Modeling Atmospheric Ammonia using Agricultural Emissions with Improved Spatial Variability and Temporal Dynamics

Abstract

Abstract. Ammonia emissions to the atmosphere have increased substantially in Europe since 1960, largely due to the intensification of agriculture as illustrated by enhanced livestock and increasing use of fertilizers. These associated emissions of reactive nitrogen, particulate matter and acid deposition have contributed to negative societal impacts on human health and terrestrial ecosystems. Due to the limited availability of measurements, emission inventories are used to assess large-scale ammonia emissions from agriculture, creating gridded annual emission maps as well as emission time profiles, both globally and regionally. The modeled emissions are in turn used in chemistry transport models to obtain ammonia concentrations and depositions. However, current emission inventories usually have relatively low spatial resolutions and coarse categorizations that do not distinguish between fertilization on various crops, grazing, animal housing, and manure storage in its spatial allocation. Furthermore, in assessing the seasonal variation of ammonia emissions, they do not take into account local climatology and agricultural management, which limits the capability to reproduce observed spatial and seasonal variations in the ammonia concentrations. This paper describes a novel ammonia emission model that quantifies agricultural emissions with improved spatial details and temporal dynamics over the year of 2010, in Germany and Benelux. The spatial allocation was achieved by embedding the agricultural emission model INTEGRATOR into MACC-III, thus accounting for differences in manure and fertilizer application on croplands and grassland, grazing, animal houses and manure storage systems. The more detailed temporal distribution comes from the integration of the TIMELINES model, which provided predictions of the timing of key agricultural operations including the day of fertilization across Europe. The emission estimates and time profiles were imported into LOTOS-EUROS to obtain surface concentrations and total columns for validation. The comparison of surface concentration time series between modeled output and in-situ measurements illustrated that the updated model has been improved significantly with respect to the temporal variation of ammonia emission, and its performance was more stable and robust. The comparison between ammonia total columns from remote sensing and simulations showed that there is an overestimation in Southern Germany and underestimation in Northern Germany, which suggested that updating ammonia emission fractions and accounting for manure transport is the direction for further improvement.

Forest–atmosphere exchange of reactive nitrogen in a remote region – Part II: Modeling annual budgets

“…Such an overestimation of NH 3 and NH + 4 in precipitation at forest monitoring sites was identified before for stations in Baden-Württemberg and Bavaria (Schaap et al, 2017). A similar systematic overestimation by the model in southern Germany in comparison to novel NH 3 satellite data has also been identified (Ge et al, 2020). This leads us to believe that the overestimation is largely due to shortcomings in the emission information (Sect.…”

“…Hence, in reality, the emission variability may be more evident under summer conditions. Currently, the detailing of crop-dependent emissions made within LOTOS-EUROS -i.e., the use of variable emission fractions within German regions in combination with the recent timing module of Ge et al (2020) -is under investigation to elucidate if these factors contribute to the measurement-model mismatches observed for the measurement site.…”

4D‐Var Inversion of European NH3 Emissions Using CrIS NH3 Measurements and GEOS‐Chem Adjoint With Bi‐Directional and Uni‐Directional Flux Schemes

“…In contrast, the Posterior_bi estimates of EU25 and the Netherlands are 2850 Gg N a −1 and 100 Gg N a −1 , respectively, much closer (<2% difference for EU25, 10% difference for the Netherlands) to the HTAP v2 and CEIP estimates and a recent improved dynamic agricultural emission estimate (95 Gg N a −1 for the Netherlands) from Ge et al. (2020). While the Posterior_bi emission estimate for the UK is significantly larger than these bottom‐up estimates by a factor of 1.3–1.8, the Posterior_bi emission estimate for Switzerland is consistently smaller than these bottom‐up estimates by a factor of 1.4–2.1.…”

Forest–atmosphere exchange of reactive nitrogen in a remote region – Part I: Measuring temporal dynamics

“…For example, in spring and summer, NH 3 had probably the largest contribution on the N r flux. Elevated NH 3 concentrations were likely caused by emissions from agricultural management in the surrounding region (Ge et al, 2020). The concentration of NH 3 was still lower than of NO 2 , but the v d of NH 3 is significantly higher than of NO 2 for woodland.…”