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Preprints
https://doi.org/10.5194/acp-2019-979
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-2019-979
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.

  02 Mar 2020

02 Mar 2020

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A revised version of this preprint is currently under review for the journal ACP.

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

Xinrui Ge1, Martijn Schaap2, Richard Kranenburg2, Arjo Segers2, Gert Jan Reinds3, Hans Kros3, and Wim de Vries3 Xinrui Ge et al.
  • 1Environmental Systems Analysis Group, Wageningen University, Wageningen, the Netherlands
  • 2Department of Climate, Air and Sustainability, TNO, Utrecht, the Netherlands
  • 3Wageningen Environmental Research, Wageningen, the Netherlands

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.

Xinrui Ge et al.

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Short summary
This article is about improving the modeling of agricultural ammonia emissions, both spatially and temporally, which is a result of actual land use, meteorology, agricultural activities and practices in real life. The article illustrates the first step for a better understanding of ammonia emission with the possibility to be applied to European scale, which is of great significance for the improvement of ammonia budget research and future policy-making.
This article is about improving the modeling of agricultural ammonia emissions, both spatially...
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