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As the medium of geological information, groundwater provides an indirect method to solve the secondary disasters of mining activities. Identifying the groundwater regime of overburden aquifers induced by the mining disturbance is significant in mining safety and geological environment protection. This study proposes the novel data-driven algorithm based on the combination of machine learning methods and hydrochemical analyses to predict anomalous changes in groundwater levels within the mine and its neighboring areas induced after mining activities accurately. The hydrochemistry analysis reveals that the dissolution of carbonate and evaporite and the cation exchange function are the main hydrochemical process for controlling the groundwater environment. The anomalous change in the hydrochemistry characteristic in different aquifers reveals that the hydraulic connection between different aquifers is enhanced by mining activities. The continuous wavelet coherence is used to reveal the nonlinear relationship between the groundwater level change and external influencing factors. Based on the above analysis, the groundwater level, precipitation, mine water inflow, and unit goal area could be considered as the input variables of the hydrological model. Two different data-driven algorithms, the Decision Tree and the Long Short-Term Memory (LSTM) neural network, are introduced to construct the hydrological prediction model. Four error metrics (MAPE, RMSE, NSE and R2) are applied for evaluating the performance of hydrological model. For the NSE value, the predictive accuracy of the hydrological model constructed using LSTM is 8% higher than that of Decision Tree algorithm. Accurately predicting the anomalous change in groundwater level caused by the mining activities could ensure the safety of coal mining and prevent the secondary disaster of mining activities. [ABSTRACT FROM AUTHOR]

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Groundwater plays an important role in ecological environment protection in arid and semi-arid areas. Therefore, understanding the characteristics of hydrochemical evolution is of great significance for the sustainable use of groundwater in the area of the Tailan River Basin. The Tailan River Basin is located in an arid, ecologically sensitive area in western China. In this study, we collected 42 groups of representative water samples from the Tailan River Basin and analyzed the chemical distribution in the groundwater using mathematical statistics, Piper and Gibbs diagrams, ion ratio analysis, and hydrogeochemical simulation methods. We also discussed the water–rock interactions in the groundwater hydrochemical evolutionary process. The results were as follows: (1) The chemical types of groundwater changed from HCO3·SO4-Ca·Na to SO4·Cl-Na·Ca, Cl·SO4-Na, and Cl-Na, and the total dissolved solids content increased from less than 1 g/L to more than 40 g/L from the gravel plain to the fine soil plain. (2) The Gibbs diagram, the ion ratio analysis, and the saturation index showed that the groundwater chemical characteristics in the study area were mainly controlled by water–rock interactions, as well as evaporation and concentration. Along the runoff of groundwater, halite and gypsum were dissolved. Nevertheless, dolomite and calcite precipitated. The relationship between the chlor-alkali index and [(Na+ + K+)-Cl−] and [(Ca2+ + Mg2+)-(HCO3− + SO42−)] indicated that cation exchange also affects the chemical composition of groundwater in the area. (3) Through reverse hydrogeochemical simulation, the main water–rock effect of the groundwater runoff process revealed by qualitative analysis was quantitatively verified. [ABSTRACT FROM AUTHOR]

Background: The determination of nutrient content in the petiole is one of the important methods for achieving cotton fertilization management. The establishment of a monitoring system for the nutrient content of cotton petioles during important growth periods under drip irrigation is of great significance for achieving precise fertilization and environmental protection. Methods: A total of 100 cotton fields with an annual yield of 4500–7500 kg/ha were selected among the main cotton-growing areas of Northern Xinjiang. The nitrate nitrogen (NO3−–N), inorganic phosphorus (PO43−–P) and inorganic potassium (K+–K) content and yield of cotton petioles were recorded. Based on a yield of 6000 kg/ha as the dividing line, a two-level and yield-graded monitoring system for NO3−–N, PO43−–P and K+–K in cotton petioles during important growth periods was established, and predictive yield models for NO3−–N, PO43−–P and K+–K in petioles during important growth periods were established. Results: The results showed found that the yields of the 100 cotton fields surveyed were normally distributed. Therefore, two yield grades were classified using 6000 kg/ha as a criterion. Under different yield-graded, the NO3−–N, PO43−–P and K+–K content of petiole at important growth stages was significantly positively correlated with yield. Further, the variation range of NO3−–N, PO43−–P and K+–K content in petioles could be used as a standard for yield-graded. In addition, a yield prediction model for the NO3−–N, PO43−–P and K+–K content of petioles was developed. The SSO-BP validation model performed the best (R2 = 0.96, RMSE = 0.06 t/ha, MAE = 0.05 t/ha) in the full bud stage, which was 12.9% higher than the BP validation model. However, the RMSE and MAE were decreased by 86.7% and 88.1%, respectively. Conclusion: The establishment of NPK nutrition monitor system of cotton petioles under drip irrigation based on yield-graded provides an important basis for nutrition monitor of cotton petiole under drip irrigation in Xinjiang. It also provides a new method for cotton yield prediction. [ABSTRACT FROM AUTHOR]

Ephedra species are beneficial for environmental protection in desert and grassland ecosystems. They have high ecological, medicinal, and economic value. To strengthen the protection of the sustainable development of Ephedra, we used occurrence records of Ephedra sinica Stapf., Ephedra intermedia Schrenk et C.A. Mey., and Ephedra equisetina Bge., combined with climate, soil, and topographic factors to simulate the suitable habitat of three Ephedra based on ensemble models on the Biomod2 platform. The results of the models were tested using AUC, TSS, and kappa coefficients. The results demonstrated that the ensemble model was able to accurately predict the potential distributions of E. sinica, E. intermedia, and E. equisetina. Eastern and central Inner Mongolia, middle and eastern Gansu, and northeastern Xinjiang were the optimum regions for the growth of E. sinica, E. intermedia, and E. equisetina, respectively. Additionally, several key environmental factors had a significant influence on the suitable habitats of the three Ephedra. The key factors affecting the distribution of E. sinica, E. intermedia, and E. equisetina were annual average precipitation, altitude, and vapor pressure, respectively. In conclusion, the results showed that the suitable ranges of the three Ephedra were mainly in Northwest China and that topography and climate were the primary influencing factors. [ABSTRACT FROM AUTHOR]

Rural settlements in oasis are primary habitations, and their changes are related to natural environment and anthropogenic activities. The spatiotemporal variations of rural settlements in an oasis are significant in arid regions. In this study, Qipan Township (QPT) and Yamansu Township (YMST) were chosen as a case study and validation case, respectively. Datasets, including Landsat images in 2002, 2010, and 2018, were collected. The cellular automata (CA)-agent-based model (ABM) and patch-generating land use simulation (PLUS) model were used to simulate the spatiotemporal dynamic variations of rural settlement and other land use types in the oasis in this study. Natural environmental, socioeconomic conditions, and human decision-making are the three driving factors that were used in the model. Human decision-making involves the actions of two types of agents: authority agent and resident agent. On the basis of land use data of 2002 and 2010, the rural settlement and other land use in 2018 were predicted using the CA-MAS and PLUS models. The following results were obtained: First, human decision-making behaviors were the leading factor in the changes of rural settlements in the CA-ABM model. Second, CA based on multiple random seed (CARS) of PLUS could better simulate the spatiotemporal variations of QPT rural settlements than CA-ABM and linear regression of PLUS. Similarly, CARS of PLUS also simulated the spatiotemporal evolution of rural settlements in YMST with high accuracy. Third, the areas of croplands, roads, and residential lands in QPT will expand to 20.7, 5.7, and 4.6 km2, respectively, in 2026, but the unused land will shrink, as predicted by CARS of PLUS. This study provides a scientific basis for the environmental protection of rural settlements in the oasis and sustainable settlement planning in arid regions. [ABSTRACT FROM AUTHOR]

Abstract: The geoheritages landscape resources in xinjiang are great in quantity, vary in type and excellent in grade. The geoheritages landscape resources can be classified into 7 different types and 28 subtypes in xinjiang. The old Model of Geoheritages management can no longer meet the demands of fast development. Therefore, An Optimization Model of Geoheritages landscape resources management is put forward in this paper based on the view of Benefit- Sharing in Xinjiang. In The Optimization Model, geological heritages landscape resources can get Optimization at last, by government centered management, capitalized management, ecosystems management conception, wellcycling protection and exploitation management model, benefit-sharing mechanism innovation and geological tourism network with geopark as its center. [Copyright &y& Elsevier]

[Display omitted] • The construction land and farmland in Jinghe County has changed dramatically. • The farmland increased significantly and expanded along with a "triangle" three sides. • The biodiversity of farmland decreased, while the fragmentation degree of forest land increased. • Human factors are the main driving force of landscape pattern change in Jinghe County. The eco-environment of the arid area is constantly fragile global-wide. The study on the spatial and temporal variation of its landscape pattern can quickly identify the driving factors, which is an effective analysis method of ecological conservation in the arid area. In this study, Jinghe County, Xinjiang, in Northwestern China, is chosen as a case study of understanding the major driving force of the oasis landscape. Landsat satellite images of 1994, 2007, and 2017 are used as the basic data sources for analysis. The moving window method, transition matrix, and factor analysis are selected as the main technical means for this paper to reveal the temporal and spatial variation of landscape pattern in Jinghe County, and to quantitatively describe its driving factors. The results of the analysis show that: (1) The landscape pattern of Jinghe County has changed dramatically, especially the construction land and farmland. Both show an increase of 351.29% and 269.01% respectively between 1994 and 2017. Farmland increased the most, with the proportion of 6.45% in Jinghe County, and expanded along with the "triangle" three sides. The area of forest land increased slightly, the area of grassland and unused land decreased, and the area of water did not change significantly. (2) The dominant obvious regional differences of landscape types in Jinghe County are mainly manifested in farmland and forest land. Taking the unused land as the boundary, the biodiversity of farmland decreased, and the aggregation degree deepened, while the fragmentation degree of forest land increased. (3) The spatial–temporal variation of landscape patterns in Jinghe County is the result of the combination of natural and human factors, but the correlation between human factors and the change of landscape area is significantly greater than that of natural factors. Thus, human factors are the main driving force of landscape pattern change in Jinghe County. The scientific management and planning of the intensity of human activities in Jinghe County should be the focus of ecological environment restoration and protection. [ABSTRACT FROM AUTHOR]