ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-18-3433-2018Comparison and evaluation of anthropogenic emissions of
SO2 and NOx over ChinaLiMengm.li@mpic.dehttps://orcid.org/0000-0001-5418-9177KlimontZbigniewZhangQiangMartinRandall V.https://orcid.org/0000-0003-2632-8402ZhengBobo.zheng@lsce.ipsl.frhttps://orcid.org/0000-0001-8344-3445HeyesChrishttps://orcid.org/0000-0001-5254-493XCofalaJanuszZhangYuxuanHeKebinMinistry of Education Key Laboratory for Earth System
Modeling, Department for Earth System Science, Tsinghua University,
Beijing, ChinaInternational Institute for Applied Systems Analysis
(IIASA), Laxenburg, 2361, AustriaState Key Joint Laboratory of Environment Simulation and
Pollution Control, School of Environment, Tsinghua University,
Beijing, ChinaDepartment of Physics and Atmospheric Science, Dalhousie
University, Halifax, Canadanow at: Max-Planck Institute for Chemistry, Mainz, GermanyMeng Li (m.li@mpic.de) and Bo Zheng (bo.zheng@lsce.ipsl.fr)8March2018185343334569July20176February201822January20184August2017This work is licensed under the Creative Commons Attribution 4.0 International License. To view a copy of this licence, visit https://creativecommons.org/licenses/by/4.0/This article is available from https://acp.copernicus.org/articles/.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/.pdf
Bottom-up emission inventories provide primary understanding of
sources of air pollution and essential input of chemical transport
models. Focusing on SO2 and NOx, we conducted
a comprehensive evaluation of two widely used anthropogenic emission
inventories over China, ECLIPSE and MIX, to explore the potential
sources of uncertainties and find clues to improve emission
inventories. We first compared the activity rates and emission
factors used in two inventories and investigated the reasons of
differences and the impacts on emission estimates. We found that
SO2 emission estimates are consistent between two
inventories (with 1 % differences), while NOx
emissions in ECLIPSE's estimates are 16 % lower than those of
MIX. The FGD (flue-gas desulfurization) device penetration rate and
removal efficiency, LNB (low-NOx burner) application
rate and abatement efficiency in power plants, emission factors of
industrial boilers and various vehicle types, and vehicle fleet need
further verification. Diesel consumptions are quite uncertain in
current inventories. Discrepancies at the sectorial and provincial levels
are much higher than those of the national total. We then examined
the impacts of different inventories on model performance by using
the nested GEOS-Chem model. We finally derived top-down emissions by
using the retrieved columns from the Ozone Monitoring Instrument
(OMI) compared with the bottom-up estimates. High correlations
were observed for SO2 between model results and OMI
columns. For NOx, negative biases in bottom-up gridded
emission inventories (-21 % for MIX, -39 % for ECLIPSE)
were found compared to the satellite-based emissions. The emission trends
from 2005 to 2010 estimated by two inventories were both consistent
with satellite observations. The inventories appear to be fit for evaluation of
the policies at an aggregated or national level; more work is needed
in specific areas in order to improve the accuracy and robustness of
outcomes at finer spatial and also technological levels. To our
knowledge, this is the first work in which source comparisons
detailed to technology-level parameters are made along with the
remote sensing retrievals and chemical transport modeling. Through
the comparison between bottom-up emission inventories and evaluation
with top-down information, we identified potential directions for
further improvement in inventory development.
Introduction
SO2 and NOx are important precursors of secondary
PM2.5, contributing to severe environmental problems
including haze and acid rain, and have been shown to be detrimental to human
health and ecosystems (Seinfeld and Pandis, 2006). China's
anthropogenic emissions have become one of the major contributors to
the global budget during the last decade (Klimont et al., 2013; Hoesly
et al., 2018). To support chemical transport modeling and provide
scientific basis for policy-making, several emission inventories
covering China have been developed (Streets et al., 2003; Ohara
et al., 2007; Zhang et al., 2007, 2009; Lu et al., 2010, 2011;
Kurokawa et al., 2013; Klimont et al., 2009, 2013; Wang et al., 2014;
Li et al., 2017; EDGAR v4.2, available at
http://edgar.jrc.ec.europa.eu).
Bottom-up emissions are estimated through comprehensive
parameterization of fuel consumption, industrial production, emission
factors and mitigation measures and spatially allocated to satisfy
the chemical and climate model requirements. Uncertainties of
emissions have been qualitatively illustrated (e.g., Granier et al.,
2011; Saikawa et al., 2017) or quantitatively analyzed (Streets
et al., 2003; Zhao et al., 2011; Guan et al., 2012; Hong et al.,
2017), inferring significant gaps in activity statistics and control
measures' assumptions in emission inventories developed for different
spatial scales (i.e., global, regional, or city scale; Zhao et al.,
2015).
Extensive comparisons of emission inventories have been conducted to
illustrate the impacts of variable emissions on the model simulation
results (e.g., Saikawa et al., 2017; Zhou et al., 2017). Although they
provide important indications on the extent of discrepancies, there
are still gaps for applying the comparison results to improve the
inventory accuracy:
Comparisons have been conducted for the total anthropogenic
sources, instead of by sectors, subsectors, and sources. Inconsistency of
source categories included in inventory models were not overviewed
or analyzed.
Few studies go into the comparisons on a specific parameter level
because the technology-based framework for each inventory was not
publicly available.
Top-down and bottom-up comparisons have not been comprehensively
combined to infer the potential uncertain parameters for all key
sectors.
To further improve the accuracy of emission estimation, we compared and
evaluated the global ECLIPSE (Evaluating the Climate and Air Quality Impacts
of Short-Lived Pollutants; Klimont et al., 2017) and MIX Asian (Li et al.,
2017) inventories due to the following reasons: (a) Up to the time of paper
preparation, ECLIPSE and MIX were the only publicly accessible gridded
emission datasets that include both SO2 and NOx covering
China for the period of 2005 and 2010; (b) both inventories have been widely
applied in atmospheric modeling and policy discussions (e.g., Stohl et al.,
2015; Duan et al., 2016; Galmarini et al., 2017; Rao et al., 2017); (c) the
technology-based framework and compiling parameters by source categories are
obtained for ECLIPSE and MIX through international collaboration, which is
not accessible for other inventories over China. The methods and data were
extensively described by a series of papers (Zheng et al., 2014; Liu et al.,
2015; Klimont et al., 2017; Li et al., 2017), supporting us for explicit
comparisons and analyses; (d) ECLIPSE (GAINS model, Greenhouse gas-Air
pollution Interactions and Synergies model; Amann et al., 2011) can be
representative of the state-of-science global emission inventory covering
China, and MIX (MEIC model, Multi-resolution Emission Inventory for China;
available at www.meicmodel.org) represents the regional inventory
compiled with advanced methods and local data. The methods, parameters and
assumptions of GAINS and MEIC are always referred to by inventory developers
(e.g., Lu et al., 2010; Fu et al., 2013; Kurokawa et al., 2013; Zhao et al.,
2013). The comparisons and validations are important to improve the accuracy
of gridded emissions and model performance over China.
Another motivation of this work is to discuss the “fitness” of current
developed inventories (specifically ECLIPSE and MIX) and modeling work
performed with them for policy-relevant discussion. The inventories and the
relevant modeling work are playing an increasingly important role for policy
discussion in Europe and most recently more and more in Asia on different
scales. However, there are no systematic and officially approved methods and
inventories but a variety of scientific products. While a lot of effort has
been made to validate emission estimates with measurements, higher source and
spatial resolution of inventories and projections will also serve discussion
about how to shape future policies to reduce impact of air pollution. In this
work, we compared the ECLIPSE and MIX emissions over China at a detailed
activity-source level. What we focused on in this paper is the bottom-up
comparison detailed to a specific parameter contributing to the differences
between the two widely used gridded emission inventories (ECLIPSE and MIX),
combined with top-down validations from the satellite observations. We
compared the activity rates and emission factors derived from several key
parameters for the largest sources for each sector and subsector.
Discrepancies in the methodologies used, data sources, technology penetration
assumptions and spatial emission patterns are discussed and illustrated.
Furthermore, we combined the bottom-up comparisons with top-down evaluations
based on observations of the OMI (Ozone Monitoring Instrument) aboard the
Aura satellite
(Levelt et al., 2006). To our knowledge, this is the first emission inventory
assessment work in which parameter-level comparison and remote sensing
evaluations are combined. OMI data provide essential constraints on emission
estimates, spatial distributions and trends (Wang et al., 2012; Liu et al.,
2016). In recent work described by Geng et al. (2017), OMI NO2
columns were applied to analyze and evaluate the sensitivities of spatial
proxies used in the emission gridding process.
Methodology and data used are summarized in Sect. 2. Bottom-up
comparisons of emissions are illustrated by decomposing the elements
of inventory development in Sect. 3.1. Section 3.2 presents the
evaluations and constraints from a satellite perspective. A summary of key
reasons leading to emission discrepancies is provided in
Sect. 3.3. Finally, Sect. 4 gives the concluding remarks.
Methodology and dataThe ECLIPSE and MIX emission inventory
Spatially specific emission inventories of air pollutants and
greenhouse gases are among key inputs for chemical transport models
(CTMs) and climate models. ECLIPSE (Klimont et al., 2017) and MIX (Li
et al., 2017) emission inventories have been applied in numerous
modeling activities at global (Stohl et al., 2015) and regional
levels, within the ECLIPSE and MICS-Asia (Model Intercomparison Study
for Asia) Phase III projects, respectively. In general, both
inventories use a dynamic technology-based methodology to estimate
anthropogenic emissions by multiplying activity rates with
technology-specific emission factors for each source by administrative
unit (province or county) (Klimont et al., 2017; Li et al., 2017). Then,
spatial proxies are used to distribute emission estimates by
province or county to grids to satisfy the needs of model simulation. The
key features of both inventories are listed in Table 1.
Key features of ECLIPSE v5a and MIX emission inventories.
ItemECLIPSE v5aMIXYear1990–2010 at 5-year intervals2005a, 2008, 2010DomainGlobalAsiaSpatial resolution0.5∘×0.5∘0.25∘×0.25∘Temporal resolutionMonthlyMonthlyActivities included for each sector Energy/powerPower plants (including combined heat and power), energyproduction–conversion (including district heating plants), fossil fuel distributionPower plants (including combined heat and power)IndustryIndustrial combustion and processesIndustrial combustion (including industrialheating plants) and industrial processesResidentialResidential combustion sourcesResidential combustion sources (includingresidential heating plants)TransportationOn-road and off-road transport sourcesbOn-road and off-road transport sourcesbAgricultureLivestock and fertilizationLivestock and fertilizationData sources of activity rates PowerInternational Energy Agency (IEA)CPED (Liu et al., 2015)IndustryInternational Energy Agency (IEA)Provincial industrial economystatistics (NBS)ResidentialInternational Energy Agency (IEA)Provincial energy statistics (NBS)TransportationInternational Energy Agency (IEA)Provincial energy statistics (NBS);Zheng et al. (2014)AgricultureUN Food and Agriculture OrganizationcProvincial statistics (NBS,Huang et al., 2012)Emission factors and technologyGAINS model (Klimont et al., 2017)MEIC modeld, process-based model for NH3 (Huang et al., 2012)Data accesshttp://www.iiasa.ac.at/web/home/research/researchPrograms/air/ECLIPSEv5a.htmlhttp://www.meicmodel.org/dataset-mix
a Developed following the same methodology of Li et al. (2017).b International air and international shipping are not
included.c FAO, http://www.fao.org/faostat/en/#home.d Zhang et al. (2009); Lei et al. (2011); Zheng
et al. (2014); Liu et al. (2015).
The ECLIPSE dataset is a global emission inventory for the
period of 1990 to 2010 extended by projections to 2050 in 5-year
intervals with monthly variations, developed with the GAINS model
(Amann et al., 2011). Primary sources of activity data are the
International Energy Agency (IEA, 2012) for fuel use and the UN Food and
Agriculture Organization for agriculture (FAO,
http://www.fao.org/faostat/en/#home). GAINS distinguishes 172
regions, including provinces for China, for which regionally specific
emission factors and technology distributions are assumed.
Emissions are distributed to grids at a specific resolution
(0.5∘×0.5∘ for ECLIPSE,
longitude × latitude) based on the percentages of spatial
proxies located in grids by source category using GIS (Geographic
Information System) techniques. For ECLIPSE, several layers were
developed as spatial proxies in line with those used in the
Representative Concentration Pathways (RCP) (Lamarque et al., 2010),
i.e., locations of energy and manufacturing facilities, road networks,
shipping routes, human and animal population density, and agricultural
land use. Spatial proxies were further developed within the Global
Energy Assessment project (GEA project; Riahi et al., 2012), including
improved population distribution, flaring in oil and gas production,
smelters, and power plants for which provincial emission layers of
MEIC were
used. Spatial proxies for both ECLIPSE and MIX are summarized in
Table S1 in the Supplement.
In this work, we use a gridded ECLIPSE v5a dataset (current legislation,
CLE;
available at
http://www.iiasa.ac.at/web/home/research/researchPrograms/air/ECLIPSEv5a.html)
for 2005 and 2010 in China for all anthropogenic sources excluding
international shipping and aviation to keep source consistency in comparison
to MIX. We developed two sensitivity cases of the ECLIPSE emissions by
changing the emission estimates or spatial proxies to study the effect of
inventory parameterization on model accuracy, as described in Sect. 3.2.1.
MIX was developed for 2008 and 2010 (including monthly
variation) by combining the up-to-date regional inventories. For
China, the monthly MEIC dataset (available at
www.meicmodel.org) and PKU-NH3 (only for NH3)
inventory are used (Li et al., 2017). The MEIC model calculates and
updates emissions for over 700 anthropogenic sources dynamically and
delivers the dataset online. Activity rates are derived from local
provincial statistics in China and emission factors are derived from
the best available local measurements and recent peer-reviewed data
for China. Power plants are treated as point sources with emissions
estimated by fuel type considering actual combustion technology and
installed control measures such as FGD (flue-gas desulfurization); this information is derived from CPED (China coal-fired
Power plant Emissions Database) as described by Liu
et al. (2015). Following the methodology of Zheng et al. (2014),
emissions of the transport sector in MEIC are estimated at the county
level based on comprehensive parameterization of vehicle ownership,
fuel consumption, temporal evolution of emission factors and
implementation of new environmental standards. Volatile organic compounds (VOCs) are speciated to
more than 1000 species and lumped to a GEOS-Chem configured mechanism
based on source-specific composite profiles and mapping tables in Li
et al. (2014).
Monthly gridded emissions of MEIC are generated by applying
source-based spatial and temporal profiles (Li et al.,
2017). Provincial emissions of MEIC are firstly distributed to counties,
then further distributed to grids. The former process was based on
statistics by county (i.e., GDP (gross domestic product), industrial
GDP, total population, urban population, rural population,
agricultural activity, vehicle population), and the latter was based
on gridded maps as spatial proxies (i.e., population density map, road
network). For power plants, locations were determined using Google
Earth following the unit-based methodology. Gridded emission products
of MEIC v1.1 at a resolution of 0.25∘×0.25∘
were integrated into MIX. In this work, we updated China's emissions
with MEIC v1.2 and extended the MIX emissions back to 2005 following
the same methodology.
GEOS-Chem
GEOS-Chem is an open-access global 3-D CTM widely used by about 100
research groups worldwide. The model is driven by the GEOS (Goddard
Earth Observing System) meteorological dataset and includes complete
NOx–Ox–HC–aerosol chemistry (“full
chemistry”), covering over 80 species and more than 300 chemical
reactions (Bey et al., 2001; Park et al., 2004).
In this work, the Asian-nested grid GEOS-Chem model v9-01-03 driven by GEOS-5
was used to simulate SO2 and the NO2
maps with different emission inventories (Chen et al., 2009). Anthropogenic
emissions for Asia were replaced with MIX and ECLIPSE variants described in
Sect. 3.2.1. The model has a horizontal resolution of 0.667∘×0.5∘ (longitude × latitude) covering Asia and 47 vertical
layers. A nonlocal scheme was applied in mixing within the planetary boundary
layer (Lin and McElroy, 2010). Global concentrations at 2.5∘×2∘ (longitude × latitude) were simulated to provide
time-varying boundary conditions to the target region. A 1-month spin-up was
conducted to reduce the effect of initial conditions. To compare with the OMI
observations consistently, we averaged the daily modeled vertical columns at
13:00–15:00 local time and resampled the model on grids that have OMI data.
Top-down emission inventory
We developed the top-down emission inventories for SO2 and
NOx based on the OMI/Aura L2 swath data. For SO2,
we obtained the planetary boundary layer SO2 total
vertical columns from GES DISC (Goddard Earth Sciences Data and
Information Services Center) of NASA (Li et al., 2006). The
mass-balance method was used to interpret the top-down anthropogenic
emissions from total columns (Martin et al., 2003; Lee et al.,
2011). For NOx, we used the tropospheric slant NO2
column data of the DOMINO v2 (Dutch OMI NO2 version 2) product
accessed from the TEMIS website (Tropospheric Emission Monitoring
Internet Service, http://www.temis.nl/) (Boersma et al.,
2011). Slant columns were converted to vertical columns using the air
mass factor (AMF), which is sensitive to the NO2 vertical
profile (Palmer et al., 2001; Lamsal et al., 2010). We revised the AMF
by replacing the a priori vertical profiles with the modeled ones to
reduce the bias in comparison following the methodology of Lamsal
et al. (2010). To reduce the retrieval uncertainties, we excluded the
OMI pixels at a solar zenith angle ≥78∘, cloud radiance
fraction >30 %, surface albedo ≥0.3 or affected by row
anomaly
(http://projects.knmi.nl/omi/research/product/rowanomaly-background.php).
Large pixels near the swath edges (10 pixels on each side) are also
rejected in spatial averaging. Furthermore, daily data of SO2
and NO2 vertical column density were developed after
allocating the OMI pixels to model grids (0.667∘×0.5∘) based on area weights.
Top-down NOx emissions were developed following the finite
difference mass balance (FDMB) methodology (Lamsal et al., 2011;
Cooper et al., 2017). We used the summer data to develop the top-down
emissions because of the stronger relationship between local emissions
and grid columns. The smearing length is around 50 km over
China in summer (assuming wind speed 5 ms-1, NO2
lifetime 3 h), comparable to the model grid size, implying
weak effects upon the inversion of horizontal mass transport between
grids. Compared to the basic mass-balance method described in Martin
et al. (2003), the FDMB method reduces the errors from nonlinearity of
NOx–OH–O3 chemistry (Gu et al., 2016; Cooper
et al., 2017). A unitless scaling factor β was introduced to
represent the sensitivity of fractional modeled NO2 columns to
the fractional anthropogenic emission changes for each grid. We
apply 15 % perturbation to emissions, simulate the NO2
column changes and calculate β following Eq. ()
(Lamsal et al., 2011; Cooper et al., 2017).
ΔEE=βΔΩΩ,
where E represents the total NOx emissions and Ω
represents the local NO2 column. ΔE is the emission
changes of anthropogenic sources, and ΔΩ is the column
changes under perturbation.
The top-down emissions were further determined based on
Eq. ().
Et=Ea1+Ωt-ΩaΩaβ,
where Et and Ea represent the top-down and priori
emissions, respectively. Ωt is the OMI-retrieved
column. Ωa is the modeled column of GEOS-Chem.
Following Cooper et al. (2017), we limited β within 0.1–10 to
avoid biases in regions with negligible low anthropogenic emissions or
columns. The absolute error in the retrieved NO2 columns is
estimated at 1×1015 molecules cm-2 (Martin
et al., 2003). We filtered out the monthly averaged retrieved columns
based on this criterion and further developed the top-down emissions
for each simulation case. Finally, summer-averaged top-down emissions
were developed and applied in the evaluations of this work.
Results and discussionComparisons of ECLIPSE and MIX
Following the framework of gridded emission inventory development, we
conducted parameter-level comparisons between ECLIPSE and MIX and
quantified the reasons causing the emission differences for each
sector. Starting from the emission comparisons for all of China in
Sect. 3.1.1, we further compared emissions by province in Sect. 3.1.2
and gridded emissions in Sect. 3.1.3.
As shown in Table 1, the activity rates were assigned independently by
two inventories. As a global emission inventory, ECLIPSE mainly relies
on international statistics of IEA. Differently, MIX obtains the
official statistics of energy consumption and industrial output from
NBS (National Bureau of Statistics) or MEP (Ministry of Environmental
Protection) of China. We can expect high independency for the
determination of emission factors between ECLIPSE (GAINS model) and
MIX (MEIC model). As two independently developed inventory models, the
source classification, technology and removal efficiencies of control
facilities in GAINS and MEIC are expected to be different, although
they both refer to up-to-date measurements and peer-reviewed
data. Different methods were developed in two inventory models for
specific sectors, including power plants, transportation, and
agriculture. For power plants, the spatial proxies were essentially
consistent between ECLIPSE and MIX. For other sectors, emissions were
gridded independently by two emission inventories (see Table S1).
China's emissions by sectors
Although a comprehensive dataset on fuel consumption and product yield,
surveys on technique penetration and measurements of emission
factors are incorporated in the inventories, there are several
additional assumptions made to characterize some sources for which
information is either incomplete or missing. For ECLIPSE and MIX,
assumptions are made independently and data sources are often
different. Particularly, MEIC developed high-resolution emissions
based on unit-based information for the power sector and county-level
emissions for transportation.
Figure 1 shows the comparisons of China's emission estimates in 2005 and 2010
between two inventories for four key sectors: power, industry, residential
and transportation. For 2010, ECLIPSE estimates about 28 Tg of
SO2 and 22 Tg of NOx (expressed in
Tg NO2 hereafter);
1 and 16 % less than MIX, respectively. On a sector level, a 40 %
difference is found for power plants (higher in ECLIPSE), 24 % for the
industry sector (lower in ECLIPSE) for SO2, and
15--21 % in
power and transportation for NOx (lower in ECLIPSE). It should be
noted that heating plants are distributed in the power and industry sectors
in ECLIPSE, while they are aggregated into industry and residential based on
the plant type of fuel combusted in MIX. Redistributing the heating emissions
by aggregating the heating emissions from the industry and residential
sectors to the power sector in MIX will reduce the differences to about
11 % (higher in ECLIPSE) in the power sector and 17 % (lower in
ECLIPSE) in industry, while increasing the difference in the residential
sector from about 10 to 32 % for SO2 and broadening the
differences in the power sector to around 30 % for NOx
(ECLIPSE lower).
Emissions of SO2 and NOx in 2005 and 2010
by sector over China. NOx emissions are shown in Tg NO2.
As shown in Fig. 1, emission trends from 2005 to 2010 are similar in
two inventories, indicating analogous assumptions of technology
evolution driven by economic growth and implemented air quality
policies in ECLIPSE and MIX. In general, MIX estimates larger changes
by sectors in the analyzed period. Specifically, for power plants,
MIX estimates a decline for SO2 by 54 %, comparable to the
45 % reduction in ECLIPSE, while for NOx both models
calculate about a 10 % increase. MIX estimates slightly larger
increasing trends for industrial emissions. For NOx
emissions from transport, ECLIPSE calculates lower overall emissions
but higher growth, 26 %, compared to 15 % in MIX.
The fuel consumptions of MIX and ECLIPSE among different sectors in
2010 are presented in Table S2. Owing to different source structure in
each of the models, there are sometimes significant discrepancies for
specific sectors. For example, for coal, the total consumption is
relatively consistent, within 10 % on a mass basis, while in the road
transport sector MIX has 28 % higher diesel fuel use. More details
along with discussion of emissions and implied emission factors are
provided below.
Coal-fired power plants
For power plants, coal combustion contributes more than 95 % of
SO2 and NOx emissions. Activity rates, assumed
heating values, capital sizes, emissions, and key parameters for
determining emission factors for coal-fired power plants are listed
and compared in Table 2.
Activity rates, emissions and emission factors for
SO2 and NOx in power plants of
China for 2005 and 2010a.
a Including both raw coal and derived coal.b National average.c We interpret the defined small units (<50 MW) in ECLIPSE to <100 MW
here by assuming that 1/3 of the units in the range of <100 MW fall into <50 MW according to Liu et al. (2015).d The raw EFSO2 is calculated following
EF=2×sulfur content×(1-sulfur
retention in ashes).
The coal consumption of ECLIPSE is 11--14 % higher than MIX in 2005 and 2010
(mass based) due to the differences in energy statistics and included
sources. As a global emission inventory model, ECLIPSE (GAINS model) relies
on the energy statistics from IEA (http://www.iea.org/), consistent
with the national Energy Balance Sheets provided by the NBS of China (Hong
et al., 2017), and also includes district heating plants. In MIX (MEIC
model), coal consumption in power plants is derived from CPED, which contains
the detailed fuel consumption rates, fuel quality, combustion and control
technology of over 7600 power-generating units in China (Liu et al., 2015).
It should be noted that the heating values of coal in China declined between
2005 and 2010, based on the CPED database (Liu et al., 2015).
ECLIPSE's SO2 emissions are 37 and 18 % higher than MIX in
2010 and 2005, respectively. In 2010, the implied SO2 emission
factor is determined as 6.1 gkg-1, 24 % higher than
4.9 gkg-1 estimated by MIX. As shown in Table 2, raw
emission factor, FGD application rate and removal efficiency all
contribute to this discrepancy. SO2 raw emission factor of
ECLIPSE is 19 % higher than MIX due to different coal quality
assumed in two inventory models. MIX assumes higher FGD penetration (87.0 % vs. 65.4 %) but lower removal
efficiency (80 % vs. 95 %)
than ECLIPSE. From 2005 to 2010, the emission
discrepancy grew larger because of the decrease in sulfur content and
sharply increasing application rates of FGD.
For NOx, the emission estimates of power plants are
similar between ECLIPSE and MIX: 7 % difference in 2005 and
15 % in 2010. Compared to MIX, the lower emission estimates of
ECLIPSE are primarily due to emission factors, which are about
20 % lower and can be explained by three factors:
Fuel distributions between large and small units.
In MIX, about 89 % of coal was consumed by large or medium
units (>100MW) in 2010, compared to 95 % in ECLIPSE,
reflecting different interpretations of mitigation strategies during the
11th Five-Year Plan of China.
Raw emission factors by technologies. The unabated
emission factor of NOx for existing large power plants
differs within 5 %, while for plants with LNBs
(low-NOx burners), emission factors are 33 % lower
in ECLIPSE. Furthermore, compared to MIX, ECLIPSE used 21 %
lower emission factors for small plants and 26 % lower emission factors for
newly built plants.
Application rates of technologies. MIX assumes
that in 2010 81 % of power plants are equipped with the LNB
techniques while only a 30 % application rate is considered in
ECLIPSE. The impact of this difference is partly offset by higher
efficiencies of LNB assumed in the latter. Neither model assumed the
implementation of selective catalytic reduction installations in this period.
Industry
Comparison of industrial emissions is most challenging since this
sector includes a multitude of sources with greatly varying emission
characteristics and different representation in the investigated
inventories. Overall, ECLIPSE calculates lower emissions, i.e., 24 and
13 % for SO2 and NOx in 2010 and 22 and
0.1 % in 2005, respectively.We compare the parameters of
the main contributing industrial sources below keeping the source
classification differences in mind.
Coal-fired industrial boilers. MIX estimates about
10.4 Tg of SO2 and 4.3 Tg of NOx emitted from
combustion in industrial boilers, nearly 123 and 71 % more than
ECLIPSE. Including fuel use in fuel conversion and the transformation
sector in ECLIPSE slightly reduces the discrepancy to 101 % for
SO2 and 70 % for NOx. While coal consumption
of MIX is 29 % larger than ECLIPSE, the key factor contributing to the
difference is varying emission factors.
Production of cement and brick. Cement production is among
the major industrial sources, contributing more than 26 % of industrial
emissions. Both inventories use the same cement production rates,
while ECLIPSE applies higher emission factors leading to 13 and
29 % higher SO2 and NOx emissions than MIX for
2010. For brick production, the ECLIPSE emission estimates of
SO2 and NOx are more than 3 times higher with a
difference in production rate of only 25 %. This sector,
however, is very uncertain, as information about fuel use is poorly
known and actual emission factors are missing.
Other sources. One of major reasons for discrepancy is due to
oil combustion in the industrial sector where ECLIPSE assumptions indicate
about 36 % lower use than MIX, resulting in the 30 %
differences for SO2 and NOx emissions. Another
systematic issue is allocation of emissions between furnaces and the
production process, where for sectors like pulp and paper, nonferrous
metal production, sinter and lime production, etc., different
approaches are used in the GAINS (ECLIPSE) and MEIC (MIX) models.
Residential
Residential combustion contributes around 10-15 % of
SO2 and 6 % of NOx emissions in the considered
period. The emission estimates are comparable between two inventories,
for example for 2010, 4.15 Tg of SO2 and 1.73 Tg
of NOx in ECLIPSE and 4.58 Tg of SO2, 1.38 Tg of
NOx in MIX. Both models use nearly identical coal
consumptions: about 302 Tg (ECLIPSE) and 306 Tg (MIX),
indicating the consistent statistics from the provincial energy balance
table and the national ones for fuel consumed in residential
boilers and/or stoves.
Transportation
Transportation sector contributes more than 25 % to the total
NOx emissions, but is negligible for SO2 emissions. In
ECLIPSE, high emitters representing an old and poorly maintained vehicle
fleet share 12 % of the total transport emissions. ECLIPSE
estimates 5.50 Tg of NOx emissions in 2010
(4.86 Tg as shown in Table 3 and 0.64 Tg from
high emitters), 21 % less than MIX. While the ECLIPSE inventory
includes province-specific fleet characteristics (Klimont et al.,
2017), MIX emissions were developed at a county level by modeling the
vehicle stock following the Gompertz function, technology
distributions in accordance with emission standards and emission
factors using an international vehicle emission model, as
documented by Zheng et al. (2014).
Comparisons of activity, emissions and emission factors
for the transport sector emission estimates of
NOxa.
a HDV-G: heavy-/medium-duty buses and trucks – gasoline
fueled; LDV-G: light-duty buses, trucks and passenger cars –
gasoline fueled; MC: motorcycle; HDV-D: heavy-/medium-duty buses and
trucks – diesel fueled;
LDV-D: light-duty buses and trucks – diesel fueled.b High emitters are not included.c Emission factors on a mass basis are converted to an energy basis with
a heating value of 43.1 MJkg-1 for gasoline and diesel.
Table 3 compares the fuel consumption, emission estimates and net
emission factors among various vehicle types in two inventories for
2005 and 2010. Parameters for 2005 show a similar difference ratio to
those in 2010. Assumptions for diesel combustion sources (on-road and
off-road) are the main contributor to emission discrepancies. In 2010,
diesel emissions of ECLIPSE are over 50 % lower than MIX
estimates.
Gasoline. There is only a 3 % difference in total gasoline
use in the transport sector between ECLIPSE and MIX. However, emission
estimates are significantly different, especially for light-duty
vehicles, which dominate the total. The consistency in the total
gasoline consumption between ECLIPSE and MIX is attributed to the
consistency in statistics. As shown in Table 3, the gasoline
consumptions by vehicle types show large differences between ECLIPSE
and MIX, indicating the different vehicle fleet assumptions in two
inventory models. Detailed data are not known and each of the
inventories (or research groups developing them) relied on their own
assumptions about fuel consumption per vehicle and mileage traveled and
combined those with the available data on the number of vehicles,
their sales and retirement rate. Owing to the reasons above, the
results can differ significantly. Light-duty vehicles are the largest
gasoline consumer (>77 %) in both inventories, with 18 %
higher gasoline consumption estimated in ECLIPSE than those of MIX in
2010. Accordingly, ECLIPSE estimates less gasoline consumed in heavy-duty vehicles (74 %) and motorcycles (32 %) than MIX. These
differences reduced from 2005 to 2010. Emission estimates of heavy-duty vehicles and motorcycles also show large differences
between two inventories. For heavy-duty vehicles, ECLIPSE estimates lower emissions
than MIX (66 % in 2010), as a result of less fuel consumption,
while higher emission factors are estimated in ECLIPSE. For motorcycles, emissions of ECLIPSE
are 64 % lower than MIX, contributed by both fuel consumption and
emission factors. It appears that the assumptions about penetration
and performance of vehicles with specific emission standards vary
between the models since the fleet average emission factor in ECLIPSE
is 69 % higher than that of MIX, i.e., 9.3 gkg-1
vs. 5.5 gkg-1.
Diesel. As shown in Table 3, the significant diesel emission
discrepancies can be primarily attributed to the differences in fuel
consumption. Compared to MIX, ECLIPSE has 22 % lower diesel use
for on-road vehicles and 39 % lower for off-road engines. While
applied emission factors are comparable for most categories, there is a
large discrepancy for light-duty vehicles, where the MIX value is 4
times larger than ECLIPSE. One possible explanation is that there
might be an issue with assumptions about fuel efficiency that were
applied when converting the native MEIC values, which are kilometers
driven for activity and gram per kilometer for emission factors (Zheng
et al., 2014).
Provincial emission estimates
Provincial emissions were developed using different methodologies for two
inventories (see Sect. 2.1). The provincial emission discrepancies
between two inventories are attributed primarily to two factors:
(i) the differences in activities, emission factors and policy
implementation assumptions at the national level (as discussed in the
previous section) and (ii) distribution of activities among the
provinces – see Sect. 2.1 for principal data sources for the latter.
Figure 2 compares emissions and the relative difference in fossil fuel
consumption by province in ECLIPSE and MIX in 2010. The differences in
provincial SO2 emissions are relatively large for a number of
provinces, especially when compared to the fluctuations of coal
consumption. This indicates significant differences in provincial
emission factors, which is mainly because of varying assumptions on
application and efficiency of abatement measures but also different
allocation of coal use between power and industry since emission
standards for these sectors are different. MIX estimates lower
emissions mainly for eastern China, including Shandong, Hebei, Henan,
Jiangsu, Sichuan, Zhejiang and Anhui.
Comparisons of emission estimates and fuel consumption by
province in China in 2010. Values out of y axis range are labeled
separately in the graphs. Abbreviations of provinces are provided in
Table S3.
For NOx, ECLIPSE estimates are systematically lower than
MIX for most provinces. For 20 provinces, mainly located in
northern and central China, such as Hebei, Shanxi, Inner Mongolia,
Liaoning, Jilin, Heilongjiang and Shandong, ECLIPSE emissions are
lower by over 20 %. For Beijing, however, ECLIPSE
emissions are 41 % higher than MIX driven by a larger estimate for
power plants (+36 %) and transport (+114 %). In general,
assumptions about diesel consumption in transport vary significantly
between inventories, highlighting the need for further validation of
the regional fuel statistics. It is important to note that, as in many
other countries, the national and regional energy use statistics
contain limited information about diesel fuel use in trucks and
non-road engines used in industries in which fuels are allocated to the
industry rather than the transport.
The sectorial distributions of emissions by province are generally
consistent between the two inventories, as presented in Fig. S1 in the Supplement. For
SO2, the emission fractions of power plants in MIX are lower,
and industrial fractions are overall higher than those of ECLIPSE due
to the differences in source classification and emission factors. The
distribution patterns of NOx provincial emissions show
relatively good consistency (within 30 % difference on the sector
level) between the two inventories.
Gridded emissions
Gridded emissions are direct inputs for atmospheric chemistry models
and climate models. We compared ECLIPSE and MIX gridded emissions by
analyzing three components: emissions by grids at 0.5∘×0.5∘ resolution in 2010, the spatial proxies used in the gridding
process and gridded emission trends from 2005 to 2010.
Gridded emissions. Figure 3 compares the gridded emissions between
ECLIPSE and MIX for SO2 and NOx. MIX emissions were
aggregated from 0.25 to 0.5∘ to be comparable with ECLIPSE. The
discrepancies in spatial distribution of gridded emissions are in line with
provincial emission differences discussed earlier. Grids located in eastern
parts of China show higher SO2 emissions in ECLIPSE compared to MIX.
NOx emissions of ECLIPSE are overall lower than MIX, except for
Beijing and Guangzhou. Correlations between the two gridded emissions are
quite good at 0.5∘×0.5∘ grids (slope =0.83,
R=0.90 for SO2, slope =0.79, R=0.98 for NOx).
Comparisons of MIX and ECLIPSE gridded emissions on
0.5∘×0.5∘ grids for 2010.
Sectorial emissions show distinct spatial characteristics (Fig. 3b).
Comparisons of industrial and residential sectors show clear
administrative boundaries as these are typically distributed from
provincial emissions using population-based proxies. Since power
plants are treated as point sources, emissions differ in grids over
the entire country, higher for SO2 and lower for
NOx in ECLIPSE. Signals of large cities are observed in
the comparison for the transportation sector because emissions are gridded
based on road network (on-road) or population distribution (off-road).
The differences of gridded emissions illustrated in Fig. 3 are
attributed to the discrepancies in emission estimates nationwide and
by provinces (Sect. 3.1.1, 3.1.2) and also method and data of
emission spatial allocations (see Sect. 2). For power plants that
were treated as point sources, emissions are gridded based on the
locations verified by Google Earth (Liu et al., 2015), consistent
between ECLIPSE and MIX. For other sectors, ECLIPSE gridded the
provincial emissions according to the source-specific layers, and MEIC
used a two-step allocation method (province to county, county to
grid). The data sources of spatial proxies also differ between the two
inventories (see Table S1). We further compared the spatial proxies by
sector in Fig. 4.
Emission distribution ratios within provinces in China in
2010 at 0.5∘×0.5∘ resolution.
Spatial proxy. Spatial proxies can play key roles in
evaluating the accuracy of emission inventories and CTM simulation (Geng
et al., 2017; Zheng et al., 2017). Proxies used in the ECLIPSE and MIX
emission gridding process are summarized in Sect. 2. Source-specific
layers were developed as spatial proxies by ECLIPSE, among which, MEIC
emissions were taken to distribute emissions for power plants (Klimont
et al., 2017). For the industry and residential sectors, emissions are
distributed mainly based on population data. Road networks and
population are used as proxies for transportation emissions. The spatial
proxies used in MIX (MEIC) have been summarized in several papers
(Geng et al., 2017; Li et al., 2017), showing that local proxies are
used in the gridding process. MIX (MEIC) uses Google Earth in verifying
the locations for each power plant. As described in Zheng
et al. (2014), for the transport sector, the China digital
road-network map is used for emission distribution. Other proxies
including the total population map (for some industrial sources),
urban population map (for industrial heating, residential coal
burning, etc.) and rural population map (for residential biofuel
burning) are in general consistent with the global inventory (Geng
et al., 2017).
In this work, we calculated the distribution ratios, reflecting the
spatial proxies used, by dividing the emissions for each grid by the
provincial emissions for each sector. The distribution ratios between
ECLIPSE and MIX in 2010 are shown in Fig. 4. Good correlations
(slope ≥0.79, R≥0.94) are observed for all sectors, which
is reasonable because similar proxy datasets were used in the two
inventories, as illustrated above. The differences for specific
sectors (e.g., residential with a slope of 0.87, transportation with a
slope of 0.79–0.81) are higher than others, mainly due to the
different population datasets and road networks used for emission
allocation of relevant sources in ECLIPSE and MIX.
Emission trend (2005–2010). Fig. 5 presents the emission changes
of SO2 and NOx estimated by ECLIPSE and MIX for the
period of 2005 to 2010. For SO2, the emission trends are similar
between the two inventories: a sharp decrease for power plants due to the
wide application of desulfurization facilities since 2006 and an overall
increase for industrial sources driven by economic growth and still low
penetration of emission control technology, consistent with the national
emission trend analyses in Sect. 3.1.1. For NOx, different
emission trends are estimated for transportation and consistent trends are
estimated for other sectors.
SO2(a) and NOx(b)
emission changes from 2005 to 2010 by sectors calculated as
E2010–E2005, at 0.5∘×0.5∘
grids. Note different color scales are used among sectors.
In MIX, decreasing emissions for the Beijing and PRD (Pearl River Delta)
regions are estimated, which are dominated by a decline in the power and
transport sectors, in contrast to the increasing emission trend of
ECLIPSE. The different trends of transportation emissions are
attributed to the different assumptions about the legislation effect on
pollution control in the two inventory systems. For Beijing, the
differences in the transportation emission trend are mainly caused by
diesel vehicles. In ECLIPSE, 47 % increases are estimated for
diesel-fueled vehicles, compared to 28 % emission decreases in
MIX. Fuel consumptions show large discrepancies in the trend from 2005 to
2010, with +54 % (ECLIPSE) compared to -20 % (MIX) for
heavy-duty vehicles and +45 % (ECLIPSE) compared to +3 %
(MIX) for light-duty vehicles. The emission factors of light-duty
vehicles increase by 5 % in ECLIPSE, whereas they decrease by 34 % in
MIX, attributed to the different assumptions about emission control
effects. As a pioneer in pollution control of China, Beijing carried
out a Euro III standard in 2005 and Euro IV standard in 2008 for light-duty vehicles. The Euro IV penetrations in 2010 in Beijing are
assumed to be
around 12 % in ECLIPSE and more than 60 % in MIX, which
might be too optimistic and should be verified with local surveys.
For the PRD region, gasoline and heavy-duty diesel vehicles contribute to the
different emission trends. Of the emission changes for light-duty gasoline buses,
a 22 % increase is estimated in ECLIPSE,
compared to 12 % emission reduction in MIX. For heavy-duty diesel
vehicles, the trends in fuel consumption (+55 % in ECLIPSE, compared to
-11 % in MIX) and technology distribution (21 % of Euro III in 2010
for ECLIPSE, compared to >50 % in MIX) are the main contributors to
the difference. In summary, survey data are urgently needed to validate the
fuel consumption, effect of legislation and trend for diesel vehicles in
pioneering regions such as Beijing and PRD.
Evaluations from the satellite perspective
In this section, we evaluated the effect of emission inventories on
the accuracy of model simulations through combing GEOS-Chem
Asian-nested modeling and OMI observations (Sect. 3.2.1). Top-down
emissions were developed for both SO2 and NOx
(Sect. 3.2.2). Due to the large uncertainties in SO2
retrievals of OMI, we mainly focused on the evaluations for
NOx emission estimates, spatial proxies and emission
trends.
Sensitivity cases for model simulations
The main purpose of this subsection is to evaluate the effect of gridded
emissions on model performance and figure out the effect of emission
estimates and spatial distributions on model performance using satellite
observations as a criterion. Therefore, we set up four sensitivity cases of
modeling, ECL-case0, ECL-case1, ECL-case2 and MIX. ECL-case0 and MIX form the
two basic cases, which apply the ECLIPSE and MIX emissions in the simulation,
respectively. ECL-case1 scales China's emissions in ECLIPSE to MIX's value by
sectors retaining original spatial distributions. ECL-case2 redistributes the
ECLIPSE emissions over China based on the spatial grid ratios of MIX, also on
the sector level. The characteristics of the emission inventory used for each
case are summarized in Table 4 and shown in Fig. S2. We processed each
inventory into model-ready inputs through regridding new emissions,
performing VOC speciation and temporal allocation. The speciation factor and
monthly profiles by sector of the MIX inventory are used for
ECL-case0–case2.
We resample the model results based on satellite observations spatially and
temporally for consistent comparison as described in Sect. 2.3.
SO2 columns simulated by GEOS-Chem in sensitivity
cases, compared to OMI observations; summer average
(June–July–August) in 2010. Unit: 1 DU =2.69×1016moleccm-2.
Description of model simulation cases and statistics of
model performance of NO2; summer average in 2010.
a Normalized Mean Bias.b Root Mean Square Error.c ECLIPSE emission estimates by sector in China.d Spatial proxies by sector based on ECLIPSE.e MIX emission estimates by sector in China.f Spatial proxies by sector based on MIX.
Figure 6 compares the SO2 columns simulated by the four model
cases and OMI SO2 columns in 2010. Although OMI data tend to
overestimate the concentrations due to the overlap in signals of
SO2 and O3 during retrieval, good correlations are
found between model results and satellite observations (R=0.633–0.667,
slope =0.842–0.863, generally consistent among sensitivity cases;
see Table S4), confirming the high accuracies of the priori
SO2 spatial emission patterns.
NO2 tropospheric vertical columns modeled for each case are
compared with the retrieved OMI columns (Fig. 7). Summer-averaged
results are shown here because of the closer connection between
emissions and columns due to short NOx lifetime. As shown
in Fig. 7, a modeled NO2 density map shows a similar spatial
pattern among cases, but different magnitudes. Higher NO2
concentrations are observed for the ECL-case1 and MIX case because
common emission estimates of MIX are used, which are higher than
ECLIPSE. Compared to OMI, all model cases underestimate the pollution
in northern China and slightly overestimate the columns over central
China. As illustrated in Table 4, the performance of the MIX case is the
best among all cases, identified by low biases (NMB=-4.72 %) and a better slope ratio (slope =0.601). The results of
ECL-case0 and ECL-case2 are similar because the differences in spatial
proxies of two inventories are small compared to the differences in
emission estimates (Sect. 3.1.3). Replacing the emission estimates of
ECLIPSE with MIX improves the model performance from bias at -12.2
to -6.19 % (ECL-case1 vs. ECL-case0).
NO2 tropospheric columns simulated by GEOS-Chem in
sensitivity cases. For each case, the specific NO2 vertical
profiles were applied in inversion; summer average
(June–July–August) in 2010.
Top-down emission evaluations
Satellite-based emission inventories were developed following the
finite difference mass balance methodology (Cooper et al.,
2017). Emissions of SO2 estimated by bottom-up and top-down
inventories are presented in Table S5. Both ECLIPSE and MIX correlate
well with the top-down estimates (R=0.722–0.896,
slope =0.539–0.923) in 2005 and 2010. Relatively high negative
biases are found (NMB=-51.0∼-29.1 %) in
the bottom-up inventories, possibly attributed to the uncertainties in the
OMI retrievals for SO2. Table 5 shows the NOx
emission estimates and correlations between bottom-up and top-down
inventories. For NOx, it can be concluded that ECLIPSE and
MIX are consistent with the top-down estimates over China. Summer-averaged bottom-up emissions show strong correlations with the
top-down ones (R>0.87 for both inventories) in 2010. The mean
biases of MIX are -21.2 % (11.9 mols-1 in RMSE),
much lower than -39.4 % of ECLIPSE (14.6 mols-1 in
RMSE). The slope ratio of MIX shows a slightly better performance than
ECLIPSE (0.73 for MIX, 0.50 for ECLIPSE), but should be interpreted
with caution since the slope can be dominated by several large point
sources.
a Only contain grids covered by OMI pixels filtered
by the criterion described in the text.
In spatial distribution, large discrepancies are observed between
bottom-up emission inventories and the top-down ones. For SO2,
bottom-up inventories tend to underestimate emissions in Shandong
Province and several southern provinces such as Guizhou, Jiangxi and
Fujian, which may be attributed to the scattered coal consumption, while
overestimating emissions in the Yangtze River Delta (YRD) region (see Fig. 8). As shown in
Fig. 9, both ECLIPSE and MIX underestimate the NOx
emission strength in northern China and parts of the YRD and PRD regions and overestimate emissions located in large
cities such as Beijing, Shanghai and Wuhan. One important reason for
the latter is associated with the limitations of currently used
spatial proxies. Using population or industry gross domestic
product as a spatial proxy may distribute too much emissions to
provincial capitals or economically developed cities. Through
sensitivity test analyses, it is concluded that treating sources as
point sources can significantly reduce the uncertainties in the emission
gridding process (Geng et al., 2017).
Comparisons among ECLIPSE, MIX and top-down emissions of
SO2; summer average in 2010.
Comparisons among ECLIPSE, MIX and top-down emissions of
NOx; summer average in 2010.
Emission changes from 2005 to 2010 were evaluated and presented in
Fig. 10 for SO2 and Fig. 11 for NOx. The maps of
SO2 emission changes are consistent in spatial patterns
between bottom-up and top-down inventories. Effective control measures
including nationwide FGD application led to a significant SO2
emission decrease between 2005 and 2010, especially in Beijing, Hebei,
Shanxi, YRD, PRD and the southwest provinces of China. The annual growth
rate of China's emissions of NOx is highly consistently
estimated by ECLIPSE, MIX and satellite-based inventories, around
4.0 % annual growth in the period of 2005 to 2010 (see Table 5).
The results are comparable with previous work using various inversion
methodologies, satellite sensors or CTMs (Gu et al., 2013; Krotkov
et al., 2016; Miyazaki et al., 2017). Figure 11 shows the gridded
emission changes from 2005 to 2010 among different inventories for
NOx. A decrease in parts of the YRD and PRD regions and shut down
of large facilities are captured by satellite, showing generally
consistency with MIX. Significantly larger growth is observed in
northern China's emissions from top-down inventories than in
estimates of ECLIPSE and MIX. In Beijing, the satellite-based
inventory shows a relatively stable trend, different from the increasing
trend of ECLIPSE or decreasing trend of MIX, indicating that
assumptions about the penetration of emission reduction technology
need further revision in both inventory models for large cities.
SO2 emission changes from 2005 to 2010, estimated by
ECLIPSE, MIX and a derived top-down inventory for each case; summer
average (JJA).
NOx emission changes from 2005 to 2010, estimated
by ECLIPSE, MIX and derived top-down inventories for each case;
summer average (JJA).
DiscussionSummary of key parameters contributing to emission
uncertainties
We address several key factors contributing to the differences between
ECLIPSE and MIX for SO2 and NOx emission
estimates: source classification, energy statistics, emission factors,
assumptions about control technology penetration and spatial proxies.
The source classification for heating plants, fuel conversion and industrial
boilers is differently defined between ECLIPSE (GAINS model) and MIX (MEIC
model), making the interpretation of comparisons for each source more
difficult and to some degree less transparent (Sect. 3.1.1). The
source-structure
differences are inevitable for emission inventory models designed for
estimates on different spatial scales. As a global inventory model, GAINS
integrates statistics from international sources (e.g., IEA, FAO). Therefore,
the source structure of GAINS is set up in accordance with the international
statistics framework. Focusing on emissions on a regional scale, MEIC set up
a calculation framework based on statistics from local agencies in China to
gain higher specificity in temporal and spatial distribution (e.g., NBS,
CPED). As illustrated and analyzed in Sect. 3.1.1, the differences by sector
should be interpreted with caution, especially for the power and industry
sectors.
The apparent emission uncertainty ratios (the ratio of the maximum
emission discrepancy to the mean value using provincial energy
statistics or national statistics) of SO2 and NOx
resulting from energy use are 30 and 16 % according to Hong
et al. (2017). For SO2, emission uncertainties are quite
sensitive to the energy use uncertainty mostly contributed by
industrial coal use (Hong et al., 2017). Based on this work, diesel
consumption in the transport sector remains highly uncertain, contributing
to the emission differences for NOx.
The FGD penetration rate in power plants, as well as assumed removal
efficiencies, significantly affect the SO2 emission estimates
and trends. Similarly, for NOx, application rates and
abatement efficiency of LNB technology installed in power plants is
significantly different in the models compared; these assumptions
should be further verified and constrained. Emission factors for
diverse industrial boiler types are the main contributors to the
uncertainty in the industrial emissions. Assumptions about vehicle
fleet, implementation of emission standards and emission factors for
various vehicle types still differ among the investigated inventory
models. More in situ measurements and local surveys are needed to
reduce these uncertainties.
Spatial proxies used in emission inventories are an important factor
contributing to the overall accuracy in model simulation. Integration
of detailed spatial information that is often included in regional
inventories like MIX should be considered as the best way to improve
the resolution and spatial allocation of emissions in global products
like ECLIPSE.
Uncertainty of top-down evaluation
In this work, moderate negative biases are observed in bottom-up
emission inventories (-21 % for MIX, -39 % for ECLIPSE),
compared to satellite-based ones. But the top-down evaluations are
subject to uncertainties from both satellite retrievals and model
simulations. The uncertainties of retrieved individual NO2
columns of the DOMINO v2.0 product are estimated at 1.0×1015 molecules cm2, +25 % mainly arising from the
AMF calculation (Boersma et al., 2007, 2011). Because of the high
aerosol loadings in eastern China, the aerosol scattering and
absorption have positive or negative effects on NO2 retrieval,
with a mean effect of 14 % (Lin et al., 2014). A negative systematic
bias of 10–20 % by season plus a random error of 30 % are
generated by model simulation using GEOS-Chem (Martin et al., 2003;
Lin and McElroy, 2010; Lin, 2012). As suggested by Ding et al. (2017),
multiple sensors can give more comprehensive and accurate constraints
on the priori spatial and temporal emission estimates, which can be
further applied in future work.
Concluding remarks
We conducted parameter-level comparisons of gridded Chinese emissions
between ECLIPSE and MIX, elucidated the effect on CTM simulations, and
evaluated the inventories based on OMI observations. This work is
important for inventory developers and modelers for understanding the
potential uncertainties in the gridded emission inventory over
China. For inventory developers, the detailed comparisons give
indications on the underlying uncertainties of parameters by source,
including the source classifications, activity rates, emission factors
and technology distributions. For modelers, the comparisons and
validations are important to understand the effect of emissions on
model performance. This work shows that our best inventories appear to
be fit for evaluation of the policies at an aggregated or national
level; more work is needed in specific areas in order to improve
accuracy and robustness of outcomes at the finer spatial and also
technological levels. The main findings are as follows.
In 2010, compared to MIX, the emission estimates of ECLIPSE
are identical for SO2 and 16 % lower for
NOx. SO2 emissions of power plants and industry
sectors differ by +40 and -24 % (ECLIPSE compared to MIX),
attributed to the differences in source classification system, FGD
penetration rates and assumed removal efficiencies. Emission
factors for diverse industrial boiler types are the main reason for
the industrial emission differences. For NOx, ECLIPSE
estimates are lower than those of MIX for all sectors. Lower
NOx emission factors for power plants and lower diesel
consumptions in the transport sector in ECLIPSE are the main reasons
for the discrepancies. Application rates and abatement efficiency of
plants equipped with LNBs should be further verified and
constrained. Assumptions about vehicle fleet, implementation of
emission standards and emission factors for various vehicle types
still differ between evaluated inventory models. Large uncertainties
should be addressed for the diesel consumptions in current inventory
models.
We modeled four sensitivity cases to investigate the effect
of emission estimates and spatial proxies of emission inventories on
model accuracy using GEOS-Chem. The model case using MIX as input
shows the best performance, with mean biases at -4.72 % (NMB,
for NOx). Increasing the ECLIPSE emission estimates to
MIX levels reduces the biases from -12.2 to -6.19 % (for
NOx). For ECLIPSE, changing the spatial pattern to that of MIX apparently does
not affect the model results, owing to the role the power
sector (ECLIPSE uses MEIC proxy already) plays in
emissions. Top-down emissions were developed based on OMI
retrievals. High correlations were observed between the bottom-up
and top-down SO2 emissions, providing evidence of the
accuracy of the spatial emission patterns in ECLIPSE and MIX. We
found moderate negative biases in bottom-up emission inventories for
NOx (-21 % for MIX, -39 % for ECLIPSE),
compared to satellite-based ones.
Both inventories show decreasing trends for SO2 and
increasing trends for NOx between 2005 and 2010 but the
spatial pattern of change differs. Signals of large power plants and
of city centers can be found. Trend analyses from the top-down
perspective indicate an annual growth rate of 4 % for
NOx, consistent with development of bottom-up emissions.
A strong NOx emission increase in northern China and
decrease in parts of the YRD and PRD regions are captured by the
satellite retrievals, similar to the MIX estimates.
ECLIPSE v5a global emissions developed based on the
GAINS model can be publicly accessed from
http://www.iiasa.ac.at/web/home/research/researchPrograms/air/ECLIPSEv5a.html.
The specific parameters of the GAINS model were retrieved from http://gains.iiasa.ac.at/models/. The MIX inventory is publicly
available from http://www.meicmodel.org/dataset-mix.html.
China's emissions in MIX are obtained from MEIC v1.2, which is
downloaded from http://www.meicmodel.org/index.html. The L2
swath SO2 column data developed by NASA are available at
https://disc.gsfc.nasa.gov/. The tropospheric NO2
column data of DOMINO v2 can be accessed from www.temis.nl.
The Supplement related to this article is available online at https://doi.org/10.5194/acp-18-3433-2018-supplement.
The authors declare that they have no conflict of
interest.
This article is part of the special issue “Global and regional
assessment of intercontinental transport of air pollution: results from HTAP,
AQMEII and MICS”. It is not associated with a conference.
Acknowledgements
This work was supported by the National Key R&D program
(2016YFC0201506), the National Natural Science Foundation of China
(41625020) and IIASA's Young Scientists Summer Program (YSSP)
sponsored by the National Natural Science Foundation of China
(41611140118). We acknowledge the free use of the NASA OMI SO2
and DOMINO v2 products.
Edited by: Christian Hogrefe
Reviewed by: two anonymous referees
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