Advertisement

Science China Earth Sciences

, Volume 56, Issue 8, pp 1422–1433 | Cite as

A method to estimate concentrations of surface-level particulate matter using satellite-based aerosol optical thickness

  • JinHua Tao
  • MeiGen Zhang
  • LiangFu Chen
  • ZiFeng Wang
  • Lin Su
  • Cui Ge
  • Xiao Han
  • MingMin Zou
Research Paper

Abstract

We propose a new method to estimate surface-level particulate matter (PM) concentrations by using satellite-retrieved Aerosol Optical Thickness (AOT). This method considers the distribution and variation of Planetary Boundary Layer (PBL) height and relative humidity (RH) at the regional scale. The method estimates surface-level particulate matter concentrations using the data simulated by an atmospheric boundary layer model RAMS and satellite-retrieved AOT. By incorporation MODIS AOT, PBL height and RH simulated by RAMS, this method is applied to estimate the surface-level PM2.5 concentrations in North China region. The result is evaluated by using 16 ground-based observations deployed in the research region, and the result shows a good agreement between estimated PM2.5 concentrations and observations, and the coefficient of determination R 2 is 0.61 between the estimated PM2.5 concentrations and the observations. In addition, surface-level PM2.5 concentrations are also estimated by using MODIS AOT, ground-based LIDAR observations and RH measurements. A comparison between the two estimated PM2.5 concentrations shows that the new method proposed in this paper is better than the traditional method. The coefficient of determination R 2 is improved from 0.32 to 0.62.

Keywords

planetary boundary layer model satellite remote sensing surface-level particulate matter aerosol optical thickness 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Li C C, Mao J T, Liu Q H, et al. Application of MODIS aerosol sroducts in the air pollution in Beijing research. Sci China Ser D-Earth Sci, 2005, 35(Suppl): 177–186Google Scholar
  2. 2.
    Li C C, Mao J T, Alexis K H L, et al. Research on the air pollution in Beijing and its surroundings with MODIS AOD Products. Chin J Atmos Sci, 2003, 27: 869–880Google Scholar
  3. 3.
    Chu D A, Kaufman Y J, Zibordi G, et al. Global monitoring of air pollution over land from the Earth Observing System-Terra Moderate Resolution Imaging Spectroradiometer (MODIS). J Geophys Res, 2003, 108: ACH4-1–ACH4-18Google Scholar
  4. 4.
    Slater J F, Dibb J E, Campbell J W, et al. Physical and chemical properties of surface and column aerosols at a rural New England site during MODIS overpass. Remote Sens Environ, 2004, 92: 173–180CrossRefGoogle Scholar
  5. 5.
    Engle-Cox J A, Holloman C H, Coutant B W. Qualitative and quantitative evaluation of MODIS satellite sensor data for regional and urban scale air quality. Atmos Environ, 2004, 38: 2495–2509CrossRefGoogle Scholar
  6. 6.
    Wang J, Christopher A. Intercomparison between satellite-derived aerosol optical thickness and PM2.5 mass: Implications for air quality studies. Geophys Res Lett, 2003, 30: 1–4Google Scholar
  7. 7.
    Gupta P, Christopher S A, Wang J. Satellite remote sensing of particulate matter and air quality assessment over global cities. Atmos Environ, 2006, 40: 5880–5892CrossRefGoogle Scholar
  8. 8.
    Li C C, Mao J T, Alexis K H L, et al. Remote sensing of high spatial resolution aerosol optical depth with MODIS data over Hong Kong. Chin J Atmos Sci, 2005, 29: 335–342Google Scholar
  9. 9.
    Koelemeijer R B A, Homan C D, Matthijsen J. Comparison of spatial and temporal variations of aerosol optical thickness and particulate matter over Europe. Atmos Environ, 2006, 40: 5304–5315CrossRefGoogle Scholar
  10. 10.
    Wang Z F, Chen L F, Tao J H, et al. Satellite-based estimation of regional particulate matter (PM) in Beijing using vertical-and-RH correcting method. Remote Sens Environ, 2010, 114: 50–63CrossRefGoogle Scholar
  11. 11.
    Engel-Cox J A, Hoff R M, Rogers R, et al. Integrating lidar and satellite optical depth with ambient monitoring for 3-dimensional particulate characterization. Atmos Environ, 2006, 40: 8056–8067CrossRefGoogle Scholar
  12. 12.
    Hutchison K D, Faruqui S J, Smith S. Improving correlations between MODIS aerosol optical thickness and ground-based PM2.5 observations through 3D spatial analyses. Atmos Environ, 2008, 42: 530–543CrossRefGoogle Scholar
  13. 13.
    Pelletier B, Sater R, Vidot J. Retrieving of particulate matter from optical measurement: A semiparametric approach. J Geophys Res, 2007, 112: 1–18Google Scholar
  14. 14.
    Pielke R A, Cotton W R, Walko R L, et a1. A comprehensive meteorological modeling system RAMS. Meteorol Atmos Phys, 1992, 49: 69–91CrossRefGoogle Scholar
  15. 15.
    Xue M, Droegemeier K K, Wong V. The advanced regional prediction system (ARPS)-A multiscale nonhydrostatic atmospheric simulation and prediction tool, part I: Model dynamics and verification. Meteorol Atmos Phys, 2000, 75: 161–193CrossRefGoogle Scholar
  16. 16.
    Fang X Y, Jiang W M, Miao S G, et a1. The multisale numerica1 modeling system for research on the relationship between urban planning and meteorological environment. Adv Atmos Sci, 2004, 21: 103–l12CrossRefGoogle Scholar
  17. 17.
    Xu X R. Physical Theory of Remote Sensing. Beijing: Peking University Press, 2005.298–299Google Scholar
  18. 18.
    Liou K N. An Introduction to Atmospheric Radiation. 2nd ed. Beijing: Meteorological Press, 2002. 31–32Google Scholar
  19. 19.
    Gerasopoulos E, Andreae M O, Zerefos C S, et a1. Climatological aspects of aerosol optical properties in Northern Greece. Atmos Chem Phys, 2003, 3: 2025–2041CrossRefGoogle Scholar
  20. 20.
    Liu X G. Monitoring and modelling research on the aerosol hygroscopicity—Taking Beijing, Pearl River Delta for example. Dissertation for the Doctoral Degree. Beijing: Peking University, 2008. 67–121Google Scholar
  21. 21.
    Pan X L. Observation Study of Atmospheric Aerosol Scattering Characteristics as a Function of Relative Humidity. Beijing: Chinese Academy of Meteorological Sciences, 2007. 3–4Google Scholar
  22. 22.
    Malm W C, Day D E, Kreidenweis S M. Light scattering characteristics of aerosol as a function of relative humidity: Part I—A comparison of measured scattering and aerosol concentrations using the theoretical models. Technical Report. J Air Waste Manage Assoc, 2000Google Scholar
  23. 23.
    Zhang L S, Shi G Y. The impact of relative humidity on the radiative property and radiative forcing of sulfate aerosol. Acta Meteorol Sin, 2002, 60: 230–237Google Scholar
  24. 24.
    Kotchenruther R A, Hobbs P V, Hegg D A. Humidification factors for atmospheric aerosol off the mid-Atlantic coast of the United States. J Geophys Res, 1999, 1043: 2239–22511CrossRefGoogle Scholar
  25. 25.
    Kotchenruther R A, Hobbs P V. Humidification factors of aerosols from biomass burning in Brazil. J Geophys Res, 1998, 103: 32081–32089CrossRefGoogle Scholar
  26. 26.
    Im J, Saxena V K, Wenny B N, et al. An assessment of hygroscopic growth factors for aerosols in the surface boundary layer for computing direct radiative forcing. J Geophys Res, 2001, 106: 20213–20224CrossRefGoogle Scholar
  27. 27.
    Magi B I, Hobbs P V. Effects of humidity on aerosols in southern Africa during the biomass burning season. J Geophys Res, 2003, 108, doi: 10.1029/2002JD002144Google Scholar
  28. 28.
    Song C H, Park M E, Lee K H, et al. An investigation into seasonal and regional aerosol characteristics in East Asia using model-predicted and remotely-sensed aerosols. Atmos Chem Phys, 2008, 8: 6627–6654CrossRefGoogle Scholar
  29. 29.
    He X, Deng Z Z, Li C C. Application of MODIS AOD in surface PM10 evaluation. Acta Sci Nat Univ Pekinensis, 2010, 46: 178–184Google Scholar
  30. 30.
    Wang Z F. research in estimating PM concentration using satellite remote sensing. Dissertation for the Doctoral Degree. Beijing: Institute of Remote Sensing Applications of Chinese Academy of Sciences, 2010. 85–86Google Scholar
  31. 31.
    Hansen J E, Travis L D. Light scattering in planetary atmospheres. Space Sci Rev, 1974, 16: 527–610CrossRefGoogle Scholar
  32. 32.
    Levy R C, Remer L A, Kleidman R G. Global evaluation of the Collection 5 MODIS dark-target aerosol products over land. Atmos Chem Phys, 2010, 10: 10399–10420CrossRefGoogle Scholar
  33. 33.
    Zhang M G. A multi-scale air quantity modeling system and its evaluation L introduction to the model system and simulation of Meteorological parameters. Chin J Atmos Sci, 2005, 29: 805–813Google Scholar
  34. 34.
    Gao Y, Zhang M G, Zhu L Y. Numerical analysis of atmospheric O3 concentrations over Beijing during the 2008 Olympic Games. Clim Environ Res, 2010, 15: 643–651Google Scholar

Copyright information

© Science China Press and Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • JinHua Tao
    • 1
    • 2
  • MeiGen Zhang
    • 1
  • LiangFu Chen
    • 2
  • ZiFeng Wang
    • 2
  • Lin Su
    • 2
  • Cui Ge
    • 1
  • Xiao Han
    • 1
  • MingMin Zou
    • 2
  1. 1.State Key Laboratory of Atmospheric Boundary Layer Physics and Atmospheric Chemistry (LAPC), Institute of Atmospheric PhysicsChinese Academy of SciencesBeijingChina
  2. 2.State Key Laboratory of Remote Sensing ScienceJointly Sponsored by Institute of Remote Sensing Applications of Chinese Academy of Sciences and Beijing Normal UniversityBeijingChina

Personalised recommendations