Improved spatial representation of a highly resolved emission inventory in China: evidence from TROPOMI measurements

Abstract

Emissions in many sources are estimated in municipal district totals and spatially disaggregated onto grid cells using empirically selected spatial proxies such as population density, which might introduce biases, especially in fine spatial scale. Efforts have been made to improve the spatial representation of emission inventory, by incorporating comprehensive point source database (e.g. power plants, industrial facilities) in emission estimates. Satellite-based observations from the TROPOspheric Monitoring Instrument (TROPOMI) with unprecedented pixel sizes (3.5 × 7 km2) and signal-to-noise ratios offer the opportunity to evaluate the spatial accuracy of such highly resolved emissions from space. Here, we compare the city-level NO x emissions from a proxy-based emission inventory named the Multi-resolution Emission Inventory for China (MEIC) with a highly resolved emission inventory named the Multi-resolution Emission Inventory for China - High Resolution (MEIC-HR) that has nearly 100 000 industrial facilities, and evaluate them through NO x emissions derived from the TROPOMI NO2 tropospheric vertical column densities (TVCDs). We find that the discrepancies in city-level NO x emissions between MEIC and MEIC-HR are influenced by the proportions of emissions from point sources and NO x emissions per industrial gross domestic product (IGDP). The use of IGDP as a spatial proxy to disaggregate industrial emissions tends to overestimate NO x emissions in cities with lower industrial emission intensities or less industrial facilities in the MEIC. The NO x emissions of 70 cities are derived from one year TROPOMI NO2 TVCDs using the exponentially modified Gaussian function. Compared to the satellite-derived emissions, the cities with higher industrial point source emission proportions in MEIC-HR agree better with space-constrained results, indicating that integrating more point sources in the inventory would improve the spatial accuracy of emissions on city scale. In the future, we should devote more efforts to incorporating accurate locations of emitting facilities to reduce uncertainties in fine-scale emission estimates and guide future policies.

Observational operator for fair model calibration with ground NO2 measurements

“…Panel h presents the same comparison focused on the NCP region. Intriguingly, there is a clear overestimation by GEOS-Chem in terms of column NO 2 for both the national scale (panel g, with a positive normalized mean bias (NMB) of 50.56%) and the NCP region (panel h, with a positive NMB value of 60.04%), as evident in panels g and h. This is potentially caused by the overestimation in the NO x emission intensity (Wu et al, 2021a), which could be estimated through assimilating the OMI observations via an emission inversion system (Jin et al, 2018(Jin et al, , 2019b.…”

City-scale analysis of annual ambient PM2.5 source contributions with the InMAP reduced-complexity air quality model: a case study of Madison, Wisconsin

“…Disaggregating county level emissions traditionally consists of using spatial proxies like highways and major roads to allocate the emissions to grid cells, but this can introduce positive urban biases and negative urban biases at finer resolutions (Zheng et al 2017). These issues are being ameliorated by research on developing more accurate emissions inventories (Wu et al 2021. Lastly, additional uncertainty is introduced based on the number and distribution of monitors used to calculate the scaling factor for each pollutant because this varies by spatial domain.…”

Trend and Interannual Variations of Reactive Nitrogen Deposition in China During 2008–2017 and the Roles of Anthropogenic Emissions and Meteorological Conditions

“…Temporal profiles of emissions are fundamental inputs for CTMs and play an important role on the model performance (Wu et al., 2019). The temporal distribution of emissions was determined by country in EMEP model (http://www.emep.int/grid/country_numbers.txt).…”