Toekomstplannen: ART v1.5 en verder

Erik Tielemans

14 April 2011

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

Large sample size

Multiple companies

Uniform measurement series

>> Small sample sizes

>> Single company

>> Heterogeneous measurement series

Weight of Evidence

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

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

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

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

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

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

Next steps

Reliability studie

ART cursussen / opzet gebruikersgroep

Samenwerking met NIOSH

Model voor dermale blootstelling

Continue verbetering van mechanistich model

Sources of uncertainty Measurement error

Model uncertainty:-Substance emission

-Efficacy RMM

-Outdoor dispersion

-Human behavior

-…..

Scenario uncertainty:Missing contextual

information