Hydrochemical Signatures, Common Pollutants and Modified Water Quality Index Using Machine Learning Model in Central Ganga Plain, India

The study highlights a fresh, moderately hard, and slightly alkaline groundwater regime with marked variation in time and space. Hydrochemical analyses indicate a unique chemical environment masked with silicate weathering, dissolution of HCO<inf>3</inf>− and SO<inf>4</inf>2−, fertilizers, and sewage pollution controlling the groundwater chemistry. Concomitant occurrences of SO<inf>4</inf>2− and NO<inf>3</inf>− are controlled by multiple common pollution sources. Elevated NO<inf>3</inf>− concentrations are found to be positively correlated with trace elements like Mn, As, Zn, and U, facilitated by oxidation of organic matter and reductive dissolution of metal oxides. Natural Background Levels (NBLs) for NO<inf>3</inf>− were 7.81 mg/L (dry season) and 21.87 mg/L (wet season), while those for Mn were 15.17 µg/L (dry season) and 10.88 µg/L (wet season), respectively. Seasonal NBL changes (+ 2.8 times for NO<inf>3</inf>−, -1.4 times for Mn) highlight their distinct mobility patterns influenced by various factors, including rainfall recharge, irrigation return flows, fertiliser application, aquifer properties, etc. A new method, Water Quality Index- Guideline Ratio (WQI<inf>GR</inf>), is employed that removes the bias of the existing WQI models in weights’ calculation. The WQI<inf>GR</inf> indicates a contaminant load increase during the wet season, with poor quality clusters linked to excess NO<inf>3</inf> and Mn. Subsequent machine learning based modelling of WQI was performed on 174 samples in an 80:20 ratio for training and validation, respectively. The model proved efficient in WQI prediction with an R2 score of 0.93, MAE RMS of 3.3991, and MLE RMS of 5.3826. The study recommends controlled fertilizer, adequate waste management, and improved sewerage systems for safe and sustainable groundwater management. © King Abdulaziz University and Springer Nature Switzerland AG 2025.