Special Issue "Water Quality Optimization"

A special issue of Water (ISSN 2073-4441). This special issue belongs to the section "Water Quality and Contamination".

Deadline for manuscript submissions: 31 March 2022.

Special Issue Editors

Dr. George Besseris
E-Mail Website
Guest Editor
Department of Mechanical Engineering, University of West Attica, Athens, Greece
Interests: mechanical processes; optimization; chemical engineering
Dr. Triantafyllos Kaloudis
E-Mail Website
Guest Editor
Athens Water Supply & Sewerage Company, EYDAP SA, 11146 Athens, Greece
Interests: Cyanotoxins; cyanobacterial metabolites; cyanobacterial blooms; detection/determination of cyanotoxins; mass spectrometry; water treatment; advanced oxidation processes; environmental chemistry
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Special Issue Information

Dear Colleagues,

The availability of water hinges upon a certain water quality level that needs to be reached in order to render water suitable for consumption, farming, and other usages valuable to humans. The accessibility of water is related to ‘Goal 6’ of the United Nations Sustainable Development Goals. For this Special Issue of Water, entitled ‘Water Quality Optimization’, we solicit articles that place emphasis on novel methods that can help us to improve water quality. Therefore, the scope of this Special Issue is broad enough to include new optimization techniques that provide decision-making support to water professionals who seek to screen and optimize any type of water operation, including desalination and wastewater treatment. Such techniques may be novel mathematical or computational models that provide robust and convenient solutions to complex water problems. Contributions on state-of-the-art computational methods are also welcome as long as they are accompanied by a detailed case study in improving a unique water process. Moreover, technological advances that may help us to improve water quality by introducing new knowledge into water chemical systems are also relevant to this Special Issue.

Editor: Dr. George Besseris

Water is probably the most precious resource on the planet. Improvement of water quality is vital to public health and well-being and the protection of the environment. Recent advances in optimization techniques can greatly benefit water utilities by improving the effectiveness and efficiency of processes; however, they have a rather slow uptake by the water sector. The main aim of this Special Issue is to promote the application of novel and advanced optimization techniques in water-related processes that target high-quality water. We welcome original research papers on water-related processes, including drinking water and wastewater treatment (experimental–laboratory, pilot, or actual-scale), the design and development of materials and components for water treatment, improvements to the monitoring of water quality, and methods of analysis.

Editor: Dr. Triantafyllos Kaloudis

Dr. George Besseris
Dr. Triantafyllos Kaloudis
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 papers will be 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. Water 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 2000 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
  • water quality optimization
  • water qualimetrics
  • statistical/empirical techniques for water quality improvement
  • case studies in water quality improvement
  • artificial intelligence, evolutionary computation and swarm intelligence in environmental aquametrics
  • optimizing desalination processes
  • optimizing wastewater treatment processes
  • drinking water/wastewater treatment

Published Papers (3 papers)

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Research

Article
Micro-Clustering and Rank-Learning Profiling of a Small Water-Quality Multi-Index Dataset to Improve a Recycling Process
Water 2021, 13(18), 2469; https://doi.org/10.3390/w13182469 - 08 Sep 2021
Viewed by 413
Abstract
The efficiency improvement of wastewater recycling has been prioritized by ‘Goal 6’ of the United Nations Sustainable Development initiative. A methodology is developed to synchronously profile multiple water-quality indices of a wastewater electrodialysis (ED) process. The non-linear multifactorial screener is exclusively synthesized by [...] Read more.
The efficiency improvement of wastewater recycling has been prioritized by ‘Goal 6’ of the United Nations Sustainable Development initiative. A methodology is developed to synchronously profile multiple water-quality indices of a wastewater electrodialysis (ED) process. The non-linear multifactorial screener is exclusively synthesized by assembling proper R-based statistical freeware routines. In sync with current trends, the new methodology promotes convenient, open and rapid implementation. The new proposal unites the ‘small-and-fast’ data-sampling features of the fractional multifactorial designs to the downsizing, by microclustering, of the multiple water quality indices—using optimized silhouette-based classification. The non-linear multifactorial profiling process is catalyzed by the ‘ordinalization’ of the regular nominal nature of the resulting optimum clusters. A bump chart screening virtually eliminates weak performances. A follow-up application of the ordinal regression succeeds in assigning statistical significance to the resultant factorial potency. The rank-learning aptitude of the new profiler is tested and confirmed on recently published wastewater ED-datasets. The small ED-datasets attest to the usefulness to convert limited data in real world applications, wherever there is a necessity to improve the quality status of water for agricultural irrigation in arid areas. The predictions have been compared with other techniques and found to be agreeable. Full article
(This article belongs to the Special Issue Water Quality Optimization)
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Article
Low Cost Activated Carbon for Removal of NOM and DBPs: Optimization and Comparison
Water 2021, 13(16), 2244; https://doi.org/10.3390/w13162244 - 17 Aug 2021
Viewed by 540
Abstract
Higher concentrations of disinfection byproducts (DBPs) in small water systems have been a challenge. Adsorption by tailored activated carbon (AC), developed from waste materials of a pulp and paper company using optimization of chemical activation by nitric acid followed by physical activation and [...] Read more.
Higher concentrations of disinfection byproducts (DBPs) in small water systems have been a challenge. Adsorption by tailored activated carbon (AC), developed from waste materials of a pulp and paper company using optimization of chemical activation by nitric acid followed by physical activation and metal coating, was tested for the removal of natural organic matter from water using synthetic and natural water. AC was coated with aluminum and iron salts in a ratio of 0.25 to 10.0% of metal: AC (wt:wt%). The optimization of dosage, pH, and time was performed to achieve the highest adsorption capacity. The modified AC of 0.75% Fe-AC and 1.0% Al-AC showed 35–44% improvement in DOC removal from natural water. An enhancement of 40.7% in THMs removal and 77.1% in HAAs removal, compared to non-modified, AC were achieved. The pseudo-second order was the best fitted kinetic model for DOC removal, representing a physiochemical mechanism of adsorption. Full article
(This article belongs to the Special Issue Water Quality Optimization)
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Article
Optimization of Water Quality Monitoring Networks Using Metaheuristic Approaches: Moscow Region Use Case
Water 2021, 13(7), 888; https://doi.org/10.3390/w13070888 - 24 Mar 2021
Viewed by 566
Abstract
Currently many countries are struggling to rationalize water quality monitoring stations which is caused by economic demand. Though this process is essential indeed, the exact elements of the system to be optimized without a subsequent quality and accuracy loss still remain obscure. Therefore, [...] Read more.
Currently many countries are struggling to rationalize water quality monitoring stations which is caused by economic demand. Though this process is essential indeed, the exact elements of the system to be optimized without a subsequent quality and accuracy loss still remain obscure. Therefore, accurate historical data on groundwater pollution is required to detect and monitor considerable environmental impacts. To collect such data appropriate sampling and assessment methodologies with an optimum spatial distribution augmented should be exploited. Thus, the configuration of water monitoring sampling points and the number of the points required are now considered as a fundamental optimization challenge. The paper offers and tests metaheuristic approaches for optimization of monitoring procedure and multi-factors assessment of water quality in “New Moscow” area. It is shown that the considered algorithms allow us to reduce the size of the training sample set, so that the number of points for monitoring water quality in the area can be halved. Moreover, reducing the dataset size improved the quality of prediction by 20%. The obtained results convincingly demonstrate that the proposed algorithms dramatically decrease the total cost of analysis without dampening the quality of monitoring and could be recommended for optimization purposes. Full article
(This article belongs to the Special Issue Water Quality Optimization)
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