Atmos. Chem. Phys., 17, 935–963, 2017
www.atmos-chem-phys.net/17/935/2017/
doi:10.5194/acp-17-935-2017
© Author(s) 2017. CC Attribution 3.0 License.
MIX: a mosaic Asian anthropogenic emission inventory under the
international collaboration framework of the MICS-Asia and HTAP
Meng Li
1,2
, Qiang Zhang
1,12
, Jun-ichi Kurokawa
3
, Jung-Hun Woo
4
, Kebin He
2,11,12
, Zifeng Lu
5
, Toshimasa Ohara
6
,
Yu Song
7
, David G. Streets
5
, Gregory R. Carmichael
8
, Yafang Cheng
9
, Chaopeng Hong
1,2
, Hong Huo
10
,
Xujia Jiang
1,2
, Sicong Kang
2
, Fei Liu
2
, Hang Su
9
, and Bo Zheng
2
1
Ministry of Education Key Laboratory for Earth System Modeling, Department of Earth System Science,
Tsinghua University, Beijing, China
2
State Key Joint Laboratory of Environment Simulation and Pollution Control, School of Environment,
Tsinghua University, Beijing, China
3
Asia Center for Air Pollution Research, 1182 Sowa, Nishi-ku, Niigata, Niigata, 950-2144, Japan
4
Department of Advanced Technology Fusion, Konkuk University, Seoul, Korea
5
Energy Systems Division, Argonne National Laboratory, Argonne, IL, USA
6
National Institute for Environmental Studies, 16-2 Onogawa, Tsukuba, Ibaraki, 305-8506, Japan
7
State Key Joint Laboratory of Environmental Simulation and Pollution Control, Department of
Environmental Science, Peking University, Beijing, China
8
Center for Global and Regional Environmental Research, University of Iowa, Iowa City,
IA 52242, USA
9
Multiphase Chemistry Department, Max Planck Institute for Chemistry, Mainz, Germany
10
Institute of Energy, Environment and Economy, Tsinghua University, Beijing, China
11
State Environmental Protection Key Laboratory of Sources and Control of Air Pollution Complex,
Beijing, China
12
Collaborative Innovation Center for Regional Environmental Quality, Beijing, China
Correspondence to: Qiang Zhang (qiangzhang@tsinghua.edu.cn)
Received: 23 November 2015 – Published in Atmos. Chem. Phys. Discuss.: 10 December 2015
Revised: 9 November 2016 – Accepted: 5 December 2016 – Published: 20 January 2017
Abstract. The MIX inventory is developed for the
years 2008 and 2010 to support the Model Inter-Comparison
Study for Asia (MICS-Asia) and the Task Force on Hemi-
spheric Transport of Air Pollution (TF HTAP) by a mosaic of
up-to-date regional emission inventories. Emissions are esti-
mated for all major anthropogenic sources in 29 countries
and regions in Asia. We conducted detailed comparisons of
different regional emission inventories and incorporated the
best available ones for each region into the mosaic inven-
tory at a uniform spatial and temporal resolution. Emissions
are aggregated to five anthropogenic sectors: power, indus-
try, residential, transportation, and agriculture. We estimate
the total Asian emissions of 10 species in 2010 as follows:
51.3 Tg SO
2
, 52.1 Tg NO
x
, 336.6 Tg CO, 67.0 Tg NMVOC
(non-methane volatile organic compounds), 28.8 Tg NH
3
,
31.7 Tg PM
10
, 22.7 Tg PM
2.5
, 3.5 Tg BC, 8.3 Tg OC, and
17.3 Pg CO
2
. Emissions from China and India dominate the
emissions of Asia for most of the species. We also estimated
Asian emissions in 2006 using the same methodology of
MIX. The relative change rates of Asian emissions for the pe-
riod of 2006–2010 are estimated as follows: −8.1 % for SO
2
,
+19.2 % for NO
x
, +3.9 % for CO, +15.5 % for NMVOC,
+1.7 % for NH
3
, −3.4 % for PM
10
, −1.6 % for PM
2.5
,
+5.5 % for BC, +1.8 % for OC, and +19.9 % for CO
2
.
Model-ready speciated NMVOC emissions for SAPRC-99
and CB05 mechanisms were developed following a profile-
assignment approach. Monthly gridded emissions at a spa-
tial resolution of 0.25
◦
× 0.25
◦
are developed and can be ac-
cessed from http://www.meicmodel.org/dataset-mix.
Published by Copernicus Publications on behalf of the European Geosciences Union.
936 M. Li et al.: MIX: a mosaic Asian anthropogenic emission inventory
1 Introduction
The Model Inter-Comparison Study for Asia (MICS-Asia)
project is currently in phase III. During the previous two
phases, studies have been focused on long-range transport
and deposition of pollutants, global inflow of pollutants to
Asia, model sensitivities to aerosol parameterization, and
emissions over Asia (Carmichael et al., 2002, 2008; Han et
al., 2008; Hayami et al., 2008; Holloway et al., 2008; Wang et
al., 2008). MICS-Asia Phase III aims to conduct further inter-
comparisons of atmospheric modeling for Asia and analyze
the disagreement of model output and relative uncertainties.
In this regard, common meteorological fields, emission data,
and boundary conditions should be used. One of the key tasks
in MICS-Asia Phase III is to develop a reliable Asian emis-
sion inventory as common input for model intercomparisons
through integration of state-of-the-art knowledge on Asian
emissions.
A reasonable understanding of anthropogenic emissions
is essential for atmospheric chemistry and climate research
(Xing et al., 2013; Keller et al., 2014). Hence, the commu-
nity has put tremendous efforts into developing better emis-
sion inventories (Granier et al., 2011). For a large geographic
region like Asia, compiling a bottom-up emission inventory
is a challenging task because it requires a huge amount of
local information on energy use, technologies, and environ-
mental regulations for many different countries.
Generally, there are two common approaches to develop a
bottom-up emission inventory at regional level. One is us-
ing a unified framework of source categories, calculating
method, chemical speciation scheme (if applicable), and spa-
tial and temporal allocations (e.g., Streets et al., 2003; Ohara
et al., 2007; Lu et al., 2011). Using the unified approach,
emissions are estimated in a consistent way with attain-
able resources. Several Asian emission inventories widely
used in the community were developed by the unified ap-
proach. Streets et al. (2003) first developed a comprehensive
Asian emission inventory for a variety of gaseous and aerosol
species for the year 2000 to support the TRACE-P (Trans-
port and Chemical Evolution over the Pacific) campaign
(Carmichael et al., 2003), which was subsequently used for
MICS-Asia Phase II. Ohara et al. (2007) developed the Re-
gional Emission inventory in Asia (REAS) version 1.1 cover-
ing emissions of major species over Asia from 1980 to 2003,
which provides estimates of Asian emissions for a long-term
period. However, with the unified approach, many region-
dependent parameters are shared among different regions due
to lack of resources and local knowledge (e.g., emission fac-
tors, chemical profiles, spatial proxies, and temporal profiles,
etc.), introducing large uncertainties in emission estimates
for a specific region (He et al., 2007; Kurokawa et al., 2009).
The other is the “mosaic” approach that harmonizes vari-
ous emission inventories of different regions into one emis-
sion data product at large scale, by normalization of source
categories, species, and spatial and temporal resolution from
different inventories and providing emission data with uni-
form format. Available emission inventories always differ in
geographic region, time period, source classification, species,
and spatial and temporal resolution, introducing complexi-
ties in intercomparisons of emissions and model results with
different emission inputs. By involving the state-of-the-art
local emission inventories developed with local knowledge
and harmonizing them to uniform format, this approach can
provide a reference on magnitude and spatial distribution of
emissions for different regions, while there is always trade-
off in spatial–temporal coverage and resolution due to incon-
sistencies among involved inventories.
Recent studies (e.g., Zhang et al., 2009; Kurokawa et
al., 2013) tend to use the mosaic approach to supplement
the Asian emission inventory developments. To support the
NASA’s INTEX-B (the Intercontinental Chemical Transport
Experiment Phase B) mission (van Donkelaar et al., 2008;
Adhikary et al., 2010), Zhang et al. (2009) developed a new
emission inventory for Asia for the year 2006 as an update
and improvement of the TRACE-P inventory (Streets et al.,
2003). Compared to the TRACE-P inventory, the INTEX-
B inventory improved emission estimates for China by in-
troducing a technology-based methodology and incorporated
several local inventories including BC and OC emissions for
India from Reddy et al. (2002a, b), a Japan emission inven-
tory from Kannari et al. (2007), and official emission inven-
tories for the Republic of Korea and Taiwan. In the updated
version 2.1 of the REAS inventory (Kurokawa et al., 2013), a
few regional inventories developed with local knowledge are
also incorporated to improve the accuracy (see Sect. 2.2.1 for
details).
In order to support the MICS-Asia III and other global and
regional modeling activities with the best available anthro-
pogenic emission dataset over Asia, we develop a new Asian
anthropogenic emission inventory, named MIX, by harmo-
nizing different local emission inventories with the mosaic
approach. The mosaic inventory developed in this work will
provide (1) a more complete and state-of-the-art understand-
ing of anthropogenic emissions over Asia with best estimates
from local inventories; (2) a reference dataset with moder-
ate accuracy and resolution that can support both scientific
research and mitigation policy-making; and (3) broader ap-
plication of the best available local inventories in modeling
studies by processing them to model-ready format and in-
cluding them in a publicly available emission dataset.
The MIX inventory is developed for 2008 and 2010, in ac-
cordance with base year simulations in MICS-Asia III and
the Task Force on Hemispheric Transport of Air Pollution
(TF HTAP). It should be noted that MIX is not compara-
ble to INTEX-B and TRACE-P to derive an emission trend
due to differences in methodology and underlying data. In
this paper, we also provided Asian emissions for 2006 us-
ing the same methodology, partly resolving the problems of
trend analysis in mosaic inventories. The gridded MIX emis-
sion data for the years 2008 and 2010 are then incorporated
Atmos. Chem. Phys., 17, 935–
963, 2017 www.atmos-chem-phys.net/17/935/2017/
M. Li et al.: MIX: a mosaic Asian anthropogenic emission inventory 937
Figure 1. Domain and component of the MIX emission inventory.
into the HTAP v2.2 global emission inventory (Janssens-
Maenhout et al., 2015) to support the modeling activities in
HTAP, providing a consistent emission input for global and
regional modeling activities.
Figure 1 presents the definition of the MIX domain and
emission datasets used for each country and region. The do-
main of MIX covers 29 countries and regions (the full list of
country and region names are listed in Table 1), stretching
from Kazakhstan in the west to Russia Far East in the east
and from Indonesia in the south to Siberia in the north. Emis-
sions are aggregated into five sectors: power, industry, resi-
dential, transportation, and agriculture. Ten chemical species
are included in the MIX inventory, including both gaseous
and aerosol species: SO
2
, NO
x
, CO, NMVOC (non-methane
volatile organic compounds), NH
3
(ammonia), PM
10
(par-
ticulate matter with diameter less than or equal to 10 µm),
PM
2.5
(particulate matter with diameter less than or equal
to 2.5 µm), BC (black carbon), OC (organic carbon), and
CO
2
. Only emissions from anthropogenic sources are in-
cluded in MIX. NMVOC emissions are speciated into model-
ready inputs for two chemical mechanisms: CB05 (the Car-
bon Bond mechanism; Yarwood et al., 2005) and SAPRC-
99 (the State Air Pollution Research Center 1999 version;
Carter, 2000) (see Tables S1 and S2 in the Supplement).
Monthly emissions are provided by sector at 0.25
◦
× 0.25
◦
resolution. Gridded emissions are available from http://www.
meicmodel.org/dataset-mix. The key features of the MIX in-
ventory are summarized in Table 1.
This paper documents the methodology and emission
datasets of the MIX Asian anthropogenic emission inven-
tory. The regional and national inventories used to develop
MIX gridded datasets and the mosaic methodology are pre-
sented in Sect. 2. Section 3 presents Asian emissions in 2010
and spatial and temporal variations in emissions. Changes in
Asian emissions between 2006 and 2010 are also discussed.
Section 4 highlights the major improvements in the new in-
ventory by comparing MIX with other Asian emission in-
ventories. Uncertainties and limitations of the inventory are
discussed in Sect. 5. Concluding remarks are provided in
Sect. 6.
2 Compilation of the MIX emission inventory
2.1 Methodology
Five emission inventories are collected and incorporated
into the mosaic inventory, as listed in the following: REAS
inventory version 2.1 for the whole of Asia (referred to
as REAS2 hereafter; Kurokawa et al., 2013), the Multi-
resolution Emission Inventory for China (MEIC) developed
by Tsinghua University (http://www.meicmodel.org), a high-
resolution NH
3
emission inventory by Peking University (re-
ferred to as PKU-NH
3
inventory hereafter; Huang et al.,
2012), an Indian emission inventory developed by Argonne
National Laboratory (referred to as ANL-India hereafter; Lu
et al., 2011; Lu and Streets, 2012), and the official Korean
emission inventory from the Clean Air Policy Support Sys-
tem (CAPSS; Lee et al., 2011).
We then selected different emission datasets for various
species for each country by the following hierarchy. REAS2
was used as the default where local emission data are ab-
sent. Emission inventories compiled by the official agencies
or developed with more local information are selected to
override REAS2, which include MEIC for mainland China,
ANL-India for India, and CAPSS for the Republic of Ko-
rea. Detailed information and advantages of these invento-
ries are presented in Sect. 2.2. As only a few species (SO
2
,
BC, OC, and power plant NO
x
) were available from ANL-
India, REAS2 was used to supplement the missing species.
A mosaic process was then used to combine ANL-India and
REAS2 into a single dataset for Indian emissions. It is worth
noting that the REAS2 has incorporated local inventories for
Japan and Taiwan, which are subsequently adopted in MIX
for these two regions. PKU-NH
3
was further used to replace
MEIC emissions for NH
3
over China, given that PKU-NH
3
was developed with a process-based model that represented
the spatiotemporal variations in NH
3
emissions. Table 2 lists
the information of each inventory used in MIX.
Figure 2 illustrates the mosaic process for the MIX
inventory development. Each dataset was reprocessed to
0.25
◦
× 0.25
◦
resolution with monthly variations when nec-
essary. We used monthly gridded emissions from each com-
ponent inventory where available and assumed no monthly
variation in emissions when the component inventory only
provided annual emissions. The monthly profiles and spa-
tial proxies used in each component emission inventories are
summarized in Tables S3 and S4.
For each regional emission inventory, emissions were ac-
quired with subsector information and then aggregated into
five sectors: power, industry, residential, transportation, and
agriculture. Table S5 presented the sectoral mapping tables
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963, 2017
938 M. Li et al.: MIX: a mosaic Asian anthropogenic emission inventory
Table 1. Summary of the MIX Asian anthropogenic emission inventory.
Item Description
Domain 29 countries and regions in Asia
Countries and regions China, Japan, Democratic People’s Republic of Korea, Republic of Korea, Mongolia, India,
Afghanistan, Bangladesh, Bhutan, Maldives, Nepal, Pakistan, Sri Lanka, Brunei, Cambodia,
Indonesia, Laos, Malaysia, Myanmar, Philippines, Singapore, Thailand, Vietnam, Kazakhstan,
Kyrgyzstan, Tajikistan, Turkmenistan, Uzbekistan, Russia (East Siberia, Far East, Ural, West
Siberia)
Species SO
2
, NO
x
, CO, NMVOC, NH
3
, PM
10
, PM
2.5
, BC, OC, CO
2
VOC speciation by chemical mechanisms: CB05, SAPRC-99
Sectors power, industry, residential, transportation, agriculture
Spatial resolution 0.25
◦
× 0.25
◦
Seasonality monthly
Year 2008, 2010
Data Access http://www.meicmodel.org/dataset-mix
Table 2. List of regional emission inventories used in this work.
MEIC v1.0 PKU-NH
3
CAPSS JEI-DB+OPRF ANL-India REAS2
Year 1990–2010 2006 2008, 2010 2008, 2010 1996–2010 2008, 2010 2000–2010
Region China China Republic of Korea Japan India India Asia
Seasonality Monthly Monthly Annual Monthly Monthly Annual Monthly
Resolution 0.25
◦∗
1 km 0.25
◦
1 km 0.1
◦
0.25
◦∗
0.25
◦∗
SO
2
X X X X X
NO
x
X X X X X
CO X X X X
NMVOC X X X X
NH
3
X X X X
PM
10
X X X X
PM
2.5
X X X
BC X X X X
OC X X X X
CO
2
X X X X
NMVOC speciation X X
∗
Power plant emissions are developed with specific geophysical locations and allocated into 0.25
◦
× 0.25
◦
grids.
Figure 2. Schematic methodology of the MIX emission inventory
development.
from subsectors to the five MIX sectors for each regional in-
ventory. For each subsector, the corresponding IPCC sectors
are also provided in Table S5. For agriculture sector, only
NH
3
emissions are provided in the MIX inventory given that
soil NO
x
emissions and agriculture PM emissions are not
available in the regional inventories used for compiling MIX.
Emissions from open biomass burning, fugitive dust, avia-
tion, and international shipping were excluded in the MIX
inventory because those emissions were only available in a
few inventories.
NMVOC emissions were speciated to SAPRC-99 and
CB05 mechanisms following the explicit species mapping
approach documented in Li et al. (2014) (see Fig. 3). Finally,
emissions were aggregated to the five MIX sectors and then
assembled to monthly emission grid maps over Asia with a
uniform spatial resolution of 0.25
◦
× 0.25
◦
.
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M. Li et al.: MIX: a mosaic Asian anthropogenic emission inventory 939
Figure 3. NMVOC speciation scheme used in the MIX inventory
development. The mapping table is derived from Carter (2013).
2.2 Components of the MIX emission inventory
2.2.1 REAS2
We used anthropogenic emissions from REAS2 (Kurokawa
et al., 2013) to fill the gap where local emission data are not
available. REAS2 updated the REAS version 1.1 for both ac-
tivity data and emission factors by each country and region
using global and regional statistics and recent regional spe-
cific studies on emissions factors. Improved from its previ-
ous version, power plant emissions in REAS2 were estimated
by combining information on generation capacity, fuel type,
running years, and CO
2
emissions from the Carbon Moni-
toring for Action database (CARMA; Wheeler and Ummel,
2008) and the World Electric Power Plants database (WEPP;
Platts, 2009). REAS2 extended the domain to include emis-
sions of Central Asia and the Asian part of Russia (referred to
as Russia Asia). Readers can refer to Kurokawa et al. (2013)
for detailed data sources of activity rates and emission fac-
tors assignments for each country and source type. REAS2
is available for the period of 2000–2008. In this work, we
updated the REAS2 to the year 2010, following the same ap-
proach documented in Kurokawa et al. (2013).
REAS2 also incorporated a few regional inventories devel-
oped by local agencies with detailed activity data and emis-
sion factors, including the JEI-DB inventory (Japan Auto-
Oil Program (JATOP) Emission Inventory-Data Base; JPEC,
2012a, b, c) for all anthropogenic sources in Japan excluding
shipping, OPRF (Ocean Policy Research Foundation; OPRF,
2012) for shipping emissions in Japan, CAPSS emission in-
ventory for Korea (Lee et al., 2011), and official emission
data from the Environmental Protection Administration of
Taiwan for Taiwan (Kurokawa et al., 2013). All these re-
gional datasets were then harmonized to the same spatial and
temporal resolution in REAS2. In this work, we processed
the CAPSS emission data separately as an individual data
source, which is presented in Sect. 2.2.5, and adopted Japan
and Taiwan emissions directly from the REAS2 product.
The REAS2 inventory is provided with monthly gridded
emission data for both air pollutants and CO
2
by sectors
at 0.25 × 0.25
◦
resolution. We aggregated the 11 REAS2
sectors to 5 sectors provided in the MIX inventory. Emis-
sions from open biomass burning, aviation, and international
shipping were excluded from the REAS2 before incorporat-
ing into MIX. Monthly variations are developed for power
plants, industry, residential sources, and cold-start emissions
from vehicles by various monthly profiles (Kurokawa et al.,
2013). In REAS2, power plants with annual CO
2
emissions
larger than 1 Tg were provided as point sources with coor-
dinates of locations, while emissions for other sectors were
processed as areal sources and gridded at 0.25 × 0.25
◦
reso-
lution using maps of rural, urban, and total populations and
road networks (see Table S4).
2.2.2 MEIC
We use anthropogenic emission data generated from the
MEIC (Multi-resolution Emission Inventory for China)
model to override emissions in mainland China. MEIC
is a bottom-up emission inventory framework developed
and maintained by Tsinghua University, which uses a
technology-based methodology to calculate air pollutant and
CO
2
emissions for more than 700 anthropogenic emitting
sources for China from 1990 to the present. With the detailed
source classification, the MEIC model can represent emis-
sion characteristics from different sectors, fuels, products,
combustion/process technologies, and emission control tech-
nologies. The MEIC model improved the bottom-up emis-
sion inventories developed by the same group (Streets et al.,
2006; Zhang et al., 2007a, b, 2009; Lei et al., 2011) and inte-
grated them into a uniform framework. The major improve-
ments include a unit-based power plant emission database
(Wang et al., 2012; Liu et al., 2015), a high-resolution ve-
hicle emission modeling approach (Zheng et al., 2014), an
explicit NMVOC speciation assignment methodology (Li et
al., 2014), and a unified, online framework for emission cal-
culation, data processing, and data downloading (available at
http://www.meicmodel.org).
Power plant emissions in MEIC were derived from the
China coal-fired Power plant Emissions Database (CPED),
in which emissions were estimated for each generation unit
based on the unit-specific parameters including fuel con-
sumption rates, fuel quality, combustion technology, and
emission control technology. With detailed information of
over 7600 generation units in China, CPED improved the
spatial and temporal resolution of the power plant emission
inventory compared to previous studies (Liu et al., 2015). For
the on-road transportation sector, MEIC used the new ap-
proach developed by Zheng et al. (2014), which estimated
vehicle emissions with high spatial resolution by using vehi-
cle population and emission factors at county level. County-
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