Enhancing Air Quality Predictions with Proximal Policy Optimization and Reinforcement Learning

This study presents a fresh strategy for accurate forecasting of the Air Quality Index (AQI) utilizing Proximal Policy Optimization (PPO), a reinforcement learning (RL) algorithm which is far more adept at accurately modeling atmospheric changes in real time compared to conventional approaches. Most existing models designed for forecasting the AQI tend to be overly simplistic because of their fixed AQI attribution assumptions. Pace responsiveness to real-world environmental changes is neither automated nor practical, thus diminishing its overall usefulness. Filling this gap requires the proposed framework to put into action a PPO agent working with a simulated environment based on key pollutant concentration: NO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>, SO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>, CO, O<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf>, and PM2.5. This simulated environment serves as a reward-penalty framework through which the agent hones its policy prediction—or improves forecast accuracy—over time by receiving negative feedback based on prediction error. The innovation consists of incorporating real-time data feeds and blended parameter streams into the feedback loops of an AQI model already able to respond to change, improving adaptability to volatile atmospheric conditions. Trained and evaluated on multidimensional datasets, this model was shown to retain its potential practicality achieving a Mean Absolute Error (MAE) of 13.88 and a final loss of 0.102 over 100 training epochs. The reward trajectory combined with the scatter plot of prediction versus actual AQI demonstrate clear model performance bounds in learning and generalization broad predictive conditions. This research improves the development of AI-based systems for environmental monitoring by enhancing proactive responsiveness and model agility. This offers a substantial advancement in public health preparedness as well as strategies for pollution mitigation.