предыдущий|следующий

Advance in the remote sensing of atmospheric aerosol composition

  • role： First author第一作者
  • Affiliation：

    State Environmental Protection Key Laboratory of Satellite Remote Sensing, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

  • Email：lizq@radi.ac.cn
  • Introduction：李正强，1977年生，男，研究员，研究方向为气溶胶遥感。E-mail：lizq@radi.ac.cn
Zhengqiang LI,  
  • role： Corresponding author通信作者
  • Affiliation：

    State Environmental Protection Key Laboratory of Satellite Remote Sensing, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

  • Email：xieys@radi.ac.cn
  • Introduction：谢一凇，1986年生，男，助理研究员，研究方向为气溶胶成分遥感。E-mail：xieys@radi.ac.cn
Yisong XIE*,  
  • Affiliation：

    State Environmental Protection Key Laboratory of Satellite Remote Sensing, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

Ying ZHANG,  
  • Affiliation：

    State Environmental Protection Key Laboratory of Satellite Remote Sensing, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

Lei LI,  
  • Affiliation：

    State Environmental Protection Key Laboratory of Satellite Remote Sensing, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

Hua XU,  
  • Affiliation：

    State Environmental Protection Key Laboratory of Satellite Remote Sensing, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

Kaitao LI,  
  • Affiliation：

    State Environmental Protection Key Laboratory of Satellite Remote Sensing, Institute of Remote Sensing and Digital Earth, Chinese Academy of Sciences, Beijing 100101, China

Donghui LI

реферат

Complex and drastic variations of atmospheric aerosol components lead to high uncertainties in climate change assessment. The remote sensing of aerosol composition is the technology often used in aerosol optical and microphysical parametric analysis. These parameters, which are derived from remote sensing measurements, can quantitatively estimate the aerosol components of the entire atmosphere. Remote sensing offers the advantages of real-time and fast detection, spatial coverage, and maintenance of natural aerosol status. This paper presents a comprehensive review of theories, observations, models, and algorithms for aerosol composition remote sensing. First, in the area of algorithm development, we analyze the main ideas and methods for establishing aerosol composition models. In particular, an advanced remote sensing categorization model, which includes black carbon, brown carbon, mineral dust, light-scattering organic matter, inorganic salts, sea salt, and water, is presented in detail. The methods are identified or distinguished according to components (i.e., sensitivity parameters, including light absorption, size distribution, particulate shape, etc.). Second, for the calculation of the refractive index, some of the typical methods suitable for different aerosol mixing states are compared, and then their impacts on composition retrieval are determined. Third, some examples of remotely sensed aerosol components are given, and a preliminary validation of the retrievals is conducted by using synchronized chemical measurements. Finally, the development tendencies of the remote sensing of atmospheric aerosol composition are summarized from the perspectives of observation capability enhancement, optimization of the categorization model, improvements in retrieval accuracy, extension of application abilities, and identification of utilization prospects in global climate change assessment.

ключеви́че слова́

aerosol;chemical components;remote sensing;black carbon;brown carbon;dust;optical and microphysical parameters;mixing states

References

  1. 1.
    Andreae M O and Gelencsér A. 2006. Black carbon or brown carbon? The nature of light-absorbing carbonaceous aerosols. Atmospheric Chemistry and Physics, 6(10): 3131–3148
  2. 2.
    Arola A, Schuster G, Myhre G, Kazadzis S, Dey S and Tripathi S N. 2011. Inferring absorbing organic carbon content from AERONET data. Atmospheric Chemistry and Physics, 11(1): 215–225
  3. 3.
    Bohren C F and Huffman D R. 1998. Absorption and Scattering of Light by Small Particles. New York: John Wiley: 213–219
  4. 4.
    Bond T C and Bergstrom R W. 2006. Light absorption by carbonaceous particles: An investigative review. Aerosol Science and Technology, 40(1): 27–67
  5. 5.
    Boucher O, Randall D, Artaxo P, Bretherton C, Feingold G, Forster P, Kerminen V M, Kondo Y, Liao H, Lohmann U, Rasch P, Satheesh S, Sherwood S, Stevens B and Zhang X. 2013. Clouds and aerosols//Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge, UK, New York, NY, USA: Cambridge University Press: 571–658
  6. 6.
    Cao J J and Li J J. 2016. Formation and toxicological effect of secondary organic aerosols. Journal of Earth Environment, 7(5): 431–441 (
  7. 7.
    Choi Y and Ghim Y S. 2016. Estimation of columnar concentrations of absorbing and scattering fine mode aerosol components using AERONET data. Journal of Geophysical Research: Atmospheres, 121(22): 13628–13640
  8. 8.
    Derimian Y, Karnieli A, Kaufman Y J, Andreae M O, Andreae T W, Dubovik O, Maenhaut W and Koren I. 2008. The role of iron and black carbon in aerosol light absorption. Atmospheric Chemistry and Physics, 8(13): 3623–3637
  9. 9.
    Dey S, Tripathi S N, Singh R P and Holben B N. 2006. Retrieval of black carbon and specific absorption over Kanpur city, northern India during 2001-2003 using AERONET data. Atmospheric Environment, 40(3): 445–456
  10. 10.
    Dubovik O, Herman M, Holdak A, Lapyonok T, Tanré D, DeuzéJ L, Ducos F, Sinyuk A and Lopatin A. 2011. Statistically optimized inversion algorithm for enhanced retrieval of aerosol properties from spectral multi-angle polarimetric satellite observations. Atmospheric Measurement Techniques, 4(5): 975–1018
  11. 11.
    Dubovik O, Holben B, Eck T F, Smirnov A, Kaufman Y J, King M D, Tanre D and Slutsker I. 2002. Variability of absorption and optical properties of key aerosol types observed in worldwide locations. Journal of the Atmospheric Sciences, 59(3): 590–608
  12. 12.
    Dubovik O, Lapyonok T, Litvinov P, Herman M, Fuertes D, Ducos F, Torres B, Derimian Y, Huang Xin, Lopatin A, Chaikovsky A, Asp etsberger M and Federspiel C. 2014. GRASP: a versatile algorithm for characterizing the atmosphere. SPIE Newsroom, 25
  13. 13.
    Dubovik O, Sinyuk A, Lapyonok T, Holben B N, Mishchenko M, Yang Ping, Eck T F, Volten H, Muñoz O, Veihelmann B, van der Zande W J, Leon J F, Sorokin M and Slutsker I. 2006. Application of spheroid models to account for aerosol particle nonsphericity in remote sensing of desert dust. Journal of Geophysical Research: Atmospheres, 111(D11): D11208
  14. 14.
    Dubovik O, Smirnov A, Holben B N, King M D, Kaufman Y J, Eck T F and Slutsker I. 2000. Accuracy assessments of aerosol optical properties retrieved from Aerosol Robotic Network (AERONET) Sun and sky radiance measurements. Journal of Geophysical Research: Atmospheres, 105(D8): 9791–9806
  15. 15.
    Erlick C, Abbatt J P D and Rudich Y. 2011. How different calculations of the refractive index affect estimates of the radiative forcing efficiency of ammonium sulfate aerosols. Journal of the Atmospheric Sciences, 68(9): 1845–1852
  16. 16.
    Feng Y, Ramanathan V and Kotamarthi V R. 2013. Brown carbon: a significant atmospheric absorber of solar radiation?. Atmospheric Chemistry and Physics, 13(17): 8607–8621
  17. 17.
    Ganguly D, Ginoux P, Ramaswamy V, Dubovik O, Welton J, Reid E A and Holben B N. 2009. Inferring the composition and concentration of aerosols by combining AERONET and MPLNET data: comparison with other measurements and utilization to evaluate GCM output. Journal of Geophysical Research: Atmospheres, 114(D16): D16203
  18. 18.
    Hao J M, Xu J Y, Wu J and Ma Q. 2017. A study of the atmospheric environmental capacity of Jingjinji and of the five northwestern provinces and autonomous regions in China. Engineering Sciences, 19(4): 13–19 (
  19. 19.
    He H, Wang X M, Wang Y S, Wang Z F, Liu J G and Chen Y F. 2013. Formation mechanism and control strategies of haze in China. Bulletin of Chinese Academy of Sciences, 28(3): 344–352 (
  20. 20.
    He K B, Jia Y T, Ma Y L, Lei Y, Zhao Q, Tanaka S and Okuda T. 2009. Regionality of episodic aerosol pollution in Beijing. Acta Scientiae Circumstantiae, 29(3): 482–487 (
  21. 21.
    Hecobian A, Zhang X, Zheng M, Frank N, Edgerton E S and Weber R J. 2010. Water-Soluble Organic Aerosol material and the light-absorption characteristics of aqueous extracts measured over the Southeastern United States. Atmospheric Chemistry and Physics, 10(13): 5965–5977
  22. 22.
    Holben B N, Eck T F, Slutsker I, Tanré D, Buis J P, Setzer A, Vermote E, Reagan J A, Kaufman Y J, Nakajima T, Lavenu F, Jankowiak I and Smirnov A. 1998. AERONET—a federated instrument network and data archive for aerosol characterization. Remote Sensing of Environment, 66(1): 1–16
  23. 23.
    Hu R M, Blanchet J P and Girard E. 2005. Evaluation of the direct and indirect radiative and climate effects of aerosols over the western Arctic. Journal of Geophysical Research: Atmospheres, 110(D11): D11213
  24. 24.
    Kandler K and Schütz L. 2007. Climatology of the average water-soluble volume fraction of atmospheric aerosol. Atmospheric Research, 83(1): 77–92
  25. 25.
    Kirchstetter T W, Novakov T and Hobbs P V. 2004. Evidence that the spectral dependence of light absorption by aerosols is affected by organic carbon. Journal of Geophysical Research: Atmospheres, 109(D21): D21208
  26. 26.
    Koch D, Bond T C, Streets D, Unger N and van der Werf G R. 2007. Global impacts of aerosols from particular source regions and sectors. Journal of Geophysical Research: Atmospheres, 112(D2): D02205
  27. 27.
    Koven C D and Fung I. 2006. Inferring dust composition from wavelength-dependent absorption in Aerosol Robotic Network (AERONET) data. Journal of Geophysical Research: Atmospheres, 111(D14): D14205
  28. 28.
    Lesins G, Chylek P and Lohmann U. 2002. A study of internal and external mixing scenarios and its effect on aerosol optical properties and direct radiative forcing. Journal of Geophysical Research: Atmospheres, 107(D10): AAC 5-1–AAC 5-12
  29. 29.
    Li L, Zheng X, Li Z Q, Li Z H, Dubovik O, Chen X F and Wendisch M. 2017. Studying aerosol light scattering based on aspect ratio distribution observed by fluorescence microscope. Optics Express, 25(16): A813–A823
  30. 30.
    Li Z, Gu X, Wang L, Li D, Xie Y, Li K, Dubovik O, Schuster G, Goloub P, Zhang Y, Li L, Ma Y and Xu H. 2013. Aerosol physical and chemical properties retrieved from ground-based remote sensing measurements during heavy haze days in Beijing winter. Atmospheric Chemistry and Physics, 13(20): 10171–10183
  31. 31.
    Li Z Q, Goloub P, Devaux C, Gu X F, Deuzé J L, Qiao Y L and Zhao F S. 2006. Retrieval of aerosol optical and physical properties from ground-based spectral, multi-angular, and polarized sun-photometer measurements. Remote Sensing of Environment, 101(4): 519–533
  32. 32.
    Li Z Q, Li L, Zhang F X, Li D H, Xie Y S and Xu H. 2015. Comparison of aerosol properties over Beijing and Kanpur: Optical, physical properties and aerosol component composition retrieved from 12 years ground-based Sun-sky radiometer remote sensing data. Journal of Geophysical Research: Atmospheres, 120(4): 1520–1535
  33. 33.
    Li Z Q, Li D H, Li K T, Xu H, Chen X F, Chen C, Xie Y S, Li L, Li L , Li W, Lyu Y, Jia L L, Zhang Y and G X F. 2015. Sun-sky radiometer observation network with the extension of multi-wavelength polarization measurements. Journal of Remote Sensing, 19(3): 495–519 (
  34. 34.
    Li Z Q, Xu H, Li K T, Li D H, Xie Y S, Li L, Zhang Y, Gu X F, Zhao W, Tian Q J, Deng R R, Su X L, Huang B, Qiao Y L, Cui W Y, Hu Y, Gong C L, Wang Y Q, Wang X F, Wang J P, Du W B, Pan Z Q, Li Z Z and Bu D. 2018. Comprehensive study of optical, physical, chemical, and radiative properties of total columnar atmospheric aerosols over China: An overview of Sun-sky radiometer Observation NETwork (SONET) measurements. Bulletin of the American Meteorological Society, 99(4): 739–755
  35. 35.
    Loeb N G and Su W Y. 2010. Direct aerosol radiative forcing uncertainty based on a radiative perturbation analysis. Journal of Climate, 23(19): 5288–5293
  36. 36.
    Morcrette J J, Boucher O, Jones L, Salmond D, Bechtold P, Beljaars A, Benedetti A, Bonet A, Kaiser J W, Razinger M, Schulz M, Serrar S, Simmons A J, Sofiev M, Suttie M, Tompkins A M and Untch A. 2009. Aerosol analysis and forecast in the European Centre for medium-range weather forecasts integrated forecast system: Forward modeling. Journal of Geophysical Research: Atmospheres, 114(D6): D06206
  37. 37.
    O’Neill N T, Dubovik O and Eck T F. 2001. Modified Ångström exponent for the characterization of submicrometer aerosols. Applied Optics, 40(15): 2368–2375
  38. 38.
    Qie L L, Li Z Q, Goloub P, Li L, Li D H, Li K T, Zhang Y and Xu H. 2017. Retrieval of the aerosol asymmetry factor from Sun-sky radiometer measurements: application to almucantar geometry and accuracy assessment. Applied Optics, 56(36): 9932–9940
  39. 39.
    Ramanathan V and Carmichael G. 2008. Global and regional climate changes due to black carbon. Nature Geoscience, 1(4): 221–227
  40. 40.
    Sato M, Hansen J, Koch D, Lacis A, Ruedy R, Dubovik O, Holben B, Chin M and Novakov T. 2003. Global atmospheric black carbon inferred from AERONET. Proceedings of the National Academy of Sciences of the United States of America, 100(11): 6319–6324
  41. 41.
    Schuster G L, Dubovik O and Arola A. 2016. Remote sensing of soot carbon - Part 1: distinguishing different absorbing aerosol species. Atmospheric Chemistry and Physics, 16(3): 1565–1585
  42. 42.
    Schuster G L, Dubovik O, Holben B N and Clothiaux E E. 2005. Inferring black carbon content and specific absorption from Aerosol Robotic Network (AERONET) aerosol retrievals. Journal of Geophysical Research: Atmospheres, 110(D10): D10S17
  43. 43.
    Schuster G L, Lin B and Dubovik O. 2009. Remote sensing of aerosol water uptake. Geophysical Research Letters, 36(3): L03814
  44. 44.
    Sun H L, Biedermann L and Bond T C. 2007. Color of brown carbon: a model for ultraviolet and visible light absorption by organic carbon aerosol. Geophysical Research Letters, 34(17): L17813
  45. 45.
    van Beelen A J, Roelofs G J H, Hasekamp O P, Henzing J S and Röckmann T. 2014. Estimation of aerosol water and chemical composition from AERONET Sun-sky radiometer measurements at Cabauw, the Netherlands. Atmospheric Chemistry and Physics, 14(12): 5969–5987
  46. 46.
    Wang L, Li Z Q, Li D H, Li K T, Tian Q J, Li L, Zhang Y, Lü Y and Gu X F. 2012. Retrieval of dust fraction of atmospheric aerosols based on spectra characteristics of refractive indices obtained from remote sensing measurements. Spectroscopy and Spectral Analysis, 32(6): 1644–1649 (
  47. 47.
    Wang L, Li Z Q, Ma Y, Li L and Wei P. 2013. Retrieval of aerosol chemical composition from ground-based remote sensing data of sun-sky radiometers during haze days in Beijing winter. Journal of Remote Sensing, 17(4): 944–958 (
  48. 48.
    Wang L, Li Z Q, Tian Q J, Ma Y, Zhang F X, Zhang Y, Li D H, Li K T and Li L. 2013. Estimate of aerosol absorbing components of black carbon, brown carbon, and dust from ground-based remote sensing data of sun-sky radiometers. Journal of Geophysical Research: Atmospheres, 118(12): 6534–6543
  49. 49.
    Wang Y S, Yao L, Wang L L, Liu Z R, Ji D S, Tang G Q, Zhang J K, Sun Y, Hu B and Xin J Y. 2014. Mechanism for the formation of the January 2013 heavy haze pollution episode over central and eastern China. Science China Earth Sciences, 44(1): 15–26 (
  50. 50.
    Xie Y S, Li D H, Li K T, Zhang L, Chen C, Xu H and Li Z Q. 2013. Aerosol optical and microphysical properties in haze days based on ground-based remote sensing measurements. Journal of Remote Sensing, 17(4): 970–980 (
  51. 51.
    Xie Y S, Li Z Q, Li L, Wang L, Li D H, Chen C, Li K T and Xu H. 2014. Study on influence of different mixing rules on the aerosol components retrieval from ground-based remote sensing measurements. Atmospheric Research, 145–146: 267–278
  52. 52.
    Xie Y S, Li Z Q, Zhang Y X, Zhang Y, Li D H, Li K T, Xu H, Zhang Y, Wang Y Q, Chen X F, Schauer J J and Bergin M. 2017. Estimation of atmospheric aerosol composition from ground-based remote sensing measurements of Sun-sky radiometer. Journal of Geophysical Research: Atmospheres, 122(1): 498–518
  53. 53.
    Wei P, Li Z Q, Wang Y, Xie Y S, Zhang Y and Xu H. 2013. Remote sensing estimation of aerosol composition and radiative effects in haze days. Journal of Remote Sensing, 17(4): 1021–1031 (
  54. 54.
    Zhang Y, Li Z Q, Cuesta J, Li D H, Wei P, Xie Y S and Li L. 2015. Aerosol column size distribution and water uptake observed during a major haze outbreak over Beijing on January 2013. Aerosol and Air Quality Research, 15(3): 945–957
  55. 55.
    Zhang Y, Li Z Q, Sun Y L, Lv Y and Xie Y S. 2018. Estimation of atmospheric columnar organic matter (OM) mass concentration from remote sensing measurements of aerosol spectral refractive indices. Atmospheric Environment, 179: 107–117

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