High-resolution hybrid inversion of IASI ammonia columns to constrain US ammonia emissions using the CMAQ adjoint model

Abstract. 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 55 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 60 constrain emission estimates . This work utilizes satellite observations from the Infrared Atmospheric Sounding Interferometer 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 U.S. 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.
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 IASI (Clarisse et al., 2009;Van Damme et 70 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 et al., 2019)) 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 75 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 80 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 took advantage of the implementation 85 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 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 90 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 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 2/3 less computational time. In the second step, emission scaling factors obtained from the IMB method with a coarser 95 resolution are used as an initial starting point for 4D-Var optimization process to reduce the overall computational time (Li et al., 2019).
In this study, the IASI-NH3 dataset was applied to optimize NH3 emission estimates from the 2011 National Emission Inventory (NEI 2011) using 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, 100 cloud process, diffusion, and advection (Zhao et al., 2019). Both process-by-process and full adjoint model https://doi.org/10.5194/acp-2020-523 Preprint. Discussion started: 29 June 2020 c Author(s) 2020. CC BY 4.0 License. evaluations show reasonable accuracy based on agreements between the adjoint sensitivities and forward sensitivities (Zhao et al., 2019). Previous inversion based NH3 emission constraint 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 NH3 emission inventory using NH3 derived from the 105 Tropospheric Emission Spectrometer (TES) 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. Besides, the hybrid inversion approach adopted in this study allows us to calculate full adjoint sensitivities online instead of using approximated sensitivities from the offline-110 simulations (Zhu et al., 2013, Cao et al., 2020. The performance of our optimized estimates and the NEI 2011 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.

IASI-NH3 observations
NH3 column densities retrieved from IASI onboard the Metop-A satellite are assimilated to constrain spatiallyresolved 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 9:30 am local standard time (LST) are used due to more 120 favorable thermal contrast and smaller errors comparing to the ones from the night pass around 9:30 pm (LST). A comparison between the IASI-NH3 data and ground-based Fourier transform infrared 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 125 Ammonia Monitoring Network (AMoN) network 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 a better performance for measurements under unfavorable conditions (Whitburn et al., 2016;Van Damme et al., 2017).

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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 density in the dataset. Small negative columns are possibleand these are valid observations, needed to reduce overall biases in the dataset. As 135 https://doi.org/10.5194/acp-2020-523 Preprint. Discussion started: 29 June 2020 c Author(s) 2020. CC BY 4.0 License. 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 ( Figure S1), we cannot quantify the relating 140 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 NH3 retrieved 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 6 grid by 6 grid CMAQ simulation grid matrix) for iterative mass 145 balance (Figure 1). The mean column density (Ωo) is calculated as the monthly arithmetic mean of all retrievals with their centroids falling in the same grid, 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 relative error (molec/cm 2 ) corresponding to mean column density in each grid is calculated following Van Damme et al.
(2014) as: where ̅ is mean relative error (molec/cm 2 ), σi is the relative error associated with each NH3 column density retrieval as reported, and Ωo is the mean column density (Van Damme et al., 2014).
The observations from April, July, and October are used to constrain the monthly NH3 emission estimates in corresponding months from 2011 NEI. Observations from winter months are not used because they are too noisy 155 when the thermal contrast is low (Dammers et al., 2016).

NH3 emission from 2011 NEI
The EPA 2011 NEI is used as 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 Emissions for other species also come from the 2011 NEI.

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 as the coarse grid hereafter) based on the ratio between the monthly-averaged observed (Ωo) and simulated (Ωa) NH3 column density at 180 the satellite overpassing time, iteratively. At each iteration, the emission in each grid is scaled by the ratio following the equation below, 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 185 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. For grids 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 190 factor (ε0) for each grid 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 6 by 6 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 emission 195 scaling factor and unity and the error weighted, squared difference between IASI-NH3 and the simulated column density, as below: for the a priori emission estimates and IASI-NH3 retrievals, respectively. The two matrices are assumed to be diagonal. For So, the grid average absolute error is used to represent the observational error. To reduce the influence of retrievals close to or below the detection limit, an estimated detection limit of 4.8×10 15 molecules/cm 2 is added to the So (Dammers et al., 2019). 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 205 negative Ωo, their original weights are multiplied by 0.01. For Sa, the uncertainty in each grid 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; 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 30 for all 3 months based on the L-curve criteria (Hansen, 1999) (Figure S2).

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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 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 215 minimum is reached.

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 the AMoN (NADP, 2014), and the NH4 + wet deposition from the National Atmospheric Deposition Program (NADP, 2019). The simulated NH3 220 concentration in ppmV is converted to 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).

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The

Optimized estimate of NH3 emissions
The monthly total NH3 emission in CONUS increases by 46% in April, 6.6% in July, and 6.9% in October for the optimized estimates, respectively. Spatially, the distribution for high emission regions shifts from Midwest in the 2011 NEI to the Southern States in the optimized estimates in April, whereas the hot spot regions remain consistent in July and October (Figure 3). Regional total emissions are summarized according to the USDA Farm Production 255 regions, which defines the areas with similar crop production activities (Cooter et al., 2012). In general, the regional In July, regional differences are smaller except for the Northern Plain and Southeast. In the Northern Plain, the NH3 emission is 66% higher in the optimized estimates, driven by the emission increase in hotspot areas with NEI captures the NH3 emission pattern in general in these two months.

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 290 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; 2) weekly average NH4 + wet deposition measured by NADP (Figure 4).
In general, the optimized NH3 emission reduces the negative NMB when comparing the CMAQ outputs with AMoN

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NH3 concentration for all three months. Yet, the NRMSE gets higher and R 2 gets lower, indicating a higher spatial variation in the residuals. This is likely due to the tendency of satellite-based inversion to over-adjust emissions in high concentration areas (Zhu et al., 2013). There is a greater improvement at the high concentration end than the low concentration end because both IASI satellite and the passive samplers at the AMoN sites have higher  (Figure S4). The fact that it is transported at higher altitude in 2 days could explain that it is not measured by ground observations at AMoN sites at biweekly resolution. The long-range transport at higher altitude may lead to an overestimation in IASI retrieved NH3 column densities which 305 assume a vertical profile with highest concentrations near the surface (Whitburn et al., 2016). Such transport is also not well represented in the hybrid inverse modeling approach where the transport effect is not included in the IMB inversion at 216 km by 216 km resolution.
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 310 improved the model performance. For October, the optimization increased the NMB from -1.8% to 10%. It 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). 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 three months in most of the CONUS.

Ambient 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., 320 2015, Zhao et al., 2019. 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 (Figure 5). The relative change of the monthly average PM2.5 concentration is over 10% in one-fifth of the CONUS, including an increase in the Northeastern, Pacific West, and Rocky Mountains regions, and a decrease in the Midwest. For most of these regions, 325 over 90% of the change is driven by the change in concentration of NH4 + and NO3 -.
Comparison of the simulated monthly average NH4 + and NO3concentration 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 underestimation 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 the Pennsylvania 330 state and surrounding areas. For the other sites, the impact of optimization is mixed but minor in general.
For the Midwest, our optimized NH3 emission is 31% lower than the 2011 NEI, leading to a 20 -30% decrease in NH4 + and NO3concentration. Overestimation of NO3in the Midwest has been recognized in previous model evaluations. Previous studies attempted to moderate the high bias by lowering the nitric acid (HNO3) concentration through either lowering both daytime and nighttime HNO3 formation rate or raising deposition removal rate (Heald 335 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 surrounding areas is due to the over-340 amplified local NH3 emissions in the optimized estimates to match the high NH3 column density in IASI-NH3 2011, as discussed earlier. It adds to the existing overestimation in NH4 + and NO3concentration as compared to ground measurements. The fact that the simulated ambient NH3 concentration, NH4 + concentration, and NH4 + wet deposition using the optimized NH3 estimates is biased high in comparison with independent ground measurements suggests the enhanced NH3 abundance observed from IASI is driven by long-range transport at higher altitudes 345 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 NO3concentrations 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 350 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 in the sites in the San Joaquin Valley, limiting the formation of NH4NO3 through gas-particle partitioning . Thus, attempts to close the gap between the simulated and monitored NH4 + and NO3concentrations by scaling NH3 emission alone are ineffective and might lead to an overestimation in local NH3 emissions.

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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 small. There are only 1% and 4% of the CONUS with a relative change in PM2.5 concentration over 10%. 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 (Nr) 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 (Figure S5) 365 https://doi.org/10.5194/acp-2020-523 Preprint. Discussion started: 29 June 2020 c Author(s) 2020. CC BY 4.0 License.
within CONUS (USGS, 2018). In total, the Nr deposition increased by 39%, 2%, and 9% 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 Great Plain region (+73%), followed by the Eastern Temperate Forest (+41%) (Figure 6). Although the overall increase is small in July and October, the increment can be high in individual ecoregions, including Southern Semiarid Highlands (+109% in July), Temperate

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Sierras (+66% in July), and Marine West Coast (+64% in October). 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).
Driven by the increase in the reduced form of Nr (NH3 and NH4 + ) deposition, a higher share of reduced form of Nr to the total Nr deposition is found in most of the ecoregions for all three months than the NEI-based estimates. More 375 detrimental impacts on sensitive species and biodiversity are expected when this change in dominant Nr form are considered in addition to the increase in magnitude because the growth of many sensitive plant species will be inhibited by a high NH4 + to NO3ratio 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 380 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.

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We use the NH3 emission 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 Midwest is overestimated and the emission in Southern states is underestimated in the 2011 NEI.
Overall, the optimized emission is 46% higher. The optimized emission estimates in July and October are slightly higher (6.6% and 6.9%) than the 2011 NEI estimates and the spatial distribution agrees well.

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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. https://doi.org/10.5194/acp-2020-523 Preprint. Discussion started: 29 June 2020 c Author(s) 2020. CC BY 4.0 License.

Figure 5
The changes in monthly average PM2.5, NH4 + , and NO3mass concentration in April due to the NH3 emission adjustment in the optimized estimates. The change is defined as concoptimizedconca priori, where concoptimized and conca priori represents the simulated monthly average mass concentration using the optimized and a priori NH3 emission estimates, respectively. The difference between the observed NH4 + , and NO3mass concentration and simulated concentrations using the a priori NH3 emission (concobsconca priori , where concobs represents the observed monthly average mass concentration) are overlaid using colored dots with the same color scheme.
640 Figure 6 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 https://doi.org/10.5194/acp-2020-523 Preprint. Discussion started: 29 June 2020 c Author(s) 2020. CC BY 4.0 License.