Artificial Intelligence (AI) and Monte Carlo Simulation-Based Modeling for Predicting Groundwater Pollution Indices and Nitrate-Linked Health Risks in Coastal Areas Facing Agricultural Intensification

Abstract Groundwater in coastal agricultural zones is highly susceptible to degradation from seawater intrusion and intensive agrochemical usage. This study assesses groundwater (GW) quality and nitrate-related health risks in the Skhirat coastal aquifer (Morocco) using a multidisciplinary approaches. A total of thirty groundwater wells were sampled and analysed for physico-chemical properties, including major ions and nutrients. Multivariate statistical analyses were employed to explore contamination sources. Pollution indices such as the Groundwater Pollution Index (GPI) and Nitrate Pollution Index (NPI) were computed, and Monte Carlo simulations were conducted to assess nitrate-related health risks through ingestion and dermal exposure. Furthermore, Random Forest modeling was applied to predict groundwater pollution indices. Results of hydrochemical facies revealed Na⁺-Cl⁻ dominance in 47% of samples, suggesting strong marine influence, while nitrate concentrations reached up to 89.3 mg/L, exceeding WHO limits in 26.7% of sites. Pollution indices indicated that 33.3% of samples exhibited moderate to high GPI values (mean = 0.93), while 36.7% of samples exceeding the threshold for NPI (mean = 1.09). Monte Carlo simulations for nitrate health risk revealed that 43% of samples posed non-carcinogenic health risks to children (HI > 1), with the 95th percentile HI reaching 3.47. Multivariate analysis identified seawater intrusion and agricultural inputs as key drivers. Random Forest outperformed other models in predicting GPI (R²=0.76) and NPI (R²=0.95). Spatial prediction maps visualized contamination hotspots aligned with intensive horticultural activity. This integrated methodology offers a robust framework to diagnose groundwater pollution sources and predict future risks, aiding in targeted mitigation strategies and sustainable groundwater management in coastal agriecosystems.