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Monitoring and Modelling of Contaminants in Water Environment
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Monitoring and Modelling of Contaminants in Water Environment

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

Deadline for manuscript submissions: 30 May 2025 | Viewed by 4338

Special Issue Editors

Dr. Hang Wan

E-Mail Website
Guest Editor
Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou 519000, China
Interests: water environment monitoring; contaminant hydrology; computational fluid dy-namics; deep learning; environmental impact
Special Issues, Collections and Topics in MDPI journals
Dr. Jingjie Feng

E-Mail Website
Guest Editor
State Key Laboratory of Hydraulics and Mountain River Engineering, Sichuan University, Chengdu 610065, China
Interests: hydraulics; water resources engineering; hydraulic modelling; environmental hy-draulics; water quality protection
Dr. Hui Li

E-Mail Website
Guest Editor
State Key Joint Laboratory of Environmental Simulation and Pollution Control, School of Environment, Beijing Normal University, Beijing 100875, China
Interests: water environment monitoring; contaminant hydrology; virtual water; environmental system analysis; water environment management
Dr. Hao Sun

E-Mail Website
Guest Editor Assistant
Institute of Chemical Industry of Forest Products, Chinese Academy of Forestry, Nanjing, China
Interests: porous materials; pollution reduction; contaminant adsorption; catalytic degradation of pollutants; adsorption kinetics; emerging pollutant

Special Issue Information

Dear Colleagues,

Affected by climate change and human activities, the water environment in watersheds has changed greatly. Water environment protection is of the utmost importance in ensuring national water security. In order to carry out the work of water environment protection more effectively, it is necessary to master the evolution process of the water environment in the basin under the new situation. Accurate monitoring and modelling of the evolution of water quality is a prerequisite for water safety protection. The Special Issue mainly focuses on water quality monitoring and the development of new technologies for water quality simulation. The proposal for the Special Issue aims to strengthen water environment control through new technologies and achieve timely control and early warning of water quality pollution. In view of that, the editors of the Special Issue encourage researchers to obtain interesting results on this topic and to submit high-quality manuscripts, including review papers, regular research articles, interesting images, and communications in the fields of monitoring and modelling of contaminants in water environments.

Papers on all relevant topics are welcome, including but not limited to the following:

  • Methods, theories, and systems for water quality monitoring;
  • Methods, theories, and systems for water quality modelling;
  • Environmental risks associated with pollutants;
  • Source and sink characteristics of pollutants in the environment;
  • Pollution reduction and water quality control.

Dr. Hang Wan
Dr. Jingjie Feng
Dr. Hui Li
Dr. Hao Sun
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

  • water environment monitoring
  • contaminant hydrology
  • climate change
  • environmental hydraulics
  • deep learning
  • emerging pollutants
  • pollution reduction
  • pollution source analysis

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (5 papers)

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Research

22 pages, 3373 KiB  
Article
High-Precision Prediction of Total Nitrogen Based on Distance Correlation and Machine Learning Models—A Case Study of Dongjiang River, China
by Yuanpei Chen, Weike Yao and Yiling Chen
Water 2025, 17(8), 1131; https://doi.org/10.3390/w17081131 - 10 Apr 2025
Viewed by 303
Abstract
Excessive total nitrogen (TN) in water bodies leads to eutrophication, algal blooms, and hypoxia, which pose significant risks to aquatic ecosystems and human health. Accurate real-time TN prediction is crucial for effective water quality management. This study presents an innovative approach that combines [...] Read more.
Excessive total nitrogen (TN) in water bodies leads to eutrophication, algal blooms, and hypoxia, which pose significant risks to aquatic ecosystems and human health. Accurate real-time TN prediction is crucial for effective water quality management. This study presents an innovative approach that combines the distance correlation coefficient (DCC) for feature selection with a coupled Attention-Convolutional Neural Network-Bidirectional Long Short-Term Memory (At-CBiLSTM) model to predict TN concentrations in the Dongjiang River in China. A dataset of 28,922 time-series data points was collected from seven sampling sites along the Dongjiang River, spanning from November 2020 to February 2023. The DCC method identified conductivity, Permanganate Index (CODMn), and total phosphorus as the most significant predictors for TN levels. The At-CBiLSTM model, optimized with a time step of three, outperformed other models, including standalone Long Short-Term Memory (LSTM), Bi-directional LSTM (Bi-LSTM), Convolutional Neural Network LSTM (CNN-LSTM), and Attention-LSTM variants, achieving excellent performance with the following metrics: mean absolute error (MAE) = 0.032, mean squared error (MSE) = 0.005, mean absolute percentage error (MAPE) = 0.218, and root mean squared error (RMSE) = 0.045. Importantly, increasing the number of input features beyond three variables led to a decline in model accuracy, underscoring the importance of DCC-driven feature selection. The results highlight that combining DCC with deep learning models, particularly At-CBiLSTM, effectively captures nonlinear temporal dependencies and improves prediction accuracy. This approach provides a solid foundation for real-time water quality monitoring and can inform targeted pollution control strategies in river ecosystems. Full article
(This article belongs to the Special Issue Monitoring and Modelling of Contaminants in Water Environment)
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20 pages, 16086 KiB  
Article
Geographic Information System-Based Database for Monitoring and Assessing Mining Impacts on Water Resources and Environmental Systems at National Scale: A Case Study of Morocco (North Africa)
by Salma Boukhari, Abdessamad Khalil, Lahcen Zouhri and Mariam El Adnani
Water 2025, 17(7), 924; https://doi.org/10.3390/w17070924 - 22 Mar 2025
Viewed by 437
Abstract
Decision-making in how to manage environmental issues around mine sites is generally a complicated task. Furthermore, the large amount of data and information involved in the management process is cumbersome. However, Decision Support Tools (DSTs) based on Geographic Information Systems (GISs) are of [...] Read more.
Decision-making in how to manage environmental issues around mine sites is generally a complicated task. Furthermore, the large amount of data and information involved in the management process is cumbersome. However, Decision Support Tools (DSTs) based on Geographic Information Systems (GISs) are of great interest to environmental managers in order to help them to make well-informed and thoroughly documented decisions regarding reclamation plans, especially for abandoned mine sites. The current study highlights the implementation of a cost-effective and efficient GIS-based database as a DST that will be used to assess and manage environmental challenges, particularly those related to water resources, such as hydrographic network issues surrounding mine sites. Based on GISs, a prototype of a national geodatabase was designed and implemented for Moroccan mine sites. It consisted of a set of GIS layers that facilitated the dissemination of an extensive array of multidisciplinary environmental data concerning Moroccan mines to decisionmakers. By applying GIS tools, such as buffer zone analysis, to environmental and hydrological datasets, high-priority mines requiring urgent intervention were identified based on their proximity to water resources, their acid mine drainage (AMD) potential, and their environmental impact on ecosystems. The results highlight the effectiveness of GIS-based approaches in assessing environmental risks, particularly concerning water resources, while also contributing to sustainable mining management in Morocco. Finally, using the GIS-based database is expected to raise the awareness of decisionmakers in government agencies and mining companies for implementing a reclamation program for mine sites. Full article
(This article belongs to the Special Issue Monitoring and Modelling of Contaminants in Water Environment)
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27 pages, 4395 KiB  
Article
Impact of Land Use Pattern and Heavy Metals on Lake Water Quality in Vidarbha and Marathwada Region, India
by Pranaya Diwate, Prasanna Lavhale, Suraj Kumar Singh, Shruti Kanga, Pankaj Kumar, Gowhar Meraj, Jatan Debnath, Dhrubajyoti Sahariah, Md. Simul Bhuyan and Kesar Chand
Water 2025, 17(4), 540; https://doi.org/10.3390/w17040540 - 13 Feb 2025
Viewed by 872
Abstract
Lakes are critical resources that support the ecological balance and provide essential services for human and environmental well-being. However, their quality is being increasingly threatened by both natural and anthropogenic processes. This study aimed to assess the water quality and the presence of [...] Read more.
Lakes are critical resources that support the ecological balance and provide essential services for human and environmental well-being. However, their quality is being increasingly threatened by both natural and anthropogenic processes. This study aimed to assess the water quality and the presence of heavy metals in 15 lakes in the Vidarbha and Marathwada regions of Maharashtra, India. To understand the extent of pollution and its sources, the physico-chemical parameters were analyzed which included pH, turbidity, total hardness, orthophosphate, residual free chlorine, chloride, fluoride, and nitrate, as well as heavy metals such as iron, lead, zinc, copper, arsenic, chromium, manganese, cadmium, and nickel. The results revealed significant pollution in several lakes, with the Lonar Lake showing a pH value of 12, exceeding the Bureau of Indian Standards’ (BIS) limit. The Lonar Lake also showed elevated levels of fluoride having a value of 2 mg/L, nitrate showing a value of 45 mg/L, and orthophosphate showing a concentration up to 2 mg/L. The Rishi Lake had higher concentrations of nickel having a value of 0.2 mg/L and manganese having a value of 0.7 mg/L, crossing permissible BIS limits. The Rishi Lake and the Salim Ali Lake exhibited higher copper levels than other lakes. Cadmium was detected in most of the lakes ranging from values of 0.1 mg/L to 0.4 mg/L, exceeding BIS limits. The highest turbidity levels were observed in Rishi Lake and Salim Ali Lake at 25 NTU. The total hardness value observed in the Kharpudi Lake was 400 mg/L, which is highest among all the lakes under study. The spatial analysis, which utilized remote sensing and GIS techniques, including Sentinel-2 multispectral imagery for land use and land cover mapping and Digital Elevation Model (DEM) for watershed delineation, provided insights into the topography and drainage patterns affecting these lakes. The findings emphasize the urgent need for targeted management strategies to mitigate pollution and protect these vital freshwater ecosystems, with broader implications for public health and ecological sustainability in regions reliant on these water resources. Full article
(This article belongs to the Special Issue Monitoring and Modelling of Contaminants in Water Environment)
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9 pages, 1318 KiB  
Article
Effects of Sublethal Exposure to Three Water Pollutants on Scototaxis in Rare Minnow (Gobiocypris rarus)
by Ning Qiu, Wenjing Li, Jianna Jia, Guoqiang Ma and Shitao Peng
Water 2024, 16(20), 2948; https://doi.org/10.3390/w16202948 - 16 Oct 2024
Viewed by 789
Abstract
The biological early warning system with fish behavior as the detection index is an efficient and rapid early warning technology for the ecological damage caused by water pollutants. However, the attempt to apply the scototaxis (dark preference) behavior of fish to biological early [...] Read more.
The biological early warning system with fish behavior as the detection index is an efficient and rapid early warning technology for the ecological damage caused by water pollutants. However, the attempt to apply the scototaxis (dark preference) behavior of fish to biological early warning is still relatively lacking. In this study, we delved into the dark and light preferences of the rare minnows (Gobiocypris rarus), employing three distinct tank configurations. Additionally, we systematically examined the modulating effects of environmental illumination, nutritional status, and the number of test subjects on this behavior, aiming to establish optimal experimental parameters for its observation. Furthermore, cadmium ions [Cd2+], tricaine methanesulfonate [MS222], and p-chloroaniline were employed as representative heavy metal ions, neuroactive agents, and organic toxicants, respectively, to test the impact of chemicals on scototaxis in gradient concentrations. The results demonstrated that the rare minnow exhibited a clear scototaxis (dark preference), and this behavior was not affected by the nutritional status of the test fish, the illumination, or the number of subjects. While the dark chamber was consistently the preferred location of rare minnows during the chemical exposure tests, the degree of scototaxis by the rare minnow significantly decreased at Cd2+ ≥ 3 mg/L, MS222 ≥ 11 mg/L, and p-chloroaniline ≥ 29 mg/L, suggesting a potential disruption of their innate behavioral patterns by these chemicals. These findings underscore the sensitivity of rare minnows to water pollutants. Therefore, the scototaxis behavior of rare minnows can be a potential and useful behavioral indicator for biological early warning, which can be used for early biological warning of sudden water pollution caused by chemicals such as Cd2+, MS222, and p-chloroaniline. Full article
(This article belongs to the Special Issue Monitoring and Modelling of Contaminants in Water Environment)
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12 pages, 3442 KiB  
Article
Assessment of Potentially Toxic Elements in Subtropical Urban Streams (Santo André, SP, Brazil)
by Rafaella M. T. Espeçoto, Marilena M. Luciano, Bruno L. Batista, Camila N. Lange, Heloísa F. Maltez, Luís C. Schiesari, Marcus V. França, Ângela T. Fushita, Lúcia H. G. Coelho and Ricardo H. Taniwaki
Water 2024, 16(18), 2681; https://doi.org/10.3390/w16182681 - 20 Sep 2024
Viewed by 1258
Abstract
Environmental contamination by potentially toxic elements (PTEs) poses a significant challenge, particularly in the metropolitan regions of developing countries. This issue arises from the high levels of pollution driven by industrial growth and the increased traffic from fossil fuel-powered vehicles. Even after the [...] Read more.
Environmental contamination by potentially toxic elements (PTEs) poses a significant challenge, particularly in the metropolitan regions of developing countries. This issue arises from the high levels of pollution driven by industrial growth and the increased traffic from fossil fuel-powered vehicles. Even after the wastewater treatment in treatment plants, PTEs often persist, posing risks to stream structure and function. This form of pollution is persistent, long-term, and irreversible, presenting a significant challenge in terms of freshwater conservation. This study aimed to assess the water quality and PTE concentrations in urban streams in Santo André, SP, Brazil, to identify the PTEs relevant to stream pollution. We analyzed the water quality in seven catchments in the Santo André municipality, in the metropolitan region of São Paulo, Brazil. The samples were collected during the dry (2021) and rainy periods (2022), and the concentrations of potentially toxic elements (PTEs) were analyzed via inductively coupled plasma–mass spectrometry (ICP-MS). The results showed elevated electrical conductivity (429 ± 211 μS·cm) and low dissolved oxygen concentrations in the streams (2.3 ± 0.95 μg·L), indicating potential problems such as eutrophication and toxicity to aquatic organisms. PTE concentrations, particularly those of Mn (30.8 ± 22.3 μg·L), Fe (91.1 ± 72.1 μg·L), and Zn (38.1 ± 28.7 μg·L), were among the highest concentrations. Seasonal variations affected the PTE concentrations, with Cr and Fe predominating during the dry season and Zn increasing during the rainy season. Associations were found between the PTE concentrations and the water pH, indicating the importance of continuous monitoring and remediation efforts. Full article
(This article belongs to the Special Issue Monitoring and Modelling of Contaminants in Water Environment)
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