Articles | Volume 20, issue 24
https://doi.org/10.5194/acp-20-16055-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/acp-20-16055-2020
© Author(s) 2020. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Modeling atmospheric ammonia using agricultural emissions with improved spatial variability and temporal dynamics
Environmental Systems Analysis Group, Wageningen University, Wageningen, the Netherlands
Martijn Schaap
Department of Climate, Air and Sustainability, TNO, Utrecht, the Netherlands
Richard Kranenburg
Department of Climate, Air and Sustainability, TNO, Utrecht, the Netherlands
Arjo Segers
Department of Climate, Air and Sustainability, TNO, Utrecht, the Netherlands
Gert Jan Reinds
Wageningen Environmental Research, Wageningen, the Netherlands
Hans Kros
Wageningen Environmental Research, Wageningen, the Netherlands
Wim de Vries
CORRESPONDING AUTHOR
Environmental Systems Analysis Group, Wageningen University, Wageningen, the Netherlands
Wageningen Environmental Research, Wageningen, the Netherlands
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Cited
13 citations as recorded by crossref.
- Ammonia emissions from agriculture and their contribution to fine particulate matter: A review of implications for human health K. Wyer et al. 10.1016/j.jenvman.2022.116285
- Impact of interannual weather variation on ammonia emissions and concentrations in Germany X. Ge et al. 10.1016/j.agrformet.2023.109432
- Spatially explicit boundaries for agricultural nitrogen inputs in the European Union to meet air and water quality targets W. de Vries et al. 10.1016/j.scitotenv.2021.147283
- 4DEnVar-based inversion system for ammonia emission estimation in China through assimilating IASI ammonia retrievals J. Jin et al. 10.1088/1748-9326/acb835
- Improving the accounting and categorization of tools of agriculture in the conditions of economic modernization K. Khatamov et al. 10.1051/e3sconf/202338903091
- Forest–atmosphere exchange of reactive nitrogen in a remote region – Part I: Measuring temporal dynamics P. Wintjen et al. 10.5194/bg-19-389-2022
- The food we eat, the air we breathe: a review of the fine particulate matter-induced air quality health impacts of the global food system S. Balasubramanian et al. 10.1088/1748-9326/ac065f
- Forest–atmosphere exchange of reactive nitrogen in a remote region – Part II: Modeling annual budgets P. Wintjen et al. 10.5194/bg-19-5287-2022
- Daily Emission Patterns of Coal-Fired Power Plants in China Based on Multisource Data Fusion N. Wu et al. 10.1021/acsenvironau.2c00014
- Comparison of ammonia air concentration before and during the spread of COVID-19 in Lombardy (Italy) using ground-based and satellite data D. Lovarelli et al. 10.1016/j.atmosenv.2021.118534
- Improving spatial and temporal variation of ammonia emissions for the Netherlands using livestock housing information and a Sentinel-2-derived crop map X. Ge et al. 10.1016/j.aeaoa.2023.100207
- 4D‐Var Inversion of European NH3 Emissions Using CrIS NH3 Measurements and GEOS‐Chem Adjoint With Bi‐Directional and Uni‐Directional Flux Schemes H. Cao et al. 10.1029/2021JD035687
- On the weekly cycle of atmospheric ammonia over European agricultural hotspots M. Van Damme et al. 10.1038/s41598-022-15836-w
13 citations as recorded by crossref.
- Ammonia emissions from agriculture and their contribution to fine particulate matter: A review of implications for human health K. Wyer et al. 10.1016/j.jenvman.2022.116285
- Impact of interannual weather variation on ammonia emissions and concentrations in Germany X. Ge et al. 10.1016/j.agrformet.2023.109432
- Spatially explicit boundaries for agricultural nitrogen inputs in the European Union to meet air and water quality targets W. de Vries et al. 10.1016/j.scitotenv.2021.147283
- 4DEnVar-based inversion system for ammonia emission estimation in China through assimilating IASI ammonia retrievals J. Jin et al. 10.1088/1748-9326/acb835
- Improving the accounting and categorization of tools of agriculture in the conditions of economic modernization K. Khatamov et al. 10.1051/e3sconf/202338903091
- Forest–atmosphere exchange of reactive nitrogen in a remote region – Part I: Measuring temporal dynamics P. Wintjen et al. 10.5194/bg-19-389-2022
- The food we eat, the air we breathe: a review of the fine particulate matter-induced air quality health impacts of the global food system S. Balasubramanian et al. 10.1088/1748-9326/ac065f
- Forest–atmosphere exchange of reactive nitrogen in a remote region – Part II: Modeling annual budgets P. Wintjen et al. 10.5194/bg-19-5287-2022
- Daily Emission Patterns of Coal-Fired Power Plants in China Based on Multisource Data Fusion N. Wu et al. 10.1021/acsenvironau.2c00014
- Comparison of ammonia air concentration before and during the spread of COVID-19 in Lombardy (Italy) using ground-based and satellite data D. Lovarelli et al. 10.1016/j.atmosenv.2021.118534
- Improving spatial and temporal variation of ammonia emissions for the Netherlands using livestock housing information and a Sentinel-2-derived crop map X. Ge et al. 10.1016/j.aeaoa.2023.100207
- 4D‐Var Inversion of European NH3 Emissions Using CrIS NH3 Measurements and GEOS‐Chem Adjoint With Bi‐Directional and Uni‐Directional Flux Schemes H. Cao et al. 10.1029/2021JD035687
- On the weekly cycle of atmospheric ammonia over European agricultural hotspots M. Van Damme et al. 10.1038/s41598-022-15836-w
Latest update: 09 Dec 2023
Short summary
This article is about improving the modeling of agricultural ammonia emissions. By considering land use, meteorology and agricultural practices, ammonia emission totals officially reported by countries are distributed in space and time. We illustrated the first step for a better understanding of the variability of ammonia emission, with the possibility of being applied at a European scale, which is of great significance for ammonia budget research and future policy-making.
This article is about improving the modeling of agricultural ammonia emissions. By considering...
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