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Impact of Emissions from Major Point Sources in Chungcheongnam-do on Surface Fine Particulate Matter Concentration in the Surrounding Area

충남지역 대형 점오염원이 주변지역 초미세먼지 농도에 미치는 영향

  • Kim, Soontae (Department of Environment & Safety Engineering, Ajou University) ;
  • Kim, Okgil (Department of Environment & Safety Engineering, Ajou University) ;
  • Kim, Byeong-Uk (Georgia Environmental Protection Division) ;
  • Kim, Hyun Cheol (NOAA/Air Resources Laboratory, College Park)
  • 김순태 (아주대학교 환경안전공학과) ;
  • 김옥길 (아주대학교 환경안전공학과) ;
  • 김병욱 (미국조지아주환경청) ;
  • 김현철 (미국국립해양대기청)
  • Received : 2017.02.03
  • Accepted : 2017.03.18
  • Published : 2017.04.30

Abstract

The Weather Research and Forecast (WRF) - Community Multiscale Air Quality (CMAQ) system was applied to investigate the influence of major point sources located in Chungcheongnam-do (CN) on surface $PM_{2.5}$ (Particulate Matter of which diameter is $2.5{\mu}m$ or less) concentrations in its surrounding areas. Uncertainties associated with contribution estimations were examined through cross-comparison of modeling results using various combinations of model inputs and setups; two meteorological datasets developed with WRF for 2010 and 2014, and two domestic emission inventories for 2010 and 2013 were used to estimate contributions of major point sources in CN. The results show that contributions of major point sources in CN to annual $PM_{2.5}$ concentrations over Seoul, Incheon, Gyeonggi, and CN ranged $0.51{\sim}1.63{\mu}g/m^3$, $0.71{\sim}1.62{\mu}g/m^3$, $0.63{\sim}1.66{\mu}g/m^3$, and $1.04{\sim}1.86{\mu}g/m^3$, respectively, depending on meteorology and emission inventory choice. It indicates that the contributions over the surrounding areas can be affected by model inputs significantly. Nitrate was the most dominant $PM_{2.5}$ component that was increased by major point sources in CN followed by sulfate, ammonium, and others. Based on the model simulations, it was estimated that primary $PM_{2.5}$ $(PPM)-to-PM_{2.5}$ conversion rates were 41.3~50.7 ($10^{-6}{\mu}g/m^3/TPY$) for CN, and 12.4~18.3 ($10^{-6}{\mu}g/m^3/TPY$) for Seoul, Incheon, and Gyeonggi, respectively. In addition, spatial gradients of PPM contributions show very steep trends. $NO_X$-to-nitrate conversion rates were 7.61~12.3 ($10^{-6}{\mu}g/m^3/TPY$) for CN, and 3.94~11.3 ($10^{-6}{\mu}g/m^3/TPY$) for the sub-regions in the SMA. $SO_2$-to-sulfate conversion rates were 4.04~5.28 ($10^{-6}{\mu}g/m^3/TPY$) for CN, and 3.73~4.43 ($10^{-6}{\mu}g/m^3/TPY$) for the SMA, respectively.

Keywords

References

  1. Anderson, J.O., J.G. Thundiyil, and A. Stolbach (2012) Clearing the air: a review of the effects of particulate matter air pollution on human health. Journal of Medical Toxicology, 8(2), 166-175. https://doi.org/10.1007/s13181-011-0203-1
  2. Bartnicki, J. (1999) Computing source-receptor matrices with the EMEP Eulerian Acid Deposition Model. EMEP MSC-W Note, 5, 99.
  3. Benjey, W., M. Houyoux, and J. Susick (2001) Implementation of the SMOKE emissions data processor and SMOKE tool input data processor in Models-3. U.S. EPA.
  4. Board of Audit and Inspection of Korea (2016) Audit Report: Air Quality Improvement Project Status over Seoul Metropolitan Area. https://www.bai.go.kr/bai/cop/bbs (accessed December 5, 2016)
  5. Boylan, J. and B.U. Kim (2012) Development of $PM_{2.5}$ interpollutant trading ratio. Presented at 2012 Community Modeling & Analysis System (CMAS) Conference, Chapel Hill, NC, October 16, 2012.
  6. Byun, D.W. and J.K.S. Ching (1999) Science algorithms of the EPA Models-3 community multiscale air quality (CMAQ) modeling system. Washington, DC, USA: US Environmental Protection Agency, Office of Research and Development.
  7. Carter, W.P.L. (2000) Implementation of the SAPRC-99 chemical mechanism in the models-3 framework.
  8. Cohan, D.S. (2004) Applicability of CMAQ-DDM to Source Apportionment and Control Strategy Development. In 3rd Annual CMAS Models-3 Users' Conference, RTP, NC.
  9. ENVIRON International Corporation (2014) User's guide to the Comprehensive Air Quality Model with Extensions Version 6.1. http://www.camx.com/files/camxusersguide_v6-10.pdf (accessed 2016.12.07.)
  10. GREENPEACE (2016) Silent Killer Fine Particulate Matter-The health impacts of current and planned coal-fired power generation in South Korea and related current policy.
  11. 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 Discussions, 6(1), 107-173. https://doi.org/10.5194/acpd-6-107-2006
  12. He, Z., Y.J. Kim, K.O. Ogunjobi, and C.S. Hong (2003) Characteristics of $PM_{2.5}$ species and long-range transport of air masses at Taean background station, South Korea, Atmospheric Environment, 37(2), 219-230. https://doi.org/10.1016/S1352-2310(02)00834-8
  13. Kim, B.U., O. Kim, H.C. Kim, and S. Kim (2016a) Influence of fossil-fuel power plant emissions on the surface fine particulate matter in the Seoul Capital Area, South Korea. Journal of the Air and Waste Management Association, 66(9), 863-873. https://doi.org/10.1080/10962247.2016.1175392
  14. Kim, J.H., D.R. Choi, Y.S. Koo, J.B. Lee, and H.J. Park (2016c) Analysis of Domestic and Foreign Contributions using DDM in CMAQ during Particulate Matter Episode Period of February 2014 in Seoul, Journal of Korean Society for Atmospheric Environment, 32(1), 82-99. (in Korean with English abstract) https://doi.org/10.5572/KOSAE.2016.32.1.082
  15. Kim, K.H., E. Kabir, and S. Kabir (2015) A review on the human health impact of airborne particulate matter, Environment international, 74, 136-143. https://doi.org/10.1016/j.envint.2014.10.005
  16. Kim, S. (2011) Ozone simulations over the Seoul Metropolitan Area for a 2007 June episode, Part I: evaluating volatile organic compounds emissions speciated for the SAPRC99 chemical mechanism. Journal of Korean Society for Atmospheric Environment, 27(5), 580-602. (in Korean with English abstract) https://doi.org/10.5572/KOSAE.2011.27.5.580
  17. Kim, S., C.H. Bae, H.C. Kim, and B.U. Kim (2017) $PM_{2.5}$ Simulations in the Seoul Metropolitan Area: (I) Model Contributions of Precursor Emissions in the CAPSS Emissions Inventory. Journal of Korean Society for Atmospheric Environment, In Press. (in Korean with English abstract)
  18. Kim, S., H.C. Kim, B.U. Kim, C.H. Bae, E.H. Kim, S.H. You, M.A. Bae, O.G. Kim, and C.W. Park (2016b) CMAQ Simulation Study to Analyze the Long-term Variations of Criteria Air Pollutants in the Seoul Metropolitan Area during 2004-2015, 17th IUAPPA World Clean Air Congress and 9th CAA Better Air Quality Conference Clean Air for Cities Perspectives and Solutions.
  19. Kim, Y.P. (2006) Air Pollution in Seoul Caused by Aerosols, Journal of Korean Society for Atmospheric Environment, 22(5), 535-553. (In Korean with English abstract)
  20. Lee, J.Y. and Y.P. Kim (2007) Source apportionment of the particulate PAHs at Seoul, Korea: impact of long range transport to a megacity, Atmospheric Chemistry and Physics, 7(13), 3587-3596. https://doi.org/10.5194/acp-7-3587-2007
  21. Leem, J.H., S. Kim, and H.C. Kim (2015) Public-health impact of outdoor air pollution for 2nd air pollution management policy in Seoul metropolitan area, Korea, Annals of occupational and environmental medicine, 27(1), 1. https://doi.org/10.1186/s40557-014-0044-x
  22. Ministry of Environment (2016) Fine dust countermeasure plan. http://www.me.go.kr/issue/finedust2 (accessed December 5, 2016).
  23. Ristovski, Z.D., B. Miljevic, N.C. Surawski, L. Morawska, K.M. Fong, F. Goh, and I.A. Yang (2012) Respiratory health effects of diesel particulate matter, Respirology, 17(2), 201-212. https://doi.org/10.1111/j.1440-1843.2011.02109.x
  24. 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 pp.

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