trip distance amazonaws com
17th AIM International Workshop 17th – 18th February, 2012 NIES, Tsukuba, JAPAN
Kyoto University
Setsunan University
Gakuji KURATA, Soichi MORIMOTO, Minna GUO, Yuzuru MATSUOKA Yoko SHIMADA 1
Transportation model
Air Pollution model
We developed a reconciliation tool for domestic and international Transport statistics to estimate the reliable past traffic 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 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. 2
framerowk of traffic demand model 【Passenger】 regional population Domestic
【Freight】 Sociopopulation/ economic population development change model scenario
World CGE Model
Domestic
International trip gene. unit by purpose
Freight generation unit
Modal share
Modal share
（Walk, Bicycle, Car, Bus, Railway, Aviation）
Trip length
Domestic Traffic Demand
International Traffic Demand
Passenger Transport demand
Production/ transport demand by commodity International
(truck, railroad, ship)
Transport distance
Transportation Policy Urban Infrastructure Plan Effect of City-Country Planning
Domestic Freight demand
International Freight demand
Freight Transport demand
3
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] 4
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] 5
【Passenger】
Task
Data
STEP1
Domestic
STEP2-P • Population • Trip generation unit • Trip distance share by type • Trip distance share by Purpose
domestic
Modal split
Domestic
Traffic Demand
Production/ transport demand by 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 Transport demand
international
Transportation Policy Urban Infrastructure Plan Effect of City-Country Planning
Domestic Freight demand
International Freight demand
Freight Transport demand
Energy consumption
Each type of Transport Total Freight Transport
STEP2-F
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
Trip generation unit modal share trip distance
Freight generation unit modal share transportation distance
• • •
Production + import Freight generation unit Distance share by type
6
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 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 2 t j h wch' VK ', j CHVK j ,t 2 wer' VK ', j ERVK j , h,t 2 h
wch' TV ', j CHTV j ,t 2 wer' TV ', j ERTV j ,h ,t 2 h
wch' PS ', j CHPS j ,t 2 wer' PS ', j ERPS j , h ,t 2 h
wch' TN ', j CHTN j ,t 2 wer' TN ', j ERTN j , h ,t 2 h
wch' ENT ', j CHENT j ,t wer' ENT ', j ERENT j , h ,t 2
2
h
wer' DS ', j ERDS j , h ,t 2 wer' LF ', j ERLFj ,h ,t 2 h
h
wer' FEV ', j ERFEV j , h ,t 2 wer' FET ', j ERFET j ,h ,t 2 h
Fuel 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 World Road Statistics 63-89, 1995,2001,2002,2006 World Develoement Indicators 2005 World Develoement Indicators 2007 Statistical Year book 47 EUROSTAT(web) ODYSSEE OECD Environmental Data Compendium 2006/2007 Transport Statistical Trends in Transport 1965-1994 Trends in the Transport Sector 1970-2005 World Bank's Railway Database Energy Balnces of OECD Countries, Energy Balnces of non-OECD Countries Globalstat World Motor Vehicle Statistics International Historical Statistics China Statistical Yearbook Korea Statistical Yearbook TEDDY(TERI Energy Data Directory & Yearbook) Land Transport Statistics Japan Automobile Transportation statistics Japan National Transportation Statistics 2008 World Railways ANNUAL BULLETIN OF TRANSPORT STATISTICS FOR EUROPE AND NORTH AMERICA Handbook of Transport Statistics in the UNECE region 2006 Malaysia Transport Statistics 2006 Statistical Handbook of Vietnam Philippine Yearbook 2006 USSR Facts & Figures Annual Statistical Yearbook Thailand Transport and Communications Yearbook 2003 Transport Statistics Great Britain 2005 China Energy Databook 2004 Statistical Abstract of Transport in Asia and the Pacific 2007 Philippine Statistical Yearbook 1991 North American Transportation Statistics Taiwan Statistical Databook 2007 Statistical Yearbook of Bangladesh 2005
Source IRF, 2003,1995,2001,2002,2006 World Bank, 2005 World Bank, 2007 UN, 2003 European Comission, webからDL(2008/4/11) ADEME, 2002 OECD, 2007 ECMT, 1998 ECMT, 2007 World Bank, 2001 IEA, 2007 enerdata, 2006 Japan Automobile Manufacturers Association, 2004, 2008 Mitchell, 2003 National Bureau of Statistics of China, 1993, 1994, 1995, 2000, 2001, 2006 Korea National Statistical Office, 2002, 2006 TERI, 2002, 2006 MLIT Japan, 2001, 2006 MLIT Japan 1987-2006 Bureau of Transportation Statistics, U.S. Department of Transportaion, 2008 Japan Railway Technical Service, 2005
UN Economic Commission for Europe, 2004, 2005, 2008 UN Economic Commission for Europe, 2006 , 2006 General Statistics Office of Vietnam, 2005, 2006 National Statistics Office, 2006 Academic International Press, 1977,1979,1987,1990,1991,1992 National Statistical Office, 1998,2000,2002,2004 SWEDISH INSTITUTE FOR TRANSPORT AND COMMUNICATIONS ANALYSIS, 2003 DEPARTMENT FOR TRANSPORT, 2005 Lawrence Berkeley National Laboratory, 2004 UN Economic and Social Commission for Asia and the Pacific, 2007 Republic of the Phillippine National Statistical Coordination Board, 1991 North American Transportation Statistics Database, 2007 Council for Economic Planning and Development Exective Yuan, R.O.C., 2007 Bngladesh Bureau of Statistics, 2005
8
Sample image of Result output p-km of passenger car (Australia)
Mil-p-km
Example of the Result
9
9
【Passenger】
Task
Data
STEP1
Domestic
STEP2-P • Population • Trip generation unit • Trip distance share by type • Trip distance share by Purpose
domestic
Modal split
Domestic
Traffic Demand
Production/ transport demand by 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 Transport demand
international
Transportation Policy Urban Infrastructure Plan Effect of City-Country Planning
Domestic Freight demand
International Freight demand
Freight Transport demand
Energy consumption
Each type of Transport Total Freight Transport
STEP2-F
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
Trip generation unit modal share trip distance
Freight generation unit modal share transportation distance
• • •
Production + import Freight generation unit Distance share by type
10
International Domestic
Domestics transport from STEP1 Demand Population Passenger Transport demand by individual attribute by each type of transport Reference Value（Japan） calibration Program to estimate the parameters of passenger transport
Output
Trip generation unit by individual attribute, purpose of trip
Modal share by type of transpiration, purpose of trip and 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
Representative Trip distance by each distance category
distance category Short Middle Long
11
International
Domestic
Each country Production + import by each commodity
from STEP1 Freight transportation amount by each type of transportation
Reference Value（Japan） calibration Program to estimate the parameters for Freight transportation
Freight generation unit by each commodity Distance category share by each type of transportation
Output Freight generation unit by commodity
Modal share by type of transportation and distance category
Representative distance for each distance category
distance category Short Middle Long
12
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 300000 waste
ton-km by mode domestic naviation
fishery product 250000
mil-ton-km
total: 5.9×1011ton-km
freight rail truck
200000 150000 100000 50000 0
～300km
100～750km
750km～
13
ton-km by commodities mining product agriculture product ceramic, soil and stone forest product other product
total: 6.8×1011ton-km
oil and coal product food product metal product textile product machine chemical product 300000
ton-km by mode
fishery product
mil-ton-km
250000
200000
domestic naviation 150000
freight rail truck
100000
50000
0 ～300km
100～750km
750km～
14
Transportation model
Air Pollution model
We developed Country level Transportation model to estimate the traffic volume which is consistent with available statistics.
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”, “transportation facility”, “personal attribute” and “trip distance”.
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.
15
Estimation of Individual Exposure to PM2.5 Time use survey
Staying duration in each micro-environment for each age group
Duration
Emission inventory
Traffic by province
Traffic by each road
WRF / CMAQ
Roadside Model
Background concentration
Concentration from road traffic
Energy / Fuel consumption in Household Information about Housing
Indoor PM Emission
Concentration
Micro-environment individual Exposure Model Exposure to each age group
16
Target Area
Target Year： 2005
27 Provinces and 4 Municipalities
Polutant ： ＰＭ2.5 17
Micro-environment individual Exposure Model Daily average exposure concentration Classification of individual attribute
E Cm T a
a m
m
Concentration in micro-environ.
×
Stay duration
54 age 0-4,5-14, 15-19, .....70-74, 75+ 15group Male / Female Employed / Unemployed
Daily average concentration a exposure の1日平均曝露濃度(μ g/m3) E a : コホート
Cm : 微環境 m の汚染物質濃度(μ Concentration in micro-environment g/m3) a m
T
a
Duration ofa stay in micro-environment m m 滞在時間率(-) : コホート の微環境 Individual attribute group : コホート 18
Considered micro-environment in this model Micro Indoor Place Purpose Emission source Environ.
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 D
Indoor
Lighting
Kerosene lamp
Night time
Indoor
‐
None
Inside Building (Home and Building)
E(H)
‐
‐
Outdoor activity (500 m from road)
Time use survey Adult (age 15 and above)： Time use survey (10 provinces) Child：from literatures
19
19
Micro-environment individual Exposure Model Single-Compartment Mass Balance Model under steady-state assumption Cm
1 v Fd
Se F vC p o V
Formulation to calculate concentration. with Indoor emission ME A、B、C
Se 1 Cm ( Fp vC0 ) V v Fd
Cm
m における大気汚染物質濃度(μ : 微環境 g/m3) (m) (μg/m3) Pollutant concentration at micro environment
Co
3 Pollutant concentration atg/m Outdoor (μg/m3) : 屋外大気汚染物質濃度(μ )
Fp : 浸透率(-) Penetration Factor (-) v : 換気回数(1/hr) Air Exchange Rate (1/hr)
w/o indoor emission ME D Fp v Cm Co v Fd
Fd
Deposition rate (1/hr) : 除去率(1/hr)
S
: 一時間当たり燃料消費量(KJ/hr) Energy consumption (KJ/hr)
Outdoor ME E
e
Emission Factor (μg/KJ) : 排出係数(μ g/KJ)
V
3 Volume of Micro : 部屋の体積(m ) Environment(m3)
Cm Co
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
40° E 50° E 60° E 70° E
90° E
110° E
130° E
150° E
170° E 180°
50° N
WRF
40° N
CMAQ 30° N
20° N
10° N
Target Area Lambert Conformal Center：112°E 21°N Standard latitude :10°, 30°
0°
10° S
WRF
CMAQ
Grid Size
80km
80km
Number of Grids (East-West) Number of Grids (North-South)
120
117
105
102
20° S
Calculation Term 1 Jan 2001
- 31 Dec 2001 22
22
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)
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： ①Whole China Case： 10km ②Beijing Fine Case： 1km • Divided a mesh to 3 classes by the distance from road. C H M
100m
M
L
L
500m
「High」： 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： 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 」
Fuel Share by Fuel type and purpose [%] 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
0 0 0 67 33 0
Agr. Residue 作物残渣
0 0 0 66 34 0
Coal 石炭 46 54 0 68 32 0
Kerosene LPG 灯油 LPG 100 0 0 0 0 100
100 0 0 100 0 0
Natural Gas Heat 天然ガス 熱 100 0 0 100 0 0
0 100 0 0 0 0
26
27
27
Population Density
Individual Exposure Concentration
from LandScan2008
Outdoor Concentration
This result is still preliminary. Absolute value of concentration is not yet validated.
+ Contribution from Indoor emission
category (Person) Population in each 曝露人口 （人）
Population Histogram in Exposure concentration.
•
3500000
Urban
3000000
Rural 2500000 2000000
•
1500000 1000000 500000
0 50 100 150 200 250 300 350 400 450 500 550 600 650 700 750 800 850 900 950
Exposure Concentration Category 年平均PM2.5曝露濃度 カテゴリー
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.
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 not yet validated.
Average PM2.5 exposure concentration for Male, age 65-69, unemployed (㎍/m3) 29
30
Comparison 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E(屋外)低 ME-E(M) (Outdoor-Mid) 微環境E(屋外)中 ME-E(H) (Outdoor-High) 微環境E(屋外)高 ME-D (Indoor-w/o-emission) 微環境D(屋内・発生源なし) ME-C (Indoor-Lighting) 微環境Ｃ(照明） ME-B (Indoor-Heating) 微環境Ｂ(暖房） ME-A (Indoor-Cooking) 微環境Ａ(調理+給湯）
天津
北京
四川
ME-E(L) (Outdoor-Low)
1800is still This result 1600 preliminary. values1400 are not 1200 yet validated. 1000 800 600 400 200 0
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
100 80 60 40 20 0
重慶
ME-E(L) (Outdoor-Low) 微環境E(屋外)低 ME-E(M) (Outdoor-Mid) 微環境E(屋外)中 ME-E(H) (Outdoor-High) 微環境E(屋外)高 ME-D (Indoor-w/o-emission) 微環境D(屋内・発生源なし) ME-C (Indoor-Lighting) 微環境Ｃ(照明） ME-B (Indoor-Heating) 微環境Ｂ(暖房） ME-A (Indoor-Cooking) 微環境Ａ(調理+給湯）
This result is still 160 preliminary. 140 values are not 120 yet validated.
Average PM2.5 exposure (Upper: Urban Lower: Rural) 30
PM2.5一日平均暴露濃度(μ g/m3)
1400
1200
3500 This result is still 1000 preliminary. 3000 values 800 are not yet2500 validated. 600 2000
400
1500
500
0 0歳 1-4歳 5-14歳 15-19歳・有職 15-19歳・無職 20-24歳・有職 20-24歳・無職 25-29歳・有職 25-29歳・無職 30-34歳・有職 30-34歳・無職 35-39歳・有職 35-39歳・無職 40-44歳・有職 40-44歳・無職 45-49歳・有職 45-49歳・無職 50-54歳・有職 50-54歳・無職 55-59歳・有職 55-59歳・無職 60-64歳・有職 60-64歳・無職 65-69歳・有職 65-69歳・無職 70-74歳・有職 70-74歳・無職 0歳 1-4歳 5-14歳 15-19歳・有職 15-19歳・無職 20-24歳・有職 20-24歳・無職 25-29歳・有職 25-29歳・無職 30-34歳・有職 30-34歳・無職 35-39歳・有職 35-39歳・無職 40-44歳・有職 40-44歳・無職 45-49歳・有職 45-49歳・無職 50-54歳・有職 50-54歳・無職 55-59歳・有職 55-59歳・無職 60-64歳・有職 60-64歳・無職 65-69歳・有職 65-69歳・無職 70-74歳・有職 70-74歳・無職
0
0歳・男 0歳 1-4歳・男 1-4歳 5-14歳・男 5-14歳 15-19歳・有職・男 15-19歳・有職 15-19歳・無職・男 15-19歳・無職 20-24歳・有職・男 20-24歳・有職 20-24歳・無職・男 20-24歳・無職 25-29歳・有職・男 25-29歳・有職 25-29歳・無職・男 25-29歳・無職 30-34歳・有職・男 30-34歳・有職 30-34歳・無職・男 30-34歳・無職 35-39歳・有職・男 35-39歳・有職 35-39歳・無職・男 35-39歳・無職 40-44歳・有職・男 40-44歳・有職 40-44歳・無職・男 40-44歳・無職 45-49歳・有職・男 45-49歳・有職 45-49歳・無職・男 45-49歳・無職 50-54歳・有職・男 50-54歳・有職 50-54歳・無職・男 50-54歳・無職 55-59歳・有職・男 55-59歳・有職 55-59歳・無職・男 55-59歳・無職 60-64歳・有職・男 60-64歳・有職 60-64歳・無職・男 60-64歳・無職 65-69歳・有職・男 65-69歳・有職 65-69歳・無職・男 65-69歳・無職 70-74歳・有職・男 70-74歳・有職 70-74歳・無職・男 70-74歳・無職 0歳・女 0歳 1-4歳・女 1-4歳 5-14歳・女 5-14歳 15-19歳・有職・女 15-19歳・有職 15-19歳・無職・女 15-19歳・無職 20-24歳・有職・女 20-24歳・有職 20-24歳・無職・女 20-24歳・無職 25-29歳・有職・女 25-29歳・有職 25-29歳・無職・女 25-29歳・無職 30-34歳・有職・女 30-34歳・有職 30-34歳・無職・女 30-34歳・無職 35-39歳・有職 35-39歳・有職・女 35-39歳・無職 35-39歳・無職・女 40-44歳・有職 40-44歳・有職・女 40-44歳・無職 40-44歳・無職・女 45-49歳・有職 45-49歳・有職・女 45-49歳・無職 45-49歳・無職・女 50-54歳・有職 50-54歳・有職・女 50-54歳・無職 50-54歳・無職・女 55-59歳・有職 55-59歳・有職・女 55-59歳・無職 55-59歳・無職・女 60-64歳・有職 60-64歳・有職・女 60-64歳・無職 60-64歳・無職・女 65-69歳・有職 65-69歳・有職・女 65-69歳・無職 65-69歳・無職・女 70-74歳・有職 70-74歳・有職・女 70-74歳・無職 70-74歳・無職・女
PM2.5一日平均暴露濃度(μ g/m3)
Comparison between all Individual attribute group
350 This result is still preliminary. 300 values are not yet250 validated.
200 ME-E(L) (Outdoor-Low) 微環境E(屋外)低 ME-E(M) (Outdoor-Mid) 微環境E(屋外)中 ME-E(H) (Outdoor-High) 微環境E(屋外)高 ME-D (Indoor-w/o-emission) 微環境D(屋内・発生源なし) ME-C (Indoor-Lighting) 微環境Ｃ(照明） ME-B (Indoor-Heating) 微環境Ｂ(暖房） 微環境Ａ(調理+給湯） ME-A (Indoor-Cooking)
150
100
50
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E(屋外)低 ME-E(L) (Outdoor-Low) 微環境Ｃ(照明） 微環境E(屋外)中 ME-E(M) (Outdoor-Mid) 微環境E(屋外)高 微環境Ｂ(暖房） ME-E(H) (Outdoor-High) 微環境D(屋内・発生源なし) ME-D (Indoor-w/o-emission) 微環境Ａ(調理+給湯） 微環境Ｃ(照明） 微環境Ｂ(暖房） ME-B (Indoor-Heating) 微環境Ａ(調理+給湯） ME-A (Indoor-Cooking) ME-C (Indoor-Lighting)
200
1000
0
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 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. Air Pollution Model 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.
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Thank you for your attention
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Kyoto University
Setsunan University
Gakuji KURATA, Soichi MORIMOTO, Minna GUO, Yuzuru MATSUOKA Yoko SHIMADA 1
Transportation model
Air Pollution model
We developed a reconciliation tool for domestic and international Transport statistics to estimate the reliable past traffic 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 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. 2
framerowk of traffic demand model 【Passenger】 regional population Domestic
【Freight】 Sociopopulation/ economic population development change model scenario
World CGE Model
Domestic
International trip gene. unit by purpose
Freight generation unit
Modal share
Modal share
（Walk, Bicycle, Car, Bus, Railway, Aviation）
Trip length
Domestic Traffic Demand
International Traffic Demand
Passenger Transport demand
Production/ transport demand by commodity International
(truck, railroad, ship)
Transport distance
Transportation Policy Urban Infrastructure Plan Effect of City-Country Planning
Domestic Freight demand
International Freight demand
Freight Transport demand
3
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] 4
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] 5
【Passenger】
Task
Data
STEP1
Domestic
STEP2-P • Population • Trip generation unit • Trip distance share by type • Trip distance share by Purpose
domestic
Modal split
Domestic
Traffic Demand
Production/ transport demand by 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 Transport demand
international
Transportation Policy Urban Infrastructure Plan Effect of City-Country Planning
Domestic Freight demand
International Freight demand
Freight Transport demand
Energy consumption
Each type of Transport Total Freight Transport
STEP2-F
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
Trip generation unit modal share trip distance
Freight generation unit modal share transportation distance
• • •
Production + import Freight generation unit Distance share by type
6
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 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 2 t j h wch' VK ', j CHVK j ,t 2 wer' VK ', j ERVK j , h,t 2 h
wch' TV ', j CHTV j ,t 2 wer' TV ', j ERTV j ,h ,t 2 h
wch' PS ', j CHPS j ,t 2 wer' PS ', j ERPS j , h ,t 2 h
wch' TN ', j CHTN j ,t 2 wer' TN ', j ERTN j , h ,t 2 h
wch' ENT ', j CHENT j ,t wer' ENT ', j ERENT j , h ,t 2
2
h
wer' DS ', j ERDS j , h ,t 2 wer' LF ', j ERLFj ,h ,t 2 h
h
wer' FEV ', j ERFEV j , h ,t 2 wer' FET ', j ERFET j ,h ,t 2 h
Fuel 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 World Road Statistics 63-89, 1995,2001,2002,2006 World Develoement Indicators 2005 World Develoement Indicators 2007 Statistical Year book 47 EUROSTAT(web) ODYSSEE OECD Environmental Data Compendium 2006/2007 Transport Statistical Trends in Transport 1965-1994 Trends in the Transport Sector 1970-2005 World Bank's Railway Database Energy Balnces of OECD Countries, Energy Balnces of non-OECD Countries Globalstat World Motor Vehicle Statistics International Historical Statistics China Statistical Yearbook Korea Statistical Yearbook TEDDY(TERI Energy Data Directory & Yearbook) Land Transport Statistics Japan Automobile Transportation statistics Japan National Transportation Statistics 2008 World Railways ANNUAL BULLETIN OF TRANSPORT STATISTICS FOR EUROPE AND NORTH AMERICA Handbook of Transport Statistics in the UNECE region 2006 Malaysia Transport Statistics 2006 Statistical Handbook of Vietnam Philippine Yearbook 2006 USSR Facts & Figures Annual Statistical Yearbook Thailand Transport and Communications Yearbook 2003 Transport Statistics Great Britain 2005 China Energy Databook 2004 Statistical Abstract of Transport in Asia and the Pacific 2007 Philippine Statistical Yearbook 1991 North American Transportation Statistics Taiwan Statistical Databook 2007 Statistical Yearbook of Bangladesh 2005
Source IRF, 2003,1995,2001,2002,2006 World Bank, 2005 World Bank, 2007 UN, 2003 European Comission, webからDL(2008/4/11) ADEME, 2002 OECD, 2007 ECMT, 1998 ECMT, 2007 World Bank, 2001 IEA, 2007 enerdata, 2006 Japan Automobile Manufacturers Association, 2004, 2008 Mitchell, 2003 National Bureau of Statistics of China, 1993, 1994, 1995, 2000, 2001, 2006 Korea National Statistical Office, 2002, 2006 TERI, 2002, 2006 MLIT Japan, 2001, 2006 MLIT Japan 1987-2006 Bureau of Transportation Statistics, U.S. Department of Transportaion, 2008 Japan Railway Technical Service, 2005
UN Economic Commission for Europe, 2004, 2005, 2008 UN Economic Commission for Europe, 2006 , 2006 General Statistics Office of Vietnam, 2005, 2006 National Statistics Office, 2006 Academic International Press, 1977,1979,1987,1990,1991,1992 National Statistical Office, 1998,2000,2002,2004 SWEDISH INSTITUTE FOR TRANSPORT AND COMMUNICATIONS ANALYSIS, 2003 DEPARTMENT FOR TRANSPORT, 2005 Lawrence Berkeley National Laboratory, 2004 UN Economic and Social Commission for Asia and the Pacific, 2007 Republic of the Phillippine National Statistical Coordination Board, 1991 North American Transportation Statistics Database, 2007 Council for Economic Planning and Development Exective Yuan, R.O.C., 2007 Bngladesh Bureau of Statistics, 2005
8
Sample image of Result output p-km of passenger car (Australia)
Mil-p-km
Example of the Result
9
9
【Passenger】
Task
Data
STEP1
Domestic
STEP2-P • Population • Trip generation unit • Trip distance share by type • Trip distance share by Purpose
domestic
Modal split
Domestic
Traffic Demand
Production/ transport demand by 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 Transport demand
international
Transportation Policy Urban Infrastructure Plan Effect of City-Country Planning
Domestic Freight demand
International Freight demand
Freight Transport demand
Energy consumption
Each type of Transport Total Freight Transport
STEP2-F
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
Trip generation unit modal share trip distance
Freight generation unit modal share transportation distance
• • •
Production + import Freight generation unit Distance share by type
10
International Domestic
Domestics transport from STEP1 Demand Population Passenger Transport demand by individual attribute by each type of transport Reference Value（Japan） calibration Program to estimate the parameters of passenger transport
Output
Trip generation unit by individual attribute, purpose of trip
Modal share by type of transpiration, purpose of trip and 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
Representative Trip distance by each distance category
distance category Short Middle Long
11
International
Domestic
Each country Production + import by each commodity
from STEP1 Freight transportation amount by each type of transportation
Reference Value（Japan） calibration Program to estimate the parameters for Freight transportation
Freight generation unit by each commodity Distance category share by each type of transportation
Output Freight generation unit by commodity
Modal share by type of transportation and distance category
Representative distance for each distance category
distance category Short Middle Long
12
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 300000 waste
ton-km by mode domestic naviation
fishery product 250000
mil-ton-km
total: 5.9×1011ton-km
freight rail truck
200000 150000 100000 50000 0
～300km
100～750km
750km～
13
ton-km by commodities mining product agriculture product ceramic, soil and stone forest product other product
total: 6.8×1011ton-km
oil and coal product food product metal product textile product machine chemical product 300000
ton-km by mode
fishery product
mil-ton-km
250000
200000
domestic naviation 150000
freight rail truck
100000
50000
0 ～300km
100～750km
750km～
14
Transportation model
Air Pollution model
We developed Country level Transportation model to estimate the traffic volume which is consistent with available statistics.
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”, “transportation facility”, “personal attribute” and “trip distance”.
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.
15
Estimation of Individual Exposure to PM2.5 Time use survey
Staying duration in each micro-environment for each age group
Duration
Emission inventory
Traffic by province
Traffic by each road
WRF / CMAQ
Roadside Model
Background concentration
Concentration from road traffic
Energy / Fuel consumption in Household Information about Housing
Indoor PM Emission
Concentration
Micro-environment individual Exposure Model Exposure to each age group
16
Target Area
Target Year： 2005
27 Provinces and 4 Municipalities
Polutant ： ＰＭ2.5 17
Micro-environment individual Exposure Model Daily average exposure concentration Classification of individual attribute
E Cm T a
a m
m
Concentration in micro-environ.
×
Stay duration
54 age 0-4,5-14, 15-19, .....70-74, 75+ 15group Male / Female Employed / Unemployed
Daily average concentration a exposure の1日平均曝露濃度(μ g/m3) E a : コホート
Cm : 微環境 m の汚染物質濃度(μ Concentration in micro-environment g/m3) a m
T
a
Duration ofa stay in micro-environment m m 滞在時間率(-) : コホート の微環境 Individual attribute group : コホート 18
Considered micro-environment in this model Micro Indoor Place Purpose Emission source Environ.
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 D
Indoor
Lighting
Kerosene lamp
Night time
Indoor
‐
None
Inside Building (Home and Building)
E(H)
‐
‐
Outdoor activity (500 m from road)
Time use survey Adult (age 15 and above)： Time use survey (10 provinces) Child：from literatures
19
19
Micro-environment individual Exposure Model Single-Compartment Mass Balance Model under steady-state assumption Cm
1 v Fd
Se F vC p o V
Formulation to calculate concentration. with Indoor emission ME A、B、C
Se 1 Cm ( Fp vC0 ) V v Fd
Cm
m における大気汚染物質濃度(μ : 微環境 g/m3) (m) (μg/m3) Pollutant concentration at micro environment
Co
3 Pollutant concentration atg/m Outdoor (μg/m3) : 屋外大気汚染物質濃度(μ )
Fp : 浸透率(-) Penetration Factor (-) v : 換気回数(1/hr) Air Exchange Rate (1/hr)
w/o indoor emission ME D Fp v Cm Co v Fd
Fd
Deposition rate (1/hr) : 除去率(1/hr)
S
: 一時間当たり燃料消費量(KJ/hr) Energy consumption (KJ/hr)
Outdoor ME E
e
Emission Factor (μg/KJ) : 排出係数(μ g/KJ)
V
3 Volume of Micro : 部屋の体積(m ) Environment(m3)
Cm Co
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
40° E 50° E 60° E 70° E
90° E
110° E
130° E
150° E
170° E 180°
50° N
WRF
40° N
CMAQ 30° N
20° N
10° N
Target Area Lambert Conformal Center：112°E 21°N Standard latitude :10°, 30°
0°
10° S
WRF
CMAQ
Grid Size
80km
80km
Number of Grids (East-West) Number of Grids (North-South)
120
117
105
102
20° S
Calculation Term 1 Jan 2001
- 31 Dec 2001 22
22
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)
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： ①Whole China Case： 10km ②Beijing Fine Case： 1km • Divided a mesh to 3 classes by the distance from road. C H M
100m
M
L
L
500m
「High」： 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： 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 」
Fuel Share by Fuel type and purpose [%] 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
0 0 0 67 33 0
Agr. Residue 作物残渣
0 0 0 66 34 0
Coal 石炭 46 54 0 68 32 0
Kerosene LPG 灯油 LPG 100 0 0 0 0 100
100 0 0 100 0 0
Natural Gas Heat 天然ガス 熱 100 0 0 100 0 0
0 100 0 0 0 0
26
27
27
Population Density
Individual Exposure Concentration
from LandScan2008
Outdoor Concentration
This result is still preliminary. Absolute value of concentration is not yet validated.
+ Contribution from Indoor emission
category (Person) Population in each 曝露人口 （人）
Population Histogram in Exposure concentration.
•
3500000
Urban
3000000
Rural 2500000 2000000
•
1500000 1000000 500000
0 50 100 150 200 250 300 350 400 450 500 550 600 650 700 750 800 850 900 950
Exposure Concentration Category 年平均PM2.5曝露濃度 カテゴリー
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.
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 not yet validated.
Average PM2.5 exposure concentration for Male, age 65-69, unemployed (㎍/m3) 29
30
Comparison 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E(屋外)低 ME-E(M) (Outdoor-Mid) 微環境E(屋外)中 ME-E(H) (Outdoor-High) 微環境E(屋外)高 ME-D (Indoor-w/o-emission) 微環境D(屋内・発生源なし) ME-C (Indoor-Lighting) 微環境Ｃ(照明） ME-B (Indoor-Heating) 微環境Ｂ(暖房） ME-A (Indoor-Cooking) 微環境Ａ(調理+給湯）
天津
北京
四川
ME-E(L) (Outdoor-Low)
1800is still This result 1600 preliminary. values1400 are not 1200 yet validated. 1000 800 600 400 200 0
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
100 80 60 40 20 0
重慶
ME-E(L) (Outdoor-Low) 微環境E(屋外)低 ME-E(M) (Outdoor-Mid) 微環境E(屋外)中 ME-E(H) (Outdoor-High) 微環境E(屋外)高 ME-D (Indoor-w/o-emission) 微環境D(屋内・発生源なし) ME-C (Indoor-Lighting) 微環境Ｃ(照明） ME-B (Indoor-Heating) 微環境Ｂ(暖房） ME-A (Indoor-Cooking) 微環境Ａ(調理+給湯）
This result is still 160 preliminary. 140 values are not 120 yet validated.
Average PM2.5 exposure (Upper: Urban Lower: Rural) 30
PM2.5一日平均暴露濃度(μ g/m3)
1400
1200
3500 This result is still 1000 preliminary. 3000 values 800 are not yet2500 validated. 600 2000
400
1500
500
0 0歳 1-4歳 5-14歳 15-19歳・有職 15-19歳・無職 20-24歳・有職 20-24歳・無職 25-29歳・有職 25-29歳・無職 30-34歳・有職 30-34歳・無職 35-39歳・有職 35-39歳・無職 40-44歳・有職 40-44歳・無職 45-49歳・有職 45-49歳・無職 50-54歳・有職 50-54歳・無職 55-59歳・有職 55-59歳・無職 60-64歳・有職 60-64歳・無職 65-69歳・有職 65-69歳・無職 70-74歳・有職 70-74歳・無職 0歳 1-4歳 5-14歳 15-19歳・有職 15-19歳・無職 20-24歳・有職 20-24歳・無職 25-29歳・有職 25-29歳・無職 30-34歳・有職 30-34歳・無職 35-39歳・有職 35-39歳・無職 40-44歳・有職 40-44歳・無職 45-49歳・有職 45-49歳・無職 50-54歳・有職 50-54歳・無職 55-59歳・有職 55-59歳・無職 60-64歳・有職 60-64歳・無職 65-69歳・有職 65-69歳・無職 70-74歳・有職 70-74歳・無職
0
0歳・男 0歳 1-4歳・男 1-4歳 5-14歳・男 5-14歳 15-19歳・有職・男 15-19歳・有職 15-19歳・無職・男 15-19歳・無職 20-24歳・有職・男 20-24歳・有職 20-24歳・無職・男 20-24歳・無職 25-29歳・有職・男 25-29歳・有職 25-29歳・無職・男 25-29歳・無職 30-34歳・有職・男 30-34歳・有職 30-34歳・無職・男 30-34歳・無職 35-39歳・有職・男 35-39歳・有職 35-39歳・無職・男 35-39歳・無職 40-44歳・有職・男 40-44歳・有職 40-44歳・無職・男 40-44歳・無職 45-49歳・有職・男 45-49歳・有職 45-49歳・無職・男 45-49歳・無職 50-54歳・有職・男 50-54歳・有職 50-54歳・無職・男 50-54歳・無職 55-59歳・有職・男 55-59歳・有職 55-59歳・無職・男 55-59歳・無職 60-64歳・有職・男 60-64歳・有職 60-64歳・無職・男 60-64歳・無職 65-69歳・有職・男 65-69歳・有職 65-69歳・無職・男 65-69歳・無職 70-74歳・有職・男 70-74歳・有職 70-74歳・無職・男 70-74歳・無職 0歳・女 0歳 1-4歳・女 1-4歳 5-14歳・女 5-14歳 15-19歳・有職・女 15-19歳・有職 15-19歳・無職・女 15-19歳・無職 20-24歳・有職・女 20-24歳・有職 20-24歳・無職・女 20-24歳・無職 25-29歳・有職・女 25-29歳・有職 25-29歳・無職・女 25-29歳・無職 30-34歳・有職・女 30-34歳・有職 30-34歳・無職・女 30-34歳・無職 35-39歳・有職 35-39歳・有職・女 35-39歳・無職 35-39歳・無職・女 40-44歳・有職 40-44歳・有職・女 40-44歳・無職 40-44歳・無職・女 45-49歳・有職 45-49歳・有職・女 45-49歳・無職 45-49歳・無職・女 50-54歳・有職 50-54歳・有職・女 50-54歳・無職 50-54歳・無職・女 55-59歳・有職 55-59歳・有職・女 55-59歳・無職 55-59歳・無職・女 60-64歳・有職 60-64歳・有職・女 60-64歳・無職 60-64歳・無職・女 65-69歳・有職 65-69歳・有職・女 65-69歳・無職 65-69歳・無職・女 70-74歳・有職 70-74歳・有職・女 70-74歳・無職 70-74歳・無職・女
PM2.5一日平均暴露濃度(μ g/m3)
Comparison between all Individual attribute group
350 This result is still preliminary. 300 values are not yet250 validated.
200 ME-E(L) (Outdoor-Low) 微環境E(屋外)低 ME-E(M) (Outdoor-Mid) 微環境E(屋外)中 ME-E(H) (Outdoor-High) 微環境E(屋外)高 ME-D (Indoor-w/o-emission) 微環境D(屋内・発生源なし) ME-C (Indoor-Lighting) 微環境Ｃ(照明） ME-B (Indoor-Heating) 微環境Ｂ(暖房） 微環境Ａ(調理+給湯） ME-A (Indoor-Cooking)
150
100
50
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E(屋外)低 ME-E(L) (Outdoor-Low) 微環境Ｃ(照明） 微環境E(屋外)中 ME-E(M) (Outdoor-Mid) 微環境E(屋外)高 微環境Ｂ(暖房） ME-E(H) (Outdoor-High) 微環境D(屋内・発生源なし) ME-D (Indoor-w/o-emission) 微環境Ａ(調理+給湯） 微環境Ｃ(照明） 微環境Ｂ(暖房） ME-B (Indoor-Heating) 微環境Ａ(調理+給湯） ME-A (Indoor-Cooking) ME-C (Indoor-Lighting)
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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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Summary
Transportation Model 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. Air Pollution Model 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.
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Thank you for your attention
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