Articles | Volume 14, issue 17
Atmos. Chem. Phys., 14, 9155–9169, 2014
Atmos. Chem. Phys., 14, 9155–9169, 2014

Research article 08 Sep 2014

Research article | 08 Sep 2014

Comparison of the predictions of two road dust emission models with the measurements of a mobile van

M. Kauhaniemi1, A. Stojiljkovic2, L. Pirjola3, A. Karppinen1, J. Härkönen1, K. Kupiainen2,4, L. Kangas1, M. A. Aarnio1, G. Omstedt5, B. R. Denby6, and J. Kukkonen1 M. Kauhaniemi et al.
  • 1Finnish Meteorological Institute, P.O. Box 503, 00101 Helsinki, Finland
  • 2Nordic Envicon Oy, Huopalahdentie 24, 00350 Helsinki, Finland
  • 3Metropolia University of Applied Sciences, Department of Technology, P.O. Box 4021, 00180 Helsinki, Finland
  • 4Finnish Environment Institute, P.O. Box 140, 00251 Helsinki, Finland
  • 5Swedish Meteorological and Hydrological Institute, 60176 Norrköping, Sweden
  • 6The Norwegian Institute for Air Research, P.O. Box 100, 2027 Kjeller, Norway

Abstract. The predictions of two road dust suspension emission models were compared with the on-site mobile measurements of suspension emission factors. Such a quantitative comparison has not previously been reported in the reviewed literature. The models used were the Nordic collaboration model NORTRIP (NOn-exhaust Road TRaffic Induced Particle emissions) and the Swedish–Finnish FORE model (Forecasting Of Road dust Emissions). These models describe particulate matter generated by the wear of road surface due to traction control methods and processes that control the suspension of road dust particles into the air. An experimental measurement campaign was conducted using a mobile laboratory called SNIFFER, along two selected road segments in central Helsinki in 2007 and 2008. The suspended PM10 concentration was measured behind the left rear tyre and the street background PM10 concentration in front of the van. Both models reproduced the measured seasonal variation of suspension emission factors fairly well during both years at both measurement sites. However, both models substantially under-predicted the measured emission values. The article illustrates the challenges in conducting road suspension measurements in densely trafficked urban conditions, and the numerous requirements for input data that are needed for accurately applying road suspension emission models.

Final-revised paper