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Groundwater Environmental Risk Perception
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Groundwater Environmental Risk Perception

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

Deadline for manuscript submissions: 10 October 2025 | Viewed by 591

Special Issue Editors

Dr. Jin Wu

E-Mail Website
Guest Editor
Faculty of Geographical Science, Beijing Normal University, Beijing 100875, China
Interests: groundwater monitoring; groundwater quality assessment; risk assessment
Special Issues, Collections and Topics in MDPI journals
Dr. Jiao Li

E-Mail Website
Guest Editor
Technical Centre for Soil, Agriculture and Rural Ecology and Environment, Ministry of Ecology and Environment, Beijing 100012, China
Interests: groundwater; big data analysis; artificial intelligence; coordinated management of soil and groundwater; risk management and control; source apportionment
Dr. Quanwei Song

E-Mail Website
Guest Editor
CNPC Research Institute of Safety and Environment Technology, Beijing 102206, China
Interests: petrochemical industries; groundwater remediation; permeable reactive barriers

Special Issue Information

Dear Colleagues,

Groundwater environmental risk perception is an extremely complex and comprehensive process that requires us to deeply understand and assess all aspects related to groundwater pollution. This includes conducting a thorough investigation of the current status of groundwater contamination, accurately identifying pollution sources, rigorously assessing potential risks, and gaining an in-depth understanding of contamination treatment methods. Collectively, all these elements constitute a comprehensive understanding of the groundwater environment and its associated risks. In this complex process, the application of advanced technologies such as machine learning, data-driven methods, and deep learning plays a crucial role. These technologies can significantly enhance our ability to assess groundwater environmental risks, ensuring that it is both comprehensive and accurate. Data-driven methods emphasize the construction of a comprehensive data framework that integrates information from various aspects such as geology, hydrology, pollution source distribution, and pollutant concentrations, thereby developing powerful risk assessment models that provide strong support for decision-making processes. However, there are still many scientific and technical problems due to the diversity of data sources and the complexity of the risk transmission process.

This Special Issue, entitled ‘Groundwater Environmental Risk Perception’, seeks to create a platform to review and present the advanced methodologies, current progress and challenges, and future opportunities in groundwater risk management within the context of big data. Topics of interest include, but are not limited to, the following:

  1. Application of advanced technologies for groundwater risk assessment;
  2. Risk assessment models for groundwater;
  3. Groundwater contamination sources, transport, and transformation;
  4. Big data applications in groundwater quality monitoring and prediction.

Dr. Jin Wu
Dr. Jiao Li
Dr. Quanwei Song
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Water is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • groundwater environmental risk
  • groundwater pollution
  • advanced technologies
  • risk assessment model
  • risk perception
  • multivariate data

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Published Papers (2 papers)

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Research

21 pages, 4854 KiB  
Article
Impact of Iron Minerals on Nitrate Reduction in the Lake–Groundwater Interaction Zone of High-Salinity Environment
by Zhen Wang, Yuyu Wan, Zhe Ma, Luwen Xu, Yuanzheng Zhai and Xiaosi Su
Water 2025, 17(9), 1241; https://doi.org/10.3390/w17091241 - 22 Apr 2025
Viewed by 204
Abstract
Nitrate is the most prevalent inorganic pollutant in aquatic environments, posing a significant threat to human health and the ecological environment, especially in lakes and groundwater, which are located in the high agricultural activity intensity areas. In order to reveal the sources of [...] Read more.
Nitrate is the most prevalent inorganic pollutant in aquatic environments, posing a significant threat to human health and the ecological environment, especially in lakes and groundwater, which are located in the high agricultural activity intensity areas. In order to reveal the sources of nitrogen pollution in lakes and groundwater, this study of the transformation mechanism of nitrogen in the interaction zone between lakes and groundwater has become an important foundation for pollution prevention and control. The coupling effect between the biogeochemical processes of nitrate and iron has been pointed out to be widely present in various water environments in recent years. However, the impact of iron minerals on nitrate reduction in the lake–groundwater interaction zone of a high-salinity environment still remains uncertain. Based on the sediment and water chemistry characteristics of the Chagan Lake–groundwater interaction zone in northeastern China (groundwater TDS: 420~530 mg/L, Na+: 180~200 mg/L, and Cl−: 15~20 mg/L and lake water TDS: 470~500 mg/L, Na+: 210~240 mg/L, and Cl−: 71.40~87.09 mg/L), this study simulated relative oxidizing open system conditions and relative reducing closed conditions to investigate hematite and siderite effects on nitrate reduction and microbial behavior. The results indicated that both hematite and siderite promoted nitrate reduction in the closed system, whereas only siderite promoted nitrate reduction in the open system. Microbial community analysis indicated that iron minerals significantly promoted functional bacterial proliferation and restructured community composition by serving as electron donors/acceptors. In closed systems, hematite addition preferentially enriched Geobacter (denitrification, +15% abundance) and Burkholderiales (DNRA, +12% abundance), while in open systems, siderite addition fostered a distinct iron-carbon coupled metabolic network through Sphingomonas enrichment (+48% abundance), which secretes organic acids to enhance iron dissolution. These microbial shifts accelerated Fe(II)/Fe(III) cycling rates by 37% and achieved efficient nitrogen removal via combined denitrification and DNRA pathways. Notably, the open system with siderite amendment demonstrated the highest nitrate removal efficiency (80.6%). This study reveals that iron minerals play a critical role in regulating microbial metabolic pathways within salinized lake–groundwater interfaces, thereby influencing nitrogen biogeochemical cycling through microbially mediated iron redox processes. Full article
(This article belongs to the Special Issue Groundwater Environmental Risk Perception)
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27 pages, 9731 KiB  
Article
Interpretable Machine Learning Based Quantification of the Impact of Water Quality Indicators on Groundwater Under Multiple Pollution Sources
by Tianyi Zhang, Jin Wu, Haibo Chu, Jing Liu and Guoqiang Wang
Water 2025, 17(6), 905; https://doi.org/10.3390/w17060905 - 20 Mar 2025
Viewed by 247
Abstract
Accurate evaluation of groundwater quality and identification of key characteristics are essential for maintaining groundwater resources. The purpose of this study is to strengthen water quality evaluation through the SHAP and XGBoost algorithms, analyze the key indicators affecting water quality in depth, and [...] Read more.
Accurate evaluation of groundwater quality and identification of key characteristics are essential for maintaining groundwater resources. The purpose of this study is to strengthen water quality evaluation through the SHAP and XGBoost algorithms, analyze the key indicators affecting water quality in depth, and quantify their impact on groundwater quality through interpretable tools. The XGBoost algorithm shows that zinc (0.183), nitrate (0.159), and chloride (0.136) are the three indicators with the highest weight. The SHAP algorithm shows that zinc (34.62%), nitrate (17.65%), and chloride (16.98%) have higher contribution values, which explains the output results of XGBoost. According to the calculation scores and classification standards of the water quality model, 49% of the groundwater samples in the study area have excellent water quality, 33% of the samples are better, and 18% of the samples are polluted. The results of positive matrix factorization (PMF) show that natural conditions, metal processing, metal smelting and mining, and agricultural activities all cause pollution to groundwater. Zinc, chloride, nitrate, and manganese were the key variables determined by the SHAP algorithm to explain the vast majority of human health risk sources. These findings indicate that interpretable machine learning not only improves the correlation of water quality assessment but also quantifies the judgment basis of each sample and helps to track key pollution indicators. Full article
(This article belongs to the Special Issue Groundwater Environmental Risk Perception)
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