Sustainable Environment and Water Resource Management

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Environmental Sciences".

Deadline for manuscript submissions: 31 May 2024 | Viewed by 6619

Special Issue Editors


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Guest Editor
Faculty of Science and Engineering, University of Nottingham Malaysia, Semenyih 43500, Malaysia
Interests: water resources management; water engineering; hydrology; hydrological modeling; environment hydrologic; hydro-environemtal engineering; flood modeling; rivers hydraulics; water quality; coastal and estuarine development
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Guest Editor
Associate Professor, Faculty of Science and Engineering, University of Nottingham Malaysia, Semenyih 43500, Malaysia
Interests: water quality; water management; water pollution; wastewater management; biomass and biochar; pyrolysis; bioretention

Special Issue Information

Dear Colleagues,

This Special Issue is devoted to sustainable environment and water resource management in various research fields. Now is the time to address the world’s pressing water and environmental issues as we aspire to achieve more significant sustainable development for the good of the future. As part of our commitment to sustainability, the University of Nottingham Malaysia organised the 1st International Conference on Water and Environment for Sustainability in collaboration with the Malaysia Chapters of the International Association for Hydro-Environment Engineering and Research (IAHR) and the International Association for Coastal Reservoir Research (IACRR). The conference took place from 7 to 9 December 2022 at the Kuala Lumpur Convention Centre, Malaysia, concurrently with ASIAWATER 2022, the region’s leading international water and wastewater event in Asia.

Alongside “Towards Sustainable Water and Environment Management for the Future” as the theme, this international conference provided the essential platform for scientists, researchers, engineers, industry players, managers, and policymakers to engage, share and exchange the latest scientific knowledge, management approaches, and engineering solutions on water and environment in support of the adoption of the 17 United Nations Sustainable Development Goals (SDGs).

Sustainable management of global water and environment issues is fundamental for the achievements of all SDGs that can positively impact our world and provide a better future for all, including the improvement to the quality of life. Therefore, this Special Issue is welcoming all experts and will be provided the opportunity to share recent scientific knowledge, management approaches, and engineering solutions from different backgrounds of water and environmental related academia, research institutions, industry companies, non-government organisations, and government agencies.

Dr. Fang Yenn Teo
Dr. Anurita Selvarajoo
Guest Editors

Manuscript Submission Information

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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. Applied Sciences 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

  • hydrology and water resources
  • river, lake, estuary, and sea
  • extreme events and climate change
  • water supply and wastewater treatment
  • environmental pollution and protection
  • sustainable technology and engineering
  • sustainable development goal
  • sustainable environment

Related Special Issue

Published Papers (5 papers)

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Research

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17 pages, 5643 KiB  
Article
Early Warning of Red Tide of Phaeocystis globosa Based on Phycocyanin Concentration Retrieval in Qinzhou Bay, China
by Yin Liu, Huanmei Yao, Huaquan Chen, Mengsi Wang, Zengshiqi Huang and Weiping Zhong
Appl. Sci. 2023, 13(20), 11449; https://doi.org/10.3390/app132011449 - 19 Oct 2023
Viewed by 784
Abstract
Phaeocystis globose (P. glo) are the most frequent harmful algae responsible for red tides in Qinzhou Bay, Guangxi. They pose a significant threat to the coastal marine ecosystem, making it essential to develop an efficient indicator method tailored to P. glo [...] Read more.
Phaeocystis globose (P. glo) are the most frequent harmful algae responsible for red tides in Qinzhou Bay, Guangxi. They pose a significant threat to the coastal marine ecosystem, making it essential to develop an efficient indicator method tailored to P. glo outbreaks. In remote sensing water quality monitoring, there is a strong correlation between P. glo and cyanobacteria, with phycocyanin (PC) serving as an indicator of cyanobacterial biomass. Consequently, existing research has predominantly focused on remote sensing monitoring of medium to high PC concentrations. However, it is still challenging to monitor low PC concentrations. This paper introduced the BP neural network (BPNN) and particle swarm optimization algorithm (PSO). It selects spectral bands and indices sensitive to PC concentrations and constructs a PC concentration retrieval model, in combination with meteorological factors, offering a comprehensive exploration of the indicative role of low PC concentrations in predicting P. glo red tide outbreaks in Qinzhou Bay. The results demonstrated that the PC concentration retrieval model, based on the backpropagation neural network optimized by the particle swarm optimization algorithm (PSO-BPNN), demonstrated better performance (MAE = 0.469, RMSE = 0.615). In Qinzhou Bay, PC concentrations were mainly concentrated around 2~5 μg/L. During the P. glo red tide event, the area with undetectable PC concentrations (PC < 0.04 μg/L) increased by 4.97 km2, with regions below 0.9 μg/L experiencing exponential growth. Considering the variations in PC concentrations along with meteorological factors, we proposed a straightforward early warning threshold for P. glo red tides: PC < 0.9 μg/L and T < 20 °C. This method, from a remote sensing perspective, analyzes the process of P. glo outbreaks, simplifies PC concentration monitoring, and provides a reasonably accurate prediction of the risk of P. glo red tide disasters. Full article
(This article belongs to the Special Issue Sustainable Environment and Water Resource Management)
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20 pages, 5424 KiB  
Article
A Multivariate Model of Drinking Water Quality Based on Regular Monitoring of Radioactivity and Chemical Composition
by Cecilia Ionela Tăban, Ana Maria Benedek, Mihaela Stoia, Maria Denisa Cocîrlea and Simona Oancea
Appl. Sci. 2023, 13(18), 10544; https://doi.org/10.3390/app131810544 - 21 Sep 2023
Viewed by 817
Abstract
From a public health perspective, the monitoring of water quality intended for human consumption belongs to the operational and audit management of the supply zones. Our study explores the spatial and temporal patterns of the parameters of drinking water in Sibiu County, Romania. [...] Read more.
From a public health perspective, the monitoring of water quality intended for human consumption belongs to the operational and audit management of the supply zones. Our study explores the spatial and temporal patterns of the parameters of drinking water in Sibiu County, Romania. We related the relevant physical-chemical parameters (ammonia, chlorine, nitrates, Al, Fe, Pb, Cd, Mn, pH, conductivity, turbidity, and oxidizability) and radioactivity (gross alpha activity, gross beta activity, and radon-222 content) from a 5-year survey to the water source (surface water and groundwater, which may be of subsurface or deep origin), space (sampling locality) and time (sampling month and year). We conducted a combined evaluation using the generalized linear mixed models (GLMMs), Pearson correlation analysis of the physical-chemical parameter, multivariate linear redundancy analysis (RDA), t-value biplots construction, and co-inertia analysis. The obtained regional model shows that the source, locality, and month of sampling are significant factors in physical-chemical parameters’ variation. Fe and turbidity have significantly higher values in surface water, and nitrates and conductivity in groundwater. The highest values are recorded in January (nitrates), March (Cl, ammonia, pH) and August (Fe, turbidity). The RDA ordination diagram illustrates the localities with particular or similar characteristics of drinking water, two of which (rural sources) being of concern. The water source is the best predictor for radioactivity, which increases from surface to ground. The gross alpha and beta activities are significantly and positively correlated, and are both correlated with conductivity. In addition, the gross alpha activity is positively correlated with nitrates and negatively with pH, while the gross beta activity is positively correlated with Mn and negatively with Fe; these relationships are also revealed by the co-inertia analysis. In conclusion, our model using multilevel statistical techniques illustrates a potential approach to short-term dynamics of water quality which will be useful to local authorities. Full article
(This article belongs to the Special Issue Sustainable Environment and Water Resource Management)
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23 pages, 2227 KiB  
Article
Multicriteria Decision Making and Water Infrastructure: An Application of the Analytic Hierarchy Process for a Sustainable Ranking of Investments
by Maria Macchiaroli, Luigi Dolores and Gianluigi De Mare
Appl. Sci. 2023, 13(14), 8284; https://doi.org/10.3390/app13148284 - 18 Jul 2023
Cited by 1 | Viewed by 1062
Abstract
The United Nations SDG6 goal of ensuring universal access to safe drinking water and sanitation by 2030 will require increased investment in the rehabilitation and maintenance of water infrastructure. In Italy, the water sector has not yet reached the performance of other European [...] Read more.
The United Nations SDG6 goal of ensuring universal access to safe drinking water and sanitation by 2030 will require increased investment in the rehabilitation and maintenance of water infrastructure. In Italy, the water sector has not yet reached the performance of other European countries. The hierarchization of investments is essential for identifying priorities and efficiently allocating resources. This issue is part of the debate on the reconciliation of public and private needs in the management of water services. The present research proposes a model based on the analytic hierarchy process (AHP). Taking into account the design alternatives considered optimal that contribute to the resolution of territorial criticalities, the model organizes them in a ranking that indicates the chronological priorities to be respected in the investments to be made. The evaluation criteria are set in compliance with the norms defined by the National Authority (ARERA). The model is tested on a water manager in the Campania region. Among the main results, it is found that the two extremes of the ranking are shared between the two actors involved in the investment strategy (the private operator and public regulator). The model represents an effective tool for identifying shared planning strategies between public and private operators. Full article
(This article belongs to the Special Issue Sustainable Environment and Water Resource Management)
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24 pages, 8990 KiB  
Article
Sequential Data Processing for IMERG Satellite Rainfall Comparison and Improvement Using LSTM and ADAM Optimizer
by Seng Choon Toh, Sai Hin Lai, Majid Mirzaei, Eugene Zhen Xiang Soo and Fang Yenn Teo
Appl. Sci. 2023, 13(12), 7237; https://doi.org/10.3390/app13127237 - 17 Jun 2023
Cited by 2 | Viewed by 982
Abstract
This study introduces a systematic methodology whereby different technologies were utilized to download, pre-process, and interactively compare the rainfall datasets from the Integrated Multi-Satellite Retrievals for Global Precipitation Mission (IMERG) satellite and rain gauges. To efficiently handle the large volume of data, we [...] Read more.
This study introduces a systematic methodology whereby different technologies were utilized to download, pre-process, and interactively compare the rainfall datasets from the Integrated Multi-Satellite Retrievals for Global Precipitation Mission (IMERG) satellite and rain gauges. To efficiently handle the large volume of data, we developed automated shell scripts for downloading IMERG data and storing it, along with rain gauge data, in a relational database system. Hypertext pre-processor (pHp) programs were built to visualize the result for better analysis. In this study, the performance of IMERG estimations over the east coast of Peninsular Malaysia for the duration of 10 years (2011–2020) against rain gauge observation data is evaluated. Moreover, this study aimed to improve the daily IMERG estimations with long short-term memory (LSTM) developed with Python. Findings show that the LSTM with Adaptive Moment Estimation (ADAM) optimizer trained against the mean square error (MSE) loss enhances the accuracy of satellite estimations. At the point-to-pixel scale, the correlation between satellite estimations and ground observations was increased by about 15%. The bias was reduced by 81–118%, MAE was reduced by 18–59%, the root-mean-square error (RMSE) was reduced by 1–66%, and the Kling–Gupta efficiency (KGE) was increased by approximately 200%. The approach developed in this study establishes a comprehensive and scalable data processing and analysis pipeline that can be applied to diverse datasets and regions encountering similar domain-specific challenges. Full article
(This article belongs to the Special Issue Sustainable Environment and Water Resource Management)
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Review

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44 pages, 1843 KiB  
Review
A Comprehensive Survey of Machine Learning Methodologies with Emphasis in Water Resources Management
by Maria Drogkoula, Konstantinos Kokkinos and Nicholas Samaras
Appl. Sci. 2023, 13(22), 12147; https://doi.org/10.3390/app132212147 - 08 Nov 2023
Cited by 5 | Viewed by 2280
Abstract
This paper offers a comprehensive overview of machine learning (ML) methodologies and algorithms, highlighting their practical applications in the critical domain of water resource management. Environmental issues, such as climate change and ecosystem destruction, pose significant threats to humanity and the planet. Addressing [...] Read more.
This paper offers a comprehensive overview of machine learning (ML) methodologies and algorithms, highlighting their practical applications in the critical domain of water resource management. Environmental issues, such as climate change and ecosystem destruction, pose significant threats to humanity and the planet. Addressing these challenges necessitates sustainable resource management and increased efficiency. Artificial intelligence (AI) and ML technologies present promising solutions in this regard. By harnessing AI and ML, we can collect and analyze vast amounts of data from diverse sources, such as remote sensing, smart sensors, and social media. This enables real-time monitoring and decision making in water resource management. AI applications, including irrigation optimization, water quality monitoring, flood forecasting, and water demand forecasting, enhance agricultural practices, water distribution models, and decision making in desalination plants. Furthermore, AI facilitates data integration, supports decision-making processes, and enhances overall water management sustainability. However, the wider adoption of AI in water resource management faces challenges, such as data heterogeneity, stakeholder education, and high costs. To provide an overview of ML applications in water resource management, this research focuses on core fundamentals, major applications (prediction, clustering, and reinforcement learning), and ongoing issues to offer new insights. More specifically, after the in-depth illustration of the ML algorithmic taxonomy, we provide a comparative mapping of all ML methodologies to specific water management tasks. At the same time, we include a tabulation of such research works along with some concrete, yet compact, descriptions of their objectives at hand. By leveraging ML tools, we can develop sustainable water resource management plans and address the world’s water supply concerns effectively. Full article
(This article belongs to the Special Issue Sustainable Environment and Water Resource Management)
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