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Hydrochemical dynamics of water quality for irrigation use and introducing a new water quality index incorporating multivariate statistics

机译:灌溉用水水质的水化学动力学,介绍多元统计的新水质指数

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Anthropogenic contamination, including the adaptation of sophisticated agricultural methodologies, has rendered a steep decline in the water and soil quality across the globe. This, in turn, has adversely affected the crop productivity. Hence, to utilize water from a source, a review assessment regarding the water suitability is essential. The present study proposes a new irrigation-based water quality index (IWQI), integrating statistics; hierarchical cluster analysis (HCA) and principal component analysis (PCA), to specific water use. Twenty-three sampling locations were chosen for analyses of 13 water quality parameters from October 2017 to February 2019 from Deepor Beel, Assam, to determine the efficacy of the proposed water quality index. HCA divided the 23 locations into 3 clusters, after which, PCA for each independent cluster provided component scores. These component scores were then used to estimate weights for each parameter for all the 23 locations. The IWQI categorized all the sampling locations as "Very Good", thereby rendering them fit for irrigation. This was validated through the use of various irrigation parameters, such as sodium adsorption ratio (SAR), Kelly's ratio (KR), soluble sodium percentage (SSP), residual sodium carbonate (RSC), permeability index (PI), and magnesium adsorption ratio (MgR). USSL and Wilcox plots were also considered for the correlation. IWQI was observed to provide an excellent association with all the parameters. Thus, the study would prove fruitful in establishing long-term and extensive management plans to the conservation of natural water resources.
机译:人为污染,包括调整复杂的农业方法,在全球的水和土壤质量方面取得了陡峭的下降。反过来,这对作物生产力产生了不利影响。因此,利用来自来源的水,关于水适宜性的审查评估至关重要。本研究提出了一种新的灌溉水质指数(IWQI),整合统计数据;分层聚类分析(HCA)和主成分分析(PCA),以特定的用水。选择了二十三个采样场所,从2017年10月到2019年2月到2019年2月到2019年2月,从Eduid Beel,Assam,确定所提出的水质指数的疗效。 HCA将23个位置划分为3个集群,之后,每个独立群集的PCA提供组件分数。然后使用这些组分分数来估计所有23个位置的每个参数的权重。 IWQI将所有采样位置分类为“非常好”,从而使它们适合灌溉。通过使用各种灌溉参数来验证,例如钠吸附比(SAR),凯利的比例(KR),可溶性钠百分比(SSP),残留碳酸钠(RSC),渗透指数(PI)和镁吸附率(MGR)。也考虑了苏联和Wilcox地块的相关性。观察到IWQI提供了与所有参数的良好联系。因此,该研究将证明为保护天然水资源的长期和广泛的管理计划而富有成效。

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