Local Air Pollution Modelling,
AIM/Air
Takeshi Fujiwara
Kyoto University
2005.3.10

Topics• Calculation of impact of traffic sector on airquality– By using traffic volume data measured in traffic
census, emission from traffic sector wasdisaggregated into line source of road networks.

• Improvement of AIM/Air: inclusion of land use
information– Based on land use information, total emission ofeach sector was disaggregated to a set of area
which is used by the sector.

[The 1st topic]
Calculation of impact ofTraffic Sector on Air Quality

Outline• The method of estimating traffic volume

on road networks by using limited numberof measurement data are applied.
• Diffusion of emission gas and particles(NOx、SOx、PM) from automobiles wascalculated.
• Line emission source was regarded as aseries of point sources.

Back ground・Research on emission from automobiles byDr. Hao et. al., which had been published
as a book 「城市机动车排放汚染控制 」
・Hao’s group have already calculated
emission diffusion from automobiles inBeijing.
・Emission condition of automobiles, such as
emission coefficient for each type of car,
was assumed based on MOBLIE 5 model.

Flow Diagram of Calculating Concentration
Related with Transport Sector
Road andTraffic Data

GIS data

Estimation ofTraffic VolumeEmission RatioMeteorology
Data

Calculation ofStability

Estimated Emission

Section ofTraffic
Estimation

Diffusion Calculation

Section of Diffusion
Calculation

Traffic Data Required
・Road data and traffic volume data measured- Road length, width, connection.
- Measured flow rate of automobile.
・Traffic volume- How many automobiles pass over per hour.
・Emission ratio of automobile- NOx emission ratio of each type ofautomobiles is used.

Traffic volume estimation• Using road network, land use, andactual traffic volume data, traffic
volume of each road was estimated.
• OD traffic volume estimation method
based on link flow was applied.
• To accelerate computation speed
cluster computer system was used.

Traffic Volume Estimation• We adopted a method to approximate unknown traffic
volume with the small number of traffic volume data.

• Traffic volume is estimated based on the following
equation, it is so called gravity model.
OD traffic volumeX ij = αA i B j t ijγ・This model requires information of trip generation (Ai) at
one node and trip attraction (Bj) at the other node.
・Land use information is used to identify thecharacteristics of the node.

Utilization of Land Use Data・A circle, which radius is 300m, shows anode of road networks.
・A sector which has the largest area in thecircle is identified.
・Initial value of trip generation (Ai) and trip
attraction (Bj) between two sectors is set.
・Traffic volume data measured are used topredict unmeasured traffic volume.

Procedure of traffic volume
estimation - 11) Traveling time at the initial condition thateach link traffic volume is zero is estimated.
2)A path with minimum traveling time
between OD pairs is searched.
3)Using DIJKSTRA algorithm, each OD traffic
volume is distributed to the path withminimum traveling time.

Procedure of traffic volume
estimation - 2-4)Update the minimum traveling time of eachlink path.

(T = T0 ⋅ 1.0 + 2.62 × (v/C )5)5)Update the link traffic volume.

v(m)a1 ⎞ (m − 1) 1⎛+va= ⎜1 − ⎟ ⋅ vam⎠m⎝6)Go back to step 2), until each link traffic
volume is converged.

推定交通量Traffic volume
estimated (台/時)(cars/hour)

Validation of Traffic Volume
Estimation
8000

Relationcoefficient
相関 係 数 0 .7 7

y = 1 .0 5 2 5 x

7000
6000
5000
4000
3000
2000
1000
00
2000
4000 6000 8000
Traffic volume
measured(cars/hour)
実測交
通量 (台/時)Diffusion Calculation• By using ArcGIS, road is divided into

fractions by 100m x 100m mesh inorder to regard emission source on
the road as a series of point sources.
• Diffusion is computed by AIM-AIR
with cluster computers.

Diffusion equation
Plume diffusion equation2 ⎞ ⎡⎛⎧ ( z − He ) 2 ⎫⎧ ( z + He ) 2 ⎫ ⎤y⎟・⎢ exp ⎨ −⋅ exp ⎜ −C ( x, y , z ) =⎬ + exp ⎨ −
⎬⎥
222⎜
⎟
2πσ y σ z u22σσ2σy ⎠ ⎢zz⎩⎭⎩⎭ ⎥⎦⎣⎝Qp

z：Height of receptor(m)Q p ：Emission at point source(Particles：kg/s，Gas：m3N/s)

u ：Wind velocity (m/s) He ：Effective stack height (m)σ y (x )： Diffusion parameter of y axis at distance x(-)σ z (x) ： Diffusion parameter of y axis at distance x(-)

C ( x, y, z ) ： Concentration at receptor (x,y,z)Demonstration• Area: the center of Beijing city
• Period: Jan 1st, 2000 ~ Jan 14th, 2000
• Time step: 1 hour
• Emission source: automobiles• Traffic pattern: hourly change in a day• Meteorology data: ECMWF• Model: Plume or Puff model for a line of point

sources (each point source covers emission fromautomobiles on road within 100m x 100m area)[The 2nd topic]
Improvement of AIM/Air: inclusionof land use information

Purpose of this study• Improvement of accuracy in emission

database• Spatial distribution of emission by using
land information• Evaluating affection of each sector on airquality• System integration of emission estimationand diffusion calculation in AIM/Air

Diffusion calculation procedureof AIM/Air
Emission estimation
by end use model
Large point source

Confirmation oflocation of LPS

Energyend usedatabase

Land use
map

Vector map
Satellite image

Area source

Disaggregation
to county level

Generation of land use data
digitized with ArcInfo

Disaggregation to30” x 30” mesh

Rearrangement ofemission based on land use

Population
distribution

Diffusion calculation of air pollutants
By using AIM/Air air quality model

Meteorology data（ECMWF）

Estimation of emission• Large point source：Power, cement, iron & steel, nonferrous
material sectors and other sectors with large emission.
• Area source：Transport, commercial and public sectors andother sectors with small emission.
発電運輸
北京市
上海市
天津市
重慶市

セメント
商業部門
鉄鋼
非鉄金属
家庭（都市部） 家庭（農村部）

その他

エンドユースモデル地域区分（31地域）
河北省
福建省
四川省
内蒙古自治区
山西省
江西省
貴州省
広西チワン族自治区
遼寧省
山東省
雲南省
寧夏回族自治区
吉林省
河南省
陝西省チベット自治区
黒竜江省
湖北省
甘粛省
新疆ウイグル自治区
江蘇省
湖南省
青海省
浙江省
広東省
安徽省
海南省

Disaggregation of Area Source Emission
to County Level• Emission of 31 provinces was disaggregated tomore detailed level, 2347 counties.

SectorPower
CementIron & Steel
Nonferros
Transport
Commerce
PublicOtherIndex to disaggregate fromprovince level to county level
GDP of secondary industries
4.24E+08

GDP of tertiary industries
Population
0.0

Distribution map of SO2 emission
from power sector（kg/yr）

Disaggregation to 30”x30” Mesh• Emission from county level was disaggregated to30” x 30” mesh by using population distribution.
Emission from county

4.24E+08

0.0

9.12E+05

人口分布データ
デPopulation
distribution

0.00.65

Distribution map of SO2 emission
from power sector（kg/yr）
0.0

Land Use Map of Urban Area
• A digital map of land use was made by overlaying land use
map scanned, location information (VMAP), and satellite
image.

Digitized land use information

Public building
公共建築
area

Commercial
商業中心区
area

School &学校&病院

Hospital area

Residential
居住areaIndustrial
area &工業&倉庫
WarehouseRearrangement by Using
Land Use Information• Emission data of each 30” x 30” cells isrearranged to actual one by using sector’s areaand population
Residential area

Population density

9.33E+04

0.0

Emission distribution by using population density（kg/yr）
4.35E+04

0.0

Redisaggregated one by using
information of residential area（kg/yr）
1.00E+05

0.0

Characteristics of AIM/AIR Air
quality model• Diffusion equations: Puff model is applied at thetime the wind speed is small (< 1m/s),otherwise plume model is applied.
• Accelerated algorithm in diffusion calculation isadopted.
• Concentration every hour is calculated.
• Emission pattern (time change of emittingpollutants) is definable for each sector.
• Meteorology data are interpolated at thereceptors where concentration is calculated.

Emission Patterns -1• Hourly change

•

0.06

Magnitude of emission– Cement, Iron & Steel, Nonferrous, Commercial
sectors: Constant emission
from 9 to 19 o’clock– Power and Public sectors:
Emission pattern was set
based on actual
consumption of electricity ina day
Daily change– Cement, Iron &steel, Nonferrous sectors: Noemission on Sat. & Sun.
Constant emission on theother days

0.05
0.04
0.03
0.02
0.01
0.00
06

12O'clock

1824

Emission Patterns -2• Monthly change– Commercial and Public
sectors: Emission fraction
corresponding to eachmonth was decided sothat heating devices
may be much used in
the month which
averaged temperatureof the month is morethan 10 degree.

• Special term or day– Cement, Iron & Steel,Non-ferrous sections:No emission on thespecial term or day

Mont h1
2
3
4
5
6
7
8
910
1112

Emission fractions
Commer ci al
Publ i c
0.447
0.243
0.121
0.1690.066
0.076
0.011
0.052
0.009
0.051
0.051
0.009
0.009
0.051
0.009
0.051
0.009
0.051
0.013
0.052
0.071
0.078
0.178
0.125
1.000
1.000

Special term or day
1-Jan
新年
5-Feb ～7-Feb
春節
29-Apr ～5-May
労働祭
29-Sep ～3-Oct
国慶祭

System Integration ofEmission Projection
and Air Quality Model• To integrate AIM/Enduse and AIM/Air,reconfirmation of LPS data base,rearrangement of AIM/Air data format,
documentation were conducted.
• Spatial disaggregation of emission to sector’sarea by using land use information wasperformed with ArcInfo and Access.
• 32 clustered computers aided diffusion
calculation.

Result of estimated emission by using
end use model, AIM/Local China
Others
23.5%

Power
41.1%

Transport
1.0%
Commerce
5.1%
Public（Urbanarea）
5.3%

Transport
12.0%

Others17.0%

Commerce
0.9%

Public（Ruralarea）
10.4%

Cement Nonferrous
4.1%
5.8%

Iron & Steel
3.7%

Share of SO2 emission by sector

Public（Urban area）
1.3%
Public（Rural area）
Cement Nonferrous
2.6%
4.4%
2.7%
Iron & Steel
7.5%

Share of NOx emission by sector• Total emission of SO2 was 18,206Kt、 total emission of NOx was•••

Power
51.6%

11,033Kt.
Contribution from power sector was the largest in both SO2 and NOxemission
Emission from public sector occupied 15.7% of total emission.
Emission from traffic sector occupied 12.0% of total emission.

General Result of Air Quality
Estimation• AIM/Air Air quality model was applied toestimation of SO2、NOx concentration of Beijing,Shanghai, and Chongqing.
• The estimated and the observed values of year
averaged concentration were compared.
SO2(mg/m3)
NOx(mg/m3)
Estimated Observed Estimated Observed
Beijing
0.11
0.08
0.08
0.14
Shanghai
0.03
0.04
0.03
0.10Chongqing
0.11
0.170.070.06*) The observed values are reported in China Energy Databook

Result of SO2 Concentration
in Beijing
Daily averaged SO2 concentration

1.2• Remarkable seasonal

1.0
PublicCommerce
Transport
OthersNonferrous
Iron & Steel
Cement
Power

0.8

0.6

0.4

0.2

0.0
1/1

3/1

5/1

7/1

9/1

11/1

Contribution to SO2 concentration

100%

80%

Public60%

change, specially highconcentration in winterseason.
• Concentration on 63 days
was over the 2nd levelstandard (0.15mg/m3)• Concentration on 35 days
was over the 3rd levelstandard (0.25mg/m3)• Commercial and power
sectors were main
contributors to SO2
concentration

Commerce
Transport
Others40%

Nonferrous

Iron & Steel
Cement20%

Power0%Jan.

Feb. Mar.

Apl.

May Jun.

Jul.

Aug. Sep. Oct. Nov. Dec.

Chongqing

Shanghai
0.09
0.08
0.07
Public0.06

Commerce
Transport

0.05

OthersNonferrous

0.04

Iron & Steel

0.03

Cement
Power

0.02
0.01

Daily averaged SO2 concentration (mg/m3)

Daily averaged SO2 concentration (mg/m3)

Comparison of SO2 Concentrations ofShanghai and Chongqing with Beijing’s

0.00
1/1

3/1

5/1

7/1

9/1

0.40

Public0.35

Commerce
0.30

Transport

0.25

OthersNonferrous

0.20

Iron & Steel

0.15

Cement
Power

0.10
0.05
0.00
1/1

11/1

3/1

5/1

7/1

9/1

11/1

80%
PublicCommerce

60%

Transport
OthersNonferrous

40%

Iron & Steel
CementPower

20%

Contribution to SO2 concentration

100%

100%

Contribution to SO2 concentration

0.45

80%

PublicCommerce
60%

Transport
OthersNonferrous

40%

Iron & Steel
CementPower20%0%0%
Jan. Feb. Mar.

Apl.

May Jun. Jul.

Aug. Sep. Oct. Nov. Dec.

Jan. Feb. Mar. Apl.

May Jun. Jul. Aug. Sep. Oct. Nov. Dec.

Daily averaged NOx concentration (mg/m3)

Result of NOx Concentration in Beijing• Seasonal change and0.30

0.25

PublicCommerce

0.20

Transport
Others0.15

Nonferrous
Iron & Steel
Cement

0.10

Power
0.05

0.00
1/1

3/1

5/1

7/1

9/1

11/1

Contribution to Nox concentration

100%

80%
PublicCommerce
60%

Transport
OthersNonferrous

40%

Iron & Steel
CementPower20%

0%Jan. Feb. Mar.

Apl.

May Jun.

Jul.

Aug. Sep. Oct. Nov. Dec.

high concentration inwinter season.
• Concentration on 64 days
was over the 2nd levelstandard (0.15mg/m3)• Concentration on 11 days
was over the 3rd levelstandard (0.25mg/m3)• Power sectors was a main
contributor to NOx
concentration.

Comparison of NOx Concentrations ofShanghai and Chongqing with Beijing’s
0.09
0.08
0.07
Public0.06

Commerce
Transport

0.05

OthersNonferrous

0.04

Iron & Steel

0.03

CementPower

0.02
0.01
0.00
1/1

3/1

5/1

7/1

9/1

0.16

Public0.14

Commerce
Transport

0.12

Others0.10

Nonferrous
Iron & Steel

0.08

Cement
Power

0.06
0.04
0.02
0.00
1/1

11/1

3/1

5/1

7/1

9/1

11/1

100%

80%
PublicCommerce

60%

Transport
OthersNonferrous

40%

Iron & Steel
CementPower

20%

Contribution to NOx concentration

100%

Contribution to NOx concentration

Chongqing

0.18

Daily averaged NOx concentration (mg/m3)

Daily averaged NOx concentration (mg/m3)

Shanghai

80%

PublicCommerce
60%

Transport
OthersNonferrous

40%

Iron & Steel
Cement
Power

20%

0%0%
Jan. Feb. Mar.

Apl.

May Jun. Jul.

Aug. Sep. Oct. Nov. Dec.

Jan. Feb. Mar. Apl.

May Jun. Jul. Aug. Sep. Oct. Nov. Dec.

Conclusion• By estimating emission from road networks,

diffusion of air pollutant from transport sector
was calculated.
• Land use information added more reality tolocation of area emission source.
• Concentration of SO2、NOｘ in Beijing, Shanghai,and Chongqing were estimated and evaluated .
• This system becomes a powerful tool to assess
impact on air pollution briefly.