Biomass Burning Plume due to Large Forest Fires over Siberia
Sonoyo Mukai1, Itaru Sano2 and Makiko Nakata2
The Kyoto College of Graduate Studies for Informatics

1

Kindai University

2

Abstract
It is well known that the biomass burning aerosols, generated by the large-scale forest fires and/or
burn agriculture, have influenced the severity of air pollution The biomass burning plumes were first
analyzed based on the global distribution of hot spots by the Rapid Fire Response System provided
by the measurements from the Moderate Resolution Imaging Spectrometer (MODIS), on board thesatellites Terra and Aqua. In particular, the focus was on the large forest fires often occurring inearly summer in Siberia. Subsequently, using the detection method of biomass burning plume fromsatellite measurements, an effective description of the aerosol model was generated. Finally, theaerosol characteristics of the biomass burning plume in Siberia were retrieved as an example, byapplying the Method of Successive Order of Scattering (MSOS) as radiation simulation in the semiinfinite atmosphere to the data by Aqua/MODIS for May 10, 2016.
Keywords: Aerosol retrieval, MSOS, GLI, ADEOS-2, MODIS, Aqua

■ 1．Introduction

report on global warming (https://www.ipcc.ch/

The JAXA/GCOM-C (the Japan Aerospace
Exploration Agency/Global Change Observation
Mission-1) satellite will be onboard the SGLI
(second generation GLI (global imager)), to belaunched in December 2017. The SGLI has
multiple channels, including near-UV (ultraviolet)
channels of 0.38 and 0.412 µm, and two polarization
channels at red and near-infrared wavelengths of
0.67 and 0.87 µm. It is worth noting that the nearUV measurements are helpful for the detection ofcarbonaceous particles, which are the main
component of aerosols from biomass burning. It is
known

that

the

biomassburning

plumes,generated by the large-scale forest fires and/or
burn agriculture, have influenced the severity of
air pollution [1]. Forest fires increase due to global
warming and climate change and vice versa. This
negative cycle decreases the quality of global
environment and human health. The 5th IPCC

report/ar5/wg1/)

https://www.google.co.jp/?gws_

rd=sslemphasizes the importance of studying
aerosol characteristics and their temporal andspatial variations, as well as mentions thewarming effect of black carbon aerosols as opposedto the cooling effect of other kinds of aerosols.
However, the aerosol properties of hazardous air
pollutants are still insufficiently understood.
Aerosol distribution varies seasonally due tofactors such as emissions, photochemical reactions
and wind direction [2] [3].
In this study, we have focused on remote sensing
for determining the extreme concentrations ofatmospheric aerosols, namely heavy aerosol
pollution. This phenomenon is called a heavy
aerosol episode, or simply an “episode”, since it isanunusualphenomenon.

Aerosol

episodescertainly deteriorate the air quality, environment
and the ecosystem. It is natural to take into
account not only the biomass burning plumes, but

NAIS Journal

13

also

other

aerosol

episodes,becausethecharacteristics and distribution of atmosphericIn general, retrievals of aerosols over land,aerosols are known to be complicated. The

based on satellite data, are made by using three-atmospheric particles originate from both, naturalchannel or two-channel algorithms in the visible

factors and human activities. Of the particles thatto near-infrared wavelengths [9]. However, it isare limited to naturally originating aerosols, thedifficult to distinguish between absorbing (e.g.,

first one is desert dust, often called dust storms.

biomass burning) and non-absorbing (e.g., sulfate)

They are the most dynamic natural phenomena

particles. Thus, the usual retrieval algorithms

which emit a large amount of mineral dust into

adopt only sulfate-type non-absorbing particles,

the atmosphere, which is then widely transported,

such as standard aerosols. This limitation could

causing serious atmospheric turbidity [4]. As far

be overcome by using near-UV and violet data [10]as Asia is concerned, it is well known that yellow[11]. The method of using shorter wavelength data

dust causes widespread and severe air pollution

has been introduced in order to detect absorbing

there [2]. It is highly likely that large-scale aerosol

aerosols, such as plumes from biomass burning

episodes owing to a mixture of both natural factors[12] [13] [14]. An algorithm based on the combined

and human activities have occurred. These issues

use of near-UV and violet data is briefly described

would possibly be considered in future studies,

here, as it has already been shown in our previous

while the present study deals with the severe

work based on ADEOS (Advanced Earth Observing

biomass burning plumes, as agricultural biomass

Satellite)-2 /GLI measurements [8]. Simply put, itburning episodes continue to survive in Southeast

is a method for detecting absorbing particles,

Asia, while the big forest fire in Siberia has

based on the ratio of reflectance at 0.40 (Band 2)

influenced the air quality in Asia [5].

and 0.38 μm (Band 1), called AAI (Absorbing■

Aerosol Index) hereafter. Fig. 1 shows the

2．Biomass Burning Plumes By Satellites

numerical values of AAI cited from our previous
work. The solid curve in Fig. 1 shows the histogram

MODIS sensor boarded on both satellites Terraof AAI from the GLI data within the region denoted

and Aqua provides the hot spots based on the Rapid

by the white square denoted in Fig. 2. It indicatesFire Response System (MYD14 Collection 5) [6] [7].

that the mean and standard deviation values of

The distribution of hot spots strongly suggests the

AAI are 0.907 and 0.095, respectively. The dashedposition and degree of the biomass burning plumes.

curve represents the same type of data as the solid

It is well known that biomass burning plume is a

curve, but for the minimum values of AAI duringseasonal phenomenon peculiar to a particular

May 2003, which would be the values on ordinary

region. Hence, the mass concentrations of aerosols

days. The mean and standard deviation values ofare frequently governed with spatial and/or

0.870 and 0.019, respectively, can therefore be

temporal variations of biomass burning episodes.

considered the standard values for ordinary, clear

In general, the values of AOT (aerosol opticaldays. Accordingly, AAI is an indicator for absorbing

thickness) vary with time and place. However, theparticles, such as biomass burning aerosols. Fromaerosol concentrations in Siberia in May 2003 canFig. 1, it can be seen that the AAI for biomass

be said to have been strongly influenced by theburning aerosols takes a value higher than 0.9,

forest fires there. Major forest fires have occurred

which means, AAI ≈ 0.9 must be the threshold of athere from late spring to early summer [8]. Our

biomass burning plume. It can be seen from Fig. 2

previous work has shown a heavy haze atmosphere,that the values of AAI are high almost all overwhich must have originated from the big forest

northeast Asia.

fire around the lake Baikal, May 20, 2003 over
Siberia using the measurements by ADEOS-2/GLI

14

and POLDER [8].

NAIS Journal

aero sol;
3. C
 P (continental pollution) represents anthropogenic
aerosols, including various species of sulfate(SO42−), nitrate- (NO3−), OC, ammonium (NH4+) andsoot;

4. DP (dirty pollution) consists of the same aerosol
types as CP, but at significantly higher levels;
5. DD (desert dust) is assumed to be mostly mineral soil; and6. PM (polluted marine) consists primarily of seasalt with traces of CP.
Firstly, we focus on the size of aerosol particles.
Fig. 1

The histogram of AAI, i.e. the ratio of reflectanceat 0.40 μ m (Band 2) and 0.38 μ m (Band 1), overnortheast Asia, derived from ADEOS-2/GLI data.
The solid and dashed curves represent thehistogram of the image shown in Fig. 2a on May
20, 2003 and the ordinary days during May 2003,
respectively. The figure is partly cited from ourprevious work [8].

The size distributions of these six aerosol types,
based on the AERONET measurements, have twomodes for small (f) and large (c) particles in abimodal log-normal distribution for the particle
volume. Considering the aerosols here arecontinental, the maritime aerosol type (PM) is
discarded in the present discussions. Therefore,
five aerosol types {BB, RU, CP, DP, DD} are taken
into account (refer to Table 1). The parameters Vf,
rf and σf are the volume concentration, mode radiusand standard deviation of small mode particles,
respectively. The corresponding parameters forlarge mode particles are Vc, rc and σc. It is clearthat while the six parameters are necessary forthe

Fig. 2■

 lobal distribution of AAI from ADEOS-2/GLI on
G
20 May, 2003.

retrieving the optimized size of aerosols on a global
scale. Therefore, effective simplification is required
for the retrieval of size distribution of global

3. AEROSOL Retrieval

3-1

definition of aerosol size, they are excessive foraerosols. An assemblage of five aerosol types {BB,RU, CP, DP, DD} is treated with the k-means

Aerosol model

Aerosol properties are represented by several

method by using four characteristic parametersparameters. The most basic parameter is spectral{rf, σf, rc, σc}. These five aerosol types are iteratively

aerosol optical thickness AOT(λ) at a wavelength

assembled into k sub-clusters [17]. As a result, theλ. Several other aerosol parameters, such as size

optimum number of final categories is two (i.e.,

distribution and refractive index, are also derivedk=2), with the two sub-clusters that were obtainedfromautomatic

being {BB, RU, CP, DP} and {DD} (refer to Table

classification of accumulated NASA/AERONET

1). This statistical result is reasonable for

data, atmospheric aerosols have been classified

understanding the optical and physical properties

into six categories [15] [16] as follows:of aerosols. The former and the latter clusters

1. BB (biomass burning) is an aged smoke aerosol

represent the small anthropogenic aerosols (AA)consisting primarily of soot and organic carbon;

and mineral dusts, respectively. This result is

2. RU (rural) is referred to as a clean continental

obvious in some ways before the cluster analysis,

AOT(λ).

Accordingtothe

NAIS Journal

15

because a cursory glance at Table 1 reveals that

following equation [18] [19].

the four characteristic parameters {rf, σf, rc, σc},
except for volume concentration {Vf, Vc}, are similar
to each other for {BB, RU, CP, DP} types. Therefore,
we can employ the fixed values of {0.144, 1.533,(2)

3.607, 2.104} for {rf, σf, rc, σc} for the AA typeaerosols. Similarly, we can focus on the volume

where ε denotes electricity, the subscripts m and jconcentrations {Vf, Vc} alone. Here, a uniquerepresent the matrix and inclusion, respectively

parameter (f) of the fine particle fraction, definedand g is the volume fraction of the inclusions, which

as f = V f / (V f + Vc ) , is introduced. Accordingly, themust be less than or equal to 0.4. With regard to thesize distribution function can be approximately

accumulated AERONET measurements [15] [16],expressed for continental anthropogenic aerosols

the values of the complex refractive index of AA

in the following simple form, with the uniquetype aerosols seems to be weakly dependent onvariable of fine particle fraction (f):the wavelength, at least for the visible wavelength
bands. It should be noted here that the spectral
absorption (i.e., the imaginary part of the refractive
index) should be taken into account at NUV
wavelengths [20]. We set the approximate valuesof 1.404–0.004i and 1.520–0.035i for the matrix
and inclusions, respectively, for applications in the(1)

Table 1 S ub-clustering of the continental aerosol types
with respect to size distribution parameters partly
cited from AERONET clustering works [15] [16].

visible band data. In this approximation, thematrix and the inclusions were assumed to be
weak absorbing particles such as CP-type andstrong absorbers such as soot, respectively [21][22].
The sub-clustering work of the AERONET

DD

BB

RU

CP

DP

rf

0.117

0.144

0.133

0.140

0.140

0.144

σf

1.482

1.562

1.502

1.540

1.540

1.533

Vf

0.077

0.040

0.013

0.032

0.032

rc

2.834

3.733

3.590

3.556

3.556

3.607

aerosol models are simply represented by two

σc

1.908

2.144

2.104

2.134

2.134

2.104

and component of AA type particles, such as

Vc

0.268

0.081

0.020

0.034

0.034

products suggests that two kinds of aerosol
properties, refractive index and size, are respectively
induced from the mixing of aerosol types with thevolume fraction of the inclusions (g) and an
approximate

log-normal

distribution,

defined by the fine particle fraction (f). In short,parameters, of f in Eq.(1) and g in Eq.(2), for the size
biomass burning aerosols, respectively.
3-2

16

bimodalExample of Biomass Burning Aerosol Retrieval

Another parameter of aerosol characteristics is

Figure 3 shows the distribution of hot spots

refractive index. It is reasonable to consider that asuperimposed on the Aqua/MODIS true color

mixture of various aerosol types exists in nature.

composite (RGB: Band1-4-3) images over the

A simple internal homogeneous mixing of twonortheastern Siberia from 7 to 10 in May, 2016.

components is adopted using the Maxwell Garnett

The hot spot distribution in Fig. 3 clearly suggests

mixing (MGM) rule. The MGM rule provides athe emission of a large amount of biomass burningcomplex refractive index calculated using theaerosols and gaseous particles due to the big forest

NAIS Journalfire. The hazy smoke flowing out of the region offunction. Thus, the two parameters, {f and g}, offire origin, through the wind, can thus beaerosol properties for {size distribution andrecognized. The aerosol retrieval for severe

refractive index} are sufficient to carry out

biomass burning plume is then treated with theseradiation simulations for the retrieval of AA type

Aqua/MODIS data. For an example, dense aerosol

aerosols, based on the MSOS. Note that the basic

concentrations were assumed for around central

formulation of MSOS has been shown in thepart of Fig.3d because of large AOT.

previous work of this Journal [23].
The dots in Fig. 4 denote the MODIS data in the(a) May 7(b) May 8

target point in Fig. 3d on May 10, 2016. The
desired aerosol properties are derived from thetwo-dimensional

interpolation

of

the

(f, g)

coordinates, whose step sizes are 0.1 and 0.01,
respectively, in order to fit the MODIS data^

denoted by the dots. For the sake of clarity, theenlarged graph of the square surrounded by the(c) May 9(d) May 10

dashed line in the original image is superimposedon the top portion of Fig. 4. From this enlarged
figure, the optimized aerosol parameters were
derived as (f*, g*) = (0.5, 0.31). The value of f = 0.5
suggests the dominance of fine particles, while g =0.31 corresponds to the absorbing carbonaceous
aerosols [24]. In addition to mention, the value of

Fig.3

Distribution of hot spots superimposed on the
Aqua/MODIS true color composite images over
Siberia from May 7 to 10, 2016.

AAI takes larger or equal 0.9 from a roughestimation from Aura/OMI measurements aroundthe present target point.

As mentioned above, the biomass burning
aerosols are described by the two parameters of f
and g alone. Therefore it is possible to derive thesetwo parameters in a two-channel diagram withwavelengths of 0.46 and 0.55 µm. In radiation
simulations in the Earth’s atmosphere, the atmosphere is assumed to be a semi-infinite, opticallythick system, consisting of AA-type aerosols. In
the case of the semi-infinite atmosphere, themethod of successive order of scattering (MSOS) is
used [1], which means, in radiation simulations by
MSOS, two parameters, f and g, are desired. In
other words, the size distribution takes the form ofEq. (1) with a unique parameter f, while thecomplex refractive index is calculated using theMaxwell Garnett internal mixing rule, represented
by Eq. (2), where g represents the volume fractionof the inclusions into the matrix. The parameter f
represents the fine particle fraction of theapproximate bimodal log-normal size distribution

Fig. 4 T he simulated values of the reflected intensity forthe proposed aerosol models, described by two
parameters (f, g) in a two-channel diagram of 0.46
µm and 0.55 µm, where the solid curves denote
the results for various values of parameters g and f,
respectively. The dots denote MODIS data on
June 10, 2016 at the target points (around central
part in Fig. 3d). The superimposed upper figure is
the enlarged graph of the square surrounded by
the dashed line in the bottom image.

NAIS Journal

17

■ 4．Summary
The results of this study demonstrate that theaerosol model can be simplified for aerosol retrieval
from Aqua/MODIS data in northeast Siberia. This
indicates that the usage of GCOM−C / SGLI willallow a variety of issues, such as the atmosphericmodel itself, the surface reflectance of earth andthe mixing of clouds, to be tractable because
GCOM−C / SGLI will have 19 channels, including
two polarization channels. It has been pointed outthat the near-UV channel satellite data are
effective for the retrieval of biomass burning
aerosols

becausethecarbonaceous

properties

aresensitiveattheaerosolnear-UV

wavelengths [20]. We handled this issue by using
0.38 and 0.40 µm data by ADEOS-2/GLI [8], whichis consistent with the present results. It is worth
mentioning that our present results are notinconsistent with the previous ones, while thecomputing procedure is much more effective thanthe previous one, as a result of the simplificationof aerosol descriptions. Consequently, synthetic
analysis based on the combination of visible databy MODIS, near-UV data by GLI and polarization
measurements by POLDER may become practical,
with the proposed procedure and results being
available for a feasibility study of GCOM-C/SGLI.
Furthermore, this work suggests that, with theusage of GCOM−C / SGLI, a variety of issues suchas the atmospheric model itself, the surface
reflectance of earth and the mixing of clouds, willbe tractable by using the simplified aerosol models
proposed in this work.
Acknowledgements
The authors thank NASA for distributing the
MODIS data and JAXA for ADEOS-2 data. This
work was supported in part by the Global Change
Observation Mission - Climate project by JAXA
(no. JX - PSPC - 434796). This study was supported
in part by the Global Environment Research Fundof the Ministry of Environment, Japan (S-12), and
JSPS KAKENHI Grant Number 15K00528.

18

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◆著者紹介

向井

苑生

Sonoyo Mukai

京都情報大学院大学応用情報技術研究科
環境リモートセンシングセンター (KCGI-REESIT) 教授

佐野到

Itaru Sano

近畿大学理工学部情報学科
同大学院総合理工学研究科教授

中田

真木子

Makiko Nakata

近畿大学総合社会学部准教授

NAIS Journal

19