Spatial assessment of groundwater quality in Mamundiyar basin, Tamil Nadu, India

Natural and anthropogenic origin of metallic contamination and health risk assessment: A hydro-geochemical study of Sehwan Sharif, Pakistan

Heavy metal contamination in groundwater is a serious threat to the environment and therefore its proper monitoring is a matter of great concern these days. In the present research, groundwater samples from Sehwan Sharif district Jamshoro, Pakistan were collected to estimate the concentration of various elements including potentially hazardous metals. Statistical analysis of the collected data based on Pearson co-relation metal clustering and Principal Component Analysis (PCA) divides the elements into three groups; Group I contains As, Cu, Ni, and Cd, Group II contains Mn, Fe, B, and Cr and Group III contains Pb and Zn. The elements Cu, Ni, As, Pb, Cd, and Zn found with higher RSD values demonstrate their anthropogenic origin whereas the lower concentration of Mn, Fe, B, and Cr indicate their natural origin (Tepanosyan et al., 2016). The histograms and box-plots of Mn, Fe, B and Cr were found normally distributed while abnormal for Cu, Ni, Pb, As, Cd and Zn. The HQs of these elements indicate their non-carcinogenic risks. However, results of individual metallic behavior indicate the highest HQ measured for B followed by HQs for Cu, and As. The toxic effects of investigated metal (loid)s calculated using HI were found to be 1.58 for adults and 1.35 for the child which is considered the medium chromic risk and cancer risk. About the toxicity of these heavy metals, their cancer risk was assessed on the levels of Cd, As, and Cr in groundwater. The carcinogenic risk of As was found to be 2.78 × 10^-4 and 1.62 × 10^-3 for child and adult, respectively. Furthermore, the values of this carcinogenic risk are 2.64 × 10^-6 and 1.54 × 10^-5 for Cd while 4.24 × 10^-3 and 2.48 × 10^-2 for Cr in child and adult, respectively. Since cancer risk exceeded the target risk of 1 × 10^-4 for As and Cr in adults and children, it can thus be considered 'non-acceptable'. The Geographic Information System (GIS) based maps were prepared using Inverse Distance Weighted (IDW) interpolation which showed the Spatial distribution of all elements throughout Sehwan Sharif from different sources of environment. Spatial maps of elements produced by ArcGIS show the hotspots of potentially hazardous elements such as the highest concentration of Pb, As, Zn, Cu, Ni, and Cd were found in urban areas of Sehwan Sharif district Jamshoro, Pakistan.

Hydrogeochemical Survey along the Northern Coastal Region of Ramanathapuram District, Tamilnadu, India

Ramanathapuram is a drought-prone southern Indian district that was selected for conducting a hydrogeochemical study. Groundwater samples from 40 locations were collected during January 2020 (pandemic interdiction according to COVID) and January 2021. The hydrogeochemical properties of the groundwater samples were evaluated and compared with drinking water regulations to assess their water quality. The order of cation dominance was as follows: Na+ > Ca2+ > K+ > Mg2+ in January 2020 and Na+ > Ca2+ > Mg2+ > K+ in January 2021 with respect to the mean value. The order of anion dominance was as follows: Cl− > HCO3− > SO42− > NO3− > F− in January 2020 and Cl− > SO42− > HCO3− > NO3− > F− in January 2021 with respect to the mean value. In the study area, the southern coastal region was identified as a groundwater-polluted zone through spatial analysis based on all analysis results. The irrigation water quality was analyzed using various calculated indices, such as Na% (percent sodium), SAR (sodium absorption ratio), PI (permeability index), MgC (magnesium risk), RSC (residual sodium concentration), and KI (Kelly ratio), demonstrating the suitability of the groundwater for irrigation in most parts of the study area. This was also confirmed by the Na% vs. EC Plot, USSL, and Doneen’s Plot for PI. In addition, the WQI results for drinking water and irrigation confirmed the suitability of the groundwater in most parts of the study area, except for the coastal regions. The dominant hydrogeologic facies of Na+-Cl−, Ca2+-Mg2+-SO42−, and Ca2+-Mg2+-Cl− types illustrated by the Piper diagram indicate the mixing process of freshwater with saline water in the coastal aquifers. Rock–water interaction and evaporation were the main controllers of groundwater geochemistry in the study area, as determined using the Gibbs plot. Ion exchange, seawater intrusion, weathering of carbonates, and the dissolution of calcium and gypsum minerals from the aquifer were identified as the major geogenic processes controlling groundwater chemistry using the Chadha plot, scatter plot, and Cl−/HCO3− ratio. Further, multivariate statistical approaches also confirmed the strong mutual relationship among the parameters, several factors controlling hydrogeochemistry, and grouping of water samples based on the parameters. Appropriate artificial recharge techniques must be used in the affected regions to stop seawater intrusion and increase freshwater recharge.

Spatiotemporal variation of groundwater quality using integrated multivariate statistical and geostatistical approaches in Amol-Babol Plain, Iran

In recent years, groundwater quality has become a global concern due to its effect on human life and natural ecosystems. To assess the groundwater quality in the Amol-Babol Plain, a total of 308 water samples were collected during wet and dry seasons in 2009. The samples were analysed for their physico-chemical and biological constituents. Multivariate statistical analysis and geostatistical techniques were applied to assess the spatial and temporal variabilities of groundwater quality and to identify the main factors and sources of contamination. Principal component analysis (PCA) revealed that seven factors explained around 75% of the total variance, which highlighted salinity, hardness and biological pollution as the dominant factors affecting the groundwater quality in the Plain. Two-way analysis of variance (ANOVA) was conducted on the dataset to evaluate the spatio-temporal variation. The results showed that there were no significant temporal variations between the two seasons, which explained the similarity between six component factors in dry and wet seasons based on the PCA results. There are also significant spatial differences (p > 0.05) of the parameters under study, including salinity, potassium, sulphate and dissolved oxygen in the plain. The least significant difference (LSD) test revealed that groundwater salinity in the eastern region is significantly different to the central and western side of the study area. Finally, multivariate analysis and geostatistical techniques were combined as an effective method for demonstrating the spatial structure of multivariate spatial data. It was concluded that multiple natural processes and anthropogenic activities were the main sources of groundwater salinization, hardness and microbiological contamination of the study area.

Fluoride contamination - Artificial neural network modeling and inverse distance weighting approach

The underground waters in the Mamundiyar basin, India, present real chemical quality problems. Their fluoride content always exceeds the recommended levels. The Inverse Distance Weighted (IDW) method has been used for spatial interpolation of various key chemical parameters. Artificial Neural Network (ANN) modeling was applied to understand the correlation and sensitivity of all chemical parameters with respect to fluorides. The correlation of all the considered parameters is found to be poor where the highest correlation observed was only 0.37. This result showed that four of the parameters, namely pH, chlorides, sulphates and calcium, were found to have greater capacity of influencing fluorides than the other eight parameters. Chlorides were found to be the parameter that was the most sensitive and most correlated to fluorides.