Journal of the Korean earth science society (한국지구과학회지)

The Korean Earth Science Society (한국지구과학회)
권호 표지
DOI QR코드

DOI QR Code

Verification and Estimation of the Contributed Concentration of CH4 Emissions Using the WRF-CMAQ Model in Korea

WRF-CMAQ 모델을 이용한 한반도 CH4 배출의 기여농도 추정 및 검증

  • Moon, Yun-Seob (Department of Environmental Education, Korea National University of Education) ;
  • Lim, Yun-Kyu (Department of Environmental Education, Korea National University of Education) ;
  • Hong, Sungwook (Satellite Analysis Division, National Meteorological Satellite Center) ;
  • Chang, Eunmi (Ziinconsulting INC.)
  • 문윤섭 (한국교원대학교 환경교육과) ;
  • 임윤규 (한국교원대학교 환경교육과) ;
  • 홍성욱 (국가기상위성센터 위성분석과) ;
  • 장은미 (지인컨설팅 부설연구소)
  • Received : 2013.01.24
  • Accepted : 2013.05.28
  • Published : 2013.06.30
Download PDF
⟨ Previous Next ⟩

Abstract

The purpose of this study was to estimate the contributed concentration of each emission source to $CH_4$ by verifying the simulated concentration of $CH_4$ in the Korean peninsula, and then to compare the $CH_4$ emission used to the $CH_4$ simulation with that of a box model. We simulated the Weather Research Forecasting-Community Multiscale Air Quality (WRF-CMAQ) model to estimate the mean concentration of $CH_4$ during the period of April 1 to 22 August 2010 in the Korean peninsula. The $CH_4$ emissions within the model were adopted by the anthropogenic emission inventory of both the EDGAR of the global emissions and the GHG-CAPSS of the green house gases in Korea, and by the global biogenic emission inventory of the MEGAN. These $CH_4$ emission data were validated by comparing the $CH_4$ modeling data with the concentration data measured at two different location, Ulnungdo and Anmyeondo in Korea. The contributed concentration of $CH_4$ estimated from the domestic emission sources in verification of the $CH_4$ modeling at Ulnungdo was represented in about 20%, which originated from $CH_4$ sources such as stock farm products (8%), energy contribution and industrial processes (6%), wastes (5%), and biogenesis and landuse (1%) in the Korean peninsula. In addition, one that transported from China was about 9%, and the background concentration of $CH_4$ was shown in about 70%. Furthermore, the $CH_4$ emission estimated from a box model was similar to that of the WRF-CMAQ model.

이 연구의 목적은 한반도에서 $CH_4$ 농도의 수치모의 검증을 통하여 $CH_4$ 배출원의 기여 농도를 추정하는 것이고, 이 수치모의에 사용된 $CH_4$ 배출량을 상자모델로부터 추정된 $CH_4$ 배출량과 비교하는 것이다. 한반도에서 2010년 4월 1일부터 8월 22일까지 $CH_4$의 평균 농도를 추정하기 위해 WRF-CMAQ 모델이 사용되었다. 모델에서 $CH_4$ 배출량은 전지구 배출량인 EDGAR와 한국에서의 온실기체 배출량인 GHG-CAPSS로부터 인위적 배출 인벤토리와 전지구 자연적 인벤토리인 MEGAN이 적용되었다. 이들 $CH_4$ 배출량은 안면도 및 울릉도에서 측정된 $CH_4$ 농도와 모델링 농도 자료를 비교함으로써 검증되었다. 울릉도에서 국내 배출원으로부터 추정된 $CH_4$의 기여 농도는 약 20%로 나타났고, 이것은 한반도 내 농장(8%), 에너지 기여 및 산업공정(6%), 일반폐기물(5%), 생체 및 토지이용(1%) 등 $CH_4$ 배출원으로부터 기원하였다. 그리고 중국으로부터 수송된 $CH_4$의 기여 농도는 약 9%였고, 나머지 배경농도는 약 70%로 나타났다. 박스모델로 추정된 $CH_4$ 배출량은 WRF-CMAQ 모델에서 사용한 $CH_4$ 배출량과 유의미한 결과를 얻었다.

Keywords

References

  1. Berrittella, C. and van Huissteden, J., 2009, Uncertainties in modeling CH4 emissions from northern wetlands in glacial climates: effect of hydrological model and CH4 model structure. Climate of the Past Discussions, 5, 817-851. https://doi.org/10.5194/cpd-5-817-2009
  2. Coats, C.J.Jr., 1995, High performance algorithms in the Sparse Matrix Operator Kernel Emissions (SMOKE) Modeling System. MCNC, Environmental Systems Division, Research Triangle Park, NC. 6 pp.
  3. Dudhia, J., 1993, A nonhydrostatic version of the penn stat/NCAR mesoscale model: validation tests and simulation of an Atlantic cyclone and cold front. Monthly Weather Review, 121, 1493-1513. https://doi.org/10.1175/1520-0493(1993)121<1493:ANVOTP>2.0.CO;2
  4. Environmental Protection Agency, 1999, Science algorithms of the EPA model-3 community multiscale air quality (CMAQ) modeling system. http://www.epa.gov/ asmdnerl/models3/doc/science/science.html.
  5. GEIA (Global Emissions Inventory Activity), http:// www.geiacenter.org
  6. Gery, M.W., Whitten, G.Z., Killus, J.P., and Dodge, M.C., 1989, A photochemical kinetics mechanism for urban and regional scale computer modeling. Journal of Geophysical Research, 94, 12925-12956. https://doi.org/10.1029/JD094iD10p12925
  7. Guenther, A., Archer, S., Greenberg, J., Harley, P., Helmig, D., Klinger, L., Vierling, L., Wildermuth, M., Zimmerman, P., and Zitzer, S., 1999, Biogenic hydrocarbon emissions and landcover/climate change in a subtropical savanna. Physical Chemistry Earth Part B-Hydrology Oceans and Atmosphere, 24(6), 659-667. https://doi.org/10.1016/S1464-1909(99)00062-3
  8. IPCC (Intergovernment Panel for Climate Change), 2001, The scientific basis. Cambridge University Press, 991pp.
  9. IPCC (Intergovernment Panel for Climate Change), 1996, Revised 1996 IPCC guidelines for national greenhouse gas inventories: Reference manual. IPCC.
  10. IPCC (Intergovernment Panel for Climate Change), 2006, 2006 IPCC guidelines for national greenhouse gas inventories. IPCC.
  11. Kim, H.R., 2009, Building up the inventory system of national greenhouse gases. Statistics Korea, 1-59. (in Korean)
  12. Koo, H.S., Kim, H.D., Kang, S.D., and Shin, D.W., 2007, A numerical study on the formation mechanism of a mesoscale low during East-Asia winter monsoon. Journal of the Korean Earth Science Society, 28(5), 613-619. (in Korean) https://doi.org/10.5467/JKESS.2007.28.5.613
  13. Korea Energy Economic Institute, 2008, Planning research for adapting to new guideline of IPCC of the national greenhouse gases for the climate change adaptation-Report I, II. Korea Energy Economic Institute, 9-361. (in Korean)
  14. Lee, H.R., Kim, K.E., and Lee, Y.H., 2009, Numerical simulation of the effects of moisture on the reinforcement of a tropopause fold. Journal of the Korean Earth Science Society, 30(5), 630-645. (in Korean) https://doi.org/10.5467/JKESS.2009.30.5.630
  15. Liu, S.C., Trainer, M., Fehsenfeld, F.C., Parrish, D.D., Williams, E.J., Fahey, D.W., Huber, G., and Murphy, P.C., 1994, Ozone production in the rural troposphere and implications for regional ozone distributions. Journal of Geophysical Research, 92, 4191-4207.
  16. McKendry, I.G., 1993, Ground-level ozone in Montreal Canada. Atmospheric Environment. 27B(1), 93-103.
  17. Moon, Y.S., Lim, Y.K., and Lee K., 2011, An estimation of concentration of Asian dust (PM10) Using WRFSMOKE-CMAQ (MADRID) during springtime in the Korean peninsula. Journal of the Korean Earth Science Society, 32(3), 276-293. (in Korean) https://doi.org/10.5467/JKESS.2011.32.3.276
  18. National Center for Atmospheric Research, 1994, Terrain and Land Use for the Fifth-Generation Penn State/ NCAR Mesoscale Modeling System (MM5): Program TERRAIN: NCAR Technical Notes. NCAR/TN-397+IA.
  19. National Center for Atmospheric Research, 2003, Tutorial class notes and user's guide: MM5 Modeling System Version 3.
  20. Prather, M.J., 1996, Natural modes and time scales in atmospheric chemistry: theory, GWPs for $CH_4$ and CO, and run away growth. Geophysical Research Letters, 23, 2597-2600. https://doi.org/10.1029/96GL02371
  21. Seok, G.S., 2009, Study on the improvement method for improving of reliability of the GHG-CAPSS. National Institute of Environmental Research, 10-150. (in Korean)
  22. Umeda, T. and Martien, P.T., 2000, Evaluation of a data assimilation technique for a mesoscale meteorological model used for air quality modeling. Journal of Applied Meteorology, 41, 21-29
  23. Wild, O., Prather, M.J., and Akimoto, H., 2001, Indirect long term global radiative cooling from NOx emission. Geophysical Research Letters, 28(9), 1719-1722. https://doi.org/10.1029/2000GL012573

Cited by

  1. Analysis of Sensitivity to Prediction of Particulate Matters and Related Meteorological Fields Using the WRF-Chem Model during Asian Dust Episode Days vol.35, pp.1, 2014, https://doi.org/10.5467/JKESS.2014.35.1.1
  2. Estimation of Chemical Speciation and Temporal Allocation Factor of VOC and PM2.5 for the Weather-Air Quality Modeling in the Seoul Metropolitan Area vol.36, pp.1, 2015, https://doi.org/10.5467/JKESS.2015.36.1.36