The application of MEIC emission inventory in air quality model

HE Bin 1 MEI Shi-long 1 LU Chen-li 1 LI Hai-jun 1 ZHOU Qiu-lin 1 SONG Liu-ming 1

(1.Jiaxing Meteorological Bureau , Jiaxing, China 314050)

【Abstract】The required techniques are investigated for applying the Tsinghua MEIC emission inventory of 2012 reference year with 0.25° × 0.25° resolution to the WRF-CHEM model. These techniques include the calculation of pollutant mass per unit area, the transformation of pollutant concentration from the lat-lon grid to the mesoscale grid, the refinement of emission inventory with model land-use data, the determination of hourly emission data in each sector, and the apportionment of major PM2.5 species in the specified regions. The effectiveness and performance of the aforementioned inventory processing techniques are assessed in the heavy pollution scenarios. It is indicated that the local accumulation and horizontal transport of pollutants can be well simulated from the WRF-CHEM model. However, the concentration in the high pollution central area is underestimated, which is closely related to the forecast bias in the meteorological fields. Additionally, the refinement of emission inventory changes the simulated pollutant concentration. The amount of change is found to vary according to the weather conditions. Under stable meteorological conditions, large changes mainly occur within and around urban areas.

【Keywords】 MEIC ; WRF-CHEM ; processing techniques of emission inventory ;


【Funds】 Major science and technology projects of Science Technology Department of Zhejiang Province (2014C03025)

Download this article


    [1] Cao G L, Zhang X Y, Gong SH L, et al. Emission sources of major particulate matter and air pollutants in China [J]. Science Bulletin, 2011, 56(3): 261–268 (in Chinese).

    [2] Zhong L J, Zheng J Y, Wang G Q, et al. Quantitative analysis for uncertainty and case study of emission inventory of air pollution sources [J]. Environmental Science Research, 2007, 20(4): 15–20 (in Chinese).

    [3] Zhang Q, Klimont Z, Streets D G, et al. Particulate emission model for anthropogenic sources in China and 2001 emission inventory estimation [J]. Natural Science Progress, 2006, 16(2): 23–231 (in Chinese).

    [4] Zhang Q, Streets D G, Carmichael G R, et al. Asian emissions in 2006 for the NASA INTEX-B mission. Atmos. Chem. Phys. Discuss. [J]. 2009, 9: 4081−4139.

    [5] Li L, Chen CH H, Huang H Y. Applying Models-3/CMAQ to study atmospheric pollution and transport in the Yangtze River Delta [J]. Shanghai Environmental Science, 2007, 26(4): 159–165 (in Chinese).

    [6] Chen B B, Lin CH CH, Yang K, et al. Fuzhou air quality prediction system based on CMAQ model [J]. China Environmental Science, 2012, 32(10): 1744–1752 (in Chinese).

    [7] Li F, Zhu B, An J L, et al. Numerical simulation of heavy haze pollution in the Yangtze River Delta and surrounding areas in early December 2013 [J]. China Environmental Science, 2015, 35(7): 1965–1974 (in Chinese).

    [8] Ma X, Chen D S, Gao Q X, et al. Application of WRF-CHEM model to simulate the effects of aerosol pollution on summer meteorological conditions in the Beijing-Tianjin-Hebei region [J]. Resource Science, 2012, 34(8): 1408–1415 (in Chinese).

    [9] Zhou G Q, Xie Y, Wu J B, et al. Reasons for PM2.5 prediction and deviation in eastern China based on WRF-CHEM model [J]. China Environmental Science, 2016, 36(8): 2251–2259 (in Chinese).

    [10] Yang P, Zhu B, Gao J H, et al. A numerical simulation of pollution-island events in the summer PM2.5 pollution in Nanjing, China [J]. China Environmental Science, 2016, 36(2): 321–330 (in Chinese).

    [11] Grell, G A, Peckham, S E, Schmitz, R, et al. Fully coupled “online” chemistry within the WRF model [J]. Atmospheric Environment, 2005, 39: 6957–6975.

    [12] Fu, X, Wang, S X, Zhao B, et al. Emission inventory of primary pollutants and chemical speciation in 2010 for the Yangtze River Delta region, China.Atmos.Environ, 2013, 70: 39–50.

    [13] Zheng J Y, Zhang L J, Zhong L J, et al. Emission inventory and spatial distribution of atmospheric area sources in Pearl River Delta [J]. China Environmental Science, 2009, 29(5): 455–460 (in Chinese).

    [14] Streets, D G, Yarber, K F, Woo, J H, et al. Biomass burning in Asia: Annual and seasonal estimates and atmospheric emissions [J]. Global Biogeochem. Cycles, 2003, 17: 1759–1768.

    [15] He M, Zheng J Y, Yin S S, et al. Trend, temporal and spatial characteristics, and uncertainties in biomass burning emissions in the Pearl River Delta, China [J]. Atmos. Environ., 2011, 45: 4051–4059.

    [16] Wu X L. Study on the emission inventory of atmospheric pollutants in the Yangtze River Delta [D]. Shanghai: Fudan University, 2009 (in Chinese).

    [17] Zhai Y R, Wang Q G, Song Y Y. Characteristics of atmospheric pollutant emissions from energy consumption in the Yangtze River Delta [J]. China Environmental Science, 2012, 32(9): 1574–1582 (in Chinese).

    [18] Ma Z H, Liang Y P, Zhang J, et al. Study on PM2.5 composition of typical emission sources in Beijing. Journal of Environmental Science, 2015, 35(12): 4043–4052 (in Chinese).

    [19] Zheng M, Zhang Y J, Yan C Q, et al. Establishment of Shanghai PM2.5 industrial source spectrum [J]. China Environmental Science, 2013, 33(8): 1354–1359 (in Chinese).

This Article



Vol 37, No. 10, Pages 3658-3668

October 2017


Article Outline


  • 1 Model settings and MEIC emission inventory processing methods
  • 2 Results and discussion
  • 3 Conclusions
  • References