Introduction
China is one of the regions with the highest concentration of PM2.5
(particulate matter with aerodynamic diameter equal to or less than
2.5 µm) in the world (van Donkelaar et al., 2015). The problem is
especially serious over the Beijing–Tianjin–Hebei (BTH) region, one of the
most populous and developed regions in China. Annual average PM2.5
concentrations in this region reached 85–110 µg m-3 during
2013-2015, which approximately triple the standard threshold
(35 µg m-3) and far exceed those in other metropolitan
regions (Wang et al., 2017). It has been estimated that the severe PM2.5
pollution leads to about 1.05–1.23 million premature deaths per year in
China (Lim et al., 2012; Burnett et al., 2014; J. D. Wang et al., 2016), and the
monetized loss over the BTH region is as high as 134 billion Chinese Yuan,
representing 2.2 % of regional gross domestic product (GDP) (Lv and Li,
2016). Additionally, PM2.5 substantially affects global and regional
climate by absorbing and scattering solar radiation and by altering cloud
properties (IPCC, 2013; Seinfeld et al., 2016; Zhao et al., 2017a), which in
turn exert an impact on regional air quality (J. D. Wang et al., 2014; Zhao et al.,
2017b).
To tackle the heavy PM2.5 pollution problem, the Chinese government issued
the Action Plan on Prevention and Control of Air Pollution in September
2013, which aimed at a 25 % reduction in PM2.5 concentrations over
the BTH region by 2017 from the 2012 levels (The State Council of the
People's Republic of China, 2013). The attainment of an ambient PM2.5
standard would further require substantial reductions in air pollutant
emissions (Wang et al., 2015, 2017). To establish emission control
strategies, many studies have apportioned the sources of PM2.5 over the
BTH region, either by mining monitoring data using the positive matrix
factorization and chemical mass balance methods (e.g., Zhang et al., 2007; Yu
et al., 2013) or by embedding chemical tracers in chemical transport models
(CTMs) (e.g., Y. J. Wang et al., 2016; Li et al., 2015; Ying et al., 2014). While
these studies can capture the current contributions of various sources to
PM2.5 concentrations, these contributions could differ significantly
from the PM2.5 reductions induced by reducing emissions from the
corresponding sources, due to highly nonlinear chemical mechanisms (Han et
al., 2016; Wang et al., 2011). Therefore, it is imperative to assess the
nonlinear response of PM2.5 to pollutant emissions from multiple
sources, which could provide direct support for the development of effective
control policies.
The most widely used technique to evaluate the responses of PM2.5
concentrations to emission changes is the “brute force” method, which
involves perturbing emissions from a certain source and repeating the solution of
a CTM (Russell et al., 1995). A number of studies have utilized the brute
force method to quantify the sensitivities of PM2.5 concentrations
over the BTH region to emissions from different spatial regions (Streets et
al., 2007; Wang et al., 2008; L. T. Wang et al., 2014; Li and Han, 2016) or different economic
sectors (Wang et al., 2008; L. T. Wang et al. 2014; Han et al., 2016; Liu et al., 2016),
either on a seasonal basis (Streets et al., 2007; Wang et al., 2008; Han et
al., 2016; Liu et al., 2016) or during a specific heavy-pollution episode (Li
and Han, 2016; L. T. Wang et al., 2014). To improve the computational efficiency,
several mathematic techniques embedded in CTMs have been developed to
simultaneously calculate the sensitivities of the modeled concentrations to
multiple emission sources, including the decoupled direct method (Yang et
al., 1997) and adjoint analysis (Sandu et al., 2005; Hakami et al., 2006).
Zhang et al. (2016) used the adjoint analysis method to examine sensitivities
of PM2.5 concentrations in the BTH region to pollutant emissions during
several pollution periods. However, all the preceding studies only quantified
first-order sensitivities and therefore could not capture the nonlinearity in
the responses of PM2.5 concentrations to pollutant emissions, which can
be extremely strong in metropolitan regions like BTH due to complex chemical
mechanisms (Wang et al., 2011). Moreover, no studies have simultaneously
evaluated the response of PM2.5 concentrations in BTH to emissions of
multiple pollutants from different sectors and regions, which we need to
consider and balance to develop cost-effective control strategies.
In light of the drawbacks of the preceding methods, the response surface
modeling (RSM) technique (denoted by “conventional RSM” hereafter
to distinguish it from the extended response surface modeling, ERSM, technique) has been developed by using advanced
statistical techniques to characterize the complex nonlinear relationship
between model outputs and inputs (U.S. Environmental Protection Agency, 2006;
Xing et al., 2011; Wang et al., 2011). This technique has been applied to the
United States (U.S. Environmental Protection Agency, 2006) and eastern
China (Wang et al., 2011) to evaluate the response of PM2.5 and its
chemical components to pollutant emissions. However, the number of emission
scenarios required to build conventional RSM depends on the variable number
via an equation of fourth or higher order (Zhao et al., 2015b). Therefore,
the required scenario number would be tens of thousands for over 15 variables
and even hundreds of thousands for over 25 variables, which is
computationally impossible for most three-dimensional CTMs. To overcome this
major limitation, we recently developed the ERSM technique (Zhao et al., 2015b), which substantially reduced
the scenario number needed to build the response surface and hence extended
its applicability to an increased number of regions, pollutants, and sectors
with an acceptable computational burden.
Given the advantage of the ERSM technique, here we apply it to over 1000
simulations by the Community Multi-scale Air Quality model with
Two-Dimensional Volatility Basis Set (CMAQ/2D-VBS) to systematically
evaluate the nonlinear response of PM2.5 to emission changes in
multiple pollutants from different sectors and regions over the BTH region.
The major sources contributing to PM2.5 and its major components are
identified, and the nonlinearity in the response of PM2.5 to emission
changes is characterized. Based on the results of this study, suggestions for
PM2.5 control policies over the BTH region are proposed.
Methodology
CMAQ/2D-VBS configuration and evaluation
The CMAQ/2D-VBS model was developed in our previous study (Zhao et al., 2016)
by incorporating the 2D-VBS model framework into CMAQv5.0.1. Compared with
the default CMAQ, the CMAQ/2D-VBS model explicitly simulates aging of
secondary organic aerosol (SOA) formed from non-methane volatile organic
compounds (NMVOCs), aging of primary organic aerosol (POA), and
photooxidation of intermediate-volatility organic compounds (IVOCs), thereby
significantly improving the simulation results of organic aerosol (OA),
particularly SOA. The model parameters within the 2D-VBS framework have been
optimized in our previous studies (Zhao et al., 2015a, 2016) based on a
series of smog-chamber experiments. Here we use the same model parameters as
those of the “high-yield VBS” configuration reported in Zhao et al. (2016),
which agrees best with surface OA and SOA observations among three model
configurations. An application in eastern China reveals that CMAQ/2D-VBS
reduces the underestimation in OA concentrations from 45 % (default
CMAQv5.0.1) to 19 %. More importantly, while the default CMAQv5.0.1
substantially underestimates the fraction of SOA in OA by 5–10 times and cannot track the oxygen-to-carbon ratio (O : C), the SOA fraction and O : C
simulated by CMAQ/2D-VBS agree fairly well with observations.
We apply the CMAQ/2D-VBS model over the BTH region. One-way, double-nesting
simulation domains are used, as shown in Fig. 1. Domain 1 covers East Asia
with a grid resolution of 36 km × 36 km; Domain 2 covers the BTH
and its surrounding regions with a grid resolution of
12 km × 12 km. We use the SAPRC99 gas-phase chemistry module and
the AERO6 aerosol module, in which the treatment of OA is replaced with the
2D-VBS framework. The aerosol thermodynamics is based on ISORROPIA-II. The
initial and boundary conditions for Domain 1 are kept constant as the model
default profile, and those for Domain 2 are extracted from the output of
Domain 1. A 5-day spin-up period is used to reduce the influence of initial
conditions on modeling results.
Double-nesting domains used in CMAQ/2D-VBS simulation (a)
and the definition of five target regions in the innermost domain, denoted by
different colors (b). The grey lines in (b) represent
the boundaries of prefecture-level cities.
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The Weather Research and Forecasting Model (WRF, version 3.7) is used to
generate the meteorological fields. The National Center for Environmental
Prediction (NCEP)'s FNL (Final) Operational Global Analysis data (ds083.2) at
1.0∘ × 1.0∘ and 6 h resolution are used to generate
the first-guess field. The NCEP's Automated Data Processing (ADP) data
(ds351.0 and ds461.0) are used in objective analysis (i.e., grid nudging).
The major physics options for WRF include the Kain–Fritsch cumulus scheme,
the Pleim–Xiu land-surface module, the Asymmetric Convective Model with
nonlocal upward mixing and local downward mixing (ACM2) for planetary
boundary layer (PBL) parameterization, the Morrison double-moment scheme for
cloud microphysics, and the Rapid Radiative Transfer Model for General Circulation Models (RRTMG)
radiation scheme. The land cover type data are obtained from the Moderate
resolution Imaging Spectroradiometer (MODIS). The simulation periods are
January, March, July, and October in 2014, representing winter, spring,
summer, and fall. We select these 4 months because the occurrence
frequencies of various meteorological types in these months are statistically
most similar to the average conditions in winter, spring, summer, and fall
during 2004–2013 (Wu, 2016).
A high-resolution anthropogenic emission inventory in 2014 has been developed
by Tsinghua University using an “emission factor method” (Fu et al., 2013;
Zhao et al., 2013b) for the BTH region. The emissions from area and mobile
sources are first calculated for each prefecture-level city based on
statistical data and subsequently distributed into the model grids according
to the spatial distribution of population, GDP, and road networks. A
unit-based method (Zhao et al., 2008) is applied to estimate and locate the
emissions from large point sources (LPSs) including power plants, iron and
steel plants, and cement plants. The anthropogenic emission inventory in
other provinces of China was originally developed for 2010 and 2012 in our
previous studies (Zhao et al., 2013a, b; S. X. Wang et al., 2014; Cai et al.,
2017); this has been updated to 2014 in this study following the same
methodology. In both the BTH and national emission inventories, the emissions
from the open burning of agricultural residue are calculated using crop
yields, straw to grain ratio, fraction of biomass burned in the open field,
and emission factors (Fu et al., 2013; Zhao et al., 2013b; Wang and Zhang,
2008). We do not include the emissions from forest and grassland fires, which
typically account for less than 5 % of the total biomass burning
emissions over the BTH region (Qin and Xie, 2011) and are not the focus of
the present study. Table S1 in the Supplement summarizes emissions of major
air pollutants in each prefecture-level city over the BTH region in 2014;
Table S2 gives the provincial emissions in the whole of China in 2014. The
emissions for other countries are obtained from the
MIX emission inventory (Li et al., 2017) for 2010, which
is the latest year available. Following our previous study (Zhao et al.,
2016), we assume IVOC emissions to be 30 times, 4.5 times, 1.5 times, and 3.0
times the POA emissions from gasoline vehicles, diesel vehicles, biomass
burning, and other emission sources, respectively, which is based on a series
of laboratory measurements (Gordon et al., 2014a, b; Hennigan et al., 2011;
Jathar et al., 2014). The biogenic emissions were calculated by the Model of
Emissions of Gases and Aerosols from Nature (MEGAN; Guenther et al., 2006).
We compared the simulation results of WRFv3.7 and CMAQ/2D-VBS with
meteorological observations obtained from the National Climatic Data Center
(NCDC), PM2.5 observations at 138 state-controlled observational sites,
and observations of major PM2.5 chemical components at seven sites within
the modeling domain. We show that the meteorological and chemical simulations
generally agree well with observations, with performance statistics mostly
within the benchmark values proposed by previous studies. Details of the
model evaluation methods and results are given in the Supplement (Sect. S1, Table S3–S5, Figs. S1–S5).
Development of ERSM prediction system
The detailed methodologies of the conventional RSM and ERSM techniques have
been described in our previous papers (Zhao et al., 2015b; Xing et al.,
2011). Here we only summarize some key components. The conventional RSM
technique characterizes the relationships between a response variable (e.g.,
PM2.5 concentration) and a set of control variables (i.e., emissions of
particular pollutants from particular sources) based on a number of randomly
generated emission control scenarios (Xing et al., 2011; Wang et al., 2011).
The PM2.5 concentration for each emission scenario is calculated with a
CTM (CMAQ/2D-VBS in this study), and the conventional RSM is subsequently
established using the maximum-likelihood estimation–empirical best linear
unbiased predictors (MLE-EBLUPs) developed by Santner et al. (2003). Due to
the limitation of the conventional RSM technique with respect to variable
number, we have developed the ERSM technique (Zhao et al., 2015b) to extend
the applicability to an increased number of variables and geographical
regions. The ERSM technique first quantifies the relationship between
PM2.5 concentrations and precursor emissions for each single region
using the conventional RSM technique as described above and then assesses
the effects of interregional transport of PM2.5 and its precursors on
PM2.5 concentration in the target region. In order to quantify the
interaction among regions, we introduce a key assumption that the emissions
of precursors in the source region affect PM2.5 concentrations in the
target region through two major processes: (1) the interregional transport
of precursors enhancing the chemical formation of secondary PM2.5 in the
target region; (2) the formation of secondary PM2.5 in the source region
followed by transport to the target region. We quantify the individual
contributions of these two processes as well as the contribution of local
emissions in the target region, which are subsequently integrated to derive
the total PM2.5 concentrations in the target region. The development of
the ERSM prediction system requires several hundred to over 1000 emission
scenarios, but once built, it enables real-time prediction of PM2.5
concentrations for any given control strategy and proves to be an efficient
and user-friendly decision making tool. Moreover, ERSM can be applied to
design a least-cost control strategy once it is coupled with control cost
models/functions that links the emission reductions with economic costs.
For the application of the RSM/ERSM techniques to the BTH region, we define five target regions in the inner modeling domain (Domain 2), i.e., Beijing,
Tianjin, Northern Hebei (N Hebei), Eastern Hebei (E Hebei), and Southern
Hebei (S Hebei), as shown in Fig. 1. The decomposition of Hebei province
is based on a preliminary analysis of the pollutant transport patterns over
the BTH region (Sect. S2). The simulation using the back-trajectory method
indicates that four major types of heavy-pollution episodes in Beijing are
primarily contributed by air mass from the south, the local area, the
northwest, and the southeast. We develop two RSM/ERSM prediction systems
(Table 1). The response variables for the first prediction system, which is
built using the conventional RSM technique, are concentrations of PM2.5,
SO42-, NO3-, and OA over the urban areas of prefecture-level
cities in the five target regions. For the second prediction system that is
established using the ERSM technique, the response variables are only
PM2.5 concentrations. The first prediction system uses 101 emission
control scenarios generated by the Latin hypercube sample (LHS) method (Iman
et al., 1980) to map atmospheric concentrations versus emissions of five
PM2.5 precursors, i.e., NOx, SO2, NH3, NMVOC + IVOC, and
POA, in all five target regions (Table 1). It is on the one hand intended for the
validation of the second system (Sect. 3.1) and, on the other hand, used to
study the source contributions of major PM2.5 components. For the second
system, the emissions of the preceding PM2.5 precursors as well as
primary inorganic PM2.5 (i.e., the chemical components of primary
PM2.5 other than POA) in each of the five regions are categorized into seven
and four control variables, respectively, resulting in 55 control variables in
total (Table 1). Note that we distinguish POA and primary inorganic
PM2.5 because the former undergoes chemical reactions and produces SOA,
while the latter is mostly chemically inert in the CMAQ/2D-VBS model. We
generate 1121 scenarios (see Table 1) to build the response surface,
following the method detailed in Zhao et al. (2015b). Specifically, the
scenarios include (1) 1 CMAQ/2D-VBS base case; (2) 200 scenarios generated by
applying the LHS method for the control variables of precursors in Beijing,
200 × 4 scenarios generated in the same way for Tianjin, Northern
Hebei, Eastern Hebei, and Southern Hebei; (3) 100 scenarios generated by
applying LHS method for the total emissions of NOx, SO2, NH3,
NMVOC + IVOC, and POA in all five regions; and (4) 20 scenarios in which one of the
control variables of primary inorganic PM2.5 emissions is set to 0.25
for each scenario. Here the scenario numbers (200 in group 2 and 100 in group
3) are determined based on numerical experiments conducted in our previous
studies (Xing et al., 2011; Wang et al., 2011), which showed that the
response surface for seven and five variables could be built with good prediction
performance (mean normalized error < 1 %; correlation
coefficient > 0.99) using 200 and 100 scenarios, respectively.
Finally, we generate 54 independent scenarios for out-of-sample validation,
which will be detailed in Sect. 3.1.
Description of the RSM/ERSM prediction systems developed in this
study.
Method
Control variables
Control scenarios
Conventional RSM technique
Five control variables: total emissions of NOx, SO2, NH3, NMVOC + IVOC, and POA.
101 control scenarios: 1. 1 CMAQ/2D-VBS base case, 2. 100a scenarios generated by applying the LHS method for the five variables.
ERSM technique
55 control variables in total: 11 control variables in each of the five regions, including seven nonlinear control variables, i.e., 1. NOx/large point sources (LPSs)b, 2. NOx/other sources, 3. SO2/LPS, 4. SO2/other sources, 5. NH3/all sources, 6. NMVOC + IVOC/all sources, 7. POA/all sources, and four linear control variables, i.e., 8. primary inorganic PM2.5/power plants, 9. primary inorganic PM2.5/industry, 10. primary inorganic PM2.5/residential and commercial, 11. primary inorganic PM2.5/transportation.
1121 control scenarios: 1. 1 CMAQ/2D-VBS base case, 2. 1000 scenarios, including 200a scenarios generated by applying LHS method for the nonlinear control variables in Beijing, 200 scenarios generated in the same way for Tianjin, 200 scenarios for Northern Hebei, 200 scenarios for Southern Hebei, and 200 scenarios for Eastern Hebei, 3. 100a scenarios generated by applying the LHS method for the total emissions of NOx, SO2, NH3, NMVOC + IVOC, and POA, 4. 20 scenarios in which one primary inorganic PM2.5 control variable is set to 0.25 for each scenario.
Overall, a 100 and 200 scenarios are needed for the response
surfaces for five and seven variables, respectively (Xing et al., 2011; Wang et al.,
2011). b LPS includes power plants, iron and steel plants, and
cement plants.
For the application of the ERSM prediction system to quantitatively characterize
the sensitivity of PM2.5 concentrations to emission changes, we define
“PM2.5 sensitivity” as the change ratio of PM2.5 concentration
divided by the reduction ratio of an emission source, following previous
studies (Zhao et al., 2015b; Wang et al., 2011).
SaX=C*-Ca/C*/1-a,
where SaX is the PM2.5 sensitivity to emission source X at its
emission ratio a; C* and Ca are PM2.5 concentrations in the
base case (when the emission ratio of X is 1) and in the control scenario, in which the emission ratio of X is a, respectively. Similar indices can be
defined for chemical components of PM2.5, such as NO3-,
SO42-, and OA.
Results and discussion
Validation of ERSM performance
The conventional RSM technique has been extensively demonstrated to have high
accuracy and stability in previous papers (Xing et al., 2011; Wang et al.,
2011), so we only describe the validation of the ERSM technique. Following
Zhao et al. (2015b), we assess the performance of the ERSM prediction system
using the “out-of-sample” and 2D-isopleth validation methods, which focus
on the accuracy and stability of the prediction system, respectively.
For out-of-sample validation, we use the ERSM prediction system to calculate
the PM2.5 concentrations for 54 out-of-sample control scenarios,
i.e., scenarios independent from those used to build the prediction system,
and compare them with the corresponding CMAQ/2D-VBS simulation results. These 54
out-of-sample scenarios (summarized in Table S6) include 40 cases
(cases 1–40) in which the control variables of precursors change but those of
primary inorganic PM2.5 stay the same as the base case, 4 cases
(cases 41–44) that are the other way around, and 10 cases (cases 45–54) in
which control variables of precursors and primary inorganic PM2.5 change
simultaneously. Most cases are generated randomly with the LHS method
(cases 4–6, 10–12, 16–18, 22–24, 28–54), and some cases are designed
in which all control variables are subject to large emission changes
(cases 1–3, 7–9, 13–15, 19–21, 25–27).
Figure 2 compares the ERSM-predicted and CMAQ/2D-VBS-simulated PM2.5
concentrations and PM2.5 responses (defined as the difference between
PM2.5 concentration in an emission control scenario and that in the base
case) for the out-of-sample scenarios using scatterplots. Table 2 summarizes
the statistics of the model performance. The definitions of normalized error
(NE), mean normalized error (MNE), and normalized mean error (NME) are given
as follows:
NE=Pi-Si/Si,MNE=1Ns∑i=1NsPi-Si/Si,NME=∑i=1NsPi-Si/∑i=1NsSi,
where Pi and Si are the ERSM-predicted and CMAQ/2D-VBS-simulated
value of the ith out-of-sample scenario; Ns is the number of out-of-sample scenarios. Figure 2 shows
that the ERSM predictions and CMAQ/2D-VBS simulations agree well with each
other. For PM2.5 concentrations, the correlation coefficients are larger
than 0.99, and the MNEs and NMEs are less than 1 % for all 4 months.
The maximum NEs could be as large as 11 % for a particular month and
region, but the 95 % percentiles of NEs are all within 4.4 %. NEs
exceeding 4.4 % happen only for the scenarios in which most control
variables are reduced substantially, indicating relatively large errors at
low emission rates, which is consistent with our previous study (Zhao et al.,
2015b). Note that all sensitivity scenarios used in Sect. 3.2–3.4 have ≤ 80 % emission reductions, which helps to avoid relatively large
errors. We also examine the errors in predicted PM2.5 response. Since
the CMAQ/2D-VBS-simulated PM2.5 responses are very close to zero in
several scenarios, their normalized errors (NEs) and mean normalized errors
(MNEs) could be extremely large even if the absolute errors are small, which
cannot properly characterize the accuracy of the ERSM technique. For this
reason, we only calculate the correlation coefficients and NMEs (Table 2).
The correlation coefficients of the PM2.5 response are larger than 0.99, and
the NMEs are within 5.6 % for all months. In summary, the out-of-sample
validation indicates an overall good agreement between ERSM predictions and
CMAQ/2D-VBS simulations.
Comparison of PM2.5 concentrations (a) and PM2.5
responses (b) predicted by the ERSM technique with out-of-sample
CMAQ/2D-VBS simulations. The dashed line is the one-to-one line, indicating
perfect agreement.
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Comparison between ERSM-predicted and CMAQ/2D-VBS-simulated
PM2.5 concentrations for 54 out-of-sample scenarios.
Month
Variable
Statistical index
Beijing
Tianjin
Northern Hebei
Eastern Hebei
Southern Hebei
Jan
PM2.5 concentration
R
0.998
0.998
0.995
0.997
0.997
MNE (%)
0.52
0.55
0.64
0.67
0.60
Maximum NE (%)
7.56
6.98
10.67
8.01
8.03
95 % percentile of NEs (%)
1.61
2.86
2.92
3.46
3.02
NME ( %)
0.44
0.46
0.57
0.53
0.53
PM2.5 response
R
0.998
0.998
0.995
0.997
0.997
NME (%)
3.36
3.48
4.25
4.00
3.88
Mar
PM2.5 concentration
R
0.999
0.996
0.998
0.995
0.999
MNE (%)
0.37
0.54
0.39
0.57
0.49
Maximum NE (%)
3.75
6.58
4.30
5.04
3.22
95 % percentile of NEs (%)
1.53
3.15
2.03
4.35
2.03
NME (%)
0.31
0.45
0.34
0.49
0.42
PM2.5 response
R
0.999
0.996
0.998
0.995
0.999
NME (%)
2.38
4.32
2.70
4.55
3.59
Jul
PM2.5 concentration
R
0.997
0.998
0.998
0.999
0.999
MNE (%)
0.94
0.54
0.46
0.37
0.47
Maximum NE (%)
5.05
5.02
4.65
1.83
3.62
95 % percentile of NEs (%)
3.47
2.33
2.17
1.49
1.87
NME ( %)
0.80
0.47
0.41
0.33
0.39
PM2.5 response
R
0.997
0.998
0.998
0.999
0.999
NME (%)
4.97
3.71
2.80
2.58
2.78
Oct
PM2.5 concentration
R
0.996
0.994
0.999
0.999
0.999
MNE (%)
0.83
0.70
0.36
0.39
0.36
Maximum NE (%)
8.90
11.19
3.79
3.90
2.46
95 % percentile of NEs (%)
3.04
3.50
1.44
2.10
1.64
NME (%)
0.67
0.58
0.30
0.35
0.32
PM2.5 response
R
0.996
0.994
0.999
0.999
0.999
NME (%)
4.51
5.64
2.20
3.29
2.79
We further examine whether the ERSM technique can capture the trends in
PM2.5 concentrations in response to continuous changes in precursor
emissions, i.e., the stability of the ERSM technique. To this end, we compare
the 2D isopleths of PM2.5 concentrations as a function of simultaneous
changes in two precursors' emissions in all five regions derived from the
ERSM and conventional RSM techniques. It should be noted that, although the
ERSM technique is applicable to a much larger number of control variables
than conventional RSM, the assumptions in the treatment of interregional
transport (Sect. 2.2) in ERSM might affect its accuracy. Nevertheless, the
predictions by conventional RSM can be regarded as proxies for real
CMAQ/2D-VBS simulations since conventional RSM has been extensively demonstrated to have
high accuracy and stability in previous studies (Xing et al., 2011; Wang et
al., 2011). For this reason, the comparison between the ERSM and conventional
RSM techniques helps to evaluate the stability of the ERSM technique. Figure 3
illustrates the PM2.5 isopleths in Beijing as a function of three
combinations of precursors, i.e., NOx and NH3, SO2 and NH3,
and VOC + IVOC and POA; the isopleths for other regions are very similar
and are thus not shown. The x and y axes of the figures represent the “emission
ratio”, defined as the ratios of the changed emissions to the emissions in
the base case. For example, an emission ratio of 0.7 means the emission of a
particular control variable accounts for 70 % that of the base case. The
color isopleths represent PM2.5 concentrations. The comparison shows
that the shapes of isopleths derived from both prediction systems generally
agree with each other. The agreement is very good for the case of
VOC + IVOC and POA,and for the cases of NOx and NH3 and
SO2 and NH3 when the emission ratios for NOx and NH3 are
larger than 0.2. Relatively large errors occur at very low
NOx / NH3 emission ratios (< 0.2) due primarily to an
extremely strong nonlinearity. Within these low emission ranges, the ERSM
technique can capture the general trends in PM2.5 concentrations in
response to emission changes, but the concentration gradients predicted by
ERSM are smaller than those given by conventional RSM. More studies are
needed to further improve the performance of ERSM at very low
NOx / NH3 emission ratios. Despite the existing errors, the
general consistency between RSM- and ERSM-predicted isopleths demonstrates the
stability of the ERSM prediction system. In other words, the discrepancies
between ERSM and CMAQ/2D-VBS cannot challenge the major conclusions on the
effectiveness of emission reductions. Finally, as stated in the last
paragraph, all sensitivity scenarios used in the following discussions have
emission ratios ≥ 0.2, since < 0.2 emission reductions are
quite rare as they are limited by the technologically feasible reduction potentials
(S. X. Wang et al., 2014).
Response of PM<inline-formula><mml:math id="M190" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> concentrations to emissions of air
pollutants
Having demonstrated the reliability of the ERSM prediction system, we employ
it to investigate the responses of PM2.5 concentrations to emissions of
various pollutants from different sectors and regions. We use PM2.5
sensitivity defined in Sect. 2.2 to quantitatively characterize the
sensitivity of PM2.5 concentrations to emission changes. Figure 4
illustrates the sensitivity of 4-month (January, March, July, and October)
mean PM2.5 concentrations to the stepped control of individual air
pollutants (a) and individual pollutant–sector combinations (b) in the BTH region, which are derived from the ERSM technique. The Fig. 4a can be obtained from both the RSM and ERSM prediction
systems, and their results are consistent, whereas Fig. 4b, as well
as the results shown in Figs. 5 and 6 can only be derived from ERSM. Among
all pollutants, the 4-month mean PM2.5 concentrations are the most sensitive
to the emissions of primary inorganic PM2.5 in all five regions, and the
PM2.5 sensitivities vary from 24 to 36 % according to region. When
primary inorganic PM2.5 emissions from various sectors are
differentiated, the industry sector is found to make the largest contribution
to PM2.5 concentrations, followed by the residential and commercial
sectors; the contribution of power plants is negligibly small because of
smaller emissions and higher stacks. The PM2.5 sensitivities to primarily
inorganic PM2.5 emissions remain constant at various reduction ratios.
Comparison of the 2-D isopleths of PM2.5 concentrations in
Beijing in response to the simultaneous changes in precursor emissions in all
five regions derived from the conventional RSM technique and the ERSM
technique. The x and y axes represent the emission ratio, defined as the
ratios of the changed emissions to the emissions in the base case. The color
contours represent PM2.5 concentrations (unit: µg m-3).
![]()
Sensitivity of 4-month mean PM2.5 concentrations to the stepped control of individual air pollutants (a) and individual
pollutant–sector combinations (b). The x axis shows the reduction
ratio (1 - emission ratio). The y axis shows PM2.5
sensitivity, which is defined as the change ratio of concentration divided by
the reduction ratio of emissions. The colored bars denote the PM2.5
sensitivities when a particular emission source is controlled while the
others stay the same as the base case; the black dotted line denotes the
PM2.5 sensitivity when all emission sources are controlled
simultaneously. The red stars represent PM2.5 concentrations in the base
case.
![]()
While primary inorganic PM2.5 makes the largest contribution to
PM2.5 concentrations among all air pollutants, the total contributions
of all precursors (NOx, SO2, NH3, NMVOC, IVOC, and POA), which
range between 31 and 48 %, exceed that of primary inorganic PM2.5
(24–36 %). Among the precursors, PM2.5 concentrations are primarily
sensitive to the emissions of NH3, NMVOC + IVOC, and POA, and their
relative importance differs according to reduction ratio. The PM2.5
sensitivity to NH3 increases substantially with the increase in
reduction ratio, primarily attributable to the transition from NH3-rich
to NH3-poor regimes when more controls are enforced. The PM2.5
sensitivities to POA and NMVOC + IVOC, however, decrease slightly with the
increase in reduction ratio. This is because, based on the gas-particle
absorptive partitioning theory, organics have a higher tendency to partition
into the particle phase at larger OA concentrations. As a result of the
nonlinearity, the PM2.5 sensitivities to POA and NMVOC + IVOC
emissions are larger than those to NH3 emissions at small reduction
ratios (e.g., 20 %), while it is the other way around at large reduction
ratios (e.g., 80 %).
The PM2.5 sensitivity to SO2 emissions is considerably smaller
compared with the three precursors above and does not change significantly as
a function of reduction ratio. From 2007 to 2014 (the base year of this
study), both SO2 emissions and SO42- concentrations in
PM2.5 have been continuously decreasing due to effective control
policies (Wang et al., 2017), which partly explains the small sensitivity of
PM2.5 to SO2 emissions. The response of PM2.5 concentrations
to NOx emissions could change from negative to positive with the
increase in reduction ratio, which has been reported in several previous
studies (Dong et al., 2014; Zhao et al., 2013c; Cai et al., 2017). Small
NOx emission reductions could lead to an increase in O3 and
HOx concentrations in several seasons owing to an NMVOC-limited
photochemical regime, which on the one hand enhances SO42- and SOA
formation and, on the other hand, could also increase NO3-
concentrations by accelerating the nocturnal formation of N2O5 and
HNO3 through the NO2 + O3 reaction at low temperatures. A
substantial reduction in NOx emissions, however, transforms the
NMVOC-limited regime to an NOx-limited regime, resulting in a successive
decline in concentrations of O3, HOx, and most PM2.5 chemical
components. Judging from our simulation results (Fig. 4), if only the
NOx emissions within the BTH region are controlled, a very large
reduction ratio of about 80 % is required to realize a reduction in
annual PM2.5 concentrations in most areas. However, the effects could be
distinctly different if NOx emissions outside the BTH region are jointly
reduced. Our previous studies using the CMAQ model (Zhao et al., 2013c; Wang
et al., 2010, 2011) have shown that uniform reductions in NOx emissions
in the whole of China by 23–50 % result in considerable annual
PM2.5 reduction over the BTH region. This is because NOx emission
reductions in upwind regions are more likely to result in a net PM2.5
decrease compared with local emission reductions, since the photochemistry
typically changes from an NMVOC-limited regime in local urban areas at the
surface to an NOx-limited regime in downwind areas or at upper levels
(Xing et al., 2011). The results shown in Fig. 4 also support the
abovementioned pattern and mechanism to some extent: even a 20 % NOx
emission reduction in BTH can lead to PM2.5 decrease in Northern Hebei
because, as the northernmost region in BTH, it is significantly affected by
emissions in other regions within BTH. Note that some recently discovered
chemical pathways are missing in the model, such as the oxidation of SO2
by NO2 in aerosol water and the SO2 heterogeneous reactions on the
dust surface (Fu et al., 2016; Cheng et al., 2016; G. H. Wang et al., 2016).
The incorporation of these processes in the model may affect the simulated
responses of PM2.5 to NOx and SO2 emissions. Regarding
emission sectors, the contributions of SO2 and NOx emissions are
dominated by “other sources” (sources other than LPS) because they emit
larger amount of pollutants at lower height compared with LPS.
The black dotted lines in Fig. 4 show the PM2.5 sensitivity when all
pollutants from all sectors are controlled simultaneously. The sum of
PM2.5 sensitivities to individual pollutant–sector combinations (stacked
columns) is mostly larger than the sensitivity to all pollutants and sectors
(black dotted lines), especially under large reduction ratios. This is mainly
attributed to the overlapping effect of two precursors (e.g., SO2 and
NH3) involved in the formation of ammonium sulfate and ammonium nitrate.
Nevertheless, at small reduction ratios, the sum of individual sensitivities
is sometimes smaller because the negative effects of reducing NOx are
mitigated when we simultaneously reduce NOx emissions from multiple
sectors as well as emissions of other air pollutants such as NMVOC. When all
pollutants and sectors are controlled together, the PM2.5 sensitivity
generally increases with reduction ratio, indicating that an additional air
quality benefit could be achieved, larger than expected from linear
extrapolation, if more control measures were implemented.
Figure 5 illustrates the PM2.5 sensitivities to individual
pollutant–sector combinations in each month. The source contribution features
are significantly discrepant in different months. The contributions of
primary inorganic PM2.5 emissions to PM2.5 concentrations are
notably higher in January than in other months, which is probably attributed
to weaker dilution and slower chemical reactions in January. Regarding
different emission sectors of primary inorganic PM2.5, the industrial
sector plays a dominant role in all months except January, when the
residential and commercial sectors make a similar or even larger contribution
as compared to the industrial sector. The higher contribution of the
residential and commercial sectors in January is on the one hand because of the
higher emissions due to heating, and, on the other hand, it is explained by weaker
vertical mixing in winter, which results in a larger relative contribution of
low-level sources. This result highlights the importance of residential and
commercial sources for PM2.5 pollution controls in the winter. The
contributions of precursors are dominated by POA and NMVOC + IVOC in
January, while in July, NOx, SO2, and NH3, which are known to
be precursors of secondary inorganic aerosols, make larger contributions than
POA and NMVOC + IVOC. The responses of PM2.5 concentrations to
NOx emissions can be the opposite in different seasons. Specifically, in
July, NOx emission reductions always induce a decrease in PM2.5
concentrations due to an NOx-limited photochemical regime. In January,
however, even an 80 % reduction in NOx emissions (roughly the maximum
technically feasible reduction ratio) could result in a net PM2.5
increase, as a result of a strong NMVOC-limited regime. To achieve a net
PM2.5 reduction in January, it would be necessary to simultaneously
reduce NOx emissions outside the BTH region.
Sensitivity of monthly mean PM2.5 concentrations to the stepped control of individual air pollutants from individual sectors in January,
March, July, and October. The meanings of x axis, y axis, colored bars,
black dotted lines, and red stars are the same as in Fig. 4.
![]()
Contributions of precursor (NOx, SO2, NH3, NMVOC,
IVOC, and POA) and primary inorganic PM2.5 emissions from individual
regions to PM2.5 concentrations. The contributions are quantified by
comparing the base case with sensitivity scenarios in which emissions from a
specific source are reduced by 80 %. This figure illustrates
contributions to 4-month mean PM2.5 concentrations and monthly mean
PM2.5 concentrations in January and July. The results for March and
October are given in Fig. S6.
![]()
We further evaluate the contributions of primary inorganic PM2.5 and
precursor emissions from various regions to PM2.5 concentrations
(Figs. 6 and S6). Here the contributions are quantified by comparing the base
case with sensitivity scenarios in which emissions from a specific source are
reduced by 80 %, which reaches the maximum technologically feasible
reduction ratios of major pollutants in most areas (S. X. Wang et al., 2014).
Obviously, the contributions of total primary inorganic PM2.5 emissions
in the BTH region are dominated by local sources, which account for over
75 % of the total primary inorganic PM2.5 contributions. When
precursor emissions are decomposed into different regions, local sources
usually also represent the largest contributors, but precursor emissions from
other regions (denoted by “regional precursor emissions” hereafter) could
also make significant contributions, depending on regions and seasons. The
precursor emissions from the northern part of BTH (e.g., Northern Hebei,
Beijing) mainly contribute to local PM2.5 concentrations, whereas those
from the southern part of BTH (e.g., Southern Hebei) significantly affect the
PM2.5 concentrations in both the local region and other regions. Over
the BTH, heavy pollution is frequently associated with southerly wind, while
strong northerly wind often blows away PM2.5 pollution (Jia et al.,
2008; Zheng et al., 2015), which explains the higher contribution of
emissions from southern BTH to other regions. Moreover, the importance of
regional precursor emissions relative to local ones is remarkably higher in
July than in January, which can be explained by the southerly monsoon and
stronger vertical mixing in summer that favors the interregional transport of
air pollutants. We also examine the contributions of emissions outside the
BTH region to PM2.5 concentrations in the five target regions. The
results reveal that these emissions contribute 24–33 % of the 4-month
mean PM2.5 concentrations, among which more than 80 % could be
attributed to precursor emissions. Among the 4 months, the contribution of
emissions outside BTH is considerably smaller in January (12–21 %) as
compared to other months (29–38 %).
Response of PM<inline-formula><mml:math id="M322" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> chemical components to emissions of air
pollutants
Ambient PM2.5 is comprised of complicated chemical components with
distinctly different formation pathways. To gain deeper insight into the
formation mechanisms and source attribution of PM2.5, we examine the
sensitivities of major PM2.5 components, including NO3-,
SO42-, and OA, to the stepped control of individual air pollutants, as
shown in Fig. 7 (January and July) and Fig. S7 (March and October).
NO3- concentrations are the most sensitive to NH3 emissions in all
months except July, when the sensitivities of NO3- concentrations to
NH3 and NOx emissions are similar. The NO3-
sensitivities to NOx emissions differ significantly according to season.
In most months, NO3- concentrations are positively correlated with
NOx emissions. In January, however, the sensitivities of NO3-
concentrations to NOx emissions are mostly negative and could be
positive at large reduction ratios, which can be explained by a very strong
NMVOC-limited photochemical regime and abundant ice water for the heterogeneous
formation of HNO3 from N2O5 at cold temperatures. The
sensitivities of NO3- to both NH3 and NOx emissions show
pronounced increasing trends with the increase in reduction ratio, in
agreement with the strong nonlinearity in these two pollutants described in
Sect. 3.2. NMVOC emissions make moderate positive contributions to
NO3-, with the largest and smallest contributions occurring in January
and July in conjunction with NMVOC-limited and NOx-limited photochemical
regimes, respectively. Finally, SO2 emissions have very small influences
on NO3- concentrations.
For SO42-, SO2 emissions represent the dominant contributor in
all months. The sensitivity of SO42- concentrations to SO2
emissions does not change significantly with respect to reduction ratio,
consistent with the results shown in Section 3.2. The contributions of
NH3 emissions to SO42- concentrations are quite small except in
October, when NH3 accounts for approximately one-fourth of the contribution
of SO2. NOx emissions affect SO42- concentrations mainly by
altering O3 and HOx concentrations, the effects of which are
positive in July at large reduction ratios and mostly negative in other
months. NMVOC emissions can make a small impact on SO42-
concentrations primarily through changing O3 and HOx
concentrations.
Sensitivity of monthly mean NO3-, SO42-, and OA
concentrations to the stepped control of individual air pollutants in January and
July. The meanings of x axis, y axis, colored bars, black dotted lines,
and red stars are the same as in Fig. 4 but for NO3-/SO42-/OA.
The results for March and October are given in Fig. S7.
![]()
The emissions of POA and NMVOC + IVOC are obviously two major contributors to
OA concentrations. The relative importance of the two is strongly dependent
on season. In July, POA and NMVOC + IVOC make similar contributions to OA
concentrations, while POA usually contributes more in other months. In
January, the contribution of POA could account for about 4 times that of
NMVOC + IVOC. The higher relative contribution of POA emissions in
January can be explained in several ways. First, the POA emissions are
relatively higher in January due to residential heating, while the NMVOC
emissions from solvent use and biogenic sources are higher in July. Second,
lower temperature in winter favors the partitioning of the semi-volatile
components comprising POA to the particle phase, whereas higher temperature
and stronger radiation in July accelerate the formation of SOA from
NMVOC + IVOC. Similar to SO42-, the impact of NOx emissions
on OA concentrations also works through two pathways. Besides the
abovementioned photochemical pathway, NOx emission reductions could lead
to OA increases due to the fact that SOA yield, defined as the ratio of SOA
formation to the consumption of a precursor, is generally higher at a
low-NOx condition than at a high-NOx condition. As an integrated
effect, the responses of OA concentrations to NOx emissions are negative
in most situations.
PM<inline-formula><mml:math id="M380" display="inline"><mml:msub><mml:mi/><mml:mn mathvariant="normal">2.5</mml:mn></mml:msub></mml:math></inline-formula> responses to emission reductions during heavy-pollution
episodes
Having shown the responses of monthly mean PM2.5 concentrations to
pollutant emissions, we are also interested in heavy-pollution episodes, in
which the source contributions could be quite different from the monthly mean
results, largely due to variations in meteorological conditions. To provide
more insight into the control strategies for heavy pollution, we use the ERSM
technique to investigate the source contribution features during three
typical heavy-pollution episodes. We first select 47 heavy-pollution episodes
over the BTH region during 2013–2015 (Table S7). Subsequently, we employ the
Hybrid Single Particle Lagrangian Integrated Trajectory (HYSPLIT) model
(Stein et al., 2015) and concentration weighted trajectory (CWT) method
(Cheng et al., 2013) to identify the potential source regions for PM2.5
during each episode and categorize these episodes according to their source
regions. We then select a representative episode from each of the three most
important pollution types in which the air mass primarily originates from
local areas (“Local” type), from the south (“South” type), and from the
southeast (“Southeast” type). We give preference to episodes within the
4-month simulation period of this study to facilitate a comparison with
the monthly mean source contribution features. For this reason, we select
(1) 5–7 January 2014, (2) 7–11 October 2014, and (3) 29–31 October 2014 as
representatives of the Local, South, and Southeast types. The
selection of heavy-pollution episodes is detailed in Sect. S2.
Figure 8 shows the contribution of precursor and primary inorganic PM2.5
emissions from individual regions to PM2.5 concentrations during the
three heavy-pollution episodes, and Fig. 9 illustrates the sensitivity of
PM2.5 concentrations to the stepped control of individual pollutant–sector
combinations. During 5–7 January 2014 (Local type), the contributions of
local emission sources to PM2.5 concentrations far exceed those from
other regions within BTH as well as from outside of BTH (Fig. 8). In contrast
to the monthly mean results (Sect. 3.2), the contributions of primary
inorganic PM2.5 emissions are comparable to, and even larger than, those
of precursor emissions in the BTH region. The total contributions of primary
PM2.5 (including POA) account for as much as 70–80 % of the
contributions of all pollutants within the BTH region, which highlights the
crucial importance of primary PM2.5 controls during this episode.
Moreover, the control of NMVOC, NH3, and SO2 emissions could
contribute moderately to reducing PM2.5 concentrations. However,
NOx emission reduction induces an increase in PM2.5 concentrations,
even at an 80 % reduction ratio. Therefore, effective temporary control
measures for this episode should focus on the control of local emissions,
with emphasis laid on primary PM2.5.
The contribution of precursor (NOx, SO2, NH3, NMVOC,
IVOC, and POA) and primary inorganic PM2.5 emissions from individual
regions to PM2.5 concentrations during three typical heavy-pollution
episodes.
![]()
Sensitivity of PM2.5 concentrations to the stepped control of
individual air pollutants from individual sectors during three
heavy-pollution episodes. The meanings of x axis, y axis, colored bars,
and black dotted lines are the same as in Fig. 4.
![]()
During 7–11 October 2014 (South type), the contributions of emissions
outside BTH to PM2.5 concentrations are as large as 33 % in Beijing and 40–50 % in other regions. Within the BTH region, the emissions from
Southern Hebei can have similar effects to local emissions on PM2.5
concentrations in Beijing, indicating a strong long-range transport from the
south. In addition, the total contributions of precursor emissions about
double those of primary inorganic PM2.5 emissions. Among all precursors,
PM2.5 concentrations are mainly sensitive to emissions of NH3,
NMVOC + IVOC, and POA. The sensitivity of PM2.5 concentrations to
NOx emissions increases dramatically with reduction ratio. Although
small NOx reductions may slightly elevate PM2.5 concentrations,
large NOx emission reduction (> 50 %) can result in
significant PM2.5 reduction. To effectively mitigate PM2.5
pollution during this episode, we should implement control measures for
precursor emissions in both the BTH region (especially the southern part) and
regions south of BTH. The NOx emissions, if controlled, should be
reduced by at least 50 % to avoid adverse side effect.
For 29–31 October 2014 (Southeast type), PM2.5 concentrations are
also significantly affected by emissions outside the BTH region. Within the
BTH region, the PM2.5 concentrations in Beijing and Northern Hebei are
about equally affected by local emissions and emissions from Eastern Hebei
and Southern Hebei, while local emissions play dominant roles in other
regions. The emissions of both precursor and primary inorganic PM2.5
within the BTH region make important contributions to PM2.5
concentrations, and the relative significance of the two is dependent on
region. All precursors except NOx can contribute considerably to
PM2.5 reductions, and the sensitivity of PM2.5 to NH3
increases rapidly with emission ratio. NOx emissions are negatively correlated
with PM2.5 concentrations in most cases. Regarding the temporary control
strategy for this episode, it is preferable to implement joint control of
primary PM2.5 and precursors both within and outside the BTH region,
with stringent measures over Eastern and Southern Hebei.
From the analysis above, we conclude that the source contributions are
tremendously different in these three episodes, which have been demonstrated
to represent some key features of the corresponding pollution types
(Local, South, and Southeast types). Therefore, episode-specific
control strategies need to be formulated based on the source contribution
features of individual pollution types. Nevertheless, the results of this
study are not yet sufficient to guide the development of temporary control
strategies for all heavy-pollution episodes because the conclusions drawn
from the three episodes may not be generalized to pollution types. In future
studies, we need to simulate more episodes to improve their classification
and to comprehensively understand the source contribution features of each
pollution type. For a coming heavy-pollution episode, we can predict its
pollution type using an air quality forecasting model and subsequently
formulate the temporary control strategies based on the source contribution
features of this specific pollution type.
Conclusion and implications
In the present study, we investigated the nonlinear response of PM2.5
concentrations to emission changes in multiple pollutants from different
sectors and regions over the BTH region, using the ERSM technique coupled
with the CMAQ/2D-VBS model.
Among all pollutants, primary inorganic PM2.5 makes the largest
contribution (24–36 %) to the 4-month mean PM2.5 concentrations.
The contribution from primary inorganic PM2.5 is especially high in
heavily polluted winter and is dominated by the industry as well as
residential and commercial sectors. The total contributions of all precursors
to PM2.5 concentrations range between 31 and 48 %. Among the
precursors, PM2.5 concentrations are primarily sensitive to the
emissions of NH3, NMVOC + IVOC, and POA. With the increase in reduction
ratio, the sensitivities of PM2.5 concentrations to pollutant emissions
remain roughly constant for primary inorganic PM2.5 and SO2,
increase substantially for NH3 and NOx, and decrease slightly for
POA and NMVOC + IVOC. The contributions of primary inorganic PM2.5
emissions to PM2.5 concentrations are dominated by local emission
sources, which account for over 75 % of the total primary inorganic
PM2.5 contributions. For precursors, however, emissions from other
regions could play similar roles to local emission sources in the summer and
over the northern part of BTH. Different PM2.5 chemical components are
associated with distinct source contribution features. The NO3- and
SO42- concentrations are the most sensitive to emissions of NH3 and
SO2, respectively. The emissions of the POA and NMVOC + IVOC are two
major contributors to OA concentrations, with their relative importance
depending on season.
The source contribution features are significantly different for three
typical heavy-pollution episodes, which belong to three distinct pollution
types. The PM2.5 concentrations in the first episode (Local type)
are dominated by local sources and primary PM2.5 emissions, while the
second episode (South type) is primarily affected by precursor emissions
from local and southern regions. The third episode (Southeast type) is
significantly influenced by emissions of both primary inorganic
PM2.5 and precursors from multiple regions. Future investigations are needed to
acquire generalized patterns for the source contributions of various
heavy-pollution types.
The results of the present study have important implications for PM2.5
control policies over the BTH region. First, the control of primary
PM2.5 emissions should be a priority in PM2.5 control strategies.
Primary PM2.5, including primary inorganic PM2.5 and POA,
contribute over half of the 4-month mean PM2.5 concentrations, which is
even higher in the winter when heavy pollution frequently occurs. The
industry sector and the residential and commercial sectors represent 85 %
of the total primary PM2.5 emissions and therefore should be the focus
of primary PM2.5 controls. In particular, we should pay special
attention to the residential and commercial sectors, which account for half
of the total contribution of primary PM2.5 emissions to PM2.5
concentrations in the winter but have been frequently neglected in China's
previous control policies. Second, the control policies for NMVOC and IVOC
emissions should be strengthened. The sensitivity of PM2.5
concentrations to NMVOC + IVOC is one of the largest among all
precursors. In particular, the control of NMVOC and IVOC emissions is very
effective for PM2.5 reduction even at the initial control stage, as
indicated by the large sensitivity at small reduction ratios. Moreover, NMVOC
reduction is also crucial for the mitigation of O3 pollution considering
an NMVOC-limited regime over the urban region and its surrounding areas (Xing et al.,
2011). Third, NOx emissions should be substantially reduced in both the
BTH and other parts of China; in the long run, the reduction ratio should
preferably approach their maximum feasible reduction levels. Fourth, more
stringent control policies should be enforced in Southern Hebei, which on the one hand suffers from the most severe PM2.5 pollution (L. T. Wang et al., 2014)
and, on the other hand, significantly affects both local and regional
PM2.5 concentrations. Last but not least, considering the distinct
source contributions in different heavy-pollution episodes, episode-specific
temporary control strategies should be formulated according to the source
contribution feature of the specific pollution type.
The present study has a few limitations. First, the establishment of ERSM
requires several hundred or over 1000 emission scenarios, although the
scenario number needed for a specific number of control variables has already
been dramatically reduced as compared to the conventional RSM technique.
Studies are needed to further reduce the scenario number but retain the
accuracy of the ERSM technique. Second, the current ERSM is developed based
on the meteorological conditions simulated for the base year and has not
considered the impact of interannual variations in meteorological conditions
on the relationships between emissions and PM2.5 concentrations. Third,
although the ERSM-predicted responses of PM2.5 concentrations to
precursor emissions have been demonstrated to agree well with chemical
transport model simulations, evaluating the predicted responses against the
actual situation in the real atmosphere still represents a major challenge because it is extremely difficult to artificially perturb emissions in the
atmosphere. Last but not the least, the NMVOC and IVOC emissions have been
lumped together in this study to reduce the number of control variables.
Considering their differences in sources and SOA formation potentials (Jathar
et al., 2014; Wu et al., 2017), a detailed quantification of the individual
contributions of NMVOC and IVOC emissions from various sources to PM2.5
concentrations is required in the future to better inform NMVOC/IVOC control
policies.