Interpolation of Missing Ozone Data Using Graph Machine Learning and Parameter Analysis Through Explainable Artificial Intelligence Comparison

Ozone (O3), a short-lived climate pollutant, continues to increase despite policies aimed at suppressing its precursors in South Korea. The government operates approximately 500 observatories to monitor O3 and trace gases. Researchers use these data to address the ongoing issue of increasing O3 levels. However, challenges in data retrieval from observatories may introduce biases in O3 studies. In this study, we developed a graph-based machine learning model to simulate missing O3 concentrations for mitigate bias. The model incorporates spatiotemporal distribution characteristics using a merged observation dataset from South Korea in 2021. Regardless of region or length of missing data, the model effectively simulates O3 variations with R2 of up to 0.9 and RMSE of 3.6. To determine the influence of input parameters on O3 interpolation, we used eXplainable AI methods. The results indicated that NO2 is the most important factor in cities, while photochemical indicators are more influential in provinces.