Operator exposure
model development
Henk Goede, Thies Oosterwijk, Marc Kennedy,
Jody Schinkel, Suzanne Spaan, Rianda Gerritsen-Ebben
Background
• Bystanders, Residents, Operator & Worker (BROWSE)
• WP1: Operator models (1st models - boom spraying,
mixing & loading)
• Multiple exposure routes (inhalation, dermal, ingestion)
• Improve on existing pesticide models – empirical, point
estimates
Modelling approach
Conceptual
Mechanistic model
framework
(algorithm)
• Identify input parameters
• Assigning categories
• Assign effect values
Database
Output data
Conceptual framework
Air
Surfaces
Source
Bulk / splashes
Clothing
Deposition
Respiratory tract
Inhalation
Oral
+
Ingestion
Surface
contact
Skin
+
Dermal
Boom spraying
Spraying
(+)
Nozzle
maintenance
(+)
Cleaning
Boom spraying
- relevant routes
• Spraying
- inhalation
- dermal (deposition)
- dermal (surface contacts – vehicle controls, interior/exterior vehicle)
• Nozzle maintenance
- dermal (surface contacts – nozzles, boom, tools)
- dermal (bulk/splashes)
Boom spraying
Inhalation parameters
Substance emission potential
= in use concentration, volatility, surface tension
Activity emission potential
= amount applied, droplet size, boom height,
crop height, vehicle speed
Localised control
= boom shields / screens
Dispersion
= wind speed / direction, temperature, humidity
Operator orientation
= operator height, operator-to-boom distance,
operator orientation to boom and wind,
number of passes [simplified Gaussian plume]
Personal enclosure
= cabin enclosure
Nozzle maintenance
Dermal parameters (surface contacts – hands/forearms)
Substance emission potential
= in use concentration, viscosity
Activity emission potential
= contact probability (frequency blocked nozzles)
intensity (nozzle type, boom height, shields)
surface contamination level (nozzles, boom)
De-/recontamination
= hand wash frequency
frequency putting gloves on/off
Boom spraying
Example of parameter inputs
Category
Description
A
No Cabin
B
Closed Cabin
C
Closed Cabin with air filtration
D
Cabin with at least one window open
Exposure reduction
0%
90 ± 10 %
98 ± 2 %
50 ± 50 %
Exploratory analyses
Boom spraying, inhalation exposure (n=236)
• Linear regression models explain ± 50% of
variance in the data (droplet size, total area (Ha),
wind speed)
• Important determinants not fitted in models include
cabin enclosure, boom height
• Some parameters not included in the model because it’s not
known in the data → boom screens/shields
Conclusion
• The conceptual framework helps to apply a systematic approach
for all scenarios
• Data quality problematic
• Mechanistic models will be useful when limited contextual
information is available in data (e.g. mixing & loading)
Way forward
• Finalise mechanistic models
- assign values to determinants in the database
- uncertainty analyses
• Improve and expand the current database
• Input cross-cutting WPs - transfer factors (PPE protection,
deposition, transfer coefficients)
• Incorporate survey data in parameters to represent work
patterns in EU, in particular operator behaviour
• Validation, calibration
Thank you for your attention
www.browseproject.eu