Machine learning-based ozone and PM2.5 forecasting: Application to multiple AQS sites in the Pacific Northwest.

ABSTRACT: Air quality in the Pacific Northwest (PNW) of the U.S has generally been good in recent years, but unhealthy events were observed due to wildfires in summer or wood burning in winter. The current air quality forecasting system, which uses chemical transport models (CTMs), has had difficulty forecasting these unhealthy air quality events in the PNW. We developed a machine learning (ML) based forecasting system, which consists of two components, ML1 (random forecast classifiers and multiple linear regression models) and ML2 (two-phase random forest regression model). Our previous study showed that the ML system provides reliable forecasts of O₃ at a single monitoring site in Kennewick, WA. In this paper, we expand the ML forecasting system to predict both O₃ in the wild

SUBMITTER: Fan K 

PROVIDER: S-EPMC9999009 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature