Unified artificial intelligence framework for modeling pollution dynamics and sustainable remediation in environmental chemistry

This deposit provides the complete, reproducible simulation data and code accompanying the manuscript "Unified Artificial Intelligence Framework for Modeling Pollution Dynamics and Sustainable Remediation in Environmental Chemistry" submitted to Scientific Reports. Cite this article Rabbi, M.F. Unified artificial intelligence framework for modeling pollution dynamics and sustainable remediation in environmental chemistry. Sci Rep 15, 36196 (2025). https://doi.org/10.1038/s41598-025-20083-w The repository includes:- EnvironmentalContaminantSimulator (Python) that generates all synthetic datasets using a fixed random seed for deterministic outputs- Primary datasets S1–S13 covering four environmental scenarios (base, high variability, seasonal dominant, trend dominant)- Summary statistics, cross-scenario validation metrics, Monte Carlo uncertainty analysis- Green chemistry multi-objective optimization data- Feature importance analysis with SHAP and LIME interpretability- Complete reproducibility framework with requirements.txt and run.py Key Framework Components:- Graph Neural Networks (GNN) for pollutant interaction modeling (R² > 0.89)- Generative Adversarial Networks (GAN) for climate scenario synthesis (94% accuracy)- Reinforcement Learning (RL) for remediation optimization (89.7% efficiency)- Green Chemistry optimization for sustainable solvent selection- Physics-Informed Neural Networks (PINN) with embedded Darcy's law- Hybrid AI-Physics models achieving 89% predictive accuracy All data are synthetic and contain no proprietary information. The framework demonstrates computational scalability from 80 to 5,000 records with 22-month deployment feasibility. Fixed random seed (42) ensures complete reproducibility across computing environments. Implementation: Python 3.12.4 with NumPy 1.26.4, pandas 2.2.2, scikit-learn 1.5.1, matplotlib 3.8.4, and SciPy 1.13.1.