Joint Retrieval of PM2i5 Concentration and Aerosol Optical Depth over China Using Multi-Task Learning on FY-4A AGRI

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外文摘要：Aerosol optical depth(AOD)and fine particulate matter with a diameter of less than or equal to 2.5 μm(PM2.5)play crucial roles in air quality,human health,and climate change.However,the complex correlation of AOD-PM2.5 and the limitations of existing algorithms pose a significant challenge in realizing the accurate joint retrieval of these two parameters at the same location.On this point,a multi-task learning(MTL)model,which enables the joint retrieval of PM2.5 concentration and AOD,is proposed and applied on the top-of-the-atmosphere reflectance data gathered by the Fengyun-4A Advanced Geosynchronous Radiation Imager(FY-4A AGRI),and compared to that of two single-task learning models—namely,Random Forest(RF)and Deep Neural Network(DNN).Specifically,MTL achieves a coefficient of determination(R2)of 0.88 and a root-mean-square error(RMSE)of 0.10 in AOD retrieval.In comparison to RF,the R2 increases by 0.04,the RMSE decreases by 0.02,and the percentage of retrieval results falling within the expected error range(Within-EE)rises by 5.55％.The R2 and RMSE of PM2.5 retrieval by MTL are 0.84 and 13.76 μg m-3,respectively.Compared with RF,the R2 increases by 0.06,the RMSE decreases by 4.55 μg m-3,and the Within-EE increases by 7.28％.Additionally,compared to DNN,MTL shows an increase of 0.01 in R2 and a decrease of 0.02 in RMSE in AOD retrieval,with a corresponding increase of 2.89％in Within-EE.For PM2.5 retrieval,MTL exhibits an increase of 0.05 in R2,a decrease of 1.76 μg m-3 in RMSE,and an increase of 6.83％in Within-EE.The evaluation suggests that MTL is able to provide simultaneously improved AOD and PM2.5 retrievals,demonstrating a significant advantage in efficiently capturing the spatial distribution of PM2.5 concentration and AOD.

外文关键词：

AODPM2.5FY-4Amulti-task learningjoint retrieval

作者：

Bo LI、Disong FU、Ling YANG、Xuehua FAN、Dazhi YANG、Hongrong SHI、Xiang'ao XIA

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作者单位：

Electronic Engineering College,Chengdu University of Information Technology,Chengdu 610225,China

Tianjin Meteorological Radar Research Trial Center,Tianjin Meteorological Bureau,Tianjin 300074,China

Key Laboratory of Middle Atmosphere and Global Environment Observation,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China

Research Centre on Meteorological Observation Engineering Technology,China Meteorological Administration,Beijing 100081,China

Key Laboratory of Atmospheric Environment and Extreme Meteorology,Institute of Atmospheric Physics,Chinese Academy of Sciences,Beijing 100029,China

School of Electrical Engineering and Automation,Harbin Institute of Technology,Harbin 150001,China

University of Chinese Academy of Sciences,Beijing 100049,China

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出版年：

2025

DOI：

10.1007/s00376-024-3222-y

大气科学进展(英文版)

中国科学院大气物理研究所

大气科学进展(英文版)

影响因子：0.741

ISSN：0256-1530

年,卷(期)：2025.42(1)