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

Abstract

Ozone (O3) is a significant contributor to air pollution and the main constituent of photochemical smog that plagues China. Nitrogen dioxide (NO2) is a significant air pollutant and a critical trace gas in the Earth’s atmosphere. The presence of O3 and NO2 has detrimental effects on human health, the ecosystem, and agricultural production. Forecasting accurate ambient O3 and NO2 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 O3 and NO2 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 O3 and NO2 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 O3 and NO2 forecasts with continuous spatiotemporal coverage.

Keywords

• Ozone
• Nitrogen dioxide
• Air Pollution Forecast
• Chemical Transport Model
• and Random Forest