Authors: Deying Zhang*, East China Normal University
Topics: Remote Sensing, Spatial Analysis & Modeling, Environment
Keywords: multiple air pollutants, satellite observations, ground-level concentrations, geographically weighted regression
Session Type: Paper
Start / End Time: 5:00 PM / 6:40 PM
Room: Madison A, Marriott, Mezzanine Level
Presentation File: No File Uploaded
People in Huaihe River Basin in China have been suffering from severe air pollution due to the development of heavy industry. Traditional ambient monitoring station measurements can provide real-time accurate data, but it is limited due to the less number of monitoring sites. Satellite observation data from remote sensing can provide a wide range concentrations of multiple air pollutants in long-time sequence. In this paper, Aura OMI Level-3 tropospheric NO2 products, planetary Boundary Layer SO2 products were used to estimate NO2, SO2 concentrations on the ground. And MOSID AOD products from Terra and Aqua were used to estimate ground-level PM10 and PM2.5 concentrations, using a geographically weighted regression model (GWR) combined with meteorological parameters. The results show that the coefficient of determination (R2) between the estimated concentration of NO2, SO2, PM10, PM2.5 with the measured value reaches: 0.72, 0.77, 0.81, 0.82, respectively. Moreover, the root mean square error (RMSE) is 7.79ug/m3, 9.43 ug/m3, 19.67 ug/m3, 12.89 ug/m3. It indicates that estimating ground-level NO2, SO2, PM10 and PM2.5 concentrations based on satellite observations is reliable. The method is useful for studying regions where air pollutant concentrations are limited.