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Holistic approach for quantification and identification of pollutant sources of a river basin by analyzing the open drains using an advanced multivariate clustering

机译:使用高级多元聚类分析明渠的整体方法量化和识别流域污染物

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Global scarcity of freshwater has been gearing towards an unsustainable river basin management and corresponding services to the humans. It needs a holistic approach, which exclusively focuses on effective river water quality monitoring and quantification and identification of pollutant sources, in order to address the issue of sustainability. These days, rivers are heavily contaminated due to the presence of organic and metallic pollutants released from several anthropogenic sources, such as industrial effluents, domestic sewage, and agricultural runoff. It is astonishing to note that even in many developing countries, most of these contaminants are carried through open drains, which enter river premises without proper treatment. Such practice not only devastates riverine ecosystem but also gives rise to deadly diseases, such as minimata and cancer in humans. Considering these issues, the present study develops a novel approach towards simultaneous identification of major sources of pollution in the rivers, along with critical pollutants and locations using an advanced hierarchical cluster and multivariate statistical analysis. A systematic approach has been developed by agglomerating both R-mode and Q-mode analysis, which develops monoplots, two-dimensional biplots, rotated component matrices, and dendrograms (using SPSS and Analyse It software) to reveal relationships among various quality parameters to identify the pollutant sources along with clustering of critical sampling sites and pollutants. A case study of the Ganges River Basin of India has been considered to demonstrate the efficacy and usefulness of the model by analyzing 85 open drains. Both organic and metallic pollutants are analyzed simultaneously as well as separately to get a holistic understanding of all the relationships and to broaden the perspective of water characterization. Results provide a comprehensive guidance to the policy makers and water managers to optimize corrective efforts, minimize further damage, and improve the water quality condition to ensure sustainable development of the river basin.
机译:全球淡水的短缺一直在朝着不可持续的流域管理和向人类提供相应服务的方向发展。它需要一种整体方法,专门解决有效的河流水质监测以及污染物源的量化和识别,以解决可持续性问题。如今,由于多种人为来源释放的有机和金属污染物(如工业废水,生活污水和农业径流)的存在,河流受到了严重污染。令人惊讶地注意到,即使在许多发展中国家,这些污染物中的大多数也是通过露天排水沟携带的,这些排水沟未经适当处理就进入河流。这种做法不仅破坏了河流生态系统,而且还引发了致命的疾病,例如人类的迷你瘤和癌症。考虑到这些问题,本研究开发了一种新颖的方法,可使用高级层次聚类和多元统计分析方法,同时识别河流中的主要污染源以及关键污染物和位置。通过聚集R模式分析和Q模式分析,已经开发出一种系统的方法,该方法可以开发单图,二维双图,旋转分量矩阵和树状图(使用SPSS和Analyze It软件)以揭示各种质量参数之间的关系以进行识别污染物来源以及关键采样点和污染物的聚集。已考虑通过对印度恒河流域的案例研究,通过分析85条明渠来证明该模型的有效性和实用性。同时分析有机污染物和金属污染物,并分别进行分析,以全面了解所有关系,并拓宽水表征的视野。结果为决策者和水管理者提供了全面的指导,以优化纠正措施,最大程度地减少进一步的损害,并改善水质状况,以确保流域的可持续发展。

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