CHARACTERIZATION OF AEROSOLS BASED ON THE

SIMULTANEOUS MEASUREMENTS

M. Nakata, T. Yokomae, T. Fujito, I. Sano & Sonoyo Mukai

Kinki University, Higashi-Osaka, Japan

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.

aerosol properties

size composition amountshape

m = n-kidV / dlnr AOT

refractive index

size dist function

opticalthickness

～ sphere

1. Classification of aerosol types

2. Correlation between AOT and PM

3. Aerosol retrieval from Aqua/MODIS

4. Summary

Contents

Clustering of global aerosols

Omar et al. 2005

present workThe 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)

Parameters:

The 5 parametersAerosol 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.

Method: Aerosols are classified into 6 categories by k-Means clustering method with AERONET data.

Our results coincide with Omar's

Desert dust Biomass burning

Continental pollution

Polluted marine Dirty pollution

Rural (background)

size distribution for 6 aerosol categories: bi-modal (fine & coarse) lognormal fn. 　

locations size distribution

Size fn. available for 6 aerosol categories is demanded in practice.

rr

( )

( )

−+

+

−=

)34.2(ln2

ln(3.42)-rlnexp

)34.2ln(2

) f-1 (

)86.1(ln2

)14.0(ln-rlnexp

)86.1ln(2

f

rln

)f V(

2

2

2

2

π

πrd

d

An approximate size distribution(the parameter to characterize aerosol size is "f" alone, where f is the fraction of fine ptl.):

1. Classification of aerosol types

2. Correlation between AOT and PM

3. Aerosol retrieval from Aqua/MODIS

4. Summary

Contents

Map of AERONET site in NASA/AERONET web page

KyotoKobe

Osaka Higashi-Osaka Nara

Kinki University Campus,Higashi-Osaka, Japan34.65°N, 135.59°E

Ground measurements at Higashi-Osaka

Photometry :AERONET sun/sky radiometer

PM sampling:PM2.5 & PM10&OBC SPM-613D

NIES/LIDAR

Location

Ground measurements at Higashi-Osaka

AOT (0.675 µm) at Higashi-Osaka from 2004 to 2010

PhotometryAERONETsun/sky radiometer

AERONET/Osaka site

PM2.5 and PMC at Higashi-Osaka from 2004 to 2010

PMC = PM10- PM2.5

PM sampling

PM samplingPM2.5 & PM10&OBC

SPM-613D

Classification results of AERONET/Osaka

Cluster-A: Large AOT & small α

Asian dust

Cluster-C: Small AOT & large α Clear atmosphere is not too often

Cluster-B & F: Small AOT & large α but slightly dirtier than clear (Cluster-C)

Background at Osaka

Cluster-D: Large AOT & Large αCluster-E: Small AOT & small α

Typical aerosol event involving small aerosols

Classification results for global as AOT (0.675µm) against α (0.44/0.87µm)

Scatter diagrams as AOT (0.675µm) against α (0.44/0.87µm) for three clusters of aerosols at Higashi-Osaka.

Cluster-2: Large AOT & Large α

Cluster-3: Large AOT & Small α

Cluster-1: Small AOT

Aerosol properties at Higashi-Osaka site are roughly reclassifies into 3 clusters

1) Cluster-1,-2 (Anthropogenic)

& 2) Cluster-3 (Asian dust)

　

The correlation between AOT and PM2.5

is improved for 2-clusters as:

PM2.5 = 62.4 AOT + 12.4 　 PM2.5 = 52.8 AOT + 9.68 　

　

2hours time shift:

PM2.5 =

95.1 AOT - 18.6

　

Estimation of PM2.5 from AOT ad vice versa

1. Classification of aerosol types

2. Correlation between AOT and PM

3. Aerosol retrieval from Aqua/MODIS

4. Summary

Contents

{rm , σ} :{0.14,1.86}

{rm , σ} :{3.42,2.34}

0.10.20.30.40.5

【 1 】 size distribution： represented by f

　

【 Retrieval Flow for dust storm】

R sim (λ) : R obs (λ)

f & m = n(λ) – k(λ) i

f*, n*(λ), k*(λ)

R(λ) ←New Radiative Transfer code (successive scattering method*)

【 2 】 refractive index: m = n(λ) – i ･ k(λ)

【 aerosol model】

* available for semi-infinite atmosphere model i.e. for optically thick heavy aerosol events

c )

ex. Yellow dust storm

on April 10 in 2006 over the Badain Jaran Desert

Aqua/MODIS image

AOT 4.0

Dust aerosol mass concentration with SPRINTARS

Retrieval of dust aerosols the Badain Jaran Desert 45N

40N

30N

25N

20N140E130E110E 120E100E90E

λ(μm)0.46 1.57 - 0.0036 i 1.55 - 0.0057 i 1.53 - 0.0080 i0.55 1.55 - 0.0024 i 1.55 - 0.0046 i 1.53 - 0.0080 i0.65 1.54 - 0.0015 i 1.55 - 0.0038 i 1.53 - 0.0080 i0.86 1.51 - 0.0011 i 1.55 - 0.0030 i 1.53 - 0.0080 i

C : d'AlmeidaA : AERONET B : RSTRrefractive index

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)(43N, 104E)

1. 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.

Summary