Methodology and applications of the GAINS integrated assessment model Markus Amann International...
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Methodology and applications of the GAINS integrated assessment model Markus Amann International Institute for Applied Systems Analysis (IIASA) 33 rd Session of the EMEP Steering Body, Geneva, September 7-9, 2009
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Transcript of Methodology and applications of the GAINS integrated assessment model Markus Amann International...
Methodology and applications of the
GAINS integrated assessment model
Markus AmannInternational Institute for Applied Systems Analysis (IIASA)
33rd Session of the EMEP Steering Body, Geneva, September 7-9, 2009
Protocols under the LRTAP Convention
1985: First Sulphur Protocol: 30% flat rate reduction of SO2 emissions relative to 1980– Economically and ecologically inefficient
1994: Second Sulphur Protocol: Country-specific SO2 reduction obligations – Derived from cost-effectiveness principle, based on calculations with
RAINS model
1999: Gothenburg multi-pollutant/multi-effect Protocol: Country-specific reductions of SO2, NOx, VOC, NH3 – Derived from effect-based environmental targets
with RAINS model
2009-2010: Revision of the Gothenburg Protocol
Cost-effectiveness needs integration
• Economic development
• Emission generating activities (energy, transport, agriculture,
industrial production, etc.)
• Emission characteristics
• Emission control options
• Costs of emission controls
• Atmospheric dispersion
• Environmental impacts (health, ecosystems)
• Systematic approach to identify cost-effective packages of
measures
Building blocks of RAINS/GAINS
Energy/agricultural projections
Emissions
Emission control options
Atmospheric dispersion
Air pollution impacts,Basket of GHG emissions
Costs
PRIMES, POLES, CAPRI,national projections
Simulation/“Scenario analysis” mode
The GAINS multi-pollutant/multi-effect framework
PM SO2 NOx VOC NH3
Health impacts: PM
O3
Vegetation damage: O3
Acidification
Eutrophication
The GAINS model: The RAINS multi-pollutant/ multi-effect framework extended to GHGs
PM SO2 NOx VOC NH3
Health impacts: PM
O3 Vegetation damage: O3
Acidification
Eutrophication
PM SO2 NOx VOC NH3 CO2 CH4 N2OHFCsPFCsSF6
Health impacts: PM
O3 Vegetation damage: O3
Acidification
Eutrophication Radiative forcing: - direct
- via aerosols - via OH
The GAINS optimization mode
Energy/agricultural projections
Emissions
Emission control options
Atmospheric dispersion
Costs
Environmental targets
OPTIMIZATION
PRIMES, POLES, CAPRI,national projections
Air pollution impacts,Basket of GHG emissions
Input of Working Groups under the Convention to GAINS
Energy/agricultural projections
Emissions
Emission control options
Atmospheric dispersion
Costs
Environmental targets
Air pollution impacts,Basket of GHG emissions
Convention bodies
Parties
EMEP TFEIP/CEIP
EGTEI
EMEP TFMM/HTAP/MSC-W
WGE THF/TFM/CCE
EB/WGSR
Environmental impacts of air pollutionGAINS estimates for 2000
PM Eutrophication Ozone
Acid, forests Acid, lakes Acid, semi-nat. ecos.
0%
25%
50%
75%
100%
125%
150%
175%
2000 2005 2010 2015 2020
GDP Primary energy use
Land-based emissionsCAFE baseline “with climate measures”, EU-25
0%
25%
50%
75%
100%
125%
150%
175%
2000 2005 2010 2015 2020
GDP Primary energy use CO2
0%
25%
50%
75%
100%
125%
150%
175%
2000 2005 2010 2015 2020
GDP Primary energy use CO2 SO2
0%
25%
50%
75%
100%
125%
150%
175%
2000 2005 2010 2015 2020
GDP Primary energy use CO2 SO2 NOx
0%
25%
50%
75%
100%
125%
150%
175%
2000 2005 2010 2015 2020
GDP Primary energy use CO2 SO2 NOx VOC
0%
25%
50%
75%
100%
125%
150%
175%
2000 2005 2010 2015 2020
GDP Primary energy use CO2 SO2 NOx VOC PM2.5
0%
25%
50%
75%
100%
125%
150%
175%
2000 2005 2010 2015 2020
GDP Primary energy use CO2SO2 NOx VOCNH3 PM2.5
Scope for further technical emission reductions 2020, CAFE baseline “with climate measures”, EU-25
0%
20%
40%
60%
80%
100%
SO2 NOx VOC NH3 PM2.5
2000 CLE-2020 MTFR-2020
Current legislation 2020
Scopefor furthermeasures
2020
Loss in statistical life expectancy attributable to fine particles [months]
Loss in average statistical life expectancy due to identified anthropogenic PM2.5Calculations for 1997 meteorology
2000 2020 2020 CAFE baseline Maximum technical
Current legislation emission reductions
0
2000
4000
6000
8000
10000
12000
14000
16000
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health improvement (Change between baseline and maximum measures)
An
nu
al C
ost
€M
illi
on
s
Costs for reducing health impacts from fine PM Analysis for the EU Clean Air For Europe (CAFE) programme
0
2000
4000
6000
8000
10000
12000
14000
16000
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health improvement (Change between baseline and maximum measures)
An
nu
al C
ost
€M
illi
on
s
Costs for reducing health impacts from fine PM Analysis for the EU Clean Air For Europe (CAFE) programme
CASE A
CASE B
CASE C
CASE A
CASE B
CASE C
CASE B
Costs and benefits of the policy scenarios for 2020(Source: Holland et al., 2005)
0
50
100
150
Case "A" Case "B" Case "C" Maximumtechnicalmeasures
Billion Euros/year
Costs for road sources SO2 costs NOx costs NH3 costsVOC costs PM costs Benefits Uncertainty
Emission reductions suggested by the Thematic Strategy for 2020 [2000=100%]
0%
20%
40%
60%
80%
100%
SO2 NOx VOC NH3 PM2.5
% of 2000 emissions
Grey range: Scope for further measures (CLE - MTFR 2020) Thematic Strategy
Current legislation 2020
Maximum reductions 2020+930 mio €
+1000 mio €+140 mio €
+2600 mio €
+650 mio €
+1900 mio € for mobile sources (NOx+PM)
Emission control costs by sectorfor achieving the air quality targets of the EU Thematic Strategy
0
1
2
3
4
5
Without Euro-VI With Euro-VI Without Euro-VI With Euro-VI
National energy projections (+3% CO2) Climate policy scenario (-20% CO2)
Bil
lio
n €
/yr
Power sector Industry Domestic Transport Agriculture
0
1
2
3
4
5
Without Euro-VI With Euro-VI Without Euro-VI With Euro-VI
National energy projections (+3% CO2) Climate policy scenario (-20% CO2)
Bil
lio
n €
/yr
Power sector Industry Domestic Transport Agriculture
Courtesy of Les White
0
2000
4000
6000
8000
10000
12000
14000
16000
0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
Health improvement (Change between baseline and maximum measures)
An
nu
al C
ost
€M
illi
on
s RAINS cost-
effectivenessapproach
“Equal technology” approach
Cost savings from the GAINS approachEstimates presented by European industry associations
0
25
50
75
100
125
National energy projections (+3% CO2 in 2020) Illustrative projections meeting the EU climatetarget (-20% CO2 in 2020)
Bill
ion
€/y
r
Indicative costs for changes in the energy system to meet climate and energy targets Costs for further measures to achieve the targets of the EU Thematic Strategy on Air PollutionCosts for implementing current air pollution legislation
0
25
50
75
100
125
National energy projections (+3% CO2 in 2020) Illustrative projections meeting the EU climatetarget (-20% CO2 in 2020)
Bill
ion
€/y
r
Indicative costs for changes in the energy system to meet climate and energy targets Costs for further measures to achieve the targets of the EU Thematic Strategy on Air PollutionCosts for implementing current air pollution legislation
Air pollution control costs to meet the EU air quality and climate targetsEU-27, 2020
0
25
50
75
100
125
National energy projections (+3% CO2 in 2020) Illustrative projections meeting the EU climatetarget (-20% CO2 in 2020)
Bill
ion
€/y
r
Indicative costs for changes in the energy system to meet climate and energy targets Costs for further measures to achieve the targets of the EU Thematic Strategy on Air PollutionCosts for implementing current air pollution legislation
Business as usualNational energy projections
(+3% CO2 in 2020)
PRIMES energy scenario with climate measures
(-20% CO2 in 2020)
€20 bn/yr
Uncertainty treatment
• Four sources of uncertainties:– Data imperfections – Model simplifications– Incomplete scientific understanding– The future!
• Uncertainty analyses in GAINS:– Quantitative uncertainty analysis (error propagation)– Robustness considered in model design – Identification of potential systematic biases– Sensitivity analyses on exogenous assumptions
Review of RAINS/GAINS methodology and input data
• Scientific peer reviews of modelling methodology in 2004 and 2006
• Bilateral consultations with experts from Member States and Industry on input data– For CAFE: 2004-2005: 24 meetings with 107 experts– For NEC review: 2006: 28 meetings with >100 experts
• GAINS GHG review workshop: March 2009
• GAINS EC4MACS review workshop: October 5, 2009
Conclusions
• Recent protocols of the Convention employ effect-based rationale, using the RAINS/GAINS cost-effectiveness approach
• GAINS integrates scientific information and quantitative data from all Working Groups under the Convention
• Recent extension to greenhouse gases highlight important synergies and trade-offs between air pollution and climate policies
• Review of GAINS and underlying information is critical for credibility and acceptance of policy results
Building blocks of GAINS
Energy/agricultural projections
Emissions
Emission control options
Atmospheric dispersion
Air pollution impacts,Basket of GHG emissions
Costs
GAINS methodology for emission calculation
pki
k
kipmki
k m
pmkikipi iefAxefAE ,,,,,,,,,,,
i, k, m, p Country, activity type, abatement measure, pollutant
Ei,p Emissions of pollutant p (for SO2, NOx, VOC, NH3, PM2.5, CO2 , CH4,
N2O, etc.) in country i
Ai,k Activity level of type k (e.g., coal consumption in power plants) in country i
efi,k,m,p Emission factor of pollutant p for activity k in country i after application of control measure m
iefi,k,p Implied emission factor of pollutant p for activity k in country i
xi,k,m,p Share of total activity of type k in country i to which a control measure m for pollutant p is applied.
Comparison of emissions reported by CLRTAP Parties to CEIP and calculated in the GAINS model
Markus Amann, Zig KlimontEMEP Centre for Integrated Assessment Modelling (CIAM)
33rd Session of the EMEP Steering Body, Geneva, September 7-9, 2009
GAINS methodology for emission calculation
pki
k
kipmki
k m
pmkikipi iefAxefAE ,,,,,,,,,,,
i, k, m, p Country, activity type, abatement measure, pollutant
Ei,p Emissions of pollutant p (for SO2, NOx, VOC, NH3, PM2.5, CO2 , CH4,
N2O, etc.) in country i
Ai,k Activity level of type k (e.g., coal consumption in power plants) in country i
efi,k,m,p Emission factor of pollutant p for activity k in country i after application of control measure m
iefi,k,p Implied emission factor of pollutant p for activity k in country i
xi,k,m,p Share of total activity of type k in country i to which a control measure m for pollutant p is applied.
Approach for comparison of emission estimates
• Comparison of estimates for 2000 and 2005:– National totals– SNAP 11 sectors– Key sectors– GNFR
• For SO2, NOx, NMVOC, NH3, and PM2.5
• For 39 countries; some EECCA countries not included yet• Data sources:
– CEIP data submitted to CLRTAP in 2009– GAINS calculation based on the data prepared within the NEC
Directive review work; last updates of historical data in 2006
• Analysis of implied emission factors• Final report in December 2009
Comparison of GAINS estimates with national submissions in 2006
0%
20%
40%
60%
80%
100%
120%
Au
stri
a
Bel
giu
m
Cyp
rus
Cze
ch R
ep.
Den
mar
k
Est
on
ia
Fin
lan
d
Fra
nce
Ger
man
y
Gre
ece
Hu
ng
ary
Irel
and
Italy
Lat
via
Lith
uan
ia
Lu
xem
bo
urg
Mal
ta
Net
her
lan
ds
Po
lan
d
Po
rtu
gal
Slo
vaki
a
Slo
ven
ia
Sp
ain
Sw
eden U
K
EU
-25
National estimates RAINS estimate
0%
20%
40%
60%
80%
100%
120%
Au
stri
a
Bel
giu
m
Cyp
rus
Cze
ch R
ep.
Den
mar
k
Est
on
ia
Fin
lan
d
Fra
nce
Ger
man
y
Gre
ece
Hu
ng
ary
Irel
and
Italy
Lat
via
Lith
uan
ia
Lu
xem
bo
urg
Mal
ta
Net
her
lan
ds
Po
lan
d
Po
rtu
gal
Slo
vaki
a
Slo
ven
ia
Sp
ain
Sw
eden U
K
EU
-25
National estimates RAINS estimate
0%
20%
40%
60%
80%
100%
120%
140%
Au
stri
a
Bel
giu
m
Cyp
rus
Cze
ch R
ep.
Den
mar
k
Est
on
ia
Fin
lan
d
Fra
nce
Ger
man
y
Gre
ece
Hu
ng
ary
Irel
and
Ital
y
Lat
via
Lit
hu
ania
Lu
xem
bo
urg
Mal
ta
Net
her
lan
ds
Po
lan
d
Po
rtu
gal
Slo
vaki
a
Slo
ven
ia
Sp
ain
Sw
eden U
K
EU
-25
National estimates RAINS estimate
0%
20%
40%
60%
80%
100%
120%
Aus
tria
Bel
giu
m
Cyp
rus
Cze
ch R
ep.
Den
mar
k
Est
onia
Finl
and
Fran
ce
Ger
man
y
Gre
ece
Hun
gar
y
Irel
and
Ital
y
Latv
ia
Lith
uani
a
Luxe
mb
ourg
Mal
ta
Net
herl
ands
Pol
and
Por
tuga
l
Slo
vaki
a
Slo
veni
a
Spa
in
Sw
eden U
K
EU
-25
National estimates RAINS estimate
SO2 NOx
NH3 NMVOC
0%
20%
40%
60%
80%
100%
120%
Au
stri
a
Bel
giu
m
Cyp
rus
Cze
ch R
ep.
Den
mar
k
Est
on
ia
Fin
lan
d
Fra
nce
Ger
man
y
Gre
ece
Hu
ng
ary
Irel
and
Italy
Lat
via
Lith
uan
ia
Lu
xem
bo
urg
Mal
ta
Net
her
lan
ds
Po
lan
d
Po
rtu
gal
Slo
vaki
a
Slo
ven
ia
Sp
ain
Sw
eden U
K
EU
-25
National estimates RAINS estimate
0%
20%
40%
60%
80%
100%
120%
Au
stri
a
Bel
giu
m
Cyp
rus
Cze
ch R
ep.
Den
mar
k
Est
on
ia
Fin
lan
d
Fra
nce
Ger
man
y
Gre
ece
Hu
ng
ary
Irel
and
Italy
Lat
via
Lith
uan
ia
Lu
xem
bo
urg
Mal
ta
Net
her
lan
ds
Po
lan
d
Po
rtu
gal
Slo
vaki
a
Slo
ven
ia
Sp
ain
Sw
eden U
K
EU
-25
National estimates RAINS estimate
0%
20%
40%
60%
80%
100%
120%
140%
Au
stri
a
Bel
giu
m
Cyp
rus
Cze
ch R
ep.
Den
mar
k
Est
on
ia
Fin
lan
d
Fra
nce
Ger
man
y
Gre
ece
Hu
ng
ary
Irel
and
Ital
y
Lat
via
Lit
hu
ania
Lu
xem
bo
urg
Mal
ta
Net
her
lan
ds
Po
lan
d
Po
rtu
gal
Slo
vaki
a
Slo
ven
ia
Sp
ain
Sw
eden U
K
EU
-25
National estimates RAINS estimate
0%
20%
40%
60%
80%
100%
120%
Aus
tria
Bel
giu
m
Cyp
rus
Cze
ch R
ep.
Den
mar
k
Est
onia
Finl
and
Fran
ce
Ger
man
y
Gre
ece
Hun
gar
y
Irel
and
Ital
y
Latv
ia
Lith
uani
a
Luxe
mb
ourg
Mal
ta
Net
herl
ands
Pol
and
Por
tuga
l
Slo
vaki
a
Slo
veni
a
Spa
in
Sw
eden U
K
EU
-25
National estimates RAINS estimate
SO2 NOx
NH3 NMVOC
0%
20%
40%
60%
80%
100%
120%
140%A
US
TB
ELG
DEN
MFI
NL
FRA
NG
ER
MG
REE
IREL
ITA
LLU
XE
NETH
PO
RT
SPA
IS
WE
UN
KI
BU
LGC
YPR
CZ
RE
ES
TO
HU
NG
LATV
LITH
MA
LTPO
LAR
OM
AS
KR
ES
LOV
ALB
AB
ELA
BO
HE
CR
OA
MA
CE
MO
LDN
OR
RU
SS
SEM
OS
WIT
TU
RK
UK
RAE
mis
sions
report
ed t
o C
EIP
rela
tive t
o G
AIN
S e
stim
ate
Comparison of NOx emissions in 2009GAINS (100%), CEIP (2000)
GAINS estimate
Sectoral contribution to NOx emissions in 2000 Source: GAINS model calculations
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
All countries EU-15 EU-12 Other
Sh
are
of
tota
l N
Ox e
mis
sio
ns
Other
Off-road
Road transport
Residential
Industry
Power plants
Implied emission factors for NOx
Heavy duty vehicles, diesel
0.0
0.2
0.4
0.6
0.8
1.0
1.2
AU
ST
BELG
DEN
MFI
NL
FRA
NG
ER
MG
REE
IREL
ITA
LLU
XE
NETH
PO
RT
SPA
IS
WE
UN
KI
BU
LGC
YPR
CZ
RE
ES
TO
HU
NG
LATV
LITH
MA
LTPO
LAR
OM
AS
KR
ES
LOV
ALB
AB
ELA
BO
HE
CR
OA
MA
CE
MO
LDN
OR
RU
SS
SEM
OS
WIT
TU
RK
UK
RA
EU
15
EU
12
Oth
er
g/M
J
Implied emission factors for NOx
Passenger cars, diesel
0.0
0.1
0.1
0.2
0.2
0.3
0.3
0.4
0.4
AU
ST
BELG
DEN
MFI
NL
FRA
NG
ER
MG
REE
IREL
ITA
LLU
XE
NETH
PO
RT
SPA
IS
WE
UN
KI
BU
LGC
YPR
CZ
RE
ES
TO
HU
NG
LATV
LITH
MA
LTPO
LAR
OM
AS
KR
ES
LOV
ALB
AB
ELA
BO
HE
CR
OA
MA
CE
MO
LDN
OR
RU
SS
SEM
OS
WIT
TU
RK
UK
RA
EU
15
EU
12
Oth
er
g/M
J
Comparison of NH3 emissions in 2009GAINS (100%), CEIP (2000)
0%
20%
40%
60%
80%
100%
120%
140%A
US
TB
ELG
DEN
MFI
NL
FRA
NG
ER
MG
REE
IREL
ITA
LLU
XE
NETH
PO
RT
SPA
IS
WE
UN
KI
BU
LGC
YPR
CZ
RE
ES
TO
HU
NG
LATV
LITH
MA
LTPO
LAR
OM
AS
KR
ES
LOV
ALB
AB
ELA
BO
HE
CR
OA
MA
CE
MO
LDN
OR
RU
SS
SEM
OS
WIT
TU
RK
UK
RA
Em
issi
ons
report
ed t
o C
EP r
ela
tive t
o G
AIN
S e
stim
ate
GAINS estimate
Implied emission factors for NH3
Dairy cows
0
5
10
15
20
25
30
35
40
45
AU
ST
BELG
DEN
MFI
NL
FRA
NG
ER
MG
REE
IREL
ITA
LLU
XE
NETH
SPA
IS
WED
UN
KI
BU
LGC
YPR
CZ
RE
ES
TO
HU
NG
LATV
LITH
MA
LTPO
LAR
OM
AS
KR
ES
LOV
ALB
AB
ELA
BO
HE
CR
OA
MA
CE
MO
LDN
OR
WPO
RT
RU
SS
SEM
OS
WIT
TU
RK
UK
RA
kg N
H3/a
nim
al
0
1000
2000
3000
4000
5000
6000
7000
8000
9000
kg m
ilk/c
ow
-year
Implied emission factor Milk yield
0%
20%
40%
60%
80%
100%
120%
140%
160%
180%
200%
AU
ST
BELG
DEN
MFI
NL
FRA
NG
ER
MG
REE
IREL
ITA
LLU
XE
NETH
PO
RT
SPA
IS
WE
UN
KI
BU
LGC
YPR
CZ
RE
ES
TO
HU
NG
LATV
LITH
MA
LTPO
LAR
OM
AS
KR
ES
LOV
ALB
AB
ELA
BO
HE
CR
OA
MA
CE
MO
LDN
OR
RU
SS
SEM
OS
WIT
TU
RK
UK
RAE
mis
sions
report
ed t
o C
EIP
rela
tive t
o G
AIN
S e
stim
ate
sComparison of PM2.5 emissions estimatesGAINS (100%), CEIP (2000, 2005)
GAINS estimate
Sectoral contribution to PM2.5 emissions in 2000 Source: GAINS model calculations
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
All countries EU-15 EU-12 Other
Contr
ibuti
on t
o t
ota
l PM
2.5
em
issi
ons
Other
Off-road
Road transport
Residential
Industry
Power plants
Implied PM2.5 emission factors Fuelwood stoves
0
200
400
600
800
1000
1200
1400
1600
1800
2000
ALB
AA
US
TB
ELA
BELG
BO
HE
BU
LGC
RO
AC
YPR
CZ
RE
DEN
MES
TO
FIN
LFR
AN
GER
MG
REE
HU
NG
IREL
ITA
LLA
TV
LITH
LUX
EM
AC
EM
ALT
NETH
NO
RW
PO
LAPO
RT
MO
LDR
OM
AR
US
SS
EM
OS
KR
ES
LOV
SPA
IS
WED
SW
ITTU
RK
UK
RA
UN
KI
g/G
J
Conclusions
• After last round of bilateral consultations in 2006, good match of GAINS estimates with national inventories.
• Since then some countries have substantially modified their inventories. Updating of GAINS databases is underway.
• Cross-country comparison of implied emission factors reveals important differences – some of them need more analysis.
Building blocks of GAINS
Energy/agricultural projections
Emissions
Emission control options
Atmospheric dispersion
Air pollution impacts,Basket of GHG emissions
Costs
PRIMES, CAPRI,national projections
Approach for atmospheric dispersion modelling in GAINS
• Based on a sample of 2000 runs of the EMEP Eulerian model (for five meteorological years), functional relationships between – national emissions and– air quality indicators at grid levelhave been developed for– (annual mean) ambient PM2.5 concentrations,– SOMO35 ozone indicator– deposition of sulfur and nitrogen compounds.
• Validation against results of full EMEP model for emissions of Thematic Strategy on Air Pollution
Modelling of PM2.5 ambient concentrations
Endpoint: annual mean concentrations of PM2.5 composed of
• Primary emissions of PM2.5 from anthropogenic sources
• Secondary inorganic aerosols (ammonium sulfate, ammonium nitrate) due to precursors SO2, NOx, NH3
• Water associated with secondary inorganics
• Secondary organic aerosols (from VOC emissions)
• Natural background (mineral, sea salt, organic matter)
• A fraction that is chemically not identified by the measurements
• Thus: calculations do not reproduce complete observed mass
Endpoint: annual mean concentrations of PM2.5 composed of
• Primary emissions of PM2.5 from anthropogenic sources
• Secondary inorganic aerosols (ammonium sulfate, ammonium nitrate) due to precursors SO2, NOx, NH3
• Water associated with secondary inorganics
• Secondary organic aerosols (from VOC emissions)
• Natural background (mineral, sea salt, organic matter)
• A fraction that is chemically not identified by the measurements
• Thus: calculations do not reproduce complete observed mass – Focus on anthropogenic fraction!
Functional relationships for PM2.5developed for GAINS
PM2.5j Annual mean concentration of PM2.5 at receptor point j
pi Primary emissions of PM2.5 in country i
si SO2 emissions in country i
ni NOx emissions in country i
ai NH3 emissions in country i
αS,Wij, νS,W,A
ij, Linear transfer matrices for reduced and oxidized
σW,Aij, πA
ij s nitrogen, sulfur and primary PM2.5, for winter, summer and annual
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Validation of functional relationship for PMfor TSAP emission scenario [μg/m3]
PM2.5, mg m-3
y = 1.0369x - 0.0377
R2 = 0.9955
0
5
10
15
20
0 5 10 15 20
Full EMEP model
GA
INS
ap
pro
xim
atio
n
Validation of the GAINS approximations of the functional relationships for PM2.5 against computations of the full EMEP model around the emission levels outlined in the Thematic Strategy for Air Pollution.
Functional relationships for deposition developed for GAINS
)( ,0,,0,,,0,,, pipi
ipjijpjp EEPDepDep
Depp,j Annual deposition of pollutant p at receptor point j
Depp,j,,0 Reference deposition of pollutant p at receptor point j
Ei,p Annual emission of pollutant p (SO2, NOx, NH3) in country I
Ei,p,0 Reference emissions of pollutant p in country I
Pi,j,p,0 Transfer matrix for pollutant p for emission changes around the reference emissions.
Sulfur deposition [mg/m2/yr]
y = 1.0076x + 2.7392
R2 = 0.9976
0
500
1000
1500
2000
2500
3000
0 500 1000 1500 2000 2500 3000
Full EMEP model
RA
INS
ap
pro
xim
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n
N deposition [mg/m2/yr]
y = 0.9944x + 11.686R2 = 0.9985
0
500
1000
1500
2000
2500
0 500 1000 1500 2000 2500
Full EMEP model
RA
INS
ap
pro
xim
atio
n
Validation of the GAINS approximations of the
functional relationships for deposition
against computations of the full EMEP model around the emission
levels outlined in the Thematic Strategy for
Air Pollution.
Functional relationship for ozone developed for GAINS
)()( 0,,0,,0, iii
liiii
lill vvVnnNO3O3
O3l Health-relevant long-term ozone indicator measured as the population-weighted
SOMO35 in receptor country l
O3l,0 Population-weighted SOMO35 in receptor country l due to reference emissions n0, v0
ni, vi Emissions of NOx and VOC in source country i
Ni,l, Vi,l Coefficients describing the changes in population-weighted SOMO35 in receptor country l due to emissions of NOx and VOC in source country i.
SOMO35, ppb.days
y = 0.985x + 88.092
R2 = 0.9627
0
500
1000
1500
2000
2500
3000
3500
4000
0 500 1000 1500 2000 2500 3000 3500 4000
Full EMEP model
GA
INS
ap
pro
xim
atio
n
Comparison of the SOMO35 indicator calculated from the reduced-form approximations of the GAINS model with the results from the full EMEP Eulerian model, for the final CAFE scenario.
Modelling urban PM2.5 in RAINSConcept
• On top of regional (50*50 km) grid average concentration of PM2.5 as computed by EMEP model,
• superimpose sub-grid “urban increment” of PM2.5 (City-Delta), calculated based on– Urban emission densities of low level PM sources (traffic,
domestic)– City-specific wind speeds– Size of urban area within grid cell
Modelling urban PM2.5 in RAINSApproach
1. Develop a functional relationship that includes important local predictors
2. Compute urban increments with three urban-scale models for seven cities
3. Derive from this data sample regression coefficients for the functional relationship
4. Compile data base on local predictors for 200 cities
5. Calculate urban increments for these 200 cities
Functional relationship for urban increment of PM2.5 The city-delta approach
Δc … concentration increment computed with the 3 models
α, β … regression coefficients
D … city diameter
U … wind speed
Q … change in emission fluxes
d … number of winter days with low wind speed
365
dQ
U
DQ
U
Dc
Emission densities (red) and computed urban increments (blue)
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30
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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
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35
Wie
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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
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35
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Assumed mineral and sea salt Regional backgroundUrban increment AIRBASE monitoring data for urban background 2004
Italy Netherlands NO Poland PT
Conclusions
• An approach has been developed to estimate PM2.5 concentrations in urban background air at the European scale.
• Validation (was) constrained by– limited availability of quality-controlled PM2.5 measurements,– uncertainties in urban emission inventories.
• Improved methodology subject of EC4MACS work plan.
