Multivariable Investigation of Physicochemical Drivers Influencing Opportunistic Pathogens in Drinking Water

Effective management requires understanding the associations between physicochemical parameters and OP occurrence. This study applied machine learning (ML) models to explore relationships between physicochemical parameters (e.g., pH, temperature), OPs (Legionella spp., Pseudomonas spp., and Mycobacterium spp.), and the free-living amoeba Vermamoeba vermiformis. eXtreme Gradient Boosting, Random Forest (RF), and Support Vector Regression were optimized using cross-validation to predict qPCR-derived cell-equivalent (CE) concentrations, and Shapley Additive exPlanations (SHAP) were used to interpret predictor contributions.

For Legionella spp., the RF model achieved the best test performance (R² = 0.80). SHAP analysis identified pH, free chlorine, and water temperature as the most influential predictors of Legionella spp. CE concentrations.