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Spatial Analysis of Major Atmospheric Aerosol Species Using Earth Observing Satellite Data

지구관측 위성자료를 이용한 주요 대기 에어러솔 성분의 공간분포 분석

  • Lee, Kwon-Ho (Dept. of Satellite Geoinfomatics Engineering, Kyungil University)
  • 이권호 (경일대학교 위성정보공학과)
  • Received : 2011.03.31
  • Accepted : 2011.05.20
  • Published : 2011.06.30

Abstract

Atmospheric aerosols, small particles in the atmosphere, are one of the important parameters in climate change and human health. Additionally, accurate estimates of aerosol species are increasingly important in environmental impact assessment studies. Recent advances in global satellite remote sensing provide powerful tool for air quality monitoring. This study explores the potential usage of satellite derived data such as atmospheric aerosols for air quality monitoring as well as climate change study. The objectives of this study is to understand the general features of the global distribution of type dependent aerosols. A detailed spatio-temporal variability of the each different satellite dataset shows the variation of the global zonal average and specific geographical regions where the strong emission sources are located. Especially, significantly large aerosol amounts are observed in Asia and Africa because of the desert dust storm, anthropogenic and biomass burning emissions.

대기 에어러솔은 대기중에 존재하는 미세입자로 인체 유해성을 지니고 있으며 기후변화에 있어서도 중요한 역할을 하는 변수로 알려져 있다. 게다가 대기 에어러솔의 주요 성분에 대한 정확한 평가는 환경에 미치는 영향에 있어서도 매우 중요하다. 이 때문에 지구관측 위성자료는 대기질 감시측면에서도 효과적인 수단으로 이용되고 있다. 따라서 본 연구에서는 인공위성 관측 자료를 이용하여 전구적인 규모의 대기 에어러솔의 주요 성분별 분포변화를 알아보고자 MODIS 에어러솔 광학두께(AOT; Aerosol Optical Thickness)와 미세입자분율(FMF; Fine Mode Fraction), 그리고 TOMS 자외선 흡수성 에어러솔 지수(AAI; UV Absorbing Aerosol Index)를 이용하여 네 가지의 주요 에어러솔의 성분(먼지, 탄소성, 황산화물, 해염)을 구분할 수 있는 방법을 제시하였다. 여기서 얻어진 결과물을 검증하기 위하여 에어러솔 예측 모델자료와 각 격자별 자료로 선형회귀분석을 수행한 결과, 본 연구에서 산출된 결과물이 에어러솔의 성분별 전반적인 패턴을 잘 표현하는 것을 확인 할 수 있었다. 이렇게 주요 에어러솔 타입으로 구분된 위성자료의 사용은 대기질 감시뿐만 아니라 기후변화연구에 있어서도 도움을 줄 것으로 사료된다.

Keywords

References

  1. 이권호, 김정은, 김영준, 서애숙, 안명환. 2002. GMS-5 인공위성 원격탐사 자료를 이용한 대기 에어러솔 모니터링. 한국지리정보학회지 5(2):1-15.
  2. 이동하, 이권호, 김영준. 2006. 에어로졸 종류 구분을 위한 MODIS 에어로졸 자료의 적용. 대한원격탐사학회지 22(6):496-505. https://doi.org/10.7780/kjrs.2006.22.6.495
  3. Andres, R.J., and A.D. Kasgnoc. 1998. A time-averaged inventory of subaerial volcanic sulfur emissions. J. Geophys. Res., 103:25251-25261. https://doi.org/10.1029/98JD02091
  4. Barnaba, F. and G.P. Gobbi. 2004. 2004. Aerosol seasonal variability over the Mediterranean region and relative impact of maritime, continental and Saharan dust particles over the basin from MODIS data in the year 2001. Atmospheric Chemistry and Physics, 4: 2367-2391. https://doi.org/10.5194/acp-4-2367-2004
  5. Chin, M., P. Ginoux, S. Kinne, B.N. Holben, B.N. Duncan, R.V. Martin, J.A. Logan, A. Higurashi, and T. Nakajima. 2002. Tropospheric aerosol optical thickness fromt he GOCART model and comparisons with satellite and sunphotometer measurements, J. Atmos. Sci. 59:461-483. https://doi.org/10.1175/1520-0469(2002)059<0461:TAOTFT>2.0.CO;2
  6. Chin, M., R.B. Rood, S.-J. Lin, J.F. Muller, and A.M. Thomspon. 2000. Atmospheric sulfur cycle in the global model GOCART: Model description and global roperties, J. Geophys. Res., 105:24,671-24,687. https://doi.org/10.1029/2000JD900384
  7. Cook, C. Liousse, H. Cachier, and J. Feichter. 1999. Construction of a fossil fuel emission data set for carbonaceous aerosol and implementation and radiative impact in the ECHAM4 model. J. Geophys. Res., 104;22137-22162. https://doi.org/10.1029/1999JD900187
  8. Dubovik, O., B.N. Holben, T.F. Eck, A. Smirnov, Y.J. Kaufman, M.D. King, D. Tanre, and I. Slutsker, 2002. Variability of absorption and optical properties of key aerosol types observed in worldwide locations, J. Atm. Sci., 59: 590-608. https://doi.org/10.1175/1520-0469(2002)059<0590:VOAAOP>2.0.CO;2
  9. Duncan, B.N., V.M. Randall, A.C. Staudt, R. Yevich and J.A. logan. 2003. Interannual and seasonal variability of biomass burning emissions constrained by satellite observations, Journal of Geophysical Research, 108, 4040, doi:10.129/2002JD002378.
  10. Glaccum W., R. Lucke, R.M. Bevilacqua, E.P. Shettle, J.S. Hornstein, D.T. Chen, J.D. Lumpe, S.S. Krigman, D.J. Debrestian, M.D. Fromm, F. Dalaudier, E. Chassefiere, C. Deniel, C.E. Raneall, D.W. Rusch, J.J. Olivero, C. Brogniez, J. Lenoble, and R. Kremer. 1996. The Polar Ozone and Aerosol Measurement instrument. J. Geophys. Res. 101: 14479-14487. https://doi.org/10.1029/96JD00576
  11. Gordon H.R. and M. Wang. 1994. Retrieval of water-leaving radiance and aerosol optical thickness over the oceans with SeaWiFS: A preliminary algorithm, Appl. Opt. 33:443-452. https://doi.org/10.1364/AO.33.000443
  12. Guenther, A., C.N. Hewitt, D. Erickson, R. Fall, C. Geron, T. Graedel, P. Harley, L. Klinger, M. Lerdau, W.A. Mckay, T. Pierce, B. Scholes, R. Steinbrecher, R. Tallamraju, J. Taylor, and P. Zimmerman. 1995. A global model of natural volatile organic compound emissions. J. Geophys. Res., 100:8873-8892. https://doi.org/10.1029/94JD02950
  13. Helfand, H.M. and J.C. and J.C. Labraga. 1988. Design of a nonsingular level 2.5 second-order closure model for the prediction of atmospheric turbulence. J. Atmos. Sci., 45:113-132. https://doi.org/10.1175/1520-0469(1988)045<0113:DOANLS>2.0.CO;2
  14. Herman, J.R., P.K. Bhartia, O. Torres, C. Hsu, C. Seftor and E. Celarier. 1997. Global distribution of UV-absorbing aerosols from Nimbus 7/TOMS data. J. Geophys. Res. 102:16911-16922. https://doi.org/10.1029/96JD03680
  15. Higurashi, A. and T. Nakajima. 2002. Detection of aerosol types over the East China Sea near Japanfrom four-channel satellite data, Geophys. Res. Lett., 29(17), 1836, doi:10.1029/2002GL015357.
  16. Hsu, N.C., J.R. Herman, O. Torres, B.N. Holben, D. Tanre, T.F. Eck, A. Smirnov, B. Chatenet, and F. Lavenu. 1999. Comparison of the TOMS aerosol Index with Sun-photometer aerosol optical thickness: Results and application. J. Geophy. Res., 23:745-748.
  17. IPCC. 2007. Climate Change: The Science Basis, Cambridge Univ. Press, New York.
  18. Jeong, M.-J., and Z. Li. 2005. Quality, compatibility, and synergy analyses of global aerosol products derived from the advanced very high resolution radiometer and Total Ozone Mapping Spectrometer, J. Geophys. Res., 110, D10S08, doi;10.1029/2004JD004647
  19. Kaskaoutis, D.G., H.D. Kambezidis, N. Hatzianastassiou, P.G. Kosmopoulos, K.V. Badarinath. 2007a. Aerosolclimatology: on the discrimination of aerosol types over four AERONET sites Atmospheric Chemistry and Physics Discussions, 7:6357-6411. https://doi.org/10.5194/acpd-7-6357-2007
  20. Kaskaoutis, D.G., P. Kosmopoulos, H.D. Kambezidis and P.T. Nastos. 2007b. Aerosol climatology and discrimination of different types over Athens, Greece, based on MODIS data, Atmospheric Environment, 41(34):7315-7329. https://doi.org/10.1016/j.atmosenv.2007.05.017
  21. Knapp, K.R. and L.L. Stowe. 2002. Evaluating the Potential for Retrieving Aerosol Optical Depth over Land from AVHRR Pathfinder Atmosphere Data, J. Atmos. Sci., 59(3):279-293. https://doi.org/10.1175/1520-0469(2002)059<0279:ETPFRA>2.0.CO;2
  22. Lee, K.H., Y.J. Kim, W. von Hoyningen-Huene, and J.P. Burrows. 2007. Spatio-Temporal Variability of Atmospheric Aerosol from MODIS data over Northeast Asia in 2004. Atmos. Environ. 41(19):3959-3973. doi:10.1016/j.atmosenv.2007.01.048.
  23. Lee, K.H., Z. Li, Y.J. Kim, A. Kokhanovshy. 2009. Aerosol monitoring from satellite observation: a history of three decades, Atmospheric and Biological Environmental Monitoring, YJ Kim, U. Platt, MB Gu, H Iwahashi(Eds.), Springer, doi:10.1007/978-1-4020-9674-7_2, pp. 13-38.
  24. Lee, K.H., Y.J.Kim. 2010. Satellite remote sensing of Asian aerosols:a case study of clean, polluted and dust storm days, Atmos. Meas. Tech., 3:1771-1784, doi:10.5194/amt-3-1771-2010.
  25. Levy, R.C., L.A. Remer, and O. Dubovik. 2007a. Global aerosol optical properties and application to Moderate Resolution Imaging Spectroradiometer aerosol retrieval over land, J. Geophys. Res., 112, D13210, doi;10.1029/2006JD007815.
  26. Levy, R.C., L. Remer, S. Mattoo, E. Vermote, and Y.J. Kaufman. 2007b. A second-generation algorithm for retrieving aerosol properties over land from MODIS spectral reflectance, J. Geophys. RES., 112, D13211.
  27. Lin, S.-J. and R.B. Rood. 1996. Multidimensional flux-form semi-Lagrangian transport schemes. Mon. Wea. Rev., 124:2046-2070. https://doi.org/10.1175/1520-0493(1996)124<2046:MFFSLT>2.0.CO;2
  28. Liss, P.S., and L. Merlivat. 1986. Air-seagas exchange rates: Introduction and synthesis. The Role of Air-Sea Exchange in Geochemical Cycling, P. Buat-Me' nard, Ed., D. Riedel, pp.113-127.
  29. Martonchik, J.V. and D.J. Diner. 1992. Retrieval of aefosol and land surface optical properties from multi-angle satellite imagery. IEEE Trans. Geosci. Remote Sens. 30: 223-230. https://doi.org/10.1109/36.134073
  30. McCormick, M.P., D.M. Winker, E.V. Browell, J.A. Coakley, C.S. Gardner, R.M. Hoff, G.S. Kent, S.H. Melfi, R.T. Menzies, C.M.R. Menzies, D.A. Randall, and J.A. Reagan. 1993. Scientific investigations planned for the Lidar In-space Technology Experiment(LITE). Bull. Amer. Meteorol. Soc. 74(2):205-214. https://doi.org/10.1175/1520-0477(1993)074<0205:SIPFTL>2.0.CO;2
  31. Muller, J.F., and G. Braseur. 1995. IMAGES: A three-dimensional chemical transport model of the global troposphere. J. Geophys. Res., 100:16445-16490. https://doi.org/10.1029/94JD03254
  32. Olivier, J. G. J. et. al. 1996. Description of EDGAR version 2.0: A set of global emission inventories of greenhouse gases and ozone-depleting substances for all anthropogenic and most natural sources on a per country basis and on 18 3 18 grid. RIVM/TNO Rep. 771060-002. 140 pp.
  33. Omar, A.H., J.-G. Won, D. M. Winker, S.-C. Yoon, O. Dubovik, and M.P. McCormick. 2005. Development of global aerosol models using cluster analysis of Aerosol Robotic Network (AERONET) measurements, J. Geophys. Res., 110, D10S14, doi:10.1029/2004JD004874.
  34. Rao, C.R.N, E.P. McClain, and L.L. Stowe. 1989. Remote sensing of aerosols over the oceans using AVHRR data theory, practice, and applications. Int. J. Remote Sens. 10(4-5):743-749. https://doi.org/10.1080/01431168908903915
  35. Remer, L.A. and Y.J. Kaufman. 1998. Dynamic aerosol model: Urban/industrial aerosol, J. Geophys. Res., 103(D12):13,859-13,872. https://doi.org/10.1029/98JD00994
  36. Remer, L.A., Y.J. Kaufman, B.N. Holben, A.M. Thompson, and D. McNamara. 1998. Biomass burning aerosol size distribution and modeled optical properties, J. Geophys. Res., 103(D24):31,879-31,892. https://doi.org/10.1029/98JD00271
  37. Remer, L.A., D. Tanre, Y.J. Kaufman, C. Inhoku, S. Mattoo, R. Levy, D.A. Chu, B. Holben, O. Dubovik, A. Smirnov, J.V. Martins, R.R. Li, and Z. Ahmad. 2002. Validation of MODIS aerosol retrieval over ocean. Geophysical Research Letters, 29(12):321-324. https://doi.org/10.1029/2002GL015098
  38. Remer, L.A. Y.J. Kaufman, D. Tanre, S. Mattoo, D.A. Chu, J.V. Martins, R.R. Li, C. Ichoku, R.C. Levy, R.G. Kleidman, T.F. Eck, E. Vermote, and B.N. Holben. 2005. The MODIS Aerosol Algorithm, Products and Validation, J. Atmos. Sci., 62:947-973. https://doi.org/10.1175/JAS3385.1
  39. Stowe, L.L. 1991. Cloud and aerosol products at NOAA/NESDIS, Paleogeogr. Paleoclimatol. Paleoecol. 90:25-32.
  40. Wesely, M.L. 1989. Parameterization of surface resistance to gaseous dry deposition in regional-scale numerical models. Atmos. Environ., 23:1293-1304. https://doi.org/10.1016/0004-6981(89)90153-4
  41. Yevich, R., and J.A. Logan. 2003. An assessment of biofuel use and burning of agricultural waste in the developing world, Global Biogeochem. Cycles, 17(4), 1095, doi:10.1029/2002GB001952.

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