Recent methodological changes in the GAINS model

M. Amann, W. Asman, I. Bertok, J. Cofala, C. Heyes, Z. Klimont, W. Schöpp, F. Wagner

Meeting of the Task Force on Integrated Assessment ModellingPrague, May 2-4, 2007

Recent methodological changes

• Update to the City-delta methodology

• RAINS cost curve-based optimization replaced by GAINS measured-based optimization

• 5-years meteorological conditions

• EC4MACS work plan

Changes to the City-delta methodology

Changes since December 2006

• New population and city-domain data (“compact” city shapes including ~70% of population)

• Target metric: population-weighted PM2.5 concentration for health impact assessment

• Refined results from the three urban models

• Revised functional relationship

• Multi-year meteorology

• Modified assumptions on urban emissions

“Compact urban shape” for which the urban increment is computed – Prague

Compact urban shapes for which the urban increments are computed

Paris London Lisbon

Krakow Milan Berlin

Urban increments computed by the three models for the 5*5 km center grid cell and population-weighted

0

2

4

6

8

10

12

Berlin Krakow Lisbon London Milan Paris Prague

Inc

rem

ent

(mic

rog

ram

/m3

)

5 km * 5 km Population-weighted

Urban increments computed by Chimere, CAMx, RCG, compared with the City-delta regression

0

2

4

6

8

10

12

14

Berlin Krakow Lisbon London Milan Paris Prague

mic

rog

ram

/m3

Chimere CAMx RCG City-delta

Hypothesis of the City-delta functional relationship

Δc … concentration increment computed with the 3 models

α. β … regression coefficients

D … city diameter

U … wind speed

Δq … change in emission fluxes

d … number of winter days with low wind speed

365

dQ

U

DQ

U

Dc

Urban per-capita emissions by SNAP sector

0.0

0.5

1.0

1.5

2.0

2.5

3.0

Wien

Sofia

Brno

Helsink

iLil

le

Toulon

Valenc

ienne

s

Mon

tpell

ier

Avigno

n

Mue

nche

n

Nuern

berg

Wup

perta

l

Bielef

eld

Chem

nitz

Kasse

l

Halle

Dortm

und

Mila

no

Genov

a

Venez

ia

Amste

rdam

Leide

n

Krako

w

Bydgo

szcz

Porto

Consta

nta

Ljublj

ana

Zarag

oza

Cordo

ba

Stock

holm

Genev

e

Man

ches

ter

Nottin

gham

Portsm

outh

Stoke

-on-

Trent

South

ampt

on

Kingsto

n up

on H

ull

kg P

M2.

5/p

ers

on

/yr

SNAP 2 (Domestic) SNAP 3 (Idustry) Snap 7 (Traffic) SNAP 8 (Other mobile sources)

Emission densities (red) and computed urban increments (blue)

0

5

10

15

20

25

30

Wie

n

Sof

ia

Brn

o

Hel

sink

i

Lille

Tou

lon

Val

enci

enne

s

Mon

tpel

lier

Avi

gnon

Mue

nche

n

Nue

rnbe

rg

Wup

pert

al

Bie

lefe

ld

Che

mni

tz

Kas

sel

Hal

le

Dor

tmun

d

Mila

no

Gen

ova

Ven

ezia

Am

ster

dam

Leid

en

Kra

kow

Byd

gosz

cz

Por

to

Con

stan

ta

Ljub

ljana

Zar

agoz

a

Cor

doba

Sto

ckho

lm

Gen

eve

Man

ches

ter

Not

tingh

am

Por

tsm

outh

Sto

ke-o

n-T

rent

Sou

tham

pton

Kin

gsto

n up

on H

ull

Urban increment (microgram PM2.5/m3) Emission density (t/km2)

Contribution of long-range transport (blue) and local primary PM emissions (red) to urban PM2.5

0

5

10

15

20

25

30

35

Wie

n

Linz

Bru

xelle

s

Gen

t

Sof

ia

Var

na

Pra

ha

Ost

rava

Arh

us

Hel

sink

i

Tur

ku

Mar

seill

e

Lille

Tou

lous

e

Nan

tes

Lens

Gre

nobl

e

Val

enci

enne

s

Met

z

Sai

nt-E

tienn

e

Ren

nes

Bet

hune

Avi

gnon

Dijo

n

Ang

ers

Bre

st

mic

rogr

am P

M2.

5/m

3

Assumed mineral and sea salt Regional backgroundUrban increment AIRBASE monitoring data for urban background 2004

AT BE Bulgaria FI France

Contribution of long-range transport (blue) and local primary PM emissions (red) to urban PM2.5

0

5

10

15

20

25

30

35

Mila

no

Rom

a

Nap

oli

Tor

ino

Pal

erm

o

Gen

ova

Bol

ogna

Fire

nze

Bar

i

Cat

ania

Ven

ezia

Ver

ona

Mes

sina

Pad

ova

Trie

ste

Rig

a

Viln

ius

Kau

nas

Am

ster

dam

Rot

terd

am

Gra

venh

age

Utr

echt

Ein

dhov

en

Leid

en

Dor

drec

ht

Tilb

urg

Hee

rlen

Gro

ning

en

Osl

o

Ber

gen

Kat

owic

e

War

szaw

a

Lodz

Kra

kow

Wro

claw

Poz

nan

Gda

nsk

Szc

zeci

n

Byd

gosz

cz

Lubl

in

Bia

lyst

ok

Gdy

nia

Cze

stoc

how

a

Rad

om

Kie

lce

Tor

un

Lisb

oa

Por

to

mic

rog

ram

PM

2.5

/m3

Assumed mineral and sea salt Regional backgroundUrban increment AIRBASE monitoring data for urban background 2004

Italy Netherlands NO Poland PT

0

5

10

15

20

25

30

35

Ess

en

Stu

ttgar

t

Mue

nche

n

Koe

ln

Due

ssel

dorf

Han

nove

r

Bon

n

Wup

pert

al

Dre

sden

Kar

lsru

he

Leip

zig

Moe

nche

ngla

dbac

h

Che

mni

tz

Reu

tling

en

Bra

unsc

hwei

g

Kie

l

Osn

abru

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Hal

le

Mue

nste

r

Mag

debu

rg

Zw

icka

u

Wue

rzbu

rg

Dor

tmun

d

The

ssal

onik

i

Deb

rece

n

mic

rog

ram

PM

2.5

/m3

Assumed mineral and sea salt Regional backgroundUrban increment AIRBASE monitoring data for urban background 2004

Contribution of long-range transport (blue) and local primary PM emissions (red) to urban PM2.5

Germany GR HU

0

5

10

15

20

25

30

35

Lond

on

Birm

ingh

am

Man

ches

ter

Leed

s

Gla

sgow

New

cast

le-u

pon-

Tyn

e

Live

rpoo

l

Not

tingh

am

She

ffiel

d

Bris

tol

Brig

hton

Edi

nbur

gh

Por

tsm

outh

Leic

este

r

Bou

rnem

outh

Rea

ding

Mid

dles

brou

gh

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ke-o

n-T

rent

Bel

fast

Cov

entr

y

Car

diff

Birk

enhe

ad

Sou

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pton

Sw

anse

a

Sou

then

d-on

-Sea

Pre

ston

Bla

ckpo

ol

Ald

ersh

ot

Der

by

Luto

n

Gill

ingh

am

Bar

nsle

y

Kin

gsto

n up

on H

ull

Ply

mou

th

mic

rog

ram

PM

2.5

/m3

Assumed mineral and sea salt Regional backgroundUrban increment AIRBASE monitoring data for urban background 2004

Contribution of long-range transport (blue) and local primary PM emissions (red) to urban PM2.5

United Kingdom

Sectoral contributions to background concentrations of primary PM2.5 components from urban sources

0

2

4

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8

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18

Wie

n

Linz

Bru

xelle

s

Gen

t

Sof

ia

Var

na

Pra

ha

Ost

rava

Arh

us

Hel

sink

i

Tur

ku

Mar

seill

e

Lille

Tou

lous

e

Nan

tes

Lens

Gre

nobl

e

Val

enci

enne

s

Met

z

Sai

nt-E

tienn

e

Ren

nes

Bet

hune

Avi

gnon

Dijo

n

Ang

ers

Bre

st

mic

rog

ram

PM

2.5

/m3

Domestic (SNAP 2) Industry (SNAP3) Traffic (SNAP7) Non-road mobile (SNAP 8)

AT BE Bulgaria FI France

Sectoral contributions to background concentrations of primary PM2.5 components from urban sources

0

2

4

6

8

10

12

14

16

18

Lond

on

Birm

ingh

am

Man

ches

ter

Leed

s

Gla

sgow

New

cast

le-u

pon-

Tyn

e

Live

rpoo

l

Not

tingh

am

She

ffiel

d

Bris

tol

Brig

hton

Edi

nbur

gh

Por

tsm

outh

Leic

este

r

Bou

rnem

outh

Rea

ding

Mid

dles

brou

gh

Sto

ke-o

n-T

rent

Bel

fast

Cov

entr

y

Car

diff

Birk

enhe

ad

Sou

tham

pton

Sw

anse

a

Sou

then

d-on

-Sea

Pre

ston

Bla

ckpo

ol

Ald

ersh

ot

Der

by

Luto

n

Gill

ingh

am

Bar

nsle

y

Kin

gsto

n up

on H

ull

Ply

mou

th

mic

rog

ram

PM

2.5

/m3

Domestic (SNAP 2) Industry (SNAP3) Traffic (SNAP7) Non-road mobile (SNAP 8)

United Kingdom

Summary

• Substantial revisions of methodology and input data

• Health impact assessment based on population-weighted increments – conservative assumption?

• Largest uncertainties associated with quality of urban emission estimates. Large discrepancies cannot be readily explained

• More plausible on emissions assumptions improve estimates

• Validation hampered by lack of quality-controlled monitoring data

• Sensitivity analysis explored implications on optimization results

Mathematical formulation of the GAINS optimization

Comparison of cost curvesExamples for Germany and Greece

Germany, SO2, NEC_NAT,2020

0

200

400

600

800

1000

1200

1400

290 340 390 440

Remaining Emissions kt/yr

To

tal

cost

ME

ur/

yr

costs-RAINS

costs-GAINS

Germany, NOx, NEC_NAT,2020

0

100

200

300

400

500

600

700

800

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660 710 760 810 860 910

Remaining Emissions kt/yr

To

tal

cost

ME

ur/

yr

costs-RAINS

costs-GAINS

Germany, PM2.5, NEC_NAT,2020

0

200

400

600

800

1000

1200

1400

75 80 85 90 95 100

Remaining Emissions kt/yr

To

tal

cost

ME

ur/

yr

costs-RAINS

costs-GAINS

Greece, SO2, NEC_NAT,2020

0

20

40

60

80

100

120

40 50 60 70 80 90

Remaining Emissions kt/yr

To

tal

cost

ME

ur/

yr

costs-RAINS

costs-GAINS

Greece, NOx, NEC_NAT,2020

0

20

40

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100

120

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180

140 150 160 170 180 190 200 210

Remaining Emissions kt/yr

To

tal

cost

ME

ur/

yr

costs-RAINS

costs-GAINS

Greece, PM2.5, NEC_NAT,2020

0

50

100

150

200

250

16 21 26 31

Remaining Emissions kt/yr

To

tal

cost

ME

ur/

yr

costs-RAINS

costs-GAINS

Multi-year meteorology

Multi-year meteorology

• Atmospheric dispersion based on meteorological conditions of 1996, 1997, 1998, 2000, 2003

• Sensitivity analysis with 2003

Loss in statistical life expectancy computed with different meteorological conditions (for 2000)

0

2

4

6

8

10

12

14

Au

stria

Be

lgiu

m

Bu

lgar

ia

Cyp

rus

Cze

ch R

ep.

De

nma

rk

Est

onia

Fin

land

Fra

nce

Ge

rma

ny

Gre

ece

Hu

nga

ry

Ire

land

Ital

y

La

tvia

Lith

uan

ia

Lu

xem

bou

rg

Ma

lta

Ne

ther

land

s

Po

land

Po

rtug

al

Ro

man

ia

Slo

vaki

a

Slo

ven

ia

Sp

ain

Sw

ede

n

UK

EU

-27

EU

-25

Cro

atia

No

rwa

y

Sw

itzer

land

Mo

nth

s

5-yrs mean 2003 1997

Estimates of mortality from ozone for year 2000 emissions for different meteorological conditions

70%

80%

90%

100%

110%

120%

130%

140%

Au

stria

Be

lgiu

m

Bu

lgar

ia

Cyp

rus

Cze

ch R

ep.

De

nma

rk

Est

onia

Fin

land

Fra

nce

Ge

rma

ny

Gre

ece

Hu

nga

ry

Ire

land

Ital

y

La

tvia

Lith

uan

ia

Lu

xem

bou

rg

Ma

lta

Ne

ther

land

s

Po

land

Po

rtug

al

Ro

man

ia

Slo

vaki

a

Slo

ven

ia

Sp

ain

Sw

ede

n

UK

EU

-27

Cro

atia

Tu

rkey

No

rwa

y

Sw

itzer

land

Mo

rta

lity

cas

es in

rel

atio

n t

o 5

-yrs

mea

n

5-yrs mean 2003 1997

Estimates of unprotected forest area for year 2000 emissions for different meteorological conditions

50%

60%

70%

80%

90%

100%

110%

120%

130%A

ust

ria

Be

lgiu

m

Bu

lgar

ia

Cyp

rus

Cze

ch R

ep.

De

nma

rk

Est

onia

Fin

land

Fra

nce

Ge

rma

ny

Gre

ece

Hu

nga

ry

Ire

land

Italy

Latv

ia

Lith

uan

ia

Luxe

mbo

urg

Ma

lta

Ne

ther

land

s

Po

land

Po

rtug

al

Ro

man

ia

Slo

vaki

a

Slo

ven

ia

Sp

ain

Sw

ede

n

UK

EU

-27

EU

-25

Cro

atia

Tu

rkey

No

rwa

y

Sw

itzer

land

km2

of

eco

sys

tem

s w

ith

aci

d d

epo

siti

on

ab

ov

e c

riti

ca

l lo

ad

s in

re

lati

on

to

19

97

5-yrs mean 2003 1997

Estimates of ecosystem area with excess nitrogen deposition for year 2000 emissions for different meteorological conditions

50%

60%

70%

80%

90%

100%

110%

120%

Au

stria

Be

lgiu

m

Bu

lgar

ia

Cyp

rus

Cze

ch R

ep.

De

nma

rk

Est

onia

Fin

land

Fra

nce

Ge

rma

ny

Gre

ece

Hu

nga

ry

Ire

land

Italy

Latv

ia

Lith

uan

ia

Luxe

mbo

urg

Ma

lta

Ne

ther

land

s

Po

land

Po

rtug

al

Ro

man

ia

Slo

vaki

a

Slo

ven

ia

Sp

ain

Sw

ede

n

UK

EU

-27

Cro

atia

Tu

rkey

No

rwa

y

Sw

itzer

land

km2

of

eco

sys

tem

s w

ith

nit

rog

en

dep

os

itio

n a

bo

ve c

riti

cal

lo

ad

s in

re

lati

on

to

19

97

5-yrs mean 2003 1997

Summary

• For EU-27, PM and ozone impacts from 5-yrs meteorology very similar to 1997. Acidification ~10% higher, eutrophication ~5% higher

• But different trends in different regions across Europe

• Implications on meaures

• 2003 produces higher health impacts for PM and ozone

EC4MACS

European Consortium for Modelling of Air Pollution and Climate Strategies

Overview of the 5 years work plan

The EC4MACS model system

GAINSPOLES PRIMES

CAPRI

TM5 EMEP

CCE-IMPACTS

TREMOVE

BENEFITS

Global/hemisphericboundary conditions

European policy drivers

Energy

Transport

Atmosphere

Agriculture

Ecosystems

GEM-E3

Cost-effectiveness

Impacts

FASOM

General work plan

• 2007: – Methodological improvements

• 2008: – Data collection – Feedbacks on methodological improvements

• 2009– Interim assessment– Methodology workshop

• 2010– Uncertainty assessment – Bilateral consultations on input data

• 2011– Final assessment