Ammonia (NH3) emissions have large impacts on air quality and nitrogen
deposition, influencing human health and the well-being of sensitive
ecosystems. Large uncertainties exist in the “bottom-up” NH3 emission
inventories due to limited source information and a historical lack of
measurements, hindering the assessment of NH3-related environmental
impacts. The increasing capability of satellites to measure NH3
abundance and the development of modeling tools enable us to better
constrain NH3 emission estimates at high spatial resolution. In this
study, we constrain the NH3 emission estimates from the widely used
2011 National Emissions Inventory (2011 NEI) in the US using Infrared
Atmospheric Sounding Interferometer NH3 column density measurements
(IASI-NH3) gridded at a 36 km by 36 km horizontal resolution. With a
hybrid inverse modeling approach, we use the Community Multiscale Air Quality Modeling System (CMAQ) and its multiphase adjoint model to optimize NH3 emission estimates in April, July, and October.
Our optimized emission estimates suggest that the total NH3 emissions
are biased low by 26 % in 2011 NEI in April with overestimation in the Midwest
and underestimation in the Southern States. In July and October, the
estimates from NEI agree well with the optimized emission estimates, despite
a low bias in hotspot regions. Evaluation of the inversion performance using
independent observations shows reduced underestimation in simulated ambient
NH3 concentration in all 3 months and reduced underestimation in
NH4+ wet deposition in April. Implementing the optimized NH3
emission estimates improves the model performance in simulating PM2.5
concentration in the Midwest in April. The model results suggest that the
estimated contribution of ammonium nitrate would be biased high in a priori
NEI-based assessments. The higher emission estimates in this study also
imply a higher ecological impact of nitrogen deposition originating from
NH3 emissions.
Introduction
Ammonia (NH3) emissions play a major role in ambient aerosol formation
and reactive nitrogen deposition (Stevens, 2019: Houlton et al., 2013).
However, our understanding of NH3 sources and sinks is limited by the
large uncertainties present in the NH3 emissions inventories (Xu et
al., 2019; McQuilling and Adams, 2015). In chemical transport models,
uncertainties in NH3 emissions propagate into the dynamic modeling of
the atmospheric transport, chemistry, and deposition of NH3, other
reactive nitrogen species, and other key atmospheric constituents associated
with NH3 (Heald et al., 2012; Paulot et al., 2013; Kelly et al., 2014;
Zhang et al., 2018b), hindering an accurate assessment of the various
NH3-related environmental impacts and the associated sources. The large
uncertainties in the NH3 emission inventories are partially due to a
lack of sufficient in situ NH3 measurements that could be used to
constrain emission estimates (Zhu et al., 2015).
Emerging satellite observations of gaseous NH3 provide a unique
opportunity to better constrain the bottom-up NH3 emission estimates
for both their spatial distribution and seasonality. Bottom-up inventories
calculate the NH3 emissions based on estimated activity levels and
corresponding emission factors, both of which are subject to high
uncertainties, particularly for agricultural sources, the major contributor
(Cooter et al., 2012; McQuilling and Adams, 2015). Several studies have
utilized NH3 column density retrieved from the Infrared Atmospheric
Sounding Interferometer (IASI) (Clarisse et al., 2009; Van Damme et al.,
2015b) or the Atmospheric Infrared Sounder (AIRS; Warner et al., 2016) as
well as the inferred surface mixing ratio of NH3 from the Cross-track
Infrared Sounder (CrIS; Shephard and Cady-Pereira, 2015; Shephard et al.,
2020) to characterize the spatiotemporal distribution of NH3. These
satellite measurements are useful for supplementing emission inventories to
identify and quantify underestimated or missing emission hotspots,
especially in intensive agricultural zones (Van Damme et al., 2018; Dammers
et al., 2019; Clarisse et al., 2019). These studies find that the
satellite-derived emission estimates are often twice as much as the
bottom-up estimates on a regional scale and can be over 10 times higher over
hotspots. However, the NH3 retrievals from satellites are also subject
to large uncertainties when the signal-to-noise ratio is low, which limits
their ability to accurately measure NH3 columns in low-emission areas
(Clarisse et al., 2010; Van Damme et al., 2015a).
Inverse-modeling-based optimization combines the information from a priori emission
inventories and observations and allows us to use the information from both.
As one of the inverse modeling methods, the four-dimensional variational
assimilation (4D-Var) method seeks the best emission estimate by minimizing
a cost function that measures the differences between observations and model
predictions, as well as the differences between a prior and adjusted
emission estimates. 4D-Var can be computationally expensive at fine model
resolutions or with a large set of observations to be assimilated (Brasseur
and Jacob, 2017). Recent studies have taken advantage of the implementation of the
adjoint technique in the chemical transport models to conduct 4D-Var for
optimizing emissions estimation (Zhu et al., 2013; Paulot et al., 2014;
Zhang et al., 2018c). The adjoint-based inversion method calculates the
gradients of a cost function analytically and searches for the solution using a
steepest-descent optimization algorithm through iterating (Brasseur and
Jacob, 2017). By testing the performance of the inverse modeling method
using artificial observational data, Li et al. (2019) proposed that a
two-step optimization process, which combines the iterative mass balance
(IMB) method and the 4D-Var method, can further reduce the computational
cost. The IMB method assumes a linear relationship between the NH3
column density and local NH3 emission and searches for the emission scaling
factors iteratively until the simulated NH3 column density converges to
the observations. At a coarse (2∘×2.5∘)
resolution, the IMB method is as effective as the 4D-Var method and requires
two-thirds less computational time. In the second step, emission scaling factors
obtained from the IMB method with a coarser resolution are used as an
initial starting point for the 4D-Var optimization process to reduce the overall
computational time (Li et al., 2019).
This work utilizes satellite observations from the IASI-NH3 column
density measurements (IASI-NH3) (Clarisse et al., 2009;Van Damme et
al., 2017), to provide a high-resolution, optimized NH3 emission
inventory for the US developed using an adjoint inverse modeling technique
(Li et al., 2019), the robustness of which is demonstrated by evaluation
against multiple independent in situ measurements. The IASI-NH3 dataset
was applied to optimize NH3 emission estimates from the 2011 National
Emissions Inventory (2011 NEI) using the Community Multiscale Air Quality Modeling System (CMAQ) and its adjoint model at a 36 km × 36 km resolution. The multiphase adjoint model for CMAQ v5.0
was developed recently, including full adjoints for gas-phase chemistry,
aerosols, cloud process, diffusion, and advection (Zhao et al., 2020). Both
process-by-process and full adjoint model evaluations show reasonable
accuracy based on agreements between the adjoint sensitivities and forward
sensitivities (Zhao et al., 2020). Previous inversion-based NH3
emission constraints using in situ measures are limited by the spatial
coverage and representativeness of the measurements (Gilliland et al., 2006;
Henze et al., 2009; Paulot et al., 2014;). Zhu et al. (2013) first attempted
to optimize the NH3 emission inventory using NH3 derived from the
Tropospheric Emission Spectrometer satellite at 2∘×2.5∘ resolution (Zhu et al., 2013). Inverse modeling at
such a coarse resolution is limited to refining regional emissions. Similar
to the inversion using CrIS NH3 measurements (Cao et al., 2020), inversion
with the IASI-NH3 dataset allows us to perform the optimization at a
finer resolution with its daily global spatial coverage. Furthermore, the hybrid
inversion approach adopted in this study allows us to calculate full adjoint
sensitivities online instead of using approximated sensitivities from the
offline simulations (Zhu et al., 2013; Cao et al., 2020). The performance of
our optimized estimates and the 2011 NEI are evaluated and compared based on
in situ observed ambient NH3 concentrations and NH4+ wet
deposition. Finally, by substituting the a priori NH3 emissions with the
optimized emissions, we assess the subsequent changes in simulated ambient
PM2.5 concentrations and nitrogen deposition exceedances.
Materials and methodsIASI-NH3 observations
NH3 column densities retrieved from IASI on board the Metop-A satellite
are assimilated to constrain spatially resolved NH3 emissions using the
2011 NEI as the a priori inventory (Clarisse et al., 2009; Van Damme et al., 2014;
USEPA, 2014). The polar sun-synchronous satellite has a 12 km diameter
footprint at nadir and a bidaily global coverage. Only observations from the
morning pass around 09:30 local standard time (LST) are used due to more
favorable thermal contrast and smaller errors as compared to the
night pass around 21:30 LST. A comparison between the IASI-NH3 data
and ground-based Fourier transform infrared (FTIR) observations shows a correlation
between the two with r=0.8 and the slope = 0.73, indicating a tendency
of IASI-NH3 to underestimate the FTIR observations (Dammers et al.,
2016). A comparison between IASI-NH3 and airborne measurements also
indicated an underestimation in California, while the comparison between
IASI-NH3 and ground observation from the Ammonia Monitoring Network (AMoN)
indicated an overestimation (Van Damme et al., 2015a; NADP, 2014).
Overall, the evaluations show broad consistency between IASI-NH3 and
other independent measurements, with no consistent biases identified. These
evaluations were based on previous datasets. Here we use a new version that
relies on another retrieval algorithm, which among other things has better
performance for measurements under unfavorable conditions (Whitburn et al.,
2016; Van Damme et al., 2017).
Specifically, the NH3 products for 2011 from ANNI-NH3-v2.2R-I
datasets were used (Van Damme et al., 2017). The algorithm relies on the
conversion of hyperspectral range indices to NH3 column density using a
neural network that takes into account 20 input parameters, characterizing
temperature, pressure, humidity, and NH3 vertical profiles. A
relative uncertainty estimate is provided along with each of the NH3
vertical column densities in the dataset. Small negative columns are possible
– and these are valid observations, needed to reduce overall biases in the
dataset. As the retrieval is unconstrained, no averaging kernels are
calculated. We therefore directly compare the IASI-NH3 column density
with the simulated column density in CMAQ. Such comparison may be biased
because the sensitivity of retrieved NH3 column densities to NH3
concentrations is height-dependent (typically peaks around 700–850 hPa)
(Dammers et al., 2017; Shephard et al., 2015). Although the CMAQ-simulated
NH3 columns are also most sensitive to NH3 concentration changes
between 700 to 900 hPa (Fig. S1), we cannot quantify the relating
uncertainties without knowing the averaging kernels. Without information on
averaging kernels, differences between NH3 vertical profiles in CMAQ
and the ones used for retrieval may also contribute to the bias between
retrieved and modeled column densities, depending on the magnitude of
differences (Whitburn et al., 2016).
The retrieved NH3 columns densities are regridded to the 36 km by 36 km
CMAQ grid for 4D-Var data assimilation and 216 km by 216 km resolution (a six-grid-by-six-grid CMAQ simulation grid matrix) for iterative mass balance
(Fig. 1). The mean column density (Ωo) is calculated as the
arithmetic mean of all retrievals with their centroids falling in the same
grid cell, following the recommendation that the unweighted mean is
preferred for the updated version of IASI-NH3 as error-weighting can
lead to biases (Van Damme et al., 2017). The error (molec cm-2)
corresponding to the mean column density in each grid is calculated as
σ‾=∑σi×Ωi2n-1,
where σ‾ is the mean error (molec cm-2), Ωi is
the ith retrieval of NH3 column density from IASI-NH3 Level 2 data, σi
is the relative error associated with each Ωi as reported, and n is
the number of retrievals within each grid cell during the defined time
period. For 4D-Var inversion and IMB inversion, daily and monthly means and
errors are calculated, respectively.
IASI monthly average NH3 column density in April, July, and
October 2011 at 36 km by 36 km (a, b, c) and 216 km by 216 km (d, e, f)
resolutions within the model simulation domain of this study. The average
relative error associated with the column density is shown in the corner of
each plot.
The observations from April, July, and October are used to constrain the
monthly NH3 emission estimates in corresponding months from 2011 NEI.
Limited by the high computational cost of adjoint-model-based inversion, the
optimization is only performed for the 3 months selected instead of a
full year. Observations from winter months are not used because they are too
noisy when the thermal contrast is low (Dammers et al., 2016).
NH3 emission from 2011 NEI
The EPA 2011 NEI is used for a priori emission estimates. Major NH3 sources
include livestock waste management, fertilizer application, mobile sources,
fire, and fuel combustion, with the majority being emitted by the first two
sources. Specifically, the emissions from livestock waste management are
estimated based on county-level animal population data and process-based
daily emission factors. Emissions from fertilizer applications are estimated
based on county-level fertilizer quantities and fixed emission factors,
following the CMU Ammonia Model (USEPA, 2015). The NH3 emissions over
Mexico and Canada are derived from the simulation results of a fully coupled
bi-directional agroecosystem and chemical transport model
(FEST_C_EPIC_CMAQ_BIDI) (Shen et al., 2020). Emissions for other species
also come from the 2011 NEI.
CMAQ and its adjoint
We use CMAQ v5.0
(Byun and Schere, 2006; USEPA, 2012) and its adjoint (Zhao et al., 2020),
driven by meteorological fields produced from the Weather Research and
Forecasting (WRF) Model v3.8.1 with grid nudging using the North American
Regional Reanalysis dataset (NOAA, 2019). The simulated
meteorological fields show good agreement with surface observations (Fig. S2) (NOAA, 2020). The CB05 chemical mechanism was adopted for gas-phase
chemistry (Yarwood et al., 2005). The model implements ISORROPIA-II in the
aerosol module (AERO06) to calculate the gas–particle partitioning of
NH3 and NH4+ (Fountoukis and Nenes, 2007). The simulation
domain covers the contiguous US (CONUS) and part of Canada and Mexico with
a 36 km by 36 km horizontal resolution and 13 vertical layers extending up
to 14.5 kPa (∼16 km) (Fig. 1). To evaluation CMAQ model
performance, the simulated gas–particle partitioning ratio of
NH3–NH4+ and NH4+ deposition is compared with
observations from AMoN, the Clean Air Status and Trends Network, and
the National Atmospheric Deposition Program (NADP) (Figs. S3 and S4).
CMAQ captures the overall spatial pattern of these governing processes for
atmospheric NH3 abundance, considering the uncertainties in emissions,
model parameters, and meteorological fields. Expanded evaluation of CMAQ
model performance in simulating gas–particle partitioning and nitrogen
deposition has been conducted in previous studies (Chen et al., 2019, 2020). Monthly simulations are conducted for April, July, and October in
2011 with a 10 d spin-up for each month.
Hybrid inversion approach
We chose the hybrid inversion approach to combine the advantage of the
faster computational speed of the mass balance method and the better
optimization performance of the 4D-Var method. The first step is to apply
the IMB approach to adjust the a priori (2011 NEI) NH3 emission at 216 km by
216 km resolution (referred to as the coarse grid hereafter) based on the ratio
between the monthly averaged observed (Ωo) and simulated
(Ωa) NH3 column density at the satellite overpassing time,
iteratively. At each iteration, the emission in each grid cell is scaled by
the ratio following the equation below:
Et=ΩoΩa×Ea,
where Et and Ea are the new and a priori emission estimates, respectively.
The method has been described in detail in previous studies (Li et al.,
2019; Cooper et al., 2017; Martin et al., 2003). The IMB is applied at the
coarse grid so that the NH3 column will be dominated by the local
emissions instead of transport from neighboring grids (Li et al., 2019). The
coarse resolution also reduces the uncertainty associated with IASI-NH3
as the number of retrievals increases in each grid cell. For grid cells with
mean relative error larger than 100 %, the satellite observations are
considered to be too noisy to provide useful constraints and the a priori emission
estimates are retained. The iteration stops when the normalized mean square
error either decreases by less than 10 % or begins to increase. The final
scaling factor (ε0) for each grid cell is the
multiplication of the scaling factors derived at each iteration and
downscaled to 36 km by 36 km resolution by assigning the same value to the six-by-six grid matrix. This scaling factor is applied to the 2011 NEI emissions
to create the revised a priori estimate for the 4D-Var inversion.
Next, the 4D-Var inversion is performed. The solution of the optimization
problem is sought iteratively by minimizing the cost function (J) defined as
the combination of error-weighted, squared difference between the emission
scaling factor and unity and the error-weighted, squared difference between
IASI-NH3 and the simulated column density, as below:
J=γεi-ε0TSa-1ε-ε0+Ωo-F(ε)TSo-1(Ωo-F(ε)).ε is the monthly emission scaling factor to be optimized at
each iteration where ε=logEt/Ea
on the 36 km by 36 km CMAQ grid, consisting of 6104 overland grid
cells in the CONUS. Sa and So are error covariance matrices for the a priori
emission estimates and IASI-NH3 retrievals, respectively. With limited
information on the spatial correlation of the error covariance, the two
matrices are assumed to be diagonal (Paulot et al., 2014; Zhu et al.,
2013). For So, the grid average absolute error is used to represent the
observational error. Our test shows that negative Ωo will lead
to a continuous decrease in the adjusted emission for the grid cell because
modeled column density cannot become negative. To limit the influence of
these negative Ωo, their original weights are multiplied by
0.01. For Sa, the uncertainty in each grid cell is assumed to be 100 %
of the a priori emissions. F(ε) is CMAQ-simulated NH3 column
density sampled at the satellite passing time if there is at least one
IASI-NH3 retrieval in that grid cell; γ is the regularization
factor balancing the relative contribution of the a priori emission inventory and
IASI-NH3 retrievals to the J value. γ is chosen to be 800 for
April and 500 for July and October based on the L-curve criteria (Hansen,
1999) (Fig. S5).
The gradients of the cost function to NH3 emissions are calculated by
the CMAQ adjoint model. In each iteration, the emission-weighted monthly
averaged sensitivities in each grid cell are supplied to the L-BFGS-B
optimization routine contained in the “optimr” package in R to find the
scaling factors that will achieve the minimum of the cost function (Zhu et
al., 1997; Byrd et al., 1995). NH3 column density is re-simulated using
adjusted emissions by the new set of scaling factors. The iteration process
is terminated when the decrease in J is less than 2 % or the local minimum
is reached (Li et al., 2019; Zhu et al., 2013).
Posterior evaluation
The posterior emissions are evaluated by comparing the model simulation from
optimized emissions with observations. Simulated results are compared with
ambient NH3 concentrations from AMoN (NADP, 2014) and the
NH4+ wet deposition from NADP (NADP, 2019). The simulated
NH3 concentration in ppmv is converted to micrograms per cubic meter (µg m-3) using local
temperature and pressure from the model meteorological inputs. For
evaluation against the NH4+ wet deposition, the simulated
deposition is scaled by the ratio between measured and simulated
precipitation to eliminate the bias introduced by precipitation fields
(Appel et al., 2011).
ResultsOptimization performance evaluation
The optimized NH3 emissions reduce the bias in the NH3 columns
between the satellite observation and the model prediction as shown by the
decrease in the values of normalized root mean square error (NRMSE) and
normalized mean biases (NMBs) in Fig. 2. There are negative biases using
2011 NEI in all 3 months, especially in areas with high emission rates.
Although the IMB inversion can lower the NRMSE, it tends to over-adjust and
introduce a positive bias, likely because of the coarse resolution and
neglect of the impact of transport. The 4D-Var inversion effectively
decreases the positive bias and further reduces the NRMSE. The cost function
value reduces by 85, 46, and 38 % with the 4D-Var inversion in
April, July, and October, respectively. We find that it is more challenging
to adjust the emissions in April than in the other 2 months because of the
greater differences in the magnitude and the spatial distribution of the
emissions. The optimized NH3 emission successfully captures the high
NH3 column density in the Southern States (Texas and Oklahoma),
reducing the NRMSE by half in that region. Despite the general improvement
in the model performance, negative biases in July increase in California's
San Joaquin Valley. Scaling up the emission in the San Joaquin Valley will
result in high NH3 concentrations downwind even when the local NH3
emissions downwind are zeroed, whereas the IASI-NH3 observed
concentrations downwind are low. The transported hotspot downwind of the San
Joaquin Valley in CMAQ only occurs in July, suggesting near-field removal
may not be captured at the current resolution, and warrants further
investigation. Grid-by-grid comparison between model-simulated NH3
column density using the a priori and optimized estimates with IASI-NH3 shows
improved agreement in both high- and low-emission grid cells after
optimization (Fig. S6). It shows that the hybrid inversion approach can
alleviate the weakness of direct 4D-Var inversion, which tends to over-adjust
high-emission regions and under-adjust low-emission regions, mainly because
the IMB inversion provides a better initial state.
CMAQ-simulated monthly average NH3 column density for April,
July, and October 2011 using the a priori emissions (a, b, c), the emissions
adjusted by IMB (d, e, f), and the final optimized emissions using the hybrid
approach (g, h, i). For comparison with the IASI-NH3 retrievals,
simulated NH3 columns at the passing time were derived when there were
observations in that grid cell. Normalized root mean square error (NRMSE)
and normalized mean bias (NMB) between the simulated values and
IASI-NH3 are provided. Residue map (IASI-NH3- simulated NH3
column densities) is shown in the corner of each plot.
The IMB inversion took three iterations to achieve the convergence condition
for each month, and subsequently the 4D-Var inversion took 10, 4, and
6 iterations for April, July, and October, respectively. Fewer iterations
are needed with the hybrid approach than the direct 4D-Var inversion, which
typically takes up to 15 to 20 iterations of adjoint simulation (Paulot et
al., 2014; Zhang et al., 2018a). The CPU time of a forward simulation is
only one-fifth of an adjoint simulation. In total, the CPU time required by the
hybrid approach is expected to be one-third to two-thirds lower than the direct 4D-Var
inversion approach.
Optimized estimate of NH3 emissions
The monthly total NH3 emission in the CONUS increases by 35 % in April,
18 % in July, and 10 % in October for the optimized estimates.
Spatially, the distribution for high-emission regions shifts
from the Midwest in the 2011 NEI to the Southern States in the optimized
estimates in April, whereas the hotspot regions remain consistent in July
and October (Fig. 3). Regional total emissions are summarized according to
the USDA farm production regions, which define the areas with similar crop
production activities (Cooter et al., 2012). In general, the regional
variation of NH3 emissions in April is dominated by fertilizer
application. The optimized estimates in the Corn Belt and Lake States
regions are lower than the 2011 NEI, where high contributions from
fertilizer applications were estimated. In contrast, the optimized estimates
are 2–3 times higher than the 2011 NEI estimates in the Delta States and
Southern States, where the a priori estimates for NH3 emission from fertilizer
application are low. The higher NH3 emission estimates in the Southern
States are driven by the enhanced NH3 column densities from IASI over
that region. IASI-NH3 column densities are higher in 2011 than those in
adjacent years (Fig. S7), which coincides with the higher surface
temperature observed in 2011 (NOAA 2019) (Fig. S8). NH3
emission will increase due to enhanced NH3 volatilization from
agricultural lands under warmer conditions (Bash et al., 2013; Shen et
al., 2020). In fact, the optimized NH3 emission pattern in April is
more consistent with the spatial pattern of inorganic nitrogen fertilizer
estimated based on plant demand (Cooter et al., 2012). NH3 emission in
2011 estimated by CMAQ with a NH3 bidirectional exchange model also predicted higher NH3 emission in the Southern States (Shen et al., 2020). The ratio between NH3 emission
estimates in Southern States and those within the CONUS is 26 and 18 % in
the optimized estimates and estimates including NH3 bidirectional
exchange, respectively. In comparison, the ratio is only 10 % in the a priori NEI
estimates, suggesting a potential low bias in 2011 NEI. In July, regional
differences are smaller except for the Northern Plain and Mountain region.
In the Northern Plain, the NH3 emission is 66 % higher in the
optimized estimates, driven by the emission increase in hotspot areas with
concentrated animal feeding operations (CAFOs) (USDA, 2012; Van Damme et al.,
2017, Clarisse et al., 2019). The potential bias in different sectors
suggests the need for sectoral inversion when a larger observational dataset
becomes available in the future. In October, the relative difference is less
than 10 % in most of the regions, indicating that the 2011 NEI
appropriately reflects the NH3 emission pattern. There is a significant
increase in the NH3 emissions in Mexico during all 3 months. Such
an emission increment is crucial to improving the model performance in both
Mexico and the southwestern US. However, it was not a goal of this study
to determine emissions biases in Mexico given the limited information on
NH3 emissions.
The spatial distribution of monthly total NH3 emission from
the a priori (a, b, c) and optimized (d, e, f) estimates in April, July, and
October. The total emission based on the a priori and optimized estimates is
summarized for each USDA farm production region (g, h, i). The source
contributions to total emission are shown for the a priori estimates.
The total NH3 emissions in the optimized estimates are 623, 564,
and 335 Gg per month in April, July, and October, respectively. In
comparison, the emission estimates in the 2011 NEI are 462, 475, and
304 Gg per month for the 3 months. Similar to a bottom-up agricultural
NH3 emission inventory (MASAGE_NH3) and two inverse-model-optimized estimates based on NH4+ wet deposition, we find a
higher emission in the spring season (Paulot et al., 2014; Gilliland et al.,
2006), while others, including the NEI, estimate a summertime peak (Zhu et
al., 2013; USEPA, 2015; Cooter et al., 2012; Cao et al., 2020). The large
variation between different inventories warrants both improved information
on bottom-up inventories and more observations to support inverse model
optimization in the spring season. Better knowledge about agricultural
activities and more independent ground and space observations are needed.
Besides the a priori emission inventory and observational constraints, the inversion
performance will also be affected by other processes (e.g., gas–particle
partition, transport, cloud and precipitation, and dry and wet deposition)
governing the atmospheric abundance of NH3. Future works refining the
pertinent processes will also help improve the optimized NH3 emission
estimates. It should also be noted that there are interannual variations in
emission inventories developed for different years. The good spatial
agreement with IASI-NH3 indicates that the 2011 NEI captures the
NH3 emission pattern in general in these 2 months. Although the
inversion is only applied for the selected 3 months, the simulated
NH3 column densities using the a priori inventory are consistently lower than
the IASI-NH3 observations in 2011 (Fig. S9), suggesting that the
NH3 emission estimates in 2011 NEI may be biased low in other months,
too.
Evaluation of the optimized emission estimates against independent
datasets
The robustness of the NH3 emission optimization is evaluated by
comparing the model outputs using both the a priori and optimized emission estimates
with independent observations. The bias and uncertainties inherited in the
CMAQ forward model and its adjoint, as well as the assumptions made about
the uncertainties of the a priori emission inventory and IASI-NH3 observations,
will all influence the robustness. Here, we choose to evaluate the outputs
against (1) biweekly average ambient NH3 concentrations measured by
AMoN and (2) weekly average NH4+ wet deposition measured by NADP
(Fig. 4).
Evaluation of the simulated NH3 surface concentration (a, b, c) and NH4+ wet deposition (d, e, f) against biweekly NH3
concentration observations from AMoN and weekly NH4+ wet
deposition observations from NADP, respectively. The orange circles and blue
dots represent comparison using the a priori and optimized NH3 emission
estimates, respectively. Summary statistics including sample size (N),
normalized mean bias (NMB), normalized root mean square error (NRMSE), least
square error regression slope and intercept, and R square (R2) for all
comparisons are listed below the plots.
In general, the optimized NH3 emission reduces the negative NMB when
comparing the CMAQ outputs with AMoN NH3 concentration for all 3
months. There is a greater improvement at the high-concentration end than
the low-concentration end because both the IASI satellite and the passive
samplers at the AMoN sites have higher uncertainties in areas with low
NH3 abundance (Van Damme et al., 2015a; Puchalski et al., 2011). Yet
the NRMSE gets higher and R2 gets lower in April, indicating a higher
spatial variation in the residuals. There is an over-adjustment for sites in
Pennsylvania in April, where there is a hotspot observed by IASI on 14 and 15 April. The hotspot possibly came from a large transported
plume at a higher altitude from the central US to Pennsylvania (Figs. S10
and S11), which is not measured by ground observations at AMoN sites
at biweekly resolution. If that is the case, the hybrid inverse modeling
framework would have difficulties in reproducing the long-range transport
contribution for two reasons. First, local emissions in Pennsylvania would
be enhanced in the IMB inversion, and inter-grid transport were neglected at
216 km by 216 km resolution. Second, the following 4D-Var inversion very
likely reached a local optimal by adjusting emissions from local and
surrounding grid cells near the observed hotspot rather than grid cells at
distance. Furthermore, the IASI-NH3 column densities may be overestimated
because vertical profiles with the highest concentrations near the surface were
assumed in the retrieval process (Whitburn et al., 2016).
For evaluation against NADP observations, there is a noticeably improved
agreement in April, with reduced negative NMB and reduced discrepancies for
most of the data pairs. For July, the emission optimization only slightly
improved the model performance. For October, the optimization increased the
NMB from -1.8 to 4.8 %. This indicates that NH3 emission is not the
dominant explanatory factor for bias in simulated NH4+ wet
deposition that is commonly observed in chemical transport models (Appel et
al., 2011; Paulot et al., 2014). A better representation of the cloud,
precipitation, and deposition processes in the WRF Model and CMAQ is
needed to close the gap between simulated and observed NH4+
deposition amount. Overall, the improved model operational performance for
ambient NH3 suggests that the inverse model optimization applied in
this study provides improvements in the NH3 emission estimates during
all 3 months in most of the CONUS, except in Pennsylvania and
surrounding regions in April. The hybrid inverse modeling technique may
over-adjust local emissions in hotspots dominated by long-range transport.
ImplicationsAmbient aerosol concentration
As a major precursor of ambient aerosol formation, the NH3 emission
inventory is believed to be a major source of uncertainty in PM2.5
assessment in several parts of the CONUS (Henze et al., 2009; Schiferl et
al., 2014; Heald et al., 2012), which can further bias the source
contribution assessments on PM2.5-related health impacts (Lee et al.,
2015; Zhao et al., 2020). Comparison of the simulated PM2.5 mass
concentration using the a priori and optimized NH3 emission estimates shows
that the NH3 emission bias in April is a major factor for bias in the
modeled PM2.5 concentration leading to high or low bias in ammonium
nitrate (NH4NO3) formation (Fig. 5). The relative change of the
monthly average PM2.5 concentration is over 5 % in one-fifth of the
CONUS, including an increase in the Northeast, the Pacific West, the Rocky
Mountains, part of Texas, and the Gulf Coast region, and a decrease in
the Midwest. For most of these regions, over 90 % of the change is driven
by the change in concentration of NH4+ and NO3-.
The changes in monthly average PM2.5, NH4+, and
NO3- mass concentration in April due to the NH3 emission
adjustment in the optimized estimates. The change is defined as
concoptimized-concapriori, where concoptimized and
concapriori represent the simulated monthly average mass
concentration using the optimized and a priori NH3 emission estimates,
respectively. The difference between the observed NH4+ and
NO3- mass concentration and simulated concentrations using the a priori
NH3 emission (concobs-concapriori, where concobs
represents the observed monthly average mass concentration) is overlaid
using colored dots with the same color scheme.
Comparison of the simulated monthly average NH4+ and
NO3- concentration using the a priori estimates against ambient monitoring
network data (USEPA, 2018) shows that there is a high bias in the Midwest
region and Pennsylvania state, and a low bias for the rest of
the sites (Table 1). Simulations using the optimized NH3 emission
estimates reduce the high bias in the Midwest region but exacerbate the high
bias in Pennsylvania state and the surrounding areas. For the other sites,
the impact of optimization is mixed but minor in general.
Statistical summary of the correlation between simulated monthly
average NH4+ and NO3- concentrations and observations in
April*.
NH4+Midwest Penn Other a priorioptimizeda priorioptimizeda priorioptimizedN47 37 115 NMB0.270.220.000.07-0.35-0.35NRMSE0.400.350.280.300.450.44slope0.520.540.410.390.600.65R20.570.650.240.180.250.28NO3-Midwest Penn Other a priorioptimizeda priorioptimizeda priorioptimizedN69 38 240 NMB0.640.550.250.43-0.39-0.38NRMSE0.960.880.660.730.630.65slope0.440.460.290.290.620.55R20.760.780.330.310.280.25
* The correlation between observed concentrations and simulated ones based on a priori and optimized NH3 emission estimates is compared. The sites are grouped as the Midwest region, Pennsylvania state and surrounding areas, and other areas.
For the Midwest, our optimized NH3 emission is 12 % lower than the
2011 NEI, leading to a 5–30 % decrease in NH4+ and
NO3- concentration. Overestimation of NO3- in the
Midwest has been recognized in previous model evaluations. Previous studies
have attempted to moderate the high bias by lowering the nitric acid (HNO3)
concentration through either lowering both the daytime and nighttime HNO3
formation rate or raising the deposition removal rate (Heald et al., 2012; Zhang
et al., 2012; Walker et al., 2012). It was found that such modification in
the model parameterization cannot fully account for the overestimation
(Heald et al., 2012; Zhang et al., 2012; Walker et al., 2012). Our study
implies that the springtime overestimation can partly be explained by the
overestimation in NH3 emissions which drives the high bias in
NH4NO3 formation.
The large increase of the NH4NO3 concentration in Pennsylvania
state and the surrounding areas is due to the over-amplified local NH3
emissions in the optimized estimates to match the high NH3 column
density in IASI-NH3 2011, as discussed earlier. It leads to a higher
magnitude of biases in NH4+ and NO3- concentration as
compared to ground measurements. The fact that the simulated ambient
NH3 concentration, NH4+ concentration, and NH4+ wet
deposition using the optimized NH3 estimates are biased high in
comparison with independent ground measurements suggests the enhanced
NH3 abundance observed from IASI is possibly driven by long-range
transport at higher altitudes instead of local surface emissions.
For the rest of the CONUS, there is only a slight impact of the optimization
on simulated NH4NO3 formation. For example, although the NH3
emission is doubled in the San Joaquin Valley in California, the modeled
NH4+ and NO3- concentrations are still biased low using
the optimized estimates. A sensitivity test using GEOS-Chem shows that the
San Joaquin Valley region is nitric-acid-limited instead of ammonia-limited
(Walker et al., 2012), suggesting that there is an underestimation in
HNO3 formation. A comparison of the simulated and measured speciated
PM2.5 shows that there is a low bias in non-volatile cation
concentrations at the sites in the San Joaquin Valley, limiting the
formation of NH4NO3 through gas–particle partitioning (Chen et
al., 2019). Thus, attempts to close the gap between the simulated and
monitored NH4+ and NO3- concentrations by scaling NH3 emission alone are ineffective and
might lead to an overestimation in local NH3 emissions.
For July and October, there is a very limited difference between the
simulated PM2.5 concentration using the optimized and a priori NH3
emission estimates, as expected, because the change in NH3 emission is
minor. There are only 1 and 4 % of the CONUS with a relative change in
PM2.5 concentration over 5 %, respectively. This result shows that the uncertainty
in NH3 emission estimates is moderate and is not a major contributor to
biases in modeled PM2.5 in July and October.
Reactive nitrogen deposition
The uncertainties in NH3 emission inventory also impact the reactive
nitrogen (Nr) deposition assessment, which informs the ecosystem impacts
evaluation and effective mitigation actions (Ellis et al., 2013). To
evaluate the impact of the NH3 emission optimization on simulated Nr
deposition, the Nr deposition amount simulated using optimized and a priori emission
estimates is analyzed in all biodiversity-protected areas designated by the
USGS (Fig. S12) within the CONUS (USGS, 2018). In total, the Nr deposition
increased by 27, 9, and 5 % on average in these protected areas in
April, July, and October, respectively. A regional comparison based on the
Level I ecoregions (Pardo et al., 2015) shows that the deposition increment
is the highest in the Tropical Wet Forests (+64 %), followed by the
Great Plain region (+46 %), in April (Fig. 6). Although the overall
increase is small in July and October, the increment can be high in
individual ecoregions, including Southern Semiarid Highlands (+95 % in
July) and Temperate Sierras (+62 % in July). In addition to the
increment in deposition amount, higher NH3 emission, especially in
intensive agriculture regions, may indicate higher source contribution from
agricultural NH3 than previous estimates (Lee et al., 2016).
The changes in the simulated monthly reactive nitrogen (Nr)
deposition amount in protected areas for biodiversity conservation caused by
the emission adjustment in April, July, and October. For each month, the
left bar is for the a priori deposition amounts and the right bar is for the
optimized deposition amounts. The deposition is grouped for 10 Level I
ecoregions defined by the Commission for Environmental Cooperation,
including Northern Forests (NF), Great Plains (GP), Northwestern Forested
Mountains (NFM), Marine West Coast Forest (MWCF), North American Deserts
(NAD), Mediterranean California (MC), Southern Semiarid Highlands (SSH),
Temperate Sierras (TS), and Tropical Wet Forests (TWF).
Driven by the increase in the reduced form of Nr (NH3 and
NH4+) deposition, a higher share of the reduced form of Nr to the
total Nr deposition is found in most of the ecoregions for all 3 months
than in the NEI-based estimates. More detrimental impacts on sensitive species
and biodiversity are expected when this change in dominant Nr form is
considered in addition to the increase in magnitude because the growth of
many sensitive plant species will be inhibited by a high
NH4+-to-NO3- ratio in soil and water (Bobbink and Hicks, 2014).
Conclusions
We apply the newly developed multiphase adjoint of the CMAQ v5.0 chemical
transport model and NH3 column observations from the satellite-borne
IASI to optimize NH3 emissions estimates in the CONUS using a hybrid
inversion modeling approach. The approach consists of a coarse-resolution
iterative mass balance inversion (216 km by 216 km) and a fine-resolution
4D-VAR inversion (36 km by 36 km) and is performed using IASI-NH3
observations in April, July, and October. The hybrid approach overcomes the
over-adjusting problem for high-emission areas in the direct 4D-Var method
and reduces the computational cost, but it may introduce over-adjustment in
special cases where the NH3 abundance is dominated by transport instead
of local emissions.
We use the NH3emission from 2011 NEI, commonly used in regional and
national simulations and assessments as the a priori emission. We find that the
optimized NH3 emission inventory differs greatly with the 2011 NEI in
April. The emission in the Midwest is overestimated and the emission in the Southern
States is underestimated in the 2011 NEI. Overall, the optimized emission is
35 % higher in April. The optimized emission estimates in July and October
are also higher (18 and 10 %) than the 2011 NEI estimates, but the
spatial distribution agrees well. The IASI-NH3 observations indicate a
consistent underestimation of NH3 emissions in California's San Joaquin
Valley in all 3 months; however, the inverse modeling fails to properly
scale up the emissions in July. The evaluation of simulation outputs against
ground measurements including ambient NH3 concentrations from AMoN and
NH4+ wet deposition from NADP shows that the optimized NH3
emission estimates reduce the NMB between model outputs and independent
observations, especially in April. The NRMSE remains high, indicating (1) the
potential to further optimize NH3 emission estimates when more
representative observations of ambient NH3 abundance become available and
(2) the need to address the uncertainties in other processes affecting the
NH3 abundance, such as gas–particle partitioning, dry and wet
deposition, and in-cloud processes.
Application of the optimized NH3 emission estimates also yields a
better agreement between the simulated and observed PM2.5 concentration
in April in the Midwest region by improving the model performance on
simulated NH4+ and NO3-. This is consistent with previous
findings that the uncertainty in NH3 emission is a key factor limiting
the model performance of PM2.5. The optimized NH3 emission
estimates in general increase the Nr deposition amount and the relative
importance of reduced-form Nr, highlighting the importance of constraining
NH3 emission estimates for accurately assessing nitrogen deposition and
ecosystem health over sensitive regions.
Data availability
The IASI/Metop-A NH3 total column Level 2 data are available at the
IASI portal provided by the AERIS data infrastructure (https://iasi.aeris-data.fr/NH3_IASI_A_data; ULB, 2018).
Independent observations for evaluation – including surface NH3
concentrations, NH4+ wet depositions, and speciated PM2.5
concentrations – are available from the NADP website and Air Quality System
(http://nadp.slh.wisc.edu/data/AMoN/, NADP, 2014; http://nadp.slh.wisc.edu/data/NTN/, NADP, 2019; https://aqs.epa.gov/aqsweb/documents/data_api.html, USEPA, 2018).
The supplement related to this article is available online at: https://doi.org/10.5194/acp-21-2067-2021-supplement.
Author contributions
AR and YC conceived the study. YC, AR, HZ, and JK contributed to the design
the method. YC conducted the inverse modeling and data analysis. LC, PFC, and
MVD are responsible for the IASI-NH3 data. SC, SZ, AH, MR, MT, DH, PP, JR,
AN, AP, SN, JB, KF, GC, CS, TC, and AR developed the adjoint model of CMAQ. YC
prepared the manuscript, with discussions and comments from HS, AR, JK, YH,
SC, SZ, JS, and GP. All authors gave approval to the final version of
the manuscript.
Competing interests
The authors declare that they have no conflict of interest.
Disclaimer
Contents of this publication are solely the responsibility of the grantee
and do not necessarily represent the official views of the supporting
agencies. Further, the US government does not endorse the purchase of any
commercial products or services mentioned in the publication.
Acknowledgements
The authors acknowledge the AERIS data
infrastructure for providing access to the IASI data in this study. ULB has
been supported by the Belgian State Federal Office for Scientific, Technical
and Cultural Affairs (Prodex arrangement IASI.FLOW). Lieven Clarisse and Martin Van Damme are
respectively research associate and postdoctoral researcher with the Belgian
F.R.S-FNRS.
Financial support
This research has been supported by the United States Environmental Protection Agency (grant no. R83588001), the National Aeronautics and Space Administration (grant no. NNX16AQ29G), and the China Scholarship Council (grant no. 201606010393).
Review statement
This paper was edited by Bryan N. Duncan and reviewed by two anonymous referees.
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