flanders brussels walloniaaqm.jrc.ec.europa.eu/fairmode/document/event/presentation/201304… · 1....
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www.vmm.be www.ibgebim.be airclimat.wallonie.be
Flanders Brussels Wallonia
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Delta-tool a MS-experience
FAIRMODE recommandations:
→ use of models :→ use of models :
• population exposure • spatial extent of exceedances
→ combined use of models and monitoringdata: (among others) spatial interpolation
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1. Belgian Interpolation technique : RIO-corine
2. Delta-tool statistics RIO-model
3. Population Exposure3. Population Exposure
4. Model Uncertainty
5. Conclusions
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1. Belgian Interpolation techniqueRIO-Corine
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Belgium: RIO-Corine
→ O3, NO2, PM10, PM2.5
→ Operational interpolation technique using- AQ measurements- Landuse information- Satellite data (AOD) PM2.5- Satellite data (AOD) PM2.5
→ IRCEL:• real-time (up-to-date) maps: website realtime• assessment (population exposure, compliance
checking)• background concentrations as input for high
resolution models
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Exposure based on RIO-Corine interpolationRemark: color of the dots are the measuredconcentrations
Remark: color of the dots are the measuredconcentrations
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Remark: color of the dots are the measured concentrations
(ug/m3)
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2. Delta-tool statistics RIO-corine
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Model evaluation: Deltatool
→ Comparison measurements – (interpolation) technique results (hourly values)
- “leaving-one-out” methodology : with n stations, interpolation with n-1,validation model results at the location of the left-out monitoring sitevalidation model results at the location of the left-out monitoring site
→ New version deltatool v3.2 :- takes measurement uncertainty into account
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Delta-tool 1.2 vs Delta-tool 3.2 PM10-hourly values
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Summary statistics: Delta-tool v.1.2 vs Delta-tool v.3.2
PM10-hourly values (2010)
Source : Lefebvre et al. (2009)
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Conclusion:
•RIO-Corine interpolation (PM10, NO2, 2009-2010) complies with model qualityobjectives DELTA-tool
•Moreover: meets also the model performance criteria DELTA-tool v3.2
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3. Population Exposure
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PM10 annual mean 2009: 2 model results :
RIO-CORINE interpolation ETC/ACM EU air quality maps B E
De Smet P,Horálek J,Kurfürst P,Schreiberová M, De Leeu w F,(2012) European air quality maps of ozone and PM10 for 2009 and theirUncertainty Analysis. ETC/ACM Technical Paper 2011/11
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Targetplot RIO-Corine scatterplot ETC/ACM (hourly values) (yearly values)
PM 10 annual mean 2009 PM 10 annual mean 2009
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Summary RIO-Corine Summary ETC/ACM (hourly values) (yearly values)
PM 10 annual mean 2009 PM10 annual mean 2009
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PM10 2009: % population exposure BE
ETC/ACM EU air quality maps BE vs. RIO-CORINE interpo lation
7%
6%
7%
8%
0%
3,30%
0%0%
1%
2%
3%
4%
5%
6%
% exposed exceedance annual limit value PM10
% exposed exceedance daily limit value PM10
ETC/ACMRIO-CORINE
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suburban background station
suburban background station
Remark: color of the dots are the measured concentrations
Source : Lefebvre et al. (2009)
urban background station
suburban background station
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RIO-Corine (4x4 km) NO2 annual mean concentration 2010 :
zoom Brussels30 %
19 %
15
20
25
30
35
Source : Lefebvre et al. (2009)
Difference of 11 % in population exposure when changing 1 ug/m³distribution around limit value is important
0
5
10
>39 ug/m³ >40 ug/m³
% population exposure
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Limit Value40 ug/m³
Modelled value withconfidenceinterval
Source : Lefebvre et al. (2009)
Modelled valuesSource: Bino Mahieu, VITO.
How many exceedances ?Or is the question: What is the probability of an exe edance ?
Model uncertainty
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4. Model uncertainty
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• Delta tool = validation statistics- comparison between measurements and model
results- for interpolation tools (via “leaving-one-out”)
• Model Uncertainty
Source : Lefebvre et al. (2009)
• Model Uncertainty - covers the whole domain- takes into account uncertainty of input data
(for Eulerian models : uncertainty emissions, meteo, ...)
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Kriging
How to calculate uncertainty of the RIO-model ?
• Contributions to the RIO-model uncertainty:
• clustering of measurements stations• measurement uncertainty• distance to the measurements stations
Source : Lefebvre et al. (2009)
• distance to the measurements stations• variance of measurements
• uncertainty introduced by de-and retrending
• Aggregation to annual averages: temporal autocorrelation is taking into account
de-andretrending
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Example of probability exceedance
Source : Lefebvre et al. (2009)Exceedances daily meanPM10 limit value 2009
Probability exceedance 2009
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3. Conclusions
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•Delta-tool statistics: RIO-interpolation tool is performing well (PM10, NO2, 2009-2010)
spatial interpolation should also beconsindered in the guidances
Source : Lefebvre et al. (2009)
consindered in the guidances(cfr. remarks BE made concering the NO2-guidance)
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•Results of exposure assessment : strongly depend on the models used : difficult for a MS to
explain these differences to the public
•Model Uncertainty or ‘probability exceedance ’ should be taken into account
Source : Lefebvre et al. (2009)
should be taken into account
•Some technical difficulties occured with the Delta-tool
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Thank you for your attention [email protected]
twitter.com/SMOG_BE
http://www.ircel.behttp://www.vmm.be
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Technical information on RIO-model:
Publications
•Jef Hooyberghs, Clemens Mensink, Gerwin Dumont and Frans Fierens, Spatial interpolation of ambient ozone concentrations from sparse monitoring points in Belgium, J. Environ. Monit., 2006, 8, 1129-1135 (doi: 10.1039/B612607N)
Source : Lefebvre et al. (2009)
•Stijn Janssen, Gerwin Dumont, Frans Fierens and Clemens Mensink, Spatial interpolation of air pollution measurements using CORINE land cover data, Atmospheric Environment 42, Issue 20, June 2008, Pages 4884-4903 (doi:10.1016/j.atmosenv.2008.02.043)
•Stijn Janssen, Gerwin Dumont, Frans Fierens, Felix Deutsch, Bino Maiheu, David Celis, Elke Trimpeneers and Clemens Mensink, Land use to characterize spatial representativeness of air quality monitoring stations and its relevance for model validation. Accepted to Atmospheric Environment (2012)