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

ABSTRACT: 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. For example, a CTM-based operational forecasting system for air quality over the Pacific Northwest, called AIRPACT, uses over 100 processors for several hours to provide 48-h forecasts daily, but struggles to capture unhealthy O₃ episodes during the summer and early fall, especially over Kennewick, WA. This research developed machine learning (ML) based O₃ forecasts for Kennewick, WA to demonstrate an improved forecast capability. We used the 2017-2020 simulated meteorology and O₃ observation data from Kennewick as training datasets. The mete

SUBMITTER: Fan K 

PROVIDER: S-EPMC8883518 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature