Jing Yang
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Jing Yang has a total of 10 co-authors. Here are the top co-authors by number of shared publications:
- Haibo Li
School of Environmental Studies, China University of Geosciences, Wuhan 430078, China
2 shared publications - Zhonghua Tang
School of Environmental Studies, China University of Geosciences, Wuhan 430078, China
2 shared publications - Dongjin Xiang
School of Mathematics and Physics, China University of Geosciences, Wuhan 430074, China
1 shared publication - Geng Liu
Beijing Delhi Technology Group Co., Ltd., Beijing 110026, China
1 shared publication - Jiale Shang
School of Environmental Studies, China University of Geosciences, Wuhan 430078, China
1 shared publication - Mengqi Liu
Nanjing Institute of Environmental Sciences, Ministry of Ecology and Environment of the People’s Republic of China, Nanjing 210042, China
1 shared publication
Latest Publications
- Open Access
Using Clustering, Geochemical Modeling, and a Decision Tree for the Hydrogeochemical Characterization of Groundwater in an In Situ Leaching Uranium Deposit in Bayan-Uul, Northern China
Uranium extraction through the in situ leaching method stands as a pivotal approach in uranium mining. In an effort to comprehensively assess the repercussions of in situ uranium leaching on groundwater quality, this study collected 12 representative groundwater samples within the Bayan-Uul mining area. The basic statistical characteristics of the water samples showed that the concentrations of SO42− and total dissolved solids (TDS) were relatively high. Through the use of cluster analysis, the water samples were categorized into two distinct clusters. Seven samples from wells W-d, W-u, N01, W10-2, W08-1, W10-1, and W13-1, situated at a considerable distance from the mining area, were grouped together. Conversely, five samples from wells W08-2, W13-2, W01-1, W02-2, and the pumping well located in closer proximity to the mining area, formed a separate cluster. A decision tree-based machine learning approach was employed to discern the influence of various hydrochemical indicators in forming these clusters, with results indicating that SO42− exerts the most substantial influence, followed by Ca2+. The mineral saturation indices from geochemical modeling indicated that, as the distance from the mining area increased, the trend of calcium minerals changed from dissolution to precipitation; iron minerals were in a precipitation state, and the precipitation trend was gradually weakening. In light of these findings, it is clear that in situ uranium leaching significantly impacted the groundwater in the vicinity of the mining area. The prolonged consumption of groundwater sourced near the study area, or its use for animal husbandry, poses potential health risks that demand heightened attention.
Open AccessUsing Clustering, Geochemical Modeling, and a Decision Tree for the Hydrogeochemical Characterization of Groundwater in an In Situ Leaching Uranium Deposit in Bayan-Uul, Northern China
Uranium extraction through the in situ leaching method stands as a pivotal approach in uranium mining. In an effort to comprehensively assess the repercussions of in situ uranium leaching on groundwater quality, this study collected 12 representative groundwater samples within the Bayan-Uul mining area. The basic statistical characteristics of the water samples showed that the concentrations of SO42− and total dissolved solids (TDS) were relatively high. Through the use of cluster analysis, the water samples were categorized into two distinct clusters. Seven samples from wells W-d, W-u, N01, W10-2, W08-1, W10-1, and W13-1, situated at a considerable distance from the mining area, were grouped together. Conversely, five samples from wells W08-2, W13-2, W01-1, W02-2, and the pumping well located in closer proximity to the mining area, formed a separate cluster. A decision tree-based machine learning approach was employed to discern the influence of various hydrochemical indicators in forming these clusters, with results indicating that SO42− exerts the most substantial influence, followed by Ca2+. The mineral saturation indices from geochemical modeling indicated that, as the distance from the mining area increased, the trend of calcium minerals changed from dissolution to precipitation; iron minerals were in a precipitation state, and the precipitation trend was gradually weakening. In light of these findings, it is clear that in situ uranium leaching significantly impacted the groundwater in the vicinity of the mining area. The prolonged consumption of groundwater sourced near the study area, or its use for animal husbandry, poses potential health risks that demand heightened attention.
- Open Access
Using Self-Organizing Map and Multivariate Statistical Methods for Groundwater Quality Assessment in the Urban Area of Linyi City, China
Groundwater holds an important role in the water supply in Linyi city, China. Investigating the hydrochemical characteristics of groundwater, and revealing the factors governing groundwater geochemistry, is a primary step for ensuring the safe and rational exploitation of groundwater resources. This study used a self-organizing map (SOM) and multivariate statistical methods to assess groundwater quality in the urban area of Linyi city. Based on the hydrochemical dataset consisting of nine parameters (i.e., pH, , , , , , , , and ) from 89 groundwater samples, the SOM was first applied to obtain the weight vectors of the output nodes. Hierarchical cluster analysis (HCA) was used for organizing the nodes into four clusters. The node cluster indices were then remapped to the groundwater samples according to the winner node for each sample. The hydrochemical characteristics and factors controlling the groundwater geochemistry of the four clusters were analyzed using principal component analysis (PCA) and graphical methods including Piper and Gibbs diagrams, as well as binary plots of the major ions in groundwater. Results indicated that groundwater geochemistry in this area is primarily governed by water–rock interactions, such as the dissolution of halite, calcite, and gypsum, along with the influence of municipal sewage and the degradation of organic matter. This study demonstrates that the integration of an SOM and multivariate statistical methods improves the understanding of groundwater geochemistry and hydrochemical evolution in complex groundwater flow systems impacted by utilization.Open AccessUsing Self-Organizing Map and Multivariate Statistical Methods for Groundwater Quality Assessment in the Urban Area of Linyi City, China
Groundwater holds an important role in the water supply in Linyi city, China. Investigating the hydrochemical characteristics of groundwater, and revealing the factors governing groundwater geochemistry, is a primary step for ensuring the safe and rational exploitation of groundwater resources. This study used a self-organizing map (SOM) and multivariate statistical methods to assess groundwater quality in the urban area of Linyi city. Based on the hydrochemical dataset consisting of nine parameters (i.e., pH, , , , , , , , and ) from 89 groundwater samples, the SOM was first applied to obtain the weight vectors of the output nodes. Hierarchical cluster analysis (HCA) was used for organizing the nodes into four clusters. The node cluster indices were then remapped to the groundwater samples according to the winner node for each sample. The hydrochemical characteristics and factors controlling the groundwater geochemistry of the four clusters were analyzed using principal component analysis (PCA) and graphical methods including Piper and Gibbs diagrams, as well as binary plots of the major ions in groundwater. Results indicated that groundwater geochemistry in this area is primarily governed by water–rock interactions, such as the dissolution of halite, calcite, and gypsum, along with the influence of municipal sewage and the degradation of organic matter. This study demonstrates that the integration of an SOM and multivariate statistical methods improves the understanding of groundwater geochemistry and hydrochemical evolution in complex groundwater flow systems impacted by utilization.