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Volume 17, issue 10 | Copyright

Special issue: East Asia emissions assessment (EA2)

Atmos. Chem. Phys., 17, 6393-6421, 2017
© Author(s) 2017. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 30 May 2017

Research article | 30 May 2017

Comparison of emissions inventories of anthropogenic air pollutants and greenhouse gases in China

Eri Saikawa1,2, Hankyul Kim2, Min Zhong1, Alexander Avramov1, Yu Zhao3, Greet Janssens-Maenhout4, Jun-ichi Kurokawa5, Zbigniew Klimont6, Fabian Wagner6,7, Vaishali Naik8, Larry W. Horowitz8, and Qiang Zhang9 Eri Saikawa et al.
  • 1Department of Environmental Sciences, Emory University, Atlanta, GA, USA
  • 2Rollins School of Public Health, Emory University, Atlanta, GA, USA
  • 3School of the Environment, Nanjing University, Nanjing, China
  • 4European Commission, Joint Research Centre, Directorate of Energy, Transport and Climate, Via Fermi, 2749, 21027 Ispra (VA), Italy
  • 5Asia Center for Air Pollution Research, 1182 Sowa, Nishi-ku, Niigata, Niigata, 950-2144, Japan
  • 6International Institute for Applied Systems Analysis, Laxenburg, Austria
  • 7Andlinger Center for Energy and the Environment, Princeton University, Princeton, NJ, USA
  • 8NOAA Geophysical Fluid Dynamics Laboratory, Princeton, NJ, USA
  • 9Center for Earth System Science, Tsinghua University, Beijing, China

Abstract. Anthropogenic air pollutant emissions have been increasing rapidly in China, leading to worsening air quality. Modelers use emissions inventories to represent the temporal and spatial distribution of these emissions needed to estimate their impacts on regional and global air quality. However, large uncertainties exist in emissions estimates. Thus, assessing differences in these inventories is essential for the better understanding of air pollution over China. We compare five different emissions inventories estimating emissions of carbon dioxide (CO2), carbon monoxide (CO), nitrogen oxides (NOx), sulfur dioxide (SO2), and particulate matter with an aerodynamic diameter of 10µm or less (PM10) from China. The emissions inventories analyzed in this paper include the Regional Emission inventory in ASia v2.1 (REAS), the Multi-resolution Emission Inventory for China (MEIC), the Emission Database for Global Atmospheric Research v4.2 (EDGAR), the inventory by Yu Zhao (ZHAO), and the Greenhouse Gas and Air Pollution Interactions and Synergies (GAINS). We focus on the period between 2000 and 2008, during which Chinese economic activities more than doubled. In addition to national totals, we also analyzed emissions from four source sectors (industry, transport, power, and residential) and within seven regions in China (East, North, Northeast, Central, Southwest, Northwest, and South) and found that large disagreements exist among the five inventories at disaggregated levels. These disagreements lead to differences of 67µgm−3, 15ppbv, and 470ppbv for monthly mean PM10, O3, and CO, respectively, in modeled regional concentrations in China. We also find that all the inventory emissions estimates create a volatile organic compound (VOC)-limited environment and MEIC emissions lead to much lower O3 mixing ratio in East and Central China compared to the simulations using REAS and EDGAR estimates, due to their low VOC emissions. Our results illustrate that a better understanding of Chinese emissions at more disaggregated levels is essential for finding effective mitigation measures for reducing national and regional air pollution in China.

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We analyze differences in existing air pollutant emission estimates to better understand the magnitude of emissions as well as the source regions and sectors of air pollution in China. We find large disagreements among the inventories, and we show that these differences have a significant impact on regional air quality simulations. Better understanding of air pollutant emissions at more disaggregated levels is essential for air pollution mitigation in China.
We analyze differences in existing air pollutant emission estimates to better understand the...