Artificial Intelligence for Environmental Sustainability: Air Quality Prediction, E-Waste Management and Smart Resource Optimization

Abstract

The research paper focuses on the conceptual framework paper rather than a definitive experimental prediction study. It proposes an integrated architecture for AI-enabled environmental sustainability, with air-quality monitoring and prediction as the primary application and e-waste management as a secondary contextual application. The framework combines environmental sensing, data preprocessing, Artificial Neural Networks (ANN), fuzzy decision support, and environmental action. A focused Chennai air-quality case study is used to illustrate how AQI, PM2.5 and SO2 observations can be organized for AI-based analysis. The numerical observations are treated as observed values, while the ANN outputs and performance statistics are explicitly identified as retrospective illustrative calculations, not as independently validated experimental evidence. Because the source research paper does not document a complete sampling period, dataset size, train-test split, validation strategy, hardware environment, or energy measurements, no claim of model generalization is made. The proposed discussion therefore emphasizes uncertainty, data provenance, model limitations, computational and energy cost, and the trade-off between the environmental benefits of AI and the environmental footprint of AI systems. The paper concludes with a research protocol for future experimental validation using independently collected, time-stamped environmental data.