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240902s2024 xx |||||o 00| ||eng c |
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|a 10.2166/wst.2024.276
|2 doi
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|a eng
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|a Benstaali, Imène
|e verfasserin
|4 aut
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|a Optimized wastewater management utilizing multivariate statistical analysis
|b a case study of the Mascara wastewater treatment plant, Algeria
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|c 2024
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|a Text
|b txt
|2 rdacontent
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|a ƒaComputermedien
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|a ƒa Online-Ressource
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|a Date Completed 31.08.2024
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|a Date Revised 31.08.2024
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|a published: Print-Electronic
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|a Citation Status MEDLINE
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|a © 2024 The Authors This is an Open Access article distributed under the terms of the Creative Commons Attribution Licence (CC BY 4.0), which permits copying, adaptation and redistribution, provided the original work is properly cited (http://creativecommons.org/licenses/by/4.0/).
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|a Effective wastewater management is crucial in regions experiencing water scarcity and environmental stressors, such as pollution and climate change. Optimizing treatment processes is essential for achieving environmental sustainability. This study aims to highlight the importance of effective wastewater management strategies, particularly in regions facing water scarcity. Our objective was to identify key factors influencing the treatment process. Therefore, we evaluated associations between physicochemical parameters using multivariate statistical methods, including Principal Component Analysis (PCA) and Hierarchical Ascendant Classification (HAC). Our findings categorize the monthly water samples into three distinct groups based on levels of organic pollution: the first group (July, August, and September) is characterized by high oxygenation levels and significantly low organic pollution, indicating optimal system operation. The second group (April, October, November, and December) exhibits low oxygenation and low organic pollution, promoting sludge settling and pollutant reduction. The third group (January, February, March, May, and June) shows significantly high organic pollution and low oxygenation, which corresponds to unfavorable environmental conditions. Our study demonstrates the effectiveness of multivariate statistical methods in optimizing wastewater treatment processes, providing crucial insights for environmental sustainability and water resource management
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|a Journal Article
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|a HAC
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|a PCA
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|a organic pollution
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|a physicochemical parameters
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|a wastewater
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|a Wastewater
|2 NLM
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|a Water Pollutants, Chemical
|2 NLM
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|a Talia, Amel
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|4 aut
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|a Benadela, Laouni
|e verfasserin
|4 aut
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| 773 |
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|i Enthalten in
|t Water science and technology : a journal of the International Association on Water Pollution Research
|d 1986
|g 90(2024), 4 vom: 31. Aug., Seite 1290-1305
|w (DE-627)NLM098149431
|x 0273-1223
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|g volume:90
|g year:2024
|g number:4
|g day:31
|g month:08
|g pages:1290-1305
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|u http://dx.doi.org/10.2166/wst.2024.276
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