methodology and applications of the rains air pollution integrated assessment model markus amann...
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![Page 1: Methodology and applications of the RAINS air pollution integrated assessment model Markus Amann International Institute for Applied Systems Analysis (IIASA)](https://reader034.vdocuments.us/reader034/viewer/2022050714/56649d2a5503460f949ffb70/html5/thumbnails/1.jpg)
Methodology and applications of the RAINS air pollution integrated assessment model
Markus AmannInternational Institute for Applied Systems Analysis (IIASA)
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Contents
• Cost-effectiveness analysis
• The RAINS concept
• Key methodologies and results
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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
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The RAINS integrated assessment model for air pollution
Energy/agricultural projections
Emissions
Emission control options
Atmospheric dispersion
Health and environmental impacts
Costs
Driving forces
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The RAINS multi-pollutant/multi-effect framework
PM SO2 NOx VOC NH3
Health impacts: PM
O3
Vegetation damage: O3
Acidification
Eutrophication
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System boundaries
Driving forces of air pollution (energy use, transport, agriculture)
• are driven by other issues, and
• have impacts on other issues too.
Critical boundaries:
• Greenhouse gas emissions and climate change policies (GAINS!)
• Agricultural policies
• Other air pollution impacts on water and soil (nitrogen deposition over seas, nitrate in groundwater, etc.)
• Quantification of AP effects where scientific basis is not robust enough (economic evaluation of benefits)
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Policy analysis with the RAINS cost-effectiveness approach
Energy/agricultural projections
Emissions
Emission control options
Atmospheric dispersion
Health and environmental impacts
Costs
Environmental targets
OPTIMIZATION
Driving forces
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Per-capita costs NEC1999 Scenario H1
EU-15
UK
Sweden
SpainPortugal
Netherlands
Luxembourg
ItalyIreland
Greece
Germany
FranceFinland
Denmark
Belgium
Austria
0
100
200
300
0.0 0.5 1.0 1.5 2.0 2.5 3.0 3.5
Average ozone population exposure index of REF(ppm.h)
Tota
l em
issi
on
co
ntr
ol c
ost
s/ca
pit
a (E
UR
O/y
r)
H1
REF
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The cost-effectiveness approach
Decision makers
Decide about•Ambition level (environmental targets)
•Level of acceptable risk
•Willingness to pay
Models help to separate policy and technical issues:
Models
Identify cost-effective and robust measures:
• Balance controls over different countries, sectors and pollutants
• Regional differences in Europe
• Side-effects of present policies
• Maximize synergism with other air quality problems
• Search for robust strategies
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RAINS policy applications
• UN ECE Convention on Long-range Transboundary Air Pollution:– Second Sulphur Protocol 1994– Gothenburg Multi-pollutant Protocol 1999
• European Union– Acidification Strategy 1997– National Emission Ceilings 1999– Clean Air For Europe 2005– Revision of National Emission Ceilings 2007
• China– National Acid Rain policy plan 2004– Multi-pollutant/multi-effect clean air policy 2007
• National RAINS implementations – Netherlands, Italy, Finland
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Review of RAINS methodology and input data
• Scientific peer review of modelling methodology in 2004
• Bilateral consultations with experts from Member States and Industry on input data– For CAFE: 2004-2005: 24 meeting with 107 experts– For NEC review: 2006: 28 meetings with > 100 experts
• The RAINS model is accessible online atwww.iiasa.ac.at/rains
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Criteria for aggregation of emission sources
RAINS applies six criteria:
• Importance of source (>0.5 percent in a country)
• Possibility for using uniform activity rates and emission factors
• Possibility of establishing plausible forecasts of future activity levels
• Availability and applicability of “similar” control technologies
• Availability of relevant data
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Calculating emissions
mkj
mkjimkjikjimkj
mkjii XeffefAEE,,
,,,,,,,,,
,,, )1(
i,j,k,m Country, sector, fuel, abatement technology
Ei,y Emissions in country i for size fraction y
A Activity in a given sector
ef “Raw gas” emission factor
effm,y Reduction efficiency of the abatement option m
X Implementation rate of the considered abatement measure
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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
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RAINS cost estimates are country- and technology-specific
Technology-specific factors:• Investments• Demand for labour, energy, by-products• Lifetime of equipment• Removal efficiency
Country-specific factors:• Prices for labour, energy, by-products, etc.• Applicability
General factors:• Interest rate
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An example cost curve for SO2
Low sulfur coal
1 % S heavy fuel oil
FGD - baseload
power plants
FGDoil fired
power plants
0.2 % S diesel oil
FGD large industrial
boilers
0.6 % S heavy fuel oil
FGD small industrial
boilers
0.01 % Sdiesel oil
Remaining measures
Present legislation
0
500
1000
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2000
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3000
0 50 100 150 200 250 300
Remaining emissions (kt SO2)
Ma
rgin
al
co
sts
(E
UR
O/t
on
SO 2
re
mo
ve
d)
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Scope for further technical emission reductionsCAFE baseline “with climate measures”, EU-25
0%
20%
40%
60%
80%
100%
SO2 NOx VOC NH3 PM2.5
% of 2000 emissions
2000 CAFE baseline 2020, current legislation Maximum technical reductions 2020
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Source-receptor relationships for PM2.5derived from the EMEP Eulerian model for primary and secondary 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
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Wiji
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Wij
iIi
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iIi
Aij
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spPM
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Estimating the loss of life expectancy in RAINSApproach
• Endpoint: – Loss in statistical life expectancy
– Related to long-term PM2.5 exposure, based on cohort studies
• Life tables provide baseline mortality for each cohort in each country
• For a given PM scenario: Mortality modified through Cox proportional hazard model using Relative Risk (RR) factors from literature
• From modified mortality, calculate life expectancy for each cohort and for entire population
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Input to life expectancy calculation
• Life tables (by country)
• Population data by cohort and country, 2000-2050
• Urban/rural population in each 50*50 km grid cell
• Air quality data: annual mean concentrations – PM2.5 (sulfates, nitrates, ammonium, primary
particles), excluding SOA, natural sources
– 50*50 km over Europe, rural + urban background
– for any emission scenario 1990-2020
• Relative risk factors
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Loss in 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
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Five stages in dynamic acidification modelling
Important time factors:• Damage delay time• Recover delay time
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Excess acid deposition to forests
Percentage of forest area with acid deposition above critical loads, Calculation for 1997 meteorology
2000 2020 2020 CAFE baseline Maximum technical
Current legislation emission reductions
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Excess nitrogen deposition threatening biodiversity
Percentage of ecosystems area with nitrogen deposition above critical loads Calculation for 1997 meteorology
2000 2020 2020 CAFE baseline Maximum technical
Current legislation emission reductions
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Vegetation-damaging ozone concentrations
AOT40 [ppm.hours]. Critical level for forests = 5 ppm.hours Calculations for 1997 meteorology
2000 2020 2020 CAFE baseline Maximum technical
Current legislation emission reductions
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Optimized emission reductions for EU-25of the CAFE policy scenarios [2000=100%]
0%
20%
40%
60%
80%
100%
SO2 NOx VOC NH3 PM2.5
% of 2000 emissions
Grey range: CLE to MTFR Case "A" Case "B" Case "C"
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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
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Courtesy of Les White
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Health improvement (Change between baseline and maximum measures)
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RAINS cost-effectivenessapproach
Equal technology approach
Cost savings from the RAINS approachEstimates presented by Concawe
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Emission control costs of the CAFE policy scenarios
0
10
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Case "A" Case "B" Case "C" Max. technical reductions
Billion Euros/year
Road sources SO2 NOx NH3 VOC PM
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The critical question on uncertainties in the policy context
• Not: What is the confidence range of the model results?
• But: Given all the shortcomings, imperfections and the goals, how can we safeguard the robustness of the model results?
Conventional scientific approaches for addressing uncertainties do either not provide policy-relevant answers or are too complex to implement. For practical reasons alternative approach required
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In RAINS, uncertainties addressed through
(1) Model construction
(2) Identification of potential biases
(3) Target setting
(4) Sensitivity analyses
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Uncertainties of intermediate results95% confidence intervals
SO2 NOx NH3
Emissions ±13 % ±13 % ±15 %
Deposition ± 14-17 %
Critical loads excess(area of protected ecosystems)
-5% - +2.5 %
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Probability for protecting ecosystems
Gothenburg Protocol 2010
80%
82%
84%
86%
88%
90%
92%
94%
96%
98%
100%
5% 10% 15% 20% 25% 30% 35% 40% 45% 50% 55% 60% 65% 70% 75% 80% 85% 90% 95%
Probability
% o
f ec
osy
stem
are
a
EU-15 Non-EU
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More advanced methods for treating uncertainties could be developed …
But:
• Are Parties ready to put increased effort into providing and, subsequently, agreeing upon the data needed for such an analysis?
• Would Parties be prepared to follow abatement strategies derived with such a method, i.e., to pay more for strategies that yield the same environmental improvements but with a higher probability of attainment?