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Current practice of PM-measurements, data processing, interpretation and visualization in Belgium Frans Fierens scientific staff member of the Flemish Environment Agency (VMM) at the Belgian Interregional Environment Agency (IRCEL) PM_lab workshop, 2010 March 4
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IRCEL-CELINE ? NL : Intergewestelijke Cel voor het Leefmilieu FR : Cellule Interr é gionale de l'Environnement EN : Belgian Interregional Environment Agency Agreement between the 3 Belgian Regions (1994) Major tasks : SMOG (winter/summer) warnings (IDPC) Interregional Calibration Bench Interregional AQ Database (3 Regions) Scientific support Reports EU-COM / Experts EU-working groups
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Contents 1.Choice of PM-Measurement locations 2.Calibration of PM-Measurements - equipment 3.Future technical development in the next 2-3 years 4.Data acquisition - Handling of PM-data 5.Spatial Interpolation of PM-point data 6.Forecast Modelling (deterministic / statistical models).
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Contents 1.Choice of PM-Measurement locations 2.Calibration of PM-Measurements - equipment 3.Future technical development in the next 2-3 years 4.Data acquisition - Handling of PM-data 5.Spatial Interpolation of PM-point data 6.Forecast Modelling (deterministic / statistical models).
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Number of PM10 and PM2.5 monitoring stations PM10 : start measurements in 1996 PM2.5 : start measurements in 2000 PM10 (telemetric stations) (>90% valid daily averages) PM2.5 (telemetric stations) (>90% valid daily averages) Beside PM : also BC and Black Smoke measurements
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PM10 : monitoring stations Location of PM10 telemetric stations Locations mostly : - Industrial - Urban or Urban Background Very few “rural” and “traffic” stations (Historical reasons)
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PM2.5 : monitoring stations Location of PM2.5 telemetric stations Locations mostly : - Industrial - (Sub) Urban Very few “rural” and “traffic” “AEI stations” : -Bruges(*) -Ghent (*) -Antwerp : 2 (*) -Brussels : 2 -Liège -Charleroi (*) not on the map
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Contents 1.Choice of PM-Measurement locations 2.Calibration of PM-Measurements - equipment 3.Future technical development in the next 2-3 years 4.Data acquisition - Handling of PM-data 5.Spatial Interpolation of PM-point data 6.Forecast Modelling (deterministic / statistical models).
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PM measuring techniques in Belgium 1.Flanders - Oscillating Micro Balans (TEOM and TEOM-FDMS) -Bèta Absorption (ESM FH62I-R) -Gravimetric : -Equivalence tests -PM2.5 (to calculate the Average Exposure Index “AEI” on urban- background locations, started in 2009) + 1 Rural background location 2.Brussels - Oscillating Micro Balans (only TEOM-FDMS since 2004-2005) 3.Wallonia - Bèta Absorption (MP101 integration time 24h) - Optical techniques (GRIMM)
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Automatic PM monitors <> EU reference method PROBLEM : automatic monitors <> EU (gravimetric) reference method NO PROBLEM : When “equivalence” is demonstrated
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Current “calibration” of PM in Belgium (*) based on the ‘guide for the demonstration of equivalence of ambient air monitoring methods’ (Excel templates from the JRC) (**) preliminary results of an equivalence program in Wallonia result in somewhat higher calibration factors
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New comparative campaign (VMM) : PM10 “calibration” factors calculated in new campaign are slightly higher than previously “Comparative PM10 and PM2.5 measurements in Flanders (Belgium)”, VMM, Period 2006 - 2007 (www.vmm.be)
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First comparative campaign (VMM) : PM2.5 Higher “calibration” factors for PM2.5 than for PM10 -> higher volatile fraction “Comparative PM10 and PM2.5 measurements in Flanders (Belgium)”, VMM, Period 2006 - 2007 (www.vmm.be)
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Spatial and temporal variation of calibration factors
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Contents 1.Choice of PM-Measurement locations 2.Calibration of PM-Measurements - equipment 3.Future technical development in the next 2- 3 years 4.Data acquisition - Handling of PM-data 5.Spatial Interpolation of PM-point data 6.Forecast Modelling (deterministic / statistical models).
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Future technical development in the next 2-3 years (1) Flanders : - More “Chemkar” campaigns ( PM10 “hotspots”,Rural vs Urban PM10 & PM2.5, Antwerp harbour, …) - Measuring the effect of Woodburning on PM (levoglucosan) - Additional measuring stations (e.g. Streetcanyon NO2/PM) - Testing of new Bèta-monitors (BAM1020, FAI SWAM 5DC) - UFP measurements (streets) - Further participating in CEN/TC264/WG15 : * revision of the PM10 standard EN12341 * revision of the PM2.5 standard EN14907
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Future technical development in the next 2-3 years (2) Brussels : - “Black Carbon” measurements - “Counting Particles” (using GRIMM monitors) Wallonia : - additional measuring stations (e.g. Tournai, Namur) - EC/OC analyser at Vielsalm (Rural background) Interregional (IRCEL-CELINE) : - further developing Interpolation techniques (eg. use of satellite observations like AOD) - higher spatial resolution modelling (forecasts + assessment) - implementation of data assessment techniques
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Contents 1.Choice of PM-Measurement locations 2.Calibration of PM-Measurements - equipment 3.Future technical development in the next 2-3 years 4.Data acquisition - Handling of PM-data 5.Spatial Interpolation of PM-point data 6.Forecast Modelling (deterministic / statistical models).
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Data acquisition of automatic measurements Monitoring station RDRC IRCEL Every hour (26’ after each hour) -> ½ - hourly measurements -> FTP to IRCEL servers -> calculation of hourly / 8-hourly / 24-hour averages. -> publication real-time data + maps on websites “Regional Data Processing Centers”
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“Real-Time” publication on websites - tables
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“Real-Time” publication on websites - maps
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Contents 1.Choice of PM-Measurement locations 2.Calibration of PM-Measurements - equipment 3.Future technical development in the next 2-3 years 4.Data acquisition - Handling of PM-data 5.Spatial Interpolation of PM-point data 6.Forecast Modelling (deterministic / statistical models).
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How to define a scientifically based methodology for assessment of spatial representativeness? CORINE land use map
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Observation: Sampling values depend on land use in (direct) vicinity of the monitoring site Consequence: Interpolation scheme needs to know this relation between land use and air quality levels Approach : Create land use indicator to express this relation RIO-Corine interpolation VITO + IRCEL developed the RIO-corine methodology
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2 km Land use indicator For each station: Determine buffer (e.g. 2km radius) Characterize land use by CORINE class distribution inside buffer RIO - Land use indicator (1)
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Land use indicator is based on CORINE class distribution Calibration of coefficients a i : multi-regression to optimize trend for mean and standard dev. of monitoring data RIO - Land use indicator (2)
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‘ Kriging’ condition = ‘spatialy’ homogeneous data Use relation between land use indicator and AQ statistics to “detrend” monitoring data: Remove local character of sampling values Kriging interpolation of “detrended” data
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1.Detrend sampling values 2.Interpolate detrended values with Ordinary Kriging 3.Determine local - value 4.Get corresponding trend shift ( C) 5.Add C to interpolation result RIO-corine methodology Correlation distance
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Compare with standard IDW and OK Valdidation – “leaving-one-out” ModelO3O3 NO 2 PM 10 RMS E BiasRMSEBiasRMSEBias IDW10.97-1.7018.174.7412.121.70 OK10.37-0.4416.851.4511.651.22 RIO9.56-0.0814.45-0.679.890.01
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Valdidation – using “independent” measurements R² = 0.90 MAE = 2.9 µg/m³ RMS = 4.3 µg/m³ Average observations : 30.6 µg/m³ Average RIO-c interpolation : 31.5 µg/m³
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Annual mean PM10 concentrations 2006 RIO-corine Ordinary Kriging
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RIO OK Annual average NO2 concentrations 2002
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RIO-corine : further developments (1) NO2 - 4x4 km NO2 - 1x1 km
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RIO-corine : further developments (2) New proxy : AOD (aerosol optical Depth) ? Source : Modis Terra satelite, 2006 Total Column AOD 2006
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RIO-corine : more info “Spatial interpolation of air pollution measurements using CORINE landcover data ” Janssen Stijn a, Dumont Gerwin b, Fierens Frans b, Mensink Clemens a a Flemish Institute for Technological Research (VITO),Boeretang 200, B-2400 Mol, Belgium b Belgian Interregional Cell Environment Agency(IRCEL), Kunstlaan 10-11, B-1210 Brussels, Belgium Atmospheric Environment 42/20 (2008) 4884-4903
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Contents 1.Choice of PM-Measurement locations 2.Calibration of PM-Measurements - equipment 3.Future technical development in the next 2-3 years 4.Data acquisition - Handling of PM-data 5.Spatial Interpolation of PM-point data 6.Forecast Modelling (deterministic / statistical models).
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Brussels Polluants : PM 10 et NO 2 Plan : 3 niveaux Wallonia Polluant : PM 10 Plan : 3 niveaux Flanders Polluant : PM 10 Plan : 1 niveau - Information of the public (see ozone EU info/alert thresholds) - Activation winter SMOG action plans (FORECASTED PM10 > 70 µg/m³, for two consecutive days) Goal of Air Quality forecasts ?
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Two different types of models 1.Deterministic models Complex input : meteo, emissions, geografical information, fysico- chemical processes Long CPU -> CHIMERE (forecasts) / BelEUROS (emission scenario’s) 2.Statistical or neural-network models Simple input : database with measurements, some simple forecasted meteo parameters Short CPU (minutes) -> SMOGSTOP (Ozone) / OVL (PM10, NO2)
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CHIMERE : simple schematic overview NOx emissions combustion Example Temperature
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CHIMERE – Example (1) Forecast for 21/6/2005 Observations 21/6/2005
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CHIMERE – Example (2)
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OVL : schematically Input: PM 10 measurements day-1 Meteoforecasts Process: Output : PM 10 daily mean day0, +1, +2, +3 and +4 Neural Network
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OVL : most important meteo-input parameter Temperature Inversion Low windspeeds Boundary Layer Height
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OVL : PM10 – winter/spring 2005 forecast day +1 R=0.7 Antwerp (monitoring station 42R801)
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OVL : more info “A neural network forecast for daily average PM10 concentrations in Belgium” Hooyberghs Jef a, Mensink Clemens a, Dumont Gerwin b, Fierens Frans b, Brasseur Olivier c a Flemish Institute for Technological Research (VITO),Boeretang 200, B-2400 Mol, Belgium b Interregional Cell for the Environment (IRCEL), Kunstlaan 10-11, B-1210 Brussels, Belgium c Royal Meteorological Institute (RMI), Ringlaan 3, B-1180 Brussels, Belgium Atmospheric Environment 39/18 (2005) 3279-3289
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Physics, chemistry and emissions taken into account Possible grid refinement Satisfying results at the scale of Belgian Regions Representation of hourly concentrations Based on dispersion in the atmosphere (BLH) Results available for specific location Satisfying results at local scale Adaptability to current emission level Reduced computing time Computing time increases with resolution High dependence on emission inventories (+ link with long- range transport) Formation of secondary PM Forecast available only at measurement stations (time series) Long-range transport not taken into account Formation of secondary PM + - CHIMEREOVL CHIMERE & OVL: advantages and disadvantages
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Dank voor uw aandacht ! Je vous remercie de votre attention ! Wir danken Ihnen für Ihre Aufmerksamkeit ! Thank you for your attention ! More info : www.ircel.be www.vmm.be
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