Groundwater pollution by metal elements is a serious issue due to its probable risks to the ecosystem and human health. In the present study, 75 groundwater samples were collected from the Ibinur Lake Basin (ILB) of NW China. The contents of As, Se, Pb, Cu, Cr(Ⅵ), Zn, Mn, and Cd were determined. The levels, pollution degrees, and potential ecological risks of metals in groundwater were systematically analyzed for the first time in this area. The potential sources of metals were also discussed. It was observed that the mean contents of metals in groundwater in the ILB were lower than the Class III thresholds of the Standard for Groundwater Quality of China (GB/T 14848–2017), whereas the maximum contents of As, Se, Pb, Cr(Ⅵ), and Mn exceeded the Class III thresholds values. The pollution index of each metal and the Nemerow comprehensive index (NPI) caused by the overall pollution by all these metals in groundwater showed the pollution-free level. The single and comprehensive potential ecological risk index of analyzed metals in groundwater showed a relatively low level of potential ecological risk. Additionally, spatial distribution patterns of contents, pollution levels, and ecological risks of metals in groundwater in the ILB were found to be substantially heterogeneous. Furthermore, As and Se in groundwater originated from anthropogenic sources such as agriculture and mining, whereas Mn mainly originated from natural factors, and Pb, Cu, Cr(Ⅵ), Zn, and Cd were correlated with both natural and anthropogenic sources. Overall, As was identified as the main pollution factor, while As and Se were identified as the main ecological risk factors in the groundwater in the ILB. These results can provide important information for groundwater management in the ILB and will guide authorities in taking the necessary measures to ensure the safety of groundwater supply in the northwestern arid regions of China.

Accurate and rapid estimation of soil organic matter (SOM) content is of great significance for advancing precision agriculture. Compared with traditional chemical methods, the hyperspectral estimation is superior in rapidly estimating SOM content. Soil grain size affects soil spectral reflectance, thereby affecting the accuracy of hyperspectral estimation. However, the appropriate soil grain size for the hyperspectral analysis is nearly unknown. This study propose a best hyperspectral estimation method for determining SOM content of farmland soil in the Ibinur Lake Irrigation Area (ILIA) of the northwest arid zones of China. The original spectral reflectance of the 20-mesh (0.85 mm) and 60-mesh (0.25 mm) sieved soil were obtained, and the feature wavebands were selected using five types of spectral transformations. Then, hyperspectral estimation models were constructed based on the partial least squares regression (PLSR), support vector machine (SVM), random forest (RF), and extreme gradient boosting (XGBoost) models. Results show that the SOM content had relatively higher correlation coefficient with spectral reflectance of the 0.85 mm sieved soil than that of the 0.25 mm sieved soil. The transformation of original spectral reflectance of soil effectively enhanced the spectral characteristics related to SOM content. Soil grain size obviously affected spectral reflectance and the accuracy of hyperspectral estimation models. The overall stability and estimation accuracy of RF model was significantly higher compared with the PLSR, SVM, and XGBoost. Finally, the RF model combined with the root mean first-order differentiation (RMSFD) of spectral reflectance of the 0.85 mm sieved soil (R² = 0.82, RMSE = 2.37, RPD = 2.27) was identified as the best method for estimating SOM content of farmland soil in the ILIA.

In order to investigate the difference in particle size distribution of soil along an urban–suburban–rural–desert (USRD) gradient in an arid zone, surface soil (0–20 cm) samples were gathered at the urban, suburban, rural, and desert gradients in Urumqi, a northwestern Chinese city. Laser diffraction technology was adopted for determining the particle size distribution of the soil. Comparisons were made regarding the particle size distribution traits and soil properties in different gradient zones based on parameters such as the mean particle size (M_G), fractal dimension (D_v), sorting coefficient (ơ_G), kurtosis (K_G), and skewness (SK_G). Results indicate that (1) particle size distribution in the urban, suburban, and rural soils was mainly sand particle sizes, whereas the desert soil was mainly composed of silt particle sizes. The average D_v value ranking for soil in each gradient is desert > suburban > urban > rural. (2) The width and peak of the soil particle size frequency curve ranged within 0–500 μm, and the width and peak of the soil particle size frequency curve of each gradient were different. (3) The M_G of rural soil was the highest, whereas the M_G of desert soil was the lowest. The ơ_G values of the surface soil of each gradient were all greater than 4.0, and the sorting performance was extremely poor. The SK_G of the desert and urban soil particle size showed extremely positive and negative skewness, respectively, while the SK_G of the rural and suburban soil particle size showed extremely negative skewness. The K_G values of the particle sizes of the rural and suburban soils exhibited narrow and medium peaks, whereas those of the urban and desert soils exhibited very broad and flat peaks. (4) The D_v of the soil in each gradient was strongly influenced by the soil particle size distribution, with the clay content of the soil playing a dominant role. Finally, the fractal dimension was identified as an indicator of the characteristics of the fine particle matter content in the soil structure. The novel contribution of this work is to clarify the fractal differences in the particle size distribution of soil along an urbanization gradient. The present research findings can offer fundamental information relating to the characteristics of soil particle size distribution along an urbanization gradient zone.