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

Protocols under the LRTAP Convention 1985: First Sulphur Protocol: 30% flat rate reduction of SO 2 emissions relative to 1980 –Economically and ecologically inefficient 1994: Second Sulphur Protocol: Country-specific SO 2 reduction obligations –Derived from cost-effectiveness principle, based on calculations with RAINS model 1999: Gothenburg multi-pollutant/multi-effect Protocol: Country-specific reductions of SO 2, NO x, VOC, NH 3 –Derived from effect-based environmental targets with RAINS model : 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 PMSO 2 NO x VOCNH 3 Health impacts: PM  O 3  Vegetation damage: O 3  Acidification  Eutrophication 

The GAINS model: The RAINS multi-pollutant/ multi-effect framework extended to GHGs PMSO 2 NO x VOCNH 3 Health impacts: PM  O 3  Vegetation damage: O 3  Acidification  Eutrophication  PMSO 2 NO x VOCNH 3 CO 2 CH 4 N2ON2O HFCs PFCs SF 6 Health impacts: PM  O 3  Vegetation damage: O 3  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 pollution GAINS estimates for 2000 PMEutrophicationOzone Acid, forestsAcid, lakesAcid, semi-nat. ecos.

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

Scope for further technical emission reductions 2020, CAFE baseline “with climate measures”, EU-25 Current legislation 2020 Scope for further measures 2020

Loss in statistical life expectancy attributable to fine particles [months] Loss in average statistical life expectancy due to identified anthropogenic PM2.5 Calculations for 1997 meteorology CAFE baseline Maximum technical Current legislation emission reductions

%10%20%30%40%50%60%70%80%90%100% Health improvement (Change between baseline and maximum measures) Annual Cost €Millions Costs for reducing health impacts from fine PM Analysis for the EU Clean Air For Europe (CAFE) programme

%10%20%30%40%50%60%70%80%90%100% Health improvement (Change between baseline and maximum measures) Annual Cost €Millions 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)

Emission reductions suggested by the Thematic Strategy for 2020 [2000=100%] Current legislation 2020 Maximum reductions mio € mio € +140 mio € mio € +650 mio € mio € for mobile sources (NOx+PM)

Emission control costs by sector for achieving the air quality targets of the EU Thematic Strategy

Courtesy of Les White %10%20%30%40%50%60%70%80%90%100% Health improvement (Change between baseline and maximum measures) Annual Cost €Millions RAINS cost- effectiveness approach “Equal technology” approach Cost savings from the GAINS approach Estimates presented by European industry associations

Air pollution control costs to meet the EU air quality and climate targets EU-27, 2020 Business as usual National energy projections (+3% CO 2 in 2020) PRIMES energy scenario with climate measures (-20% CO 2 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: : 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 i, k, m, p Country, activity type, abatement measure, pollutant E i,p Emissions of pollutant p (for SO 2, NO x, VOC, NH 3, PM2.5, CO 2, CH 4, N 2 O, etc.) in country i A i,k Activity level of type k (e.g., coal consumption in power plants) in country i ef i,k,m,p Emission factor of pollutant p for activity k in country i after application of control measure m ief i,k,p Implied emission factor of pollutant p for activity k in country i x i,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 Klimont EMEP Centre for Integrated Assessment Modelling (CIAM) 33 rd Session of the EMEP Steering Body, Geneva, September 7-9, 2009

GAINS methodology for emission calculation i, k, m, p Country, activity type, abatement measure, pollutant E i,p Emissions of pollutant p (for SO 2, NO x, VOC, NH 3, PM2.5, CO 2, CH 4, N 2 O, etc.) in country i A i,k Activity level of type k (e.g., coal consumption in power plants) in country i ef i,k,m,p Emission factor of pollutant p for activity k in country i after application of control measure m ief i,k,p Implied emission factor of pollutant p for activity k in country i x i,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 SO 2, NO x, NMVOC, NH 3, 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

Comparison of NO x emissions in 2009 GAINS (100%), CEIP (2000) GAINS estimate

Sectoral contribution to NO x emissions in 2000 Source: GAINS model calculations

Implied emission factors for NO x Heavy duty vehicles, diesel

Implied emission factors for NO x Passenger cars, diesel

Comparison of NH 3 emissions in 2009 GAINS (100%), CEIP (2000) GAINS estimate

Implied emission factors for NH 3 Dairy cows

Comparison of PM2.5 emissions estimates GAINS (100%), CEIP (2000, 2005) GAINS estimate

Sectoral contribution to PM2.5 emissions in 2000 Source: GAINS model calculations

Implied PM2.5 emission factors Fuelwood stoves

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 level have 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 SO 2, NO x, NH 3 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 SO 2, NO x, NH 3 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 PM2.5 developed for GAINS PM2.5 j Annual mean concentration of PM2.5 at receptor point j p i Primary emissions of PM2.5 in country i s i SO 2 emissions in country i n i NO x emissions in country i a i NH 3 emissions in country i α S,W ij, ν S,W,A ij, Linear transfer matrices for reduced and oxidized σ W,A ij, π A ij s nitrogen, sulfur and primary PM2.5, for winter, summer and annual

Validation of functional relationship for PM for TSAP emission scenario [μg/m 3 ] 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 Dep p,j Annual deposition of pollutant p at receptor point j Dep p,j,,0 Reference deposition of pollutant p at receptor point j E i,p Annual emission of pollutant p (SO 2, NO x, NH 3 ) in country I E i,p,0 Reference emissions of pollutant p in country I P i,j,p,0 Transfer matrix for pollutant p for emission changes around the reference emissions. 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 O3 l Health-relevant long-term ozone indicator measured as the population-weighted SOMO35 in receptor country l O3 l,0 Population-weighted SOMO35 in receptor country l due to reference emissions n 0, v 0 n i, v i Emissions of NO x and VOC in source country i N i,l, V i,l Coefficients describing the changes in population-weighted SOMO35 in receptor country l due to emissions of NO x and VOC in source country i. 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 RAINS Concept 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 RAINS Approach 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

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

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

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