1365 Hourly PM2.5 Estimates from Different Measurements of a Geostationary Satellite Using an Ensemble Learning Algorithm

Wednesday, 15 January 2020
Hall B (Boston Convention and Exhibition Center)
Jianjun Liu, Environmental Model and Data Optima Laboratory, Laurel, MD

Most studies have focused on making daily PM2.5estimations using polar-orbiting satellite retrieved aerosol optical depth (AOD) (e.g., MODIS), which are inadequate for understanding the evolution of PM2.5. This study developed an ensemble learning model to estimate hourly PM2.5concentrations from both Himawari AOD and top-of-atmosphere reflectance with someauxiliary parameters. The estimated PM2.5concentrations from AOD and reflectance are comparable each other and agree well with ground measurements. Satellite-estimated PM2.5concentrations over central East China display a north-to-south decreasing gradient with the highest concentration in winter and the lowest concentration in summer. Diurnally, concentrations are higher in the morning and lower in the afternoon. PM2.5concentrations exhibit a significant spatial agglomeration effect in central East China. The errors in AOD do not necessarily affect the retrieval accuracy of PM2.5proportionally, especially if the error is systematic. High-frequency spatiotemporal PM2.5variations can improve our understanding of the formation and transportation processes of regional pollution episodes.
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