Advertisement

Comparison of spatial patterns of ammonia concentration and dry deposition flux between a regional Eulerian chemistry-transport model and a local Gaussian plume model

  • Niramson Azouz
  • Jean-Louis DrouetEmail author
  • Matthias Beekmann
  • Guillaume Siour
  • Roy Wichink Kruit
  • Pierre Cellier
Article
  • 29 Downloads

Abstract

Agricultural activities are the principal sources of ammonia (NH3) emitted into the atmosphere. High ammonia deposition flux may impact sensitive ecosystems. Regional models of NH3 dispersion, transport and deposition may under- or overestimate NH3 fluxes. We compared NH3 dry deposition fluxes simulated with local and regional models on different theoretical scenarios characterised by varying the values of several input factors: grid cell sizes, characteristics of the NH3 sources such as location and emission rate, characteristics such as canopy resistance (Rc) or roughness length (z0) at the NH3 sinks, and meteorological conditions such as wind speed and direction. Our results showed that, for a given grid cell size, both models provide similar predictions of average NH3 concentration and dry deposition flux over the whole simulation domain. A sensitivity analysis of NH3 concentration and dry deposition flux to wind speed and to surface resistance also showed a similar behaviour between both models. However, the differences of model formalism and changes in the values of the input factors, especially grid cell size and vertical resolution, provide different spatial patterns of NH3 dry deposition flux and concentration. Our results would suggest that regional models operating with large grid cell sizes (e.g. larger than 1 km) could not predict accurately patterns of NH3 dry deposition fluxes close to the sources (e.g. a few tens or hundreds of metres) on heterogeneous landscapes in terms of NH3 fluxes.

Keywords

Dispersion model Atmospheric ammonia Dry deposition flux Sub-grid variability Landscape 

Notes

Acknowledgments

We gratefully acknowledge L. Menut, C. Flechard and N. Flipo for useful comments on the design of the research and on results. We also thank F.J. Sauter, O. Maury and M.R. Theobald for their help in programming support, model coding and data formatting.

Funding information

This work was supported by the French National Institute for Agricultural Research (Environment and Agronomy Division), the EU ECLAIRE project (grant no. FP7-Environment 282910), and the French Research Agency (ANR), ESCAPADE project (ANR-12-AGRO-0003).

Supplementary material

11869_2019_691_MOESM1_ESM.pdf (676 kb)
ESM 1 (PDF 675 kb)

References

  1. Asman WAH, van Jaarsveld HA (1992) A variable-resolution transport model applied for NHx in Europe. Atmos Environ 26:445–464CrossRefGoogle Scholar
  2. Asman WAH, Pinksterboer EF, Maas HFM, Erisman JW, Waijersypelaan A, Slanina J, Horst TW (1989) Gradients of the ammonia concentration in a nature reserve: model results and measurements. Atmos Environ 23:2259–2265CrossRefGoogle Scholar
  3. Asman WAH, Sutton MA, Schjørring JK (1998) Ammonia: emission, atmospheric transport and deposition. New Phytol 139:27–48CrossRefGoogle Scholar
  4. Bobbink R, Hicks K, Galloway J, Spranger T, Alkemade R, Ashmore M, Bustamante M, Cinderby S, Davidson E, Dentener F, Emmett B, Erisman JW, Fenn M, Gilliam F, Nordin A, Pardo L, De Vries W (2010) Global assessment of nitrogen deposition effects on terrestrial plant diversity: a synthesis. Ecol Appl 20:30–59CrossRefGoogle Scholar
  5. De Pue D, Roet D, Lefebvre W, Buysse J (2017) Mapping impact indicators to link airborne ammonia emissions with nitrogen deposition in Natura 2000 sites. Atmos Environ 166:120–129CrossRefGoogle Scholar
  6. Dore AJ, Carslaw DC, Braban C, Cain M, Chemel C, Conolly C, Derwent RG, Griffiths SJ, Hall J, Hayman G, Lawrence S, Metcalfe SE, Redington A, Simpson D, Sutton MA, Sutton P, Tang YS, Vieno M, Werner M, Whyatt JD (2015) Evaluation of the performance of different atmospheric chemical transport models and inter-comparison of nitrogen and Sulphur deposition estimates for the UK. Atmos Environ 119:131–143CrossRefGoogle Scholar
  7. Dragosits U, Theobald MR, Place CJ, Lord E, Webb J, Hill J, ApSimon HM, Sutton MA (2002) Ammonia emission, deposition and impact assessment at the field scale: a case study of sub-grid spatial variability. Environ Pollut 117:147–158CrossRefGoogle Scholar
  8. Erisman JW, Vermetten AWM, Pinksterboer EF, Asman WAH, Waijers-Ypelaan A, Slanina J (1987) Atmospheric ammonia: distribution, equilibrium with aerosols and conversion rate to ammonium. In: Ammonia and acidification. RIVM/TNO, Bilthoven, pp 59–72Google Scholar
  9. Geels C, Andersen HV, Ambelas Skjøth C, Christensen JH, Ellermann T, Løfstrøm P, Gyldenkærne S, Brandt J, Hansen KM, Frohn LM, Hertel O (2012) Improved modelling of atmospheric ammonia over Denmark using the coupled modelling system DAMOS. Biogeosciences 9:2625–2647CrossRefGoogle Scholar
  10. Jones L, Nizam MS, Reynolds B, Bareham S, Oxley ERB (2013) Upwind impacts of ammonia from an intensive poultry unit. Environ Pollut 180:221–228CrossRefGoogle Scholar
  11. Loubet B, Milford C, Sutton MA, Cellier P (2001) Investigation of the interaction between sources and sinks of atmospheric ammonia in an upland landscape using a simplified dispersion-exchange model. J Geophys Res Atmos 106:24183–24195CrossRefGoogle Scholar
  12. Loubet B, Asman WAH, Theobald MR, Hertel O, Tang YS, Robin P, Hassouna M, Daemmgen U, Génermont S, Cellier P, Sutton MA (2009) Ammonia deposition near hot spots: processes, models and monitoring methods. In: Atmos Ammonia. Springer, Dordrecht, pp 205–267CrossRefGoogle Scholar
  13. McGinn SM, Janzen HH, Coates TW, Beauchemin KA, Flesch TK (2016) Ammonia emission from a beef cattle feedlot and its local dry deposition and re-emission. J Environ Qual 45:1178–1185CrossRefGoogle Scholar
  14. Menut L, Bessagnet B, Khvorostyanov D, Beekmann M, Blond N, Colette A, Coll I, Curci G, Foret G, Hodzic A, Mailler S, Meleux F, Monge JL, Pison I, Siour G, Turquety S, Valari M, Vautard R, Vivanco MG (2013) CHIMERE 2013: a model for regional atmospheric composition modelling. Geosci Model Dev 6:981–1028CrossRefGoogle Scholar
  15. Peduzzi E, Pisoni E, Clappier A, Thunis P (2018) Multi-level policies for air quality: implications of national and sub-national emission reductions on population exposure. Air Qual Atmos Health 11:1121–1135CrossRefGoogle Scholar
  16. Pitcairn CE, Leith ID, van Dijk N, Sheppard LJ, Sutton MA, Fowler D (2009) The application of transects to assess the effects of ammonia on woodland groundflora. In: Atmos Ammonia. Springer, Dordrecht, pp 59–69CrossRefGoogle Scholar
  17. Sauter F, van Jaarsveld JA, Van Zanten M, van der Swaluw E, Aben J, de Leeuw F (2015) The OPS-model. Description of OPS 4.4.4. RIVM Report. National Institute for Public Health and the Environment (RIVM), BilthovenGoogle Scholar
  18. Shen J, Chen D, Bai M, Sun J, Coates T, Lam SK, Li Y (2016) Ammonia deposition in the neighbourhood of an intensive cattle feedlot in Victoria, Australia. Sci Rep 6:32793CrossRefGoogle Scholar
  19. Skamarock W, Klemp J, Dudhia J, Gill D, Barker D, Wang W, Powers J (2007) A description of the advanced research WRF version 2. NCAR Technical Note, BoulderGoogle Scholar
  20. Sutton MA, Milford C, Dragosits U, Place CJ, Singles RJ, Smith RI, Pitcairn CER, Fowler D, Hill J, ApSimon HM, Ross C, Hill R, Jarvis SC, Pain BF, Phillips VC, Harrison R, Moss D, Webb J, Espenhahn SE, Lee DS, Hornung M, Ullyett J, Bull KR, Emmett BA, Lowe J, Wyers GP (1998) Dispersion, deposition and impacts of atmospheric ammonia: quantifying local budgets and spatial variability. Environ Pollut 102:349–361CrossRefGoogle Scholar
  21. Theobald MR, Løfstrøm P, Walker J, Andersen HV, Pedersen P, Vallejo A, Sutton MA (2012) An intercomparison of models used to simulate the short-range atmospheric dispersion of agricultural ammonia emissions. Environ Model Softw 37:90–102CrossRefGoogle Scholar
  22. Theobald MR, Simpson D, Vieno M (2016) Improving the spatial resolution of air-quality modelling at a European scale - development and evaluation of the air quality re-gridder model (AQR v1.1). Geosci Model Dev 9:4475–4489CrossRefGoogle Scholar
  23. Thunis P, Degraeuwe B, Pisoni E, Meleux F (2017) Analyzing the efficiency of short-term air quality plans in European cities, using the CHIMERE air quality model. Air Qual Atmos Health 10:235–248CrossRefGoogle Scholar
  24. Valari M, Menut L (2010) Transferring the heterogeneity of surface emissions to variability in pollutant concentrations over urban areas through a chemistry transport model. Atmos Environ 44:3229–3238CrossRefGoogle Scholar
  25. van der Swaluw E, de Vries W, Sauter F, Aben J, Velders G, van Pul A (2017) High-resolution modelling of air pollution and deposition over the Netherlands with plume, grid and hybrid modelling. Atmos Environ 155:140–153CrossRefGoogle Scholar
  26. van Jaarsveld JA (2004) The operational priority substances model. Description and validation of OPS-Pro 4.1. RIVM-report 500045001. RIVM, BilthovenGoogle Scholar
  27. van Pul WAJ, van Jaarsveld JA, Vellinga OS, van den Broek M, Smits MCJ (2008) The VELD experiment: an evaluation of the ammonia emissions and concentrations in an agricultural area. Atmos Environ 42:8086–8095CrossRefGoogle Scholar
  28. Vivanco MG, Bessagnet B, Cuvelier C, Theobald MR, Tsyro S, Pirovano G, Aulinger A, Bieser J, Calori G, Ciarelli G, Manders A, Mircea M, Aksoyoglu S, Briganti G, Cappelletti A, Colette A, Couvidat F, D'Insidoro M, Kranenburg R, Meleux F, Menut L, Pay MT, Rouïl L, Silibello C, Thunis P, Ung A (2017) Joint analysis of deposition fluxes and atmospheric concentrations of inorganic nitrogen and sulphur compounds predicted by six chemistry transport models in the frame of the EURODELTAIII project. Atmos Environ 151:152–175CrossRefGoogle Scholar
  29. Wichink Kruit RJ, Aben J, de Vries W, Sauter F, van der Swaluw E, van Zanten MC, van Pul WAJ (2017) Modelling trends in ammonia in the Netherlands over the 1990-2014. Atmos Environ 154:20–30CrossRefGoogle Scholar

Copyright information

© Springer Nature B.V. 2019

Authors and Affiliations

  1. 1.UMR ECOSYS, INRA, AgroParisTechUniversité Paris-SaclayThiverval-GrignonFrance
  2. 2.LISA/IPSL, Laboratoire Interuniversitaire des Systèmes Atmosphériques, UMR CNRS 7583Université Paris Est Créteil (UPEC) et Université Paris Diderot (UPD)CréteilFrance
  3. 3.RIVMBilthovenThe Netherlands

Personalised recommendations