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Toekomstplannen: ART v1.5 en verder
Erik Tielemans
14 April 2011
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ART approach
Model
Bayesian process
to combine data
and model output
Exposure estimates
for risk assessment
Similarity module to
select data for risk
assessment
Exposure database,
with contextual
information
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Large sample size
Multiple companies
Uniform measurement series
>> Small sample sizes
>> Single company
>> Heterogeneous measurement series
Weight of Evidence
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Outcome mechanistic model: bottom loading
0 1 2 3 4 5 6 7
Concen
tration (
mg/m
3)
0,01
0,1
1
10
ART prediction:
GM: 0.32 mg/m3
0.17-0.62 mg/m3
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Outcome mechanistic model: bottom loading + bayesian
update 3 observations 1 worker
0 2 4 6
Concen
tration (
mg/m
3)
0,01
0,1
1
10
ART prediction:
GM: 0.32 mg/m3
0.21-0.52 mg/m3
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Outcome mechanistic model: bottom loading + bayesian
update 20 observations 1 worker
0 2 4 6
Concen
tration (
mg/m
3)
0,01
0,1
1
10
ART prediction:
GM: 0.32 mg/m3
0.23-0.47 mg/m3
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Outcome mechanistic model: bottom loading + bayesian
update 20 observations 20 workers
0 2 4 6
Concen
tration (
mg/m
3)
0,01
0,1
1
10
ART prediction:
GM: 0.32 mg/m3
0.23-0.44 mg/m3
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Outcome mechanistic model: bottom loading + bayesian
update 20 observations 20 workers, 20 companies
0 1 2 3 4 5 6 7
Concen
tration (
mg/m
3)
0,01
0,1
1
10
ART prediction:
GM: 0.32 mg/m3
0.27-0.38 mg/m3
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Outcome mechanistic model: bottom loading + bayesian
update 20 observations 20 workers, 20 companies,
more variability in dataset
0 1 2 3 4 5 6 7
Concentr
ation (
mg/m
3)
0,01
0,1
1
10
ART prediction:
GM: 0.32 mg/m3
0.25-0.41 mg/m3
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Next steps
Reliability studie
ART cursussen / opzet gebruikersgroep
Samenwerking met NIOSH
Model voor dermale blootstelling
Continue verbetering van mechanistich model
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Sources of uncertainty Measurement error
Model uncertainty:-Substance emission
-Efficacy RMM
-Outdoor dispersion
-Human behavior
-…..
Scenario uncertainty:Missing contextual
information