Impact of Emission Inventory Choices on PM10 Forecast Accuracy and Contributions in the Seoul Metropolitan Area

배출량 목록에 따른 수도권 PM10 예보 정합도 및 국내외 기여도 분석

  • Bae, Changhan (Department of Environmental & Safety Engineering, Ajou University) ;
  • Kim, Eunhye (Department of Environmental & Safety Engineering, Ajou University) ;
  • Kim, Byeong-Uk (Georgia Environmental Protection Division) ;
  • Kim, Hyun Cheol (Air Resources Laboratory, National Oceanic and Atmospheric Administration) ;
  • Woo, Jung-Hun (Department of Advanced Technology Fusion, Konkuk University) ;
  • Moon, Kwang-Joo (National Institute of Environmental Research) ;
  • Shin, Hye-Jung (National Institute of Environmental Research) ;
  • Song, In Ho (National Institute of Environmental Research) ;
  • Kim, Soontae (Department of Environmental & Safety Engineering, Ajou University)
  • 배창한 (아주대학교 환경공학과) ;
  • 김은혜 (아주대학교 환경공학과) ;
  • 김병욱 (미국 조지아주 환경청) ;
  • 김현철 (미국 국립해양대기청) ;
  • 우정헌 (건국대학교 신기술융합학과) ;
  • 문광주 (국립환경과학원 대기환경연구과) ;
  • 신혜정 (국립환경과학원 대기환경연구과) ;
  • 송인호 (국립환경과학원 대기환경연구과) ;
  • 김순태 (아주대학교 환경공학과)
  • Received : 2017.09.02
  • Accepted : 2017.10.16
  • Published : 2017.10.31


This study quantitatively analyzes the effects of emission inventory choices on the simulated particulate matter (PM) concentrations and the domestic/foreign contributions in the Seoul Metropolitan Area (SMA) with an air quality forecasting system. The forecasting system is composed of Weather Research and Forecasting (WRF)-Sparse Matrix Operator Kernel Emissions (SMOKE)-Community Multi-Scale Air Quality (CMAQ). Different domestic and foreign emission inventories were selectively adopted to set up four sets of emissions inputs for air quality simulations in this study. All modeling cases showed that model performance statistics satisfied the criteria levels (correlation coefficient >0.7, fractional error <50%) suggested by previous studies. Notwithstanding the apparently good model performance of total PM concentrations by all emission cases, annual average concentrations of simulated total PM concentrations varied up to $20{\mu}g/m^3$ (160%) depending on the combination of emission inventories. In detail, the difference in simulated annual average concentrations of the primary PM coarse (PMC) was up to $25.2{\mu}g/m^3$ (6.5 times) compared with other cases. Furthermore, model performance analyses on PM species showed that the difference in the simulated primary PMC led to gross model overestimation in general, which indicates that the primary PMC emissions need to be improved. The contribution analysis using model direct outputs indicated that the domestic contributions to the annual average PM concentrations in the SMA vary from 44% to 67%. To account for the uncertainty of the simulated concentration, the contribution correction factor method proposed by Bae et al. (2017) was applied, which resulted in converged contributions(from 48% to 57%). We believe this study shows that it is necessary to improve the simulated concentrations of PM components in order to enhance the accuracy of the forecasting model. It is deemed that these improvements will provide more accurate contribution results.


Supported by : 국립환경과학원


  1. Bae, C.H., H.C. Kim, B.U. Kim, and S. Kim (2015) Improvement of PM forecast using PSAT based customized emission inventory over Northeast Asia, 14th Annual CMAS Models-3 Users' Conference, October 5-7, Chapel Hill, NC.
  2. Bae, C.H., B.U. Kim, H.C. Kim, C. You, and S. Kim (2016) Sensitivity of particulate matter in the Seoul Metropolitan Area to emission reduction from source sectors, 17th IUAPPA World Clean Air Congress and 9th CAA Better Air Quality Conference Clean Air for Cities Perspectives and Solutions.
  3. Bae, C.H., C. You, B.U. Kim, H.C. Kim, and S. Kim (2017) $PM_{2.5}$ simulations for the Seoul Metropolitan Area: (III) application of the modeled and observed $PM_{2.5}$ ratio on the contribution estimation, Journal of Korean Society for Atmospheric Environment, 33(5), 445-457. (In Korean with English abstract).
  4. Bartnicki, J. (1999) Computing source-receptor matrices with the EMEP Eulerian acid deposition Model, EMEP MSC-W Note, 5, 99.
  5. Benjey, W., M. Houyoux, and J. Susick (2001) Implementation of the SMOKE emission data processor and SMOKE tool input data processor in Models-3, US EPA.
  6. Boylan, J.W. and A.G. Russell (2006) PM and light extinction model performance metrics, goals, and criteria for three-dimensional air quality models, Atmospheric Environment, 40, 4946-4959.
  7. Byun, D.W. and K.L. Schere (2006) Review of the governing equations, computational algorithms, and other components of the Models-3 Community Multiscale Air Quality (CMAQ) modeling system, Applied Mechanics Reviews, 59(2), 51-77.
  8. Carlton, A.G., P.V. Bhave, S.L. Napelenok, E.O. Edney, G. Sarwar, R.W. Pinder, G.A. Pouliot, and M. Houyoux (2010) Model representation of secondary organic aerosol in CMAQv4. 7, Environmental Science and Technology, 44(22), 8553-8560.
  9. Carter, W.P.L. (1999) Documentation of the SAPRC-99 chemical Mechanism for VOC reactivity assessment, Report to California Air Resources Board, Contracts 92-329 and 95-308.
  10. Digar, A., D.S. Cohan, and M.L. Bell (2011) Uncertainties influencing health-based prioritization of ozone abatement strategies, Environmental Science and Technology, 45, 7761-7767.
  11. Emery, C., Z. Liu, A.G. Russell, M.T. Odman, G. Yarwood, and N. Kumar (2016) Recommendations on statistics and benchmarks to assess photochemical model performance, Journal of the Air and Waste Management Association,
  12. Guenther, A., T. Karl, P. Harley, C. Wiedinmyer, P.I. Palmer, and C. Geron (2006) Estimates of global terrestrial isoprene emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature), Atmospheric Chemistry and Physics, 6(1), 107-173.
  13. In, H.J. and Y.P. Kim (2010) Estimation of the aerosol optical thickness distribution in the Northeast Asian forest fire episode in May 2003: Possible missing emissions, Atmospheric Research, 98(2), 261-273.
  14. Jeong, J.H., Y.K. Kim, Y.S. Moon, and M.K. Hwang (2007) Intercomparison of wind and air temperature fields of meteorological model for forecasting air quality in Seoul metropolitan area, Journal of Korean Society for Atmospheric Environment, 23(6), 640-652. (In Korean with English abstract)
  15. Kim, B.U., C.H. Bae, H.C. Kim, E. Kim, and S. Kim (2017b) Spatially and chemically resolved source apportionment analysis: Case study of high particulate matter event, Atmospheric Environment, 162, 55-70,
  16. Kim, H.C., E. Kim, C.H. Bae, J.H. Cho, B.U. Kim, and S. Kim (2017a) Regional contributions to particulate matter concentration in the Seoul Metropolitan Area, Korea: seasonal variation and sensitivity to meteorology and emissions inventory, Atmospheric Chemistry and Physics, 17, 10315-10332,
  17. Kim, H.C., S. Kim, B.U. Kim, C.S. Jin, S. Hong, R. Park, S.W. Son, C.H. Bae, M.A. Bae, C.K. Song, and A. Stein (2017d) Recent increase of surface particulate matter concentrations in the Seoul Metropolitan Area, Korea, Scientific Reports, 7.
  18. Kim, S., C.H. Bae, H.C. Kim, and B.-U. Kim (2017c) $PM_{2.5}$ simulations in the Seoul Metropolitan Area: (I) model contributions of precursor emissions in the CAPSS emissions inventory, Journal of Korean Society for Atmospheric Environment, 33(2), 139-158. (In Korean with English abstract)
  19. Kim, S., N. Moon, and D.W. Byun (2008) Korea emissions inventory processing using the US EPA's SMOKE system, Asian Journal of Atmospheric Environment, 2(1), 34-46.
  20. Kurokawa, J., T. Ohara, T. Morikawa, S. Hanayama, G. Janssens-Maenhout, T. Fukui, and H. Akimoto (2013) Emissions of air pollutants and greenhouse gases over Asian regions during 2000-2008: Regional Emission inventory in ASia (REAS) version 2, Atmospheric Chemistry and Physics, 13(21), 11019-11058.
  21. Lee, D.-G., Y.-M. Lee, K. Jang, C. Yoo, K. Kang, J.-H. Lee, S. Jung, J. Park, S.-B. Lee, J. Han, J. Hong, and S. Lee (2011) Korean national emissions inventory system and 2007 air pollutant emissions, Asian Journal of Atmospheric Environment, 5(4), 278-291, doi:10.5572/ajae.2011.5.4.278.
  22. Li, M., Q. Zhang, J. Kurokawa, J.-H. Woo, K.B. He, Z. Lu, T. Ohara, Y. Song, D.G. Streets, G.R. Carmichael, Y.F. Cheng, C.P. Hong, H. Huo, X.J. Jiang, S.C. Kang, F. Liu, H. Su, and B. Zheng (2017) MIX: a mosaic Asian anthropogenic emission inventory for the MICS-Asia and the HTAP projects, Atmospheric Chemistry and Physics, 17(2), 935.
  23. Lu, Z., Q. Zhang, and D.G. Streets (2011) Sulfur dioxide and primary carbonaceous aerosol emissions in China and India, 1996-2010, Atmospheric Chemistry and Physics, 11, 9839-9864, doi:10.5194/acp-11-9839-2011.
  24. NIER (2011a) Study on the construction of high resolution weather-atmosphere modeling for urban scale fine dust forecasting (III). (in Korean)
  25. NIER (2011b) Evaluation of impacts of long-transport pollutants in Northeast Asia (II). (in Korean)
  26. NIER (2014) Improve accuracy of ozone forecasting and improve emission processing model (I). (in Korean)
  27. NIER (2015) National Institute of Environmental Research annual report. (in Korean)
  28. NOAA (2005) (accessed on Aug. 16, 2017).
  29. MOE (2012) A Study on Improvement and Expansion of Urban Scale $PM_{2.5}$ Forecasting System. (in Korean)
  30. MOE (2017) Emergency action plan for high-concentration PM in the Seoul metropolitan area, (accessed on Aug. 23, 2017). (in Korean)
  31. MSIP (2015) Development of Korean air quality simulation system for $PM_{2.5}$ forecasting. (in Korean)
  32. Skamarock, W.C., J.B. Klemp, J. Dudhia, D.O. Gill, D.M. Barker, M.G. Duda, X. Huang, W. Wang, and J.G. Powers (2008) A description of the advanced research WRF version 3, NCAR Tech. Note NCAR/TN-475+STR, National Center for Atmospheric Research, Boulder, CO, 125.
  33. Tonnesen, G.S. and R.L. Dennis (2000) Analysis of radical propagation efficiency to assess ozone sensitivity to hydrocarbons and $NO_x$: 1. Local indicators of instantaneous odd oxygen production sensitivity, Journal of Geophysical Research: Atmospheres, 105(D7), 9213-9225.
  34. Zhang, Q., D. Streets, G. Carmichael, K. He, H. Huo, A. Kannari, Z. Klimont, I. Park, S. Reddy, J. Fu, D. Chen, L. Duan, Y. Lei, L. Wang, and Z. Yao (2009) Asian emissions in 2006 for the NASA INTEX-B mission, Atmospheric Chemistry and Physics Discussions, 9(1), pp. 4081-4139.

Cited by

  1. Review of Shandong Peninsular Emissions Change and South Korean Air Quality vol.34, pp.2, 2018,