Air pollution reaching hazardous levels in many Chinese cities
has been a major concern in China over the past decades. New policies have
been applied to regulate anthropogenic pollutant emissions, leading to
changes in atmospheric composition and in particulate matter (PM)
production. Increasing levels of atmospheric ammonia columns have been
observed by satellite during recent years. In particular, observations from
the Infrared Atmospheric Sounding Interferometer (IASI) reveal an increase of
these columns by 15 % and 65 % from 2011 to 2013 and 2015, respectively,
over eastern China. In this paper we performed model simulations for 2011,
2013 and 2015 in order to understand the origin of this increase and to
quantify the link between ammonia and the inorganic components of particles:
NH4(p)+/SO4(p)2-/NO3(p)-. Interannual change of
meteorology can be excluded as a reason: year 2015 meteorology leads to
enhanced sulfate production over eastern China, which increases the ammonium
and decreases the ammonia content, which is contrary to satellite
observations. Reductions in SO2 and NOx emissions
from 2011 to 2015 of 37.5 % and 21 % respectively, as constrained from
satellite data, lead to decreased inorganic matter (by 14 % for
NH4(p)++SO4(p)2-+NO3(p)-). This in turn leads to
increased gaseous NH3(g) tropospheric columns by as much as 24 %
and 49 % (sampled corresponding to IASI data availability) from 2011 to
2013 and 2015 respectively and thus can explain most of the observed
increase.
Introduction
Particulate matter (PM) pollution poses serious health
concerns all over the world and particularly over China
. Among PM precursors, several studies
pointed
out the importance of ammonia (NH3(g)), whose main source is
agriculture and which acts as a limiting species in the formation of fine
particulate matter PM2.5, particulate matter with an
aerodynamic diameter less than 2.5 µm;. Balance
between SO2, NOx and NH3 emissions will
define cation- or anion-limited regimes of inorganic particulate matter
formation, and this is key for PM control policies
. Yet, an increase in the ammonia atmospheric
content over China has been observed by the satellite instrument AIRS, with a
trend of +2.27 % yr-1 from 2002 to 2016 . Infrared
Atmospheric Sounding Interferometer instrument (IASI)
satellite observations also show an increased density of the NH3(g) column over China between 2011 and 2015, with a
sharper trend between 2013 and 2015. Several factors could explain this
enhancement, including an increase of NH3(g) emissions in China,
whereby agricultural activities represent 80 %–90 % of total ammonia
emissions in China . However, ammonia emissions appear to
have reached a maximum in 2005 and have been almost constant since
. Another study estimates a 7 %
increase of NH3 emissions between 2011 and 2015, much lower than
the observed trends. Other sources, such as biomass burning and associated
ammonia emissions, do not show particular trends .
Consequently, the increase of atmospheric NH3(g) concentrations
over China does not seem to be explained by changes in ammonia emissions. The
rise of ammonia concentrations over China could be explained by increased
NH3(g) evaporation from inorganic PM due to a rise in
temperature, as shown by . Meteorological variations would
change both the NH3(g) volatilization and the equilibrium between
ammonia, ammonium nitrate (NH4NO3) and nitric acid
(HNO3). Finally, a decrease in sulfate and total nitrate
availability caused by SOx (SO2+SO4(p)2-) and NOx (NO+NO2,) emission reductions could leave
more ammonia in the gas phase, since less ammonium is required to neutralize
particle-phase acids, following a mechanism already observed by
over the United States. Such a decrease in SO2
emissions has also occurred over China since 2011 . Its
impact on atmospheric ammonia concentrations has not been quantified yet.
examined the change of Chinese sulfate–nitrate–ammonium
aerosols due to anthropogenic emission changes of SO2 and
NOx from 2000 to 2015. However, they assumed an increase of
NOx emissions from 2006 to 2015 and not the large decrease
observed since 2013.
IASI instrument NH3(g) total columns over eastern China
(110–125∘ E, 20–45∘ N), in
1016 molecules cm-2 for (a) 2011, (b) 2013 and
(c) 2015. In this figure, the available observations have been
averaged on a 0.25∘× 0.25∘ grid.
A recent study by suggests that ammonia increase mainly comes
from SO2 emission policies. They found that the changes in
NOx emissions decreased the NH3 column
concentrations in their study period. Conversely, have
shown that SO2 and NO2 emissions control was an important
factor affecting the significant enhancement of NH3 column
concentrations over China during the period 2011–2014. In addition, our
study also presents a comparison to NH3 IASI satellite
observations. Our goal here is to understand the factors controlling
atmospheric ammonia concentrations over China in recent years. In order to
identify the drivers of NH3(g) variability over China, we conducted
different sensitivity studies with a regional chemistry-transport model,
isolating the impacts of (i) meteorological conditions and (ii) decrease of
anthropogenic SO2 and NOx emissions. Section 2
presents the regional chemistry-transport model (CTM) CHIMERE used in this
study and the different settings of the performed sensitivity tests. It also
presents IASI NH3 column and surface PM observations.
Section 3 gives the results of the sensitivity tests and shows the impacts of
meteorological conditions and emission changes on ammonia and ammonium
concentrations. The simulations are also evaluated by comparison of the
modelled PM2.5 concentrations with surface measurements. Finally,
the modelled NH3(g) column interannual variability is compared to
that retrieved from satellite (IASI) data.
Material and methodIASI satellite observations
NH3(g) column observations from space are provided by the Infrared
Atmospheric Sounding Interferometer instrument (IASI), operating between 3.7 and 15.5 µm, on board the Metop-A European satellite
. The algorithm used to retrieve
NH3 columns from the radiance spectra is described in
and .
For this study we used the dataset ANNI-NH3-v2.2R-I, relying on ERA-Interim
ECMWF (European Centre for Medium-Range Weather Forecasts) meteorological
input data rather than the operationally provided EUMETSAT IASI Level 2 (L2)
data used for the standard near-real-time version. An analysis of
ANNI-NH3-v2.1 time series revealed sharp discontinuities coinciding with IASI
L2 version changes . With the ECMWF ERA-Interim
reanalysis, the time series is now coherent in time (except for the cloud
coverage flag) and can therefore be used to study interannual
NH3(g) variability over eastern China between 2011 and 2015
(Fig. ). We used the daily satellite information from
morning orbits (at ∼09:30 LT) to have daily information on IASI data
availability. The IASI total columns are first averaged into daily
“super-observations” (average of all individual IASI data within the
0.25∘× 0.25∘ resolution of CHIMERE). The annual
gridded means of Fig. are calculated from these
gridded daily super-observations. In this study, and as suggested in
, we do not apply a selection to the IASI observations.
In 2011, a mean value of 4.7×1015 molecules cm-2 is
observed for eastern China, increasing to
5.36×1015 molecules cm-2 in 2013 (+15 %) and
7.76×1015 molecules cm-2 in 2015 (+65 %).
The chemistry-transport CHIMERE model and updated NOx and SO2 emissions
CHIMERE (2014b version) is a three-dimensional chemistry-transport regional
model (; http://www.lmd.polytechnique.fr/chimere/, last access:
15 September 2018) run here over a
0.25∘× 0.25∘ regular grid, on a domain completely
covering China's territory (72∘30′–145∘ E,
17∘30′–55∘ N). The domain includes 290
(longitude) grid cells × 150 (latitude) grid cells and 17 vertical layers, with
altitude going from the ground to 200 hPa (about 12 km), with eight layers
within the first 2 km. Meteorological fields are provided by ECMWF
meteorological forecasts . To
prescribe atmospheric boundaries and initial composition, climatological
values are used from the LMDZ-INCA global model . Biogenic
emissions are calculated taking into account meteorological parameters with
the MEGAN-v2 model . We use the EDGAR-HTAP-v2.2 inventory
delivered for the year 2010 , to prescribe the
anthropogenic emissions for the simulation. Chinese emissions in
EDGAR-HTAP-v2.2 are derived from the Multi-resolution Emission Inventory for China (MEIC) developed by Tsinghua
University, the NH3(g) emission inventory from Peking University
and the Regional Emission inventory in Asia (REAS) to fill the
remaining gaps. The respective total annual emissions of SO2,
NO and NH3(g) for 2010 are 42.4, 25.2 and 20.3 Mt
(Table ), and the spatial distributions of these
emissions are represented in Fig. .
Annual budgets of the EDGAR-HTAP-v2.2 inventory and of emissions
corrected from the OMI instrument (this work), for SO2, NO
and NO2, over Asia and over eastern China (in Mt). The Asian domain
corresponds to our full domain (72∘30′–145∘ E,
17∘30′–55∘ N), and the eastern China domain corresponds
to a smaller domain (110–125∘ E, 20–45∘ N), displayed by
a black rectangle in Fig. .
EDGAR-HTAP-v2.2 emissions for the year 2010 for
(a)SO2, (b)NO and
(c)NH3. Units are tons per cell (t cell-1) (cell size of
0.25∘× 0.25∘). The black rectangle shows the eastern China domain.
We used the most recent HTAP emission inventory built for the year 2010 to
simulate emissions of the year 2011, assuming that both years have similar
emissions. Initially, a comparison of the OMI and CHIMERE NO2
columns for 2011 shows a Pearson correlation coefficient of 0.78 for daily
values and an annual mean bias of -7 %. To understand the impact of the
NOx and SO2 emission reductions observed over China
in 2013 and 2015, the model emissions need to be updated. The observed
variability of satellite columns has been used to update emissions, as in
. Here NO, NO2 and SO2 emissions have
been updated following the variability observed by OMI between 2011, 2013 and
2015. We assume that NO2 variations are controlled by
NOx emissions changes. The yearly gridded relative
variabilities seen by the OMI instrument between 2011 and 2013 and between
2011 and 2015 are applied to daily prior anthropogenic SO2,
NO and NO2 emissions. H2SO4 emissions represent
only a small fraction of SOx (1 %) and, consequently, are
not updated here. Equation () has been applied to each pixel of
our regional grid for 2015 and 2013, where i can be SO2,
NO2 or NO and j the pixel number:
Emis(year,i,j)=Emis(2010,i,j)×(Col(year,i,j)Col(2011,i,j)).
Emis(2010) represents the reference HTAP emission inventory, and Col(2011) and Col(year) the respective OMI satellite
observation values for 2011 and 2015 (or 2013). Corrections for the emission
inventory are shown in Fig. . Values of derived
emissions have been limited to 500 % of initial values, to avoid outlier
values located in North Korea. It should be noted that our update based on
annual averages does not modify the seasonal cycle for NOx
and SO2 (with a maximum in winter; see Fig. S1 in the Supplement).
In the model, spatial distributions for NOx and
SO2 emissions appear to have a similar structure (see
Fig. ). Ammonia emissions show a significant seasonal
cycle with emissions higher during summer than during the other seasons. On a
molar basis, NH3(g) emissions are low compared to SO2 and
NOx emissions, except in summer when NH3(g)
emissions are in excess compared to SO2 alone (Fig. S1 in the
Supplement).
Annual differences between the EDGAR-HTAP-v2.2 inventories for the
year 2010 and the emissions corrected from OMI for the year 2015, for
(a)SO2, (b)NO and
(c)NO2. Units are tons per cell (t cell-1) (cell size of
0.25∘× 0.25∘).
When applying Eq. (), the temporal evolution of OMI
NO2 columns leads to a decrease in NOx emissions
(Figs. S3 and S4 in the Supplement), particularly after 2013 (+1 % in 2013
and -21 % in 2015 as compared to 2011). derived similar
NOx emission changes with the exponentially modified
Gaussian method of (e.g. decrease of 21 % of
NOx emissions within Chinese cities between 2011 and 2015).
We have also compared our updated inventories for NOx and
SO2 with the DECSO v5 inventories calculated with an inverse
modelling method based on an extended Kalman filter
(; http://www.globemission.eu/, last access:
4 July 2018). For DECSO, the a priori
NOx anthropogenic emissions are taken from the EDGAR-v4.2
inventory, whereas our prior emissions come from EDGAR-HTAP-v2.2. EDGAR-v4.2
does not take into account shipping emissions, as shown in Fig. S2 in the
Supplement. Our annual evolution rates are consistent with DECSO trend estimates
(+1 % in 2013 and -14 % in 2015, compared to DECSO in 2011). A recent
study from evaluated NOx emissions and
found a change of -17.4 % from 2011 to 2015, similar to our -24 %
change. The OMI SO2 trends imply (following Eq. ) a
continuous decrease of SO2 emissions from 2011 to 2015 (-24 %
in 2013 and -37 % in 2015 compared to 2011; see
Table and Fig. a). This
decreasing trend is consistent with trend estimates of the SO2 in
DECSO, -11 % in 2013 and -25 % in 2014 compared to 2011.
Nevertheless, our total SO2 emissions in 2013 seem to be
underestimated compared to the DECSO annual estimates (-15 % in 2011,
-25 % in 2013). It should be noted here that our method to update
emissions has some limitations. All the variability of satellite tropospheric
columns is attributed to emissions, without taking into account variability
associated to meteorology, transport, chemistry or instrumental degradation.
However, the comparison with independent emission estimations shows good
consistency. found SO2 emissions change of
-41.9 % between 2011 and 2015, again similar to our -37.5 %
evolution. Hence, we consider our estimated emission inventories realistic
enough to conduct sensitivity tests.
The emission update helped correctly reproduce the column changes for SO2-44 % (CHIMERE) and -53 % (OMI) between 2011 and 2015 and for
NO2-31 % (CHIMERE) and -23 % (OMI) between 2013 and 2015.
ISORROPIA
The composition and phase state of inorganic aerosol in thermodynamic
equilibrium with gas-phase precursors are calculated using the ISORROPIA
V2006 module . The CHIMERE CTM calculates the
thermodynamical equilibrium of the system: sulfate–nitrate–ammonium–water
for a given temperature (in a range [260–312 K]; increment:
ΔK =2.5 K) and relative humidity (RH, in a range [0.3–0.99];
increment: ΔRH =0.05). Considering temperature, relative humidity,
TN (TN =NO3(p)-+HNO3; total nitrates), TA (TA
=NH4(p)++NH3(g); total ammonia) and TS (TS =SO4(p)2-+H2SO4; total sulfates), the gas to
particle partitioning of NH4(p)+/NH3(g) and
HNO3(g)/NO3(p)- is calculated using tabulated values.
Depending on the equilibrium calculation, both the absorption and desorption of
ammonia are represented in the model. Within CHIMERE, a kinetic approach is also
added to simulate transport barriers for the gas to the particle phase and
vice versa. Over Europe, a recent study, conducted with CHIMERE and the
ISORROPIA V2006 module, showed that higher temperature and lower RH will
promote NH3(g) at the expense of NH4(p)+.
Set-up of the performed sensitivity tests
Six experiments were performed to discriminate factors that control the
ammonia atmospheric budget over China over recent years. Configurations
corresponding to the six experiments are described in
Table . The 2011 reference simulation serves
as a baseline for comparison with the other simulations (under different
meteorological and emissions scenarios). The simulations labelled “A” are
used to quantify the influence of meteorological parameters, the simulation
labelled “B” to quantify the influence of SO2 emissions variations
and simulations labelled “C” to quantify the influence of both
SO2 and NOx emissions variations. The 2013A and
2015A simulations were performed keeping emissions at 2010 levels, but the
meteorology was updated to isolate effects of meteorological variability on
simulated ammonia levels. Among scenarios performed in our study, the 2015B
simulation uses the same meteorology as 2015A but with 2015 SO2
emission giving information on the role of Chinese SO2 emission
reduction in the NH3(g) atmospheric content. Finally, the 2015C
simulation uses the same meteorology as 2015A but using both
SO2 and NOx emissions corrected with 2015 OMI
observations (see Table ). As the updated 2013
NOx emissions are similar to the initial EDGAR-HTAP-v2.2
emissions used for the 2011 reference simulation, we expect similar results
for the 2013B and 2013C simulations. Consequently, the 2013B simulation was
not conducted. Hence, 2013C and 2015C are the key simulations of our study
showing the combined effect of SO2 and NOx
emission reductions and of meteorology on atmospheric ammonia.
Description of the different experiments performed with the regional
chemistry-transport model CHIMERE.
NameMeteorologySO2 emissionsNOx emissionsObjectives of the simulation2011AECMWF 2011EDGAR-HTAP-v2.2 2010EDGAR-HTAP-v2.2 2010Baseline simulation2013AECMWF 2013EDGAR-HTAP-v2.2 2010EDGAR-HTAP-v2.2 2010Sensitivity to meteorology2013CECMWF 2013Deduced from OMI for 2013Deduced from OMI for 2013Sensitivity to SO2 and NOx emission reduction2015AECMWF 2015EDGAR-HTAP-v2.2 2010EDGAR-HTAP-v2.2 2010Sensitivity to meteorology2015BECMWF 2015Deduced from OMI for 2015EDGAR-HTAP-v2.2 2010Sensitivity to SO2 emission reduction2015CECMWF 20115Deduced from OMI for 2015Deduced from OMI for 2015Sensitivity to SO2 and NOx emission reductionResults and discussionImpact of meteorological conditions on the ammonia/ammonium–sulfate–nitrate system
Three scenario simulations using the same emissions but different meteorological conditions
(2011A, 2013A and 2015A) were performed to understand the role of meteorology
in ammonia atmospheric concentrations. As our study uses satellite
observations giving tropospheric trace gas columns for different purposes,
the model results will be generally presented as vertical columns (reaching
from the ground to 12 km height). However, most of the column content can
generally be found within the first 2.5 km, close to the ground (more than
90 % of NH3(g) is located within the first 2.5 km in CHIMERE).
Hence, to study the meteorological influence on ammonia, we restrict the
comparison to 0 to 2.5 km (which corresponds to about 720 hPa) partial
columns, as we want to average meteorological parameters for a height that
should be representative of conditions where pollutants are located.
Figure shows NH3(g) columns in 2011
(Fig. a) and simulated variations depending on
meteorological conditions for 2013 (Fig. b) and 2015
(Fig. c). It can be observed that the eastern China
area includes regions with the highest NH3(g) values (except the
Indo-Gangetic Plain). Over the
eastern China area, meteorological conditions affect NH3(g)
columns: an increase of 4 % is simulated in 2013, whereas a decrease of
7 % is simulated in 2015. In addition, it can be observed that over the
southern China area (see black rectangle in
Fig. b–c) ammonia decreases for 2013 and 2015.
(a) Ammonia columns (molecules cm-2) for the 2011A simulation; (b) relative differences (%) of ammonia columns between
2013A and 2011A; (c) relative differences between 2015A and 2011A.
Note that as values over the sea mostly represent only small variations, we
do not show them here and in the following figures.
Gaseous and
particulate inorganic species variation for the eastern China region. Disk
surfaces are proportional to column amount (molecules cm-2) and colours
indicate relative evolution rates compared to 2011A (%).
Figure shows that ammonia changes caused by
meteorological variations are opposite to sulfate and ammonium changes in
both cases, for 2013 and 2015. Meteorological conditions in 2015 (Fig. S5 in
the Supplement) promoted the formation of ammonium and sulfates (+6 %
and +12 % respectively) in 2015A compared to 2011A and a decrease of
nitrates (-13 %; Fig. ). For 2013, changes of
NH3(g) are opposite to ammonium (-7 %) and sulfates
(-7 %), with a slight increase of NH3(g) (+4 %). It appears
that changes in the NH3(g)/NH4(p)+ ratio are correlated with
SO4(p)2- changes. This can be explained from well-known
neutralization reactions in the gas (Reaction ) or
aqueous phase (Reactions and ):
R12NH3(g)+H2SO4(g)⇌(NH4)2SO4(p)R2NH3(aq)+H2SO4(aq)⇌NH4(aq)++HSO4(aq)-R3NH3(aq)+HSO4(aq)-⇌NH4(aq)++SO4(aq)2-.
SO2 oxidation to SO4(p)2- can happen in the gas phase,
from process Reaction (), in which OH is the oxidant, produced
from Reaction (), that is linked to humidity:
R4O(1D)+H2O(g)→2OHR5OH+S(+IV)O2(g)→H2S(+VI)O4(g).
Sulfate production can also occur in the aqueous phase, by SO2(aq)
oxidation with O3 or H2O2, yielding sulfuric acid
which can then be neutralized to form ammonium sulfate . The
extent of this reaction chain depends on cloud liquid water content. It is
initiated by a solution of SO2(g) in the water phase
(SO2⚫H2O(aq)):
R6SO2(aq)+H2O(aq)⇌SO2⚫H2O(aq)R7SO2⚫H2O(aq)+O3(aq)→HSO4(aq)++H++O2.
(a) Monthly variation of NH3(g) partial columns
and relative humidity in 2013 and 2015 compared to 2011. (b) Monthly
variation of NH3(g) partial columns and temperature in 2013 and
2015 compared to 2011.
While the aqueous-phase pathway is globally dominant, the gas-phase pathway
can also be important under dry conditions e.g.. In
our study, we did not investigate the relative importance of both pathways
because this would have required inclusion of specific tagging. Nevertheless,
for both pathways, RH increase favours SO4(p)2- production,
either through increased production of OH radicals in the gaseous
phase (for a given temperature, so that specific humidity also increases) or
through a larger cloud liquid water content . In 2013,
annual mean temperature and annual mean RH increased by +0.7 K and
decreased by -0.9 % respectively compared to 2011; cloud liquid water
relative variation also shows a decrease of -7 %. In 2015, temperature
and annual mean RH increased by 1 K and increased by +1.3 % respectively
compared to 2011, and cloud liquid water relative variation increased by
+20 %. Accordingly, the total sulfate–nitrate–ammonium (called pSNA
hereafter) production is increased by +7 % in 2015A compared to 2011A,
which explains the decrease in the NH3(g) columns of -7 %. Conversely, meteorological conditions in 2013 (decrease of RH and liquid
water) depressed the formation of pSNA (Fig. ).
Consequently, its production was lower by about 6 % in 2013A compared to
2011A, and NH3(g) columns were larger by +4 %. These
relationships between meteorological parameters and NH3(g) columns
can also be documented by correlation statistics
(Fig. ). An inverse correlation between monthly RH
and NH3(g) column variations over the eastern China domain is shown in
Fig. a, with Pearson correlation coefficients of
-0.47 and -0.56 in 2013 and 2015, respectively. An even more pronounced
negative correlation is also observed on a daily basis, with correlation
coefficients for 2013 and 2015 of -0.71 and -0.61 respectively. When RH
increases, the production of NH4(p)+ from NH3(g) also
increases. The largest difference between 2013A and 2015A is observed in
November and December 2013 and 2015, when RH variations and NH3(g)
column variations are opposite (Fig. a).
Figure b shows that temperature changes do not
control the NH3(g) variation, as the Pearson correlation
coefficients are -0.04 and 0.09 in 2013 and 2015 respectively. It should
also be noted that decreases of ammonia and ammonium are observed over areas
showing an increase in rainfall frequencies (see Fig. S6 in the Supplement)
in 2015 and 2013 (in the south of China, Guangxi and Guangdong Province; see
the black box in Fig. ). Conversely, with
rainfall frequencies lower than 90 d yr-1 (for rainfall above
1 mm d-1), and small changes in rainfall frequencies over the North China
Plain for 2011 and 2013, changes in wet deposition do not seem to impact
ammonia levels significantly. Indeed, low correlation is found between
monthly rainfall frequency variations and monthly ammonia variations over
eastern China (Pearson correlation coefficients of -0.12 and -0.18 for 2013
and 2015 respectively).
Finally it has been observed in this study that total NH3(g)+NH4(p)+ columns will vary depending on meteorological
conditions. It should be noted that if NH3(g) is favoured, as for
2015, the total content will decrease as NH3(g) lifetime is shorter
than NH4(p)+ lifetime due to faster deposition.
Impact of SO2 and NOx emission reduction on NH3 columns and inorganic aerosolImpact of SO2 and NOx emission reduction on NH3 columns
Figure b represents the impact of the SO2
emission reduction on NH3(g) columns for the 2015B simulations. For
the year 2013, the comparison is made from the 2013C simulation (see
Table , and Fig. S7 in the Supplement), since
NOx emissions between 2011 and 2013 are similar. The
SO2 emission reduction (-24 % for 2013 and -37 % for 2015
as compared to 2011) strongly affects NH3(g) columns, which
increase by +10 % over eastern China in the 2013C simulation compared to
2013A and by about +36 % in the 2015B simulation compared to 2015A. Thus,
the effect of SO2 reduction on NH3(g) columns appears to
be non-linear because NH3(g) interactions are not limited to
SO2. It should be noted that the column change is mainly controlled
by changes between the surface and the first few kilometres of altitude, as
much of column content (>90 %) is located between the surface and 2.5 km
of altitude; but as IASI and OMI satellite provide full column information,
we present the entire CHIMERE column to be as consistent as possible with
observations. In the two cases, the decrease of ammonia over western China
and Mongolia (between 0 % and -15 %; Fig. b),
where NH3(g) values are initially low
(Fig. a), remains small.
Figure c shows the additional impact of
NOx emission reductions, of about -21 % between 2015 and
2011, on the NH3(g) amount over eastern China (with the 2015C
simulation, compared to 2015B). The additional increase of ammonia columns in
the 2015C simulation, is about 15 % compared to the 2015B simulation
(SO2 emission decrease only) in the northern part of the eastern China
subdomain. This statement on NOx emission evolution impacts
is different from that in , in which NOx
emission reduction is considered not responsible for the NH3
increase between 2011 and 2015.
(a) Ammonia columns (molecules cm-2) for the
2015A simulation, (b) relative differences of ammonia columns between the
simulations from 2015B and 2015A, in %, and (c) additional relative
differences of ammonia columns between the simulations from 2015C minus 2015B
compared to 2015A, in %. The black rectangle is for the eastern China domain.
Figure b represents the impact of the SO2
emission reductions on NH4(p)+ columns with a decrease of about
14 % in the 2015B simulation compared to 2015A. The reduction of
NOx emissions between 2015B and 2015C leads to an additional
decrease of ammonium levels in the 2015C simulation, -4 % compared to
2015B for eastern China, where the decrease is most pronounced
(Fig. c). In addition, ammonium columns have decreased
by about 2 % over eastern China in the 2013C simulation compared to 2013A. The
spatial anti-correlation observed between NH4(p)+ and
NH3(g) is explained by less production of NH4(p)+ from
the NH3(g) due to emission modifications. It is interesting to note
that the NOx and SO2 emission reduction impacts on
NH3(g) and NH4(p)+ columns can differ depending on the
area (see Fig. S8 in the Supplement). In the 2015B simulation the ammonium
production decreases most strongly in the Sichuan Province and Chongqing municipality
(black rectangle, Fig. b), and there is a large-scale
decrease around SO2 sources (Fig. a). In the
2015C simulation, we can observe a larger decrease in the northern China region (red
rectangle, Fig. c), where ammonium nitrate is produced
with freshly formed HNO3(g), following Reaction ():
NH3(g)+HNO3(g)⇌NH4NO3(p).
These relationships between sources and impacted regions are explained by the
time needed for SO4(p)2- and NO3(p)- formation from
SO2 and NOx precursors of several days and several
hours to 1 d respectively. Thus, SO2 to H2SO4
oxidation is more a regional process (unless it happens in the aqueous
phase), whereas nitrate formation proceeds closer to the sources. However,
for conditions of weak atmospheric dispersion or high humidity, as in the
Sichuan Province and Chongqing municipality (located in an orographic
depression), sulfates can be formed closer to sources. In this area,
sulfates contribute as much as 32 % to the PM column, compared to
23 % over eastern China, SO4(p)2- (Fig. S8 in the Supplement).
(a) Ammonium columns (molecules cm-2) for the
2015A simulation, (b) relative differences of ammonium columns between the
simulations from 2015B and 2015A, in %, and (c) additional relative
differences of ammonium columns between the simulations from 2015C minus
2015B compared to 2015A, in %. The black rectangle is for the Sichuan
Province and Chongqing municipality, and the red rectangle is for northern China.
Impact of SO2 and NOx emission reduction on pSNA production
The emission update leads to changes in the pSNA production, as already
suggested by changes in the NH4(p)+ columns. As SO2
columns are strongly decreased (-40 % for 2015B; -41 % for 2015C),
less ammonium (-14 % for 2015B; -18 % for 2015C) is formed in the
particulate phase from the reaction with sulfuric acid
(Reactions , , ), and more
NH3(g) remains in the gas phase (+36 % for 2015B; +51 % for
2015C; see Figs. and S9 in the Supplement). These
higher NH3(g) levels trigger a larger conversion
(Reaction ) of gaseous nitric acid into particulate
NO3(p)- (+33 %; Figs. and S10). In
the 2015C simulation, the increase of NO3(p)- is less notable
(+11 % because NOx emissions decrease), and ammonia
columns show a bigger increase than in 2015B over eastern China. On the whole,
the reduction of emissions in the 2015B and 2015C simulations leads to a
reduction of the total pSNA PM production (e.g. -16.6 % and
-18.5 %, respectively, compared to 2015A), mainly promoted by the
reduction of SO2 emissions. Among PM components, a decrease
of the sulfate molar fraction is observed (from 32 % to 29 %), and the
NO3(p)- fraction increases, from 12 % to 15 %, while
the NH4(p)+ molar fraction stays stable around 56 % (21 % of
pSNA PM mass). It can also be observed in these scenarios that for
similar emissions and meteorology,
TA =[NH3(g)]+[NH4(p)+] decreases in 2015B and
slightly more in 2015C (Fig. S9 in the Supplement). The reasons are that
ammonia is favoured compared to 2015A and that deposition is a more efficient
process for ammonia.
Gaseous and particulate inorganic species variation for the eastern China region. Disk surfaces are proportional to column amount
(molecules cm-2), and colours indicate relative evolution rates compared to 2015A
(%).
NH3(g): a key role in the regulation of PM pollution as a limiting reactant
The regime of nitrate production, limited either by NH3(g) or
HNO3(g), can be evaluated using the Gratio calculation
(; Gratio two-dimensional distribution is displayed in
Fig. S10 in the Supplement). A negative value of the
Gratio indicates that TA low availability is strongly limiting
nitrate production in competition to sulfate production, while a value
in the range [0–1] indicates a TA-limited situation, and a value greater than 1
indicates that TA is in excess. In our study, as we want to know if we are
facing a cation- or anion-limited regime, we chose to adapt the
Gratio to a cation / anion ratio (C/Aratio),
easier to interpret with no negative values, as follows:
C/Aratio=TATN+2×[SO4(p)2-],
where TA =[NH3(g)]+[NH4(p)+] is the total ammonia
reservoir, and TN =[HNO3(g)]+[NO3(p)-] is the total
nitrate reservoir. When the C/Aratio is in the range [0–1]
molC molA-1, we are in a cation-limited regime,
and when C/Aratio is larger than
1 molC molA-1, we are in an anion-limited
regime. The C/Aratio has been calculated for columns, partial
columns (up to 1 km) and for the surface, and results for various scenarios
are displayed in Fig. . It should be noted that the
ratio has an important month-to-month variability (normalized standard
deviation of 10 %), as displayed in Fig. d for the
East Asia region. Note that NH4(p)+ is the only cation
considered here; the potential role of other cations is discussed below.
Figure d presents the C/Aratio for the
simulations considering several altitudes, at the surface, the 0–1 km
column and the CHIMERE total column. C/Aratio is highly variable
depending on the vertical layer considered, with a significantly lower
C/Aratio considering a total column than a reduced layer close to
the surface, a statement also observable in . Close to sources
(surface), more ammonium will be present, leading often in the 2015B and 2015C
scenarios to an excess cation regime. If we consider vertical columns, as
0–1 km for example (which corresponds roughly to the atmospheric mixing
layer), the ratio is below 1 (anion-limited regime) for most months.
Considering the entire tropospheric column, a cation-limited regime occurs
for all months. This decrease in the C/Aratio with altitude can be
explained by the fact that sulfuric and nitric acid need some time to be
formed from precursor gases, while the major cation NH4+ is
directly emitted at the surface. In addition, a slight increase of
C/Aratio is observed from January to May, when a maximum is
reached. It is probably due to the increase of NH3 emissions during
this period compared to decreasing SO2 and NOx
emissions (Fig. S1 in the Supplement). Then, for July and August, the
cation / anion ratio drops. This change is not explained by a change in emissions
because these months display emissions close to June emissions. A probable
explanation is the following: first, July and August correspond to the
monsoon season, with higher water vapour content and solar radiation over the
study area. This leads to enhanced OH radical concentrations (up to
twice the annual mean) to form H2SO4(g) and HNO3(g).
Second, higher water content induces more SO2(g) dissolution in
the aqueous phase. Both factors induce more SO4(p)2- formation
, which decreases the C/Aratio.
Partial column (0–1 km) cation / anion ratio
(molC molA-1) over China for the simulations (a) 2015A, (b) 2015B and
(c) 2015C and (d) monthly variation
of C/Aratio over eastern China. Full lines represent ratios derived
from columns (up to 12 km), dashed lines represent ratios derived from the 0–1 km column and dotted lines represent ratios derived from surface concentrations.
Black rectangles represent central China, and red rectangles northern China.
C/Aratio two-dimensional distributions are shown in
Fig. for the 0–1 km column. The atmosphere is
mainly cation-limited over eastern China in the 2015A initial scenario. As
expected from the decrease in anion precursor emissions (i.e SO2
and NOx), the C/Aratio is higher in the 2015C and
2015B simulations than in the 2015A simulation, as for example in the Sichuan
Province and Chongqing municipality (black square in
Fig. b) and northern China regions (red rectangle in
Fig. c). Reductions of SO2 and
NOx emissions led to a C/Aratio increase and
change in the limitation regime close to NH3(g) source areas
(Fig. a and c). In the future, emission reductions for
NH3 and anion precursors should reduce NH4NO3(p) and
(NH4)2SO4(p) formation, reducing observed PM levels,
which was already suggested in . It should also be noted that
cations such as Ca2+ or Mg2+ (from dust or anthropogenic
emissions) are not included in CHIMERE chemistry. They could induce a bias in
our analysis, underestimating the C/Aratio. Nevertheless, we can
reasonably assume here that the NH4(p)+ molar content is
generally much higher than Ca2+ and Mg2+ content. A study
by with measurements in Beijing (from September 2006 to
August 2007) shows that in winter, when the lowest NH4(p)+
concentrations are met, the NH4(p)+ content (about
0.5 µmol m-3) still exceeds 3 times the (Ca2+ +
Mg2+) amount (about 0.15 µmol m-3). Another recent
study measuring soluble ions of PM2.5 in Beijing shows a large
excess of NH4(p)+ compared to Ca2+or Mg2+ in
summer and winter 2014 . Still, not taking into account this
chemistry for mineral cation species can lead to simulated cation-limited
situations instead of a cation-excess situation for restricted times of the
year and areas, when the ratio reaches values close to 1.
Time evolution of inorganic PM and precursor species between 2011 and 2015
The combined impacts of meteorology and emission reductions on gaseous and
particulate species are shown in Fig. using
simulations 2011A, 2013C and 2015C including meteorology and updated
emissions for the three corresponding years. The time evolution between 2011
and 2015 is qualitatively similar to that presented in the previous section
for emission changes alone. The impact of changing meteorology is to damp the
negative changes of pSNA (Sect. ,
Figs. and S11 in the Supplement present
two-dimensional distribution of pSNA changes) and the positive changes in
NH3(g) due to emission reductions. As a result, in our simulations,
NH3(g) columns increased by as much as +14 % in 2013 and by
41 % in 2015 over eastern China, as compared to 2011, combining both
meteorological and emission changes.
Evaluation against PM2.5 surface measurements
The evolution of surface PM2.5, induced by changes in SO2
and NOx emissions in our simulations, is evaluated here
against independent daily PM2.5 surface measurements. Scores for
normalized bias, normalized RMSE (NRMSE), ratios of model and observed
variability and Pearson correlation coefficients are displayed in Table 3. We
used data from the U.S. Department of State Air Quality
Monitoring Program over China (e.g. in Beijing, Chengdu, Guangzhou, Shanghai and
Shenyang; http://www.stateair.net/, last access: 5 October 2018), for
the years 2013 and 2015. For the year 2011, data are available only for
Beijing, so this year was discarded. We present results for updated
inventories (simulations 2013C and 2015C). We also present changes between
simulations 2013A and 2015A and 2013C and 2015C in Table .
The PM2.5 values measured in Chinese cities show large amplitudes,
ranging from 0 to 500 µg m-3, and CHIMERE correctly
represents both these amplitudes and the strong day-to-day variability of
PM2.5 surface concentrations, as illustrated by good correlation
coefficients (Pearson's coefficient averaging 0.7) between time series and a
ratio of standard deviations close to unity (except for Shenyang; Table 3).
Our 2013C and 2015C simulations overestimate average PM2.5
concentrations for four cities (Beijing, Shanghai, Guangzhou and Chengdu) and
underestimate them for Shenyang (bias =-13 %). For the Beijing,
Shanghai, Guangzhou and Chengdu stations, we observe a slight decrease of
PM2.5 means (-4.3, -2.2, -1.1 and
-11.5µg m-3) between our reference simulations (2013A and
2015A) and simulations with modified inventories (2013C and 2015C). This
means that updating the emissions improves agreement with observations by
reducing biases and errors (NRMSE). We also find a slight decrease at
Shenyang station (-1.1µg m-3), making the already negative
bias slightly worse. The strongest improvement is observed in Chengdu
(central China), an area where inorganic PM mainly depends on sulfate
and ammonium.
(a) Evolution of NOx(g),
SO2(g), NH3(g) and HNO3(g) tropospheric columns
(molecules cm-2) for 2011A, 2013C and 2015C over the eastern China
domain. (b) Evolution of NH4(p)+, SO4(p)2-,
NO3(p)- and pSNA tropospheric columns (molecules cm-2)
from 2011 to 2015 over the eastern China domain.
Daily PM2.5 comparison between model and measurements for
2013C and 2015C simulations. “Changes” corresponds to differences between
2013C and 2015C comparisons on the one hand and 2013A and 2015A on the other
(i.e. biaschanges= biasC- biasA).
Bias and NRMSE are normalized using the measurement mean. R corresponds to
the Pearson correlation coefficient and n represents the number of available
daily means.
StationsPM2.5 measurement meanPM2.5 model mean (µg m-3)/Bias (%)/NRMSE (%)/σCHIMEREσobsR/n(µg m-3)changes (µg m-3)changes (%)changes (%)changesBeijing92.3115.7/-4.3+25%/-05%64%/-03%1.00.77/=730Shanghai55.368.6/-2.2+24%/-04%57%/-04%1.10.76/=723Guangzhou47.659.8/-1.1+26%/-02%64%/-01%1.00.54/=719Chengdu83.8127.6/-11.5+52%/-14%71%/-12%1.00.72/+0.03687Shenyang74.064.4/-1.0-13%/-02%61%/-01%0.60.68/+0.02570
PM2.5 observations show a decrease from 2013 to 2015 for Beijing,
Chengdu, Guangzhou and Shanghai of -19 %, -20 %, -30 % and -15 %
respectively and an increase for Shenyang of +18 %. These changes are not
fully reproduced by CHIMERE, possibly because emissions have been modified
for NOx and SO2 only and not for organic or other
inorganic species. In CHIMERE, PM2.5 decreases are calculated for
Beijing and Chengdu (-3.6 % and -10 % respectively), no significant
change is simulated for Guangzhou and increases are modelled for Shanghai and
Shenyang (+12.5 % and +3.7 % respectively). All changes are calculated
filtering simulation results according to the availability of measurements.
The increase in Shenyang can be explained by a lack of data for a significant
fraction of the sampling periods between 2013 and 2015 (229 d available
against 341). In Shanghai, the modelled increase is not explained by
PM2.5 inorganic components, which present a +1 % trend at
surface, but is due to the effects of meteorological conditions on other
PM2.5 components, which present larger increases.
Correspondences between CHIMERE simulations and IASI NH3(g) column observations
Data retrieved from the IASI instrument allow us to compare satellite
observations to simulations and to verify the simulated trends.
Figure shows the spatial distributions of ammonia
over China for IASI and CHIMERE for 2011, 2013 and 2015, presenting a similar
spatial pattern and a good correlation (∼RIASI-CHIMERE=0.91
over eastern China) and an acceptable daily correlation
(∼RIASI-CHIMERE=0.55). Nevertheless, simulations underestimate
ammonia levels with a bias of -39 %, mainly because of the comparison over the sea
area. As described above, IASI observations show a +65 % increase between
2011 and 2015. Interestingly, our model results between 2011A and 2015C,
taking into account emission and meteorology changes, show a rather similar
difference of +49 % of NH3(g) (when CHIMERE simulations are
sampled on daily IASI observations' availability). For the intermediate year
2013, the IASI satellite observed a +15 % increase in NH3(g)
columns, while CHIMERE simulations showed a +24 % increase in the 2013C
scenario (again sampled on IASI observations). Conversely, simulations
with unmodified emissions only show small changes for both years (+6 % in
2013A; -3 % in 2015A). estimated a +35 % NH3
column increase over the North China Plain, between 2011 and 2015, taking
account of SO2 emissions decreases, a value close to our result for
this case (+27 % between 2011A and 2015B). This suggests that the observed
increase for ammonia by IASI can be explained to a large extent by changes in
atmospheric chemistry induced by SO2 but also by
NOx emission reductions, with less ammonium present within
inorganic aerosol and more ammonia remaining in the gas phase.
Ammonia column evolution for IASI (a, b, c) and CHIMERE (d, e, f)
in (a) 2011, (b) 2013, (c) 2015, (d) 2011A,
(e) 2013C and (f) 2015C (molecules cm-2).
Conclusion
Sensitivity tests with the regional chemistry-transport model CHIMERE have
been performed to understand the evolution of the NH3(g)
atmospheric content over China, with an increase observed by IASI
measurements over eastern China of +15 % between 2011 and 2013 and of
+65 % between 2011 and 2015. One of the main results of this study is
that the strong observed changes in the NH3(g) atmospheric content
are mainly associated with a reduction of anthropogenic SO2
emissions and, to a lesser extent, with a reduction in anthropogenic
NOx emissions and with interannual changes in meteorological
conditions. With SO2 emissions reduced by 24 % between 2011 and
2013 and by 37.5 % between 2011 and 2015, and an additional
NOx emission reduction of -21 % between 2011 and 2015,
CHIMERE reproduces an increase in NH3(g) of +24 % between 2011
and 2013 and of +49 % between 2011 and 2015 (when filtering simulations
according to IASI observations' availability). Also, it should be recalled
that NH3 emissions have remained constant in our scenarios, as no
precise information on NH3 emission changes was available to us, but
suggested a 7 % increase between 2011 and 2015, which
could partly explain the difference between IASI and CHIMERE increases.
SO2 and NOx emission reductions have been inferred
from OMI satellite observations, so our study is to a large degree
constrained by observations. Simulations then allow us to state that observed
decreases in SO2 and NOx columns and increases in
NH3 are mutually consistent. The cation / anion ratio shows an
interesting height dependence. It is below unity for total columns, above
unity for surface and near unity for the first kilometre of the atmosphere. The
latter is probably most relevant for inorganic aerosol formation affecting
air quality. Thus it appears that in addition to SO2 and
NOx reductions, NH3 emission reductions would also
be efficient to reduce inorganic aerosol formation. The reduction of
SO2 and NOx emissions also leads to a decrease of
inorganic pSNA production (-14 % in the tropospheric columns between 2011
and 2015), which is a major contributor to PM2.5 concentrations
(about 50 % of surface PM2.5 and 33 % of column PM). A shift
from sulfate to nitrate is simulated due to stronger SO2 reduction
than NOx reduction, with more ammonia thus available for
nitrate formation. Finally, based on our work, it appears that the changes in
gaseous precursors must be updated each year to understand changes in
PM. Current bottom-up inventories are not updated quickly enough. The
method we used to derive inventories for 2013 and 2015 from satellite data
provides an interesting first estimation but presents uncertainties when
pollutants are transported or eliminated. It would be interesting to use
inverse methods operating in synergy between regional CTM and atmospheric
observations i.e, DECSO; to better represent
NOx and SO2 emissions. Inverse modelling systems
could also be used to quantify NH3 emissions, as IASI space-based
NH3 observations have shown considerable potential to reveal the high
spatio-temporal variability of NH3 emissions
.
Data availability
Data are available by contacting the author.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-6701-2019-supplement.
Author contributions
ML and AFC designed the experiments, and ML
carried them out. LC, CC, PFC and MVD were responsible for the satellite
retrieval algorithm development and the processing of the IASI NH3
dataset. GS prepared meteorological and emission data. AFC prepared emission
update and satellite data. ML adapted the model code, performed the
simulations and prepared the paper. All authors contributed to the text and
interpretation of the results and reviewed the paper.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
We acknowledge the free use of tropospheric NO2 column data from
the OMI sensor from http://www.temis.nl/index.php (last access: 3 May
2019). The thesis of Mathieu Lachatre was funded by Sorbonne Universités,
and this study was funded by PolEASIA ANR project under the allocation
ANR-15-CE04-0005. This work was granted access to the HPC resources of TGCC
under the allocation A0030107232 made by GENCI. PM2.5 measurements
were provided by the U.S. Department of State Air Quality Monitoring Program,
Mission China. IASI is a joint mission of EUMETSAT and the Centre National
d'Études Spatiales (CNES, France). The authors acknowledge the Aeris data
infrastructure (https://www.aeris-data.fr/, last access: 3 May 2019)
for providing access to the IASI Level-2 NH3 data used in this
study. The French scientists are grateful to CNES and Centre National de la
Recherche Scientifique (CNRS) for financial support. The research in Belgium
is also funded by the Belgian State Federal Office for Scientific, Technical
and Cultural Affairs and the European Space Agency (ESA Prodex IASI Flow
project).
Review statement
This paper was edited by Kostas Tsigaridis and reviewed by
two anonymous referees.
ReferencesAnsari, A. S. and Pandis, S. N.: Prediction of multicomponent inorganic
atmospheric aerosol behavior, Atmos. Environ., 33, 745–757,
10.1016/S1352-2310(98)00221-0, 1999.Banzhaf, S., Schaap, M., Wichink Kruit, R. J., Denier van der Gon, H. A. C., Stern, R., and Builtjes, P. J. H.: Impact of emission
changes on secondary inorganic aerosol episodes across Germany, Atmos. Chem. Phys., 13, 11675–11693, 10.5194/acp-13-11675-2013, 2013.Bauer, S. E., Tsigaridis, K., and Miller, R.: Significant atmospheric aerosol
pollution caused by world food cultivation, Geophys. Res. Lett., 43,
5394–5400, 10.1002/2016GL068354,
2016.Beirle, S., Boersma, K., Platt, U., Laurence, M., and Wagner, T.: Megacity
emissions and lifetimes of nitrogen oxides probed from space, Science, 333,
1737–1739, 10.1002/2016GL068354, 2011.Chen, Q., Song, S., Stefan, H., Yuei-An, L., Zhu, W., and Jingyang, Z.:
Assessment of ZTD derived from ECMWF/NCEP datawith GPS ZTD over China, GPS
Solut., 15, 415–425, 10.1007/s10291-010-0200-x, 2010.Chen, R., Cheng, J., Lv, J., and Wu, L.: Comparison of chemical compositions
in air particulate matter during summer and winter in Beijing, China,
Environ. Geochem. Hlth., 39, 913–921,
10.1007/s10653-016-9862-9, 2017.Clarisse, L., Clerbaux, C., Dentener, F., Hurtmans, D., and Coheur, P. F.:
Global ammonia distribution derived from infrared satellite observations,
Nat. Geosci., 2, 479–483, 10.1038/ngeo551,
2009.Cohen, A. J., Brauer, M., Burnett, R., Anderson, H. R., Frostad, J., Estep, K.,
Balakrishnan, K., Brunekreef, B., Dandona, L., Dandona, R., Feigin, V.,
Freedman, G., Hubbell, B., Jobling, A., Kan, H., Knibbs, L., Liu, Y., Martin,
R., Morawska, L., Pope, C. A., Shin, H., Straif, K., Shaddick, G., Thomas,
M., van Dingenen, R., van Donkelaar, A., Vos, T., Murray, C. J., and
Forouzanfar, M. H.: Estimates and 25-year trends of the global burden of
disease attributable to ambient air pollution: an analysis of data from the
Global Burden of Diseases Study 2015, The Lancet, 389, 1907–1918,
10.1016/S0140-6736(17)30505-6,
2017.de Foy, B., Lu, Z., and Streets, D.: Satellite NO2 retrievals suggest China
has exceeded its NOx reduction goals from the twelfth Five-Year Plan,
Sci. Rep.-UK, 6, 35912, 10.1038/srep35912, 2016.Ding, J., Miyazaki, K., van der A, R. J., Mijling, B., Kurokawa, J.-I., Cho, S., Janssens-Maenhout, G., Zhang, Q., Liu, F., and
Levelt, P. F.: Intercomparison of NOx emission inventories over East Asia, Atmos. Chem. Phys., 17, 10125–10141, 10.5194/acp-17-10125-2017, 2017.Fortems-Cheiney, A., Dufour, G., Hamaoui-Laguel, L., Foret, G., Siour, G.,
Van Damme, M., Meleux, F., Coheur, P.-F., Clerbaux, C., Clarisse, L., Favez,
O., Wallasch, M., and Beekmann, M.: Unaccounted variability in NH3
agricultural sources detected by IASI contributing to European spring haze
episode, Geophys. Res. Lett., 43, 5475–5482,
10.1002/2016GL069361,
2016.Fu, X., Wang, S., Xing, J., Zhang, X., Wang, T., and Hao, J.: Increasing
Ammonia Concentrations Reduce the Effectiveness of Particle Pollution Control
Achieved via SO2 and NOX Emissions Reduction in East China, Environ.
Sci. Tech. Let., 4, 221–227,
10.1021/acs.estlett.7b00143,
2017.Guenther, A., Karl, T., Harley, P., Wiedinmyer, C., Palmer, P. I., and Geron, C.: Estimates of global terrestrial isoprene
emissions using MEGAN (Model of Emissions of Gases and Aerosols from Nature), Atmos. Chem. Phys., 6, 3181–3210, 10.5194/acp-6-3181-2006, 2006.Hedegaard, G. B., Brandt, J., Christensen, J. H., Frohn, L. M., Geels, C., Hansen, K. M., and Stendel, M.: Impacts of
climate change on air pollution levels in the Northern Hemisphere with special focus on Europe and the Arctic, Atmos. Chem. Phys., 8, 3337–3367, 10.5194/acp-8-3337-2008, 2008.Hoyle, C. R., Fuchs, C., Järvinen, E., Saathoff, H., Dias, A., El Haddad, I., Gysel, M., Coburn, S. C., Tröstl, J.,
Bernhammer, A.-K., Bianchi, F., Breitenlechner, M., Corbin, J. C., Craven, J., Donahue, N. M., Duplissy, J., Ehrhart, S., Frege, C.,
Gordon, H., Höppel, N., Heinritzi, M., Kristensen, T. B., Molteni, U., Nichman, L., Pinterich, T., Prévôt, A. S. H., Simon, M., Slowik, J. G.,
Steiner, G., Tomé, A., Vogel, A. L., Volkamer, R., Wagner, A. C., Wagner, R., Wexler, A. S., Williamson, C., Winkler, P. M., Yan, C.,
Amorim, A., Dommen, J., Curtius, J., Gallagher, M. W., Flagan, R. C., Hansel, A., Kirkby, J., Kulmala, M., Möhler, O., Stratmann, F.,
Worsnop, D. R., and Baltensperger, U.: Aqueous phase oxidation of sulphur dioxide by ozone in cloud droplets, Atmos. Chem. Phys., 16, 1693–1712, 10.5194/acp-16-1693-2016, 2016.Huang, X., Song, Y., Li, M., Li, J., Huo, Q., Cai, X., Zhu, T., Hu, M., and
Zhang, H.: A high-resolution ammonia emission inventory in China, Global
Biogeochem. Cy., 26, GB1030, 10.1029/2011GB004161, 2012.Janssens-Maenhout, G., Crippa, M., Guizzardi, D., Dentener, F., Muntean, M., Pouliot, G., Keating, T., Zhang, Q.,
Kurokawa, J., Wankmüller, R., Denier van der Gon, H., Kuenen, J. J. P., Klimont, Z., Frost, G., Darras, S., Koffi, B.,
and Li, M.: HTAP_v2.2: a mosaic of regional and global emission grid maps for 2008 and 2010 to study hemispheric transport of air pollution, Atmos. Chem. Phys., 15, 11411–11432, 10.5194/acp-15-11411-2015, 2015.Jingjing, L., Jianping, H., Bin, C., Tian, Z., Hongru, Y., Hongchun, J.,
Zhongwei, H., and Beidou, Z.: Comparisons of PBL heights derived from CALIPSO
and ECMWF reanalysis data over China, J. Quant. Spectrosc.
Ra., 153, 102–112,
10.1016/j.jqsrt.2014.10.011,
2015.Kang, Y., Liu, M., Song, Y., Huang, X., Yao, H., Cai, X., Zhang, H., Kang, L., Liu, X., Yan, X., He, H., Zhang,
Q., Shao, M., and Zhu, T.: High-resolution ammonia emissions inventories in China from 1980 to 2012, Atmos. Chem. Phys., 16, 2043–2058, 10.5194/acp-16-2043-2016, 2016.Koukouli, M. E., Theys, N., Ding, J., Zyrichidou, I., Mijling, B., Balis, D., and van der A, R. J.:
Updated SO2 emission estimates over China using OMI/Aura observations, Atmos. Meas. Tech., 11, 1817–1832, 10.5194/amt-11-1817-2018, 2018.Kurokawa, J., Ohara, T., Morikawa, T., Hanayama, S., Janssens-Maenhout, G., Fukui, T., Kawashima, K., and Akimoto, H.: Emissions of air pollutants and greenhouse
gases over Asian regions during 2000–2008: Regional Emission inventory in ASia (REAS) version 2, Atmos. Chem. Phys., 13, 11019–11058, 10.5194/acp-13-11019-2013, 2013.Landrigan, P. J., Fuller, R., Acosta, N. J., Adeyi, O., Arnold, R., Basu, N.,
Baldé, A. B., Bertollini, R., Bose-O'Reilly, S., Boufford, J. I.,
Breysse, P. N., Chiles, T., Mahidol, C., Coll-Seck, A. M., Cropper, M. L.,
Fobil, J., Fuster, V., Greenstone, M., Haines, A., Hanrahan, D., Hunter, D.,
Khare, M., Krupnick, A., Lanphear, B., Lohani, B., Martin, K., Mathiasen,
K. V., McTeer, M. A., Murray, C. J., Ndahimananjara, J. D., Perera, F.,
Potočnik, J., Preker, A. S., Ramesh, J., Rockström, J., Salinas,
C., Samson, L. D., Sandilya, K., Sly, P. D., Smith, K. R., Steiner, A.,
Stewart, R. B., Suk, W. A., van Schayck, O. C., Yadama, G. N., Yumkella, K.,
and Zhong, M.: The Lancet Commission on pollution and health, The Lancet,
391, 10119, 10.1016/S0140-6736(17)32345-0, 2017.Lelieveld, J., Evans, J. S., Fnais, M., Giannadaki, D., and Pozzer, A.: The
contribution of outdoor air pollution sources to premature mortality on a
global scale, Nature, 525, 367–371, 10.1038/nature15371, 2015.Li, X., Zhou, W., and Ouyang, Z.: Forty years of urban expansion in Beijing:
What is the relative importance of physical, socioeconomic, and neighborhood
factors?, Appl. Geogr., 38, 1–10, 10.1016/j.apgeog.2012.11.004,
2013.Liu, F., Beirle, S., Zhang, Q., van der A, R. J., Zheng, B., Tong, D., and He, K.: NOx emission trends over Chinese cities estimated
from OMI observations during 2005 to 2015, Atmos. Chem. Phys., 17, 9261–9275, 10.5194/acp-17-9261-2017, 2017.Liu, M., Huang, X., Song, Y., Xu, T., Wang, S., Wu, Z., Hu, M., Zhang, L., Zhang, Q., Pan, Y., Liu, X., and Zhu, T.: Rapid SO2 emission
reductions significantly increase tropospheric ammonia concentrations over the North China Plain, Atmos. Chem. Phys., 18, 17933–17943, 10.5194/acp-18-17933-2018, 2018.Mailler, S., Menut, L., Khvorostyanov, D., Valari, M., Couvidat, F., Siour,
G., Turquety, S., Briant, R., Tuccella, P., Bessagnet, B., Colette, A.,
Létinois, L., Markakis, K., and Meleux, F.: CHIMERE-2017: from urban to
hemispheric chemistry-transport modeling, Geosci. Model Dev., 10, 2397–2423,
10.5194/gmd-10-2397-2017, 2017.Menut, L., Bessagnet, B., Khvorostyanov, D., Beekmann, M., Blond, N.,
Colette, A., Coll, I., Curci, G., Foret, G., Hodzic, A., Mailler, S., Meleux,
F., Monge, J.-L., Pison, I., Siour, G., Turquety, S., Valari, M., Vautard,
R., and Vivanco, M. G.: CHIMERE 2013: a model for regional atmospheric
composition modelling, Geosci. Model Dev., 6, 981–1028,
10.5194/gmd-6-981-2013, 2013.Mijling, B., van der A, R. J., and Zhang, Q.: Regional nitrogen oxides emission trends in East Asia observed from space, Atmos. Chem. Phys., 13, 12003–12012, 10.5194/acp-13-12003-2013, 2013.
Nenes, A., Pilinis, C., and Pandis, S.: ISORROPIA: A new thermodynamic model
for inorganic multicomponent atmospheric aerosols, Aquat. Geochem.,
4, 123–152, 1998.Owens, R. G. and Hewson, T.: ECMWF Forecast User Guide, 10.21957/m1cs7h,
2018.Palmer, P. I., Abbot, D. S., Fu, T.-M., Jacob, D. J., Chance, K., Kurosu,
T. P., Guenther, A., Wiedinmyer, C., Stanton, J. C., Pilling, M. J.,
Pressley, S. N., Lamb, B., and Sumner, A. L.: Quantifying the seasonal and
interannual variability of North American isoprene emissions using satellite
observations of the formaldehyde column, J. Geophys. Res.-Atmos., 111, D12315, 10.1029/2005JD006689,
2006.Paulot, F., Ginoux, P., Cooke, W. F., Donner, L. J., Fan, S., Lin, M.-Y., Mao, J., Naik, V., and Horowitz, L. W.:
Sensitivity of nitrate aerosols to ammonia emissions and to nitrate chemistry: implications for present and future nitrate optical depth, Atmos. Chem. Phys., 16, 1459–1477, 10.5194/acp-16-1459-2016, 2016.Petetin, H., Sciare, J., Bressi, M., Gros, V., Rosso, A., Sanchez, O., Sarda-Estève, R., Petit, J.-E., and Beekmann, M.:
Assessing the ammonium nitrate formation regime in the Paris megacity and its representation in the CHIMERE model, Atmos. Chem. Phys., 16, 10419–10440, 10.5194/acp-16-10419-2016, 2016.Pinder, R. W., Gilliland, A. B., and Dennis, R. L.: Environmental impact of
atmospheric NH3 emissions under present and future conditions in the eastern United States,
Geophys. Res. Lett., 35, L12808, 10.1029/2008GL033732,
2008.Pozzer, A., Tsimpidi, A. P., Karydis, V. A., de Meij, A., and Lelieveld, J.: Impact of agricultural emission reductions on
fine-particulate matter and public health, Atmos. Chem. Phys., 17, 12813–12826, 10.5194/acp-17-12813-2017, 2017.Riddick, S., Ward, D., Hess, P., Mahowald, N., Massad, R., and Holland, E.: Estimate of changes in agricultural terrestrial nitrogen pathways and
ammonia emissions from 1850 to present in the Community Earth System Model, Biogeosciences, 13, 3397–3426, 10.5194/bg-13-3397-2016, 2016.Schaap, M., van Loon, M., ten Brink, H. M., Dentener, F. J., and Builtjes, P. J. H.: Secondary inorganic aerosol simulations for Europe with special
attention to nitrate, Atmos. Chem. Phys., 4, 857–874, 10.5194/acp-4-857-2004, 2004.Schiferl, L. D., Heald, C. L., Van Damme, M., Clarisse, L., Clerbaux, C., Coheur, P.-F., Nowak, J. B., Neuman, J. A., Herndon, S. C., Roscioli, J. R.,
and Eilerman, S. J.: Interannual variability of ammonia concentrations over the United States: sources and implications, Atmos. Chem. Phys., 16, 12305–12328, 10.5194/acp-16-12305-2016, 2016.
Seinfeld, J. H. and Pandis, S. N.: Atmospheric Chemistry and Physics: From
Air Pollution to Climate Change, 2nd edn., John Wiley & Sons, New York, USA,
2006.Stockwell, W. R. and Calvert, J. G.: The mechanism of the HO-SO2 reaction,
Atmos. Environ., 17, 2231–2235,
10.1016/0004-6981(83)90220-2, 2016.Szopa, S., Foret, G., and Menut, L., and Cozic, A.: Impact of large scale
circulation on European summer surface ozone: consequences for modeling,
Atmos. Environ., 43, 1189–1195,
10.1016/j.atmosenv.2008.10.039, 2008.Van Damme, M., Whitburn, S., Clarisse, L., Clerbaux, C., Hurtmans, D., and
Coheur, P.-F.: Version 2 of the IASI NH3 neural network retrieval
algorithm: near-real-time and reanalysed datasets, Atmos. Meas. Tech., 10,
4905–4914, 10.5194/amt-10-4905-2017, 2017.Van Damme, M., Clarisse, L., Whitburn, S., Hadji-Lazaro, J., Hurtmans, D.,
Clerbaux, C., and Coheur, P.-F.: Industrial and agricultural ammonia point
sources exposed, Nature, 564, 99–103, 10.1038/s41586-018-0747-1, 2018.
Wang, Y., Zhang, Q. Q., He, K., Zhang, Q., and Chai, L.:
Sulfate-nitrate-ammonium aerosols over China: response to 2000–2015
emission changes of sulfur dioxide, nitrogen oxides, and ammonia, Atmos.
Chem. Phys., 13, 2635–2652, 10.5194/acp-13-2635-2013, 2013.Warner, J. X., Dickerson, R. R., Wei, Z., Strow, L. L., Wang, Y., and Liang,
Q.: Increased atmospheric ammonia over the world's major agricultural areas
detected from space, Geophys. Res. Lett., 44, 2875–2884,
10.1002/2016GL072305,
2017.Whitburn, S., Van Damme, M., Clarisse, L., Turquety, S., Clerbaux, C., and
Coheur, P. F.: Doubling of annual ammonia emissions from the peat fires in
Indonesia during the 2015 El Niño, Geophys. Res. Lett., 43,
11007–11014, 10.1002/2016GL070620, 2016.Wu, J., Kong, S., Wu, F., Cheng, Y., Zheng, S., Yan, Q., Zheng, H., Yang, G.,
Zheng, M., Liu, D., Zhao, D., and Qi, S.: Estimating the open biomass burning
emissions in central and eastern China from 2003 to 2015 based on satellite
observation, Atmos. Chem. Phys., 18, 11623–11646,
10.5194/acp-18-11623-2018, 2018.Zhang, L., Chen, Y., Zhao, Y., Henze, D. K., Zhu, L., Song, Y., Paulot, F.,
Liu, X., Pan, Y., Lin, Y., and Huang, B.: Agricultural ammonia emissions in
China: reconciling bottom-up and top-down estimates, Atmos. Chem. Phys., 18,
339–355, 10.5194/acp-18-339-2018, 2018.Zheng, B., Tong, D., Li, M., Liu, F., Hong, C., Geng, G., Li, H., Li, X.,
Peng, L., Qi, J., Yan, L., Zhang, Y., Zhao, H., Zheng, Y., He, K., and Zhang,
Q.: Trends in China's anthropogenic emissions since 2010 as the consequence
of clean air actions, Atmos. Chem. Phys., 18, 14095–14111,
10.5194/acp-18-14095-2018, 2018.