1. Road Vehicle Exhaust Emissions
An age of uncertainty
Dr Glyn Rhys-Tyler
Dispersion Modellers User Group 2017
April 6th 2017
Holiday Inn, Kensington, London

2. Overview
We are living through a period of significant change, in terms of
knowledge, technologies, behaviour, attitudes, and legislation.
The Chair has expressed a particular interest in the relationship between
vehicle dynamics and exhaust emissions.
The first part of this presentation will address some issues of particular
relevance to the Strategic Road Network (SRN), and potential lessons
from recent Dutch experience.
Time permitting, the latter part will touch on some relevant (and
probably familiar) areas of ongoing uncertainty in emissions modelling.

3. Current UK guidance
• Interim Advice Note 185/15: Updated traffic, air quality and noise
advice on the assessment of link speeds and generation of vehicle data
into ‘speed-bands’ for users of DMRB Volume 11, Section 3, Part 1 ‘Air
Quality and Volume 11, Section 3. Part 7 Noise.
• Asserts that congestion on motorways tends to occur when speeds
drop below 50mph (80kph). Assumes >50mph equates to ‘free flow’.
• The advice note identifies that during periods of congestion on the
motorway, traffic emissions per vehicle increase relative to free flow
conditions.
• Note: IAN 185/15 also provides guidance for Urban / Rural (Non-
Motorway) Roads

4. Motorway speed band descriptors & emissions (IAN 185/15)
NOx emissions (g/km per vehicle) – IAN 185/15
NB. Speed and SD
metrics derived from
MIDAS data
Analysis in 2014
indicated that EFT
tended to underestimate
emissions in congested
conditions compared to
free flow (circa 36%),
hence the publication of
IAN 185/15.

5. Example EFT speed / emission relationship
On a motorway, with varying degrees of
congestion (speed & acceleration), what
really happens to exhaust emissions?

6. Vehicle dynamics and emission rates
Two key areas of uncertainty:
• Measuring and quantifying vehicle dynamics (speed & acceleration),
and:
• Quantifying vehicle exhaust emission rates in different phases of
vehicle dynamics.

7. The TNO (Dutch) approach (1)
• Netherlands Organisation for Applied Scientific Research (TNO)
www.tno.nl/en/
• TNO report R10188 “On-road determination of average Dutch driving
behaviour for vehicle emissions” (2016)
• Study to investigate and quantify the significance of driving behaviour
as a factor in the determination of exhaust emission rates.
• Implemented a test program in 2015 to determine driving behaviour
by randomly following / shadowing light vehicles across the
Netherlands highway network using an instrumented vehicle.
• Instantaneous speed, acceleration and position were recorded at 1Hz.

8. The TNO (Dutch) approach (2)
• In this way, ‘average’ driving behaviour for Dutch drivers on Dutch
roads (in terms of instantaneous speed and acceleration) was
determined across different road types, traffic situations, and levels of
congestion, as at 2015.
• Professional driver used to ‘shadow’ a sample of light vehicles
• 108 hours of total driving time, covering a distance of 6640 km,
comprising 180 trips (motorway, rural, urban).
• Output set of ‘driving vectors’ (‘q’), describing and quantifying the mix
of passenger car driver behaviour (vehicle speed and acceleration)
across different road types, levels of congestion, speed limits, and
modes of speed enforcement.

9. The TNO (Dutch) approach (3)
• These ‘vectors’ (‘q’) are then associated with average exhaust
emission rates, so that total exhaust emissions for a particular
passenger car type can be estimated, using TNO’s VERSIT+ emissions
model.
• ‘q’ values quantify the fraction of driving time at different velocities
and accelerations normalized to 1 km of total distance travelled.
• Driving dynamics are defined by the dynamic variable ‘w’, defined as:
w = a + 0.014v, where ‘a’ is in units of m/s2, and v is in units of kph.
• Emission factors (EF) in g/km are a function of the emission map ‘u’
and the driving vector ‘q’:
EF(g/km) = (q1*u1) + (q2*u2) + (q3*u3) + … + (q9*u9) + (q10*u10)

10. TNO ‘w’ values and ‘q’ vectors
Comment:
‘w’ values;
‘q’ values;
Terminology

11. UK ‘ad hoc’ data collection
Vehicle dynamics
data collected on an
‘ad hoc’ informal
basis, Oct 2016 to
Apr 2017.
Speed (kph) and
position at 10Hz
217 hours of data,
circa 10,000 miles,
circa 70 ‘trips’
Majority on the M1
& A1(M), but also
other routes.
Map base ©
Google Earth Pro

12. Sample of vehicle speed data – M1 J14 to J21 (northbound)
Acknowledgement: Transport Systems Catapult for graphic

13. Observed speed and S.D. (70mph speed limit)

14. Observed speed and S.D. (50mph average speed cameras)

15. Comparison of Dutch and UK ‘q’ values
• Dutch 120kph closest speed
limit to UK 70mph (113kph);
• M1 J14 to J16 a close match to
the Dutch average ‘q’ values;
• Observations from M1 J19 to
J21 a little faster than the
Dutch average ‘q’ values;

16. Where can we obtain corresponding ‘u’ (emission rate) values?
• Options are limited;
• For the purpose of presenting a worked example (only), reference is
made to the data collected by DfT in late 2015;
• DfT collected laboratory, on-track, and on-road emissions data from
a range of diesel passenger cars;
• Here we will use some laboratory measurements of NOx (g/sec) from
a sample of Euro 6 diesel cars operating over the WLTC drive cycle.
• N.B. DfT reported that on-road (PEMS) NOx emissions were around
3.8 times higher than the WLTC cycle laboratory values for this
sample of vehicles. Be careful….

17. WLTC drive cycle (Class 3)

18. WLTC drive cycle – DfT 2015 laboratory tests
NOx emissions (g/sec)
over the WLTC cycle for 9
Euro 6 diesel cars
1800 second (30 minute)
drive cycle; data at 1Hz
Note locations and
magnitude of transient
peaks (variability).

19. NOx emission rates derived from laboratory WLTC drive cycle
• Derived from a sample of nine Euro 6 diesel passenger cars;
• Within speed bands, mean values ‘generally’ increase monotonically with load;
• N.B. Lack of data within ‘q8’ vector using this (WLTC) drive cycle.

20. Example 1 – 70mph limit (congested)

21. Example 2 – 50mph limit average speed cameras (congested)

22. Example 3 – 13 consecutive suburban roundabouts

23. DfT 2015 PEMS ‘on-road’ test route
On-track and on-
road PEMS data;
Potentially a more
representative
source of emissions
data for ‘real world’
driving;
Possible initial
source for more
generally
applicable ‘u’
values……
Map base © Google Earth
Pro

24. Some other areas of uncertainty in exhaust emission modelling
• The ongoing primary NO2 (f-NO2) problem. Still ongoing….
• Observed variability in exhaust emissions performance across vehicle
manufacturers. A challenge and an opportunity….
• Exhaust emissions from vans (N1) up to 3.5 tonnes. Deserves more
attention….
• Age / mileage /maintenance related deterioration in performance of
emissions control technologies. Significant uncertainty….
• The legacy challenge. Perhaps the biggest challenge….

25. Passenger cars – Total oxides of nitrogen
Source: Carslaw & Rhys-Tyler
(2013)

26. Passenger cars – Nitrogen dioxide emissions
Source: Carslaw & Rhys-Tyler
(2013)

27. Variability in NOx emissions ratios by diesel car manufacturer

28. Variability in NOx emissions ratios by diesel van manufacturer

29. References
• Interim Advice Note 185/15: Updated traffic, air quality and noise advice on the
assessment of link speeds and generation of vehicle data into ‘speed-bands’ for
users of DMRB Volume 11, Section 3, Part 1 ‘Air Quality and Volume 11, Section
3. Part 7 Noise.
• Carslaw D. and Rhys-Tyler G. (2013). Remote sensing of NO2 exhaust emissions
from road vehicles. Department for Environment Food and Rural Affairs (DEFRA).
• DfT (2016). Vehicle Emissions Testing Programme (Cm 9259). April 2016.
• TNO report R10188 “On-road determination of average Dutch driving behaviour
for vehicle emissions” (2016)

30. Contact
Dr Glyn Rhys-Tyler
Email: admin@glynrhys-tyler.com
Web: www.glynrhys-tyler.com