Introducing Shairstreet

by | Feb 6, 2020 | Science & Engineering

Traffic pollution is known to have a greater impact in urban areas where vehicle congestion and the resulting exhaust emissions from motor vehicles are high. In these areas where street canyons exist (buildings flanking the sides of a street), air pollution can be effectively trapped leading to high-risk exposure for these densely populated and trafficked areas.

What is Shairstreet?

The Shair modeling system showing where in the architecture Shairstreet is located.

Shairstreet calculates the concentrations of traffic related pollutants, such as PM₂.₅ and NO₂, from our realtime traffic emissions model, taking into account traffic congestion, vehicle speed, meteorology and building effects.

Street canyons and air pollution

Illustration of vehicle emissions in a street canyon and the recirculation vortex that can be created when the wind above the buildings is flowing perpendicular to the street.

The speed of the vehicles traveling in a street canyon also affects the level of pollution. Cars and trucks that are moving very fast generate large turbulence near the ground level which helps disperse the pollutants vertically making the air cleaner than if it was a traffic jam. Shairstreet uses Shair’s realtime traffic flow and fleet distribution (cars vs. trucks) estimates to model the effect of dynamic traffic for street-level pollution.

Accounting for all the buildings

Below is an example of our work in the City of Richmond, CA where lidar data was spatially merged with building footprint data to obtain a 3D building database for the city. Wind speed and direction are calculated at the top of every street canyon road segment. Lidar data is not prerequisite for Shairstreet modeling, as other methods can be used to obtain building heights for urban areas.

An example of building geometry input that is used in Shairstreet from Richmond, CA.

Dispersion away from roadways

The prevalent method for modeling road line sources breaks the street link into infinitesimally small chunks, so that each one can be modeled effectively as a point source. This is because there is an exact analytical (mathematical) solution for Gaussian dispersion from a point source. Such a method, however would not allow Shairstreet to model millions of road links frequently, since it is computationally very slow.

Instead of breaking a road segment into infinitesimally small point sources, Shairstreet can use only the start and end points of each road link in order to approximate Gaussian dispersion away from the roadway line source. This is possible since the road links Shairstreet models are relatively short in length, typically less than 50 meters. The advantage is that the calculations are very fast, as each road link is computationally effectively only a single point source instead of hundreds or thousands.

Shairstreet output for PM₂.₅ in Richmond, CA during morning rush hour commute.

The figure here shows an example of Shairstreet raw model output for PM₂.₅ in Richmond, CA at 10x10m resolution. You can see the major highways lighting up the map. Over 100,000 roadway links were modeled in this area.

How is traffic contributing to air pollution in my neighborhood?

Modeled PM₂.₅ in Richmond, CA using the Shair modeling system’s GUI. Barrett Avenue is a highly trafficked street in Richmond.

Shairstreet can also answer questions of how limiting traffic in certain streets can affect air quality, allowing cities to make strategic planning decisions with air quality in mind.

Find out more!