuracy improving by approximately 95.23%. Additionally, while the partial least squares regression (PLSR) model also offered improved accuracy over MLR, the RF model still outperformed PLSR in terms of prediction accuracy and robustness. Additionally, it maximized the utilization of existing geochemical survey data, enabling cross-species studies for the first time and avoiding redundant evaluations of different types of agricultural products in the same region. In this investigation, we selected the Xining-Ledu region of Qinghai Province, China, as the study area and employed a random forest model to predict the crop F content in soils, providing a new methodological framework for crop production that effectively enhances agricultural quality and efficiency.

Soil quality assessment in low human activity disturbance zones: a study on the Qinghai-Tibet Plateau.

The Qinghai-Tibet Plateau, located at the Third Pole and known as the "Asian water tower," serves as a crucial ecological barrier for China. Grasping the soil quality on the Qinghai-Tibet Plateau holds paramount importance for the rational and scientific exploitation of soil resources within the region and is essential for vegetation restoration and ecological reconstruction. This study, conducted in Maqin County, Qinghai Province, collected 1647 soil samples (0-20 cm) within a study area of 6300 km2. Sixteen soil indicators were selected that were split into beneficial (N, P, S, and B), harmful (Cr, Hg, As, Pb, Ni, and Cd), and essential (Cu, Zn, Se, Ga, K, and Ca) elements. The Soil Quality Index (SQI) was computed to assess soil quality across diverse geological contexts, land cover classifications, and soil profiles. The results indicate that the overall SQI in the study area was comparatively high, with most regions having an SQI between 0.4 and 0.6, categorized as moderately to highly satisfactory. Among the different geological backgrounds, the highest SQI was found in the Quaternary alluvium (0.555) and the lowest in the Precambrian Jinshuikou Formation (0.481). Regarding different land-use types, the highest SQI was observed in glacier- and snow-covered areas (0.582) and the lowest in other types of grassland (0.461). The highest SQI was recorded in typical alpine meadow soil (0.521) and the lowest in leached brown soil (0.460). The evaluation results have significant reference value for the sustainable utilization and management of soil in Maqin County, Qinghai Province, China.