Journal Article

Enhanced Forecasting and Assessment of Urban Air Quality by an Automated Machine Learning System: The AI‐Air

Yang, Jiayu, Wang, Yaqiang, Mo, Jingyue, Ke, Huabing, Gong, Sunling, Zhou, Chunhong, You, Yan, Zhang, Lei

2025

Overview

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. Plain Language Summary Currently, artificial intelligence (AI) technology provides an innovative technological way to solve air quality problems with its excellent capability. This work develops an advanced automated air quality forecasting system (AI‐Air), based on the China Meteorological Administration Unified Atmospheric Chemical Environmental Model (CUACE). By comparing the forecasting results with the existing numerical models, the AI‐Air system shows its excellent performance in both overall performance evaluation and case‐specific forecasting. The AI‐Air system not only surpasses the conventional methods in forecasting accuracy but also demonstrates its fine forecasting ability in detail. In addition, this study provides an in‐depth discussion of the key factors affecting air quality in different types of cities and conducts a feature importance analysis. This analysis deepens the understanding of the intrinsic mechanisms of air quality changes in different urban environments and provides a scientific basis for formulating more precise air quality management strategies. Overall, the development and application of the AI‐Air system not only improves the science and accuracy of air quality prediction, but also provides strong technical support for urban environmental management and policy formulation. Key Points An automated ML system (AI‐Air) is developed for urban air quality forecasting Operational analyses show effective correction of under‐/overestimation phenomena Explanatory analyses explore key influencing factors in inland and coastal cities

• Publisher: John Wiley & Sons, Inc,American Geophysical Union (AGU)
• Subject: Accuracy / Aerosols / Air pollution / Air quality / Algorithms / Artificial intelligence / Atmospheric chemistry / automated air quality forecasting system (AI‐air) / Automation / Climatic conditions / Correlation coefficient / Data assimilation / Emissions / Environmental modeling / explanatory analyses / Forecasting / key meteorological factors / Machine learning / Meteorological satellites / Neural networks / Outdoor air quality / Particulate matter / Pollutants