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

Mansoor A Baluch,Hashim Nisar Hashmi

doi:10.1155/2019/4307251

Mansoor A Baluch, Hashim Nisar Hashmi

Open Access

https://doi.org/10.1155/2019/4307251

Copy DOI

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.

Highlights

One of the most influential elements regarding environment concerns all over the world is the anthropogenic distribution, e.g., sewage discharge into the river, reclamation process of the land, and climate change due to atmospheric deposition effects [1]
Much attention is paid by various researchers in assessing the quality of surface water because of its direct connection with human beings and with other species [2]. e major factors affecting the quality of river water usually are morphology of the basin and regional atmosphere along with climate change, and both natural and anthropogenic effects are responsible for governing these effects [3]
Due to high-dispersionmeasured quality parameter’s data for all regions (i.e., M1–M6), as shown in Figure 2, it is extremely difficult to rely on either mean value or variance for assessing the water quality. erefore, coefficient of variation (CV) of quality parameters focused in this study is calculated from the ratio pH | 2

Summary

Introduction

One of the most influential elements regarding environment concerns all over the world is the anthropogenic distribution, e.g., sewage discharge into the river, reclamation process of the land, and climate change due to atmospheric deposition effects [1]. Statistical technique provides a tool for water flow management system along with the solution of pollution issues It has been used for characterizing and evaluating the parameters between surface and the fresh quality of water and helpful for exploring the effect of anthropogenic sources on the spatial and temporal variations river quality [23,24,25]. Bhattacharyya et al [28] have used principal component analysis (PCA)/factor analysis (FA) and cluster analysis (CA) along with correlation analysis multivariate statistical technique for calculating the water quality index of the Damodar River in India. Shrestha and Kazama [29] used cluster analysis (CA), principal component analysis (PCA), factor analysis (FA), and discriminant analysis (DA) for the evaluation of spatial/temporal variations and huge complex water quality index for the Fuji River Basin. Khan et al and Zafar et al [30, 31]

Methods

Results

Conclusion