Source characterization of trace elements and impacts of salinization using geochemical modelling with Machine Learning approach and Monte-Carlo based health risk assessment in coastal aquifer zones.

Key Points

• This study aims to characterize trace elements and assess the impacts of salinization using geochemical modeling and Monte Carlo simulations.
• Utilized Principal Component Analysis (PCA) and Self-Organizing Maps (SOM) to identify major contributors to trace elements.
• Applied Machine Learning methods for predictive modeling of pollution levels.
• Conducted health risk assessments using Monte Carlo simulations to evaluate water resources in the coastal zones.
• Identified seawater mixing mechanism contributing to higher levels of Fe and Mn within the coastal aquifer.
• Demonstrated the impact of redox conditions enhancing the dissolution of geogenic sources like arsenic, barium, and iron.
• Highlighted pollution levels, indicating a significant health risk associated with trace element exposure in the region.