AI-Based Frameworks for Indoor Air Quality Management in Smart Cities
This chapter presents a computational framework for intelligent indoor environmental management through artificial intelligence integration in smart urban infrastructure. The work systematically develops mathematical models for pollutant dynamics using mass balance principles and state space representation, followed by machine learning and deep learning approaches for predictive forecasting of indoor environmental conditions. Reinforcement learning and multi objective optimization techniques are introduced to balance air quality performance with energy efficiency objectives. The framework incorporates sensor networks, edge computing architectures, digital twin simulation environments, and explainable AI mechanisms to enable real time adaptive control. By integrating physical modeling with data driven intelligence, the proposed approach transforms indoor environmental regulation from static rule based operation into a predictive and self optimizing urban decision system aligned with sustainability and resilience goals.