Presented at WCTR 2016 by PhD candidate Haneen Khries www.wctrs-conference.com http://www.its.leeds.ac.uk/people/h.khreis

1. Institute for Transport Studies
FACULTY OF ENVIRONMENT
Critical Issues in Estimating Human
Exposure to Traffic-related Air Pollution:
Advancing the Assessment of Road
Vehicle Emissions Estimates
Haneen Khreis
@WCTR 2016 Shanghai, 10-15 July 2016

2. Acknowledgements
James Tate
Karen Lucas
Tony May
Mark Nieuwenhuijsen
WCTRS Ph.D. Innovation Grant

3. o Road traffic is a dominant
source of urban air pollution
o Historic & current trends
suggest ongoing increases in
vehicle fleet and number of
people exposed to traffic-
related air pollution (TRAP)
o Traffic-related emissions and
air pollution difficult to quantify
and control
Background

4. Increased awareness of the adverse
health effects associated with TRAP
o All-cause and cardiovascular mortality,
cardiovascular morbidity, respiratory
morbidity, lung function, COPD, birth
outcomes, cancer, cognitive and
psychomotor development, congenital
anomalies, fertility rates, obesity, diabetes
Background

5. Some open questions
o Which (multi-)pollutants?
o Which vehicle fleet (fuels,
emission standards, vehicle
classes)?
o Where to intervene?
o What is the accuracy and
precision of the health effect
estimates?
o Are vehicle emission standards
adequate?
o Are air quality guidelines
adequate?

6. Open questions
Compliance,
effectiveness
Atmospheric transport,
chemical transformation,
and deposition
Human time-activity in relation
to indoor and outdoor air quality;
Uptake, deposition, clearance, retention
Susceptibility factors;
mechanisms of damage
and repair, health outcomes
Regulatory
action
Traffic
emissions
Traffic air
pollution
Exposure/
dose
Human
healthHEI, 2003
At the mercy of the
emission inputs/ factors

7. Current emission estimation
methodology
o Vehicle emissions
(g/km), for a certain
pollutant and a
vehicle type, are
functions of average
speed over a trip
E= f ( ҧ𝑣)
o In Europe, functions
are sourced from
COPERT 
functions unreliable,
especially at lower
average speeds
A typical range of the variability of individual measurements for emission factors for
gasoline passenger cars of Euro 3 technology

8. New emission estimation
methodology
o Develop a new set of average-speed emission functions  transparent
and replicable
o Better account for real-world driving conditions e.g. urban stop-start
driving/ congestions (where emissions are underestimated)
o Explore real-world driving conditions effects on emissions  road
gradient
o Compare the new functions with the current/ standard emission
estimation methodology and highlight differences
o Apply both methodologies to the traffic air pollution  exposure/dose 
human health effects (childhood asthma) chain
Regulatory action
Traffic emissions
Exposure/ dose
Human health
Traffic air pollution

9. Study area

10. Test routes

11. Logging real-world driving
+

12. Emission estimation
Emissionstandard(Pre-EurotoEuro5)
Roadgradient–separateruns
Second-by-second emission estimates for each simulated vehicle
Gradient = 0 Gradient = X

13. Average-speed emission
functions development
Average
emissions
g/km
Average
emissions
g/km
Average
emissions
g/km
Average
emissions
g/km
Average
emissions
g/km
Average
emissions
g/km
Average
emissions
g/km
Average
emissions
g/km
Average
emissions
g/km
Average
emissions
g/km
Average
emissions
g/km
Average
emissions
g/km
Average
emissions
g/km
Average
emissions
g/km
Average
emissions
g/km

14. Results

15. Results
Statistic Micro-trip distance (km) Micro-trip time (s)
Micro-trip average
speed (km/h)
Minimum 0.001389 2.00 0.2857
1st quartile 0.034583 29.00 4.3604
Median 0.157222 59.00 11.7600
Mean 0.467699 77.95 15.0534
3rd quartile 0.590556 98.00 24.6097
Maximum 12.694220 919.00 78.9278
33 hours of driving  1406 micro-trips

16. Results
o For each vehicle class, fuel type
and Euro emission standard,
(micro-trip) average-speed
emission function was developed
o R2 ranging from 0.54 to 0.98
o Low relationships are a product
of the inability of the statistical
models to address large scatter
o Including road grade in model
estimates highly increases the
scatter and decreases R2

17. Gradient vs non-gradient
scenarios

18. Comparison with COPERT
Passenger car – diesel pre-Euro Passenger car – diesel Euro 1 Passenger car – diesel Euro 2
Passenger car – diesel Euro 3 Passenger car – diesel Euro 4 Passenger car – diesel Euro 5
Diesel passenger cars (-1325%, 33%; DE5)

19. Comparison with COPERT
Passenger car – petrol pre-Euro Passenger car – petrol Euro 1 Passenger car – petrol Euro 2
Passenger car – petrol Euro 3 Passenger car – petrol Euro 4 Passenger car – petrol Euro 5
Petrol passenger cars (-338%, 32%; DE5)

20. Comparison with COPERT
SD Buses diesel pre-Euro SD Buses diesel Euro 1 SD Buses diesel Euro 2
SD Buses diesel Euro 3 SD Buses diesel Euro 4 SD Buses diesel Euro 5
Single Decker buses (-4287%, 39%; DE5 - SCR)
0
20
40
60
80
100
0 50 100
COPERT SD Buses EURO 0
New SD Buses EURO 0
0
20
40
60
80
100
0 50 100
COPERT SD Buses EURO 1
New SD Buses EURO 1
0
20
40
60
80
100
0 50 100
COPERT SD Buses EURO 2
New SD Buses EURO 2
0
20
40
60
80
100
0 50 100
COPERT SD Buses EURO 3
New SD Buses EURO 3
0
20
40
60
80
100
0 50 100
COPERT SD Buses EURO 4
New SD Buses EURO 4
0
20
40
60
80
100
0 50 100
COPERT Euro 5 SCR
New Euro 5 SCR

21. Gradient vs non-gradient
scenario

22. Summary
• New average-speed emission functions developed tailored for Bradford
driving and underpinned by:
• High resolution real-world driving cycles ++
• Modelled emissions (PHEM; verified but a model) -
• And a micro-trip averaging approach +?
• Results confirm different emission estimates at lower average speeds
• Implications to practice unclear  modelled journey times are
consistently faster than observed, suggesting that congestion is under-
represented in traffic models
• To minimize errors in the road traffic emission inventory, a series of
improvements regarding activity data should be implemented and
vehicle class specific driving cycles should be obtained
• Inaccurate emissions and TRAP estimates can bias exposure-response
functions  downward bias?

23. Questions?