Predicting Air Quality Index (AQI) Using ML and Time-Series Forecasting

Aarya Patel

Abstract: Air pollution presents a formidable global challenge, significantly impacting public health and environmental integrity. Accurate and timely air quality forecasting is thus indispensable for proactive environmental management. This paper meticulously synthesizes recent advancements in applying machine learning (ML) and deep learning (DL) algorithms to predict air quality and ambient pollutant concentrations. Drawing insights from comprehensive analyses, this systematic review powerfully demonstrates how these sophisticated computational techniques are alleged to surpass traditional statistical methods in capturing the intricate, non-linear, and comprehensive dynamics inherent in atmospheric data. Key findings underscore the exceptional value of diverse architectural innovations, especially for time-series forecasting (predicting things that change over time), and advanced machine learning models like recurrent neural networks (for example, LSTMs and GRUs) are incredibly effective. These models are designed to learn from past data, like historical pollution levels and weather information, to predict future conditions accurately. This strong ability to predict, along with model interpretability (meaning we can understand why the model made a certain prediction, perhaps using a tool like SHAP), provides major advantages for various real-world applications. For businesses, this means they can make smarter choices about industrial operations; for cities, it helps with better urban planning; and for everyone, it boosts public health initiatives. Despite data scarcity and computational demands, these cutting-edge ML/DL methodologies provide scalable, precise solutions, fundamentally enhancing predictive capabilities for smarter, sustainable urban ecosystems.

Keywords: AQI (Air Quality Index), ML (Machine Learning), DL (Deep Learning), LSTM (Long Short-Term Memory), GRU (Gated Recurrent Unit), CNN (Convolutional Neural Network)