Inversion of atmospheric PM2.5 mass concentration in China from 2011 to 2020 using MCD19-A2 data and GWR model

Temporal and spatial transformation characteristics of China’s land-based atmospheric aerosol were obtained from the high spatial resolution MODIS MCD19-A2 data from 2011 to 2020 using remote sensing and geographically weighted regression model (GWR). This article was adopted: 1) Vertical-humidity revision: The vertical revision of aerosol optical depth (AOD) was carried out under the different spatial distributions of satellite and PM2.5 concentration data. The AOD value was obtained from the MODIS, and then divided by the atmospheric boundary layer height data, in order to obtain the AOD value close to the ground level. The humidity was revised to remove the influence of relative humidity on the AOD value. The vertically revised “dry” AOD value was divided to obtain a vertical-humidity revised “wet” AOD by the humidity impact factor, according to the calculated humidity impact factor in each city. There was an increase from 0.365 to 0.779 in the correlation coefficient between the “wet” AOD value and PM2.5 concentration data. 2) Multiple colinear tests: multiple colinear tests were carried out to verify the GWR fitting using the eight variables of the model. Variance inflation factor (VIF) was selected to test the multiple colinear all over the eight variables for the better fitting of the GWR model. 3) GWR model was established to inspect the accuracy: The processed PM2.5 and auxiliary variables data of Chinese cities were selected to establish 10 annual GWR models and the fitting of each model using the Geographically Weighted Regression function in Modeling Spatial Relationships tool of ArcGIS. SPSS software was used to verify the accuracy of the fitting in each model, with the adjusted coefficient of determination (R2) and root mean square error (RMSE). Conclusions were drawn as follows: 1) The spatial and temporal distribution of aerosol was basically conformed to the “low in the west and high in the east, decreasing year by year”. There was an outstanding seasonal difference in the AOD value, where the highest value was 0.294 in spring, followed by 0.262 in summer, and the lower values were 0.194 and 0.223 in autumn and winter, respectively. 2) The VIF demonstrated that the strongest multicollinearity was observed in 2018, indicating the monthly scale GWR model of each city. The VIF variables were close to 1, which fully met the requirements of the GWR model. 3) The better fitting of the model was achieved, where the best year was 2013 (R2=0.933), and the worst year was 2018 (R2=0.761). There was a basically consistent trend in the spatiotemporal distribution of PM2.5 concentration and AOD in each year, indicating the high applicability of MCD19-A2 data in the GWR model. 4) In terms of PM2.5 visualization, the distribution of PM2.5 was inverted by the Spline, Inverse Distance Weighted, Kriging and Natural Neighbor interpolation in 2013. The spatial distribution and concentration range were basically consistent, compared with the “Distribution of 2013 mean PM2.5 concentrations in China” data provided by National Earth System Science Data Center. IDW and Natural Neighbor more accurately described the high-value areas of PM2.5 concentration around the Tarim Basin in Xinjiang and the Beijing-Tianjin-Hebei region.