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Application of Modeling and Assessment in Sustainable Water Quality Management

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Water Management".

Deadline for manuscript submissions: closed (29 February 2024) | Viewed by 9605

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Special Issue Editor

Dr. Usman Safder

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Guest Editor

School of Chemical and Bioprocess Engineering, University College Dublin, Dublin, Ireland
Interests: water quality monitoring; WWTP design; optimization; water management; water-energy nexus; sustainability
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Water, a vital resource for human life, is threatened by the global climate crisis, which is now accepted as reality even by skeptics. Changes in the global climate has already influenced the availability of freshwater and will have a severe effect on water quality. Human activity has affected water resources, notably in the last 50 years. Furthermore, it is widely understood that monitoring and analyzing water quality is critical for the successful management of all water resources and for providing the best possible conditions for the people and communities that rely on a vibrant water sector for their daily requirements. For sustainable water management, the effective and integrated water quality management as well as the sustainable design of water treatment technologies with strict environmental consideration (sustainability) must be implemented with resolve.

This Special Issue focuses on cutting-edge research on the modeling, optimization, analysis, methodologies, and practical applications of wastewater treatment, water resources, and sustainable systems. It seeks to provide a platform for academics, developers, and practitioners from both academia and industry to share cutting-edge findings and promote smart water treatment and sustainable systems.

Research areas may include (but are not limited to) the following:

Assessing and interpreting global energy emissions.
Current advancements and anticipated developments in sustainable water quality monitoring and assessment technologies.
Sustainable technology for water quality measurement and monitoring.
Multi-objective optimization analyses energy, environmental, and economic impacts.
Environmental sustainability and the rapid digitalization.
Role of IoT, AI, and big data in water quality assessment, design, and monitoring.
Automation, optimization, or sustainability of wastewater treatment facilities.
Waste reuse techniques and strategies that are progressive and innovative.

I look forward to receiving your contributions.

Dr. Usman Safder
Guest Editor

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. Sustainability 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 2400 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

clear technologies
artificial intelligence
environmental sustainability
digitalization
modeling and design
industrial, commercial, and residential applications
water quality assessment

Published Papers (6 papers)

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Research

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20 pages, 18430 KiB

Open AccessArticle

Design of Non-Structural Practices for Sustainable Water Quality Improvement in an Urban River: A Case Study of South Korea

by Taesung Kang, Nayeong Yu, Minhwan Shin, Kyoungsoo Na, Kyoung Jae Lim and Jonggun Kim

Sustainability 2024, 16(6), 2298; https://0-doi-org.brum.beds.ac.uk/10.3390/su16062298 - 11 Mar 2024

Viewed by 689

Abstract

Urban rivers exhibit characteristics of low flow and significant water quality fluctuations, making them susceptible to pollution from various sources such as untreated sewage, non-point pollution within the urban area, and unknown inflows. To address water quality management in urban rivers, precise investigations into background water quality, pollution levels, and the characteristics of pollution sources are essential. Following the identification of pollution sources, sustainable river management strategies, incorporating both structural and non-structural measures, are crucial. This study aims to develop continuous and long-term river management strategies, considering the characteristics of urban river basins, through citizen participation governance and non-structural approaches. Citizen networks were formed for each target urban river, and activities for water quality improvement were proposed and implemented. This study provides phased approaches to citizen participation governance, and activities include citizen-led water quality monitoring, the purification and monitoring of riverbank pollution sources, and water-related education. It emphasizes the importance of local residents’ interest in urban river water quality improvement and underscores the need for sustained activities through local citizen networks. Additionally, active participation and investments from the local government, government agencies, and various experts are deemed essential. Full article

(This article belongs to the Special Issue Application of Modeling and Assessment in Sustainable Water Quality Management)

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15 pages, 4553 KiB

Open AccessFeature PaperArticle

The Risk of Water Quality Deterioration with Urban Flood Control—A Case in Wuxi

by Pan Hu and Lan Feng

Sustainability 2024, 16(1), 185; https://0-doi-org.brum.beds.ac.uk/10.3390/su16010185 - 25 Dec 2023

Cited by 2 | Viewed by 804

Abstract

There is a demand for flood control in densely populated river network areas. Therefore, small floodgates are used for long-term and rapid water flow regulation in such contexts. However, people often disregard these floodgates’ potential interference with the natural water environment. This study focused on an urban floodgate-controlled reach and monitored the monthly data of four main pollutant indicators (TN, TP, COD_Mn, and NH₃-N) from 2016 to 2018 at six fixed sampling points (S1–S6). The difference analysis and cluster analysis results indicated that floodgate adjustments were the dominant driving factor of water quality changes in the reach, with pollutant concentration differences observed between the floodgate opening and closing periods. The results of the Canadian Council of Ministers of the Environment Water Quality Index evaluation showed that the water quality of the floodgate-controlled reach was categorized as “marginal” or “poor”. It is particularly important to note that the concentration of nitrogen compounds exceeded the allowable limits. The results of the Mann–Kendall trend and time series analyses revealed an overall upward trend in NH₃-N concentration and a localized upward trend in TP concentration and presented periodic concentration fluctuations of four pollutants (TN, TP, COD_Mn, and NH₃-N). This study highlights that flood control management using small floodgates can pose a risk of deteriorating water quality. Therefore, it is necessary to develop scientific water quality management methods. Full article

(This article belongs to the Special Issue Application of Modeling and Assessment in Sustainable Water Quality Management)

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18 pages, 8096 KiB

Open AccessArticle

An Empirical Modal Decomposition-Improved Whale Optimization Algorithm-Long Short-Term Memory Hybrid Model for Monitoring and Predicting Water Quality Parameters

by Binglin Li, Hao Xu, Yufeng Lian, Pai Li, Yong Shao and Chunyu Tan

Sustainability 2023, 15(24), 16816; https://0-doi-org.brum.beds.ac.uk/10.3390/su152416816 - 13 Dec 2023

Viewed by 680

Abstract

Prediction of water quality parameters is a significant aspect of contemporary green development and ecological restoration. However, the conventional water quality prediction models have limited accuracy and poor generalization capability. This study aims to develop a dependable prediction model for ammonia nitrogen concentration in water quality parameters. Based on the characteristics of the long-term dependence of water quality parameters, the unique memory ability of the Long Short-Term Memory (LSTM) neural network was utilized to predict water quality parameters. To improve the accuracy of the LSTM prediction model, the ammonia nitrogen data were decomposed using Empirical Modal Decomposition (EMD), and then the parameters of the LSTM model were optimized using the Improved Whale Optimization Algorithm (IWOA), and a combined prediction model based on EMD-IWOA-LSTM was proposed. The study outcomes demonstrate that EMD-IWOA-LSTM displays improved prediction accuracy with reduced RootMean Square Error (RMSE) and Mean Absolute Percentage Error (MAPE) in comparison to the LSTM and IWOA-LSTM approaches. These research findings better enable the monitoring and prediction of water quality parameters, offering a novel approach to preventing water pollution rather than merely treating it afterwards. Full article

(This article belongs to the Special Issue Application of Modeling and Assessment in Sustainable Water Quality Management)

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23 pages, 3312 KiB

Open AccessArticle

Assessment of the Performance of a Water Treatment Plant in Ecuador: Hydraulic Resizing of the Treatment Units

by Jonathan I. Mendez-Ruiz, María B. Barcia-Carreño, Lisbeth J. Mejía-Bustamante, Ángela K. Cornejo-Pozo, Cristian A. Salas-Vázquez and Priscila E. Valverde-Armas

Sustainability 2023, 15(2), 1235; https://0-doi-org.brum.beds.ac.uk/10.3390/su15021235 - 9 Jan 2023

Cited by 3 | Viewed by 3598

Abstract

Granting access to drinking water has been a challenge because 47% of the worldwide population is not connected to a drinking water distribution network in rural settlements. This study aimed to evaluate the contaminant removal efficiency in a conventional water treatment facility in the Austro region of Ecuador, Paute, to identify the treatment units requiring hydraulic resizing. Water samples were collected from each treatment unit to characterize the physical-chemical and microbiological parameters, and the dimensions of the treatment ponds for hydraulic evaluation purposes. Water hardness, electrical conductivity, SO₄²⁻, and Fe²⁺ were the main issues found in the water, which failed to comply with Ecuadorian technical guidelines. The treatment units, such as the flocculator, rapid sand filter, and storage tank, were resized to meet the demand of the future population. In addition, the residual free chlorine was measured as insufficient in the community’s tap water, showing an unprotected water distribution system to microbiological contamination. No disinfection by-products were found despite the existence of biodegradable organic matter. The findings of this research propose improvements in the deployed treatment practices to provide the community with drinking water in accordance with the Sustainable Development Objectives (SDG 3 and SDG 6). Full article

(This article belongs to the Special Issue Application of Modeling and Assessment in Sustainable Water Quality Management)

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Review

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26 pages, 4683 KiB

Open AccessReview

Advancement in Supercapacitors for IoT Applications by Using Machine Learning: Current Trends and Future Technology

by Qadeer Akbar Sial, Usman Safder, Shahid Iqbal and Rana Basit Ali

Sustainability 2024, 16(4), 1516; https://0-doi-org.brum.beds.ac.uk/10.3390/su16041516 - 10 Feb 2024

Cited by 1 | Viewed by 1113

Abstract

Supercapacitors (SCs) are gaining attention for Internet of Things (IoT) devices because of their impressive characteristics, including their high power and energy density, extended lifespan, significant cycling stability, and quick charge–discharge cycles. Hence, it is essential to make precise predictions about the capacitance and lifespan of supercapacitors to choose the appropriate materials and develop plans for replacement. Carbon-based supercapacitor electrodes are crucial for the advancement of contemporary technology, serving as a key component among numerous types of electrode materials. Moreover, accurately forecasting the lifespan of energy storage devices may greatly improve the efficient handling of system malfunctions. Researchers worldwide have increasingly shown interest in using machine learning (ML) approaches for predicting the performance of energy storage materials. The interest in machine learning is driven by its noteworthy benefits, such as improved accuracy in predictions, time efficiency, and cost-effectiveness. This paper reviews different charge storage processes, categorizes SCs, and investigates frequently employed carbon electrode components. The performance of supercapacitors, which is crucial for Internet of Things (IoT) applications, is affected by a number of their characteristics, including their power density, charge storage capacity, and cycle longevity. Additionally, we provide an in-depth review of several recently developed ML-driven models used for predicting energy substance properties and optimizing supercapacitor effectiveness. The purpose of these proposed ML algorithms is to validate their anticipated accuracies, aid in the selection of models, and highlight future research topics in the field of scientific computing. Overall, this research highlights the possibility of using ML techniques to make significant advancements in the field of energy-storing device development. Full article

(This article belongs to the Special Issue Application of Modeling and Assessment in Sustainable Water Quality Management)

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28 pages, 1363 KiB

Open AccessReview

A Brief Insight into the Toxicity Conundrum: Modeling, Measuring, Monitoring and Evaluating Ecotoxicity for Water Quality towards Environmental Sustainability

by Paulina Vilela, Gabriel Jácome, Wladimir Moya, Pouya Ifaei, Sungku Heo and Changkyoo Yoo

Sustainability 2023, 15(11), 8881; https://0-doi-org.brum.beds.ac.uk/10.3390/su15118881 - 31 May 2023

Cited by 1 | Viewed by 1972

Abstract

In view of the continuous increment of industrial residues, the risk associated with chemical toxicity in the environment has piqued the interest of researchers in pursuit of an alternative methodology for mitigating the apparent toxicity of chemicals. Over the past decade, the applicability of toxicity models and the evaluation of the apparent toxicity of chemicals have been examined for achieving sustainability of the environment and improving water quality. The prediction of toxicant effects with reasonable accuracy in organisms of water bodies and other environmental compartments lies in the application of a chemical toxicity model with further risk assessment analysis. This review summarizes well-known and recent advances of modeling techniques to evaluate and monitor toxicity in the environment. Chemical toxicity models such as the individual-based concentration addition (CA), independent action (IA) and whole-mixture-based concentration addition-independent action (CAIA) are considered, as well as their environmental applications, specific case studies, and further research needs towards sustainability. The gap that needs to be overcome in toxicity studies for the environmental sustainability is noted based on the aspects of environmental chemistry and ecotoxicology, sufficient laboratory equipment, data availability and resources for relevant social parameters needed for investigation. Full article

(This article belongs to the Special Issue Application of Modeling and Assessment in Sustainable Water Quality Management)

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