The Greenhouse Agricultural Operator Exposure …...greenhouse application types, particularly for:...
Transcript of The Greenhouse Agricultural Operator Exposure …...greenhouse application types, particularly for:...
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The Greenhouse Agricultural Operator
Exposure Model (Greenhouse AOEM)
Sabine Martin
Federal Institute for Risk Assessment
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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· mainly based on data for outdated equipment and practices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
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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
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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
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Database
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Database
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Database
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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
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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
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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)
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Statistical evaluation
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)
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Statistical evaluation
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Statistical evaluation
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Validation
• Robustness (Cross validation)
• Prediction
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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
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Models
M/L - Exposure models predicting the 75th percentile [µg/person]
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Models
Application - Exposure models predicting the 75th percentile [µg/person]
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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
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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