A simulation of Aerosols in Asia with the use of ADAM2 and CMAQ

Soon-Ung Park*, Joeng Hoon Cho, Moon-Soo Park

Center for Atmospheric and Environmental Modeling

Seoul National University Research Park RM 515

San 4-2, Bongcheon-dong, Gwanak-gu, Seoul, 151-919

REPUBLIC OF KOREA *supark@snu.ac.kr http://www.caem.re.kr

Abstract: The Aerosol Modeling System (AMS) has been developed to estimate aerosol concentrations of both

natural (dust) and anthropogenic aerosols and implemented to simulate the observed PM10 concentration for

the period from 10 to 17 March 2010 when a couple of Asian dust events are observed in South Korea. The

system is based on the Asian Dust Aerosol Model 2 (ADAM2) for the dust aerosol and the Community

Multiscale Air Quality (CAMQ) modeling system of the US Environmental Protection Agency (EPA) for the

anthropogenic aerosol. The AMS has been employed to simulate aerosol concentrations in the Asian domain at

27 km horizontal grid spacing with the Intercontinental Chemical Transport Experiment – Phase B (INTEX-B)

pollutant emission data in Asia and in the nested East Asian domain at 9 km horizontal grid spacing with

emission data from the INTEX-B in Asia and the Clear Air Policy Supporting System (CAPSS) of Korea

Ministry of Environment in the South Korean region. It is found that the AMS model with the 27 km horizontal

grid scheme (Model 1) tends to underestimate the concentrations of both the dust (natural) and the

anthropogenic aerosols, whereas the model with the 9 km horizontal grid scheme (Model 2) quite successfully

simulates the observed Asian dust and anthropogenic aerosol concentrations. This suggests that AMS has a

great potential for the use of Asian aerosol forecast model in Asia.

Key-Words: Aerosol Modeling System (AMS), ADAM2, Anthropogenic aerosol, Asian dust aerosol, CMAQ

1 Introduction In recent years, atmospheric aerosols have become

an emerging issue due to their important role in

atmospheric processes, including their number

concentration, their mass, size, chemical

composition, and aerodynamic and optical

properties. Of these, size is the most important; it

not only reflects the nature of the source of the

aerosols but also relates to their health effects (Bates

et al., 1966; Pope et al., 1992; Dockery et al., 1992,

1993; Balásházy et al., 2003) and to their aesthetic

and climate effects via their light scattering

properties (Chang and Park, 2004; Park et al., 2005;

IPCC, 1996; Jacobson, 2001; Kanfman et al., 2002;

Penner et al., 2004; Crutzen, 2004).

Current estimates suggest that anthropogenic

aerosols and biomass burning have climate forcing

enough to offset warming caused by greenhouse

gases such as carbon dioxide (Kiehl and Briegle,

1993). However, a large uncertainty of key

properties of aerosols makes it difficult to correctly

quantify the radiative forcing (Chang and Park,

2004). The uncertainties are due to in part to the

limited data on aerosol climatology, and in part to

the lack of our understanding on the processes

responsible for the production, transport, physical

and chemical evolution and the removal of aerosols

at various spatial and time scales that are largely

dependent on the aerosol size distribution (Clarke et

al., 2004).

East Asia is a major source of natural (Asian

dust) and anthropogenic aerosols over the Northern

Hemisphere due to rapid economic expansion in

many Asian countries. Asian dust (Hwangsa in

Korean) which is a typical example of mineral

aerosol occurs in the Sand desert, Gobi desert and

Loess plateau in northern China and Mongolia more

frequently during the spring season (Park and In,

2003; In and Park, 2003; Park and Lee, 2004).

Tropospheric aerosols that originate in China are the

complex mixture of various aerosols such as Asian

dust and anthropogenic particles from a variety of

sources. The dust particles raised in the source

regions of inland China will experience pollutants

emitted from the industrialized regions that are

heavily concentrated in the eastern part of China

during long-range transport of dust. Consequently, a

significant transformation of chemical composition

of the dust is expected as indicated by observations

(Saxena and Seigneur, 1983; Gao et al., 1991; Kang

and Sang, 1991; Park et al., 2005).

Recently Park et al. (2010a) have developed the

Asian Dust Aerosol Model 2 (ADAM2) and used to

Advances in Fluid Mechanics and Heat & Mass Transfer

ISBN: 978-1-61804-114-2 258

simulate successfully the Asian dust events (Park et

al., 2010b, Park et al., 2010 c). However, this model

cannot be used to simulate the anthropogenic

aerosols. Therefore, the Community Multiscale Air

Quality (CAMQ) model (Otte et al., 2005; Byun and

Schere, 2006) has been coupled with ADAM2 to

simulate all kinds of aerosols including natural and

anthropogenic aerosols.

The purpose of this study is to simulate aerosols

in Asia using the Aerosol Modeling System (AMS)

that entails coupling the ADAM2 model with the

CAMQ model for the period of 10 to 17 March

2010 when Asian dust events occurred in Asia and

to test the performance of the AMS model.

2 Model description

2.1 Meteorological Model The meteorological model used in this study is the

fifth generation mesoscale model of non-hydrostatic

version (MM5, Pennsylvania State University/

National Center for Atmospheric Research) defined

in the x, y, and σ coordinate (Grell et al., 1994;

Dudhia et al., 1998).

The model domain (Fig. 1) has horizontal

resolution of 27 x 27 km2 for the Asian domain (Fig.

1a) with nested domains of 9 x 9 km2 for the East

Asia domain (Fig. 1b). Each domain has 25 vertical

layers. For our convenience the model with the

Asian domain is referred to “Model 1” and that of

the East Asian domain to “Model 2”.

Fig. 1 Model domains and the Asian dust source

region with surface soil types (Gobi , Sand ,

Loess and Mixed ) in (a) Model 1 (Asian

domain) and (b) Model 2 (East Asian domain).

2.2 Aerosol Modeling System (AMS) The Aerosol Modeling System (AMS) is consisted

of the Asian Dust Aerosol Model 2 (ADAM2, Park

et al., 2010a), the Community Multiscale Air

Quality (CMAQ) modeling system (http://www.

cmaq-model.org) with pollutants emission data.

2.2.1 ADAM2 model The ADAM2 model is an Eulerian dust transport

model that includes the specifications of the dust

source regions, delineated by the statistical analysis

of the World Meteorological Organization (WMO)

dust reporting data and statistically derived dust

emission conditions in Sand, Gobi, Loess and mixed

soil surface in the domain in Fig. 1a. The model

uses the suspended particle-size distribution

parameterized by the several log-normal

distributions in the source regions, based on the

concept of the minimally and fully dispersed

particle-size distribution. It has 11-size of bins with

near the same logarithm interval for particles of

0.15-37 µm in radius (Park and In, 2003; Park and

Lee, 2004). The model can be run for a whole year

round with a temporally varying emission reduction

factors derived statistically using the normalized

difference vegetation index (NDVI) in the different

surface soil types in the Asian dust source region.

The detailed description is given in Park et al.

(2010a).

2.2.2 CMAQ model

The EPA Community Multiscale Air Quality

(CMAQ) modeling system (http://www.cmaq-

model. org) is a three-dimensional Eulerian

atmospheric chemistry and transport modeling

system that simulates ozone, particulate matter,

toxic airborne pollutants, visibility, and acidic and

nutrient pollutant species throughout the

troposphere (University of North Carolina, 2010).

The aerosol component of the CMAQ model has

the particle size distribution as the superposition of

three lognormal subdistributions, called modes. Fine

particles with diameters less than 2.5 µm (PM2.5) are

represented by two subdistributions called the

Aitken and accumulation modes. The Aitken mode

includes particles with diameters up to

approximately 0.1 µm for mass distribution and the

accumulation mode covers the mass distribution in

the range from 0.1 to 2.5 µm. The coarse mode

covers the mass distribution in the range from 2.5

µm to 10 µm. The model includes the processes of

coagulation, particle growth by the addition of mass

Advances in Fluid Mechanics and Heat & Mass Transfer

ISBN: 978-1-61804-114-2 259

and new particle formation (Binkowski and Roselle

2003).

2.2.3 Emission data

Air pollutant emissions in Asia (Fig. 2) in the year

2006 are obtained from the Intercontinental

Chemical Transport Experiment – Phase B (INTEX

B, Zhang et al., 2009) that include

anthropogenic sources, excluding biomass burning.

The estimated total Asian (China

genic emissions in the year 2006 are 47.1 Tg (31.0

Tg) SO2, 36.7 Tg (20.8 Tg) NOX, 54.6 Tg (23.2 Tg)

VOC, 298.2 Tg (166.9 Tg) CO, 29.2 Tg

PM10, 22.2 Tg (13.3 Tg) PM2.5, suggesting more

than 60 % of the total anthropogenic emissions

being contributed by China.

Fig. 2 Horizontal distributions of emission rate (t

day-1 grid

-1) of (a) SO2, (b) NOx, (c) NH

PM10 (1 grid: 27x27 km2) on March 2006.

Air pollutant emissions in South Korea (Fig. 3) in

the year 2007 are obtained from the Clean Air

Policy Supporting System (CAPSS, Korean

Ministry of Environment) in a 1 x 1 km

scheme. These emission data over South Korea are

used for the simulation of aerosols in

3 A simulation of aerosols for the

period of 10 to 17 March 2010An aerosol simulation has been done with the use of

AMS for the period of 10 to 17 March 2010 in the

Asian domain (Fig. 1a) with the use of emission

data in Fig. 2 and the nested East Asia domain (Fig.

1b) with emission data in Figs. 2 and 3.

Binkowski and Roselle,

Air pollutant emissions in Asia (Fig. 2) in the year

2006 are obtained from the Intercontinental

hase B (INTEX-

ncludes all major

anthropogenic sources, excluding biomass burning.

The estimated total Asian (China’s) anthropo-

genic emissions in the year 2006 are 47.1 Tg (31.0

, 54.6 Tg (23.2 Tg)

VOC, 298.2 Tg (166.9 Tg) CO, 29.2 Tg (18.2 Tg)

, suggesting more

than 60 % of the total anthropogenic emissions

Horizontal distributions of emission rate (t

, (b) NOx, (c) NH3, and (d)

) on March 2006.

Air pollutant emissions in South Korea (Fig. 3) in

the year 2007 are obtained from the Clean Air

Policy Supporting System (CAPSS, Korean

Ministry of Environment) in a 1 x 1 km2 grid

scheme. These emission data over South Korea are

used for the simulation of aerosols in Model 2.

A simulation of aerosols for the

period of 10 to 17 March 2010 has been done with the use of

AMS for the period of 10 to 17 March 2010 in the

Asian domain (Fig. 1a) with the use of emission

data in Fig. 2 and the nested East Asia domain (Fig.

1b) with emission data in Figs. 2 and 3.

Fig. 3 Horizontal distributions

day-1 grid

-1) of (a) SO2, (b) NOx, (c) NH

PM10 (1 grid: 3x3 km2) on March 2006.

Fig. 4 shows the spatial distributions of the near

surface total PM10 concentration (Fig. 4a) and the

anthropogenic aerosol (PM

4b) simulated by Model 1

2010.

(a) Total PM10

(b) Anthropogenic PM 10

Fig. 4 Horizontal distribution of near surface (a)

total PM10 concentration and (b) anthropogenic

aerosol (PM10) concentration simulated by Model 1

at 00 UTC 16 March 2010.

Horizontal distributions of emission rate (t

, (b) NOx, (c) NH3, and (d)

) on March 2006.

Fig. 4 shows the spatial distributions of the near

concentration (Fig. 4a) and the

anthropogenic aerosol (PM10) concentration (Fig.

simulated by Model 1 at 00 UTC 16 March

Horizontal distribution of near surface (a)

concentration and (b) anthropogenic

) concentration simulated by Model 1

UTC 16 March 2010.

Advances in Fluid Mechanics and Heat & Mass Transfer

ISBN: 978-1-61804-114-2 260

The anthropogenic aerosols include SO42-, NO3

-,

NH4+, OC, BC, sea salt, emitted PM10. It is quite

clear that the contribution of the anthropogenic

aerosols to the total aerosols is quite significant

especially in the south eastern part of China were

the pollutant emission rate is large. The enhanced

anthropogenic aerosol in this region may cause the

frequent occurrence of the haze.

Fig. 5 shows the spatial distributions of the near

surface total PM10 concentration (Fig. 5a) simulated

by Model 2 at the same time in Fig. 4.

The total PM10 concentration in the nested

domain is enhanced compared with that in the Asian

domain (Figs. 4 and 5) toward the downwind region

due to the enhanced wind speed in the lower level

that results in the enhancement of dust rise in the

source regions in northeastern China.

Fig. 5 The same as in Fig. 4 except for by Model 2.

To examine more closely the impact of the nested

grid domain on the aerosol concentration, the time

series of observed and modeled PM10 concentrations

estimated by Model 1 and Model 2 are compared at

several sites located in the downwind region of

South Korea.

Fig. 6 shows the time series of the observed total

PM10 concentration, the modeled total PM10

concentration and the anthropogenic aerosol

concentration estimated by Model 1 and Model 2 at

Seoul, Jeonju, Ulsan and Busan in Korea (Fig. 1b).

Fig. 6 Time series of observed (solid-dot line), total

(solid line), anthropogenic (dotted line) PM10

concentration simulated by Model 1 and Model 2 at

(a) Seoul, (b) Jeonju, (c) Ulsan, and (d) Busan.

Advances in Fluid Mechanics and Heat & Mass Transfer

ISBN: 978-1-61804-114-2 261

It is quite clear that the observed aerosol

concentrations more than 100 µg m-3 are mainly

contributed by the Asian dust aerosol and the Model

2 simulates much better peak aerosol contribution

during the dust event period and the anthropogenic

aerosol concentration during the non-dust event

period than those by the Model 1 at all sites (Fig. 6),

suggesting the importance of the nested grid model

for the simulation of the total aerosols.

Fig. 7 shows the time series of modeled total

aerosol concentration and the anthropogenic aerosol

concentration simulated by Model 1 and Model 2 at

Beijing in China. Although the direct comparison of

aerosol concentrations simulated by Model 1 and

Model 2 is difficult without the observed data, the

time series of wind speed (not shown) implies better

result of Model 2 than that of Model 1. The

anthropogenic aerosol concentration can be more

than 200 µg m-3 during the non-dust event period

causing a haze problem. The dust event occurred in

the later afternoon on 15 March (Fig. 7) may be

associated with the dust event observed in the

morning on 16 March in Korea (Fig. 6).

Fig. 7. Time series of total PM10 concentration

(solid line) and anthropogenic PM10 concentration

(dotted line) simulated by (a) Model 1 and (b)

Model 2.

4 Conclusion The presently developed Aerosol Modeling System

(AMS) on the basis of the ADAM2 model for the

Asian dust aerosols and the CMAQ model for the

anthropogenic aerosols has been employed to

estimate the total aerosol concentration (natural +

anthropogenic aerosols) in Asia for the period from

10 to 17 March 2010 when a couple of dust events

are observed in Korea.

It is found that the AMS model has demonstrated

its ability to simulate quite reasonably the observed

PM10 concentration and to distinguish the

contribution of anthropogenic aerosol concentration

from the total observed PM10 concentration. The

observed high PM10 concentrations exceeding 100

µg m-3 in Korea are found to be mainly contributed

by the Asian dust aerosol whereas the anthropogenic

aerosol concentrations exceeding 200 µg m-3 are

often found in the southeastern part of China where

the pollutant emissions are highest. This may cause

frequent occurrences of haze events in this region.

It is also found that Model 2 (horizontal grid

distance of 9 km) simulates much better total

aerosol concentration by simulating better

meteorological fields than Model 1 (horizontal grid

distance of 27 km).

The present study mainly pertains to the

performance test of the AMS model for a short time

period. More accurate prediction of the aerosol

concentration requires a proper parameterization of

interaction processes between the aerosol and

gaseous pollutants and among different aerosols.

This is now on hand.

Acknowledgments: This work was funded by the Korea Meteorological

Administration Research and Development Program

under grant CATER 2012-2050.

References:

[1] Balásházy I, Hofmann W, Heistracher T, Local

particle deposition pattern may play a key role

in the development of lung cancer, Journal of

Applied Physiology, 94, 2003, 1719-1725.

[2] Bates DV, Fish BR, Hatch TF, Mercer TT,

Morrow PE, Deposition and retension models

for internal dosimetry of the human respiratory

tract, Heath Physics, 12, 1966, 173-178.

[3] Binkowski FS, Roselle SJ, Models-3

Community Multiscale Air Quality (CMAQ)

model aerosol component 1. Model description,

Journal of Geophysical Research, 108(D6),

2003, 4183.

[4] Byun D, Schere KL, Review of the governing

equations, computational algorithms, and other

components of the Models-3 Community

Multiscale Air Quality (CMAQ) Modeling

System, Applied Mechanics Reviews, 59, 2006,

51-77.

[5] Chang L-S, Park S-U, Direct radiative forcing

due to anthropogenic aerosols in East Asia

during April 2001, Atmospheric Environment,

38, 2004, 4467-4482.

Advances in Fluid Mechanics and Heat & Mass Transfer

ISBN: 978-1-61804-114-2 262

[6] Clarke AD, Shinozuka Y, Kapustin VN,

Howell S, Huebert B, Doherty S, et al., Size

distributions and mixtures of dust and black

carbon aerosol in Asian outflow, Journal of

Geophysical Research 109, 2004, D15S09.

[7] Crutzen P, New directions: the growing urban

heat and pollution island effect - impact on

chemistry and climate, Atmospheric

Environment, 38, 2004, 3539-3540.

[8] Dockery DW, Pope CA, Xu XP, Spengler JD,

Ware JH, Fay ME et al., An association

between air-pollution and mortality in six

United States cities, New England Journal of

Medicine, 329, 1993, 1753-1759.

[9] Dockery DW, Schwartz J, Spengler JD, Air

pollution and daily mortality: associations with

particulates and acid aerosols, Environmental

Research, 59, 1992, 362-373.

[10] Dudhia J, Grill D, Guo Y-R, Hausen D,

Manning K, Wang W, PSU/NCAR mesoscale

modelling system tutorial class notes (MM5

modelling system version 2), 1998.

[11] Grell GA, Dudhia J, Stauffer DR, A description

of 5th generation Penn State/NCAR mesoscale

model (MM5), NCAR TECH, Note NCAR/TN-

398, 1994,117p.

[12] In H-J, Park S-U, The soil particle size

dependent emission parameterization for an

Asian dust (yellow sand) observed in Korea on

April 2002. Atmospheric Environment, 37,

2003, 4625-4636.

[13] IPCC (Intergovernmental Panel on Climate

Change), Climate Change 1995: The science of

Climate change, Cambridge University Press,

1996.

[14] Jacobson MZ, Strong radiative heating due to

the mixing state of black carbon in atmospheric

aerosols, Nature, 409, 2001, 695-697.

[15] Kanfman YJ, Tanré D, Boucher O, A satellite

view of aerosols in the climate system, Nature,

419, 2002, 215-223.

[16] Kang KH, Sang SE, Influence of yellow sand

on TSP in Seoul, Proceeding of the 2nd

IUAPPA, Regional Conference on Air

Pollution, II, Seoul, Korea.

[17] Kiehl JT, Briegle BP, The relative roles of

sulfate aerosols and greenhouse gases in

climate forcing, Science, 260, 1993, 311-314.

[18] Otte TL, Pouliot G, Pleim JE, Young JO,

Schere KL, Wong DC, et al., Linking the Eta

Model with the Community Multiscale Air

Quality (CMAQ) modelling system to build a

national air quality forecasting system, Weather

& Forecasting, 20, 2005, 367-384.

[19] Park S-U, Chang L-S, Lee E-H, Direct

radiative forcing due to aerosols in East Asia

during a Hwangsa (Asian dust) event observed

in 18-23 March 2002 in Korea, Atmospheric

Environment, 39, 2005, 2593-2606.

[20] Park S-U, Choe A, Lee E-H, Park M-S, Song X,

The Asian dust aerosol model 2 (ADAM2)

with the use of normalized difference

vegetation data (NDVI) obtained from the

spot4/vegetation data, Theoretical and Applied

Climatology, 101, 2010a, 191-208.

[21] Park S-U, Choe A, Park M-S, Estimates of

Asian dust deposition over the Asian region by

using ADAM2 in 2007, Science of the Total

Environment, 408, 2010b, 2347-2356.

[22] Park S-U, Choe A, Park M-S, Chun Y,

Performance tests of the Asian Dust Aerosol

Model 2 (ADAM2), Journal of Sustainable

Energy & Environment, 1, 2010c, 77-83.

[23] Park S-U, In H-J, Parameterization of dust

emission for the simulation of the yellow sand

(Asian dust) observed in March 2002 in Korea,

Journal of Geophysical Research, 108(D19),

2003, 4618.

[24] Park S-U, Lee E-H, Parameterization of Asian

dust (Hwangsa) particle-size distributions for

use in dust emission model, Atmospheric

Environment, 38, 2004, 2155-2162.

[25] Penner JEP, Dong X, Chen Y, Observational

evidence in radiative forcing due to the indirect

aerosol effect, Nature, 427, 2004, 231-234.

[26] Pope CA, Schwartz J, Ransom MR, Daily

mortality and PM10 pollution in Utah valley,

Archives of Environmental Health, 47, 1992,

211-217.

[27] Saxena P, Seigneur C, Modeling of multiphase

atmospheric aerosol, Atmospheric Environment,

17, 1983, 1315-1329.

[28] University of North Carolina, Operational

Guidance for the Community Multiscale Air

Quality (CMAQ) Modeling System,

Community Modeling and Analysis System

Institute for the Environment, 2010.

[29] Zhang Q, Streets DG, Carmichael GR, He KB,

Huo H, Kannary A, et al., Asian emissions in

2006 for the NASA INTEX-B mission,

Atmospheric Chemistry and Physics, 9, 2009,

5131-5153.

Advances in Fluid Mechanics and Heat & Mass Transfer

ISBN: 978-1-61804-114-2 263