Assessment of water quality in and around Jia-Bharali river basin, North Brahmaputra Plain, India, using multivariate statistical technique

被引:15
作者
Khound, Nayan J. [1 ]
Bhattacharyya, Krishna G. [2 ]
机构
[1] Digboi Coll, Dept Chem, Tinsukia 786171, India
[2] Gauhati Univ, Dept Chem, Gauhati 781014, India
关键词
Shallow aquifer; Multivariate statistical techniques; Hierarchical cluster; Principal component; Hydrochemistry; GROUNDWATER QUALITY; SURFACE-WATER; MINING AREA; CHEMISTRY; GEOCHEMISTRY; DISTRICT; EVOLUTION; CAMEROON; PRADESH; METAL;
D O I
10.1007/s13201-018-0870-z
中图分类号
TV21 [水资源调查与水利规划];
学科分类号
081501 ;
摘要
The present study envisages the application of multivariate analysis, water utility class and conventional graphical representation to reveal the hidden factor responsible for deterioration of water quality and determine the hydrochemical facies of water sources in Jia-Bharali river basin, North Brahmaputra Plain, India. Fifty groundwater and 35 surface water samples were collected and analyzed for 15 parameters viz pH, TDS, hardness, COD, Ca2+, Mg2+, Na+, K+, Fe, HCO3-, Cl-, SO42-, NO3-, PO43- and F- for a period of 3 hydrological years (2009-2011) in six different seasons (three wet and three dry). The results were evaluated and compared with WHO and BIS water quality standards. Except Fe (>0.3mg/L), all parameters were found well within the desirable limit of WHO and BIS for drinking water. Ca2+ and HCO3- were dominant ions among cations and anions. The piper trilinear diagram classified majority of water samples for both seasons fall in the fields of Ca2+-Mg2+-HCO3- water type indicating temporary hardness. Varimax factors extracted by principal component analysis indicates anthropogenic (domestic and agricultural runoff) and geogenic influences on the trace elements. Hierarchical cluster analysis grouped water sources into three statistically significant clusters based on the similarity of water quality characteristics. This study illustrates the usefulness of multivariate statistical techniques for analysis and interpretation of complex datasets, and in water quality assessment, identification of pollution sources/factors and understanding temporal/spatial variations in water quality for effective water quality management.
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页数:21
相关论文
共 67 条
[1]  
Akoto Osei, 2014, Lakes & Reservoirs Research and Management, V19, P174, DOI 10.1111/lre.12066
[2]   Water quality and heavy metal monitoring in water and sediment samples of the KuAA1/4k‡ekmece Lagoon, Turkey (2002-2003) [J].
Altun, Oemer ;
Sacan, Melek Tuerker ;
Erdem, Ayten Kimiran .
ENVIRONMENTAL MONITORING AND ASSESSMENT, 2009, 151 (1-4) :345-362
[3]  
[Anonymous], 1999, POLL RES
[4]  
[Anonymous], POLL RES
[5]  
[Anonymous], 2006, STANDARD METHODS EXA, DOI DOI 10.5860/CHOICE.37-2792
[6]  
[Anonymous], 1985, STUDY INTERPRETATION
[7]  
[Anonymous], 2004, World Health Organization Guidelines for Drinking Water Quality Third Edition, V1
[8]  
[Anonymous], 2018, Guidelines for Drinking Water Quality
[9]  
Berner ElizabethKay., 1987, GLOBAL WATER CYCLE G
[10]  
Bhattacharyya, 2012, INT J APPL SCI ENG R, V1, P512, DOI [10.6088/ijaser.0020101052, DOI 10.6088/IJASER.0020101052]