3. the advanced reach tool (art)
DESCRIPTION
The third lecture on modelling exposure for a course held in Norway 2011TRANSCRIPT
WORKING FOR A HEALTHY FUTURE
INSTITUTE OF OCCUPATIONAL MEDICINE . Edinburgh . UK www.iom-world.org
The Advanced REACH Tool
John Cherrie
Summary…
• Background• The concept of ART• The mechanistic model tool• Dispersion, an example of the work to underpin
the model
• Bayesian updating• Model output
Background…
• ART is a generic higher tier tool• Included in the REACH Guidance• Applicable for exposure to dust, vapour, mist, and
fumes• Co-development project funded by:• Shell, GSK, Eurometaux, Cefic• HSE, Dutch government, Afsset• BOHS
• Delivered by:• TNO, IOM, HSL, BAuA, UU and NRCWE
The conceptual basis…
• Based on the premise that both models and measurement data can be informative about exposure
• Set within a Bayesian framework
Combining data reduces uncertaintyU
ncer
tain
ty
+ BDA
Model Data Updated estimate
The 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
Modelling the exposure distribution
Scenario
Median exposure
Exposure variability
Mechanistic model
Information from meta-analysis
Model specification…
jklikijiijkli wcY )()()()()( )(ln
- Between-company variability
- Between-worker variability
- Within-worker variability
Underpinning the mechanistic model…
• The early work by Cherrie, Schneider et al• Developed a model for use in epidemiological studies• Multiplicative deterministic model• Near-field/Far-field• Relied on the assessor to choose the parameters• Validated in a number of different situations
• Dispersion parameters derived from a 2-box model
Cherrie and Schneider. (1999) Validation of a new method for structured subjective assessment of past concentrations. The Annals of Occupational Hygiene; 43 (4): 235-245.
Cherrie. (1999) The effect of room size and general ventilation on the relationship between near and far-field concentrations. Applied occupational and environmental hygiene; 14 (8): 539-46.
An example of the use of this model…
0.001
0.01
0.1
1.0
10.0
100.0
1000.0
0.0 0.0 0.1 1.0 10.0 100.0
Measured exposures (ppm)
Esti
mate
d e
xp
osu
res (
pp
m)
101
Validation of the model…
The ART mechanistic model…
The mechanistic model…
Source
LCIR
Source barrier
Far-field
Personal barrier Surfaces
Near-field
RPE
2nd
Personal behaviour
Inhalation boundry
Nine modifying factors…
• Intrinsic emission potential (E)• Activity emission potential (H)• Local controls (LC)• Separation (Sep) • Segregation (Seg)• Surface contamination (Su)• Dilution (D)• Personal behavior (P)• RPE
Three basic equations…
RPECCC ffnft )(
nfnfnfnfnfnfnf DSuPLCHEC )(
ffffffffffffffff SepDSuSegLCHEC )(
Substance emission potential…
Category Relative weight
Firm granule 0.01
Granule 0.03
Coarse dust 0.1
Fine dust 0.3
Extremely fine dust
1.0
Substance emission potential…
iiimixi pp ,
Partial vapour pressure of substance i in mixture
Vapour pressure of substance i
Mole fraction
Activity coefficient
UNIFAC
Activity emission potential…
Powders
Liquids
Solid objects
Hot / molten metal
Local controls…
• No localized controls• Suppression techniques• Wetting at the point of release• Knockdown suppression
• Containment (non-extracted)• Local ventilation systems• Receiving hoods• Capturing hoods
• Enclosing hoods
Efficacy = 0.0001
Efficacy = 0.5
Dispersion…
Q
FF Q
NF eT
Indoor
Outdoor
Spray booth
Calibration with measurements…
• Using multiple sources• Also including measurements in the low range• Only good quality data: N~2,500• Quantification in units (mg/m3)• Provides model uncertainty• Separate predictions for:• Dust• Vapour• Mist• Fumes
Bayesian updating…
• Bayesian techniques allow for different types of information to be integrated in a theoretically rigorous way via probability theory
• Bayes approach takes account of the relative strength of the different information sources
• Bayesian module is integrated in ART to update model estimate with user’s own data
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
0.7
0.75
0.8
-4 -3 -2 -1 0 1 2 3
Data
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
0.7
0.75
0.8
-4 -3 -2 -1 0 1 2 3
Data Prior
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
0.4
0.45
0.5
0.55
0.6
0.65
0.7
0.75
0.8
-4 -3 -2 -1 0 1 2 3
Data Prior Posterior
ART output…
• Explicit treatment of variability and uncertainty• Percentile of distribution• Uncertainty of estimate
Variability
Uncertainty
90th , 75th , 50th percentile
Inter-quartile, 80 %, 90 %, 95 % CI
Bayesian update
Key scientists involved in ART…
• Erik Tielemans (TNO)• John Cherrie (IOM) • Wouter Fransman (TNO)• Hans Marquart (TNO) • Jody Schinkel (TNO) • Thomas Schneider (NRCWE)• Martin Tischer (BaUA• Martie van Tongeren (IOM)• Nick Warren (HSL)
Acknowledgements…
• S. Bailey (GSK, UK)• D. Brouwer (TNO, The Netherlands)• M. Byrne (University of Galway, Ireland)• D. Dahmann (Bochum University, Germany)• J. Dobbie (BP, UK)• D. Gazzie (Industrial Health Control, UK)• A. Gillies (Gillies Associates Ltd, UK)• E. Joseph (GSK, UK)• I. Kellie (OHS Scotland Ltd, UK)• D. Mark (HSL, UK)• P. McDonnell (University of Galway, Ireland)• M. Newell (Bayer Crop Sciences, UK)• M. Nicas (University of California, USA)• D. O’Malley (Genesis Environmental Ltd, UK)• M. Piney (HSL, UK)• A. Robertson (IOM, UK)• J. Saunders (HSL, UK)• M. Smith (Bayer Crop Sciences, UK)• H. Tinnerberg (Lund University, Sweden)• H. Westberg (Örebro University Hospital, Sweden)• A. Wolley (Wolley Associates, UK)• J. Wren (GSK, UK)• D. Iddon (Lilly, UK)• D. Noy (Dow chemicals, NL)• J. Urbanus (Shell, NL)• R. Battersby (EBRC consulting, Germany)