Post on 10-Jan-2017
AIR POLLUTION AND HEALTH EFFECTS: THE CONTRIBUTION
OF TRAFFIC?The uncertainties in the full chain of traffic activity-fleet
composition-vehicle emissions-air pollution dispersion-
individual exposure- health effects
Session: Jenga - The suspense builds as the stakes get higher
Haneen Khreis, 2nd International Conference on
Transport and Health, San Jose, 13-15 June, 2016
Estimating the Human Exposure to Traffic-Related Air Pollution
■ Monitoring stations and personal measurements
(relatively reliable)
■ Monitors: locations based on regulatory, not
scientific, purposes and are limited
■ Personal measurements: small
timeframes/populations
■ Logistic and costs concerns
TRAP is highly heterogeneous■ Experiment: walk across
the designated route to
the left (12 trips: 6 AM
peak, 6 PM peaks) - stop
at each circle
(microenvironment) for 20
seconds, ultrafine
particles measurement,
device: handheld
Condensation Particle
Counter 3007
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Estimating the Human Exposure to Traffic-Related Air Pollution
■ Surrogates for Human Exposure
– Proximity to ‘major roads’.
– Vehicles counts (& composition).
– Land Use Regression models.
– Geostatistical interpolation (GIS).
– Hybrid models (mixture, activity diaries, home monitors, indices)
(2).
– And dispersion models…….
Dispersion modelling
■ Estimate levels of TRAP exposures used in health
effects analysis most commonly come from routine air
quality monitoring stations and land-use regression
models
■ These are assumed to be traffic-related pollutants
■ There are only a few studies using air pollution
dispersion modelling in the literature, perhaps due to
relatively severe data demands and the laborious
process associated with the modelling
■ When using dispersion modelling, evaluating the
health effects of TRAP exposures becomes a part of a
broad effort which starts with the assessment of
source (vehicle) activity and associated emissions
Air pollution: the contribution of traffic?
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
Transport
policy
Emissions
Ambient air
quality
Exposure/
dose
Human
healthHEI, 2003
Step 1
■ Step 1: Determine traffic
activity using a traffic
assignment model to
provide, for each road in the
network, data on traffic flow
and speed and vehicle
kilometres driven (source
activity rate)
Transport
policy
Emissions
Ambient air
quality
Exposure/
dose
Human
healthHEI, 2003
Step 1
Step 11. A road network (supply); The road network specifies
the physical structure of the roads upon which trips
occur
2. A trip matrix (demand); The trip matrix specifies the
number of trips from zone i to zone j for all zones
modelled in the network
■ Trips are then allocated to routes based on the
total flows along links in the network and
corresponding network costs (e.g. times or average
generalized cost) calculated
■ The simulation model then takes the route allocated
traffic and creates another more accurate cost-flow
relationships by working out the delays that will occur
at the junctions due to these assigned flows
■ The assignment and simulation loops run iteratively,
until an equilibrium point is reached at which the
costs (e.g., times) are optimised
Step 1
■ Outputs
– Traffic average speed (AM peak hour, Inter-peak hour, PM peak hour)
– Traffic flow (passenger car units/hour)
■ Acceptance criteria (in comparison to real life):
■ Traffic flow: G𝐸𝐻 =2(𝑀−𝐶)2
𝑀+𝐶
– GEH of less than ‘5.0’ is considered a good match between the modelled and
observed hourly volumes, and 85% of the volumes in a traffic model should
achieve this criteria (240 links)
■ Traffic speed (52 journeys)
– Average difference between modelled and observed journey times within 15%
for all time periods
Modelled journey times are consistently faster than observed, in all time periods,
suggesting that congestion is under-represented in the models
Time
period
Test
1 2 3 4 5
AM 40% 77% 40% 88% 73%
IP 46% 89% 46% 95% 78%
PM 42% 73% 42% 82% 63%
Conclusions
■ SATURN is a static equilibrium model, intended to provide long-run estimates of vehicle flow, rather than a snapshot of what happened on a particular day
■ Does not consider the effect of acceleration and deceleration at junctions, and has a uniform emission through a junction based on average speed
■ Validation is difficult because the cost of obtaining network wide, long-run observed counts is prohibitive
■ And because there is usually significant uncertainty in the observed count data
■ Validation is conducted at the project scale
■ Sensitivity testing on the model show that the model is remarkably stable at the aggregate level, but less so at the link level
Step 2
■ Step 2: Determine vehicle
proportions on the road in
preparation for emission
factoring
Transport
policy
Emissions
Ambient air
quality
Exposure/
dose
Human
healthHEI, 2003
Step 2
Step 2
■ Typical sources of this information
– Automatic number plate recognition (limited in place and time, date of registration usually used to determine emission standards)
– Local data collected by the council (counters, limited in place and time, no emission standard available)
– National data collected at counter points and complied with local data (same limitations as local data)
– Solicited projects (limited in space and time, can be biased due to measurement technique)
– National atmospheric emission inventories national average (National data and projections)
Step 3
■ Step 3: Using/developing an
emission model that applies
emission factors for the
vehicle activity rate, to
calculate pollutant loads
emitted by vehicles
Transport
policy
Emissions
Ambient air
quality
Exposure/
dose
Human
healthHEI, 2003
Step 3
Step 3
Current/standard approach
Average-speed emission functions
sourced from the “Computer
Programme to Calculate Emissions
from Road Transport”, referred to
as COPERT 4
COPERT 4 (v10.0) is a model
developed and coordinated by the
European Commission's Joint
Research Centre and the European
Environment Agency
COPERT used by most European
Countries in the compilation of
national emission inventories
Passenger cars (diesel and petrol, 5 emission standards)
Light good vehicles (diesel and petrol, 5 emission standards, 3 weight classes)
Heavy good vehicles (diesel, 5 emission standards, 13 weight classes)
Buses (diesel, 6 emission standards/ technology, 3 weight classes)
Coaches (diesel, 6 emission standards/ technology, 2weight classes)
Step 3
Driving cycle NEDC (worked)WLTP class 3a
(proposed)Bradford real-world driving cycle
Duration (s) 1,220 (20.3 minutes) 1,800 (30 minutes) 117,471 (1958 minutes ≈33 hours)
Distance (km) 11 23 657
% idling b time 26 13 31
% acceleration time 20 44 27
% deceleration time 15 42 25
% cruising c time 39 1 17
Average speeds with stops (km/h) 32.5 46.3 20.1
Average speeds without stops (km/h) 44.7 53.2 29.9
Maximum speed (km/h) 120 131 100
Maximum acceleration (m/s) 1.06 1.88 4.72
Maximum deceleration (m/s) -1.39 -1.52 -6.67
% time spent at 0-40 km/h 65 49 81
% time spent at >40-60 km/h 16 19 18
% time spent at > 60 km/h 19 32 1
0
0.2
0.4
0.6
0.8
1
1.2
1.4
1.6
1.8
2
0 10 20 30 40 50 60 70 80
COPERT Diesel Passenger Car EURO 4 New Diesel Passenger Car EURO 4
Emission standard
Diesel passenger cars
PHEM0 PHEMG
Average (g/km) Range (g/km) Average (% change from PHEM0) Range (% change from PHEM0)
Euro 0 488.5 165.4 - 2131.5 507.3 (3.84) 81.02 (-51.02) - 2159.4 (1.31)
Euro 1 448.4 153.0 - 1932.7 464.6 (3.61) 74.89 (-51.05) - 1952.1 (1.00)
Euro 2 417.8 138.0 - 1803.9 430.1 (2.94) 70.74 (-48.74) - 1825.5 (1.20)
Euro 3 488.3 157.3 - 2087.5 501.5 (2.70) 92.9 (-40.94) - 2110.9 (1.12)
Euro 4 499.5 155.3 - 2134.6 510.9 (2.28) 108.5 (-30.14) - 2132.2 (-0.11)
Euro 5 381.2 145.9 - 1244.5 390.2 (2.35) 88.95 (-39.03) - 1258.9 (1.15)
Euro 6 362.6 142.4 - 1062.5 371.8 (2.53) 90.31 (-36.58) - 1064.0 (0.14)
Emission standard
Petrol passenger cars
PHEM0 PHEMG
Average (g/km) Range (g/km) Average (% change from PHEM0) Range (% change from PHEM0)
Euro 0 827.1 202.0 - 3470.4 842.2 (1.83) 176.4 (-12.67) - 3513.6 (1.24)
Euro 1 613.8 168.3 - 2852.2 624.6 (1.76) 112.4 (-33.21) - 2885.7 (1.17)
Euro 2 583.7 161.0 - 2725.4 592.3 (1.47) 110.3 (-31.49) - 2760.6 (1.29)
Euro 3 579.3 161.8 - 2693.8 589.0 (1.67) 107.6 (-33.50) - 2726.5 (1.21)
Euro 4 632.1 157.4 - 3032.0 640.8 (1.38) 122.4 (-22.24) - 3058.0 (0.86)
Euro 5 381.6 141.1 - 1286.8 390.2 (2.25) 104.6 (-25.87) - 1285.4 (-0.11)
Euro 6 330.7 129.5 - 959.9 339.5 (2.66) 95.5 (-26.25) - 979.6 (2.05)
STEP 3
Conclusions ■ Current emission factors unreliable, especially at lower speeds
■ There is a lack of clarity about the underlying data?
■ Emission factors are based on limited observations, which differ from the activities to which
they are applied
■ Using a small vehicle sample to represent a vehicle fleet of millions
■ Driving cycle not representative
■ Not considering all actual environmental conditions in emission estimation
■ Validation of urban emission models through comparison of observed–predicted values has
never been done, as it is not feasible to monitor emissions from all vehicles travelling on a
road
■ Neither is it possible to accurately quantify uncertainties in emissions due to the complex and
varied nature of the data and calculations associated with emission estimation
Step 4
■ Step 4: Using a dispersion
model to simulate emissions
dispersion across study area
Transport
policy
Emissions
Ambient air
quality
Exposure/
dose
Human
healthHEI, 2003
Step 4
Step 4
Modelled and measured hourly
concentrations of NOx between
12th and 16th November 1996
at Birmingham West
Conclusions■ Inaccuracies in results of dispersion modelling arise due to:
– Inherent variability caused by random turbulence
– Accuracy and coverage of the meteorological and emission input data
– The effect of complex surface features such as buildings
– Not accounting for street canyon effects
– Accuracy of background emission inventories
– Unrealistic diurnal profiles
– Spatial misalignment
– and inherent model uncertainty (i.e., is the model formulation correct and appropriate to the required output?)
■ The way in which model errors are dealt with depends on the error type
■ Sometimes not dealt with/ validated
■ Systematic errors (bias), where the same error trend is apparent at all times, can be addressed by calibrating the model using a fudge factor
■ Random error (uncertainty) may still remain after model calibration and comparison of model add an error band around the modelled mean in which the true value is thought to lie
Step 5
■ Step 5: Assign/temporally
resolve modelling outputs to
addressed of interest
Transport
policy
Emissions
Ambient air
quality
Exposure/
dose
Human
healthHEI, 2003
Step 5
Step 5
– Home location Standard
– Work/study location Sometimes
– Commute Rarely
– Elsewhere Rarely
■ E.g. (systematic review of 38 studies ) TRAP exposures almost exclusively assigned based on the participants’ residential addresses (35 studies – 3 looked at school and daycare centres)
■ Only 8 recent studies, 5 of which published after 2014, considered children’s mobility at ages when exposure at the residential address is less relevant and assigned time-weighted TRAP exposures at daycare-centres and schools alongside residence
Conclusions
■ Exposures at the home address not necessarily true exposure
■ Exposure variation may be masked
■ Limited knowledge on effects of TRAP peaks/short term exposures
■ Very difficult to disentangle effects of different exposure windows well correlated
Step 6
■ Step 6: Assess associations
between exposures and
health outcomes
Transport
policy
Emissions
Ambient air
quality
Exposure/
dose
Human
healthHEI, 2003Step 6
Final conclusions
■ Sources of error
– Traffic activity
– Vehicle proportions
– Vehicle emission standards
– Emission factors (non-linear functions of traffic speed)
– Spatial alignment of sources and addresses
– Space-time resolution
– Application and compatibility of models
– Dispersion modelling configuration
– Modelled vs true exposures
– Reliability of health outcomes diagnosis
Recommendations
■ More effort into exhaustive exercises of the chain’s validation – at each step and
overall
■ Communicating sources of error and implications to people using modelling outputs
– epidemiologists and policy makers
■ There is a need of improving the accuracy of input at each step of the chain –
explore data sets available and other options
■ Are these results good enough to be used in policy?