Development of a Machine Learning Approach for Local-Scale Ozone Forecasting: Application to Kennewick, WA

Chemical transport models (CTMs) are widely used for air quality forecasts, but these models require large computational resources and often suffer from a systematic bias that leads to missed poor air pollution events. This research developed machine learning (ML) based O3 forecasts for Kennewick, WA to demonstrate an improved forecast capability. Using 2017–2020 simulated meteorology and O3 observation data as training datasets, two ML models were developed: ML1 uses random forest (RF) classifier and multiple linear regression (MLR) models, while ML2 uses a two-phase RF regression model with best-fit weighting factors. The ML models showed improved forecast skill for high-O3 events and provided reliable forecasting capability with much less computational resources compared to traditional CTMs.