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Spatial proxies used in bottom-up emission inventories toderive the spatial distributions of emissions are usually empirical andinvolve additional levels of uncertainty. Although uncertainties in currentemission inventories have been discussed extensively, uncertaintiesresulting from improper spatial proxies have rarely been evaluated. In thiswork, we investigate the impact of spatial proxies on the representation ofgridded emissions by comparing six gridded NOx emission datasets overChina developed from the same magnitude of emissions and different spatialproxies. GEOS-Chem-modeled tropospheric NO2 vertical columnssimulated from different gridded emission inventories are compared withsatellite-based columns. The results show that differences between modeledand satellite-based NO2 vertical columns are sensitive to the spatialproxies used in the gridded emission inventories. The total populationdensity is less suitable for allocating NOx emissions than nighttimelight data because population density tends to allocate more emissions torural areas. Determining the exact locations of large emission sources couldsignificantly strengthen the correlation between modeled and observedNO2 vertical columns. Using vehicle population and an updated roadnetwork for the on-road transport sector could substantially enhance urbanemissions and improve the model performance. When further applyingindustrial gross domestic product (IGDP) values for the industrial sector,modeled NO2 vertical columns could better capture pollution hotspots inurban areas and exhibit the best performance of the six cases compared tosatellite-based NO2 vertical columns (slope  =  1.01 and R2 = 0. 85). This analysis provides a framework for information from satelliteobservations to inform bottom-up inventory development. In the future, moreeffort should be devoted to the representation of spatial proxies to improvespatial patterns in bottom-up emission inventories

Impact of spatial proxies on the representation of bottom-up emission inventories: A satellite-based analysis