Introduction
A significant portion of the population in China has been
exposed to severe air pollution in recent decades as the consequence of
intensive energy use without efficient control measures. Based on ambient air
pollution data published by the China National Environmental Monitoring
Center (CNEMC), most of the major cities are in violation of the Chinese
Ambient Air Quality Standards grade II standard (35 µgm-3)
for annual average particulate matter with diameter of 2.5 µm or
less (PM2.5; Zhang and Cao, 2015; Y. Wang et al., 2014), with a mean
population weighted PM2.5 concentration of over 60 µgm-3
during 2013–2014. Long-term exposure to such high levels of PM2.5
greatly threatens public health in China. Recent studies have suggested that
approximately more than 1 million premature deaths can be attributed to
outdoor air pollution each year in China (Lelieveld et al., 2015; Liu et al.,
2016; Hu et al., 2017a).
Accurate exposure estimates are required in health effect studies. Ambient
air quality is usually measured at monitoring sites and used to represent
the exposure of the population in the surrounding areas. A routine central
monitoring network in China has been operating since 2013, but it is still
limited in spatial coverage and lacks detailed information of the chemical
composition, PM size fractions, and source origins of air pollutants.
Chemical transport models (CTMs) have been widely used in health effect
studies to overcome the limitations in central monitoring measurements for
exposure estimates (Philip et al., 2014; Lelieveld et al., 2015; Liu et al., 2016; Laurent et al., 2016a, b; Ostro et al., 2015).
However, the accuracy of the predictions from CTMs is largely affected by
the accuracies of the emission inventories (Wang et al., 2010), meteorological fields (Hu et al., 2010), and numerical solutions
to the equations that describe various atmospheric processes (Hu et al., 2006; Yu et al., 2005). Several emission inventories have been created to
cover China. Different emission inventories focus on specific geographical
regions in the urban, regional (Zhao et al., 2012; Zhang et al., 2008), and national or continental (Zhang et al., 2009; Kurokawa et al., 2013)
scales, and/or focus on specific pollutants (Su et al., 2011; Ou et al., 2015) and specific sectors (Zhao et al., 2008; Xu et al., 2017).
Despite great efforts in improving the accuracy of emission inventories in
China, large uncertainties remain. Generally, emissions of pollutants are
estimated as the product of activity levels (such as industrial production
or energy consumption), unabated emission factors (i.e., mass of emitted
pollutant per unit activity level), and the efficiency of emission controls.
Large uncertainties are associated with activity levels, emission source
fractions, and emission factors (Akimoto et al., 2006; Lei et al., 2011a).
For a Pearl River delta (PRD) inventory in 2006, SO2 emission has low
uncertainties of -16 to +21 % from power plant sources
quantified by Monte Carlo simulations, while NOx has medium-to-high
uncertainties of -55 to +150 % and VOC, CO, and PM have
even higher uncertainties (Zheng et al., 2009). For an
inventory for the Yangtze River delta (YRD) region, the overall
uncertainties for CO, SO2, NOx, PM10, PM2.5, VOCs, and
NH3 emissions are ±47.1, ±19.1, ±27.7, ±117.4, ±167.6, ±133.4, and
±112.8 %, respectively (Huang et al., 2011). A comprehensive quantification study by Zhao et al. (2011) using Monte Carlo simulations showed that the
uncertainties of Chinese emissions of SO2, NOx, PM2.5, BC,
and OC in 2005 are -14 to +13, -13 to +37, -17 to +54, -25 to +136, and -40 to +121 %, respectively.
The uncertainties in emission inventories are carried into CTMs simulations,
leading to uncertainties in air quality predictions, which need to be
carefully evaluated to identify the useful information for health effect
studies (Hu et al., 2017b, 2014c, b, 2015b; Tao et al., 2014). An evaluation
of 1-year air pollutants predictions using the Weather Research and
Forecasting (WRF) / Community Multi-scale Air Quality (CMAQ) modeling
system with the Multi-resolution Emission Inventory for China (MEIC) has been
reported (Hu et al., 2016a). The model predictions of O3 and
PM2.5 generally agree with ambient measured concentrations, but the
model performance varies in different regions and seasons. In some regions,
such as Northwest China, the model significantly underpredicted
PM2.5 concentrations. A recent study compared a few anthropogenic
emission inventories in China during 2000–2008 (Saikawa et al., 2017), but
detailed evaluation of model results based on these inventories has not been
performed.
Ensemble techniques are often used to reduce uncertainties in model
predictions from combining multiple data sets. They have been widely used in
climate predictions (Murphy et al., 2004; Tebaldi and Knutti, 2007), and
have been adopted recently in air quality predictions (Delle Monache et al., 2006; Huijnen et al., 2010). The methods to utilize the strength of
different emission inventories to get improved air quality predictions for
China have not been reported in the literature. The aim of this study is to
create an improved set of air quality predictions in China by using an
ensemble technique. First, four sets of 1-year air quality predictions
were conducted using the WRF/CMAQ modeling system with four different
anthropogenic emission inventories for China in 2013. In addition to MEIC,
the three other emission inventories are the Emissions Database for Global
Atmospheric Research (EDGAR), Regional Emission inventory in Asia version 2 (REAS2), and Emission Inventory for China developed by School of Environment
at Tsinghua University (SOE). The model performance of PM2.5 and
O3 with different emission inventories was then evaluated against
available observation data for 60 cities in China. The differences among air
quality predictions with the four inventories were also compared and
identified. Finally, an ensemble technique was developed to minimize the
bias of model predictions and to create improved exposure predictions. To
the authors' best knowledge, this is the first ensemble model study in China
using multiple emission inventories. The ensemble predictions of this study
are available for public health effect analyses upon request to the
corresponding author.
This paper is organized as follows. The CMAQ model, emissions and other
inputs for the model, observational data sets used for model performance
evaluation, and the method for ensemble calculation are described in Sect. 2. Section 3 discusses the model performance on gaseous and particulate
pollutants simulated with the four emission inventories, as well as the
performance of the ensemble predictions in different regions/cities and with
different averaging times. The major findings are summarized in the
Conclusion section.
Method
Model description
In this study, the applied CMAQ model is based on CMAQ v5.0.1 with changes to
improve the model's performance in predicting secondary organic and inorganic
aerosols. The details of these changes can be found in previous studies
(Hu et al., 2016a, 2017c) and the references therein; therefore, only a brief
description is summarized here. The gas-phase photochemical mechanism
SARPC-11 was modified to better treat isoprene oxidation chemistry (Ying et
al., 2015; Hu et al., 2017c). Formation of secondary organic aerosol (SOA)
from reactive uptake of dicarbonyls, methacrylic acid epoxide, and isoprene
epoxydiol through surface pathways (Li et al., 2015; Ying et al., 2015) was
added. Corrected SOA yields due to vapor wall loss (Zhang et al., 2014) were
adopted. Formation of secondary nitrate and sulfate through heterogeneous
reactions of NO2 and SO2 on particle surface (Ying et al.,
2014) was also incorporated. It has been shown that these modifications
improved the model performance on secondary inorganic and organic PM2.5
components.
The WRF/CMAQ modeling domain and the regions in China. The dots
represent the 60 cities where observational data are available for ensemble
analysis. The x and y axis is the Lambert projection grid numbers in the west–east and south–north direction. NCP represents the North China Plain region. The provinces included in each region are as follows: NCP: Beijing, Tianjin, Hebei, Shandong, and Inner Mongolia; Northeast: Liaoning, Jilin, and Heilongjiang; YRD: Shanghai, Jiangsu, and Zhejiang; Central China: Shanxi, Henan, Hubei, Anhui, Hunan, Jiangxi; Northwest: Xinjiang, Qinghai, Ningxia, Gansu, and Shaanxi; Sichuan Basin: Sichuan and Chongqing; Southwest: Tibet, Yunnan, Guizhou, Guangxi; PRD: Guangdong, Hong Kong, and Macau; Fujian, Hainan, and Taiwan are grouped as the “Other” region.
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Anthropogenic emissions
The CMAQ model was applied to study air pollution in China and surrounding
countries in eastern Asia using a horizontal resolution of 36 km. The modeling
domain is shown in Fig. 1. The anthropogenic emissions are from four
inventories: MEIC, SOE, EDGAR, and REAS2. MEIC was developed by a research
group in Tsinghua University (http://www.meicmodel.org). Compared with other
inventories for China, e.g., INTEX-B (Zhang et al., 2009) or TRACE-P (Streets et al., 2003), the major
improvements include a unit-based inventory for power plants (Wang et al., 2012) and cement plants (Lei et al., 2011b), a county-level high-resolution vehicle
inventory (Zheng et al., 2014), and a novel non-methane VOC (NMVOC) speciation approach (Li et al., 2014). The VOCs were speciated
to the SAPRC-07 mechanism. As the detailed species to model species mapping
of the SAPRC-11 mechanism is essentially the same as the SAPRC-07 mechanism (Carter and Heo, 2012), the speciated VOC emissions in the
MEIC inventory were directly used in the simulation.
The SOE emission inventory was developed using an emission factor method (Wang et al., 2011; Zhao et al., 2013b). The sectorial emissions in
different provinces were calculated based on activity data, technology-based
and uncontrolled emissions factors, and penetrations of control technologies
(fractions of pollutants not collected). Elemental carbon (EC) and organic
carbon (OC) emissions were calculated based on PM2.5 emissions and
their fractions in PM2.5 in source-specific speciation profiles. The
sectorial activity data and technology distribution were obtained using an
energy demand modeling approach with various Chinese statistics and
technology reports. More details, including the spatiotemporal
distributions and speciation of NMVOC emissions, can be found in previous
publications (Zhao et al., 2013a, b; Wang et al., 2011).
Since MEIC and SOE emission inventories only cover China, emissions from
other countries and regions were based on REAS2 (Kurokawa et al., 2013).
Version 4.2 of EDGAR emissions (http://edgar.jrc.ec.europa.eu/overview.php?v=42) has a
spatial resolution of 0.1 × 0.1∘. The EDGAR inventory
contains annual emissions from different sectors based on IPCC designations.
REAS2 has a spatial resolution of 0.25 × 0.25∘ for all of Asia. The inventory contains monthly emissions of pollutants
from different source categories. Detailed information regarding these
inventories can be found in the publications presenting them. Table S1 in the Supplement shows
the total emissions of major pollutants within China in a typical workday of
each season. In general, large differences exist among different inventories
for China. MEIC has the highest CO emissions in winter while REAS2 has the
highest in other seasons. MEIC has the highest NOx emissions while
REAS2 has the highest emissions of VOCs in all seasons. EDGAR predicts the
highest SO2 emissions, which are approximately a factor of 2 higher
than those estimated by SOE. SOE has the highest NH3 emissions while
EDGAR has much lower NH3 emissions than the other three. EDGAR also has
the lowest EC and OC emissions, but the total PM2.5 emissions are the
highest. Standard deviations indicate that winter has the largest
uncertainties for all species except SO2 and NH3. Winter has the
lowest SO2 uncertainties while summer has the largest NH3
uncertainties.
All emissions inventories were processed with an in-house program and
re-gridded into the 36 km resolution CMAQ domain when necessary.
Representative speciation profiles based on the SPECIATE 4.3 database
maintained by the U.S. EPA were applied to split NMVOC of EDGAR and REAS2
into the
SAPRC-11 mechanism, and PM2.5 of all inventories was split into AERO6
species. Monthly emissions were temporally allocated into hourly files using
temporal allocation profiles from previous studies (Chinkin et al., 2003;
Olivier et al., 2003; Wang et al., 2010). More details regarding EDGAR can
be found in D. Wang et al. (2014), while those for REAS2 can be found in Qiao
et al. (2015).
Other inputs
The Model for Emissions of Gases and Aerosols from Nature (MEGAN) v2.1 was
used to generated biogenic emissions (Guenther et al., 2012). The 8-day
Moderate Resolution Imaging Spectroradiometer (MODIS) leaf area index (LAI)
product (MOD15A2) and the plant function type (PFT) files used in the Global
Community Land Model (CLM 3.0) were applied to generate inputs for MEGAN.
The readers are referred to Qiao et al. (2015) for more
information. Open biomass burning emissions were generated using a satellite-observation-based fire inventory developed by NCAR (Wiedinmyer et al., 2011). The dust emission module was
updated to be compatible with the 20-category MODIS land use data (Hu et al., 2015a) for inline dust emission processing, and sea salt emissions were
also generated in-line during CMAQ simulations.
The meteorological inputs were generated using WRF v3.6.1 (Skamarock et al., 2008). The initial and boundary conditions for
WRF were downloaded from the NCEP FNL Operational Model Global Tropospheric
Analyses data set. WRF configurations details can be found in Zhang et al. (2012). WRF performance has been evaluated by comparing predicted
2 m above surface temperature and relative humidity, and 10 m wind speed and
wind direction with all available observational data at ∼ 1200
stations from the National Climate Data Center (NCDC). The model performance
is generally acceptable and detailed evaluation results can be found in a
previous study (Hu et al., 2016a).
The initial and boundary conditions representing relatively clean
tropospheric concentrations were generated using CMAQ default profiles.
Model evaluation
Model predictions with the four emission inventories were evaluated against
available observation data in China. Hourly observations of PM2.5,
PM10, O3, CO, SO2, and NO2 from March to
December 2013 at 422 stations in 60 cities were obtained from CNEMC
(http://113.108.142.147:20035/emcpublish/), but no observations were
available for January and February. Observations at multiple sites in one
city were averaged to calculate the average concentrations of the city.
Detailed quality control of the data can be found in previous studies (Hu et
al., 2014a, 2016a; Y. Wang et al., 2014). Statistical matrix of
mean normalized bias (MNB), mean normalized error (MNE), mean fractional bias
(MFB), and mean fractional error (MFE) were calculated using Eqs. (1)–(4):
MNB=1N∑i=1NCm-CoCo,MNE=1N∑i=1NCm-CoCo,MFB=1N∑i=1NCm-CoCo+Cm2,MFE=1N∑i=1NCm-CoCo+Cm2,
where Cm and Co are the predicted and observed city average
concentrations, respectively, and N is the total number of observation data.
MNB and MNE are commonly used in evaluation of model performance of
O3, and MFB and MFE are commonly used in evaluation of model
performance of PM2.5 (Tao et al., 2014). The U.S. EPA previously
recommended O3 model performance criteria of within ±0.15 for MNB
and less than 0.30 for MNE (as shown in Fig. 1), and PM model performance
criteria of within ±0.60 for MFB and less than 0.75 for MFE (U.S. EPA,
2001). Figure 2 includes the criteria and goals for PM as a function of PM
concentration, as suggested by Boylan and Russell (2006), which have been
widely used in model evaluation.
Performance of predicted O3, CO, NO2, and SO2 for
different months (top two rows) and regions based on simulations with
individual inventories. The blue dashed lines on the O3 plots are
±0.15 for MNB and 0.3 for MNE as suggested by the U.S. EPA (2001).
Changes of colors show the months from March to December in top two rows (3 refers to March, 12 to December, etc.),
while showing regions from NCP to Other in the bottom two rows.
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Ensemble predictions
The four sets of predictions with the different inventories were combined
linearly to calculate the ensemble predictions, as shown in Eq. (5):
Cpred,ens=∑m=1NmwmCpred,m,
where Cpred,ens is the ensemble prediction,
Cpred,m is the predicted concentration from the mth
simulation, Nm is the number of simulations in the ensemble (Nm=4),
and wm is the weighting factor of the mth simulation. The weighting
factor for each set of predictions was determined by minimizing the objective
function Q in Eq. (6):
Q=∑iNcityCiobs-∑m=1NmwmCipred,m2,
where Ciobs is the observed PM2.5 or O3
concentration at the ith city, Ncity is the total number of
cities with observation (N=60), Cipred,m is the predicted
concentration at the ith city from the mth simulation, and Nm is the
number of simulations in the ensemble (Nm=4). The weight factor wm
of the mth simulation to be determined is within the range of [0, 1], with
w=0 represents no influence of the individual simulation on the ensemble
prediction, and w=1 indicates that concentrations of the individual
simulation are fully accounted for in the ensemble prediction. The
observation data were the same as used in the model evaluation. Ensemble
predictions were performed for PM2.5 and O3 in this study. A
MATLAB program was developed to solve above equation and determine the
weighting factors using the linear least squares solver “lsqlin”.
Overall model performance of gas and PM species in 2013 using
different inventories. Obs is observation, MFB is mean fractional bias, MFE
is mean fractional error, MNB is mean normalized bias, and MNE is mean
normalized error. The indices were calculated with hourly observations and
predictions. The best performance is indicated by the bold numbers.
Prediction
MFB
MFE
MNB
MNE
O3
Mean Obs: 51.70 ppb
MEIC
49.83
-0.08
0.35
0.02
0.33
SOE
44.51
-0.2
0.38
-0.09
0.32
EDGAR
49.82
-0.04
0.28
0.03
0.28
REAS2
51.17
-0.04
0.33
0.05
0.33
CO
Mean Obs: 0.96 ppm
MEIC
0.31
-0.92
0.96
-0.57
0.63
SOE
–
–
–
–
–
EDGAR
0.23
-1.12
1.16
-0.66
0.73
REAS2
0.42
-0.72
0.82
-0.41
0.59
NO2
Mean Obs: 21.45 ppb
MEIC
10.12
-0.79
0.93
-0.41
0.66
SOE
11.59
-0.65
0.81
-0.33
0.61
EDGAR
6.82
-1.02
1.07
-0.6
0.67
REAS2
9.3
-0.81
0.92
-0.46
0.63
SO2
Mean Obs: 17.21 ppb
MEIC
12.5
-0.51
0.87
0.01
0.87
SOE
12.76
-0.44
0.83
0.06
0.86
EDGAR
15.86
-0.16
0.73
0.31
0.88
REAS2
15.15
-0.23
0.74
0.23
0.86
PM2.5
Mean Obs: 70.01 µgm-3
MEIC
56.39
-0.32
0.64
-0.02
0.63
SOE
59.77
-0.24
0.61
0.09
0.67
EDGAR
52.59
-0.3
0.59
-0.05
0.56
REAS2
60.35
-0.21
0.59
0.08
0.63
PM10
Mean Obs: 118.61 µgm-3
MEIC
62.7
-0.63
0.79
-0.32
0.61
SOE
63.32
-0.6
0.76
-0.3
0.6
EDGAR
55.76
-0.67
0.78
-0.38
0.58
REAS2
71.41
-0.49
0.7
-0.21
0.59
Results
Model performance on gaseous and particulate pollutants
Table 1 summarizes the overall model performance on O3, CO,
NO2, SO2, PM2.5, and PM10 with different
inventories using the averaged observations in 60 cities in 2013. Model
performance meets the O3 criteria for all inventories. O3
from SOE is 7.2 parts per billion (ppb) lower than the mean observed
concentration while the underpredictions of the other three inventories are
less than 2 ppb. CO, NO2, and SO2 are underpredicted by all
inventories, indicating potential emission underestimation of these species
in the inventories. CO predictions from three inventories (SOE inventory does
not include CO) are substantially lower than observations, with the best
performance (lowest MNB and MNE) from REAS2. The overall performance of
NO2 is similar to CO. However, MEIC and SOE yield the lowest MNB,
while EDGAR yields the highest MNB for CO. SO2 performance is better
than CO and NO2, and MEIC and SOE yield the lowest MNB, while MNE
values of the four inventories are very similar. PM2.5 and PM10
predictions using all inventories meet the performance criteria with similar
MFB and MFE values. REAS2 yields slightly better PM2.5 and PM10
performance, but all inventories underpredict the concentrations generally.
The difference in model performance with the four inventories also varies
seasonally and spatially. Figure 2 shows the comparison of model performance
for hourly gaseous species (O3, CO, NO2, and SO2) in
each month from March to December 2013. The MNB values of O3 in most
months are within the criteria for all inventories except for SOE, which
underpredicts O3 concentrations. March has the worst performance of
O3 for all inventories with MNE values larger than 0.4 for MEIC, SOE,
and EDGAR. No significant performance difference among different inventories
in different months is found, but large differences exist in various regions
of China (see the definition of regions of China in Fig. 1). O3
predicted using MEIC, EDGAR, and REAS2 meets the performance criteria in most
regions except for YRD by MEIC and PRD by EDGAR. O3 predicted using
SOE only meets the criteria in the Northwest (NW) and other region (Other) of
China. CO and NO2 are underpredicted in all regions, with the
largest underpredictions in NW and Other. This pattern is similar among the
results with all inventories. SO2 is generally underpredicted in all
regions but overpredicted in the Sichuan Basin (SCB) by all inventories.
SO2 is also overpredicted by EDGAR in the PRD region. SO2 in
Northeast (NE) is substantially underpredicted by MEIC and REAS2. In
general, model performance in the more developed regions such as YRD,
and PRD are relatively better, compared to NW and Other.
Figure 3 illustrates the PM2.5 and PM10 performance statistics of
MFB and MFE as a function of absolute concentrations in different months of
2013 and in different regions. PM2.5 predictions based on each inventory
are within the performance goal of MFB and between the goal and criteria of
MFE in all months. There are no significant differences among inventories.
Half of monthly averaged PM10 MFB values fall within the goal while the
rest are between the goal and criteria. MFE values of PM10 are all
between the goal and criteria. From the regional perspective, PM2.5
performance for NE by SOE fails the MFB criteria, while that for SCB by
MEIC, SOE, and REAS2 fails the MFE criteria. MFB values of PM10 in
all regions meet the criteria except NW, due to underestimation of windblown
dust emissions in NW.
Performance of predicted PM2.5 and PM10 for different
months (a–d) and regions (e–h) based on simulations with individual
inventories. The x axis is the observed concentrations. The model
performance criteria (solid black lines) and goals (dash blue lines) are
suggested by Boylan and Russell (2006). The model performance
goals represent the level of accuracy considered to approximate the
best a model could be expected to achieve, and the model performance criteria
represent the level of accuracy that is considered to be acceptable for
modeling applications. Changes in colors show the months from March to December in the top two rows (3 refers to March, 12 to December, etc.), while they show regions from NCP to Other in the bottom two rows.
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Spatial differences of model-predicted annual average gas species
concentrations (in the horizontal panels) with different inventories (in the
vertical panels). Units are ppb. The color bars of the first column are
different to better show the spatial distribution of different species.
White indicates zero, while blue, green, yellow, and red mean concentrations
from low to high. The color bars for the other three columns are same; white
indicates zero and blue and green mean values less than zero, while yellow,
purple, and red mean values larger than zero. O3-1h represents daily
maximum 1 h O3 and O3-8h represents daily maximum 8 h mean O3.
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Spatial differences of model-predicted seasonal averaged PM2.5
concentrations (in the horizontal panels) with different inventories (in the
vertical panels). Units are µgm-3. In the first column, white
indicates zero, while blue, green, yellow, and red mean concentrations from
low to high. The color bars for the other three columns are same; white
indicates zero and blue and green mean values less than zero, while yellow,
purple, and red mean values larger than zero.
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Spatial variations in predicted gaseous and particulate pollutants
Figure 4 shows the spatial distribution of annual average daily maximum 1 h
O3 (O3-1h), 8 h mean O3 (O3-8h),
NO2, and SO2 predicted by MEIC and the differences between
predictions of SOE, EDGAR, and REAS2 against those of MEIC. MEIC-predicted annual O3-1h concentrations are ∼ 60 ppb in most parts
of China with the highest values of ∼ 70 ppb in SCB. SOE predicts lower
O3-1h values than MEIC, with ∼ 5 ppb differences in the SCB,
central China (CNT), and North China Plain (NCP) regions and 2–3 ppb
differences in regions other than the above three regions. EDGAR also predicts 2–3 ppb lower
O3-1h in most regions than MEIC but its O3-1h predictions in
the Tibetan Plateau, NCP, and ocean regions are 2–3 ppb higher than MEIC
predictions. REAS2-predicted O3-1h values are lower than MEIC for
scattered areas in the NE, NW, and CNT regions but are slightly higher in
other regions. MEIC, SOE, and REAS2 have similar results for regions out of
China (the difference is generally less than 1 ppb) since the simulations
used same emissions for those regions. O3-8h shows similar spatial
distributions as O3-1h among inventories with slightly less
differences. NO2 concentrations are 10–15 ppb in developed areas of
the NCP and YRD regions, and are greater than 5 ppb in other urban areas as
predicted by MEIC. SOE predicts 2–3 ppb lower NO2 concentrations in
most areas except the vast NW region. EDGAR predicts lower NO2 (more
than 5 ppb difference) in urban areas of the NCP and YRD areas but higher
concentrations in the entire western part of China by approximately 1–2 ppb.
REAS2 has the closest NO2 with MEIC as the 1–2 ppb underestimation
or overestimation are almost evenly distributed in the whole country.
SO2 concentrations are up to 20 ppb in the NCP, CNT, and SCB
regions,
while they are less than 5 ppb in other regions. SOE generally predicts 2–3 ppb
lower SO2 in the eastern half of China with the largest difference of
-10 ppb in the CNT region. EDGAR and REAS2 have very similar differences in
SO2 concentrations with MEIC, i.e., more than 5 ppb higher
concentrations in the NCP and YRD than MEIC, ∼ 2 ppb higher
concentrations in the PRD, 2–3 ppb lower concentrations in the NE, and up to
5 ppb lower concentrations in the CNT and SCB.
Spatial differences of model-predicted annual PM2.5 components
(in the horizontal panels) with different inventories (in the vertical
panels). Units are µgm-3. “OTHER” represents the other implicit components (OTH). Colors are used as in Fig. 5.
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Figure 5 shows the seasonal distribution of PM2.5 total mass predicted
by MEIC and differences between predictions by the other three inventories
and those by MEIC. In spring, MEIC-predicted PM2.5 concentrations are
∼ 50 µgm-3 in eastern and southern China. Southeast
Asia has the highest value of ∼ 100 µgm-3. SOE predicts
5–10 µgm-3 lower PM2.5 than MEIC in north China and
< 5 µgm-3 higher values in southern China and along the
coastline. EDGAR predicts > 20 µgm-3 lower values in NCP
and ∼ 10 µgm-3 lower values in NE, CNT, and SCB, but up
to 20 µgm-3 higher values in PRD. REAS2 predicts higher
PM2.5 values in most parts of China except underpredictions in NE and
SCB. The difference in PM2.5 in YRD and NCP is up to 20–30 µgm-3. In summer, the high PM2.5 regions are much smaller compared
to spring with ∼ 50 µgm-3 in NCP, northern part of YRD and
SCB and 20–30 µgm-3 in other parts. Generally, SOE predicts
< 10 µgm-3 lower values in most regions. EDGAR predicts
lower values in NCP and SCB but 5–10 µgm-3 higher values in
southern China. REAS2 predicts higher values in almost all the regions except
some scattered areas in NCP, YRD, and SCB.
In fall, PM2.5 concentrations are larger than 50 µgm-3
in most regions except NW and are ∼ 100 µgm-3 in part of
NCP, CNT, and SCB. SOE predicts lower values than MEIC in northern China but
higher in southern China. EDGAR predicts up to 30 µgm-3 lower
values in NCP and SCB while up to 20 µgm-3 higher values in
YRD. REAS2 again estimates similar values as MEIC with less than 5 µgm-3 differences in most regions and up to 20 µgm-3
higher values in scattered areas in YRD and SCB. In winter, MEIC-predicted
PM2.5 concentrations are up to 200 µgm-3 in NCP, CNT,
YRD, and SCB, while PRD has concentrations of ∼ 50 µgm-3. SOE-predicted concentrations are severely lower by 30 µgm-3 in most regions with high PM2.5 concentrations but by
< 10 µgm-3 higher in only coast areas. EDGAR also predicts
30 µgm-3 lower PM2.5 concentrations in NE, NCP, CNT, and
SCB, but 20 µgm-3 higher in the YRD region. The regions with
lower values by REAS2 compared to MEIC are in the regions of NE, NCP, CNT, and
SCB, similar to EDGAR but with much smaller areas.
Figure 6 shows the annual average concentrations of PM2.5 components
predicted by MEIC and the differences between predictions by the other three
inventories and those by MEIC. Annual average particulate sulfate
(SO42-) concentrations with MEIC are 20–25 µgm-3 in
NCP, CNT, and SCB, and about 10 µgm-3 in other regions in the
southeastern China. SOE-predicted concentrations are ∼ 10 µgm-3 lower in the high-concentration areas and 2–3 µgm-3 lower in other areas. EDGAR-predicted SO42- are ∼ 5 µgm-3 higher in southeastern China and 2–3 µgm-3 lower in SCB. REAS2-predicted SO42- concentrations
are generally 2–3 µgm-3 lower than those of MEIC in most areas
except the coastal areas. MEIC predicts the highest particulate nitrate
(NO3-) concentrations of up to 30 µgm-3 in NCP and
YRD and concentrations in other regions are 5–10 µgm-3 except
northwest China. SOE-predicted nitrate concentrations are
< 5 µgm-3 lower in the high-concentration areas and ∼ 2 µgm-3 higher values in coastal areas. EDGAR uniformly
predicts lower NO3- values than MEIC with the largest difference
of 10 µgm-3. REAS2 has similar results to SOE. Particulate
ammonium (NH4+) concentrations predicted by MEIC have a peak value of
15 µgm-3 and are mostly less than 10 µgm-3 in
eastern and southern China. SOE predicts slightly lower concentrations except for
the coastal areas in PRD, where the SOE predictions are 1–2 µgm-3 higher.
EC concentrations are generally low compared to other components as predicted
by MEIC. The concentrations are less than 10 µgm-3 in NCP,
CNT, and SCB. All other three inventories predict 1–2 µgm-3
lower EC values than MEIC throughout the country. Primary organic aerosol
(POA) concentrations predicted by MEIC are 20–30 µgm-3 in NCP, CNT, and SCB,
and ∼ 10 µgm-3 in other areas in eastern and southern China. SOE-predicted concentrations are up to 5 µgm-3 higher in most areas, but in scattered places the SOE predictions
are ∼ 2 µgm-3 lower than MEIC. EDGAR and REAS2
predictions are up to ∼ 10 µgm-3 lower except for
coastal areas. SOA concentrations are low in northern China and are up to 10 µgm-3 in eastern and southern China. All three other inventories
predict ∼ 2 µgm-3 lower SOA concentrations than MEIC.
For other implicit components (OTH), the highest concentrations are ∼ 15 µgm-3 in NW and NCP, while other regions have concentrations lower than
5 µgm-3. In NW, the major sources of OTH are
windblown dust generated in-line by CMAQ simulations; thus, almost no
differences are observed among the four simulations. SOE and EDGAR predict
lower OTH vales in northern China (∼ 2 µgm-3 and slightly
higher values in southern and eastern China (∼ 5 µgm-3). REAS2
predicts higher OTH values in eastern China uniformly with up to 10 µgm-3 differences in the NCP, YRD, and SCB regions.
Additional comparisons of the model predictions in different regions and
some major cities in China are shown in Figs. S1–S4 in the Supplement.
The weighting factors (w) of each inventory in the ensemble
predictions of PM2.5 when using daily, monthly, and annual averages in
the objective function (Eq. 5).
Daily
Monthly
Annual
MEIC
0.07
0.13
0.31
SOE
0.14
0.16
0.24
EDGAR
0.38
0.23
0.20
REAS2
0.49
0.63
0.36
MFB (a) and MFE (b) of predicted PM2.5 for with an averaging time of
24 h, 1 month, and 1 year based on the individual inventories and the
ensemble.
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Performance of daily PM2.5 (MFB and MFE) and O3-1h (MNB
and MNE) in different regions of China based on individual inventories and
the ensemble. The weighting factors (w) used to calculate the ensemble of
each region are also included. The best performance is indicated by the bold
numbers.
Region
MEIC
SOE
EDGAR
REAS2
ensemble
(No. of cities)
w
MFB
MFE
w
MFB
MFE
w
MFB
MFE
w
MFB
MFE
MFB
MFE
PM2.5
NE (4)
0.16
-0.23
0.44
0.21
0.38
0.68
0.20
-0.30
0.43
0.43
-0.12
0.43
-0.08
0.42
NCP (14)
0.00
-0.30
0.47
0.52
-0.34
0.46
0.14
-0.40
0.51
0.56
-0.20
0.41
-0.12
0.40
NW (6)
0.00
-0.87
0.90
0.20
-0.80
0.84
0.59
-0.85
0.87
1.00
-0.81
0.83
-0.49
0.66
YRD (20)
0.05
-0.29
0.45
0.00
-0.27
0.43
0.61
-0.23
0.40
0.35
-0.13
0.40
-0.18
0.38
CNT (5)
0.09
-0.10
0.46
0.18
-0.05
0.41
0.50
-0.27
0.40
0.22
0.09
0.44
-0.14
0.37
SCB (2)
0.00
0.10
0.48
0.64
0.23
0.48
0.00
-0.10
0.39
0.08
0.07
0.43
-0.15
0.40
SOUTH (9)
0.10
-0.35
0.51
0.00
-0.18
0.41
0.59
-0.07
0.45
0.30
-0.25
0.44
-0.16
0.41
CHINA (60)
0.07
-0.34
0.52
0.14
-0.26
0.50
0.38
-0.33
0.49
0.49
-0.22
0.46
-0.20
0.45
w
MNB
MNE
w
MNB
MNE
w
MNB
MNE
w
MNB
MNE
MNB
MNE
O3-1h
NE
0.09
0.44
0.50
0.00
0.16
0.34
0.45
0.41
0.47
0.27
0.42
0.48
0.14
0.31
NCP
0.29
0.33
0.47
0.12
0.23
0.44
0.06
0.46
0.59
0.42
0.47
0.56
0.25
0.43
NW
0.00
0.65
0.72
0.82
0.54
0.62
0.00
0.70
0.77
0.00
0.68
0.74
0.25
0.46
YRD
0.00
0.20
0.41
0.53
0.14
0.38
0.00
0.25
0.45
0.45
0.27
0.44
0.17
0.39
CNT
0.27
0.27
0.47
0.18
0.16
0.43
0.10
0.35
0.53
0.36
0.35
0.52
0.18
0.42
SCB
0.44
0.59
0.68
0.14
0.42
0.58
0.28
0.59
0.70
0.00
0.60
0.72
0.33
0.53
SOUTH
0.84
0.39
0.50
0.00
0.29
0.46
0.00
0.38
0.51
0.00
0.42
0.53
0.16
0.37
CHINA
0.19
0.34
0.49
0.20
0.23
0.44
0.00
0.39
0.54
0.51
0.41
0.53
0.21
0.42
Ensemble predictions
The above analyses indicate that model performance with different
inventories varies for different pollutants and in different regions. Table
S2 shows the observed annual average concentrations of PM2.5 in the 60
cities and the predictions from the four inventories as well as the weighted
ensemble predictions. The weighting factors for predictions using MEIC,
REAS2, SOE, and EDGAR are 0.31, 0.36, 0.24, and 0.20, respectively (Table 2).
The ensemble predictions greatly reduce MFB to a value of -0.11, compared to
the MFB values of -0.25 to -0.16 using the annual average concentrations in
the individual simulations. Also, the ensemble prediction yields an MFE
value of 0.24, lower than any MFE values of 0.26–0.31 based on individual
simulations (Fig. 7). The ensemble predictions of annual O3-1h have
MNB and MNE of 0.03 and 0.14, respectively, improved from MNB of 0.06–0.19 and MNE of 0.16–0.22 in the individual predictions.
To further evaluate the ability of the ensemble method in improving
predictions at locations where observational data are not available,
ensemble predictions were made using a data withholding method. For each
city, the observations at the other 59 cities were used to determine the
weighting factors in E6 and the ensemble prediction at the city was
calculated. Performance of the ensemble predictions at the city was
calculated using the withheld observations to evaluate the performance. The
evaluation process was repeated for each of the 60 cities and the
performance was compared to that with individual inventories (shown in Table S3). The results show that the ensemble predictions are better than those
with EDGAR, MEIC, REAS2, and SOE at 36, 37, 32, and 40 cities for PM2.5,
and 39, 39, 43, and 38 cities for O3-1h, respectively. The ensemble
predictions are better than ≥ 2 of the individual predictions at 45 and
41 cities for PM2.5 and O3-1h, respectively. Out of the 15 cities
for which the ensemble PM2.5 is only better than one or none of the
individual predictions, 10 cities have MFB within ±0.25 and MFE less
than 0.25. Out of the 19 cities for which the ensemble O3-1h is only better
than one or none of the individual predictions, 14 cities still have MNB
within ±0.2 and MNE less than 0.2. The results demonstrate that the
ensemble can improve the predictions even at locations with no observational
data available.
Spatial distributions of PM2.5 and its components in the
ensemble predictions. Units are µgm-3. The scales of the
panels are different. White indicates zero, while blue, green, yellow, and red
mean concentrations from low to high. “OTHER” represents the other implicit components (OTH).
![]()
Previous studies have revealed that CTMs predictions agree more when
averaging over longer periods of time (i.e., annual vs. monthly vs. daily averages; Hu et al., 2014b, 2015b). Ensemble predictions were also
calculated with daily and monthly averages for PM2.5, in addition to
the calculation with annual averages discussed above. The weighting factors
and the performance of ensemble predictions are shown in Table 2 and Fig. 7, respectively. The weighting factors vary largely with the averaging
times, suggesting that the prediction optimization needs to be conducted
separately when using different time averages. The ensemble predictions
improve the agreement with observations in all averaging time cases, with
lower MNB and MNE than any of the individual predictions. In general, EDGAR
and REAS2 have large weights for daily and monthly ensemble calculations, and
MEIC and SOE have large weights for annual ensemble calculations. This
result indicates that the annual total emission rates of MEIC and SOE are
likely accurate, but the temporal profiles to allocate the annual total
emissions rates to specific days/hours need to be improved.
Table 3 shows the ensemble prediction performance on PM2.5 and
O3-1h in different regions of China using the daily average
observations and daily average predictions with individual inventories. The
weighting factors vary greatly among regions, reflecting the substantial
difference in the spatial distributions of PM2.5 and O3 when using
different inventories. The MNB and MNE values of ensemble predictions are
reduced in all regions for both pollutants, suggesting the ensemble
predictions improve the accuracy and can be better used in further health
effect studies. The similar findings are also found with the monthly
average observations and predictions (shown in Table S4).
Figure 8 shows spatial distributions of PM2.5 and its components from
the ensemble predictions using the weighting factors of annual averages. The
ensemble of PM2.5 components was calculated using the same weighting
factors as for PM2.5 in total mass. Concentrations of over 80 µgm-3 annual average
PM2.5 are estimated in NCP, CNT, YRD, and SCB regions in
2013. Secondary inorganic aerosols (SO42-, NO3-, and
NH4+) account for approximately half of PM2.5, and exhibit
similar spatial patterns. Carbonaceous aerosols (EC, POA, and SOA) account
for about 30 %, but POA and SOA have quite different spatial
distributions. High POA concentrations are mainly distributed in NCP, CNT, and
SCB, while high SOA concentrations are found in southern China. By
considering the spatial distributions of population and ensemble PM2.5,
the population-weighted annual average PM2.5 concentration in China in
2013 is 59.5 µgm-3, which is higher than the estimated value
of 54.8 µgm-3 by Brauer et al. (2016).
The results of the current study can be further applied in health effect
studies. The first such analysis used annual PM2.5 ensemble
predictions to assess the spatial distribution of excess mortality due to
adult (> 30 years old) ischemic heart disease (IHD), cerebrovascular
disease (CEV), chronic obstructive pulmonary disease (COPD), and lung cancer
(LC) in China caused by PM2.5 exposure (Hu et al., 2017a). Any health
studies requiring human exposure information to different pollutants would
benefit from this study. Even though the weighted factors vary depending on
the regions, averaging times and different study years, the ensemble method
proposed in this study minimizes the difference between predictions and
observations and can be applied in different studies. The way to calculate
the weighting factors depends on the objectives of specific studies. But in
general, the more observation data used in the calculation, the more accurate
the ensemble prediction will be.