Determination of processes affecting groundwater quality in the coastal aquifer beneath Puri city, India: a multivariate statistical approach

Abstract

Variability of groundwater quality parameters is linked to various processes such as weathering, organic matter degradation, aerobic respiration, iron reduction, mineral dissolution and precipitation, cation exchange and mixing of salt water with fresh water. Multivariate statistical analyses such as principal component analysis (PCA) and hierarchical cluster analysis (HCA) were applied to the standardized data set of eleven groundwater quality parameters (i.e. pH, Ca2+, Mg2+, Na+, K+, Fe3+, alkalinity, NO3-, Cl-, SO4(2-), TDS) collected during the post-monsoon and the summer seasons in order to elicit hydrologic and biogeochemical processes affecting water quality in the unconfined aquifer beneath Puri city in eastern India. The application of PCA resulted in four factors explaining 73% variance in post-monsoon and 81% variance in summer. The HCA using Ward's method and squared Euclidean distance measure classified the parameters into four clusters based on their similarities. PCA and HCA allowed interpretation of processes. During both post-monsoon and summer seasons, anthropogenic pollution and organic matter degradation/Fe(III) reduction were found dominant due to contribution from on-site sanitation in septic tanks and soak pits in the city. Cation exchange and mineral precipitation were possible causes for increase in Na+ and decrease in Ca2+ concentration in summer. Fresh water recharge during monsoon and Sea water intrusion in summer are attributed as significant hydrologic processes to variations of the groundwater quality at the study site.

A critical analysis of physiochemical properties influencing pit latrine emptying and feacal sludge disposal in Kampala Slums, Uganda

“…In addition, the four principal components were retained because their eigenvalues were greater than one (Figure 7). Similar findings have been reported by Mohapatra et al (2011), Han et al (2012), and Mayanja (2014. These four principal components have eigenvalues greater than or close to unity and explain 29.6, 18.4, 15.6 and 14% of the total variance contained in the dataset (Table 3).…”

Evaluation of seasonal variation of water quality using multivariate statistical analysis and irrigation parameter indices in Ajakanga area, Ibadan, Nigeria

“…All parameters are in unit of mg/L except pH (no unit) and EC in μs/cm Sample pH (Mohapatra et al 2011). PC2 accounted for 21.9% of the total variance, showing strong positive loading on SO 2− 4 and this may be due to anthropogenic/organic wastes, atmospheric deposition, agricultural wastes, fertilizers and bacterial oxidation from dumpsite (Sidle et al 2000) or atmospheric deposition (Wayland et al 2003).…”

Evaluation of Factors Influencing the Groundwater Chemistry in a Small Tropical Island of Malaysia

“…This classification is done objectively without prior assumptions regarding the data so as to uncover structure or underlying patterns of the original data set [27]. Here, cluster analysis was applied to the physical and chemical parameters of groundwater.…”

“…This can lead to the deterioration of groundwater by seawater intrusion, which has been identified as one of the major problems affecting groundwater [4]. In addition, complex interactions of multiple factors such as geology, mineral composition of an aquifer, weathering, water-rock interactions, topography, tidal effects, climate and anthropogenic activities are important determinants of groundwater quality [6,7,8]. Therefore, effective pollution control and sustainable water resources management are necessary to tackle such challenges of water quality [9].…”