Integrated assessment of groundwater hydrogeochemistry and quality using multivariate statistical analysis, self-organizing maps, and water quality indices in District Bagh, AJK, Pakistan - Usman Basharat; Wenjing Zhang; Arshad Abbasi; Sehrish Mahroof; Cuihong Han; Shoukat Husain Khan; Shuxin Li (Ecotoxicology and Environmental Safety)
Groundwater is a critical resource in the District Bagh, Azad Jammu & Kashmir, Pakistan, but its quality is deteriorating due to anthropogenic and natural impacts. This study evaluates the hydrochemistry and quality of groundwater using an integrated approach that includes Multivariate Statistical Analysis, Self-Organizing Maps, Weighted Arithmetic Water Quality Index, Multiple Linear Regression and Irrigation Water Quality Indices. The study revealed significant differences in physicochemical characteristics, such as cations and anions in the groundwater samples, suggesting both natural and anthropogenic impacts. Pearson shows EC has a strong positive correlation with Na+ (r = 0.75) and Cl- (r = 0.73), along with a robust correlation between Na+ and Cl- (r = 0.98), highlighting their significant roles in groundwater's ionic content. Geochemical interactions, salinity, ion exchange, and anthropogenic activities are identified as essential factors influencing groundwater quality using factor analysis. Hydrogeochemistry is significantly influenced by lithology: halite dissolution Cl⁻ input (Na⁺/Cl⁻ = 1.039); rock formation promotes silicate weathering and Na⁺ release (Na⁺/Cl⁻ > 1); gypsum dissolution (Ca²⁺/SO₄²⁻ = 12.085); and carbonate weathering (Ca²⁺ + Mg²⁺/HCO₃⁻ = 1.232). Chloro-alkaline indices (CAI-I = −0.039, CAI-II = 0.007) suggest carbonate undersaturation. The SOM results showed that 64 neurons generated the hydrochemical properties of groundwater and were organized into four clusters, identifying areas with varied salinity, ionic content, and buffering capability. The computed WA-WQI scores vary from 8.45 to 122.03. MLR was applied to create a new WQI equation that includes four variables. The model shows that turbidity has the highest beta coefficient of 0.40 and is a major contributor to the classification of groundwater. The irrigation water quality investigation revealed that most of the irrigation indexes are suitable for agricultural use, with minor hazards of soil sodicity and permeability difficulties. These findings highlight the importance of ongoing monitoring and specific management measures for ensuring sustainable groundwater supplies.