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Hydrochemical Dynamics and Environmental Impacts of Mining on Water Quality

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Hydrogeology".

Deadline for manuscript submissions: 30 April 2026 | Viewed by 608

Special Issue Editor


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Guest Editor
School of Resources and Geosciences, China University of Mining and Technology, Xuzhou 221116, China
Interests: mine hydrogeology and engineering geology; mine geological disasters; mine envi-ronmental disasters; mine water disaster prevention; overburden failure caused by mining; coal mining under water-containing environments
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Special Issue Information

Dear Colleagues,

Mining activities are essential for resource extraction but pose significant risks to groundwater systems, a critical global freshwater resource. The release of contaminants (e.g., heavy metals, acids, sulfates, radionuclides) from active and abandoned mines alters natural hydrogeochemical processes, leading to the long-term degradation of groundwater quality. Understanding the mechanisms of contaminant release, transport, and transformation, as well as the resulting hydrochemical evolution, is vital for environmental protection, sustainable mining practices, and effective remediation strategies. The basin-scale analysis and modeling of these processes are crucial for predicting impacts, guiding mitigation efforts, and safeguarding water security. With advances in interdisciplinary approaches—including geochemical modeling, isotopic tracing, artificial intelligence, remote sensing, and real-time monitoring technologies—there are new opportunities that can be used to deepen our understanding of mining-induced hydrochemical dynamics.

Despite progress, fundamental challenges remain in predicting contaminant behavior, assessing long-term evolution, and developing scalable remediation solutions for mining-affected aquifers. The purpose of this Special Issue is to present cutting-edge research on the impacts of mining on groundwater quality and hydrochemical evolution. We seek contributions that advance theoretical frameworks, methodological innovations, field validation, and technological applications in this critical field.

Dr. Qiqing Wang
Guest Editor

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Keywords

  • resource extraction
  • geochemical modeling
  • water–rock interaction
  • mining-induced hydrochemical dynamics
  • water quality
  • environmental protection
  • impact of mining on aquifers

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Published Papers (1 paper)

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Research

17 pages, 4353 KB  
Article
A KPCA-ISSA-SVM Hybrid Model for Identifying Sources of Mine Water Inrush Using Hydrochemical Indicators
by Xikun Lu, Qiqing Wang, Baolei Xie and Jingzhong Zhu
Water 2025, 17(19), 2859; https://doi.org/10.3390/w17192859 - 30 Sep 2025
Viewed by 408
Abstract
Early identification of mine water inrush types and determination of water sources are prerequisites for water disaster monitoring and early warning. A mine water source identification model is proposed to improve the accuracy of water source prediction based on Kernel Principal Component Analysis [...] Read more.
Early identification of mine water inrush types and determination of water sources are prerequisites for water disaster monitoring and early warning. A mine water source identification model is proposed to improve the accuracy of water source prediction based on Kernel Principal Component Analysis (KPCA) and Support Vector Machine (SVM) models optimized by the Improved Sparrow Search Algorithm (ISSA). Nine conventional hydrochemical indicators are selected, including Ca2+, Mg2+, Na++K+, HCO3, Cl, SO42−, total hardness, alkalinity, and pH. KPCA can realize the dimensionality reduction to eliminate the redundancy of information between discriminant indices, simplify the model structure, and enhance the calculation speed of the predicted model. The penalty factor C and kernel parameter g of the SVM model are optimized by the Sparrow Search Algorithm (SSA). In addition, comparative analysis with the SVM, SSA-SVM, and ISSA-SVM models demonstrates that the KPCA and ISSA significantly enhance the classification performance of the SVM model. The KPCA-ISSA-SVM model outperforms three contrastive models in terms of accuracy, precision, recall, Kappa coefficient, Matthews Correlation Coefficient, and geometric mean values of 90.75%, 0.90, 0.88, 0.89, 0.87, and 0.89, respectively. These outcomes underscore the superior performance of the KPCA-ISSA-SVM hybrid model and its potential for effectively identifying mine water sources. This research can serve to identify the mine water sources. Full article
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