A first set of optimized scenarios from RAINS: Exploring the range between Current Legislation and...
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A first set of optimized scenarios from RAINS: Exploring the range between Current Legislation and Maximum Technically Feasible Reductions for 2020 M. Amann, I. Bertok, R. Cabala, J. Cofala, F. Gyarfas, C. Heyes, Z. Klimont, F. Wagner, W. Schöpp
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Transcript of A first set of optimized scenarios from RAINS: Exploring the range between Current Legislation and...
A first set of optimized scenarios from
RAINS:
Exploring the range between
Current Legislation and
Maximum Technically Feasible Reductions
for 2020
M. Amann, I. Bertok, R. Cabala, J. Cofala, F. Gyarfas, C. Heyes,
Z. Klimont, F. Wagner, W. Schöpp
General assumptions
• All calculations for 2020
• CAFE baseline scenario “with climate measures”
• Maximum technically feasible emission reductions (MTFR) as presented to WGTS in November
• All results for regional scale calculations (50*50 km resolution), urban effects ignored
• Impact assessment for 1997 meteorology
• Assumptions on health impact assessment as presented earlier
• Scope and cost-effectiveness of EURO-V/VI not considered – all
calculations exclude further measures for mobile sources
Caveats
• Limited quality control of the initial results
• New functional relationships not yet formally documented; validation not fully completed
• New approach for linearized approximation of critical loads exceedance not fully validated with new EMEP model
• City-Delta results not yet included!
• Uncertainty analysis not yet performed
Functional relationships for PM
PM2.5j Annual mean concentration of PM2.5 at receptor point j
I Set of emission sources (countries)
J Set of receptors (grid cells)
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, σW,Aij, πA
ij Linear transfer matrices for reduced and oxidized nitrogen, sulfur and primary PM2.5, for winter, summer and annual
)2**2),1**32
14*1**1,0min(max(*5.0
)**(*5.0
**5.2
jiIi
Wijji
Ii
Wiji
Ii
Wij
iIi
Siji
Ii
Sij
iIi
Aij
Iii
Aijj
knckscac
na
spPM
Implications of neglecting urban differences
For PM:
• Systematic underestimation of PM (and health impacts) in urban areas
• For optimization, bias towards regional scale components of PM (secondary inorganic aerosols).
• Proper inclusion of City-Delta would give more weight to low-level urban emissions of primary PM (e.g., from traffic)
For ozone:
• Systematic overestimate of O3 (and health impacts) in urban areas
• For optimization, importance of regional and urban-scale VOC reductions is underestimated
Target setting
Remaining problem areas in 2020CAFE Baseline with Climate Measures (Light blue = no risk)
Forests – acid dep. Semi-natural – acid dep. Freshwater – acid dep.
Health - PM Health+vegetation - ozone Vegetation – N dep.
Option 1 for PM target: Absolute limitAbsolute limit on PM concentrations or life expectancy losses
[Country-average PM2.5, μg/m3]
0
2
4
6
8
10
12
14
16
18
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PM
Residual Baseline > MTFR NEC > Baseline Max. gap closure
Option 2 for PM target: Gap closure to base year Uniform improvement of PM effects in relation to 2000
[100% = 2000]
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Aus
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Bel
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Cyp
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Den
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onia
Fin
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PM
Residual Baseline > MTFR 2000 > Baseline Max. gap closure
Option 3 for PM target: Gap closure to baseline (CLE)Uniform improvement of PM effects in relation to baseline 2020
[100% = 2020]
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Aus
tria
Bel
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Den
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onia
Fin
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Fra
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Ger
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and
Ital
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Latv
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Lith
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Luxe
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Mal
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and
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tuga
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PM
Residual Baseline > MTFR NEC > Baseline Max. gap closure
Ozone: Scope for target setting
0
1000
2000
3000
4000
5000
6000
Aus
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Bel
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Cyp
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Den
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Fra
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Ozone
Residual Baseline > MTFR NEC > Baseline Max. gap closure
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Aus
tria
Bel
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Cyp
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Den
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Ger
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and
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Ozone
Residual Baseline > MTFR 2000 > Baseline Max. gap closure
Absolute targets (ppb.days) Gap closure relative to 2000
Acidification: Scope for target setting
Absolute targets (eq/ha) Gap closure relative to 2000
0
200
400
600
800
1000
1200
Aus
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Acidification
Residual Baseline > MTFR NEC > Baseline Max. gap closure
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Aus
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Acidification
Residual Baseline > MTFR 2000 > Baseline Max. gap closure
Eutrophication: Scope for target setting
Absolute targets (eq/ha) Gap closure relative to 2000
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Aus
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Cyp
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Eutrophication
Residual Baseline > MTFR 2000 > Baseline Max. gap closure
0
200
400
600
800
1000
1200
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Eutrophication
Residual Baseline > MTFR NEC > Baseline Max. gap closure
Definition of gap closure
Effect indicator
MTFR from EU25 excluding EURO5/6
Base year exposure (2000/1990)
Baseline 2020 (Current legislation)
MTFR from EU25MTFR from EU-25 + shipping
MTFR from all Europe + shipping
No-effect level (critical load/level)
Zero exposure
Gap concept used for NEC
3 ambition levels within this rangeused for illustrative 2005 calculations
NEC 2010
0%
20%
40%
60%
80%
100%
SO2 NOx VOC NH3 PM2.5
2000 CLE-2020 "with climate measures"CLE 2020 "Illustrative climate" MTFR-2020 "with climate measures"MTFR 2020 "Illustrative climate"
Scope for further technical emission reductions“Illustrative Climate” vs. “Climate Policy” scenario, EU-25
Summary
• Due to the updated constellation of scope for emission reductions, atmospheric dispersion, effect estimates, etc:– Using absolute effect levels as uniform targets or
– Using uniform relative improvements between base year/baseline and no-effect levels
do not appear as meaningful options for setting practical interim targets.
• For illustrative calculations, three ambition levels dividing the space between – baseline (current legislation 2020) and
– maximum technically feasible reductions (for EU-25 only, EURO-V/VI excluded)
have been used.
Questions
• Do you agree so far?
• Any preferences for target setting?
First optimized scenarios
Optimized scenarios
Four environmental endpoints:• Loss in life expectancy attributable to PM• Cases of premature deaths attributable to ozone• Accumulated excess deposition over CL for acidification• Accumulated excess deposition over CL for eutrophication
Three ambition levels:• 25 %• 50 %• 75 %
between the effects of baseline 2020 and of MTFR – excluding EURO-V/VI, ships and non-EU countries
Emission reductions for health impacts from PM Scenarios A1
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Au
stri
a
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lgiu
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.
De
nm
ark
Est
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Fin
lan
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Fra
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Ge
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Hu
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Ire
lan
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La
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Lith
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bo
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Ma
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Ne
the
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Pol
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Po
rtug
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SO2 25% GC 50% GC 75% GC
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Au
stri
a
Be
lgiu
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.
De
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ark
Est
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Fin
lan
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Fra
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Ge
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Gre
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Hu
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Ire
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La
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Lith
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nia
Lu
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Ma
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the
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Po
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NOx 25% GC 50% GC 75% GC
0%
20%
40%
60%
80%
100%
120%
140%
Au
stri
a
Be
lgiu
m
Cyp
rus
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ch R
ep
.
De
nm
ark
Est
on
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Fin
lan
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Fra
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Ge
rma
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Gre
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Hu
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ary
Ire
lan
d
Ita
ly
La
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Lith
ua
nia
Lu
xem
bo
urg
Ma
lta
Ne
the
rla
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s
Po
lan
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Po
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ga
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Sp
ain
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NH3 25% GC 50% GC 75% GC
0%
10%
20%
30%
40%
50%
60%
70%
80%
90%
100%
Au
stri
a
Be
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m
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ep
.
De
nm
ark
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Fin
lan
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Fra
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Ge
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Gre
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Hu
ng
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Ire
lan
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Ita
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La
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Lith
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Lu
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Ma
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the
rla
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lan
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PM2.5 25% GC 50% GC 75% GC
vainima
Split to 4 -- can't read
Total (per-capita) costs for the PM scenarios A[€/person/year]
0
100
200
300
400
500
600
700
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-25
Total Costs (Euro/person/yr)CLE mobile CLE stationary 25% GC 50% GC 75% GC MTFR
Total (per-capita) costs for the PM scenarios AAdditional costs on top of the baseline 2020 [€/person/year]
0
5
10
15
20
25
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35
40
45
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-25
Total Costs (Euro/person/yr) 25% GC 50% GC 75% GC
Costs related to GDP (MER) [% of GDP – Market Exchange Rate]
0.0%
0.5%
1.0%
1.5%
2.0%
2.5%
3.0%
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-25
Total costs % GDP 2020, MER CLE mobile CLE stationary 25 % GC 50% GC 75% GC MTFR
Costs related to GDP (PPS) [% of GDP – Purchasing Power Standards]
0.0%
0.2%
0.4%
0.6%
0.8%
1.0%
1.2%
1.4%
1.6%
Aus
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-25
Total costs % GDP 2020, PPS CLE mobile CLE stationary 25% GC 50% GC 75% GC MTFR
Costs related to GDP (MER) Additional costs on top of the baseline 2020 [% of GDP]
0.0%
0.1%
0.2%
0.3%
Aus
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-25
Total costs % GDP 2020, MER 25 % GC 50% GC 75% GC
Costs related to GDP (PPS) Additional costs on top of the baseline 2020 [% of GDP ]
0.00%
0.04%
0.08%
0.12%
Aus
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Bel
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-25
Total costs % GDP 2020, PPS 25% GC 50% GC 75% GC
Emission control costs by pollutantof baseline 2020 - current legislation [€/person/year]
0
100
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400
500
600
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-25
Costs Baseline (Euro/person/yr) Mobile SO2 NOx NH3 VOC PM
Emission control costs by pollutantAdditional cost for the 25% scenario A1/1 [€/person/year]
0.0
0.5
1.0
1.5
2.0
2.5
3.0
3.5
4.0
4.5
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Costs Low Amb. (Euro/person/yr) SO2 NOx NH3 VOC PM
Emission control costs by pollutantAdditional costs for the 50% scenario A1/2 [€/person/year]
0
2
4
6
8
10
12
14
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-25
Costs Med. Amb. (Euro/person/yr) SO2 NOx NH3 VOC PM
Emission control costs by pollutantAdditional costs for the 75% scenario A1/3 [€/person/year]
0
5
10
15
20
25
30
35
40
45
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-25
Costs High Amb. (Euro/person/yr) SO2 NOx NH3 VOC PM
Loss in life expectancy attributable to anthropogenic PM [months]
0
1
2
3
4
5
6
7
8
9
10
Aus
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Bel
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Cze
ch R
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Cyp
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Tot
al E
U-2
5
Loss in life expectancy (months) Residual MTFR A1/3 A1/2 A1/1
Optimizations for other environmental endpoints
Emission control costs vs. loss in life expectancy[billion €/yr]
REFA1/1A1/2
A1/3
MTFR
0
10
20
30
40
3.5 4 4.5 5 5.5
Loss in life expectancy (months)
A1-PM A2-ozone A3-acidification A4-eutrophication
Emission control costs vs. premature deaths attributable to ozone [billion €/yr]
0
10
20
30
40
14000 15000 16000 17000 18000
Cases of premature deaths from ozone
A1-PM A2-ozone A3-acidification A4-eutrophication
Emission control costs vs. forest ecosystems area with acid deposition above CL [billion €/yr]
0
10
20
30
40
5 7 9 11 13 15 17
Percent of ecosystems area with acid deposition above CL
A1-PM A2-ozone A3-acidification A4-eutrophication
Conclusions and questions
• PM and acidification imply similar emission reductions– Scope for joint optimization
– Increases robustness versus uncertainty in health impacts of secondary inorganic aerosols
• PM/acidification and ozone are complementary– Joint consideration increases robustness versus the ignored health
impacts from secondary organic aerosols
• Target setting needs further examination– Difficulties in reaching improvements of small effects and/or in
peripheral regions may imply unproportional measures
– Dialogue with effects community and benefit analysis
• Additional costs for 25% and 50% ambition levels are significantly lower than earlier legislation (e.g., NEC) – More analysis around the 75% ambition level?
Possible next stepsShopping list
Further scenario runs:• Joint optimization for all effects for present model set-up
(Scenario A5)• Introduce City-Delta – with this revised set B:
– Scope for EURO-V/VI– Include shipping emissions in the optimization– Sensitivity analysis for illustrative climate projection– Sensitivity analysis for national energy projections– PM2.5 air quality limit values – different ambition levels
• Explore alternative target setting rules• Uncertainty/sensitivity analyses• Analysis for 2015
City-Delta
Present State of work
Key question
• “Can the urban signal of PM and O3 be expressed as a generalized correlation for all urban areas for ready incorporation into RAINS?”
For the health-relevant end-points:
• PM: Annual mean concentrations of total PM2.5 in urban background air
• O3: SOMO35 (Sum over daily eight-hour mean concentrations exceeding 35 ppb, accumulated over full year) in urban background air
City-Delta model intercomparison
• 17 models, 8 cities, 7 scenarios
• Coordination: IIASA, JRC, MSC-W
• Identify differences in model results (deltas) across– Scales (50 km vs. 2/5/10 km)– Emission scenarios (2010, MFR)– Cities– Models
– Pollutants (O3 and PM)
Key findings from City-Delta: PM
• Validation of PM is hampered by the lack of reliable (chemically speciated) observations
• All models underestimate total PM mass, both at urban and large scale. This is due to limited understanding of sources and processes.
• Models agree that a large part of PM found in urban background originates from the regional background.
• Models agree that the urban increment can be described by a linear relation between primary PM emission densities and concentrations.
“Urban impact” on PM2.5 in ViennaSource: Puxbaum et al., 2003
0
1
2
3
4
5
6
7
Jun-
99
Jul-9
9
Aug-9
9
Sep-9
9
Oct-99
Nov-9
9
Dec-9
9
Jan-
00
Feb-0
0
Mar
-00
Apr-0
0
May
-00
µg
/m³ NH4, SO4
Na, CaOCBC
Urban impact PM2.5 (difference between urban and rural twin site)
0
1
2
3
4
5
6
7
Jun-
99
Jul-9
9
Aug-9
9
Sep-9
9
Oct-99
Nov-9
9
Dec-9
9
Jan-
00
Feb-0
0
Mar
-00
Apr-0
0
May
-00
µg
/m³ NH4, SO4
Na, CaOCBC
PM10 observations in London as a function of emission density primary PM10 from road transport
PM10 concentration as a function of emission density,
primary PM10 from UK road transport (2001)
0
5
10
15
20
25
30
35
40
0 1 2 3 4 5
PM10 emission density, t km -2 yr-1
5 km x 5 km
[PM
10],
ug
m-3
Roadside sites
Urban sites
Rural sites
PM10 concentration as a function of emission density,
primary PM10 from UK road transport (2001)
0
5
10
15
20
25
30
35
40
0 1 2 3 4 5
PM10 emission density, t km -2 yr-1
5 km x 5 km
[PM
10],
ug
m-3
Roadside sites
Urban sites
Rural sites
Primary PM10 concentrations vs. emission densityCity-Delta model results, Berlin
Data processed by JRC-IES
Modelled relation between PM2.5 and emission densities - Berlin
-4
-2
0
2
4
6
8
-5 0 5 10 15 20
Difference between sub-grid and grid-average emission density (t/km2)
Dif
fere
nc
e b
etw
ee
n s
ub
-gri
d a
nd
gri
d-a
ve
rag
e
PM
2.5
co
nc
en
tra
tio
n
(mic
rog
ram
s/m
3 )
Base case CLE MFR
Berlin Slope R2
Base case 0.44 0.70
CLE case 0.46 0.67
MFR case 0.46 0.67
All cases 0.44 0.69
Paris Slope R2
Base case 0.23 0.83
CLE case 0.22 0.82
MFR case 0.22 0.82
All cases 0.23 0.82
Slopes of individual cities against EMEP wind speed in city grid
0.00
0.25
0.50
0.75
1.00
1.25
1.50
1.75
2.00
1.00 1.25 1.50 1.75 2.00 2.25 2.50
EMEP Wind Speed m/s
Slo
pe
µg
/m3
/t(P
M2.
5)/k
m2
PM2.5 Unadjusted Wind
y = -1.9623x + 4.6786
0.00
0.25
0.50
0.75
1.00
1.25
1.50
1.75
2.00
1.00 1.25 1.50 1.75 2.00 2.25 2.50
EMEP Wind Speed m/s
Slo
pe
µg
/m3
/t(P
M2.
5)/k
m2
PM2.5 Adjusted Wind PM2.5 Unadjusted Wind Linear (PM2.5 Adjusted Wind)
ΔPMsub-grid = (EDsub-grid - EDEMEP) * (k1 - k2*Vwind)
ΔPMsub-grid .. Difference in PM concentration between sub-grid (urban/rural) area and EMEP grid average
EDx … Emission density for low sources (x=urban/rural/EMEP grid average)
Vwind … Annual mean wind speed in EMEP grid cell
k1, k2 … Parameters derived from the City-Delta ensemble model
Functional relationship for PM
ΔPMsub-grid = (EDsub-grid – EDEMEP) * (k1 - k2*Vwind)=
= (EDEMEP * (PDsub-grid / PDEMEP ) – EDEMEP) * (k1 - k2*Vwind) =
= EDEMEP * (PDsub-grid / PDEMEP – 1) * (k1 - k2*Vwind)
Necessary input data:
• Emission densities of low level sources in an EMEP cell (for an
emission control scenario)
• Population densities in urban and rural areas for an EMEP cell
• Annual mean wind speed in an EMEP grid cell
PM implementation in RAINS
Input data
Emission densities
Population density ratios
Wind speed
Urban increments
Anthropogenic contribution to PM2.5Grid average vs. urban increments, 2000 [µg/m3]
Validation against observationsUrban background PM2.5 [μg/m3]
0
5
10
15
20
25
30
35
40
45
Wie
n (A
)
Gra
z (A
)
Linz
(A
)
Ant
wer
p (B
)
Gen
t (B
)
Bas
el (
CH
)
Ber
n (C
H)
Dui
sbur
g (D
)
Erf
urt
(A)
Koe
ln C
horw
eile
r (D
)
Hal
le (
D)
HE
LSIN
KI
(FIN
)
Gre
nobl
e (F
)
Lille
(F
)
Nan
tes
(F)
Par
is (
F)
Str
assb
ourg
(F
)
Bol
ogna
(I)
Pav
ia (
I)
Mila
no (
I)
Am
ster
dam
(N
L)
Um
ea (
S)
Goe
tebo
rg (
S)
Mal
moe
(S
)
Sto
ckho
lm (
S)
Upp
sala
(S
)
Väx
jö (
S)
Tar
tu (
ES
T)
Ipsw
ich
(UK
)
Lond
on (
UK
)
Lond
on B
loom
sbur
y (U
K)
Nor
wic
h (U
K)
Alb
acet
e(S
P)
Bar
celo
na (
SP
)
Hue
lva
(SP
)
Ovi
edo
(SP
)
Tar
rago
na (
SP
)
BE
RLI
N (
D)
CLE
RM
ON
T-F
(F
)
MA
RS
EIL
LE (
F)
Mon
tpel
lier
(F)
PR
AG
(C
Z)
ZU
ER
ICH
(C
H)
Mineral & sea salt Secondary inorganics Primary PM, regionalPrimary PM, urban increment Observation TEOM data or traffic site
Population density Prague Source: LandScan 2002
Conclusions
• A first approach for a addressing urban air quality for Europe-wide health impact assessment has been developed – based on observations and City-Delta results
• First results are promising, further refinement is necessary
• More PM2.5 monitoring data is necessary for validation
• Uncertainty and sensitivity analyses not yet performed
• Only for health impact assessment, not yet suitable for compliance with AQ limit values
Key findings from City-Delta Ozone
• Model results are highly sensitive to the quality of the emission inventories. High quality emission inventories are necessary for good model performance.
• Generally, – model reproduce well the present situation,– they agree on the ozone changes expected from current legislation in
2010, – and there is agreement on relatively little scope for further improvements
from emission controls beyond CLE.
Urban ozone and urban emission densitySOMO35, transect through Paris
Data processed by JRC-IES
