Uncovering Key Sources of Regional Ozone Simulation Biases Using Machine Learning and SHAP Analysis.

Atmospheric chemical transport models (CTMs) are widely used in air quality management, but still have large biases in simulations. Accurately and efficiently identifying key sources of simulation biases is crucial for model improvement. However, traditional approaches, such as sensitivity and uncertainty analyses, are computationally intensive and inefficient, as they require multiple model runs.