Introduction
Nitrogen emissions in the form of ammonia (NH3), which largely derive
from agriculture, have been associated with acidification and eutrophication
of soils and surface waters (Krupa, 2003; Vitousek et al., 1997), which may
reduce biodiversity in vulnerable ecosystems (Bobbink et al., 1998, 2010).
Ammonia also reacts with nitric acid and sulfuric acid to form ammonium
salts, which account for a large fraction of particulate matter
concentrations (Schaap et al., 2004). Particulate matter is a major
contributor to smog and is related to negative health impacts (Pope III et al.,
2009). Moreover ammonium salts play an important role in the radiance
balance of the Earth, thus having an impact on climate change (Charlson et
al., 1991; Erisman et al., 2007). It has been shown that reduced nitrogen also
plays a role in the fixation of carbon dioxide (CO2) (Reay et al.,
2008). Human activities have increased the global emissions of
reactive
nitrogen (Nr)
to the atmosphere (Holland et al., 1999). Current global Nr
emissions have been estimated to be almost 4 times larger than
pre-industrial levels (Fowler et al., 2013), with NH3 emissions
amounting to 49.3 Tg in 2008 (EDGAR – Emission Database for Global Atmospheric
Research, 2011). Consequently this has led to large increases in atmospheric
nitrogen deposition (Rodhe et al., 2002; Dentener et al., 2006). Biomass
burning was found to account for 11 % of the global emission budget of
NH3 (Bouwman et al., 1997). While agricultural emissions dominate
in the Northern Hemisphere, biomass burning is one of the main sources of
NH3 concentration in the Southern Hemisphere.
Despite its central role in many environmental threats, little is known
about the ammonia budget and its distribution across the globe.
Uncertainties in global and regional emission rates are large, with errors of
more than 50 % (Erisman et al., 2007; Sutton et al., 2013). Ammonia
concentrations have a large variability in time and space and a short lifetime
in the order of hours. The lack of globally distributed observations
hampers our understanding. Surface observations are available, but these are
not homogenously distributed over the globe, with most observation sites
located in the Northern Hemisphere. Most sites provide data with a poor
temporal resolution (e.g. many observation networks use passive samplers
with a sampling time of 2 or 4 weeks (Thijsse et al., 1998; Puchalski et
al., 2011)), whereas emission and deposition dynamics affect concentrations
on the scale of hours to days. Systems with higher sampling frequency such
as the AMANDA, MARGA and (denuder) filter packs are available, but the
number of measurement networks using these instruments is limited as they
are often costly to operate (Erisman et al., 2001; Thomas et al., 2009;
Mount et al., 2002; Hansen et al., 2003). Moreover, measuring NH3 is
challenging and existing in situ measurement techniques are often prone to
sampling artefacts (von Bobrutzki et al., 2010). Recent advances in open-path
remote sensing techniques, like (mini-)differential optical absorption spectroscopy (DOAS) systems and open-path quantum cascade laser (QCL) instruments, show great potential in overcoming part of these
sampling issues (Volten et al., 2012; Miller et al., 2014), but are still in
the development stage and not widely applied yet. Another aspect is the lack
of vertical information, as most instruments only measure surface
concentrations (Erisman et al., 2007, 2008; Van Damme et al., 2015a). Some
recent airborne measurements have been made (Nowak et al., 2007, 2010; Leen
et al., 2013), but only during dedicated campaigns with limited temporal and
spatial coverage. In short, it is very difficult to obtain detailed
knowledge on the global ammonia budget using field observations that are currently available.
Remote sensing products from atmospheric satellite sounders such as the
Infrared Atmospheric Sounding Interferometer (IASI), the Tropospheric
Emission Spectrometer (TES) and the Cross-track Infrared Sounder (CrIS) (Van
Damme et al., 2014a; Shephard et al., 2011, 2015) have become available and
show good promise to improve NH3 concentration monitoring (Van Damme et
al. 2014b; Luo et al., 2015; Whitburn et al., 2015). However, these data sets
are constrained by the overpass time of the satellite and the atmospheric
conditions (cloud coverage, thermal contrast, etc.). Moreover, the
uncertainties associated to the data are relatively large, which calls for a
detailed evaluation of the data. A recent study (Van Damme et al., 2015a)
showed a number of challenges related to the validation. First, reliable
hourly in situ data are sparse. Second, when not using optimal estimation
satellite products, as is the case for the IASI-NH3 retrieval, one has
to assume a vertical profile to link surface concentrations to a column
value. Third, the ground-based observations are often influenced by local
sources, whereas satellite observations have a footprint of the order of
tens of kilometres. A recent study by Shephard et al. (2015) shows the
potential of an instrument that can be used for profile comparisons. In the
study, instruments on an aircraft were used to measure a vertical profile of
NH3; these measurements were used as a validation tool for the NH3-profile
observations of TES. Hence, a measurement methodology that provides
columnar and vertical profiles of ammonia concentrations at a high temporal
resolution would be highly beneficial for evaluating the merits of the novel
satellite products. Fourier transform infrared spectrometry (FTIR) provides
this methodology. Atmospheric sounders have a long history for validation of
satellite products. FTIR observations are already commonly used for the
validation of satellite products of, among others, carbon monoxide (CO),
methane (CH4) and nitrous oxide (N2O) (Wood, 2002;
Griesfeller et al., 2006; Dils et al., 2006; Kerzenmacher et al., 2012).
FTIR spectrometry is a well-established remote sensing technique for the
observation of atmospheric trace gases (Rao and Weber, 1992). FTIR has so
far been used to estimate ammonia emissions from fires (Yokelson et al.,
1997, 2007; Paton-Walsh et al., 2005), but only on a campaign basis, not
through long-term monitoring. There are several monitoring stations with FTIR
instruments that are operated on a regular basis, providing long-term time series for
a suite of key tropospheric and stratospheric species, including carbon
dioxide (CO2), carbon monoxide (CO) and ozone (O3). So far nobody
has systematically analysed the FTIR measurements for NH3. We have
developed a NH3-retrieval strategy for four Network for the Detection of
Atmospheric Composition Change (NDACC) FTIR stations, spanning very
different concentration conditions (polluted and remote sites), in order to
obtain time series of NH3 total columns and show their value for
describing temporal variations.
First we present the measurement sites and the retrieval strategies in
Sect. 2. We describe the characteristics of the retrieval in Sect. 3.1.1
and the uncertainty budget in Sect. 3.1.2. Section 3.2 constitutes of an
interpretation of the results in combination with a comparison with existing
data sets of CO total columns and temperature to distinguish between emission
sources. We summarize the results in Sect. 4.
Measurement sites and retrieval strategies
Sites description
Ground-based FTIR instruments measure the solar absorption spectra under
cloud-free conditions by using a Fourier transform spectrometer. These
spectra can be analysed by using a line-by-line model (Pougatchev et al.,
1995; Hase et al., 2004, 2006), which models the spectroscopic absorption
lines by using known parameters from a spectroscopic database (e.g. HITRAN,
Rothman et al., 2013) in combination with the radiative state of the
atmosphere, and an optimal estimation inversion scheme (Rodgers, 2000).
Information on vertical concentration profiles can be retrieved using the
pressure broadening of the absorption lines. For the NDACC network the
spectral region measured is the near- to mid-infrared domain (740 to 4250 cm-1, i.e. 13.5 to 2.4 µm), with a HgCdTe or InSb cooled detector
(Zander et al., 2008) and a suite of optical filters being used to optimize
the signal-to-noise ratio in the complementary spectral regions. Instruments
in the network are routinely checked and characterized using laboratory
measurements of HBr lines and the line-fit software (Hase et al., 1999) to
assess the instrument line shape, alignment and measurement noise levels.
Four NDACC stations are used in our study, two in each hemisphere:
The site of Bremen (53.10∘ N, 8.85∘ E) is especially suitable to measure
variations in ammonia concentrations as the surrounding state, Lower Saxony, is a region with intensive agricultural activities with high and
temporal variable emissions (Dämmgen and Erisman, 2005). In short, the
ammonia total columns (moleculescm-2) at Bremen are expected to reach
high values compared to background stations. The University of Bremen
operates a Bruker 125HR spectrometer and a solar tracker by Bruker GmbH,
directly on the university campus.
The Jungfraujoch station (46.55∘ N, 7.98∘ E) is a high-altitude
station (3580 m a.s.l.) located in Switzerland (Zander et al., 2008). There are no sources of large
emissions surrounding the station itself as it is located in
the free troposphere. At Jungfraujoch, a Bruker 120HR instrument has been in
operation since the early 1990s. For the current study, specific for the
Jungfraujoch site, we used a subset of spectra recorded during the 2004–2013
time period with apparent solar zenith angles (SZA) between 70 and
85∘ to increase the capability to retrieve the very low ammonia
concentrations.
The Lauder (45.04∘ S, 169.68∘ E) National Institute of Water and Atmospheric
Research (NIWA) atmospheric research station in Central Otago, New Zealand,
is situated at an altitude of 370 m a.s.l. Long-term operations started in 1991 with
a Bruker 120M (Griffith et al., 2003). This instrument was replaced with a
Bruker 120HR in October 2001. Ammonia emissions in the surrounding valley
are mostly due to livestock grazing on the pastures and are a by-product of
seasonal fertilizer application. In recent years there has been an increase
in cattle grazing and crop cultivation (EDGAR – Emission Database for Global
Atmospheric Research, 2011).
Réunion Island (20.9∘ S, 55.50∘ E) is located in the Indian Ocean to the east of
Madagascar. The station is located at the University campus of Saint-Denis on
the north side of the island. Agricultural activities are mostly related to
sugar cane production. The island is prone to some local biomass burning and
wild fire events, which are known to emit ammonia. It is also very close to
Madagascar, a region with frequent and intense biomass burning events, and
it has been found, using backward trajectory, that the emissions in Madagascar
can be transported to Réunion Island within 1 day (Vigouroux et al.,
2009). The measurements used in this study are performed with a Bruker 120M
spectrometer. Details on the measurements can be found in Senten et al. (2008) and Vigouroux et al. (2012).
These stations are expected to provide significant differences in
variability and levels of ammonia, making them suitable to demonstrate the
strength of our retrieval scheme for application across the whole network. A
summary of the station descriptions is given in Table 1. CO columns were
obtained from the NDACC database to be used for comparison in Sect. 3.
FTIR stations used in the analysis. The location,
longitude, latitude and altitude are given for each station as well as the
instrument used for the measurements. Some station specifics are given in
the last column.
Station
Location
Longitude
Latitude
Altitude (m a.s.l.)
Instrument
Station specifics
Bremen
Germany
8.85∘ E
53.10∘ N
27
Bruker 125 HR
City, fertilizers, livestock
Lauder
New Zealand
169.68∘ E
45.04∘ S
370
Bruker 120 HR
Fertilizers, livestock
Réunion
Indian Ocean
55.5∘ E
20.90∘ S
85
Bruker 120 M
Fertilizers, fires
Jungfraujoch
Switzerland
7.98∘ E
46.55∘ N
3580
Bruker 120 HR
High altitude, no large sources
Micro-windows used in the NH3 retrieval at the four
stations.
Stations
Micro-window
Spectral range (cm-1)
Interfering species (profile-retrieved species in bold)
Signal-to-noise ratio (SNR)
Bremen and Lauder
MW1
930.32–931.32
NH3, H2O, O3, CO2, N2O, HNO3, SF6, CFC-12, solar lines
Bremen – real SNR mean value of 450
MW2
966.97–967.68
NH3, H2O, O3, CO2, N2O, HNO3, solar lines
Lauder – real SNR mean value of 250
Réunion
MW1
929.4–931.4
NH3, H2O, O3, CO2, N2O, HNO3, SF6, CFC-12
Réunion – real SNR mean value of 365
MW2
962.1–970.0
NH3, H2O, O3, CO2, N2O, HNO3, HDO, 686O3, solar lines
Jungfraujoch
MW1
929.4–931.4
NH3, H2O, O3, CO2, N2O, HNO3, SF6, CFC-12
MW2
962.1–970.0
NH3, H2O, O3, CO2, N2O, HDO, 686O3, solar lines
Jungfraujoch – fixed at 250
Calculated spectrum for both spectral windows measured with the
125HR in Bremen on the 19 April 2010 at 09:59 UTC, corresponding to a
total column of 18.83×1015 molecules cm-2. The top two panels show the
individual contributions of the different species in the first (MW1) and
second (MW2) spectral windows. The second row shows the same calculated
spectra but now with the y-axis scaled to show the minor interfering
species.
![]()
Example of a synthetic atmospheric spectrum for both spectral
windows at Réunion Island, computed for 5 June 2011, and a total
column of 1.07×1015 molecules cm-2. The top panel shows the individual
contributions of the main species in the first spectral window. The bottom
panel shows the second spectral window.
![]()
<inline-formula><mml:math display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">NH</mml:mi><mml:mn mathvariant="normal">3</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> retrieval strategies
The ammonia absorption lines from its υ2 vibrational band can
be observed in the 700–1350 cm-1 wavenumber range and they are also used
in the retrieval of satellite products of ammonia (e.g. Clarisse et al.,
2009; Van Damme et al., 2014a). In this spectral range the FTIR spectra can
be measured using a potassium bromide (KBr) beam splitter in combination
with a mercury–cadmium–telluride (MCT) nitrogen-cooled detector (Zander et
al., 2008). The retrieval scheme of trace gas concentrations from FTIR
spectra is built on the use of a set of spectral micro-windows containing
absorption lines of the targeted species, with minimum interference by other
atmospheric species or solar lines. Two slightly different sets of spectral
micro-windows were used at the four stations, but both sets use the same
main NH3 absorption lines. The target and interfering species are
summarized in Table 2, with the profile-retrieved species indicated in bold.
To properly estimate ammonia, interfering species like O3 and water
vapour (H2O) that overlap NH3 lines in the υ2
vibrational band have to be accounted for. Two micro-windows were chosen
that contain as few interfering species as possible. In both sets, the
first micro-window (MW1) covers the NH3 absorption line at 930.75 cm-1.
At Bremen/Lauder, the choice was to use only isolated NH3
absorption features to avoid possible problems due to line mixing, therefore
the spectral window MW1 is only 1 cm-1 wide (930.32–931.32; MW1).
Figure 1 shows an example of a synthetic spectrum calculated to fit a
observation that was measured with the 125HR in Bremen on the 19 April 2010 at 09:59 UTC (solar zenith angle of 45∘). The NH3
concentrations on this day were slightly higher than average, resulting in
slightly stronger NH3 absorption features in the spectra. The top two
figures show the absorption contributions of the absorbing species in both
micro-windows. The bottom two panels show an enlarged version of the figure
to distinguish the interfering species with smaller absorption features. At
Réunion Island/Jungfraujoch, MW1 was extended (929.4–931.4; MW1) to cover
another NH3 line at 929.9 cm-1. This improved the retrieval for
Réunion Island because at this location the NH3 concentration levels
are much lower than at Bremen and the water vapour concentrations are much
higher. In this high humidity condition, the 930.75 cm-1 line is not
isolated from H2O, and it improved the retrieval to add the more
isolated one at 929.9 cm-1 (see Fig. 2). The main interfering species
in MW1 are CO2, N2O and H2O. Minor interfering species are
SF6 and CFC-12. The second window spans the NH3 line at
967.35 cm-1. Again, different widths are used for Bremen/Lauder
(966.97–967.68; MW2) and Réunion Island/Jungfraujoch (962.7–970; MW2). The
very weak absorption signatures at Réunion Island and Jungfraujoch are close
to the noise level and therefore the whole NH3 absorption shape is
retrieved (about 964–968 cm-1; see Fig. 2) rather than a single line.
The main interfering species in MW2 are O3, CO2 and H2O for
all sites. At Réunion Island HDO also interferes in MW2 as well as the
isotopologue 686O3 (i.e. 16O–18O–16O), which has
been fitted in addition to the main 666O3. At Jungfraujoch apart
from CO2, two O3 isotopologues (the most abundant and 686O3)
and water vapour, which are the main interferences, N2O,
CFC-12, SF6 and HDO absorptions are also retrieved. Typical NH3
absorptions are weak, on the order of a few tenths of a percent. The typical
measurement noise (signal-to-noise ratio) differs per spectra and site but
ranges between ∼250 at Lauder and ∼450 at
Bremen. Channelling was not an issue in any of the spectra and did not need
to be fitted.
Except at Jungfraujoch where SFIT2 is used, the retrieval is performed using
the more recent SFIT4.0.9.4 algorithm (Pougatchev et al., 1995; Hase et al.,
2004, 2006). Both versions use a form of the optimal estimation method
(Rodgers, 2000) to retrieve the volume mixing ratios and total
columns of NH3 and make use of a priori information (profile and
covariance matrix). For Bremen, Lauder and Jungfraujoch the used NH3
a priori volume mixing ratios are based on balloon observations (Toon et al.,
1999, NH3 available in data set but not reported). The shape of the
balloon measurements' profile was kept constant but extended and scaled to
expected surface concentrations. The a priori surface volume mixing ratio is
estimated to be 10 ppb for Bremen (Dämmgen and Erisman, 2005). Although the
shape of NH3 profiles do change through time, the largest share of
NH3 is expected to be in the mixing layer, which is represented by the
lowest layers in the calculation (Van Damme et al., 2015a; Nowak et al.,
2010). At Réunion Island, the a priori profile was taken from the MOZART
model (L. Emmons, private communication, 2014). The a priori profile peaks at
a higher altitude (4–5 km) instead of the boundary layer as in Bremen, as
NH3 is expected to originate mainly from the transport of biomass burning
emissions at this location. At all stations, the a priori profiles of the
interfering species were taken from the Whole Atmosphere Community Climate
Model (Chang et al., 2008).
At Bremen and Lauder, the a priori covariance matrices only have diagonal
values, corresponding to standard deviations of 100 % for all layers with
no interlayer correlation, chosen in relation to the large range of possible
concentrations and variations between layers. At Jungfraujoch and Réunion
Island, we did not use the a priori covariance matrix as an optimal
estimation; however, the Tikhonov-type L1 regularization (e.g. Sussmann et
al., 2009) was adopted for the Jungfraujoch retrievals. After several tests,
values of 50 and 250 were adopted for the alpha parameter and the signal to
noise for inversion, respectively. A Tikhonov regularization with an alpha
parameter value of 50 was also adopted for the Réunion retrievals. The
signal to noise ratio is calculated for each of the spectra, the mean value being 365.
Daily temperature and pressure profiles for the meteorological variables
were taken from NCEP (National Centers for Environmental Prediction). For the
radiative transfer calculations the profiles were split into about 50
levels, depending slightly on the station, from ground up to 80 km
(100 km in the case of Jungfraujoch and Réunion Island). The layers
have a typical thickness of 500 m in the troposphere, up to 2 km for the
higher layers. For the line spectroscopy we use the HITRAN 2012 database
(Rothman et al., 2013) in combination with a number of corrections for
CO2 (ATMOS, Brown et al., 1996) (except for Jungfraujoch for which the
HITRAN lines are used) and sets of pseudo-lines generated by G. C. Toon
(NASA-JPL) to account for broad unresolved absorptions by heavy molecules
(e.g. CFC-12, SF6).
Measured and calculated spectrum for both spectral windows measured
with the 125HR in Bremen on the 19 April 2010 at 09:59 UTC, corresponding to a total column of 18.83×1015 molecules NH3 cm-2.
The top two panels show the observed (blue line) and calculated (green line)
spectra for MW1 (left) and MW2 (right). The bottom two figures show the
residuals of the fits in both spectral windows.
![]()
Top panels: the retrieved NH3 profile (blue) and the a priori
profile (green) in order from left to right: Bremen (left), Lauder (Left
middle), Réunion Island (right middle) and Jungfraujoch (right). Horizontal
lines indicate the standard deviation in all observations for each layer.
Bottom panels: the normalized averaging kernel for each of the stations.
![]()
Figure 3 shows an example of the fit in both micro-windows for the same
measured spectra as used in Fig. 1. The top two and bottom two panels show
the calculated (green line) and measured spectrum (blue line) and the
residual of both micro-windows. The simultaneous fits are good with a
standard deviation of 0.15 % in both cases.
Results of the FTIR retrievals
Characteristics of the <inline-formula><mml:math display="inline"><mml:mrow class="chem"><mml:msub><mml:mi mathvariant="normal">NH</mml:mi><mml:mn mathvariant="normal">3</mml:mn></mml:msub></mml:mrow></mml:math></inline-formula> retrievals
Vertical information
The retrieved vertical information differs from station to station. The top
of Fig. 4 shows the average NH3 volume mixing ratios (VMR) for
each of the retrieved layers (blue line) and the a priori
profile that was used as input in the retrieval (green line), for the four stations. The bottom of
Fig. 4 shows the averaging kernels for each of the four stations averaged
over all available observations. As mentioned earlier most of the NH3
at Bremen is in the lowest layers. In Fig. 4 this is also observed as the
averaging kernel shows the most sensitivity in the lowest layers (red and
green lines for the layers 0.03–0.5 and 0.5–1 km). The combination of the
two spectral micro-windows contains, on average, 1.9 degrees of freedom of
signal (DOFS) for the Bremen spectra, which means around two independent
vertical layers can be retrieved. The two separate layers consist of a layer
covering ground–1 km and one that covers 1–6 km height, which can be
observed in Fig. 4. It must be taken into account, however, that the
averaging kernels shown are a mean of all observations and thus the retrievable
number of layers and combined layer depths vary from spectra to spectra. On
average, the Lauder spectra have a DOFS of 1.4. There is only vertical
information for multiple layers during periods with increased NH3 total
columns, which mostly occur during summer. Similar to Bremen, averaging
kernels peak near the surface. At Réunion Island, only a DOFS of 1.0 is achieved,
with almost no vertical information available. All the averaging kernels
peak at the same altitude (about 5 km), which is also the peak of the a
priori profile (Fig. 4). Similar to the Réunion spectra, the Jungfraujoch
spectra do not have vertical information with a DOFS of 1.0.
Random and systematic uncertainties used in the error
calculation.
Version (stations)
SFIT 4 (Bremen, Lauder, Réunion)
Version (stations)
SFIT 2 (Jungfraujoch)
Parameter
Random uncertainty
Systematic uncertainty
Parameter
Random uncertainty
Systematic uncertainty
Temperature
2 K troposphere
2 K troposphere
Temperature
1.5 K 0–20 km
5 K stratosphere
5 K stratosphere
2.0 K 20–30 km
5.0 K 30 km –
Solar line shift
0.005 cm-1
0.005 cm-1
Line intensity
20.0 %
Solar line strength
0.1 %
0.1 %
Line T broadening
10.0 %
Solar zenith angle
0.01∘
0.01∘
Line P broadening
10.0 %
Phase
0.001 radian (rad)
0.001 rad
Interfering species
HITRAN2012: varies
Zero level
0.01
0.01
Instrumental line shape (ILS)
10 %
Background curvature
0.001 cm-2
Influence of a priori profiles
Calculated
Field of view
0.001
Solar zenith angle (SZA)
0.2∘
Line intensity
20.0 %
Line T broadening
10.0 %
Line P broadening
10.0 %
Interfering species
HITRAN2012: varies
Mean random and systematic errors for each of the
individual NH3 retrieval parameters. The table is split into two
sections to cover both the error calculation using SFIT4 (Bremen, Lauder,
Réunion) and SFIT2 (Jungfraujoch). At the bottom the errors are summarized
into total mean errors for each of the stations.
Station
Bremen
Lauder
Réunion
Jungfraujoch
Parameter
Mean
Mean
Mean
Mean
Mean
Mean
Parameter
Mean
Mean
random
systematic
random
systematic
random
systematic
random
systematic
error (%)
error (%)
error (%)
error (%)
error (%)
error (%)
error (%)
error (%)
Temperature
4.9
4.9
3.6
3.6
2.7
2.9
Temperature
15.2
Solar zenith angle
1.6
1.6
Solar zenith angle
1.9
Phase
1.0
1.0
1.1
1.1
Instrumental line shape
1.4
Zero level
5.0
5.0
6.8
6.8
Measurement noise
4.5
8.4
10.9
Measurement noise
18.2
Interfering species
1.3
2.4
0.9
8.7 (H2O
Interfering species
1.4
line pressure
broadening)
Retrieval parameters
0.1
0.1
Model parameters
1.4
Background curvature
1.1
1.2
0.3
Forward model
1.0
Smoothing error
2.8
8.1
10.3
Smoothing
5.4
Spectroscopy
21.0
22.7
17.8
Spectroscopy
20.1
NH3 a priori
6.1
Influence of a priori
6.6
profiles (H2O & HDO)
Subtotal error
9.1
23.5
12.0
27.0
15.3
20.0
Subtotal error
25.3
23.1
Total error
25.8
30.2
25.2
Total
34.2
Statistics of the NH3 columns. (No.: number of data
points, DOFS: degrees of freedom of signal, mean ± the error of the
mean, RMSE: root-mean-square error.). Total columns are given in 1×1015 molecules
NH3 cm-2.
Station
No.
Mean
Mean
Median
RMSE
DOFS
(molecules ×1×1015)
(molecules ×1×1015)
(molecules ×1×1015)
Bremen
554
1.9
13.75±4.24
9.51
20.22
Lauder
2412
1.4
4.17±1.40
2.85
5.95
Réunion
1262
1.0
0.80±0.54
0.56
1.14
Jungfraujoch
2702
1.0
0.18±0.07
0.15
0.22
Uncertainties budget
For the error analysis the posteriori error calculation included in the
SFIT4 package is used. The error calculation is based on the error
estimation approach by Rodgers (2000). It allows the calculation of the
error by attributing errors to each of the parameters used in the retrieval.
The error budget can be divided into three contributions: the error due to
the forward model parameters, the measurement noise and the error due to the
vertical resolution of the retrieval (smoothing error). The assumed
uncertainties for the parameters used in the retrieval are listed in Table 3
for the parameters used in the calculation for Bremen, Lauder and Réunion.
For Jungfraujoch, the error computation was performed using the perturbation
method, the spectra of 2009 to 2011 and the Rodger formalism as explained
e.g. in Franco et al. (2015). For Réunion Island, the covariance matrix used
for the smoothing error has diagonal elements representing 150 %
variability from the a priori profile. To reflect the error in the NCEP
temperature profiles, we assume an uncertainty of about 2 K in the
troposphere and a 5 K uncertainty in the stratosphere. For the uncertainty
in the NH3 line parameters we assume values as stated in the HITRAN
2012 database. We assume a conservative 20 % uncertainty for the intensity
and 10 % for both the temperature and pressure broadening coefficients.
The results of the error calculation are listed in Table 4. Combining the
systematic and random errors, we have a mean total error of 25.8 % for all
the spectra measured at Bremen (N=554), 30.2 % for the spectra at
Lauder (N=2412), 25.2 % for the Réunion spectra (N=1262) and 34.2 for
the Jungfraujoch spectra (N=2702). The errors are dominated by
uncertainties in the spectroscopy. In detail, the random error sources
amount to a mean error of 9.1 % for the Bremen spectra, which is mostly
due to uncertainty in temperature, measurement noise and the zero level of
the sensor (i.e. an instrument property). In the case of the
systematic
error, with a mean error of 23.5 %, the error is for the largest part due
to the spectroscopy (i.e. line parameters), with smaller contributions of the
temperature, zero level, phase and the smoothing error. The results are
similar for the Lauder, Réunion and Jungfraujoch spectra, with most of the
uncertainty coming from the line parameters. Hence, line intensity
parameters of the ammonia absorption lines are critical for the NH3
concentrations.
Time series of retrieved NH3 columns (in molecules NH3
cm-2). From top to bottom the figure shows the Bremen (blue), Lauder
(red), Réunion (green) and Jungfraujoch (yellow) total columns. The bars
reflect the errors in the individual observations.
![]()
2004–2013 monthly averaged columns for NH3, CO and
temperature. The top two panels show the monthly NH3 column
concentrations (molecules NH3 cm-2) for each of the four
stations. Vertical lines indicate the mean monthly error. The bottom two
panels show additional column concentrations of CO (bottom, left) and
temperature (bottom, right).
![]()
Time series
Figure 5 shows the NH3 total columns retrieved from all available
spectra from 2004 to 2013. Table 5 gives a summary of statistics of the
retrieved NH3 columns. Individual measurements at Bremen (blue) show
high concentrations, especially in spring, with an overall mean column total
of 13.7×1015 molecules NH3 cm-2 and a root-mean-square error (RMSE) of
20.22 indicating a large variability in the observations. The amplitude of
the spring peaks varies throughout the years, with maxima in 2010 and 2013,
reaching ∼93×1015 and 85×1015 molecules NH3 cm-2. The
variability through the years is caused by changes in meteorology, emissions
and timing of the measurements. Gaps in the data are due to days with
overcast and instrument downtimes. The individual observed columns are
sorted into monthly averages to analyse the seasonal variability and to
understand the processes driving the NH3 concentrations. This is shown
in Fig. 6, together with monthly averages of surface temperature and CO
total columns. NH3 column total concentrations at Bremen (Blue line)
have a seasonal cycle with highest levels during spring, the summer months
and autumn. The maximum concentrations occur around April, which is
consistent with temporal emission patterns for manure application reported
for this region (Friedrich and Reis, 2004; Van Damme et al., 2015b; Paulot et
al., 2014). The baseline variability with higher concentrations in summer
can be explained by an increase in volatilization rates of NH3, emitted
from livestock housing, which is driven by animal activity and temperature
(Gyldenkærne et al., 2005). A comparison is made with CO to distinguish
between agricultural and fire emissions sources. A correlation between
NH3 and CO columns is not observed, which is consistent with
agriculture being the dominant source of ammonia.
On average the measurements at Lauder (Fig. 5, red line, top panel) yield
a column total of 4.17×1015 molecules NH3 cm-2. These levels are
about one-third of the concentrations measured at Bremen (blue, top panel).
Spectra from Lauder are available for most days in the retrieved time
series, which makes it easier to discern peaks and variability. Distinctive
peaks are only visible in each summer. Maxima during springtimes are not
often observed. The peak values are similar in between years, with maxima
typically around 30×1015 molecules NH3 cm-2. The RMSE of 5.95
reflects a large variability in the observations between individual
retrievals. The average error is 1.34×1015 molecules NH3 cm-2, which
is around a quarter of the mean. Figure 6 shows the seasonal cycle of Lauder
(red line, top left panel). The seasonal variation of NH3 coincides
with that of the atmospheric temperature (red line, bottom right panel) and
with the livestock emissions in the surrounding region, which are strongly
correlated with temperature.
The third panel of Fig. 5 shows the observations from Réunion (green
symbols, bottom panel). The mean column total observed at Réunion is 0.80×1015
molecules NH3 cm-2. The concentrations are low during most of the
year. However, peaks reaching densities of ∼6×1015 molecules
NH3 cm-2 can be observed during the end of each year. The peaks in
September–November coincide with the dry season, indicating that emissions
are mostly due to biomass burning and large fire events (Vigouroux et al.,
2012). This is supported by the increased CO concentrations, which are also
observed in October and November (see, bottom left panel, Fig. 6).
NH3 surface concentration measurements are not available for this
region but a recent paper by Van Damme et al. (2015b), which uses
IASI-NH3 observations, shows similar seasonal cycles for the south-eastern parts of Africa (Madagascar). Temperature is almost constant
throughout the year and is not a major factor in the seasonality of Réunion.
Observations from Jungfraujoch have the lowest mean concentration of all
four stations (Fig. 5, orange line), with a mean of 0.18×1015 molecules
NH3 cm-2. The low concentrations at Jungfraujoch are expected, as
the station is located in the free troposphere high above the surrounding
valleys. Transport of NH3 from the valleys only occurs sporadically
during days with intense vertical mixing. This was also observed in an
earlier study of CO concentrations (Barret et al., 2003). The Jungfraujoch
observations show almost no seasonal effects with only a minimal increase
during the summer months. The low concentrations measured at Jungfraujoch
support our assumption on the vertical distribution of the ammonia
concentrations with low values in the troposphere that were used in our
a priori profiles.
Conclusions and perspectives
In this study we presented a new method to retrieve ammonia total columns
from ground-based FTIR solar spectra. Observations from four complementary
stations were used to illustrate the capabilities of the retrieval method.
NH3 total columns ranging 3 orders of magnitude were obtained with
high abundances at Bremen (mean of 13.7×1015 molecules cm-2, with a mean
DOFS of 1.9) to low columns at Jungfraujoch (mean of 0.18×1015 molecules
cm-2, with a mean DOFS of 1.0). The very low levels obtained at Jungfraujoch demonstrate the sensitivity of the retrieval method we
developed. A separate error calculation shows random errors in the order of
10 % and systematic errors of 25 % for individual observations. The
errors are dominated by uncertainties in spectroscopy, atmospheric
temperature and deviations in instrumental parameters. For conditions with
high surface concentrations of ammonia, as in Bremen, it is possible to
retrieve information on the vertical gradient as two layers can be
discriminated. At Bremen, the retrieval is most sensitive to ammonia
in the planetary boundary layer, where most of the ammonia is expected. For
conditions with lower concentrations there is not enough information to
discriminate individual layers. Station-specific seasonal cycles were found
to be consistent with known seasonal cycles of the dominant ammonia sources
in the station surroundings. For example, highest levels in Bremen were
observed during springtime when manure is applied to the fields, with column
total concentrations reaching up to 93×1015 molecules cm-2.
Remote sensing techniques avoid sampling artefacts common to other
techniques such as filter packs (Puchalski et al., 2011; von Bobrutzki et al.,
2010). For in situ observations, open-path remote sensing techniques, e.g. DOAS and QCL instruments, are starting to be used (Volten et al., 2010;
Miller et al., 2014). The FTIR-NH3 observations would be an excellent
addition to these approaches as it provides the NH3 total column and
profiles, including vertical information, for sites sampling high ammonia
levels. With a mean error of ∼25 % for all observations in
high ammonia source areas, the accuracy of the FTIR retrievals is comparable
to that reported for satellite products (TES, IASI, CrIS). Compared to the
in situ open-path remote sensing methods, the FTIR method has a higher
uncertainty, but this is a trade-off for the ability to retrieve vertical
information. To improve the accuracy of the FTIR-NH3 retrieval, a
reassessment of the spectral line parameters is necessary.
Observations from existing networks commonly represent daily or even monthly
averaged concentration values, which severely complicates any attempt to
validate satellite observations. The novel FTIR-NH3 observations enable
a direct validation of satellite products. As the FTIR- NH3 product
provides averaging kernels, a direct comparison can be made with optimal
estimation satellite retrievals while taking account of the a priori
information and vertical sensitivity of both instruments (Rodgers and Connor,
2003). A dedicated field campaign was executed at the Cabauw Experimental
Site for Atmospheric Remote Sensing (CESAR) in the Netherlands (spring and
summer 2014) to validate the IASI-NH3 using a range of instruments,
including mini-DOAS instruments and a Bruker IFS-66 instrument.
The uncertainty in the emission distributions hampers the performance and
prediction capabilities of air quality and climate models (Heald et al.,
2012). Emissions are usually based on nationally reported yearly emission
inventories (Pouliot et al., 2012) and gridded by distributing the emissions
according to animal populations and agricultural land use (Bouwman et al., 2002;
Kuenen et al., 2011). To improve on static emission time profiles, a new
direction is to include the impact of the meteorological variability of ammonia
emissions in modelling systems (Sutton et al., 2013). Recently, such an
improvement was shown to greatly enhance the performance of air quality
models (Skjøth et al., 2011). Satellite observations in combination with
chemical transport models (CTM) have been used to provide a top-down
constraint on ammonia emissions (e.g. Zhu et al., 2013). Similar to
satellite observations, FTIR total columns in combination with surface and
satellite observations could provide the means to evaluate the emission
modelling through comparing trends and concentration anomalies within and
between years. For this purpose continuous time series are necessary. Due to
the lack of continuous data (i.e. more than one observation per hour) we
could not derive a typical diurnal cycle in this study, whereas this would
be highly useful for model evaluation. Improved knowledge on the diurnal
cycles may also greatly help to interpret model evaluation results compared
to satellite data, as they provide snapshots, e.g. daily observations by IASI at
09:30 local time. Also, the model–measurement comparison would be less
sensitive to modelling errors in the turbulent vertical exchange as the
ammonia is integrated vertically.
The developed retrieval methodology from FTIR instruments provides a new way
of obtaining vertically and temporally resolved measurements of ammonia
concentrations. FTIR-NH3 observations may prove very valuable for
satellite and model validation and may provide a complementary source of
information to constrain the global ammonia budget.