Ammonia (NH3), as an alkaline gas in the atmosphere, can cause direct
or indirect effects on the air quality, soil acidification, climate change
and human health. Estimating surface NH3 concentrations is
critically important for modeling the dry deposition of NH3 and for
modeling the formation of ammonium nitrate, which have important impacts on
the natural environment. However, sparse monitoring sites make it
challenging and difficult to understand the global distribution of surface
NH3 concentrations in both time and space. We estimated the global
surface NH3 concentrations for the years of 2008–2016 using
satellite NH3 retrievals combining vertical profiles from
GEOS-Chem. The accuracy assessment indicates that the satellite-based
approach has achieved a high predictive power for annual surface NH3
concentrations compared with the measurements of all sites in China, the US and
Europe (R2=0.76 and RMSE = 1.50 µg N m-3). The
satellite-derived surface NH3 concentrations had higher consistency
with the ground-based measurements in China (R2=0.71 and RMSE = 2.6 µg N m-3) than the US (R2=0.45 and RMSE = 0.76 µg N m-3) and Europe (R2=0.45 and RMSE = 0.86 µg N m-3) at
a yearly scale. Annual surface NH3 concentrations higher than 6 µg N m-3 are mainly concentrated in the North China Plain of China and
northern India, followed by 2–6 µg N m-3 mainly in southern and
northeastern China, India, western Europe, and the eastern United States (US).
High surface NH3 concentrations were found in the croplands in China, the
US and Europe, and surface NH3 concentrations in the croplands in China
were approximately double those in the croplands in the US and Europe.
The linear trend analysis shows that an increase rate of surface NH3
concentrations (> 0.2 µg N m-3 yr-1) appeared in eastern China during 2008–2016, and a middle increase rate (0.1–0.2 µg N m-3 yr-1) occurred in northern Xinjiang over China. NH3
increase was also found in agricultural regions in the central and eastern US
with an annual increase rate of lower than 0.10 µg N m-3 yr-1.
The satellite-derived surface NH3 concentrations help us to determine
the NH3 pollution status in the areas without monitoring sites and to
estimate the dry deposition of NH3 in the future.
Introduction
Ammonia (NH3), emitted primarily by agricultural activities and biomass
burning, is an important alkaline gas in the atmosphere (Van Damme et
al., 2018; Warner et al., 2017). Excessive surface NH3 concentrations
can cause chronic or acute damage to the plant (such as reduced growth and
bleached gray foliage) when its capacity of detoxification is exceeded
(Eerden, 1982; Sheppard et al., 2008). Estimation of surface
NH3 concentrations is critically important in modeling the dry
deposition of NH3, which may comprise a large part of atmospheric
nitrogen (N) deposition, and could cause acidification in the soil,
eutrophication in the aquatic ecosystems, and contamination in drinking
water (Basto et al., 2015; Kim et al., 2014; Lamarque et al., 2005; Larssen
et al., 2011; Reay et al., 2008). In addition, NH3 can also react with
nitric acid and sulfuric acid to form ammonium salts (Li et al., 2014; Y. Li
et al., 2017), which are important components of particulate matter (PM)
and have negative impacts on air quality and human health (Xu et al.,
2017; Schaap et al., 2004; Wei et al., 2019).
Several national monitoring programs can measure surface NH3
concentrations, including the Chinese Nationwide Nitrogen Deposition
Monitoring Network (NNDMN) established in 2004, the Ammonia Monitoring
Network in China (AMoN-China) established in 2015, the Ammonia
Monitoring Network in the US (AMoN-US), and the European Monitoring
and Evaluation Programme (EMEP). However, there are still relatively large
uncertainties of estimating global surface NH3 concentrations,
resulting from the sparse monitoring sites as well as the limited spatial
representativeness (Liu et al., 2017a, b). Satellite
NH3 retrievals are an important complement to gain the global
distribution of NH3 concentrations with a high spatial resolution
(Van Damme et al., 2014b). NH3 can be measured by several
satellite instruments including the Infrared Atmospheric Sounding
Interferometer (IASI), Atmospheric Infrared Sounder (AIRS), Cross-track
Infrared Sounder (CrIS) and tropospheric emission spectrometer (TES). TES
using the thermal infrared spectral range has sparser spatial coverage
compared to IASI, CrIS and AIRS (Shephard et al., 2011; L. Zhang et al.,
2018). A recent study (Kharol et al., 2018)
reported the dry NH3 depositions in North America, and found -15 %
underestimation in CrIS surface NH3 concentrations (using three fixed
NH3 profiles considering unpolluted, moderate and polluted conditions)
compared with the measurements from AMoN-US during the warm months (from
April to September). Warner et al. (2017) reported the global AIRS NH3
concentrations at 918 hPa (approximately 700–800 m) at 1∘ latitude × 1∘ longitude grids, and found NH3 concentrations
increased in the major agricultural regions during 2003–2015
(Warner et al., 2017). The IASI NH3 measurements have
been validated with NH3 columns measured by Fourier transform
infrared (FTIR) spectroscopy, ground-based NH3 measurements, NH3
emissions and atmospheric chemistry transport models (CTMs) (Dammers et
al., 2016; Van Damme et al., 2014b, 2015a; Whitburn et al.,
2016).
Apart from satellite retrievals, CTMs are also powerful tools to investigate
spatiotemporal variability of surface NH3 concentrations in the
atmosphere. Schiferl et al. (2016) evaluated the modeled NH3 concentrations
during 2008–2012 from GEOS-Chem, and found an approximately 26 %
underestimation compared with the ground-based measurements, which can be
related to the relatively large uncertainties in NH3 emissions used for
driving GEOS-Chem (Schiferl et al., 2016). Zhu et al. (2013) used the
GEOS-Chem constrained by TES measurements to estimate surface NH3
concentration during 2006–2009, and found an improvement in comparison with
the ground-based measurements in the United States. Schiferl et al. (2014) used the airborne observations to validate
the simulated NH3 concentrations in 2010 from GEOS-Chem, and revealed
reasonably simulated NH3 vertical profiles compared with the aircraft
measurements but with an underestimation in surface NH3 concentrations
in California (Schiferl et al., 2014). A number of previous studies have
used satellite NO2 columns to estimate the surface NO2
concentrations combining NO2 vertical profiles from CTMs (Geddes et
al., 2016; Lamsal et al., 2013; Nowlan et al., 2014; Liu et al., 2017c). The
methods of using the vertical profiles to convert satellite-retrieved
columns to surface concentrations have been proven successful for SO2
and NO2 (Geddes et al., 2016; Geng et al., 2015; Lamsal et al.,
2008; Nowlan et al., 2014). CTMs can provide valuable information of NH3
vertical profiles (Whitburn et al., 2016; Liu et al., 2017b), and
IASI-derived surface NH3 concentrations combining NH3 vertical
profiles from CTMs in China and Europe were evaluated previously (Liu et
al., 2017b; van der Graaf et al., 2018). This study followed these studies to
estimate the satellite-derived global surface NH3 concentrations using
IASI NH3 retrievals and the vertical profiles from GEOS-Chem, and the
present study aims to estimate the global surface NH3 concentration
from a satellite perspective.
Data and methodsIASI NH3 measurements
The Infrared Atmospheric Sounding Interferometer (IASI) is a passive
instrument measuring infrared radiation within the spectral range of
645–2769 cm-1. The IASI-A instrument is on board the MetOp-A
satellite launched in 2006 covering the globe twice a day with an elliptical
spatial resolution of approximately 12 km by 12 km, and crosses the
Equator at 09:30 and 21:30 local time (Van Damme et al., 2014a). We
used the daytime IASI NH3 measurements due to the larger positive
thermal contrast detected by satellite instruments leading to smaller errors
compared to the nighttime data (Van Damme et al., 2014a). In this
work, we used the IASI NH3 column products (ANNI-NH3-v2.2R-I) during
2008–2016 (Van Damme et al., 2017) to estimate the global surface
NH3 concentrations. The ANNI-NH3-v2.2R-I datasets were developed by
converting spectral HRI (hyperspectral range index) to NH3 columns
through an Artificial Neural Network for IASI (ANNI) algorithm
(Whitburn et al., 2016). This algorithm considered the
influence of the NH3 vertical profiles, pressure, humidity and
temperature. The NH3 vertical profile information used to
generate the ANNI NH3 columns was retrieved from GEOS-Chem, which
integrates H2SO4-HNO3-NH3 aerosol thermodynamics
mechanisms (Whitburn et al., 2016; Van Damme et al., 2017). The
ANNI-NH3-v2.2R-I datasets used the ANNI algorithm and took account of the
influence of NH3 vertical profiles, pressure, humidity and
temperature, which were to make the columns accurate. There is no
information on NH3 vertical profiles in the ANNI-NH3-v2.2R-I
datasets. The NH3 vertical profiles used in this paper was to convert
the columns to surface concentrations and to make the surface NH3 estimates accurate. The IASI NH3 columns used in this study were
processed into monthly data on 0.25∘ latitude × 0.25∘ longitude grids by the arithmetic averaging method (Van
Damme et al., 2017; Whitburn et al., 2016; Liu et al., 2017a).
Surface NH3 measurements
To evaluate our satellite-derived global surface NH3 concentrations, we
collected available surface NH3 measurements on a regional scale in
2014. In China, we used the national measurements from the Chinese
Nationwide Nitrogen Deposition Monitoring Network (NNDMN) including 10 urban
sites, 22 rural sites and 11 background sites. Surface NH3
concentrations in the NNDMN were measured by both ALPHA (Adapted Low-cost
Passive High Absorption) and DELTA (DEnuder for Long-Term Atmospheric
sampling) systems. The bias for monthly measurements at a site using the DELTA
system is as below approximately 10 % (Sutton et al., 2001),
and the correlation between the ALPHA and DELTA measurements was highly
significant (R2=0.919, p<0.001)
(Xu
et al., 2015). The detailed descriptions on the NNDMN have been described in
a previous study
(Xu
et al., 2015). In the US, we used the measurements of 67 sites from
AMoN-US, downloaded from the website http://nadp.sws.uiuc.edu/AMoN/, last access: 21 September 2018.
Surface NH3 concentrations in the AMoN-US were measured by a radiello™
diffusive sampler (http://www.radiello.com, last access: 21 September 2018) as a simple diffusion-type
sampler collected every 2 weeks, and these sites were generally distributed
at rural sites (Li et al., 2016). We calculated annual surface NH3
concentrations by averaging all the measurements since we compared the
measured surface NH3 concentrations with satellite-derived surface
NH3 concentrations at a yearly scale. In Europe, we used the
measurements of 43 sites from the EMEP network
(https://www.nilu.no/projects/ccc/emepdata.html, last access: 21 September 2018). EMEP is composed of
multiple national networks in Europe; thus the measurement systems differ
among different national networks. The overall bias of the different
instruments in EMEP varied from -30 % to 10 % for all sites
(von Bobrutzki et al., 2010). Most sites in China, the US and
Europe were set to a height of 1–50 m above the ground (Xu et al., 2015; Li
et al., 2016; Puchalski et al., 2011).
GEOS-Chem model
We used GEOS-Chem version 11-01 as the chemical transport model to calculate
global NH3 vertical profiles (using the year 2014 as a case study in
the results and discussion). We performed spin up for 5 months, which well
exceeds the typical lifetime of atmospheric NH3 (typically within 24 h) and aerosol ammonium ions (typically within a week)
(Pye et al., 2009). It has a spatial resolution of
2∘ latitude × 2.5∘ longitude × 47
vertical layers spanning over Earth's surface and about 80 km above it. It
is driven by the meteorological field data of the GEOS-FP
(forward-processing) products, which were produced by NASA GMAO (Global
Modeling and Assimilation Office) (https://gmao.gsfc.nasa.gov/, last access: 21 September 2018). Here we
modeled the NH3 vertical profiles using GEOS-Chem, and used the
monthly averages for analysis. The global NH3 emissions in GEOS-Chem
are based on EDGAR (Emissions Database for Global Atmospheric Research)
v4.2 (http://edgar.jrc.ec.europa.eu/overview.php?v=42, last access: 21 September 2018), while the regional
emissions are replaced with the MIX inventory for East Asia (M. Li et al.,
2017) (http://www.meicmodel.org/dataset-mix.html, last access: 21 September 2018), EMEP inventory for
Europe (http://www.emep.int/, last access: 21 September 2018), NEI (National Emissions Inventory) for
the US (https://www.epa.gov/air-emissions-inventories, last access: 21 September 2018) and CAC (Criteria Air
Contaminant) inventory for Canada
(https://www.canada.ca/en/services/environment/pollution-waste-management/national-pollutant-release-inventory.html, last access: 21 September 2018).
The main difference between the regional inventories and EDGAR is that
seasonality of emissions is included in regional inventories. The
seasonality of regional emissions inventories is embedded as an integral part
of the inventory except EMEP (Janssens-Maenhout et al., 2015; Crippa et
al., 2018; Lenhart and Friedrich, 1995). The biomass burning emissions are
from the Global Fire Emissions Database (GFED4) including agricultural fires,
wildfire and pre-scribed burning (Giglio et al., 2013). The
GEOS-Chem simulates a comprehensive atmospheric NOx-O3–volatile organic compound–aerosol
system (Mao et al., 2013). The thermodynamic equilibrium of the
NH3-H2SO4-HNO3 system is simulated by the ISORROPIA II
model (Fountoukis and Nenes, 2007; Pye et al.,
2009). The modeling of wet deposition is described by a previous study
(Liu et al., 2001) with updates from other studies
(Amos et al., 2012; Wang et al., 2011). Dry deposition
of particles follows the size-segregated treatment (Zhang et
al., 2001) and gaseous dry deposition follows the framework (Wesely,
1989) with updates from a previous study (Wang et al., 1998). We
archive the output daily averages of NH3 concentrations as well as the
averages between 09:00 and 10:00, which corresponds to the local crossing time
of IASI (09:30). The local time is the time in a particular region or area
expressed with reference to the meridian passing through it. The
relationship between NH3 concentration at 09:00–10:00 and the daily
averages derived from the GEOS-Chem was used to convert the satellite-observed NH3 column to daily averages (Nowlan et al., 2014).
Estimation of surface NH3 concentrations
We estimated global surface NH3 concentrations using the IASI NH3
columns as well as GEOS-Chem. We took into account the advantages of
IASI NH3 columns with high spatial resolutions and GEOS-Chem with
vertical profiles. The IASI NH3 data we gained are column data, and
there is no information on the vertical information. To convert the columns
to surface concentrations, we used the widely used modeled vertical
profiles from GEOS-Chem. The GEOS-Chem outputs include 47 layers, which are
not continuous in the vertical direction. To gain the continuous vertical
NH3 profile, we used the Gaussian function to fit the 47 layers'
NH3 concentrations. The main advantage to simulating the vertical
profiles is that the NH3 concentration at any height indicated by
satellite can be obtained. On the other hand, the simulated profile function
has a general rule, which can convert the columns indicated by satellite to
surface concentration simply and quickly for many years. The height of each
grid box used here was calculated at the middle height of each layer rather
than the top height of each layer. A three-parameter Gaussian function was
used to fit NH3 vertical profiles at each grid box from GEOS-Chem
in the previous studies (Whitburn et al., 2016; Liu et al., 2017b):
ρ=ρmaxe-Z-Z0σ2,
where ρ is NH3 concentrations at the layer height Z; ρmax is the
maximum NH3 concentrations at the height z0; σ is
an indicator for the spread or thickness of the NH3 concentrations.
This study expanded Eq. (1) to fit NH3 vertical profiles at
each grid box by the following equation (Liu et al., 2017b):
ρ=∑i=1nρmax,ie-Z-Z0,iσi2,
where n ranges from 1 to 6. If n=1, Eq. (2) is the same as
Eq. (1); if n>1, Eq. (2) is multiple
three-parameter Gaussian items. We determined the value of n that can
simulate the NH3 vertical profiles with the best performance at each grid
box using the determining coefficients of R square (R2). Once the
NH3 vertical profiles were determined at each grid box, we could
extrapolate NH3 concentrations at any height from GEOS-Chem
(GGEOS-Chem).
We then aggregated the IASI NH3 columns ΩIASI
(0.25∘ latitude × 0.25∘ longitude) to the
GEOS-Chem grid size Ω‾IASI (2∘ latitude × 2.5∘ longitude) by the averaging method. We obtain the
following equation (Lamsal et al., 2008):
GIASI9-10‾=GGEOS-ChemΩGEOS-Chem×ΩIASI9-10‾,
where GIASI9-10‾ is the satellite-derived surface
NH3 concentrations at a GEOS-Chem grid size at 09:00–10:00;
GGEOS-ChemΩGEOS-Chem is the ratio of surface NH3
concentrations to NH3 columns calculated from GEOS-Chem; ΩIASI9-10‾ is the average IASI NH3 columns at a
GEOS-Chem grid at 09:00–10:00.
We found a high correlation (R= 0.96 and p= 0.000) between the surface
NH3 concentrations and NH3 columns based on the GEOS-Chem outputs
(Fig. S1 in the Supplement). Then we used the satellite-derived scaling factor to
downscale the satellite-derived surface NH3 concentrations at
a GEOS-Chem grid by using the following ratio:
4RIASI=ΩIASIΩIASI‾,5GIASI9-10=GIASI9-10‾×RIASI,
where RIASI is the scaling factor. GIASI9-10 is
the satellite-derived surface NH3 concentrations at a satellite IASI
grid size (0.25∘ latitude × 0.25∘ longitude) at
09:00–10:00.
To convert the instantaneous satellite-derived surface NH3
concentrations GIASI to daily average surface NH3 concentrations,
we used the following equations (Nowlan et al., 2014):
GIASI∗=GGEOS-Chem1-24GGEOS-Chem9-10×GIASI9-10,
where GIASI∗ is the daily average surface NH3
concentrations, and GGEOS-Chem1-24GGEOS-Chem9-10 is
the ratio of the GEOS-Chem surface NH3 concentrations at the daily
average to the average of 09:00–10:00.
Results and discussionNH3 vertical profiles from GEOS-Chem
NH3 emitted from the surface can be transported horizontally or
vertically, and its concentrations may show a certain gradient in the
vertical and horizontal directions (Preston et al., 1997; Rozanov et al.,
2005). There are generally two types of shapes of NH3 vertical profiles
(Fig. S2) from aircraft measurements (Y. Li et al.,
2017; Tevlin et al., 2017) and CTMs (Whitburn et al., 2016; Liu et al.,
2017b). One is representative for the vertical profile with maximum NH3
concentrations at a certain height (z0>0) and the other is
representative for the vertical profile with maximum NH3 concentrations
near the Earth surface (z0=0). In this study, the vertical profiles
of NH3 were fitted based on the 47 layers' outputs by GEOS-Chem in 2014
at a monthly scale. Figure S3 shows the spatial distribution of
NH3 concentrations in the first and fifth layers simulated by GEOS-Chem
in January 2014. NH3 concentrations in the fifth layer are
significantly lower than those in the first layer, suggesting that NH3
concentrations decrease with increasing layers (or altitude), especially in
NH3 hot spot regions (such as eastern China, India, western Europe and
the eastern US). The average difference of NH3 concentrations between the
first and fifth layers on the land is 0.34 µg N m-3. The average
NH3 concentrations in the first and fifth layers in eastern China,
India, western Europe and the eastern US were 2.76, 7.28, 0.55 and 0.31 µg N m-3, respectively.
To more vividly depict the vertical profiles of NH3, we show NH3
vertical concentrations with a cross section drawn at 37∘ N in January
2014 (Fig. S4). High NH3 concentrations are mainly
concentrated in layers 1–10, and show a significant decreasing trend with increasing altitude, which is consistent with the aircraft measurements
(Preston et al., 1997; Lin et al., 2014; Levine et al., 1980; Shephard and
Cady-Pereira, 2015; Y. Li et al., 2017; Tevlin et al., 2017). NH3 vertical
profiles were fitted by a Gaussian function (two to six terms) based on the 47 layers' NH3 concentrations from GEOS-Chem, and the fitting accuracy
was determined by R2. We found that the NH3 vertical profiles on land between 60∘ N and 55∘ S can be well modeled
using a Gaussian function (R2 higher than 0.90) (Fig. 1).
Previous studies also found high accuracy using the Gaussian function to
simulate the NH3 vertical profiles in China and globally (Whitburn
et al., 2016; Liu et al., 2017b).
R2 of Gaussian fit for NH3 vertical profiles.
Validation of satellite-derived surface NH3 concentrations
NH3 vertical profiles were used to convert IASI NH3 columns to
surface NH3 concentrations. Figure 2 shows the IASI-derived
global surface NH3 concentrations on land for the 0.25∘
latitude × 0.25∘ longitude grids in 2014. IASI-derived
surface NH3 concentrations capture the general spatial pattern of
surface NH3 concentrations fairly well in 2014 in regions with
relatively intensive monitoring sites (R2=0.76 and RMSE = 1.50 µg N m-3 in Figs. 2 and 3). Overall, 72.85 % of
observations (including China, the US and Europe) were within a factor of
2 of the satellite-derived surface NH3 concentrations. In China, approximately 71.43 % and 77.27 % of observations were within a
factor of 2 of the satellite-derived surface NH3 concentrations in
urban and rural areas, respectively. There is no big difference in the
accuracy of satellite-derived surface NH3 concentrations between urban
and rural areas. In the US, the monitoring sites were generally
distributed at rural sites (http://www.radiello.com, last access: 21 September 2018) (Li et al., 2016),
and, in Europe, there is no information to indicate the land use of each
site (https://projects.nilu.no//ccc/, last access: 21 September 2018)
(Tørseth et al., 2012a). The overall mean
of satellite-derived surface NH3 concentrations in 2014 at the measured
sites was 2.52 µg N m-3 and was close to the average of measured
surface NH3 concentrations (2.51 µg N m-3) in 2014.
IASI-derived surface NH3 concentrations gained higher consistency with
the ground-based measurements in China (R2=0.71 and RMSE = 2.6 µg N m-3 for 43 sites) than the US (R2=0.45 and RMSE = 0.76 µg N m-3 for 67 sites) and Europe (R2=0.45 and RMSE = 0.86 µg N m-3 for 43 sites) at a yearly scale. This might be due to the fact
that high concentrations in a region (associated with high thermal
contrast) can be more reliably detected by IASI (Van Damme et al.,
2015a). The accuracy of IASI-retrieved surface NH3 concentrations in
different regions is highly linked with the thermal contrast (TC) and
atmosphere NH3 abundance (Whitburn et al., 2016).
The lowest uncertainties occur when high columns and high TC coincide. In
case either of them decreases, the uncertainty will gradually increase. In
case both the TC and column are low, all sensitivity to NH3 is lost.
When high TC and high NH3 columns (high HRI) occur, the major
contribution to the uncertainty results from the thickness of the NH3
layer, the surface temperature and the temperature profile
(Whitburn et al., 2016). The simulation of NH3
mixing from GEOS-Chem may also have different accuracy in different regions,
and thus can cause uncertainty to the different accuracy of IASI-retrieved
surface NH3 concentrations in different regions. Notably, we compared
the surface NH3 concentrations at the monitoring stations with the
grid values of satellite-derived estimates directly. This point-to-grid
verification strategy may cause uncertainty since the monitoring site
location may not be representative of a given grid cell for an average
retrieved value. The satellite-derived NH3 has a detection limit of
0.0025 µg N m-3 (2.5 ppb) (van der Graaf et al., 2018; Van Damme et al.,
2015a). Similarly, we also compared the surface NH3 concentrations (at
the first layer) simulated by GEOS-Chem with the monitoring results
(R2=0.54 and RMSE = 2.14 µg N m-3 in Fig. 3). In
general, IASI-derived surface NH3 concentrations had better consistency
with the ground-based measurements than those from GEOS-Chem over China, the
US and Europe. The relatively low accuracy from GEOS-Chem was likely due to
the coarse model resolutions as well as the poor spatiotemporal
representations of NH3 emissions, as suggested by a previous study
(Y. Zhang et al., 2018).
Spatial distribution of satellite-derived and measured
surface NH3 concentrations in 2014.
Comparison of satellite-derived and GEOS-Chem modeled
surface NH3 concentrations with measured concentrations in China, the US
and Europe.
A known limitation of IASI NH3 retrievals is lack of vertical
profile information. A previous study (Van Damme et al., 2015a) used
fixed profiles on land to convert the IASI NH3 columns to surface
NH3 concentrations. Using the fixed profiles can cause large
uncertainties for estimating surface NH3 concentrations. In this work,
we utilized the advantages of CTMs and considered the spatial variability of
the vertical profiles, and we prove that IASI NH3 columns are powerful to
predict the surface NH3 concentrations by combining the vertical profiles
simulated by Gaussian function.
Through the Gaussian simulation of NH3 vertical profiles, we are able
to evaluate the sensitive regions of surface NH3 concentrations with
respect to different heights. Figure S5 shows the spatial
distribution of the difference of NH3 concentrations between 40 and
60 m (about the middle height of the first layer in GEOS-Chem). In general,
in strong NH3 emission regions, there is a relatively large difference
in surface NH3 concentrations such as, for instance, in eastern China
and northwestern India (can be up to 3 µg N m-3); subsequently, a
middle difference (2–3 µg N m-3) occurs in eastern and middle
China, northern India, and northern Italy. Except for the abovementioned regions,
the difference of NH3 concentrations between 40 and 60 m is generally
lower than 0.5 µg N m-3.
Spatial distribution of IASI-derived surface NH3
concentrations and N fertilizer plus N manure (from N application) in
China, Europe and the US.
Spatial distributions of satellite-derived surface NH3 concentrations
Figure 4 shows the spatial distributions of surface NH3
concentrations in China, the US and Europe in 2014. The overall mean surface
NH3 concentrations over China were 2.38 µg N m-3, with the
range of 0.22–13.11 µg N m-3. We found large areas in eastern
China (109–122∘ E, 28-41∘ N), Sichuan Basin, Hubei (including
Wuhan, Xiangyang and Yichang), Shaanxi (including Xi'an, Baoji, Hanzhong,
Weinan), Gansu (Lanzhou and its surrounding areas), Shanxi (including
Yuncheng and Changzhi) and northwestern Xinjiang with surface NH3
concentrations greater than 8 µg N m-3 yr-1, which were in
agreement with the spatial distributions of the croplands in China
(Fig. S6). It is not surprising that high surface NH3
concentrations occurred in eastern China and the Sichuan Basin because the major
Chinese croplands are distributed there, as the major source of NH3
emissions with frequent N fertilizer applications. In addition, N manure is
another major source of NH3 emissions in China, and the percentage of N
manure to NH3 emissions exceeds 50 % (Kang et
al., 2016). Overall, there was a significant linear correlation between
surface NH3 concentration and N fertilization plus N manure in China
(R2=0.69, p= 0.000 in Fig. 5). The hot spots also occurred
in northwestern Xinjiang surrounding the cropland areas, which may be
related to the dry climate that can maintain NH3 in the gaseous state
for a longer time, providing climate conditions for the long-distance
transmission of NH3. Recent national measurement work (Pan et al.,
2018) also revealed high surface NH3 concentrations in northwestern
Xinjiang, confirming the rationality of the IASI-derived estimates.
Comparison of satellite-derived surface NH3 concentrations and N fertilizer plus N manure (from N application) in
China, the US and Europe. The spatial resolution of satellite-derived surface
NH3 concentrations and N fertilizer plus N manure is 0.25∘ and
0.5∘, respectively. We first resampled the satellite-derived surface
NH3 concentrations to 0.5∘ grids and then compared them with N
fertilizer plus N manure by each grid cell. We obtained the N fertilizer and
N manure data produced from McGill University (Potter et al.,
2010).
In the US, the overall mean surface NH3 concentrations were 1.52 µg N m-3 yr-1, with the range of 0.14–10.60 µg N m-3. The
surface NH3 hot spots were generally distributed in croplands in the
central US (such as Ohio, Illinois, South Dakota, Nebraska,
Kansas, Minnesota and North Dakota), as well as in some small areas in
western coastal regions (such as California and Washington). In particular,
the San Joaquin Valley (SJV) in California (agricultural land) had the
highest surface NH3 concentrations greater than 4 µg N m-3.
The NH3 source in the SJV was livestock and mineral N fertilizer,
which accounted for 74 % and 16 % of total NH3 emissions,
respectively (Simon et al., 2008). Except for the SJV in California, the
annual surface NH3 concentrations in croplands were mostly within
the range of 1–3 µg N m-3, which were much lower than those in
eastern China (mostly within the range of 4–10 µg N m-3). Compared
with the spatial distribution of N fertilization plus N manure, the hot spots
of surface NH3 concentration can basically reflect the distribution of
high N fertilization (R2=0.37, p= 0.000 in Figs. 4 and 5).
In Europe, the overall mean surface NH3 concentrations were 1.8 µg N m-3, with the range of 0.04–9.49 µg N m-3. High surface
NH3 concentrations were distributed in croplands,
especially in the western regions with values greater than 4 µg N m-3, such as northern Italy (Milan and its surrounding areas),
Switzerland, central and southern Germany, eastern France (Paris and its
surrounding areas), and Poland. According to the Emissions Database for Global
Atmospheric Research (EDGAR), N manure and N fertilization account for
53 % and 43 % of the NH3 emissions in western Europe. Overall,
there was also a significant linear correlation between surface NH3
concentration and N fertilization plus N manure (R2=0.39, p= 0.000)
in Europe, reflecting the importance of N fertilization on surface NH3
concentration.
NH3 is the most abundant alkaline gas in the atmosphere, and has
implications to neutralize acidic species (such as H2SO4 and
HNO3) to form ammonium salts (such as (NH4)2SO4 and
NH4NO3). Ammonium salts are the important inorganic N components
in PM2.5, which can reduce regional visibility and contribute to human
disease burden (Van et al., 2015; Yu et al., 2007). Comparing surface
NH3 concentrations with PM2.5 can benefit the understanding of the
sources and the mixture of air pollution. The spatial distribution of
satellite-derived PM2.5 (dust and sea salt removed) in 2014
(Fig. S7) gained from a previous study (Van et al.,
2016) was compared with the satellite-derived surface NH3
concentrations in 2014. On the other hand, NO2 is also an important
precursor of nitrate salts in PM2.5. We also included the
satellite-derived surface NO2 concentrations (Fig. S7) from a
previous study (Geddes et al., 2016) to compare with surface NH3
and PM2.5 concentrations.
The hot spots of surface NH3 concentrations were highly linked with the
hot spots of PM2.5. The most severe pollution occurred in eastern
China with annual average PM2.5 exceeding 50 µg m-3 (much
higher than 35 µg m-3 as the level 2 annual PM2.5 standard
set by World Health Organization Air Quality Interim Target-1) and annual
average surface NH3 and NO2 concentrations greater than 8 µg N m-3 and 4 µg N m-3, respectively. A previous study (Xu
et al., 2017) reported that the secondary inorganic aerosols of
NH4+ and NO3- can account for 65 % of PM2.5 based
on the measurements at three sites in Beijing. NH3 and NO2 are the
most important precursors of nitrate salts and ammonium salts, and certainly
contribute to the severe pollution in eastern China. The second severe
pollution episode occurred in northern India with annual average PM2.5 and
surface NH3 concentrations exceeding 40 µg m-3 and 4 µg N m-3, respectively (surface NO2 concentrations less than 1 µg N m-3). The major source of NH3 in northern India was
agricultural activities and livestock waste management (Warner et al.,
2016). The hot spots of surface NH3 concentrations in the central and
eastern US were highly related to the hot spots of PM2.5. The annual
average PM2.5 is less than 10 µg m-3 (the first level set by the
World Health Organization) in most areas of the US, and only small areas
had PM2.5 greater than 10 µg m-3. Similarly, in western
Europe, the hot spots of high surface NH3 and NO2 concentrations
(greater than 3 µg N m-3) were consistent with the hot spots of
PM2.5 (greater than 20 µg m-3).
Seasonal variations in satellite-derived surface NH3 concentrations
To investigate the seasonal variations in surface NH3 concentrations,
we took the monthly surface NH3 concentrations in 2014 as a case study
(Fig. 6) and analyzed the seasonal surface NH3 concentrations
in hot spot regions including eastern China (ECH), Sichuan and Chongqing (SCH),
Guangdong (GD), northeastern India (NEI), the eastern US (EUS), and western Europe (WEU)
(Fig. 7).
Global surface NH3 concentrations in January, April,
July and October in 2014. The red rectangular regions include eastern China
(ECH), Sichuan and Chongqing (SCH), Guangdong (GD), northeastern India (NEI),
the eastern US (EUS), and western Europe (WEU).
Monthly variations in surface NH3 concentrations in
hot spot regions including eastern China (ECH), Sichuan and Chongqing (SCH),
Guangdong (GD), northeastern India (NEI), the eastern US (EUS), and western Europe (WEU).
Seasonal mean IASI-derived surface NH3 concentrations vary by more than 2 orders of magnitude in hot spot regions, such as eastern China and
the eastern US. In China, high surface NH3 concentrations occurred in
spring (March, April and May) and summer (June, July and August) in eastern
China (ECH), Sichuan and Chongqing (SCH), and Guangdong (GD). This may be due to
two major reasons. First, mineral N fertilizer or manure
application occurred in summer or spring in the croplands
(Paulot et al., 2014). A previous study (Huang et
al., 2012) also suggested a summer peak in NH3 emissions in China,
which was consistent with the summer peak in surface NH3
concentrations. Second, the temperature in warm months is highest in one
year, which favors the volatilization of ammonium
(NH4++OH-→NH3+H2O). Notably, there is a
difference in the seasonal variations in surface NH3 concentrations
between ECH (peaking in June and July) and GD (peaking in March), which was
likely related to different crop planting, N fertilization time and
meteorological factors (Van Damme et al., 2015a, b; Shen et al., 2009). In the eastern US (EUS), high surface NH3
concentrations appeared in warm months (from March to August, Fig. 7) with the maximum in May due to higher temperature and emissions in vast
croplands, where the agricultural mineral N fertilizers dominate the
NH3 emissions. A previous study also implied a spring peak in NH3
emissions in the eastern US (Gilliland et al., 2006). Since
the spatial patterns of high surface NH3 concentrations are highly
linked with the spatial distributions of croplands, seasonal surface
NH3 concentrations mainly reflect the timing of N fertilizers in the
croplands. In western Europe, surface NH3 concentrations are low in
January and February, rise in March and reach their maximum, remain high
from March to June, and then decline from June to December (Fig. 7).
High NH3 concentrations appeared from March to June, mainly affected by
higher temperature and frequent N fertilization (Van Damme et al.,
2014a, 2015b; Paulot et al., 2014; Whitburn et al., 2015).
To identify the major regions of biomass burning that may affect the spatial
distribution of surface NH3 concentrations, we used the fire products
from the Moderate Resolution Imaging Spectroradiometer (MODIS) on board
NASA Aqua and Terra. The MODIS climate modeling grid (CMG) global monthly
fire location product (level 2, collection 6) developed by the University of
Maryland included the geographic location of fire, raw count of fire pixels and
mean fire radiative power (Giglio et al., 2015). We used the Aqua and
Terra CMG fire products on a monthly scale at a spatial resolution of
0.5∘ latitude × 0.5∘ longitude in 2014, and the
fire pixel counts were used to identify the hot spot regions of biomass
burning. In the major hot spots with frequent fires (mostly in the Southern
Hemisphere), the biomass burning controlled the seasonal surface NH3
concentrations (Figs. S8 and S9), such as, for instance, Africa
north of the Equator, Africa south of the Equator and central South America. Apart
from the large areas with frequent fires in the Southern Hemisphere, we also
demonstrated the relationship of biomass burning and surface NH3
concentrations in China, the US, and Europe and selected six typical regions in
China (CH1 and CH2), the US (US1 and US2) and Europe (EU1 and EU2) (Fig. 8) to analyze the monthly variations in fire counts and surface NH3
concentrations.
MODIS fire counts in 2014. (a) Spatial distributions of
MODIS fire counts. (b) Monthly variations in fire counts and surface
NH3 concentrations in biomass burning regions in China, the US and
Europe.
In China, the first region (CH1) covers the major cropland areas in northern
China including Shandong, Henan and northern Jiangsu provinces. The fire
counts were mainly from the crop straw burning, which contributes a large amount to
surface NH3 concentrations. Both surface NH3 concentrations and
fire counts were found in June likely related to the crop straw burning in
these agricultural regions. Notably, despite a decline in fire counts in
July, the surface NH3 concentrations in July did not decrease, probably
due to mineral N fertilization for new planted crops (CH1 is typical for
spring and summer corn rotations) as well as the high temperature favoring
NH3 volatilization in July. The second region (CH2) is typical for the
rice cultivation area in southern China, where the rice was normally
planted in June or July with frequent mineral N fertilization. Thus, the
highest surface NH3 concentrations occurred in June and July. This
region is also typical for the winter wheat and summer rice rotations, and
the wheat is normally harvested from May to July, which can lead to frequent
fire counts there. Despite the more frequent fires in the second region than
the first region, the surface NH3 concentrations in CH2 were much lower
than those in CH1. This may be due to the wetter climate and more frequent
precipitation events in CH2 than in CH1, resulting in higher scavenging of
surface NH3 concentrations into water.
US1 is a region typical for forest land in the US, and the fire counts are
certainly from the forest fires or anthropogenic biomass burning. The
monthly variations in surface NH3 concentrations were consistent with
the monthly variations in MODIS fire counts, which peaked in August with
high temperature. Instead, US2 is a region typical for mixed agricultural
and forest lands, which can be influenced by both potential mineral N
fertilization and anthropogenic biomass burning or forest fires. It is clear
to see that there is a peak in surface NH3 concentrations in October
resulting from biomass burning because of the same peak in fire counts
in October. However, there is also an apparent peak in surface NH3
concentrations in May, which may result from the mineral N fertilization in
this region. In Europe, the selected two regions of EU1 and EU2 are mainly
covered by crops, vegetables and forests. For EU2, the monthly
variations in surface NH3 concentrations were consistent with the
monthly variations in MODIS fire counts, which peaked in August with high
temperature, implying that the biomass burning may control the seasonal
surface NH3 concentrations. For EU1, there were two peaks of surface
NH3 concentrations including April and August. The August peak can be
related to the biomass burning because of the high fire counts, while the
April peak may be related to the agricultural fertilizations for the spring
crops.
Trends in surface NH3 concentrations in China, the US and Europe
Time series of 9 years (2008–2016) of IASI-derived surface NH3
concentrations have been fitted using the linear regression method
(Geddes et al., 2016; Richter et al., 2005) for all grids on land.
The annual trend (the slope of the linear regression model) is shown in
Fig. 9. A significant increase rate of surface NH3
concentrations (> 0.2 µg N m-3 yr-1) appeared in
eastern China, and a middle positive trend (0.1–0.2 µg N m-3 yr-1) occurred in northern Xinjiang, corresponding to its frequent
agricultural activities for fertilized crops and dry climate (Warner et
al., 2017; Liu et al., 2017b; Xu et al., 2015; Huang et al., 2012). The large
increase in eastern China was consistent with the results revealed by AIRS
NH3 data (Warner et al., 2017). The increase in
surface NH3 concentrations in eastern China was consistent with the
trend of NH3 emission estimates by a recent study (Zhang et
al., 2017). China's NH3 emissions increased significantly from 2008 to
2015, with an increase rate of 1.9 % yr-1, which was mainly driven by
eastern China (Zhang et al., 2017). Approximately 85 %
of the inter-annual variations were due to the changes of human activities,
and the remaining 15 % resulted from air temperature changes. Agricultural
activities are the main driver of NH3 emission increase, of which
43.1 % and 36.4 % were contributed by livestock manure and fertilizer
application (Zhang et al., 2017). In addition, the increase in
surface NH3 concentrations in eastern China may also be linked with the
decreased NH3 removal due to the decline in acidic gases (NO2 and
SO2) (Liu et al., 2017a; Xia et al., 2016). NH3 can react with
nitric acid and sulfuric acid to form ammonia sulfate and ammonia nitrate
aerosols. The reduction of acidic gases leads to the reduction of NH3
conversion to ammonia salts in the atmosphere, which may lead to the
increase in NH3 in the atmosphere (Liu et al., 2017a; Y. Li et al.,
2017). China's SO2 emissions decreased by about 60 % in 2008–2016,
which led to a 50 % decrease in surface SO2 concentrations simulated
by the WRF model and then resulted in a 30 % increase in surface NH3
concentrations (Liu et al., 2018).
Trends of IASI-derived surface NH3 concentrations
between 2008 and 2016. A linear regression was performed to calculate the
trends. The significance value (p) and R2 for the trends can be found
in Fig. S10.
In the US, the NH3 increase was found in agricultural regions in middle
and eastern regions with an annual increase rate of lower than 0.10 µg N m-3 yr-1, which was consistent with the results of AIRS NH3
data for a longer time period (2003–2016) (Warner et al.,
2017), while we examined the time span of 2008–2016 from IASI retrievals.
Based on the simulation data of the CMAQ model, it is also found that NH3
increased significantly in the eastern US from 1990 to 2010, which is
inconsistent with the significant downward trend of NOx emissions
(Y. Zhang et al., 2018). This inconsistency between NH3 and
NOx trends in the US was mainly due to different emission control
policies. Over the past 2 decades, due to the implementation of effective
regulations and emission reduction measures for NOx, NOx
emissions in the US decreased by nearly 41 % between 1990 and 2010
(Hand et al., 2014). However, this NH3 increase in the
eastern US is likely due to the lack of NH3 emission control policies as
well as the decreased NH3 removal due to the decline in acidic gases
(NO2 and SO2) (Warner et al., 2017; Li et al., 2016). As
NH3 is an uncontrolled gas in the US, NH3 emissions have
continuously increased since 1990, and by 2003 NH3 emissions had begun
to dominate the inorganic N emissions (NOx plus NH3)
(Y. Zhang et al., 2018). For western Europe, the trend was close to
0 in most regions, although we can observe the NH3 increase at many
points with small positive trends lower than 0.1 µg N m-3 yr-1. Compared with the trend of surface NH3 concentrations in
China and the US, the change of surface NH3 concentrations in western
Europe is more stable, which may be related to the mature NH3 reduction
policies and measures in Europe. Since 1990, Europe has implemented a series
of agricultural NH3 emission reduction measures, and NH3 emissions decreased by about 29 % between 1990 and 2009
(Tørseth et al., 2012b). For example, due to serious N
eutrophication, the Netherlands has taken measures to reduce NH3 emissions by nearly a factor of 2 in the past 20 years, while maintaining a
high level of food production (Dentener et al., 2006). The N fertilizer
use in Europe has decreased according to the data from the World
Bank (http://data.worldbank.org/indicator/AG.CON.FERT.ZS, last access: 18 September 2018) with an annual
decrease of -8.84 to ∼-17.7 kg ha-1 yr-1 in
fertilizer use in Europe (Warner et al., 2017).
Conclusions
The IASI-derived global surface NH3 concentrations during 2008–2016
were inferred based on IASI NH3 column measurements as well as NH3
vertical profiles from GEOS-Chem in this study. Global NH3 vertical
profiles on land from GEOS-Chem can be modeled well by the Gaussian
function between 60∘ N and 55∘ S with R2 higher
than 0.90. The IASI-derived surface NH3 concentrations were compared to
the in situ measurements over China, the US and Europe. One of the major
findings is that a relatively high predictive power for annual surface
NH3 concentrations was achieved through converting IASI NH3
columns using modeled NH3 vertical profiles, and the validation with
the ground-based measurements shows that IASI-derived surface NH3
concentrations had higher accuracy in China than the US and Europe. High
surface NH3 concentrations were found in croplands in China, the US, and
Europe, and surface NH3 concentrations in croplands in China were
approximately double those in the US and Europe. Seasonal mean
IASI-derived surface NH3 concentrations vary by more than 2 orders of
magnitude in hot spot regions, such as eastern China and the eastern US. The
linear trend analysis shows that a significant positive increase rate of
above 0.2 µg N m-3 yr-1 appeared in eastern China during
2008–2016, and a middle increase trend (0.1–0.2 µg N m-3 yr-1)
occurred in northern Xinjiang Province. In the US, the NH3 increase was
found in agricultural regions in middle and eastern regions with an annual
increase rate of lower than 0.10 µg N m-3 yr-1.
Data availability
The IASI NH3 satellite datasets are available at
http://iasi.aeris-data.fr/NH3 (Van Damme et al., 2017, 2018). The ground-based NH3 measurements in the
Chinese Nationwide Nitrogen Deposition Monitoring Network (NNDMN) can be
requested from Xuejun Liu at the China Agricultural University. The
ground-based NH3 measurements from AMoN-US can be downloaded from
the website http://nadp.sws.uiuc.edu/AMoN/ (Li et al., 2016). The ground-based NH3
measurements from the EMEP network can be gained from
https://www.nilu.no/projects/ccc/emepdata.html (von Bobrutzki et al., 2010). The IASI-derived surface
NH3 used in this study is available from the corresponding author upon
request.
The supplement related to this article is available online at: https://doi.org/10.5194/acp-19-12051-2019-supplement.
Author contributions
LL and XZ designed the research; WX and XL's group conducted the fieldwork
in China; LL prepared IASI NH3 products; LL and AYHW conducted model
simulations; LL, WX, LZ, XW and ZW performed the data analysis and prepared
the figures; LL, AYHW and XZ wrote the paper, and all coauthors contributed to
the revision.
Competing interests
The authors declare that they have no conflict of interest.
Acknowledgements
We acknowledge the free use of IASI NH3 data from the Atmospheric
Spectroscopy Group at Université libre de Bruxelles (ULB). We thank
Jeffrey A. Geddes for the help of using GEOS-Chem in this work. This
study is supported by the National Natural Science Foundation of China (nos. 41471343, 41425007, 41101315 and 41705130) and Doctoral Research Innovation
Fund (2016CL07) as well as the Chinese National Programs on Heavy Air
Pollution Mechanisms and Enhanced Prevention Measures (project no. 8 in the
second special program).
Financial support
This research has been supported by the National Natural Science Foundation of China (grant nos. 41471343, 41425007, 41101315 and 41705130).
Review statement
This paper was edited by Frank Dentener and reviewed by two anonymous referees.
ReferencesAmos, H. M., Jacob, D. J., Holmes, C. D., Fisher, J. A., Wang, Q., Yantosca, R. M., Corbitt, E. S., Galarneau, E., Rutter, A. P., Gustin, M. S., Steffen, A., Schauer, J. J., Graydon, J. A., Louis, V. L. St., Talbot, R. W., Edgerton, E. S., Zhang, Y., and Sunderland, E. M.: Gas-particle partitioning of atmospheric Hg(II) and its effect on global mercury deposition, Atmos. Chem. Phys., 12, 591–603, 10.5194/acp-12-591-2012, 2012.Basto, S., Thompson, K., Phoenix, G., Sloan, V., Leake, J., and Rees, M.:
Long-term nitrogen deposition depletes grassland seed banks, Nat.
Commun., 6, 1–6, 10.1038/ncomms7185, 2015.Crippa, M., Guizzardi, D., Muntean, M., Schaaf, E., Dentener, F., van
Aardenne, J. A., Monni, S., Doering, U., Olivier, J. G. J., Pagliari, V.,
and Janssens-Maenhout, G.: Gridded emissions of air pollutants for the
period 1970–2012 within EDGAR v4.3.2, Earth Syst. Sci. Data, 10, 1987–2013,
10.5194/essd-10-1987-2018, 2018.Dammers, E., Palm, M., Van Damme, M., Vigouroux, C., Smale, D., Conway, S.,
Toon, G. C., Jones, N., Nussbaumer, E., Warneke, T., Petri, C., Clarisse,
L., Clerbaux, C., Hermans, C., Lutsch, E., Strong, K., Hannigan, J. W.,
Nakajima, H., Morino, I., Herrera, B., Stremme, W., Grutter, M., Schaap, M.,
Wichink Kruit, R. J., Notholt, J., Coheur, P. F., and Erisman, J. W.: An
evaluation of IASI-NH3 with ground-based Fourier transform infrared
spectroscopy measurements, Atmos. Chem. Phys., 16,
10351–10368, 10.5194/acp-16-10351-2016, 2016.
Dentener, F., Drevet, J., Lamarque, J., Bey, I., Eickhout, B., Fiore, A.,
Hauglustaine, D., Horowitz, L., Krol, M., and Kulshrestha, U.: Nitrogen and
sulfur deposition on regional and global scales: a multimodel evaluation,
Global Biogeochem. Cy., 20, 1–21, 2006.
Eerden, L. J. M. V. D.: Toxicity of ammonia to plants, Agr.
Environ., 7, 223–235, 1982.Fountoukis, C. and Nenes, A.: ISORROPIA II: a computationally efficient thermodynamic equilibrium model for K+–Ca2+–Mg2+–NH4+–Na+–SO42-–NO3-–Cl-–H2O aerosols, Atmos. Chem. Phys., 7, 4639–4659, 10.5194/acp-7-4639-2007, 2007.Geddes, J. A., Martin, R. V., Boys, B. L., and van Donkelaar, A.: Long-term
trends worldwide in ambient NO2 concentrations inferred from satellite
observations, Environ. Health Persp., 124, 281–289, 2016.Geng, G., Zhang, Q., Martin, R. V., Donkelaar, A. V., Huo, H., Che, H., Lin,
J., and He, K.: Estimating long-term PM2.5 concentrations in China using
satellite-based aerosol optical depth and a chemical transport model, Remote
Sens. Environ., 166, 262–270, 2015.Giglio, L., Randerson, J. T., and van der Werf, G. R.: Analysis of daily,
monthly, and annual burned area using the fourth-generation global fire
emissions database (GFED4), J. Geophys. Res.-Biogeo.,
118, 317–328, 10.1002/jgrg.20042, 2013.
Giglio, L., Csiszar, I., and Justice, C. O.: Global distribution and
seasonality of active fires as observed with the Terra and Aqua Moderate
Resolution Imaging Spectroradiometer (MODIS) sensors, J. Geophys.
Res.-Biogeo., 111, 17–23, 2015.Gilliland, A. B., Wyat Appel, K., Pinder, R. W., and Dennis, R. L.: Seasonal
NH3 emissions for the continental united states: Inverse model
estimation and evaluation, Atmos. Environ., 40, 4986–4998,
10.1016/j.atmosenv.2005.12.066, 2006.
Hand, J. L., Schichtel, B. A., Malm, W. C., Copeland, S., Molenar, J. V.,
Frank, N., and Pitchford, M.: Widespread reductions in haze across the
United States from the early 1990s through 2011, Atmos. Environ.,
94, 671–679, 2014.
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, 1–14, 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.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.Kharol, S. K., Shephard, M. W., McLinden, C. A., Zhang, L., Sioris, C. E.,
O'Brien, J. M., Vet, R., Cady-Pereira, K. E., Hare, E., Siemons, J., and
Krotkov, N. A.: Dry Deposition of Reactive Nitrogen From Satellite
Observations of Ammonia and Nitrogen Dioxide Over North America, Geophys.
Res. Lett., 45, 1157–1166, 10.1002/2017GL075832, 2018.
Kim, T. W., Lee, K., Duce, R., and Liss, P.: Impact of atmospheric nitrogen
deposition on phytoplankton productivity in the South China Sea, Geophys.
Res. Lett., 41, 3156–3162, 2014.
Lamarque, J. F., Kiehl, J., Brasseur, G., Butler, T., Cameron-Smith, P.,
Collins, W., Collins, W., Granier, C., Hauglustaine, D., and Hess, P.:
Assessing future nitrogen deposition and carbon cycle feedback using a
multimodel approach: Analysis of nitrogen deposition, J. Geophys.
Res.-Atmos. (1984–2012), 110, 1–21, 2005.Lamsal, L. N., Martin, R. V., van Donkelaar, A., Steinbacher, M., Celarier,
E. A., Bucsela, E., Dunlea, E. J., and Pinto, J. P.: Ground-level nitrogen
dioxide concentrations inferred from the satellite-borne Ozone Monitoring
Instrument, J. Geophys. Res.-Atmos., 113, 1–15,
10.1029/2007JD009235, 2008.Lamsal, L. N., Martin, R. V., Parrish, D. D., and Krotkov, N. A.: Scaling
relationship for NO2 pollution and urban population size: a satellite
perspective, Environ. Sci. Technol., 47, 7855–7861, 2013.
Larssen, T., Duan, L., and Mulder, J.: Deposition and leaching of sulfur,
nitrogen and calcium in four forested catchments in China: implications for
acidification, Environ. Sci. Technol., 45, 1192–1198, 2011.
Lenhart, L. and Friedrich, R.: European emission data with high temporal
and spatial resolution, Water Air Soil Pollut., 85, 1897–1902, 1995.Levine, J. S., Augustsson, T. R., and Hoell, J. M.: The vertical
distribution of tropospheric ammonia, Geophys. Res. Lett., 7,
317–320, 10.1029/GL007i005p00317, 1980.Li, M., Zhang, Q., Kurokawa, J.-I., Woo, J.-H., He, K., Lu, Z., Ohara, T., Song, Y., Streets, D. G., Carmichael, G. R., Cheng, Y., Hong, C., Huo, H., Jiang, X., Kang, S., Liu, F., Su, H., and Zheng, B.: MIX: a mosaic Asian anthropogenic emission inventory under the international collaboration framework of the MICS-Asia and HTAP, Atmos. Chem. Phys., 17, 935–963, 10.5194/acp-17-935-2017, 2017.
Li, Y., Schwandner, F. M., Sewell, H. J., Zivkovich, A., Tigges, M., Raja,
S., Holcomb, S., Molenar, J. V., Sherman, L., and Archuleta, C.:
Observations of ammonia, nitric acid, and fine particles in a rural gas
production region, Atmos. Environ., 83, 80–89, 2014.
Li, Y., Schichtel, B. A., Walker, J. T., Schwede, D. B., Chen, X., Lehmann,
C. M., Puchalski, M. A., Gay, D. A., and Collett, J. L.: Increasing
importance of deposition of reduced nitrogen in the United States,
P. Natl. Acad. Sci. USA, 113, 5874–5879, 2016.Li, Y., Thompson, T. M., Van Damme, M., Chen, X., Benedict, K. B., Shao, Y., Day, D., Boris, A., Sullivan, A. P., Ham, J., Whitburn, S., Clarisse, L., Coheur, P.-F., and Collett Jr., J. L.: Temporal and spatial variability of ammonia in urban and agricultural regions of northern Colorado, United States, Atmos. Chem. Phys., 17, 6197–6213, 10.5194/acp-17-6197-2017, 2017.
Lin, J. T., Martin, R. V., Boersma, K. F., Sneep, M., Stammes, P., Spurr,
R., Wang, P., Van Roozendael, M., Clémer, K., and Irie, H.: Retrieving
tropospheric nitrogen dioxide from the Ozone Monitoring Instrument: effects
of aerosols, surface reflectance anisotropy, and vertical profile of
nitrogen dioxide, Atmos. Chem. Phys., 14, 1441–1461,
10.5194/acp-14-1441-2014, 2014.Liu, H., Jacob, D. J., Bey, I., and Yantosca, R. M.: Constraints from 210Pb
and 7Be on wet deposition and transport in a global three-dimensional
chemical tracer model driven by assimilated meteorological fields, J. Geophys. Res.-Atmos., 106, 12109–12128,
10.1029/2000JD900839, 2001.Liu, L., Zhang, X., Xu, W., Liu, X., Li, Y., Lu, X., Zhang, Y., and Zhang, W.: Temporal characteristics of atmospheric ammonia and nitrogen dioxide over China based on emission data, satellite observations and atmospheric transport modeling since 1980, Atmos. Chem. Phys., 17, 9365–9378, 10.5194/acp-17-9365-2017, 2017a.
Liu, L., Zhang, X., Xu, W., Liu, X., Lu, X., Wang, S., Zhang, W., and Zhao,
L.: Ground Ammonia Concentrations over China Derived from Satellite and
Atmospheric Transport Modeling, Remote Sens., 9, 1–19, 2017b.Liu, L., Zhang, X., Zhang, Y., Xu, W., Liu, X., Zhang, X., Feng, J., Chen,
X., Zhang, Y., Lu, X., Wang, S., Zhang, W., and Zhao, L.: Dry Particulate
Nitrate Deposition in China, Environ. Sci. Technol., 51,
5572–5581, 10.1021/acs.est.7b00898, 2017c.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.
Mao, J., Paulot, F., Jacob, D. J., Cohen, R. C., Crounse, J. D., Wennberg,
P. O., Keller, C. A., Hudman, R. C., Barkley, M. P., and Horowitz, L. W.:
Ozone and organic nitrates over the eastern United States: Sensitivity to
isoprene chemistry, J. Geophys. Res.-Atmos., 118,
11256–11268, 2013.
Nowlan, C., Martin, R., Philip, S., Lamsal, L., Krotkov, N., Marais, E.,
Wang, S., and Zhang, Q.: Global dry deposition of nitrogen dioxide and
sulfur dioxide inferred from space-based measurements, Global Biogeochem.
Cy., 28, 1025–1043, 2014.
Pan, Y., Tian, S., Zhao, Y., Zhang, L., Zhu, X., Gao, J., Huang, W., Zhou,
Y., Song, Y., and Zhang, Q.: Identifying ammonia hotspots in China using a
national observation network, Environ. Sci. Technol., 52, 3926–3934, 2018.Paulot, F., Jacob, D. J., Pinder, R. W., Bash, J. O., Travis, K., and Henze,
D. K.: Ammonia emissions in the United States, European Union, and China
derived by high-resolution inversion of ammonium wet deposition data:
Interpretation with a new agricultural emissions inventory
(MASAGE_NH3), J. Geophys. Res.-Atmos., 119, 4343–4364, 10.1002/2013JD021130, 2014.Potter, P., Ramankutty, N., Bennett, E. M., and Donner, S. D.:
Characterizing the Spatial Patterns of Global Fertilizer Application and
Manure Production, Earth Interact., 14, 1–22, 10.1175/2009EI288.1, 2010.Preston, K. E., Jones, R. L., and Roscoe, H. K.: Retrieval of NO2
vertical profiles from ground-based UV-visible measurements: Method and
validation, J. Geophys. Res.-Atmos., 102, 19089–19097,
10.1029/97JD00603, 1997.
Puchalski, M. A., Sather, M. E., Walker, J. T., Lehmann, C. M. B., Gay, D.
A., Johnson, M., and Robarge, W. P.: Passive ammonia monitoring in the
United States: comparing three different sampling devices, J.
Environ. Monitor., 13, 3156–3167, 2011.Pye, H. O. T., Liao, H., Wu, S., Mickley, L. J., Jacob, D. J., Henze, D. K.,
and Seinfeld, J. H.: Effect of changes in climate and emissions on future
sulfate-nitrate-ammonium aerosol levels in the United States, J.
Geophys. Res.-Atmos., 114, 1–18, 10.1029/2008jd010701, 2009.
Reay, D. S., Dentener, F., Smith, P., Grace, J., and Feely, R. A.: Global
nitrogen deposition and carbon sinks, Nat. Geosci., 1, 430–437, 2008.
Richter, A., Burrows, J. P., Nüß, H., Granier, C., and Niemeier, U.:
Increase in tropospheric nitrogen dioxide over China observed from space,
Nature, 437, 129–132, 2005.Rozanov, A., Bovensmann, H., Bracher, A., Hrechanyy, S., Rozanov, V.,
Sinnhuber, M., Stroh, F., and Burrows, J. P.: NO2 and BrO vertical profile
retrieval from SCIAMACHY limb measurements: Sensitivity studies, Adv.
Space Res., 36, 846–854, 10.1016/j.asr.2005.03.013,
2005.
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., Nowak, J. B., Holloway, J. S., Neuman, J. A.,
Bahreini, R., Pollack, I. B., Ryerson, T. B., Wiedinmyer, C., and Murphy, J.
G.: An investigation of ammonia and inorganic particulate matter in
California during the CalNex campaign, J. Geophys. Res.-Atmos., 119, 1883–1902, 2014.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.Shen, J. L., Tang, A. H., Liu, X. J., Fangmeier, A., Goulding, K. T. W., and
Zhang, F. S.: High concentrations and dry deposition of reactive nitrogen
species at two sites in the North China Plain, Environmental Pollut., 157,
3106–3113, 10.1016/j.envpol.2009.05.016,
2009.Shephard, M. W. and Cady-Pereira, K. E.: Cross-track Infrared Sounder (CrIS) satellite observations of tropospheric ammonia, Atmos. Meas. Tech., 8, 1323–1336, 10.5194/amt-8-1323-2015, 2015.Shephard, M. W., Cady-Pereira, K. E., Luo, M., Henze, D. K., Pinder, R. W.,
Walker, J. T., Rinsland, C. P., Bash, J. O., Zhu, L., Payne, V. H., and
Clarisse, L.: TES ammonia retrieval strategy and global observations of the
spatial and seasonal variability of ammonia, Atmos. Chem. Phys., 11,
10743–10763, 10.5194/acp-11-10743-2011, 2011.
Sheppard, L. J., Leith, I. D., Crossley, A., Dijk, N. V., Fowler, D.,
Sutton, M. A., and Woods, C.: Stress responses of Calluna vulgaris to
reduced and oxidised N applied under “real world conditions”, Environ.
Pollut., 154, 404–413, 2008.
Simon, H., Allen, D. T., and Wittig, A. E.: Fine particulate matter
emissions inventories: comparisons of emissions estimates with observations
from recent field programs, J. Air Waste Manage., 58, 320–343, 2008.
Sutton, M. A., Tang, Y. S., Miners, B., and Fowler, D.: A New Diffusion
Denuder System for Long-Term, Regional Monitoring of Atmospheric Ammonia and
Ammonium, Water Air Soil Pollut., 1, 145–156, 2001.Tørseth, K., Aas, W., Breivik, K., Fjæraa, A. M., Fiebig, M.,
Hjellbrekke, A. G., Lund Myhre, C., Solberg, S., and Yttri, K. E.:
Introduction to the European Monitoring and Evaluation Programme (EMEP) and
observed atmospheric composition change during 1972–2009, Atmos.
Chem. Phys., 12, 5447–5481, 10.5194/acp-12-5447-2012, 2012a.Tørseth, K., Aas, W., Breivik, K., Fjæraa, A. M., Fiebig, M., Hjellbrekke, A. G., Lund Myhre, C., Solberg, S., and Yttri, K. E.: Introduction to the European Monitoring and Evaluation Programme (EMEP) and observed atmospheric composition change during 1972–2009, Atmos. Chem. Phys., 12, 5447–5481, 10.5194/acp-12-5447-2012, 2012b.
Tevlin, A. G., Li, Y., Collett, J. L., Mcduffie, E. E., Fischer, E. V., and
Murphy, J. G.: Tall Tower Vertical Profiles and Diurnal Trends of Ammonia in
the Colorado Front Range, J. Geophys. Res.-Atmos., 122, 12468–12487,
2017.Van, D. A., Martin, R. V., Spurr, R. J., and Burnett, R. T.: High-Resolution
Satellite-Derived PM2.5 from Optimal Estimation and Geographically Weighted
Regression over North America, Environ. Sci. Technol., 49,
10482–10491, 2015.
Van, D. A., Martin, R. V., Brauer, M., Hsu, N. C., Kahn, R. A., Levy, R. C.,
Lyapustin, A., Sayer, A. M., and Winker, D. M.: Global Estimates of Fine
Particulate Matter using a Combined Geophysical-Statistical Method with
Information from Satellites, Models, and Monitors, Environ. Sci.
Technol., 50, 3762–3772, 2016.Van Damme, M., Clarisse, L., Heald, C. L., Hurtmans, D., Ngadi, Y., Clerbaux, C., Dolman, A. J., Erisman, J. W., and Coheur, P. F.: Global distributions, time series and error characterization of atmospheric ammonia (NH3) from IASI satellite observations, Atmos. Chem. Phys., 14, 2905–2922, 10.5194/acp-14-2905-2014, 2014a.Van Damme, M., Wichink Kruit, R., Schaap, M., Clarisse, L., Clerbaux, C.,
Coheur, P. F., Dammers, E., Dolman, A., and Erisman, J.: Evaluating 4 years
of atmospheric ammonia (NH3) over Europe using IASI satellite
observations and LOTOS-EUROS model results, J. Geophys. Res.-Atmos., 119, 9549–9566, 2014b.Van Damme, M., Clarisse, L., Dammers, E., Liu, X., Nowak, J. B., Clerbaux, C., Flechard, C. R., Galy-Lacaux, C., Xu, W., Neuman, J. A., Tang, Y. S., Sutton, M. A., Erisman, J. W., and Coheur, P. F.: Towards validation of ammonia (NH3) measurements from the IASI satellite, Atmos. Meas. Tech., 8, 1575–1591, 10.5194/amt-8-1575-2015, 2015a.Van Damme, M., Erisman, J. W., Clarisse, L., Dammers, E., Whitburn, S.,
Clerbaux, C., Dolman, A. J., and Coheur, P. F.: Worldwide spatiotemporal
atmospheric ammonia (NH3) columns variability revealed by satellite,
Geophys. Res. Lett., 42, 8660–8668, 2015b.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.van der Graaf, S. C., Dammers, E., Schaap, M., and Erisman, J. W.: Technical note: How are NH3 dry deposition estimates affected by combining the LOTOS-EUROS model with IASI-NH3 satellite observations?, Atmos. Chem. Phys., 18, 13173–13196, 10.5194/acp-18-13173-2018, 2018.von Bobrutzki, K., Braban, C. F., Famulari, D., Jones, S. K., Blackall, T., Smith, T. E. L., Blom, M., Coe, H., Gallagher, M., Ghalaieny, M., McGillen, M. R., Percival, C. J., Whitehead, J. D., Ellis, R., Murphy, J., Mohacsi, A., Pogany, A., Junninen, H., Rantanen, S., Sutton, M. A., and Nemitz, E.: Field inter-comparison of eleven atmospheric ammonia measurement techniques, Atmos. Meas. Tech., 3, 91–112, 10.5194/amt-3-91-2010, 2010.Wang, Q., Jacob, D. J., Fisher, J. A., Mao, J., Leibensperger, E. M., Carouge, C. C., Le Sager, P., Kondo, Y., Jimenez, J. L., Cubison, M. J., and Doherty, S. J.: Sources of carbonaceous aerosols and deposited black carbon in the Arctic in winter-spring: implications for radiative forcing, Atmos. Chem. Phys., 11, 12453–12473, 10.5194/acp-11-12453-2011, 2011.Wang, Y., Logan, J. A., and Jacob, D. J.: Global simulation of tropospheric
O3-NOx-hydrocarbon chemistry: 2. Model evaluation and global ozone
budget, J. Geophys. Res.-Atmos., 103, 10727–10755, 1998.Warner, J. X., Wei, Z., Strow, L. L., Dickerson, R. R., and Nowak, J. B.: The global tropospheric ammonia distribution as seen in the 13-year AIRS measurement record, Atmos. Chem. Phys., 16, 5467–5479, 10.5194/acp-16-5467-2016, 2016.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.
Wei, J., Li, Z., Peng, Y., and Sun, L.: MODIS Collection 6.1 aerosol optical
depth products over land and ocean: validation and comparison, Atmos.
Environ., 201, 428–440, 2019.
Wesely, M.: Parameterization of surface resistances to gaseous dry
deposition in regional-scale numerical models, Atmos. Environ., 23,
1293–1304, 1989.Whitburn, S., Van Damme, M., Kaiser, J. W., van der Werf, G. R., Turquety,
S., Hurtmans, D., Clarisse, L., Clerbaux, C., and Coheur, P. F.: Ammonia
emissions in tropical biomass burning regions: Comparison between
satellite-derived emissions and bottom-up fire inventories, Atmos.
Environ., 121, 42–54, 10.1016/j.atmosenv.2015.03.015,
2015.Whitburn, S., Van Damme, M., Clarisse, L., Bauduin, S., Heald, C. L.,
Hadji-Lazaro, J., Hurtmans, D., Zondlo, M. A., Clerbaux, C., and Coheur, P.
F.: A flexible and robust neural network IASI-NH3 retrieval algorithm,
J. Geophys. Res.-Atmos., 121, 6581–6599,
10.1002/2016JD024828, 2016.Xia, Y., Zhao, Y., and Nielsen, C. P.: Benefits of China's efforts in
gaseous pollutant control indicated by the bottom-up emissions and satellite
observations 2000–2014, Atmos. Environ., 136, 43–53, 10.1016/j.atmosenv.2016.04.013, 2016.Xu, W., Luo, X. S., Pan, Y. P., Zhang, L., Tang, A. H., Shen, J. L., Zhang, Y., Li, K. H., Wu, Q. H., Yang, D. W., Zhang, Y. Y., Xue, J., Li, W. Q., Li, Q. Q., Tang, L., Lu, S. H., Liang, T., Tong, Y. A., Liu, P., Zhang, Q., Xiong, Z. Q., Shi, X. J., Wu, L. H., Shi, W. Q., Tian, K., Zhong, X. H., Shi, K., Tang, Q. Y., Zhang, L. J., Huang, J. L., He, C. E., Kuang, F. H., Zhu, B., Liu, H., Jin, X., Xin, Y. J., Shi, X. K., Du, E. Z., Dore, A. J., Tang, S., Collett Jr., J. L., Goulding, K., Sun, Y. X., Ren, J., Zhang, F. S., and Liu, X. J.: Quantifying atmospheric nitrogen deposition through a nationwide monitoring network across China, Atmos. Chem. Phys., 15, 12345–12360, 10.5194/acp-15-12345-2015, 2015.
Xu, W., Song, W., Zhang, Y., Liu, X., Zhang, L., Zhao, Y., Liu, D., Tang, A., Yang, D., Wang, D., Wen, Z., Pan, Y., Fowler, D., Collett Jr., J. L., Erisman, J. W., Goulding, K., Li, Y., and Zhang, F.: Air quality improvement in a megacity: implications from 2015 Beijing Parade Blue pollution control actions, Atmos. Chem. Phys., 17, 31–46, 10.5194/acp-17-31-2017, 2017.Yu, Y., Xu, M., Yao, H., Yu, D., Qiao, Y., Sui, J., Liu, X., and Cao, Q.:
Char characteristics and particulate matter formation during Chinese
bituminous coal combustion, P. Combust. Inst., 31,
1947–1954, 10.1016/j.proci.2006.07.116, 2007.Zhang, L., Gong, S., Padro, J., and Barrie, L.: A size-segregated particle
dry deposition scheme for an atmospheric aerosol module, Atmos.
Environ., 35, 549–560, 10.1016/S1352-2310(00)00326-5, 2001.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.Zhang, X., Wu, Y., Liu, X., Reis, S., Jin, J., Dragosits, U., Van Damme, M.,
Clarisse, L., Whitburn, S., Coheur, P.-F., and Gu, B.: Ammonia Emissions May
Be Substantially Underestimated in China, Environ. Sci.
Technol., 51, 12089–12096, 10.1021/acs.est.7b02171, 2017.Zhang, Y., Mathur, R., Bash, J. O., Hogrefe, C., Xing, J., and Roselle, S. J.: Long-term trends in total inorganic nitrogen and sulfur deposition in the US from 1990 to 2010, Atmos. Chem. Phys., 18, 9091–9106, 10.5194/acp-18-9091-2018, 2018.
Zhu, L., Henze, D. K., Cady-Pereira, K. E., Shephard, M. W., Luo, M.,
Pinder, R. W., Bash, J. O., and Jeong, G. R.: Constraining U.S. ammonia
emissions using TES remote sensing observations and the GEOS-Chem adjoint
model, J. Geophys. Res.-Atmos., 118, 3355–3368, 2013.