Journal of Korean Society for Atmospheric Environment (한국대기환경학회지)

Korean Society for Atmospheric Environment (한국대기환경학회)
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Application of MODIS Satellite Observation Data for Air Quality Forecast

MODIS 인공위성 관측 자료를 이용한 대기질 예측 응용

  • Lee, Kwon-Ho (Advanced Environmental Monitoring Research Center (ADEMRC) Department of Environmental Science & Engineering, Gwangju Institute of Science & Technology (GIST)) ;
  • Lee, Dong-Ha (Advanced Environmental Monitoring Research Center (ADEMRC) Department of Environmental Science & Engineering, Gwangju Institute of Science & Technology (GIST)) ;
  • Kim, Young-Joon (Advanced Environmental Monitoring Research Center (ADEMRC) Department of Environmental Science & Engineering, Gwangju Institute of Science & Technology (GIST))
  • 이권호 (광주과학기술원 환경공학과.환경모니터링신기술 연구센터) ;
  • 이동하 (광주과학기술원 환경공학과.환경모니터링신기술 연구센터) ;
  • 김영준 (광주과학기술원 환경공학과.환경모니터링신기술 연구센터)
  • Published : 2006.12.31
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Abstract

Satellites have been valuable tool for global/regional scale atmospheric environment monitoring as well as emission source detection. In this study, we present the results of application of satellite remote sensing data for air quality forecast in Seoul metropolitan area. AOT (Aerosol Optical Thickness) data from TERRA/MODIS (Moderate Resolution Imaging Spectre-radiometer) satellite were compared to ground based $PM_{10}$ mass concentrations, and used to estimate the possibility of the aerosol forecasting in Seoul metropolitan area. Although correlation coefficient (${\sim}0.37$) between MODIS AOT products and surface $PM_{10}$ concentration data was relatively low, there was good correlation between MODIS AOT and surface PM concentration under certain atmospheric conditions, which supports the feasibility of using the high-resolution MODIS AOT for air quality forecasting. The MODIS AOT data with trajectory forecasts also can provide information on aerosol concentration trend. The success rate of the 24 hour aerosol concentration trend forecast result was about 75% in this study. Finally, application of satellite remote sensing data with ground-based air quality observations could provide promising results for air quality monitoring and more exact trend forecast methodology by high resolution satellite data and verification with long term measurement dataset.

Keywords

References

  1. Burrows, J.P. (1999) Current and future passive remote sensing techniques used to determine atmospheric constituents. In A.F. Bouwman, editor, Approaches to scaling of trace gas fluxes in ecosystems. Amsterdam: Elsevier, 317-347
  2. Chu, A.D., Y.J. Kaufman, C. Ichoku, L.A. Remer, D. Tanre, and B.N. Holben (2002) Validation of MODIS aerosol optical depth retrieval over land, Geophys. Research Ltr., 29, 10.1029/2001GL013205
  3. Draxler, R.R. and G.D. Rolph (2003) HYSPLIT (HYbrid Single-Particle Lagrangian Integrated Trajectory) Model access via NOAA ARL READY Website (http://www.arl.noaa.gov/ready/hysplit4.html) NOAA Air Resources Laboratory, Silver Spring, MD
  4. Engel-Cox, J.A., C.H. Holloman, B.W. Coutant, and R.M. Hoff (2004b) Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality, Atmos. Environ., 38 (16), 2495-2509 https://doi.org/10.1016/j.atmosenv.2004.01.039
  5. Engel-Cox, J.A., R.M. Hoff, and A. Haymet (2004a) Recommendations on the Use of Satellite Remote-Sensing Data for Urban Air Quality, J. of Air and Waste Management, 54, 1360-1371 https://doi.org/10.1080/10473289.2004.10471005
  6. Hutchison, K.D. (2003) Applications of MODIS satellite data and products for monitoring air quality in the state of Texas, Atmos. Environ., 37(17), 2403-2412 https://doi.org/10.1016/S1352-2310(03)00128-6
  7. Hutchison, K.D., S. Smith, and S. Faruqui (2004) The use of MODIS data and aerosol products for air quality prediction, Atmos. Environ., 38(30), 5057-5070 https://doi.org/10.1016/j.atmosenv.2004.06.032
  8. Kaufman, Y.J. and R.S. Fraser (1983) Light Extinction by Aerosols During Summer Air Pollution, J. Appl. Meteor., 22, 1694-1706 https://doi.org/10.1175/1520-0450(1983)022<1694:LEBADS>2.0.CO;2
  9. Kaufman, Y.J., D. Tanre, L.A. Remer, E.F. Vermote, D.A. Chu, and B.N. Holben (1997) Operational remote sensing of tropospheric aerosol over the land from EOS-MODIS, J. Geophys. Res., 102, 17,051-17,061 https://doi.org/10.1029/96JA02043
  10. King, M.D., Y.J. Kaufman, D. Tanre, and T. Nakajima (1999) Remote sensing of tropospheric aerosols from space: Past, present, and future, Bull. Am. Meteorol. Soc., 80, 2229-2259 https://doi.org/10.1175/1520-0477(1999)080<2229:RSOTAF>2.0.CO;2
  11. Kittaka, C., J. Szykman, B. Pierce, J. Al-Saadi, D. Neil, A. Chu, L. Remer, E. Prins, and J. Holdzkom (2004) Utilizing MODIS Satellite Observations to Monitor and Analyze Fine Particulate Matter, $PM_{2.5}$, Transport Event. Sixth Conference on Atmospheric Chemistry: Air Quality in Megacities, Seattle, Washington, American Meteorological Society
  12. Lee, K.H., Y.J. Kim, W. von Hoyningen-Huene, and J.P. Burrows (2006) Characteristics of aerosol observed during two severe haze events over Korea in June and October 2006. Atmos. Environ., accepted
  13. Malm, W.C., J.F. Sisler, D. Huffman, R.A. Eldred, and T.A. Cahill (1994) Spatial and seasonal trends in particle concentration and optical extinction in the United States, J. Geophys. Res., 99, 1357-1370
  14. Ramon, D., R. Santer, and J. Vidot (2003) Determination of fine particulate matter from MERIS and SeaWiFS aerosol data, Proceeding of 'MERIS users Workshop', ESA-ESRIN, Frascati, Italy, 10-13 November
  15. Remer, L.A., D. Tanre, Y.J. Kaufman, C. Ichoku, 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, Geophys. Research Lett., 29(12), 10.1029/2001GL013204
  16. Rolph, G.D. (2003) Real-time Environmental Applications and Display System (READY) Website (http://www.arl.noaa.gov/ready/hysplit4.html). NOAA Air Resources Laboratory, Silver Spring, MD
  17. Schwartz, S.E. (1996) The white house effect: Shortwave radiative forcing of climate by anthropogenic aerosols: An overview, J. Aerosol Sci., 27(3), 359-382 https://doi.org/10.1016/0021-8502(95)00533-1
  18. Smirnov, A., B.N. Holben, D. Savoie, J.M. Prospero, Y.J. Kaufman, D. Tanre, T.F. Eck, and I. Slutsker (2000) Relationship between column aerosol optical thickness and in situ ground based dust concentrations over Barbados, Geophys. Res. Lett., 27, 1643-1646 https://doi.org/10.1029/1999GL011336
  19. Stowe, L., A. Ignatov, and R. Singh (1997) Development, validation, and potential enhancements to the second-generation operational aerosol product at the National Environmental Satellite, Data, and Information Service of the National Oceanic and Atmospheric Administration, J. Geophys. Res., 102 (D14), 16,923-16,934 https://doi.org/10.1029/96JD02132
  20. Szykman, J., J. White, B. Pierce, J. Al-Saadi, D. Neil, C. Kittaka, A. Chu, L. Remer, L. Gumley, and E. Prins (2004) Utilizing MODIS Satellite Observations in Near-real-time to Improve AIRNow Next Day Forecast of Fine Particulate Matter, PM2.5. Sixth Conference on Atmospheric Chemistry: Air Quality in Megacities, Seattle, Washington, American Meteorological Society
  21. Tanre, D., Y.J. Kaufman, M. Herman, and S. Mattoo (1997) Remote sensing of aerosol properties over oceans using the MODIS/EOS spectral radiances, J. Geophys. Res., 102, 16,971-16,988 https://doi.org/10.1029/96JD03437
  22. Wang, J. and S.A. Christopher (2003) Intercomparison between satellite-derived aerosol optical thickness and PM2.5 mass: Implications for air quality studies, Geophys. Res. Lett., 30(21), 2095, doi:10.1029/2003GL018174