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 intercomparisons 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 emission 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 community has put tremendous efforts into developing
better emission 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 environmental regulations for many different countries.
Generally, there are two common approaches to develop a bottom-up emission
inventory at regional level. One is using a unified framework of source
categories, calculating method, chemical speciation scheme (if applicable),
and spatial 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 attainable resources. Several Asian
emission inventories widely used in the community were developed by the
unified approach. 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 (Transport 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 Regional Emission
inventory in Asia (REAS) version 1.1 covering 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 factors, 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 various emission
inventories of different regions into one emission data product at large
scale, by normalization of source categories, species, and spatial and
temporal resolution from different inventories and providing emission data
with uniform format. Available emission inventories always differ in
geographic region, time period, source classification, species, and spatial
and temporal resolution, introducing complexities 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 inconsistencies 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 introducing 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
inventory from Kannari et al. (2007), and official emission inventories 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 anthropogenic emission dataset over Asia,
we develop a new Asian anthropogenic emission inventory, named MIX, by
harmonizing 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 understanding of anthropogenic emissions over Asia with
best estimates from local inventories; (2) a reference dataset with moderate
accuracy and resolution that can support both scientific research and
mitigation policy-making; and (3) broader application of the best available
local inventories in modeling studies by processing them to model-ready
format and including them in a publicly available emission dataset.
The MIX inventory is developed for 2008 and 2010, in accordance 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
comparable 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 using the same methodology, partly
resolving the problems of trend analysis in mosaic inventories. The gridded
MIX emission data for the years 2008 and 2010 are then incorporated 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.
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
SO2, NOx, CO, NMVOC, NH3, PM10, PM2.5, BC, OC, CO2
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
Figure 1 presents the definition of the MIX domain and emission datasets
used for each country and region. The domain 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. Emissions are
aggregated into five sectors: power, industry, residential, transportation,
and agriculture. Ten chemical species are included in the MIX inventory,
including both gaseous and aerosol species: SO2, NOx, CO, NMVOC
(non-methane volatile organic compounds), NH3 (ammonia), PM10
(particulate matter with diameter less than or equal to 10 µm),
PM2.5 (particulate matter with diameter less than or equal to 2.5 µm),
BC (black carbon), OC (organic carbon), and CO2. Only emissions
from anthropogenic sources are included in MIX. NMVOC emissions are
speciated into model-ready inputs for two chemical mechanisms: CB05 (the
Carbon 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 inventory are summarized in Table 1.
Domain and component of the MIX emission inventory.
This paper documents the methodology and emission datasets of the MIX Asian
anthropogenic emission inventory. The regional and national inventories used to
develop MIX gridded datasets and the mosaic methodology are presented 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
inventory by comparing MIX with other Asian emission inventories.
Uncertainties and limitations of the inventory are discussed in Sect. 5.
Concluding remarks are provided in Sect. 6.
Compilation of the MIX emission inventory
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 NH3 emission
inventory by Peking University (referred to as PKU-NH3 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 System (CAPSS; Lee et al., 2011).
List of regional emission inventories used in this work.
MEIC v1.0
PKU-NH3
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∘∗
SO2
X
X
X
X
X
NOx
X
X
X
X
X
CO
X
X
X
X
NMVOC
X
X
X
X
NH3
X
X
X
X
PM10
X
X
X
X
PM2.5
X
X
X
BC
X
X
X
X
OC
X
X
X
X
CO2
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.
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 absent. 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 Korea. Detailed information and advantages of
these inventories are presented in Sect. 2.2. As only a few species
(SO2, BC, OC, and power plant NOx) 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-NH3 was further used to replace MEIC emissions
for NH3 over China, given that PKU-NH3 was developed with a
process-based model that represented the spatiotemporal variations in
NH3 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 necessary. We
used monthly gridded emissions from each component inventory where
available and assumed no monthly variation in emissions when the component
inventory only provided annual emissions. The monthly profiles and spatial
proxies used in each component emission inventories are summarized in Tables S3 and S4.
Schematic methodology of the MIX emission inventory development.
For each regional emission inventory, emissions were acquired with
subsector information and then aggregated into five sectors: power,
industry, residential, transportation, and agriculture. Table S5 presented
the sectoral mapping tables from subsectors to the five MIX sectors for
each regional inventory. For each subsector, the corresponding IPCC sectors
are also provided in Table S5. For agriculture sector, only NH3
emissions are provided in the MIX inventory given that soil NOx
emissions and agriculture PM emissions are not available in the regional
inventories used for compiling MIX. Emissions from open biomass burning,
fugitive dust, aviation, 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∘.
NMVOC speciation scheme used in the MIX inventory development. The
mapping table is derived from Carter (2013).
Components of the MIX emission inventory
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 activity data and emission factors by each country and
region using global and regional statistics and recent regional specific
studies on emissions factors. Improved from its previous version, power
plant emissions in REAS2 were estimated by combining information on
generation capacity, fuel type, running years, and CO2 emissions from
the Carbon Monitoring for Action database (CARMA; Wheeler and Ummel, 2008)
and the World Electric Power Plants database (WEPP; Platts, 2009). REAS2
extended the domain to include emissions 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 factors
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 approach documented in Kurokawa et al. (2013).
REAS2 also incorporated a few regional inventories developed by local
agencies with detailed activity data and emission 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 inventory 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 regional 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 CO2 by sectors at
0.25 × 0.25∘
resolution. We aggregated the 11 REAS2 sectors to 5 sectors provided in
the MIX inventory. Emissions from open biomass burning, aviation, and
international shipping were excluded from the REAS2 before incorporating
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
CO2 emissions larger than 1 Tg were provided as point sources with
coordinates of locations, while emissions for other sectors were processed
as areal sources and gridded at 0.25 × 0.25∘ resolution using
maps of rural, urban, and total populations and road networks (see Table S4).
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 CO2 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 emission characteristics
from different sectors, fuels, products, combustion/process technologies,
and emission control technologies. The MEIC model improved the bottom-up
emission inventories developed by the same group (Streets et al., 2006;
Zhang et al., 2007a, b, 2009; Lei et al., 2011) and integrated them into
a uniform framework. The major improvements include a unit-based power plant
emission database (Wang et al., 2012; Liu et al., 2015), a high-resolution
vehicle emission modeling approach (Zheng et al., 2014), an explicit NMVOC
speciation assignment methodology (Li et al., 2014), and a unified, online
framework for emission calculation, 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
consumption 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 approach developed by Zheng
et al. (2014), which estimated vehicle emissions with high spatial
resolution by using vehicle population and emission factors at county level.
County-level emissions were further allocated to high-resolution grids based
on a digital road map and weighting factors of vehicle kilometers traveled
by vehicle and road type.
MEIC provides lumped speciated NMVOC emissions for different chemical
mechanisms, e.g., SAPRC-99, SAPRC-07, CBIV, CB05, and RADM2. Following the
speciation assignment approach developed by Li et al. (2014), emissions of
individual NMVOC species were calculated for each source category by
splitting the total NMVOC emissions with corresponding source profiles.
Emissions were then assigned to various mechanisms using species mapping
tables.
MEIC delivers monthly emissions at various spatial resolutions through an
open-access, online framework (http://www.meicmodel.org).
Monthly variations and gridded emissions were generated by sector using
different temporal profiles and spatial proxies. Users can define the
metadata (species, domain range, time period, sectors, spatial resolution,
and chemical mechanisms), calculate gridded emissions, and download data
from the website. Monthly emissions at 0.25∘ × 0.25∘ generated from MEIC v1.0 (referred to as MEIC
hereafter) were used in MIX. Emissions were aggregated to four MIX sectors:
power, industry, residential, and transportation. NH3 emissions in MEIC
were replaced by PKU-NH3, which will be discussed in the next section.
PKU-NH3 for China
We used a high-resolution NH3 emission inventory in China compiled by
Peking University (PKU-NH3;
Huang et al., 2012) to replace China's
NH3 emissions in MEIC. MEIC used annual and regional average NH3
emission factors to calculate emissions from each source category, while
PKU-NH3 used a process-based model to estimate NH3 emissions which
parameterized the spatial and temporal variations of emission factors with
consideration of ambient temperature, soil property, and other factors. For
NH3 emissions from fertilizer applications, fertilizer type, soil
property, fertilizer application method, application rate, and ambient
temperature were used to develop monthly and gridded emission factors. For
livestock wastes, emissions were estimated based on a mass-flow methodology
by tracing the migration and volatilization of nitrogen from each stage of
livestock manure management.
PKU-NH3 estimated NH3 emissions in China (including mainland China
and Hong Kong, Macao, and Taiwan) in 2006 for the following sources:
livestock wastes, farmland ecosystem, biomass burning, excrement from rural
population, chemical industry, waste disposal, and transportation. Open
biomass burning was excluded from the MIX inventory aggregation since the
MICS-Asia III project uses GFED dataset for biomass burning. PKU-NH3 is
available at 1 km × 1 km resolution with monthly variation. We then
regridded PKU-NH3 monthly emissions to 0.25∘ × 0.25∘. In the MIX inventory, 2006 emissions from
PKU-NH3 are used for both 2008 and 2010 since 2006 is the most recent
year for emissions in PKU-NH3 when the MIX inventory was developed. As
the major drivers of NH3 emissions, synthetic fertilizer consumption
and animal population increased by 4 and 9 % from 2006 to 2010,
respectively, much smaller than the growth rates of coal consumption and
vehicle population for the same period.
ANL emission inventories for India
A high-resolution Indian emission inventory developed by ANL (referred to as
ANL-India hereafter; Lu et al., 2011) was used in the MIX inventory.
ANL-India used a technology-based methodology to estimate SO2, BC, and
OC emissions in India for the period of 1996–2010. Major anthropogenic
sources including both fossil-fuel and biofuel combustion are covered in
ANL-India. Time-dependent trends in emission factors were developed by
taking account of the impact of technology changes on emissions (Habib et
al., 2004; Venkataraman et al., 2005). Lu and Streets (2012) further updated
power plant emissions in India by calculating emissions at the generating
unit level (∼ 800 units in total) based on information from
the reports of the Central Electricity Authority (CEA), including
geographical location, capacity, fuel type, electricity generation, time the
plant was commissioned/decommissioned, etc. The exact location of each power
plant was obtained from the Global Energy Observatory
(http://globalenergyobservatory.org) and crosschecked through Google Earth.
The updated unit-based power plant emissions in ANL-India are available for
SO2, NOx, BC, and OC.
ANL-India is available for the period of 1990–2010 at 0.1∘ × 0.1∘ resolution with monthly variations.
Emissions are presented by sectors, i.e., power, industry, residential,
transportation, and open biomass burning. Monthly variations in ANL-India
were developed by sector using various surrogates (Lu et al., 2011). As
ANL-India only covers some of the required MIX species (SO2, BC, and OC
for all sectors, NOx for power plants), monthly emissions by sector
(excluding open biomass burning) from ANL-India were first regridded to
0.25∘ × 0.25∘ and then merged with
REAS2 before being implemented in MIX to cover all species. The merge
process is presented in Sect. 2.3.
CAPSS inventory for the Republic of Korea
For the Republic of Korea, we used the CAPSS emission inventory developed by
the National Institute of Environmental Research of Korea (Lee et al.,
2011). CAPSS estimated emissions with four levels of source classifications.
We mapped emissions from 12 first-level aggregated source categories (SCC1)
to five sectors in MIX. The CAPSS inventory included emissions for CO2
and five regulated air pollutants, SO2, NOx, CO, NMVOC, and
PM10. We derived sector-specific emission ratios between PM10 and
the other aerosol components from Lei et al. (2011) and applied those ratios
to estimate PM2.5, BC, and OC emissions. In the MIX inventory, we used
the 2008 and 2009 CAPSS inventories to represent 2008 and 2010 emissions of
the Republic of Korea, because 2009 is the most recent year of CAPSS
inventory at the time the MIX inventory was developed. In the CAPSS
inventory, point sources, area sources, and mobile sources were processed
using different spatial allocation approaches (Lee et al., 2011). We used
the 0.25∘ × 0.25∘ emission product
from CAPSS as input for the MIX inventory. Only annual total emissions were
presented in the CAPSS inventory. In the MIX inventory, we assume no monthly
variation in emissions in the Republic of Korea.
Mosaic of Indian emission inventory
ANL-India is available for SO2, BC, and OC for all sectors as well as
NOx for power plants. In this work, REAS2 is used to supplement the
missing species in ANL-India. To reduce possible inconsistencies from
implementation of the two different inventories, we have reprocessed
ANL-India and REAS2 emissions over India in the following two steps.
First, for power plants, because ANL-India used CEA reports to derive
information of individual power generation units while REAS2 used the CARMA
and WEPP databases to get similar information, direct merging of the two
products could introduce inconsistency due to a mismatch of unit information
in the two databases. In this work, we directly used ANL-India for SO2,
NOx, BC, and OC emissions and used REAS2
for CO, NMVOC, PM2.5,
PM10, and CO2 but redistributed the total magnitudes of REAS2
power plant emissions by using the spatial distribution of power plants in
the ANL-India inventory. We generated the spatial proxies of fuel
consumption for each fuel type (coal, oil, and gas) at 0.25 × 0.25∘ by aggregating fuel consumptions of each unit in the ANL-India
inventory. We then applied the spatial proxy to the REAS2 estimates by fuel
type for species that were not included in ANL-India.
Second, we used BC and OC emissions from ANL-India but used PM2.5 and
PM10 emissions from REAS2. In certain grids, the sum of BC and OC
emissions may exceed PM2.5 emissions because the two inventories may
use different activity data, emission factors, and spatial proxies. The
so-called “PMfine” species in chemical transport models are usually
calculated by subtracting BC and OC emissions from total PM2.5
emissions, leading to negative emissions of PMfine in those grids. In
this case, we adjusted the emissions of PM2.5 to the sum of BC and OC
emissions for each sector.
NMVOC speciation of the MIX inventory
In the MIX inventory, we provide model-ready speciated NMVOC emissions over
Asia (except the Republic of Korea) for both CB05 and SAPRC-99 chemical
mechanisms, by using the explicit species mapping approach and updated NMVOC
profiles developed in Li et al. (2014), as illustrated in Fig. 3. Following
Li et al. (2014), NMVOC emissions for CB05 and SAPRC-99 species are
calculated as follows:
EVOC(i,k,m)=∑j=1nEVOC(i,k)×X(i,j)mol(j)×C(j,m),
where k is the region, m is species type in CB05 or
SAPRC-99 mechanisms, and n is the number of species emitted from source
i. EVOC is the total NMVOC emissions by source type. In
this work, emissions in China and other Asian countries were derived from
MEIC and REAS2 respectively. X(i,j) is the mass fraction of species
j in the total NMVOC emissions for source i, which is
taken from the profiles developed by Li et al. (2014). Those profiles were
constructed by grouping and averaging multiple profiles from both local
measurements and the SPECIATE database (Hsu and Divita, 2009; Simon et al.,
2010). Mol(j) is the mole weight of species j and
C(j,m) is the conversion factor between j and m
obtained from the mapping tables in Carter (2013).
For the Republic of Korea, the SMOKE-Asia model developed by Woo et al. (2012) was
used to calculate model-ready NMVOC emissions for both CB05 and
SAPRC-99 mechanisms. NMVOC emissions from the CAPSS were mapped to Source
Classification Codes (SCCs) and country–state–county (FIPS) code in
SMOKE-Asia model and speciated NMVOC emissions were then calculated by
linking emissions to speciation profiles with cross references.
Emission distributions among sectors in Asia in 2010.
Monthly profiles
We directly used monthly emissions from each regional emission inventory
when compiling the MIX inventory. We assume no monthly variation in
emissions when monthly profiles are absent from the regional emission
inventories. Table S3 presents the monthly profiles used in each component
emission inventory for MIX. In summary, monthly profiles for power plant
emissions usually developed based on monthly statistics of power generation.
Monthly profiles of industrial emissions are derived from monthly output of
industrial products or industrial GDP. Residential monthly profiles are
estimated from stove operation time based on ambient temperatures by regions
(Streets et al., 2003).
Spatial proxies
We used gridded emissions from each regional emission inventory to compile
the grid maps of emissions. Locations of emitting facilities were used to
derive gridded emission for large sources, while spatial proxies such as
population density, road networks, and land use information are used to
allocate emissions of areal sources. Table S4 summarized spatial proxies
used in developing gridded emissions for each regional inventory.
Results
Asian anthropogenic emissions in 2010
Based on the mosaic approach and candidate inventories described in Sect. 2,
gridded anthropogenic emissions for 10 species were generated over Asia and
called the MIX emission inventory. In the MIX inventory, Asian
anthropogenic emissions in 2010 are estimated as follows: 51.3 Tg SO2,
52.1 Tg NOx, 336.6 Tg CO, 67.0 Tg NMVOC, 28.8 Tg NH3, 31.7 Tg
PM10, 22.7 Tg PM2.5, 3.5 Tg BC, 8.3 Tg OC, and 17.3 Pg CO2.
Figure 4 presents the emission distributions among sectors over Asia in
2010. Among the different sectors, the industrial sector has the largest
contribution to SO2 (50 % of total), NMVOC (38 %), PM10
(48 %), and CO2 (40 %) emissions. Power plants have significant
contributions for SO2 (38 % of total), NOx (29 %), and
CO2 (34 %) emissions.
National anthropogenic emissions in the MIX emission inventory in
2010 (units: Tg for CO2 and Gg for other species). Bold values are the total emissions for Asian regions.
Countries
SO2
NOx
CO
NMVOC
NH3
PM10
PM2.5
BC
OC
CO2
Chinaa
28 663
29 071
170 874
23 619
9804
16 615
12 200
1765
3386
10 124
Japan
708
1914
4278
1178
479
114
81
20
8
1107
Korea, DPR
211
238
4488
138
111
264
115
14
17
71
Korea, Republic of
418
1062
838
851
190
124
87
24
8
541
Mongolia
99
62
735
47
97
109
46
2
4
15
Other East Asiab
1437
3275
10 339
2215
875
610
328
60
37
1735
India
9259
9565
67 423
16 892
9871
7093
5216
1019
2530
2277
Afghanistan
3
178
456
141
143
21
20
8
10
2
Bangladesh
133
368
2575
788
1016
342
234
33
121
84
Bhutan
5
13
302
50
41
26
21
4
14
5
Maldives
3
8
151
9
1
0
0
0
0
2
Nepal
30
83
2109
443
254
150
139
27
105
34
Pakistan
1397
946
9279
2112
1859
600
558
114
390
263
Sri Lanka
133
116
1321
367
122
152
111
15
59
31
Other South Asiab
1704
1712
16 194
3910
3435
1290
1082
200
699
421
Brunei
11
12
6
32
8
1
0
0
0
9
Cambodia
26
47
1025
211
134
59
56
11
44
17
Indonesia
1964
2570
23 749
7970
1945
1182
947
178
692
554
Laos
150
41
397
85
87
24
22
4
16
6
Malaysia
365
631
3731
1765
255
216
132
16
35
201
Myanmar
67
91
2705
814
425
165
156
31
125
49
Philippines
503
361
2347
869
413
193
123
15
68
119
Singapore
175
116
162
334
10
7
5
1
1
43
Thailand
614
809
8572
2327
649
495
275
35
149
297
Vietnam
575
442
8231
2234
665
710
562
87
322
232
Southeast Asiab
4449
5120
50 925
16 640
4592
3051
2278
378
1452
1527
Kazakhstan
1050
559
3348
544
41
442
222
13
28
204
Kyrgyzstan
27
35
371
40
12
62
28
2
3
6
Tajikistan
14
25
192
30
15
22
13
1
1
4
Turkmenistan
64
124
417
238
14
64
30
2
3
52
Uzbekistan
493
228
899
310
50
373
165
3
11
121
Central Asiab
1648
971
5227
1162
133
963
458
21
46
387
East Siberia
1649
534
2874
394
23
368
198
14
20
184
Far East
358
489
2681
303
18
223
123
22
25
120
Ural
1480
456
4005
591
22
1047
598
19
75
186
West Siberia
677
926
6045
1310
42
465
269
32
52
340
Russia Asiab
4164
2405
15 605
2597
105
2103
1188
87
172
830
Asia
51 324
52 118
336 588
67 034
28 816
31 726
22 749
3530
8322
17 301
a Hong Kong, Macao, and Taiwan are included.
b The Asian region includes the set of countries listed in the section.
Asian emissions by sector in 2010 for each region (units: Tg for
CO2 and Gg for other species). Bold values are the total emissions for Asian regions.
Regions
SO2
NOx
CO
NMVOC
NH3
PM10
PM2.5
BC
OC
CO2
China
28 663
29 071
170 874
23 619
9804
16 615
12 200
1765
3386
10 124
Power
8166
9455
2077
255
0
1389
893
2
0
3245
Industry
16 775
11 218
71 276
14 461
239
9451
6061
575
530
4928
Residential
3489
1140
76 579
6349
450
5246
4737
908
2752
1266
Transportation
234
7257
20 942
2553
76
529
509
281
104
684
Agriculture
9040
Other East Asia
1437
3275
10 339
2215
875
610
328
60
37
1735
Power
427
460
70
13
3
162
64
0
0
617
Industry
644
649
3329
1475
26
285
142
20
6
584
Residential
189
361
1402
259
143
77
47
8
18
233
Transportation
176
1805
5538
467
23
87
74
32
13
300
Agriculture
680
India
9259
9565
67 423
16 892
9871
7093
5216
1019
2530
2277
Power
5476
2391
2676
125
8
2029
842
1
3
886
Industry
2959
973
18 164
3372
172
1284
931
217
200
585
Residential
685
968
34 317
7311
2185
2946
2640
709
2275
659
Transportation
139
5233
12 267
6085
9
834
802
92
52
147
Agriculture
7496
Other South Asia
1704
1712
16 193
3910
3436
1290
1082
200
699
421
Power
774
290
51
6
0
24
12
0
1
79
Industry
546
165
2536
635
38
400
231
18
75
93
Residential
153
395
12 148
2600
724
777
753
147
594
199
Transportation
231
863
1459
669
1
90
87
34
29
49
Agriculture
2673
Southeast Asia
4449
5120
50 925
16 640
4592
3051
2278
378
1452
1527
Power
1596
1136
580
110
10
540
157
2
0
393
Industry
2101
748
4309
3182
133
902
566
28
245
450
Residential
364
396
26 804
5792
1139
1478
1430
286
1140
424
Transportation
388
2840
19 233
7556
15
131
126
63
66
260
Agriculture
3296
Central Asia
1648
971
5227
1162
133
963
458
21
46
387
Power
1066
379
40
7
1
4
1
0
0
155
Industry
407
134
354
623
7
930
428
6
32
105
Residential
154
131
576
159
85
3
3
1
2
98
Transportation
20
327
4257
373
1
26
26
15
12
29
Agriculture
39
Russia Asia
4164
2405
15 605
2597
105
2103
1188
87
172
830
Power
1981
1149
478
26
6
27
15
2
9
517
Industry
1996
179
2727
1520
14
1949
1050
21
103
180
Residential
101
87
1056
236
58
23
20
4
16
63
Transportation
86
990
11
,344
815
2
105
103
59
44
71
Agriculture
25
Asia
51 324
52 118
336 588
67 034
28 816
31 726
22 749
3530
8322
17 301
Power
19 487
15 260
5972
543
28
4175
1984
7
13
5893
Industry
25 429
14 065
102 695
25 267
629
15 200
9409
884
1192
6925
Residential
5134
3478
152 882
22 707
4784
10 551
9630
2063
6798
2944
Transportation
1274
19 316
75 040
18 517
126
1800
1727
576
320
1540
Agriculture
23 249
Emissions distributions by Asian regions in 2010.
Asian emissions in 2010 for 10 species are listed in Table 3 by country and
the shares of 2010 emissions by each subregion are presented in Fig. 5.
China is the largest contributor for most species except NH3, with more
than 50 % contribution for SO2, NOx, CO, PM10, PM2.5,
and CO2 emissions. Following China, India is the largest contributor
for NH3 emissions (34 % of total) and the second largest contributor
for all other species. As shown in Fig. 5, Southeast Asia and Other South
Asia contribute more than 20 % to NMVOC, NH3, OC, and CO emissions
and around 10 % for other species, representing, in particular, a high
contribution from biofuel emissions. Contributions from other Asian regions
are less than 10 % for all species.
Table 4 presents Asian 2010 emissions by region and by sector. Emissions by
country and by sector can be downloaded from the MIX website
(http://www.meicmodel.org/dataset-mix.html). China's anthropogenic emissions
in 2010 are estimated as follows: 28.7 Tg SO2, 29.1 Tg NOx, 170.9 Tg CO,
23.6 Tg NMVOC, 9.8 Tg NH3, 16.6 Tg PM10, 12.2 Tg PM2.5, 1.8 Tg BC, 3.4 Tg OC, and 10.1 Pg CO2. Overall, industry is
the largest emitter of China's anthropogenic emissions, contributing 49 %
of the total CO2 emissions and 59, 39, 61, and 50 % of
SO2, NOx, NMVOC, and PM2.5 emissions respectively. The
dominance of the industrial sector on China's anthropogenic emissions
reflects the fact that China has developed a huge industrial capacity, which
has led to very high levels of energy use and emissions. For example, China
produced 44 and 70 % of global iron and cement, respectively, in 2010
(World Steel Association, 2011; United Nations, 2011). As a result,
industrial SO2 emissions in China in 2010 surpassed SO2 emissions
from the US and Europe combined. Power plants contributed 32 % of the
total CO2 emissions and 28, 33, and 7 % of SO2,
NOx, and PM2.5 emissions respectively. Emission ratios of
SO2 / CO2 and PM2.5 / CO2 are lower in power plants than in
the industrial sector, reflecting better emission control facilities
operated in power plants, such as flue-gas desulfurization devices (FGD).
The residential sector dominates emissions for pollutants from incomplete
combustion, given that large amounts of solid fuels (coal and biomass) were
burned in small stoves in China's homes. The residential sector shared
13 % of China's total CO2 emissions in 2010, but contributed to
45 % of CO, 27 % of NMVOC, 51 % of BC, and 81 % of OC emissions
respectively. The transportation sector accounted for 25, 12,
11, and 16 % of NOx, CO, NMVOC, and BC emissions respectively.
The contribution of the transportation sector to China's CO and NMVOC
emissions has substantially decreased during recent years, which will be
further discussed in the next section.
In the MIX inventory, Indian emissions in 2010 are estimated as follows: 9.3 Tg SO2,
9.6 Tg NOx, 67.4 Tg CO, 16.9 Tg NMVOC, 9.9 Tg NH3,
7.1 Tg PM10, 5.2 Tg PM2.5, 1.0 Tg BC, 2.5 Tg OC, and 2.3 Pg
CO2. In India, the industrial sector has much lower contributions to
emissions compared to China, while higher emission contributions from the
residential sector are estimated. The differences of the emission patterns
between China and India can be attributed to differences in the stage of
economic development and the composition of the energy structure. In India,
the residential sector is the second largest contributor for CO2
emissions and the largest contributor for CO, NMVOC, PM2.5, BC, and OC
emissions, in which more than 70 % of those emissions are contributed by
biofuel combustion. With the rapid growth of coal-fired generation units,
SO2 emissions from Indian power plants are estimated to be 5.5 Tg in
2010, contributing 59 % of the total Indian SO2 emissions. The
SO2 / CO2 emission ratio in Indian power plants is significantly
higher than that of China, representing the low penetration rates of FGD in
Indian power plants (Lu et al., 2011). The transportation sector contributes
55 % of NOx and 36 % of NMVOC emissions in India. These large
shares are caused by the high emission factors used in REAS2, in which
relatively poor emission control measures are in place (Kurokawa et al.,
2013).
Per capita emissions by sector for 2010 in MIX, ranked by GDP per
capita for each country.
Emission ratios of SO2 to CO2, and CO to CO2 by
Asian regions. CHN is China, OEA is Other East Asia, IND is India,
OSA is Other South Asia, SEA is Southeast Asia, CA is Central Asia, and
RA is Russia Asia.
Figure 6 compared per capita emissions by sector and by species in 2010 for
each country. Emissions are ranked by GDP per capita of each country. The
correlations between emission intensity (per capita emissions) and economic
development (GDP per capita) at country level are not always significant
because emission intensities are affected by not only economic level but
also by other factors such as industrial structure and dominant fuel type.
Nevertheless, the changes in emission intensities in general follow the
pattern of Kuznets curve for most species except NH3, BC, and OC.
Emission intensities tend to increase following the GDP growth first and
then tend to decrease for high-income countries. For BC and OC, per capita
emissions are higher in developing countries than in developed countries
because low-income countries with low incomes tend to use biofuels in which
emitted more BC and OC than other fuel types.
Ratios of different species were widely used to inform emission
characteristics. For example, SO2 / CO2 ratio was used as an
indicator of coal combustion and emission control levels (Li et al., 2007),
and ratios of CO / CO2 were used to inform combustion efficiency (Wang et
al., 2010). Figure 7 compares regional emission ratios of SO2 / CO2
and CO / CO2 estimated by the MIX inventory. Emission ratios of
SO2 / CO2 are lowest in Other East Asia among different regions,
which could be attributed to small share of coal use and high penetration of
emission control facilities, while high emissions ratios of
SO2 / CO2 were found in Russia Asia and Central Asia due to high
fraction of coal use and less emission controls. Other East Asia also has
the lowest emission ratios of CO / CO2 among different regions, owing
to a
high contribution from industrial and transportation emissions. In contrast,
high emissions from small residential combustions led to low combustion
efficiencies and high emission ratio over India and Southeast Asia.
Asian emissions in 2006 and trends between 2006 and 2010 (units for
emissions: Tg for CO2 and Gg for other species)∗.
Regions
SO2
NOx
CO
NMVOC
NH3
PM10
PM2.5
BC
OC
CO2
China
34 597 (0.83)
23 719 (1.23)
179 626 (0.95)
20 715 (1.14)
11 203 (0.88)
19 342 (0.86)
13 752 (0.89)
1771 (1.00)
3486 (0.97)
7827 (1.29)
Japan
838 (0.85)
2352 (0.81)
5888 (0.73)
1538 (0.77)
507 (0.94)
149 (0.76)
109 (0.74)
32 (0.63)
12 (0.68)
1241 (0.89)
Korea, DPR
233 (0.91)
293 (0.81)
5430 (0.83)
175 (0.79)
108 (1.02)
319 (0.83)
139 (0.83)
16 (0.86)
18 (0.94)
84 (0.85)
Korea, Republic of
446 (0.94)
1270 (0.84)
827 (1.01)
794 (1.07)
184 (1.03)
65 (1.91)
42 (2.04)
15 (1.55)
12 (0.69)
510 (1.06)
Mongolia
71 (1.39)
45 (1.37)
523 (1.4)
37 (1.29)
103 (0.93)
75 (1.46)
31 (1.49)
1 (1.58)
2 (1.72)
11 (1.38)
Other East Asia
1588 (0.90)
3961 (0.83)
12 668 (0.82)
2544 (0.87)
903 (0.97)
607 (1.01)
321 (1.02)
65 (0.92)
44 (0.84)
1846 (0.94)
India
7476 (1.24)
7484 (1.28)
55 910 (1.21)
14 685 (1.15)
9015 (1.09)
5874 (1.21)
4327 (1.21)
887 (1.15)
2415 (1.05)
1892 (1.20)
Afghanistan
2 (1.33)
111 (1.60)
279 (1.64)
96 (1.46)
131 (1.10)
14 (1.49)
13 (1.48)
5 (1.48)
7 (1.37)
2 (1.25)
Bangladesh
102 (1.30)
283 (1.30)
2332 (1.10)
711 (1.11)
889 (1.14)
283 (1.21)
203 (1.15)
30 (1.09)
113 (1.07)
67 (1.25)
Bhutan
4 (1.32)
11 (1.21)
256 (1.18)
43 (1.15)
42 (0.99)
21 (1.23)
18 (1.18)
3 (1.14)
12 (1.13)
4 (1.17)
Maldives
3 (0.97)
8 (0.98)
144 (1.05)
7 (1.21)
0 (1.07)
0 (1.28)
0 (1.27)
0 (1.45)
0 (1.40)
2 (1.00)
Nepal
28 (1.08)
72 (1.16)
1985 (1.06)
405 (1.09)
242 (1.05)
138 (1.08)
128 (1.08)
25 (1.08)
98 (1.08)
32 (1.07)
Pakistan
1128 (1.24)
816 (1.16)
8298 (1.12)
1871 (1.13)
1543 (1.20)
542 (1.11)
503 (1.11)
103 (1.11)
358 (1.09)
231 (1.14)
Sri Lanka
108 (1.23)
120 (0.96)
1274 (1.04)
347 (1.06)
112 (1.09)
123 (1.23)
98 (1.13)
15 (1.00)
58 (1.02)
29 (1.08)
Other South Asia
1376 (1.24)
1421 (1.20)
14 568 (1.11)
3481 (1.12)
2959 (1.16)
1121 (1.15)
964 (1.12)
181 (1.10)
647 (1.08)
365 (1.15)
Brunei
9 (1.22)
10 (1.15)
6 (0.91)
32 (0.98)
7 (1.13)
1 (0.56)
1 (0.58)
0 (0.67)
0 (0.53)
8 (1.18)
Cambodia
26 (0.99)
46 (1.00)
976 (1.05)
198 (1.07)
121 (1.11)
55 (1.06)
53 (1.06)
11 (1.04)
42 (1.04)
16 (1.04)
Indonesia
1676 (1.17)
1999 (1.29)
19 379 (1.23)
6134 (1.30)
1634 (1.19)
1237 (0.96)
944 (1.00)
164 (1.08)
663 (1.04)
520 (1.07)
Laos
133 (1.13)
35 (1.17)
388 (1.02)
80 (1.06)
78 (1.12)
24 (1.01)
22 (1.01)
4 (1.02)
16 (1.00)
6 (1.03)
Malaysia
290 (1.26)
505 (1.25)
3117 (1.20)
1504 (1.17)
222 (1.15)
191 (1.13)
126 (1.05)
14 (1.13)
33 (1.05)
175 (1.15)
Myanmar
71 (0.94)
76 (1.21)
2594 (1.04)
654 (1.25)
392 (1.08)
155 (1.06)
149 (1.04)
30 (1.04)
121 (1.03)
47 (1.05)
Philippines
474 (1.06)
288 (1.26)
2269 (1.03)
812 (1.07)
404 (1.02)
160 (1.21)
114 (1.08)
15 (0.97)
70 (0.98)
94 (1.27)
Singapore
191 (0.92)
112 (1.03)
138 (1.18)
290 (1.15)
12 (0.85)
7 (0.96)
6 (0.97)
1 (1.08)
1 (1.12)
39 (1.08)
Thailand
796 (0.77)
740 (1.09)
7555 (1.13)
2031 (1.15)
533 (1.22)
508 (0.97)
285 (0.96)
33 (1.06)
134 (1.11)
271 (1.09)
Vietnam
463 (1.24)
337 (1.31)
7419 (1.11)
1584 (1.41)
624 (1.07)
594 (1.20)
485 (1.16)
79 (1.09)
302 (1.06)
184 (1.26)
Southeast Asia
4129 (1.08)
4149 (1.23)
43 841 (1.16)
13 319 (1.25)
4027 (1.14)
2933 (1.04)
2184 (1.04)
352 (1.07)
1383 (1.05)
1360 (1.12)
Kazakhstan
1775 (0.59)
499 (1.12)
2107 (1.59)
423 (1.28)
39 (1.06)
381 (1.16)
192 (1.16)
10 (1.26)
24 (1.17)
191 (1.07)
Kyrgyzstan
30 (0.90)
27 (1.32)
224 (1.66)
30 (1.34)
12 (1.00)
60 (1.03)
27 (1.05)
1 (1.44)
2 (1.21)
5 (1.19)
Tajikistan
11 (1.24)
16 (1.62)
122 (1.57)
25 (1.17)
15 (1.06)
28 (0.80)
15 (0.86)
1 (1.78)
1 (1.34)
3 (1.03)
Turkmenistan
45 (1.42)
97 (1.28)
298 (1.40)
174 (1.37)
13 (1.10)
54 (1.18)
25 (1.20)
2 (1.37)
2 (1.28)
42 (1.22)
Uzbekistan
590 (0.84)
241 (0.94)
808 (1.11)
287 (1.08)
53 (0.94)
325 (1.15)
143 (1.15)
3 (1.03)
9 (1.13)
129 (0.94)
Central Asia
2451 (0.67)
879 (1.10)
3558 (1.47)
940 (1.24)
131 (1.01)
847 (1.14)
402 (1.14)
17 (1.27)
39 (1.17)
370 (1.04)
East Siberia
1711 (0.96)
482 (1.11)
2437 (1.18)
351 (1.12)
24 (0.97)
380 (0.97)
199 (0.99)
11 (1.28)
19 (1.06)
178 (1.03)
Far East
349 (1.02)
410 (1.19)
2284 (1.17)
268 (1.13)
20 (0.91)
228 (0.98)
120 (1.03)
17 (1.34)
22 (1.13)
109 (1.10)
Ural
1510 (0.98)
412 (1.11)
3757 (1.07)
551 (1.07)
22 (0.99)
1042 (1.01)
580 (1.03)
17 (1.09)
69 (1.09)
174 (1.07)
West Siberia
647 (1.05)
815 (1.14)
5399 (1.12)
1206 (1.09)
43 (0.99)
484 (0.96)
275 (0.98)
27 (1.19)
50 (1.03)
308 (1.10)
Russia Asia
4217 (0.99)
2119 (1.13)
13 878 (1.12)
2376 (1.09)
108 (0.97)
2132 (0.99)
1173 (1.01)
72 (1.21)
160 (1.07)
770 (1.08)
Asia
55 832 (0.92)
43 732 (1.19)
324 049 (1.04)
58 059 (1.15)
28 348 (1.02)
32 857 (0.97)
23 124 (0.98)
3345 (1.06)
8174 (1.02)
14 430 (1.20)
∗ numbers in the parentheses represent emission ratios of 2010 to
2006. Bold values are the total emissions and ratios for Asian regions.
Changes of Asian emissions from 2006 to 2010
In this work, we also developed Asian emissions for 2006 and 2008 following
the same approach of MIX, to illustrate the changes in Asian emissions from
2006 to 2010. Table 5 presents Asian emissions in 2006 and emission ratios
of 2010 to 2016 by country. For the whole of Asia, emission growth rates
from 2006 to 2010 are estimated as follows: -8.1 % for SO2,
+19.2 % for NOx, +3.9 % for CO, +15.5 % for NMVOC,
+1.7 % for NH3, -3.4 % for PM10, -1.6 % for PM2.5,
+5.5 % for BC, +1.8 % for OC, and +19.9 % for CO2. Growth in
CO2 emissions represent the continuously increasing energy use across
Asia during 2006–2010, while different trends among species represents
differences in the emission control level among sectors and regions.
Compared to the increasing emission trends of all species during 2001–2006
(Zhang et al., 2009), the relatively flat or even decreasing emission trends
in many species indicate the effectiveness of emission control measures in
recent years (Gu et al., 2013; Lin et al., 2010; Wang et al., 2013).
Emission changes from 2006 to 2010 by Asian regions for
SO2 (a) and CO (b). Left panel: emissions in 2006 and 2010 by region. Y axis
represents emissions by region. X axis represents accumulative emission
contribution of regions. The dotted and solid lines represent emissions in
2006 and 2010 respectively. Right panel: the shares of emissions by sectors
over China and India in 2006 and 2010.
Comparison of emission trends of NOx, SO2, and CO over
Asia with satellite observations.
Species
Regions
Study
Method
Period
AGR (% yr-1)a
NOx
China
Berezin et al. (2013)
Inverse modeling
2001–2008
11.5
China
Gu et al. (2013)
Inverse modeling
2005–2010
4.0
China
Miyazaki et al. (2016)
Inverse modeling
2005–2010
3.7
East China
Mijling et al. (2013)
Inverse modeling
2007–2011
9.0
East China
Krotkov et al. (2016)
Satellite
2005–2010
5.4
Central East China
Itahashi et al. (2014)
Satellite
2000–2010
11.0
China
This work
Inventory
2006–2010
5.2
India
Krotkov et al. (2016)
Satellite
2005–2010
4.6
India
Miyazaki et al. (2016)
Inverse modeling
2005–2010
3.2
India
This work
Inventory
2006–2010
6.3
SO2
East China
Krotkov et al. (2016)
Satellite
2005–2010
-6.9
China
This work
Inventory
2006–2010
-4.6
India
Krotkov et al. (2016)
Satellite
2005–2010
16.5
India
This work
Inventory
2006–2010
5.5
CO
China
Yumimoto et al. (2014)
Inverse modeling
2005–2010
-3.1
China
Yin et al. (2015)
Inverse modeling
2002–2011
-1.1
East China
Worden et al. (2013)
Satellite
2000–2012
-1.6, -1.0b
China
This work
Inventory
2006-2010
-1.2
a AGR is annual growth rate.
b Results are developed using MOPITT and AIRS.
During 2006–2010, CO2 emissions increased in China (+29.4 %), India
(+20.4 %), Other South Asia (+15.2 %), and Southeast Asia
(+12.3 %) and remained relatively stable for other regions. The increases in
CO2 emissions are driven by energy consumption growth stimulated by
economic development over Asian regions, especially for China and India. As
reported by IEA (International Energy Agency), the total primary energy
consumption of Asia has increased by 20.6 % during the period of 2005 and
2010 (IEA, 2013). During the same period, SO2 emissions decreased in
China (-17.2 %), Other East Asia (-9.5 %), and Central Asia (-32.8 %)
due to effective emission control, while they increased in India (+23.9 %),
Other South Asia (+23.9 %), and Southeast Asia (+7.8 %) due to
growth in coal use and absence of desulfurization devices. The decrease in
SO2 emissions changes in Asian is dominated by changes in China and
India. Figure 8a demonstrates the changes in SO2 emissions among
Asian regions from 2006 to 2010. Wide installation of FGD in China's coal-fired power plants is the main driving
factor of SO2 emission changes over Asia. SO2 emissions in China's
power plants decreased from 17.2 Tg in 2006 to 8.2 Tg in 2010, contributing
to most of the total SO2 emission reduction over Asia. In contrast,
SO2 emissions in India increased by 27 % during 2006–2010, owing to
the
dramatic construction of new power plants and the lack of emission control
facilities (Garg et al., 2001, 2006). As a consequence, the Indian share of
the total Asian SO2 emissions increased from 13 % in 2006 to 18 %
in 2010. NOx and NMVOCs emissions were increased in all Asian regions
except Other East Asia (-17.3 % for NOx and -13.0 % for NMVOCs
respectively), indicating lack of effective control measures for those two
species over Asia. Increases of NOx and NMVOC emissions are mainly
driven by growth in industrial activities and vehicle population. For
NOx, remarkable emission increases are observed for China
(+22.6 %), India (+27.8 %), Other South Asia (+20.5 %), and
Southeast Asia (+23.4 %) during 2006–2010. For NMVOC, emissions
increased by 14.0, 15.0, 12.3, 24.9, 23.6, and 9.3 %
for China, India, Other South Asia, Southeast Asia, Central Asia, and Russia
Asia respectively. Emission changes of other species are relatively small
(i.e., within 6 %) during 2006–2010. For CO, PM10, and PM2.5,
emission reductions in China were partly offset by increases of emissions in
the South and Southeast Asian regions. CO emissions in China decreased by
5 % during 2006–2010 (see Fig. 8b), mainly due to improved combustion
efficiency, recycling of industrial coal gases, and strengthened vehicle
emission standards. The implementation of new vehicle emission standards and
retirement of old vehicles has reduced China's transportation CO and NMVOC
emissions by 20 and 30 %, respectively, during 2006–2010. While in
India, Other South Asia, and Southeast Asia, CO emissions increased by
21, 11, and 16 %, respectively, between 2006 and 2010.
Speciated NMVOC emissions for the year 2010 by chemical group and
by Asian regions. Alkanes: ethane, propane, butanes, pentanes, hexanes,
higher alkanes and their isomers. Alkenes: ethane, propene, isoprene,
terpenes, higher alkenes and their isomers. Alkynes: ethyne and other
alkynes. Aromatics: benzene, toluene, xylene, trimethylbenzene, other
aromatics and their isomers. OVOCs: aldehydes (formaldehyde, acetaldehyde,
and higher aldehydes), ketones (acetone and higher ketones), alcohols
(methanol, ethanol, and higher alcohols), ethers, and acids. “Others”:
halogenated hydrocarbons, unidentified species, etc.
Satellite observations have shown promising capabilities in detect trends in
surface emissions (Streets et al., 2013). The increases in NOx
emissions over China and India were confirmed by satellite-based inversions
and the growth rates in satellite-based NOx emission trends during
2006–2010 are generally comparable to our estimates in emission inventories
(Table 6). For SO2 emissions, the downward trend over China and upward
trend over India were also observed by satellite remote sensing, while
higher growth rates were detected by OMI than the bottom-up emission
inventory (Krotkov et al., 2016). The downward trend of CO emissions over
China in recent years has been confirmed by both in situ and satellite
observations (Wang et al., 2010; Worden et al., 2013; Yumimoto et al., 2014;
Yin et al., 2015). The decreasing rate of CO emissions over China is
estimated to be -1.2 % yr-1 from 2006 to 2010 in the MIX
inventory, consistent with the rates observed by multiple satellites in
range of -1.0 to -3.1 % yr-1 during 2000–2012 (Table 6).
Speciated NMVOC emissions
Figure 9 presents 2010 Asian NMVOC emissions of different chemical groups by
region and by sector. Similar to Asian emissions estimated in previous work
(Klimont et al., 2002; Li et al., 2014), alkanes and alkenes are the largest
contributors to the total Asian NMVOC emissions in 2010 (27 and 26 %
of the total respectively), followed by oxygenated volatile organic compounds (OVOCs; 20 %),
aromatics (17 %), and alkynes (7 %).
Regionally, shares of alkanes and aromatics are higher in China, Other East
Asia, Central Asia, and Russia Asia than other regions, due to large
contributions from the industrial sector. Shares of alkynes in Central Asia
and Russia Asia are significantly lower than other regions due to a low
contribution from biofuel emissions. Sectoral contribution of emissions
varies significantly by different chemical groups. Over Asia, the industrial
sector is the major source of emissions of alkanes and aromatics. Alkanes
emissions from industrial sector are mainly contributed by gas production
and distribution (19.8 % of total industrial emissions), coal combustion
(17.1 %), and oil refinery (15.0 %), and aromatics emissions are mainly
contributed by architectural paint use (21.0 % of total industrial
emissions), other industrial paint use (16.6 %), and gas production and
distribution (10.6 %). The residential sector has a high contribution of
OVOCs, alkynes, and alkenes, among which mainly contributed by biofuel
combustions. The sectoral contribution to different chemical groups also
varies with region. For example, the residential sector dominates emissions
for all species in the Other South Asia region, as a consequence of the low
economic development in that region.
Among different regions, China, India, and Southeast Asia are the largest
contributors to NMVOC emissions in Asia, with contributions varying by
chemical groups. China contributes more than 40 % of alkanes, alkynes, and
aromatics in Asia, compared to 35 % contribution of the total Asian NMVOC
emissions. India contributes high to emissions of alkenes, alkynes, and
OVOCs, constituting about 30 % of Asian emissions. The high emissions of
alkenes in India (and Other South Asia) are mainly from contributions of
biofuel combustions and motorcycles, and OVOC emissions in India are
dominant by biofuel combustions. Southeast Asia shares around 20 % of the
emissions of alkanes, alkenes, aromatics, and OVOCs.
Monthly variations of Asian SO2, CO, PM2.5, and
CO2 emissions by sector for the year 2010.
Seasonality
Monthly emissions by sector and by Asian region are provided in Tables S6–S14. Monthly profiles in emissions are highly sector dependent given
that monthly activity rates vary among different sectors. Figure 10
illustrates the monthly variations of Asian SO2, CO, PM2.5, and
CO2 emissions by sector for the year 2010. Different species generally
show similar monthly emission patterns within the same sector, indicating
that monthly emission profiles of each sector are dominated by monthly
variations in activity rates. For example, industrial emissions are higher
in the second half of the year induced by larger industrial productions to
meet the annual total production target. The most significant monthly
variation with a winter peak was found in the residential sector, reflecting
the higher energy demand for residential heating in winter. Residential
SO2 emissions in winter are even higher than other species, because
SO2 emissions from China dominate residential emissions in Asia (70 %
of total), of which coal consumption in winter is higher than other regions
for heating. Monthly profiles of CO emissions are different from other
species for the transportation sector. This is because the CO emission
factor in winter is higher than in other seasons due to additional emissions
from the cold-start process (Kurokawa et al., 2013; Zheng et al., 2014).
Monthly variations of SO2, CO, PM2.5, and CO2
emissions by Asian region for the year 2010.
Figure 11 presents monthly variations of SO2, CO, PM2.5, and
CO2 emissions by Asian region. Compared to other species, CO emissions
are much higher in winter in high-latitude regions due to residential
heating and additional vehicle emissions from cold starts. Winter PM2.5
emissions in China are higher than other regions, representing large
emissions from solid fuel use in residential homes.
Grid maps for gaseous (a) and aerosol (b) species in the MIX
Asian emission inventory, 2010.
Intercomparisons of total anthropogenic emissionsa among MIX,
REAS2, and EDGAR v4.2 for 2008.
Unit: Tg yr-1
SO2
NOx
CO
NMVOC
NH3
PM10
PM2.5
BC
OC
CO2
Asiab
MIX
49.00
46.38
317.11
60.26
27.66
30.16
21.71
3.40
8.04
15 145
REAS2
52.82
46.17
344.20
65.94
32.74
34.21
23.51
2.95
7.55
15 271
EDGAR v4.2
63.26
36.73
212.16
53.43
20.08
31.08
15 282
China
MIX
31.41
26.55
175.64
22.10
9.80
17.63
12.74
1.76
3.38
8955
REAS2
33.58
25.55
202.71
27.78
15.00
21.69
14.57
1.60
3.09
9085
EDGAR v4.2
41.35
20.66
106.10
22.60
11.11
14.76
8647
India
MIX
8.42
8.86
61.80
15.95
9.42
6.65
4.88
0.98
2.48
2103
REAS2
10.08
9.68
61.80
15.95
9.42
6.65
4.88
0.71
2.29
2103
EDGAR v4.2
8.42
6.37
45.58
10.58
4.14
10.80
2307
a Including power, industry, residential, transportation, and
agriculture.
b “Asia” refers to all Asian regions excluding the Russia Asia in MIX.
Intercomparisons of emission estimates between MIX, REAS2, and
EDGAR v4.2 by Asian regions and sectors. (a) Absolute differences of
emission estimates. (b) Ratio of emission estimates. Grey shaded grids
indicate that the comparison is not available due to absence of emission
estimates in EDGAR v4.2. Abbreviations of Asian countries and regions are
the same as in Fig. 7. Abbreviations of sectors are as follows: POW is
power plants; INDU is industry; RES is residential; TRA is
transportation; AGR is agriculture; SUM is total. Russia Asia is not
included in the comparison.
Gridded emissions
In the MIX inventory, gridded emissions for 10 gaseous and aerosol species
were developed at 0.25 × 0.25∘ resolution. Emission maps of
all species in 2010 are shown in Fig. 12. Compared to the previous gridded
Asian emission inventories, we believe the spatial patterns are improved
because several local high-resolution emission datasets are incorporated,
such as CPED for China and JEI-DB and OPRF for Japan. However, for sectors
in which emissions are dominated by spatially scattered sources (e.g.,
residential combustion, solvent use), the spatial distributions in emissions
are still uncertain.
MIX emission inventory can be accessed publicly from the website of
http://www.meicmodel.org/dataset-mix. Both 2008 and 2010
emissions of 10 species with monthly variation at a spatial resolution of
0.25 × 0.25∘ are available from the website, including
SO2, NOx, CO, NH3, NMVOC, PM10, PM2.5, BC, OC, and
CO2. Speciated NMVOC Emissions for CB05 and SAPRC-99 chemical
mechanisms are provided at the same spatial and temporal resolution. The MIX
inventory has been regridded to 0.1 × 0.1∘ resolution using
the area-weighting approach and then incorporated to the HTAP v2 gridded
emission inventory (Janssens-Maenhout et al., 2015). The HTAP v2 emission
inventory can be downloaded from the EDGAR website
(http://edgar.jrc.ec.europa.eu/htap_v2/index.php?SECURE=_123).
Comparison with other inventories
MIX, REAS2, and EDGAR v4.2 over Asia
A comprehensive intercomparison among different emission inventories over
Asia was conducted by Kurokawa et al. (2013). In this work, we compare the
MIX inventory with REAS2 and EDGAR v4.2 (EC-JRC/PBL, 2011), two widely used
inventories, to highlight the new findings from the mosaic inventory and
identify the potential sources of uncertainties. We choose the year of 2008
to conduct the comparison because emissions after 2008 are not available in
either REAS2 or EDGAR v4.2. Russian Asia was excluded from comparison. Asian
anthropogenic emissions of MIX, REAS2, and EDGAR v4.2 in 2008 are tabulated
in Table 7. Over Asia, MIX and REAS differ within 10 % for most species,
except for NH3 (18 % higher in REAS), PM10 (13 % higher), and
BC (13 % lower). It is not surprising that the total Asian emission
budgets in MIX and REAS2 are similar given that MIX used emissions
estimates in REAS2 for Asian regions except China and India. However, REAS2 has incorporated several recent emission inventories for China
(Kurokawa et al., 2013). The differences between REAS and MIX over China and
India will be discussed in the following sections.
Comparison of 2008 power plants emission estimates between MEIC
v1.0, REAS2, and EDGAR v4.2 for Shanxi province, China. (a) Spatial
distribution of CO2 emissions and (b) emission ratios of SO2 to
CO2. CO2 emissions are grouped by colors.
Remarkable differences are observed between MIX and EDGAR v4.2. Compared to
MIX, 2008 Asian emissions in EDGAR are 29 % higher for SO2, but 20,
33, 11, and 27 % lower for NOx, CO, NMVOC, and NH3
respectively. PM10 and CO2 emissions agree well between the two
inventories with differences within 3.2 %. Figure 13 details the differences by region and
by sector. Regionally, the differences can be largely attributed to
disagreements in emission estimates for China and India, as presented in
Table 7. Discrepancies are relatively large at the sector level compared to
total emissions. EDGAR's estimates for SO2 emissions from power plants
are 60 % higher than estimates in MIX. For China, 70 % of power
generation capacities were equipped with FGD and the average SO2 removal
efficiency was 78 % (Liu et al., 2015). The high estimates in EDGAR v4.2
are most likely due to underestimation of FGD penetration or SO2 removal
efficiencies of FGD (Kurokawa et al., 2013). Remarkable differences for the
residential and transportation sectors are found for NOx, CO, and NMVOC
estimates in the two inventories. For instance, EDGAR v4.2 estimates lower
NOx emissions of transportation sector by 27 and by 48 % for the
residential sector compared to MIX. Similarly, residential CO emissions in
EDGAR v4.2 are 33 % lower than in MIX, leading to 33 % lower
estimates of CO emissions in EDGAR v4.2 compared to MIX. Underestimates of CO
emissions in EDGAR v4.2 inventory have been confirmed by top-down constraints
(Pétron et al., 2004; Fortems-Cheiney et al., 2011). As the statistical
differences of energy use are usually within 30 % at sector level (Guan
et al., 2012), the discrepancy by sector could only be attributed to
differences in the raw emission factors and abatement measures. Although a
point-by-point comparison of emission factors between EDGAR v4.2 and MIX is
not feasible, we can still speculate that EDGAR v4.2 may overestimate the
combustion efficiency and emission control measures in Asia by using an
emission factor database from developed countries. NH3 emissions in
EDGAR v4.2 are 26 % lower than in MIX, with a large difference in
residential emissions. The differences are mainly from high emission
estimates of wastewater treatment sources in REAS2, which were incorporated
into MIX for Asian regions except China. MIX estimated 3.4 Tg NH3
emissions from wastewater treatment in Asia in 2008, which are more than 2
orders of magnitude higher than EDGAR v4.2 estimates. Differences in
PM10 emissions at the sector level are also large; similar estimates of
PM10 emissions in the two datasets are rather a coincidence than real
agreements.
For CO2 emissions, good agreements are found among MIX, REAS2, and EDGAR
v4.2 inventories in Asia with differences in total emissions less than
1 %. CO2 emissions from biofuel combustion are included in the
intercomparison. CO2 emission estimates in MIX and REAS2 only differ
in China because REAS2 are used in MIX for regions other than China. Total
CO2 emissions of China
in MIX and REAS2 are quite similar (1.5 % higher in
REAS2), while REAS2 estimated higher emissions for power sector (+386 Tg
or +13.6 % compared to MIX) but lower emissions for industry (-293 Tg,
-6.8 %) emissions. EDGAR v4.2 estimates lower CO2 emissions for China
(-308 Tg, -3.4 %) and higher emissions for other regions: +102 Tg
(+5.6 %) for Other East Asia, +83.9 Tg (+5.6 %) for Southeast
Asia, +204 Tg (+9.7 %) for India, +29.5 Tg (+7.5 %) for Other
South Asia, and +25.3 Tg (+6.4 %) for Central Asia. At sector level,
EDGAR v4.2 estimates are 29 % (+833 Tg) higher for power, 22 % (-944 Tg) lower for industry, and 31 % (-192 Tg) lower for transportation over
China compared to MIX. For Other East Asia, differences between EDGAR v4.2
and MIX are mainly contributed by power sector, with 20 % higher emissions
(+130 Tg) in EDGAR v4.2. Residential sector is the main contributor to the
differences between EDGAR v4.2 and MIX over India, with 28 % (+177 Tg)
higher emissions estimated in EDGAR v4.2. The relatively large discrepancy
at sector level can be attributed to differences in energy statistics and
emission factors (Guan et al., 2012; Liu et al., 2015) as well as
differences in sector definitions. In particularly, EDGAR v4.2 used fuel
consumption data from IEA statistics while MIX and REAS2 used provincial
level data from Chinese Energy Statistics, which can differ by 20 % at
sector level (Hong et al., 2016). Emissions from heating plants are
aggregated to the industrial sector in the MIX inventory, while in EDGAR
v4.2 heating plants are aggregated to the energy sector and then compared
to the power sector in the MIX inventory. In the future, harmonizing the
sector (subsector) definition among global and regional inventories would
help to reduce the discrepancy of emission estimates at sector level.
China
Power plants
Both MIX and REAS2 processed power plants emissions as point sources. As
presented in Sect. 2.2, MIX used a high-resolution emission database for
China (CPED; Liu et al., 2015) to derive emissions and locations of China's
power plant emissions at unit level. In REAS2, emissions of individual power
plants are estimated by combining information from two global databases,
CARMA and WEPP. MIX and REAS2 showed good agreements on power plant
emissions in China for SO2 and CO2 (8 %
differences for
SO2 and 14 %
for CO2) in 2008, implying similar estimates in
energy consumption and emission factors in two inventories. Compared to MIX,
REAS2 estimates higher
emissions of NOx, PM10, and PM2.5 by more
than 20 %, mainly due to the differences in the emission factors used in
compiling China's emissions. Liu et al. (2015) found that CARMA has omitted
information of small plants and overestimated emissions from large plants by
wrongly allocating fuel consumptions of small plants to large ones. REAS2
included 380 power plants for China, compared to 2411 plants in MIX, while
power plants in REAS2 are large ones which contributed 72 % of CO2
emissions in China.
Comparisons of spatial distribution of NH3 agricultural
emissions between MEIC v1.0 and PKU-NH3. Provinces that included in
tropical zones are Fujian, Guangdong, Hainan, Guangxi, Guizhou, Hubei,
Hunan, Yunnan, Sichuan, Jiangxi, Anhui, Zhejiang, and Jiangsu. Other
provinces are treated as temperate ones.
Figure 14a compares CO2 emissions from power plants between MEIC and
REAS2 in Shanxi province where a large amount of coal is extracted and
combusted in power plants. EDGAR emissions are also presented in Fig. 14a
as a reference. For Shanxi province, MIX, REAS2, and EDGAR included 134, 22,
and 24 coal-fired power plants, respectively, demonstrating the omission of
many small power plants in REAS2 and EDGAR. In REAS2, only plants with
annual CO2 emissions higher than 1 Tg were processed as point sources
(Kurokawa et al., 2013). In the three datasets, a total of 6, 13, and 12 power plants in Shanxi province
have annual CO2 emissions higher than 5 Tg, respectively, indicating significant emission overestimates for large
plants in REAS2 and EDGAR. Moreover, the locations of power plants are not
accurate in EDGAR given that CARMA used city centers as the approximate
coordinates of power plants (Wheeler and Ummel, 2008). In contrast,
coordinates in CPED are obtained from official sources and crosschecked by
Google Earth (Liu et al., 2015); the positions of large power plants in
REAS2 are also checked manually (Kurokawa et al., 2013).
Figure 14b further compares the emission ratios of SO2 / CO2 in
the three inventories for individual power plants over Shanxi. Large
deviations of SO2 / CO2 ratios in MIX are driven by variations of
fuel quality, combustion efficiency, and FGD removal efficiency in each
plant, which are precisely represented in CPED. In CPED, there is a tendency
towards a decrease in SO2 / CO2 emission ratio with increase of
plant size (corresponding to higher CO2 emissions), in accordance with
the legislation that large units were required to be equipped with FGD
during 2005–2010 (Zhang et al., 2012). Smaller deviations in
SO2 / CO2 emission ratios are found in REAS2, because power plant
SO2 emissions in REAS2 were estimated by using the average FGD
penetration rates at provincial level (Kurokawa et al., 2013). The constant
ratios for all power plants in EDGAR indicate that (a) the emission factors
are not varied within China and (b) the spatial distribution treats all
power plants equal, which does not take the variations among power plants
into consideration.
NH3 agriculture emission estimates for China.
Unit: Tg-NH3 yr-1
PKU-NH3
MEIC v1.0
REAS2
EDGAR v4.2
MASAGE_NH3
Year
2006
2008
2008
2008
2005–2008∗
Fertilizer application
3.20
4.40
9.40
8.26
3.64
Live stock
5.30
5.30
2.80
2.31
5.83
∗ Averaged estimates during 2005–2008.
Agriculture
The agriculture sector is a dominant source of NH3 emissions, mainly
contributed by fertilizer applications and manure managements. MIX
incorporated the PKU-NH3 inventory for China, which estimated
agricultural NH3 emissions using a process-based model to represent the
dynamic impact of fertilizer use patterns, meteorological factors, and soil
properties (Huang et al., 2012). The new inventory improved on previous
studies which used uniform emission factors across time and region. Table 8
compares agricultural NH3 emissions in China estimated in different
emission inventories. Compared to other work, PKU-NH3 yields lower
estimates for fertilizer application but higher estimates for manure
management. The differences are mainly because PKU-NH3 used local
correction factors for fertilizer volatilization and manure loss rate (Huang
et al., 2012). Top-down inversion of NH3 emissions by adjoint model and
deposition fluxes agrees well with Huang et al. (2012), confirming the
validity of the process-based model (Paulot et al., 2014).
Besides the magnitude of emissions, a process-based model may also better
represent the spatial and temporal variations in emissions. As an example,
Fig. 15 compares NH3 agricultural emissions for MEIC and the
PKU-NH3 inventory for different climate zones. MEIC agrees well with
PKU-NH3 in temperate zones but is significantly higher than
PKH-NH3 in tropical zones. The differences in spatial distributions can
be explained by the discrepancies in derived emission factors in the two
inventories given that they used the same activity data from the National
Bureau of Statistics of China (NBSC). MEIC used a higher loss rate of
NH3 (20 % for urea) for tropical zones and a lower one (15 %) for
temperate zones following Klimont (2001). With full consideration of
fertilization method and soil acidity by grids and by month, PKU-NH3
estimated 9 % average NH3 loss rate for urea for tropical zones and
14 % for temperate zone.
Other sectors
This section further discusses the differences between MIX and REAS2 over
China. EDGAR is not compared here because references to the detailed
underlying data used in EDGAR are not available. Figure S1 in the Supplement compares MIX and
REAS2 estimates for China for 2008 by species and by sector. The two
inventories generally agree well given that both MEIC and REAS2 incorporate
the most recent advances in emission inventory studies in China. The major
differences between the two inventories are discussed below with explanation
for possible reasons.
REAS2 estimates higher CO and PM emissions than MEIC for the industrial
sector. This is probably because REAS2 underestimates the emission control
progress in China's industrial sector after 2005. During the 11th
Five-Year Plan (2005–2010), China has implemented a series of new standards
to restrict industrial emissions, leading to a downward trend in emission
factors after 2005 (Zhao et al., 2013). Emission standards implemented
during 2005–2010 are summarized in Table S15. For the industrial sector,
REAS2 adopted CO and PM emission factors from Streets et al. (2006) and Lei
et al. (2011), respectively, which represent the real-world emission
characteristics before the year 2005. Using those emission factors may have
overestimated industrial emissions. Moreover, REAS2 estimated an increasing
trend in China's CO emissions during 2005–2008, which is opposite to the
downward trend derived from satellite-based constraints for the same period
(Yumimoto et al., 2014; Yin et al., 2015), confirming that REAS2 may
overestimate CO emissions in China after 2005. Transportation emissions in
MEIC and REAS2 differ significantly for different species. Compared to
REAS2, MEIC estimates much lower emissions for CO and NMVOC (dominated by
gasoline vehicles) but higher emissions for NOx and PM (dominated by
diesel vehicles).
India
For India, MIX used ANL-India for SO2, BC, and OC emissions and REAS2
for other species. Here we compare ANL-India and REAS2 for SO2, BC, and
OC emissions, to evaluate the impact of using ANL-India. Both ANL-India and
REAS2 used energy consumption data from IEA, and hence the differences are
mainly from emission factors. Reasonable agreements are found in total
emissions over India (differing by 8–28 %), while discrepancies are large
at the sector level. REAS2 estimates 50 % higher SO2 estimates for
all sectors except power plants, most likely from different assumptions
about the sulfur content of fuels. For BC and OC, the ratio between REAS2
and ANL-India varies from 0.4 to 11.8 at the sector level, indicating large
differences in emission factor selections. ANL-India used emission factors
from a global database (Bond et al., 2004) with updates of a few recent
measurements (Lu et al., 2011), while REAS2 used a local database developed
many years ago (Reddy and Venkataraman, 2002a, b). It should be noted
that local emission measurements in India are still too few to support
accurate emission estimates. More measurements should be conducted in the
future to remedy this situation.
When implementing REAS2 to MIX over India, power plant emissions were
redistributed using spatial distributions derived from ANL-India at
0.25∘ × 0.25∘ resolution (see Sect. 2.3). We believe that it will improve the accuracy because power plant
emissions in ANL-India were estimated by each unit and allocated manually by
Google Earth. A total of 68 power plants are identified in REAS2, compared
to 145 plants in ANL-India. The two inventories generally agree well for the
grids in which both inventories allocate power plant emissions. Lu and
Streets (2012) found that the magnitudes and locations of power plant
NOx emissions (from ANL-India) are matched well with satellite-based
observations over India, providing confidence to the accuracy of ANL-India
estimates. From all the comparisons discussed above, we can conclude that
emissions are well depicted in MIX due to integration of the most recent
regional inventories.
Uncertainty in emission estimates by Asian regions in 2010 (95 %
confidence intervals; unit: %).
Regions
SO2
NOx
CO
NMVOC
NH3
PM10
PM2.5
BC
OC
CO2
References
China
±12
±31
±70
±68
±132
±130
±208
±258
Zhang et al. (2009)
±91
±107
±187
±229
Lei et al. (2011)
-14–13
-13–37
-14–45
-17–54
-25–136
-40–121
Zhao et al. (2011)
-16–17
-43–93
-43–80
Lu et al. (2011)
±31
±37
±86
±78
±153
±114
±133
±176
±271
±31
Kurokawa et al. (2013)
India
-15–16
-41–87
-44–92
Lu et al. (2011)
±32
±49
±114
±137
±144
±120
±145
±178
±233
±49
Kurokawa et al. (2013)
Others
±35
±47
±131
±111
±148
±194
±208
±257
±286
±44
Kurokawa et al. (2013)
Uncertainties and limitations
The MIX emission inventory subjects to uncertainties and several
limitations. Emission estimates from bottom-up inventories are uncertain due
to lack of complete knowledge of human activities and emission from
different sources. Uncertainty ranges of an emission inventory could be
estimated using propagation of error or Monte Carlo approaches (e.g.,
Streets et al., 2003; Zhao et al., 2011). However, in a mosaic emission
inventory like MIX, a normalized quantitative assessment of uncertainty
ranges is difficult because detailed information for emission inventory
development is not collected. Table 9 summarized the uncertainty range
estimates for China, India, and other Asian regions in different regional
emission inventories. It should be noted that those ranges are not directly
comparable due to differences in methods (propagation of error or Monte
Carlo simulation). However, those numbers might roughly represent the
uncertainty ranges in the MIX inventory as it was compiled from several
inventories listed in Table 9. In general, uncertainty ranges are relatively
small for species which emissions are dominated my large-scale combustion
sources (e.g., SO2, NOx, and CO2) but larger for species whose
emissions are mainly from small-scale and scattered sources (e.g., CO,
NMVOC, and carbonaceous aerosols). More detailed discussions on the
uncertainty sources of Asian emission inventories can be found in previous
literatures (e.g., Lu et al., 2011; Zhao et al., 2011; Kurokawa et al.,
2013).
As indicated by Janssens-Maenhout et al. (2015), the mosaic process could
introduce additional and undesired uncertainties when compiling a gridded
emission inventory from different datasets. The uncertainties may arise from
inconsistencies among datasets, including missing species in specific
datasets, closure of mass balances for aerosols, and inconsistency on the
country boarders.
When species in a specific inventory were missing, alternative estimates or
datasets were used to fill the gap in which may involve additional
uncertainties. In the MIX inventory, PM2.5, BC, and OC emissions for
Republic of Korea were roughly estimates from PM10 emissions in the
CAPSS inventory and sector-specific emission ratios between PM10 and
other aerosol components from Lei et al. (2011). For India, we used
ANL-India for SO2, BC, and OC for all sectors and NOx for power
plants. REAS2 was used to fill the gap where emissions from ANL-India were
absent (see Sect. 2.3 for detailed process procedure). For above cases where
estimates of different species in the same country were obtained from
various sources, the ratios between species may be less reliable and should be
used with caution.
When using different datasets for different types of aerosols in the same
country, additional uncertainties for aerosol emissions might be introduced
from the inconsistency in mass balance closure due to differences in spatial
proxies (Janssens-Maenhout et al., 2015), which is the case for Indian
emissions in this study. In the MIX inventory, BC and OC emissions were
obtained from ANL-India while PM2.5 and PM10 emissions were taken
from REAS2. During the mosaic process of Indian emissions, an additional check
was performed by grid for each sector and emissions of PM2.5 were
adjusted to the sum of BC and OC emissions for the grids where the sum of BC
and OC emissions exceeds PM2.5 emissions.
For a mosaic emission inventory, inconsistencies could occur at country
boarders when emissions of the two adjacent countries were obtained from
different datasets (Janssens-Maenhout et al., 2015). In the MIX inventory,
the inconsistencies are expected at the country boarder of China and India.
However, low populations and emissions are observed along the border of
China, reducing the impact of cross-border grids on the accuracy of
emissions. Also, deriving country totals from the gridded emissions is not
appropriate for small countries due to the impact from cross-border grids,
especially for those grids with large point source emissions
(Janssens-Maenhout et al., 2015).
The current MIX inventory also has several limitations. Firstly, it provides
emissions with aggregated sectoral information, which may be sufficient for
the base case model but insufficient for targeted policy cases. Secondly,
the MIX inventory is provided with moderate spatial resolution (i.e.,
0.25∘ × 0.25∘), which could
support global and regional models but is still too coarse for urban models.
Finally, yet importantly, gridded emissions are only available for 2008 and
2010 to support base years modeling activities in MICS-Asia and HTAP. For
other years, modelers could use available global and regional inventories with
more complete year coverage (e.g., EDGAR v4.2, REAS2) or extrapolate the
gridded MIX inventory to the neighboring years. Developing a complete time
series of gridded emission dataset with the best available local inventories is
a challenging task because it requires extensive international collaboration
to coordinate various resources. Continuous efforts under international
collaboration frameworks (e.g., MICS-Asia, HTAP) could help to deliver
improved and updated emission inventories over Asia continuously.