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Environments
Special Issues
Monitoring and Control of Water Pollution for Environmental Sustainability
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Monitoring and Control of Water Pollution for Environmental Sustainability

A special issue of Environments (ISSN 2076-3298). This special issue belongs to the section "Environmental Monitoring and Management".

Deadline for manuscript submissions: 15 December 2026 | Viewed by 2518

Editors

Dr. Wan Zakiah Wan Ismail

E-Mail Website
Guest Editor
Faculty of Engineering and Built Environment, Universiti Sains Islam Malaysia, Nilai, Negeri Sembilan, Malaysia
Interests: optical and electronics sensing; artificial intelligence
Dr. Nor Azlina Abd Aziz

E-Mail Website
Guest Editor
Faculty of Engineering and Technology, Multimedia University, Melaka, Malaysia
Interests: soft computing and its application in engineering problems
Special Issues, Collections and Topics in MDPI journals
Dr. Anith Khairunnisa Ghazali

E-Mail Website
Guest Editor
Faculty of Engineering and Technology (FET), Multimedia University (MMU), Melaka, Malaysia
Interests: control systems; sustainability; optimization; intelligent systems; energy management systems; regenerative braking; electric vehicle; instrumentation and engineering applications

Special Issue Information

Dear Colleagues,

Water is a vital element that supports our lives, animals, and ecosystems. There are many types of pollutants present in water that can harm our ecosystems, such as heavy metals, fertilizers, pesticides, parasites, faecal waste, nitrates, and phosphates, among others. The pollutants can be detected by systematic data collection, including physical, biological and chemical parameters, using lab analysis and advanced sensors. Then, water quality can be analyzed and assessed to identify pollution sources from industrial, agricultural runoff, or household sources. Authorities need to ensure regulatory compliance and inform policy, while control measures should focus on pollutant source reduction and treatments for cleaner water and improved public health. Additionally, modern approaches leverage real-time monitoring, biosensors, nanotechnology, and cloud platforms for faster detection and better management.

Dr. Wan Zakiah Wan Ismail
Dr. Nor Azlina Abd Aziz
Dr. Anith Khairunnisa Ghazali
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Environments is an international peer-reviewed open access monthly 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 1800 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

  • types of pollutants
  • traditional or modern methods for detecting pollutants
  • monitoring pollutants
  • preserving environmental data

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

Published Papers (2 papers)

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Review

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12 pages, 1300 KB  
Review
Critical Assessment on the Current Situation of the Atoyac and Salado Rivers from Oaxaca State, Mexico
by Florencio Montellano-Jiménez, Edwin A. Zelaya-Benavidez, Victor A. Franco-Luján, Virginia Hernández-Montoya, Marbella Sánchez-Soriano and Heriberto Cruz-Martínez
Environments 2026, 13(6), 344; https://doi.org/10.3390/environments13060344 - 17 Jun 2026
Viewed by 539
Abstract
Rivers worldwide are increasingly affected by pollution from untreated wastewater, industrial effluents, agricultural runoff, and solid waste. In Mexico, the Atoyac and Salado rivers in Oaxaca present critical cases of environmental degradation. This study provides a comprehensive assessment of their current condition by [...] Read more.
Rivers worldwide are increasingly affected by pollution from untreated wastewater, industrial effluents, agricultural runoff, and solid waste. In Mexico, the Atoyac and Salado rivers in Oaxaca present critical cases of environmental degradation. This study provides a comprehensive assessment of their current condition by integrating a review of scientific studies reported in the literature with updated field measurements conducted at representative sampling points. The results reveal severe deterioration in water quality, with key physicochemical parameters—such as total dissolved solids, chemical oxygen demand, total nitrogen, and fats and oils—consistently exceeding the permissible limits established in the standard NOM-001-SEMARNAT-2021. For instance, chemical oxygen demand reached values as high as 2112 mg/L, approximately 10 times higher than the regulatory limit (210 mg/L), highlighting the severity of organic pollution in these rivers. These findings indicate a high load of organic and inorganic pollutants associated with anthropogenic activities, including urbanization, industrial discharges, and inadequate wastewater management. In contrast, pH and temperature values remain within acceptable ranges. Most heavy metals and cyanides were found below regulatory limits, suggesting no immediate risk from these contaminants. However, continuous monitoring of these heavy metals and cyanides is necessary, as they could represent a public health problem in the future. Overall, this study underscores the urgent need for integrated and multidisciplinary strategies to restore and sustainably manage these river systems, considering both environmental and socio-environmental dimensions. Full article
(This article belongs to the Special Issue Monitoring and Control of Water Pollution for Environmental Sustainability)
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20 pages, 840 KB  
Systematic Review
Water Quality Monitoring and Assessment Using Machine Learning: A Review of Formulation, Modeling Approaches, and Explainable Artificial Intelligence
by Mohd Akmal Ab Karim, Wan Zakiah Wan Ismail, Farrah Masyitah Mohd Shuib, Nor Azlina Ab Aziz and Anith Khairunnisa Ghazali
Environments 2026, 13(5), 267; https://doi.org/10.3390/environments13050267 - 11 May 2026
Cited by 1 | Viewed by 1612
Abstract
Water pollution poses significant risks to human health and environmental sustainability, highlighting the need for accurate water quality assessment and prediction. This review examines the application of machine learning (ML) in Water Quality Index (WQI) assessments, focusing on WQI formulation, predictive modelling approaches, [...] Read more.
Water pollution poses significant risks to human health and environmental sustainability, highlighting the need for accurate water quality assessment and prediction. This review examines the application of machine learning (ML) in Water Quality Index (WQI) assessments, focusing on WQI formulation, predictive modelling approaches, and explainable artificial intelligence (XAI) techniques. A structured literature review is conducted using major scientific databases, including ScienceDirect, Springer, and other relevant sources, following a systematic study selection process. The review analyzes commonly used water quality parameters and highlights how the deterministic structure of WQI influences machine learning modelling, often leading to high predictive performance that reflects predefined formulations rather than independent pattern learning. A comprehensive comparison of single, hybrid, and ensemble ML models is presented, showing that hybrid approaches generally provide improved robustness and accuracy in complex water quality scenarios. In addition, the role of XAI methods in enhancing model interpretability and supporting transparent decision-making is discussed. Key challenges, including limited generalization, model complexity, and interpretability constraints, are identified, and future research directions are proposed to develop more reliable and practical AI-based water quality monitoring systems. Overall, this review provides insights into the integration of machine learning and WQI, emphasizing the importance of balancing predictive accuracy with interpretability for sustainable water resource management. Full article
(This article belongs to the Special Issue Monitoring and Control of Water Pollution for Environmental Sustainability)
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