Current practice of PM-measurements, data processing, interpretation and visualization in Belgium Frans Fierens scientific staff member of the Flemish

Current practice of PM-measurements, data processing, interpretation and

visualization in Belgium

Frans Fierensscientific 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 LeefmilieuFR : Cellule Interrégionale 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







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)

0

10

20

30

40

50

60

1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

# P

M10

sta

tio

ns

Wallonia

Brussels

Flanders

0

10

20

30

40

50

60

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009#

PM

2.5

stat

ion

s

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







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)

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

PM10 currently used calibration factors Equivalence after "correction" (*)ESM 1.37 yesTEOM 1.47 yesTEOM-FDMS 1 yesGRIMM 1 yes (**)

PM2.5 currently used calibration factors Equivalence after "correction" (*)ESM 1.46 yes TEOM 1.75 yes TEOM-FDMS 1 yes GRIMM 0.85 yes (**)

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)

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)

Spatial and temporal variation of calibration factors

Contents







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







Data acquisition of automatic measurements

Monitoringstation

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







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 theRIO-corine methodology

43R240

2 km

0 5 10 15 20 25 30 35 400

50

100

150

200

250

300

350

400

CORINE Class

Num

ber

of g

rid c

ells

in b

uffer

42N016

43N073

43R240

Land use indicatorFor 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 ai :

multi-regression to optimize trend for mean and standard dev. of monitoring data

RIO - Land use indicator (2)

0 0.5 1 1.50

20

40

60

80

100

120

140

NO

2 [

g/m

3 ]

week

rural

urb back

urbind

traff

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 1.110

15

20

25

30

35

40

45

50

55

60

PM

10 [

g/m

3 ]

week

rural

urb back

urbind

traff

<PM10>

<NO2>

‘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

0 0.2 0.4 0.6 0.8 1 1.2 1.40

10

20

30

40

50

60

70

80

90

C

[µg/

m³]

C

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

LegendRIO map [µg/m³]

34 - 43

44 - 52

53 - 61

62 - 68

69 - 75

76 - 126

LegendKriging [µg/m³]

78 - 85

86 - 87

88 - 90

91 - 95

96 - 99

100 - 109

Legenddelta C [µg/m³]

-4 - 0

1 - 21

22 - 27

28 - 33

34 - 39

40 - 52

(1) Kriging map (2) DC map

(3) RIO map

0 0.2 0.4 0.6 0.8 1 1.2 1.40

10

20

30

40

50

60

70

80

90

C

[µg/

m³] C

Correlation <-> distance

Compare with standard IDW and OK

Valdidation – “leaving-one-out”

Model O3 NO2 PM10

RMSE

Bias RMSE Bias RMSE Bias

IDW 10.97 -1.70 18.17 4.74 12.12 1.70

OK 10.37 -0.44 16.85 1.45 11.65 1.22

RIO 9.56 -0.08 14.45 -0.67 9.89 0.01

Valdidation – using “independent” measurements

0

10

20

30

40

50

60

70

80

90

20

04

06

26

20

04

07

01

20

04

07

06

20

04

07

11

20

04

07

16

20

04

07

21

20

04

07

26

20

04

11

26

20

04

12

01

20

04

12

06

20

04

12

11

20

04

12

16

20

04

12

21

20

04

12

26

20

04

12

31

20

05

01

05

20

05

01

10

observations

RIO-corine interpolated

R² = 0.90MAE = 2.9 µg/m³RMS = 4.3 µg/m³

Average observations : 30.6 µg/m³Average RIO-c interpolation : 31.5 µg/m³

Annual mean PM10 concentrations 2006

RIO-corineOrdinary Kriging

RIOOK

Annual average NO2 concentrations 2002

LegendNO2 [µg/m³]

error

1 - 10

11 - 12

13 - 14

15 - 17

18 - 20

21 - 23

24 - 26

27 - 29

30 - 33

> 33

LegendNO2 [µg/m³]

error

1 - 10

11 - 12

13 - 14

15 - 17

18 - 20

21 - 23

24 - 26

27 - 29

30 - 33

> 33

LegendNO2 [µg/m³]

error

< 10

11 - 12

13 - 14

15 - 17

18 - 20

21 - 23

24 - 26

27 - 29

30 - 33

> 33

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 ColumnAOD 2006

RIO-corine : more info

“Spatial interpolation of air pollution measurements using CORINE landcover data ”Janssen Stijna, Dumont Gerwinb, Fierens Fransb, Mensink Clemensa

aFlemish Institute for Technological Research (VITO),Boeretang 200, B-2400 Mol, BelgiumbBelgian Interregional Cell Environment Agency(IRCEL), Kunstlaan 10-11, B-1210 Brussels, Belgium

Atmospheric Environment 42/20 (2008) 4884-4903

Contents







Brussels

Polluants : PM10 et NO2

Plan : 3 niveaux

Wallonia

Polluant : PM10

Plan : 3 niveaux

Flanders

Polluant : PM10

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 emissionscombustion

ExampleTemperature

CHIMERE – Example (1)

Forecast for 21/6/2005

Observations 21/6/2005

CHIMERE – Example (2)

OVL : schematically

Input:•PM10 measurements day-1

•Meteo forecasts

Process:

Output : PM10 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 2005forecast day +1

0

20

40

60

80

100

120

140

01/01/05 01/02/05 01/03/05 01/04/05

µg/m

³

metingen

OVL modelR=0.7

Antwerp (monitoring station 42R801)

OVL : more info

“A neural network forecast for daily average PM10 concentrations in Belgium”Hooyberghs Jefa, Mensink Clemensa, Dumont Gerwinb, Fierens Fransb, Brasseur Olivierc

aFlemish Institute for Technological Research (VITO),Boeretang 200, B-2400 Mol, BelgiumbInterregional Cell for the Environment (IRCEL), Kunstlaan 10-11, B-1210 Brussels, BelgiumcRoyal Meteorological Institute (RMI), Ringlaan 3, B-1180 Brussels, Belgium

Atmospheric Environment 39/18 (2005) 3279-3289

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.bewww.vmm.be

Documents

Current practice of PM-measurements, data processing, interpretation and visualization in Belgium Frans Fierens scientific staff member of the Flemish