1. Title Why do we underestimate Elemental Carbon in PM?
Progress with MSC-W modelling of EC
Presented by Svetlana Tsyro
TFMM 7-th meeting, Helsinki, May2006

2. EC emissions, EMEP 2002 (Kt) EC fine EC coar Power generation 6.5 6
Non-industrial combustion
210 91
Industrial combustion 5 3
Production processes 30 20
Extraction & distribution of fossil fuels
Solvent and other product use
Road transport
192 54
Other mobile sources and machinery
109 8
Waste treatment and disposal 4
Agriculture
(*) PM emission speciation based on Kupiainen, K. and Klimont (2004)
Meteorologisk Institutt met.no

3. EC concentrations Model calculated for 2002 Observed
Fine and coarse particles are distinguished in order to account for the different dry and wet removal rates.
EMEP OC/EC campaign
(July 2002-June 2003)
14 sites, 1 day a week
Meteorologisk Institutt met.no

4. Model vs. Measurements July 2002-March 2003
Bias=- 41%
Corr= 0.84
Individual stations
Bias:
from -80% (PT01) to +7% (NO01)
Temporal correlation: ~ 0.54
from (UK) to (NL)
Fine and coarse particles are distinguished in order to account for the different dry and wet removal rates.
Meteorologisk Institutt met.no

5. Outline: Possible reasons for EC underestimation
Missing emission sources
Uncertainties in modelling of EC transport
Uncertainties in anthropogenic emissions of EC/PM
Fine and coarse particles are distinguished in order to account for the different dry and wet removal rates.
Meteorologisk Institutt met.no

6. 1. Missing EC emission sources
Wildland fires
Monthly EC emissions from
Global Fire Emission Database (GFED2)
Fine and coarse particles are distinguished in order to account for the different dry and wet removal rates.
Meteorologisk Institutt met.no

7. EC emissions in 2002 1. Wildland fires Anthropogenic Wildfires (GFED2)
Fine and coarse particles are distinguished in order to account for the different dry and wet removal rates.
Units: tonnes/grid
Meteorologisk Institutt met.no

8. Contribution to total EC (%) Concentrations (ng/m3)
1. Wildland fires
EC from wildfires in 2002
Contribution to total EC (%)
Concentrations (ng/m3)
Fine and coarse particles are distinguished in order to account for the different dry and wet removal rates.
Meteorologisk Institutt met.no

9. For the sites affected:
1. Wildland fires
For the sites affected:
DE02
FI17
NO01
PT01
SE12
SK04
Bias fires
-41
-42 13
-69
-31
Bias, w/o fires
-43
-53 7
-80
-38
-74
R fires
0.68
0.58
0.39
0.43
0.44
0.28
R, w/o fires
0.65
0.55
0.61
0.70
0.40
0.24
Fine and coarse particles are distinguished in order to account for the different dry and wet removal rates.
Bias = %  %
Corr =
Meteorologisk Institutt met.no

10. 2. Uncertainties in modelling EC transport
So far EC was assumed to be in internal mixture –
hydrophilic
Freshly emitted EC is mostly hydrophobic
EC ”ageing” - getting coated with soluble material,
becoming hydrophilic
Fine and coarse particles are distinguished in order to account for the different dry and wet removal rates.
Meteorologisk Institutt met.no

11. Hygroscopic properties of EC affect its removal
2. Uncertainties in modelling EC transport
Hygroscopic properties of EC affect its removal
EC wet deposition
No scavenging of hydrophobic EC from clouds
EC dry deposition –
Smaller dry deposition velocity of “fresh” EC
(no hygroscopic growth of hydrophobic EC)
Fresh EC is less efficiently removed that the aged EC
More EC remains in the air
Meteorologisk Institutt met.no

12. Sensitivity tests on the effect of EC ageing:
2. Uncertainties in modelling EC transport
Sensitivity tests on the effect of EC ageing:
1. Internally mixed – hydrophilic
Assume: 80% of emitted EC is hydrophobic
2. Ageing: hydrophobic  hydrophilic:
turnover rate 2.5% h-1 (e-time scale τ  1 day) (Cook and Wilson, JGR, 1996)
variable turnover rate (Riemer et al. ACP, 2004)
daytime summer, winter: τ = 8 h (below 250m)
---- # # # τ = 2 h (above 250m)
night τ = 30 h ( h)
3. Externally mixed – hydrophobic (no in-cloud scavenging)
Meteorologisk Institutt met.no

13. Effect of EC ageing 2. Uncertainties in modelling EC transport PHIL
AGEC
AGEV
PHOB
Relative bias (%)
-46
-38
-42
-28
Spatial correlation
0.89
0.91
0.90
0.92
PHIL - hydrophilic EC
AGEC - ageing EC with constant rate
AGEV - ageing EC with variable rate,
PHOB - hydrophobic EC
Fine and coarse particles are distinguished in order to account for the different dry and wet removal rates.
Meteorologisk Institutt met.no

14. Effect of EC ageing on results
2. Uncertainties in modelling EC transport
Effect of EC ageing on results
Fine and coarse particles are distinguished in order to account for the different dry and wet removal rates.
UrbBG
rural
UrbBG
Near-city
Meteorologisk Institutt met.no

15. Even with no EC removal we still underestimate EC by 2% !!!
2. The effect of EC ageing
 Rather limited for the sites considered
 However, the effect is expected to be larger in other regions with different pollution and precipitation regime
Fine and coarse particles are distinguished in order to account for the different dry and wet removal rates.
 Even extreme case of hydrophobic EC yields a negative bias of 28%
Even with no EC removal we still underestimate EC by 2% !!!
Meteorologisk Institutt met.no

16. EC emissions (EMEP, 2002) 3. Uncertainties in anthropogenic emissions
EC fine (Ktonnes)
EC total (Ktonnes)
Power generation
6.5
6.8
Non-industrial combustion
210
300.8
Industrial combustion 5
8.3
Production processes
29.9
49.4
Extraction & distribution of fossil fuels
Solvent and other product use
Road transport
192.3
245.9
Other mobile sources and machinery
108.9
162.4
Waste treatment and disposal
52.3
56.2
Agriculture 20
33.8
Meteorologisk Institutt met.no

17. Sensitivity tests for EC emissions
3. Uncertainties in anthropogenic emissions
EC emissions between 1 and 2.5 mkm
Underestimation of residential combustion emissions
Underestimation of traffic emissions
Sensitivity tests for EC emissions
Base
EC1 x1.15
S2 x4.5
S7 x2
Relative bias (%)
-41
-36 -6
-19
Spatial correlation
0.90
0.89
Fine and coarse particles are distinguished in order to account for the different dry and wet removal rates.
Meteorologisk Institutt met.no

18. Effect of EC properties
3. Uncertainties in anthropogenic emissions
Fine and coarse particles are distinguished in order to account for the different dry and wet removal rates.
Near-city
rural
Urb BG
Urb BG
Meteorologisk Institutt met.no

19. “Flat” underestimation
3. Uncertainties in anthropogenic emissions
Monthly series
“Flat” underestimation
Fine and coarse particles are distinguished in order to account for the different dry and wet removal rates.
Mixed
Meteorologisk Institutt met.no

20. Underestimation -larger in winter Lager underestimation in summer
3. Uncertainties in anthropogenic emissions
Underestimation -larger in winter
Fine and coarse particles are distinguished in order to account for the different dry and wet removal rates.
Lager underestimation in summer
Meteorologisk Institutt met.no

21. Results suggest: Emissions from mobile sources can be underestimated
3. Uncertainties in anthropogenic emissions
Underestimation larger in summer
The EC underestimation is generally the same over the seasons, some places larger in summer than in winter
Fine and coarse particles are distinguished in order to account for the different dry and wet removal rates.
Residential combustion cannot explain such underestimation (low emissions in summer)
Results suggest: Emissions from mobile sources can be underestimated
Meteorologisk Institutt met.no

22. Conclusions:
Natural biomass burning emissions – are not sufficiently widespread over Europe to explain the 40% underestimation of EC concentrations (improvements of bias by 2-10%)
EC hygroscopity ++ has a small effect over central European areas and does not provide a feasible explanation for the EC underestimation
The results suggest that anthropogenic EC/PM emissions are underestimated
Fine and coarse particles are distinguished in order to account for the different dry and wet removal rates.
Meteorologisk Institutt met.no

23. Uncertainties in measured EC (analytical procedures)
Conclusions:
EC emission underestimation ++ analysis of temporal correlations results indicates that the underestimation is generally the same for different seasons.
This points out the importance of traffic and off-road mobile sources
Are emission factors for diesel underestimated?
Can these be re-evaluated (ref. US EPA)
Measurements needed !
Fine and coarse particles are distinguished in order to account for the different dry and wet removal rates.
Uncertainties in measured EC (analytical procedures)
Meteorologisk Institutt met.no

24. Thank you!