Predictive modeling of heavy metal pollution and ecological risk for sustainable water quality management in the NY-NJ harbor system.

Evaluating and forecasting surface water quality is essential for protecting aquatic ecosystems and improving water resource management. This study introduces a novel paradigm that integrates machine learning (ML) with the potential ecological risk index (PERI) to dynamically forecast, rather than statically assess, ecological risks from heavy metal contamination in an urban estuarine environment. Surface water samples from the Lower Passaic River in New Jersey, USA, were analyzed for copper (Cu), lead (Pb), and mercury (Hg) across multiple sites and sampling campaigns. Concentrations ranged from 3.1 to 42.6 µg/L for Cu, 1.8 to 25.4 µg/L for Pb, and 0.12 to 1.36 µg/L for Hg, corresponding to PERI values spanning from 85.7 to 672.3, indicating moderate to very high ecological risk levels. Four ML algorithms, random forest (RF), support vector machine (SVM), extreme gradient boosting (XGBoost), and artificial neural network (ANN), were employed to model PERI based on pollution indices. According to the findings, model performance was ranked as ANN > RF > SVM > XGBoost. The ANN model demonstrated superior performance, achieving the lowest error (MAE = 2.25) with excellent predictive accuracy (a testing R2 of 0.981), and was identified as the most effective model. Variable importance analysis confirmed Hg as the dominant risk driver. This integrated approach demonstrates that coupling ML with ecological risk indices can effectively capture complex, nonlinear relationships between metal concentrations and ecological impact, significantly improving risk forecasting for dynamic urban rivers and providing a mechanistic understanding of the dominant risk drivers. The findings provide a transferable framework for identifying contamination hotspots, guiding remediation efforts, and supporting adaptive management.