Xuefei Hu

TL;DR: Li et al. as discussed by the authors developed a two-stage spatial statistical model using the MODIS Collection 6 aerosol optical depth (AOD) and assimilated meteorology, land use data, and PM2.5 concentrations from China's recently established ground monitoring network.

TL;DR: A national-scale geographically weighted regression model was developed to estimate daily PM2.5 concentrations in China with fused satellite AOD as the primary predictor and confirmed satellite-derived AOD in conjunction with meteorological fields and land use information can be successfully applied to extend the ground PM 2.5 monitoring network in China.

TL;DR: A random forest model incorporating aerosol optical depth data, meteorological fields, and land use variables to estimate daily 24 h averaged ground-level PM2.5 concentrations over the conterminous United States in 2011 is developed.

TL;DR: A geographically weighted regression model was developed to examine the relationship among PM(2.5), aerosol optical depth, meteorological parameters, and land use information, and suggested that North American Land Data Assimilation System could be used as an alternative of North American Regional Reanalysis to provide some of the meteorological fields.

TL;DR: In this paper, a new aerosol product with 1 km spatial resolution derived by the Multi-Angle Implementation of Atmospheric Correction (MAIAC) algorithm was examined using a two-stage spatial statistical model with meteorological fields and land use parameters (e.g., forest cover, road length, elevation, and point emissions).