1. The EuroDelta project - Sectoral approach to IAM -
Kees Cuvelier, Philippe Thunis, Pete Roberts
LW, LP, LT, AN, ST, RS, AK, LR, BB, RB, MS, GB, PB, AMG
TFIAM-TFMM-TFHTAP June 2009

2. EuroDelta
Inter-comparison exercise involving some important models used for AQ policy applications in Europe.
Several Phases of work
Model Comparison with Data (validation, performance)
Model Comparison with Model (understand formulation effects)
Model Scenario studies (response to changes in input)
Model zooming studies (response on different scale)
Coordinated (inputs and outputs) by JRC Ispra
Preparation of input data
Common system of managing results
Pictorial display of information
Data processing for detailed outputs.

3. ED Participants Models common to all phases: CHIMERE EMEP LOTOS MATCH
REM/CALGRID
Participants
JRC-Ispra (EC)
CONCAWE (B)
INERIS (F) - CHIMERE
MET.no (N) - EMEP
TNO (NL) - LOTOS
SMHI (S) - MATCH
FUB (G) - REM/CALGRID

4. ED phase I ( )
Examined the performance of Regional AQ models in predicting:
recent (2000; for validation) and
future (2020) AQ in Europe
Used 1999 and 2001 Meteorology to test sensitivity
Same input data (emission inventory)
Different meteorological drivers, grids and domains.

5. ED phase I ( ) cont’
Investigation of 2020 emission reductions for
NOx, SO2, VOC, NH3, PPM2.5 independently in three countries:
FR, DE, IT
NOx and SOx in sea areas
North Sea, Med. Sea
Emission reductions applied proportionately across SNAP-sectors.
Calculated Source Receptor Relationships (SRR)
Country to Country
Country to Europe (EU26, EUall)

6. Phase I Results
Model inter-comparison (model to data and model to model) was very informative and scientifically rewarding.
Model developments
insights into several aspects of model performance.
Learning points on standardisation of input data.
SRR comparison was a “first”
Ensemble approach
Spread of ensemble indicates degree of consistency between models ~ measure of ‘uncertainty’
Proximity of model to centre of ensemble indicates degree of bias.

7. Emission reductions
The emission reductions are distributed over all SNAP sectors in proportion to their contribution.
This type of SRR (Country => Grid => Country/EU) is used in the IIASA/RAINS approach to Integrated Assessment (IA) for the Protocol.

8. ED Phase II (2006-)
Main objective: to study if reducing emissions from individual sectors produced the same SRR as a proportional reduction across all sectors.
Stimulated by questions about large point source treatments in models (source heights vs model vertical structure)
International shipping as a sector open to control
Recognition (City Delta) that some sectors may be more important than others requiring ‘uplift’ of PM concentrations in grid squares with significant urban area.
Proximity of Sources and Populations
Clearly it is computationally impossible to have full sectoral SRRs in IAM so
First look to see if there was any effect
Second look to see if sectors could be grouped and make the computational process more tractable.

9. ED Phase II (2006-) con’t Run in two tranches Tranche 1
Explored results for GB, FR, ES, DE
Med. Sea Scenarios
Published EUR EN – 2008
Tranche 2
PL, IT, Benelux, Po Valley
North Sea, Baltic Sea and Atlantic
Added some finer resolution studies for Marseilles and Piraeus
Total of 80 land-based and 15 sea-based scenarios

10. Presentation of Results
Each calculation involves an emission reduction in a country.
EuroDelta tool is used to calculate the consequential difference in [say] concentration at each domain grid square:
SRR: Country => Country
SRR: Country => EU
This difference can be population weighted if needed.
Emission reductions are done:
on a sector basis
on a proportional basis across sectors
Emission reductions compared using an effectiveness parameter

11. Emission reduction (kt / year)
Emission reduction overview (F)
Emission reduction (kt / year)
Scen
Country
Sectors
Pollutant
NOx
PM2.5
SOx
VOC
NH3
BASE CASE 2020 CLE 1
France
All
NOx+PM2.5
230 62 2
SOx+VOC
110
150 3
SNAP 1 40 4
SNAP1/4 30 5
SNAP 2 45 6
SNAP 3
100 7
SNAP3/6 70
120 8
SNAP 4 10 9
SNAP 7 90
SNAP 10
250 11
Comb.
40/100/90
3/45/2/10/2 12
40/70
30/120

12. Example Scenario (France input data)
To simplify modelling and reduce number of runs
NOx and PPM2.5 reductions modelled together
SO2 and VOC modelled together
Scenarios
ALL scenario
National Emission Reductions applied across all sectors (proportional to inventory)
SECTOR scenario
Emission reduction to sector only
COMBINED
Selected sectors run together
Sum of emission reductions is the same for ALL and COMBINED

13. Effectiveness of Emission Reduction for (population weighted) PM2.5
EER = Pollutant reduction / kt. of pollutant abated
= Δ Concentration*POP / Δ Emission
Relative effectiveness (RE) :
= Single Sector EER / ALL sector EER
RE of PPM2.5 reduction on PM2.5 for EMEP model

14. Relative Effectiveness of PPM2.5 reduction on PM2.5 (EMEP model)FR
0.40
1.02
1.08
1.47 DE
0.39
1.09
0.44
1.45
1.07 ES
0.26
1.96
0.41
0.80
1.12 UK
0.33
1.03
0.45
1.38
1.61 IT
0.27
1.20
0.38 PL
0.37
1.11
1.30
BNL
0.21
0.81
1.54

15. Summary for PPM2.5 reduction on PM2.5
Results
Reducing emission on Sector 1 is less effective than reducing ALL
Reducing emission on Sector 3 is less effective than reducing ALL
Reducing emission on Sector 7 is more effective than reducing ALL
Reducing emission on Sectors 2 and 4 is mixed
Significance:
if IAM on basis of ALL modelling determines reduction of X is necessary but if implementation means that burden of reduction falls on Sector 1/3 then policy will underachieve.
If burden falls on Sector 7 then policy will over-achieve
Most effective policy likely to be one that accounts for the sectoral effect.

16. Other Countries and Models
Next slides show:
Graphic of data from table
Impacts in the different countries/regions (country=>country, =>EU26, => EUall)
Predictions of the individual models as bars – denoting range, EMEP as cross
Single Sector
First attempts at grouping sectors +

17. PM2.5
POP = 0
PPM2.5, SOx, NOx
Absolute impact

18. Country => Country

19. Country => EU26

20. Country => EUall

21. PM2.5:
POP = 0 1
PPM2.5, NOx

22. Country => EU26
POP=0
POP=1

23. YOLL:
PPM2.5, SOx, NOx
Absolute impact

24. Country => Country

25. Country => EU26

26. Country => EUall

27. Total Deposition:
POP = 0
NOx, SOx, NHx

28. Country => Country

29. Country => EU26

30. Country => EUall

31. Conclusions
Some results of the EuroDelta project have been presented that show that there are important differences in the effectiveness of emission reductions between sectors.
Broadly speaking
Reducing emissions in sectors 1 and 3 has less effect than the ALL scenario in which national emissions are reduced proportionately.
Reducing emissions in sector 7 has more effect
For some sectors (2, 4) the effect can be less or more depending on country
Findings are consistent across models.

32. Recommendation ED-II
It is recommended that validation calculations are carried out as part of the Policy process to examine if the implied sectoral reductions are able to deliver the intended benefits.
If sectoral weights could be incorporated into the IA itself, then this may lead to :
Different emission ceilings for the same environmental
benefits
Different distribution of emission reductions per sector.

33. More information
The first phase of this work is reported and is available from JRC
Contact:
Full model results will be published after the Summer.
More work to be done!

34. Referencies
Vautard, Van Loon, Schaap, Bergstrom, Bessagnet, Brandt, Builtjes, Christensen, Cuvelier, Graff,
Jonson, Krol, Langner, Roberts, Rouïl, Stern, Tarrason, Thunis, Vignati, White, Wind.
Is regional air quality model diversity representative of uncertainty for ozone simulation?
Geophysical Research Letters 2007
R Vautard, M. Schaap, R Bergström, B. Bessagnet, J. Brandt,P.J.H. Builtjes,J. H. Christensen,
C. Cuvelier, V. Foltescu, A. Graf, A. Kerschbaumer, M. Krol, P. Roberts, L. Rouïl, R. Stern, L. Tarrason,
P. Thunis, E. Vignati, P. Wind.
Skill and uncertainty of a regional air quality model ensemble.
Atmospheric Environment 2008
M. Schaap, R. Vautard, M. van Loon, R. Bergström, B. Bessagnet, L. Rouil, V. Foltescu, J. Langner,
J. Brandt, J. Kristensen, E. Vignati, M. Krol, P.J.H. Builtjes, R. Stern, A. Graff, P. Wind, J.E. Jonson,
L. Tarrasón, L. White, P. Roberts, C. Cuvelier, P. Thunis:
Evaluation of long term aerosol simulations from seven regional air quality models and their ensemble in
the EURODELTA study.
Atmospheric Environment 2007
Van Loon, Vautard, Schaap, Bergstrom, Bessagnet, Brandt, Builtjes, Christensen, Cuvelier, Graff,
Evaluation of long-term ozone simulations from seven regional air quality models and their ensemble.
Thunis P, Cuvelier C (editors), Roberts P, White L, Post L, Tarrason L, Tsyro S, Stern R, Kerschbaumer A,
Rouil L, Bessagnet B, Bergström R, Schaap M, Boersen G, Boersen P,. EURODELTA II –
Evaluation of a Sectoral Approach to Integrated Assessment Modelling Including the Mediterranean Sea.
EUR EN. Luxembourg (Luxembourg): OPOCE; JRC41801

35. Future of EuroDelta New phase of EuroDelta will start in autumn.
Involvement of more Modelling groups.
50 km x 50 km => Finer scale => Zooming.
High resolution Int & Val, using HR emission inventories.
Vertical profiles.
Past-casting: 2004 => 2009 with 2009-models.
Int & Val, impact of meteo variability.
Organic aerosols – Int & Val of PM speciation
Biogenic emissions in relation to ozone and organics
(natural VOCs)
Regional – Hemispheric integration (background, climate)
Data assimilation
Exposure
Southern Europe / Mediterranean
Application to the Ambient air-quality directive process
(Ex: impact of (local) measures, change to biofuels,…)

36. Future of EuroDelta New phase of EuroDelta will start in autumn.
Involvement of more Modelling groups.
50 km x 50 km => Finer scale => Zooming.
High resolution Int & Val, using HR emission inventories.
Vertical profiles.
Past-casting: 2004 => 2009 with 2009-models.
Int & Val, impact of meteo variability.
Organic aerosols – Int & Val of PM speciation
Biogenic emissions in relation to ozone and organics
(natural VOCs)
Regional – Hemispheric integration (background, climate)
Data assimilation
Exposure
Southern Europe / Mediterranean
Application to the Ambient air-quality directive process
(Ex: impact of (local) measures, change to biofuels,…)
Policy-supporting AQ modelling activities

37. Thank you for your attention!

38. SNAP97 emission activity sectors:1
Combustion in energy and transformation industry (Power plants) 2
Non-industrial combustion plants (Residential heating) 3
Combustion in manufacturing industry (Industrial areas) 4
Production processes 6
Solvent and other product use (VOCs from industry and domestic activity) 7
Road transport 8
Other mobile sources and machinery 10
Agriculture
The emission reductions are distributed over all SNAP sectors in proportion to their contribution. This type of SRR (Country => Grid => Country) is used in the IIASA/RAINS approach to Integrated Assessment (IA) for the CAFE programme.

39. Report ED II +
3 DVDs
To receive a copy:
Let me know

43. Conclusions ED II Particulate matter
All models agree that there are differences in
effectiveness of emission reductions between sectors
The differences between sectors is greater for
population weighted compared with non-weighted
concentrations
True for C=>G=>C, and C=>G=>EU
The sectoral response is not the same in all countries
and is different for each pollutant
Ozone (SOMO35)
There are large country differences in the response of
SOMO35 to NOx reductions from different sectors
(same for VOC reductions)
SOMO35 is defined as the sum, over the year, of the maximum of the 8 hours daily means
over 35 ppb. [ppb*days]

44. Main Conclusion ED II
The study has shown that there are important differences between sectors in the amount of concentration (deposition) reduction obtained by changing a pollutant emission.
This difference is not accounted for in the present process used to evaluate future national emissions ceiling reductions for both beneficial effect and cost-effectiveness.
This raises the possibility that, when national bodies consider how to implement an emission ceiling, choices might be made that are less effective than expected.

45. Effectiveness of Emission Reduction for population weighted PM2.5
EER = Pollutant reduction per kt. of pollutant abated
= Δ Concentration*POP / Δ Emission
Relative effectiveness (RE) :
= Single Sector ERR / ALL sector ERR

46. Example Scenario (France input data)
To simplify modelling and reduce number of runs
NOx and PPM2.5 reductions modelled together
SO2 and VOC modelled together
Scenarios
ALL scenario
National Emission Reductions applied across all sectors (proportional to inventory)
SECTOR scenario
Emission reduction to sector only
COMBINED
Selected sectors run together
Sum of emission reductions is the same for ALL and COMBINED

47. Emission reduction (kt / year)
Emission reduction overview
Emission reduction (kt / year)
Scen
Country
Sectors
Pollutant
NOx
PM2.5
SOx
VOC
NH3
BASE CASE 2020 CLE 1
France
All
NOx+PM2.5
230 62 2
SOx+VOC
110
150 3
SNAP 1 40 4
SNAP1/4 30 5
SNAP 2 45 6
SNAP 3
100 7
SNAP3/6 70
120 8
SNAP 4 10 9
SNAP 7 90
SNAP 10
250 11
Comb.
40/100/90
3/45/2/10/2 12
40/70
30/120

48. ED Phase I ( )
Examination of common performance of Regional AQ
models in predicting recent (2000; for validation) and
future (2020) AQ in Europe for 1999/2001 Meteorology
AQ models:
CHIMERE (F), RCG-REM (G), EMEP, MATCH (S),
LOTOS (NL), TM5 (NL,Ispra)
Investigation of 2020 emission reductions for
NOx, SO2, VOC, NH3, PPM2.5 independently in
FR, GE, IT (Source-Receptor Relationships)
NOx and SOx in sea areas (NorthSea, MedSea).
The model Ensemble was used to measure robustness
of the predictions.

49. ED Phase II (2006-)
Study of the impact of emission reductions in individual
emission sectors.
First look at whether there are differences in the impact of
emission reductions if they are applied to single sectors
compared with all sectors.
Aims to assess the usefulness of introducing sectoral SRR
in IA.
Total of 80 scenarios for FR, SP, GE, UK, PL, IT, PV, BNL.
Total of 15 sea-scenarios for NorthSea, MedSea, zoom into
Marseille and Athens

50. Effectiveness measure of emission reduction =
Pollutant reduction per kTon of pollutant abated =
Δ Concentration*POP / Δ Emission
Relative effectiveness =
Eff.sector / Eff.All

51. Emissions reductions in kton/year
Emission reduction overview
Emissions reductions in kton/year
Scen
Country
Sectors
Pollutant
NOx
PM2.5
SOx
VOC
NH3
BASE CASE 2020 CLE 1
France
All
NOx+PM2.5
230 62 2
SOx+VOC
110
150 3
SNAP 1 40 4
SNAP1/4 30 5
SNAP 2 45 6
SNAP 3
100 7
SNAP3/6 70
120 8
SNAP 4 10 9
SNAP 7 90
SNAP 10
250 11
Comb.
40/100/90
3/45/2/10/2 12
40/70
30/120

52. Rel.eff of sectoral PPM25 reductions on PM25*POP for EMEP:
Country => Grid => Country impacts
PPM25 S1 S2 S3 S4 S7 FR
0.40
1.02
1.08
1.47 DE
0.39
1.09
0.44
1.45
1.07 ES
0.26
1.96
0.41
0.80
1.12 UK
0.33
1.03
0.45
1.38
1.61 IT
0.27
1.20
0.38 PL
0.37
1.11
1.30
BNL
0.21
0.81
1.54

53. Rel.eff of sectoral NOx reductions on PM25*POP for EMEP:
Country => Grid => Country impacts
NOx S1 S2 S3 S4 S7 FR
0.70
1.16 DE
0.69
0.74
1.14 ES
0.48
0.80
1.26 UK
0.55
0.66
1.44 IT
0.61
0.77
0.87
1.04 PL
1.09
0.95
0.92
1.20
BNL
0.60
0.78
1.52

54. Rel.eff of sectoral SOx reductions on PM25*POP for EMEP:
Country => Grid => Country impacts
SOx S1 S2 S3 S4 S8 FR
0.60
0.86 DE
0.76
0.88 ES
0.94 UK
0.75
0.82 IT
0.64
0.81
1.46 PL
0.91
0.92
BNL
0.51
0.59
1.54

58. Results Relative Effectiveness
ratio of change per unit reduction in one sector to change per unit reduction across sectors.
Country => Self SRR
EMEP model results (other models similar)
Look at effect of pollutant reductions on PM2.5
Primary PM2.5
NOx
SO2
Population weighted but no urban increment

59. Relative Effectiveness of NOx reduction on PM2.5FR
0.70
1.16 DE
0.69
0.74
1.14 ES
0.48
0.80
1.26 UK
0.55
0.66
1.44 IT
0.61
0.77
0.87
1.04 PL
1.09
0.95
0.92
1.20
BNL
0.60
0.78
1.52

60. Relative Effectiveness of SOx reduction on PM2.5FR
0.60
0.86 DE
0.76
0.88 ES
0.94 UK
0.75
0.82 IT
0.64
0.81
1.46 PL
0.91
0.92
BNL
0.51
0.59
1.54

61. Conclusions(2) Significance
Policy objectives set using the ALL sectors approach may not be met if the sectoral reduction burden is not aligned with the modelling assumptions.
There may be ways of meeting policy targets that are more efficient (require less reductions) than could be currently assessed
A way to take some account of sectoral effects in IAM should be sought.

62. Observations
Quite some consistency between models and countries for role of Sector 1 and Sector 3
More variation between countries for Sector 4, models consistent
More variation between both models and countries for sectors 2 and 7
Groupings not perfect – not done for all countries
grouping appears to reduce variation
Adding sector 9 to 1 and 3 appears to increase variation
Adding sector 4 9 and 10 cannot judge as individual countries not run in single sector tests.

63. Conclusions (1)
Quite some consistency between models and countries for role of Sector 1 and Sector 3
More variation between countries for Sector 4, models consistent
More variation between both models and countries for sectors 2 and 7