Green development of agriculture is important for achieving coordinated and high-quality regional development for China. Using provincial data from 1990 to 2020, this work explored the dynamics of agricultural green development efficiency of 31 provinces in China, its spatiotemporal characteristics, and its driving factors using a super-efficiency slacks-based measure (Super-SBM), the Malmquist productivity index (MPI), spatial autocorrelation, and a geographic detector. Results showed that the overall agricultural green development efficiency showed a U-shaped trend, suggesting a low level of efficiency. Although a gradient difference was visible among eastern, central, and western regions, the efficiency gap narrowed each year. Technological progress and efficiency both promoted agricultural green development efficiency, especially technological progress. Agricultural green development efficiency had significant spatial aggregation characteristics, but Moran's I result showed a downward trend from 2015 to 2020, indicating a risk of spatial dispersion in the later stage. The provinces with high agricultural green development efficiency were mainly concentrated in the eastern region, while those with low efficiency were concentrated in the central and western regions. Agricultural green development efficiency was influenced by various factors, which showed differences according to time and region. The impact of the labor force's education level and technological progress increased during the study period, and significantly facilitated agricultural green development efficiency in the eastern region, while the central and western regions were still affected by the scale level and environmental regulation, reflecting the advantages of the eastern region in terms of economy and technology. In the future, strengthening agricultural scientific and technological innovation and deepening interprovincial cooperation can help further improve the level of green agricultural development. In addition, local governments should formulate more precise local agricultural support policies based on macro-level policies and local conditions.

Water risk early warning systems based on the water environmental carrying capacity (WECC) are powerful and effective tools to guarantee the sustainability of rivers. Existing work on the early warning of WECC has mainly concerned the comprehensive evaluation of the status quo and lacked a quantitative prejudgement and warning of future overload. In addition, existing quantitative methods for short-term early warning have rarely focused on the integrated change trends of the early warning indicators. Given the periodicity of the socioeconomic system, however, the water environmental system also follows a trend of cyclical fluctuations. Thus, it is meaningful to monitor and use this periodicity for the early warning of the WECC. In this study, we first adopted and improved the prosperity index method to develop an integrated water risk early warning framework. We also constructed a forecast model to qualitatively and quantitatively prejudge and warn about the development trends of the water environmental system. We selected the North Canal Basin (an essential connection among the Beijing- Tianjin-Hebei region) in China as a case study and predicted the WECC in 25 water environmental management units of the basin in 2018-2023. We found that the analysis of the prosperity index was helpful in predicting the WECC, to some extent. The result demonstrated that the early warning system provided reliable prediction (root mean square error of 0.0651 and mean absolute error of 0.1418), and the calculation results of the comprehensive early warning index (CEWI) conformed to the actual situation and related research in the river basin. From 2008 to 2023, the WECC of most water environmental management units in the basin had improved but with some spatial differences: the CEWI was generally poor in areas with many human disturbances, while it was relatively good in the upstream regions with higher forest and grass covers as well as in the downstream areas with larger water volume. Finally, through a sensitivity analysis of the indicators, we proposed specific management measures for the sustainability of the water environmental system in the North Canal Basin. Overall, the integrated water risk early warning framework could provide an appropriate method for the water environmental administration department to predict the WECC of the basin in the future. This framework could also assist in implementing corresponding management measures in advance, especially for the performance evaluation and the arrangement of key short-term tasks in the River Chief System in China.

Lakes play important roles in sustaining the ecosystem and economic development in Inner Mongolia Autonomous Region of China, but the spatial patterns and driving mechanisms of water quality in lakes so far remain unclear. This study aimed to identify the spatial changes in water quality and the driving factors of seven lakes (Juyanhai Lake, Ulansuhai Lake, Hongjiannao Lake, Daihai Lake, Chagannaoer Lake, Hulun Lake, and Wulannuoer Lake) across the longitudinal axis (from the west to the east) of Inner Mongolia. Large-scale research was conducted using the comprehensive trophic level index (TLI (Σ)), multivariate statistics, and spatial analysis methods. The results showed that most lakes in Inner Mongolia were weakly alkaline. Total dissolved solids and salinity of lake water showed obvious zonation characteristics. Nitrogen and phosphorus were identified as the main pollutants in lakes, with high average concentrations of total nitrogen and total phosphorus being of 4.05 and 0.21 mg/L, respectively. The values of TLI (Σ) ranged from 49.14 to 71.77, indicating varying degrees of lake eutrophication, and phosphorus was the main driver of lake eutrophication. The lakes of Inner Mongolia could be categorized into lakes to the west of Daihai Lake and lakes to the east of Daihai Lake in terms of salinity and TLI (Σ). The salinity levels of lakes to the west of Daihai Lake exceeded those of lakes to the east of Daihai Lake, whereas the opposite trend was observed for lake trophic level. The intensity and mode of anthropogenic activities were the driving factors of the spatial patterns of lake water quality. It is recommended to control the impact of anthropogenic activities on the water quality of lakes in Inner Mongolia to improve lake ecological environment. These findings provide a more thorough understanding of the driving mechanism of the spatial patterns of water quality in lakes of Inner Mongolia, which can be used to develop strategies for lake ecosystem protection and water resources management in this region.

Investigating the effect of geocells on the erosion and deposition distribution of ephemeral gullies in the black soil area of Northeast China can provide a scientific basis for the allocation of soil and water conservation measures in ephemeral gullies. In this study, an artificial simulated confluence test and stereoscopic photogrammetry were used to analyze the distribution characteristics of erosion and deposition in ephemeral gullies protected by geocells and the effect of different confluence flows on the erosion process of ephemeral gullies. Results showed that when the confluence flow was larger, the effect of geocell was more evident, and the protection against ephemeral gully erosion was stronger. When the confluence flow rates were 0.6, 1.8, 2.4, and 3.0 m³/h, ephemeral gully erosion decreased by 37.84%, 26.09%, 21.40%, and 35.45%. When the confluence flow rates were 2.4 and 3.0 m³/h, the average sediment yield rate of the ephemeral gully was close to 2.14 kg/(m²?min), and the protective effect of ephemeral gully erosion was enhanced. When the flow rate was higher, the surface fracture of the ephemeral gully was more serious. With an increase in confluence flow rate, the ratio of erosion to deposition increased gradually, the erosion area of ephemeral gullies was expanded, and erosion depth changed minimally. In conclusion, geocell measures changed erosion patterns by altering the rill erosion/deposition ratio, converting erosion from rill erosion to sheet erosion.

Visible and near-infrared (vis-NIR) spectroscopy technique allows for fast and efficient determination of soil organic matter (SOM). However, a prior requirement for the vis-NIR spectroscopy technique to predict SOM is the effective removal of redundant information. Therefore, this study aims to select three wavelength selection strategies for obtaining the spectral response characteristics of SOM. The SOM content and spectral information of 110 soil samples from the Ogan-Kuqa River Oasis were measured under laboratory conditions in July 2017. Pearson correlation analysis was introduced to preselect spectral wavelengths from the preprocessed spectra that passed the 0.01 level significance test. The successive projection algorithm (SPA), competitive adaptive reweighted sampling (CARS), and Boruta algorithm were used to detect the optimal variables from the preselected wavelengths. Finally, partial least squares regression (PLSR) and random forest (RF) models combined with the optimal wavelengths were applied to develop a quantitative estimation model of the SOM content. The results demonstrate that the optimal variables selected were mainly located near the range of spectral absorption features (i.e., 1400.0, 1900.0, and 2200.0 nm), and the CARS and Boruta algorithm also selected a few visible wavelengths located in the range of 480.0-510.0 nm. Both models can achieve a more satisfactory prediction of the SOM content, and the RF model had better accuracy than the PLSR model. The SOM content prediction model established by Boruta algorithm combined with the RF model performed best with 23 variables and the model achieved the coefficient of determination (R²) of 0.78 and the residual prediction deviation (RPD) of 2.38. The Boruta algorithm effectively removed redundant information and optimized the optimal wavelengths to improve the prediction accuracy of the estimated SOM content. Therefore, combining vis-NIR spectroscopy with machine learning to estimate SOM content is an important method to improve the accuracy of SOM prediction in arid land.

Growth-promoting bacteria (GPB) have shown promising effects on serving plants against environmental constraints such as drought. Nevertheless, simultaneous effects of different GPB have less been considered for arid land plants and under field conditions. We investigated the effects of single and combined application of GPB, including free-living nitrogen-fixing bacteria (NFB), phosphate solubilizing bacteria (PSB), potassium solubilizing bacteria (KSB), a combination of NFB, PSB, and KSB (NPK), and control, at three drought stress treatments. In order to better understand the interactions between drought and GPB, we measured the morphological, biochemical, and physiological plant traits. The target plant was salt tree (Halimodendron Halodendron (Pall.) Voss), a legume shrub native to arid lands of Central and West Asia. All biofertilizer treatments enhanced the growth, physiology, and biochemistry of salt tree seedlings, and there were significant differences among the treatments. KSB and PSB treatments increased photosynthetic pigments, but KSB treatment was more efficient in transpiration rate and stomatal regulation and increased the soluble carbohydrates. PSB treatment had the highest effect on root traits, such as taproot length, root volume, cumulative root length, and the ratio of root to shoot. NFB treatment enhanced root diameter and induced biomass translocation between root systems. However, only the application of mixed biofertilizer (i.e., NPK treatment) was the most significant treatment to improve all plant morphological and physiological characteristics of salt tree under drought stress. Therefore, our results provided improvement of some specific plant traits simultaneous with application of three biofertilizers to increase growth and establishment of salt tree seedlings in the degraded arid lands.

Since Pb is a non-biodegradable inorganic pollutant and a non-essential metal, its long-term presence in soil poses a great threat to the environment. Iris lactea Pall. var. chinensis (Fisch.) Koidz., a perennial dense bush herb with high resistance of Pb and wide adaptability, was used in pot experiments to study the effects of exogenous nitrate N ($\text{NO}_{3}^{-}-\text{N}$) on the absorption and transportation of Pb and plant growth under different Pb concentrations. Then, the mechanism of $\text{NO}_{3}^{-}-\text{N}$ affecting Pb and nutrient uptake and transport was explored. The concentration of Pb in the experiment ranged from 0 to 1600 mg/kg, and the added concentration of $\text{NO}_{3}^{-}-\text{N}$ was 0.0-0.3 g/kg. The results showed that I. lactea was highly tolerant to Pb, and the shoot fraction was more sensitive to varied Pb concentrations in the soil than the root fraction. This protective function became more pronounced under the condition of raised Pb concentration in the soil. When the concentration of Pb in the soil reached 800 mg/kg, the highest Pb content of I. lactea was found under the condition of 0.1 g/kg of $\text{NO}_{3}^{-}-\text{N}$ addition. When Pb concentration in the soil increased to 1600 mg/kg, the increase in $\text{NO}_{3}^{-}-\text{N}$ addition promoted Pb uptake by the root. To ensure the well growth of I. lactea and the effect of remediation of Pb-contaminated soil, the recommended concentration of $\text{NO}_{3}^{-}-\text{N}$ in the soil is 0.1 g/kg. This result provides a theoretical basis for exogenous N regulation of phytoremediation of Pb-contaminated soil.