high-resolution and real-world emission model and …...2016/4/14 high-resolution and real-world...

2016/4/14

High-resolution and real-world emission model and inventory for urban vehicle fleets in China

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International Workshop on “Mobile source emission modeling and emission reduction strategies” Ye Wu School of Environment, Tsinghua University March 3-4, 2016

Outline

Background and Research Motivation

Methodology framework and data source

Emission measurement and model development –PEMS measurement –Chasing measurement –Multiscale emission model development

Traffic dynamics and high-resolution emission inventory –Urban traffic flow – Inter-city traffic flow –High-resolution emission inventory: case studies of Macao, Nanjing and Beijing

Mesoscale air quality modeling

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Vehicle emissions are one of the most important local PM2.5 sources for many megacities in China.

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Beijing

Shanghai

Hangzhou

Guangzhou

Shenzhen

31%

29% 28%

22% 52%

Vehicle/Mobile sourcesCoal combustionDustIndustrialResidential, argicultural and others

Beijing (81 μg/m3)

Shanghai (53 μg/m3) Hangzhou (55 μg/m3)

Guangzhou (39 μg/m3) Shenzhen (30 μg/m3)

Background

Sectoral allocation of local sources

Data source: Ministry of Environmental Protection. Note: For Shanghai and Shenzhen, their mobile source sectors include off-road sources and ships.

Background

Beijing is the leader in vehicle emission controls within China.

“Vehicle-Fuel-Road” integrated control system.

– Now, traffic measures are increasingly important now in the system, including the notable restrictive policies on registration and usage (e.g., license control, regular traffic restriction, odd-even restriction).

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Vehicle emission control

New vehicle control

In-use vehicle control

Better fuel

quality

Traffic measures

Economic measures

Research Motivation

Significant climate forcing and health impacts due to key components (e.g., Black carbon, BC; Polycyclic aromatic hydrocarbon, PAHs)

Uncertainty from in-use high emitters and high off-cycle emissions

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Polycyclic hydrocarbons

Ash

Sulphate

Black carbon (soot)

Emissions distribution for truck fleets in Beijing and Chongqing (tested by chasing)

Wang et al., 2012

Research Motivation

High-resolution emission inventory is an irreplaceable tool compared with conventional inventory technology (registration data based). – High-resolution: hourly, link-level, vehicle techno. group (category, fuel, standard, model year)

Impacts from urban traffic congestion and inter-city freight transportation are of great concerns.

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Urban congestion Inter-city transportation

Location: Jing-Zang Expressway Location: Guomao Bridge

Beijing Total area: ~16,000 km2

Urban area: ~1,000 km2

Outline






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Outline






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High-resolution vehicle emissions in Beijing based on real-world urban and inter-city traffic data Wu Center Group Meeting

FPS-400 Exhaust dilution system


Portable Emission Measurement System We are dedicated to developing PEMS methods for high-resolution and real-

world measurements of PM, BC, VOCs and unregulated species (e.g., PAHs).

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EcoStar: Exhaust flow meter and gas analyzers

Micro proportional sampling system


ELPI+, PSD, CPMA Real-time particle instruments

Ion-Molecule Reaction mass spectrometer

PM Physical Characteristics (number, size distribution, density)

Exhaust inlet

Real-time BC measurement

Particle sampler Summa Canister

Laboratory chemical analysis of PM, VOCs and SVOCs

SVOCs sampler

Aethalometer

Real-time measurement of specified VOCs

GPS data logger

OBD decoder

Exhaust outlet

Instantaneous operating conditions


PM measurement of HDDVs: real-world BC emissions

Improvements from less congested traffic and more strict emission standard

Significantly higher BC emissions from mechanical pump injection engine equipped HDDVs

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Aver

age

BC e

mis

siso

ns (m

g kg

-1)

0

1000

2000

3000

4000FreewaysCongested roadsEntire trip

Euro II Euro III Euro IV Euro V

(a) fuel-based

Operating mode bin

Bin

0B

in1

Bin

11B

in12

Bin

13B

in14

Bin

15B

in16

Bin

17B

in18

Bin

21B

in22

Bin

23B

in24

Bin

25B

in26

Bin

27B

in28

Bin

35B

in36

Bin

37B

in38

Aver

age

BC e

mis

sion

rate

s (m

g s-1

)

0

3

6

9

12

15

18

Euro IVEuro III Euro II

Average fuel consumption based BC emissions Average BC emission rates according to operating mode


PM measurement of HDDVs: real-world PAHs emissions

We are expanding functions and applicability of the PEMS method to more unregulated pollutants.

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Average particle-borne total PAHs emissions Comparison of specified PAHs emissions by engine type


PEMS measurement of alternative fuels and advanced vehicles

PEMS can play an essential role in understanding the real-world emission complexity from powertrain (e.g., hybrid vs. ICEV), engine (e.g., lean burn vs stoichiometric), after-treatment (e.g., SCR) and traffic conditions (e.g., congestion).

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Hybrid electric gasoline vehicles CNG, LNG and hybrid urban buses


Plum chasing measurement: Improved understanding of high-emitters based on large-sized vehicle samples

Significantly higher BC emissions discerned by chasing for non-local trucks in Beijing.

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Targeted vehicle Mobile platform

Schematic on-road plume chasing

Comparison with PEMS testing for one truck

BC emissions based on chasing tests


Multiple-Scale emission models for Urban vehicle fleets E.g., the Emission factor Model for BEijing Vehicle fleet (Version 2.0)

– Beijing’s official model (Beijing Environmental Protection Bureau, since 2010) – Other city-level emission factor models: Macao, Guangzhou, Nanjing, etc.

Data fundamental and key method – Dynamometer tests for thousands of light-duty vehicles and PEMS tests for hundreds of both light-

duty and heavy-duty vehicles – Speed correction functions: dynamo. tests over various cycles; operating binning method; micro-trip

method

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Average emission factors for urban transit buses (g/km, an average speed of 18 km/h )

Speed-correction function for China IV urban bus

y = 10.219x-0.797 R² = 0.58

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1

2

3

4

5

6

0 10 20 30 40 50 60 70

Spee

d co

rrec

tion

func

tion

Average speed (km/h)

More information about the EMBEV: Wu et al., ACP, 2012; Zhang et al., Atmos. Environ., 2014; Zhang et al., Appl. Energy, 2014; Wu et al., Environ. Pollu., 2016, under review.


Multiple-Scale emission models for urban vehicle fleets The National Emission Inventory Guidebook (MEP, 2015)

– Its archetype is the EMBEV model with full considerations in regional distinctions – Applied by many cities for source apportionment

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See detailed information via Wu et al., ACP, 2012; Zhang et al., Atmos. Environ., 2014; Zhang et al., Appl. Energy, 2014; Yue et al., 2015; Zheng et al., 2015; Wu et al., Environ. Pollu., 2016, under review.

Diesel Fuel quality

Vehicle weight

High-emitters

Traffic flow

EMBEV


Outline






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Urban Traffic Flow: High-resolution traffic volume and fleet composition by radio-frequency identification (RFID) technology

In Nanjing, nearly 600 RFID detection stations have been built, covering +90% of local vehicles. Detailed vehicle specifications (e.g., manufacturer, vehicle model, emission

standard, fuel type, vehicle size).

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How to work

RFID Tag

RFID detector

Vehicle Management System

Infrastructure Data

Urban Traffic Flow: Resolving congestion maps into dynamic hourly speed profiles

The congestion map is technically supported by the floating car system using more than 60,000 taxis as probe vehicles (GPS based).

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Real-time congestion map in Beijing based on the floating car system

Hourly speed (kph) Typical weekday, 2013

Urban Traffic Flow: Individual vehicle usage data

Nearly 500 private passenger cars in Beijing have been investigated for one to six months.

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• Comparable habitual travel distance and lower random travel distance in Beijing than German cities.

• Lower fraction of random travel in Beijing

• Fundamental to evaluate mileage threshold for BEVs and electrified mileage for PHEVs

• Improve emission inventory regarding traffic conditions, cold start and evaporative emissions.

Case 1: High-resolution emission inventory in Macao (Generation 1)

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≤ 170

171~410

411~720

721~1020

1021~1420

1421~1770

1771~2710

≥ 2711

HC emission density （g/h）

2010 Typical weekday Traffic volume Manual camera record & TransCAD modeling

Vehicle speed GPS field investigation

Fleet composition Remote sensing

database

Case 2: High-resolution emission inventory in Nanjing (Generation 2A)

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Traffic volume RFID data validated by traffic loop detectors and camera record

Vehicle speed Floating car plus RFID

Fleet composition RFID

Nanjing

Features: higher temporal (real-time) and fleet (vehicle specifications) resolutions based on RFID data

Case 3: High-resolution emission inventory in Beijing (Generation 2B)

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PM2.5, 2013 weekday Features: Integrated urban and inter-city traffic data

Traffic volume Urban: Annual average hourly traffic volume, real-world traffic count, traffic density model Inter-city: highway traffic monitoring

Vehicle speed Urban: Real-time congestion map, GPS survey Inter-city: highway traffic monitoring

Fleet composition Urban: Vehicle remote sensing and registration data; traffic count Inter-city: highway traffic monitoring Real-world recognition of non-local trucks

Traffic demand by vehicle category and region in Beijing

67% of light-duty passenger vehicles, 73% of public buses and 78% of taxis within the 5th Ring Rd. Preliminary results show 78% of local heavy-duty trucks and 90% of non-local duty trucks outside the 5th

Ring Rd. (40%~50% concentrated within the 5th and 6th Ring Rds. )

Good agreement (i.e., within ±6% for most category) with mileage travelled data from vehicle inspection database (weekends combined); Annual average mileage of LDVs was 14,500 km per year.

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0.0E+00

4.0E+07

8.0E+07

1.2E+08

1.6E+08

2.0E+08

Outside the 6th Ring Rd.Between the 5th and 6th Ring Rds.Within the 5th Ring Rd

Daily traffic activity by vehicle category and region (veh·km, 2013 weekday)

Comparison with annual mileage data (km, 2013)

0.0E+00

3.0E+04

6.0E+04

9.0E+04

1.2E+05

1.5E+05

LDPV MDPV HDPV LDT HDT(local)

Taxi Publicbus

Estiamted based on traffic dataVehicle inspection data

Possibly contributed by non-local passenger transportation

Outline






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Mesoscale Simulation of primary air pollutants

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Mesoscale air quality modeling with AERMOD – Pollutants: CO, BC, NOX/NO2 (oxidized by ozone) – Emissions domain: within 6th Ring Rd in 4728 grids – Receptors domain: within 5th Ring Rd in 3779 fine grids – Input data: gridded emissions, ground and high-altitude

meteorological data (wind speed, direction, sunshine, cloud, etc.), geographic information (e.g., land use)

– Mechanisms: dispersion plus NO/NO2 chemical 0

10

20

30

40

50

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223

Typi

cal w

ind

spee

d (×

10 m

/s) Janurary

AprilJulyOctober2014 APEC

01020304050607080

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223

Aver

age

Ozo

ne co

ncen

trat

ion

(ppb

)

Wind speed

Ozone concentration

Within 5th Ring Rd: 3779 fine grid (0.5 km*0.5 km)

In between 5th and 6th Ring Rds: 949 coarse grid (1.5 km*1.5 km)

Results: NO2 simulation results

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0

20

40

60

80

100

0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223

Hou

rly

aver

age

NO

2 co

ncen

trat

ion

(ug

/m3)

Jan Apr Jul Oct

Simulated contribution by vehicles, 2013 weekday (ug/m3, wind direction is SW)

Spatial distribution of daily contribution, 2013 October

Simulated daily NO2 concentrations were 26 to 42 ug/m3, responsible for 56% of urban ambient NO2 concentration. Two peaks associated with traffic rush hours and low concentration in afternoon due to high O3 level.

Actual peak hour

Results: BC simulation results (preliminary)

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Simulated contribution by vehicles (ug/m3, wind direction is NE)

Spatial distribution of daily contribution, 2013 Jan

Daily average BC contribution was 3.5 ug/m3 in the urban area for 2013 weekday. Significantly higher BC contribution in the nighttime, consistent with other measured data

High BC emissions from HDTs and bad dispersion conditions

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0 1 2 3 4 5 6 7 8 9 1011121314151617181920212223Hour

ly a

vera

ge B

C co

ncen

trat

ion（

ug/m

3 ）

2013年9月29日 2013年同期工作日 APEC会议期间 2013 congested day

2013 weekday 2014 APEC

Relevant publications Vehicle emission measurement

– (HDDVs NOx) Wu et al. The challenge to NOx emission control for heavy-duty diesel vehicles in China. Atmos. Chem. Phys., 2012.

– (HDDVs Black Carbon) Zheng et al. Characteristics of On-road Diesel Vehicles: Black Carbon Emissions in Chinese Cities Based on Portable Emissions Measurement. Environ. Sci. Techno., 2015

– (Bus CO2) Zhang et al. Real-world fuel consumption and CO2 emissions of urban public buses in Beijing. Appl. Energy, 2014a.

– (Alternative fuel and NOx) Zhang et al. Can Euro V heavy-duty diesel engines, diesel hybrid and alternative fuel technologies mitigate NOx emissions? New evidence from on-road tests of buses in China. Appl. Energy, 2014b

– (Light-duty CO2) Zhang et al. Real-world fuel consumption and CO2 (carbon dioxide) emissions by driving conditions for light-duty passenger vehicles in China. Energy, 2014.

– (Chasing)Wang et al. On-road diesel vehicle emission factors for nitrogen oxides and black carbon in two Chinese cities. Atoms. Environ., 2012.

Emission model development and control strategies – (Strategy) Wu et al. On-road vehicle emission control in Beijing: past, present, and future. Environ. Sci. Techno., 2011 – (EMBEV) Zhang et al. Historic and future trends of vehicle emissions in Beijing, 1998-2020: A policy assessment for the

most stringent vehicle emission control program in China. Atmos. Environ., 2014 – (National emission inventory) Wu et al. Assessment of vehicle emission programs in China during 1998-2013:

achievement, challenges and implications. Environ. Pollu., 2016 (under review).

Traffic dynamics and high-resolution emission inventory – (Macao case) Zhang et al. High-resolution simulation of link-level vehicle emissions and concentrations for air pollutants

in a traffic-populated East Asian city.. Atoms. Chem. Phys. Discussion, 2016.

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Thanks for your attention!

Ye Wu, Professor

School of Environment, Tsinghua University, Beijing 100084, China

E-mail contact: [email protected]

Tel: +86-10-62796947

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mailto:[email protected]

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