Enhanced Forecasting and Assessment of Urban Air Quality by an Automated Machine Learning System: The AI‐Air - Jiayu Yang; Huabing Ke; Sunling Gong; Yaqiang Wang; Lei Zhang; Chunhong Zhou; Jingyue Mo; Yan You (Earth and Space Science)
Abstract An automated air quality forecasting system (AI‐Air) was developed to optimize and improve air quality forecasting for different typical cities, combined with the China Meteorological Administration Unified Atmospheric Chemistry Environmental Model (CUACE), and used in a typical inland city of Zhengzhou and a coastal city of Haikou in China. The performance evaluation results show that for the PM2.5 forecasts, the correlation coefficient (R) is increased by 0.07–0.13, and the mean error (ME) and root mean square error (RMSE) is decreased by 3.2–3.5 and 3.8–4.7 μg/m³. Similarly, for the O3 forecasts, the R value is improved by 0.09–0.44, and ME and RMSE values are reduced by 7.1–22.8 and 9.0–25.9 μg/m³, respectively. Case analyses of operational forecasting also indicate that the AI‐Air system can significantly improve the forecasting performance of pollutant concentrations and effectively correct underestimation, or overestimation phenomena compared to the CUACE model. Additionally, explanatory analyses were performed to assess the key meteorological factors affecting air quality in cities with different topographic and climatic conditions. The AI‐Air system highlights the potential of AI techniques to improve forecast accuracy and efficiency, and with promising applications in the field of air quality forecasting.