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Institute for Transport StudiesFACULTY 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
Acknowledgements
James Tate
Karen Lucas
Tony May
Mark Nieuwenhuijsen
WCTRS Ph.D. Innovation Grant
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
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
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?
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
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
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
Study area
Test routes
Logging real-world driving
+
Emission estimation
Em
issio
n s
tan
da
rd (
Pre
-Eu
ro to
Eu
ro 5
)
Roa
d g
rad
ien
t –
se
pa
rate
run
s
Second-by-second emission estimates for each simulated vehicle
Gradient = 0 Gradient = X
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
Results
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
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
Gradient vs non-gradient
scenarios
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)
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)
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
Gradient vs non-gradient
scenario
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?
Questions?
