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

Gakuji KURATA,
Soichi MORIMOTO,
Minna GUO,
Yuzuru MATSUOKA

Setsunan University

Yoko SHIMADA
1

Transportation
model

Air Pollutionmodel

We developed a reconciliation tool fordomestic and international Transport
statistics to estimate the reliable pasttraffic volume.

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

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

In the exposure model, we developed
roadside model to consider the highconcentration 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.
2

framerowk of traffic demand model【Freight】【Passenger】
regional
population
Domestic

Sociopopulation/
economic
population
development
scenariochange model

Domestic

International

Domestic
Freight
demandInternational
Freight
demandtrip gene.
unit
by purpose

Freight
generation
unit

Modal share

Modal share

Trip length
Domestic
Traffic
DemandWorld CGE
Model

International（Walk, Bicycle,
Car, Bus, Railway,
Aviation）

International
Traffic
DemandPassenger TransportdemandProduction/transport demandby commodity(truck, railroad,ship)

Transport
distance

Transportation Policy
Urban Infrastructure
Plan
Effect of City-Country
Planning

Freight Transportdemand3

i: transportation type(domestic or international)
pa: personal attribute
pt: purpose of trip
d: representative distance
m: modePTV: passenger transportation volume[p-km]
POP: effective population[person]
PTG: trip generation rate[number/person]
PTS: modal share[-]
DIS: trip distance[km]4i: transportation type(domestic or international)
com: commodity
d: representative distance
m: modeFTV: 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]5【Passenger】

Task

Data

STEP1

Domestic

STEP2
STEP2-P• Population• Trip generation unit
• Trip distance shareby type• Trip distance shareby Purpose

domestic

Modal split

Domestic

Traffic Demand

Production/transport demandby commodity

World CGE
Model
(35 region)

International
trip gene.
unit
by purpose

Reconciliation of traffic data
1971-2005,
Possession of vehicle, transport, Total transport,
energy consumption using statistical data

Each type of Transport
Total Passenger Transport

population/
population
change model

regional
population

Traffic Statistics
Energy Statistics

Each type of transport
Total
Possession
transportation【Freight】
Socioeconomic
development
scenario

Freight
generation
unit

Modal Split（Walk, Bicycle,
Car, Bus, Railway,
Aviation）(truck, railroad,ship)

Trip length

Transport
distance

International
Traffic Demand

Passenger Transportdemandinternational

Transportation Policy
Urban Infrastructure Plan
Effect of City-Country
Planning

Domestic
Freight demandInternational
Freight demandFreight TransportdemandEnergyconsumption

Each type of Transport
Total Freight Transport

STEP2-F

Decomposition ofPassenger Transport
Estimation of the parametersof passenger transportation
at 2005

Decomposition of
Freight Transport
Estimation of the parameterof Freight transportation
at 2005

Trip generation unit
modal sharetrip distanceFreight generation unit
modal sharetransportation distance•••

Production + import
Freight generation unit
Distance share by type6



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 thesevalues ( 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 timeseries.
Numbers of Vehicle
Objective Function


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)ER    wch' ST ', j  CHST j ,t 2  wer' ST ', j   ERST j , h,t 2tj h wch' VK ', j  CHVK j ,t 2  wer' VK ', j   ERVK j , h,t 2h wch' TV ', j  CHTV j ,t 2  wer' TV ', j   ERTV j , h ,t 2h wch' PS ', j  CHPS j ,t 2  wer' PS ', j   ERPS j , h ,t 2h wch' TN ', j  CHTN j ,t 2  wer' TN ', j   ERTN j , h,t 2h wch' ENT ', j  CHENT j ,t  wer' ENT ', j   ERENT j , h ,t22h wer' DS ', j   ERDS j , h ,t 2  wer' LF ', j   ERLFj , h ,t 2hh

 wer' FEV ', j   ERFEV j , h ,t 2  wer' FET ', j   ERFET j , h ,t 2hFuel consumption(ktoe / vehicle, ktoe / km person , ktoe / km t)

Average trip distance(Passenger),
Average transport distance(Freight)h wer' ATD ', j   ERATD j , h ,t 2  wer' AFD ', j   ERAFD j , h ,t 2 
h
h




min

7

Used International and domestic statics for transportation
Statistics Name
Name㻌World Road Statistics 63-89, 1995,2001,2002,2006㻌
World Develoement Indicators 2005㻌
World Develoement Indicators 2007㻌
Statistical Year book 47㻌
EUROSTAT(web)㻌
ODYSSEE㻌
OECD Environmental Data Compendium 2006/2007 Transport㻌
Statistical Trends in Transport 1965-1994㻌
Trends in the Transport Sector 1970-2005㻌
World Bank's Railway Database㻌Energy Balnces of OECD Countries,
Energy Balnces of non-OECD Countries㻌
Globalstat㻌
World Motor Vehicle Statistics
International Historical Statistics㻌
China Statistical Yearbook㻌
Korea Statistical Yearbook㻌
TEDDY(TERI Energy Data Directory & Yearbook) 㻌Land Transport Statistics Japan
Automobile Transportation statistics Japan㻌
National Transportation Statistics 2008㻌
World Railways㻌
ANNUAL BULLETIN OF TRANSPORT STATISTICS FOR EUROPE ANDNORTH AMERICA㻌
Handbook of Transport Statistics in the UNECE region 2006㻌
Malaysia Transport Statistics 2006㻌
Statistical Handbook of Vietnam㻌
Philippine Yearbook 2006㻌
USSR Facts & Figures Annual㻌
Statistical Yearbook Thailand㻌
Transport and Communications Yearbook 2003㻌
Transport Statistics Great Britain 2005㻌
China Energy Databook 2004㻌
Statistical Abstract of Transport in Asia and the Pacific 2007㻌
Philippine Statistical Yearbook 1991㻌
North American Transportation Statistics㻌
Taiwan Statistical Databook 2007㻌
Statistical Yearbook of Bangladesh 2005㻌

Source㻌IRF, 2003,1995,2001,2002,2006㻌
World Bank, 2005㻌
World Bank, 2007㻌
UN, 2003㻌
European Comission, webからDL(2008/4/11)㻌
ADEME, 2002㻌
OECD, 2007㻌
ECMT, 1998㻌
ECMT, 2007㻌
World Bank, 2001㻌
IEA, 2007㻌
enerdata, 2006㻌
Japan Automobile Manufacturers Association, 2004, 2008
Mitchell, 2003㻌
National Bureau of Statistics of China, 1993, 1994, 1995, 2000, 2001, 2006㻌
Korea National Statistical Office, 2002, 2006㻌
TERI, 2002, 2006㻌
MLIT Japan, 2001, 2006
MLIT Japan 1987-2006㻌
Bureau of Transportation Statistics, U.S. Department of Transportaion, 2008㻌
Japan Railway Technical Service, 2005㻌

UN Economic Commission for Europe, 2004, 2005, 2008㻌
UN Economic Commission for Europe, 2006㻌
, 2006㻌
General Statistics Office of Vietnam, 2005, 2006㻌
National Statistics Office, 2006㻌
Academic International Press, 1977,1979,1987,1990,1991,1992㻌
National Statistical Office, 1998,2000,2002,2004㻌
SWEDISH INSTITUTE FOR TRANSPORT AND COMMUNICATIONS ANALYSIS, 2003㻌
DEPARTMENT FOR TRANSPORT, 2005㻌
Lawrence Berkeley National Laboratory, 2004㻌
UN Economic and Social Commission for Asia and the Pacific, 2007㻌
Republic of the Phillippine National Statistical Coordination Board, 1991㻌
North American Transportation Statistics Database, 2007㻌
Council for Economic Planning and Development Exective Yuan, R.O.C., 2007㻌
Bngladesh Bureau of Statistics, 2005㻌

8

Sample image of Result output
p-km of passenger car (Australia)

Mil-p-km

Exampleof the Result

99

【Passenger】

Task

Data

STEP1

Domestic

STEP2
STEP2-P• Population• Trip generation unit
• Trip distance shareby type• Trip distance shareby Purpose

domestic

Modal split

Domestic

Traffic Demand

Production/transport demandby commodity

World CGE
Model
(35 region)

International
trip gene.
unit
by purpose

Reconciliation of traffic data
1971-2005,
Possession of vehicle, transport, Total transport,
energy consumption using statistical data

Each type of Transport
Total Passenger Transport

population/
population
change model

regional
population

Traffic Statistics
Energy Statistics

Each type of transport
Total
Possession
transportation【Freight】
Socioeconomic
development
scenario

Freight
generation
unit

Modal Split（Walk, Bicycle,
Car, Bus, Railway,
Aviation）(truck, railroad,ship)

Trip length

Transport
distance

International
Traffic Demand

Passenger Transportdemandinternational

Transportation Policy
Urban Infrastructure Plan
Effect of City-Country
Planning

Domestic
Freight demandInternational
Freight demandFreight TransportdemandEnergyconsumption

Each type of Transport
Total Freight Transport

STEP2-F

Decomposition ofPassenger Transport
Estimation of the parametersof passenger transportation
at 2005

Decomposition of
Freight Transport
Estimation of the parameterof Freight transportation
at 2005

Trip generation unit
modal sharetrip distanceFreight generation unit
modal sharetransportation distance•••

Production + import
Freight generation unit
Distance share by type10

International
Domestic

Domestics transport
from STEP1
DemandPopulation
Passenger Transport demand
by individual attributeby each type of transport
Reference Value（Japan）
calibration
Programto estimate the parameters ofpassenger transport

OutputTrip generation unit
by individual attribute,
purpose of trip

Modal shareby type of transpiration,
purpose of trip anddistance category

Trip generation unit
by individual attribute andPurpose of trip
Distance shareby the type of transportation
Distance shareby Purpose of trip

Representative Trip distanceby each distance category

distance
category
 Short Middle
 Long11

International

Domestic

Each country
Production + importby each commodity

from STEP1
Freight transportation amountby each type of transportation

calibration
Programto estimate the parametersfor Freight transportation

Reference Value（Japan）
Freight generation unit
by each commodity
Distance category shareby each type of transportation

OutputFreight generation unit
by commodity

Modal shareby type of transportationand distance category

Representative distancefor each distance category

distance
category
 Short Middle
 Long12

ton-km by commodities

mining productother productforest productmachinefood productagriculture productceramic, soil and stone
oil and coal productmetal productpaper and pulp
chemical producttextileproduct
300000waste

ton-km by mode
domestic naviation

fishery
product
250000

mil-ton-km

total: 5.9×1011ton-km

freight railtruck

20000015000010000050000
0～300km100～750km

750km～

13ton-km by commodities
mining productagriculture productceramic, soil and stone
forest productother producttotal: 6.8×1011ton-km

oil and coal productfood productmetal producttextile productmachinechemical product300000fishery productton-km by mode

mil-ton-km

250000200000domestic naviation

150000freight rail
truck

10000050000
0～300km100～750km

750km～

14

Transportation
model

Air Pollution
model

We
developed
Country
level
Transportation model to estimate thetraffic volume which is consistent withavailable statistics.

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

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

In the exposure model, roadside model toconsider the high concentration from roadtraffic.

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

15

Estimation of Individual Exposure to PM2.5
Time usesurveyStaying duration ineach
micro-environment
for each age group

Duration

Emission
inventory

Traffic byprovince

Traffic by
each road

WRF /
CMAQ

Roadside
Model

Backgroundconcentration

Concentrationfrom road traffic

Energy / Fuelconsumptionin Household
Information
about Housing
Indoor PM
Emission

Concentration

Micro-environment individual Exposure Model
Exposure to each age group

16

Target Area

Target Year：㻌 200527 Provinces and4 Municipalities

Polutant㻌 ：㻌 ＰＭ2.5
17Micro-environment individual Exposure Model
Daily average exposure concentrationaE C Tm

Concentrationin micro-environ.

mam

× Stayduration

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

Daily average
concentration
a exposureの㻝日平均曝露濃度㻔μ㼓㻛㼙㻟㻕E a 㻦㻌コホート

Cm 㻦 微環境
m の汚染物質濃度㻔μConcentrationin micro-environment
㼓㻛㼙㻟㻕
a
m

T

Duration ofa
stay
in micro-environmentmm 滞在時間率㻔㻙㻕㻦㻌コホートの微環境

aIndividual attribute group㻦㻌コホート18

Considered micro-environment in this model
Micro
IndoorPlace Purpose Emission source
Environ.

Duration of stayA

IndoorCooking
Hot water

Oven,
Cooking Stove

Cooking

B

IndoorHeatingHeating Stove,
Fireplace

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

C
D

IndoorLighting

Kerosene lamp

Night time

Indoor‐None

Inside Building (Home and Building)㻌 E(H)
㻌 E(M) Outdoor
㻌 E(L)‐‐

Outdoor activity (<100 m from road)

‐
‐

‐
‐

Outdoor activity ( 100-500 m from road)
Outdoor activity (>500 m from road)

Time use survey
Adult (age 15 and above)：
Time use survey (10 provinces)Child：from literatures19

19

Micro-environment individual Exposure Model
Single-Compartment Mass Balance Model under steady-state
assumption

Cm

1 
Se 
F
vC

 p o

v  Fd V Formulation to calculate concentration.
with Indoor emission ME A、B、C

Cm

Se
1
( Fp vC0  )V v  Fd 

Cm 㻌 㻦㻌 微環境
m における大気汚染物質濃度㻔μ㼓㻛㼙㻟㻕㻌 (m) (μg/m3)
Pollutantconcentration at micro environment㻟Co 㻌 㻦㻌 屋外大気汚染物質濃度㻔μPollutant concentration at㼓㻛㼙
Outdoor(μg/m3)
㻕㻌

Fp 㻌 㻦㻌 浸透率㻔㻙㻕㻌
Penetration Factor (-)

w/o indoor emission㻌 ME D
Fp v
Cm 
Co
v  FdS㻌㻦㻌 一時間当たり燃料消費量㻔㻷㻶㻛㼔㼞㻕㻌Energy consumption (KJ/hr)

Outdoor ME Ee㻌Emission Factor(μg/KJ)
㻦㻌 排出係数㻔μ㼓㻛㻷㻶㻕㻌

Cm  Cov 㻌 㻦㻌 換気回数㻔㻝㻛㼔㼞㻕㻌
Air Exchange Rate (1/hr)

Fd 㻌 㻦㻌 除去率㻔㻝㻛㼔㼞㻕㻌
Deposition rate (1/hr)㻟V 㻌 㻦㻌 部屋の体積㻔㼙
Volume of Micro㻕㻌 Environment(m3)20

20

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)21㻠㻜㼻㻌㻱 㻡㻜㼻㻌㻱 㻢㻜㼻㻌㻱 㻣㻜㼻㻌㻱㻥㻜㼻㻌㻱㻝㻝㻜㼻㻌㻱

㻝㻟㻜㼻㻌㻱

㻝㻡㻜㼻㻌㻱

㻝㻣㻜㼻㻌㻱 㻝㻤㻜㼻㻌

㻡㻜㼻㻌㻺

㼃㻾㻲

㻠㻜㼻㻌㻺

㻯㻹㻭㻽
㻟㻜㼻㻌㻺

㼀㼍㼞㼓㼑㼠㻌㻭㼞㼑㼍㻌

㻞㻜㼻㻌㻺

㻌

㻸㼍㼙㼎㼑㼞㼠㻌㻯㼛㼚㼒㼛㼞㼙㼍㼘㻌㻯㼑㼚㼠㼑㼞：㻝㻝㻞°㻱㻌㻞㻝°㻺㻌㻿㼠㼍㼚㼐㼍㼞㼐㻌㼘㼍㼠㼕㼠㼡㼐㼑㻌㻦㻝㻜°㻘㻌㻟㻜°㻌

㻝㻜㼻㻌㻺

WRF

CMAQ

Grid Size

80km

80km

Number of Grids(East-West)
Number of Grids(North-South)

120

117

105

102

㻜㼻㻌

㻝㻜㼻㻌㻿

㻞㻜㼻㻌㻿

㻯㼍㼘㼏㼡㼘㼍㼠㼕㼛㼚㻌㼀㼑㼞㼙㻌㻌㻝㻌㻶㼍㼚㻌㻞㻜㻜㻝㻌㻌㻌㻌㻙㻌㻌㻟㻝㻌㻰㼑㼏㻌㻞㻜㻜㻝㻌
22

22



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



WRF ARW(Advanced Research WRF)㻌 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)







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

23

Roadside Model

Road Network Data:
OpenStreetMap• Calculation case and mesh size：㻌 㻌 㻌 㻌 ①Whole China Case： 10km㻌 㻌 㻌 㻌 ②Beijing Fine Case： 1km• Divided a mesh to 3 classes by the
distance from road.
C

L

M

H

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

100m

M

L

500m

24

Definition of the buffer area of Roadside Model

Road : OpenStreetMap

In the case of Beijing 1 km mesh case.

25

25

Indoor Fuel Consumption
Considered Fuel：㻌 Biomass, Coal, petroleum, kerosene, LPG, natural Gas
Energy consumption per capita in Household㻌 ・㻌 Household energy consumption both Urban/Rural：㻌 㻌 㻌 㻌 㻌 㻌 「Labor and population statistics」、「Chinese Energy statistics」
㻌 ・㻌 Household Biomass consumption in Rural area：㻌 㻌 㻌 㻌 㻌 㻌 㻌 「Chinese Rural energy statistics 」㻌
Fuel Share by Fuel type and purpose [%]㻌Purpose
用途
Cooking
調理・給湯Heating
都市 暖房
Urban
Lighting
照明
Cooking
料理・給湯
Rural
Heating
農村 暖房
Lighting
照明

Biomass
バイオマス
Wood
薪

Agr.
Residue
作物残渣

㻜
㻜
㻜
㻢㻣
㻟㻟
㻜

㻜
㻜
㻜
㻢㻢
㻟㻠
㻜

Coal
石炭

Kerosene
LPG
灯油
㻸㻼㻳

㻠㻢
㻡㻠
㻜
㻢㻤
㻟㻞
㻜

㻝㻜㻜
㻜
㻜
㻜
㻜
㻝㻜㻜

㻝㻜㻜
㻜
㻜
㻝㻜㻜
㻜
㻜

NaturalGas Heat
天然ガス
熱
㻝㻜㻜
㻜
㻜
㻝㻜㻜
㻜
㻜

㻜
㻝㻜㻜
㻜
㻜
㻜
㻜

26

27

27

Population Density

Individual Exposure Concentration

from
LandScan2008

OutdoorConcentration

This result is still
preliminary.
Absolute value ofconcentration is
not yet validated.

+ Contribution from Indoor emission

Population in eachcategory (Person)
曝露人口 （人）Population Histogram in Exposure concentration.

•

3500000

Urban

3000000

Rural

2500000
2000000•

1500000
1000000
500000050 100 150 200 250 300 350 400 450 500 550 600 650 700 750 800 850 900 950

Exposure Concentration㻌 Category
年平均PM2.5曝露濃度 カテゴリー

In the Urban area, Major
contributor is “indoor w/oemission”, this mean source ofpollutants is outdoor, and stay
duration to indoor is much longer
than duration at outdoor.
Still exposure in rural area ishigher than urban area due to thehigh usage of biomass fuel in
Kitchen and Heating.

28

Result from Exposure Model

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

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

This result is still
preliminary.
values are notyet validated.

Average PM2.5 exposure concentration for Male, age 65-69,
unemployed (㎍/m3)2930Comparison between all province (Urban / Rural)

甘粛

青海

寧夏

新疆

甘粛

青海

寧夏

新疆

陝西

チベット

雲南

貴州

陝西

チベット

雲南

貴州

四川

重慶

海南

広西

広東

湖南

湖北

河南

山東

江西

福建

安徽

浙江

江蘇

上海

黒龍江

吉林

遼寧

内モンゴル

山西

河北

微環境㻱㻔屋外㻕低
ME-E(M) (Outdoor-Mid)微環境㻱㻔屋外㻕中
ME-E(H) (Outdoor-High)微環境㻱㻔屋外㻕高
ME-D (Indoor-w/o-emission)
微環境㻰㻔屋内・発生源なし㻕
ME-C (Indoor-Lighting)
微環境Ｃ㻔照明）
ME-B (Indoor-Heating)
微環境Ｂ㻔暖房）
ME-A (Indoor-Cooking)
微環境Ａ㻔調理㻗給湯）

天津

北京

四川

ME-E(L) (Outdoor-Low)㻝㻤㻜㻜is still
This result㻝㻢㻜㻜preliminary.
values㻝㻠㻜㻜are not㻝㻞㻜㻜
yet validated.
㻝㻜㻜㻜
㻤㻜㻜
㻢㻜㻜
㻠㻜㻜
㻞㻜㻜
㻜

海南

広西

広東

湖南

湖北

河南

山東

江西

福建

安徽

浙江

江蘇

上海

黒龍江

吉林

遼寧

内モンゴル

山西

河北

天津

北京

㻝㻜㻜
㻤㻜
㻢㻜
㻠㻜
㻞㻜
㻜

重慶

ME-E(L) (Outdoor-Low)
微環境㻱㻔屋外㻕低
ME-E(M) (Outdoor-Mid)微環境㻱㻔屋外㻕中
ME-E(H) (Outdoor-High)微環境㻱㻔屋外㻕高
ME-D (Indoor-w/o-emission)
微環境㻰㻔屋内・発生源なし㻕
ME-C (Indoor-Lighting)
微環境Ｃ㻔照明）
ME-B (Indoor-Heating)
微環境Ｂ㻔暖房）
ME-A (Indoor-Cooking)
微環境Ａ㻔調理㻗給湯）This result is still㻝㻢㻜
preliminary.
㻝㻠㻜values are not㻝㻞㻜
yet validated.

Average PM2.5 exposure(Upper: Urban Lower: Rural)㻌
30

㻼㻹㻞㻚㻡一日平均暴露濃度㻔μ 㼓㻛㼙㻟㻕

1400

1200

㻟㻡㻜㻜
This
result is still
1000
preliminary.
㻟㻜㻜㻜values800 are notyet㻞㻡㻜㻜
validated.
600
㻞㻜㻜㻜

400

200
㻝㻡㻜㻜

㻡㻜㻜

㻜
㻜歳
㻝㻙㻠歳
㻡㻙㻝㻠歳
㻝㻡㻙㻝㻥歳・有職㻝㻡㻙㻝㻥歳・無職㻞㻜㻙㻞㻠歳・有職㻞㻜㻙㻞㻠歳・無職㻞㻡㻙㻞㻥歳・有職㻞㻡㻙㻞㻥歳・無職㻟㻜㻙㻟㻠歳・有職㻟㻜㻙㻟㻠歳・無職㻟㻡㻙㻟㻥歳・有職㻟㻡㻙㻟㻥歳・無職㻠㻜㻙㻠㻠歳・有職㻠㻜㻙㻠㻠歳・無職㻠㻡㻙㻠㻥歳・有職㻠㻡㻙㻠㻥歳・無職㻡㻜㻙㻡㻠歳・有職㻡㻜㻙㻡㻠歳・無職㻡㻡㻙㻡㻥歳・有職㻡㻡㻙㻡㻥歳・無職㻢㻜㻙㻢㻠歳・有職㻢㻜㻙㻢㻠歳・無職㻢㻡㻙㻢㻥歳・有職㻢㻡㻙㻢㻥歳・無職㻣㻜㻙㻣㻠歳・有職㻣㻜㻙㻣㻠歳・無職
㻜歳
㻝㻙㻠歳
㻡㻙㻝㻠歳
㻝㻡㻙㻝㻥歳・有職
歳・有職
㻝㻡㻙㻝㻥歳・無職
歳・無職㻞㻜㻙㻞㻠歳・有職
歳・有職
㻞㻜㻙㻞㻠歳・無職
歳・無職㻞㻡㻙㻞㻥歳・有職
歳・有職
㻞㻡㻙㻞㻥歳・無職
歳・無職㻟㻜㻙㻟㻠歳・有職
歳・有職
㻟㻜㻙㻟㻠歳・無職
歳・無職㻟㻡㻙㻟㻥歳・有職
歳・有職
㻟㻡㻙㻟㻥歳・無職
歳・無職㻠㻜㻙㻠㻠歳・有職
歳・有職
㻠㻜㻙㻠㻠歳・無職
歳・無職㻠㻡㻙㻠㻥歳・有職
歳・有職
㻠㻡㻙㻠㻥歳・無職
歳・無職㻡㻜㻙㻡㻠歳・有職
歳・有職
㻡㻜㻙㻡㻠歳・無職
歳・無職㻡㻡㻙㻡㻥歳・有職
歳・有職
㻡㻡㻙㻡㻥歳・無職
歳・無職㻢㻜㻙㻢㻠歳・有職
歳・有職
㻢㻜㻙㻢㻠歳・無職
歳・無職㻢㻡㻙㻢㻥歳・有職
歳・有職
㻢㻡㻙㻢㻥歳・無職
歳・無職㻣㻜㻙㻣㻠歳・有職
歳・有職
㻣㻜㻙㻣㻠歳・無職
歳・無職

㻜

0歳・男
㻜歳
1-4歳・男㻝㻙㻠歳
5-14歳・男㻡㻙㻝㻠歳
15-19歳・有職・男㻝㻡㻙㻝㻥歳・有職
15-19歳・無職・男㻝㻡㻙㻝㻥歳・無職
20-24歳・有職・男㻞㻜㻙㻞㻠歳・有職
20-24歳・無職・男㻞㻜㻙㻞㻠歳・無職
25-29歳・有職・男㻞㻡㻙㻞㻥歳・有職
25-29歳・無職・男㻞㻡㻙㻞㻥歳・無職
30-34歳・有職・男㻟㻜㻙㻟㻠歳・有職
30-34歳・無職・男㻟㻜㻙㻟㻠歳・無職
35-39歳・有職・男㻟㻡㻙㻟㻥歳・有職
35-39歳・無職・男㻟㻡㻙㻟㻥歳・無職
40-44歳・有職・男㻠㻜㻙㻠㻠歳・有職
40-44歳・無職・男㻠㻜㻙㻠㻠歳・無職
45-49歳・有職・男㻠㻡㻙㻠㻥歳・有職
45-49歳・無職・男㻠㻡㻙㻠㻥歳・無職
50-54歳・有職・男㻡㻜㻙㻡㻠歳・有職
50-54歳・無職・男㻡㻜㻙㻡㻠歳・無職
55-59歳・有職・男㻡㻡㻙㻡㻥歳・有職
55-59歳・無職・男㻡㻡㻙㻡㻥歳・無職
60-64歳・有職・男㻢㻜㻙㻢㻠歳・有職
60-64歳・無職・男㻢㻜㻙㻢㻠歳・無職
65-69歳・有職・男㻢㻡㻙㻢㻥歳・有職
65-69歳・無職・男㻢㻡㻙㻢㻥歳・無職
70-74歳・有職・男㻣㻜㻙㻣㻠歳・有職
70-74歳・無職・男㻣㻜㻙㻣㻠歳・無職
0歳・女㻜歳
1-4歳・女㻝㻙㻠歳
5-14歳・女歳・女㻡㻙㻝㻠歳
15-19歳・有職・女
歳・有職・女㻝㻡㻙㻝㻥歳・有職
15-19歳・無職・女歳・無職・女㻝㻡㻙㻝㻥歳・無職
20-24歳・有職・女
歳・有職・女㻞㻜㻙㻞㻠歳・有職
20-24歳・無職・女歳・無職・女㻞㻜㻙㻞㻠歳・無職
25-29歳・有職・女
歳・有職・女㻞㻡㻙㻞㻥歳・有職
歳・無職・女25-29歳・無職・女㻞㻡㻙㻞㻥歳・無職
30-34歳・有職・女
歳・有職・女㻟㻜㻙㻟㻠歳・有職
30-34歳・無職・女歳・無職・女㻟㻜㻙㻟㻠歳・無職㻟㻡㻙㻟㻥歳・有職
35-39歳・有職・女
歳・有職・女㻟㻡㻙㻟㻥歳・無職
35-39歳・無職・女歳・無職・女㻠㻜㻙㻠㻠歳・有職
40-44歳・有職・女
歳・有職・女㻠㻜㻙㻠㻠歳・無職
40-44歳・無職・女歳・無職・女㻠㻡㻙㻠㻥歳・有職
45-49歳・有職・女
歳・有職・女㻠㻡㻙㻠㻥歳・無職
歳・無職・女45-49歳・無職・女㻡㻜㻙㻡㻠歳・有職
50-54歳・有職・女
歳・有職・女㻡㻜㻙㻡㻠歳・無職
50-54歳・無職・女歳・無職・女㻡㻡㻙㻡㻥歳・有職
55-59歳・有職・女
歳・有職・女㻡㻡㻙㻡㻥歳・無職
歳・無職・女55-59歳・無職・女㻢㻜㻙㻢㻠歳・有職
60-64歳・有職・女
歳・有職・女㻢㻜㻙㻢㻠歳・無職
60-64歳・無職・女歳・無職・女㻢㻡㻙㻢㻥歳・有職
65-69歳・有職・女
歳・有職・女㻢㻡㻙㻢㻥歳・無職
歳・無職・女65-69歳・無職・女㻣㻜㻙㻣㻠歳・有職
70-74歳・有職・女
歳・有職・女㻣㻜㻙㻣㻠歳・無職
70-74歳・無職・女㻼㻹㻞㻚㻡一日平均暴露濃度㻔μ 㼓㻛㼙㻟㻕Comparison between all Individual attribute group

㻟㻡㻜
This
result is still
preliminary.
㻟㻜㻜values are notyet㻞㻡㻜
validated.

㻞㻜㻜

㻝㻡㻜
ME-E(L) (Outdoor-Low)
微環境㻱㻔屋外㻕低
ME-E(M) (Outdoor-Mid)微環境㻱㻔屋外㻕中
ME-E(H) (Outdoor-High)微環境㻱㻔屋外㻕高
ME-D (Indoor-w/o-emission)
微環境㻰㻔屋内・発生源なし㻕
ME-C (Indoor-Lighting)
微環境Ｃ㻔照明）
ME-B (Indoor-Heating)
微環境Ｂ㻔暖房）
微環境Ａ㻔調理㻗給湯）
ME-A (Indoor-Cooking)

㻝㻜㻜

㻡㻜

微環境E(屋外)
PM2.5 average Exposure concentration in Each age group ( Beijing, Uraban)
微環境D(屋内・発生源なし)
微環境㻱㻔屋外㻕低
ME-E(L) (Outdoor-Low)
微環境Ｃ(照明）
微環境㻱㻔屋外㻕中
ME-E(M) (Outdoor-Mid)微環境㻱㻔屋外㻕高
微環境Ｂ(暖房）
ME-E(H) (Outdoor-High)微環境㻰㻔屋内・発生源なし㻕
ME-D (Indoor-w/o-emission)
微環境Ａ(調理+給湯）
微環境Ｃ㻔照明）
微環境Ｂ㻔暖房）
ME-B (Indoor-Heating)
微環境Ａ㻔調理㻗給湯）
ME-A (Indoor-Cooking)
ME-C (Indoor-Lighting)

㻝㻜㻜㻜

0

㻌 黒龍江省・農村におけるコホート別PM2.5曝露濃度31

Summary

Transportation Model We developed the reconciliation method to estimate more reliableinternational and domestic traffic data.
 We are developing the method to decompose the passenger andfreight 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.
Air Pollution Model We developed the individual exposure model to estimate thehuman 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 theroadside model. More detail co-benefit analysis will be possible.

32

Thank you for your attention

33