1 laxenburg, 16 nov. 2006 tfiam-tfmm joint workshop(1) similarities and differences in particle...

Laxenburg, 16 Nov. 2006TFIAM-TFMM joint workshop (1)1

Similarities and Differences in Particle Characteristics across Europe

Jean-Philippe Putaud

EC – DG Joint Research CentreInstitute for Environment and Sustainability

TFIAM – TFMM joint workshop on cost-effective control of urban air pollution

Laxenburg, 16 Nov. 2006TFIAM-TFMM joint workshop (2)2 fr

om T

NO

FE

L (N

L),

Rob

les-

Gon

zale

z et

al.,

Geo

phys

. R

es.

Let

t.,

955

-958

, 20

00.

London

Dusseldorf

Bruxelles

Krakow

Paris

Wien

Munchen

Budapest

Milano

Beograd

3 5 N

4 0 N

4 5 N

5 0 N

5 5 N

6 0 N

6 5 N

7 0 N

-1 0 W -0 5 W 0 0 W 0 5 W 1 0 W 1 5 W 2 0 W 2 5 W 3 0 W

Aerosol optical depth at 555 nm in August 1997 measured from space


http://air-climate.eionet.eu.int/databases/EuroAirnet/


PM

10 m

ass

conc

entr

atio

n (µ

g/m

³)


Doc

kery

et

al.,

N.

Eng

l. J.

Med

. 32

9,

175

3 (1

993

)


Rel

ativ

e R

isk

0.99

1.00

1.01

1.02

Ath

ens

Bar

celo

na

Bas

el

Birm

ingh

am

Bud

apes

t

Kra

kow

Erf

urt

Gen

eva

Hel

sink

i

Lond

on

Lyon

Mad

rid

Mila

n

Par

is

Pra

gue

Rom

e

Sto

ckho

lm

Tel

Avi

v

Tep

lice

Tur

in

Zur

ich

City-specific estimates (95% CL) of relative risk for mortality per 10 μg/m3 increase in PM10 levels.

Le T

ertr

e et

al.,

Ris

k A

naly

sis,

25,

711

-718

, 20

05

May such differences be due to different PM characteristics ?


In existing aerosol data bases:

Very few datasets incl. more than just PM mass. e.g. EMEP regard areas with high population density

Other short- or long-term datasets:

• cover various types of sites (from remote to kerbside)

• cannot be easily reached by “users”

A European Aerosol phenomenology:

Synthetising in a single document aerosol datasets from

various sources including PM mass concentrations and

particle size distributions or chemical compositions.


http://ccu.jrc.it/downloaded 741 times


Synthesising physical and chemical aerosol characteristics which may be relevant to:

Aerosols and Health:

Are there differences in particle characteristics which may explain differences in health effects?

Aerosols and Climate:- Particles (and ozone) are two radiative forcing agents- synergies and trade-offs between climate change mitigation and air pollution abatement policies

Aerosol ModellingModels need testing and validation against fundamental data such as aerosol particle size distributions and chemical compositions.

E


natural rural near-city urban kerbside

34 sites

full year data set seasonal data set


0

20

40

60

80

100

120

Jung

frau

joch

(C

H)

Sev

ettij

arvi

(F

IN)

Skr

eada

len

(N

)

Birk

enes

(N

)

Asp

vret

en (

S)

Cha

umon

t (C

H)

Mon

agre

ga (

E)

Illm

itz (

A)

Har

wel

l (G

B)

Hoh

enpe

isse

nber

g (D

)

Mel

pitz

(D

)

Ispr

a (I

)

Waa

smun

ster

(B

)

Zue

rich

(CH

)

Bas

el (

CH

)

Lond

on-B

(G

B)

Leip

zig

97-9

9 (D

)

Gen

t (B

)

Bol

ogna

(I)

Bar

celo

na (

E)

Ber

n (C

H)

Lond

on-M

(G

B)

Wie

n (A

)

PM

2.5

(µg/

m³)

average

median

USA annual standard

PM

2.5

(µg/

m³)

PM mass concentration annual statistics

0

20

40

60

80

100

120

Jung

frau

joch

(C

H)

Sev

ettij

arvi

(F

IN)

Skr

eada

len

(N)

Birk

enes

(N

)

Asp

vret

en (

S)

Cha

umon

t (C

H)

Mon

agre

ga (

E)

Illm

itz (

A)

Har

wel

l (G

B)

Hoh

enpe

isse

nber

g (D

)

Mel

pitz

(D

)

Ispr

a (I

)

Waa

smun

ster

(B

)

Zue

rich

(CH

)

Bas

el (

CH

)

Lond

on-B

(G

B)

Leip

zig

97-9

9 (D

)

Gen

t (B

)

Bol

ogna

(I)

Bar

celo

na (

E)

Ber

n (C

H)

Lond

on-M

(G

B)

Wie

n (A

)

PM

10 (

µg/

m³)

average

median

EU 24-hr limit value

2005 EU annual standard

2010 EU annual standard

PM

10 (

µg/

m³)



0

10

20

30

40

50

60

70

80

90

100

110

120

0 10 20 30 40 50 60 70 80 90 100 110 120

PM 10 (µg/m³)

PM

2.5

(µ

g/m

³)Sevettijarvi (FIN)

Skreadalen (N)

Birkenes (N)

Chaumont (CH)

Waasmunster (B)

Gent (B)

Bologna (I)

Zuerich (CH)

Basel (CH)

Barcelona (E)

Bern (CH)

PM2.5 and PM10 mass concentrations are correlated (R² = 0.95).

PM2.5/PM10 ratios range from 0.57 to 0.85.


Median number size distribution in summerduring the morning, afternoon and night periods

Near-City

Free Troposphere

X axis particle diameter between 0.001 and 1 micrometerY axis number concentration as dN/dlog(Dp) between 1.E+2 and 1.E+5 cm-3

morning trafficafternoonnight

Fig. 6a: summer

Natural Rural Urban Kerbside

1. E+02

1. E+03

1. E+04

1. E+05

0.001 0.01 0.1 1

Melpitz (D)

1. E+02

1. E+03

1. E+04

1. E+05

0.001 0.01 0.1 1

Aspvreten (S)

1. E+02

1. E+03

1. E+04

1. E+05

0.001 0.01 0.1 1

J ungfraujoch (CH)

1. E+02

1. E+03

1. E+04

1. E+05

0.001 0.01 0.1 1

Leipzig 99-01 (D)

1. E+02

1. E+03

1. E+04

1. E+05

0.001 0.01 0.1 1

Marylebone (GB)

1. E+02

1. E+03

1. E+04

1. E+05

0.001 0.01 0.1 1

Harw ell (GB)

1. E+02

1. E+03

1. E+04

1. E+05

0.001 0.01 0.1 1

Hohenpeissenberg (D)

1. E+02

1. E+03

1. E+04

1. E+05

0.001 0.01 0.1 1

Bloomsbury (GB)

1. E+02

1. E+03

1. E+04

1. E+05

0.001 0.01 0.1 1

Ispra (I)

1. E+02

1. E+03

1. E+04

1. E+05

0.001 0.01 0.1 1

Bresso (I)


0

10000

20000

30000

40000

50000

60000

Jung

frau

joch

(C

H)

Asp

vret

en (

S)

Har

wel

l (G

B)

Hoh

enpe

isse

nber

g(D

)

Mel

pitz

(D

)

Ispr

a (I

)

Lond

on-B

(G

B)

Leip

zig

(D)

Mila

no-B

ress

o (I

)

Lond

on-M

(G

B)

pa

rtic

le n

um

be

r co

nce

ntr

atio

n (

#/c

m³)

morning

afternoon

night

Natural Rural Near-city Urban Kerbside

= = <



0

10000

20000

30000

40000

50000

60000

0 10 20 30 40 50 60PM2.5 , µg/m³

Par

ticle

num

ber

(Dp>

10 n

m),

#/c

m³

Any relationship betweenPM mass and particle number concentrations ?

Marseille (F)

Milano (I)

Leipzig (D)

London (GB)

0

1000

2000

3000

4000

5000

6000

7000

8000

0 10 20 30 40 50 60PM2.5, µg/m³

Par

ticle

num

ber

(Dp>

100

nm),

#/c

m³


0%

20%

40%

60%

80%

100%

Sev

ettij

arvi

(F

IN)

Skr

eada

len

(N)

Birk

enes

(N

)

Cha

umon

t (C

H)

Mon

agre

ga (

E)

Illm

itz (

A)

Waa

smun

ster

(B

)

Mel

pitz

96-

99 (

D)

Ispr

a (I

)

Zue

rich

(CH

)

Bas

el (

CH

)

Gen

t (B

)

Bol

ogna

(I)

Bar

celo

na (

E)

Ber

n (C

H)

Wie

n (A

)

PM

2.5

com

posi

tion

BC

OM

unacc.

NO3

NH4

nssSO4

sea salt

min. dust

PM2.5 annual average chemical composition



0%

20%

40%

60%

80%

100%

Sev

ettij

arvi

(F

IN)

Skr

eada

len

(N)

Birk

enes

(N

)

Cha

umon

t (C

H)

Mon

agre

ga (

E)

Illm

itz (

A)

Waa

smun

ster

(B

)

Mel

pitz

96-

99 (

D)

Ispr

a (I

)

Zue

rich

(CH

)

Bas

el (

CH

)

Gen

t (B

)

Bol

ogna

(I)

Bar

celo

na (

E)

Ber

n (C

H)

Wie

n (A

)

PM

10 -

PM

2.5

com

posi

tion

BC

OM

unacc.

NO3

NH4

nssSO4

sea salt

min. dust

PMcoarse annual average chemical composition



PM10 PM2.5 PM10-PM2.5

Na

tura

l &R

ura

l Bck

gr

Ne

ar-

City

&U

rba

n

Bck

gr

Ke

rbsi

de

Na

tura

l &R

ura

l Bck

gr

Ne

ar-

City

&U

rba

n B

ckgr

Ke

rbsi

de

Na

tura

l &R

ura

l Bck

gr

Ne

ar-

City

&U

rba

n B

ckgr

Ke

rbsi

de

Black Carbon 6 6 13 8 8 17 4 3 7

Organic Matter 17 23 22 24 23 29 4 8 10

Nitrate 8 17 10 5 18 11 8 7 8

Ammonium 7 8 4 8 11 7 1 1 0Sulfate 20 15 10 30 18 13 3 5 5

Sea salt 9 5 3 3 3 1 20 9 6

Mineral dust 11 11 19 5 7 8 21 23 37Unknown (*) 30 31 19 21 18 15 40 47 28TOTAL 109 115 100 107 106 100 102 104 100


Some conclusions

• At a given site PM10 and PM2.5 are well correlated

but PM2.5 / PM10 ranges from 0.55 to 0.85

• NO useful correlation between PM mass and particle

number concentrations

• same PM2.5 → particle number twice larger

• same particle number → PM2.5 three times larger

• PM chemical composition quite similar across Europe

More differences between background and kerbside sites

Secondary inorganics and POM vs. POM and dust

(PM10)

POM and EC

(PM2.5)


Some comments

• Lots of relevant data sets are not included in this

compilation

• The lack of harmonisation in sampling and analytical

techniques makes comparisons between sites difficult.

COST633 WG1 Task

2

COST633 WG1 Task

1

633


• Make use of the COST633 action structure to

- more systematically compile the existing aerosol data

- assess comparability of various analytical methods

• Make the existing aerosol data available to a larger

community through the WDCA

Perspective:

A European aerosol phenomenology – stage 2

633


Among 18 + 2 parties

1. metadata have been provided by:

• GR, SI, AT, FI, ES, PT and HU (7) : comprehensive• UK, DE, CZ, IT, FR and IS (5) : partial information

2. data have been reported by:

• AU, HU, GR, UK, DK, ES, FI, NL, NO, SI, and JRC (9+1)


• Avoid wasting money for decreasing PM mass

without effects on health impact

• Integrated assessment exercises are generally limited

by data availability

• A consistent (temporal and spatial) monitoring effort

is needed

• There is already a huge amount of data available

across Europe but a lack of reporting to accessible

data banks

• Attempts (COST Action 633 + TFMM PM assessment

report) going on

Final statements:

THANKS !


Supplementary material633


EMEP particulate matter data: T

ransb

ou

nd

ary Particu

late Matter in

Eu

rop

e: Statu

s Rep

ort 2005


0

10

20

30

40

50

60

19

85

19

86

19

87

19

88

19

89

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

0

5

10

15

20

25

30

(NH4)2SO4 NH4NO3

0

10

20

30

40

50

60

19

85

19

86

19

87

19

88

19

89

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

0

5

10

15

20

25

30

PM10 PM10-((NH4)2SO4+NH4NO3)(NH4)2SO4 NH4NO3

0

10

20

30

40

50

60

19

85

19

86

19

87

19

88

19

89

19

90

19

91

19

92

19

93

19

94

19

95

19

96

19

97

19

98

19

99

20

00

20

01

20

02

20

03

20

04

0

5

10

15

20

25

30

PM10 PM10-((NH4)2SO4+NH4NO3)(NH4)2SO4 NH4NO3

Variations in PM10 and PM10 components (µg/m³)over 20 years at IT04 (Ispra):


0

2

4

6

8

10

12

14

16

18

0.05-0.09 0.09-0.16 0.16-0.30 0.30-0.60 0.60-1.0 1.0-1.8 1.8-3.1 3.1-6.2 6.2-9.9

aerodynamic diameter (µm)

dM

/dL

og

Dp

(µ

g/m

³)

BC

OM

unacc.

NH4

nssSO4

sea salt

min. dust

Gent, Belgium

0

5

10

15

20

25

0.06-0.13 0.13-0.25 0.25-0.50 0.50-1.0 1.0-2.0 2.0-4.0 4.0-8.0 8.0-16


dM

/dL

og

Dp

(µ

g/m

³)

BC

OM

unk

NO3

NH4

nssSO4

sea salt

min dust

Marseille, France

0

5

10

15

20

25

30

35

40

0.05-0.14 0.14-0.42 0.42-1.2 1.2-3.5 3.5-10


dM

/dL

og

Dp

(µ

g/m

³)

BC

OM

unacc.

NO3

NH4

nssSO4

sea salt

min. dust

Bologna, Italy


natural and ruralbackground

PM10 near-city and urban grd average

NH47%

NO313%

unacc.30%

OM19%

BC5%

nssSO412%

sea salt4%

min. dust10%

PM10 natural & rural grd average

nssSO419%

NH46%NO3

7%

OM16%

unacc.28%

BC6%

min. dust10%

sea salt8%

PM10 kerbside grd average

NH44%

NO310%unacc.

19%

OM22%

BC13%

nssSO410%

sea salt3%

min. dust19%

PM

10near-city and urban

backgroundkerbside

PM2.5 near-city and urban grd average

NH410%

NO317%

unacc.17%

OM23%

BC7%

nssSO417%

sea salt3%

min. dust6%

PM2.5 kerbside grd average

NH47%

NO311%

unacc.15%

OM28%

BC17%

nssSO413%

sea salt1%

min. dust8%

PM2.5 natural & rural grd average

nssSO427%

NH48%NO3

5%

OM22%

unacc.20%

BC8%

min. dust5% sea salt

5%

PM

2.5

1 laxenburg, 16 nov. 2006 tfiam-tfmm joint workshop(1) similarities and differences in particle...

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intdatabaseseuroairnet

space slide

seasonal data set slide

particle characteristics

pm10 mass concentrations