Introduction
Aerosol information, particularly PM2.5 concentration and aerosol optical depth (AOD), is essential for inquiries pertaining to air quality, human health impacts, and climate change. Previous approaches to estimate PM2.5 concentration from remote sensing data have largely relied on AOD retrieval techniques. However, the complexity of the AOD–PM2.5 interplay and limitations of existing algorithms pose significant challenges to achieving precise co-estimations of the two quantities 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). Using the Multi-Angle Atmospheric Correction (MAIAC) AOD and ground-based PM2.5 observations as references, the performance of the proposed MTL model is evaluated and compared to that of two single-task learning (STL) models, namely, random forest (RF) and deep neural network (DNN). Specifically, the MTL model estimates that the coefficient of determination (R2) of AOD is 0.88 and the root mean squared error (RMSE) is 0.10, while the estimated R2 of PM2.5 is 0.84, and the RMSE is 13.76 μg·m–3. Compared with the RF model, the R2 of the MTL model for estimating AOD and PM2.5 increased by 0.04 and 0.06, respectively, and the RMSE decreased by 0.02 and 4.55 μg·m–3, respectively. In addition, compared with the DNN model, the R2 of the MTL model for estimating AOD and PM2.5 increased by 0.01 and 0.05, respectively, and the RMSE decreased by 0.02 and 1.76 μg·m–3, respectively. The evaluation suggests that the MTL model is able to provide simultaneously improved AOD and PM2.5 retrievals, offering a critical advantage in capturing the actual distribution of fine particulate matter with high time efficiency, and thereby substantially advancing satellite retrieval techniques for air quality research and climate change studies.
Citation
Li, Bo,Disong Fu, Ling YANG, Xuehua Fan, Dazhi Yang, Hongrong Shi, Xiang-Ao XIA. 2024: Joint Retrieval of PM2.5 Concentration and Aerosol Optical Depth over China Using Multi-Task Learning on FY-4A AGRI. Adv. Atmos. Sci., https://doi.org/10.1007/s00376-024-3222-y.
Acknowledgments
The work was supported by the Opening Foundation of Key Laboratory of Atmospheric Sounding, the China Meteorological Administration and the CMA Research Center on Meteorological Observation Engineering Technology (U2021Z03), the National Natural Science of Foundation of China (Grant No. 42375138, 42030608, 42105128), the Opening Foundation of Key Laboratory of Atmospheric Chemistry, China Meteorological Administration (2022B02).
Source Data
The ERA5 data can be found at https://cds.climate.copernicus.eu/#!/home. The MODIS data can be found at https://www.earthdata.nasa.gov/. The AERONET data can be accessed from https://aer onet.gsfc.nasa.gov/. The FY-4A datasets are obtained from http://data.nsmc.org.cn. Hourly PM2.5 concentration data for 2018 were downloaded from the Data Center of the Ministry of Environmental Protection of China (http://datacenter.mep.gov.cn/index). The hourly PM2.5 concentration data at 15 air quality monitoring stations in Hong Kong were downloaded from the Environmental Protection Department (EPD) website (http://www.epd.gov.hk/epd/).