Comparative Analysis of Air Quality in Major Cities vs. Minor Cities in India (2022-2025)

. Air quality assessment and forecasting is a vital field that connects environmental science, data engineering, and artificial intelligence, particularly due to the growing health risks posed by pollution-related respiratory and cardiovascular diseases. To address this challenge, comprehensive frameworks are required for monitoring and predicting pollutant concentrations, which in turn support regulatory agencies in enforcing standards like the Air Quality Index (AQI) and shaping effective mitigation policies. This study draws on diverse data sources such as ground-based monitoring stations and satellite observations applying preprocessing techniques to handle missing data, fix errors, and spatial-temporal inconsistencies. Advanced feature engineering highlights temporal trends, spatial variations, and pollutant-weather interactions, while methods like time-series decomposition, spatial interpolation, and correlation analysis help explain variability. Predictive modelling combines traditional statistical tools (e.g., ARIMA, Prophet) and machine learning approaches (e.g., Logistic Regression). Additionally, ensemble approaches that integrate data-driven insights with physical dispersion models improve accuracy, and model performance is validated using statistical measures such as mean absolute error, root mean squared error, R², and AQI category hit rates to ensure both precision and practical interpretability.