Deposition of reactive nitrogen causes detrimental environmental effects, including biodiversity loss, eutrophication, and soil acidification. Measuring and modelling the biosphere–atmosphere exchange of ammonia, the most abundant reduced nitrogen species, is complex due to its high reactivity and solubility, often leading to systematic discrepancies between model predictions and observations. This study aims to determine whether three state-of-the-art exchange schemes for NH3 can accurately model NH3 exchange in a dune ecosystem (Solleveld) and detect factors causing the uncertainties in these schemes. The selected schemes are DEPAC (DEPosition of Acidifying Compounds) by van Zanten et al. (2010) and the schemes by Massad et al. (2010) and Zhang et al. (2010). Validation against 1 year of gradient flux measurements revealed that the Zhang scheme represented the NH3 deposition at Solleveld best, whereas the DEPAC scheme overestimated the total deposition, while the Massad scheme underestimated the total deposition. Yet none of these schemes captured the emission events at Solleveld, pointing to considerable uncertainty in the compensation point parameterization and possibly in the modelling of NH3 desorption processes from wet surface layers. The sensitivity analysis further reinforced these results, showing how uncertainty in essential model parameters in the external resistance (Rw) and compensation point parameterization propagated into diverging model outcomes. These outcomes underscore the need to improve our mechanistic understanding of surface equilibria represented by compensation points, including the adsorption–desorption mechanism at the external water layer, and specific recommendations are provided for future modelling approaches and measurement setups to support this goal.

This study investigates single pixel nadir viewing detection limit of atmospheric ammonia for a range of area flux mapping satellite infrared sensor spectral resolutions (0.05-2.0 cm(-1)) and measurement noise levels. The detection level of ammonia is computed directly from simulated satellite ammonia spectral signatures, which has the advantage of being independent of retrieval methodologies. Information on the frequency of a given detection limit, and the cumulative probability of detection, are provided as a function of instrument spectral resolution and noise. For example, a Cross-track Infrared Sounder-like instrument with a modest spectral resolution of 0.625 cm(-1) and excellent signal-to-noise ratio of similar to 1600 would be able to detect ammonia on average similar to 70% of the time; these same instrument specifications will have a detection limit of 0.2 ppbv (surface) or 1.6x10(15) molec cm(-2) (total column) that can be achieved at a detection rate of 10% as it requires favorable infrared remote sensing conditions (large thermal contrast). Under more typical atmospheric states a detection limit of 0.5 ppbv (3.5 x 10(15) molec cm(-2)) is achieved at a 50% detection rate. This detection limit information is valuable for applications that incorporate remote sensing data in conditions when the atmospheric ammonia amounts are below the detection limit of the satellite sensor (e.g., nongrowing season in crop fertilizer source regions). As the simulations use real-world atmospheric state observations as inputs the results can be used to provide general guidance on the detection limits of past, current, and potential new environmental flux mapping instruments used for ammonia monitoring covering large geographical regions.

The flux divergence approach (FDA) is a popular technique for deriving NOx emission estimates from tropospheric NO2 columns measured by the TROPOspheric Monitoring Instrument (TROPOMI) satellite sensor. An attractive aspect of the FDA is that the method simplifies three-dimensional atmospheric chemistry and transport processes into a two-dimensional (longitude–latitude) steady-state continuity equation for columns that balances local NOx emissions with the net outflow and chemical loss of NOx. Here we test the capability of the FDA to reproduce known NOx emissions from synthetic NO2 column retrievals generated with the LOTOS-EUROS chemistry transport model over the Netherlands at high spatial resolution of about 2×2 km during summer. Our results show that the FDA captures the magnitude and spatial distribution of the NOx emissions to high accuracy (absolute bias <9 %), provided that the observations represent the NO2 column in the boundary layer, that wind speed and direction are representative for the boundary layer (PBL) column, and that the high-resolution spatiotemporal variability of the NO2 lifetimes and NOx:NO2 ratio is accounted for in the inversion instead of using single fixed values. The FDA systematically overestimates NOx emissions by 15 %–60 % when using tropospheric NO2 columns as the driving observation, while using PBL NO2 columns largely overcomes this systematic error. This merely reflects the fact that the local balance between emissions and sinks of NOx occurs in the boundary layer, which is decoupled from the NO2 in the free troposphere. Based on the recommendations from this sensitivity test, we then applied the FDA using observations of NO2 columns from TROPOMI, corrected for contributions from free-tropospheric NO2, between 1 June and 31 August 2018. The NOx emissions derived from the default TROPOMI retrievals are biased low over cities and industrialized areas. However, when the coarse 1×1° TM5-MP NO2 profile used in the retrieval is replaced by the high-resolution profile of LOTOS-EUROS, the TROPOMI NOx emissions are enhanced by 22 % and are in better agreement with the inventory for the Netherlands. This emphasizes the importance of using realistic high-resolution a priori NO2 profile shapes in the TROPOMI retrieval. We conclude that accurate quantitative NOx emissions estimates are possible with the FDA, but they require sophisticated, fine-scale corrections for both the NO2 observations driving the method and the estimates of the NO2 chemical lifetime and NOx:NO2 ratio. This information can be obtained from high-resolution chemistry transport model simulations at the expense of the simplicity and applicability of the FDA.

The amount of methane released to the atmosphere from the Nord Stream subsea pipeline leaks remains uncertain, as reflected in a wide range of estimates1–18. A lack of information regarding the temporal variation in atmospheric emissions has made it challenging to reconcile pipeline volumetric (bottom-up) estimates1–8 with measurement-based (top-down) estimates8–18. Here we simulate pipeline rupture emission rates and integrate these with methane dissolution and sea-surface outgassing estimates9,10 to model the evolution of atmospheric emissions from the leaks. We verify our modelled atmospheric emissions by comparing them with top-down point-in-time emission-rate estimates and cumulative emission estimates derived from airborne11, satellite8,12–14 and tall tower data. We obtain consistency between our modelled atmospheric emissions and top-down estimates and find that 465 ± 20 thousand metric tons of methane were emitted to the atmosphere. Although, to our knowledge, this represents the largest recorded amount of methane released from a single transient event, it is equivalent to 0.1% of anthropogenic methane emissions for 2022. The impact of the leaks on the global atmospheric methane budget brings into focus the numerous other anthropogenic methane sources that require mitigation globally. Our analysis demonstrates that diverse, complementary measurement approaches are needed to quantify methane emissions in support of the Global Methane Pledge19. Modelling of the evolution of atmospheric methane emissions from the 2022 Nord Stream subsea pipeline leaks shows that the event emitted the largest recorded amount of methane from a single transient event.

Ammonia (NH3) is an essential form of reactive nitrogen whose emissions originate primarily from manure and fertilizer. Atmospheric ammonia is a major precursor of anthropogenic aerosols, for example ammonium nitrate, and negatively impacts both human and ecosystem health. While important, the overall budget of ammonia remains highly uncertain, as in-situ measurements of ammonia are still sparse and typically only provide a very coarse spatial-temporal coverage. Satellite ammonia observations provide a tool to study the budget in detail, however NH3 satellite products validation studies are challenging due to the small scale spatiotemporal variability of atmospheric ammonia. Furthermore, the validation studies typically only focus on a single satellite product and/or comparison to in-situ surface concentrations. This limits such studies to more of an evaluation on how well a satellite represents a surface point measurement instead of validating the satellite vertical sounding (e.g. profile and total column) observations. The ground-based Fourier Transform infrared (FTIR)-NH3 product is an excellent dataset that can more directly evaluate the satellite column and profile type products as it shares a similar sounding measurement. Here we present an update to the FTIR-NH3 record, which includes an extension in both the number of sites as well as the observation period of these stations. Furthermore, we present the latest evaluation and intercomparison results for the most current CrIS (1, and 2), IASI(-A, -B and -C), AIRS and GOSAT(1) ammonia products.

The oil sands in Alberta, Canada, are a significant source of air pollution. Observations from the Ozone Monitoring Instrument (OMI) on the NASA Aura satellite have been used to quantify NOx emissions from the surface mining region of the oil sands. Two related emissions methods were utilized, one for point and one for area sources, where OMI vertical column densities of NO2 were combined with winds from a meteorological reanalysis and a two-dimensional exponentially modified Gaussian (EMG) plume model. This work better connects the two (point and area) emissions methods and discusses the interpretation of fit parameters and the ability of OMI (and other sensors) to resolve emissions between neighbouring sources.The two methods employed, in good agreement with each other, indicated an increase in emissions from about 55 to 80 kt [NO2] yr-1 between 2005-2011 and a flat trend thereafter. Reported emissions were within 15 % of reported emissions, consistent to within uncertainties. In an extension of this methodology, OMI observations were combined with reported point source emissions to derive the more uncertain emissions component from the large off-road mining fleet. These were found to make up about 60 % of total NOx emissions, also consistent with reported emissions. The OMI-derived 0.9 % yr-1 increase in fleet emissions and the 5.5 % yr-1 increase in bitumen mined, generally a good proxy for fleet emissions, can be reconciled by considering the evolution of the mine fleet over this period. OMI is therefore able to track the transition from US EPA Tier 1 standards, through Tier 4 standards, to the present and in doing so demonstrates the efficacy of this policy. Furthermore, this analysis shows that had the fleet remained at Tier 1, this source would currently be emitting an additional 40 kt [NO2] yr-1.

The exposure to elevated levels of ozone contributes to respiratory diseases and ecosystem degradation. Mediterranean countries are among those most affected by high ozone concentrations, which are generally overestimated by chemistry transport models underscoring the importance of improving the accuracy of air quality modelling. This study introduces an enhanced Mediterranean dry deposition description within the LOTOS-EUROS model framework, focusing on refining key vegetation parameters for the Mediterranean climate zone, with the goal to better estimate deposition and connected concentration values. Adjustments were made to the vegetation type dependent Jarvis functions for temperature and vapour pressure deficit, as well as to the maximum stomatal conductance across four land use types: arable land, crops, deciduous broadleaf forest, and coniferous evergreen forest. The model’s baseline run showed a widespread overestimation of ozone. Adjustments to the dry deposition routines reduced this overestimation, but the model simulation incorporating all changes still showed elevated ozone levels. Both runs displayed moderate spatial correlation with observations from 117 rural background monitoring stations, and most stations exhibited a temporal correlation between 0.5 and 0.8. An improved RMSE and bias were noted at the majority of the stations (114 out of 117) for the model simulation incorporating all changes. The monthly analysis indicated consistent overestimation at two Portuguese sites beginning in March. The model effectively tracked temporal changes overall. However, the diurnal analysis revealed site-specific differences: an overestimation at the station closest to highly populated areas at night, while rural stations aligned better with observed values. These results highlight the benefits of region-specific model adaptations and lay the groundwork for further advancements, such as incorporating detailed vegetation classifications and seasonal variations.

Understanding whether nitrogen (N), phosphorus (P) or potassium (K) (co)limit productivity across biodiverse herbaceous habitats is crucial to guide management. Therefore, we investigated for 386 plots representing 13 nutrient-limited habitat types across Europe whether community N:P:K stoichiometry and limitation types differ along wide-ranging gradients in soil development, moisture and pH. Results indicate P/P+N as frequent as N limitation. K/K+N limitation occurs not where K availability is minimal, but in species-impoverished habitats with excess N and P. Overall, P emerges as primary driver of stoichiometry, strongly driven by the environment: at optimal pH of 6, N:P and P/P+N limitation are minimal and N limitation maximal, despite also good conditions for N availability. At pH<5 and >7, N:P is high and P/P+N limitation common. Our findings emphasize soil pH control on nutrient limitation through influence on P. Studies reporting widespread K (co)limitation in temperate herbaceous vegetation likely sampled anthropogenically P/N-enriched communities. ### Competing Interest Statement The authors have declared no competing interest.

Over the past century, ammonia (NH3) emissions have increased with the growth of livestock and fertilizer usage. The abundant NH3 emissions lead to secondary fine particulate matter (PM2.5) pollution, climate change, and a reduction in biodiversity, and they affect human health. Up-to-date and spatially and temporally resolved information on NH3 emissions is essential to better quantify their impact. In this study we applied the existing Daily Emissions Constrained by Satellite Observations (DECSO) algorithm to NH3 observations from the Cross-track Infrared Sounder (CrIS) to estimate NH3 emissions. Because NH3 in the atmosphere is influenced by nitrogen oxides (NOx), we implemented DECSO to estimate NOx and NH3 emissions simultaneously. The emissions are derived over Europe for 2020 on a spatial resolution of 0.2°×0.2° using daily observations from both CrIS and the TROPOspheric Monitoring Instrument (TROPOMI; on the Sentinel-5 Precursor (S5P) satellite). Due to the limited number of daily satellite observations of NH3, monthly emissions of NH3 are reported. The total NH3 emissions derived from observations are about 8 Tg yr−1, with a precision of about 5 %–17 % per grid cell per year over the European domain (35–55° N, 10° W–30° E). The comparison of the satellite-derived NH3 emissions from DECSO with independent bottom-up inventories and in situ observations indicates a consistency in terms of magnitude on the country totals, with the results also being comparable regarding the temporal and spatial distributions. The validation of DECSO over Europe implies that we can use DECSO to quickly derive fairly accurate monthly emissions of NH3 over regions with limited local information on NH3 emissions.

Uncertainty in ammonia (NH3) emissions causes the inaccuracy of fine particulate matter simulations, which is associated with human health. To address this uncertainty, in this work, we employ the iterative finite difference mass balance (iFDMB) technique to revise NH3 emissions over East Asia using the Cross-track Infrared Sounder (CRIS) satellite for July, August, and September 2019. Compared to the emissions, the revised NH3 emissions show an increase in China, particularly in the North China Plain (NCP) region, corresponding to agricultural land use in July, August, and September and a decrease in South Korea in September. The enhancement in NH3 emissions resulted in a remarkable increase in concentrations of NH3 by 5 ppb. in July and September, there is an increase in ammonium (NH4+) and nitrate (NO3-) concentrations by 5 μg/m3, particularly in the NCP region, while in August, both NH4+ and NO3- concentrations exhibit a decrease. For sulfate (SO42-), in August and September, the concentrations decreased over most regions of China and Taiwan, as a result of the production of ammonium sulfate; increased concentrations of SO42-, however, were simulated over South Korea, Japan, and the southern region of Chengdu, caused by higher relative humidity (RH). In contrast, during the month of July, our simulations showed an increase in SO42- concentrations over most regions of China. To gain a more comprehensive understanding, we defined a sulfur conversion ratio (SCR=(Spost-Sprior)/(SO42-post-SO42-prior)), which explains how changes in sulfur in the gas phase affect changes in sulfate concentrations. A subsequent sensitivity analysis performed in this study indicated the same relationship between changes in ammonia and its effect on inorganic fine particulate matter (PM2.5). This study highlights the challenge of controlling and managing inorganic PM2.5 and indicates that reducing the emissions of air pollutants do not necessarily lead to a reduction in their concentrations.

Emissions from biomass burning are a significant source of air pollution, which can adversely impact air quality and ecosystems thousands of kilometres downwind. These emissions can be estimated by a bottom-up approach that relies on fuel consumed and standardized emission factors. Emissions are also commonly derived with a top-down approach, using satellite-observed fire radiative power (FRP) as a proxy for fuel consumption. Biomass burning emissions can also be estimated directly from satellite trace gas observations, including carbon monoxide (CO). Here, we explore the potential of satellite-derived CO emission rates from biomass burning and provide new insights into the understanding of satellite-derived fire CO emissions globally, with respect to differences in regions and vegetation type. Specifically, we use the TROPOMI (Tropospheric Monitoring Instrument) high-spatial-resolution satellite datasets to derive burning CO emissions directly for individual fires between 2019 and 2021 globally. Using synthetic data (with known emissions), we show that the direct emission estimate methodology has a 34 % uncertainty for deriving CO emissions (and a total uncertainty of 44 % including wind and CO column uncertainty). From the TROPOMI-derived CO emissions, we derive biome-specific emission coefficients (emissions relative to FRP) by combining the direct emission estimates and the satellite-observed FRP from the Moderate Resolution Imaging Spectrometer (MODIS). These emission coefficients are used to establish annual top-down CO emission inventories from biomass burning, showing that Southern Hemisphere Africa has the highest CO biomass burning emissions (over 25 % of global total of 300–390 Mt(CO) yr−1 between 2003–2021), and almost 25 % of global CO biomass burning emissions are from broadleaved evergreen tree fires. A comprehensive comparison between direct estimates, top-down and bottom-up approaches, provides insight into the strengths and weaknesses of each method: FINN2.5 has higher CO emissions, by a factor between 2 and 5, than all other inventories assessed in this study. Trends over the past 2 decades are examined for different regions around the globe, showing that global CO biomass burning emissions have, on the whole, decreased (by 5.1 to 8.7 Mt(CO) yr−1), where some regions experience increased and others decreased emissions.

Ammonia (NH3) is an essential form of reactive nitrogen whose emissions originate primarily from manure and fertilizer. Atmospheric ammonia is a major precursor of anthropogenic aerosols, for example ammonium nitrate, and negatively impacts both human and ecosystem health. While important, the overall budget of ammonia remains highly uncertain, as in-situ measurements of ammonia are still sparse and typically only provide a very coarse spatial-temporal coverage. Satellite ammonia observations provide a tool to study the budget in detail, however NH3 satellite products validation studies are challenging due to the small scale spatiotemporal variability of atmospheric ammonia. Furthermore, the validation studies typically only focus on a single satellite product and/or comparison to in-situ surface concentrations. This limits such studies to more of an evaluation on how well a satellite represents a surface point measurement instead of validating the satellite vertical sounding (e.g. profile and total column) observations. The ground-based Fourier Transform infrared (FTIR)-NH3 product is an excellent dataset that can more directly evaluate the satellite column and profile type products as it shares a similar sounding measurement. Here we present an update to the FTIR-NH3 record, which includes an extension in both the number of sites as well as the observation period of these stations. Furthermore, we present the latest evaluation and intercomparison results for the most current CrIS (1, and 2), IASI(-A, -B and -C), AIRS and GOSAT(1) ammonia products.

NOx is an important primary air pollutant of major environmental concern which is predominantly produced by anthropogenic combustion activities. NOx needs to be accounted for in national emission inventories, according to international treaties. Constructing accurate inventories requires substantial time and effort, resulting in reporting delays of 1 to 5 years. In addition to this, difficulties can arise from temporal and country-specific legislative and protocol differences. To address these issues, satellite-based atmospheric composition measurements offer a unique opportunity for the independent and large-scale estimation of emissions in a consistent, transparent, and comprehensible manner. Here we test the multi-source plume method (MSPM) to assess the NOx emissions over Germany in the COVID-19 period from 2019–2021. For the years where reporting is available, the differences between satellite estimates and inventory totals were within 75–100 kt (NO2) NOx (<10 % of inventory values). The large reduction in the NOx emissions (∼15 %) concurrent with the COVID-19 lockdowns was observed in both the inventory and satellite-derived emissions. The recent projections for the inventory emissions of 2021 pointed to a recovery of the 2021 emissions towards pre-COVID-19 levels. In the satellite-derived emissions, however, such an increase was not observed. While emissions from the larger power plants did rebound to pre-COVID-19 levels, other sectors such as road transport did not, and the change in emissions is likely due to a reduction in the number of heavier transport trucks compared to the pre-COVID-19 numbers. This again illustrates the value of having a consistent satellite-based methodology for faster emission estimates to guide and check the conventional emission inventory reporting. The method described in this work also meets the demand for independent verification of the official emission inventories, which will enable inventory compilers to detect potentially problematic reporting issues, bolstering transparency and comparability, which are two key values for emission reporting.

Presented is a methodology to explicitly identify and account for cloud-free satellite measurements below a sensor's measurement detection level. These low signals can often be found in satellite observations of minor atmospheric species with weak spectral signals (e.g., ammonia (NH3)). Not accounting for these non-detects can high-bias averaged measurements in locations that exhibit conditions below the detection limit of the sensor. The approach taken here is to utilize the information content from the satellite signal to explicitly identify non-detects and then account for them with a consistent approach. The methodology is applied to the CrIS Fast Physical Retrieval (CFPR) ammonia product and results in a more realistic averaged dataset under conditions where there are a significant number of non-detects. These results show that in larger emission source regions (i.e., surface values > 7.5 ppbv) the non-detects occur less than 5% of the time and have a relatively small impact (decreases by less than 5%) on the gridded averaged values (e.g., annual ammonia source regions). However, in regions that have low ammonia concentration amounts (i.e., surface values < 1 ppbv) the fraction of non-detects can be greater than 70%, and accounting for these values can decrease annual gridded averaged values by over 50% and make the distributions closer to what is expected based on surface station observations.

Abstract. Emissions from wildfires are a significant source of air pollution, which can adversely impact air quality and ecosystems thousands of kilometers downwind. These emissions can be estimated by a bottom-up approach, using inputs such fuel type, burned area, and standardized emission factors. Emissions are also commonly derived with a top-down approach, using satellite observed fire radiative power (FRP) as proxy for fuel consumption. More recently, wildfire emissions have been demonstrated to be estimated directly from satellite observations, including carbon monoxide (CO). Here, we explore the potential of satellite-derived CO emission rates from wildfires and provide new insights into the understanding of satellite-derived fire CO emissions globally, with respect to differences in regions and vegetation type. Specifically, we use the TROPOMI (Tropospheric Monitoring Instrument) high spatial-resolution satellite datasets to create a global inventory database of burning emissions CO emissions between 2019 and 2021. Our retrieval methodology includes an analysis of conditions under which emission estimates may be inaccurate and filters these accordingly. Additionally, we determine biome specific emission coefficients (emissions relative to FRP) and show how combining the satellite derived CO emissions with satellite observed FRP from the Moderate Resolution Imaging Spectrometer (MODIS) establishes an annual CO emission budget from wildfires. The resulting emissions totals are compared to other top-down and bottom-up emission inventories over the past two decades. In general, the satellite-derived emissions inventory values and bottom-up emissions inventories have similar CO emissions totals across different global regions, though the discrepancies may be large for some regions (Southern Hemisphere South America, Southern Hemisphere Africa, Southeast Asia) and for some bottom-up inventories (e.g. FINN2.5, where CO emissions are a factor of 2 to 5 higher than other inventories). Overall, these estimates can help to validate emission inventories and predictive air quality models, and help to identify limitations present in existing bottom-up emissions inventory estimates.

Within Copernicus Atmosphere Monitoring Service (CAMS) an ensemble of 9 chemistry transport models provides daily analyses and forecasts, as well as annual reanalyses over Europe. The CAMS_61 project was developed to provide development plans, guidelines and tools for the upgrade of the models within this regional air quality service. In this study we will present the main outcomes of the CAMS_61 project with special focus on the generic data assimilation tool developed to promote the uptake of Sentinel 4 and 5 satellite observations within the service. The tool consists of a pre- and post-processor tool for the download, selection, conversion and visualisation of the satellite data and an observation operator source code that could in principle be included in any data assimilation system for the simulation of the observations using model variables. The tool has been tested on TROPOMI data within different model/assimilation systems and on a set of simulated Sentinel 4 data.

Scattering of light in the atmosphere and low sea surface albedo decrease the sensitivity of satellites to air pollution close to the sea surface. To reliably retrieve tropospheric nitrogen dioxide (NO2) columns using the TROPOspheric Monitoring Instrument (TROPOMI), it is therefore necessary to have good a priori knowledge of the vertical distribution of NO2. In this study, we used an aircraft of the Royal Belgian Institute of Natural Sciences, part of the Belgian coastguard structure, which was already equipped with a sniffer sensor system, measuring CO2, NOx and SO2. This instrumentation enables us (1) to capture pollution plumes originating from ships sailing within an Emission Control Area, and (2) to validate TROPOMI tropospheric NO2 columns over the polluted North Sea in summer 2021 and (3) to evaluate vertical profile shapes from several chemical models. We observe multiple clear signatures of ship plumes from seconds after emission to multiple kilometers downwind. Besides that, our results show that the chemical transport model TM5, which is used in the retrieval of the operational TROPOMI data, tends to underestimate surface level pollution while overestimating NO2 at higher levels over the polluted North Sea. The higher horizontal resolutions in the regional CAMS ensemble mean and LOTOS EUROS improve the surface level pollution estimates, but the models still systematically overestimate NO2 levels at higher altitudes, indicating exaggerated vertical mixing in the models. When replacing the TM5 a priori NO2 profiles with the aircraft-measured NO2 profiles in the air mass factor (AMFs) calculation, we find that recalculated AMFs reduce, and the retrieved NO2 columns increase by 20%. This indicates a significant low bias in TROPOMI tropospheric NO2 measurements over the North Sea. This low bias has important implications for estimating emissions over the sea. While TROPOMI NO2 low biases caused by the TM5 a priori profiles have previously also been reported over land, the reduced vertical mixing and smaller surface albedo over sea makes this issue especially relevant over sea and coastal regions.

Ammonia is one of the most impactful pollutants emitted from agricultural activities, harming human health and contributing to biodiversity loss. In ammonia emission inventories, the spatial distribution of annual emissions is mostly approximated by constant empirical emission fractions, which do not account for spatial variability, nor for temporal variability within a year or between years caused by weather variations. Besides, factors like manure properties, soil properties, and manure application techniques also lead to differences in the amount of ammonia emitted into the atmosphere. By not or only partly accounting for these factors, significant uncertainties are introduced into ammonia emission estimates at regional and national scales. In this study, we applied the empirical ALFAM2 model to derive spatially explicit slurry application emission fractions from cropland for use in the large-scale INTEGRATOR model, using the information on slurry properties (dry matter content and pH), manure application rate, application technique, incorporation time, air temperature, wind speed, and rainfall rate. In addition, the impact of weather on the ammonia emissions from animal housing and manure storage systems was included through a temperature-dependent scaling. We applied the method to investigate the year-to-year spatio-temporal variabilities of ammonia emissions and modeled concentrations across Germany from 2015 to 2018. Through the comparison with in situ measurements and satellite-derived observations, we studied how surface concentrations and total columns relate to local meteorology. We found that the spatio-temporal variability in emission fractions improves the ability to reproduce the interannual variability observed in ammonia concentration and total column measurements. This study shows that the developed approach to derive spatially explicit emission fractions can significantly improve ammonia emission modeling and is of great importance for studying the temporal variability between years.