Groundwater contamination characterization using multivariate statistical analysis and geostatistical method

Abstract

The aim of the present study is to identify sources of groundwater contamination in Rupnagar district, Punjab, using an integrated approach of exploratory factor analysis (EFA) and ordinary kriging (OK). For this, a 13 physico-chemical parameter data set at 14 sampling locations for a period of over 25 years was assessed. The correlation was statistically examined amongst parameters. A five-factor model is proposed which explains over 89.11% of total groundwater quality variation. Three semi-variogram models, namely exponential, Gaussian, and spherical, fitted well for the data set and are cross-validated using predictive statistics. Spatial variability maps of all the parameters and factor scores are generated and are in good agreement with each other. The variation seen in groundwater quality is mainly due to various hydrogeochemical, anthropogenic, and geogenic processes occurring in the region. Thus, this study indicated that there is need to treat the industrial and municipal wastewater before discharging it (directly/indirectly) into nearby streams and pits and to encourage sustainable agricultural practices to prevent adverse health effects and minimize further environmental degradation in the study region.

GIS‐based groundwater quality assessment using GQIs and fuzzy‐logic approach

“…From the hydrogeological point of view, the area is primarily filled with quaternary sediments, mainly of fluvial nature. The main geological units in the region include newer alluvium, older alluvium, upper and lower shiwalik (Figure ) (Chaudhry et al, 2019). The oldest alluvium known as the Ludhiana formation (the mid to late Pleistocene) consists of materials such as clay, fine sand, gravel and silt.…”

“…Additionally, FL acts as an important method for transmitting outcomes in a very clear language format (also known as linguistic variables) to the public and beneficiaries. Several studies have used FL in assessing the quality of groundwater (Abbasnia et al, 2018; Chaudhry et al, 2019; Liu et al, 2021) and have suggested addressing complex water quality issues. All these studies implemented the FL GIS‐based GQI (FGQI) technique using point‐based short‐term data (Dahiya et al, 2007; Selvaraj et al, 2020; Vadiati et al, 2016).…”

Groundwater Fluoride Contamination and Probabilistic Health-risk Assessment in Fluoride Endemic Areas of the Lower Satluj Basin, Punjab, India

“…Given that no other natural source of fluoride exists in the study region, various anthropogenic activities also contribute to increasing the fluoride concentration in the region (i.e. brick kilns, industrial and domestic wastewater discharge, increase in the use of fertilizers, mining activities, and extreme irrigation practices) (Chaudhry et al 2019a). The broad gap between the groundwater residence time and the charging area alongside Ca 2+ insufficient groundwater level is responsible for Na + -HCO3level groundwater in the region.…”

“…Singh et al (2011) reported a significant range of fluoride (0.05-0.65 mg/L) in the region. Chaudhry et al (2019a) reported that fluoride in the region is due to an increase in anthropogenic activities and geogenic processes such as weathering and the breakdown of rocks. In the study conducted by the Geological Survey of India (GSI) for the northern region (GSI 2017), metamorphic and igneous rocks (i.e.…”

Groundwater quality assessment using principal component analysis and hierarchical cluster analysis in Alaçam, Turkey

“…Recently, with the increasing number of physical and chemical variables of groundwater, a wide variety of statistical methods are now used for accurate analysis and interpretation of data (Subyani & Al Ahmadi 2010). Multivariate statistical analysis methods have been used frequently in fields such as soil and water resources in the last years (Masoud et al 2018;Chaudhry et al 2019;Dash & Kalamdhad 2021). Cluster analysis (CA), factor analysis (FA), principal components analysis (PCA), discriminant analysis (DA) and several other multivariate statistical analyses are widely used to present spatial variations in groundwater quality and to determine impact factors (Ma et al 2014).…”