Environmental pollution involves complex nonlinear interactions, chemical variability, and physical constraints that challenge traditional models. A unified artificial intelligence framework integrating Graph Neural Networks, Generative Adversarial Networks, Reinforcement Learning, Green Chemistry optimization, and Physics Informed Neural Networks with embedded Darcy's law and a hybrid AI physics model. This framework simulates contaminant transport, generates climate scenarios, and optimizes sustainable remediation strategies across four calibrated environmental scenarios.
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