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 R