  Tropospheric ozone (O3) is a greenhouse gas which can absorb heat and make
the weather even hotter during extreme heatwaves. Besides, it is an influential
ground-level air pollutant which can severely damage the environment. Thus
evaluating the importance of various factors related to the O3 formation
process is essential. However, O3 simulated by the available climate models
exhibits large variance in different places, indicating the insufficiency of
models in explaining the O3 formation process correctly. In this paper, we aim
to identify and understand the impact of various factors on O3 formation and
predict the O3 concentrations under different pollution-reduced and climate
change scenarios. We employ six supervised methods to estimate the observed O3
using fourteen meteorological and chemical variables. We find that the deep
neural network (DNN) and long short-term memory (LSTM) based models can predict
O3 concentrations accurately. We also demonstrate the importance of several
variables in this prediction task. The results suggest that while Nitrogen
Oxides negatively contributes to predicting O3, solar radiation makes a
significantly positive contribution. Furthermore, we apply our two best models
on O3 prediction under different global warming and pollution reduction
scenarios to improve the policy-making decisions in the O3 reduction.
