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

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

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).

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).

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

PM10 : monitoring stations Location of PM10 telemetric stations Locations mostly : - Industrial - Urban or Urban Background Very few “rural” and “traffic” stations (Historical reasons)

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

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).

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 ) 3.Wallonia - Bèta Absorption (MP101 integration time 24h) - Optical techniques (GRIMM)

Automatic PM monitors <> EU reference method PROBLEM : automatic monitors <> EU (gravimetric) reference method NO PROBLEM : When “equivalence” is demonstrated

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

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 (

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 (

Spatial and temporal variation of calibration factors

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).

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

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

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).

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”

“Real-Time” publication on websites - tables

“Real-Time” publication on websites - maps

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).

How to define a scientifically based methodology for assessment of spatial representativeness? CORINE land use map

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

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)

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)

‘ 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

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

Compare with standard IDW and OK Valdidation – “leaving-one-out” ModelO3O3 NO 2 PM 10 RMS E BiasRMSEBiasRMSEBias IDW OK RIO

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

Annual mean PM10 concentrations 2006 RIO-corine Ordinary Kriging

RIO OK Annual average NO2 concentrations 2002

RIO-corine : further developments (1) NO2 - 4x4 km NO2 - 1x1 km

RIO-corine : further developments (2) New proxy : AOD (aerosol optical Depth) ? Source : Modis Terra satelite, 2006 Total Column AOD 2006

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)

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).

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 ?

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)

CHIMERE : simple schematic overview NOx emissions combustion Example Temperature

CHIMERE – Example (1) Forecast for 21/6/2005 Observations 21/6/2005

CHIMERE – Example (2)

OVL : schematically Input: PM 10 measurements day-1 Meteoforecasts Process: Output : PM 10 daily mean day0, +1, +2, +3 and +4 Neural Network

OVL : most important meteo-input parameter Temperature Inversion Low windspeeds Boundary Layer Height

OVL : PM10 – winter/spring 2005 forecast day +1 R=0.7 Antwerp (monitoring station 42R801)

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)

 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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