Abstract:
In order to understand the effects of air pollution on health, we use advanced predictive modeling to examine the relationship between the Air Quality Index (AQI) and lung cancer mortality across major urban areas in the United States. We concentrate on important pollutants that are known to contribute to poor air quality, like PM2.5 and NO2. We use statistical analysis and machine learning techniques, such as linear regression and XGBoost, to accurately predict health outcomes through an extensive dataset analysis, which includes historical air quality readings from the Environmental Protection Agency (EPA) and health records from the Centers for Disease Control and Prevention (CDC). Our results show a strong correlation between increased lung cancer mortality and high levels of particulate pollutants. This relationship emphasizes how important it is to implement focused public health initiatives to lower exposure to these dangerous contaminants. In order to mitigate the negative health effects of air pollution, we support legal changes based on our findings, such as stronger standards for air quality and improved mechanisms for monitoring pollution.This study not only establishes the foundation for future research examining more comprehensive environmental and public health policies, but it also gives vital information about the risks associated with air pollution to U.S. lawmakers and health professionals. By demonstrating the direct relationship between air quality and healthcare outcomes, Our study is an essential resource for enhancing the dynamics of urban health, especially in the quickly growing metropolitan areas of the United States.
