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

Deadline for manuscript submissions: closed (31 May 2024) | Viewed by 27715

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Special Issue Editors

Prof. Dr. Fang Yenn Teo

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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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Dr. Anurita Selvarajoo

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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

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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

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Related Special Issue

Emerging Approaches in Hydrology and Water Resources in Applied Sciences (2 articles)

Published Papers (7 papers)

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Research

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18 pages, 23027 KB

Open AccessArticle

Research on the Jiamusi Area’s Shallow Groundwater Recharge Using Remote Sensing and the SWAT Model

by Xiao Yang, Changlei Dai, Gengwei Liu and Chunyue Li

Appl. Sci. 2024, 14(16), 7220; https://doi.org/10.3390/app14167220 - 16 Aug 2024

Cited by 1 | Viewed by 1413

Abstract

Jiamusi is situated in Heilongjiang Province, China, in the center of the Sanjiang Plain. The 1980s’ overplanting of paddy fields resulted in a decrease in groundwater levels, scarcity of groundwater resources, and frequent earth collapses. Examining and safeguarding the groundwater resources in this region has emerged as a crucial subject. In light of this, this paper uses the remote sensing water balance method and the SWAT distributed hydrological model to calculate groundwater resources in the Jiamusi area. It also conducts scientific experiments by examining various factors, including rainfall, the degree of water supply, soil type, and land use. The measured monthly runoff of Jiamusi City’s Tongjiang and Fuyuan City’s hydrology stations was utilized to establish the model parameters for the SWAT model. A preliminary assessment of the distribution features of shallow groundwater in the Jiamusi area is conducted using the two methodologies mentioned above, and the following results are reached: (1) Tongjiang Hydrological Station and Fuyuan Hydrological Station both had good runoff modeling results, with R² and NS values of 0.81, 0.77, and 0.77, 0.75, respectively. (2) The SWAT model works well for assessing groundwater resources. Between 2010 and 2016 (two preheating years), Jiamusi’s average groundwater recharge was 61.03 × 10⁸ m³, with a recoverable amount of 27.4 × 10⁸ m³. (3) Based on the remote sensing water balancing approach, the average exploitable quantity of groundwater recharge in the Jiamusi area between 2008 and 2016 is 23.94 × 10⁸ m³, while the average recharge in the area is 53.2 × 10⁸ m³. (4) The Jiamusi metropolitan area is the core of the groundwater phreatic reservoir water reserves, which progressively decline in both the northeast and southeast directions. It falls to the southwest as Fuyuan City’s center. The Songhua River’s main stream area near Tongjiang City has the least volume of water reserves in the phreatic layer, and the area’s groundwater reserves converge to the southeast and northwest, where surface water makes up the majority of the water resources. Full article

(This article belongs to the Special Issue Sustainable Environment and Water Resource Management)

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28 pages, 2684 KB

Open AccessArticle

Exploring Factors Influencing Recreational Experiences of Urban River Corridors Based on Social Media Data

by Lin Shi, Sreetheran Maruthaveeran, Mohd Johari Mohd Yusof and Chenyang Dai

Appl. Sci. 2024, 14(10), 4086; https://doi.org/10.3390/app14104086 - 11 May 2024

Cited by 7 | Viewed by 2864

Abstract

River corridors, recognized as “blue–green infrastructure,” have become a crucial support system for urban sustainability in contemporary urbanized societies. Understanding the factors influencing the recreational experience along urban river corridors is paramount for enhancing visitors’ health and well-being. This study focuses on the Hutuo River Corridor in Shijiazhuang, China, collecting 3006 valid reviews from Dianping, a prominent review platform. We developed a text-based thematic model and conducted content analysis using this dataset. The main social (visiting time, duration of stay, motivation, safety, and visitors’ types and activities) and physical (natural elements, artificial facilities, maintenance and management, accessibility, distance, models of transportation, weather, and seasons) factors associated with recreational experiences were identified. We assessed visitor perceptions of urban river corridors and elucidated facilitators or barriers through textual content analysis in reviews. The results indicate the feasibility of employing social media data to study visitors’ recreational experiences along urban river corridors. This comprehensive exploration from a qualitative ecological perspective contributes valuable insights for urban planning and management. Moreover, the findings hold significant implications for understanding the usage patterns of river corridors in China and potentially in other countries. Full article

(This article belongs to the Special Issue Sustainable Environment and Water Resource Management)

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17 pages, 5643 KB

Open AccessArticle

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

Cited by 2 | Viewed by 1827

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 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 KB

Open AccessArticle

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

Cited by 5 | Viewed by 2015

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. 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 KB

Open AccessArticle

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 11 | Viewed by 2788

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 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 KB

Open AccessArticle

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 9 | Viewed by 2627

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 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 (

M S E

) 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%,

M A E

was reduced by 18–59%, the root-mean-square error (

R M S E

) was reduced by 1–66%, and the Kling–Gupta efficiency (

K G E

) 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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44 pages, 1843 KB

Open AccessReview

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 - 8 Nov 2023

Cited by 66 | Viewed by 12899

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 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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