Spatial Analysis of the Increased Arsenic Concentration in Groundwater of Southern Punjab, Pakistan

Anum Iftikhar; Shi Hongbo; Maqsood  Ahmad; Nasir  Ali; Saddam Hussain

doi:10.54692/ajss.2020.04041253

Anum Iftikhar Department of Statistics & Finance, Shanxi University of Finance and Economics , Taiyuan, China.
Shi Hongbo Department of Statistics, Shanxi Universityof Finance and Economics, Taiyuan, China.
Maqsood Ahmad Department of Statistics,Faculty of Science University of Okara, Okara, Punjab, Pakistan
Nasir Ali Department of Humanity and Social Sciences, National Textile University Faisalabad, Faisalabad, Punjab, Pakistan.
Saddam Hussain Department of Statistics, Shanxi university of finance and economics , Taiyuan, China.

DOI: https://doi.org/10.54692/ajss.2020.04041253

Keywords: Health Risk, Water Quality, Factor Analysis, Southern Punjab, Multivariate Statistics

Abstract

Extensive pumping and less recharge of groundwater increase concentration of Arsenicin it; thus, make it injurious for human health. This study explores the water quality indicators of groundwater of the arid and populous areas of Southern Punjab – Pakistan, which experiences extensive pumping for irrigation and drinking purposes. Spatial data of 550 locations from the three major cities of this area namely Multan, Muzaffargarh, and Khanewal were collected to observe the 8 well known water quality indicators set by Pakistan Environmental Protection Agency (PEPA) and World Health Organization (WHO). Firstly, we computed pairwise correlation coefficients among these indicators and later on contamination load for this data was also calculated by using factor analysis. Several regression models were fitted and the established equations were evaluated based on their coefficient of determination for pairs of variables. Factor analysis suggested 3 groups of indicators that were chemically correlated and showed concurrence with regression analysis. Among the water samples, 27% showed undesired Arsenic concentration that violated thePEPA and WHO standards. Lastly, we spatially mapped these indicators to predict the unmonitored locations which will enable stakeholders and policymakers to take decisions in the best interest of the health of community living in these areas.