Heterogeneous Graph Network (HGN) for Binary Spatio-Chemical-Source Classification of Per- and Polyfluoroalkyl Substances (PFAS) Contamination: An Optimized Approach

Per- and Polyfluoroalkyl Substances (PFAS) pose a pervasive global environmental and public health challenge due to their persistence and widespread use. Accurate assessment of their contamination is crucial for risk assessment and remediation planning. Traditional machine learning (ML) and conventional geospatial models often fail to fully capture the complex, non-Euclidean interactions between chemical properties, environmental variables, geographical proximity (e.g., groundwater flow and atmospheric transport), and anthropogenic sources. This paper introduces an Optimized Heterogeneous Graph Network (HGN) designed as an alternative approach for the binary classification of PFAS contamination exceeding regulatory thresholds. The HGN models the assessment landscape as a graph where nodes represent sampling locations (spatial features), PFAS compounds (chemical features), and contaminant sources (e.g., industrial facilities), connected by weighted edges reflecting geographic distance, chemical similarity, and source associations. The model utilizes a multi-relational attention mechanism to differentially weigh node features and edge types, thus capturing intricate dependencies more effectively than existing ML approaches, such as ensemble models. Furthermore, we incorporate Bayesian Optimization (BO) for hyperparameter tuning, ensuring peak performance and model stability. Results show that the HGN framework offers a promising alternative in PFAS research by providing a superior representation of the underlying transport, exposure, and source mechanisms, potentially enabling efficient and actionable identification of contamination hotspots through binary outcomes.