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Geoscientific Model Development An interactive open-access journal of the European Geosciences Union
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Volume 4, issue 1
Geosci. Model Dev., 4, 47–68, 2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.
Geosci. Model Dev., 4, 47–68, 2011
© Author(s) 2011. This work is distributed under
the Creative Commons Attribution 3.0 License.

Development and technical paper 25 Jan 2011

Development and technical paper | 25 Jan 2011

SMOKE for Europe – adaptation, modification and evaluation of a comprehensive emission model for Europe

J. Bieser1,2, A. Aulinger1, V. Matthias1, M. Quante1,2, and P. Builtjes3,4 J. Bieser et al.
  • 1Helmholtz-Zentrum Geesthacht, Institute of Coastal Research, 21502 Geesthacht, Germany
  • 2Leuphana University Lüneburg, Institute of Ecology and Environmental Chemistry, 21335 Lüneburg, Germany
  • 3Institut für Meteorologie, Freie Universität Berlin, 12165 Berlin, Germany
  • 4TNO Built Environment and Geosciences, Air Quality and Climate Team, 3508 TA Utrecht, The Netherlands

Abstract. The US EPA regional emission model SMOKE was adopted and modified to create temporally and spatially distributed emission for Europe and surrounding countries based on official reports and public domain data only. The aim is to develop a flexible model capable of creating consistent high resolution emission data for long-term runs of Chemical Transport Models (CTMs). This modified version of SMOKE, called SMOKE for EUROPE (SMOKE-EU) was successfully used to create hourly gridded emissions for the timespan 1970–2010.

In this paper the SMOKE-EU model and the underlying European datasets are introduced. Emission data created by SMOKE-EU for the year 2000 are evaluated by comparison to data of three different state-of-the-art emission models. SMOKE-EU produced a range of values comparable to the other three datasets. Further, concentrations of criteria pollutants calculated by the CTM CMAQ using the four different emission datasets were compared against EMEP measurements with hourly and daily resolution. Using SMOKE-EU gave the most reliable modelling of O3, NO2 and SO42−. The amount of simulated concentrations within a factor of 2 (F2) of the observations for these species are: O3 (F2 = 0.79, N = 329 197), NO2 (F2 = 0.55, N = 11 465) and SO42− (F2 = 0.62, N = 17 536). The lowest values were found for NH4+ (F2 = 0.34, N = 7400) and NO3 (F2 = 0.25, N = 6184). NH4+ concentrations were generally overestimated, leading to a fractional bias (FB) averaged over 22 measurement stations of (FB = 0.83 ± 0.41) while better agreements with observations were found for SO42− (FB = 0.06 ± 0.38, 51 stations) and NO3 (FB = 0.13 ± 0.75, 18 stations).

CMAQ simulations using the three other emission datasets were similar to those modelled using SMOKE-EU emissions. Highest differences where found for NH4+ while O3 concentrations were almost identical.

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