Optimized wastewater management utilizing multivariate statistical analysis: a case study of the Mascara wastewater treatment plant, Algeria.

IF 2.5 4区 环境科学与生态学 Q3 ENGINEERING, ENVIRONMENTAL Water Science and Technology Pub Date : 2024-08-01 Epub Date: 2024-08-12 DOI:10.2166/wst.2024.276
Imène Benstaali, Amel Talia, Laouni Benadela
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Abstract

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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利用多元统计分析优化废水管理:阿尔及利亚 Mascara 废水处理厂案例研究。
有效的废水管理对于缺水地区以及面临污染和气候变化等环境压力的地区至关重要。优化处理工艺对于实现环境可持续性至关重要。本研究旨在强调有效废水管理策略的重要性,尤其是在面临水资源短缺的地区。我们的目标是找出影响处理过程的关键因素。因此,我们采用主成分分析法(PCA)和层次递升分类法(HAC)等多元统计方法评估了理化参数之间的关联。我们的研究结果根据有机物污染程度将月度水样分为三个不同的组别:第一组(7 月、8 月和 9 月)的特点是含氧量高,有机物污染程度明显较低,表明系统处于最佳运行状态。第二组(4 月、10 月、11 月和 12 月)显示出低含氧量和低有机污染,促进了污泥沉淀和污染物减少。第三组(1 月、2 月、3 月、5 月和 6 月)显示出明显的高有机污染和低含氧量,与不利的环境条件相对应。我们的研究证明了多元统计方法在优化废水处理工艺方面的有效性,为环境可持续性和水资源管理提供了重要启示。
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来源期刊
Water Science and Technology
Water Science and Technology 环境科学-工程:环境
CiteScore
4.90
自引率
3.70%
发文量
366
审稿时长
4.4 months
期刊介绍: Water Science and Technology publishes peer-reviewed papers on all aspects of the science and technology of water and wastewater. Papers are selected by a rigorous peer review procedure with the aim of rapid and wide dissemination of research results, development and application of new techniques, and related managerial and policy issues. Scientists, engineers, consultants, managers and policy-makers will find this journal essential as a permanent record of progress of research activities and their practical applications.
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