g. pirovano – cesiricerca, italy comparison and validation of long term simulation of pm10 over 7...

G. Pirovano – CESIRICERCA, Italy

Comparison and validation of long term simulation of PM10 over 7 European cities in the

frame of Citydelta project

Bedogni M. , Costa M.P., Casadei S.AMA Mobility and Environment Agency of Milan, Milan, Italy

Pirovano G. CESI RICERCA Spa, Milan, Italy

AMA - MI


http://aqm.jrc.it/citydelta/

Phase III


CAMx version 4.21CAMx version 4.21

• Horizontal advection: Bott Scheme• Resistance Based Dry Deposition• Wet Deposition• Photolysis rates adjusted as a function of cloud cover,

total ozone column and turbidity• Gas phase chemistry

– Mechanism: CBIV99 Solver: CMC• Aerosol phase chemistry

– SIA: Isorropia SOA: SOAP algorithm– Coarse/Fine bins No dynamic processes


Introduction...Introduction...Vautard R. et al., Evaluation and intercomparison of Ozone and PM10 simulations

by several chemistry-transport models over 4 european cities within the City-Delta project, Atm. Env., 2006, in press.

City-delta IIICity-delta III• Base year 2004• Only CAMx model• 5+2 cities: Berlin, Lisbon, London, Milan, Paris, (Prague, Krakow)• MM5 meterological fields at 10 km (Milan 5km, Lisbon 2km)• Kv computation following CMAQ approach• EMEP Emission Inventory & local inventories spatial distribution• Biogenic emissions• CHIMERE continental fields as B.C.• No specific tuning for each city

...We may look at this exercise with......We may look at this exercise with...


……a “synoptic” point viewa “synoptic” point view

……in order to discuss a couple of interesting topicsin order to discuss a couple of interesting topics

• Operational evaluation of the modelling system

• PM10 mass and composition


Stations subsetsStations subsets

- 2 . 5 - 2 - 1 . 5 - 1 - 0 . 5 0 0 . 5 1 1 . 5

- 2 . 5 - 2 - 1 . 5 - 1 - 0 . 5 0 0 . 5 1 1 . 5

5 0 . 5

5 1

5 1 . 5

5 2

5 2 . 5

5 3

5 0 . 5

5 1

5 1 . 5

5 2

5 2 . 5

5 3

2 1

2 7

3 03 4 3 7

3 13 2

3 9

2 6

400 450 500 550 600 650

UTM (km )

4900

4950

5000

5050

5100

5150

UT

M (

km)

M ILANO

G ENO VA

TRENTO

BO LO G NA

VERO NA

PIACENZA

M O DENA

BRESCIA

VARESE

BERG AM O

SO ND RIO

PARM A

NO VARA

ALESSANDRIA

b e r l i s l o n m i l p a r

0

0 . 1

0 . 2

0 . 3

0 . 4

0 . 5

0 . 6

0 . 7

0 . 8

0 . 9

1

ber lis lon mil par

0

20

40

60

80

100

[km

]

NOX Emission density

Distance from city centre


Yearly Mean

ber lis lon mil par

0

5

10

15

20

25

30

35

40

[pp

b]

ber lis lon mil par

0

10

20

30

40

50

60

[pp

b]

ber lis lon mil par

0

10

20

30

40

50

60

70

[g

/m3]

NO2 (24h r.a.) O3 (8h daytime r.a. - summer)

PM10 (24h r.a.)

ber lis lon mil par

0

10

20

30

40

50

60

70

[pp

b]

Ox (24h r.a.)

Obs CAMx


BIAS

NO2 O3

PM10

ber lis lon mil par

-50

-40

-30

-20

-10

0

10

20

30

40

50

[]

ber lis lon mil par

-50

-40

-30

-20

-10

0

10

20

30

40

50

[]

ber lis lon mil par

-50

-40

-30

-20

-10

0

10

20

30

40

50

[]


Sigma ratio

NO2 O3

PM10


0

0 . 2

0 . 4

0 . 6

0 . 8

1

1 . 2

1 . 4

1 . 6


0

0 . 2

0 . 4

0 . 6

0 . 8

1

1 . 2

1 . 4

1 . 6

ber lis lon mil par

0

0.5

1

1.5

2

2.5

3

3.5

4


Correlation

NO2 O3

PM10


0

0 . 1

0 . 2

0 . 3

0 . 4

0 . 5

0 . 6

0 . 7

0 . 8

0 . 9

1


0

0 . 1

0 . 2

0 . 3

0 . 4

0 . 5

0 . 6

0 . 7

0 . 8

0 . 9

1


0

0 . 1

0 . 2

0 . 3

0 . 4

0 . 5

0 . 6

0 . 7

0 . 8

0 . 9

1


01020304050607080

1/1 1/2 1/3 1/4 1/5 1/6 1/7 1/8 1/9 1/10 1/11 1/12

[pp

b]

Time series - NO2 daily mean (city average)

0

10

20

30

40

50

60

70

80

1/1 1/2 1/3 1/4 1/5 1/6 1/7 1/8 1/9 1/10 1/11 1/12

[pp

b]

Milan

Lisbon

0

10

20

30

40

50

60

70

80

1/1 1/2 1/3 1/4 1/5 1/6 1/7 1/8 1/9 1/10 1/11 1/12

[pp

b]

Paris

Obs

CAMx


0

20

40

60

80

100

120

1/1 1/2 1/3 1/4 1/5 1/6 1/7 1/8 1/9 1/10 1/11 1/12

[pp

b]

0

20

40

60

80

100

120

1/1 1/2 1/3 1/4 1/5 1/6 1/7 1/8 1/9 1/10 1/11 1/12

[pp

b]

0

20

40

60

80

100

120

1/1 1/2 1/3 1/4 1/5 1/6 1/7 1/8 1/9 1/10 1/11 1/12

[pp

b]

Time series - O3 daily max (city average)

Milan

Lisbon

Paris

Obs

CAMx


020406080

100120140160180

1/1 1/2 1/3 1/4 1/5 1/6 1/7 1/8 1/9 1/10 1/11 1/12

[ug

/m3]

0

20

40

60

80

100

120

140

160

180

1/1 1/2 1/3 1/4 1/5 1/6 1/7 1/8 1/9 1/10 1/11 1/12

[ug

/m3]

020406080

100120140160180

1/1 1/2 1/3 1/4 1/5 1/6 1/7 1/8 1/9 1/10 1/11 1/12

[ug

/m3]

Time series - PM10 daily mean (city average)

Milan

Lisbon

Paris

Obs CAMx


PM10 - total mass (yearly mean)

SIAPPM10

PM10

ber kra lis lon mil par pra

0

10

20

30

40

50

60

70[

g/m

3]

C ity C entre

U rban A rea


0

10

20

30

40

50

60

70

[g

/m3

]

C ity C entre

U rban A rea


0

10

20

30

40

50

60

70

[g

/m3]

C ity C entre

U rban A rea


PM10 - relative composition

Urban areas

City centre


0

0.2

0.4

0.6

0.8

1

SO A

N H4+

SO 4=

N O 3-

PPM 10


0

0.2

0.4

0.6

0.8

1

SO A

N H4+

SO 4=

N O 3-

PPM 10


PM10 - concentrations vs emissions

Primary PM10 (urban area)

vsUrban area Emix density

0

5

10

15

20

25

0 1 2 3 4 5 6 7[ton/km2]

[ug

/m3]

Lis

ParMil

0

2

4

6

8

10

12

14

16

0 1 2 3 4 5 6 7 8 9[ton/km2]

[ug

/m3]

Nitrate (urban area)

vsDomain Emix density (NOX ) Lis

Mil

Lon

NH3/NOX ratio(Domain Emix density)

0

0.1

0.2

0.3

0.4

0.5

0.6

ber lis lon mil par pra

G. Pirovano – CESIRICERCA, Italyppm10 sulf nitr ammo other

0

10

20

30

40

50

60

70

[g

/m3

]

YearJanuary

Critical episodes - January 2004Critical episodes - January 2004

MilanCity centre

ppm10 sulf nitr ammo other

0

5

10

15

20

25

30

[g

/m3

]

YearJanuary


0

5

10

15

20

25

30

[g

/m3

]

YearJanuary

Urban area

Paris


0

10

20

30

40

50

60

70[

g/m

3]YearJanuary


• Good reliability of emission inventories

• Heterogeneous measured data set can mask model discrepancies

• Photochemistry is well reproduced (also in Milan) at domain scale

• Local scale effects on NO2/O3 induce BIAS of 5-15%

• Performances for PM10 are less reliable and more heterogeneous:10-20 % of mass is missing and variability is not well described

• Fine scale meteorology has clearly enhanced model performances

• PM10 yearly mean is around 20 [g/m3] (…not in Milan!)

• Milan: combined effect of low ventilation and chemistry on PM10

• Lisbon: ….a particular case…

• Sever episodes: errors may be emphasised (critical for exceedances)

ConclusionsConclusions


• Evaluation of critical episodes (exceedances)

• Having established several datasets, we are planning to:• perform source-apportionment analysis (OSAT & PSAT)• perform systematic sensitivity analysis (PA & DDM)

Next steps...Next steps...

• Following the “spirit” of Citydelta and the

ACCENT network we are surely interested and

willing to join projects based on model

ensemble approach CAMx


The authors would like to thank CONCAWE (London), IIASA (Vienna) and JRC (Ispra) to have funded and promoted the phase III of Citydelta

modelling exercise.

The authors would also like to thank Claudio Carnevale of the University of Brescia for his valuable contribution in the development

of the emission processor, TERRARIA (Milan) to have provided biogenic emissions, and ARPA Lombardia to have performed some simulations .

Mobility and Environment Agency technical contribution has been sustained by the Municipality of Milan.

[email protected]

Thanks to…


Paris StationsParis Stations

0

5

10

15

20

25

30

35

1 2 3 4 5 6 7 8 9 10 11 12

month

[pp

b]

CERGY

GENNEVI

MELUN

PARIS_1

PARIS_18

VITRY_SS

NO2

0

5

10

15

20

25

30

1 2 3 4 5 6 7 8 9 10 11 12month

[pp

b]

CERGY

GENNEVI

MELUN

PARIS_1

PARIS_18

VITRY_SS

PM10

g. pirovano – cesiricerca, italy comparison and validation of long term simulation of pm10 over 7...

Documents

italy pirovano

pirovano cesiricerca

o3o3 pm10 slide

italy bias

italy comparison

italy correlation

italy introduction

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