Forecasting O3 and NO2 concentrations with spatiotemporally continuous coverage in southeastern China using a Machine learning approach

Ozone (O 3 ) is a significant contributor to air pollution and the main constituent of photochemical smog that plagues China. Nitrogen dioxide (NO 2 ) is a significant air pollutant and a critical trace gas in the Earth’s atmosphere. The presence of O 3 and NO 2 has detrimental effects on human health, the ecosystem, and agricultural production. Forecasting accurate ambient O 3 and NO 2 concentrations with full spatiotemporal coverage is pivotal for decision-makers to develop effective mitigation strategies and prevent harmful public exposure. Existing methods, including chemical transport models (CTMs) and time series at air monitoring sites, forecast O 3 and NO 2 concentrations either with nontrivial uncertainty or without spatiotemporally continuous coverage. In this research, we adopted a forecasting model that integrates the random forest algorithm with NASA’s Goddard Earth Observing System “Composing Forecasting” (GEOS-CF) product. This approach offers spatiotemporally continuous forecasts of O 3 and NO 2 concentrations across southeastern China for up to five days in advance. Both overall validation and spatial cross-validation revealed that our forecast framework significantly surpassed the initial GEOS-CF model for all validation metrics, substantially reducing the errors in the GEOS-CF forecast data. Our model could provide accurate near-real-time O 3 and NO 2 forecasts with continuous spatiotemporal coverage.