Assessment of groundwater quality for drinking and irrigation in semi-arid regions of Andhra Pradesh, Southern India, using multivariate statistical analysis

2021 ◽  
Vol 14 (19) ◽  
Author(s):  
Veeraswamy Golla ◽  
Pradeep Kumar Badapalli ◽  
Prasad Mannala
Keyword(s):  
Statistical Analysis ◽  
Groundwater Quality ◽  
Multivariate Statistical Analysis ◽  
Andhra Pradesh ◽  
Arid Regions ◽  
Southern India ◽  
Multivariate Statistical ◽  
Drinking And Irrigation ◽  
Semi Arid

scholarly journals Identification of the dominant hydrogeochemical processes and characterization of potential contaminants in groundwater in Qingyuan, China, by multivariate statistical analysis

RSC Advances ◽  
2018 ◽  
Vol 8 (58) ◽  
pp. 33243-33255 ◽  
Author(s):  
Zuobing Liang ◽  
Jianyao Chen ◽  
Tao Jiang ◽  
Kun Li ◽  
Lei Gao ◽  
...  
Keyword(s):  
Statistical Analysis ◽  
Multivariate Statistical Analysis ◽  
Water Source ◽  
Hydrogeochemical Processes ◽  
Karst Aquifers ◽  
Multivariate Statistical ◽  
Drinking And Irrigation ◽  
Karst Areas

In karst areas, groundwater is an important water source for drinking and irrigation purposes; however, karst aquifers are vulnerable and recovery from damage is difficult.

scholarly journals Application of multivariate statistical analysis in the assessment of groundwater quality of Tan Thanh district, Ba Ria – Vung Tau province

2017 ◽  
Vol 1 (M2) ◽  
pp. 66-72
Author(s):  
Au Hai Nguyen ◽  
Ngan Thi Khanh Phan ◽  
Thuy Thi Thanh Hoang ◽  
Ngoc Nguyen Hong Phan
Keyword(s):  
Cluster Analysis ◽  
Statistical Analysis ◽  
Groundwater Quality ◽  
Multivariate Statistical Analysis ◽  
Anthropogenic Pollution ◽  
Dry Season ◽  
Wet Season ◽  
Multivariate Statistical ◽  
Rock Interaction ◽  
And Cluster Analysis

In the present study, Multivariate Statistical Analysis (MSA) such as Principle Component Analysis (PCA) and Cluster Analysis (CA) were applied to determine the temporal and spatial variations of groundwater quality in Tan Thanh district, Ba Ria – Vung Tau province. Groundwater samples were collected from 18 monitoring wells in April (dry season) and October (wet season) during the year 2012. Fifteen parameters (pH, TH, TDS, Cl-, F-, NO3-, SO42-, Cr6+, Cu2+, Ca2+, Mg2+, Na+, K+, HCO3- and Fe2+) were selected for MSA. PCA identified a reduced number of mean three latent factors of groundwater quality. Three factors called salinization, water-rock interaction and anthropogenic pollution explanined 70,5% (dry season) and 71.28% (wet season) of the variances. Cluster analysis revealed two main different groups of similarities between the sampling sites. This study presents the necessity of MSA in order to extract more precise information from a huge minitoring data, which will be usefull to groundwater quality management.

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