Abstract. We have numerically evaluated how effective selected potential
measures would be for reducing the impact of road dust on ambient air
particulate matter (PM10). The selected measures included a reduction of
the use of studded tyres on light-duty vehicles and a reduction of the use of
salt or sand for traction control. We have evaluated these measures for a
street canyon located in central Helsinki for four years (2007–2009 and
2014). Air quality measurements were conducted in the street canyon for two
years, 2009 and 2014. Two road dust emission models, NORTRIP (NOn-exhaust Road TRaffic Induced Particle emissions) and FORE (Forecasting Of Road dust Emissions), were
applied in combination with the Operational Street Pollution Model (OSPM), a street canyon dispersion model, to
compute the street increments of PM10 (i.e. the fraction of PM10
concentration originating from traffic emissions at the street level) within
the street canyon. The predicted concentrations were compared with the air
quality measurements. Both road dust emission models reproduced the
seasonal variability of the PM10 concentrations fairly well but under-predicted
the annual mean values. It was found that the largest reductions of
concentrations could potentially be achieved by reducing the fraction of
vehicles that use studded tyres. For instance, a 30 % decrease in the
number of vehicles using studded tyres would result in an average decrease
in the non-exhaust street increment of PM10 from 10 % to 22 %,
depending on the model used and the year considered. Modelled contributions
of traction sand and salt to the annual mean non-exhaust street increment of
PM10 ranged from 4 % to 20 % for the traction sand and from 0.1 % to 4 % for the traction salt. The results presented here can be used
to support the development of optimal strategies for reducing high
springtime particulate matter concentrations originating from road dust.
Modelling road dust emission abatement measures using the NORTRIP model: Vehicle speed and studded tyre reduction
