1 13 th International Emission Inventory Conference June 7-10, 2004 - - PowerPoint PPT Presentation

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13th International Emission Inventory Conference June 7-10, 2004 Clearwater, Florida

Session 7 Data Management Session 7 Data Management

Design of Design of Georeference Georeference-

• Based

Based Emission Activity Modeling Emission Activity Modeling System (G System (G-

• BEAMS) for Japanese

BEAMS) for Japanese Emission Inventory Management Emission Inventory Management

Keisuke Nansai Keisuke Nansai, Noriyuki Suzuki, , Noriyuki Suzuki, Kiyoshi Tanabe, Shinji Kobayashi and Kiyoshi Tanabe, Shinji Kobayashi and Yuichi Yuichi Moriguchi Moriguchi

National Institute for Environmental Studies, National Institute for Environmental Studies, JAPAN JAPAN

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Outlines Outlines

• 1. Background
• 2. Objective
• 3. Materials and Methods

System functions System configuration Emission calculation Spatial distribution Temporal distribution

• 4. Conclusions

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Background Background

A systematic emission inventory is needed

– to improve accuracy of emission inventory – to manage data and methodologies on emission estimations, and – to quantify effect of countermeasure to reduce pollutants.

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Objective Objective

• The aims of this study are to design a

methodology for systematizing an emission inventory building and to develop the emission inventory system actually.

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Necessities for emission inventory Necessities for emission inventory

For emission management and analysis For environmental fate models

・Macro total emission ・Source contribution ・Annual change ・Emission projection ・Quantification of emission reduction measures (fuels change, new tech.) ・Pollutants to be managed (GHG, Air pollutants) ・Emission within calculation domain ・Spatial emission distribution ・Temporal emission distribution ・emitted media (air, water, soil) ・emission condition (height, temp. , velocity) ・Chemical species and physical properties of pollutants

Different concerns Needs for emission inventory system

・Inventories for various types of environmental burdens ・Easy update of emission factors and activity data ・Use of top-down and bottom up methods for emission estimation ・Combination of existing emission data with estimation ・Finding of data to be modified for accuracy improvement ・Open access to data and methods

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Outlines Outlines

• 1. Background
• 2. Objective
• 3. Materials and Methods

System functions System configuration Emission calculation Spatial distribution Temporal distribution

• 4. Conclusions

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System System functions and tools functions and tools

Inventory generator Inventory generator

(Emissions, Spatial and temporal distributions)

Data converter for calculation Data converter for calculation

(Activity data, Factors)

User interface User interface Original data storage Original data storage

(Statistics, Actual emission survey)

Database software Programming functions

Systematizing tools

GIS

System functions

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Recipe of emission inventory Recipe of emission inventory

System User

Recipes of emission inventory

Database Methods Emission calculations Emission inventory

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User’s request Pollutant Year Geometry Domain

• Add. info

Recipes of estimation methods Recipe of emission estimation Recipe of estimation

• f emission’s

temporal distribution Recipe of additional info. Recipe of estimation

• f emission’s spatial

distribution Emission estimation Spatial distribution Emission by actual survey Temporal distribution

Read recipe

Requested area

• Add. Info.

EF Activity

• Chem. spec.
• Phy. spec.

SWF Emission survey GWF Other info. Database

• Geographic. Info.

Conversion into

• utput geometry

Select

Output Control factors Control factors Recipe of control factors Adding emission conditions Source Time Time Emission source Geometry

Flow of building emission inventory Flow of building emission inventory

Generator

Select Select Select Select Select

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Outlines Outlines

• 1. Background
• 2. Objective
• 3. Materials and Methods

System functions System configuration Emission calculation Spatial distribution Temporal distribution

• 4. Conclusions

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System configuration System configuration

• 3. Estimation methods

(Programming function)

• 1. Emission activity

(Database software)

• 2. Location

(GIS)

Predefined geometry given unique number (LinkID)

100001 100002 100004 100005 100006 100007 100008 100009 100010 100011 100012 200001 200003 200004 200005 300001 300002 100003

13 standard layers HSCC LinkID

Estimation of quantity and distribution of emissions Emission activity data with HSCC and LinkID

Method 1 Method 2 +Data Input/Output Input/Output

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The standard layers The standard layers

Grid type Polygonal type

・Prefecture ・City ・Basin ・Agricultural village ・Sea ・Lake ・80km-by-80km ・10km-by-10km ・5km-by-5km ・1km-by-1km

Linear type

・River ・Road

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Outlines Outlines

• 1. Background
• 2. Objective
• 3. Materials and Methods

System functions System configuration Emission calculation Spatial distribution Temporal distribution

• 4. Conclusions

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Main steps of emission inventory Main steps of emission inventory

• 1. Emission by source
• 1. Emission by source
• 3. Spatial distribution
• 3. Spatial distribution
• 4. Temporal
• 4. Temporal

distribution distribution

Source A Source B Emission quantity Source A Source B Emission quantity

• 2. Chemical and physical speciation
• 2. Chemical and physical speciation

time

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Two approaches to building an emission Two approaches to building an emission inventory inventory

Top-down approach

Conversion of emissions on large geometries basis into emissions on smaller geometries basis

Bottom-up approach

Conversion of emissions on small geometries basis into emissions on larger geometries basis

Layer 1 Whole Japan

80 10 10 10 10 20 20 Large geometries

Layer 1 Whole Japan Layer 2 Grids

80 10 10 10 10 20 20 Larger geometries Small geometries

Layer 2 Grids

Smaller geometries

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Recipe for emission calculation Recipe for emission calculation

The inventory recipe format applicable to the top-down and bottom-up approaches

LinkID HSCC Emission Function Input order File name

<Source> <Location> 100100100 100100100 25 25 F1.exe F1.exe 1 2 Activity.mdb EF.mdb

Table name

NOxEF Coal 100100200 100100200 25 25 F2.exe F2.exe 1 2 Activity.mdb Activity.mdb BurnRt Naphtha <Method> <Variations

• rder>

<Database> 100100200 25 F2.exe 3 EF.mdb NOxEF F1[a1,a2].exe F1= a1 x a2 F1[a1,a2].exe F1= a1 x a2 a1=15 a2=2 EF Activity 30 F2[a1,a2, a3].exe F2= a1 x a2 x a3 a1=20 a2=0.5 a3=5 50

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Main steps of emission inventory Main steps of emission inventory

• 1. Emission by sources
• 1. Emission by sources
• 3. Spatial distribution
• 3. Spatial distribution
• 4. Temporal
• 4. Temporal

distribution distribution

Source A Source B Emission quantity Source A Source B Emission quantity

• 2. Chemical and physical speciation
• 2. Chemical and physical speciation

time

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Speciation of pollutant Speciation of pollutant

Emissions Chemical property A (0.3) Chemical property A (0.3) X = 200 x 0.3 = 60 Pollutant [200] Pollutant [200] Chemical property B (0.6) Chemical property B (0.6) X = 200 x 0.6 = 120 Chemical property C (0.1) Chemical property C (0.1) X = 200 x 0.1 = 20 Sum (0.3+0.6+0.1) = 1

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Main steps of emission inventory Main steps of emission inventory

• 1. Emission by sources
• 1. Emission by sources
• 3. Spatial distribution
• 3. Spatial distribution
• 4. Temporal
• 4. Temporal

distribution distribution

Source A Source B Emission quantity Source A Source B Emission quantity

• 2. Chemical and physical speciation
• 2. Chemical and physical speciation

time

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Spatial distribution Spatial distribution

Characteristics of calculation method

• Emission conversion based on the spatial weighting factor (SWF)

– SWF is defined by geometry on a layer. – SWF is normalized value in each standard layer, or the sum of SWF for all geometries on the layer equals 1. – SWF represents the magnitude of emission activity for a geometry.

• The cascade weighting method

– It considers the relationship between geographical resolution and uncertainty of public statistics. – It enables us to convert estimated emission based on a layer to emission based

• n other layer using SWF for each geometry.
• The hybrid weighting method

– Emissions from actual emission survey, existing emission inventory and emission report can be used as emissions at a geometry in preference to emission estimated by the cascade weighting method.

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Uncertainty and resolution Uncertainty and resolution

The number of Source category Types of statistical data Geographical resolution Country Prefecture Grid

Geographical level

City

Many (Large) Little (Small) High Low

• Fig. Relationship between types, sector categories and

geographical resolution of Japanese statistics applicable as emission activity data

Country Grid 40 20 City Uncertainty (%) Uncertainty

20 % decrease 20 % decrease

• 1. Direct conversion from country level

into gird level 1 x (1-0.4) = 0.6 (60 %)

• 2. Cascading conversion from country

level into gird level 1 x (1-0.2) x (1-0.2) = 0.64 (64%)

Where, original information quantity is 1

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The cascade weighting method The cascade weighting method

Layer A

∑

∈ =

⋅ =

j i m m i j i

SWF SWF W X

∑ ∑ ∑

∈ = ∈ = ∈ =

⋅ ⋅ = ⋅ =

i h n n h j i m m i j i h n n h i h

SWF SWF SWF SWF W SWF SWF X Y

Wj Xi

(SWFi) (SWFi+1)

Xi+1

• 1. Conversion of emissions on

layer A into emissions on layer B

Layer B Layer C Yh Yh+2 (SWFh) (SWFh+2 ) Yh+1 Yh+3 (SWFh+1 ) (SWFh+3 )

• 2. Conversion of emissions on

layer B into emission on layer C W, X, Y: Emissions for each geometry SWF: Spatial weighting factor : Geometry i geographically

• verlaps geometry j

j i∈

Uncertainty of SWFs

Low High

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Example of the cascade weighting method Example of the cascade weighting method

[Conversion to Grid b21] 48×0.06/(0.06+0.09 +0.09) = 12

Country (100) Country (100)

• Pref. A (40)
• Pref. A (40)
• Pref. B (60)
• Pref. B (60)

City b1 (12) City b2 (48)

Grid b21(12)

0.4 0.6

0.3 0.1

0.09

City a1 (16) City a2 (24)

0.2 0.4

Grid b22(18) Grid b23(18) Gird b11 Grid b12 Grid b13 Grid a21 Grid a22 Grid a23 Grid a11 Grid a12 Grid a13 0.07 0.08 0.06 0.09 0.09 0.07 0.15 0.06 0.06 0.09 0.09

Spatial weighting factor

Initial estimation

Spatial resolution

Low High

Surrogate validity of spatial weighting factor

High

Cascade weighting

[Conversion to pref. B] 100×0.6/(0.4+0.6) = 60 [Conversion to City b2] 60×0.4/(0.4+0.1) = 48

Low

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Spatial distribution Spatial distribution

Characteristics of calculation method

• Emission conversion based on the spatial weighting factor (SWF)

– SWF is defined by geometry on a layer. – SWF is normalized value in each standard layer, or the sum of SWF for all geometries on the layer equals 1. – SWF represents the magnitude of emission activity for a geometry.

• The cascade weighting method

– It considers the relationship between geographical resolution and uncertainty of public statistics. – It enables us to convert estimated emission based on a layer to emission based

• n other layer using SWF for each geometry.
• The hybrid weighting method

– Emissions from actual emission survey, existing emission inventory and emission report can be used as emissions at a geometry in preference to emission estimated by the cascade weighting method.

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( ) ( )

∑ ∑ ∑

∈ = ∈ = ∈ =

− − ⋅ ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ − + =

j i m m m i i i h n n j i h n n i

r SWF r SWF a W a X 1 1

( ) ( )

∑ ∑

∈ = ∈ =

− − ⋅ ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ − + =

i h n n n h h i h n n i h h

r SWF r SWF a X a Y 1 1

Wj Xi

(SWFi) (SWFi+1)

Xi+1

Layer B Layer C Yh Yh+2 (SWFh) (SWFh+2 ) Yh+1 Yh+3 (SWFh+1 ) (SWFh+3 )

(rh)

value for extrapolation to geometry h

∑ ∑

∈ = ∈ =

=

i h n n i h n n n i

SWF SWF r r

≥ − ∑

∈ = i h n n i

a X

(1) (2)

∑

∈ =

⋅ =

i h n n h i h

SWF SWF X Y

< −

(1) when (2) when

∑

∈ = i h n n i

a X

: Geometry i geographically

• verlaps geometry j

j i

ah

a: Extrapolated emissions r: Rate of domination of SWF related to extrapolated emissions to total SWF of the extrapolated geometry ∈

ah+1 (rh+1)

The hybrid weighting method The hybrid weighting method

W, X, Y: Emissions for each geometry SWF: Spatial weighting factor

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Example of the hybrid weighting method Example of the hybrid weighting method

Note 0.01 0.008 0.012 SWF: Spatial weighting factor

Emission 100 Emission 50 Emission 150

Layer 1

Extrapolating emission of 10

• btained by actual survey to Grid 4

Preferentially 10 is determined as emission of Grid 4 within the total 100, assuming that the ratio of SWF of extrapolated emission to SWF of the total emission at Grid 4 is 1.

Emission allocated to Grid 1 (100-10)×0.01/(0.01+0.008+0.012) = 30

Grid 1 Grid 2 Grid 3 Grid 4

(30) (36) (24) (10)

Emission allocated to Grid 2 (100-10)×0.008/(0.01+0.008+0.012) = 24 Emission allocated to Grid 3 (100-10)×0.012/(0.01+0.008+0.012) = 36

In the case that extrapolated emission is more than the total emission to be allocated on the layer 2, the extrapolated emission is deducted from the total emission of the larger geometry in the layer 2 layers above layer 3. Layer 2 Layer 3

Emission <Calculation process> Note

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Method of transforming spatial weighting factors Method of transforming spatial weighting factors between polygonal geometries between polygonal geometries

• 1. Transformation of SWFs

∑

∈ =

=

i j p i p i

y x Y

∑

∈ =

⋅ =

j i k B A B A j i j

k j i j

SWF SWF X y x

( )

i i j

B B A ∈

Bi

(Yi, SWFBi)

Area A1 projected

• nto B1: A1B1

Layer A Layer B

Aj (Xj)

AjBi AjBi+1

( )

i i j B B A

B B A Ar SWF SWF

i i j

/ ⋅ =

( )

1 1 /

1

+ +

⋅ =

+

i i j B B A

B B A Ar SWF SWF

i i j

• 2. Emission xjyi of projected area

AjBi

Bi+1 (Yi+1, SWFBi+1) Area A1 projected

• nto B2: A1B2
• 3. Emission Yi of geometry Bi

Ar(G1/G2): Area ratio of geometry G1 to geometry G2 X, Y: Emissions for each geometry SWF: Spatial weighting factor : Geometry i geographically

• verlaps geometry j

j i∈

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Method of transforming spatial weighting factors Method of transforming spatial weighting factors between polygonal and linear geometries between polygonal and linear geometries

• 1. Transformation of SWFs

Line Aj projected

• nto Bi: AjBi

Layer A Layer B

Line Aj projected onto Bi+1: AjBi+1 Aj (Xj)

Aj+1Bi AjBi AjBi+1

Aj+1 (Xj+1) Bi

(Yi, SWFBi)

Bi+1 (Yi+1, SWFBi+1)

∑

∈ =

⋅ =

j i k B A B A j i j

k j i j

SWF SWF X y x

⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ ⋅ =

∑

∈ = i j s i s i j B B A

B A B A Lr SWF SWF

i i j

/

⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛ ⋅ =

∑

+ ∈ = + +

+ +

1 1 1 /

1 1

i j t i t i j B B A

B A B A Lr SWF SWF

i i j

• 2. Emission xjyi of projected line

AjBi

• 3. Emission Yi of geometry Bi

Lr(G1/G2): Length ratio of geometry G1 to geometry G2 X, Y: Emissions for each geometry

∑

∈ =

=

i j p i p i

y x Y

SWF: Spatial weighting factor : Geometry i geographically

• verlaps geometry j

j i∈

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Main steps of emission inventory Main steps of emission inventory

• 1. Emission by sources
• 1. Emission by sources
• 3. Spatial distribution
• 3. Spatial distribution
• 4. Temporal
• 4. Temporal

distribution distribution

Source A Source B Emission quantity Source A Source B Emission quantity

• 2. Chemical and physical speciation
• 2. Chemical and physical speciation

time

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Temporal distribution Temporal distribution

Characteristics of calculation method

• Emission allocation based on the temporal

weighting factor (TWF)

– Fundamental methodology is the same as the US EPA’s method (Ryan, 2003). – TWF is defined by time unit (year, month, week, day, hour). – TWF is normalized value in each time unit, or the sum

• f TWF for each time unit equals 1.

– SWF represents the magnitude of emission activity for the time.

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Schematic graphs of temporal weighting Schematic graphs of temporal weighting factors by time unit factors by time unit

12 Sat. 24 5 1 1 1 1

Month Week Day of week Hour

31 1

Date

P 1

Year

∑

=

=

12 1

1

m m

TWF

∑

=

=

p a a

TWF

1

1

∑

=

=

7 1

1

d d

TWF

∑

=

=

5 1

1

w w

TWF

∑

=

=

31 1

1

da da

TWF

∑

=

=

24 1

1

h h

TWF

h d w m a p h d w m

TWF TWF TWF TWF TWF X X ⋅ ⋅ ⋅ ⋅ ⋅ =

, , , h da m a p h da m

TWF TWF TWF TWF X X ⋅ ⋅ ⋅ ⋅ =

, ,

• 1. Emission on h hour of d day of w week in m month
• 2. Emission on h hour of da date in m month

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Outlines Outlines

• 1. Background
• 2. Objective
• 3. System functions

System configuration Emission calculation Spatial distribution Temporal distribution

• 4. Conclusions

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Conclusions Conclusions

• 1. This study proposed methodologies to systematize

emission inventory with GIS and database software.

• 2. Defining geographical position by geometries on a

layer of GIS is useful to systematize main procedures

• f emission estimations.
• 3. The cascade weighting method and the hybrid

weighting method using SWFs were developed to determine spatial emission distribution.

• 4. In our system, temporal emission distribution is

calculated by the same method as the US EPA using TWFs.

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Than Thank you for k you for your attention! your attention! Questions by Questions by slowly speaking slowly speaking and easy words! and easy words!

Keisuke Nansai

nansai.keisuke@nies.go.jp

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Environmental fate model

Emission inventory Health risk Ecological risk

Policy options

• Pollutant Release and

Transfer Register (PRTR) Environmental monitoring data Social and economic data (Population, Production) Emission factor Activity data

D Driving force

riving force

P Pressure

ressure

E Effect

ffect

R Response

esponse

Field and mechanism studies

Countermeasures

Hazard database Epidemiology

Data disclosure

Emission test and investigation

• Traffic census
• Energy data
• Chemical statistics

Emission model

Environmental pollution level

S State

tate

Emission Data

Exposure model

Discussion

(Geographic information system)

GIS

• Chassis dynamo test
• Tunnel experiment
• Emission experiment

Environmental fate model

Emission inventory Health risk Ecological risk

Policy options

• Pollutant Release and

Transfer Register (PRTR) Environmental monitoring data Social and economic data (Population, Production) Emission factor Activity data

D Driving force

riving force

P Pressure

ressure

E Effect

ffect

R Response

esponse

Field and mechanism studies

Countermeasures

Hazard database Epidemiology

Data disclosure

Emission test and investigation

• Traffic census
• Energy data
• Chemical statistics

Emission model

Environmental pollution level

S State

tate

Emission Data

Exposure model

Discussion

(Geographic information system)

GIS

• Chassis dynamo test
• Tunnel experiment
• Emission experiment
• Fig. Schematic of the Virtual World