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 PM2.5 j Annual mean concentration of PM2.5 at receptor point j ISet of emission sources (countries) JSet of receptors (grid cells) p i Primary emissions of PM2.5 in country i s i SO 2 emissions in country i ni NO x emissions in country i a i NH 3 emissions in country i α S,W ij, ν S,W,A ij, σ W,A ij, π A ij Linear transfer matrices for reduced and oxidized nitrogen, sulfur and primary PM2.5, for winter, summer and annual

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 O 3 (and health impacts) in urban areas For optimization, importance of regional and urban-scale VOC reductions is underestimated

Target setting

Remaining problem areas in 2020 CAFE Baseline with Climate Measures (Light blue = no risk) Forests – acid dep. Semi-natural – acid dep.Freshwater – acid dep. Health - PMHealth+vegetation - ozoneVegetation – N dep.

Option 1 for PM target: Absolute limit Absolute limit on PM concentrations or life expectancy losses [Country-average PM2.5, μg/m 3 ]

Option 2 for PM target: Gap closure to base year Uniform improvement of PM effects in relation to 2000 [100% = 2000]

Option 3 for PM target: Gap closure to baseline (CLE) Uniform improvement of PM effects in relation to baseline 2020 [100% = 2020]

Ozone: Scope for target setting Absolute targets (ppb.days) Gap closure relative to 2000

Acidification: Scope for target setting Absolute targets (eq/ha) Gap closure relative to 2000

Eutrophication: Scope for target setting Absolute targets (eq/ha) Gap closure relative to 2000

Definition of gap closure Effect indicator MTFR from EU25 excluding EURO5/6 Base year exposure (2000/1990) Baseline 2020 (Current legislation) MTFR from EU25 MTFR 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 range used for illustrative 2005 calculations NEC 2010

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

Total (per-capita) costs for the PM scenarios A [€/person/year]

Total (per-capita) costs for the PM scenarios A Additional costs on top of the baseline 2020 [€/person/year]

Costs related to GDP (MER) [% of GDP – Market Exchange Rate]

Costs related to GDP (PPS) [% of GDP – Purchasing Power Standards]

Costs related to GDP (MER) Additional costs on top of the baseline 2020 [% of GDP]

Costs related to GDP (PPS) Additional costs on top of the baseline 2020 [% of GDP ]

Emission control costs by pollutant of baseline current legislation [€/person/year]

Emission control costs by pollutant Additional cost for the 25% scenario A1/1 [€/person/year]

Emission control costs by pollutant Additional costs for the 50% scenario A1/2 [€/person/year]

Emission control costs by pollutant Additional costs for the 75% scenario A1/3 [€/person/year]

Loss in life expectancy attributable to anthropogenic PM [months]

Optimizations for other environmental endpoints

Emission control costs vs. loss in life expectancy [billion €/yr]

Emission control costs vs. premature deaths attributable to ozone [billion €/yr]

Emission control costs vs. forest ecosystems area with acid deposition above CL [billion €/yr]

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 steps Shopping 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 O 3 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 O 3 : 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 (O 3 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 Vienna Source: Puxbaum et al., Jun - 99 Jul - 99 Aug - 99 Sep - 99 Oct - 99 Nov - 99 Dec - 99 Jan - 00 Feb - 00 Mar - 00 Apr - 00 May - 00 µg/m³ NH4, SO4 Na, Ca OC BC Urban impact PM2.5 (difference between urban and rural twin site) Jun - 99 Jul - 99 Aug - 99 Sep - 99 Oct - 99 Nov - 99 Dec - 99 Jan - 00 Feb - 00 Mar - 00 Apr - 00 May - 00 µg/m³ NH4, SO4 Na, Ca OC BC

PM10 observations in London as a function of emission density primary PM10 from road transport

Primary PM10 concentrations vs. emission density City-Delta model results, Berlin Data processed by JRC-IES

Modelled relation between PM2.5 and emission densities - Berlin BerlinSlopeR2R2 Base case CLE case MFR case All cases ParisSlopeR2R2 Base case CLE case MFR case All cases

Slopes of individual cities against EMEP wind speed in city grid

ΔPM sub-grid = (ED sub-grid - ED EMEP ) * (k1 - k2*V wind ) ΔPM sub-grid..Difference in PM concentration between sub-grid (urban/rural) area and EMEP grid average ED x …Emission density for low sources (x=urban/rural/EMEP grid average) V wind …Annual mean wind speed in EMEP grid cell k1, k2 …Parameters derived from the City-Delta ensemble model Functional relationship for PM

ΔPM sub-grid = (ED sub-grid – ED EMEP ) * (k1 - k2*V wind )= = (ED EMEP * (PD sub-grid / PD EMEP ) – ED EMEP ) * (k1 - k2*V wind ) = = ED EMEP * (PD sub-grid / PD EMEP – 1) * (k1 - k2*V wind ) 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 PM2.5 Grid average vs. urban increments, 2000 [µg/m 3 ]

Validation against observations Urban background PM2.5 [μg/m 3 ]

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 density SOMO35, transect through Paris Data processed by JRC-IES