Investigating Ground-level Ozone Formation: A Case Study in Taiwan
This research provides insights into ozone formation and prediction for policymakers in Taiwan to improve O3 reduction strategies.
This paper investigates ground-level ozone (O3) formation in Taiwan by employing six supervised methods with fourteen meteorological and chemical variables. Deep Neural Networks (DNN) and Long Short-Term Memory (LSTM) models accurately predict O3 concentrations, revealing that Nitrogen Oxides negatively contribute while solar radiation positively contributes to O3 prediction.
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.