气候变化领域集成服务门户(CCPortal): Capturing synoptic-scale variations in surface aerosol pollution using deep learning with meteorological data

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DOI	10.5194/acp-23-375-2023
	Capturing synoptic-scale variations in surface aerosol pollution using deep learning with meteorological data
	Feng, Jin; Li, Yanjie; Qiu, Yulu; Zhu, Fuxin
发表日期	2023
ISSN	1680-7316
EISSN	1680-7324
起始页码	375
结束页码	388
卷号	23 期号:1 页码:14
英文摘要	The estimation of daily variations in aerosol concentrations using meteorological data is meaningful and challenging, given the need for accurate air quality forecasts and assessments. In this study, a 3x50-layer spatiotemporal deep learning (DL) model is proposed to link synoptic variations in aerosol concentrations and meteorology, thereby building a deep Weather Index for Aerosols (deepWIA). The model was trained and validated using 7 years of data and tested in January-April 2022. The index successfully reproduced the variation in daily PM2.5 observations in China. The coefficient of determination between PM2.5 concentrations calculated from the index and observation was 0.72, with a root mean square error (RMSE) of 16.5 mu g m(-3). The DeepWIA performed better than Weather Forecast and Research (WRF)-Chem simulations for eight aerosol-polluted cities in China. The simulating power of the model also outperformed commonly used PM2.5 concentration retrieval models based on random forest (RF), extreme gradient boost (XGB), and multilayer perceptron (MLP). The index and the DL model can be used as robust tools for estimating daily variations in aerosol concentrations.
学科领域	Environmental Sciences; Meteorology & Atmospheric Sciences
语种	英语
WOS研究方向	Environmental Sciences & Ecology ; Meteorology & Atmospheric Sciences
WOS记录号	WOS:000911428800001
来源期刊	ATMOSPHERIC CHEMISTRY AND PHYSICS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/273570
作者单位	Chinese Academy of Sciences; Institute of Atmospheric Physics, CAS
推荐引用方式 GB/T 7714	Feng, Jin,Li, Yanjie,Qiu, Yulu,et al. Capturing synoptic-scale variations in surface aerosol pollution using deep learning with meteorological data[J],2023,23(1):14.
APA	Feng, Jin,Li, Yanjie,Qiu, Yulu,&Zhu, Fuxin.(2023).Capturing synoptic-scale variations in surface aerosol pollution using deep learning with meteorological data.ATMOSPHERIC CHEMISTRY AND PHYSICS,23(1),14.
MLA	Feng, Jin,et al."Capturing synoptic-scale variations in surface aerosol pollution using deep learning with meteorological data".ATMOSPHERIC CHEMISTRY AND PHYSICS 23.1(2023):14.