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Motor vehicle contributions to urban air pollution: emission measurements to inform effective policy
Andy Grieshop Assistant Professor
Civil, Construction & Environmental Engineering North Carolina State University
U.S. – Iran Symposium on Air Pollution in Megacities Beckman Center, Irvine, CA
September 3, 2013 1
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Vehicle
Emissions
(multiple
pollutants)
A range of policy objectives and approaches
Technology &
performance
standards
Fuel type and
quality
Operation and
activity
Inspection and
Maintenance
Policy ‘levers’
Urban /
regional air
pollution
Near and on-
road human
exposure
Climate
impacts
Multi-scale / multi-
pollutant impacts
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An example: vehicle PM control policies may have health and climate co-benefits or co-impacts.
Source: Reynolds, Grieshop and Kandlikar, IGES, 2011 3
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Emission standards are a primary policy approach to address emissions
Source: Reynolds, Grieshop and Kandlikar, IGES, 2011
Heavy duty vehicle PM Emission standards
4
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But how does standard adoption translate into fleet-wide emissions?
Many sources of uncertainty in estimates
• Fleet makeup/turnover – Inter-vehicle variability
• Actual vehicle activity and conditions – Intra-vehicle variability, fuel quality, upkeep
• Measurement uncertainty
Which dominates?
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Vehicle emission testing to meet different objectives
• Technology development/assessment – Policy planning or evaluation
• Emission inventory development – Emission factors
– Emission models (Empirical functions of vehicle/fleet age, composition, activity and meteorology, etc.)
• Vehicle source profiles for source apportionment efforts/model input
Emission = Emission Factor x Activity x (1 - control efficiency)
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Various emission testing approaches
Image: Sensors, Inc. Image: Franco et al, Atm Env. 2013
Image: nasa.gov
Fresh
Air Supply
Exhaust
To Ambient
Sampling
Location
Squirrel Hill Tunnel
Schematic
Ventilation Tunnels
Traffic Tunnel
(West bound)
Portable Emission Measurement Systems (PEMS)
Chassis Dynamometer
Traffic Tunnel Studies
Roadside remote sensing
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There is no ‘perfect’ approach to quantify all aspects of fleet emissions.
Emissions measured in detail
Activity-emission linkage
Realistic vehicle activity
Inter-vehicle
variation
Resource per sample
size
Engine Dyno + + - - - Chassis Dyno + + o o/- - PEMS Study - + + o o
Roadside remote sensing
- - o + +
Tunnel Study + - o o/- +
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Case study: CNG Fueling in Delhi, India
Photo courtesy Josh Apte
ईको फै्रण्डली सेवा “Eco-friendly
Service”
Compressed natural gas (CNG) fuel
Collaboration with Conor Reynolds, Dan Boland, Brian Gouge, Steve Rogak and Milind Kandlikar (UBC) and Josh Apte (UC Berkeley)
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Delhi’s Switch to CNG
• Supreme Court directive: “clean fuel”
• Compressed natural gas (CNG) – mostly methane • All public transport:
90,000 vehicles • Taxis, Buses, Auto-Rickshaws
• Timeline: 2001-2003 • no marked drop in PM levels
CNG buses in Delhi (Photo: Conor Reynolds) 10
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Indian Auto-rickshaw Project (IARP)
• Auto-rickshaws fill key transportation niche in many developing countries
• No existing measurements of in-use emissions
• Goal: measurements that contribute to both policy questions and cutting-edge science Photo: C. Reynolds
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IARP research objectives
• How effective are fuel/technology switching/phase-out?
• Measured ‘criteria’ pollutants and climate-forcing agents with focus on PM emissions
• Activity based emission model • Organic PM ‘fingerprints’ and volatility
distributions for unmeasured source types
2-stroke 4-stroke
Gasoline
(Petrol) n/a
PET-4S
N = 11
CNG CNG-2S
N = 14
CNG-4S
N = 17
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Laboratory measurements
• Vehicle testing lab near Delhi
• 30 in-use auto-rickshaws (42 tests)
Approach:
– Chassis-dyno - operate vehicles on “Indian Drive Cycle” (IDC)
– Emissions instrumentation for real-time: CO2, CO, HC, CH4, NOx , PM
– Dilution tunnel and filters for PM sampling
Data:
• Fuel- and distance- based emission factors (g pollutant per kg fuel or km)
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Very high PM2.5 emissions from 2-stroke engines
Source: Reynolds, Grieshop and Kandlikar, ES&T, 2011 14
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2-strokes are a clear loser for climate also.
CH
4 (
g/k
g)
0
100
200
300
400
010
020
03
00
40
0
CNG-4S PET-4S CNG-2S
CH4 Emission Factors
Fuel-
based E
F (
g k
g-1
)
CNG-4S PET-4S CNG-2S
(N=17) (N=11) (N=13) CNG-4S PET-4S CNG-2S
(N=16) (N=10) (N=13)
GW
C-A
ll (g
/kg)
0
2000
4000
6000
8000
10000
12000
020
00
60
00
100
00
CNG-4S PET-4S CNG-2S
CO2 equivalent* emission factor (incl. CH4, CO, BC and OC)
*Calculated based on 100 year GWPs Source: Reynolds, Grieshop and Kandlikar, ES&T, 2011
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CNG is a not a winner for auto rickshaws
16
• From a local AQ perspective
– CNG provides some improvement over gasoline but….
– Changing engine type is more important – simply getting rid of the 2 strokes (~10% of fleet) would give same benefit as the conversion project!
• From climate perspective
– CNG 2 strokes worse from a climate perspective (by a factor of 2.5)
– Fuel choice does not matter for 4 strokes – almost identical GWC (CO vs. CH4 tradeoff)
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Inter-vehicle variability is a large source of uncertainty in fleet-wide emission factor
0
200
400
600
800
1000
1200
1400
0 5 10 15
PM
2.5
Em
issi
on
Fac
tor
(mg
/kg)
Test Index
running mean
running median
(CNG-4S Tests)
±1σ from mean
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Activity-based model to examine activity influence on emission factors
Source: Grieshop et al, Atm Env, 2012 18
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Fuel Based EF
Drive Cycle FC CO2 CO NO THC PM2.5
units kg 100km-1
g kg-1
g kg-1
g kg-1
g kg-1
g kg-1
IDC 2.2 2498 84 21.4 56 0.55
DMD 2.5* 2497* 78* 18.7* 73* 0.56
MMSD 2.4* 2497 82 16* 60 0.61
MMDC 2.8* 2603 61* 10.7* 85* 0.60
Ratio to IDC
DMD 1.15* 1* 0.93* 0.87* 1.3* 1.01
MMSD 1.1* 1.00 0.98 0.75* 1.07 1.10
MMDC 1.27* 1.04 0.73* 0.5* 1.51* 1.08
Ratio to IDC – Mean of Delhi GPS data modeling
Delhi GPS 1.13# 0.97
# 0.92
# 0.85
# 1.24
# 0.99
Delhi GPS Activity Data
Manila Activity Data
Manila Drive Cycle
Emission model indicates activity can have significant effects on emission factor estimates.
Source: Grieshop et al, Atm Env, 2012 19
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IARP: developing organic PM “fingerprints”
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Volatility distributions from IARP tests (filter plus sorbent tubes) for chemistry models
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Conclusions
• Vehicle emissions testing can provide essential information for policy decisions
• Choosing testing method involves trade-offs
• Delhi auto-rickshaw testing
– Inter-vehicle variability dominates uncertainty
– Gave clear signal concerning policy (2-strokes)
– Detailed aerosol data for further modeling.
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Colleagues: UBC: Conor Reynolds, Milind Kandlikar, Hadi Dowlatabadi (IRES),
Steve Rogak, Dan Boland (MechE), Michael Brauer, Winnie Chu, Cris Barzan, Jenn Shum (Env. Health)
CMU CAPS: Allen Robinson, Ngoc Nguyen, Chris Hennigan
IDS, Delhi: Rajendra Ravi
ICAT Manesar
Funding: Auto21 Network of Centres of Excellence
ExxonMobil Educational Fund
BC Environmental and Occupational Health Research Network (BCEOHRN)
Acknowledgements:
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THANK YOU!
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BACK UP SLIDES
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Lookup table NOx of emission rates (mg s-1) CNG-4S vehicles
Vehicle Acceleration (m s-2
)
Number of
data points
Color
Scale
-1.7 -1.5 -1.3 -1.1 -0.9 -0.7 -0.5 -0.3 -0.1 0.1 0.3 0.5 0.7 0.9 1.1 1.3 1.5 0
Veh
icle
Sp
eed
(m
/s)
0 2.06 2.06 2.06 2.06 2.06 2.06 1.85 1.64 0.41 0.22 0.24 0.23 0.20 0.21 0.21 0.21 0.21 1
1 2.35 2.35 2.35 2.35 2.35 2.35 2.14 1.54 0.88 0.54 0.29 0.22 0.16 0.16 0.16 0.16 0.16 2
2 2.71 2.71 2.71 2.71 2.71 2.71 2.80 1.52 1.02 0.76 0.16 0.19 0.16 0.16 0.16 0.16 0.16 3
3 3.02 3.02 3.02 3.02 3.02 3.02 2.20 0.73 0.91 1.32 0.19 0.19 0.16 0.16 0.16 0.16 0.16 4
4 2.33 2.33 2.33 2.33 2.33 2.33 1.02 0.39 0.79 1.47 1.17 1.10 1.34 1.45 1.55 1.66 1.76 5
5 3.03 3.03 3.03 3.03 3.03 3.03 3.15 0.36 0.38 0.33 1.64 3.13 3.45 4.23 5.02 5.80 6.58 10
6 3.10 3.10 3.10 3.10 3.10 3.10 2.82 2.98 2.90 3.71 3.21 3.41 3.46 3.70 3.94 4.17 4.41 15
7 2.93 2.93 2.93 2.93 2.93 2.93 2.56 2.35 2.73 2.62 3.07 3.89 4.12 4.58 5.04 5.50 5.96 20
8 2.88 2.88 2.88 2.88 2.88 2.88 2.69 3.10 2.32 2.78 3.21 3.84 4.11 4.43 4.76 5.09 5.41 25
9 2.47 2.47 2.47 2.47 2.47 2.47 2.47 5.05 4.32 4.14 3.69 4.14 4.08 4.04 3.99 3.95 3.91 30
10 2.28 2.28 2.28 2.28 2.28 2.28 2.28 5.56 6.87 5.19 7.42 5.87 6.00 6.14 6.28 6.41 6.55 50
11 4.93 4.93 4.93 4.93 4.93 4.93 4.93 4.93 4.93 4.79 6.08 5.27 5.30 5.33 5.36 5.39 5.42 70
125
Veh
icle
Spee
d (
m/s
)
26
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Example of intra-vehicle variability
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Can lessons learned be used to improve air pollution mitigation approaches?
Source: WMO/IGAC Impacts of Megacities on Air Pollution and Climate, 2012 28
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Auto-rickshaw climate-warming emissions in CO2-equivalents*
*Using 100 year Global Warming Potential (GWP) values 29
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Simple policy question…
• From a PM emission perspective, would getting current 2-strokes off the road do more than the gasoline to CNG switch? – ~10% of current Delhi autorickshaws are 2-strokes (2009)
– Based on median of measured emission factors, PM reductions:
Extant CNG 2-s to CNG 4-s:
0.10*80k*30k*(170 – 9) = 39 tonnes/year
All past gasoline 4-s to CNG 4-s:
0.90*80k*30k*(33 – 9) = 52 tonnes/year
Replacing remaining (~8k) 2-strokes would have nearly the same effect as the gasoline to CNG switch on ~70k 4-stroke vehicles…
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International Centre for Automotive Technology (iCAT): – Chassis-dyno - operate vehicles on “Indian Drive Cycle”
– Emissions instrumentation for real-time gaseous species
– CO2, CO, HC, CH4, NOx
– Dilution tunnel and filters for PM sampling
– Air toxics analysis (aldehydes and ketones)
Lab sampling at iCAT, Manesar, India
Additional PM measurements
● Organic/Elemental Carbon (OC/EC)
analysis
● Organic PM & VOC species, volatility
● DustTrak (real-time PM mass)
● Thermophoretic sampling (PM size and
morphology) 31
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Delhi: compressed natural gas (CNG) fueling for public transport
• Mandated by Indian Supreme Court in response to public petition
• Completed 2003 – no marked drop in PM levels
• Research goal: integrated assessment of the impacts of this policy
– Health and climate impacts
– Source characterization
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PM2.5 in Delhi
Photo: C. Reynolds
Data: Chowdhury et al., 2007
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No response to CNG switch in Delhi’s Ambient PM levels?
(Source: Kandlikar 2007)
WHO
Guideline
(2005) for
PM10: 24-hr mean: 50 g/m3
Annual mean: 20 g/m3
India NAAQS:
Annual mean: 60 g/m3
PM
10 (
g/m
3)
Year
(from a single monitoring site)
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Source apportionment of PM2.5 in Delhi
23% 24% 19%
19%
• Data from 2001
• Analysis uses mostly U.S. source profiles
• Secondary sources poorly understood
Source: Chowdhury et al. 2007
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Climate impacts of CNG?
(Source: Reynolds and Kandlikar, 2008)
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Chromatographic vs. ‘physical’ approaches
Slide courtesy Ngoc Nguyen, CMU 37
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Weekday traffic has regular diurnal pattern
3:30 8:30 13:30 18:30 23:30
0
20
40
60
80
0
1000
2000
3000
4000
0.0
0.1
0.2
0.3
0.4
Ave
rag
e S
pe
ed
Ve
hic
les P
er
Ho
ur
HD
DV
Fu
el F
ractio
n
Early morning Mid-day
Rush hour 89%
11%
RUSH HOUR (7 - 9 AM)
81%
19%
HDDV
LDV
MID-DAY (10AM - 4:30 PM)
64%
36%
LATE NIGHT (12 - 6AM)Truck Fuel % Car Fuel %
Early Morning (12 – 6 AM)
89%
11%
RUSH HOUR (7 - 9 AM)
81%
19%
HDDV
LDV
MID-DAY (10AM - 4:30 PM)
64%
36%
LATE NIGHT (12 - 6AM)
Rush Hour (7 – 9 AM)
89%
11%
RUSH HOUR (7 - 9 AM)
81%
19%
HDDV
LDV
MID-DAY (10AM - 4:30 PM)
64%
36%
LATE NIGHT (12 - 6AM)
Mid-day (10 AM – 4:30 PM)
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NOx emission factor vs. fleet composition: a clear separation of vehicle classes
0.0 0.2 0.4 0.6 0.8 1.0
0
10
20
30
40
50
Sample period NOx EF
LD/HDDV EF from literature
Linear regression fit
69% Confidence Limits
Literature value averages
NO
x (
g N
O2 k
g-1)
HDDV Fuel Fraction
Adj. R2 = 0.74
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Large sample sizes to constrain PM EFs
Source: Subramanian et al, EST 2009
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Dyno testing: Indian Drive Cycle
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