Abstract
Air pollution is a significant global environmental concern with detrimental impacts on human health and ecosystems. In this research project, we analyze global air pollution data sourced from various regions worldwide. The dataset includes information on air quality index (AQI) categories, particulate matter (PM2.5) levels, and other relevant parameters. Our study employs Pandas, Matplotlib, and Seaborn libraries in Python for data processing, visualization, and analysis. We begin by filtering out data points associated with "Unhealthy for Sensitive Groups" AQI category to focus on broader air quality trends. Subsequently, we explore the basic characteristics of the dataset, including summary statistics and missing value detection. Visualization techniques such as count plots and correlation heat maps are utilized to illustrate the distribution of AQI categories and assess relationships between different variables, respectively. Our findings contribute to a better understanding of global air pollution patterns, which can inform policy-making and environmental management strategies aimed at mitigating the adverse effects of air pollution on public health and the environment. Index Terms— Particulate matter (PM2.5), Air quality index (AQI), Public health, Environmental management, Air pollution, Global
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