1

Gakuji KURATA, Soichi MORIMOTO, Minna GUO, Yuzuru MATSUOKA

17th AIM International Workshop 17th – 18th February, 2012 NIES, Tsukuba, JAPAN

Kyoto University

Setsunan University Yoko SHIMADA

2

Transportation model

Air Pollution model

We developed a reconciliation tool for domestic and international Transport statistics to estimate the reliable past traffic volume.

We broke down domestic passenger and freight transport for each “purpose”, “type of Transportation”, “personal attribute” and “trip distance”.

We developed human exposure model of PM2.5 both indoor and outdoor for Chinese 31 province.

In the exposure model, we developed roadside model to consider the high concentration from road traffic.

By connecting these two models, detail estimation of co-benefit of LCS countermeasure to the reduction of air pollution impact, especially by transportation related LCS policy.

3

framerowk of traffic demand model

regional population

Domestic Traffic

Demand

trip gene. unit

by purpose

Modal share （Walk, Bicycle,

Car, Bus, Railway, Aviation）

Trip length

International Traffic

Demand

Passenger Transport demand

Domestic International

population/ population

change model

Production/ transport demand

by commodity

Domestic Freight

demand

Freight generation

unit

Modal share (truck, railroad,

ship)

Transport distance

International Freight

demand

Freight Transport demand

Domestic International

World CGE Model

【Passenger】 【Freight】

Socio-economic

development scenario

Transportation Policy Urban Infrastructure

Plan Effect of City-Country

Planning

4

i: transportation type(domestic or international)pa: personal attribute pt: purpose of trip d: representative distance m: mode PTV: passenger transportation volume[p-km] POP: effective population[person] PTG: trip generation rate[number/person] PTS: modal share[-] DIS: trip distance[km]

5

i: transportation type(domestic or international) com: commodity

d: representative distance

m: mode

FTV: freight transportation volume[ton-km]

PI: production and import volume of commodity[US$]

FTG: freight transport generation rate[ton/US$]

FTS: modal share[-]

DIS: freight transport distance[km]

6

STEP1

STEP2-F STEP2-P

Reconciliation of traffic data 1971-2005,

Possession of vehicle, transport, Total transport, energy consumption using statistical data

Decomposition of Passenger Transport

Estimation of the parameters of passenger transportation

at 2005

Decomposition of Freight Transport

Estimation of the parameter of Freight transportation

at 2005

Traffic Statistics Energy Statistics

Each type of Transport Total Passenger Transport

Each type of Transport Total Freight Transport

STEP2• Population • Trip generation unit • Trip distance share by type • Trip distance share by Purpose

• Production + import • Freight generation unit • Distance share by type

Freight generation unitmodal share

transportation distance

Trip generation unitmodal share trip distance

Task Data

Possession Total transportation

Energy consumption

Each type of transport

regional population

DomesticTraffic Demand

trip gene. unit

by purpose

Modal split（Walk, Bicycle,

Car, Bus, Railway, Aviation）

Trip length

International Traffic Demand

Passenger Transportdemand

Domestic International

population/population

change model

Production/transport demand

by commodity

DomesticFreight demand

Freight generation

unit

Modal Split(truck, railroad,

ship)

Transport distance

International Freight demand

Freight Transport demand

domestic international

World CGE Model

(35 region)

【Passenger】 【Freight】

Socio-economic

development scenario

Transportation PolicyUrban Infrastructure Plan

Effect of City-Country Planning

7

A lot of errors and losses are included in Traffic statistics.

Minimizing the errors from reference values, such as possession, total mileage, freight amount, energy consumption and ratios between these values ( mileage, load coefficient, fuel consumption per distance etc)

Reference value is derived from statistics and literatures.

Constrain the trend from large swing to avoid the discontinuous of time series.

Numbers of Vehicle Total transport (km)

Total transport (person・km, t・km) Total transport (person) Total transport (t) Energy consumption travel distance (km/year/vehicle)

Load coefficient (person/vehicle, t/vehicle)

Fuel consumption (ktoe / vehicle, ktoe / km person , ktoe / km t)

Average trip distance(Passenger), Average transport distance(Freight)

2 2' ', , ' ', , ,

2 2' ', , ' ', , ,

2 2' ', , ' ', , ,

2 2' ', , ' ', , ,

2' ', ,

ST j j t ST j j h tt j h

VK j j t VK j j h th

TV j j t TV j j h th

PS j j t PS j j h th

TN j j t

ER wch CHST wer ERST

wch CHVK wer ERVK

wch CHTV wer ERTV

wch CHPS wer ERPS

wch CHTN wer

2

' ', , ,

2 2' ', , ' ', , ,

2 2' ', , , ' ', , ,

2 2' ', , , ' ', , ,

2' ', , , ' ',

TN j j h th

ENT j j t ENT j j h th

DS j j h t LF j j h th h

FEV j j h t FET j j h th h

ATD j j h t AFD j

ERTN

wch CHENT wer ERENT

wer ERDS wer ERLF

wer ERFEV wer ERFET

wer ERATD wer ERAF

2, , minj h t

h hD

Objective Function

8

Statistics Name SourceWorld Road Statistics 63-89, 1995,2001,2002,2006 IRF, 2003,1995,2001,2002,2006World Develoement Indicators 2005 World Bank, 2005World Develoement Indicators 2007 World Bank, 2007Statistical Year book 47 UN, 2003EUROSTAT(web) European Comission, webからDL(2008/4/11)ODYSSEE ADEME, 2002OECD Environmental Data Compendium 2006/2007 Transport OECD, 2007Statistical Trends in Transport 1965-1994 ECMT, 1998Trends in the Transport Sector 1970-2005 ECMT, 2007World Bank's Railway Database World Bank, 2001Energy Balnces of OECD Countries, Energy Balnces of non-OECD Countries

IEA, 2007

Globalstat enerdata, 2006World Motor Vehicle Statistics Japan Automobile Manufacturers Association, 2004, 2008International Historical Statistics Mitchell, 2003China Statistical Yearbook National Bureau of Statistics of China, 1993, 1994, 1995, 2000, 2001, 2006Korea Statistical Yearbook Korea National Statistical Office, 2002, 2006TEDDY(TERI Energy Data Directory & Yearbook) TERI, 2002, 2006Land Transport Statistics Japan MLIT Japan, 2001, 2006Automobile Transportation statistics Japan MLIT Japan 1987-2006National Transportation Statistics 2008 Bureau of Transportation Statistics, U.S. Department of Transportaion, 2008World Railways Japan Railway Technical Service, 2005ANNUAL BULLETIN OF TRANSPORT STATISTICS FOR EUROPE AND NORTH AMERICA

UN Economic Commission for Europe, 2004, 2005, 2008

Handbook of Transport Statistics in the UNECE region 2006 UN Economic Commission for Europe, 2006Malaysia Transport Statistics 2006 , 2006Statistical Handbook of Vietnam General Statistics Office of Vietnam, 2005, 2006Philippine Yearbook 2006 National Statistics Office, 2006USSR Facts & Figures Annual Academic International Press, 1977,1979,1987,1990,1991,1992Statistical Yearbook Thailand National Statistical Office, 1998,2000,2002,2004Transport and Communications Yearbook 2003 SWEDISH INSTITUTE FOR TRANSPORT AND COMMUNICATIONS ANALYSIS, 2003Transport Statistics Great Britain 2005 DEPARTMENT FOR TRANSPORT, 2005China Energy Databook 2004 Lawrence Berkeley National Laboratory, 2004Statistical Abstract of Transport in Asia and the Pacific 2007 UN Economic and Social Commission for Asia and the Pacific, 2007Philippine Statistical Yearbook 1991 Republic of the Phillippine National Statistical Coordination Board, 1991North American Transportation Statistics North American Transportation Statistics Database, 2007Taiwan Statistical Databook 2007 Council for Economic Planning and Development Exective Yuan, R.O.C., 2007Statistical Yearbook of Bangladesh 2005 Bngladesh Bureau of Statistics, 2005

Statistics Name

Used International and domestic statics for transportation

9 9

Sample image of Result output

Mil-

p-km

p-km of passenger car (Australia) Example of the Result

10

STEP1

STEP2-F STEP2-P

Reconciliation of traffic data 1971-2005,

Possession of vehicle, transport, Total transport, energy consumption using statistical data

Decomposition of Passenger Transport

Estimation of the parameters of passenger transportation

at 2005

Decomposition of Freight Transport

Estimation of the parameter of Freight transportation

at 2005

Traffic Statistics Energy Statistics

Each type of Transport Total Passenger Transport

Each type of Transport Total Freight Transport

STEP2• Population • Trip generation unit • Trip distance share by type • Trip distance share by Purpose

• Production + import • Freight generation unit • Distance share by type

Freight generation unitmodal share

transportation distance

Trip generation unitmodal share trip distance

Task Data

Possession Total transportation

Energy consumption

Each type of transport

regional population

DomesticTraffic Demand

trip gene. unit

by purpose

Modal split（Walk, Bicycle,

Car, Bus, Railway, Aviation）

Trip length

International Traffic Demand

Passenger Transportdemand

Domestic International

population/population

change model

Production/transport demand

by commodity

DomesticFreight demand

Freight generation

unit

Modal Split(truck, railroad,

ship)

Transport distance

International Freight demand

Freight Transport demand

domestic international

World CGE Model

(35 region)

【Passenger】 【Freight】

Socio-economic

development scenario

Transportation PolicyUrban Infrastructure Plan

Effect of City-Country Planning

11

Population by individual attribute

Passenger Transport demand by each type of transport

Program to estimate the parameters of

passenger transport

Modal share by type of transpiration, purpose of trip and distance category

Trip generation unit by individual attribute, purpose of trip

Representative Trip distance by each distance category

Trip generation unit by individual attribute and Purpose of trip

Distance share by the type of transportation

Distance share by Purpose of trip

calibration

Output

from STEP1

Reference Value（Japan）

Domestics transport Demand

Domestic

International

distance category Short Middle Long

12

Production + import by each commodity

Freight transportation amount by each type of transportation

Program to estimate the parameters for Freight transportation

Modal share by type of transportation and distance category

Freight generation unit by commodity

Representative distance for each distance category

Freight generation unit by each commodity

Distance category share by each type of transportation

calibration

Output

from STEP1

Reference Value（Japan）

Each country Domestic

International

distance category Short Middle Long

13 0

50000

100000

150000

200000

250000

300000

～300km 100～750km 750km～

mil-

ton-

km

ton-km by mode

domestic naviation

freight rail

truck

total: 5.9×1011ton-km

ton-km by commodities mining product

other product

forest product

machine

food product

agriculture product

ceramic, soil and stone

oil and coal product

metal product

paper and pulp

chemical product

textile product

waste

fishery product

14

ton-km by commodities

mining product

agriculture product

ceramic, soil and stone

forest product

other product

oil and coal product

food product

metal product

textile product

machine

chemical product

fishery product

total: 6.8×1011ton-km

0

50000

100000

150000

200000

250000

300000

～300km 100～750km 750km～

mil-

ton-

kmton-km by mode

domestic naviation

freight rail

truck

15

Transportation model

Air Pollution model

We developed Country level Transportation model to estimate the traffic volume which is consistent with available statistics.

We broke down domestic passenger and freight transport for each “purpose”, “transportation facility”, “personal attribute” and “trip distance”.

We developed human exposure model of PM2.5 both indoor and outdoor for Chinese 31 province.

In the exposure model, roadside model to consider the high concentration from road traffic.

By connecting these two models, detail estimation of co-benefit of LCS countermeasure to reduction of air pollution impact, especially by transportation relate LCS policy.

16

Emission inventory

Traffic by province Time use

survey

Energy / Fuelconsumption in Household

Micro-environment individual Exposure Model

Traffic by each road

Roadside Model

Background concentration

Concentration from road traffic

Staying duration in each

micro-environment for each age group

Indoor PM Emission

Concentration

Information about Housing

Estimation of Individual Exposure to PM2.5

Exposure to each age group

WRF / CMAQ

Duration

17

Target Area

Target Year： 2005

Polutant ： ＰＭ2.5

27 Provinces and 4 Municipalities

18

Daily average exposure concentration

Micro-environment individual Exposure Model

a am m

mE C T

コホート の 日平均曝露濃度 μ

微環境 の汚染物質濃度 μ

コホート の微環境 滞在時間率

コホート

aE

amTa

a

a mmC m

Classification of individual attribute 54 age 0-4,5-14, 15-19, .....70-74, 75+ 15group Male / Female Employed / Unemployed

× Concentration in micro-environ.

Stay duration

Daily average exposure concentration

Concentration in micro-environment

Duration of stay in micro-environment m

Individual attribute group

19 19

Considered micro-environment in this model Micro

Environ. Place Purpose Indoor Emission source Duration of stay

A Indoor Cooking Hot water

Oven, Cooking Stove Cooking

B Indoor Heating Heating Stove, Fireplace

Home (except sleeping time) (When average outdoor temperature < 10℃)

C Indoor Lighting Kerosene lamp Night time

D Indoor ‐ None Inside Building (Home and Building)

E(H) Outdoor

‐ ‐ Outdoor activity (<100 m from road) E(M) ‐ ‐ Outdoor activity ( 100-500 m from road) E(L) ‐ ‐ Outdoor activity (>500 m from road)

Time use survey Adult (age 15 and above)： Time use survey (10 provinces) Child：from literatures

20

with Indoor emission ME A、B、C

w/o indoor emission ME D p

m od

F vC C

v F

Micro-environment individual Exposure Model

0

1 ( )em p

d

SC F vCv F V

Formulation to calculate concentration.

Outdoor ME E

m oC C

1m p o

d

SeC F vCv F V

Single-Compartment Mass Balance Model under steady-state assumption

20

mC 微環境mにおける大気汚染物質濃度 μ

oC 屋外大気汚染物質濃度 μ

pF 浸透率

v 換気回数

dF 除去率

S 一時間当たり燃料消費量

e 排出係数 μ

V 部屋の体積

Pollutant concentration at micro environment (m) (μg/m3)

Penetration Factor (-)

Pollutant concentration at Outdoor (μg/m3)

Air Exchange Rate (1/hr)

Deposition rate (1/hr)

Energy consumption (KJ/hr)

Emission Factor (μg/KJ)

Volume of Micro Environment(m3)

21

Emission inventory of Air pollutants Used data： Large Point Source: from International database (Power plant, Iron & steel, Cement, Petrochemical) GDP, Road length, Rail length：China annual statistics Population, Population by industry： Histrical Population database fo China(2000)

Mothodology： Based on Yasuhuku(2011) Output species: NOx, SOx, CO,

PM2.5, PM10, NMHC Output resolution: 30sec x 30sec ( ≒ 1km)

PM2.5 emission (ton/mesh・year)

22 22

： ° °° °

WRF CMAQ

Grid Size 80km 80km

Number of Grids (East-West)

120 117

Number of Grids (North-South)

105 102

23

Meso-scale Meteorological Model developed by NCAR and Researchers community.

WRF ARW(Advanced Research WRF) version 3.1.1 Input Data

Terrain and Landuse data : USGS(U.S. Geological Survey) Initial and Boundary Meteorological Data: JRA-25 (Re-Analysis by JMA)

scale Meteorological Model developed by NCAR and Researchers

Multi scale Chemical Transport Model developed by US EPA CMAQ version 4.7 Input Data

Emission Mesh Data

Chemical Reaction Mechanism Gas Phase: CB05 (51 chemical Species, 156 Reactions) Aerosol : AERO5

24

Roadside Model

• Calculation case and mesh size： ①Whole China Case： 10km②Beijing Fine Case： 1km

• Divided a mesh to 3 classes by the distance from road.

100m

500m

「High」： <100m 「Middle」：100m～500m 「Low」 ： >500m

C

H M

L M

L

Road Network Data: OpenStreetMap

25 25

Definition of the buffer area of Roadside Model

In the case of Beijing 1 km mesh case.

Road : OpenStreetMap

26

Considered Fuel： Biomass, Coal, petroleum, kerosene, LPG, natural Gas

Energy consumption per capita in Household ・ Household energy consumption both Urban/Rural：

「Labor and population statistics」、「Chinese Energy statistics」 ・ Household Biomass consumption in Rural area：

「Chinese Rural energy statistics 」

薪 作物残渣

調理・給湯暖房照明料理・給湯暖房照明

都市

農村

灯油 天然ガス 熱用途バイオマス

石炭

Indoor Fuel Consumption

Fuel Share by Fuel type and purpose [%]

Purpose Biomass

Wood Agr. Residue Coal Kerosene LPG Natural Gas Heat

Urban

Rural

Cooking Heating Lighting

Cooking Heating Lighting

27 27

28

This result is stillpreliminary.Absolute value of concentration is not yet validated.

Individual Exposure Concentration Population Density

from LandScan2008

0

500000

1000000

1500000

2000000

2500000

3000000

3500000

50 100 150 200 250 300 350 400 450 500 550 600 650 700 750 800 850 900 950

曝露人口

（人）

年平均PM2.5曝露濃度 カテゴリー

Urban

Rural

Exposure Concentration Category

Population Histogram in Exposure concentration.

Popu

latio

n in

eac

h ca

tego

ry (P

erso

n)

+ Contribution from Indoor emission

• In the Urban area, Major contributor is “indoor w/o emission”, this mean source of pollutants is outdoor, and stay duration to indoor is much longer than duration at outdoor.

• Still exposure in rural area is higher than urban area due to the high usage of biomass fuel in Kitchen and Heating.

Outdoor Concentration

29

Average PM2.5 exposure concentration for Female, age 60-64 unemplyed (㎍/m3)

Result from Exposure Model

Average PM2.5 exposure concentration for Male, age 65-69, unemployed (㎍/m3)

This result is still preliminary. values are not yet validated.

This result is still preliminary. values are not yet validated.

30

北京

天津

河北

山西

内モンゴル

遼寧

吉林

黒龍江

上海

江蘇

浙江

安徽

福建

江西

山東

河南

湖北

湖南

広東

広西

海南

重慶

四川

貴州

雲南

チベット

陝西

甘粛

青海

寧夏

新疆

微環境 屋外 低

微環境 屋外 中

微環境 屋外 高

微環境 屋内・発生源なし

微環境Ｃ 照明）

微環境Ｂ 暖房）

微環境Ａ 調理 給湯）

北京

天津

河北

山西

内モンゴル

遼寧

吉林

黒龍江

上海

江蘇

浙江

安徽

福建

江西

山東

河南

湖北

湖南

広東

広西

海南

重慶

四川

貴州

雲南

チベット

陝西

甘粛

青海

寧夏

新疆

微環境 屋外 低微環境 屋外 中微環境 屋外 高微環境 屋内・発生源なし微環境Ｃ 照明）微環境Ｂ 暖房）微環境Ａ 調理 給湯）

Average PM2.5 exposure (Upper: Urban Lower: Rural)

Comparison between all province (Urban / Rural) 30

This result is still preliminary. values are not yet validated.

This result is stillpreliminary. values are not yet validated.

ME-E(L) (Outdoor-Low)ME-E(M) (Outdoor-Mid)ME-E(H) (Outdoor-High)ME-D (Indoor-w/o-emission)ME-C (Indoor-Lighting)ME-B (Indoor-Heating)ME-A (Indoor-Cooking)

ME-E(L) (Outdoor-Low)ME-E(M) (Outdoor-Mid)ME-E(H) (Outdoor-High)ME-D (Indoor-w/o-emission)ME-C (Indoor-Lighting)ME-B (Indoor-Heating)ME-A (Indoor-Cooking)

31

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一日平均暴露濃度

μ

微環境 屋外 低微環境 屋外 中微環境 屋外 高微環境 屋内・発生源なし微環境Ｃ 照明）微環境Ｂ 暖房）微環境Ａ 調理 給湯）

Comparison between all Individual attribute group

PM2.5 average Exposure concentration in Each age group ( Beijing, Uraban)

歳 歳 歳

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一日平均暴露濃度

μ

微環境 屋外 低微環境 屋外 中微環境 屋外 高微環境 屋内・発生源なし微環境Ｃ 照明）微環境Ｂ 暖房）微環境Ａ 調理 給湯）

0

200

400

600

800

1000

1200

1400

0歳・男

1-4歳・男

5-14歳・男

15-1

9歳・有職・男

15-1

9歳・無職・男

20-2

4歳・有職・男

20-2

4歳・無職・男

25-2

9歳・有職・男

25-2

9歳・無職・男

30-3

4歳・有職・男

30-3

4歳・無職・男

35-3

9歳・有職・男

35-3

9歳・無職・男

40-4

4歳・有職・男

40-4

4歳・無職・男

45-4

9歳・有職・男

45-4

9歳・無職・男

50-5

4歳・有職・男

50-5

4歳・無職・男

55-5

9歳・有職・男

55-5

9歳・無職・男

60-6

4歳・有職・男

60-6

4歳・無職・男

65-6

9歳・有職・男

65-6

9歳・無職・男

70-7

4歳・有職・男

70-7

4歳・無職・男

0歳・女

1-4歳・女

5-14歳・女

15-1

9歳・有職・女

15-1

9歳・無職・女

20-2

4歳・有職・女

20-2

4歳・無職・女

25-2

9歳・有職・女

25-2

9歳・無職・女

30-3

4歳・有職・女

30-3

4歳・無職・女

35-3

9歳・有職・女

35-3

9歳・無職・女

40-4

4歳・有職・女

40-4

4歳・無職・女

45-4

9歳・有職・女

45-4

9歳・無職・女

50-5

4歳・有職・女

50-5

4歳・無職・女

55-5

9歳・有職・女

55-5

9歳・無職・女

60-6

4歳・有職・女

60-6

4歳・無職・女

65-6

9歳・有職・女

65-6

9歳・無職・女

70-7

4歳・有職・女

70-7

4歳・無職・女

微環境E(屋外)微環境D(屋内・発生源なし)微環境Ｃ(照明）微環境Ｂ(暖房）微環境Ａ(調理+給湯）

0歳・女

1-4歳・女

5-14歳・女

15-1

9歳・有職・女

歳・女

歳・有職・女

歳・女

15-1

9歳・無職・女

20-2

4歳・有職・女

歳・無職・女

歳・有職・女

歳・無職・女

20-2

4歳・無職・女

25-2

9歳・有職・女

歳・無職・女

歳・有職・女

歳・無職・女

25-2

9歳・無職・女

30-3

4歳・有職・女

歳・無職・女

歳・有職・女

歳・無職・女

30-3

4歳・無職・女

35-3

9歳・有職・女

歳・無職・女

歳・有職・女

歳・無職・女

35-3

9歳・無職・女

40-4

4歳・有職・女

歳・無職・女

歳・有職・女

歳・無職・女

40-4

4歳・無職・女

45-4

9歳・有職・女

歳・無職・女

歳・有職・女

歳・無職・女

45-4

9歳・無職・女

50-5

4歳・有職・女

歳・無職・女

歳・有職・女

歳・無職・女

50-5

4歳・無職・女

55-5

9歳・有職・女

歳・無職・女

歳・有職・女

歳・無職・女

55-5

9歳・無職・女

60-6

4歳・有職・女

歳・無職・女

歳・有職・女

歳・無職・女

60-6

4歳・無職・女

65-6

9歳・有職・女

歳・無職・女

歳・有職・女

歳・無職・女

65-6

9歳・無職・女

70-7

4歳・有職・女

歳・無職・女

歳・有職・女

歳・無職・女

70-7

4歳・無職・女

黒龍江省・農村におけるコホート別PM2.5曝露濃度

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PM2.5 average Exposure concentration in Each age group ( Beijing, Uraban)

ME-E(L) (Outdoor-Low)ME-E(M) (Outdoor-Mid)ME-E(H) (Outdoor-High)ME-D (Indoor-w/o-emission)ME-C (Indoor-Lighting)ME-B (Indoor-Heating)ME-A (Indoor-Cooking)

ME-E(L) (Outdoor-Low)ME-E(M) (Outdoor-Mid)ME-E(H) (Outdoor-High)ME-D (Indoor-w/o-emission)ME-C (Indoor-Lighting)ME-B (Indoor-Heating)ME-A (Indoor-Cooking)

This result is still preliminary. values are not yet validated.

This result is still preliminary. values are not yet validated.

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Summary We developed the reconciliation method to estimate more reliable

international and domestic traffic data. We are developing the method to decompose the passenger and

freight transportation demand by individual attribute, trip purpose, type of transportation and trip distance.

This analysis may improve the future prediction of transportation demand and its structure.

We developed the individual exposure model to estimate the

human exposure from both indoor and outdoor air pollution, using WRF, CMAQ, roadside model and Exposure model.

We calculated individual exposure at the 1km or 10km mesh, so that we can estimate the population histogram of exposure concentration.

We developed the emission data from each road segment and the roadside model. More detail co-benefit analysis will be possible.

We developed the reconciliation method Transportation Model

We developed the individual exposure model to estimate the Air Pollution Model

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Thank you for your attention