A research team led by Prof. XIE Pinhua from the Hefei lnstitutes of Physical Science of the Chinese Academy of Sciences has developed a novel prediction model for surface ozone concentration in the North China Plain (NCP) and Yangtze River Delta (YRD) regions. The model leverages a sequential convolutional long short-term memory network framework (CNN-LSTM) to integrate spatiotemporal meteorological features, addressing key limitations in existing forecasting methods.

The research results, published in Environmental Science & Technology, provided a new technical approach for ozone pollution early warning.

Surface ozone has become a major summer air pollutant, often linked to high temperatures and low humidity. However, ozone levels are also influenced by complex meteorological factors such as atmospheric circulation, solar radiation, boundary layer height, and cloud cover—making accurate forecasting a persistent challenge. Conventional machine learning models often heglect these spatiotemporal dynamics, while numerical models suffer from high computational costs and limited ability to predict high-concentration ozone episodes.

In this study, the researchers constructed a multi-scale mapping model using meteorological forecast data and CNN-LSTM architecture. By incorporating meteorological fields across various spatiotemporal scales, the model achieved high prediction accuracy—with hit rates of 83% in the NCP and 56% in the YRD for high-concentration ozone events (MDA8 ≥ 160 μg/m³), and an R² exceeding 0.85 in explaining daily ozone variability.

The model also successfully quantified the impact of typhoon position shifts on regional ozone levels, further proving its robustness. 

"We've gained a clearer picture of how weather patterns drive ozone pollution, which can really support better early warnings for high-risk ozone days," said Prof. XIE Pinhua.

 CNN-LSTM Machine Learning Framework Integrating Spatiotemporal Evolution Characteristics of Weather Processes (Image by HU Feng)