Integrated machine learning-bayesian receptor modelling framework for uncertainty-aware source apportionment in a non-stationary fluoride-contaminated coastal aquifer.

Fluoride contamination in coastal aquifers is difficult to evaluate because its mobilisation is controlled by overlapping and seasonally dynamic non-stationary hydrogeochemical processes influenced by seawater intrusion, lithological heterogeneity, recharge, and anthropogenic activity. Therefore, this study aimed to develop and apply an integrated, uncertainty-aware framework to identify the dominant processes controlling groundwater fluoride, quantify their relative contributions, and relate them to lithology and land use factors. To address these complexities, a Bayesian-Absolute Principal Component Scores-Multiple Linear Regression (APCS-MLR) receptor model, integrated with Self-Organizing Maps (SOM), was proposed to achieve uncertainty-aware source apportionment. Data-informed priors combined with prior-posterior checks and No-U-Turn Sampler-based (NUTS) inference produced credible, physically consistent source contribution intervals within the Bayesian model. The framework was applied to an underexplored eastern coastal aquifer system of India, where seasonal monitoring of 171 stations revealed emerging F