Data for "Quantifying the Missing Half of Daily NOx Emissions over South, Southeast and East Asia"

Fig_1_Mask_ROI.mat is the area mask and the standard deviation of OMI NO2 over the ROI. Fig_2_PC_EOF.mat is the EOF map and PC time series of the first 4 mode of OMI NO2 dataset. Fig_3_EOF_PC_4_Typical_Urban_And_BB.mat s the 2 typical EOF map and extreme time series extracted for Urban areas and BB areas, respectively. Fig_4_typical_daily_NOx_estimation.mat is 4 typical daily NOx emission in this paper. Fig_5_NOx_Mean_Ratio_4_Estimation_EI.mat is the mean and ratio of Mean to standard deviation of NOx estimation and the existing emission inventories(FINN+EDGAR, both in EOF and Mask regions). Fig_6_Timeseries_of_NOx_Est_EOF_Mask_4_Urban_BB.mat is the time series of NOx estimation, NOx emission inventory over EOF region and NOx emission inventory over mask region in Fig.1 for Urban and BB areas. Fig_7_NOx_Estimation_PDF_Geolocation_4_LCT_types.mat is the NOx estimation and geolocation distribution of 3 land cover types. Fig_8_NOx_Estimation_Comparison_4_Sensitivity_Runs.mat is the differences between Base+40%/Base-40% and Base, plus the time series comparisons of the three runs (Base+40%, Base-40% and Base) in both Urban and BB areas. Sup_Fig_1_NO2_mean_ROI.mat is the mean of OMI NO2 over the ROI. Sup_Fig_2_Lai_ROI.mat is the land cover types of MODIS over the ROI. Sup_Fig_3_EOF_PC_4_SouthernCSA.mat s 3 typical EOF map and extreme time series extracted for Southern Continental Southeast Asia. noxEst_year_2016.mat is the estimated NOx dataset for 2016 within this work. estimateGridCoefEmission.m is the core MATLAB code used to fit the coefficients with Least Square Method and estimate the emission with bootstrap method. Demo.zip is one example to help test the core code of this work.