Chemistry Reference
In-Depth Information
Schaap et al. [
145
] proposed a simple methodology to account for traffic
re-suspension emissions in a large-scale modelling application by means of the
3D chemistry-transport model LOTOS-EUROS [
150
]:
C
clim
C
rs
X
veh
X
F
trs
ΒΌ
EF
veh
;
road
D
veh
;
road
;
road
distinguishing two key factors: car type and road type. The first resembles the
aerodynamic properties of the cars and differentiates between heavy duty (HDV)
and light duty (LDV) vehicles. The second factor accounts for the dust reservoir as
function of traffic characteristics and road surrounding. The method is based upon a
vehicle-driven kilometre map (
D
veh,road
) over Europe, typified for light duty (LDV)
and heavy duty (HDV) traffic and three road type classes (rural roads, urban roads
and highways), with corresponding emission factors (EF
veh,road
). The standard
emission factor was based on data for central Europe. However, the spatial variation
of emission strength is due to the variability in regional climate conditions (aridity)
and winter time practices concerning road sanding and studded tyre use. Account-
ing for these factors is important to translate the emission factors above to a
methodology which can also be used in southern Europe and during winter in
Scandinavia. Therefore, the methodology applies two factors,
C
clim
and
C
rs
,to
account for variability of traffic resuspension due to the variability of climate
conditions and road sanding activities scale.
Modelled contributions were validated against mineral dust observations across
Europe, revealing a significant improvement in spatial variation when compared to
the mineral dust modelling without emission without road dust resuspension. The
temporal variation needs improvement as the dependency of resuspension source
strength on day-to-day meteorology is crudely parameterized.
With the abovementioned technique Hendriks et al. [
30
] estimated that road dust
resuspension contribute around 10-15% of modelled PM
10
on a European scale and
up to 30% in densely populated area of South Europe (Fig.
5
). These estimates are
relative to the modelled concentrations, which are lower than observed ones due to
uncertainties in primary organic emissions and the lack of secondary organic
compounds. However, this overestimate is probably compensated by the fact that
in the model, peak contributions (in cities) are not captured due to the model
resolution (25
25 km).
3.2 Wear Emissions
Wear emissions comprise abraded particles from brake linings, tyres and road
pavement. Details on chemical and physical properties of wear particles can be
found in Thorpe and Harrison [
3
]. Kousoulidou et al. [
151
] showed clear evidence
that non-exhaust sources become increasingly important as no emission control
strategies are taken by Member states. Among them, road pavement wear is
