Abstract
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.
万方数据