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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

agrieshop@ncsu.edu

U.S. – Iran Symposium on Air Pollution in Megacities Beckman Center, Irvine, CA

September 3, 2013 1

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

An example: vehicle PM control policies may have health and climate co-benefits or co-impacts.

Source: Reynolds, Grieshop and Kandlikar, IGES, 2011 3

Emission standards are a primary policy approach to address emissions

Source: Reynolds, Grieshop and Kandlikar, IGES, 2011

Heavy duty vehicle PM Emission standards

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?

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)

Various emission testing approaches

Image: Sensors, Inc. Image: Franco et al, Atm Env. 2013

Image: nasa.gov

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

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

Engine Dyno + + - - - Chassis Dyno + + o o/- - PEMS Study - + + o o

Roadside remote sensing

- - o + +

Tunnel Study + - o o/- +

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)

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

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

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

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

• Fuel- and distance- based emission factors (g pollutant per kg fuel or km)

Very high PM2.5 emissions from 2-stroke engines

Source: Reynolds, Grieshop and Kandlikar, ES&T, 2011 14

2-strokes are a clear loser for climate also.

CNG-4S PET-4S CNG-2S

CH4 Emission Factors

based E

(N=17) (N=11) (N=13) CNG-4S PET-4S CNG-2S

(N=16) (N=10) (N=13)

CO2 equivalent* emission factor (incl. CH4, CO, BC and OC)

*Calculated based on 100 year GWPs Source: Reynolds, Grieshop and Kandlikar, ES&T, 2011

CNG is a not a winner for auto rickshaws

• 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)

Inter-vehicle variability is a large source of uncertainty in fleet-wide emission factor

0 5 10 15

Test Index

running mean

running median

(CNG-4S Tests)

±1σ from mean

Activity-based model to examine activity influence on emission factors

Source: Grieshop et al, Atm Env, 2012 18

Fuel Based EF

Drive Cycle FC CO2 CO NO THC PM2.5

units kg 100km-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

IARP: developing organic PM “fingerprints”

Volatility distributions from IARP tests (filter plus sorbent tubes) for chemistry models

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.

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:

THANK YOU!

BACK UP SLIDES

Lookup table NOx of emission rates (mg s-1) CNG-4S vehicles

Vehicle Acceleration (m s-2

Number of

data points

-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

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

Example of intra-vehicle variability

Can lessons learned be used to improve air pollution mitigation approaches?

Source: WMO/IGAC Impacts of Megacities on Air Pollution and Climate, 2012 28

Auto-rickshaw climate-warming emissions in CO2-equivalents*

*Using 100 year Global Warming Potential (GWP) values 29

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…

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

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

PM2.5 in Delhi

Photo: C. Reynolds

Data: Chowdhury et al., 2007

No response to CNG switch in Delhi’s Ambient PM levels?

(Source: Kandlikar 2007)

Guideline

(2005) for

PM10: 24-hr mean: 50 g/m3

Annual mean: 20 g/m3

India NAAQS:

Annual mean: 60 g/m3

(from a single monitoring site)

Source apportionment of PM2.5 in Delhi

23% 24% 19%

• Data from 2001

• Analysis uses mostly U.S. source profiles

• Secondary sources poorly understood

Source: Chowdhury et al. 2007

Climate impacts of CNG?

(Source: Reynolds and Kandlikar, 2008)

Chromatographic vs. ‘physical’ approaches

Slide courtesy Ngoc Nguyen, CMU 37

Weekday traffic has regular diurnal pattern

3:30 8:30 13:30 18:30 23:30

ractio

Early morning Mid-day

Rush hour 89%

RUSH HOUR (7 - 9 AM)

MID-DAY (10AM - 4:30 PM)

LATE NIGHT (12 - 6AM)Truck Fuel % Car Fuel %

Early Morning (12 – 6 AM)

LATE NIGHT (12 - 6AM)

Rush Hour (7 – 9 AM)

LATE NIGHT (12 - 6AM)

Mid-day (10 AM – 4:30 PM)

NOx emission factor vs. fleet composition: a clear separation of vehicle classes

0.0 0.2 0.4 0.6 0.8 1.0

Sample period NOx EF

LD/HDDV EF from literature

Linear regression fit

69% Confidence Limits

Literature value averages

HDDV Fuel Fraction

Adj. R2 = 0.74

Large sample sizes to constrain PM EFs

Source: Subramanian et al, EST 2009

Dyno testing: Indian Drive Cycle

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