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 Salmi Finnish Meteorological Institute, Air Quality Research, P.O. Box 503, FI Helsinki, Finland Boreal Env. Res. 11: (freely downloadable!)

Location of the stations

PM10 ”the spring peak”

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.

 PM 2.5 and PM 10 (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 station of Kumpula (urban background, Helsinki) SMEAR III

Hourly averaged PM 2.5 (  g/m 3 ), Kumpula, Feb – May 2006 An episode of 12 days in April – May

Hourly averages of PM 2.5 at Virolahti (regional background), South-Eastern Finland, during – 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).

The measured composition of PM 2.5 at Kumpula,16 April – 10 May 2006 EC = Elemental carbon OC = Organic carbon POM = Particulate organic matter SO 4 = sulphate WIS = water insoluble WS = 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 PM 2.5 emission flux originated from wildland fires during 20.4 – , computed based on the measured MODIS temperature anomalies, unit: tons PM. Extensive fire areas in Western Russia

The computed PM 2.5 concentrations from fires during one selected hour, at 12:00 on 27 April 2006 (unit: µg/m 3 ). The concentrations were transported over a wide area that extended from the Black Sea to Spitzbergen.

The computed PM 2.5 concentrations from fires at 12:00 on 9 May 2006 (unit: µg/m 3 ).

2007: SILAM-model results (forecast) used for forecasting regional scale concentrations Ref:Saarikoski, S., et al., Major biomass burning episode in northern Europe in spring 2006: the chemical composition and atmospheric chemistry of aerosols. Atmos.Environ., /j.atmosenv

Comparison of measured and predicted concentrations (Kumpula). The predicted primary PM 2.5 and sulphate from all European sources has also been shown.

Anthrop.: Birch: ENVISAT: Fires: Multi-component modelling, SILAM, 9 May 2006 Predicted PM 2.5 from forest fires Predicted birch pollen concentrations Satellite observations, ENVISAT Predicted anthropogenic primary PM 2.5

Operational air quality forecasts  Emissions: EMEP 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 The synoptic situation at 21 August 00UTC over Europe

MODIS rapid system 21.8 klo UTC Smoke plume MODIS rapid system 7.8 klo. 9.25UTC

PM 2.5 concentrations August 2006

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

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 ;