Study number

Assessing (dermal) exposure loading in the evaluation process of biocidal products

TNO Quality of Life

Joop J. van Hemmen

TNO Senior Research Fellow in Occupational Toxicology TNO Chemistry

Th is i s a sh ip a nd the pa int co nta ins

a b ioc ide

Contents

• Use patterns and exposure

• Modelling exposure loading

• Current predictive dermal exposure models

• Uncertainties in the risk assessment process

• Data needs

• Current developments

Use patterns and exposure

• Exposure is strongly related to a task but there are exceptions

• There are many different tasks

• Duration of the task (and/or amount handled)

• Frequency of the task occurring

Emission, transfer, deposition

Adapted from Schneider et al. (1999)

Source Surface

Contaminan t Layer

Air Floor,

worktables, machinery,

tools

Outer clothing

Inner clothing

Phases of activity / time budget

• Manufacturing

• Formulating

• Mixing/loading

• Application/handling

• Post-application (re-entry)

• Removal

Modelling exposure loading

• Single study of a task (or series of tasks)

• Single survey (representative and robust)

• Several surveys

Exposure distributions

“width” and “typical value”

Individual data for boom sprayers

0 10 20 30 40 50 60 70 80

1 10 100 1000 10000 100000

Exposure µg/kg a.s. applied

S tud y num be r

Indicative distribution exposure model (I)

R ate P ro file

L ow M e d ium H ig h V er y h ig h

N arrow C ab b ed orch ard sp ra yin g

TP T (so lven t)

N et d ep lo ym en t An tifo ula nt b ru sh in g

M ed ium TP T (w ater)

AF m ix & lo ad

AF sp ra y S h eep

dip pin g

W id e P H I sp ra yin g

D ip p in g

R e m e dial

b io cid e sp ra ying U n ca bb ed

o rc ha rd sp ra yin g

Structure of BEAT

• Integration of expert judgement and objective measurements

• Based on what an expert would do anyhow, whenever he/she thinks

• BEAT consists of 3 elements:

• Bayesian framework

• Rule base (expert judgement)

• Database (with measurements)

Rule base

• Algorithm describing the similarity / analogy between exposure scenarios

• Algorithm contains the most important determinants of exposure

• Every determinant contains an “uncertainty factor”

• When the uncertainty factors increase, the similarity

between scenarios decreases

Rule base

Process

Work practice Working

environment Controls

Agent

Scenario

Exposure

distribution

Rule base

Determinant 1 Determinant 2 Determinant 3 Determinant n

Determinant 1 Determinant 2 Determinant 3 Determinant n

Rul e ba se

Scenario A Scenario B

Analogy score

Database

• Quantitative measurements for different

scenarios with background information on

exposure determinants (over 1,000 data

points)

Structure of BEAT

Scenario X

Database

Bayesian framework

Rule base

Posterior exposure distribution

Measurement data

Analogy

Models / databases

• Study / survey results, i.e. set of data points

• Several similar studies / surveys, i.e. database

• Humanexposition bei Holzschutzmitteln

• Mechanistic / theoretical model

• SprayExpo: Inhalative und dermale Expositionsdaten für das Versprühen von flüssigen Biozid-Produkten

• Combined mechanistic and database model

Technical Notes for Guidance (TNsG)

• Examples: http://ECB.JRC.IT/biocides

User Guidance

• Revisited set of models

Guidance for choice of surrogate value (percentile)

• PT 8 and PT 14 (rodenticides and wood

preservatives)

Geometric standard deviation

2 4 6 8 10

10 1.49 2.22 2.81 3.31 3.77

20 1.33 1.76 2.08 2.33 2.56

50 1.20 1.43 1.59 1.71 1.81

Sample size

100 1.13 1.29 1.39 1.46 1.52

Table 1: Scaling factors to obtain a 90% confidence interval for a 75 ^th

percentile with a variety of sample sizes and GSDs

variance

Variability

mean

mean

Uncertainty

variance

Determines magnitude & frequency of effects

Determines confidence limits for effects

variance

Variability

mean

mean

Uncertainty

variance

Key variables for control (risk mitigation)

Key variables for research (prioritisation)

Sensitivity analysis

Sensitivity analysis

Variability and uncertainty

Knowledge about both variability and uncertainty is essential for reliable

predictions

Variability and uncertainty (2)

• Variation

• What fraction of the population is above a certain value (AOEL/MAC/TLV)?

• A point on a cumulative distribution curve

• Uncertainty

• What is the confidence in this estimate?

• Expressed as confidence limits or error bars

• How do you assess these?

• Monte Carlo (probabilistic) analysis

1.0 10.0 100.0 1000.0 Dos e [µg/kg/day]

0 10 20 30 40 50 60 70 80 90 100

Percentile

1.0 10.0 100.0 1000.0

Dose [µg/kg/day]

0 10 20 30 40 50 60 70 80 90 100

Percentile

0 10 20 30 40 50 60 70 80 90 100

Percentile

Variability inner loop

Uncertainty outer loop

Median

90% confidence

limits

Determinants of processes

(Deliverable 36 RISKOFDERM)

Categories

• substance/ product characteristics

• tasks

• process, techniques/

equipment

• exposure control

• worker characteristics

• area and situation

Examples ( for deposition)

• physical state

• amount of substance

• type of aerosol generation

• ventilation

• position relative to source

• wind direction and speed

Types of situations studied: examples 1

Handling contaminated objects

Manual dispersion of products

Dispersion with a

hand-held tool

Types of situations studied: examples 2

Spray dispersion of products

Immersion of objects

Mechanical treatment

of solid objects

Extensive body contact?

Application rate (L/min)

Results – rate (µL/min) Percentile

Results - mass or volume

Duration (min)

No

75.0

50 0.5

Percentile distribution

646.

58.8

Median

1750.

160.

Percentile distribution

Yes

Median

25.0

50 0.068

Percentile distribution

297.

744.

Median

109.

274.

Percentile distribution Median

Hands Body

Example model

wiping

Example model spray painting

Direction spraying

Proximity to source

Results – rate (µL/min) Percentile

Results - mass / volume Duration (min)

Level

75.0

60 0.12

Percentile distribution

4.54 12.

Median

15.7 51.4

Percentile distribution Median

Away from the worker

Air flow

Up to 1 meter No Indoors

Indoors/outdoors

Not highly volatile

Segregation Volatility

Liquid

Liquid/solid

Application rate (L/min)

Hands

Body

RISKOFDERM dermal model

• The RISKOFDERM Dermal Exposure Model

• Version 2.0 – Guidance Document

• © TNO, HSL, November 2005

• Excel model available

• Web-based version: next month

REACH

• Exposure Scenario

• Chemical Safety Assessment

• Certify ‘Safe Use’

• Chemical Safety Report

• ES Material safety data sheets

• Downstream user must be in compliance

• To be done by Industry

• Evaluation by Agency

Exposure Scenario

• REACH text (proposed legislation):

“An exposure scenario is the set of conditions that describe how the substance is manufactured or used during its life-cycle and how the manufacturer or

importer controls, or recommends downstream users

to control exposures of humans and the environment.”

Exposure assessment

• Tiered approach

• First Tier

• ECETOC TRA Tool (to be adapted)

https://www.ecetoc-tra.org/public/login/index.asp

• Second Tier

• Advanced integrated tool (replacing EASE)

Source – Receptor model (Cherrie)

• The potential exposure pathways from source to workplace to worker (source-receptor

model) are evaluated in compartments,

i.e. the source, breathing zone air, respiratory

tract, room air, outside air, surfaces, clothing

and skin contaminant layer

Advanced integrated tool

Deterministic model, incl. Monte

Carlo module

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

Development of worked

examples for exposure scenarios of biocidal products to humans

TNO RIVM

HSE / HSL

BAuA

Objectives

• Generation of a reference set of models and approaches for exposure prediction

• Production of a technical guidance on

exposure prediction for all biocidal product

types with worked examples

The Work

Relevant data will be collected, evaluated and presented, on the basis of extensive surveys of the available literature and information

gathered from competent authorities and industries in Europe and North America Use patterns; exposure data; models;

formulation types; secondary exposures

Development of worked examples for each relevant model and approach, as well as for each product type

* Primary exposure

* Secondary exposure

* Linkage between models and product

types/use patterns

• One axis: PTs (consumers and workers) Formulations; application techniques; use patterns; general issues

• Other axis: Exposure data and modelling;

PPE/controls

• Linkage of models and PTs

• Ultimate guidance with ‘worked examples’

Effective Personal Protective Equipment (PPE)

Discussion paper on the use of PPE

for handling of agrochemical, microbiological and biocidal pesticides

Rianda (M.G.) Gerritsen-Ebben, Derk H. Brouwer, Joop J. van Hemmen

CONSULTATION DOCUMENT

March 2006 (08-03-06)

Q&A

• Thank you for your attention

• Vielen Dank für eueren Andacht

• Dank voor uw

aandacht