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CHARACTERIZATION OF AEROSOLS BASED ON THE SIMULTANEOUS MEASUREMENTS

CHARACTERIZATION OF AEROSOLS BASED ON THE SIMULTANEOUS MEASUREMENTS. M. Nakata, T. Yokomae, T. Fujito, I. Sano & Sonoyo Mukai Kinki University, Higashi-Osaka, Japan. aerosol properties. size. amount. composition. shape. optical thickness. refractive index. size dist function.

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CHARACTERIZATION OF AEROSOLS BASED ON THE SIMULTANEOUS MEASUREMENTS

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  1. CHARACTERIZATION OF AEROSOLS BASED ON THE SIMULTANEOUS MEASUREMENTS M. Nakata, T. Yokomae, T. Fujito, I. Sano &Sonoyo Mukai Kinki University, Higashi-Osaka, Japan

  2. aerosol properties size amount composition shape optical thickness refractive index size dist function ～sphere AOT m = n-ki dV / dlnr Introduction Studying aerosol characteristics is an important subject especially in urban areas. In this work, we classify aerosol properties by utilizing the ground observations and investigate characterization of aerosols over Higashi-Osaka, Japan. Then the obtained results are examined for aerosol retrieval with Aqua/MODIS.

  3. Contents 1. Classification of aerosol types 2. Correlation between AOT and PM 3. Aerosol retrieval from Aqua/MODIS 4. Summary

  4. Clustering of global aerosols Our results coincide with Omar's Method:Aerosols are classified into6 categories by k-Means clustering method with AERONET data. Parameters: Omar et al. 2005 present work • The 26 parameters • Complex refractive index (8) • Mean radius (2) • (fine and coarse) • Standard deviation (2) • (fine and coarse) • Mode total volumes (2) • (fine and coarse) • Single scattering albedo (4) • (441, 673, 873 and 1022 nm) • Asymmetry factor (4) • (441, 673, 873 and 1022 nm) • Extinction/backscatter ratio (4) • (441, 673, 873 and 1022 nm) • The 5 parameters • Aerosol optical thickness(3) • (440, 675 and 870 nm) • Angstrom exponent (2) • (440/870 and 440/675)  Fewer essential parameters can make the interpretation of resultant clusters easier.

  5. Desert dust Biomass burning Rural (background) Continental pollution Polluted marine Dirty pollution size distribution for 6 aerosol categories: bi-modal (fine & coarse) lognormal fn. locations size distribution

  6. An approximate size distribution (the parameter to characterize aerosol size is "f" alone, where f is the fraction of fine ptl.): Size fn. available for 6 aerosol categories is demanded in practice.   r r

  7. Contents 1. Classification of aerosol types 2. Correlation between AOT and PM 3. Aerosol retrieval from Aqua/MODIS 4. Summary

  8. Ground measurements at Higashi-Osaka Location Kyoto Kobe Higashi -Osaka Osaka Nara Map of AERONET site in NASA/AERONET web page Ground measurements at Higashi-Osaka Photometry :AERONET sun/sky radiometer PM sampling:PM2.5 & PM10&OBC SPM-613D NIES/LIDAR Kinki University Campus, Higashi-Osaka, Japan 34.65°N, 135.59°E

  9. AERONET/Osaka site AOT (0.675 µm) at Higashi-Osaka from 2004 to 2010 Photometry AERONET sun/sky radiometer

  10. PM sampling PM2.5 and PMC at Higashi-Osaka from 2004 to 2010 PMC = PM10- PM2.5 PM sampling PM2.5 & PM10&OBC SPM-613D

  11. Classification results of AERONET/Osaka • Cluster-A: Large AOT & small a •  Asian dust • Cluster-C: Small AOT & large a • Clear atmosphere is not too often • Cluster-B & F: Small AOT & large a but slightly dirtier than clear (Cluster-C) • Background at Osaka • Cluster-D: Large AOT & Large a • Cluster-E: Small AOT & small a •  Typical aerosol event involving small aerosols Classification results for global as AOT (0.675mm) against a (0.44/0.87mm)

  12. Aerosol properties at Higashi-Osaka site are roughly reclassifies into 3 clusters Cluster-1: Small AOT Cluster-2: Large AOT & Large a Cluster-3: Large AOT & Small a Scatter diagrams as AOT (0.675mm) against a (0.44/0.87mm) for three clusters of aerosols at Higashi-Osaka.

  13. 2hours time shift: PM2.5 = 95.1 AOT - 18.6 PM2.5 = 62.4 AOT + 12.4 PM2.5 = 52.8 AOT + 9.68 The correlation between AOT and PM2.5 is improved for 2-clusters as: Estimation of PM2.5 from AOT ad vice versa & 2) Cluster-3 (Asian dust) 1) Cluster-1,-2 (Anthropogenic)

  14. Contents 1. Classification of aerosol types 2. Correlation between AOT and PM 3. Aerosol retrieval from Aqua/MODIS 4. Summary

  15. 0.1 0.2 0.3 0.4 0.5 【aerosol model】 【Retrieval Flow for dust storm】 【1】 size distribution ：represented by f f& m = n() –k()i R(l)←New Radiative Transfer code (successive scattering method*) R sim (l) : R obs (l) {rm , s} : {3.42,2.34} {rm , s} : {0.14,1.86} 【2】 refractive index: m = n() – i･k() f*, n*(), k*() * available for semi-infinite atmosphere model i.e. for optically thick heavy aerosol events

  16. ex. Yellow dust storm on April 10 in 2006 over the Badain Jaran Desert Aqua/MODIS image c ) Dust aerosol mass concentration with SPRINTARS AOT 4.0

  17. (43N, 104E) the heavy yellow dust storm can be interpreted by the large sized aerosol model with f=0.094 and refractive index (m) derived from AERONET data at Dalanzadgad in the Gobi Desert (41N, 105E) Retrieval of dust aerosols the Badain Jaran Desert refractive index

  18. Summary • Aerosol properties are classified with a clustering method by utilizing the ground measurements by AERONET. 2. The size distribution available for every aerosol category is proposed. 3. The cluster information can be used to improve estimation of PM2.5 from AOT. 4. New algorithms for aerosol retrieval based on the proposed aerosol models and the semi-infinite radiative transfer simulations are available for the yellow dust storm with Aqua/MODIS.

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