ACPAtmospheric Chemistry and PhysicsACPAtmos. Chem. Phys.1680-7324Copernicus PublicationsGöttingen, Germany10.5194/acp-16-5467-2016The global tropospheric ammonia distribution as seen in the 13-year AIRS
measurement recordWarnerJuying X.juying@atmos.umd.eduWeiZigangStrowL. Larrabeehttps://orcid.org/0000-0001-5999-3519DickersonRussell R.https://orcid.org/0000-0003-0206-3083NowakJohn B.https://orcid.org/0000-0002-5697-9807Department of Atmospheric and Oceanic Science, University of Maryland
College Park, College Park, MD 20742, USADepartment of Physics and Joint Center for Environmental Technology,
University of Maryland Baltimore County, Baltimore, MD 21250, USAAerodyne Research, Inc., Billerica, MA 01821, USAJuying X. Warner (juying@atmos.umd.edu)2May20161685467547913October201521December20158April201610April2016This work is licensed under a Creative Commons Attribution 3.0 Unported License. To view a copy of this license, visit http://creativecommons.org/licenses/by/3.0/This article is available from https://acp.copernicus.org/articles/16/5467/2016/acp-16-5467-2016.htmlThe full text article is available as a PDF file from https://acp.copernicus.org/articles/16/5467/2016/acp-16-5467-2016.pdf
Ammonia (NH3) plays an increasingly important role in the global
biogeochemical cycle of reactive nitrogen as well as in aerosol formation and
climate. We present extensive and nearly continuous global ammonia
measurements made by the Atmospheric Infrared Sounder (AIRS) from the Aqua
satellite to identify and quantify major persistent and episodic sources as
well as to characterize seasonality. We examine the 13-year period from
September 2002 through August 2015 with a retrieval algorithm using an
optimal estimation technique with a set of three, spatially and temporally
uniform a priori profiles. Vertical profiles show good agreement
(∼ 5–15 %) between AIRS NH3 and the in situ profiles from the
winter 2013 DISCOVER-AQ (DISCOVER-Air Quality) field campaign in central
California, despite the likely biases due to spatial resolution differences
between the two instruments. The AIRS instrument captures the strongest
consistent NH3 concentrations due to emissions from the anthropogenic
(agricultural) source regions, such as South Asia (India/Pakistan), China,
the United States (US), parts of Europe, Southeast (SE) Asia
(Thailand/Myanmar/Laos), the central portion of South America, as well as
Western and Northern Africa. These correspond primarily to irrigated
croplands, as well as regions with heavy precipitation, with extensive animal
feeding operations and fertilizer applications where a summer maximum and a
secondary spring maximum are reliably observable. In the Southern Hemisphere
(SH) regular agricultural fires contribute to a spring maximum. Regions of
strong episodic emissions include Russia and Alaska as well as parts of South
America, Africa, and Indonesia. Biomass burning, especially wildfires,
dominate these episodic NH3 high concentrations.
Introduction
Global ammonia (NH3) emissions are increasing due to the increased
agricultural livestock numbers coupled with the increasing use of nitrogen
fertilization. Atmospheric ammonia has impacts upon local scales,
acidification and eutrophication of the ecosystems, and international
(transboundary), as well as local, scales through formation of fine ammonium
containing aerosols (Sutton et al., 2007, 2008; Galloway et al., 2008; Erisman et al., 2013). Ammonia reacts rapidly with
sulfuric (H2SO4), nitric (HNO3), and hydrochloric (HCl) acids
to form a large fraction of secondary aerosols, i.e., fine particulate matter
(PM2.5) (particles less than 2.5 micrometers in diameter) (Malm et al.,
2004). These ammonium containing aerosols affect Earth's radiative balance,
both directly by scattering incoming radiation and indirectly by acting as
cloud condensation nuclei (e.g., Adams et al., 2001; Martin et al., 2004;
Abbatt et al., 2006; Wang et al., 2008; Henze et al., 2012). A large
percentage of PM2.5 can penetrate human respiratory systems and deposit
in the lungs and alveolar regions, thus endangering public health (e.g., Pope
et al., 2002). Ammonia deposition modifies the transport lifetimes, and
deposition patterns of sulfur dioxide (SO2) and nitrogen dioxide
(NOx) (Wang et al., 2008; Henze et al., 2012). Additionally, ammonia
increases the concentrations of the greenhouse gas nitrous oxide (N2O)
(EPA, 2011) and, together with NH4+ content in soils, NH3 is
involved in CH4 production and release (Fowler et al., 2009). NH3
can also contribute to increases in radiative forcing through conversion of
organic carbon (OC) into brown carbon (BrC) (Updyke et al., 2012). Therefore,
monitoring NH3 global distribution of sources is important to human
health, with respect to air and water quality, and climate change.
Atmospheric ammonia concentrations have been modeled from a three-dimensional
coupled oxidant-aerosol model (GEOS-Chem) (Bey et al., 2001) to estimate
natural and transboundary pollution influences on sulfate-nitrate-ammonium
aerosol concentrations in the United States (US) (Park et al., 2004). We used
the simulated NH3 fields from GEOS-Chem as the retrieval a priori for
this study. A number of ammonia-related science studies and top-down
inventory studies are based on GEOS-Chem and its adjoint (Henze et al., 2007;
Heald et al., 2012; Paulot et al., 2013, 2014; Zhu et al., 2013; Paulot and
Jacob, 2014). The model's ammonia emissions were based on annual data from
the 1990 1∘× 1∘ GEIA inventory of Bouwman et
al. (1997). Table 1b from Park et al. (2004) shows a summary of global and
contiguous US ammonia emissions for 2001. The inventory's categories include
anthropogenic sources: domesticated animals, fertilizers, human bodies,
industry, fossil fuels, and natural sources: oceans, crops, soils, and wild
animals. Additional emissions from biomass burning and biofuel used were
computed using the global inventories of Duncan et al. (2003) and Yevich and
Logan (2003), with an emission factor of 1.3 g NH3 per kilogram dry
mass burned (Andreae and Merlet, 2001). For the emissions from domesticated
animals and soils, the GEOS-Chem model used the exponential dependencies on
temperature reported by Aneja et al. (2000) and Roelle and Aneja (2002),
respectively. Ammonia emissions from crops and fertilizers were assumed to
vary seasonally with the number of daylight hours (Adams et al., 1999).
Seasonal variations in biomass burning and biofuel emissions in the model
were specified based on satellite observations (Duncan et al., 2003) and the
heating degree-days approach (Park et al., 2004). The GEOS-Chem model can be
used to generate 3-D global monthly mean fields of NH3 concentrations,
or higher temporal resolutions (e.g., daily or hourly), for various years.
Satellite remote sensing offers unique opportunities to monitor environmental
variables with relatively high temporal and spatial coverages. Ammonia
measurements with large, daily global coverage are challenging and lacking,
partly due to the relatively short (hours to a day) lifetime of NH3 near
the Earth's surface, and partly because its retrievals require high
sensitivity that can be only obtained from areas with high thermal contrast
(TC, the temperature difference between that of the surface temperature and
of the first discernable atmospheric layer) near the surface (Clarisse et
al., 2010). First measurements of ammonia from space were reported over
Beijing and San Diego, CA areas, as examples, with the Tropospheric Emission
Spectrometer (TES, Beer et al., 2008) and in biomass burning plumes with the
Infrared Atmospheric Sounding Interferometer (IASI, Coheur et al., 2009)
satellite. Shephard et al. (2011) documented the TES ammonia retrieval
methodology. TES NH3 data have been utilized jointly with GEOS-Chem in
various emission source studies (e.g. Alvarado et al., 2011; Pinder et al.,
2011; Walker et al., 2012; Zhu et al., 2013). Luo et al. (2015) compared TES
NH3 versus carbon monoxide (CO) ratios, using data from the year 2007,
to those of the GEOS-Chem model with a focus on biomass burning emissions
using TES representative volume mixing ratio values (Shephard et al., 2011).
The first global map of ammonia was created from IASI measurements by
correlating observed brightness temperature differences between strong
NH3 absorbing channels and weak ones to NH3 total columns using
averaged data sets from 2008 (Clarisse et al., 2009). It was later concluded
that this method tends to underestimate the global emission inventories at a
number of global NH3 hotspots using IASI radiances. Clarisse et
al. (2010) examined the ammonia amounts in the San Joaquin Valley of
California in the US using an optimal estimation (OE) retrieval method
(Rodgers, 2000) with a global uniform a priori and IASI radiances and
compared them with TES measurements. They studied the factors influencing the
ability to use satellite infrared (IR) instruments to retrieve accurate
NH3 columns and concentrations, finding that the main factors were
NH3 concentrations and thermal contrast. They concluded that through
retrieval and forward radiative transfer model runs, if both of the NH3
concentrations and thermal contrast are large enough, it is possible to
quantify ammonia near the lowest level of the atmosphere. R'Honi et
al. (2013) discussed the elevated concentrations of NH3 and HCOOH
emitted by the 2010 Russian wildfires. Additional IASI NH3 algorithm
approaches and validations are also discussed by Van Damme et al. (2014,
2015) and Whitburn et al. (2015). Heald et al. (2012) used IASI ammonia
products jointly with the GEOS-Chem output to study inorganic aerosol loading
and atmospheric ammonia concentrations over the US.
Global ammonia sources and variability based on continuous monitoring with
longer than a decade record (13 years) have not been hitherto available. This
study introduces a newly developed daily and global ammonia product from the
Atmospheric Infrared Sounder (AIRS) on the NASA EOS Aqua satellite, spanning
September 2002 through August 2015. The AIRS orbit covers nearly the entire
globe twice daily, and due to cloud clearing, recovers up to 70 % of
cloudy coverage (Susskind et al., 2003; Warner et al., 2013). Additionally,
AIRS is in the afternoon Equator crossing time; and therefore, it offers high
sensitivity due to higher surface temperature and provides higher thermal
contrast to NH3 measurements.
In the next section, we detail the methodology used to develop the global
products of NH3 and present the discussions for data quality. In
Sect. 3, we show examples of validation cases using in situ data from a
recent NASA aircraft mission – DISCOVER-AQ (Crawford et al., 2014)
(http://discover-aq.larc.nasa.gov). Section 4 illustrates the global
distributions of the NH3 sources. We demonstrate the seasonal
variability of NH3 concentrations using AIRS 13 year measurements in
Sect. 5, before summarizing results in Sect. 6.
Methodology
The AIRS instrument is a grating spectrometer with 2378 separate spectral
channels between 650 and 2670 cm-1 (15.3–3.8 µm) with a
spectral resolving power on the order of 1200. Twelve channels of the AIRS
radiances in the window regions (860–875, 928–932, and 965–967 cm-1)
are currently used to retrieve NH3. These channels are carefully
selected so that the retrievals are based on the NH3 sensitivity, while
the effects of the surface and overlapping gases are minimized. AIRS cloud
clearing, described by Susskind et al. (2003), increases the data coverage
significantly to nearly 50–70 % of the total measurements, instead of
the pure clear coverage of approximately 10–15 % at a 13.5 km2
single-view pixel size (Warner et al., 2013). AIRS NH3 retrievals are
based on the cloud-cleared radiances (CCRs) from AIRS L2 products. The
averaging kernel (AK) peaks at about 918 hPa giving AIRS good sensitivity to
lower tropospheric NH3 because the planetary boundary layer generally
extends above this altitude at the overpass local time of 1:30 p.m.
The algorithm used in this AIRS NH3 study was based on a retrieval
module developed for AIRS carbon monoxide (CO) products (Warner et al.,
2010). This module was built upon and added to the current AIRS operational
system or team algorithm (Susskind et al., 2003), but used a different
minimization method. The NH3 module uses AIRS Version 6 (V6) Level 2
(L2) profiles and errors from the previous retrieval steps (i.e., surface,
clouds, water vapor, ozone, methane, CO) as input to the AIRS forward model
– the stand-alone AIRS radiative transfer algorithm (SARTA) (Strow et al.,
2003). We used SARTA with the addition of NH3 as a variable gas, which
was carried out by co-author Strow and co-workers, since the official AIRS
forward model does not include NH3 absorption as a variable. AIRS
NH3 retrievals use an OE method following the formulations given by
Rodgers (2000), and also described by Pan et al. (1998). The OE retrieval
output quantities not only include the NH3 concentrations, but also
provide the AKs, the error covariance, and the degrees of freedom for signal
(DOFS), which benefit model verifications and data assimilation by using
well-quantified errors.
Given a model of the instrument's signals, in the OE method, the forward
equation for the NH3 profile retrieval problem can be written as
y=f(x,b)+nε,
where y is the vector of measured radiances, x is the state
vector (variables to be retrieved from the measurements), b
represents all other parameters used by the forward model, f(x,b) is the forward model function, and nε is the
instrument noise. For the variables that obey a Gaussian distribution, this
inverse problem is equivalent to the maximum likelihood solution. By using a
Newtonian iteration; the solution to Eq. (1) can be written as (Rodgers,
2000)
xn+1=xa+CaKnT(KnCaKnT+Ce)-1[y-yn-Kn(xa-xn)]
where n is the order of iteration and Ce is the
measurement error covariance matrix.
Kn=∂f(x,b)/∂x is the
jacobian matrix for iteration n, which is the sensitivity matrix of
the forward model to the state vector x. xa is the
mean of the a priori distribution and Ca is the a priori
error covariance matrix for xa.
As defined by the retrieval formulations, the AKs are computed using the
following equation:
A=CaKT(KCaKT+Ce)-1K
and,
x′≈Ax+(I-A)xa,
where I represents the identity matrix and x is the true
state. Equation (4) states that in the absence of other error sources the
retrieved state is a weighted mean of the true state and the a priori state,
with the weight A for the true state and I-A
for the a priori. This shows the importance of AKs as diagnostics of the
retrieval. The closer the matrix A is to the identity matrix the
more the retrieved state resembles the true state.
The a priori profiles and the square root of the diagonal terms of
the error covariance matrices for the low pollution (left panel), the
moderate pollution (middle panel), and the high pollution scenarios (right
panel), respectively.
The optimal estimation method requires an a priori mean profile and a
corresponding error covariance matrix that represent the current knowledge of
the geophysical property, i.e., NH3, prior to the retrieval. Due to the
high spatial variability and short lifetime of NH3, a simple fixed a
priori for all emission scenarios is not appropriate. We developed a global
mean, multi-year averaged (2003–2012), three-tier a priori from GEOS-Chem
model (v9-02) simulations for high, moderate, and low pollution. We used
GEOS-5 MERRA data sets from the NASA Global Modeling and Assimilation Office
(Rienecker et al., 2011) to drive the meteorological fields in the GEOS-Chem
simulations. Figure 1 shows the a priori mean profiles (solid curve with
squares) and the error covariance matrices (horizontal bars) for the low
(left panel), the moderate (middle panel), and the high pollution (right
panel), respectively. The high pollution range was defined by profiles with
volume mixing ratios (VMRs) greater than or equal to 5
parts-per-billion-volume (ppbv) at the surface. The moderate pollution range
includes the profiles with surface VMRs greater than or equal to 1 ppbv but
less than 5 ppbv, or greater than 1 ppbv at any level between the surface
and 500 hPa. The low pollution is then defined as being lower than the lower
bounds of the moderate pollution range. The profiles were adjusted to match
AIRS forward model levels. The modeled profiles are extrapolated near the
surface with additional constraints to eliminate high values in the model
near the surface, which are likely seen by satellite sensors.
The same set of the three-tier a priori profiles is used globally and
throughout the AIRS data record. Thus, any spatial and temporal NH3
variations detected using this algorithm are from AIRS measurements. To
select one of the three a priori profiles for each AIRS pixel, we examine the
brightness temperature difference between a strong and a weak channel,
divided by the measurement noise of the strong channel, defined as a
“difference of brightness temperature index” (DBTI). This is similar to the
method used by TES NH3 and described by Shephard et al. (2011). The
DBTIs vary with meteorological conditions and, most importantly, the thermal
contrast at the surface. To take these effects into account, we simulate
the relationship between the brightness temperature differences and TC under
various meteorological conditions using SARTA. We randomly picked
13 790 profiles from AIRS L2 products over land from the months of
January, April, July, and October in years 2003, 2008, and 2011. We then
perturbed the NH3 values spanning the three a priori mean profiles using
the range of 0–100 ppbv multiplied by a random number for each atmospheric
profile. The observed brightness temperatures are compared with the simulated
values at a given TC to determine the level of a priori for the full
retrievals. Figure 2 depicts a relationship between the DBTI and DOFS for the
three emission levels with low emissions in blue, moderate emissions in
green, and high emissions in red. The higher DBTIs are correlated with higher
DOFS, which represent higher surface thermal contrast (Deeter et al., 2007).
Correlation between the DBTI (difference of brightness temperature
index) and DOFS (degrees of freedom for signal) for the three emission
scenarios with low pollution in blue, moderate pollution in green, and high
pollution in red.
AIRS NH3 validation against CRDS (the cavity ring down
spectrometer) spiral profiles collected during the DISCOVER-AQ CA
(16 January–6 February 2013). The red curves represent AIRS retrieved
profiles, gray curves are the a priori profiles, green solid lines are in
situ spiral profiles, and the blue dashed lines are the convolved profiles
using AIRS NH3 AKs. The x axis is linear from 0 to 25 ppbv and
logarithmic from 25 to 150 ppbv.
The NH3 retrieval quality assurance levels are determined based on the
retrieval sensitivities under various meteorological and surface conditions
using the AKs and the DOFS. We also take into account the performance of the
retrievals against surface thermal contrasts from AIRS products.
Additionally, we examine the retrieval residuals, χ2, and the number
of iterations to set proper quality assurance flags. The retrieval residuals
in Kelvin (K) are defined by the square root of the mean variance of the
observed brightness temperatures minus calculated. The NH3 retrieval
quality is affected by the meteorological properties, such as the vertical
temperature and water vapor profiles, surface temperatures, and emissivity,
which are used to model the atmosphere. We also adapt the error information
provided by the AIRS CCR for the relevant channels, which includes
meteorological quantities that are used in deriving the AIRS CCR
(http://disc.sci.gsfc.nasa.gov/AIRS/documentation/v6_docs/v6releasedocs-1/V6_Level_2_Cloud_Cleared_Radiances.pdf).
This error information is flagged by Q0, Q1, and Q2 with Q0 having the
highest quality and Q2 being unusable. In the remaining discussions of this
study, we used χ2 between 0.9 and 27, considering that the channels
used are not all spectrally independent. The number of iterations limit was
set at 10, meanwhile, only the cases with retrieval residuals less than 1 K
are used. We also excluded cases with the surface thermal contrast between
-4 and +4 K, to avoid ambiguous a priori levels; however, this primarily
affects areas over the global oceans. Any additional screening of the data
for higher quality requirements, e.g., the use of DOFS, will be discussed
case by case. Although we have developed AIRS NH3 products for all
available data sets, only the daytime and land cases are discussed in this
study. Additionally, only radiances with quality flag as Q0 are selected for
the discussions in the following sections to ensure the best accuracy.
Validation with in situ measurements
Validations of retrievals using in situ measurements are vital to quantifying
uncertainties in the concentrations, sources, transport patterns, and trends
using satellite data. Direct measurements of tropospheric NH3 are
relatively sparse and in situ measurements above the ground level, necessary
to validate satellite retrievals, are available for only limited locations
and time periods (e.g. Nowak et al., 2007, 2010, 2012). Validation of AIRS
NH3 data sets with available in situ measurements is a continuous effort
as more in situ measurements become available. As an example of our
validation effort, we use the DISCOVER-AQ NH3 measurements over
California
(https://www-air.larc.nasa.gov/cgi-bin/ArcView/discover-aq.ca-2013).
The sampling inlet and NH3 calibration set-up used during DISCOVER AQ
with the cavity ring down spectrometer (CRDS) (G2103, Picarro Inc.) is the
same as used with the Chemical Ionization Mass Spectrometry (CIMS) and
described in Nowak et al. (2007). The CRDS, aboard the NASA P-3B aircraft
during DISCOVER-AQ CA, data period covers 16 January to 6 February 2013. The
in situ NH3 vertical profiles were made in the Southern San Joaquin Valley
of California. This region inside the central valley of California, between
the coastal mountains in the west and the Sierra Nevada Mountains in the
east, consists largely of farmland with scattered dairy farms. Although most
of the area is rural, the profiles were made near the small cities of Hanford
and Corcoran. We only select spiral profiles from the flights within 45 km
of the center of the retrieved AIRS profiles, for the closest match, and
within 3 h of the measurement window, similar to the method used for AIRS CO
validation (Warner et al., 2006).
(upper panel) AIRS global NH3 VMRs at 918 hPa, averaged from
September 2002 through August 2015. The colorbar is linear from 0 to 5 ppbv
and 5 to 10 ppbv, but with different increments. (lower panel) The total
occurrences (number of days) of high concentrations (VMRs > 1.0 ppbv at
918 hPa) in the 13-year period. Red/blue colors indicate relatively high/low
occurrences of high concentrations, respectively.
Figure 3 shows four retrieval profiles that show high NH3 concentrations
and meet the matching criteria, where the red curves represent AIRS retrieved
profiles, gray curves are the a priori profiles, green solid lines are in
situ spiral profiles, and the blue dashed lines are the convolved in situ
profiles by AIRS NH3 AKs. The in situ spiral profiles are taken by
flying an aircraft in the spiral shape in descending or ascending order near
a central location, hence are the closest to being the true vertical
profiles. Note that in Fig. 3, the x axis is linear from 0 to 25 ppbv and
logarithmic from 25 to 150 ppbv. The convolved in situ profiles take into
account satellite retrieval sensitivities, making them appropriate to compare
against satellite retrievals (Rodgers and Connor, 2003). The convolution
calculations follow Eqs. (3) and (4) in Sect. 2. The top left panel shows a
case measured on 16 January 2013 with the retrieval quality at 0, DOFS at
0.64, χ2 at 1.91, the retrieval residual at 0.07 K, and the
measurement time differences at 1.31 h. The distance between the in situ
profile and the center of the AIRS profile is approximately 13.5 km. The top
right panel shows four in situ profiles from 21 January 2013 with AIRS
retrieved profile quality at 0, DOFS at 0.66, χ2 at 1.26, the
retrieval residual at 0.07 K, the time differences ranging from 0.58 to
1.68 h, and the distance differences at approximately 56 km for all four
profiles. The two profiles in the bottom left panel are also from
21 January 2013, with quality at 0, DOFS at 0.83, χ2 at 0.31, the
retrieval residual at 0.06 K. The time differences to the AIRS retrieved
profile are 1.02 and -1.25 h, and the distances are 38.3 and 38.7 km,
respectively. In the bottom right panel, there are four profiles taken from
4 February 2013, with the retrieval profile quality at 0, DOFS at 0.84,
χ2 at 1.1, and the retrieval residual at 0.05 K. The time differences
between the in situ and the retrieved profiles are 1.63, 1.40, -0.47, and
-0.71, and the distances are 5.1, 45.2, 4.9, and 45.2 km, respectively.
Some of the AIRS retrievals collocate with several in situ profiles, and
these show substantial spatial variability.
Over regions with high NH3 in situ concentrations, the convolved in situ
profiles agree with the retrievals within > 1 to ∼ 3 ppbv
(∼ 5–15 %) near the top of the boundary layer, as seen in the top
two panels in Fig. 3. These two AIRS NH3 profiles show good retrieval
sensitivities with DOFS at approximately 0.64 and 0.66, χ2 at 1.91
and 1.26, and the residual at 0.07 K, respectively. The top left in situ
profile is relatively close (13.5 km) to the center of the AIRS pixel,
whereas the top right in situ profiles are further away (∼ 46 km) from
the center of the AIRS pixel. When the NH3 amount is low and there is
very little sensitivity in AIRS measurements, the convolved profiles converge
to the a priori profiles, as seen in the profiles with low NH3
concentrations in the top right panel and in the bottom left panel. In the
bottom right panel, there are four in situ profiles close to the AIRS profile
– the AIRS pixel measures the average effect of the area represented by the
four in situ profiles. Below 925–950 hPa in height, the in situ NH3
mixing ratios are significantly higher than the retrieved profiles,
indicating a limitation of satellite remote sensing in capturing near surface
composition properties. Note again that each AIRS profile covers a surface
area of 45 km2 where in situ observed NH3 amounts can vary by a
factor of ten. The aircraft in situ flights sometimes are biased by their
proximity to strong local point sources. Therefore, the differences between
the retrievals and in situ measurements are likely due to sampling issues,
although the retrieved profile matches the average of the in situ profiles as
discussed above. Nonetheless, the vertical profiles show good agreement
(∼ 5–15 %) between AIRS NH3 and the in situ profiles in the
examples given above.
AIRS NH3 DOFS values averaged over September 2002–August 2015
period. Red/blue colors indicate relatively high/low DOFS, respectively.
Global ammonia concentrations
The AIRS global NH3 VMRs at 918 hPa, averaged from September 2002
through August 2015, are shown in the upper panel of Fig. 4. The lower panel
in Fig. 4 shows the total occurrences of elevated concentrations (VMRs ≥ 1.0 ppbv at 918 hPa) for the same data set. The occurrences, in numbers
of days, are good indicators of the types of emission sources either due to
recurring agricultural practices or episodic forest fires. It is important to
analyze the NH3 VMRs together with the occurrences to identify major
emission sources. Another important quantity used in the NH3 source
analysis is the retrieval DOFS. Figure 5 shows the AIRS NH3 DOFS values
being in a range of 0.1 to slightly above 1.0. The regions with DOFS greater
than 0.4 are generally associated with high NH3 concentrations and
strong signal to noise ratios. We used a threshold level of DOFS of 0.1 to
screen the retrievals in the Fig. 4 top panel to eliminate noise and to focus
on where AIRS sensitivity is high. Areas with DOFS < 0.1 in the whole
data record are indicated in white. The AIRS retrievals are sensitive to
NH3 concentrations in the lowest layer of the atmosphere between
850 hPa and the surface, with sensitivity peaking at approximately 918 hPa
based on the retrieved AKs (not shown). Therefore, we use NH3 VMRs at
this level for all discussions in this study. There are diurnal variations in
the data sets (not shown) that may be due to a number of factors including the
day–night differences of emissions and chemical reactions and possibly
measurement sensitivities, which are beyond the scope of this paper and will
be studied at a later time. Also note that the missing data over land in
certain regions are either due to high elevation (above the 918 hPa altitude
level), and therefore not shown, or persistent cloudy days.
Globally, AIRS shows strong NH3 hotspots from biogenic and anthropogenic
sources including South Asia (India/Pakistan), East Asia (China), the central
US, parts of Europe, Southeast Asia (Thailand/Myanmar/Laos), the central
portion of South America, and Western and Northern Africa, where both the
NH3 VMRs and the frequent occurrences are high. The primary sources for
these regions are from human activities, e.g., livestock waste management and
other agricultural activities. The NH3 concentrations over these hot
spots vary from ∼ 2.5 to above 10 ppbv, averaged over 13 years
covering both strong and weak emission periods. Also seen are large regions
of high NH3 concentrations due to biomass burning events over Russia,
Alaska, South America, Africa, and Indonesia, represented by high VMRs and
low occurrences. High concentrations of NH3 are persistent over South
America and reflect emissions from biomass burning that are trapped by the
Andes
http://earthobservatory.nasa.gov/IOTD/view.php?id=8033&eocn=image&eoci=related_image.
The hot spot over South Asia corresponds to the heavily populated
Indo-Gangetic Plain with plentiful, fertile croplands and extensive
livestock, and bounded on the north by the Himalayas (Yamaji et al., 2004).
The absolute maximum on Fig. 4 is found over the Punjab which has the highest
population density in Pakistan.
(top panel) The NH3 VMRs from the persistent sources filtered
with the collocated occurrences of elevated concentrations (≥ 1.4 ppbv) using a threshold of greater than 40 days; (middle
panel) pasture and Cropland Map (http://OurWorldInData.org); and
(bottom panel) irrigated agricultural land areas
(http://data.worldbank.org). Footnote: The World Bank provided the
statement that the maps displayed on the World Bank web site are for
reference only and do not imply any judgment on the legal status of any
territory, or any endorsement or acceptance of such boundaries.
AIRS NH3 VMRs at 918 hPa averaged between September 2002 and
August 2015 for December–January–February (DJF, upper left panel),
March–April–May (MAM, upper right panel), June–July–August (JJA, lower
left panel), and September–October–November (SON, lower right panel), with
DOFS greater than 0.1 and no cutoff limit for the VMRs. Red/purple colors
indicate relatively high/low NH3 VMRs.
As in Fig. 7 except for the occurrences of high concentrations (VMRs
≥ 1 ppbv). Red/blue colors indicate relatively high/low occurrences of
high concentrations.
To understand the persistent emission sources, we filtered the NH3 VMRs
with the collocated occurrences of elevated concentrations (≥ 1.4 ppbv) greater than 40 days; and the results are shown in Fig. 6 top
panel. Although a sufficient concentration (≥ 1.4 ppbv) threshold is
used to calculate occurrences of the persistent sources, we used all VMR
values, with DOFS greater than 0.1, for the VMR maps. The persistent NH3
sources not only include those large regions listed above, but also include
small geographical areas such as in the San Joaquin Valley of central
California in the US (with low sulfur emissions and where livestock are
plentiful); the Po Valley, Italy; Fergana Valley, Uzbekistan; Azerbaijan; the
Nile Delta and along the banks of the Nile River in Egypt; and the Sichuan
Basin in China. Some of these source locations are consistent with those
previously reported by Clarisse et al. (2009). These emission hotspots are
compared with the “Pasture and Cropland Map” (see middle panel in Fig. 6),
posted by http://OurWorldInData.org, located at the Institute for New
Economic Thinking at the Oxford Martin School. AIRS NH3 source regions
are strongly correlated with cropland areas, e.g., over India, China, the
middle US, Western Africa, eastern South America, and Europe. Note that four
of the strongest emission regions correspond to high percentage irrigated
agricultural areas (see bottom panel in Fig. 6), i.e., over Pakistan, India,
northern Italy, and Azerbaijan adjacent to the Caspian Sea. The irrigated
agricultural land includes that irrigated by controlled flooding. These data
are provided by the World Bank (http://data.worldbank.org) where the
color values are the percent agricultural irrigated land of total
agricultural land. These irrigation activities are associated with periods of
fertilization and ammonia release. Sommer et al. (2004) studied the
relationship between the fertilizing time and the ammonia release time and
indicated that the fertilizers applied in March can be released in the June
to August time frame depending on the amount of precipitation. The irrigation
practices may have the same effect as high amounts of precipitation.
Over China, the AIRS retrieval can match high-resolution inventories
distinguishing the two major animal husbandry areas in east-central China
(Henan, Shandong, and Hebei provinces) as well as Sichuan to the southwest
(Huang et al., 2012). Additional weaker, but persistent, NH3 sources are
also seen in the Fig. 6 top panel that are likely related to livestock and
agriculture practices. These source regions include areas in eastern North
Carolina (consistent with Wu et al., 2007), Arizona near Phoenix, in the east
coast of Spain near Barcelona and Águilas, and over large areas in the
Netherlands, in Mozambique in Africa, and the Gambela National Park region
between Ethiopia and South Sudan.
The NH3 monthly mean variations (solid line) in the NH (upper
panel) and the SH (lower panel), respectively. The long-dash lines show the
1σ standard deviation (SD); and the shaded areas represent the
maximum and minimum range of each data set.
Seasonal variability
Seasonal variations are shown in Fig. 7 in the four NH3 VMR maps,
averaged between September 2002 and August 2015, for
December–January–February (DJF, upper left panel), March–April–May (MAM,
upper right panel), June–July–August (JJA, lower left panel), and
September–October–November (SON, lower right panel), respectively, with
DOFS greater than 0.1 and no cutoff for the VMRs. Globally, the highest
concentrations are in the NH summer and spring seasons, with the exception
from strong biomass burning (BB) sources, i.e., over South America, Southeast
Asia, and Russia in the NH fall season. The highest NH3 concentrations
over non-BB dominant regions occur over India, China, the Mid-West US, and
parts of Europe in the summer months. The longest high concentration seasons
are over northern India, collocated with the measurement of high NH4+
in the precipitation over India reported by Kulshrestha et al. (2005). The
seasonal NH3 VMR distributions in China, Europe, and the US are also
consistent, to a large extent, with the Paulot et al. (2014) study of
agricultural emissions inventory derived by high-resolution inversion of
ammonium wet deposition data. This is especially true for the spring season,
as seen in Fig. A1 of Paulot et al. (2014), showing MASAGE_NH3 (Magnitude
and Seasonality of Agricultural Emissions for NH3) emissions of NH3
from fertilizers.
High average concentrations (Fig. 7) with low frequencies of occurrences
(Fig. 8) generally indicate NH3 from biomass burning (BB). The greatest
emissions from BB in the NH appear in the summer months over Siberia and
eastern Russia as well as over Alaska, US. The highest concentrations due to
BB in the SH appear over South America in September to November (spring for
the SH) when precipitation is minimal and burning extensive (Oliveras et al.,
2014). Over SE Asia where the dry season and most BB occur in March to May,
we find another local maximum (Lin et al., 2013). Over Africa high
concentrations from BB occur in the Western and Central regions, although
both high concentrations and frequencies of occurrences appear in the Sahel
just south of the Sahara in the NH winter. In that region persistent burning
of agricultural waste has been reported (Haywood et al., 2008); see also
http://rapidfire.sci.gsfc.nasa.gov/cgi-bin/imagery/firemaps.cgi.
Ammonia seasonal variations are presented (Fig. 9) using the monthly mean
VMRs averaged over the 13-year period. Simple hemispheric averages of
NH3 concentrations for all cases do not accurately reflect the
seasonality of the important agricultural activities in the NH, due to the
mixing with BB cases and low NH3 regions, as well as regions with
missing values due to weeks of persistent cloud cover. To understand how
NH3 emissions vary seasonally due to human activities, we focus on the
NH3 concentrations from the continuous emission sources. As in the case
of Fig. 6, where we showed continuous sources using screening by the
occurrences of elevated concentrations, we select the occurrence thresholds
at concentration levels higher than 1.4 ppbv on at least 40 days of the
13-year record. Figure 9 shows the monthly mean variations of NH3 (solid
line) in both the NH (upper panel) and the SH (lower panel); the dashed lines
show the ±1σ (standard deviation, SD) and the shaded areas
represent the maximum and minimum range of each data set. In the NH, the high
emission period starts in April and the NH3 concentrations peak in June.
The NH average of the VMR concentrations from April through July is in the
range of 3.7–4.0 ppbv; and it gradually decreases to the minimum of below
2 ppbv in November–December–January. The range of monthly mean variability
between different years is also larger from April to September (at
∼ 1 ppbv) than in the winter months (at ∼ 0.4 ppbv). The SD
decreases from the summer values of 0.6 to 0.3 ppbv in the winter.
Seasonal variation in the SH (lower panel in Fig. 9) shows that the primary
sources of NH3 emission are from BB, as was seen in the NH3
seasonal maps (e.g., Fig. 7). Although the filtering for the continuous
emission sources eliminated some large occasional fires (i.e., over
Indonesia), there are still regularly occurring fires, such as those over the
central part of South America. The NH3 concentrations in the SH peaks in
September with an average value near 3.5 ppbv and decreases sharply after the
SH spring season. The season of high concentrations in the SH is much shorter
than in the NH, as demonstrated by the widths of the seasonal distribution
curves. The largest SD occurs in September with a magnitude of 2 ppbv, but
the variation between different years in the winter is very small
(∼ 0.25 ppbv).
Summary
The AIRS ammonia (NH3) measurements with a 13-year data record provide
global daily maps, identify major source regions, and show seasonal cycles.
This enables studies for detailed locations of the sources and their spatial
and temporal variations. The AIRS NH3 products using the optimal
estimation (OE) retrievals provide retrieval sensitivity properties, in
addition to NH3 concentrations, such as the averaging kernels (AKs),
error covariance matrices, and the degrees of freedom for signal (DOFS). This
will facilitate sensor inter-comparisons, model verifications, and data
assimilation of satellite retrievals. AIRS measurements can not only capture
high biomass burning emissions (e.g., over Russia, Alaska, South America,
Africa, and Indonesia) and/or accumulated concentrations such as in various
valleys (e.g., San Joaquin Valley, California in the US, the Po Valley,
Italy, Fergana Valley, Uzbekistan, and the Sichuan Basin in China), but also
emissions due to routine animal feeding and agriculture activities (e.g.,
Azerbaijan, Nile Delta and along the banks of the Nile River in Egypt, the
Mid-West US, North Carolina, US, the east coast of Spain, in the Netherlands,
in Mozambique and Ethiopia, Africa, and especially the Indo-Gangetic Plain of
South Asia). Over China, the AIRS retrieval can match high-resolution
inventories distinguishing the two major animal husbandry areas in
east-central China and the Sichuan Basin. Preliminary validation results show
excellent agreement with in situ airborne measurements (to within 5–15 %
of the retrieved profiles). Note that since each AIRS profile covers a
surface area of 45 km2 where the NH3 amounts can vary largely, the
simple numerical differences may not be the optimal way to validate satellite
ammonia products.
We use frequent occurrences of NH3 elevated concentrations to select
persistent sources. This distinguishes the NH3 emissions due to human
activities versus occasional fires or retrieval noise. We show the
persistent ammonia sources correlate well with cropland usage, particularly
in regions where irrigation is a routine practice. We show that the hemispheric
seasonal variation using sources screened by the high NH3 frequent
occurrences. The NH high NH3 concentrations occur in the spring and
summer with highest from April to July and lowest in November through
January. In the SH, the NH3 concentration is highest in September, this
is most likely due to BB emissions shown by the high VRMs and relatively low
frequent occurrences.
Detailed examinations of specific regions are needed and will be included in
future studies to improve our understanding of the processes that control the
NH3 distribution and variability. The recent NH3 trends from AIRS
13-year measurements will also be a subject of future studies since the scope
of this paper is to focus on the algorithm details and the global
distributions. Results in this study are focused on land and daytime only.
Future studies will include more complicated surface types, i.e., ocean
surfaces and regions with lower thermal contrast. The diurnal variations will
also be an important topic in the future studies. We have used the pixels
with the highest quality cloud-cleared radiances (at 45 km2 spatial
resolution) defined by the earlier steps of AIRS retrievals, while a future
direction will be to also use the higher spatial resolution single-view
pixels (at 13.5 km2) under clear-sky conditions (Warner et al., 2013).
Data availability
Data used for this study are included in the first author Warner's webpage
(http://www.atmos.umd.edu/~juying/ACP_2015_819/) under Data Links.
Acknowledgements
This study was funded by NASA's The Science of Terra and Aqua program under
grant numbers NNX11AG39G and NNX12AJ05G. We wish to acknowledge the GEOS-Chem
team, AIRS science team, DISCOVER-AQ team. MERRA data used in this
study/project have been provided by GMAO at NASA Goddard Space Flight Center
through the NASA GES DISC online archive. Resources supporting this work were
provided by the NASA High-End Computing (HEC) Program through the NASA Center
for Climate Simulation (NCCS) at Goddard Space Flight Center. Edited by: W. Lahoz
References
Abbatt, J. P. D., Benz, S., Cziczo, D. J., Kanji, Z., Lohmann, U., and
Mohler, O.: Solid Ammonium Sulphate Aerosols as Ice Nuclei: A Pathway for
Cirrus Cloud Formation, Science, 313, 1770–1773, 2006.
Adams, P. J., Seinfeld, J. H., and Koch, D. M.: Global concentrations of
tropospheric sulfate, nitrate, and ammonium aerosol simulated in a general
circulation model, J. Geophys. Res.-Atmos., 104, 13791–13823, 1999.Adams, P. J., Seinfeld, J. H., Koch, D., Mickley, L., and Jacob, D.: General
circulation model assessment of direct radiative forcing by the
sulfate-nitrate-ammonium-water inorganic aerosol system, J. Geophys.
Res.-Atmos., 106, 1097–1111, 10.1029/2000JD900512, 2001.Alvarado, M. J., Cady-Pereira, K. E., Xiao, Y., Millet, D. B., and Payne, V.
H.: Emission Ratios for Ammonia and Formic Acid and Observations of Peroxy
Acetyl Nitrate (PAN) and Ethylene in Biomass Burning Smoke as Seen by the
Tropospheric Emission Spectrometer (TES), Atmosphere, 2, 633–654,
10.3390/atmos2040633, 2011.
Andreae, M. O. and Merlet, P.: Emissions of trace gases and aerosols from
biomass burning, Global Biogeochem. Cy., 15, 955–966, 2001.
Aneja, V. P., Chauhan, J. P., and Walker, J. T.: Characterization of
atmospheric ammonia emissions from swine waste storage and treatment lagoons,
J. Geophys. Res., 105, 11535–11545, 2000.Beer, R., Shephard, M. W., Kulawik, S. S., Clough, S. A., Eldering, A.,
Bowman, K. W., Sander, S. P., Fisher, B. M., Payne, V. H., Luo, M., Osterman,
G. B., and Worden, J. R.: First satellite observations of lower tropospheric
ammonia and methanol, Geophys. Res. Lett., 35, L09801,
10.1029/2008GL033642, 2008.
Bey, L., Jacob, D. J., Yantosca, R. M., Logan, J. A., Field, B., Fiore, A.
M., Li, Q., Liu, H., Mickley, L. J., and Schultz, M.: Global modeling of
tropospheric chemistry with assimilated meteorology: Model description and
evaluation, J. Geophys. Res., 106, 23073–23096, 2001.Bouwman, A. F., Lee, D. S., Asman, W. A. H., Dentener, F. J., VanderHoek, K.
W., and Olivier J. G. J.: A global high-resolution emission inventory for
ammonia, Global Biogeochem. Cy., 11, 561–587, 10.1029/97GB02266, 1997.Clarisse, L., Clerbaux, C., Dentener, F., Hurtmans, D., and Coheur, P.-F.:
Global ammonia distribution derived from infrared satellite observations,
Nat. Geosci., 2, 479–483, 10.1038/ngeo551, 2009.Clarisse, L., Shephard, M. W., Dentener, F., Hurtmans, D., Cady-Pereira, K.,
Karagulian, F., Damme, M. V., Clerbaux, C., and Coheur P.-F.: Satellite
monitoring of ammonia: A case study of the San Joaquin Valley, J. Geophys.
Res., 115, D13302, 10.1029/2009JD013291, 2010.Coheur, P.-F., Clarisse, L., Turquety, S., Hurtmans, D., and Clerbaux, C.:
IASI measurements of reactive trace species in biomass burning plumes, Atmos.
Chem. Phys., 9, 5655–5667, 10.5194/acp-9-5655-2009, 2009.Crawford, J. H., Dickerson, R. R., and Hains, J. C.: DISCOVER-AQ:
Observations and early results, Environ. Manage., available at:
http://awma.org, last access: September 2014.Deeter, M. N., Edwards, D. P., Gille, J. C., and Drummond, J. R.: Sensitivity
of MOPITT observations to carbon monoxide in the lower troposphere, J.
Geophys. Res., 112, D24306, 10.1029/2007JD008929, 2007.Duncan, B. N., Martin, R. V., Staudt, A. C., Yevich, R., and Logan, J. A.:
Interannual and seasonal variability of biomass burning emissions constrained
by satellite observations, J. Geophys. Res., 108, 4100,
10.1029/2002JD002378, 2003.EPA: Reactive Nitrogen in the United States, A Report to the EPA Science
Advisory Board, 164 pp., available at:
http://yosemite.epa.gov/sab/sabproduct.nsf/02ad90b136fc21ef85256eba00436459/c83c30afa4656bea85256ea10047e1e1!OpenDocument&TableRow=2.2
(11/5/10 Draft), 2011.Erisman, J. W., Galloway, J. N., Seitzinger, S., Bleeker, A., Dise, N. B.,
Petrescu, R., Leach, A. M., and de Vries, W.: Consequences of human
modification of the global nitrogen cycle, Philos. T. R. Soc. B, 368, 1621,
10.1098/rstb.2013.0116, 2013.
Fowler, D., Pilegaard, K., Sutton, M. A., Ambus, P., Raivonen, M., Duyzer,
J., Simpson, D., Fagerli, H., Fuzzi, S., Schjoerring, J. K., Granier, C.,
Neftel, A., Isaksen, I. S. A., Laj, P., Maione, M., Monks, P. S., Burkhardt,
J., Daemmgen, U., Neirynck, J., Personne, E., Wichink-Kruit, R.,
Butterbach-Bahl, K., Flechard, C., Tuovinen, J. P., Coyle, M., Gerosa, G.,
Loubet, B., Altimir, N., Gruenhage, L., Ammann, C., Cieslik, S., Paoletti,
E., Mikkelsen, T. N., Ro-Poulsen, H., Cellier, P., Cape, J. N., Horvath, L.,
Loreto, F., Niinemets, U., Palmer, P. I., Rinne, J., Misztal, P., Nemitz, E.,
and Nilsso, D.: Atmospheric composition change: Ecosystems-Atmosphere
interactions, Atmos. Environ., 43, 5193–5267, 2009.
Galloway, J. N., Townsend, A. R., Erisman, J. W., Bekunda, M., Cai, Z.,
Freney, J. R., Martinelli, L. A., Seitzinger, S. P., and Sutton, M. A.:
Transformation of the nitrogen cycle: Recent trends, questions, and potential
solutions, Science, 320, 889–892, 2008.Haywood, J. M., Pelon, J., Formenti, P., Bharmal, N. A., Brooks, M. E.,
Capes, G., Chazette, P., Chou, C., Christopher, S. A., and Coe, H.: Overview
of the Dust and Biomass-burning Experiment and African Monsoon
Multidisciplinary Analysis Special Observing Period-0, J. Geophys. Res., 113,
D00C17,
10.1029/2008JD010077, 2008.Heald, C. L., Collett Jr., J. L., Lee, T., Benedict, K. B., Schwandner, F.
M., Li, Y., Clarisse, L., Hurtmans, D. R., Van Damme, M., Clerbaux, C.,
Coheur, P.-F., Philip, S., Martin, R. V., and Pye, H. O. T.: Atmospheric
ammonia and particulate inorganic nitrogen over the United States, Atmos.
Chem. Phys., 12, 10295–10312, 10.5194/acp-12-10295-2012, 2012.Henze, D. K., Hakami, A., and Seinfeld, J. H.: Development of the adjoint of
GEOS-Chem, Atmos. Chem. Phys., 7, 2413–2433, 10.5194/acp-7-2413-2007,
2007.Henze, D. K., Shindell, D. T., Akhtar, F., Spurr, R. J. D., Pinder, R. W.,
Loughlin, D., Kopacz, M., Sing, K., and Shim, C.: Spatially refined aerosol
direct radiative forcing efficiencies, Environ. Sci. Technol., 46,
9511–9518, 10.1021/es301993s, 2012.Huang, X., Song, Y., Li, M., Li, J., Huo, Q., Cai, X., Zhu, T., Hu, M., and
Zhang, H.: A high-resolution ammonia emission inventory in China, Global
Biogeochem. Cy., 26, GB1030, 10.1029/2011GB004161, 2012.
Kulshrestha, U. C., Granat, L., Engardt, M., and Rodhe, H.: Review of
precipitation monitoring studies in India – a search for regional
patterns, Atmos. Environ., 39, 4419–4435, 2005.Lin, N. H., Tsay, S. C., Maring, H. B., Yen, M.-C., Sheu, G.-C., Wang, S.-H.,
Chi, K. H., Chuang, M.-T., Ou-Yang, C.-F., Fu, J. S., Reid, J. S., Lee,
C.-T., Wang, L.-C., Wang, J.-L., Hsu, C. N., Sayer, A. M., Holben, B. N.,
Chu, Y.-C., Nguyen, X. C., Sopajaree, K., Chen, S.-J., Cheng, M.-T., Tsuang,
B.-J., Tsai, C.-J., Peng, C.-M., Schnell, R. C., Conway, T., Chang, C.-T.,
Lin, K.-S., Tsai, Y. I., Lee, W.-J., Chang, S.-C., Liu, J.-J., Chiang, W.-L.,
Huang, S.-J., Lin, T.-H., and Liu, G.-R.: An overview of regional experiments
on biomass burning aerosols and related pollutants in Southeast Asia: From
BASE-ASIA and the Dongsha Experiment to 7-SEAS, Atmos. Environ., 78, 1–19,
10.1016/j.atmosenv.2013.04.066, 2013.Luo, M., Shephard, M. W., Cady-Pereira, K. E., Henze, D. K., Zhu, L., Bash,
J. O., Pinder, R. W., Capps, S. L., Walker, J. T., and Jones, M. R.:
Satellite observations of tropospheric ammonia and carbon monoxide: Global
distributions, regional correlations and comparisons to model simulations,
Atmos. Environ., 106, 262e277, 10.1016/j.atmosenv.2015.02.007, 2015.Malm, W. C., Schichtel, B. A., Pitchford, M. L., Ashbaugh, L. L., and Eldred,
R. A.: Spatial and monthly trends in speciated fine particle concentration in
the United States, J. Geophys. Res., 109, D03306, 10.1029/2003JD003739,
2004.Martin, S. T., Hung, H.-M., Park, R. J., Jacob, D. J., Spurr, R. J. D.,
Chance, K. V., and Chin, M.: Effects of the physical state of tropospheric
ammonium-sulfate-nitrate particles on global aerosol direct radiative
forcing, Atmos. Chem. Phys., 4, 183–214, 10.5194/acp-4-183-2004, 2004.Nowak, J. B., Neuman, J. A., Kozai, K., Huey, L. G., Tanner, D. J., Holloway,
J. S., Ryerson, T. B., Frost, G. J., McKeen, S. A., and Fehsenfeld, F. C.: A
chemical ionization mass spectrometry technique for airborne measurements of
ammonia, J. Geophys. Res., 112, D10S02, 10.1029/2006JD007589, 2007.Nowak, J. B., Neuman, J. A., Bahreini, R., Brock, C. A., Middlebrook, A. M.,
Wollny, A. G., Holloway, J. S., Peischl, J., Ryerson, T. B., and Fehsenfeld,
F. C.: Airborne observations of ammonia and ammonium nitrate formation over
Houston, TX, J. Geophys. Res., 115, D22304, 10.1029/2010JD014195, 2010.Nowak, J. B., Neuman, J. A., Bahreini, R., Middlebrook, A. M., Holloway, J.
S., McKeen, S. A., Parrish, D. D., Ryerson, T. B., and Trainer, M.: Ammonia
sources in the California South Coast Air Basin and their impact on ammonium
nitrate formation, Geophy. Res. Lett., 39, L07804, 10.1029/2012GL051197,
2012.
Oliveras, I., Anderson, L. O., and Malhi, Y.: Application of remote sensing
to understanding fire regimes and biomass burning emissions of the tropical
Andes, Global Biogeochem. Cy., 28, 480–496, 2014.
Pan, L., Gille, J. C., Edwards, D. P., Bailey, P. L., and Rodgers, C. D.:
Retrieval of tropospheric carbon monoxide for the mopitt experiment, J.
Geophys. Res., 103, 32277–32290, 1998.Park, R. J., Jacob, D., Field, B. D., Yantosca, R. M., and Chin, M.: Natural
and transboundary pollution influences on sulfate-nitrate-ammonium aerosols
in the United States: implications for policy, J. Geophys. Res., 109, D15204,
10.1029/2003JD004473, 2004.
Paulot, F. and Jacob, D. J.: Hidden Cost of U.S. Agricultural Exports:
Particulate Matter from Ammonia Emissions, Environ. Sci. Technol., 48,
903–908, 2014.
Paulot, F., Jacob, D. J., and Henze, D. K.: Sources and processes
contributing to nitrogen deposition: An adjoint model analysis applied to
biodiversity hotspots worldwide, Environ. Sci. Technol., 47, 3226–3233,
2013.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.Pinder, R. W., Walker, J. T., Bash, J. O., Cady-Pereira, K. E., Henze, D. K.,
Luo, M., Osterman, G. B., and Shephard, M. W.: Quantifying spatial and
seasonal variability in atmospheric ammonia with in situ and space-based
observations, Geophys. Res. Lett., 38, L04802, 10.1029/2010GL046146,
2011.
Pope, C. A., Burnett, R. T., Thun, M. J., Calle, E. E., Krewski, D., Ito, K.,
and Thurston, G. D.: Lung Cancer, Cardiopulmonary, Mortality, and Long-term
Exposure to Fine Particulate Air Pollution, J. Am. Med. Assoc., 287,
1132–1141, 2002.R'Honi, Y., Clarisse, L., Clerbaux, C., Hurtmans, D., Duflot, V., Turquety,
S., Ngadi, Y., and Coheur, P.-F.: Exceptional emissions of NH3 and HCOOH
in the 2010 Russian wildfires, Atmos. Chem. Phys., 13, 4171–4181,
10.5194/acp-13-4171-2013, 2013.Rienecker, M. M., Suarez, M. J., Gelaro, R., Todling, R., Bacmeister, J.,
Liu, E., Bosilovich, M. G., Schubert, S. D., Takacs, L., Kim, G.-K., Bloom,
S., Chen, J., Collins, D., Conaty, A., da Silva, A., Gu, W., Joiner, J.,
Koster, R. D., Lucchesi, R., Molod, A., Owens, T., Pawson, S., Pegion, P.,
Redder, C. R., Reichle, R., Robertson, F. R., Ruddick, A. G., Sienkiewicz,
M., and Woollen, J.: MERRA: NASA's Modern-Era Retrospective Analysis for
Research and Applications, J. Climate, 24, 3624–3648,
10.1175/JCLI-D-11-00015.1, 2011.
Rodgers, C. D.: Inverse Methods for Atmospheric Sounding, Theory and
Practice, World Sci., River Edge, N.J., 2000.Rodgers, C. D. and Connor, B. J.: Intercomparison of remote sounding
instruments, J. Geophys. Res., 108, 4116, 10.1029/2002JD002299, 2003.
Roelle, P. A. and Aneja, V. P.: Environmental Simulation Chambers:
Application to Atmospheric Chemical Processes, Springer, 13 January 2006,
Science, 457 pp., 2002.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.
Sommer, S. G., Schjoerring, J. K., and Denmead, O. T.: Ammonia Emission from
Mineral Fertilizers and Fertilized Crops, Adv. Agron., 82, 557–622, 2004.
Strow, L., Hannon, S., Machado, S., Motteler, H., and Tobin, D.: An Overview
of the AIRS Radiative Transfer Model, IEEE T. Geosci. Remote Sens., 41,
303–313, 2003.
Susskind, J., Barnet, C. D., and Blaisdell, J. M.: Retrieval of atmospheric
and surface parameters from AIRS/AMSU/HSB data in the presence of clouds,
IEEE T. Geosci. Remote Sens., 41, 390–409, 2003.Sutton, M., Erisman, J., Dentener, F., and Moller, D.: Ammonia in the
environment: From ancient times to the present, Environ. Pollut., 156,
583–604, 10.1016/j.envpol.2008.03.013, 2008.Sutton, M. A., Nemitz, E., Erisman, J. W., Beier, C., Bahl, K. B., Cellier,
P., de Vries, W., Cotrufo, F., Skiba, U., Di Marco, C., Jones, S., Laville,
P., Soussana, J. F., Loubet, B., Twigg, M., Famulari, D., Whitehead, J.,
Gallagher, M. W., Neftel, A., Flechard, C. R., Herrmann, B., Calanca, P. L.,
Schjo- erring, J. K., Daemmgen, U., Horvath, L., Tang, Y. S., Emmett, B. A.,
Tietema, A., Penuelas, J., Kesik, M., Brueggemann, N., Pilegaard, K., Vesala,
T., Campbell, C. L., Olesen, J. E., Dragosits, U., Theobald, M. R., Levy, P.,
Mobbs, D. C., Milne, R., Viovy, N., Vuichard, N., Smith, J. U., Smith, P.,
Bergamaschi, P., Fowler, D., and Reis, S.: Challenges in quantifying
biosphere-atmosphere exchange of nitrogen species, Environ. Pollut., 150,
125–139, 10.1016/j.envpol.2007.04.014, 2007.
Updyke, K. M., Nguyen, T. B., and Nizkorodov, S. A.: Formation of brown
carbon via reactions of ammonia with secondary organic aerosols from biogenic
and anthropogenic precursors, Atmos. Environ., 63, 22–31, 2012.
Van Damme, M., Wichink Kruit R.J., Schaap M., Clarisse L., Clerbaux C.,
Coheur P.-F., Dammers E., Dolman A.J., Erisman J.W., 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, 2014.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, doi:10.5194/amt-8-1575-2015, 2015.Walker, J. M., Philip, S., Martin, R. V., and Seinfeld, J. H.: Simulation of
nitrate, sulfate, and ammonium aerosols over the United States, Atmos. Chem.
Phys., 12, 11213–11227, 10.5194/acp-12-11213-2012, 2012.
Wang, J., Jacob, D. J., and Martin, S. T.: Sensitivity of sulfate direct
climate forcing to the hysteresis of particle phase transitions, J. Geophys.
Res., 113, D11207, 10.1029/2007JD009368, 2008.Warner, J., Carminati, F., Wei, Z., Lahoz, W., and Attié, J.-L.:
Tropospheric carbon monoxide variability from AIRS under clear and cloudy
conditions, Atmos. Chem. Phys., 13, 12469–12479,
10.5194/acp-13-12469-2013, 2013.Warner, J. X., Comer, M. M., Barnet, C. D., McMillan, W. W., Wolf, W., Maddy,
E., and Sachse, G.: A Comparison of Satellite Tropospheric Carbon Monoxide
Measurements from AIRS and MOPITT During INTEX-A, J. Geophys. Res., 112,
D12S17, 10.1029/2006JD007925, 2006.Warner, J. X., Wei, Z., Strow, L. L., Barnet, C. D., Sparling, L. C., Diskin,
G., and Sachse, G.: Improved agreement of AIRS tropospheric carbon monoxide
products with other EOS sensors using optimal estimation retrievals, Atmos.
Chem. Phys., 10, 9521–9533, 10.5194/acp-10-9521-2010, 2010.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.
Wu, S.-Y., Krishnanb, S., Zhang, Y., and Aneja, V.: Modeling atmospheric
transport and fate of ammonia in North Carolina – Part I: Evaluation of
meteorological and chemical predictions, Atmos. Environ., 42, 3419–3436,
2007.Yamaji, K., Ohara, T., and Akimoto, H.: Regional-specific emission inventory
for NH3, N2O, and CH4 via animal farming in south, southeast, and
East Asia, Atmos. Environ., 38, 7111–7121, 2004.Yevich, R. and Logan, J. A.: An assessment of biofuel use and burning of
agricultural waste in the developing world, Global Biogeochem. Cy., 17, 1095,
10.1029/2002GB001952, 2003.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, 10.1002/jgrd.50166, 2013.