Statistical Modeling of Air Pollutants for Predicting AQI Levels

Air quality is a critical factor influencing public health, with pollutants such as particulate matter (PM10, PM2.5), ground-level ozone (O<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">3</inf>), carbon monoxide (CO), sulfur dioxide (SO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>), and nitrogen dioxide (NO<inf xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</inf>) posing significant health risks. Understanding the relationship between these pollutants is essential for effective air quality management. In this study, we apply a comprehensive range of statistical models, including Multivariate Analysis of Variance (MANOVA), Canonical Correlation Analysis (CCA), Factor Analysis, post-hoc tests, linear regression, and other techniques to evaluate the correlation between PM2.5 and key air pollutants. By employing these methods, we aim to identify the most significant contributors to PM2.5 levels and provide insights to inform air quality control strategies. Our findings offer a robust statistical framework for predicting PM2.5 concentrations, enhancing the ability of policymakers and environmental agencies to mitigate pollution-related health risks.