The MAGIC project aims to develop integrated models to simulate urban air quality and energy consumption at microscales. It uses computational fluid dynamics (CFD) software and data assimilation to model pollutant dispersion from traffic emissions. Field, wind tunnel and laboratory experiments are used to validate the models. Exposure analyses show highly variable pollutant concentrations at microscales. Momentary peak exposures of seconds to minutes may impact total exposure, but their health effects are unclear. Ultra-fast measurements of nitrogen oxides show concentration spikes corresponding to vehicles. CFD simulations of a crossroads reproduce observed hotspots at junctions from queuing traffic. Tracking vehicle movements through simulations could help identify scenarios leading to acute exposures that need mitigation.

1. Simulating the dispersion of traffic
emissions at the microscale
DMUG 24/02/2021
Huw Woodward
Imperial College London

2. The MAGIC project
• Funded by the EPSRC – “Global Grand Challenges”
• Goal:
– Develop an integrated suite of models that allow the city
design to become its own Heating, Ventilation and Cooling
(HVAC) system
• Methodology:
– Air quality modelling using CFD software Fluidity
– Data assimilation
– Reduced order modelling
– Using EnergyPlus to model energy consumption
– Validation through laboratory, wind tunnel and in field
measurements

3. Indoor
Managing Air in reen Inner Cities
Field study
Computational Fluid Dynamics (CFD)
Laboratory experiment

4. Field study
Laboratory experiment
Computational Fluid Dynamics (CFD)
Outdoor

5. Exposure analysis at the
microscale
• Health impact analyses depend on estimates of
exposure integrated over time - “acute
exposure” means > 1 hour.
• Exposure studies of pedestrians and cyclists
show highly variable concentration fields with
standard deviations of same order as mean.
• What is the impact of repeated momentary
peak exposures (seconds, minutes) at the
microscale on total exposure and health
impacts?
• Should we be concerned about hotspots at
junctions?
https://www.londonair.org.uk/london/asp/annualmaps.asp
Kingham et al 2013. Enironmental Pollution 181 211-218.

6. Cambustion ultra-fast ambient NO
concentration measurements
https://www.youtube.com/watch?v=ipcxc4kVoaM&feature=youtu.be

7. Cambustion ultra-fast ambient NO
concentration measurements
https://www.youtube.com/watch?v=ipcxc4kVoaM&feature=youtu.be

8. Simple time series analysis
Aligning spikes with vehicles –
central island
Cambustion
Car
Van
Bus/coach
Lorry
1pm, 20th
Sep

9. • Open-source CFD software developed at
Imperial College London
http://fluidityproject.github.io/
• Finite-element method
• Unstructured mesh
• Mesh adaptivity
• Large Eddy Simulation (LES) approach
• Synthetic eddy method used to apply
turbulent velocity profile at inlet

10. Fluidity traffic model
• Vehicles modelled as
second viscous fluid
displacing air as they move
through the domain.
• Instantaneous emissions
model used to calculate
exhaust emission source at
each time step.
• PTV Vissim traffic flow
model used to provide
traffic movement data as
input to the model.
Traffic modelling, Pavlidis,
2011

11. Crossroad test case
• Crossroads formed by the intersection of two canyons
• Traffic movement simulated using PTV Vissim
• NOx emissions modelled as a function of velocity and
acceleration. Model from 2006 representative of Euro 0 to 3
vehicles (Int Panis et al. 2006, Sci. Total Environ.)
• Low wind conditions and neutral boundary layer
A: 400
cars/hour
D: 200 buses/hour
C: 400 cars/hour
B: 200
cars/hour
Wind
direction
Inlet
velocity

12. Single car emissions
Emissions at crossroads
Real world NO2 emissions
1200ppb
0ppb
A
Wind
direction
B C
D (bus lane)
Single bus emissions

13. Bird’s eye view

14. Roadside concentrations at
junction
Wind
direction
D (bus lane)

15. Bird’s eye view of NOx concentrations at z=1m
Side view

16. View from the bus stop

17. Next… traffic tracking
• Tracking software developed at
Cambridge by Dr Anna
Schroeder.
• Extract traffic counts, vehicle
velocity and acceleration.

18. Summary
• Exposure assessments tend to be based on mean
concentrations averaged over hours/days/years – what
is the impact of the variation about the mean?
• Are there scenarios which lead to acute exposures
which we can avoid – e.g. cycling behind a bus, waiting
at a bus stop.
• We know that junctions are emission hotspots – this if
often not accounted for in modelling.
• Understanding the dispersion of peak emissions may
allow the effective application of passive pollution
control measures such as well-placed green
infrastructure.

19. Thank you
huw.woodward@imperial.ac.uk