Multivariate statistical analysis approach to assess groundwater quality in two selected mandals of Vizianagaram district, Andhra Pradesh, India

Gvsr, Pavan Kumar; Rao, KSrinivasa; Yadav, Arunendra; Kumar, M. Vinodh; Dora, HSainadh

doi:10.1016/j.jics.2021.100338

Cited by 3 publications

(1 citation statement)

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“…The comprehensive assessment of groundwater quality requires studying the effect of these factors, and statistical methods are a powerful tool to perform this. Application of multivariate statistical methods for groundwater quality evaluation and identification of the natural and artificial sources of groundwater pollution is widely reported in the literature, For example: 20,[25][26][27][28][29] . The methods mentioned above may indicate the amount of contamination in groundwater; however, they cannot reflect any possible risks that the contaminants pose to human health.…”

Section: Introductionmentioning

confidence: 99%

Multi-criterion approach for assessment of hydrochemical characteristics and human health risk of groundwater in Khartoum State, Sudan

Mohammed

Eltijani

Szabó

et al. 2023

Preprint

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Recently, groundwater has been recognized as one of the primary sources of water supply in Sudan. However, groundwater quality continues to deteriorate due to natural and human-induced activities. This research employed groundwater quality index (GWQI), multivariate statistical methods and human health risk assessment to investigate the suitability of groundwater for domestic uses in northern Khartoum state, Sudan. The groundwater samples were analyzed for eleven physiochemical parameters, including pH, EC, TDS, TH, Cl-, SO4-2, NO3-, Ca+2, Mg+2, Na+, HCO3- and the primary investigation indicated the deviation of these parameters from World Health Organization (WHO) standards. The hydrochemical analysis revealed different groundwater facies with the dominance of Ca-Mg-HCO3 water type. Consequently, the groundwater samples were classified, based on GWQI, into three categories as 76.4 % of the samples fall in the excellent water class, 17.6 % are projected in the good water class, and 5.9 % of groundwater samples are considered unsuitable for human consumption. The multivariate statistical methods, including Pearson's correlation analysis, hierarchical cluster analysis (HCA), and principal component analyses (PCA), were applied to determine groundwater quality data's structure and the primary factors influencing groundwater quality. These techniques revealed that groundwater in the study area is mainly controlled by rock-water interaction and agricultural practice. Additionally, they were used to categorize groundwater samples based on their chemical content. As a result, three types of groundwater were identified low, medium, and highly mineralized. In the final stage, the non-carcinogenic human health risk was assessed based on the concentration of NO3- and the obtained hazard quotient for children indicated that 64.7 % of groundwater samples are beyond the permissible limit (1<) and the use of these samples may result in health consequences. As a result, remedial measures are suggested for the sustainable use of groundwater.

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