Recent Finnish PM studies / 2 examples

Characterizing temporal and spatial patterns of urban PM10 using six years of Finnish monitoring data

Pia Anttila and Timo SalmiFinnish Meteorological Institute, Air Quality Research, P.O. Box 503, FI-00101 Helsinki, Finland

Boreal Env. Res. 11:463-479

www.borenv.net (freely downloadable!)

Location of the stations

PM10”the springpeak”

ruralbackground

0

25

50

75

2002 2003 2004 2005

µg/m

3

PM10 Virolahti

PM2.5 Virolahti

urbantraffic

0

25

50

75

2002 2003 2004 2005

µg/m

3

PM10 Oulu centre

PM2.5 Oulu centre

urbanbackground

0

25

50

75

2002 2003 2004 2005

µg/m

3

PM10 Helsinki Kallio

PM2.5 Helsinki Kallio

urbantraffic

0

25

50

75

2002 2003 2004 2005

µg/m

3

PM10 HelsinkiMannerheimintie

PM2.5 HelsinkiMannerheimintie

Co-located PM10 & PM2.5 :Very few continuously monitoring stations at the moment!

Regional scale modeling

As a practical example of FMI-SILAM application we present an analysis of an exceptionally severe and wide-spread pollution episode in (Northern) Europe in spring of 2006.

PM2.5 and PM10 (TEOM) Ion composition: Particle into

Liquid Sampler (PILS),

high time resolution (15 min) Black carbon: aethalometer, 5 min Filter samples,12 or 24 hours

elemental and organic carbon,

water-soluble organic carbon water-soluble ions levoglucosan

Aerosol measurements at the stationof Kumpula (urban background, Helsinki)

SMEAR III

PM2.5

0

10

20

30

40

50

60

70

4.2

.06

14

.2.0

6

24

.2.0

6

6.3

.06

16

.3.0

6

26

.3.0

6

5.4

.06

15

.4.0

6

25

.4.0

6

5.5

.06

15

.5.0

6

25

.5.0

6

µg

/m3

Hourly averaged PM2.5 (g/m3), Kumpula, Feb – May 2006

An episode of 12 days in April – May

Virolahti

-20

0

20

40

60

80

100

120

140

1601

.1.2

00

6

15

.1.2

00

6

29

.1.2

00

6

12

.2.2

00

6

26

.2.2

00

6

12

.3.2

00

6

26

.3.2

00

6

9.4

.20

06

23

.4.2

00

6

7.5

.20

06

21

.5.2

00

6

4.6

.20

06

18

.6.2

00

6

2.7

.20

06

16

.7.2

00

6

30

.7.2

00

6

13

.8.2

00

6

µg

/m3

PM2.5 HourlyHourly averages of PM2.5 at Virolahti (regional background), South-Eastern Finland, during 1.1.2006 – 13.8.2006

Two forest fire episodes, in April – May, and in July - August

Visibility degradation: Views over North-Eastern Helsinki during and after the forest fire episode in April – May, 2006

The specific tracer of wood combustion, levoglucosan, was also clearly elevated during episodic periods (Kumpula).

Episode-carbonaceous

10.4

.06

12.4

.06

14.4

.06

16.4

.06

18.4

.06

20.4

.06

22.4

.06

24.4

.06

26.4

.06

28.4

.06

30.4

.06

2.5.

06

4.5.

06

6.5.

06

8.5.

06

10.5

.06

12.5

.06

14.5

.06

16.5

.06

18.5

.06

µg/

m3

0

5

10

15

20

Episode-mass closure

µg/

m3

0

10

20

30

40

50

60

ECWISOCWSOC

EC

PM2.5

POMOther ions SO4

The measured composition of PM2.5 at Kumpula,16 April – 10 May 2006

EC = Elemental carbonOC = Organic carbonPOM = Particulate organic matter SO4 = sulphateWIS = water insolubleWS = water soluble

Organic carbon is elevated during episodes

Mass closure varies from 69 to 76 %. Assumption: POM = 1.6 OC

Modelling

Emissions:

Near real-time information of fires from the MODIS

instrument (onboard Aqua and Terra satellites)

New computational method developed based on MODIS-

measured temperature anomalies

Atmospheric dispersion:

Lagrangian dispersion model SILAM

Meteorological input data from HIRLAM and ECMWF

Total predicted PM2.5 emission flux originated from wildland fires during 20.4 – 15.5.2006, computed based on the measured MODIS temperature anomalies, unit: tons PM.

Extensive fire areas in Western Russia

The computed PM2.5 concentrations from fires during one selected hour, at 12:00 on 27 April 2006 (unit: µg/m3).

The concentrations were transported over a wide area that extended from the Black Sea to Spitzbergen.

The computed PM2.5 concentrations from fires at 12:00 on 9 May 2006 (unit: µg/m3).

2007: SILAM-model results (forecast) used for forecasting regional scale concentrations

Ref:Saarikoski, S., et al., 2006. Major biomass burning episode in northern Europe in spring 2006: the chemical composition and atmospheric chemistry of aerosols. Atmos.Environ., http://dx.doi.org

10.1016/j.atmosenv.2006.12.053 Measured vs. modelled

24.3.

26.3.

28.3.

30.3. 1.4.

3.4. 5.4.

7.4. 9.4.

11.4.

13.4.

15.4.

17.4.

19.4.

21.4.

23.4.

25.4.

27.4.

29.4. 1.5.

3.5. 5.5.

7.5. 9.5.

µg

/m3

0

20

40

60

80

100

PM2.5 measuredPM2.5 Fire-modelledPM2.5 Primary-modelledSulfate-modelled

Comparison of measured and predicted concentrations (Kumpula). The predicted primary PM2.5 and sulphate from all European sources has also been shown.

Anthrop.:

Birch:

ENVISAT:

Fires:

Multi-component modelling, SILAM, 9 May 2006

Predicted PM2.5 from forest fires

Predicted birch pollen concentrations

Satellite observations, ENVISAT

Predicted anthropogenicprimary PM2.5

Operational air quality forecasts http://silam.fmi.fi

Emissions: EMEP 2003 + forest fires (MODIS in near real time)

Dispersion model SILAM

Whole Europe, resolutions 1 hour, 30 km (Finland :9 km)

Forecast horizon 54 hours

Updates: daily, about noon

PM2.5, PM10, SO2, SO4

The SILAM program is publicly available from the above www address.

Problems only in spring ? No!

The summer of 2006 was exceptionally dry

Easterly winds were prevailing for an exceptional long period in August 2006

Frequently occurring wild land fires, especially in Russia and Estonia led to elevated fine particle concentrations from 18 July to 28 August;

H. Nyman 21.8.2007

The synoptic situation at 21 August 00UTC over Europe

MODIS rapid system 21.8 klo. 9.40 UTC

Smoke plume

MODIS rapid system 7.8 klo. 9.25UTC

PM2.5 concentrations August 2006

0102030405060708090

100110120130140150160170180

August 2006

PM

2.5

(µg

/m³)

UtöVirolahtiKumpula

8. 10. 12. 16. 20. 22. 26. 28.14. 18. 24.6.

SILAM operational simulations

for the August episode

Research needs in forecasting fire plumes

The propagation of fires in terrain, fire type (flaming, smouldering) and intensity

The evaluation of PM emissions based on satellite data

o Cloudiness can prevent the detection

o The distinction of wild land and other fires

o Combined use of data from several instruments

The evaluation of initial dispersion, including plume rise

Systematic evaluation of forecasts against data

Conclusions

Exceptionally intensive wild-land fires in 2006 in Europe, especially in Western Russia

Pronounced visibility degradation due to particulate matter from fires

The concentrations of biomass burning tracers - levoglucosan, potassium and oxalate, and that of particulate organic matter substantially increased during the fires

The model computations (spring) showed that the fire plumes covered an extensive area of Europe, extending, e.g., to Spitzbergen in the north

Multicomponent modelling is needed to differentiate the various PM components

Episode-carbonaceous

10.4

.06

12.4

.06

14.4

.06

16.4

.06

18.4

.06

20.4

.06

22.4

.06

24.4

.06

26.4

.06

28.4

.06

30.4

.06

2.5.

06

4.5.

06

6.5.

06

8.5.

06

10.5

.06

12.5

.06

14.5

.06

16.5

.06

18.5

.06

µg

/m3

0

5

10

15

20

Episode-mass closure

µg

/m3

0

10

20

30

40

50

60

ECWISOCWSOC

EC

PM2.5

POMOther ions SO4

Ongoing: development and evaluation of SILAM-model

PM2.5 (PM1/PM10 ) data from Europe needed for 4- 8 /2006

Invitation : please consider participating (writer/acknowl.) in the evaluation paper by providing your own PM2. 5 data

contact:mikhail.sofiev@fmi.fi ; ari.karppinen@fmi.fi