Methodology and applications of the RAINS air pollution integrated assessment model

Markus AmannInternational Institute for Applied Systems Analysis (IIASA)

Contents

• Cost-effectiveness analysis

• The RAINS concept

• Key methodologies and results

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

The RAINS integrated assessment model for air pollution

Energy/agricultural projections

Emissions

Emission control options

Atmospheric dispersion

Health and environmental impacts

Costs

Driving forces

The RAINS multi-pollutant/multi-effect framework

PM SO2 NOx VOC NH3

Health impacts: PM

O3

Vegetation damage: O3

Acidification

Eutrophication

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)

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

Per-capita costs NEC1999 Scenario H1

EU-15

UK

Sweden

SpainPortugal

Netherlands

Luxembourg

ItalyIreland

Greece

Germany

FranceFinland

Denmark

Belgium

Austria

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Average ozone population exposure index of REF(ppm.h)

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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

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

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

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

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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GDP Primary energy use

Land-based emissionsCAFE baseline “with climate measures”, EU-25

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GDP Primary energy use CO2

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GDP Primary energy use CO2 SO2

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GDP Primary energy use CO2 SO2 NOx

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GDP Primary energy use CO2 SO2 NOx VOC

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GDP Primary energy use CO2 SO2 NOx VOC PM2.5

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GDP Primary energy use CO2SO2 NOx VOCNH3 PM2.5

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

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

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Remaining emissions (kt SO2)

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Scope for further technical emission reductionsCAFE baseline “with climate measures”, EU-25

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SO2 NOx VOC NH3 PM2.5

% of 2000 emissions

2000 CAFE baseline 2020, current legislation Maximum technical reductions 2020

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

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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

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

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

Five stages in dynamic acidification modelling

Important time factors:• Damage delay time• Recover delay time

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

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

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

Optimized emission reductions for EU-25of the CAFE policy scenarios [2000=100%]

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SO2 NOx VOC NH3 PM2.5

% of 2000 emissions

Grey range: CLE to MTFR Case "A" Case "B" Case "C"

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Health improvement (Change between baseline and maximum measures)

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Costs for reducing health impacts from fine PM Analysis for the EU Clean Air For Europe (CAFE) programme

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

Emission control costs of the CAFE policy scenarios

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Case "A" Case "B" Case "C" Max. technical reductions

Billion Euros/year

Road sources SO2 NOx NH3 VOC PM

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

In RAINS, uncertainties addressed through

(1) Model construction

(2) Identification of potential biases

(3) Target setting

(4) Sensitivity analyses

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 %

Probability for protecting ecosystems

Gothenburg Protocol 2010

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Probability

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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?