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TitlePM2.5:Comparison of modelling and measurements
Presented by Hilde FagerliSB, Geneva, September 7-9, 2009
OUTLINE
Meteorologisk Institutt met.no
To what extend do the models in use reproduce the background PM2.5 measurements?
What are the main systematic biases and unknowns?
What kind of mistakes in policy advice could the models be accountable for?
PM2.5 in the EMEP Unified Model
Meteorologisk Institutt met.no
Anthropogenic
SIA: SO42-, NO3
-, NH4+
PPM2.5: (OC, EC*, dust)
Natural
Sea salt Mineral dust
Water
Emissions
EMEP (SOx, NOx, NH3, NMVOC, PM2.5, PM10
EC/OC factors based on Kupiainen & Klimont, 2006
Parameterisations of production in the model
EQSAM
The recent changes in model runs affecting PM results
Meteorologisk Institutt met.no
Change of meteorological driver –
from 10-year old HIRLAM version (PARLAM 50 km) to up-to-date version of HIRLAM (0.2x0.2º)
Resulted in concentration decrease for all aerosols,
e.g. PM2.5 is 20 to 40% lower
Model revision –
revised scheme for night-time formation of HNO3
Resulted 10-35% decrease of NO3 and NH4
Meteorologisk Institutt met.no
?5 - 10dust
8 (12)< 5 (10-20 coast)Na+
?
- 30 (24)
- 28 (35)
- 44 (-13)- 34 (7)
?Bias%
5 - 25PPM2.5
5 - 15NH4+
5 - 15water
5 - 25NO3-
15 - 35 SO42-
35 - 55SIA
Relative contributions to PM2.5 based on model calculations for 2007 (SOA excluded)
PM2.5: Bias = -41% (-23)
In brackets: 2006 results with PARLAM-PS meteorology and ACID chemistryNote that NO3- and NH4+ are filter pack measurements
Meteorologisk Institutt met.no
In Tsyro et al. (2007), model calculated EC were compared with observations from EMEP EC/OC and CARBOSOL campaigns for July 2002 – Oct 2004
EC was underestimated by 30% on average
The results consistently indicated possible inaccuracies in EC/OC emission estimates from wood burning: overestimation for northern countries underestimation for southern countries
The results were not so conclusive with regard to EC (PM) emissions from road traffic and other mobile sources, as we did not have enough information to draw conclusions from…
Primary PM
Seasonal analysis: winter
Meteorologisk Institutt met.no
The results suggest: overestimation of wood burning emissions in northern Europe underestimation of wood burning emissions in
central/southern Europe Emissions spatial distribution … ?
Unaccounted local sources … ?
EC underestimation by 30-60% at 7 sites in central and southern EuropeMain sources: road traffic and other mobile sources
Our results indicate that these emissions may be underestimated Problems with dispersion? Other sources?.... Forest fires Agricultural burning
Seasonal analysis: summer
Extra info
Model bias for SIA (2007)
Meteorologisk Institutt met.no
-100
-50
0
50
100
150
200
AT
02
DE
01
DE
02
DE
03
DE
07
DE
09
HU
02
IE05
IE06
IE08
IT01
LV10
LV16
NL0
8
NL0
9
NL1
0
NO
15
NO
39
NO
42
NO
55
PL0
2
PL0
3
PL0
4
RU
18
SK
04
SK
06
SIA SO4 NO3 NH4
EMEP intensive measurements: June-06 Jan-07 Bias SO4_PM25
-100
-80
-60
-40
-20
0
20
40
IT01 DE44 NO01 ES17 FI17 IT04
%
Bias NO3_PM25
-150
-100
-50
0
50
100
150
200
250
IT01 DE44 NO01 ES17 FI17 IT04
%
Meteorologisk Institutt met.no
ES17 !!! only 2-3 days with data per
month
Bias NH4_PM25
-75
-50
-25
0
25
50
75
100
125
150
IT01 DE44 NO01 ES17 FI17 IT04
%
Bias EC_PM25
-100
-75
-50
-25
0
25
50
75
100
IT01 DE44 NO01 ES17 FI17 IT04
%
Bias Na_PM25
-100
-75
-50
-25
0
25
IT01 DE44 NO01 ES17 FI17 IT04
%
Bias PM25
-100
-80
-60
-40
-20
0
20
40
IT01 DE44 NO01 ES17 FI17 IT04
%
Model bias for PM2.5 and SIA for 2007
(only 3 EMEP sites)
Meteorologisk Institutt met.no
-80
-60
-40
-20
0
20
AT02 DE02 DE03
PM25
SIA
*) SIA includes also coarse aerosols
SIA
Low modelled PM2.5: No SOA, underestimated SIA
Meteorologisk Institutt met.no
Water Accuracy depends on the accuracy of SIA calculations
Lack of measurements for verification
Natural
On average - minor components of PM2.5
Not regulated, but necessary for PM2.5 mass closure
sea salt - intensive data show a considerable underestimation which is not seen in EMEP monitoring sites – look at 2008-09 data
dust – practically no observations chemical speciation (Ca, Mg, K…) would help
Meteorologisk Institutt met.no
Contribution of OC to PM1
From Zhang et al, 2007
Pie charts show the average mass concentration and chemical composition:Organics: Green, sulfate (red), nitrate (blue), ammonium (orange) and chloride (purple)
SOA• SOA theories/models still changing rapidly and
dramatically• Still strong need to constrain theories/models with
ambient measurements /14C, levoglucosan, AMS, etc cf EMEP, EUCAARI campaigns
• Examples: estimates of global BSOA production:– 0-180 Tg(C)/yr (Best estimate: 88) Hallquist et al,
2009– 9-50 Tg(C)/yr Kanakidou et al., 2009
Note that the best estimate of Hallquist et al. lies outside the range of uncertainty in the Kanakidou estimate
Uncertainty in SOA modelling
Results from the EMEP model with different VBS-based SOA approaches.BSOA: Biogenic SOA, ASOA: anthropogenic SOA, WOOD: OC from domestic/residential wood-burning, FFUEL: OC from fossil-fuel sources, GBND: background OC.
Overview PM2.5
?SOA?Water?Dust≈0 (?)Na+NegativeNH4
+
NegativeNO3-
NegativeSO42-
NegativeSIA?PPM2.5BiasComponent
Implications for policy
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•Variable performance (or unknown) of model results for PM constituents and/or missing components results in inaccurate calculations of PM2.5 chemical composition
•Difficult to design the optimal reduction strategyUnderestimation of the background levels of PM2.5
not stringent enough emission reduction measures too little effective formation of SIA => underestimate
effect of emission reductions (?)• affects calculations of SR relationships and scenarios (not enough long range transport?)
The end
Comparison with EMEP observations for 2006
Meteorologisk Institutt met.no
12
24
35
-13
7
- 23
Bias
0.79
0.85
0.83
0.74
0.81
0.82
R RMSEModObsNsite
4.479.011.622PM2.5
0.471.31.024NH4+
0.611.00.922Na+
1.252.51.827NO3-
0.711.82.158SO42-
2.125.65.220SIA
Note: Aerosol model based on ACID.rv2_7_10; PARLAM meteorology
Meteorologisk Institutt met.no
Quality of emission data for PPM2.5 is crucial for the accuracy of model results for PPM2.5
Sound description of removal processes, esp. wet scavenging
Boundary conditions (?)
Primary PM: What is needed for improvement of modelling:
Meteorologisk Institutt met.no
SO4 formation…
NO3 formation….
NH4 formation…
Sound description of removal processes, esp. wet scavenging
Boundary conditions (?)
Appropriate observations for validation of results
SIA: What is needed for improved modelling
Comparison with EMEP observations for 2007
Meteorologisk Institutt met.no
8
- 30
- 28
- 44
- 34
- 41
Bias
0.76
0.77
0.83
0.69
0.78
0.60
R RMSEModObsNsite
8.676.811.521PM2.5
0.430.630.933NH4+
0.651.00.926Na+
0.871.21.727NO3-
1.051.01.854SO42-
2.283.14.625SIA
Note: filter-pack measurements for SIA components and Na, i.e. no size cut-off
PM2.5 measurements: not all sites use reference gravic method