Use of Multivariate Analytical Methods in Assessment of River Water Quality

Siddhartha Sharma
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Abstract

: This study is focused on the assessment of water quality of river Satluj in North Indian state of Punjab and evaluation of 34 physico-chemical variables monitored during the period 2015–2016, at 3 different sampling locations. Multivariate analytical techniques, such as Principal Component Analysis (PCA)/ Factor Analysis (FA) were applied to the water quality data set to identify characteristics of water quality in the studied catchment. PCA/FA was applied for source identification to data sets pertaining to 3 spatial groups (upper catchment, middle catchment and lower catchment) responsible for the data structure. These factors are conditionally named soil structure and soil erosion; domestic, municipal and industrial effluents; agricultural activities (fertilizers, livestock waste etc.) and seasonal effect factors.In the current study usefulness of multivariate analysis for evaluation of river Satluj water quality assessment and identification of dominant factors and pollution sources for effective water quality management and determination of spatial and temporal variations in water quality illustrated.
多元分析方法在河流水质评价中的应用
本研究的重点是对印度北部旁遮普邦Satluj河的水质进行评估,并对2015-2016年期间在3个不同采样点监测的34个理化变量进行评估。采用主成分分析(PCA)/因子分析(FA)等多变量分析技术对水质数据集进行分析,以确定研究流域的水质特征。采用PCA/FA对数据结构的3个空间组(上、中、下三个空间组)数据集进行源识别。这些因素有条件地称为土壤结构和土壤侵蚀;家庭、城市和工业污水;农业活动(化肥、畜禽废弃物等)和季节性影响因素。在目前的研究中,说明了多变量分析对Satluj河水质评价的有效性,以及对有效水质管理和确定水质时空变化的主导因素和污染源的识别。
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