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

Abstract

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.

Gamma-Ray Modified Polymer/Clay Composites: Synthesis, Characterization, and Formulation Optimization Using Multivariate Calculus and Graph Theory

“…The eigenvalues (obtained after performing PCA) corresponding to the active variable are listed in Table 7, and it is obvious that only the first two sets are sufficient to explain the information contained in the original data. The percentage variance for the first two sets of Eigenvalues is 95%, which is sufficient for classification purposes [28] and confirmed the applicability of PCA with confidence for the analysis of data. PC-1 shows 69.75% of total variance with strong positive characteristic loading (i.e., > 0.75) for OI and Gx and weak loading for Xc (%).…”

“…A user-friendly code in Mathematica was developed for performing the graph theory analysis. The details about the multivariate statistical analysis and graph theory can be found elsewhere [28][29][30].…”

Sustainable water resources development and management in large river basins: an introduction

“…The water quality in the Indus Basin meets the WHO drinking water standard, but it may deteriorate soil quality and degrade the downstream water quality for irrigation purposes due to high concentrations of major ions (Rehman Qaisar et al 2018 ). Study has shown that human activities and soil erosion are the main causes of water pollution (Baluch and Hasmi 2019 ). Research conducted during the COVID-19 period in the Buddha Nala River, a tributary of Sutlej, has shown that industry and the household sewage do affect the water quality in the midstream of the river (Das et al 2021 ).…”

Assessment of Heavy Metal Concentrations in Seawater in the Coastal Areas around Daerah Istimewa Yogyakarta Province, Indonesia

“…It indicates that the Pb content in seawater in the study area was also estimated from Pb contained in iron sand (a part of Quarterly Alluvium) eroded due to waves and wind then mixed in seawater. However, further research on the source of heavy metal, whether due to natural or anthropogenic factors, needs to be investigated further using Multivariate Statistical Analysis (Baluch et al 2019). The sketch of the study result is shown in Fig.…”