China is one of the largest agricultural countries in the world. Thus,
NH3 emission from agricultural activities in China considerably affects
the country's regional air quality and visibility. In this study, a
high-resolution agricultural NH3 emission inventory compiled on 1 km × 1 km horizontal resolution was applied to calculate the NH3
mass burden in China and reliably estimate the influence of NH3 on
agriculture. The key parameter emission factors of this inventory were
enhanced by considering many experiment results, and the dynamic data of
spatial and temporal information were updated using statistical data of
2015. In addition to fertilizers and husbandry, farmland ecosystems,
livestock waste, crop residue burning, wood-based fuel combustion, and other
NH3 emission sources were included in this inventory. Furthermore, a
source apportionment tool, namely, the Integrated Source Apportionment Method
(ISAM) coupled with the air quality modeling system Regional Atmospheric
Modeling System and Community Multiscale Air Quality, was applied to capture
the contribution of NH3 emitted from total agriculture (Tagr) in China.
The aerosol mass concentration in 2015 was simulated, and results showed
that the high mass concentration of NH3 exceeded 10 µg m-3
and mainly appeared in the North China Plain, Central China, the Yangtze River
Delta, and the Sichuan Basin. Moreover, the annual average contribution of TagrNH3 to PM2.5 mass burden was 14 %–22 % in China. Specific to
the PM2.5 components, TagrNH3 contributed dominantly to ammonium
formation (87.6 %) but trivially to sulfate formation (2.2 %). In
addition, several brute-force sensitivity tests were conducted to estimate the
impact of TagrNH3 emission reduction on PM2.5 mass burden. In
contrast to the result of ISAM, even though the TagrNH3 only provided
10.1 % contribution to nitrate under the current emission scenario, the
reduction of nitrate could reach 95.8 % upon removal of the TagrNH3
emission. This deviation occurred because the contribution of NH3 to
nitrate should be small under a “rich NH3”environment and large under
a “poor NH3” environment. Thus, the influence of NH3 on nitrate
formation would be enhanced with the decrease in ambient NH3 mass
concentration.
Introduction
Ammonia (NH3) is an important pollution species that is a principal
neutralizing agent for acid aerosols,
SO42- and
NO3-, which are formed from SO2 and
NOx (Chang, 1989; McMurry et al., 1983). In addition, NH3
influences the rate of particle nucleation (Ball et al., 1999; Kulmala et
al., 2002) and enhances secondary organic aerosol (SOA) yield (Babar et al.,
2017). Widespread haze events have frequently occurred in most regions of
eastern China in recent years, and several studies have reported that
secondary inorganic salts, including sulfate, nitrate, and ammonium, form
the majority of total aerosols in the urban and rural regions (Tao et al.,
2014; G. Wang et al., 2016; Zhang et al., 2012; K. Zhang et al.,
2018; Lai et al., 2016). Therefore, in addition to the heavy emissions of SO2 and
NO2, NH3 emissions from agricultural activities are
non-negligible.
China is one of the largest agricultural countries in the world. Even though
a decrease appeared from 2006 to 2012, the annual NH3 emission budget,
which reached 9.7–12 Tg (Kang et al., 2016; Xu et al., 2016; Zhou et al.,
2015), remains huge and leads to high NH3 ambient concentration. This
massive NH3 emission considerably affects regional air quality and
horizontal visibility. First, the major PM2.5 components,
(NH4)2SO4, (NH4)3H(SO4)2,
NH4HSO4, and NH4NO3, were partially or fully yielded
from neutralizing H2SO4 and HNO3 via NH3 reaction
(Tanner et al., 1981; Brost et al., 1988; Quan et al., 2014; Zhao et al.,
2013; Zhang et al., 2014). Studies also showed that NH3 improves
H2SO4 nucleation by 1–10 times (Benson et al., 2011) and provides
enough new particles to alter the number and size distributions. Thus,
NH3 and its secondary product NH4+
play an important role in the formation of air pollution and haze days. Some
research has shown that approximately 80 % of total anthropogenic NH3
emissions is derived from agricultural sources and that livestock manure
provides more contributions than that of synthetic fertilizers (Kang et al.,
2016; Zhou et al., 2016). The Chinese government has taken several control
strategies to reduce particle pollution and its precursors; some examples of
these systems include catalytic reduction systems in the power sector (Xia
et al., 2016) and measures to change coal to gas for residents' life and
heating (Ren et al., 2014). Related observations have shown that the mass
burden of SO2 and NOx have distinctly decreased in recent years
(De Foy et al., 2016; Wang et al., 2015; Zheng et al., 2018). However, no
specific measure for the control of agricultural NH3 emission has yet
been implemented, and the total agricultural NH3 emission budget was
not considerably changed from 2010 to 2017 (Zheng et al., 2018).
In addition, accurate information of agriculture NH3 emission is
important for estimating the NH3 mass burden and its environmental
effect. Several studies have focused on NH3 emissions from agricultural
activities in China or East Asia. The second version of the Regional
Emission Inventory in Asia (REAS) has established an anthropogenic emission
inventory, which includes the source of agricultural NH3 (fertilizer
application and livestock) (Kurokawa et al., 2013). This inventory, which
targeted the years 2000–2008, has a 0.25×0.25∘ spatial
resolution with monthly variation. MASAGE_NH3 (Magnitude
and Seasonality of Agricultural Emissions model for NH3) developed a
bottom-up NH3 emission inventory by using the adjoint of the GEOS-Chem
chemical transport model (Paulot et al., 2014). The network data for
NH4+ wet deposition fluxes from 2005 to 2008 were inversed to
optimize China's NH3 emission in this inventory. Fu et al. (2015) used
the Community Multiscale Air Quality (CMAQ) model coupled with an
agroecosystem to estimate the NH3 emissions with high spatial and
temporal resolution in 2011; the model could obtain hourly emission features
through online model calculations. These NH3 emission inventories have
provided useful datasets for understanding the distribution features of
NH3 mass burden in China. However, with the migration of population,
economic growth, and the increase in the consumption of agricultural
products, the spatial distribution and strength of agricultural NH3
emission changed remarkably in China during the last decade (Xu et al.,
2017); thus, a reliable emission information based on the recent year is
also necessary for estimating the NH3 mass burden.
Previous studies have investigated the influence of NH3 emission on
aerosol loading in several areas of China. Wu et al. (2008) conducted
sensitivity studies to assess the impact of livestock NH3 emissions on
the PM2.5 mass concentration in North China by using MM5-CMAQ modeling
system. The results showed that livestock NH3 provides >20 % contribution to nitrate and ammonium but provides minimal
contribution to sulfate. Wang et al. (2011) used the response surface
modeling technique to estimate the contribution of NH3 emission in eastern
China and found that the total NH3 emission contributes 8 %–11 %
to PM2.5 concentration and that the nonlinear effects are significant during
the transition between NH3 rich and poor conditions. Fu et al. (2017)
and Zhao et al. (2017) also investigated the impact of NH3 emission on
PM2.5 in eastern China and the Hai River Basin. However, related research
remains scarce and is mainly focused on the local regions, and most of it
generally uses the brute-force sensitivity method to estimate the NH3
impact on the basis of the chemistry model, which reflects the particle
concentration change with emission reduction (Koo et al., 2009).
Model domain used in this study and the geographic locations of
Beijing–Tianjin–Hebei (BTH), Northeast China (NEC), Yangtze River Delta
(YRD), Pearl River Delta (PRD), Sichuan Basin (SCB), Central China (CNC), and
Shandong Province (SDP). The locations of observation data are also shown in
the model domain.
A comprehensive high-resolution NH3 emission inventory PKU-NH3,
which is based on the year 2015, is applied in this study to capture the
agricultural NH3 mass concentration in China. In addition, the
contribution to PM2.5 particle is estimated via the air quality
modeling system Regional Atmospheric Modeling System (RAMS)–CMAQ, coupled
with the online source tagged module Integrated Source Apportionment Method
(ISAM). Compared with previous studies, this high-resolution agricultural
NH3 emission inventory is more accurate and reflects the latest spatial
and temporal distribution features (Liu et al., 2019). Major trace gases and
aerosol species in 2015 are simulated via the modeling system and evaluated
using substantial observation data. The contribution to pollutant
concentrations can be tagged and quantified by RAMS–CMAQ–ISAM under the
current scenario (Wang et al., 2009). Then, several brute-force sensitivity
tests are conducted to estimate the effect of reducing agricultural NH3
emission on the PM2.5 mass burden. The results from the source
apportionment simulation and brute-force sensitivity tests in January,
April, July, and October are presented, and the detailed features of seven
major populated areas (as shown in Fig. 1) of China are discussed.
Methodology
The emission inventory is described as follows: first, the NH3 emission
data in China were provided by the PKU-NH3 emission inventory (Kang et
al., 2016; L. Zhang et al., 2018). This inventory was developed on the basis of
previous studies (Huang et al., 2012) and improved horizontal resolution and
accuracy. It was compiled on 1 km × 1 km horizontal resolution with
monthly statistical data in 2015. One of the most uncertain parameters
of the emission factors applied in this inventory was enhanced by
considering as many native experiment results as possible with ambient
temperature, soil acidity, and other factors of change. In addition, this
inventory not only includes the fertilizer and husbandry emissions from
agricultural activities but also collects the emission data of farmland
ecosystems, livestock waste, biomass burning (forest and grassland fires,
crop residue burning, and fuel wood combustion), and other sources
(excrement waste from rural populations, the chemical industry, waste
disposal, NH3 escape from thermal power plants, and traffic sources).
Second, the anthropogenic emission of primary aerosols and the precursors
were obtained from the MIX Asian emission inventory (base year 2012)
prepared by the Model Inter-Comparison Study for Asia (MICS-ASIA III) (Lu et
al., 2011; Lei et al., 2011). The anthropogenic emission sources of
SO2, NOx, volatile organic compounds (VOCs), black carbon, organic
carbon, primary PM2.5, and PM10 were obtained from the
monthly MIX inventory with 0.25∘× 0.25∘
spatial resolution. The REAS version 2 (Kurokawa et al., 2013) and Global
Fire Emissions Database version 3 (van der Werf et al., 2010) were used to
provide data on VOCs, nitrogen oxides from flight exhaust, lighting, paint,
wildfires, savanna burning, and slash-and-burn agriculture.
The modeling system RAMS–CMAQ was applied to simulate the transformation
and transport of pollutants in the atmosphere. The CMAQ (version 5.0.2)
released by the US Environmental Protection Agency (Eder et al., 2009;
Mathur et al., 2008) was the major component of the RAMS–CMAQ modeling
system. In this model, the CB05 (version CB05tucl) chemical mechanism
(Whitten, 2010) was used to treat the gas-phase chemical mechanism. The
simulation of O3 in urban plumes, which could impact the NOx
chemical transformation and fine particle mass predictions, was updated in
this version to obtain reasonable results. The sixth-generation model CMAQ
aerosol model (AERO6), which added nine new PM2.5 species and updated
the SOA yield parametrization and primary organic aerosol aging processes,
was used to simulate the formation and dynamic processes of aerosols. The
ISORROPIA model (version 2.1) (Fountoukis and Nenes, 2007) was used to
describe the thermodynamic equilibrium of gas–particle transformation. The
highly versatile numerical model RAMS, which can capture the boundary
layer and the underlying surface well, was applied to provide the meteorological
fields for CMAQ (Cotton et al., 2003). The European Centre for Medium-Range
Weather Forecasts reanalysis datasets (1∘× 1∘
spatial resolution) were used to supply the background fields and sea
surface temperatures. The model domain (Fig. 1) is 6654 km × 5440 km with 64 km2 fixed grid cells and uses a rotated polar stereographic
map projection, which covers the entire mainland of China and its
surrounding regions. The model has 15 vertical layers, and half of them are
located in the lowest 2 km to provide a precise simulation of the
atmospheric boundary layer.
The ISAM is a flexible and efficient online source apportionment
implementation that was used to track multiple pollutants emitted from
different geographic regions and source types. Compared with its previous
version (i.e., tagged species source apportionment), the processes of
tracking tagged tracer transport and precursor reaction were optimized to
balance the computational requirements and reliable representation of
physical and chemical evolution. To reduce the nonlinear effect during phase
transformation and relative chemical interactions, a stand-alone subroutine
“wrapper” approach was applied in ISAM to apportion the secondary PM
species and their precursor gases during the thermodynamic equilibrium
simulation; a hybrid approach, which uses LU decomposition triangular
matrices (Yang et al., 1997), was developed for describing gas-phase
chemical interactions. In this study, ISAM was coupled into RAMS–CMAQ and
set to trace the transport and chemical reactions of NH3 from the
fertilizer and husbandry emission sectors and quantitatively estimate the
contribution of agriculture NH3 emission to the PM2.5 mass
concentration in China.
Model evaluation
To evaluate the performance of the model, substantial observation data are
used for comparison with the simulation results. Meteorological factors are
important to capture the formation processes and transport of secondary
aerosols. Thus, in this study, the observed meteorological data from surface
stations of the Chinese National Meteorological Center were collected to
evaluate the performance of the model. Detailed information is provided in
Appendix A. Furthermore, the observed SO2, NO2, and PM2.5
released from the Ministry of Environmental Protection of China were applied
to evaluate the modeled mass concentration of these pollutants. The hourly
observation data in January, April, July, and October at six stations
located in Beijing, Jinan, Shijiazhuang, Nanjing, Guangzhou, and Zhengzhou
were collected in this study. The scatterplots of comparison are shown in
Fig. 2, and the statistical parameters between the observations and
simulations are listed in Tables 1–3. Most of the scatter points broadly
gather around the 1 : 1 solid line. Most of the correlation coefficients in
Tables 1–3 are higher than 0.5, indicating that the model can capture the
regional variation features of measurements. The standard deviations between
the observed and simulated results are similar in most cases as well. The
simulation results performed better in winter compared with those in summer
because the diffusion condition was strong and the mass concentration
changed noticeably during summer. The modeled PM2.5 generally
performed well due to relatively high correlation coefficients. The evident
deviation of the modeled mean, which was higher than that of the
observation, was between the observed and modeled SO2. The emission of
SO2 reduced rapidly because of the control measures from 2013 in China.
However, the emission inventory may not reflect this feature and may
slightly overestimate the mass burden.
The scatterplots between the modeled and the observed hourly
SO2, NO2, and PM2.5 in January, April, July, and October 2015.
The solid lines are 1 : 1, and the dashed lines are 2 : 1 or 1 : 2.
Statistical summary of the comparisons of the monthly average
PM2.5 between simulation and observation.
a Number of samples.
b Total mean of observation.
c Total mean of simulation.
d Standard deviation of observation.
e Standard deviation of simulation.
f Correlation coefficient between daily observation and simulation.
Statistical summary of the comparisons of the monthly average
NO2 between simulation and observation.
The horizontal distributions of the modeled monthly NH3 mass concentration in January, April, July, and October in 2015.
The horizontal distributions of the modeled monthly NH3 mass
concentration in January, April, July, and October in 2015 are shown in
Fig. 3. Pan et al. (2018) provided the distributions of satellite NH3
total column distribution and the surface NH3 concentrations at several
observation sites in Fig. 1 of their paper. Their results showed that the
highest mass burden is concentrated in the North China Plain (NCP), Central
China (CNC), Yangtze River Delta (YRD), and Sichuan Basin (SCB). The
simulation results in this study broadly reflect these distribution
features. The values of NH3 concentrations in these regions could reach
10–25 µg m3 in Pan et al. (2018); these results coincided well
with the simulation results. However, some considerable deviation appeared
in areas of the eastern part of Gansu Province. In this study, the modeled
NH3 in these regions was slightly higher than those of the observations
in Pan et al. (2018). L. Zhang et al. (2018) also presented the NH3 mass
concentration in four seasons over China through simulation (horizontal
distribution) and ground-based measurements (point values) in Fig. 9 of
their paper. In addition to the regions maintained in Pan et al. (2018), the
high mass burden of NH3 also appeared in Northeast China (NEC), as
shown by the simulation and observation results in K. Zhang et al. (2018).
Generally, this distribution feature should be reasonable because the Three
River Plain located in NEC is an important agricultural base in China, and
the NH3 emission in this region can be strong during spring and summer.
The simulation results in this study also followed the seasonal variation
feature of NH3 mass burden, as shown in L. Zhang et al. (2018); the
feature was higher in summer and lower in winter, and the magnitudes were also
close to each other. Thus, the modeled NH3 concentration measured by
RAMS–CMAQ is reliable and can be applied for the analysis in this study.
Results and discussions
The horizontal distributions of modeled monthly PM2.5 mass
concentrations in January, April, July, and October in 2015 and the surface
wind field are shown in Fig. 4. Over the eastern part of China, the heavy
PM2.5 pollution happened in January, and the relatively high air
quality appeared in July. The large PM2.5 mass burden exceeded 200 µg m3 in January and was mainly concentrated in the NCP, the Yangtze
River Valley of CNC, and SCB; these observations broadly coincided with the
regions covered by a high mass burden of NH3, as shown in Fig. 3. The
wind speed in the regions mentioned above was relatively weak, implying that
the diffusion condition was poor and that more aerosols can be trapped in these
regions. In addition, the PM2.5 mass burden (50–150 µg m-3)
in July was lower than that of other months. Considering that NH3
emission is mainly concerned with secondary inorganic aerosols (SNAs), such
as sulfate, nitrate, and ammonium formation, the analysis hereafter mainly
focuses on SNA. Figure 5 presents the modeled monthly SNA mass concentration
in January, April, July, and October in 2015. The mass loading of SNA
generally provided 40 %–60 % of the total PM2.5 in the eastern
part of China; this result is comparable with previous studies (Cao et al.,
2017; Chen et al., 2016; Lai et al., 2016; H. Wang et al., 2016). The
distribution pattern and seasonal variation of SNA also followed the
features of PM2.5, and the high mass concentration of SNA could exceed
100 µg m-3 in January.
The horizontal distributions of the modeled monthly PM2.5
mass concentration in January, April, July, and October in 2015. Also shown
are the surface wind fields.
The horizontal distributions of the modeled monthly SNA mass
concentration in January, April, July, and October in 2015.
The horizontal distributions of the contribution percentage of
NH3 emissions to SNA mass concentration (%) in January, April, July, and October.
Then, the contributions of NH3 from multiple agricultural emissions
(including fertilizer, husbandry, farmland ecosystems, livestock waste, crop
residue burning, and excrement waste from rural populations) to aerosols
were calculated using RAMS–CMAQ–ISAM; the monthly average contribution
percentage of total agricultural activities (Tagr) in January, April, July,
and October is shown in Fig. 6. Generally, the TagrNH3 provided
30 %–50 % contribution to SNA in January and October and 20 %–40 %
contribution in April and July over most parts of eastern China. The
relatively low value mainly appeared in April.
The regional percent (%) of TagrNH3 contribution to
sulfate, nitrate, ammonium, and SNA mass concentration.
The regional average percentage of Tagr contribution to sulfate, nitrate,
ammonium, SNA, and PM2.5 are shown in Table 4. As shown in this table,
the annual average TagrNH3 provided major contributions, which reached
approximately 90 %, to ammonium and relatively small contributions
(5 %–10 %) to nitrate mass burden. However, the contribution to sulfate
was minimal because sulfate formation from SO2 can occur in various
ways, in addition to neutralization by NH3, such as being oxidized by
H2O2, O3, or peroxyacetic acid. The seasonal variation of
ammonium was evident; it could be higher than 99 % in January but lower
than 70 % in July. Most of the differences shown in Table 4 could
exceed 10 % because the NH3 emitted from other sources (anthropogenic
and natural sources) was substantial in these regions during summer. The
annual average TagrNH3 provided 20 %–40 % contribution to SNA
mass concentration, and the contributions in January were larger than that
in July. The seasonal variation and spatial features of TagrNH3
contribution to PM2.5 mass concentration were similar to the features
of SNA and generally provided approximately 14 %–22 % contribution to
the total PM2.5 mass concentration in these places. By contrast, the
annual contribution in China was higher than those in the regions mentioned
above. This feature indicates that the TagrNH3 provided a higher
contribution compared to other sources over regions with weaker
anthropogenic activities.
The horizontal distributions of SNA mass concentration (µg m-3) variation associated with agriculture NH3 removal in January, April, July, and October.
The variation percentage (%) of sulfate, nitrate, ammonium, and SNA
mass concentration associated with agriculture NH3 removal.
In addition, the brute-force method (zero-out sensitivity test), which can
capture the effect of emission change on aerosol mass burden, was applied to
investigate the impact of the removal of TagrNH3 emission. In contrast
to online source apportionment, the brute-force method mainly reflects the
disparity of chemical balance caused by the emission change, which could
considerably alter secondary pollutant formation. Several sensitivity tests
were conducted, and the results are shown in Fig. 7 and Table 5. Figure 7
presents the mass burden variation of SNA associated with the TagrNH3
removal. Figure 7 shows that the reduction pattern and seasonal variation of
the aerosol were broadly followed by those of their mass burden. The
considerable reduction of SNA mainly appeared in the high-concentration
regions and generally exceeded 25 µg m-3. Table 5 shows the
percentage of the variation of sulfate, nitrate, ammonium, SNA, and
PM2.5. Compared with Table 4, the variation percentage of SNA and
PM2.5 reached 30 %–60 % and 24 %–42 %, respectively, and they were
approximately 2 times higher than those of the contribution percentage.
This remarkable distinction was mainly caused by the variation of nitrate;
that is, the contribution of TagrNH3 to nitrate was generally below
10 %, as shown in Table 4, but the reduction of nitrate associated with
removing TagrNH3 emission could exceed 90 %, as shown in Table 5.
This difference between the results of ISAM and brute-force tests was
expected due to the high nonlinearity in the NOx chemistry. The nitrate
formation could become more sensitive when the “rich NH3” environment
shifts to a “poor NH3” environment, which means the decrease in
nitrate mass burden would accelerate with NH3 emission reduction.
Therefore, it can be deduced that the contribution of NH3 to nitrate
should be remarkably lower under rich NH3 environments compared
to that under poor NH3 environments. A similar phenomenon was
also reported by some previous studies (Wang et al., 2011; Xu et al., 2016).
To prove this point, more brute-force sensitivity tests were conducted. The
variation of sulfate, nitrate, ammonium, and SNA mass burden associated with
the reduction of NH3 emission (80 %, 50 %, 40 %, 30 %, 20 %,
and 10 % TagrNH3 emission, respectively) is shown in Fig. 8. The
decline of nitrate mass concentration was more rapid than that of ammonium,
and the trend became slightly faster with the reduction of NH3 emission
(signified from rich NH3 to poor NH3 environments) in most
regions. The acceleration of nitrate mass burden decline was substantial in
regions with strong NH3 emission. Furthermore, this acceleration
stopped when 20 % of NH3 emission remained, as shown in Fig. 8.
The variation (%) of sulfate, nitrate, ammonium, and SNA mass
burden associated with the NH3 emission reduction (%).
Conclusions
The emission budget of agricultural NH3 was huge and played an
important role in regional particle pollution in China. As a precursor
of the secondary aerosol, the reasonable estimation of the nonlinear
processes of secondary aerosol formation should be the key point for
capturing the contribution of NH3 to particle pollution. In this study,
the air quality modeling system RAMS-CMAQ was applied to simulate the
spatial–temporal distribution of trace gas and aerosols in 2015. In
addition, the PKU-NH3 emission inventory, which was compiled on 1 km × 1 km horizontal resolution with monthly data, was applied
to capture the features of agricultural NH3 emission in China
accurately. Then, the source apportionment module ISAM was coupled into this
modeling system to estimate the contribution of agricultural NH3 to
PM2.5 mass burden quantitatively. Brute-force sensitivity tests were
also conducted to discuss the impact of agricultural NH3 emission
reduction. The meteorological factors and mass concentration of NH3,
SO2, NO2, and PM2.5 from simulation were evaluated and showed
consistency with the observation data. Some interesting results were
explored and summarized as follows.
The high mass burden of NH3 could exceed 10 µg m-3 and
mainly appeared in the NCP, CNC, YRD, and SCB. These regions had high
coincidence with regions that are heavily covered with particle pollution.
Therefore, it can be deduced that the influence of agricultural NH3 on
the PM2.5 mass concentration is crucial.
The results from ISAM simulation showed that the TagrNH3 provided
17 %–23 % and 15 %–22 % contribution to the PM2.5 in January
and July, respectively, in most parts of eastern China, and the largest
annual average contribution appeared in CNC (17.5 %). Specific to SNA
components, the annual and regional average contributions of TagrNH3
to ammonium, nitrate, and sulfate in China were 87.6 %, 10.1 %, and
2.2 %, respectively. Therefore, agricultural NH3 emission contributes
considerably to ammonium formation but minimally to sulfate due to various
means of sulfate formation.
The brute-force sensitive test could reflect the effect of changing TagrNH3 emission on PM2.5 mass burden. The results indicated that the
reduction percentage of PM2.5 mass burden due to removal TagrNH3
emission could reach 24 %–42 % in most parts of eastern China; these
values are approximately 2 times higher than the contribution. The nitrate
reduction percentage that exceeded 90 % was the major reason for this
remarkable difference. In addition, further analysis proved that the ambient
NH3 mass burden could affect its contribution to SNA formation, that
is, the NH3 contribution to nitrate should be low under rich
NH3 environments and high under poor NH3 environments.
Therefore, the influence of NH3 would be enhanced with the decrease in
ambient NH3 mass concentration.
This study suggests that the NH3 influence on the PM2.5 mass
burden is complex because of the nonlinearity of secondary aerosol
formation. Substantial deviation exists between the results of the ISAM and
brute-force methods; thus, these two kinds of results should be
distinguished and applied to explain different issues: the contribution
under the current scenario and the effect due to emission reduction. The
modeling system is a versatile tool that allows us to investigate valuable
information for choosing efficient strategies for reducing the impact of
agricultural NH3 and improving air quality.
The daily average temperature, relative humidity, wind speed, and maximum
wind direction in January, April, July, and October 2015 were compared with
the surface shared data from the Chinese National Meteorological Center
(http://data.cma.cn/, last access: 23 July 2020) at 9 stations. The comparison results are shown in
Figs. A1–A4. These stations are located in the East China, where the high
NH3 emission regions. Generally, the modeled temperature was in good
agreement with the observed data, and can reflect the large fluctuation and
seasonal variation of relative humidity as well, except that some of the
extreme high or low values appeared abruptly. As shown in Fig. A3, most of
the daily average wind speed was lower than 3 m s-1 at Zhengzhou,
Miyun, Tianjin, and Baoding station (all located in the North China Plain),
which means the diffusion condition was not good due to the stable weather.
Otherwise, the relatively strong wind appeared at Nanjing, Chaoyang, Nanning,
and Jinan. The modeled wind speed generally reproduced all these features.
A direct comparison between observed and modeled wind direction that can
be easily influenced by the surrounding surface features is difficult.
Nevertheless, the prevailing wind direction in different seasons can be
captured by the simulation results for all stations.
In addition, Fig. A5 presents the regional average NH3 emission flux
(g s-1 per grid) of different sectors, including fertilizer, husbandry, biomass
burning, farmland ecosystems, waste disposal, and other sectors over each
region in January, April, July, and October. Furthermore, the percent (%)
of each NH3 emission sector is shown in Fig. A6. All the information
was obtained from the PKU-NH3 emission inventory directly. It can be
seen that the emission flux was higher in summer and lower in winter. The
strongest emission flux mainly appeared in BTH, SDP and CNC.
These features generally followed the distribution pattern of NH3 mass
concentration as shown in Fig. 3. On the other hand, the major proportion
was provided by husbandry and fertilizer and was relatively high in spring
and summer.
Observed and modeled daily average temperatures (K) in January,
April, July, and October 2015.
Same as Fig. A1 but for relative humidity (%).
Same as Fig. A1 but for wind speed (m s-1).
Same as Fig. A1 but for daily maximum wind direction (∘).
The regional average NH3 emission flux (g s-1 per grid) of different agriculture sectors over each region in January, April, July, and October.
The percent (%) of different NH3 emission sectors over
each region in January, April, July, and October.
Data availability
All data used in this paper are available upon
request from the corresponding author Lingyun Zhu (zhlyun@126.com).
Author contributions
XH and LZ designed and performed this study. MZ co-designed the study and provided a lot of valuable advice about the model setting and operation. ML and YS provided the PKU-NH3 emission inventory.
Competing interests
The authors declare that they have no conflict of
interest.
Acknowledgements
The Chinese National Meteorological Center and the China National Environmental Monitoring Centre are acknowledged for providing observational data sets used in this study.
Financial support
This research has been supported by the National Key R&D Programs of China (grant no. 2017YFC0209803), the National Natural Science Foundation of China (grant no. 41830109), and the Strategic Priority Research Program of the Chinese Academy of Sciences (grant no. XDA19040204).
Review statement
This paper was edited by Leiming Zhang and reviewed by two anonymous referees.
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