Using Regression Models to Predict Death Caused by Ambient Ozone Pollution (AOP) in the United States

Air pollution is a significant environmental
challenge with far-reaching consequences for public
health and the well-being of communities worldwide. This
study focuses on air pollution in the United States,
particularly from 1990 to 2017, to explore its causes,
consequences, and predictive modeling. Air pollution data
were obtained from an open-source platform and
analyzed using regression models. The analysis aimed to
establish the relationship between "Deaths by Ambient
Ozone Pollution" (AOP) and various predictor variables,
including "Deaths by Household Air Pollution from Solid
Fuels" (HHAP_SF), "Deaths by Ambient Particulate
Matter Pollution" (APMP), and "Deaths by Air
Pollution" (AP). Our findings reveal that linear
regression consistently outperforms other models in
terms of accuracy, exhibiting a lower Mean Absolute
Error (MAE) of 0.004609593 and Root Mean Squared
Error (RMSE) of 0.005541933. In contrast, the Random
Forest model demonstrates slightly lower accuracy with a
MAE of 0.02133121 and RMSE of 0.03016053, while the
Huber Regression model falls in between with a MAE of
0.02280993 and RMSE of 0.04360869. The results
underscore the importance of addressing air pollution
comprehensively in the United States, emphasizing the
need for continued research, policy initiatives, and public
awareness campaigns to mitigate its impact on public
health and the environment.