  The recent uptake in popularity in vehicles with zero tailpipe emissions is a
welcome development in the fight against traffic induced airborne pollutants.
As vehicle fleets become electrified, and tailpipe emissions become less
prevalent, non-tailpipe emissions (from tires and brake disks) will become the
dominant source of traffic related emissions, and will in all likelihood become
a major concern for human health. This trend is likely to be exacerbated by the
heavier weight of electric vehicles, their increased power, and their increased
torque capabilities, when compared with traditional vehicles. While the problem
of emissions from tire wear is well-known, issues around the process of tire
abrasion, its impact on the environment, and modelling and mitigation measures,
remain relatively unexplored. Work on this topic has proceeded in several
discrete directions including: on-vehicle collection methods; vehicle tire-wear
abatement algorithms and controlling the ride characteristics of a vehicle, all
with a view to abating tire emissions. Additional approaches include access
control mechanisms to manage aggregate tire emissions in a geofenced area with
other notable work focussing on understanding the particle size distribution of
tire generated PM, the degree to which particles become airborne, and the
health impacts of tire emissions. While such efforts are already underway, the
problem of developing models to predict the aggregate picture of a network of
vehicles at the scale of a city, has yet to be considered. Our objective in
this paper is to present one such model, built using ideas from Markov chains.
Applications of our modelling approach are given toward the end of this note,
both to illustrate the utility of the proposed method, and to illustrate its
application as part of a method to collect tire dust particles.
