Open AccessSurface and groundwater quality assessment based on multivariate statistical techniques in the vicinity of Mohanpur, Bangladesh
Author(s) -
Md. Mahtab Ali Molla,
Narottam Saha,
Sayed M A Salam,
M. Rakib-uz-Zaman
Publication year - 2015
Publication title -
international journal of environmental health engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-9183
DOI - 10.4103/2277-9183.157717
Subject(s) - principal component analysis , water quality , alkalinity , contamination , atomic absorption spectroscopy , groundwater , environmental science , surface water , sampling (signal processing) , multivariate statistics , environmental chemistry , heavy metals , hydrology (agriculture) , environmental engineering , chemistry , mathematics , statistics , geology , ecology , physics , geotechnical engineering , organic chemistry , filter (signal processing) , quantum mechanics , computer science , computer vision , biology
Aims: This work evaluated the surface and groundwater quality of Mohanpur area, Rajshahi district, Bangladesh. Multivariate statistical techniques were also applied to determine the possible sources of water contamination. Materials and Methods: Water samples were collected from randomly selected ten different sampling sites for analyzing the chemical parameters including pH, electrical conductivity, total dissolved solids, total hardness, total alkalinity, Cl− , NO3− and some heavy metals such as Mn, Pb, Cd, and As concentrations. Concentrations of heavy metals were determined using atomic absorption spectrometer (AAS). Results: Based on hydrochemical characteristics, surface and groundwater in the study area were, in general, fresh, hard, and alkaline in nature. All chemical parameters were within the WHO water quality guidelines. Whereas, among four analyzed heavy metals Pb, and Cd concentrations exceeded the WHO recommended values. Pearson correlation matrix showed a number of statistically significant associations (P < 0.01 and P < 0.05) among the examined water quality parameters. Moreover, principal component (PC) analysis (PCA) and cluster analysis (CA) were used to analyze the water quality dataset. PCA analysis identified two PCs as responsible for the data structure explaining 72.53% of the total variance in water quality. PCA indicated that the water quality variations were mainly of anthropogenic origin through agricultural and municipal discharges. Results of CA revealed three significant groups of similarity among the 10 sampling sites. Conclusions: It could be deduced from the present results that water contamination was occurred to some extent throughout the area, and is likely to be continued in the near future. Improvement of local sanitation system along with frequent training and awareness programs can help in developing water quality in the studied area