Air Quality Analysis: A Data-Driven Study of Indian Cities

Air pollution remains a critical public health issue, particularly in rapidly urbanizing regions such as India. While existing studies have analyzed air quality globally, limited large-scale, data-driven research has been conducted exclusively for India. This study presents a novel approach by leveraging big data analytics and machine learning to examine the World Health Organization (WHO) Ambient Air Quality Database. Using Exploratory Data Analysis (EDA), we detect inconsistencies, validate assumptions, and analyze correlations between key pollutants, including particulate matter (PM2.5, PM10) and nitrogen dioxide (NO₂). Unlike traditional studies focusing on static trends, this research employs K-means clustering to dynamically classify air quality levels and identify pollution hotspots across Indian cities. By integrating meteorological parameters and utilizing advanced visualization techniques, we provide a scalable, data-driven framework for real-time air quality monitoring. Experimental results demonstrate that predictive analytics can effectively pinpoint critical pollution zones, offering actionable insights for policymakers to enhance air quality management. This study bridges a significant research gap by applying machine learning-driven analytics exclusively to India's air quality, providing a foundation for future policy interventions.