title: Estimating national-scale ground-level PM25 concentration in China using geographically weighted regression based on MODIS and MISR AOD.
abstract: Taking advantage of the continuous spatial coverage, satellite-derived aerosol optical depth (AOD) products have been widely used to assess the spatial and temporal characteristics of fine particulate matter (PM2.5) on the ground and their effects on human health. However, the national-scale ground-level PM2.5 estimation is still very limited because the lack of ground PM2.5 measurements to calibrate the model in China. In this study, a national-scale geographically weighted regression (GWR) model was developed to estimate ground-level PM2.5 concentration based on satellite AODs, newly released national-wide hourly PM2.5 concentrations, and meteorological parameters. The results showed good agreements between satellite-retrieved and ground-observed PM2.5 concentration at 943 stations in China. The overall cross-validation (CV) R (2) is 0.76 and root mean squared prediction error (RMSE) is 22.26 μg/m(3) for MODIS-derived AOD. The MISR-derived AOD also exhibits comparable performance with a CV R (2) and RMSE are 0.81 and 27.46 μg/m(3), respectively. Annual PM2.5 concentrations retrieved either by MODIS or MISR AOD indicated that most of the residential community areas exceeded the new annual Chinese PM2.5 National Standard level 2. These results suggest that this approach is useful for estimating large-scale ground-level PM2.5 distributions especially for the regions without PMs monitoring sites.
authors.affiliation: Institute of Meteorology and Oceanography, PLA University of Science and Technology, No.60, Shuanglong Street, Nanjing, 211101, China.
authors.forename: Wei
authors.initials: W
authors.lastname: You
authors.affiliation: Institute of Meteorology and Oceanography, PLA University of Science and Technology, No.60, Shuanglong Street, Nanjing, 211101, China. zzlqxxy@163.com.
authors.forename: Zengliang
authors.initials: Z
authors.lastname: Zang
authors.affiliation: Institute of Meteorology and Oceanography, PLA University of Science and Technology, No.60, Shuanglong Street, Nanjing, 211101, China.
authors.forename: Lifeng
authors.initials: L
authors.lastname: Zhang
authors.affiliation: Institute of Meteorology and Oceanography, PLA University of Science and Technology, No.60, Shuanglong Street, Nanjing, 211101, China.
authors.forename: Yi
authors.initials: Y
authors.lastname: Li
authors.affiliation: Institute of Meteorology and Oceanography, PLA University of Science and Technology, No.60, Shuanglong Street, Nanjing, 211101, China.
authors.forename: Weiqi
authors.initials: W
authors.lastname: Wang
mesh_terms: D000336:Aerosols / Q000032:analysis; D000393:Air Pollutants / Q000032:analysis*; D000397:Air Pollution / Q000706:statistics & numerical data*; D002138:Calibration; D002681:China; D004784:Environmental Monitoring / Q000379:methods*; D006801:Humans; D008962:Models, Theoretical; D052638:Particulate Matter / Q000032:analysis*; D063809:Satellite Imagery; D000068916:Spatial Regression
chemical_list: D000336:Aerosols; D000393:Air Pollutants; D052638:Particulate Matter
keywords: Aerosol optical depth; Geographically weighted regression; MISR; MODIS; PM2.5