We developed a top-down methodology combining the inversed chemistry
transport modeling and satellite-derived tropospheric vertical column of
NO2 and estimated the NOx emissions of the Yangtze River Delta (YRD)
region at a horizontal resolution of 9 km for January, April, July, and
October 2016. The effect of the top-down emission estimation on air quality
modeling and the response of ambient ozone (O3) and inorganic aerosols
(SO42-, NO3-, and NH4+, SNA) to the changed
precursor emissions were evaluated with the Community Multi-scale Air
Quality (CMAQ) system. The top-down estimates of NOx emissions were
smaller than those (i.e., the
bottom-up estimates) in a national emission inventory, Multi-resolution Emission Inventory for China (MEIC), for all the 4 months, and the monthly mean was
calculated to be 260.0 Gg/month, 24 % less than the bottom-up one. The
NO2 concentrations simulated with the bottom-up estimate of NOx
emissions were clearly higher than the ground observations, indicating the
possible overestimation in the current emission inventory, attributed to its
insufficient consideration of recent emission control in the region. The
model performance based on top-down estimate was much better, and the
biggest change was found for July, with the normalized mean bias (NMB) and
normalized mean error (NME) reduced from 111 % to -0.4 % and from
111 % to 33 %, respectively. The results demonstrate the improvement of
NOx emission estimation with the nonlinear inversed modeling and
satellite observation constraint. With the smaller NOx emissions in the
top-down estimate than the bottom-up one, the elevated concentrations of
ambient O3 were simulated for most of the YRD, and they were closer to
observations except for July, implying the VOC (volatile organic
compound)-limited regime of O3 formation. With available ground
observations of SNA in the YRD, moreover, better model performance of
NO3- and NH4+ was achieved for most seasons, implying
the effectiveness of precursor emission estimation on the simulation of
secondary inorganic aerosols. Through the sensitivity analysis of O3
formation for April 2016, the decreased O3 concentrations were found
for most of the YRD region when only VOC emissions were reduced or the reduced
rate of VOC emissions was 2 times of that of NOx, implying the
crucial role of VOC control in O3 pollution abatement. The SNA level
for January 2016 was simulated to decline 12 % when 30 % of NH3
emissions were reduced, while the change was much smaller with the same
reduced rate for SO2 or NOx. The result suggests that reducing
NH3 emissions was the most effective way to alleviate SNA pollution of the
YRD in winter.
Introduction
Nitrogen oxides (NOx= NO2+ NO) play an important role in the
formation of ambient ozone (O3) and inorganic aerosols
(SO42-, NO3-, and NH4+, SNA). The NOx
emission inventory is a necessary input of the air quality model (AQM) and
has a great influence on NO2, O3, and SNA simulation (Zhou et al.,
2017; Chen et al., 2019a). Moreover, it is crucial for exploring the sources
of atmospheric pollution of O3 and fine particles (particles with
aerodynamic diameter smaller than 2.5 µm, PM2.5) with the AQM. The
inventories were usually developed with a bottom-up method, in which the
emissions were calculated based on the activity data (e.g., fuel consumption
and industrial production) and emission factors (the emissions per unit of
activity data) by source category and region. Bias existed commonly in the
bottom-up inventories, due mainly to the uncertainty of economic and energy
statistics and to the fast changes in the emission control measures,
especially in developing countries like China (Granier et al., 2011; Saikawa
et al., 2017; Zhang et al., 2019).
To improve the emission estimation, an inversed top-down method has been
developed based on satellite observations and the AQM (Martin et al., 2003; Zhao
and Wang, 2009; Zyrichidou et al., 2015; Yang et al., 2019a). The emissions
were corrected based on the difference between the modeled and observed
tropospheric vertical column densities (TVCDs) of NO2 and the response
coefficient of NO2 TVCDs to emissions (Martin et al., 2003; Cooper et
al., 2017). With higher temporal and spatial resolution than other
instruments, the NO2 TVCDs from the Ozone Monitoring Instrument (OMI) were
frequently used (Kurokawa et al., 2009; Gu et al., 2014; de Foy et al.,
2015; Kong et al., 2019; Yang et al., 2019a).
Currently, top-down methods are mainly developed at the global or
national scale with relatively coarse horizontal resolution (Martin et al.,
2003; Miyazaki et al., 2012; Jena et al., 2014; Gu et al., 2014). At the
global scale, for example, Martin et al. (2003) and Miyazaki et al. (2012)
estimated the NOx emissions at the horizontal resolution of
2∘× 2.5∘ and 2.8∘× 2.8∘, respectively. Martin et al. (2003) found that the
satellite-derived NOx emissions for 1996–1997 were 50 %–100 % larger
than the bottom-up estimates in the Po Valley, Tehran, and Riyadh urban
areas. Miyazaki et al. (2012) suggested that the bottom-up method
underestimated the NOx emissions over eastern China, the eastern United
States, southern Africa, and central–western Europe. At the national scale
with the horizontal resolution of 0.5∘× 0.5∘,
the annual NOx emissions in India 2015 derived with the top-down method
were 7 %–60 % smaller than various bottom-up estimates (Jena et al., 2014).
With the TVCDs from OMI and another instrument (Global Ozone Monitoring
Experiment, GOME), the difference in national NOx emissions for China
was quantified to be 0.4 Tg N/yr (5.8 % relative to OMI) at the resolution of
70 km × 70 km (Gu et al., 2014). Compared to national and regional
ones, limited estimates were available at the regional scale with finer
resolution. In China, great differences exist in the levels and patterns of
air pollution across the regions, attributed partly to a big variety of air
pollutant sources across the country. To achieve the target of air quality
improvement required by the central government, varied air pollution control
plans were usually developed and implemented at the city/provincial level.
Therefore, the top-down estimates in NOx emissions at finer horizontal
resolution are greatly needed for understanding the primary sources of
NO2 pollution and demonstrating the effect of emission control at the
regional scale.
At present, the reliability and rationality of the top-down emission
estimates are commonly evaluated with the AQM and satellite observations. For
example, the bias between the NO2 TVCDs from OMI observations and the AQM
based on the top-down NOx emission estimation was -30.8 ± 69.6 × 1013 molec. cm-2 in winter in India (Jena et
al., 2014). The linear correlation coefficient (R2) between OMI and the AQM
with the top-down emission estimates could reach 0.84 in Europe (Visser et
al., 2019). Compared to the satellite observations with relatively large
uncertainty (Yang et al., 2019b; Liu et al., 2019), surface concentrations
that better represent the effect of air pollution on human health and the
ecosystems were less applied in the evaluation of the top-down estimates of
NOx emissions. Limited assessments were conducted at the national
scale. For example, Liu et al. (2018) found that the normalized mean error
(NME) between the observed and simulated NO2 concentrations based on
the top-down estimate of NOx emissions could reach 32 % in China at
the resolution of 0.25∘× 0.25∘. Besides
NO2, the estimation of NOx emissions also plays an important and
complicated role in secondary air pollutant simulation including O3 and
SNA, and the response of secondary pollution to the primary emissions was
commonly nonlinear. The simulated O3 concentrations in Shanghai (the
most developed city in eastern China) could increase over 20 %, with a
60 % reduction in NOx emissions in summer 2016, implying a clear VOC (volatile organic
compound)-limited pattern for the O3 formation in the megacity (Wang et
al., 2019). For the response of SNA to NOx emissions, the
NH4+ and SO42- concentrations at an urban site in
another megacity Nanjing in eastern China were simulated to increase
1.9 % and 2.8 %, with a 40 % abatement of NOx emissions in autumn
2014, respectively, due to the weakened competition of SNA formation against
SO2 (Zhao et al., 2020). To the best of our knowledge, however, the relatively new
information from the inversed modeling of NOx emissions has not been
sufficiently incorporated into the SNA and O3 analyses with the AQM in
China.
Located in eastern China, the Yangtze River Delta (YRD) region, including the
city of Shanghai and the provinces of Anhui, Jiangsu, and Zhejiang, is one of
the most developed and heavy-polluted regions in the country. The air
quality for most cities in the YRD failed to meet the National Ambient Air Quality
Standard (NAAQS) Class II in 2016 (MEPPRC, 2017). NOx emissions made
great contributions to the severe air pollution in the region. Based on an
offline sampling and measurement study, for example, the annual average of
the NO3- mass fraction to total PM2.5 reached 19 % in
Shanghai in 2014, and it was significantly elevated in the pollution event
periods (Ming et al., 2017). In this study, we chose the YRD to estimate the
NOx emissions with the inversed method and to explore their influence
on the air quality modeling. The top-down estimates of NOx emissions
were firstly obtained with the nonlinear inversed method and OMI-derived
NO2 TVCDs for 2016. The advantage of the top-down estimation against
the bottom-up one was then evaluated with the AQM and abundant ground-based
NO2 concentrations. The influences of the top-down estimation in
NOx emissions were further detected on O3 and SNA modeling.
Sensitivity analyses were conducted by changing the emissions of precursors
to investigate the sources and potential control approaches of O3 and
SNA pollution for the region.
Data and methodsThe top-down estimation of NOx emissions
The top-down estimation of NOx emissions was conducted for January,
April, July, and October of 2016, representing the situations of the four
seasons in the YRD region, and the horizontal resolution was 9 km ×9 km. The inversed method assumed a nonlinear and variable correlation between
NOx emissions and NO2 TVCDs (Cooper et al., 2017), and the a
posterior daily emissions (top-down estimates) were calculated with the
following equation:
Et=Ea1+Ωo-ΩaΩoβ,
where Et and Ea represent the a posterior and the a prior daily
NOx emissions, respectively; Ωo and Ωa represent the observed and simulated NO2 TVCDs, respectively; β represents the response coefficient of the simulated NO2 TVCDs to a
specific change in emissions and was calculated based on the simulated
changes in TVCDs (ΔΩ) from a 10 % change in emissions
(ΔE).
The inversed method assumed that the daily emissions were similar. For a
given month, the a posterior daily emissions were used as the a priori
emissions of the next day, and the monthly top-down estimate of the NOx
emissions was scaled from the average of the a posterior daily emissions of
the last 3 d in the month, as the top-down estimate of daily NOx
emissions usually converged within a 1-month simulation period (Zhao and
Wang, 2009; Yang et al., 2019b). In our previous work (Yang et al., 2019b),
we demonstrated the robustness of the method, by applying the “synthetic”
TVCDs from air quality simulation based on a hypothetical “true” emission
inventory, instead of those from satellite observations. We found that
sufficient iteration times could result in a relatively constant emission
estimate (the top-down estimate) close to the “true” emission input. From
a bottom-up perspective, the difference in NOx emissions between
weekdays and the weekend was within 5 % in the YRD region (Zhou et al., 2017),
indicating an insignificant bias from the ignorance of the daily variation
of emissions.
The NO2 TVCDs were from OMI on board the Aura satellite. It crosses the
Equator at 13:30 local time. The horizontal resolution of OMI was 24 km × 13 km at nadir (Levelt et al., 2006), one of the finest
resolutions available for NO2 TVCD observations before October 2017. We
applied the Peking University Ozone Monitoring Instrument NO2 product
(POMINO v1; Lin et al., 2014, 2015) to constrain the NOx
emissions. POMINO v1 modified the retrieval methodology of the Dutch Ozone
Monitoring Instrument NO2 product (DOMINO v2) in China and provided
better linear correlation of NO2 TVCDs between the satellite and
available ground-based observations using multi-axis differential optical
absorption spectroscopy (MAX-DOAS) (Lin et al., 2015). The original NO2
TVCDs from POMINO v1 (level 2) were resampled into an 18 km × 18 km grid system based on the area weight method and then downscaled to
9 km × 9 km with Kriging interpolation. As an example, the NO2
TVCDs for July 2016 in the YRD are shown in Fig. S1 in the Supplement, and
larger TVCDs were found in the east–central YRD.
The Models-3 Community Multi-scale Air Quality (CMAQ) version 5.1 was used
to conduct the inversed modeling of NOx emission estimation and to
simulate the ground-level concentrations of NO2, O3, and SNA. As a
three-dimensional Eulerian model, CMAQ includes complex interactions of
atmospheric chemistry and physics and is one of the most widely applied AQMs
to evaluate the sources and processes of air pollution in China (UNC, 2012;
Xing et al., 2015; Zheng et al., 2017). As shown in Fig. 1, the two nested
modeling domains were applied with their horizontal resolutions set to 27 km and 9 km, respectively. The mother domain (D1, 177 cells × 127 cells) included
most parts of China, and the second (D2, 118 × 121 cells) covered
the YRD region. The model included 28 vertical layers, and the height of the
first layer (ground layer) was approximately 60 m. The carbon bond gas-phase
mechanism (CB05) and AERO6 aerosol module were used in the CMAQ. The initial
concentrations and boundary conditions for the D1 were derived from the
default clean profile, while those of D2 were extracted from the CMAQ
Chemistry Transport Model (CCTM) outputs of its mother domain. The first 5 d of each simulated month were chosen as the spin-up period. Details on
model configuration were described in Zhou et al. (2017) and Yang and Zhao
(2019).
The Multi-resolution Emission Inventory for China (MEIC; http://www.meicmodel.org/, last access: 20 January 2020) for 2015 was applied as the initial input of
anthropogenic emissions in D1 and D2, with an original horizontal resolution
of 0.1∘× 0.1∘. In this study, the MEIC
emissions from residential sources were downscaled to the horizontal
resolution of 9 km × 9 km, based on the spatial density of population,
and those from power, industry, and transportation based on the spatial
distribution of gross domestic product (GDP). The NOx emissions from
soil were originally obtained from Yienger and Levy (1995) and were doubled
as advised by Zhao and Wang (2009). The emissions of Cl, HCl, and lightning
NOx were collected from the Global Emissions Initiative (GEIA; Price et
al., 1997). Biogenic emissions were derived from the Model Emissions of
Gases and Aerosols from Nature developed under the Monitoring Atmospheric
Composition and Climate project (MEGAN MACC; Sindelarova et al., 2014).
Meteorological fields were provided by the Weather Research and Forecasting (WRF)
model version 3.4, a state-of-the-art atmospheric modeling system
designed for both numerical weather prediction and meteorological research
(Skamarock et al., 2008). The simulated parameters from WRF for D2 in
January, April, July, and October 2016 were compared with the observation
dataset of the US National Climate Data Center (NCDC), as summarized in Table S1
in the Supplement. The index of agreement (IOA) of wind speed for the 4
months between the two datasets was larger than 0.8. The root mean square
error (RMSE) of wind directions for the 4 months was smaller than
40∘, and the index of agreement (IOA) of temperature and relative
humidity between the two datasets was larger than 0.8 and 0.7, respectively.
The simulated meteorological parameters in D2 could reach the benchmarks
derived from Emery et al. (2001) and Jiménez et al. (2006).
The hourly NO2 and O3 concentrations were observed at 230
state-operated stations of air quality monitoring in 41 cities within the
YRD region, and they were applied to evaluate the model performance.
Locations of the stations are indicated in Fig. 1, and the observation
data were derived from the China National Environmental Monitoring Center
(CNEMC; http://www.cnemc.cn/, last access: 20 January 2020). The observations of SO42-,
NO3-, and NH4+ (SNA) concentrations in PM2.5 for the
YRD region during 2015–2017 were collected and applied to evaluate the
influence of the top-down estimation of NOx emissions on SNA
simulation. In particular, the hourly SNA concentrations of PM2.5 at
the Jiangsu Provincial Academy of Environmental Science, an urban site in the
capital city of Jiangsu Province, Nanjing (JSPAES; Chen et al., 2019b), were
observed with the Monitor for Aerosols and Gases in ambient Air (MARGA;
Metrohm, Switzerland) for January, April, July, and October 2016. Meanwhile,
the daily average concentrations of SNA were also available from MARGA
measurement for the 4 months at the Station for Observing Regional
Processes and the Earth System, a suburban site in eastern Nanjing (SORPES;
Ding et al., 2019). In addition, the seasonal average concentrations of SNA were
available at another four sites in the YRD, including the Nanjing University of
Information Science & Technology site in Nanjing (NUIST; Zhang, 2017),
and three sites respectively in the cities of Hangzhou (HZS; Li, 2018),
Changzhou (CZS; Liu et al., 2018), and Suzhou (SZS; Wang et al., 2016).
Details of the collected SNA measurement studies are summarized in Table S2
in the Supplement, and the locations of those sites are illustrated in
Fig. 1.
Scenario setting of sensitivity analysis
In general, there are two categories of chemical regimes (VOC-limited and
NOx-limited) in O3 formation (Wang et al., 2010; Jin et al., 2017). In
the VOC-limited regime, growth in O3 concentrations occurs with
increased VOC emissions and declined NOx emissions, while the
increased NOx emissions result in enhancement of O3 concentrations
in the NOx-limited regime. To explore the sources and potential control
approaches of O3 pollution, the sensitivity of O3 formation to its
precursor emissions for April was analyzed with CMAQ modeling in the YRD
region. In the YRD, the peaking time of O3 concentration has gradually
moved from summer to late spring, and the mean observed O3
concentration in April was 72.5 µg/m3, slightly higher than that
in July (71.9 µg/m3). In addition, the model performance of
O3 was better for April than that for July in this work (see details in
Sect. 3.2). Therefore, we selected April to explore the sensitivity
analysis of O3 formation in the region. As summarized in Table S3 in
the Supplement, eight cases were set besides the base scenario with the
top-down NOx estimates for April 2016. Cases 1 and 6 reduced only the
NOx emissions by 30 % and 60 %, and Cases 2 and 7 reduced only the
VOC emissions by 30 % and 60 %, respectively. To explore the
co-effect of VOCs and NOx emission controls on O3 concentrations,
cases with different reduction rates of VOCs and NOx emissions were
designed. The emissions of NOx and VOCs in Case 4 were decreased by
30 % and 60 % and in Case 5 by 60 % and 30 %, respectively. Both
NOx and VOC emissions were reduced 30 % and 60 % in Cases 3 and 8,
respectively.
The response of SNA concentrations to the changes in precursor emissions was
influenced by various factors including the abundance of NH3, atmospheric oxidation, and the chemical regime of O3 formation (Wang
et al., 2013; Cheng et al., 2016; Zhao et al., 2020). To explore the
sensitivity of SNA formation to its precursor emissions, four cases were set
besides the base scenario for January 2016, the month with the largest
observed SNA concentrations. As shown in Table S4 in the Supplement, the
emissions of NOx, SO2, and NH3 were reduced by 30 % in
Cases 9–11, respectively, and the emissions of NOx, SO2, and
NH3 were simultaneously decreased by 30 % in Case 12.
The bottom-up and top-down estimates of NOx emissions by
month for the YRD region in 2016.
Results and discussionEvaluation of the bottom-up and top-down estimates of NOx emissions
Figure 2 compares the magnitude of the NOx emissions estimated based on
the bottom-up (MEIC) and top-down methods by month in the YRD region. The
top-down estimates were smaller than the bottom-up ones for all the
4 months concerned, and the average of the monthly NOx emissions
was calculated to be 260.0 Gg/month for 2016 with the top-down method, 24 % smaller than the bottom-up estimation. The comparison indicates a probable
overestimation in NOx emissions with the current bottom-up methodology,
attributed partly to the insufficient consideration of the effect of recent
control on emission abatement. Stringent measures have gradually been
conducted to improve the local air quality in the YRD region. For example,
the “ultra-low” emission policy for the power sector started in 2015,
requiring the NOx concentration in the flue gas of coal-fired units
to be the same as that of gas-fired units. Technology retrofitting on power
units has been widely conducted, significantly improving the NOx removal
efficiencies of selective catalytic reduction (SCR) systems. These detailed
changes in emission control, however, could not be fully incorporated in a timely manner into the national emission inventory that relies more on the
routinely reported information and policy of environmental management over
the country. With the online data from continuous emission monitoring
systems (CEMS) incorporated, NOx emissions from the power sector were
estimated to be 53 % smaller than MEIC for China in 2015 in our
previous work (Zhang et al., 2019). The bias between the top-down and
bottom-up estimates could be larger in earlier years and reduced more
recently. According to Yang et al. (2019b) and Qu et al. (2017), for
example, the top-down NOx emissions were 44 % and 31 % smaller than
bottom-up ones for the YRD region and the whole of China in 2012. Benefiting
from the better data availability, the bottom-up inventory has been improved
with the inclusion of more information on individual power and industrial
plants for recent years (Zheng et al., 2018).
The spatial differences between the bottom-up and top-down
estimates of NOx emissions for January, April, July, and October 2016
(top-down minus bottom-up, in units of mol N/s).
The differences in the spatial distribution of NOx emissions between
the bottom-up and top-down estimates are illustrated by month for the YRD in
Fig. 3. The top-down estimates were commonly smaller than the bottom-up
ones in the east–central YRD, with intensive manufacturing industry and
high population, and larger than those in most of Zhejiang Province, with more
hilly and suburban regions. The bias might result from the following issues.
From a bottom-up perspective, on the one hand, more stringent control measures
were preferentially conducted for power and industrial plants in regions
with heavier air pollution like the east–central YRD. As mentioned above, the
effects of such actions were difficult to fully track in the bottom-up
inventory, leading to the overestimation of emissions for those regions. Due
to the lack of precise locations of individual industrial plants (except for
large point sources), moreover, the spatial allocation of the emissions
relied commonly on the densities of population and economy, assuming a
strong correlation with emissions for them. Such an assumption, however, would
not still hold in recent years, as a number of factories in the relatively
developed region have been moved to less developed suburban regions (e.g.,
southern Zhejiang) for both environmental and economic purposes. The
insufficient consideration of the moving of emission sources is thus
expected to result in the overestimation of emissions for developed regions and
underestimation for the less developed. On the other hand, the
satellite-derived TVCDs were relatively small in southern Zhejiang (Fig. S1), and a larger error in the satellite retrieval, and thereby emissions
constrained with the inversed modeling, was expected.
The observed and simulated hourly NO2 concentrations based on
the bottom-up and top-down NOx emissions for January, April, July, and
October 2016.
Figure 4 illustrates the observed and simulated hourly NO2
concentrations using the bottom-up and top-down estimates of NOx
emissions in the CMAQ by month. The NO2 concentrations simulated with
the bottom-up estimates were clearly larger than the observations in all the
4 months concerned, with the largest and smallest normalized mean bias
(NMB) reaching 111 % and 34 % for July and January, respectively. The
result suggests again the overestimation in NOx emissions in the
current bottom-up inventory for the YRD. The model performance based on the
top-down estimates was much better than that based on the bottom-up ones,
indicating that the inversed modeling with the satellite observation constraint
effectively improved the estimation of NOx emissions. The biggest
improvement was found for July, with the NMB reduced from 111 % to
-0.4 % and the NME reduced from 111 % to 33 %. As shown in Fig. 2, a relatively big reduction from the bottom-up to top-down estimation in
NOx emissions was found for July compared to most of the other months.
Scatter plots of the annual means of the observed and simulated surface
NO2 concentrations are shown in Fig. S2 in the Supplement. The slope
between the observations and simulation with the top-down estimate (0.99) was
much closer to 1 than that with the bottom-up one (1.57), indicating clearly
the advantage of the top-down method for constraining the magnitude of
the total emissions in the YRD region. The difference in the two slopes
implies that the surface NO2 concentrations simulated with the
bottom-up estimation were over 50 % larger than those based on top-down
ones. As a comparison, the total emissions in the bottom-up inventory were
only 30 % larger than the top-down estimation for the whole YRD region.
The larger overestimation in the concentrations than the emissions from the
bottom-up inventory could result partly from the bias of the locations of
state-operated ground observation sites. Most of these sites were located in
the urban areas where excess emissions were allocated according to the high
density of economy and population, and elevated concentrations were thus
simulated compared to rural areas. The similar correlation coefficients (R)
suggested that the spatial distribution of NOx emissions was not
greatly improved in the top-down estimation on an annual basis of urban
observations. Uncertainty existed in the satellite observations: the NMB
between NO2 TVCDs in POMINO and available ground-based MAX-DOAS
observations was 21 % on cloud-free days (Liu et al., 2019). Due mainly to
the NOx transport, moreover, a bias of 13 %–33 % on the spatial
distribution of emissions was estimated for the inversed method at the
horizontal resolution of 9 km or finer (Yang et al., 2019b). Inclusion of
more available observations in rural areas helps improve the comprehensive
evaluation of emission estimation.
The spatial distribution of the simulated monthly mean NO2
concentration with the top-down estimates and differences between the
simulations with the top-down and bottom-up NOx emissions in January,
April, July, and October 2016 (top-down minus bottom-up).
Figure 5 illustrates the spatial distribution of monthly mean NO2
concentrations simulated based on the top-down estimates and the differences
between the simulations with the top-down and bottom-up ones. The larger
NO2 concentrations existed in the east–central YRD for all the months
(left column in Fig. 5), and the difference in the spatial distribution of
NO2 concentrations (right column in Fig. 5) was similar to that in
NOx emissions (Fig. 3). Larger reduction in NO2 concentrations
based on the top-down estimates was commonly found in the east–central YRD,
while the increased concentrations were found in most of Zhejiang.
The observed and simulated hourly O3 concentrations with the
bottom-up and top-down NOx emission estimates for January, April, July,
and October 2016.
Evaluation of the O3 simulation based on the top-down NOx
estimates
Figure 6 shows the observed and simulated hourly O3 concentrations
based on the bottom-up and top-down estimates of NOx emissions by
month. Indicated by the smaller NMBs and NMEs, the model performance of
O3 based on the top-down estimates was better than that based on the
bottom-up ones for most months. It suggests that the constrained NOx
emissions with satellite observations could play an important role in the
improvement of O3 simulation. The largest improvement was found in
January, for which the NMB and NME were changed from -44 % and 49 % to
13 % and 40 %, respectively, attributed to the biggest change in
NOx emissions between the top-down and bottom-up estimates for the
month. The worse O3 modeling performance was found for July when the
top-down estimate instead of the bottom-up one was applied in the
simulation, indicated by the increased NMB and NME. Since the top-down
estimation of NOx emissions was justified by the improved NO2
simulation in July (Fig. 4c), the worse O3 simulation might result from
the uncertainty in emissions of the volatile organic compounds (VOCs) and
the chemical mechanism of the AQM in summer. As suggested by Li (2019), the
biogenic VOC (BVOC) emissions of the YRD region could be overestimated by
121 % in summer attributed to ignoring the effect of droughts, and such
overestimation might elevate the O3 concentrations in the AQM. In order to
explore the influence of the uncertainty of BVOC emissions on O3 model
performance, we conducted an extra case in which the BVOC emissions were
cut by 50 % in CMAQ. As shown in Fig. S3 in the Supplement, the NMB
between the observed and simulated O3 based on the top-down estimate of
NOx emissions and the reduced BVOC emissions declined 27 % in July.
However, it was still larger than the NMB at 1.1 % when the bottom-up
estimate of NOx emissions was applied (Fig. 4c). This comparison thus
suggested that the complicated mechanism for summer O3 formation was
insufficiently considered in the current model. A recent study conducted an
intercomparison of surface-level O3 simulation from 14 state-of-the-art
chemical transport models and implied that the larger overestimation of
summer O3 than winter for eastern China resulted possibly from the
uncertainty in the photochemical treatment in models (Li et al., 2019).
The spatial distribution of the simulated monthly mean O3
concentration with the top-down NOx estimates and the spatial
differences between the simulations with the top-down and bottom-up NOx
emissions in January, April, July, and October 2016 (top-down minus
bottom-up).
The model performance statistics of daily maximum 8 h averaged
(MDA8) O3 concentrations in January, April, July, and October 2016 with
the bottom-up and top-down NOx emissions.
Table 1 summarizes the observed and simulated daily maximum 8 h averaged
(MDA8) O3 concentrations based on the bottom-up and top-down estimates
of NOx emissions and summarized by month for the YRD region. The MDA8
O3 concentrations simulated with the top-down estimates were larger
than those with the bottom-up ones and were closer to the observations for
most months. As most of the YRD was identified as a VOC-limited region (Li
et al., 2012; Zhou et al., 2017), the reduced NOx emissions with the
top-down method enhanced the O3 levels in the AQM. Similar to the
hourly concentrations, the most significant improvement for MDA8 was found
in January, with the NMB and NME reduced from -35 % and 39 % to 11 % and 28 %, respectively. Moreover, the improvement of April and October for
MDA8 was larger than that for the hourly concentrations, indicating that the
improved NOx emissions were more beneficial for the simulation of
daytime peak O3 concentrations in spring and winter. Figure 7
illustrates the spatial distribution of the monthly mean O3
concentrations simulated based on the top-down NOx estimates and the
differences between the simulations with the top-down and bottom-up
estimates by month. In contrast to NO2, the smaller O3
concentrations existed in the east–central YRD for most months, as it was
identified as a VOC-limited region with a relatively high NO2 level
(Wang et al., 2019). Larger O3 concentrations were found for the
surrounding regions in the YRD, e.g., southern Zhejiang, attributed partly
to the relatively abundant BVOC emissions (Li, 2019). An exception existed
for July, with clearly larger O3 concentrations in the east–central YRD.
With the largest population density and the most developed economy in the YRD, the
area contains a large number of chemical industrial plants and solvent
storage, transportation, and usage (Zhao et al., 2017). High temperature in
summer promoted the volatilization of chemical products and solvent and
thereby enhanced the seasonal VOC emissions more significantly compared to
other less developed YRD regions. Moreover, the lowest NO2
concentration found in summer helped increase the O3 concentration for
the region (Gu et al., 2020). Regarding the simulation difference with two
emission estimates, application of the top-down estimates instead of the
bottom-up ones elevated the O3 concentrations in most of the YRD
region. In particular, the big reduction in NOx emissions for the
east–central YRD (Fig. 3) was expected to be responsible for the evident
growth in O3 concentrations. As the east–central YRD was identified as a
VOC-limited region in terms of O3 formation, the O3 concentration
in the region would be elevated along with the reduced NOx emissions,
reflecting the negative effect of NOx control on O3 pollution
alleviation (Wang et al., 2019).
Comparison of observed and simulated NO3-, NH4+,
and SO42- concentrations by site and season in 2016 (unit: µg/m3). The information on SNA observation sites is provided in Table
S2 in the Supplement. BU and TD indicate the CMAQ modeling with the
bottom-up and top-down estimate of NOx emissions, respectively.
Spring Summer Autumn Winter NO3-NH4+SO42-NO3-NH4+SO42-NO3-NH4+SO42-NO3-NH4+SO42-JSPAES19.116.512.75.79.310.510.36.19.731.116.520.3CMAQ (BU)20.78.512.014.46.09.110.95.09.025.69.312.8CMAQ (TD)22.39.012.211.85.49.511.65.29.126.29.412.8SORPES14.18.613.27.56.611.58.85.28.323.013.415.7CMAQ (BU)18.57.38.012.24.35.29.34.05.423.68.710.9CMAQ (TD)18.07.07.48.33.75.09.84.25.423.68.810.1NUIST16.911.015.96.87.113.1N/AN/AN/A20.914.316.8CMAQ (BU)20.07.99.914.05.87.524.39.011.3CMAQ (TD)21.88.59.911.85.37.824.69.111.3HZS19.96.619.91.92.86.212.78.313.325.36.619.5CMAQ (BU)14.15.78.85.01.52.18.33.66.518.56.69.1CMAQ (TD)16.06.38.63.71.32.89.33.96.619.96.88.9CZSN/AN/AN/A5.15.110.9N/AN/AN/A20.411.810.9CMAQ (BU)11.64.97.123.19.111.3CMAQ (TD)10.75.07.323.19.111.3SZS17.810.214.77.98.014.914.29.013.123.212.515.1CMAQ (BU)14.56.07.113.35.37.16.22.96.319.67.811.7CMAQ (TD)15.56.37.111.75.07.76.93.06.319.97.911.7Mean17.610.615.35.86.511.211.57.111.124.012.516.4CMAQ (BU)17.67.19.111.74.66.38.73.96.822.58.411.2CMAQ (TD)18.77.49.19.74.36.79.44.16.822.98.511.0Evaluation of SNA simulation based on the top-down NOx estimates
Shown in Table 2 is the comparison between the observed and simulated SNA
(SO42-, NO3-, and NH4+) concentrations by
season. Larger observed and simulated SNA concentrations were found in
winter and spring, and smaller concentrations were found in summer and autumn. For most
seasons, the simulations of NO3- concentrations were moderately
improved with the top-down estimates of NOx emissions for all the
YRD cities concerned, with an exception of Nanjing in autumn. The largest
improvement was found in summer, with the mean bias between the simulation
and observations reduced 35 % for all the cities involved. Compared to the
bottom-up inventory, the commonly smaller NOx emissions in the top-down
estimates limited the NO2 concentration and suppressed the formation
of NO3-, while the enhanced O3 from the reduced NOx
emissions promoted it (Cai et al., 2017; Huang et al., 2020). In summer, the
former dominated the process, with the most evident improvement in NO2
simulation (Fig. 4); thus the reduced NO3- concentrations that
were closer to observations were simulated for all the cities.
The simulations with both top-down and bottom-up estimates of NOx
emissions underestimated the NH4+ concentrations for most cases,
and such underestimation was slightly corrected with the application of the
top-down estimates except for summer. The average change in NH4+
concentrations was 2.3 %, much smaller than that of NO3- at
14 %. The moderate improvement in NH4+ simulation with the
reduced NOx emissions in the top-down estimates resulted partly from
the enhancement of the simulated O3 concentrations and thereby the
promoted NH4+ formation. In summer, however, the significant drop
in the simulated NO2 concentration was assumed to reduce the
NO3- and NH4+ formation and to weaken the consistency
between the simulated and observed NH4+. The difference between
the simulated SO42- with the bottom-up and top-down NOx
emission estimates was small for most seasons, implying a limited benefit
of improved NOx emissions on SO42- modeling. Besides emission
data, the chemical mechanisms included in the model should be important for
the model performance. For example, adding SO2 heterogeneous oxidation
in the model could largely improve the sulfate simulation in Nanjing (Sha et
al., 2019).
The spatial differences between the simulated SNA concentrations
with the bottom-up and top-down NOx emission estimates for January,
April, July, and October 2016 (top-down minus bottom-up).
Figure 8 shows the differences in the spatial distribution of SNA
concentrations simulated with the bottom-up and top-down estimates of
NOx emissions by month. In most of the region, the differences of
NO3- concentrations were larger than those of NH4+ and
SO42- for all seasons, and they were mainly controlled by the
changed ambient NO2 or O3 level. The difference in spatial pattern
of NO3- was similar to that of O3 for January, and the larger
growth attributed to the application of the top-down estimates was found in
northern Anhui and eastern Zhejiang (Fig. 8a). The result implies that the
change in NO3- concentration in winter could result partly from
the improved O3simulation; i.e., the elevated O3 was an
important reason for the enhanced formation of SNA in winter (Huang et
al., 2020). Similarly, the increased NO3- was found for more than
half of the YRD region in April, along with the growth of O3
concentrations (Fig. 8d). For July, however, the difference in spatial
pattern of NO3- (Fig. 8g) was similar to NO2 (Fig. 5g), and
the larger reduction attributed to the application of the top-down estimates
was found in the northern YRD. The result suggests that the declining NOx
emissions and thereby NO2 concentration dominated the reduced
NO3- formation in summer. This was mainly because the reduction of
the top-down NOx emission estimate from the bottom-up emission inventory
was much larger for July compared to spring or autumn (Fig. 2). In addition,
the VOC-limited mechanism in O3 formation was found to be weaker in summer
than winter (see Fig. 7e and g), resulting in less O3 formation
and thereby nitrate aerosol through oxidation. In October, the growth in
NO3- concentrations was found again in most of the YRD when the top-down
estimates were applied (Fig. 8j). The growth in the north resulted mainly
from the increased O3 level, while that in the south was associated
with the increased NO2. The differences in spatial patterns of
simulated NH4+ concentrations were similar to those of
NO3- for the 4 months, suggesting that the change in
NH4+ was associated with formation and decomposition of
NH4NO3. However, the changes of spatial distribution of
SO42- were similar to those of O3 concentration. Since
NH4+ was preferred to react with SO42- rather than
NO3- (Wang et al., 2013), the formation of SO42- was
mainly influenced by the atmospheric oxidizing capacity when only NOx
emissions were changed.
The observed and simulated hourly NO3- concentrations
with the bottom-up and top-down NOx emission estimates for January,
April, July, and October 2016 at JSPAES.
Figure 9 illustrates the observed and simulated hourly NO3-
concentrations based on the bottom-up and top-down estimate of NOx
emissions by month at JSPAES. The NMBs and NMEs for simulation with the
top-down emissions were smaller than those with bottom-up ones in January
and July, implying the benefit of the improved NOx emissions on hourly
NO3- concentration simulation in winter and summer. The best model
performance with the top-down estimates was found in January, with the
hourly variation commonly caught with the AQM. However, the NO3-
concentration was seriously overestimated, and the model failed to catch the
hourly variations in summer indicated by the large NMB and NME. As shown in
Fig. S4 in the Supplement, both the NO2 and O3 concentrations at
JSPAES were significantly overestimated for July except O3 with the
bottom-up NOx emission estimate, and this partly explained the elevated
NO3- level from the CMAQ simulation.
Figures S5 and S6 in the Supplement compare the observed and simulated
hourly concentrations at JSPAES by month for NH4+ and
SO42-, respectively. The NMBs and NMEs for NH4+
simulation with the top-down estimates were smaller than those with the
bottom-up ones for most months, while the changes in SO42-
concentration were small. The NH4+ and SO42-
concentrations were largely underestimated with the top-down estimates in
January, indicated by the NMB at -44 % and -38 %, respectively.
Meanwhile, as shown in Fig. S7 in the Supplement, the SO2
concentrations were overestimated by 61 % at the site. The results thus
imply a great uncertainty in the gas–particle partitioning of
(NH4)2SO4 formation in the model in winter, attributed
probably to the missed oxidation mechanisms of SO2 (Chen et al.,
2019c).
The changed percentages of ozone concentration based on the
sensitivity analysis for April 2016.
No reduction-30 % VOC emissions-60 % VOC emissionsNo reduction–-8.9 % (Case 2)-19.5 % (Case 7)-30 % NOx emissions14.2 % (Case 1)7.1 % (Case 3)-2.1 % (Case 4)-60 % NOx emissions23.7 % (Case 6)19.8 % (Case 5)14.5 % (Case 8)Sensitivity analysis of O3 and SNA formation in the YRD region
Table 3 summarizes the relative changes in the simulated O3
concentrations for April 2016 in different cases. The mean O3
concentration would decline by 8.9 % and 19.5 % with 30 % and 60 % VOC emissions off (Cases 2 and 7), while it would increase by 14.2 % and
23.7 % with 30 % and 60 % NOx emissions off (Cases 1 and 6),
respectively. The result confirmed the VOC-limited regime of O3
formation in the YRD region: controlling VOC emissions was an effective way
to alleviate O3 pollution, while reducing NOx emissions alone
would aggravate O3 pollution.
The changed percentages of NO3-, NH4+, and
SO42- concentrations based on the sensitivity analysis for January
2016.
The growth of O3 concentrations was also found when the reduction rate
of NOx emissions was equal to or larger than that of VOCs. The O3
concentration would increase by 7.1 % and 14.5 % respectively when both
NOx and VOC emissions were reduced by 30 % and 60 % (Cases 3 and
8), and it would increase by 19.8 % when NOx and VOC emissions were
respectively declined by 60 % and 30 % (Case 5). In contrast, small
abatement of O3 concentrations (2.1 %) was achieved from the 30 % and 60 % reduction of emissions respectively for NOx and VOCs (Case
4), implying that the O3 level could be restrained when the reduction
rate of VOCs was 2 times that of NOx or more. To control the
O3 pollution effectively and efficiently, therefore, the magnitude of
VOC and NOx emission reduction should be carefully planned and
implemented. In actual fact, controlling VOCs is more difficult than
NOx. Compared to NOx that come mainly from fossil fuel combustion
(Zheng et al., 2018), it is more complicated to identify the sources of
specific VOC species that are most active in O3 formation (Wei et al.,
2014; Zhao et al., 2017). Moreover, substantial VOC emissions are from fugitive sources, for which emission control technology can hardly be
effectively applied. Therefore, it is a big challenge to control O3
pollution by reducing more VOCs than NOx.
The spatial differences of monthly mean O3 concentrations
between the simulations based on the base case (top-down estimates) and
sensitivity cases in April 2016 (sensitivity case minus base case).
Figure 10 illustrates the differences in spatial patterns of the simulated
monthly mean O3 concentrations between the base and sensitivity cases
in April. The O3 concentrations were expected to decline for the whole
YRD region in the cases of 30 % and 60 % VOC emissions off (Fig. 10b
and d), indicating the VOC-limited regime of O3 formation for the
entire YRD. For other cases, the O3 concentrations were clearly
elevated in the central–eastern YRD with relatively large population and
developed industry, particularly for the cases with NOx control only
(Fig. 10a and c) or relatively large NOx abatement together with VOC
control (Fig. 10f and g). Even for the case with 60 % of VOC reduction
and 30 % of NOx (Fig. 10h), there was still a small increase in O3
concentration in the central–eastern YRD, in contrast to the slight O3
reduction found for most areas of the YRD. These results reveal the extreme
difficulty in O3 pollution control for the region. In southwestern
Zhejiang, the O3 concentrations were found to decline in the cases with
large abatement of NOx emissions (Fig. 10c, f and g), suggesting a
shift from a VOC-limited to a NOx-limited region for the O3
formation.
Table 4 summarizes the change in the simulated monthly means of SNA
(NO3-, NH4+, and SO42-) concentrations between
the base case and sensitivity cases in January. The SNA concentrations were
decreased in most cases, implying that the reduction in precursor emissions
was useful for mitigating the SNA pollution. Compared to that of precursor
emissions, however, the reduction rate of SNA was much smaller, attributed to
the strong nonlinearity of SNA formation. The largest reductions were found
at 11.7 % and 12.4 % when emissions of NH3 and all the three
precursors were decreased by 30 % (Cases 11 and 12), respectively. In
contrast, the SNA concentrations declined slightly by 1 % and increased by
0.5 % when NOx and SO2 emissions were reduced by 30 % (Cases 9
and 10), respectively. The results suggest that most of the YRD was in an
NH3-neutral or even NH3-poor condition in winter, consistent with
the judgment through the AQM based on an updated NH3 emission inventory
(Zhao et al., 2020), as the NH3 volatilization in winter was much
smaller than other seasons. Reducing NH3 emissions was the most
efficient way to control SNA pollution for the region in winter. In Case 11
with NH3 control only, the reduction in NO3- and NH4+ concentrations was much larger than that of SO42-. As NH3 reacted with
SO2 prior to NOx, NH4NO3 was assumed easier to decompose
than (NH4)2SO4when NH3emissions were reduced.
The growth of NO3- concentrations was found for Case 10 (SO2
control only), since the free NH3 from the reduced SO2 emissions
could react with NOx in the NH3-poor condition. Similarly, the
SO42- concentrations increased for Case 9 (NOx control
only), as the elevated O3 attributed to the reduction of NOx
emissions promoted the SO42- formation.
Summary
From a top-down perspective, we have estimated the monthly NOx
emissions for the YRD region in 2016, based on nonlinear inversed
modeling and NO2 TVCDs from POMINO, and the bottom-up and top-down
estimates of NOx emissions were evaluated with the AQM and ground NO2
observations. Due to insufficient consideration of improved controls on power
and industrial sources, the NOx emissions were probably overestimated
in the current bottom-up inventory (MEIC), resulting in significantly higher
simulated NO2 concentrations than the observations. The simulated
NO2 concentrations with the top-down estimates were closer to the
observations for all four seasons, suggesting the improved emission
estimation with satellite constraint. Improved O3 and SNA simulations
with the top-down NOx estimates for most months indicate the importance
role of precursor emission estimation in secondary pollution modeling for
the region. Through the sensitivity analysis of O3 formation, the mean
O3 concentrations were found to decrease for most of the YRD when only VOC emissions were reduced or the reduced rate of VOCs was 2 times that of NOx,
and the result indicates the effectiveness of controlling VOC emissions in
O3 pollution abatement for the region. For part of southern Zhejiang,
however, the O3 concentrations were simulated to decline with the
reduced NOx emissions, implying the shift from a VOC-limited to
a NOx-limited region. Compared to reducing NOx or SO2 only,
larger reduction in SNA concentrations was found when 30 % of emissions
were cut for NH3 or all the three precursors (NO2, NH3, and
SO2). The result suggests that reducing NH3 emissions was crucial
to alleviate SNA pollution of the YRD in winter.
Limitations remain in this study. Due to the limited horizontal resolution
of OMI, a relatively big bias existed in the spatial distribution of the
constrained NOx emissions at the regional scale compared to national or
continental ones, and the uncertainty could exceed 30 % for the YRD region
(Yang et al., 2019b). Therefore the improvement in the top-down estimates of
NOx emissions can be expected when more advanced and reliable
products of satellite observations become available at a finer horizontal
resolution (e.g., TROPOspheric Monitoring Instrument, TROPOMI). In addition,
more SNA observations from online measurement are recommended for a better
space coverage and temporal resolution to explore more carefully the
response of SNA to the changes in emissions of NOx and other
precursors.
Data availability
All data in this study are available from the authors upon request.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-1191-2021-supplement.
Author contributions
YY developed the strategy and methodology of the work and wrote the draft.
YZ improved the methodology and revised the manuscript. LZ provided useful
comments on the methodology. JZ and XH provided observation data of
secondary inorganic aerosols. XZ, YZ, MX, and YL provided comments on air
quality modeling.
Competing interests
The authors declare that they have no conflict of interest.
Special issue statement
This article is part of the special issue “Regional assessment of air pollution and climate change over East and Southeast Asia: results from MICS-Asia Phase III”. It is not associated with a conference.
Acknowledgements
This work was sponsored by the National Natural Science Foundation of China (91644220 and
41575142), the National Key Research and Development Program of China
(2017YFC0210106), and the Key Program for Coordinated Control of PM2.5
and Ozone for Jiangsu Province (2019023). We would also like to thank
Tsinghua University for the free use of national emissions data (MEIC) and
Peking University for the support of satellite data (POMINO v1).
Financial support
This research has been supported by the National Natural Science Foundation of China (grant nos. 91644220 and 41575142), the National Key Research and Development Program of China (grant no. 2017YFC0210106), and the Key Program for Coordinated Control of PM2.5 and Ozone for Jiangsu Province (2019023).
Review statement
This paper was edited by Hang Su and reviewed by two anonymous referees.
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