Enhanced Forecasting and Assessment of Urban Air Quality by an Automated Machine Learning System: The AI‐Air
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...
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| Format: | Article |
| Language: | English |
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American Geophysical Union (AGU)
2025-01-01
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| Series: | Earth and Space Science |
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| Online Access: | https://doi.org/10.1029/2024EA003942 |
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| author | Jiayu Yang Huabing Ke Sunling Gong Yaqiang Wang Lei Zhang Chunhong Zhou Jingyue Mo Yan You |
| author_facet | Jiayu Yang Huabing Ke Sunling Gong Yaqiang Wang Lei Zhang Chunhong Zhou Jingyue Mo Yan You |
| author_sort | Jiayu Yang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-e5d96fc346094a7e910f82da3ecbb0dd |
| institution | Kabale University |
| issn | 2333-5084 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | American Geophysical Union (AGU) |
| record_format | Article |
| series | Earth and Space Science |
| spelling | doaj-art-e5d96fc346094a7e910f82da3ecbb0dd2025-01-28T11:08:40ZengAmerican Geophysical Union (AGU)Earth and Space Science2333-50842025-01-01121n/an/a10.1029/2024EA003942Enhanced Forecasting and Assessment of Urban Air Quality by an Automated Machine Learning System: The AI‐AirJiayu Yang0Huabing Ke1Sunling Gong2Yaqiang Wang3Lei Zhang4Chunhong Zhou5Jingyue Mo6Yan You7State Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA Chinese Academy of Meteorological Sciences Beijing ChinaState Key Laboratory of Severe Weather & Institute of Artificial Intelligence for Meteorology Chinese Academy of Meteorological Sciences Beijing ChinaState Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA Chinese Academy of Meteorological Sciences Beijing ChinaState Key Laboratory of Severe Weather & Institute of Artificial Intelligence for Meteorology Chinese Academy of Meteorological Sciences Beijing ChinaState Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA Chinese Academy of Meteorological Sciences Beijing ChinaState Key Laboratory of Severe Weather & Key Laboratory of Atmospheric Chemistry of CMA Chinese Academy of Meteorological Sciences Beijing ChinaPublic Meteorological Service Center China Meteorological Administration Beijing ChinaNational Observation and Research Station of Coastal Ecological Environments in Macao Macao Environmental Research Institute Macau University of Science and Technology Macao ChinaAbstract 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.https://doi.org/10.1029/2024EA003942automated air quality forecasting system (AI‐air)machine learningexplanatory analyseskey meteorological factors |
| spellingShingle | Jiayu Yang Huabing Ke Sunling Gong Yaqiang Wang Lei Zhang Chunhong Zhou Jingyue Mo Yan You Enhanced Forecasting and Assessment of Urban Air Quality by an Automated Machine Learning System: The AI‐Air Earth and Space Science automated air quality forecasting system (AI‐air) machine learning explanatory analyses key meteorological factors |
| title | Enhanced Forecasting and Assessment of Urban Air Quality by an Automated Machine Learning System: The AI‐Air |
| title_full | Enhanced Forecasting and Assessment of Urban Air Quality by an Automated Machine Learning System: The AI‐Air |
| title_fullStr | Enhanced Forecasting and Assessment of Urban Air Quality by an Automated Machine Learning System: The AI‐Air |
| title_full_unstemmed | Enhanced Forecasting and Assessment of Urban Air Quality by an Automated Machine Learning System: The AI‐Air |
| title_short | Enhanced Forecasting and Assessment of Urban Air Quality by an Automated Machine Learning System: The AI‐Air |
| title_sort | enhanced forecasting and assessment of urban air quality by an automated machine learning system the ai air |
| topic | automated air quality forecasting system (AI‐air) machine learning explanatory analyses key meteorological factors |
| url | https://doi.org/10.1029/2024EA003942 |
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