Remote Sensing, Vol. 9, Pages 950: Estimating Ground Level NO2 Concentrations over Central-Eastern China Using a Satellite-Based Geographically and Temporally Weighted Regression Model (Remote Sensing)

People in central-eastern China are suffering from severe air pollution of nitrogen oxides. Top-down approaches have been widely applied to estimate the ground concentrations of NO2 based on satellite data. In this paper, a one-year dataset of tropospheric NO2 columns from the Ozone Monitoring Instrument (OMI) together with ambient monitoring station measurements and meteorological data from May 2013 to April 2014, are used to estimate the ground level NO2. The mean values of OMI tropospheric NO2 columns show significant geographical and seasonal variation when the ambient monitoring stations record a certain range. Hence, a geographically and temporally weighted regression (GTWR) model is introduced to treat the spatio-temporal non-stationarities between tropospheric-columnar and ground level NO2. Cross-validations demonstrate that the GTWR model outperforms the ordinary least squares (OLS), the geographically weighted regression (GWR), and the temporally weighted regression (TWR), produces the highest R2 (0.60) and the lowest values of root mean square error mean (RMSE), absolute difference (MAD), and mean absolute percentage error (MAPE). Our method is better than or comparable to the chemistry transport model method. The satellite-estimated spatial distribution of ground NO2 shows a reasonable spatial pattern, with high annual mean values (>40 ?g/m3), mainly over southern Hebei, northern Henan, central Shandong, and southern Shaanxi. The values of population-weight NO2 distinguish densely populated areas with high levels of human exposure from others.