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

Meteorologisk Institutt met.no

•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