With the gradual improvement in coal mining efficiency, the disturbance of groundwater systems caused by high-intensity mining also increases, leading to challenges in maintaining mine safety and protecting water resources in mining areas. How to accurately describe the dynamic changes in the groundwater system under mining and quantitatively predict mine water inflow are currently major problems to be addressed. Based on a full analysis of the response characteristics of a groundwater system to the extraction disturbance, this paper presents a new method to establish a mine hydrogeological conceptual model that can accurately represent the water inrush process. The unstructured-grid package of MODFLOW is used to accurately characterize the formation structure and finally make accurate water inflow predictions. Taking the Caojiatan coal mine in Shaanxi Province, China, as an example, a numerical model of unstructured water inflow is established, and the changes in the water inflow source and intensity are quantitatively evaluated. Compared with the traditional water inflow prediction method, the prediction accuracy of the new model is improved by 12–17%, which is achieved by detailing the response of the complex groundwater system under high-intensity mining conditions. The method presented in this paper has great significance and applicatory value for obtaining a comprehensive understanding of the disturbance characteristics of human underground engineering activities (e.g., coal mining) on groundwater systems, as well as accurately predicting water inflow.

There are numerous coal mines around the Nansi Lake Provincial Nature Reserve, and the mineral resources are extremely rich. Therefore, it is necessary to effectively assess the impact of mining activities on the topsoil. Based on a focused investigation of the contamination status and ecological risks of the Nansi Lake Nature Reserve assisted by GIS, principal component analysis was combined with positive matrix factorization to quantitatively identify the sources and contributions of heavy metal(loid)s in the topsoil before conducting uncertainty analysis. The results showed that coal mining caused higher Cu, Zn, and As contamination levels, while Hg and Cd had higher eco-toxicity and biological sensitivity. Meanwhile, principal component analysis (PCA) and positive matrix factorization (PMF) modeling displayed that Hg (59.2%) was primarily generated by industrial sources (fossil fuel combustion and mercury-containing wastewater); As (70.2%), Ni (65.6%), Cr (63.5%), Pb (61.3%), Cu (60.3%), and Zn (55.8%) were generated mainly from coal mining and processing, coal fossil fuel combustion, and coal gangue dumps; Cd (79.8%) came mainly from agricultural sources. Through uncertainty analysis, the contribution of contamination sources to the heavy metal(loid)s in the topsoil, as estimated by the PMF model, was shown to be quite different. Moreover, heavy metal(loid)s with lower contributions had errors in source identification and factor quantification. This study innovatively warned management to control the hazards of heavy metal(loid)s caused by mining engineering to protect the environment of the Nansi Lake Nature Reserve and revealed the potential harmful pathways of heavy metal(loid)s.

With the gradual improvement of coal mining efficiency, the disturbance of groundwater system caused by the high-intensity mining mode also increases, which is a more severe challenge to the prevention of mine safety and the protection of water resources in mining areas. How to accurately describe the dynamic changes of the groundwater system under mining and quantitatively predict mine water inflow is a major problem in the current research. Based on the full analysis of the response characteristics of groundwater system to the extraction disturbance, this paper forms a kind of method to establish a mine hydrogeological conceptual model that can accurately represent the water inrush process, and uses the MODFLOW non-structural division grid to accurately characterize the formation structure, and finally make accurate water inflow prediction. Taking the Caojiatan Coal Mine in Shaanxi Province, China as an example, a numerical model of unstructured mine water inrush was established for the first time, and the changes of water inflow source and water inflow intensity were quantitatively evaluated. Compared with the traditional water inflow prediction method, it is found that the prediction accuracy is improved by 12%~17% by detailing the response characteristics of complex groundwater system under high-strength coal seam mining conditions. The method is of great significance and promotion value for the comprehensive understanding of the disturbance characteristics of human underground engineering activities, such as coal mining, on the groundwater system, and for accurately predicting water inflow.