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Investigating the Impact of Anthropogenic and Natural Sources of Pollution on Quality of Water in Upper Indus Basin (UIB) by Using Multivariate Statistical Analysis

机译:使用多元统计分析来研究人为污染污染污染污染源的影响

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Water quality of the Indus River around the upper basin and the main river was evaluated with the help of statistical analysis. In order to analyze the similarities and dissimilarities for identifying the spatial variations in water quality of the Indus River and sources of contamination, multivariate statistical analysis, i.e., principle component analysis (PCA), cluster analysis, and descriptive analysis, was done. Data of 8 physicochemical quality parameters from 64 sampling stations belonging to 6 regions (labeled as M1, M2, M3, M4, M5, and M6) were used for analysis. The parameters used for assessing the water quality were pH, dissolved oxygen (DO), oxygen reducing potential (ORP), electrical conductivity (EC), total dissolved solids (TDS), salinity (%), and concentration of arsenic (As) and lead (Pb), respectively. PCA assisted in extracting and recognizing the responsible variation factors of water quality over the region, and the results showed three underlying factors including anthropogenic source pollution along with runoff due to rain and soil erosion were responsible for explaining the 93.87% of total variance. The parameters which were significantly influenced by anthropogenic impact are DO, EC, TDS (negative), and concentration of Pb (positive), while the concentration of As, % salinity, and ORP are affected by erosion and runoff due to rain. The worst pollution situation for regions M1 and M6 was due to the concentration of As which was approximately 400?μg/l (i.e., 40 times higher than minimum WHO recommendation). Furthermore, the results also indicated that, in the Indus River, three monitoring stations and five quality parameters are sufficient to have a reasonable confidence about the quality of water in this most important reserve of Pakistan.
机译:在统计分析的帮助下评估了上部盆地和主河周围的印度河水的水质。为了分析用于鉴定indus河流水质的空间变化和污染源,多变量统计分析,即原理成分分析(PCA),聚类分析和描述性分析的相似之处。来自属于6个区域的64个采样站的8个物理化学质量参数(标记为M1,M2,M3,M4,M5和M6)的数据进行分析。用于评估水质的参数是pH,溶解氧(DO),氧还原电位(ORP),导电性(EC),总溶解固体(TDS),盐度(%)和砷(AS)和浓度铅(PB)分别。 PCA辅助在该地区提取和认识到水质的负责任变化因素,结果表明,包括人为源污染的三种潜在因素以及由于雨水和土壤侵蚀导致的径流,负责解释总方差的93.87%。受到人为影响的显着影响的参数是,EC,TDS(阴性)和Pb(阳性)的浓度,而浓度为,%盐度和ORP受到雨水侵蚀和径流的影响。地区M1和M6的最差污染情况是由于大约400Ω·μg/ L(即,高于最低建议的40倍)的浓度是由于约400Ω·μg/ l的浓度。此外,结果还表明,在印度河流中,三个监测站和五个质量参数足以对巴基斯坦最重要的储备中的水质量具有合理的信心。

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