KeywordsMODIS; AOD; remote sensing; United States; retrieval accuracy; satellite coverage; Gaofen-1; aerosol optical depth; deep blue; Wuhan; urban aerosol; aerosol; CAPI; DFS; retrieval error; seasonal GWR models; auxiliary variable selection; geographically weighted model; MODIS AOD; PM2.5 concentrations; Yangtze River Delta; AOD; VIIRS; heavy aerosol loading; retrieval algorithm; remote sensing; aerosol; VIIRS; MODIS; sun photometer; AERONET; aerosol optical depth; aerosol models; VIIRS; NCP region; intercomparison; differential absorption lidar; ozone; satellite measurements; IASI; upper troposphere; lower stratosphere; NH3; satellite; CTM; spatial; ground; AOD; MODIS; dark target; deep blue; air quality; UV cameras; SO2 emissions rates; DOAS; plume segmentation; NO2; ground-level concentrations; OMI; CMAQ; profile shape; AOD; coastal water; MODIS; retrieval; surface NO2 volume mixing ratio; NO2; OMI; multiple regression; planetary boundary layer; PM2.5; air pollution; Lidar; warming hole; air quality; southeast US; global warming; climate change; aerosols; aerosol; MODIS; retrieval bias; eastern China; AERONET; air pollution; geostationary satellite; Himawari-8; hourly AOD; hourly PM2.5; nitrogen dioxide; sulphur dioxide; concentrations; linear trends; non-linear trends; Mann-Kendall test; Southern Hemisphere; NO2; ground level; OMI; GTWR; China
