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Volume 14, issue 2
Atmos. Chem. Phys., 14, 939-955, 2014
https://doi.org/10.5194/acp-14-939-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.
Atmos. Chem. Phys., 14, 939-955, 2014
https://doi.org/10.5194/acp-14-939-2014
© Author(s) 2014. This work is distributed under
the Creative Commons Attribution 3.0 License.

Research article 27 Jan 2014

Research article | 27 Jan 2014

Sensitivity of air pollution simulations with LOTOS-EUROS to the temporal distribution of anthropogenic emissions

A. Mues1,*, J. Kuenen2, C. Hendriks2, A. Manders2, A. Segers2, Y. Scholz3, C. Hueglin4, P. Builtjes1,2, and M. Schaap2 A. Mues et al.
  • 1Freie Universität Berlin, Carl-Heinrich-Becker-Weg 6–10, 12165 Berlin, Germany
  • 2TNO, Dept. of Climate, Air and Sustainability, P.O. Box 80015, 3508 TA Utrecht, the Netherlands
  • 3Deutsches Zentrum für Luft- und Raumfahrt (DLR), Institut für Technische Thermodynamik Systemanalyse und Technikbewertung Pfaffenwaldring 38–40, 70569 Stuttgart, Germany
  • 4EMPA, Swiss Federal Laboratories for Materials Science and Technology, Überlandstraße 129, 8600 Dübendorf, Switzerland
  • *now at: IASS Potsdam, Institute for Advanced Sustainability Studies e.V., Berliner Strasse 130, 14467 Potsdam, Germany

Abstract. In this study the sensitivity of the model performance of the chemistry transport model (CTM) LOTOS-EUROS to the description of the temporal variability of emissions was investigated. Currently the temporal release of anthropogenic emissions is described by European average diurnal, weekly and seasonal time profiles per sector. These default time profiles largely neglect the variation of emission strength with activity patterns, region, species, emission process and meteorology. The three sources dealt with in this study are combustion in energy and transformation industries (SNAP1), nonindustrial combustion (SNAP2) and road transport (SNAP7). First of all, the impact of neglecting the temporal emission profiles for these SNAP categories on simulated concentrations was explored. In a second step, we constructed more detailed emission time profiles for the three categories and quantified their impact on the model performance both separately as well as combined. The performance in comparison to observations for Germany was quantified for the pollutants NO2, SO2 and PM10 and compared to a simulation using the default LOTOS-EUROS emission time profiles. The LOTOS-EUROS simulations were performed for the year 2006 with a temporal resolution of 1 h and a horizontal resolution of approximately 25 × 25km2.

In general the largest impact on the model performance was found when neglecting the default time profiles for the three categories. The daily average correlation coefficient for instance decreased by 0.04 (NO2), 0.11 (SO2) and 0.01 (PM10) at German urban background stations compared to the default simulation. A systematic increase in the correlation coefficient is found when using the new time profiles. The size of the increase depends on the source category, component and station. Using national profiles for road transport showed important improvements in the explained variability over the weekdays as well as the diurnal cycle for NO2. The largest impact of the SNAP1 and 2 profiles were found for SO2. When using all new time profiles simultaneously in one simulation, the daily average correlation coefficient increased by 0.05 (NO2), 0.07 (SO2) and 0.03 (PM10) at urban background stations in Germany. This exercise showed that to improve the performance of a CTM, a better representation of the distribution of anthropogenic emission in time is recommendable. This can be done by developing a dynamical emission model that takes into account regional specific factors and meteorology.

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