An Improved Hybrid GC-LSTM Framework for Hourly Nowcasting of Ground-Level NO2 Concentrations Over Beijing-Tianjin-Hebei Region
文献类型：期刊论文
| 作者 | Han, Zongfu1,5,6; Fan, Meng6; Song, Shipeng3,4; Liang, Xiaoxia2; Song, Meina1,5; He, Guangyan7; Tao, Jinhua6; Chen, Liangfu6 |
| 刊名 | IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING
 |
| 出版日期 | 2025 |
| 卷号 | 63页码:14 |
| 关键词 | Atmospheric modeling Monitoring Predictive models Satellites Long short term memory Forecasting Accuracy Air pollution Spatial resolution Graphical models Grained cascade forest (gcForest) ground-level nitrogen dioxide (NO2) long short-term memory (LSTM) nowcasting tropospheric monitoring instrument (TROPOMI) |
| ISSN号 | 0196-2892 |
| DOI | 10.1109/TGRS.2024.3514158 |
| 通讯作者 | Fan, Meng(fanmeng@aircas.ac.cn) |
| 英文摘要 | Nitrogen dioxide (NO2) is a critical air pollutant with significant health and environmental implications, particularly in urban areas where high levels of emissions are prevalent. Accurate nowcasting of ground-level NO2 concentrations is essential for effective air quality management and timely public health interventions. Traditional methods often struggle with balancing the spatial accuracy of ensemble learning models and the temporal forecasting strengths of time-series models like long short-term memory (LSTM) networks. In this study, we propose an improved hybrid framework, GC-LSTM, to nowcast regional ground-level NO(2 )concentrations on an hourly scale based on satellite-derived NO2 vertical column densities (VCDs), meteorological data, and on-site observations. GC-LSTM integrates the spatial learning capabilities of grained cascade forest (gcForest) with the temporal prediction strengths of LSTM networks, leveraging the strengths of both spatial inference and time-series prediction. This study focuses on the Beijing-Tianjin-Hebei (BTH) region, one of China's most polluted areas, as a case study. Our results indicate that the GC-LSTM framework performs a strong correlation between predicted and observed ground-level NO2 concentrations, with an R-2 of 0.746 and a mean absolute percentage error (MAPE) of 18.4% at a 1-h prediction interval. Even as the prediction intervals extended to 2 and 3 h, the GC-LSTM consistently outperforms the gcForest model across all evaluated metrics, with R-2 values higher by 0.097 and 0.117, and root mean square error (RMSE) values lower by 0.666 and 1.76 mu g/m(3) than those nowcasted by using the standalone gcForest model, respectively, highlighting its robustness and adaptability. Furthermore, the capacity of the GC-LSTM framework for continual learning and adaptation ensures its effectiveness in dynamic environments, making it a valuable tool for real-time air quality forecasting and environmental management. |
| WOS关键词 | AIR-QUALITY ; TROPOSPHERIC NO2 ; NITROGEN-DIOXIDE ; EMISSIONS ; OMI ; VALIDATION ; PREDICTION ; PRECURSOR ; NETWORK ; TROPOMI |
| 资助项目 | National Key Research and Development Program of China[2022YFC3700102] ; National Natural Science Foundation of China[42375132] ; Yongxing Laboratory Organized Research Project Funding[2024KJGG18] |
| WOS研究方向 | Geochemistry & Geophysics ; Engineering ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001383068400012 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 资助机构 | National Key Research and Development Program of China ; National Natural Science Foundation of China ; Yongxing Laboratory Organized Research Project Funding |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/211930]  |
| 专题 | 中国科学院地理科学与资源研究所 |
| 通讯作者 | Fan, Meng |
| 作者单位 | 1.Beijing Univ Posts & Telecommun, Sch Natl Pilot Software Engn, Beijing 100876, Peoples R China 2.Capital Normal Univ, Coll Resource Environm & Tourism, Beijing 100048, Peoples R China 3.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Beijing 100101, Peoples R China 5.Beijing Univ Posts & Telecommun, Sch Comp Sci, Beijing 100876, Peoples R China 6.Chinese Acad Sci, Aerosp Informat Res Inst, State Key Lab Remote Sensing Sci, Beijing 100101, Peoples R China 7.Tianfu Yongxing Lab, Pollut Reduct & Carbon Reduct Synergies, Chengdu 610213, Peoples R China |
| 推荐引用方式 GB/T 7714 | Han, Zongfu,Fan, Meng,Song, Shipeng,et al. An Improved Hybrid GC-LSTM Framework for Hourly Nowcasting of Ground-Level NO2 Concentrations Over Beijing-Tianjin-Hebei Region[J]. IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,2025,63:14. |
| APA | Han, Zongfu.,Fan, Meng.,Song, Shipeng.,Liang, Xiaoxia.,Song, Meina.,...&Chen, Liangfu.(2025).An Improved Hybrid GC-LSTM Framework for Hourly Nowcasting of Ground-Level NO2 Concentrations Over Beijing-Tianjin-Hebei Region.IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING,63,14. |
| MLA | Han, Zongfu,et al."An Improved Hybrid GC-LSTM Framework for Hourly Nowcasting of Ground-Level NO2 Concentrations Over Beijing-Tianjin-Hebei Region".IEEE TRANSACTIONS ON GEOSCIENCE AND REMOTE SENSING 63(2025):14. |
入库方式： OAI收割
来源：地理科学与资源研究所
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