Atmospheric Environment 42 (2008) 5939–5955

Long-range transport of acidifying substances

in East Asia—Part I

Model evaluation and sensitivity studies

Meiyun Lina,, Taikan Okia, Tracey Hollowayb, David G. Streetsc,

Magnus Bengtssond

, Shinjiro Kanaea

a

Institute of Industrial Science, University of Tokyo, Tokyo, Japan b

Center for Sustainability and the Global Environment, University of Wisconsin-Madison, WI, USA c

Argonne National Laboratory, Argonne, IL, USA d

Institute for Global Environmental Strategies, Kanagawa, Japan

Received 22 November 2007; received in revised form 4 April 2008; accepted 18 April 2008

Abstract

This study has conducted a comprehensive model evaluation to help identify major uncertainties of regional air quality

model in predicting long-range transport and deposition of acidifying substances in East Asia. Annual predictions of the

Community Multiscale Air Quality (CMAQ) model are carried out at two horizontal scales: an 81 km domain over East

Asia and a 27 km domain over Northeast Asia. The model successfully reproduces the magnitudes and diurnal variations

of SO2 mixing ratios at most sites of the Acid Deposition Monitoring Network in East Asia (EANET). Through the

comparison with tropospheric NO2 columns from the Global Ozone Monitoring Experiment (GOME), the model is shown

to be able to capture major spatial and seasonal variations of NO2 observed from space over East Asia. Regarding the

magnitudes, however, CMAQ underpredicts the GOME retrieval over industrial area of eastern China in March and

December, and over the remote western China in July. Primary reasons for the discrepancy over eastern China are the

uncertainties both in emission inventory and in the GOME retrieval in wintertime. For the wet season the soil-biogenic NO

emission estimates need to be reviewed regarding the intensity and timing of fertilizer applications, and the magnitude of

rain-induced pulsing. The sensitivities of predicted NO2 columns, NOx mixing ratios, and wet nitrate deposition to 50%

increase of NOx emissions are studied. Due to the underpredictions of NOx and also to the uncertainty in modeled

precipitation and nitrate formation, CMAQ has a tendency to underpredict annual wet deposition loads of nitrate

observed by the EANET network.

r 2008 Elsevier Ltd. All rights reserved.

Keywords: Nitrogen oxides; Satellite data; Acid deposition; Multiscale; East Asia

1. Introduction

Nitrogen oxides ðNOx 1⁄4 NO þ NO2Þ are emitted

into the atmosphere by natural processes such as

lightning or soil emissions, and by human activities

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www.elsevier.com/locate/atmosenv

1352-2310/$ - see front matter r 2008 Elsevier Ltd. All rights reserved.

doi:10.1016/j.atmosenv.2008.04.008

Corresponding author. Tel.: +81 3 5452 6382;

fax: +81 3 5452 6383.

E-mail address: lin@rainbow.iis.u-tokyo.ac.jp (M. Lin).
related to power generation, industry, traffic, and

open biomass burning. Emissions of NOx over Asia

have experienced a rapid increase in the last decade

(Naja and Akimoto, 2004), with China and India

being the largest contributors. Richter et al. (2005)

have reported that the vertical column densities

(VCDs) of tropospheric NO2 in satellite measurements over the industrial area of China increased

about 50% from 1996 to 2004 with an accelerating

trend in annual growth rate. The principal sink of

tropospheric NOx is oxidation to nitric acid

ðHNO3Þ by reaction of NO2 with OH during

daytime and by reaction of NO2 with ozone ðO3Þ

followed by hydrolysis of N2O5 on aerosols at night

(Dentener et al., 1993; Evans and Jacob, 2005).

Recent ground-based monitoring data indicate that

the relative contribution of HNO3 to the precipitation acidity in parts of East Asia is gradually

increasing (e.g. Seto et al., 2002; Xie, 2002).

The long-range transport and fate of Asian

pollutants in the atmosphere is an area of increasing

scientific interest and political concern. Hemispheric

transport of CO and O3 have been examined

through global chemical transport modeling and

analysis of ozone soundings (e.g. Liu et al., 2002;

Naja and Akimoto, 2004) and aircraft measurements (e.g. Bey et al., 2001). A large set of global

chemical transport models (CTMs) has been used to

discuss the relationship between NOx emissions and

satellite NO2 VCDs (e.g. van Noije et al., 2006;

Wang et al., 2007; Martin et al., 2007). These global

scale studies will be useful for assessing regional air

quality, but they do not replace the need for

regional scale analysis. Regional CTMs are needed

to estimate source–receptor (S/R) relationships at

regional scales, and to examine pollution and

precursor outflow to other areas (Carmichael

et al., 2007). Due to lack of measurement data,

one of the key problems of regional air quality

modeling in East Asia is reducing the uncertainties

in the boundary conditions (BCs). By linking a

global CTM to regional modeling in East Asia, the

present study seeks to quantify the impact of global

inflows as well as regional sources on the deposition

load of sulfur and reactive nitrogen in East Asia.

The state-of-the-art regional Community Multiscale

Air Quality model (CMAQ v4.5.1) (Byun and

Ching, 1999; Byun and Schere, 2006) is employed

at two horizontal scales: a 81 km domain over East

Asia and a nested 27 km domain over Northeast

Asia. To suppress the inaccuracy of lateral-fixed BC

and also to quantify the global inflows, the global

Model for Ozone and Related Chemical Tracers

(MOZART v2.4) (Horowitz et al., 2003) is coupled

to provide BCs for the 81 km domain of CMAQ.

In addition, we have developed a new emission

processing model to incorporate various emission

data with different resolutions to the uniform model

grids.

To support the application of the multiscale

model system to predict S/R relationships, a

comprehensive model evaluation has been carried

out by using in situ monitoring and satellite

measurements. We present our results of model

evaluation and S/R relationships for acidifying

substances as two parts. The current paper, Part I,

describes the modeling system and presents the

evaluation of CMAQ annual predictions with

ground-based data from the Acid Deposition

Monitoring Network in East Asia (EANET)

(EANET, 2002), and with satellite data from the

Global Ozone Monitoring Experiment (GOME)

instrument (Richter et al., 2005). The Part II paper

(Lin et al., 2008b) presents the results of source

region attribution for sulfur and reactive nitrogen

deposition in East Asia. In addition, we have also

employed the same models to examine the budget

and photochemical sensitivity of O3 production,

which is presented in a separate paper (Lin et al.,

2008a).

The current paper is structured as follows.

Section 2 provides a brief overview of CMAQ

v4.5.1 and related data including emissions, meteorology, BCs, and observations. Section 3 evaluates

spatial, seasonal, and diurnal variations of primary

gas SO2 with the EANET surface measurements.

Section 4 presents an integrated evaluation of

tropospheric NO2 columns and surface NOx concentrations by using the combination of in situ and

satellite measurements. Section 5 analyzes the

chemical components in rainwater and the deposition load of sulfate, nitrate and ammonium.

2. Data and methods

2.1. Model setup and meteorological inputs

CMAQ v4.5.1 contains several updates from the

previous release of the model. A new global mass

continuity scheme is implemented. This scheme is

globally mass-conserving and uses the piecewise

parabolic method (PPM) (Colella and Woodward,

1984) advection scheme for horizontal advection.

The minimum value for the eddy diffusivity (KzÞ has

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5940 M. Lin et al. / Atmospheric Environment 42 (2008) 5939–5955
an important impact on nighttime concentrations.

A new option for minimum Kz is introduced in

CMAQ v4.5.1. The new version interpolates between urban and non-urban land cover to calculate

minimum Kz which can vary from 0.5 to 2:0 m2 s1.

Gas-phase chemistry is simulated with the CarbonBond IV (CBIV) (Gery et al., 1989) mechanism with

the Euler Backwards Iterative (EBI) solver. CMAQ

is configured with the AERO-3 aerosol module

(Binkowski and Roselle, 2003), in which sea salt and

the interactions between the fine- and coarse-mode

particles are not treated.

The study region is centered at ð110

E; 20NÞ on a

Lambert projected map of East Asia (Fig. 1). The

modeling domains consist of a coarse 81 km domain

that covers the wide areas of East Asia and a oneway nested 27 km domain that covers an area of

Northeast Asia including central east China, the

Korea Peninsula, and Japan. The meteorological

fields for both domains are generated by the MM5

model (Grell et al., 1994) with four-dimensional

data assimilation. MM5 initialization uses the

NCEP/NCAR global meteorological reanalysis

data sets (6 h interval and 2:5  2:5 resolution).

A three-dimensional (3-D) grid nudging technique

(6 h interval) is used both for the coarse- and finegrid simulations. No surface nudging is performed.

The Noah land surface model was adopted to

simulate land surface interactions, the cloud scheme

of Grell et al. (1994) was chosen for the physical

parameterization, and the MRF scheme of Hong

and Pan (1996) was employed for planetary

boundary layer (PBL) parameterization. A spin-up

period of two months (November–December 2000)

is carried out to allow clouds and other process to

spin up in MM5. We find that the spin-up process

significantly improves the simulated precipitation in

Southeast Asia during the beginning three months

of 2001.

There are 23 sigma levels from surface to 15 km

for the MM5 meteorological predictions. The MM5

output files are post-processed with the MeteorologyChemistry Interface Processor (MCIP) version 3.0

for CMAQ. In order to alleviate the computational

cost of CMAQ for S/R calculations associated with a

large number of vertical layers, vertical layer collapsing is performed to reduce the vertical layers from

23 MM5 sigma levels to nine sigma levels in CMAQ

with a fine resolution in the PBL. The CMAQ layers

correspond to sigma levels of 1.00, 0.98, 0.96, 0.92,

0.88, 0.80, 0.60, 0.30, and 0.00. The height of first

CMAQ layer is set to 73 m, which was also used in

the Chemical Weather Forecasting System (CFORS)

(Carmichael et al., 2003) and was shown to be robust

for regional scale studies. The intercomparison

study of tropospheric NO2 columns from 17 global

models with vertical layers ranging from 19 levels

to 52 levels (van Noije et al., 2006) suggested that

the model derived NO2 vertical columns are not

sensitive to model vertical resolutions.

2.2. Emission data

Emission data applied in this study include

anthropogenic, biomass burning, volcanic SO2, and

biogenic emissions. Anthropogenic emissions of SO2,

NOx, NH3, CO and VOCs (volatile organic compounds) were taken from Streets et al. (2003a) derived

for the TRACE-P experiment as well as emission

database for global atmospheric research (Olivier

et al., 2005) over regions for which TRACE-P

estimates are unavailable. The TRACE-P inventory

investigated the monthly variation of emissions of

the major species at provincial scale in China. This

addressed additional emissions during the winter

heating season in China and the greater intensity of

evaporative emissions (NH3 and CH4) during the

warmer months. Biomass burning emissions with

seasonal and inter-annual variability were taken from

van der Werf et al. (2006). The SO2 emissions from

the largest erupting volcano of Miyakejima, located

to the south of Tokyo, were taken from Kajino et al.

(2004) and Kazahaya et al. (2004). Seasonal emission

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80 ̊

90 ̊

90 ̊

100 ̊

100 ̊

110 ̊

110 ̊

120 ̊

120 ̊

130 ̊

130 ̊ 140 ̊

10 ̊

10 ̊

20 ̊

20 ̊

30 ̊

30 ̊

40 ̊

40 ̊ 1

2

34

5

6

7

8

9

10

11

12

13

15 14

17

16

18

19

20

21

22

25 26 27

28 29

30 31 32

33

3435 36 37

38

Fig. 1. Model domains (81-km and 27-km horizontal resolution)

for the application of CMAQ in East Asia. Locations of EANET

monitoring sites are also shown.

M. Lin et al. / Atmospheric Environment 42 (2008) 5939–5955 5941
flux of NO from soils (Yienger and Levy, 1995) as

well as CO and hydrocarbons (i.e. isoprene, monoterpene, and acetone) from vegetation in the year

2000 were obtained from Olivier et al. (2003). It

should be noted that emissions of lightning produced

NOx, wind blown sea salt and dust are not included

currently since the influence of anthropogenic

sources to acid deposition is the primary focus of

this study. The exclusion of these emissions is

assumed not to have significant influence on the

results of S/R relationships for acid deposition.

The widely used SMOKE emission processing

model requires a large set of surrogate data and

sector/regional dependent temporal profiles. These

data are probably not available in regions other than

North America. Thus, we have developed a new

emission processing model to import and transform

aforementioned inventory data by gridded emission

control (for S/R perturbation runs), spatial interpolation, temporal allocation, and chemical speciation. This new model is an effective alternative to

the SMOKE model, and is well suited for adapting

existing emission data in regions other than North

America. The new emission processing model is

publicly available upon request.

For the current study, inventory data with

different spatial resolutions are sampled and interpolated to the model grids of 81- and 27-km,

respectively. All large point sources (LPS) are

injected into model layers according to stack

parameters, with the stack height of volcanoes

assumed to be 1500 m and anthropogenic LPSs

(power plants and large industries) assumed to be

300 m. It is assumed that 90% of anthropogenic

NOx emissions are in the form of NO and 10% in

the form of NO2. For VOCs speciation, firstly the

national (provincial scale in China) speciation

profiles from the TRACE-P inventory are allocated

to each grid cell based on grid region ID. Secondly,

the gridded speciation profiles are used to split

VOCs emissions into individual pollutants. Thirdly,

the mechanism dependent speciation profiles

(information available at http://pah.cert.ucr.edu/

carter/emitdb/emitdb.xls) are imported to lump

the inventory pollutants to the photochemical

species of CBIV. For the speciation of primary

particulate matters (PM2.5), the mass ratios of

primary sulfate and nitrate are assumed to be 18%

and 8%, respectively, for fossil fuel burning, while

1% and 0.3% for biofuel fuel or biomass burning

(Wang et al., 2006). The chemical processing

module included in the emission model is general

to work for the CBIV, SAPRC99 (Carter, 2000),

and other user-defined mechanisms.

2.3. Initial and bouindary conditions

Initial conditions for the 81 km domain of CMAQ

use the default concentration profiles which are

intended to represent relatively clean air conditions.

A spin-up period of two weeks (15 December 2000–1

January 2001) is used to minimize the influences of

initial conditions. CMAQ with the 81-km grid

spacing is then continuously run from January to

December in 2001.

Driving regional CTMs with BCs derived from

global models tends to provide more realistic spatial

and temporal variability than with the lateral-fixed

profiles. In this study we adopted monthly mean

results of the global model MOZART to provide

BCs for the CMAQ 81 km domain. In addition, the

CMAQ 27 km simulation adopted hourly varying

BCs derived from the CMAQ coarse simulations.

The hourly varying BCs intend to provide more

realistic temporal variability than monthly mean

BCs from MOZART. The MOZART simulations

were driven with NCEP reanalysis meteorology for

2000–2002 and were evaluated in Holloway et al.

(2007). Horizontal interpolation and vertical linear

interpolation based on layer height are carried out

to convert data on the MOZART coordinate

(1:9  1:9

, 28 vertical hybrid sigma levels) to the

CMAQ coordinate (81  81 km, eight vertical sp

layers). The MOZART outputs for BCs consisted of

19 species including O3, peroxyacetyl nitrate (PAN),

long-lived hydrocarbons, aerosols, and other gases.

It should be noted that CMAQ uses lateral BCs

only. No upper BCs of O3 are employed in CMAQ.

2.4. Observation data

Table 1 and Fig. 1 give the location and

characteristics of EANET monitoring sites, labeled

by identification numbers in comparison figures.

Automatic monitoring method is used for the

hourly air concentration data in Japan. For wet

deposition, precipitation samples were collected

using a wet-only sampler on a daily basis for most

sites and on a weekly basis for the sites in

Philippines and Vietnam (EANET, 2002). Ion

chromatography is a major analytical method for

SO2

4 , NO3 , and NHþ

4 concentrations in rainwater.

Tropospheric NO2 column densities derived from

the CMAQ regional CTM are evaluated with the

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5942 M. Lin et al. / Atmospheric Environment 42 (2008) 5939–5955
most recent version of GOME retrieval by Richter

et al. (2005) from Bremen University. GOME is an

instrument on board the ERS-2 satellite, which was

launched by the European Space Agency in April

1995. The GOME instrument observes the atmosphere in nadir view and global coverage is achieved

in every 3 days after 43 orbits with a spatial

resolution of 40 km latitude by 320 km longitude.

Measurements of the satellite instrument GOME

have been used to retrieve tropospheric columns of

NO2 based on analysis of observations of solar

radiation backscattered from the nadir over the

423–451 nm spectral region where NO2 has an

important absorption feature (Burrows et al., 1999).

3. Sulfur dioxide

3.1. Distribution and seasonal variations

Seasonal averaged surface concentrations of SO2

in winter and in summer are shown in Fig. 2. Large

spatial and seasonal variations are found for surface

concentrations of SO2 in East Asia. The seasonal

cycle of SO2 surface concentrations is attributed to

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Table 1

EANET site locations and characteristics

ID Site name Characteristics Lat. Lon. Height (m) Country

1 Guanyinqiao Urban 29.583 106.533 262 China

2 Jinyunshan Rural 29.817 106.367 800

3 Shizhan Urban 34.233 108.950 400

4 Weishuiyuan Rural 34.367 108.867 360

5 Jiwozi Remote 33.833 108.800 2100

6 Hongwen Urban 24.467 118.133 50

7 Xiaoping Remote 24.850 118.033 686

8 Xiang Zhou Urban 22.267 113.567 40

9 Zhuxian Caver Urban 22.200 113.517 45

10 Rishiri Remote 45.117 141.233 40 Japan

11 Tappi Remote 41.250 141.350 105

12 Sado-seki Remote 38.250 138.400 110

13 Happo Remote 36.683 137.800 1850

14 Ijira Rural 35.567 136.700 140

15 Oki Remote 36.283 133.183 90

16 Banryu Urban 34.667 131.700 60

17 Yusuhara Remote 32.733 132.983 225

18 Hedo Remote 26.783 128.233 50

19 Ogasawara Remote 27.083 142.217 230

20 Kanghwa Rural 37.700 126.283 150 South Korea

21 Imsil Rural 35.600 127.183 0

22 Cheju Remote 33.300 126.167 72

25 Mondy Remote 51.667 101.000 2000 Russia

26 Irkutsk Urban 52.233 104.250 400

27 Listvyanka Rural 51.850 104.900 700

28 Ulaanbaatar Urban 47.900 106.817 1282 Mongolia

29 Terelj Remote 47.983 107.483 1540

31 Hoa Binh Rural 20.817 105.333 23 Vietnam

32 Hanoi Urban 21.017 105.850 5

30 Mae Hia Rural 18.767 98.933 350 Thailand

33 Khao Lam Dam Remote 14.767 98.583 170

34 Bangkok Urban 13.767 100.533 2

36 Patumthani Rural 14.033 100.767 2

37 Metro Manila Urban 14.633 121.067 54 Philippines

38 Los Banos Rural 14.183 121.250 35

M. Lin et al. / Atmospheric Environment 42 (2008) 5939–5955 5943
both variations in emissions and meteorological

conditions. In winter due to the highest emissions

from residential heating and strong boundary layer

inversion with little mixing and subsidence, surface

concentrations of SO2 reach more than 15 ppb over

central eastern China and Sichuan Basin. Large

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90 ̊

100 ̊ 110 ̊ 120 ̊ 130 ̊

10 ̊

20 ̊

30 ̊

40 ̊

Winter

90 ̊

100 ̊ 110 ̊ 120 ̊ 130 ̊

10 ̊

20 ̊

30 ̊

40 ̊

Summer

0.1 0.6 2.0 4.0 6.0 8.0 10.0 11.5 13.0 15.0 18.0 20.0

SO2 (ppb)

Fig. 2. Distributions of seasonal averaged surface concentration of SO2 in winter and in summer.

10−2

10−1

100

101

102

SO2 (ppb)

obs, urban obs, rural obs, remote sml

X

observation sites

Winter RMSE=2.1 (ppb) R=0.762

observation sites

Spring RMSE=2.5 (ppb) R=0.419

10−2

10−1

100

101

102

10−2

10−1

100

101

102

10−2

10−1

100

101

102

SO2 (ppb)

X X

observation sites

Summer RMSE=2.2 (ppb) R=0.443

X X X

observation sites

Autumn RMSE=2.5 (ppb) R=0.499

2 4 6 8 10 11 12 13 14 15 16 17 18 19 25 26 27 28 29 30 31 32 34 35 37 38

2 4 6 8 10 11 12 13 14 15 16 17 18 19 25 26 27 28 29 30 31 32 34 35 37 38

2 4 6 8 10 11 12 13 14 15 16 17 18 19 25 26 27 28 29 30 31 32 34 35 37 38

2 4 6 8 10 11 12 13 14 15 16 17 18 19 25 26 27 28 29 30 31 32 34 35 37 38

Fig. 3. Comparison between observed (bars) and predicted (circles) SO2 concentration with 81-km grid spacing for four seasons. RMSE is

root mean square error and R is correlation coefficient. X indicates that no monitoring data were available.

5944 M. Lin et al. / Atmospheric Environment 42 (2008) 5939–5955
amount of SO2 emissions are kept in the nearsurface boundary layer over these regions. In

summer with strong boundary layer mixing and

convection, more SO2 and its oxidants are transported away or efficiently scavenged by the precipitation. SO2 concentrations over large areas of

eastern China decrease to 10 ppb in summer.

Fig. 3 compares the modeled and observed results

for seasonal averaged concentrations of SO2 at

the EANET monitoring sites. In order to investigate

the correlation of model bias with measurement

area characteristics, the urban, rural and remote

sites are identified with different colors as shown in

Fig. 3. The model captures reasonably well the

spatial distribution of the high and low SO2

concentration areas. Underpredictions of SO2

are found at a few urban sites. The uncertainty

in local emission estimates tends to have larger

effect on urban areas where the sub-grid variations

(e.g. pair sites 28–29 of Mongolia and sites 34–35

of Thailand) may not be resolved in 81-km

grid resolution. A systematic overprediction of

SO2 is found at several rural sites, i.e. Weishuishan

(site 4) of China, Ijira (site 14) of Japan, Mae

Hia (site 30) of Thailand, and Los Banos (site 38) of

Philippines. This behavior probably reflects

the rather coarse resolution of the model and

resultant inability to distinguish the gradient for

rural sites that may be located in or nearby grid

cells containing large sulfur sources (Carmichael

et al., 2007). Overpredictions are also found at two

remote mountain sites Happo (site 13) of Japan and

Terelj (site 29) of Mongolia. The overprediction at

the mountain sites is likely associated with sub-grid

scale wind mixing influenced by local topography,

which is strongly smoothed for a horizontal resolution of 81 km. This is validated by the comparison

at the Happo site between the coarse- and fine-grid

simulations shown in Fig. 4.

Observations at Irkutsk (site 27) of Russia clearly

show the impact of fossil fuel heating during winter

season. It should be noted that SO2 emissions in

Russia from the global data set (Olivier et al., 2005)

do not have a seasonal variation. For this reason,

SO2 concentration at Irkutsk is underpredicted in

winter, while overpredicted in summer season.

3.2. Daily and diurnal variations

Fig. 4 shows the hourly time series of observed

and predicted SO2 mixing ratios at Japanese

EANET sites. CMAQ successfully reproduces the

diurnal variations and magnitudes of SO2 for most

days at all sites. Especially with the 27-km grid

spacing, model calculation well captures the finescale dynamic structures of SO2 mixing ratios in

March at the remote site Oki and the urban site

Banryu. For the mountain site Happo, the difference between the coarse- and fine-grid simulations

probably reflects the decreasing in local wind mixing

associated with smoothing of topography. For the

urban site Banryu, SO2 mixing ratios generally peak

at daytime during 16–24 March. The predicted

mixing ratios of SO2 differ appreciably in terms of

both magnitudes and diurnal variations with the

two horizontal grid spacings, reflecting the sub-grid

variations of SO2 emissions and possible influence

of minimum Kz on nighttime predictions. For the

remote sites Sado-seki, Happo, Oki, and Yusuhara,

SO2 concentrations depend primarily on transport

processes. The good agreement between predicted

and observed results implies that the SO2 emissions,

wind fields, transport processes, and chemical

transformations were reasonably well reproduced

by the modeling system. Several studies have

indicated that middle latitude cyclones forming or

intensifying near eastern Asia is a major transport

mechanism of Asian pollutants in the springtime

(e.g. Carmichael et al., 2003). During March 2001,

two eastward traveling low-pressure systems on

17–24 are the dominant meteorological features.

The model’s ability to capture the variations caused

by short-term changes of meteorological conditions

is largely attributed to observational data ingested

into the reanalysis of the large-scale meteorological

fields used for MM5 initialization.

4. Tropospheric nitrogen oxides

4.1. Comparison with satellite measurements

In contrast to in situ data from EANET, which

measures the concentration near the ground, the

GOME remote sensing measurements, after correction for vertical sensitivity, yield the column amounts

integrated over the troposphere. For comparison, 3-

D gridded hourly average NO2 mixing ratios and

MM5-predicted vertically resolved temperature and

pressure are used to calculate the NO2 VCDs in

molecule cm2 units as follows:

VCD 1⁄4 X

8

l1⁄41

Pl  DZl  A  1⁄2NO2ppm;l

Rg  Tl  106  104 (1)

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M. Lin et al. / Atmospheric Environment 42 (2008) 5939–5955 5945
where [NO2ppm;l is the NO2 mixing ratio in ppm in

layer l, DZl is the height of layer l in m, Pl and Tl are

the pressure in Pa and temperature in K, respectively,

in layer l, Rg is the gas constant ð8:34 J mole1

K1Þ,

A is the Avogadro’s number ð6:02213  1022Þ.

Monthly averages of model values at 03:00 UTC,

which is close to overpass time of the ERS-2 satellite

in East Asia, are computed for each grid cell.

Fig. 5 compares satellite measurements of NO2

column from GOME and the calculated value for

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0

4

8

12

16

20

SO2 (ppb)

Sado−seki (38.2N, 138.4E)

Obs 27km 81km

0

2

4

6

8

10

SO2 (ppb)

Happo (36.7N, 137.8E)

0

2

4

6

8

10

SO2 (ppb)

Oki (36.3N, 133.2E)

0

4

8

12

16

20

SO2 (ppb)

Banryu (34.7N, 131.7E)

0

2

4

6

8

10

SO2 (ppb)

Yusuhara (32.7N, 133.0E)

0

2

4

6

8

10

SO2 (ppb)

3Mar 5Mar 7Mar 9Mar 11Mar 13Mar 15Mar 17Mar 19Mar 21Mar 23Mar 25Mar 27Mar

Local Time (JST)

Hedo (26.8N, 128.2E)

Fig. 4. Hourly time series of observed and predicted SO2 mixing ratios in March at six EANET sites in Japan.

5946 M. Lin et al. / Atmospheric Environment 42 (2008) 5939–5955
the selected months March, July, and December.

As a result of relatively short lifetime (less than a

day) of NO2 in the PBL, the abundance of NO2 is

largely controlled by regional emissions and photochemistry while less by long-range transport. The

large NO2 seasonal cycle is explained by the

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90 ̊

100 ̊ 110 ̊ 120 ̊ 130 ̊

10 ̊

20 ̊

30 ̊

40 ̊

March

90 ̊

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10 ̊

20 ̊

30 ̊

40 ̊

July

90 ̊

100 ̊ 110 ̊ 120 ̊ 130 ̊

10 ̊

20 ̊

30 ̊

40 ̊

December

GOME

90 ̊

100 ̊ 110 ̊ 120 ̊ 130 ̊

10 ̊

20 ̊

30 ̊

40 ̊

March

90 ̊

100 ̊ 110 ̊ 120 ̊ 130 ̊

10 ̊

20 ̊

30 ̊

40 ̊

July

90 ̊

100 ̊ 110 ̊ 120 ̊ 130 ̊

10 ̊

20 ̊

30 ̊

40 ̊

December

CMAQ

Tropospheric NO2 density [1014 molec cm−2]

1 5 10 20 30 40 50 75 100 150 200 250

Fig. 5. Satellite measurements of NO2 columns from GOME (left panels) and the calculated values from CMAQ (right panels) for the

selected months, March, July and December.

M. Lin et al. / Atmospheric Environment 42 (2008) 5939–5955 5947
seasonal variation of the lifetime of NOx in the

boundary layer (Kunhikrishnan et al., 2004), related

variations in meteorological conditions, and possibly also by higher winter emissions (Jaegle ́ et al.,

2005). Both GOME and model estimated NO2

columns highlight the areas of intense pollution in

industrialized regions such as central east China,

South Korea, and Japan. Regarding the magnitudes

of NO2 columns in March and December, the

discrepancy between model and retrieval is particularly pronounced over central eastern China. However, the summer minimums of the NO2 columns

over this region are well reproduced, suggesting that

the bias over central eastern China cannot be

associated with systematic error of the basic

inventory data. Below, we analyze sources of

discrepancy between model and retrieval in detail

for each selected months.

(1) Sources of discrepancy in July: As shown in

Fig. 5, the model underestimates the GOME

retrieval in July by 60% over the remote western

China and Mongolia. Martin et al. (2007) estimated

that the NO2 columns from lightning NOx emissions are lower than 2:0  1014 molecule cm2 over

western China. Soil-biogenic emissions from grassland and scrubland are expected to dominate the

NO2 sources during the wet season over western

China as a result of a low population density. The

present study adopted emission estimates of soilbiogenic NO from Yienger and Levy (1995). They

constructed a global, temperature and precipitation

dependent, empirical model of soil-biogenic NOx

emissions. NO emission flux related with N-fertilizer

stimulation and rain-induced pulsing, an increase in

NOx measured after a shower of rain, have also

been considered. Yienger and Levy (1995) introduced a synoptic-scale modeling of rain-induced

NO emissions based on daily cumulative precipitation, but did not include the possibility of pulsing in

arid scrubland/desert regions. These areas might

provide significant short-term pulses when wetted.

The Yienger and Levy algorithm may underestimate

the magnitude of rain-induced pulsing, especially

over arid scrubland/desert regions on mid-latitudes

of East Asia.

(2) Sources of discrepancy in March: Previous

study by Wang et al. (2007) has proposed that the

underestimation of NO2 sources over central eastern

China in springtime may be associated with microbial decomposition of organic waste and with

intensive use of chemical fertilizer. Agriculture

modeling of Yienger and Levy (1995) did not

resolve different cropping practices and resultant

inability to capture the timing of fertilizer application. Yienger and Levy (1995) did not apply any

field measurement data obtained from agricultural

soils in Asia. The seasonality of agricultural soil

emissions is attributed to the timing of fertilizer

application and to the influence of seasonal

meteorological conditions including temperature

and precipitation. Associated with these reasons,

they may underestimate NO emissions from agricultural soils in springtime which would partly

explain the discrepancy of NO2 columns in March

over central east China. In addition, biomass

burning inventory by van der Werf et al. (2006),

which is adopted in this study, relies heavily on

satellite data. Compared with the Asian biomass

burning inventory by Streets et al. (2003b), this data

set may not include smaller agriculture fires (e.g. the

burning of crop residues) that cannot be detected

from space.

(3) Sources of discrepancy in December: In

December, CMAQ underestimates the GOME

NO2 retrieval by a factor of two over central

eastern China. The missing of lightning NOx is

unlikely responsible for discrepancy in wintertime

when lightning activities are weak in this region.

Primary reasons for the discrepancy may come from

the errors of anthropogenic emission estimates and

the GOME retrieval scheme. A growing body of

evidence suggests that anthropogenic NOx emissions from eastern China are likely underestimated

in the bottom-up emission inventory by Streets et al.

(2003a). Recent top-down emission inventories

using satellite retrievals (Wang et al., 2007; Jaegle ́

et al., 2005), and inverse modeling with aircraft

measurements (Wang et al., 2004) suggested that

errors in the bottom-up combustion sources of NOx

from China were likely to be constrained not to

exceed 50% seasonally. Except the underestimate of

NOx emissions, satellite retrieval might be less

reliable in wintertime associated with inadequate

treatment of satellite scenes with temporary snow

cover (Wang et al., 2007). van Noije et al. (2006)

compared three different retrievals by researchers at

Bremen University (Richter et al., 2005), Dalhousie

University/SAO (Martin et al., 2002), and BIRA/

KNMI (Boersma et al., 2004). They found that the

differences among three different retrievals are

particularly pronounced in wintertime. The Bremen

retrieval, which is used in this study, shows 35%

higher values over central east China in December

than observed in other two retrievals. GOME

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5948 M. Lin et al. / Atmospheric Environment 42 (2008) 5939–5955
retrieval errors have been discussed in detail in

several publications (e.g. Boersma et al., 2004).

4.2. Sensitivity studies

To investigate the impact of anthropogenic NOx

emissions on tropospheric NO2 columns, NOx surface

concentrations and NO1

3 wet deposition, a new

simulation was performed in which NOx emissions

were increased by 50% for March. The results are

evaluated with the EANET in situ measurements in

Japan as well as the GOME data. Fig. 6 shows the

hourly time series of observed and predicted NOx

mixing ratios in March at the EANET sites from the

baseline calculations with both grids and the sensitivity simulation. For Tappi and Sado-seki, the base

simulations well reproduces the diurnal variations and

magnitudes of NOx on most days. For Oki and

Yusuhara, although most variation signals at the Oki

site are captured, the magnitudes of NOx mixing

ratios are systematically underpredicted. The agreement between model and EANET measurements is

better in northern Japan than in central south Japan.

This spatial behavior is also seen in the comparison

for NO2 vertical columns and for surface NOx in

December (results not shown). Unlike SO2, model

calculation with the fine grid spacing does not improve

the predictions for NOx very much. A 50% increase

of NOx emissions moves the results in the right

direction, but does not eliminate the problems. The

underpredictions on most days are considerably

reduced, but there are some overpredictions of the

peak values of NOx mixing ratios on some days. Most

daily maximums at Sado-seki are overpredicted by the

calculation with 50% increase of NOx emissions.

In response to 50% increase of NOx emissions,

the estimated NO2 columns increase by 50–80%

over eastern China, the Korean Peninsula, and

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0

3

6

9

12

15

NOx (ppb)

Tappi (41.2N, 141.3E)

Obs 27km 81km 1.5[NOx] 81km

0

3

6

9

12

15

NOx (ppb)

Sado−seki (38.2N, 138.4E)

0

4

8

12

16

20

NOx (ppb)

Oki (36.3N, 133.2E)

0

3

6

9

12

15

NOx (ppb)

Local Time (JST)

Yusuhara (32.7N, 133.0E)

3Mar 5Mar 7Mar 9Mar 11Mar 13Mar 15Mar 17Mar 19Mar 21Mar 23Mar 25Mar 27Mar 29Mar

Fig. 6. Hourly time series of observed and predicted NOx mixing ratios in March at four EANET sites in Japan.

M. Lin et al. / Atmospheric Environment 42 (2008) 5939–5955 5949
Japan. The sensitivity results in March agree well

with the GOME retrieval over central east China,

whereas overpredict the retrieval over central Japan

and South Korea by 20–30%. In addition, increasing NOx emissions by 50% causes 30–60% increase

of NO1

3 wet deposition in March and reduces the

model bias approximately by 30% with the EANET

measurements in China. Follow up the analysis for

March, another new simulation was carried out for

December in which NOx emissions were increased

by 50% again over central east China whereas by

25% over South Korea and Japan. We find that

50% increase of NOx emissions over central east

China in December is not enough to constrain the

NO2 retrieval over this region. Increasing Japanese

emissions by 25% in December well reproduces the

GOME retrieval values over Japan. It would be

valuable in the future to further explore the impact

of increasing NOx emissions on O3 formation, and

verify the error range of emission estimates when

more in situ monitoring data are available and well

maintained in China.

5. Precipitation and wet deposition

5.1. Precipitation fields

The ability of the model to predict concentrations

in precipitations and wet depositions is limited by

the accuracy of the precipitation fields used in the

model. Thus we start with showing the MM5

simulated precipitation fields in winter and in

summer (Fig. 7). The seasonality and spatial

distribution of precipitation fields are generally

captured by MM5. The predominant weather

pattern in the study domain is the Asian monsoon

system. In winter, the northwesterly monsoon from

the Asian continent to Japan results in heavy

snowfalls on the coastal areas of the Japan Sea.

The northwesterly monsoon provides a transport

path of polluted air mass from the Eurasia, northern

China, Korea, and Japan to the eastern Pacific. In

summer, the southwesterly monsoonal flow dominates southeast Asia and South China. Asian

pollutants are transported along the Pacific Rim

toward the northeast.

5.2. Comparison with observations

Fig. 8 shows scatter plots of the modeled versus

observed results for accumulated precipitation

and ionic concentrations in precipitation. The

comparison shows that on average MM5 is able to

reproduce the precipitation pattern reasonably well

with the majority of sites within a factor of 2 of

observations. However, the precipitation amounts

in the summer months are generally underpredicted

within a factor of 5. This may be related to the

81-km horizontal resolution used in MM5 calculations. The precipitation field pattern is very patchy

(e.g. influenced by local topography), and the

regional scale model is unable to resolve this subgrid scale distribution.

Non-sea-salt sulfate ðnssSO2

4 Þ has the best agreement against observation among all three ions in

precipitation shown in Fig. 8. The majority of

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90 ̊ 100 ̊ 110 ̊ 120 ̊ 130 ̊

10 ̊

20 ̊

30 ̊

40 ̊

Winter

90 ̊ 100 ̊ 110 ̊ 120 ̊ 130 ̊

10 ̊

20 ̊

30 ̊

40 ̊

Summer

1 5 10 20 25 30 40 50 60 80 100 150

Precip. (cm month−1)

Fig. 7. MM5-predicted distribution fields of monthly accumulated precipitation in winter and in summer.

5950 M. Lin et al. / Atmospheric Environment 42 (2008) 5939–5955
Chinese and Japanese sites fall within the reference

lines for a factor of 2. In general, the model’s lesser

ability to predict concentrations in Southeast Asia is

related to larger uncertainties in the emission

estimates and simulated precipitation fields. During

the winter months, nssSO2

4 , NO3 , and NHþ

4 in

rainwater are found to be systematically underpredicted at the sites located in South Korea (sites 20

and 21) and in northern Japan (sites 10, 11, and 12).

Except the errors in predicted precipitation, the

wintertime underpredictions of nssSO2

4 and NO3 at

these sites are likely caused by the underestimation

in residential heating emissions and their seasonality

from the local and upper wind regions including

central east China, northeast China and Russia.

This is consistent with previous discussions about

the underpredictions of wintertime SO2 concentrations at the Russian sites and NO2 columns over

central east China in winter.

Fig. 9 shows the distributions of annual wet

deposition of sulfate, nitrate, and ammonium in

2001. The strong continental outflow of Asian

pollutants is clearly depicted. These maps provide an

Asia-wide perspective on acidic deposition, which is

due predominately to sulfur species at present, but

with a growing contribution from nitrate. On annual

average, the spatial variations and magnitudes of

sulfate and ammonium deposition have been well

reproduced by the model. Regarding the wet

deposition of nitrate, however, there is a moderate

underestimation at all EANET sites. In addition to

the uncertainties in related meteorology for nitrate

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10−1

100

101

102

Model (cm month−1)

10−1 100 101 102

Observation (cm month−1)

6

31 38

20

21

37

11

12

38

34

36

37

1

2

3

5

12

1832

34 36

3

7 89

18

19

21

31

34

1

3

7

1220

36

7

19

21

22

33

3

37

38

34

38

16

18

20 34

21

30

Winter

Spring

Summer

Autumn

6

10

12 20

16

11

12

10

12 20

21

33

10

30

5

3

30

3

9

10

16

19

30

34

5

30

33

3038

10

32

37

6

10

12

20

30

34

37

Winter

Spring

Summer

Autumn

10−1

100

101

102

103

Model (μmol L−1)

10−1 100 101 102 103 10−1

100

101

102

103

Model (μmol L−1)

10−1 100 101 102 103

Observation (μmol L−1) Observation (μmol L−1)

10−1

100

101

102

103

Model (μmol L−1)

10−1 100 101 102 103

Observation (μmol L−1)

6

10

20

31

37

11

12

31

37 38

10

11

12

20

21

33

34 36

37 38

4

10

34

5

34

1

4 3

5

7 36

1

3

33

36

37

3

6 7

8 9

10

15

17

19

31

893434

36

10

31 32

36

37

43

10

12

20

34

37

Winter

Spring

Summer

Autumn

10

18

20

31

32

1

11

12

18

32

38 10

11

21

37 38

32

35

18

371831 31

16

17

18

19

31

5

18

31

33

13

16

18

19

31

33

31 32

37

18

19

20

30

34

37

Winter

Spring

Summer

Autumn

Fig. 8. Scatter plots of modeled versus observed results for (a) accumulated precipitation and concentrations of (b) nssSO2

4 , (c) NO3 and

(d) NHþ

4 in precipitation. Values are monthly pairs with identification color for four seasons. 1:5, 1:2, and 1:1 reference lines are provided.

Sites outside the 1:5 reference lines are labeled with identification ID shown in Fig. 1.

M. Lin et al. / Atmospheric Environment 42 (2008) 5939–5955 5951
formation, several possible causes may be responsible

for the significant underpredictions in nitrate deposition. The primary reason is the underestimates of NOx

emissions in some parts of East Asia as suggested by

the analysis of tropospheric NO2 columns in Section

4. In addition, some uncertainties exist in the model

treatments for governing processes for nitrate

formation including thermodynamic equilibrium and

gas-to-particle partitioning between gaseous HNO3

and aerosol nitrate. Gaseous HNO3 might be dry

deposited quickly and thus reduce the wet deposition

of NO1

3 . Previous studies in North America (Yu

et al., 2005; Zhang et al., 2006) suggested that the

aerosol module of CMAQ has a tendency to underestimate the formation of nitrate aerosol. The

aqueous-phase chemistry and some heterogeneous

reactions for nitrate formation, such as HNO3 uptake

by aerosols, are currently not treated in CMAQ

(Zhang et al., 2006). There is also high uncertainty in

the possibility of heterogeneous reactions of N2O5 on

the surface of aerosols to produce HNO3 (Evans and

Jacob, 2005). Finally, Asian emissions of NH3, which

plays a key role in gas/aerosol partitioning, have not

yet been extensively evaluated.

6. Conclusions

The CMAQ regional CTM is employed at two

horizontal scales to simulate transport and chemistry of acid deposition and related precursors over

East Asia using meteorological fields from MM5.

We have developed a new emission processing

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90 ̊

100 ̊ 110 ̊ 120 ̊ 130 ̊

10 ̊

20 ̊

30 ̊

40 ̊

1 2 6 10 15 20 25 30 35 40 45 50

90 ̊

100 ̊ 110 ̊ 120 ̊ 130 ̊

10 ̊

20 ̊

30 ̊

40 ̊

1 356 7 8 10 12 14 16 20 30

90 ̊

100 ̊ 110 ̊ 120 ̊ 130 ̊

10 ̊

20 ̊

30 ̊

40 ̊

1 3 5 6 7 8 10 12 14 16 20 30

10−1

100

101

102

103

Model [kg ha−1 yr−1]

10−1 100 101 102 103

Observation [kg ha−1 yr−1]

nssSO4

2−

NO3

−

NH4

+

Fig. 9. Annual wet deposition load of sulfate, nitrate, and ammonium in 2001. The filled circles show measurements as observed by the

EANET network. (a) nssSO2

4 , (b) NO3 , (c) NHþ

4 and (d) scatter plot.

5952 M. Lin et al. / Atmospheric Environment 42 (2008) 5939–5955
model to transform available emission data to

model resolution, and an interface to link the

MOZART global CTM to the CMAQ regional

CTM for boundary conditions. Although measurement data in East Asia are relatively scarce, model

predictions are compared with the recently available

EANET measurements and GOME satellite retrieval to better understand major uncertainties of

regional modeling in East Asia.

In evaluating tropospheric NO2 VCDs, we found

that the model significantly underestimated the

GOME retrieval over industrial area of eastern

China in March and December. Sensitivity simulations with 50% increase of anthropogenic NOx

emissions agree well the retrieval in March, but are

still not enough to constrain the retrieval values in

December. The pronounced discrepancy over eastern China is not only due to the uncertainties in the

bottom-up emission inventory but also to the

reliability of the GOME retrieval scheme in

wintertime. The comparison for July also suggests

that the soil-biogenic NO emission estimates by

Yienger and Levy (1995) need to be reviewed

regarding the intensity and timing of fertilizer

applications, and the magnitude of rain-induced

pulsing during the wet season. Due to the underpredictions of NOx and also to the uncertainty in

nitrate formation, CMAQ has a tendency to

underpredict wet deposition of nitrate observed by

the EANET network. More integrated observations

of reactive nitrogen gases and aerosols are needed to

evaluate the aerosol components of CMAQ.

Regarding the sensitivity of model resolutions, we

find that the model calculation with 27-km grid

spacing better captures the fine-scale dynamic

structure of SO2 hourly mixing ratios. But even

with the 81 km grid spacing, the coarse calculation

reasonably well reproduces the magnitudes of

seasonal SO2 at most EANET sites with the root

mean square error lower than 2.5 ppb. The coarse

calculation also successfully reproduces major

spatial and seasonal variations of NO2 vertical

columns observed from space.

Although the sub-grid variations of emissions in

urban and rural areas may not be resolved in the

coarse resolution, comparison with monthly averaged and hourly observations demonstrates that a

computationally efficient model configuration

(employing 81 km grid cell and eight layers in the

vertical) of the CMAQ regional CTM is capable of

representing many of the main features of the

distributions of acid precursors and deposition.

Thus, we employed the model to calculate the S/R

relationships of acid deposition on regional scale as

presented in Lin et al. (2008b).

Acknowledgments

This research has been supported by Japan

International Cooperation Agency and Grants-inAid for Scientific Research, Japan Society for the

Promotion of Science (19106008). We thank

Dr. Richter at Bremen University for providing

the GOME NO2 retrieval, EANET for providing

surface measurements, and two anonymous reviewers

for helpful suggestions.

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