Reinforcement Learning for Urban Air Quality Management

This document outlines a novel deep reinforcement learning (DRL) framework for optimizing the placement of air purification booths in metropolitan areas, using Delhi, India as a case study. The research highlights the limitations of traditional pollution mitigation strategies and proposes a data-driven approach that integrates various urban factors like population density, traffic patterns, and industrial influence. By employing the Proximal Policy Optimization (PPO) algorithm, the framework aims to maximize air quality improvement (AQI) while ensuring equitable spatial coverage and adhering to practical constraints. The study compares this AI-driven solution against random and greedy placement methods, demonstrating its superiority in achieving a balanced and effective distribution of air purification infrastructure for smarter, healthier cities.