Download pdf - The Greenhouse Agricultural Operator Exposure …...greenhouse application types, particularly for: - Low crop applications (normal and dense scenario) - High crop applications (normal

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The Greenhouse Agricultural Operator

Exposure Model (Greenhouse AOEM)

Sabine Martin

Federal Institute for Risk Assessment

Sabine Martin, Interzonal workshop on harmonisation of risk assessment in toxicology - Vienna, June 2015 Page 2

Introduction

Current situation:

· different models used in risk assessment for PPPs

· mainly based on data for outdated equipment and practices

Plant protection

product

� no harmonised risk assessment in EU-MS

Exposure


Development of a new, harmonised operator exposure model for greenhouse application types, particularly for:

- Low crop applications (normal and dense scenario)- High crop applications (normal and dense scenario)

→ data based on the Southern European Greenhouse Project,→ appropriate deterministic model for regular exposure estimations

in authorisation procedures, → based on values from valid exposure studies analysed according to the

present scientific knowledge,→ transparent evaluation – publication of a detailed project report.

Scope of the new model

Project group- ANSES (French Agency for Food, Environmental and Occupational Health & Safety)- BfR (Federal Institute for Risk Assessment)- BPI (Benaki Phytopathological Institute)- BVL (Federal Office of Consumer Protection and Food Safety)- HSE (Health and Safety Executive)- INSHT (National Institute of Safety and Hygiene at Work )- JKI (Federal Research Centre for Cultivated Plants) and- ECPA (European Crop Protection Association) observed by EFSA


Scope of the new model

Quality criteria for exposure studies

• Compliance with OECD Series No. 9

• Trained operators working in accordance with Good Agricultural Practice

• Data recording and observations according to current scientific knowledge

• Compliance with GLP

• Consistent field recovery

• Suitable data form

• Whole body dosimetry for dermal exposure

• Appropriate inhalation fraction samplers for inhalation exposure

• Representative application methods and application techniques


Database


Database


Database


Types of dosimeters

Head dosimeter (face/neck wipes)

Outer body dosimeter(work clothes, coveralls)

Inner hand exposure (hand rinse/wash)

Outer Hand exposure(protective gloves)

Inner body dosimeter (long underwear)

Personal air sampler


Database

Number of mixing/loading and application data available for model development

Inhalation Total handsProtected

handsTotal body

Protected body

Head

Mixing/Loading

Tank 1) 161 / 70 229 / 69 206 / 70 129 / - 129 / -129 (33) /30 (11) 5)

Knapsack 40 / - 49 / - 49 / - 40 / - 40 / - 40 (40) / -

Total 271 347 325 169 169 199 5)

Application

LCHH 10+29 2) 10+30 10+30 10+2010 +

30 (10) 2),4)

10 (10)+30 (12) 2),5)

HCHH 30+32 2) 28 (8)+19 (14) 3) 22+18 30+10

30 + 32 (22) 2),4)

29 (16)+31 (31) 2),5)

Total 101 87 80 70 102 1001) outdoor / indoor data 4) in brackets beneath rain clothes2) normal + dense scenario 5) in brackets with face shields/masks3) in brackets from unprotected hands


Statistical evaluation

LCHH (normal scenario)

HCHH (normal scenario)

LCHH (dense scenario)

HCHH (dense scenario)

EO = DEOML(H) + DEOML(B) + DEOML(C) + IEOML + DEOA(H) + DEOA(B) + DEOA(C) + IEOA

Tank ML

LCHH A (normal scenario)

HCHH A (normal scenario)

LCHH A (dense scenario)

HCHH A (dense scenario)

+ =

ML scenario Application scenarios Model scenarios

log-linear model: log X = α · log A + Σ [Fi] (0 < α ≤ 1)



Method: Quantile Regression (75. percentile)

Comparison of outdoor and greenhouse mixing/loading data for tank equipment

Red: outdoorGreen: greenhouseo: WG ∆: WP �: WP (sachets) +: liquid

(grey, outdoor only)






Validation

• Robustness (Cross validation)

• Prediction


Validation

• Prediction

• Robustness (Cross validation)

black: normal culturered: dense culture green: dense culture with water repellent clothingempty circles: validation data filled circles: model prediction (75th; solid lines)

High crop application

Low crop application


Models

M/L - Exposure models predicting the 75th percentile [µg/person]


Models

Application - Exposure models predicting the 75th percentile [µg/person]


Limitations

� no liquid formulations

� no body exposure for ML

� different levels of PPE used

� no studies in the central or the northern zone

� limited range of active substance used in low crops

� no data for application using knapsacks


Conclusions

� New model for typical greenhouse scenarios

� Exposure factors selected by statistical analysis

� Log linear model (quantile regression, 75th and 95th percentile)

� Validation

� Tiered approach possible

� Model suitable for risk assessment for

zonal and national applications

� Draft report is provided to MS,

comments and remarks are appreciated by the end of August

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Thank you for your attention

Dr. Sabine Martin

Federal Institute for Risk Assessment

Max-Dohrn-Str. 8-10 � 10589 Berlin, GERMANY

Tel. +49 30 - 184 12 - 4589

[email protected] � www.bfr.bund.de