TitlePerformance of the EMEP aerosol model:current results and further

needs

Presented by Svetlana Tsyro (EMEP/MSC-W)

EMEP workshop on Particulate Matter Measurements & Modelling, New Orleans, April 20-23, 2004

Outline

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• Short description of the aerosol model

• Model performance evaluation

Comparison with observations of calculated :

PM10 and PM2.5 masses

PM chemical composition, particle-bound water

particle numbers

• Identified needs for the model further improvement

and validation

• Summary / conclusions

EMEP Aerosol model (UNI-AERO):

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Aerosol components: SO42-, NO3

-, NH4+, OC, EC, dust , sea salt

+ aerosol water (not yet included: SOA, primary biogenic OC, wind blown dust)

Aerosol size distribution - 4 monodisperse size modes:

nucleation, Aitken, accumulation, coarse

Assumption: particles in the same mode have the same size and the same chemical composition (internally mixed)

Accounts for aerosol dynamics (MM32): nucleation, condensation, coagulation, ‘mode merging’ Output: size resolved aerosol mass and number concentrations

Resolution: 50 x 50 km2 , 20 layers up to 100 hPa

SO4, HNO3 / NO3 NH4 / NH3, Na, Cl

PM emissions

Gas emissions

aerosolgases

OC EC DustAitken, accum.

Dust coarse

N, M, D

H2SO4SOx

NOx

Irreversible chemistry(gaseous and aqueous)

EQSAMGas/aerosol & aerosol water

NH3

Aerosol dynamics (MM32)Nucleation H2SO4-H2O SO4

Condensation H2SO4 SO4

Coagulation

Mode merging

CoarsePM

PM2.5

sea salt

Dry deposition

Wet scavenging

PM-bound water

Output: number and mass size distribution, chemical composition, PM2.5, PM10 (PM1)

Schematic computational structure of UNI-AERO

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Annual mean concentrations of PM in 2001

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PM2.5 PM10

Aerosol model

EMEP obs

Systematic underestimation

Measure-mentsspatial

coverage

Annual mean PM10 and PM2.5 (2001, EMEP)

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Spain: bias = - 67%, corr = 0.44

wind eroded & Saharan dust

N=17

Bias=-46%

Corr=0.61

N=25

Bias=-51%

Corr=0.15

The model underestimates measured PM10 and PM2.5

PM10 – complex pollutant. To explain the discrepancies between calculated and measured PM10 verification of the individual components is needed.

elevated

C. Europe: bias = - 41% corr = 0.59

Annual mean SIA (2001, EMEP)

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Bias=-19%

Corr=0.81

Bias= 15%

Corr=0.89

Sites without PM10 measurements

Does not help to explain the discrepancy between

modelled and measured PM

Sites with PM10 measurements

For that, co-located and concurrent measurements of ‘all’ aerosol

components is needed

SIA (2001): model vs. EMEP measurements

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Bias = 9%Corr = 0.71

Bias = 19%Corr = 0.91

Bias = 7%Corr = 0.84

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What is needed:

“component-wise” verification of modelled PM

EXAMPLE for Birkenes, Norway (2001)

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PM10 components

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PM10 components

PM emissions validation

One more example: Vienna

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Daily PM2.5 (June 1999 - June 2000) :

Daily series of SO4, NO3 and NH4 in PM2.5

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NO3

NH4

SO4

OC

EC

Na

EC PM emissions validation

Chemical composition of PM2.5 and PM10 (1):

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Non-C atoms in organic aerosol

Particle-bound water

Measurement artefacts

Full chemical mass closure is rarely achieved .

Unaccounted PM mass - up to 35-40%

Gravimetric method (Reference, EU and EMEP) for determining PM mass requires 48-h conditioning of dust-loaded filters at T=20C and Rh=50% -does not remove all water! At Rh=50% particles can contain 10-30% water

Gravimetrically measured PM mass does not represent dry PM mass!!!

Chemical composition of PM2.5 and PM10 (2):To what extend can particle-bound water explain the model

underestimation of measured PM?

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Spain

0

5

10

15

20

25

Obs Bemantes

M od Bemantes

Obs M onagrega

M od M onagrega

Obs M ontseny

M od M ontseny

ND/waterotherdustSSOC+ECNH4NO3SO4

Unaccounted PM mass in obs

Aerosol water in model results

Vienna Streithofen

PM2.5 Austria,1-6/2000 (AUPHEP)

PM10 PM25

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Modelled dry PM2.5 vs. Identified PM2.5 mass

Modelled water in PM2.5 vs. Unaccounted PM2.5 mass

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Model calculated dry PM2.5 (blue) and

PM2.5 including aerosol water (black)

vs. measured PM2.5 (red)

Accounting for water in modelled PM2.5 gives better agreement with measurements

BUT: verification of model calculated aerosol water with measurements is needed

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Accounting for particle-bound water in PM2.5

Model calculations vs. gravimetric PM2.5 (EMEP, 2001)

Dry PM2.5

N=13

Bias=- 47%

Corr=0.69

N=13

Bias=-28%

Corr=0.68

Dry PM2.5 + water

Smaller negative bias

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Dry PM10

N=13

Bias=- 64%

Corr=0.26

N=13

Bias=-38%

Corr=0.29

Dry PM10 + water

Smaller negative bias

Slightly improved correlation

Accounting for particle-bound water in PM10

Model calculations vs. gravimetric PM10 (EMEP, 2001)

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Verification of daily PM2.5 with EMEP measurements

Sites Obs.MeanBias

dry PM2.5Bias

PM2.5+water

CorrelationPM2.5+water

AT02 Illmitz 19.54 -51 -35 0.56

DE02 Langenbrügge 12.46 -14 14 0.69

DE03 Schauinsland 7.93 16 55 0.15

DE04 Deuselbach 11.71 -8 25 0.59

CH02 Payerne 14.80 -50 -33 0.47

CH04 Chaumont 8.12 -8 22 0.41

IT04 Ispra 32.01 -60 -49 0.42

NO01 Birkenes 4.04 -43 -24 0.57

ES07 Viznar 12.46 -68 -57 0.37

ES08 Niembro 11.16 -48 -27 0.34

ES09 Campisabalos 9.02 -48 -27 0.21

ES10 Cabo de Creus 12.09 -51 -36 0.29

ES11 Barcarrota 11.36 -58 -40 0.39

ES12 Zarra 8.89 -44 -23 0.40

ES13 Penausende 9.70 -43 -23 0.46

ES14 Els Torms 12.41 -53 -36 0.50

ES15 Risco Llano 8.46 -42 -21 0.05

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Daily PM2.5 vs. EMEP measurements

Hourly PM2.5

Aspvreten, SE

2000

Aitken number (102/cm3) at Hyytiälä, Finland, 2000

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Hourly

Daily

Hourly, october

Hourly, december

Hourly Aitken number: nucleation effect

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Nucleation events (June 10-22, 2000) No nucleation (July 12-17, 2000)

Prediction of nucleation events

Number of nucleated particles

Growth of newly formed particles

Hyytiälä, Finland

BIOFOR

Accumulation (0.1 – 0.5 μm) particle number, 2000 (10-2/cm3)

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Aspvreten, hourly

Värriö, hourly

Aspvreten, daily

Värriö, daily

Daily total particle number, Austria (AUPHEP)

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Viennaurban

Streithofenrural

Emissions + meteorology

Summary on the model performance

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• The EMEP aerosol model underestimates PM2.5 and PM10 (SOA and natural dust not yet included)

• Accounting for particle-bound water improves the agreement between model calculated and gravimetrically determined PM mass

Verification of model calculated aerosol water

• Largest discrepancy: OC, EC, mineral dust

Implementation of SOA, wind blown dust.

Emissions chemical speciation!

• Particle number – more difficult (esp. Aitken): Emissions size disaggregation! Aerosol dynamics

Measurement needs

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• Information on PM chemical composition is essential for further improvement of PM mass calculations

Co-located concurrent measurements are needed:

(process understanding, source allocation)

• Particle-bound water

• Particle number concentration: size distribution

• Particle fluxes (dry deposition) over different land-use types, size resolved

• Wet scavenging

• Vertical profiles