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Sustainability
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Water Quality: Current State and Future Trends
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Water Quality: Current State and Future Trends

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Environmental Sustainability and Applications".

Deadline for manuscript submissions: closed (15 November 2022) | Viewed by 20028

Special Issue Editors

Prof. Dr. Angela Gorgoglione

E-Mail Website
Guest Editor
Department of Fluid Mechanics and Environmental Engineering (IMFIA), School of Engineering, Universidad de la República, Montevideo 11300, Uruguay
Interests: surface hydrology; hydrologic and water-quality modeling; impact assessment of land use and climate change; urban hydrology and water-quality
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Pablo E. Santoro

E-Mail Website
Guest Editor
Institute of Fluid Mechanics and Environmental Engineering (IMFIA), School of Engineering, Universidad de la República, Montevideo, Uruguay
Interests: sediment transport; hydrodynamic modeling; fluid mechanics
Prof. Dr. Fabian A. Bombardelli

E-Mail Website
Guest Editor
Department of Civil and Environmental Engineering, University of California, Davis，CA 95616，USA
Interests: development and use of computational fluid dynamics and computational hydraulics techniques to address problems belonging to the field of environmental fluid mechanics

Special Issue Information

Dear Colleagues,

Surface-water quality plays a crucial role in the health of aquatic ecosystems as well as in the economy of countries, being one of the sources of drinking water and supporting agro-industrial activities. However, in the last century, surface-water quality has been threatened by several anthropogenic activities (e.g., urbanization, agriculture). Therefore, the need for effective management strategies to minimize stream pollution and protect aquatic ecosystems raised. Water-quality models at different scales were born with this aim. In 1925, the classic Streeter-Phelps model able to simulate dissolved oxygen in a U.S. river was developed. After the ’70s, nonlinear system models that include N and P cycling systems were implemented. Only after 1975, water-quality models were coupled with watershed models to simulate non-point source pollution as a system variable. In the last decades, this field has expanded by including several types of pollutants, major water bodies (e.g., natural and artificial lakes, deep and shallow rivers, estuaries, coastal zones), physico-chemical processes at watershed scale (source, mobilization, delivery), and external stressors (e.g., climate change, human activities) that can affect and/or threaten water quality. This Special Issue on “Water quality: current state and future trends” of the Journal Sustainability is designed to draw attention to the body of knowledge that aims at providing direction and concepts to carry this field into its next stages of evolution. Water-quality aspects that interface with other environmental matrices (e.g., atmosphere, lithosphere, biosphere) and with the anthroposphere in all its aspects (e.g., socio-economical, land-use change) are welcome. Potential topics include, but are not limited to:

  • In situ and remote sensing observations of water-quality processes and their changes in space and time.
  • Point and non-point source pollution modeling.
  • Connecting measurements and models at several scales.
  • Pollution source control and management.
  • Occurrence, fate, and transport of contaminants in water bodies.
  • Control and modeling of pollution mitigation processes.

Prof. Dr. Angela Gorgoglione
Prof. Dr. Pablo E. Santoro
Prof. Dr. Fabian A. Bombardelli
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. 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

  • water quality statuses and trends
  • water quality monitoring
  • water quality data analysis
  • water quality modeling
  • rivers
  • lakes
  • estuaries
  • coastal zones
  • watersheds
  • sustainable water management
  • climate change
  • anthropogenic changes
  • water quality policies
  • water pollution control

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Published Papers (5 papers)

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Research

23 pages, 3346 KiB  
Article
Modeling Multistep Ahead Dissolved Oxygen Concentration Using Improved Support Vector Machines by a Hybrid Metaheuristic Algorithm
by Rana Muhammad Adnan, Hong-Liang Dai, Reham R. Mostafa, Kulwinder Singh Parmar, Salim Heddam and Ozgur Kisi
Sustainability 2022, 14(6), 3470; https://doi.org/10.3390/su14063470 - 16 Mar 2022
Cited by 37 | Viewed by 2575
Abstract
Dissolved oxygen (DO) concentration is an important water-quality parameter, and its estimation is very important for aquatic ecosystems, drinking water resources, and agro-industrial activities. In the presented study, a new support vector machine (SVM) method, which is improved by hybrid firefly algorithm–particle swarm [...] Read more.
Dissolved oxygen (DO) concentration is an important water-quality parameter, and its estimation is very important for aquatic ecosystems, drinking water resources, and agro-industrial activities. In the presented study, a new support vector machine (SVM) method, which is improved by hybrid firefly algorithm–particle swarm optimization (FFAPSO), is proposed for the accurate estimation of the DO. Daily pH, temperature (T), electrical conductivity (EC), river discharge (Q) and DO data from Fountain Creek near Fountain, the United States, were used for the model development. Various combinations of pH, T, EC, and Q were used as inputs to the models to estimate the DO. The outcomes of the proposed SVM–FFAPSO model were compared with the SVM–PSO, SVM–FFA, and standalone SVM with respect to the root mean square errors (RMSE), the mean absolute error (MAE), Nash–Sutcliffe efficiency (NSE), and determination coefficient (R2), and graphical methods, such as scatterplots, and Taylor and violin charts. The SVM–FFAPSO showed a superior performance to the other methods in the estimation of the DO. The best model of each method was also assessed in multistep-ahead (from 1- to 7-day ahead) DO, and the superiority of the proposed method was observed from the comparison. The general outcomes recommend the use of SVM–FFAPSO in DO modeling, and this method can be useful for decision-makers in urban water planning and management. Full article
(This article belongs to the Special Issue Water Quality: Current State and Future Trends)
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16 pages, 5856 KiB  
Article
Accessing the Impact of Floating Houses on Water Quality in Tonle Sap Lake, Cambodia
by May Phue Wai, Vibol Chem, Khy Eam Eang, Rattana Chhin, Sokly Siev and Rina Heu
Sustainability 2022, 14(5), 2747; https://doi.org/10.3390/su14052747 - 26 Feb 2022
Cited by 4 | Viewed by 3571
Abstract
The floating houses in Tonle Sap Lake might be one of the main factors for degradation of water quality since the people in floating houses discharge sewage and waste from their households into the lake. Therefore, the government of Cambodia has decided to [...] Read more.
The floating houses in Tonle Sap Lake might be one of the main factors for degradation of water quality since the people in floating houses discharge sewage and waste from their households into the lake. Therefore, the government of Cambodia has decided to move the floating houses in Chhnok Tru to the upland regions, and more than 90% of the floating houses in Chhnok Tru have already been moved in accordance with the government’s plan. However, the scientific information on water quality before and after moving the floating houses in Tonle Sap Lake is limited. Thus, this paper aimed to evaluate differences in basic water quality such as temperature, pH, dissolved oxygen (DO), oxidation–reduction potential (ORP), conductivity (Cond), and nitrate (NO3−) before and after the floating houses were moved and to reveal the relationships between the floating houses and basic water quality. The water quality parameters were measured at 18 sampling sites in Chhnok Tru using an EXO sensor and NO3− was analyzed by ion chromatography (IC). Statistical analyses such as t-tests, correlation analysis, principal component analysis (PCA), and structural equation modeling (SEM) were used. The results show that the water quality was better after moving the floating houses; however, some parts of the study area were still polluted. In addition, the percentage of floating house distribution was significantly correlated with the temperature and ORP in the study area during dry and wet seasons. The obtained results are useful for making management decisions to sustainably manage the water quality in the area. Full article
(This article belongs to the Special Issue Water Quality: Current State and Future Trends)
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20 pages, 4285 KiB  
Article
Predictive Modeling Approach for Surface Water Quality: Development and Comparison of Machine Learning Models
by Muhammad Izhar Shah, Wesam Salah Alaloul, Abdulaziz Alqahtani, Ali Aldrees, Muhammad Ali Musarat and Muhammad Faisal Javed
Sustainability 2021, 13(14), 7515; https://doi.org/10.3390/su13147515 - 6 Jul 2021
Cited by 30 | Viewed by 4072
Abstract
Water pollution is an increasing global issue that societies are facing and is threating human health, ecosystem functions and agriculture production. The distinguished features of artificial intelligence (AI) based modeling can deliver a deep insight pertaining to rising water quality concerns. The current [...] Read more.
Water pollution is an increasing global issue that societies are facing and is threating human health, ecosystem functions and agriculture production. The distinguished features of artificial intelligence (AI) based modeling can deliver a deep insight pertaining to rising water quality concerns. The current study investigates the predictive performance of gene expression programming (GEP), artificial neural network (ANN) and linear regression model (LRM) for modeling monthly total dissolved solids (TDS) and specific conductivity (EC) in the upper Indus River at two outlet stations. In total, 30 years of historical water quality data, comprising 360 TDS and EC monthly records, were used for models training and testing. Based on a significant correlation, the TDS and EC modeling were correlated with seven input parameters. Results were evaluated using various performance measure indicators, error assessment and external criteria. The simulated outcome of the models indicated a strong association with actual data where the correlation coefficient above 0.9 was observed for both TDS and EC. Both the GEP and ANN models remained the reliable techniques in predicting TDS and EC. The formulated GEP mathematical equations depict its novelty as compared to ANN and LRM. The results of sensitivity analysis indicated the increasing trend of input variables affecting TDS as HCO3− (22.33%) > Cl− (21.66%) > Mg2+ (16.98%) > Na+ (14.55%) > Ca2+ (12.92%) > SO42− (11.55%) > pH (0%), while, in the case of EC, it followed the trend as HCO3− (42.36%) > SO42−(25.63%) > Ca2+ (13.59%) > Cl− (12.8%) > Na+ (5.01%) > pH (0.61%) > Mg2+ (0%). The parametric analysis revealed that models have incorporated the effect of all the input parameters in the modeling process. The external assessment criteria confirmed the generalized outcome and robustness of the proposed approaches. Conclusively, the outcomes of this study demonstrated that the formulation of AI based models are cost effective and helpful for river water quality assessment, management and policy making. Full article
(This article belongs to the Special Issue Water Quality: Current State and Future Trends)
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17 pages, 3883 KiB  
Article
Water-Quality Data Imputation with a High Percentage of Missing Values: A Machine Learning Approach
by Rafael Rodríguez, Marcos Pastorini, Lorena Etcheverry, Christian Chreties, Mónica Fossati, Alberto Castro and Angela Gorgoglione
Sustainability 2021, 13(11), 6318; https://doi.org/10.3390/su13116318 - 2 Jun 2021
Cited by 38 | Viewed by 5455
Abstract
The monitoring of surface-water quality followed by water-quality modeling and analysis are essential for generating effective strategies in surface-water-resource management. However, worldwide, particularly in developing countries, water-quality studies are limited due to the lack of a complete and reliable dataset of surface-water-quality variables. [...] Read more.
The monitoring of surface-water quality followed by water-quality modeling and analysis are essential for generating effective strategies in surface-water-resource management. However, worldwide, particularly in developing countries, water-quality studies are limited due to the lack of a complete and reliable dataset of surface-water-quality variables. In this context, several statistical and machine-learning models were assessed for imputing water-quality data at six monitoring stations located in the Santa Lucía Chico river (Uruguay), a mixed lotic and lentic river system. The challenge of this study is represented by the high percentage of missing data (between 50% and 70%) and the high temporal and spatial variability that characterizes the water-quality variables. The competing algorithms implement univariate and multivariate imputation methods (inverse distance weighting (IDW), Random Forest Regressor (RFR), Ridge (R), Bayesian Ridge (BR), AdaBoost (AB), Hubber Regressor (HR), Support Vector Regressor (SVR) and K-nearest neighbors Regressor (KNNR)). According to the results, more than 76% of the imputation outcomes are considered “satisfactory” (NSE > 0.45). The imputation performance shows better results at the monitoring stations located inside the reservoir than those positioned along the mainstream. IDW was the model with the best imputation results, followed by RFR, HR and SVR. The approach proposed in this study is expected to aid water-resource researchers and managers in augmenting water-quality datasets and overcoming the missing data issue to increase the number of future studies related to the water-quality matter. Full article
(This article belongs to the Special Issue Water Quality: Current State and Future Trends)
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21 pages, 2891 KiB  
Article
Source Apportionment of Inorganic Solutes in Surface Waters of Lake Baikal Watershed
by Mikhail Y. Semenov, Yuri M. Semenov, Anton V. Silaev and Larisa A. Begunova
Sustainability 2021, 13(10), 5389; https://doi.org/10.3390/su13105389 - 12 May 2021
Cited by 4 | Viewed by 2318
Abstract
The aim of this study was to obtain a detailed picture of the origin of the anthropogenic and natural inorganic solutes in the surface waters of the Lake Baikal watershed using limited data on solute sources. To reveal the origin of solutes, the [...] Read more.
The aim of this study was to obtain a detailed picture of the origin of the anthropogenic and natural inorganic solutes in the surface waters of the Lake Baikal watershed using limited data on solute sources. To reveal the origin of solutes, the chemical composition of water was considered as a mixture of solutes from different sources such as rocks and anthropogenic wastes. The end-member mixing approach (EMMA), based on the observation that the element ratios in water uncorrelated with one another are those that exhibit differences in values across the different types of rocks and anthropogenic wastes, was used for source apportionment. According to the results of correlation analysis, two tracers of sources of most abundant ions present in riverine waters were selected. The first tracer was the ratio of combined concentration of calcium and magnesium ions to concentration of potassium ion ((Ca2+ + Mg2+)/K+), and the second tracer was the ratio of sulfate and bicarbonate ion concentrations (SO42−/HCO3−). Using these tracers, three sources of main ions in water, such as sulfide-bearing silicate rocks, non-sulfide silicate rocks and carbonate rocks, were apportioned. The results of cluster analysis showed the possibility of using the ratios of strontium, iron, manganese, molybdenum, nickel, and vanadium concentrations (Sr/Fe, Sr/Mn, Ni/V, Mo/V) as tracers of the trace element sources. The use of these tracers and the obtained data on sources of main ions showed the possibility of identifying the natural trace element sources and distinguishing between natural and anthropogenic trace element sources. Full article
(This article belongs to the Special Issue Water Quality: Current State and Future Trends)
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