BROWSE - Fera Science · 2014-11-13 · BROWSE . Bystanders, Residents, Operators and WorkerS...

Grant Agreement No: 265307

Project start date: 1st January 2011

Project end date: 30rd June 2014

BROWSE Bystanders, Residents, Operators and WorkerS Exposure models for

plant protection products SEVENTH FRAMEWORK PROGRAMME

Theme: Environment (including climate change)

Technical report WP1.4: Operator exposure: Boom spraying, Mixing/loading, Orchard spraying and

Hand Held Applications

Title of Deliverable

Technical report WP1: Operator exposure: boom spraying, mixing/loading , orchard spraying and hand held applications

Deliverable number

1.4

Related WP and Tasks

WP1

Type of Document

Public Report

Authors Partners WP1 Compiled by Henk Goede, Suzanne Spaan, Thies Oosterwijk, Neleida Marrufo, Agathi Charistou,

Victoria Roelofs, Clare Butler-Ellis, Richard Glass, Kiki Machera, Marc Kennedy, Helen Owen, Dav Stott, Mark Fee, Andy Hart, Rianda Gerritsen-Ebben

Date 31/10/2014

Project coordinator Dr Andy Hart

Fera, United Kingdom E-mail: [email protected]

http://www.browseproject.eu

1

Table of Contents Abbreviations .......................................................................................................................................... 4

1 Introduction .................................................................................................................................... 5

2 Scope and Aim ................................................................................................................................. 6

3 BROWSE WP1 operator models ...................................................................................................... 7

3.1 User interface .................................................................................................................................. 7

3.2 Exposure models ............................................................................................................................. 7

3.2.1 Boom spraying inhalation model .................................................................................. 12

3.2.2 Boom spraying dermal model ....................................................................................... 26

3.2.3 Mixing and loading inhalation model ........................................................................... 37

3.2.4 Mixing and loading dermal models ............................................................................. 44

3.2.5 Orchard spraying inhalation model .............................................................................. 71

3.2.6 Orchard spraying dermal models .................................................................................. 79

3.2.7 Handheld Application inhalation model ....................................................................... 90

3.2.8 Handheld Application Dermal model ............................................................................ 95

3.3 Aggregated exposure .................................................................................................................. 130

3.3.1 External exposure ....................................................................................................... 130

3.3.2 Internal exposure ........................................................................................................ 133

3.4 Quality assurance and testing procedures .................................................................................. 137

4 Comparison with existing models ............................................................................................... 140

5 Model outcome interpretation and level of conservatism ......................................................... 141

5.1 Exposure outcomes ..................................................................................................................... 141

5.2 Routes and sources of exposure included .................................................................................. 141

5.3 Exposure scenarios included ....................................................................................................... 141

5.4 Population ................................................................................................................................... 142

5.5 Representativeness of model outcomes versus “real world” ..................................................... 142

2

5.6 Level of uncertainty and variability ............................................................................................. 142

5.7 Model comparison as indication for level of conservatism ........................................................ 143

5.8 Overall level of conservatism ...................................................................................................... 143

6 Conclusion ................................................................................................................................... 144

7 Recommendations for future development and refinement ..................................................... 146

8 References ................................................................................................................................... 148

3

Abbreviations

a.s. Active substance

ASE Airborne spray emission

AR Volume spray rate

BS Boom spraying

CTS Closed Transfer Systems

HHA Handheld application

LAI Leaf Index Area

ML Mixing & loading

MTD Mechanical Transfer Devices

PPE Personal protective Equipment

PPP Plant Protection Product

PPPs Plant Protection Products

RA Risk Assessment

RPE Respiratory Protective Equipment

SA Sensitivity analysis

4

1 Introduction

This document provides an overview of the operator models (WP 1) developed as part of the

Bystander, Resident, Operator and WorkerS (BROWSE) project. BROWSE is an EU 7th Framework

Programme and is scheduled from 2011 to mid 2014.

The main objectives of the project are to:

• Review, improve and extend the models currently used in risk assessment (RA) of Plant

Protection Products (PPPs) to evaluate the exposure of operators

• Development of new models for operator exposure scenarios

• Involve all relevant stakeholders and end users and take account of gender issues and regional

differences

The expected impact of the project is to contribute to the implementation of Regulation (EC)

1107/2009 and Dir. 2009/128/EC on the Sustainable Use of PPPs.

A Deliverable 1.1 was compiled to review existing models and data for operator exposure

(https://secure.fera.defra.gov.uk/browse/project/deliverables-&-reports.cfm). A common finding for

PPPs models is that they are based on outdated empirical data (non GLP and GLP) and only cover

limited scenarios. PPP models also derive surrogate exposure values using different percentiles and

using different normalisation parameters. Generally speaking, the models estimate external

inhalation as well as external dermal (potential and/or actual) exposures. A large variation in the use

of defaults for personal protective equipment is applied. Biocide and industrial chemicals models are

an exception, where probabilistic task-based models such as BEAT and ART are used.

It was concluded that more and reliable exposure data are required for modeling purposes. The

collation of the exposure data found in the open (public), grey literature and in data(bases) of

existing and emerging models, revealed a significant amount of papers (or reports) with quantitative

exposure data. However, although most of the collated exposure data-sources identified are publicly

available, only about one third of the sources have raw data (detailed measurement data) that is

readily accessible and only about half of these consist of data for plant protection products (PPPs).

It became clear at an early stage of the BROWSE database development that the available data does

not provide the desired level of contextual information required for modeling purposes. In order to

develop probabilistic models, new and good quality data is required. Unfortunately the BROWSE

consortia did not get access to new/recent and contextually-rich data. 5

Nevertheless, within the BROWSE framework the following options were considered for the further

development of a model for operator exposure:

• Improvement of the existing empirical models with more data

• Combination of an empirical and mechanistic modeling approach

• Development of a mechanistic model

• Inclusion of probabilistic modeling, taking into account probability distributions and uncertainty

in exposure

The aim and scope of WP1 was formulated after a comprehensive review of the currently available

PPPs, biocide and other exposure models, and considering the scenarios that are covered in current

PPP models.

2 Scope and Aim

The following aims are proposed for the operator models (WP1):

• Develop a single, new and improved modeling framework, integrating all available exposure data

• Incorporate recently-generated, high-quality exposure data if available

• Take explicit account of key factors and mechanisms influencing exposure, account for non-

linearity, strengthen estimation where data are limited and to produce probability distributions

of exposure

• Use data on operator practices in different Member States to take account of controls &

protective equipment

• Take account of regional & gender differences where possible

The following scenarios were selected for WP1 operator model development:

• boom spraying

• mixing & loading (liquids and solids)

• orchard spraying

• hand-held spraying

6

3 BROWSE WP1 operator models

3.1 User interface

See relevant software documentation. The relevant user inputs required for the user-interface are

presented for each model in the respective sections of this report.

3.2 Exposure models

Conceptual model

A generic conceptual model has been developed (Figure 1) to support the development of models

for each scenario. It describes the transport of a PPP from the source to the receptor (operator). This

model considers three main routes of personal exposure, i.e. (i) inhalation (via respiratory tract), (ii)

dermal (via skin) and (iii) ingestion (via mouth). The model consists of different compartments

through which the pesticide can pass. These compartments can broadly be divided into three steps:

• Emission from source (source, local source zone)

• Transport between source and operator (air-, surface- and bulk- zone)

• Transport at the operator (respiratory protective equipment (RPE), outer clothing contaminant

layer, inner clothing contaminant layer, skin contaminant layer, (pre-)oral /mouth)

The transport of PPPs occur through 4 mechanisms, (i) separation from gas/vapour or solid particles

from parent material (source strength), (ii) transport between compartments, (iii) loss of pesticide

from compartments due to sinks, and (iv) uptake by the receptor (Tielemans et al, 2008). The latter

mechanism (uptake) is only included as an estimate after modeling the different routes of exposure.

The inhalation exposure models accounts for the most important determinants that are involved in

the airborne transfer of PPPs from the formulation. The dermal exposure is the most complex route

of exposure and occurs through three potential pathways, i.e. (i) deposition from the air, (ii) contacts

between surfaces and the body, and (iii) bulk transfer through splashes or dripping (from liquids) and

impaction (from solids) (Schneider et al, 1999). It should be noted that the three potential dermal

pathways of exposure are scenario-specific and not always relevant (and therefore not included in

all the models). And lastly, a generic estimate is made of ingestion exposure that considers

contamination of the hands or gloves and the probability that it is transferred into the mouth and

oral route.

7

Figure 1: Conceptual model for operator exposure to PPPs

Modeling approach

For each scenario and exposure route, the most important determinants that influence the transport

of a PPP from the source to the receptor were identified and, where appropriate, included in the

models. Their effect sizes and correlations were evaluated and determined by means of data

analyses, literature reviews and expert opinion. Subsequently, mechanistic algorithms were

developed using the available evidence to underpin the models.

In general, the following modeling procedure was followed for each scenario:

• Collation of available evidence from the literature

• Statistical (regression and correlation) analyses of the available exposure data

• Identifying exposure determinants and allocating effect sizes and distributions (incl. information

from EFSA and BROWSE survey data, e.g. Glass et al, 2012)

• Adopt experimental data as input for the models (where available and/or appropriate)

Surface zone

Source

Local source zone

Skin contaminant layer(different body parts)

Outer clothing contaminant layer

Inner clothing contaminant layer

Air zone Bulk zone

Respiratory tract Oral

Respiratory protective equipment

Deposition Splashes/impaction Surface contact

Inhalation Ingestion Dermal

8

• Developing mechanistic algorithms

• Coding the datasets with the (missing) model input parameters, and introducing imputed data

where appropriate using statistical techniques (e.g. bootstrapping)

• Sensitivity analysis (SA) of the model algorithms

• Comparison and testing of model outputs with data (no calibration or fitting/transforming was

performed due to data quality issues)

• Comparing the model outputs with existing model outputs

Depending on the available evidence of exposure determinants of a given scenario, available

experimental data and existing models, the most useful type of model was selected. This resulted in

different levels of complexity of the respective mechanistic algorithms. Examples include:

• Using models of other BROWSE work packages (input from WP3 for the BS inhalation model)

• Using experimental data (surface contamination and hand/body exposure levels for the BS hands,

ML hands and ML body models)

• Using existing models (Advanced REACH tool (ART) for ML inhalation)

• Using extrapolation (BS body extrapolated from BS hands model)

Table 1 gives an overview of the principal inputs of each model and the data used for testing.

Table 1: Key model inputs and data used

Model Examples of key inputs Derived from Exposure data used for testing

BS inhalation

Spray volume rate WP3 estimate (airborne fraction estimate) Vehicle-sprayer type (dispersion estimate)

User input Experimental spray drift data & WP3 model Literature review

BROWSE database Inhalation data

BS hands Surface contamination levels (% of total spray volume) Contact event frequency

Vehicle-sprayer contamination studies User input

BROWSE database Bare hand & Protected hand

BS body Extrapolation BS hands model Contact event frequency

Literature review User input

BROWSE database Whole body (excl. hands)

*ML inhalation ART model

Existing calibrated model (based on literature review and expert elicitation procedure)


*ML hands

Container contamination levels Hand contamination levels

Experimental studies Experimental studies

BROWSE database Bare hand &

9

Model Examples of key inputs Derived from Exposure data used for testing

Contact event frequency User input Protected hand

*ML body Body contamination levels Contact event frequency

Experimental studies User input


Orchard spraying Inhalation

Spray volume rate Fraction of overspray Row spacing Vehicle/ forward speed Wind speed

User input Experimental studies User input User input User input


Orchard spraying hands

Surface contamination levels (% of total spray volume) Contact event frequency

Vehicle-sprayer contamination studies User input

BROWSE database Bare hand & Protected hand

Orchard spraying total body

Deposition estimation (based on cockpit contamination)

Vehicle-sprayer contamination studies


Hand-held models

Spray volume rate Surface contamination level Application efficiency Air Concentration

User input Studies on Surface Contamination of the Equipment when ML Literature review Existing calibrated model (based on literature review and expert elicitation procedure)

BROWSE database Hands and Whole body (excluding hands)

*Note a distinction is made between manual and mechanical mixing and loading (ML) models (not indicated in this Table)

Sensitivity analysis

Sensitivity analysis (SA) was performed for the model algorithms of each scenario. Using the input

ranges of the model algorithm a Latin hypercube sampling space of 400 points are generated across

the inputs. Due to several discrete inputs the samples are converted from a discrete uniform

distribution. Preferably, separate sensitivity analyses (SA) are required for different combinations of

categorical variables in the models.

Model testing with data and imputation of data

For each of the models described in this report we have some empirical data available which consists

of information on the inputs for the models (e.g. concentration of active substance, wind speed etc.)

10

along with the measured output (e.g. potential dermal exposure on the hands and / or body etc.)

from the field study. For each of these datasets we run the relevant model and compare the model

outputs with the measured output. However, several datasets are not complete and contain missing

pieces of information. Where this was the case the missing input or inputs were imputed by

empirical sampling from existing values for that input in the available data. As a result, this report

distinguishes between ‘true datasets’ and ‘imputed datasets’. To summarise the output we calculate

the geometric mean, median, 2.5th percentile and 97.5th percentile. We then plot the geometric

mean of the output against the true measured values that we have from the available data and plot

the 95% confidence intervals, medians and geometric means of the output against the true values

(all on log10 scale) for comparison. Except where indicated otherwise we use 10,000 iterations of

each model. Where relevant, a clear distinction is made between the measured values or results for

the model runs when the data set used was complete and when input values needed to be imputed

in the respective plots.

To test the models, two types of plots / graphs are presented:

• The measured potential exposure under specific conditions (y-axis) against the geometric mean

of the model outputs (x-axis) under the same conditions (i.e. same model inputs)

• The individual measured potential exposure (dots) and estimated model outputs (grey lines)

under the same conditions (i.e. same model inputs) plotted with the exposure level ( y-axis). We

present two of these plots with the results sorted in two different ways. The first plot is sorted on

the median model output and the second is sorted on the measured exposure values.

The models were only compared or tested with available exposure data. Thus no calibration or

transforming/fitting was performed in any of the models.

Reasons for not calibrating or fitting the models were:

(1) Considerable variation in both the data and model outputs, and

(2) Sub-standard data quality of the BROWSE database (e.g. limited or missing contextual data)

Regression analyses will not be robust when based on small datasets (the regression will

automatically remove any datasets with missing values). Although imputation is used to replace the

missing values, it’s not clear what effect this will have on the outputs.

11

Dermal sampling techniques

For potential dermal exposures, a preliminary analysis of the data showed no conclusive differences

in hand exposure data using different sampling techniques, i.e. bare hands and protected gloves.

‘Bare hands’ data refer to the measured amount of potential dermal exposure on unprotected hands

(no protective gloves worn) or on sampling (non-protective) gloves, e.g. cotton or Tyvek gloves.

‘Protective gloves’ data refer to the measured amount of potential dermal exposure on protective

gloves. When testing the hand models, the data used for hand exposure were based on both these

sampling techniques (depending on the data available). So combining the sampling techniques in the

data used for model testing may result in increased or unknown variability and outputs.

Background calculations

Various background calculations are performed in the software in order to calculate model inputs.

Typical examples include an estimate of the spraying time (to estimate the spray volume rate), the

ML periods and the number of containers used (Appendix L).

3.2.1 Boom spraying inhalation model

(a) Description

The boom spraying scenario represents field sprayers using hydraulic nozzles, but excludes air-

assisted operations. Operator inhalation exposure is assumed to originate from the spraying

equipment that releases airborne droplets during boom spraying (BS) activities. The inhalation

model focuses on droplet spray and predicts the air concentration of droplets in the breathing zone

of the operator. The dispersion process behind a moving vehicle is complex, and the available

mechanistic models that are used in drift prediction (e.g. individual drop trajectory and Gaussian

models) are not suitable to predict transfer from the boom to the vehicle. However, these models

may be useful to estimate the amount (or fraction) of airborne spray at the boom.

To estimate inhalation exposure of operators during BS, the transfer mechanisms associated with

wake and plume are considered (see Appendix A).

Proposed model:

The model consists of three parts that represents the three consecutive phases of the source-

receptor model:

(i) Local airborne spray emission (ASE)

12

(ii) Dispersion from boom to vehicle (to estimate Cvehicle)

(iii) Dispersion from vehicle to operator using cabin efficiencies (If)

More details of input variables of the model are presented in Tables 2 and 3.

(i) Local airborne spray emission (ASE)

The amount of airborne spray around the vehicle is divided into (and the sum of) two local airborne

spray emission estimates (ASEwakein & ASEplumein). The spray emission rate (in L/s) implicitly takes

account of the spraying time and assumes that the resulting air concentration is steady over time.

It describes the dispersion from the boom to the vehicle via wakes (ASEwakein) (boom nozzles directly

behind the vehicle-sprayer) and via a plume (ASEplumein) (boom nozzles located on outer boom part).

Mass balance inputs to estimate the ASE is the spray volume rate (l/s), derived from the total

volume applied and the spraying time. The fraction of spray volume that remains airborne in close

proximity of the boom is expressed as a factor (fairborne spray). This estimate is obtained from the

droplet trajectory model (Browse WP3 model) and used to estimate the fairborne spray for different

droplet qualities, i.e. fine, medium, course and very course (Appendix A). The output of the model is

based on fixed variables for distance (at 2 meters downwind of the boom), at a worst case 90

degrees wind angle. The model also includes factors such as wind speed, boom height (above crop),

crop height and vehicle speed. The WP3 output is a factor that represents the estimated fraction of

airborne droplets. It is derived from the WP3 emulator by dividing the value by 2 and then adjusted

these values to be in line with the intended fairborne spray estimate.

(ii) Dispersion from boom to vehicle (to estimate Cvehicle)

To estimate the dispersion of spray from the boom to the operator, the previously described ASE

estimates are used as input. Both the mass balance inputs of ASEwakein and ASEplumein inputs are

affected by the presence of boom shields (bs), Table 7.

The dispersion of droplet spray by plume (ASEplumein) from the outer part of boom (not directly

affected by wake effects) is determined by a plume factor (pf) based on an estimate of the

probability of plume reaching the vehicle based on different wind speeds, wind directions, driving

directions and the fraction expected to reach the vehicle-sprayer (Appendix A). Both the ASEwakein

and ASEplumein estimates are assumed to be relevant as droplet spray around the vehicle-sprayer. The

dispersion of droplet spray around the vehicle-sprayer is determined by its size (height, width,

13

length) and the vehicle speed. The rationale behind this is that the area around the vehicle-sprayer is

continuously dispersed by the vehicle speed (vvehicle) through a surface area (i.e. the width and height

of the vehicle-sprayer, Avehicle) that is perpendicular to the driving direction (Tables 4 and 5). The

length of the vehicle-sprayer (d), distinguished as vehicle-mounted/self-propelled and trailer-

mounted rigs, represents the distance between the vehicle and the boom (Table 6).

All these parameters are used to estimate the concentration of droplet spray around the vehicle

Cvehicle (see algorithms).

(iii) Dispersion from vehicle to operator using cabin efficiencies (If)

Personal enclosures on vehicles (e.g. cabins) may affect the transfer of spray to the operator.

The categories proposed for cabins are presented in Table 8 (Appendix D).

(b) Algorithms

Ipde = Cvehicle * If * Ccoi

Ipde Potential inhalation exposure concentration to the active substance (in droplet

spray) (µg/m3)

Ccoi Concentration of a.s. in spray volume (µg/l)

Cvehicle Concentration of droplet spray at the vehicle (l/m3)

If Cabin efficiency (factor)

where

Cvehicle = (ASEwakein + (ASEplumein ∗ 𝑝𝑓)) ∗ 𝑏𝑠

Avehicle ∗ vvehicle ∗ 𝑑

ASEwakein flux of spray from boom nozzles in close proximity of vehicle affected by wake (l/s)

ASEplumein flux of spray from nozzles further along the boom affected by a plume effect (l/s)

pf fraction of plume that reaches the vehicle-sprayer

bs effect of boom shielding

Avehicle surface area perpendicular to the vehicle’s driving direction where air exchange

occurs (m2) based on w (width of vehicle-sprayer) and h (height of vehicle-sprayer)

14

vvehicle vehicle speed (m/s)

d vehicle-sprayer length categories based on vehicle-mounted and self-propelled (VM)

and trailer-mounted (TM) rigs

With

ASEwakein = AR ∗ fairborne spray ∗wlb

And

ASEplumein = AR ∗ fairborne spray ∗𝑙𝑏 − 𝑤2 ∗ 𝑙𝑏

AR spray volume rate (L/s)

fairborne spray fraction of spray volume that remain airborne in close proximity of the boom

immediately after spray emission (factor) [from WP3 model] where:

• airChild = (emulatorValue / 2#) * vehicle speed (m/s)/ nozzle flow rate (ml/s) / number of

nozzles

• airAdult = (emulatorValue / 2#) * 2 *vehicle speed (m/s)/ nozzle flow rate (ml/s) / number

of nozzles

• fAirborneSpray = airAdult – airChild

w width of vehicle-sprayer (= 2 times vehicle/sprayer width, m)

lb total boom width (m)

Note: if w>lb, then w=lb where the boom width (lb) is assumed true (it is therefore

reasonably assumed that the boom width is at least the width of the virtual space

(2x vehicle-sprayer width) – in order to avoid using negative values)

# a fixed default value is applied assuming a 90 degree wind angle, equivalent to

following wind for the operator, divided by 2 on the assumption that the wind will

only be following for half of the time as the tractor will either have a head wind or

tail wind.

(c) Sensitivity analysis

Sensitivity analysis (SA) was carried out on the output representing potential dermal exposure on

the body, for the boom spraying and mixing/loading model. SA is designed to show which inputs or 15

groups of inputs has the greatest impact on the output, given their individual ranges and variability.

SA can also show the nature of the input/output relationships and provided a useful check on the

model behavior. For this reason, it has been used as part of our testing process to assess whether

the input/output relationship is realistic for individual modules. An example of this is given below in

Figure 2. The inputs which have the most influence are wind speed (m/s), concentration of a.s. in

spray volume (g/l), spray volume rate (l/s) and cabin factor. As the first three increase, the

log(exposure) generally increases. For cabin factor, as the cabin factor increases from no cabin to a

cabin with pressurized/filtered ventilation, the log(exposure) decreases.

For this example, the partitioning of output variance is also presented in Table 9. This partitioning

shows how each of the variability distributions, assigned to individual model inputs, contributes to

the induced total output variation. SA contributes to the testing of the model, in addition to the

more detailed testing described below. The details of the SA method with results from the latest

Browse model and some conclusions are given in Appendix M.

(d) Model testing

Please refer to the type of plots/graphs and explanation under item 3.2.

For the testing of the BS inhalation model, the value of boom shielding (bs) was fixed at 1, assuming

the absence of boom shielding in the data. Figure 3 shows the model estimates plotted against all

the available data (including all imputed datasets). Using a different plot type, Figure 4 indicates

whether the data concerns vehicle-(rear-mounted) and self-propelled sprayers (circles) or trailer-

mounted sprayers (squares). The output suggests that the model, in some cases, under-predicts for

trailer-mounted sprayers and over-predicts for vehicle/self-propelled sprayers. However, most of

these outputs are based on imputed data (blue) and this assumption may therefore not be

conclusive.

Figures 5 and 6 show the performance of the model using complete datasets only (with only boom

shielding imputed with a fixed value), which indicates that the model outputs fit better with the

complete datasets than the imputed datasets.

16

(e) Tables

Table 2: Boom spraying inhalation model - model inputs*

No Inputs Lower Upper Type Based on 1 Concentration of a.s. in spray

volume (g/l) 0.05 50 selected value BROWSE data

2 Spray volume rate (l/s) 0.01 1.5 selected value BROWSE data 3 Cabin factor (3 categories) 0.0001 1 Normal Literature review

(Appendix D) 4 Vehicle sprayer type

height*width (8 categories) 2.4 32 selected value Different sources

(Appendix B) 5 Vehicle or trailer mounted –

length/distance (3 categories) 0.01 1 Uniform DEFRA SID5 report

(Appendix B) 6 Boom Height above crop (m) 0.2 1.2 selected value WP3 & BROWSE data 7 Boom width (m) 2 45 selected value WP3 & BROWSE data 8 Wind speed (m/s) 0.5 10 selected value WP3 & BROWSE data 9 Plume factor NA NA fixed at 0.01 Expert estimate

(Appendix A) 10 Forward speed (km/h) 4 25 selected value WP3 & BROWSE data 11 Boom shielding# NA NA fixed at 0.7 if boom

shielding present and 1 if absent

Literature review (Appendix C)

* separate models developed using (i) different spray qualities (fine, medium, course, very course) and (ii) different crop heights (0.1, 0.5, 1.0, 1.5m)

# fixed at 1 for the testing in the absence of contextual data Table 3: Boom spraying inhalation model - input variables

Input variables Unit User input Model input From/for Concentration of a.s. in spray volume

g/l User input User input Mass balance input

Cabin factor n/a (i) No cabin (ii) Cabin without

pressurized/filtered ventilation

(iii) Cabin with


1 Sample from U(0.1, 0.25) with probability 0.25, from U(0.25, 0.5) with probability 0.5 and from U(0.5, 0.9) with probability 0.25 Sample from U(0.0001, 0.05) with probability 0.25, from U(0.05, 0.15) with probability 0.5 and from U(0.15, 0.3) with probability 0.25

Literature review Table 7 Appendix D

Wind speed km/h From WP3: 1-20 km/h User input converted for use in WP3 model

WP3 model (to estimate fairborne spray)

17

Input variables Unit User input Model input From/for Boom height above crop

M From WP3: 0-1.2 m range for users to choose from

User input WP3 model (to estimate fairborne spray)

Crop Height M From WP3: User can choose from 0.1, 0.5, 1.0, 1.5

Use selected value WP3 model (to estimate fairborne spray)

Volume applied L Estimated from total area sprayed, dose, final mixing & loading concentration

Estimated from total area sprayed, dose, final mixing & loading concentration

As input for spray volume rate (AR)

Spraying time Min Estimated from total area sprayed, boom width and vehicle speed

Estimated from total area sprayed, boom width and vehicle speed (((areaSprayedHa*10000)/(totalBoomWidthM*dForward_SpeedKMH*16.67)*1.25))

As input for spray volume rate (AR)

Vehicle-sprayer width

M Categories: S, M, L, U, sSP, mSP, lSP, O

Sampled from respective categories

Literature review Table 4 Appendix B

Vehicle-sprayer height

M Categories: S, M, L, U, sSP, mSP, lSP, O

Sample from respective categories


Vehicle-sprayer length/distance

M Categories: vehicle-mounted and self-propelled (VM), trailer-mounted (TM), unknown (O)



Boom width (m)

M User input User input To estimate ASEwakein and ASEplumein (see algorithm)

Forward speed km/h 4-25 km/h User input WP3 model and dispersion estimate

Droplet Size n/a (i) Fine (ii) Medium (iii) Coarse (iv) Very coarse

Use selected droplet size WP3 model (to estimate fairborne spray)

Wind Angle ° 90° Fixed Emulator value will be divided by 2 for adult and child before taking the difference


Number of nozzles

- None For WP3 estimate: Total number of nozzles calculated with nozzle spacing and number of passes. Estimated from rounding down (boom width*2)


18

Input variables Unit User input Model input From/for Distance used to estimate fairborne spray)

M None Fixed at 2m downwind for WP3 estimate


Plume factor - None Fixed at 0.01 Expert estimate Appendix A

Boom shielding - User input Shielding absent (1) Shielding present (0.7)

Literature review Table 7 Appendix C

Table 4: Vehicle widths categories

Vehicle type* Codes Width of the vehicle-sprayer (m)1

Width of vehicle-sprayer relevant for wake (m)2

Range (m)

Small S 1.3 3 2.0-3.0

Average, Unimog, small&medium self-propelled

M, U, sSP, mSP 2.3 5 4.0-6.0

Large, large self-propelled L, lSP 3.3 7 6.0-8.0

Unknown O - - 2-8 1 proposed width of vehicle 2 ~2 times vehicle width (to take account of area around sprayer affected by wake effects) * The following vehicle-sprayers are included in the model (Appendix B): S = quad, golf cart, mini tractor (e.g. John Deere compact 1 series) M = average-sized tractor (e.g. John Deere 2 series) L = large-sized tractor, 4WD (e.g. John Deere 9R/RT series) U = unimog or similar sSP = small self-propelled (≤1000L tank) mSP = average sized self-propelled (>1000-3000L tank) lSP = large self-propelled (≥3000L tank) O = unknown

Table 5: Height dimensions of vehicles used for boom spraying

Vehicle type Codes Height (m) Range (m)

Small S 1.5 1.2-2.0

Average, Unimog, Small&medium self-propelled M, U, sSP, mSP 2.5 2.0-3.0

Large, large self-propelled L, lSP 3.5 3.0-4.0

Unknown O - 1.2-4.0

19

Table 6: Length / distance categories based on vehicle and spraying rig configurations1

1 based on literature review (Appendix B) ^ vehicle-sprayer configurations with front-fitted booms (>0m) are currently assigned with a fixed value

of 1, assuming no reduced effect compared with drawn booms

Table 7: Proposal of multipliers/factors for boom shields1

Category Description % Drift reduction Range Multiplier

A No shields 0 n/a 1

B Boom shields (any type) 30 10 – 70 0.7

1 based on literature review (Appendix C) Note: For testing, the current dataset used for model development has no information on boom shields. For

now, a factor of 1 are assigned to all data entries

Table 8: Proposal cabin categories and associated multipliers/factors1

Category Description % Cabin efficiency Range (%) Range (factor)

Lower Upper A No cabin 1 n/a Fixed 1 B Cabin without

pressurized/filtered ventilation, OR with ventilation but without filtration

60 10 - 90 0.1 0.9

C Cabin with pressurized / filtered ventilation*

90 70 - >99 0.0001 0.3

1 based on literature review (Appendix D) * criteria will be used in the user interface to verify ventilation & filtration compliances (ASAE S-525)

Type Code Length, distance^ (range, m)

Range (factor) *

Lower Upper

Front-fitted booms FB 0 Fixed 1.0 -

Vehicle-mounted and self-propelled VM 1-6 0.1 0.9 Trailer-mounted TM 4-12 0.01 0.25

Unknown O 0-12 0.01 1.0

20

Table 9: Effect of variance explained by the model inputs (for fine nozzle, crop height 0,1m only)*

Number Input Total effect of

variance individually (%)

Total effect of variance including joint effects

(%) 1 Concentration of a.s. in spray volume (g/l) 18.70 20.37

2 Spray volume rate (l/s) 17.14 19.29

3 Cabin factor 13.46 13.92

4 Boom Height above crop (m) 6.25 7.49

5 Boom width (m) 0.29 0.35

6 Wind speed (m/s) 30.49 32.70

7 Vehicle sprayer 4.56 6.12

8 Vehicle or trailer mounted 0.06 0.07

9 Plume factor 0.88 1.01

10 Forward speed (km/h) 0.91 1.10

11 Boom shielding 2.26 3.31

* Please note that GEM-SA may be outdated. The SA is intended for continuous inputs, so re-runs required for each categorical input

21

(f) Figures

Figure 2: Example of SA output: Plot of main effects for crop height 0.1m and fine nozzle type

W

W

W

W

W

W

W

W

WW

W

0.0 0.2 0.4 0.6 0.8 1.0

45

67

standardised input value

mai

n ef

fect

C

C

C

C

C

C

CC C C

C

A

A

A

A

A

A

AA A A A

F

F

F

F

F

F

F

F

F

F

F

Mean effect95% probability intervalLeast important mean effects

W Wind speed C Concentration of a.s. in spray volume A Spray volume rate (l/s) F Cabin factor

22

Figure 3: Inhalation exposure: Plot of log10(all data*) (measured potential inhalation exposure all

data*) and log10(geometric mean(model outputs)) *including results based on datasets where some inputs were imputed datasets (blue) and results where all inputs were known (apart from boom shielding) (red).

0.001 0.01 0.1 1 10 100 1000 10000 1000000.001

0.01

0.1

1

10

100

1000

10000

100000

log10 (Geometric mean(Model outputs(µg/m3)))

log 10

(Mea

sure

d po

tent

ial i

nhal

atio

n ex

posu

re(µ

g/m

3 ))

23

Figure 4: Inhalation exposure: Plot of 95% CIs (grey lines), geometric means (red lines) and medians

(black dashed lines) for the log10(model outputs) with the log10(measured potential inhalation exposure) [blue=where some inputs were imputed; red=complete set of inputs to the model except for fixed boom shielding imputation; circles=vehicle-mounted and self-propelled; squares=trailer-mounted]. The index refers to the ordering of the model outputs and measured values when sorted on the medians of the model outputs.

0 20 40 60 80 100 120 140 160 1800.0001

0.001

0.01

0.1

1

10

100

1000

10000

100000

Index

log 10

(Pot

entia

l inh

alat

ion

expo

sure

(µg/

m3 )

)

24

Figure 5: Inhalation exposure: Plot of 95% CIs (grey lines), geometric means (red lines) and medians

(black dashed lines) for the log10(model outputs) with the log10 (measured potential inhalation exposure [red dots represent complete sets of model inputs except for fixed boom shielding imputation]). The index refers to the number assigned to the model outputs and measured inhalation exposure when they are sorted based on the measured inhalation exposure.

0 10 20 30 40 50 600.0001

0.001

0.01

0.1

1

10

100

1000

Index

log 10

(Pot

entia

l inh

alat

ion

expo

sure

(µg/

m3 )

)

25

Figure 6: Inhalation exposure: Plot of 95% CIs (grey lines), means (red lines) and medians (black

dashed lines) for the log10(model outputs) with the log10 (measured potential inhalation exposure for datasets which had a complete set of inputs except for fixed boom shielding imputation = red dots) The index refers to the sorting on the medians of the model output.

3.2.2 Boom spraying dermal model

(a) Description

The boom spraying dermal models consist of a hand model (Dph) and whole body model (Dpw). The

following activities of an operator during the boom spraying scenario are included:

• Operating the vehicle (vehicle cockpit with & without cabin)

• Stepping into/out of vehicle (contact with the vehicle interior & exterior)

• Incidental activities with the spraying rig, e.g. mounting/dismounting the rig, manual (un-)

folding of boom, maintenance of nozzles.

The models consider important determinants of dermal exposure associated with field sprayer

operations, e.g. type of spraying equipment (trailer, rear-mounted), number of unplugged nozzles

and number of application tasks (Lebailly et al, 2009). As high dermal exposures are associated with

0 10 20 30 40 50 600.0001

0.001

0.01

0.1

1

10

100

1000

Index

log 10

(Pot

entia

l inh

alat

ion

expo

sure

(µg/

m3 )

)

26

incidental activities with contaminated spraying rigs and nozzles (as opposed to the vehicle cockpit),

the model includes both the vehicle cockpit and spraying rig as sources of exposure. The cleaning of

vehicles and spraying equipment are not included as a parameter in the models because the effect

of this activity on exposure is not known.

Hand model

The hand model considers two routes of exposure, i.e. surface contacts and deposition. An estimate

of exposure from surface contacts is based on two key parameters as described by Gorman et al

(2012), i.e. frequency of contacts and surface contamination levels. The estimate of deposition onto

the hands is based on the inhalation estimate (Ipde). The input parameters and ranges of the models

are presented in Table 10.

(i) Frequency of surface contacts

The hand model applies event frequencies to provide an indication of surface contact frequencies.

The following event frequencies are included in the hand model (see Table 10):

• Surface contacts with the vehicle cockpit is represented by events of stepping into/out of the

vehicle, i.e. the number of mixing & loading periods (Fmlp) and number of times leaving the

vehicle during trouble-shooting or nozzle maintenance (Fnr)

• Surface contacts with the delivery system/rig is represented by (i) trouble-shooting and nozzle

maintenance events (Fnr) and (ii) incidental contacts such as (de-)mounting and boom

(un)folding prior to and after spraying (Frig)

The mixing & loading periods (Fmlp) are estimated from the volume applied and the tank volume. The

volume applied parameter is estimated from user inputs and are based on the total area sprayed

(Ha), dose (kg/Ha) and final mixing & loading concentration (g/l).

To determine the frequency of nozzle maintenance (Fnr), information on the frequency of trouble

shooting/nozzle maintenance during boom spraying (e.g. Lebailly et al, 2009; Bell & Lloyd, 1988) was

consulted. For the BS dermal hand model, a default of 3 nozzle maintenance events during spraying

operations is proposed. When testing the model with data (with no information of nozzle

maintenance frequencies), it is assumed that contact events with the nozzles during spraying (Fnr)

are between 0 and a worst case of 15, with a 90% probability of between 0 and 5 and 10%

probability of between 6 and 15.

27

Other events that may occur prior to or after spraying activities include nozzle replacement, (de-)

mounting of the rig and manual boom (un)folding. A fixed event frequency with the rig and nozzles

before and after spraying (Frig) is set at 1. Frig are not included as user input assuming that this is not

a known input.

These values may be updated with evidence of frequencies of different activities during boom

spraying with different vehicle-sprayer types (e.g. Lebailly et al, 2009).

(ii) Surface contamination levels

The surface contamination level (µg/cm2 of a.s.) on field sprayers are highly variable and limited

evidence is available on the determinants influencing the deposition on vehicle and sprayer surfaces

of field sprayers. Ideally the surface contamination levels should be derived from data for different

vehicle-sprayer configurations, different spray qualities, etc. However, this information is only

available in a few specific settings (e.g. Balsari & Marucco, 2003). As result, it was decided to use a

broad range of contamination levels as input for the model (Table 11 & Appendix E). For this

purpose, contamination levels are based on three ‘Volume applied’ categories based on the

percentage of the total spray volume that deposits on the vehicle and delivery system.

Other parameters included in the model algorithm are spray quality, wind speed, sprayer type (or

distance/length) and cabins.

Spray quality is considered to distinguish between flat fan low drift nozzles (VMD=220um) and flat

fan conventional nozzle (VMD=165um) (Balsari & Marucco, 2003). For wind speed, a distinction is

made between wind speeds of <1m/s and >1m/s, assuming an increase in external contamination

with higher wind speed (van de Zande et al, 2007).

Self–propelled sprayers are known to have significantly higher external contamination levels

compared to vehicle-mounted and trailer-mounted spraying rigs (Ramwell, et al, 2004). Other field

trials indicate that dermal exposure during boom spraying applications are higher among farmers

using rear-mounted sprayers compared to trailer-mounted sprayers (Lebailly et al, 2009). In the

absence of conclusive evidence, it is assumed that the distance between the boom and operator

affects dispersion and deposition. For this purpose, the model distinguishes between vehicle-

mounted and self-propelled sprayers (VM) and trailer-mounted sprayers (TM). As the surface

contamination level estimate on the vehicle cockpit concerns the vehicle exterior, a cabin efficiency

factor is also included. The effect sizes proposed for distance (vehicle- & trailer-mounted) and cabin

28

efficiency in the boom spraying inhalation model are adopted for this model (Tables 6 and 8

respectively).

In addition, the transfer efficiency from surfaces to body parts (Cf) (Appendix H), body surfaces areas

(BP) and the affected surface area of body parts (lcn/BP) (Appendix I) are included in the algorithm.

The surface-to-hand transfer efficiency (Cf) is corrected to account for multiple contacts, for both

contacts with the cockpit (Cftot_cockpit) and rig (Cftot_Rig) (Appendix J).

(iii) Deposition

Deposition on the hands is estimated by using the inhalation estimate as input (Ipde), while

considering the droplet settling velocity (vdep) and application time (t) (see table 10). One side of

both hands is considered relevant as the affected surface area of the hands (50%).

Whole body (excl. hands) model

To estimate the whole body dermal exposure, the hand model is used with an extrapolation based

on a review of body part distributions during boom spraying activities (Appendix F). The

extrapolation distinguishes between the presence (Fnr >0) or absence (Fnr = 0) of hand exposure due

to trouble shooting and nozzle maintenance. The extrapolation assumes that in the absence of

trouble-shooting and nozzle maintenance (Fnr = 0), the hands are less contaminated compared to the

whole body (see algorithm).

(b) Algorithms

I Hand exposure model (Dph)

Dph= ((Sccockpit * Ccoi * Sq * Ws * d * If) * (Cftot_cockpit * BPhands*lcn/BPpalms) + (Scrig * Ccoi * Sq * ws) *

(Cftot_rig * BPhands*lcn/BPpalms) + Ddep

Where

𝐷𝑑𝑒𝑝 =𝐼𝑝𝑑𝑒 ∗ 𝑡 ∗ 𝑣𝑑𝑒𝑝 ∗ 𝐵𝑃ℎ𝑎𝑛𝑑𝑠 ∗ 𝑙𝑐𝑛

𝐵𝑃ℎ𝑎𝑛𝑑𝑠_𝑡𝑜𝑝

And

29

𝐶𝑓𝑡𝑜𝑡_𝑐𝑜𝑐𝑘𝑝𝑖𝑡 = � �𝐶𝑓 0.5(𝑘−1)�

𝐹𝑚𝑙𝑝+𝐹𝑛𝑟

𝑘=1

𝐶𝑓𝑡𝑜𝑡_𝑟𝑖𝑔 = � �𝐶𝑓 0.5(𝑘−1)�

𝐹𝑛𝑟+𝐹𝑟𝑖𝑔

𝑘=1

Dph potential dermal exposure to a.s. in µg on the hands

Dph_cockpit potential dermal exposure to a.s. in µg on the hands (from vehicle/cockpit surfaces)

Dph_rig potential dermal exposure to a.s. in µg on the hands (from rig, boom, nozzles)

Sccockpit surface contamination levels (l/m2) of vehicle/cockpit

Scrig surface contamination levels (l/m2) of rig

Ccoi concentration of a.s. in spraying volume (µg/l)

Sq droplet quality (SqFine = 2.8; SqMediumCourseVeryCourse = 1)

ws ws ≤ 1m/s (factor 0,5), ws > 1m/s (factor 1)

Fmlp number of mixing & loading periods (= tankfulls applied) (assuming that the operator

stepped in/out of the vehicle during M&L periods)

Fnr number of nozzle maintenance events (indicating both (i) frequency of stepping i/o

of vehicle (contacts with cockpit), and (ii) contacts with boom/nozzles)

Frig frequency of incidental contacts with the rig/boom excl. nozzle maintenance

(assuming a default of 1 contact event during (de-)mounting or boom folding) prior

to and after spraying

d distance effect (mounted/self-propelled (VM) and trailer mounted (TM) sprayers)

If cabin factor

BPhands total surface area of hands (m2)

lcn/BPpalms affected surface area of hands (m2 of BP) (palms)

Cf transfer efficiency for the grasping of smooth surfaces = triangular distribution with

lower value 0.23, mode 0.45 and upper 0.68

Cftot_cockpit corrected transfer efficiency for multiple contacts (maximum: 0.89 with Cf = 0.45;

Fmlp + Fnr = 6) (Appendix H)

Cftot_Rig corrected transfer efficiency for multiple contacts (maximum: 0.89 with Cf = 0.45; Fnr

+ Frig = 6) (Appendix H)

Ddep Potential dermal hand exposure to droplets via deposition (µg)

30

Ipde Inhalation exposure estimate from inhalation model - a.i. in air (µg/m3)

t Total application time (s)

vdep droplet settling velocity average deposition speed of droplets in the air (m/s)

lcn/ BPhands_top Affected surface area of hands (m2 of BP)

Rationale

(Fmlp + Fnr) represent the frequency of surface contact events with the vehicle cockpit (Sccockpit)

when stepping into/out of the vehicle during trouble-shooting and mixing & loading

periods

(Fnr + Frig) represent the frequency of surface contact events with the delivery system/rig (Scrig)

during trouble-shooting and incidental contacts ((de-)mounting, boom folding) prior


II Whole body (excl. hands) exposure model (Dpw)

The following equation is used to estimate the potential dermal exposure (Dpw) of the whole body in

µg:

Dpw = Dph * Ew

Dpw potential dermal exposure to a.s. in µg on the whole body (excl. hands)

Dph potential dermal exposure to a.s. in µg on the hands (from hand exposure model)

Ew extrapolation from hand exposure model (ew-nozzle or ew-nonozzle) to body exposure

ew-nozzle hand exposure incl. nozzle maintenance extrapolated to whole body

(=0,2), where Fnr >0; assuming 80% of total dermal exposure is

distributed on the hands and 20% on the whole body

ew-nonozzle hand exposure excl. nozzle maintenance extrapolated to whole body

(=0,35), where Fnr = 0; assuming 65% of total dermal exposure is

distributed on the hands and 35% on the whole body

Note: for testing purposes, Ew, is estimated using the Fnr as indicated in Table 10 (90% probability

between 0-5, and 10% probability between 6-15). Software: default 3

(c) Model testing

Please refer to the type of plots/graphs and explanation under item 3.2

31


The available data is not suitable for testing the BS hand model, because of data gaps of important

parameters. For example, the number of contact events with the nozzles is not known in the data.

Assuming a frequency of nozzle maintenance (Fnr) of 90% probability between 0 and 5 and 10%

probability between 6 and 15, the model predicts a conservative exposure compared to the broad

range of data (Figure 7). A default nozzle maintenance frequency is set at 3 in the software.

Unfortunately, inputting a distribution for nozzle events will not be feasible as the probabilistic

output of the model (using iterations) will not be able to produce a single estimate with repeated

runs of the model.


The whole body exposure model was compared with the dataset using the extrapolation of the hand

model (Figures 1).

(d) Tables

Table 10: Boom spraying dermal models - main user and model inputs* No User inputs Model inputs Lower Upper Type Based on 1 Concentration of a.s.

in spray volume (g/l) Concentration of a.s. in spray volume, Ccoi (g/l)

0.05 50 selected value BROWSE data

2 Volume applied (estimated from total area sprayed, dose, final mixing&loading concentration)

Volume applied (l), 3 categories

50 25000 selected value BROWSE data

3 n/a Cockpit surface contamination levels, Sccockpit (l/m2)

1E-4 5E-2 uniform distributions

Field trial data Table 11 Appendix E

4 n/a Rig surface contamination levels, Scrig (l/m2)

1E-2 2.5E-1 uniform distributions

Field trial data Table 11 Appendix E

5 Number of M&L periods (estimated with volume applied and tank size)

Number of M&L periods (tankfulls applied) (Fmlp)


6 Frequency of nozzle maintenance (Fnr);

Frequency of nozzle maintenance (Fnr);

0 15 skew (90% probability

Field studies

32

No User inputs Model inputs Lower Upper Type Based on user selected: 0 - 5; >5; default in software = 3

(With 90% probability between 0-5) (for testing with data only)

between 0 and 5 and 10% probability between 6 and 15)

7 n/a Frequency of contacts with rig prior to/after spraying (Frig)

Fixed at 1

- Fixed Expert opinion Field studies

8 Cabin factor (3 categories)

Cabin factor (3 categories with ranges)

0.0001 1 see inhalation model


9 Vehicle or trailer mounted (3 categories – VM, TM, O-unknown))

Vehicle-sprayer length (d) Vehicle-mounted (0.1 – 0.9) Trailer-mounted (0.01 - 0.25) Front-fitted booms fixed at 1

0.01

1 uniform distributions

Field trial data Table 6 Appendix B

10 Spray quality (sq) (4 categories)

Spray quality (sq)(4 categories); SqFine = 2.8; SqMedium / Coarse / VeryCoarse = 1

1 2.8 selected value Field trials

11 Wind speed (ws) Wind speed (ws) Ws ≤ 1m/s (factor 0,5), Ws > 1m/s (factor 1)

0.5 1.0 selected value Field trials

12 n/a Transfer efficiency from surface to the hands (Cf); e.g. 1st contact = 0.45; 2nd-6th contact (*0.5); see text

0.23 0.68 triangular distribution between 0.23 and 0.68 with a mode at 0.45

IOM database Literature review Appendix H & J

13 Gender (male, female) Surface area of hands (BP) (m2)

- - lognormal distributions (Males: log(SA) ~ Normal (-2.2319, 0.109922) Females: log(SA) ~ Normal(-2.4313, 0.10362))

Appendix I

14 n/a Fraction affected surface area of hands (palms) (lcn/BPpalms)

Fixed 0.5

- Fixed Literature review Appendix I

15 From BS inhalation Inhalation exposure / See previous

33

No User inputs Model inputs Lower Upper Type Based on model air concentration, Ipde

(µg/m3) section

16 n/a Time (s), t

(spraying time)

Fixed at user input

Derived from background calculations (Appendix….)

17 n/a Droplet setting velocity, vdep

0.078 0.313 Uniform Example: Nuyttens et al. 2009

18 n/a Affected surface area of hands (back of hands), (lcn/BPhands_top)

Fixed 0.5

Literature review Appendix I

19 n/a Body model (extrapolation from hands); with nozzle maintenance (Fnr>0) and without nozzle maintenance (Fnr=0)

0.2 0.35 fixed to 0.2 if Fnr > 0 or 0.35 if Fnr = 0 (based on 2 Fnr situations)

Literature review Appendix F

* Models distinguish between Volumes applied and contamination levels of different surfaces

Table 11: Surface contamination levels using Volume Applied categories

Category Volume applied Contamination level cockpit, Sccockpit (l/m2)*+

Contamination level delivery system/rig, Scrig (l/m2)*^

Lower Upper Lower Upper Lower Upper 1 <50 1000 1.0E-4 5.0E-3 1.0E-2 2.5E-1 2 1000 5000 1.0E-4 2.50E-2 1.0E-2 2.5E-1 3 5000 >10000 1.0E-4 5.0E-2 1.0E-2 2.5E-1

* broad categories extrapolated from field sprayer and orchard sprayer contamination studies using the % of total spray volume depositing on the vehicle and delivery system (e.g. Balsari & Marucco, 2003, Ramwell et al, 2005, Michielsen et al, 2012).

+ for Sccockpit, a lower estimate of category 2 is adopted for categories 1 and 3 to account for a minimum contamination level at the start of the operation

^ for Scrig, categories 2 is used as reference for categories 1&3 to account for surface loading on the rig and surface loading over time. A worst case highly contaminated delivery system/rig contamination (incl. potential dripping from nozzles) is applied as an upper range for all categories

34

(e) Figures

Figure 1: Hand exposure: Plot of log10(measured potential dermal exposure (hands)*) and log10(geometric mean(model outputs)) (assuming a 90% probability of 0-5 nozzle maintenance events) *including data sets which have model inputs that were imputed

35

Figure 2: Whole body exposure: Plot of log10(measured potential dermal exposure (body) *) and

log10(geometric mean(model outputs)) * including data sets which have model inputs that were imputed

36

3.2.3 Mixing and loading inhalation model

(a) Using the ART model

Although often considered to be negligible, inhalation exposure to pesticides during the mixing &

loading phase cannot be ruled out, since pure formulation is handled. Therefore, inhalation

exposure will also be modeled for the mixing/loading scenario. Until now only a limited amount of

specific measurement data is available for this activity. However, the activities as performed during

mixing/loading are considered to be comparable as the ones performed with chemicals in general,

for which an exposure model for the estimation of inhalation exposure exists, namely the Advanced

REACH Tool (ART) (www.advancedreachtool.com), which is developed based on a mechanistic model

for inhalation exposure. Therefore it was decided to use this model to estimate inhalation exposure

during mixing/loading activities with PPPs.

The ART mechanistic model is based on a conceptual framework that adopts a source receptor

approach, which describes the transport of a contaminant from the source to the receptor and

defines seven independent principal modifying factors: substance emission potential, activity

emission potential, localized controls, segregation, personal enclosure, surface contamination, and

dispersion. ART currently differentiates between three different exposure types: vapours, mists, and

dust (fumes, fibres, and gases are presently excluded). Various sources were used to assign

numerical values to the multipliers to each modifying factor. The evidence used to underpin this

assessment procedure was based on chemical and physical laws. In addition, empirical data obtained

from literature were used. Where this was not possible, expert elicitation was applied for the

assessment procedure. Multipliers for all modifying factors were peer reviewed by leading experts

from industry, research institutes, and public authorities across the globe. In addition, several

workshops with experts were organized to discuss the proposed exposure multipliers (Fransman et

al., 2011).

37

The model consists of one algorithm to estimate the contribution from near-field (NF) [equation 1]

and one for estimating the contribution from far-field (FF) sources [equation 2]. Personal exposure

from a near-field source (Cnf) is a multiplicative function of substance emission potential (E), activity

emission potential (H), (primary) localized control (LC1), secondary localized control (LC2; in case two

localized controls are used simultaneously, and dispersion (D). The algorithm for a far-field source

(Cff) also includes segregation (Seg) and personal enclosure/separation (Sep).

The level of surface contamination (Su) for each activity depends on the location of the source, i.e.

whether there is (i) a near-field source only [equation 3], (ii) a far-field source only [equation 4], or

(iii) both near- and far-field sources [in which case the surface contamination in the near-field is

assumed to dominate that of the far-field, see equation 3].

38

Subsequently, the overall exposure is estimated by algorithm equation (5). The algorithm considers

multiple activities [and exposure time (texposure)] within an 8-h work shift (ttotal) and also allows

periods with assumingly zero exposure (tnon-exposure).

With regard to dispersion, the following assumptions are made:

• In case of outdoor mixing/loading, it is assumed that this takes places close to buildings

• In case of mixing/loading under a shelter/covering/roof, it is assumed that these conditions are

comparable to ‘outdoors – close to buildings’

• In case of indoor mixing, only good natural ventilation is assumed

For use within BROWSE, the above algorithms are included in the total algorithm for the estimation

of exposure during mixing loading. To do so, the relevant modifying factors within the ART

mechanistic model were considered relevant for the mixing & loading scenario. If possible, defaults

were chosen, and to do so, the relevant modifying factors per exposure scenario were identified.

It is assumed that far field exposure is not relevant for mixing & loading PPPs, since in practice one

operator is performing all the activities, and thus no secondary exposure is assumed. Also the

distance of the operator to the source is assumed to be <1 meter.

For the estimation of inhalation exposure with the ART model, it is necessary to take into account

the duration of the various activities that together form the mixing & loading scenario. To be able to

do so, assumptions are made with regard to the relative contribution of the various activities

(transport, opening/closing, decanting) to the total duration of a mixing & loading period, both for

solids and liquids (Table 14).

(b) Inhalation exposure estimates and testing

The inhalation model was not tested using the exposure dataset. However, the model has been

calibrated as described in Schinkel et al. (2011). The mechanistic model output provides a

(dimensionless) relative score for the GM exposure of a scenario and is fitted to exposure

39

measurements to ‘translate’ these scores to a quantitative exposure estimate in mg/m3 with the

following equation:

Yijk is the exposure level for the kth measurement within the jth company in the ith scenario. Xijk is the

ln-transformed exposure level; ln(α) is the intercept (natural logarithm of the slope on the natural

scale); δi represents the random effect of the ith scenario, cij represents the random effect of the jth

company in the ith scenario and εijk is the residual error term. It is assumed that δi, cij and εijk values

are normally distributed with mean equal to zero and variances representing the between-scenario,

between-company (or work-site), and within-company components of variance. The companies are

nested within scenarios. With this method the relative ART mechanistic model scores are still

proportional to actual exposure levels and importantly the effects of individual MFs are preserved.

The intercept (ln(α)) represents the estimated exposure if the ART model score is 1.

The calibration results presented in Table 15 are used to estimate inhalation exposure for mixing &

loading activities based on the relative ART-scores for specific scenarios (based on broad model).

The calibration provided insight into the uncertainty of the estimated GM for specific scenarios. This

uncertainty is expressed as an UF, and this UF is used to calculate confidence limits around the

estimated GM exposure.

The analyses indicate that the model could estimate with 90% confidence GM exposure levels within

a factor between two and six of the measured GM exposure levels (depending on the form of

exposure). The total percentage of explained variance is 61% for the abrasion exposure form (not

stated here), 64% for dust (non-abrasive), 60% for vapours (not stated here) and 30% for mist.

Two models were developed and presented below, i.e. ML liquids and ML solids.

3.2.3.1 Mixing and loading liquids

Main model and user inputs are presented in Tables 12 and 13. It is assumed that only low-volatile

substances are used in liquid formulations (in ART framework cut off point: vapour pressure <10 Pa),

and thus only exposure to mists is taken into account.

In case of mechanical application, inhalation exposure during the mixing of the diluted formulation

in the tank of the spray equipment is considered negligible, since it is assumed that this occurs in the

40

far field, with low-volatile substances, with relatively undisturbed surfaces (no aerosol formation)

and a limited open surface area (opening tank).

In case of liquid formulations it is assumed that a liter of liquid formulation weighs one kg

(comparable to water), to be able to use the concentration of active substance in the formulation (in

g/L) as the weight fraction (g/kg).

3.2.3.2 Mixing and loading solids

Main model and user inputs are presented in Tables 16. Dustiness classifications in ART were

adapted for pesticide formulations as shown in Table 17.

(a) Tables

Table 12: Mixing & loading liquids inhalation model - main user and model inputs*

No Model inputs Lower Upper Type Based on 1 Concentration of a.s. of formulation in

container/packaging, Cfcoi

(µg/l) 5

1000 selected value *

2 Viscosity (fixed medium) Fixed 0.3 - Fixed * 3 Partial vapour pressure - - fixed

10Pa/30000Pa *

4 M&L activities (3 categories) – (i) transport; (ii) opening & closing; (iii) emptying & pouring

- - Fixed *

5 Splash loading (emptying) Fixed 3 - Fixed * 6 Contaminated surface area (0,3-1m2)

(transport, opening&closing) Fixed 0.001 - Fixed *

7 Duration of activities (based on number of containers used * default time)

- - selected value *

8 (i) Indoor (natural ventilation, large room) or outdoors

(ii) Indoor (natural ventilation, small room) (iii) Outdoors (only close to building or under

shelter)

0.75 2.7 selected value *

9 Localized controls ((i) extraction (canopy hood) (0.5); (ii) extraction (other) (1); (iii) none (emptying only) (1), default = 1

0.5 1 selected value *

10 Use rate (l/min) ((based on total number of containers used * container size (l) / duration of activities (min)) (emptying only)

0.01 0.001 selected value *

11 Level of contamination (transport & opening/closing) (default of <10% of surface)

Fixed 0.1 - Fixed *

12 Level of containment (emptying only) 0.3 1 selected value * * Fransman et al 2011; Schinkel et al, 2011; Tielemans et al 2008 Table 13: Default values used for M&L (liquids) inhalation (ART) model for three activities

41

Parameter Description Transport Opening & dosing Emptying / pouring

Viscosity Low 1 1 1

medium 0,3 0,3 0,3

Use rate 10-100 l/min - - 0,01

1-101 l/min - - 0,003

0,1-1 l/min - - 0,001

Level of containment Open process - - 1

Product-to-air interface - - 0,3

Type of application Splash loading - - 3

Contaminated surface area 0,3-1m2 0,001 0,001 -

Level of contamination 10-90% of surface 0,3 0,3 -

<10% of surface 0,1 0,1 -

Localized controls None 1 1 -

Local ventilation - - 0,5

Dispersion Indoor large room 0,9 0,9 0,9

Indoor small room 2,7 2,7 2,7

Outdoor close to buildings 0,75 0,75 0,75

Surface contamination Housekeeping practices 0,01 0,01 0,01

Table 14: Use of duration in the M&L (liquids) inhalation model

Scenario/ activity Activity class ART Total mix / load period Assumed duration (in min)

Transport Handling of contaminated objects 1 min per event 1 min per container Opening / closing Handling of contaminated objects 0.5 min opening, 0.5

min closing 1 min per container

Pouring / emptying Transfer of liquid products – falling liquids 1.5 min per event 1.5 min per event

Table 15: Calibration results of the ART model

Exposure form ln(α) σ2bs (95% CI) a σ2

bc (95% CI) b σ2residual (95% CI) c σ2

total d

Dust 3.01 0.81 (0.25-1.36) 0.38 (0.12-0.64) 2.29 (2.03-2.55) 3.48

Mist 10.23 1.14 (0.17-2.10) 1.65 (0.94-2.36) 2.62 (2.06-3.18) 5.41 a between-scenario component of variance, CI = confidence interval b between-company component of variance c residual error component of variance d total variance

42

Table 16 Mixing & loading solids inhalation model - main user and model inputs*(see Appendix O for detailed information) No Model inputs Lower Upper Type 1 Concentration of a.s. of formulation in container/packaging,

Cfcoi

(µg/kg) Sample data Sample data selected value

2 M&L activities (3 categories) – (i) transport; (ii) opening & closing; (iii) emptying & pouring

- - fixed

3 Dustiness (see Table 17) 0.01 1.0 selected value 4 Moisture content Fixed 1 - fixed 5 Level of contamination - handling of slightly contaminated

(layers of less than few grams) objects (transport, opening & closing)

Fixed 0.03 - fixed

6 Carefulness of handling (transport, emptying); routine/normal Fixed 1 - fixed 7 Dropping height (drop height ≥ 0.5 m); emptying only Fixed 3 - fixed 8 Level of containment (open) Fixed 1 - fixed 9 Duration of activities

(based on number of containers used * default time) (see Table 3)

- - selected value

10 (i) Indoor (natural ventilation, large room) or outdoors (0.8991)

(ii) Indoor (natural ventilation, small room) (2.7) (iii) Outdoors (only close to building or under shelter) (0.75)

0.75 2.7 selected value

11 Localized controls ((i) extraction (canopy hood)(0.5); (ii) extraction (other) (1) ; (iii) none (emptying only), default = none (1)

0.5 1 selected value

12 Use rate (l/min) ((based on total number of containers used * container size (l) / duration of activities (min)) (emptying only) (i) Transferring 10-100 kg/min (3) (ii) Transferring 1-10 kg/min (1)

1 3 selected value

13 Surface contamination (housekeeping practices) Fixed 0.01 - fixed * Fransman et al 2011; Schinkel et al, 2011; Tielemans et al 2008

Table 17 Dustiness classification for M&L (solids) inhalation model

Description Classification (data, user interface) All activities# (i) Firm granules, flakes or pellets

Macro granule (GG) 0,01

(ii) Granules, flakes or pellets

Fine granule (FG) Wettable granule (WG)* Water soluble granule (SG)*

0,03

(iii) Coarse dust 0,1 (iv) Fine/course dust or powder

Wettable powder (WP)* (Water-soluble) powder for dry seed treatment (DS, SS) Flo-dust (GP) Dustable powder (DP)

0,3 (default)*

(v) Extremely fine and light powder NA 1 # transport; opening & closing; emptying / dumping * fine/course dust is considered reasonable worst case, to be applied as default

43

3.2.4 Mixing and loading dermal models

3.2.4.1 Mixing and loading liquids

(a) Description

This scenario includes mixing and loading (ML) of liquids during the ground boom, broadcast, hand-

held spraying and aerial spraying operations. Based on the different M&L procedures and the

available information and data, the following models were developed:

• Open pour to tank top or induction bowl

- Hands model

- Whole body (excl. hands) model

• Transfer with a mechanical transfer/coupling devices (MTD) fitted on induction bowls, incl. closed

transfer systems (CTS)

- Total body model (hands & body)

The following activities of an operator during the mixing & loading scenario were considered in the

model development:

• Transport / collection of containers

• Opening and closing of containers

• Emptying and decanting

• Using premixing containers and measuring jugs

The models emphasize the principle route of exposure as surface contacts with containers used.

Deposition of droplets during ML scenarios is assumed negligible.

The models use quantitative values derived from experimental data as mass balance inputs, i.e. (i)

surface contamination levels and/or (ii) body part contamination levels. These inputs differentiate

between ML methods, container sizes and probability of spillages for single-event ML activities. To

develop a mechanistic algorithm, other important parameters are included to estimate operator

dermal exposure, e.g. concentration of a.s. in the formulation, frequency of surface contacts,

transfer efficiency from surfaces to body and affected body parts.

44

Model 1a: Open pour to tank top or induction bowl

I Hand exposure model

The algorithm used to estimate dermal exposure during mixing and loading is based on two key

parameters associated with surface contacts as described by Gorman et al (2012), i.e. frequency of

contacts and surface contamination levels. The input parameters and ranges of the models are

presented in Table 16. In order to incorporate exposure through splashes and dripping, the model

algorithm includes experimental hand contamination data of a standardized emptying procedure of

the respective container sizes. Dermal hand exposure is first estimated per ML period (Dph_mlp) in

µg/hands, which is assumed a summation of hand-to-surface exposure to the container exterior

(Dph_container-ext, see item ii) and direct emission from splashes or dripping (Dph_es, see item iii).

Subsequently, if more than one ML period is relevant, the model estimates a total hand exposure for

multiple ML periods, expressed as (see algorithms). For this purpose, the frequency of hand contacts

with containers are used (item i).

(i) Frequency of surface contacts


The following event frequencies are included in the hand model:

• Surface contacts based on the total number of containers used incl. re-use (Ftnc)

• Number of ML periods (Fmlp) (assuming a worst case where the 1st container used during each

ML period is being re-used, unsealed and/or contaminated)

Ftnc is based on the number of containers used per ML period and gives an indication of the contact

events. The number of ML periods (Fmlp) is included in the algorithm to estimate the total number of

containers used per ML period. The total number of containers used per ML period (Ftnc/Fmlp) is

considered representative of contact events, with a distribution derived from the data of ≈1-15.

Ftnc and Fmlp is estimated with the tank size (l), final ML concentration of a.s. in spray volume (g/l),

container size (l), formulation concentration (g/l) and total spray volume applied (l).

The hand model algorithm includes the transfer efficiency from surface to the hands (Appendix H),

as well as the affected surface area of the hands involved in contacts (Appendix I).

The surface-to-hand transfer efficiency (Cf) is corrected to account for multiple contacts, expressed

as Cftot-container-ext (Appendix J).

45

(ii) Surface contamination levels

Surface contamination levels were derived from various experimental studies (Glass et al, 2009;

Gilbert et al, 2000; Mathers et al, 1999). The data distinguish between two ML methods (tank-top

and induction bowl pour) and four container sizes (1L, 5L, 10L, 20L).

Surface contamination levels (lower and upper range, Sccontainer-ext) were derived for the different

container sizes and expressed in different categories, each with a probability. The probabilities are

based on the probability frequencies in the experimental data (Table 17). The derived values are

therefore indicative and only provide information about the relative performance of the different

container sizes when used with a particular filling technique (Appendix G).

(iii) Spillages

In order to incorporate exposure through splashes and dripping, the model algorithm includes

experimental hand contamination data of a standardized emptying procedure. Hand contamination

levels (es) were derived from the available experimental studies assuming that a significant amount

of contamination occurred through spillages (using the standard CSL container pouring test) (Table

18). These values were derived for tank-top and induction bowl ML methods using the four

container size categories. Probability distributions of splashes occurring on the hands during the use

of different container sizes and ML methods are also available and included in the hand

contamination estimate. The derived values are based on limited experimental data and are only

indicative input of hand contamination levels in practice.

Model 1b: Open pour to tank top or induction bowl

II Whole body exposure (excl. hands) model

Whole body contamination levels (eopen pour) were derived from experimental studies for tank-top

pour and induction bowl pour ML methods using four container size categories (Table 19). These

values were derived from whole body contamination (coveralls) based on the standard CSL container

pouring test protocol (for the pouring into a simulated induction hopper). In the absence of data for

tank-top pouring, the latter values were adopted from induction bowl pouring. The derived values

are based on limited experimental data and are only indicative input of whole body contamination

levels in practice.

46

Model 2: Mechanical transfer devices (MTD) and closed systems

I Total body (incl. hands) exposure model

Limited data is available for mechanical transfer devices (MTD) and closed transfer systems (CTS).

These ML methods are also more complicated due to the wide range of systems used and protocols

followed for each system. The experimental data available distinguish between six systems, where a

number of the systems had a common design of valve connection to the pesticide kegs and dry

break coupling connecting the system to a mock sprayer.

In the absence of consistent experimental data for different body parts, it was decided to derive the

total operator contamination from all the available systems sampled, giving a rough estimate of

operator contamination (Table 19).

(b) Algorithms

Model 1: Open pour to tank top or induction bowl


Per M&L period

Dph_mlp = Dph_container-ext + Dph_es

Where

Dph_container-ext = Cfcoi * (Sccontainer-ext * Cftot_container-ext* BPhands * lcn/BPpalms))

Dph_es = Cfcoi * (es * (Ftnc/Fmlp))

𝐶𝑓𝑡𝑜𝑡_𝑐𝑜𝑛𝑡𝑎𝑖𝑛𝑒𝑟_𝑒𝑥𝑡 = � �𝐶𝑓 0.5��𝐹𝑡𝑛𝑐𝐹𝑚𝑙𝑝

�−1��

𝐹𝑡𝑛𝑐𝐹𝑚𝑙𝑝

𝑘=1

and 𝐹𝑡𝑛𝑐𝐹𝑚𝑙𝑝

≥ 1

𝐹𝑡𝑛𝑐𝐹𝑚𝑙𝑝

is always an integer ≥ 1 as Ftnc is an integer * Fmlp

Dph_container-ext Hand exposure from hand contacts with container exteriors ((µg/hands)

Dph_es Hand exposure from direct emissions such as splashes and dripping from containers

(µg/hands)

47

For combined ML periods Dph = Dph_mlp * Fmlp

Dph_mlp dermal hand exposure per ML period (µg/hands)

Sccontainer-ext surface contamination level for different container sizes and M&L methods (l/cm2)

Cfcoi concentration of a.s. in formulation / packaging (µg/l)

Ftnc total number of containers used incl. repeated use (Ftnc)

Fmlp number of ML periods

es hand contamination representing dripping/splashes after standard emptying

operation (separate for tank-top (es-tanktop) and induction bowl (es-induction bowl)

BP total surface area of hands (cm2)

lcn/BP affected surface area of hands (cm2 of BP)

Cf transfer efficiency from surface to the hands

Cftot-container-ext corrected transfer coefficient for multiple contacts (maximum: 0.89 with Cf = 0.45; 𝐹𝑡𝑛𝑐𝐹𝑚𝑙𝑝

= 6) (Appendix H)



Dpw = Cfcoi * eopen pour * Ftnc

Dpw potential dermal exposure to a.s. in µg on the whole body (excl. hands)

Cfcoi concentration of a.s. of formulation in container/packaging (µg/l)

eopen pour whole body exposure during a standard emptying operation / per container used

(l/whole body) as per container size and ML method

Ftnc total number of containers used incl. re-use of un-emptied containers

Model 2: Mechanical transfer devices (MTD) using induction hoppers and closed transfer systems

(CTS)

I Total body (incl. hands) exposure model (Dpw)

Dpw = Cfcoi * emtd * Fmlp 48

Dpw potential dermal exposure to a.s. in µg on the total body (incl. hands)

Cfcoi concentration of a.s. of formulation in container/packaging (µg/l)

emtd indicative dermal exposure during the use of MTS and CTS per ML period (l/whole

body incl. hands)

Fmlp frequency of contacts as number of ML periods

(c) Model testing

Please refer to the type of plots/graphs and explanation under item 3.2. Also, the number of data

available for single and multiple M&L periods vary and are different in the respective plots.


I Hand exposure model

Only ML data was used (excl. application) to test the hand model. As shown in the algorithms, the

model first estimates hand exposure for a single ML period (Figure 13 & 14). As shown in Figure 14,

model estimates tend to over-estimate for the ML of boom sprayers and under-estimate for hand-

held spraying operations. However, this may be due to the imputed data used. The ML model then

estimates the exposures of all the ML periods combined (Figure 15 & 16), which includes ML data

with both single and multiple ML periods. Mixing & loading during hand-held spraying operations, in

particular, shows a high variability and broad range in exposures (also see Appendix K). The final

model estimates of all the ML periods combined (Figure 16) appear to be conservative but do not

capture the large variation in the exposure data. However, many of the outliers may be ascribed to

less reliable imputed datasets.


II Whole body exposure model

Testing of the whole body model with exposure data is plotted in Figures 15 and 16.

Model 2: Mechanical transfer devices (MTD) and closed systems

I Total body exposure model

Too few datasets are available to compare model outputs with data.

49

(d) Tables

Table 16: Mixing & loading dermal models - main user and model inputs*

No User inputs Model inputs Lower Upper Type Based on 1 Concentration of a.s. of

formulation in container/packaging (g/l)

Concentration of a.s. of formulation in container/packaging, Cfcoi (g/l)

5

1000 selected value

BROWSE data

2 Container size – 4 categories: ≤2,5l; >2,5l - <7,5l; 7,5l - <15l; >15l

Container size – 4 categories based on experimental data (1, 5, 10 & 20L)

0.5

25 selected value

Experimental studies Appendix G

3 M&L method (3 categories): - Tank-top (open) pour - Induction bowl (open)

pour - Mechanical

transfer/coupling devices (MTD) and closed transfer systems (CTS)

M&L method (3 categories)

- - selected value

Experimental studies Appendix G

4 n/a Container contamination levels, Sccontainer-ext

(l/cm2), incl. frequency distributions (Table 19)

2.0E-10 9.0E-7 uniform distributions

Experimental studies Table 17 Appendix G

5 n/a Hand exposure after standard emptying operation, es (l/hands), as per container size and M&L method, incl. frequency distributions (Table 20)

1.0E-6 2.0E-4 uniform distributions

Experimental studies Table 18 Appendix G

6 (Volume applied/tank volume) roundup

Frequency of contacts as number of M&L periods (Fmlp)

1 15 selected value

BROWSE data

7 (Volume applied, tank size, in-use concentration (Ccoi), container size, formulation concentration (Cfcoi)) roundup

Frequency of contacts as total number of containers used incl. repeated use (Ftnc)

1 45 selected value

BROWSE data

8 n/a Dermal exposure during the use of MTSs and CTS (l/total body), emtd,

1E-7 1.4E-3 uniform Experimental studies Table 19 Appendix G

9 n/a Body exposure (eopen pour) during standard operation per container (L/body) as per container size and M&L method (Table 21)

1E-7 2.0E-4 uniform Experimental studies Table 19 Appendix G

10 n/a Transfer efficiency from surface to the hands (Cf);

0.16 0.47 triangular distribution

IOM database Literature review

50

No User inputs Model inputs Lower Upper Type Based on

1st contact = 0.31; 2nd-6th contact (*0.5)

between 0.16 and 0.47 with a mode of 0.31

Appendix H&J

11 Gender (male/female) Surface area of hands, cm2 (BP) (testing: fixed male)

- lognormal distribution – see BS dermal model

Appendix I

12 n/a Fraction affected surface area of hands (palms) (lcn/BPpalms)

Fixed 0.42

- selected value

Appendix I Expert opinion

* models distinguish between container sizes and M&L methods

51

Table 17: Surface contamination levels (Sccontainer-ext,L/cm2)^ and associated frequency distributions

(%) derived for (manual) open pour M&L methods* for tank-top pour and induction bowl

pour using four container size categories

Tank-top category 1 category 2 category 3

lower Upper P* lower > upper < P* lower upper P*

ml/container 0,001 0,01 0,01 1 1 1,5 – 4,5

Container size (L)

1 2,00E-09 2,00E-08 70 2,00E-08 2,00E-06 30

5 6,00E-10 6,00E-09 35 6,00E-09 6,00E-07 40 6,00E-07 9,00E-07 25

10 4,00E-10 4,00E-09 5 4,00E-09 4,00E-07 60 4,00E-07 5,00E-07 35

20 2,00E-10 2,00E-09 5 2,00E-09 4,00E-07 50 4,00E-07 9,00E-07 45

Induction bowl category 1 category 2 category 3

lower Upper P* lower > upper < P* lower upper P*

ml/container 0,001 0,01 0,01 1 1 1,5 – 4,5

Container size (L)

1 2,00E-09 2,00E-08 70 2,00E-08 2,00E-06 30

5 6,00E-10 6,00E-09 25 6,00E-09 6,00E-07 70 6,00E-07 9,00E-07 5

10 4,00E-10 4,00E-09 35 4,00E-09 4,00E-07 60 4,00E-07 5,00E-07 5

20 2,00E-10 2,00E-09 10 2,00E-09 4,00E-07 55 4,00E-07 9,00E-07 35

* Frequency distributions

Container surface area (defaults, cm2)

1 600

5 1754

10 2785

20 4421 ^ Indicative ranges were derived from an analysis of surface contamination levels of different container sizes

after a standard pouring procedure (Mathers et al 2000, Gilbert et al, 1999; Gilbert et al, 2000, Glass et al, 2009). Indicative frequency probabilities were derived from Glass et al (2009)

52

Table 18: Hand contamination levels (es, L/hands) and frequency distributions (%) derived for

(manual) open pour M&L methods using four container size categories ~, tank-top and

induction bowl respectively

Tank-top category 1 category 2

lower Upper P* lower upper P*

Container size (L)

1 1,00E-06 1,00E-05 99 1,00E-05 2,00E-04 1

5 1,00E-06 1,00E-05 95 1,00E-05 2,00E-04 5

10 1,00E-06 1,00E-05 70 1,00E-05 2,00E-04 30

20 1,00E-06 1,00E-05 60 1,00E-05 2,00E-04 40

Induction bowl category 1 category 2

lower Upper P* lower upper P*

Container size (L)

1 1,00E-06 1,00E-05 99 1,00E-05 2,00E-04 1

5 1,00E-06 1,00E-05 99 1,00E-05 2,00E-04 1

10 1,00E-06 1,00E-05 80 1,00E-05 2,00E-04 20

20 1,00E-06 1,00E-05 70 1,00E-05 2,00E-04 30 ^ Indicative ranges were derived from an analysis of hand contamination levels during a standard

pouring procedure (Mathers et al 2000, Gilbert et al, 1999; Gilbert et al, 2000, Glass et al, 2009). Indicative frequency distribution were derived from Glass et al (2009)

53

Table 19: Whole body contamination levels (eopen pour) derived for (manual) open pour M&L methods

using four container size categories, and total body exposure during use of mechanical

transfer devices (emtd)

Category Container size (L) Whole body contamination (excl. hands) after open pour1, eopen pour (l/whole body)^

Total body contamination after using mechanical transfer devices (MTD)2, emtd (l/total body)^

Lower Upper Mean Lower Upper Mean* Lower Upper 1 0.5 2.5 1.0E-7 1.0E-8 1.0E-6 - - -

2 >2.5 7.5 1.5E-6 1.5E-7 1.5E-5 - - -

3 >7.5 15 5.0E-6 5.0E-7 5.0E-5 - - -

4 >15 25 2.0E-5 2.0E-6 2.0E-4 - - -

5 25 - - - 7E-5 1.0E-7 1.5E-3 1 derived from whole body contamination (coveralls) after a standard pouring procedure (induction bowl). These

values were also adopted for tank-top pour. Note: If raw data was not available for all combinations of methods and container sizes, values were adopted from other similar-sized container sizes

2 operator contamination based on different body parts depending on type of loading systems used. Measurements taken after one protocol of six different mechanical transfer devices including both induction hoppers and closed transfer systems (including volumes of 5l-25l)

^ Lower and upper ranges were derived using the mean values with a factor 0,1 and 10 respectively (as standard deviations could not be calculated from the studies)

Note Derived from: eopen pour (Mathers et al 2000, Gilbert et al, 1999, Gilbert et al, 2000; Glass et al, 2009) and emtd (Glass et al, 2009)

54

(e) Figures

Figure 13: Hand exposure: Plot of log10(potential dermal exposure (hands)*) and log10(geometric mean(model outputs)) (single M&L period) *including data sets which have model inputs that were imputed

0.01 0.1 1 10 100 1000 10000 100000 1e+06 1e+07 1e+080.01

0.1

1

10

100

1000

10000

100000

1e+06

1e+07

1e+08

log10 (Geometric mean(Model outputs(µg/hands)))

log 10

(Mea

sure

d po

tent

ial d

erm

al e

xpos

ure(

µg/h

ands

))

55

Figure 14: Hand exposure: Plot of 95% CIs (grey lines), geometric means (red lines) and medians

(black dashed lines) for the log10(model outputs) with the log10(measured potential dermal exposure (hands)*) (purple squares=boom sprayer, orange crosses=hand-held sprayers) (single M&L period) *including data sets which have model inputs that were imputed

0 10 20 30 40 50 601

10

100

1000

10000

100000

1e+06

1e+07

Index

log 10

(Pot

entia

l der

mal

exp

osur

e (µ

g/ha

nds)

)

56

Figure 15: Hand exposure: Plot of log10(measured potential dermal exposure (hands)*) and

log10(geometric mean(model outputs)) (all M&L periods combined) * including data sets which have model inputs that were imputed

0.01 0.1 1 10 100 1000 10000 100000 1e+06 1e+07 1e+080.01

0.1

1

10

100

1000

10000

100000

1e+06

1e+07

1e+08

log10 (Geometric mean(Model outputs(µg/hands)))

log 10

(Mea

sure

d po

tent

ial d

erm

al e

xpos

ure(

µg/h

ands

))

57


(black dashed lines) for the log10(model outputs) with the log10(measured potential dermal exposure (hands)*) (blue circles=aerial, purple squares=boom sprayer, red diamonds=broadcast sprayer, orange crosses=hand-held sprayers) (all M&L periods combined) * including data sets which have model inputs that were imputed

0 20 40 60 80 100 120 1400.01

0.1

1

10

100

1000

10000

100000

1e+06

1e+07

Index

log 10

(Pot

entia

l der

mal

exp

osur

e (µ

g/ha

nds)

)

58

Figure 17: Whole body exposure: Plot of log10(measured potential dermal exposure (body)) and

log10(geometric mean(model outputs)) (Red circles= datasets where no model inputs needed to be imputed; blue circles= data sets which have model inputs that were imputed) (all M&L periods combined)

0.01 0.1 1 10 100 1000 10000 100000 1e+06 1e+070.01

0.1

1

10

100

1000

10000

100000

1e+06

1e+07

log10 (Geometric mean (Model outputs(µg/body)))

log 10

(Mea

sure

d po

tent

ial d

erm

al e

xpos

ure(

µg/b

ody)

)

59

Figure 18: Whole body exposure: Plot of 95% CIs (grey lines), geometric means (red lines) and

medians (black dashed lines) for the log10(model outputs) with the log10(measured potential dermal exposure (body)*) (blue circles=aerial, purple squares=boom sprayer, red diamonds=broadcast sprayer, orange crosses=hand-held sprayers) (all M&L periods combined) * including data sets which have model inputs that were imputed

0 20 40 60 80 100 120 140 1600.001

0.01

0.1

1

10

100

1000

10000

100000

1e+06

1e+07

Index

log 10

(Pot

entia

l der

mal

exp

osur

e (µ

g/bo

dy))

60

3.2.4.2 Mixing and loading solids

(a) Description

The mixing and loading (ML) of solids scenario includes all operations where PPPs with a solid

formulation (e.g. granules or powders) are mixed and loaded. These mixing/loading operations may

occur in combination with for instance ground boom, broadcast, hand-held and aerial spraying

operations, but are considered a separate activity in view of estimating exposure. The following

models were developed:

• Manual handling and emptying solid formulations from packaging (≤25kg), which includes collection, carrying, opening/closing, weighing and scooping: - Hands model - Whole body (excl. hands) model

The models apply quantitative values derived from experimental data as mass balance inputs, i.e.

body part contamination levels. It is assumed that the most important routes of exposure associated

with dermal exposure are represented in the derived data, i.e. deposition on the skin and surface-to-

hand contacts.

Hand and body exposure models

The algorithm used to estimate dermal exposure during mixing and loading of solids (see paragraph

‘algorithms’) is based on experimental data (body part contamination values) in combination with

model input parameters such as the concentration of a.s. in the formulation, number of packaging

(e.g. bags/boxes) handled used per M&L period, amount of formulation used, dustiness of the

product, the use of local ventilation, wind speed and the body part surface areas (see Table 20).

(i) Body part contamination levels Indicative body part contamination levels were derived from an experimental study (Veldhof et al,

2006) with separate estimates for the hands (eh_w&e), arms (ea_w&e), torso (et_w&e) and legs (el_w&e). It is

assumed that the sub-activity of emptying (or dumping) of solids is the most important activity in

terms of dust emissions. As such, the experimental data used to derive body contamination levels

are based on a single emptying event of a given amount of product, but it also include a sequence of

other sub-activities such as collection, carrying, opening/closing of bags/boxes, and weighing and

scooping. The data distinguish between two key determinants of exposure, i.e. (1) the amount of

61

formulation used and (2) the dustiness of the formulation during single events (emptying). Based on

regression analysis, these two parameters were found to have the most significant effect on dermal

exposure (Veldhof et al, 2006). The experimental data include the following amounts used: 200g,

1kg, 5kg, 10kg and 15kg. Considering the detailed analysis of the data (Veldhof et al, 2006), four

categories were proposed for this exposure model (<1kg, 1,0-7,5kg, 7,5-12,5kg, >12,5-25kg).

The dustiness of the formulation type is categorized as granule, powder and fine powder.

The dustiness classification of the experimental test product was translated to a corresponding

classification of PPP products.

The contamination levels for hands, arms, torso and legs are presented in Table 20. The derived

values are indicative values based on limited data only. More details on the experimental set-up and

data derivation is presented in Appendix P.

(ii) Frequency of events

Both the hand and whole body models apply event frequencies that provide an indication of the

frequency that a single emptying event will occur. The following event frequencies are included in

the hand model:

• Total number of packaging used excl. re-use (Fnc) • Number of ML periods (Fmlp) Since the body part contamination estimates are based on experimental data during a single event

of emptying (a given amount of) solids, these contamination estimates are multiplied with the

number of these events in the model. To derive a body part contamination estimate based on the

‘amount used’ during a single event, a background calculation is used to estimate the amount of

product used per M&L period (and per packaging if multiple bags/boxes are used) (see background

calculations, Appendix L). For example, the hand contamination estimate (eh_w&e) is multiplied with

the number of packaging used per M&L period (Fnc/Fmlp). No evidence is available on how body part

contamination during a single event will increase with multiple M&L periods or use of multiple

packaging. In line with experimental skin loading studies (Appendix J), a skin loading constant (fdl) is

applied to simulate a gradual increase with multiple M&L periods (see algorithms).

(iii) Other factors

Although the experimental data used to derive hand contamination levels (eh_w&e) are corrected for

amounts used (<1kg – 25kg) and formulation type (granule, powder, fine powder), it does not take 62

account of potentially important parameters such as wind speed / air velocity (and wind direction)

and local exhaust ventilation (indoors). These parameters are included in the algorithm as shown in

Table 20.

Local exhaust ventilation was not used in the experimental study. Although extraction ventilation is

not commonly used during the mixing and loading of powders in the agricultural sector, the use of

LEV cannot not be excluded either. A distribution of efficiency values for local exhaust capturing

hoods (indoors) is proposed (i.e. efficacy values of 0.01-1) based on dumping activities in industry

(from approximately 55% effectiveness) (Marquart et al, 2003).

The experimental data represent exposure conditions of an indoor setting with relatively little

airflow and unknown wind direction. To account for uncertainty regarding dispersion that is caused

by wind speed and wind direction in diverse workplace settings (both indoors and outdoors), a broad

range of effects are proposed (both increasing as decreasing) depending on the wind direction.

Considering the limited information that will be available as model inputs, a broad distinction is

made between low wind speeds (≤1m/s; efficacy values of 0.3-3) and higher wind speeds of greater

than 1m/s (efficacy values of 0.1-10).

Distributions of body surfaces areas (BP, cm2) for hands, arms, torso/trunk and legs are included in

the algorithm, which differentiate between male and female distributions (see Appendix I). These

body surface areas roughly correspond to the body surface areas used in the experimental study on

which the body contamination levels are based. Based on the experimental data, it is assumed that

the exposure to the rest of the body is negligible (Appendix P).

(b) Algorithms


Per M&L period

Dph = Cfcoi * ((eh_w&e * BPhands* Fnc/Fmlp) * vlev * ws) (1)

M&L periods combined

Dpht = Dph * fdl (2)

where constant in fdl = 0.1 with each consecutive Fnc/Fmlp event (see previous runs with fdl)

63

Dph dermal hand exposure per M&L period (µg/hands)

Dpht total dermal hand exposure of all M&L periods combined (µg/hands)

Cfcoi concentration of a.s. of formulation in packaging (µg/kg)

eh_w&e hand contamination during handling and emptying activities (kg/cm2) based on the

amount used per emptying event (kg/event)

Fnc total number of containers used

Fmlp number of M&L periods (if Fnc/Fmlp <1 then equal to 1)

vlev local exhaust extraction (canopy) hood used (indoors)

ws wind speed, air velocity

BPhands surface area of the hands (cm2)

II Whole body exposure model (Dpb)

Per M&L period

Dpb = Cfcoi * (((ea_w&e * BParm + el_w&e * BPlegs + et_w&e * BPtorso) * Fnc/Fmlp) * vlev * ws) (1)

M&L periods combined

Dpbt = Dpb * fdl (2)

where constant in fdl = 0.1 with each consecutive Fnc/Fmlp event

Dph dermal whole body exposure per M&L period (µg/whole body) (assuming that only

the arms, torso and legs are potentially exposed)

Dpbt total dermal whole body exposure of all M&L periods combined (µg/whole body)

Cfcoi concentration of a.s. of formulation in packaging (µg/kg)

ea_w&e arm contamination during handling and emptying activities (kg/cm2) based on the


el_w&e leg contamination during handling and emptying activities (kg/cm2) based on the


et_w&e torso contamination during handling and emptying activities (kg/cm2) based on the


Fnc total number of containers used

64

Fmlp number of M&L periods (if Fnc/Fmlp <1 then equal to 1)

vlev local exhaust extraction (canopy) hood used (indoors)

ws wind speed, air velocity (indoors & outdoors)

BParm surface area of the arms (cm2)

BPlegs surface area of the legs (cm2)

BPtorso surface area of the torso / trunk (cm2)

(c) Model testing Please refer to the type of plots/graphs and explanation under item 3.2. Note that the number of

data available for single and multiple M&L periods vary and are different in the respective plots.

I Hand dermal exposure model

Only ML data was used (excl. application) to test the hand model. As shown in the algorithms, the

model first estimates hand exposure for a single ML period, however, the data was plotted with

model outputs with all ML combined, as shown in the algorithms. As indicated in Figures 19 and 20

(hand models) and Figures 21 and 22 (whole body models), very little data is available to evaluate

the model outputs. Overall, it is clear that too little data is available to perform a proper testing to

evaluate the variance explained by the models. A few issues that may determine the accuracy of the

models are the sub-activities that are part of the exposure scenario of the experimental data

(weighing, scooping), that are not always relevant in the dataset with PPP exposure data as tested.

(d) Tables Table 20 Main user and model inputs * (also see Appendix P)

No User inputs Model inputs Lower Upper Type Based on 1 Concentration of

a.s. of formulation in container / packaging (mg/kg)

Concentration of a.s. of formulation in packaging, Cfcoi

(µg/kg)

from data

from data

selected value

BROWSE data

2 Amount used per packaging <=1kg >1kg - <7.5kg 7.5kg - <12.5kg >12.5kg – 25kg

Container size – 4 categories based on experimental data (200g, 1kg-5kg, 10kg, 15kg)

0.2

25 selected value

Experimental data (Appendix G)

3 Input required to Hand exposure after single # # Uniform Experimental

65

calculate: (i) amount used, (ii) product type^

scooping & emptying operation^, eh_w&e

(µg/cm2)

data, Table 2a Appendix G Appendix L

4 Input required to calculate: (i) amount used, (ii) product type^

Arms exposure after scooping & emptying operation^, ea_w&e

(µg/cm2)

# # Uniform Experimental data, Table 2 Appendix G Appendix L


Legs exposure after scooping & emptying operation^, el_w&e

(µg/cm2)

# # uniform Experimental data, Table 2 Appendix G Appendix L


Torso exposure after scooping & emptying operation^, et_w&e

(µg/cm2)

# # uniform Experimental data, Table 2 Appendix G Appendix L

7 (see parameters required in Appendix L)

Total number of containers used (Fnc)

1 from data / calcs

selected value

BROWSE data Appendix L

8 (see parameters required in Appendix L)

Number of M&L periods (Fmlp) (Fnc/Fmlp max 6 events)

from data

from data

selected value

BROWSE data

9 LEV used (indoors only)

Extraction capturing hood used (indoors only) (yes=0.45-0.01; no=1)

0.01 1.0 normal Marquart et al, 2003

10 Wind speed, direction (indoors & outdoors)

If ws ≤ 1m/s → 0.3 – 3.0; If ws > 1m/s → 0.1 – 10

0.1 10 uniform Expert opinion

11 Gender (male/female)

Surface area of body part, cm2 (hands=BPhands; arms=BParms; legs=BPlegs; torso/trunk=BPtorso); (testing: fixed male)

* * lognormal distribution

Appendix I*

# see Table 2a/b, dependent on formulation type, etc. ^ includes dustiness of product (granule, powder, fine powder)

66

(e) Figures Figure 19: Hand exposure: Plot of log10(measured potential dermal exposure (hands)*) and

log10(geometric mean(model outputs)) (all M&L periods combined)

*including data sets which have model inputs that were ‘complete’ (red) and imputed (blue)

67


(black dashed lines) for the log10(model outputs) with the log10(measured potential dermal

exposure (hands)*) (all M&L periods combined)


68

Figure 21: Whole body exposure: Plot of log10(measured potential dermal exposure (whole body)*) and log10(geometric mean(model outputs)) (all M&L periods combined)


69


medians (black dashed lines) for the log10(model outputs) with the log10(measured potential

dermal exposure (whole body)*) (all M&L periods combined)


70

3.2.5 Orchard spraying inhalation model

(a) Description

Operator inhalation exposure is assumed to primarily originate from the spraying equipment that

releases airborne droplets during orchard spraying (OS) activities. The inhalation model focuses on

droplet spray and predicts the air concentration of droplets in the breathing zone of the operator

much like the Boom spraying model. This air concentration is converted to the air concentration of

the given active substance (a.s.) based on the concentration of a.s. in the spraying volume.

To estimate inhalation exposure of operators during Orchard spraying, the following three

mechanisms are considered (please see appendix A for more information):

• The airborne spray: The overspray factor is defined as the fractions of spray that remains

airborne between the tree rows of the orchard and to which the operator could get exposed.

Cross and colleagues1 2 3 found that the fraction of overspray is dependent on the spray quality

but not on the height of the trees, the application rate or the air flow. Based on the available

evidence the model predicts the total amount of overspray based on the spray quality and the

application rate only.

• The refreshment of air around the vehicle: As the vehicle moves forward it constantly drives into

fresh air. This effect is modeled by g refreshing the air around the vehicle at the rate of the

driving speed and a using a factor for the wind speed. The area of fresh air into which the vehicle

is moving is described by the vehicle height and the tree row spacing.

• The wake and dispersion toward the operator: The turbulence around the vehicle and spraying

equipment (in particular relevant for spray from nozzles close to the vehicle) will have an

1 Cross, J. V., P. J. Walklate, R. A. Murray, and G. M. Richardson. 2003. Spray deposits and losses in different sized apple trees from an axial fan orchard sprayer: 3. effects of air volumetric flow rate. Crop Protection 22 (2): 381-94 2 Cross, J. V., P. J. Walklate, R. A. Murray, and G. M. Richardson. 2001. Spray deposits and losses in different sized apple trees from an axial fan orchard sprayer: 1. effects of spray liquid flow rate. Crop Protection 20 (1): 13-30. 3 Cross, J. V., P. J. Walklate, R. A. Murray, and G. M. Richardson. 2001. Spray deposits and losses in different sized apple trees from an axial fan orchard sprayer: 2. effects of spray quality. Crop Protection 20 (4): 333-43

71

influence on the dispersion of spray from the spray equipment to the operator. Analysis of the

data showed that the wake and dispersion is well explained by using the vehicle size and vehicle

speed to describe the spray dispersion towards the operator.

Proposed model

The model consists of three parts that represents the three consecutive phases of the source-

receptor model:

(i) The airborne spray

(ii) Dispersion around the vehicle (Wake and air Refreshment)

(iii) Dispersion from spray around the vehicle to operator using cabin efficiencies (If)

Input variables of the model are represented in Tables 1 and 2.

(i) The airborne spray

The amount of spray emitted at the spray boom that potentially remains airborne around the vehicle

(that is not deposited on the trees or past the tree row as drift) is defined as the overspray. Based on

the spray quality, the fraction of spray that deposits on the ground between trees in relation to the

total volume sprayed rate. This fraction is called the overspray fraction. Together with the total

volume spray rate it provided the model input for the total amount of spray airborne around the

vehicle.

For determination of the total overspray:

- The spray volume rate (SPR) is used as input

- The fraction of spray that remains airborne between the tree rows (foverspray) is based on

experimental data. The foverspray value is derived for 3 spray qualities (medium, fine, very fine) and

is based on the fraction of spray that deposits on the ground in relation to the total airborne

spray. (Appendix A and Table 3).

(ii) Dispersion around the vehicle (Wake and air Refreshment)

The dispersion around the vehicle is described by two processes:

Firstly, the reduction of the air concentration of spray by air refreshment due to the driving of the

vehicle into fresh air and by the wind. The total amount of overspray is divided by the area over

which are refreshment takes place (perpendicular to the driving direction) multiplied by the vehicle

72

speed and 85% of the wind speed. The area is defined by the area made up by the vehicle height and

the row spacing of the trees. The 85 percentage of wind speed is assumed to describe the wind

direction that adds to the air refreshment (only the 15% more or less equal to the driving direction is

excluded).

The dispersion of spray from the spray equipment area to the rest of the vehicle is described by the

wake factor (wf) (a factor for the turbulence) and the dilution of spray over a volume described by

the vehicle distance category (d) (Table 5) and the vehicle speed (vvehicle). The vehicle speed is used

to describe the imaginable increased distance from the operator to the spray equipment due to

displacement of the operator away from the equipment during spraying.

(iii) Dispersion from spray around the vehicle to operator using cabin efficiencies (If)

Personal enclosures on vehicles (e.g. cabins) may affect the transfer of spray to the operator.

The categories proposed for cabins are presented in Table 7.

(b) Algorithms

Ipde = Cvehicle * If * Ccoi

Ipde Potential inhalation exposure concentration to the active substance (in droplet

spray) (g/m3)

Ccoi Concentration of a.s. in spray volume (g/l)

Cvehicle Concentration of droplet spray at the vehicle (l/m3)


where

Cvehicle = 𝐴𝑅 ∗ �𝑓𝑜𝑣𝑒𝑟𝑠𝑝𝑟𝑎𝑦�

𝐻𝑉𝑒ℎ𝑖𝑐𝑙𝑒 ∗ 𝑑𝑟𝑜𝑤 ∗ (vvehicle + 0.85 ∗ 𝑣𝑤𝑖𝑛𝑑)∗

𝑤𝑓(𝑑 + vvehicle)3

AR Application rate (L/s)

fairborne spray overspray fraction: fraction of spray volume that remains airborne between the tree

rows

73

Hvehicle Vehicle height (m)

drow Row spacing (m)

vvehicle vehicle speed (m/s)

vwind wind speed (m/s)

d vehicle distance category based on vehicle-mounted and self-propelled (VM) and

trailer-mounted (TM) rigs

wf wake factor

(c) Testing and calibration

Model Testing

Please refer to the type of plots/graphs and explanation under item 3.2. for the testing of the

Orchard Spraying inhalation model. Since no data points were complete for all the required inputs all

data are imputed. Imputed data were based on expected values compared to all other datapoints.

Figure 1 shows the model estimates plotted against all the available data. Figure 2 shows the model

estimated against the data points with confidence intervals. Due to the imputed data used for

testing, no conclusions can be drawn from the correlation found between the data and model

outputs, except that the models predict exposures in the overall expected range. Figures 3 shows

the same results but with increasing data values.

74

(d) Tables

Table 1: Orchard spraying inhalation model - input variables

Abbr Input variables Unit User input Model input From/for Cc.o.i. Concentration of

a.s. in spray liquid g/l User input User input Mass balance input


n/a (iv)No cabin (v) Cabin without


(vi)Cabin with


1 Sample from U(0.1, 0.25) with probability 0.25, from U(0.25, 0.5) with probability 0.5 and from U(0.5, 0.9) with probability 0.25 Sample from U(0.0001, 0.05) with probability 0.25, from U(0.05, 0.15) with probability 0.5 and from U(0.15, 0.3) with probability 0.25


AR Application rate l/s User input User input foverspra

y

Overspray fraction

n/a Medium spray quality Fine spray quality Very fine spray quality Unknown

Sample from U(0.01-0.04) Sample from U(0.02-0.08) Sample from U(0.03-0.22) Sample from U)0.01-0.22) lognormal right skewed (mean at 0.05)

Literature review Table …… Appendix …..

Hvehicle Vehicle height m Categories: S, M, L, U, sSP, mSP, lSP, O

Sample from respective categories

drow Row spacing m Check in data User input Browse data vvehilce Vehicle speed m/s 1-8 m/s User input vwind Wind speed m/s 1-7 m/s User input d Vehicle distance

category m Categories: vehicle-

mounted and self-propelled (VM), trailer-mounted (TM), unknown (O)



75

Table 2: Proposal cabin categories and associated multipliers/factors

Category Cabin Type % Cabin

efficiency Range (%)

Range (factor)

Lower Upper

A No cabin 1 n/a Fixed 1

B Cabin without pressurized/filtered

ventilation, OR with ventilation but

without filtration

60 10 - 90 0.1 0.9

C Cabin with pressurized/filtered

ventilation*

90 70 - >99 0.0001 0.3

1 based on literature review (Appendix D)

* criteria will be used in the user interface to verify ventilation & filtration compliances (ASAE

S-525)

Table 3: Overspray fraction

1 based on literature review (Appendix …..)

Table 4: Vehicle Height

Spray quality1 Code Overspray factor Range (factor) *

Lower Upper

Medium M 0.025 0.01 0.04

Fine F 0.05 0.02 0.08

Extra fine XF 0.125 0.03 0.22

Unknown 0 0.05 0.01 0.22

Type Code Length, distance^

(range, m)

Range (factor) *

Lower Upper

No Cabin A 1.5 1 2

Cabin B,C 2.5 2 3

Unknown O 2 1 3

76

Table 5: Length / distance categories based on vehicle and spraying rig configurations1

1 based on literature review (Appendix B)

(e) Figures

Figure 1: Inhalation exposure: Plot of the (fitted) log10(data) and log10(mean(model outputs))

Figure 2: Inhalation exposure: Plot for the lo10(fitted model outputs) with the log10(data) for the

complete datasets (with increasing model values). (Plot of 95% CIs (grey lines), means (red lines),

medians (black lines), and imputed datasets (blue dots).

1e-06 1e-05 0.0001 0.001 0.01 0.1 1 10 1001e-06

1e-05

0.0001

0.001

0.01

0.1

1

10

100

log10 (Mean(Model outputs(µg/l)))

log 10

(Dat

a(µg

/l))

Type Code Length, distance^

(range, m)

Range (factor) *

Lower Upper

Vehicle-mounted and

self-propelled VM 3.5 1 6

Trailer-mounted TM 8 4 12

Unknown O 6.5 1 12

77

figure 3: Inhalation exposure: Plot for the lo10(fitted model outputs) with the log10(data) for the

complete datasets (with increasing data values). (Plot of 95% CIs (grey lines), means (red lines),

medians (black lines), and imputed datasets (blue dots).

0 10 20 30 40 50 60 70 80 90 1001e-06

1e-05

0.0001

0.001

0.01

0.1

1

10

Data index

log 10

(Mod

el o

utpu

ts (µ

g/l))

0 10 20 30 40 50 60 70 80 90 1001e-06

1e-05

0.0001

0.001

0.01

0.1

1

10

log 10

(Mod

el o

utpu

ts (µ

g/l))

78

3.2.6 Orchard spraying dermal models

Description

The Orchard spraying dermal models consist of a hand model (Dph) and whole body model (Dpw). The

following activities of an operator during the orchard spraying scenario are included:

• Operating the vehicle (vehicle cockpit with & without cabin)

• Stepping into/out of vehicle (contact with the vehicle interior & exterior)

• Incidental activities with the spraying rig, adjustment of spraying rig and maintenance of

nozzles.

The models consider important determinants of dermal exposure associated with orchard sprayer

operations, e.g. type of spraying equipment (trailer, rear-mounted), the size of the spraying activity

and number of application tasks. The model predicts the exposure based on 2 routes of exposure:

deposition from airborne spray and surface contacts with contaminated surfaces. As high dermal

exposures are associated with incidental activities with contaminated spraying rigs and nozzles (as

opposed to the vehicle cockpit), the model includes both the vehicle cockpit and spraying rig as

sources of exposure for the surface contact route. The cleaning of vehicles and spraying equipment

are not included as a parameter in the models because the effect of this activity on exposure is not

known.

Hand model

Deposition

The deposition of spray on the hand is modelled by using vehicle contamination data. It is assumed

that the contamination of the control area (cabin or vehicle controls) of the vehicle occurs through

spray deposition only. The amount of deposition on this part of the vehicle is assumed to deposition

on the upper parts of the hands of the operator as well. (the palms will be more exposed to surfaces

rather than spray deposition). Deposition of spray on the hand therefore is only assumed to occur on

the top of the hands and their respective surface area (BPapplicationtop) (Appendix H). The level of

exposure due to deposition is calculated directly from the relevant vehicle contamination levels

(Table 7 and Appendix E) and the exposed surface area. The last term of the hand exposure model

algorithm represents the deposition.

79

�𝑆𝑐𝑐𝑜𝑐𝑘𝑝𝑖𝑡 ∗ 𝐶𝑐𝑜𝑖 ∗ 𝐼𝑓 ∗ 𝐵𝑃ℎ𝑎𝑛𝑑𝑠 ∗ 𝑙𝑐𝑛

𝐵𝑃𝑎𝑝𝑝𝑙𝑖𝑐𝑎𝑡𝑖𝑜𝑛𝑇𝑜𝑝�

Surface contacts

The algorithm used to estimate dermal exposure during orchard spraying is based on two key

parameters associated with surface contacts as described by Gorman et al (2012), i.e. frequency of

contacts and surface contamination levels. The input parameters and ranges of the models are

presented in Table 6.

(iv) Frequency of surface contacts


The following event frequencies are included in the hand model (see Table 6):

• Surface contacts with the vehicle cockpit exterior is represented by events of stepping into/out

of the vehicle, i.e. the number of mixing & loading periods (Fmlp) and number of times leaving

the vehicle during trouble-shooting or nozzle maintenance (Fnr)

• Surface contacts with the delivery system/rig is represented by (i) trouble-shooting and nozzle

maintenance events (Fnr) and (ii) incidental contacts such as (de-)mounting and boom

(un)folding prior to and after spraying (Frig)

The mixing & loading periods (Fmlp) are estimated from the volume applied and the tank volume. The

volume applied parameter is estimated from user inputs and are based on the total area sprayed

(Ha), dose (kg/Ha) and final mixing & loading concentration (g/l).

Since no information on the frequency of nozzle maintenance and incidental contacts was available,

defaults from boom spraying were adopted. These are between 0 and a worst case of 15, with a 90%

probability of between 0 and 5 and 10% probability of between 6 and 15.

Other events that may occur prior to or after spraying activities include nozzle replacement and

boom adjustment. A fixed event frequency with the rig and nozzles before and after spraying (Frig) is

set at 1. Frig are not included as user input assuming that this is not a known input.

(v) Surface contamination levels

The surface contamination level (µg/cm2 of a.s.) on field sprayers are highly variable and limited

evidence is available on the determinants influencing the deposition on vehicle and sprayer surfaces

80

of field sprayers. Ideally the surface contamination levels should be derived from data for different

vehicle-sprayer configurations, different spray qualities, etc. However, this information is only

available in a few specific settings (e.g. Balsari & Marucco, 2003). As result, it was decided to use a

broad range of contamination levels as input for the model (Table 7 & Appendix E). For this purpose,

contamination levels are based on four ‘Volume applied’ categories based on the percentage of the

total spray volume that deposits on the vehicle and delivery system.

As the surface contamination level estimate on the vehicle cockpit concerns the vehicle exterior, a

cabin efficiency factor is also included. The effect sizes proposed for cabin efficiency in the orchard

and boom spraying inhalation model are adopted for this model (Error! Reference source not

found.).

In addition, the transfer efficiency from surfaces to body parts (Cf) (Appendix H), body surfaces areas

(BP) and the affected surface area of body parts (lcn/BP) (Appendix I) are included in the algorithm.

The surface-to-hand transfer efficiency (Cf) is corrected to account for multiple contacts, for both

contacts with the cockpit (Cftot_cockpit) and rig (Cftot_Rig) (Appendix J). Surface contact are only assumed

to occur with the palms of the hands (BPpalms) (Appendix H).

Whole body (excl. hands) model

To estimate the whole body dermal exposure, it is assumed that whole body exposure occurs only

due to deposition during application. The mechanism for whole body deposition is similar as for the

hand model except it is applied to the whole body surface area. Whole body exposure during

incidental contacts might be relevant but where excluded from the models due to a lack of data.

Moreover the relatively high air-concentration during application (compared to for example boom

spraying) make other sources of exposure less significant. Whole body exposure during stepping in

and out of the vehicle is also not modelled partly because exposure to the whole body seems less

likely during this activity.

Algorithms


𝐷𝑝ℎ = ��𝑆𝑐𝑐𝑜𝑐𝑘𝑝𝑖𝑡 ∗ 𝐶𝑐𝑜𝑖 ∗ 𝐶𝑓𝑡𝑜𝑡_𝑐𝑜𝑐𝑘𝑝𝑖𝑡� + �𝑆𝑐𝑓𝑎𝑛 ∗ 𝐶𝑐𝑜𝑖 ∗ 𝐶𝑓𝑡𝑜𝑡_𝑟𝑖𝑔�� ∗ �𝐵𝑃ℎ𝑎𝑛𝑑𝑠 ∗ 𝑙𝑐𝑛𝐵𝑃𝑝𝑎𝑙𝑚𝑠

�

+ �𝑆𝑐𝑐𝑜𝑐𝑘𝑝𝑖𝑡 ∗ 𝐶𝑐𝑜𝑖 ∗ 𝐼𝑓 ∗ 𝐵𝑃ℎ𝑎𝑛𝑑𝑠 ∗ 𝑙𝑐𝑛

𝐵𝑃𝑎𝑝𝑝𝑙𝑖𝑐𝑎𝑡𝑖𝑜𝑛𝑇𝑜𝑝�

81

𝐶𝑓𝑡𝑜𝑡_𝑐𝑜𝑐𝑘𝑝𝑖𝑡 = � �𝐶𝑓 0.5(𝑘−1)�

𝐹𝑚𝑙𝑝+𝐹𝑛𝑟

𝑘=1

𝐶𝑓𝑡𝑜𝑡_𝑟𝑖𝑔 = � �𝐶𝑓 0.5(𝑘−1)�

𝐹𝑛𝑟+𝐹𝑟𝑖𝑔

𝑘=1

𝐶𝑓𝑚𝑎𝑥 = ��𝐶𝑓 0.5(𝑘−1)�𝐹=6

𝑘=1

Dph potential dermal exposure to a.s. in µg on the hands

Sccockpit surface contamination levels (l/m2) of cockpit

Ccoi concentration of a.s. in spraying volume (ug/l)

Cftot_cockppit corrected transfer coefficient for multiple contacts (maximum: 0.89 with Cf =

0.45; Fmlp + Fnr = 6)

Scfan surface contamination levels (l/m2) of the fan

Cftot_rig corrected transfer coefficient for multiple contacts (maximum: 0.89 with Cf =

0.45; Fnr + Frig = 6)

BPhands total surface area of hands (m2)

lcn/BPpalms affected surface area of hands (m2 of BP) (palms)

If cabin factor

Cfmax corrected transfer coefficient for a maximum amount of contacts

(0.89 with Cf = 0.45: F= 6)

lcn/BPApplicationTop affected surface area of hands (m2 of BP) (top of hands)

Cf transfer coefficient from surface to the hands

FmLp number of mixing & loading periods

Fnr number of nozzle maintenance events

Frig frequency of incidental contacts with the rig/boom

Rationale

(FmLp + Fnr) represent the frequency of surface contacts with the vehicle cockpit (Sccockpit) when

stepping into/out of the vehicle during trouble-shooting and mixing & loading

periods

82

(Fnr + Frig) represent the frequency of surface contacts with the delivery system/rig (Scrig)

during trouble-shooting and incidental contacts ((de-)mounting, boom folding) prior



The following equation is used to estimate the potential dermal exposure (Dpw) of the whole body in

ug:

𝐷𝑝𝑤 = 𝑆𝑐𝑐𝑜𝑐𝑘𝑝𝑖𝑡 ∗ 𝐶𝑐𝑜𝑖 ∗ 𝐼𝑓 ∗ 𝐵𝑃𝑤ℎ𝑜𝑙𝑒 𝑏𝑜𝑑𝑦

Dpw potential dermal exposure to a.s. in µg on the whole body excluding hands

Sccockpit surface contamination levels (l/m2) of cockpit

Ccoi concentration of a.s. in spraying volume (ug/l)

BPwhole body total surface area of the whole body (m2)

Model testing

Please refer to the type of plots/graphs and explanation under item 3.2


The available data is not suitable for testing the Orchard spraying hand model, because of data gaps

of important parameters. For example, the number of contact events with the nozzles is not known

in the data. Assuming a frequency of nozzle maintenance (Fnr) of 90% probability between 0 and 5

and 10% probability between 6 and 15, the model predicts a conservative exposure compared to the

broad range of data (figure 4 and figure 5).

A default nozzle maintenance frequency is set at 3 in the software. Unfortunately, inputting a

distribution for nozzle events will not be feasible as the probabilistic output of the model (using

iterations) will not be able to produce a single estimate with repeated runs of the model.


The whole body exposure model was compared with the dataset using the deposition of spray on

the operator body (either inside or outside the cabin) only (figure 6 and figure 7).

83

(f) Tables

Table 6: Orchard spraying dermal models - main user and model inputs*

abbr User inputs Model inputs Lower Upper Type Based on

Cc.o.i Concentration of a.s.

in spray volume (g/l)

Concentration of

a.s. in spray

volume (g/l)


Volume applied

(estimated from total

area sprayed, dose,

final mixing&loading

concentration)

Volume applied (l),

4 categories


Sccockpit n/a Cockpit surface

contamination

levels (l/m2)

0.001 0.25 uniform

distributions

Field trial data

Table 11

Appendix E

Scrig n/a Rig surface

contamination

levels (l/m2)

0.036 0.25 Uniform

Distributions

Field trial data

Table 11

Appendix E

Fmlp Number of M&L

periods (estimated

with volume applied

and tank size)

Number of M&L

periods (tankfulls

applied)


Fnr Frequency of nozzle

maintenance (Fnr);

user selected: 0 - 5;

>5;

default in software = 3

Frequency of

nozzle

maintenance;

(With 90%

probability

between 0-5) (for

testing with data

only)

0 5 Sample from

discrete uniform

distribution

between 0 - 5

Field studies

Frig n/a Frequency of

contacts with rig

Fixed

at 1

- Fixed Expert opinion

Field studies

84


prior to/after

spraying

Cabin factor (3

categories)

Cabin factor (3

categories with

ranges)

0.0001 1 see inhalation

model

Literature

review

Table 8

Appendix D

Vehicle or trailer

mounted (3 categories

– VM, TM, O-

unknown))

Vehicle-sprayer

length (d)

Vehicle-mounted

(0.1 – 0.9)

Trailer-mounted

(0.01 - 0.25)

Front-fitted booms

fixed at 1

0.01

1 uniform

distributions

Field trial data

Table 6

Appendix B

Cf n/a Transfer efficiency

from surface to the

hands (Cf); e.g. 1st

contact = 0.45; 2nd-

6th contact (*0.5);

see text

0.23 0.68 triangular

distribution

between 0.23

and 0.68 with a

mode at 0.45

IOM database

Literature

review

Appendix H &

J

BPhands Gender (male, female) Surface area of

hands (m2)

- - lognormal

distributions

(Males: log(SA)

~ Normal (-

2.2319,

0.109922)

Females: log(SA)

~ Normal(-

2.4313,

0.10362))

Appendix I

85


BPwholeb

ody

Gender (male, female) Surface area of the

whole body (m2)

- - Lognormal

distributions

(Males:

Appendix I

LCn/

BPapplicati

on top

n/a Fraction of

affected surface

area of hands (top)

Fixed

0.5

- Fixed Literature

review

Appendix I

lcn/

BPpalms

n/a Fraction affected

surface area of

hands (palms)

Fixed

0.5

- Fixed Literature

review

Appendix I

* Models distinguish between Volumes applied and contamination levels of different surfaces

Table 7: Surface contamination levels using Volume Applied categories

Category Volume applied Contamination level cockpit,

Sccockpit (l/m2)*

Contamination level delivery

system/rig, Scrig (l/m2)*

Lower Upper Lower Upper Lower Upper

1 <50 500 0.001 0.025 0.036 0.25**

2 500 1000 0.003 0.051 0.18 0.25**

3 1000 3000 0.006 0.152 0.25** 0.25**

4 3000 >10000 0.017 0.25** 0.25** 0.25**

* broad categories extrapolated from orchard sprayer contamination studies using the % of total spray volume depositing on the vehicle and delivery system (e.g. Balsari & Marucco, 2003, Ramwell et al, 2005, Michielsen et al, 2012).

** A maximum surface loading of 0.25 liters per square meter is assumed

86

(g) Figures

Figure 4: Hand exposure: Plot of log10(measured potential dermal exposure (hands) and

log10(geometric mean(model outputs)). Assuming a 90% probability of 0-5 nozzle maintenance

events. All data sets included imputed values.

0.1 1 10 100 1000 10000 100000 1e+060.1

1

10

100

1000

10000

100000

1e+06

log10 (Mean(Model outputs(µg/hands)))

log 10

(Dat

a(µg

/han

ds))

87


(black dashed lines) for the log10(model outputs) with the log10(measured potential dermal

exposure (hands). Assuming a 90% probability of 0-5 nozzle maintenance events. All data sets

included imputed values.

0 20 40 60 80 100 120 1400.1

1

10

100

1000

10000

100000

1e+06

Data index

log 10

(Mod

el o

utpu

ts (µ

g/ha

nds)

)

88

Figure 6: Whole body exposure: Plot of log10(measured potential dermal exposure (body) and

log10(geometric mean(model outputs). All data sets included imputed values.

10 100 1000 10000 100000 1e+06 1e+0710

100

1000

10000

100000

1e+06

1e+07

log10 (Mean (Model outputs(µg/body)))

log 10

(Dat

a(µg

/bod

y))

89


medians (black dashed lines) for the log10(model outputs) with the log10(measured potential

dermal exposure (body). All datasets included imputed values.

3.2.7 Handheld Application inhalation model

(a) Description

The hand-held application (HHA) scenario represents an operator applying PPP by using a spray

lance or gun either connected to a tank by a hose, or connected to a backpack The HHA model

accounts for indoor (greenhouses) as well as outdoor (soft fruit trees, vineyards, vegetables crops,

etc.) environments.

Operator inhalation exposure is assumed to primarily originate from the spraying equipment that

releases airborne droplets HHA activities. The inhalation model focuses on droplet spray and

predicts the air concentration of droplets in the breathing zone of the operator much like the Boom

and Orchard spraying models. This air concentration is converted to the air concentration of the

given active substance (a.s.) based on the concentration of a.s. in the spraying volume. For the use of

the ART model, it was assumed that far field exposure is not relevant for mixing & loading PPPs,

0 20 40 60 80 100 120 140 160 180 200

100

1000

10000

100000

1e+06

Data index

log 10

(Mod

el o

utpu

ts (µ

g/bo

dy))

90

since in practice one operator is performing all the activities, and thus no secondary exposure is

assumed. Also the distance of the operator to the source is assumed to be <1 meter.

Furthermore, it is assumed that only low-volatile substances are used liquid formulations (in ART

framework cut off point: vapour pressure <10 Pa), and thus only exposure to mists is taken into

account. In case of liquid formulations it is assumed that a litter of liquid formulation weighs one kg

(comparable to water), to be able to use the concentration of active substance in the formulation (in

g/L) as the weight fraction (g/kg).

With regard to dispersion, the following assumptions are made:

• In case of outdoor application, it is assumed that this takes places close to buildings

• In case of application under a shelter/covering/roof, it is assumed that these conditions are

comparable to ‘outdoors – close to buildings’

• In case of application inside greenhouses only good natural ventilation is assumed

Main ART model inputs (default values) are presented in Table 1.

(b) Model testing

The model was tested with a dataset containing empirical data from which the ART score was

derived for each observation (e.g. weight fraction) along with the measured output (e.g. inhalation

exposure). The dataset used to test the model contained more than 600 data points. Where this was

the case, the missing inputs were imputed by empirical sampling from existing values in the available

data. To summarize the output, the geometric mean, median, 2.5th percentile and 97.5th percentile

were calculated. Thereafter, the geometric mean of the output against the true measured values

with 95% confidence intervals, medians and geometric means of the output against the true values

(all on log10 scale) were plotted for comparison. For each sub-scenario, 10,000 iterations were used.

The models were only compared or tested with available exposure data. Thus no calibration or

transforming/fitting was performed.

Results from the model testing are presented in figures 1 to 4. For all the sub-scenario, The y-axis

represents the measured potential inhalation exposure. The x-axis represents the geometric mean of

91

the model outputs. Blue dots indicate imputed values, red dots indicate that art scores were

available.

Figures 1 to 4 show the result of the model testing. Art default values for HHA air concentration

(Table 1) were tested against measured data representative of HHA application activities.

(c) Tables

Table 1: Default values used for HHA Inhalation (ART) model

Modifying factor Relevant parameter(s) Description Multiplier Coupling with BROWSE user inputs

Substance emission potential (E)

Viscosity Low viscosity (like water) 1.0 Relate to formulation type (EC, SC) division into categories Default

E = 10/30000 * 1.0 *

weight fraction

Weight fraction % active substance in spray liquid

Exactly Is considered to be known (input), or can be derived from other known information like the formulation, the amount used, etc.

Activity emission potential (H)

Use rate High application rate (> 3 L/min)

3

Spray application of liquids – surface

Moderate application rate (0.3-3 L/min)

1 Default

spraying of liquids Low application rate (0.03-0.3 L/min)

0.3

H= 1 * 3 * 1 Very low application rate (<0.03 L/min)

0.1

Direction of application Any direction (including upward)

3 Default Spray technique Spraying with no or low

compressed air use 1 Default

Localized controls (LC) LC=1.0

No localized controls No localized controls 1.0 Default

Dispersion (D)

Indoor: Room volume and air changes per hour

Natural ventilation, large room volume (greenhouse)

0.8991

Indoor: D=0.90 Outdoor: D=0.2 Outdoor: Distance of the Far from buildings 0.2 Default source from buildings Close to buildings 0.75 Surface contamination (Su) Su=0.01

Housekeeping practices (Default level: no specific cleaning practices, no protective clothing that repel spills, process not fully enclosed)

0.01 Default

92

(d) Figures

Figure 1. Inhalation exposure. Plot of the log10 (geometric mean of the concentration of a.i. in air*) against the log10 (measured inhalation exposure(both in ug/m3)). Blue dots indicate imputed values, red dots indicate that art scores were available. (Backpack Downwards scenario) *including a dataset which have art model inputs that were imputed

Figure 2. Inhalation exposure. Plot of the log10 (geometric mean of the concentration of a.i. in air*)

against the log10 (measured inhalation exposure(both in ug/m3)). Blue dots indicate imputed values, red dots indicate that art scores were available. (Backpack Up and Downwards scenario) *including a dataset which have art model inputs that were imputed

1e-05 0.0001 0.001 0.01 0.1 1 10 100 1000 10000 1000001e-05

0.0001

0.001

0.01

0.1

1

10

100

1000

10000

100000

log10(GM of concentration in air (µg/m3))

log 10

(Dat

a(µg

/m3 )

)

1e-05 0.0001 0.001 0.01 0.1 1 10 100 1000 10000 1000001e-05

0.0001

0.001

0.01

0.1

1

10

100

1000

10000

100000


log 10

(Dat

a(µg

/m3 )

)

93

Figure 3. Inhalation exposure. Plot of the log10 (geometric mean of the concentration of a.i. in air*)

against the log10 (measured inhalation exposure(both in ug/m3)). Blue dots indicate imputed values, red dots indicate that art scores were available. (Gun and Hose Downwards scenario) *including a dataset which have art model inputs that were imputed

Figure 4.Inhalation exposure. Plot of the log10 (geometric mean of the concentration of a.i. in air*) against the log10 (measured inhalation exposure(both in ug/m3)). Blue dots indicate imputed values, red dots indicate that art scores were available. (Gun and Hose Up and Downwards scenario) *including a dataset which have art model inputs that were imputed

1e-05 0.0001 0.001 0.01 0.1 1 10 100 1000 10000 1000001e-05

0.0001

0.001

0.01

0.1

1

10

100

1000

10000

100000


log 10

(Dat

a(µg

/m3 )

)

1e-05 0.0001 0.001 0.01 0.1 1 10 100 1000 10000 1000001e-05

0.0001

0.001

0.01

0.1

1

10

100

1000

10000

100000


log 10

(Dat

a(µg

/m3 )

)

94

3.2.8 Handheld Application Dermal model

(a) Description

Previous studies suggest that the fraction of the applied volume available for deposition onto the

worker, as well as the body surface area affected will be influenced by the spraying direction

(Brouwer et al. (2001); Berger-Preiss et al. (2005) Hughes et al. 2008) Leona et al. 1992) Marquart,

(2003)). Likewise, the equipment used will determine the degree of exposure as well as the

distribution of contamination over the operator body. (Dosemeci (2002); Leonas et al. (1992);

Machera (2003); Matuo & Matuo (1998); Nuyttens (2009); Sanjrani (1990); Wicke (1999))

Therefore, the HHA model describes four sub-scenarios based on the application direction and the

equipment used, for which separate models for hand PDE and whole body (excluding hands) PDE are

developed. The following sub-scenarios are distinguished:

1. Backpack – Downward spraying: The application method consists of a handheld spray lance

or gun connected to a backpack or knapsack. The spraying direction is exclusively

downwards because the crop is shorter than the average height of the hands above the floor

(0.7m), or because the type of treatment does not require upwards application (e.g.

herbicides).

2. Backpack – Up- and Downward spraying: This scenario includes crops higher than 0.7m, for

which the application direction can be downwards, horizontal and/or upwards. The

application method consists in a handheld spray lance or gun connected to a backpack or

knapsack.

3. Gun and hose – Downward spraying: The application method consists in a handheld spraying

lance or gun connected to a tank by a hose. The tank can be either fixed or tractor mounted.

The spraying direction is exclusively downwards since the crop height or the type of

treatment does not require upwards application.

4. Gun and hose – Up- and Downward spraying. This scenario involves handheld application on

crops higher than 0.7m by using a spraying gun or lance connected to a fixed or mobile tank

95

by a hose. This scenario includes crops higher than 0.7m so that the application direction can

be downwards, horizontal and/or upwards.

Operator dermal exposure originates from the spraying equipment that releases airborne droplets

during HHA activities. A certain fraction of the spray will deposit over the operator hands and body.

Upon incidental contact with the crop a percentage of the volume applied will be transferred from

the crop to the operator. Likewise, the fraction deposited on the equipment can be transferred to

the operator hands or body by direct contact. Splashes produced when loading the backpack

represent a source of contamination by transfer from the backpack to the operator back or hands.

The bulk exposure pathway for handheld application activities includes three kinds of events, namely

leakages from the equipment, splashes from the backpack and drifting from the treated crop. Even

when the bulk pathway may have an important contribution in case an incidental event occurs, the

mentioned unintended events can be avoided by following the best agricultural practices (FAO,

2013) (FAO, (2001a); (2001b)). It is assumed that operators take into account the Best Agricultural

Practices, and follow the PPP label, therefore there is not need to estimate exposure due to misuse

(BROWSE, 2011).

Regarding bulk exposure from the treated crop, the model assumes that the optimal application rate

is assessed and low drift equipment is used. In case that drifting occurs, the fate of the contaminant

should be the area below the crop. Since the operator applies the product from a distance large

enough in which he has freedom of mobility, this event can be neglected.

It is known that even when the best agricultural practices are followed, some types of contacts

cannot be avoided. Crops are usually designed to maximize productivity by increasing the yield per

unit area. In that case, the operator may not be able to freely move between two adjacent crop

canopies leading to frequent direct contact with the crop. This pathway of exposure represent a

common event when applying PPP by using HHA techniques, therefore contacts with the treated

crop has been modelled as part of the “Direct Contact” pathway

Likewise, splashes are likely to occur during mixing and loading periods. Although the mixing and

loading activity has been modelled in a different scenario, the operator can be exposed to the

96

splashes produced during this activity by direct contact with the equipment, and therefore this

source of contamination has been included within the HHA model.

Tables 1 and 2 offer an overview of the pathways included in each scenario for both models, Hands

exposure and Whole Body (excluding hands) exposure Similar equations apply for the different sub-

scenarios based on three key parameters associated with surface contacts as described by Gorman

et al (2012) (frequency of contacts and surface contamination levels), Brouwer et al. (2001) and

Cohen Hubal et al (2005) (transfer efficiencies after continuous contacts). The model algorithms

include the transfer efficiency from the donor surface to the hands and body (Appendix H), as well as

the affected surface area of the hands (Appendix I and Table 5) or body (Table 5) involved in

contacts. The surface-to-hand transfer efficiency (Cf) is corrected to account for multiple contacts,

expressed as Cfmax (maximum number of contact with the spray gun), Cfbackpack (transfer efficiency

after multiple contacts with the backpack) or Cfcrop (transfer efficiency after multiple contacts with

the treated crop) (Appendix J).

Model inputs are different for each sub-scenario (Tables 2).

HAND MODEL:

The model consists of two pathways of exposure:

(i) Deposition of airborne droplets

(ii) 1. Contact with the crop

2. Contact with the equipment

The input parameters and ranges of the models are presented in Table3.

(i) Deposition of airborne droplets (Ddhapp)

The air concentration (Cair) (derived from the ART model) together with the droplet setting velocity

(vdep) and the application time (t) will determine the degree of exposure via deposition

(𝐸𝑞𝑢𝑎𝑡𝑖𝑜𝑛 1). The Stoke law’s defines the droplet setting velocity as a factor directly proportional to

the diameter of the droplet (m) multiplied by the gravitational acceleration and by the differences in

97

gv ag ··4

·92 2

ηδδφ −

=

density between the droplet and the air (kg∙m-3). This velocity is inversely proportional to the

dynamic viscosity of the air (Kg m-1∙s-1) (see equation 7). Droplets <100 µm are regarded as prone to

drift to non-target surfaces (Arvidsson et al., (2011); Miller & Ellis, (2000); Salyani & Cromwell,

(1992)). On the other hand, droplets <50 µm take a long time to deposit (Sundaram & Sundaram

1991). Moreover, they can evaporate in a time shorter than 1.8 seconds under certain climatic

conditions (Hofman & Solseng, 2001) and therefore they were not considered relevant for the

assessment of potential dermal exposure. Inputs parameter used to estimate the droplet setting

velocity are presented in Table 4.

The proportion of droplets sizes relies among other factors on the nozzle type, application pressure

and technique used (Nuyttens et al., 2007). Since the user is unlikely to be acquainted with this

information, a uniform distribution of droplets sizes was considered. As mentioned before, the PPP

spraying solutions density is assumed to be 1000 Kg/m3 (comparable to water). When considering

standard conditions, the droplet setting velocity ranges from 0.078 to 0.303 m•s-1 (see Table 4). The

droplet setting velocity assessed by using the mentioned equation coincides with the sedimentation

velocity estimated by Nuyttens et al. 2009 with a similar approach.

ii.1) Contact with the treated crop ( 𝑫𝒄𝒉𝒄𝒓𝒐𝒑):

ii.1.1 The surface contamination level of the crop, D

It is known that the deposition efficiency of PPP application (E) tends to be less than 100%. Spray

deposition efficiency is dependent upon multiple factors like leaf morphology, droplet size (Smith

(2000); Abdelbagi & Adams, (1987); Uk, (1977), crop height, (Abdelbagi & Adams, 1987), the

equipment used (Juste et al. 1990; Sánchez-Hermosilla et al., 2011), volume applied (Uk, (1977);

Sánchez-Hermosilla et al.,( 2011)) and the use of commercial adjuvants (van Zyl et al.,2010) etc.

Additionally the PPP is not homogeneously distributed over the crop (Sánchez-Hermosilla et al.,

2011). Moreover, the amount of spray reaching the inside zones is remarkably lower than that on

the outside zones (Juste et al., 1990). It was decided to take a broad range of spray deposition

98

efficiency in which the worst case scenario (spray deposition efficiency of 100%) was included (L.

R. Ahuja, (2000); Zabkiewicz, (2007)).

The total leaf area determines the volume in which the product applied will distribute. The Leaf

Area Index (LAI) is defined as the leaf surface area per unit of soil surface area, reflecting a close

relationship between the deposition and distribution of the applications (Rincón, Páez, &

Sánchez-Hermosilla, 2009). To determine this index, a model that estimates this index from the

crop height was used (equation 8). The correlation between the canopy height (H) and the LAI

provides a correlation coefficient of 0.91 when used to estimate the LAI of crops with a regular

distribution of nodes and leaf development.

LAI=0.99∙(H)1.49 (Rincón et al., 2009) (Equation 8)

The average PPP per leaf area can be assessed by relating the amount of active substance of PPP

that reaches the target surface with the LAI by integrating the model by Rincón et al (2009) with

the formula developed by Gil et al. (2007) to estimate the spray deposition efficiency ( equation

5.1). Ccoi is the concentration of active substance of PPP in the air (µg/m3). Ar is the Application

rate (L/m2).

𝐷 =𝐸 ∗ 𝐶𝑐𝑜𝑖 ∗ 𝐴𝑟

0.99 ∗ 𝐻1.49 ∗ 103 (𝑒𝑞𝑢𝑎𝑡𝑖𝑜𝑛 5.1)

Once the crop surface contamination is known, it is also needed to consider the frequency of

contacts, the transfer efficiency from the treated crop to the operator hands, as well as the

affected surface area of the operator hands in order to estimate hands dermal exposure (see

equation 5).

ii.1.2 Surface contacts with the treated crop, (𝑪𝒇𝒄𝒓𝒐𝒑)

This factor has been linked to the row space (Fr), where row space is defined as the space between

the outer leaves of two opposite crop canopies (see Table 3). According to Brouwer (1999) and

99

Cohen Hubal, (2005, 2008), after the 6th contact with the donor surface the dermal loading does not

increase with further contacts, therefore the upper limit of contacts per application episode is set at

the 6th contact event (𝑒𝑞𝑢𝑎𝑡𝑖𝑜𝑛 5.2).

* Considering that the hands of the operator are unlikely to be in contact with crops shorter than

0.7m, dermal hand exposure by direct contact with the crop applies only to scenarios 2 and 4.

ii.2 Contact with the equipment

ii.2.1Surface contact with the spray gun 𝑫𝒄𝒉𝒈𝒖𝒏

As it was stated before, the equation to estimate dermal exposure by surface contact with the gun

relies on the frequency of contacts, the transfer after continuous contacts from the gun to the

operator hands, and the gun surface contamination (See equation 2)

ii.2.1.1 Frequency of contact with the spray gun or lance (and with the hose

that connects the spray gun with the tank in scenarios 1 and 2) (𝑪𝒇𝒎𝒂𝒙):

The operator holds the spray lance or gun during the whole spray process. The model considers a

new contact event each time that the operator changes the position in which he/she holds the spray

lance or gun, namely after a mixing and loading period, and when changing the spraying row since

the operator tends to pull the hose toward him/herself. Considering that a typical crop area contains

at least 6 rows (expert judgement), the upper limit for number of contacts per application episode

was taken in order to simplify the model (see equation 2.2)

ii.2.1.2 Spray gun surface contamination level (𝑺𝒄𝒈𝒖𝒏 ):

The HHA model considers that the main source of contamination is the deposition of airborne

droplets from the air (See equation 2.1). Since the operator is assumed to follow the good

agricultural practices, additional sources of contamination (leakages) as consequence of odd

behavior are neglected. The deposition rate relies on the air concentration (𝐶𝑎𝑖𝑟 ) multiplied by the

application time (t) and by the droplet setting velocity (𝑣𝑑𝑒𝑝).

100

ii.2.2 Surface contact with the backpack (𝐃𝐜𝐡𝐛𝐚𝐜𝐤𝐩𝐚𝐜𝐤)

Hand exposure by direct contact with the backpack considers the frequency of contacts as well as

the variation in the transfer efficiency from the backpack to the operator hands (See equation 3)

ii.2.2.1 Frequency of contact with the backpack (𝑪𝒇𝒃𝒂𝒄𝒌𝒑𝒂𝒄𝒌 ):

Represented by events of putting on/taking off the backpack before and after a mixing/loading

event (𝐹𝑚&𝑙). The mixing & loading periods are either an user input, or estimated from the volume

applied and the tank volume in case this value is unknown by the user. The volume applied

parameter is estimated from user inputs and are based on the total area sprayed (Ha), dose (kg/Ha)

and final mixing & loading concentration (g/l) (See equation 3.2). The surface-to-hand transfer

efficiency (Cf) is corrected to account for multiple contacts, expressed as Cfbackpack. An upper limit of

6th contact events for application episode was taken.

ii.2.2.2 Backpack surface contamination level (𝑺𝒄𝒃𝒂𝒄𝒌𝒑𝒂𝒄𝒌_𝑻𝒐𝒕):

Represented by deposition of airborne droplets onto the backpack, as well as splashes produced

when loading the backpack during a mixing and loading period. The contribution of the deposition

pathway to the total tank surface contamination relies on the air concentration (𝐶𝑎𝑖𝑟 ) multiplied by

the application time (t) and by the droplet setting velocity (𝑣𝑑𝑒𝑝) (See equation 3.1).

Due to the lack of information on spraying tank surface contamination, an estimation of this was

derived from experimental studies on surface contamination levels of induction hoppers (Glass et al,

2009; Gilbert et al, 2000; Mathers et al, 1999). The induction hopper is a receptacle similar in size to

the backpack and the method of filling is similar for both containers. Even when the opening size of

the induction bowl can be up to twice as much bigger as the opening of the spraying backpack is,

field observations often showed that operators tend to be careful when loading the backpack,

therefore both situations are comparable. The tank contamination due to splashes was estimated

from the container size (L) and the concentration of active substances in the spray volume (µg/L).

Container size varies from 0 to 7 liters, each with a probability. The probabilities are based on the

probability frequencies in the experimental data (see Table 7). The derived values are therefore

indicative and only provide information about the relative performance of the different container

sizes when used with a particular filling technique (Appendix G).

101

Data was transformed from contamination levels per operation/container to contamination levels

per square centimeter (L/cm2) for a given container size. For this purpose, surface contamination

levels were estimated using surface areas of containers. First, values were derived for the container

exterior and container upper (lid, cap) of different container sizes. The derived values are therefore

indicative and only provide information about the relative performance of the different container

sizes when used with a particular filling technique.

* PDE by direct contact with the backpack applies only to scenarios 1 and 2

In addition, the transfer efficiency from surfaces to body parts (Cf) (Appendix H), body surfaces

areas (BP) and the affected surface area of body parts (lcn/BP) (Appendix I) are included in the

algorithms. The surface-to-hand transfer efficiency (Cf) is corrected to account for multiple

contacts with an upper limit of six contacts.

Whole body (excluding hands) model

The whole body (excluding hands) model consists of two pathways of exposure:

(i) Deposition of airborne droplets

(ii) 1. Contact with the crop

2. Contact with the equipment

Similarly as for the dermal hand model, the whole body (excl. hands) model considers that transfer

from the bulk to the operator is not relevant (and therefore not included in the model) since

operators are assumed to follow the best management practice in order to avoid the occurrence of

incidental events such a splashes, leakages and drifting (Appendix N).

To model the whole body (excl. hands) exposure, similar equations are used, nevertheless the

pathways included in whole body exposure (see Table 2) as well as the model inputs vary from one

to another model. Similarly, the body surface area is extended in this model. Table 4 summarizes the

proportion of the whole body affected according to this approach. This model takes into account a

102

maximum affected body surface area of 0.94 considering that the hands and head are excluded from

this model.

The model assumes that the whole body (excl. hands) is exposed to deposition of droplets (equation

4).Whole body exposure by direct contact with the treated crop is linked to the crop height.

(𝑒𝑞𝑢𝑎𝑡𝑖𝑜𝑛 5.4). During a typical application performance by using a spraying knapsack or backpack,

just the back and the hands of the operator are prompt to be exposed by contact with the

equipment, therefore the equation to model this pathway considers that just the back body surface

is relevant (𝑒𝑞𝑢𝑎𝑡𝑖𝑜𝑛 6)*

* PDE by direct contact with the backpack applies only to scenario 1and 2

(a) Model testing

Please refer to section 3.2.7 for detailed information about the database used to test the model as

well as the statistical analysis performed. Result of the model testing are presented in figures 1to 10.

Figures 1 and 2 show the result from the sensitivity analysis performed to probe the suitability of the

four sub-scenarios proposed for the hand and whole body dermal exposure models. Results of the

hand and whole body models testing are presented in figures 3 to 10.

The graphical representations for all the sub-scenarios depict the measured potential dermal

exposure (y-axis) against the geometric mean of the model outputs (x-axis). The individual measured

potential dermal exposure (dots) and estimated model outputs (grey lines) plotted with the

exposure level ( y-axis).

(b) Algorithms

Scenario 1: Backpack – Downward spraying

I. Hand exposure model:

Dph = Ddhapp + Dchgun + Dchbackpack

Dph Potential dermal hand exposure a.s (µg)

Ddhapp Potential dermal hand exposure to droplets via deposition (µg)

103

Dch gun Potential dermal hand exposure via surface contacts with the gun (µg)

Dch backpack Potential dermal hand exposure via surface contacts with the bacpack (µg)

Deposition

𝐷𝑑ℎ𝑎𝑝𝑝 =𝐶𝑎𝑖𝑟 ∗ 𝑡 ∗ 𝑣𝑑𝑒𝑝 ∗ 𝐵𝑃ℎ𝑎𝑛𝑑𝑠 ∗ 𝑙𝑐𝑛

𝐵𝑃ℎ𝑎𝑛𝑑𝑠_𝑡𝑜𝑝 (𝑒𝑞𝑢𝑎𝑡𝑖𝑜𝑛 𝐼)

Cair Concentration of a.i. in air (µg/m3)


vdep Droplet settling velocity average deposition speed of droplets in the air (m/s)

BPhands Total surface area of the hands (m2)


Surface contacts with the gun

𝐷𝑐ℎ𝑔𝑢𝑛 =𝑆𝑐𝑔𝑢𝑛 ∗ 𝐶𝑓𝑚𝑎𝑥 ∗ 𝐵𝑃ℎ𝑎𝑛𝑑𝑠 ∗ 𝑙𝑐𝑛

𝐵𝑃ℎ𝑎𝑛𝑑𝑠_𝑝𝑎𝑙𝑚𝑠 (𝑒𝑞𝑢𝑎𝑡𝑖𝑜𝑛 2)

𝑆𝑐𝑔𝑢𝑛 = (𝑆𝑐𝑇𝑎𝑛𝑘 ∗ 𝐶𝑐𝑜𝑖) + (𝐶𝑎𝑖𝑟 ∗ 𝑡 ∗ 𝑣𝑑𝑒𝑝) (𝑒𝑞𝑢𝑎𝑡𝑖𝑜𝑛 2.1)

𝐶𝑓𝑚𝑎𝑥 = �𝐶𝑓𝑔𝑟𝑎𝑠𝑝 ∗ 0.5(𝐾−1)𝐹=6

𝐾=1

(𝑒𝑞𝑢𝑎𝑡𝑖𝑜𝑛 2.2)

Scgun = Surface contamination of the gun

Cfmax Corrected transfer efficiency for maximum amount of contacts (maximum:

0.89 with Cf = 0.45; Fgrasp= 6) (Appendix H)

104

Cfgrasp Transfer efficiency for grasping of smooth surfaces (triangular distribution

with lower value 0.23, mode 0.45 and upper 0.68)

lcn/ BPhands_palms Affected surface area of hands (m2 of BP)

Surface contact with the backpack

𝐷𝑐ℎ𝑏𝑎𝑐𝑘𝑝𝑎𝑐𝑘 =𝑆𝑐𝑏𝑎𝑐𝑘𝑝𝑎𝑐𝑘_𝑇𝑜𝑡 ∗ 𝐶𝑓𝑏𝑎𝑐𝑘𝑝𝑎𝑐𝑘 ∗ 𝐵𝑃ℎ𝑎𝑛𝑑𝑠 ∗ 𝑙𝑐𝑛


𝑆𝑐𝑏𝑎𝑐𝑘𝑝𝑎𝑐𝑘_𝑇𝑜𝑡 = (𝑆𝑐𝑏𝑎𝑐𝑘𝑝𝑎𝑐𝑘 ∗ 𝐶𝑐𝑜𝑖) + (𝐶𝑎𝑖𝑟 ∗ 𝑡 ∗ 𝑣𝑑𝑒𝑝) (𝑒𝑞𝑢𝑎𝑡𝑖𝑜𝑛 3.1)

𝐶𝑓𝑏𝑎𝑐𝑘𝑝𝑎𝑐𝑘 = � 𝐶𝑓𝑔𝑟𝑎𝑠𝑝 ∗ 0.5(𝐹𝑚&𝑙−1)

𝐹𝑚&𝑙

𝐾=1


Scbackpack Surface contamination level of the backpack (l/cm2)

Cfbackpack Corrected transfer efficiency for multiple contacts (maximum: 0.89 with Cf =

0.45; Fgrasp = 6) (Appendix H)

Fm&l Frequency of M&L periods

Ccoi Concentration of a.s in spraying volume (µg/L)

II. Whole body exposure model:

Dpw = Ddwapp + Dcwcrop + Dcwequipment

Dpw Potential dermal whole body exposure a.s (µg)

Ddwapp Potential dermal whole body exposure to droplets via deposition (µg)

Dcwcrop Potential dermal whole body exposure via surface contacts with the crop

(µg)

Dcwbackpack Potential dermal whole body exposure via surface contacts with the

backpack (µg)

105

Deposition

𝐷𝑑𝑤𝑎𝑝𝑝 = 𝐶𝑎𝑖𝑟 ∗ 𝑡 ∗ 𝑣𝑑𝑒𝑝 ∗ 𝐵𝑃𝑤ℎ𝑜𝑙𝑒 𝑏𝑜𝑑𝑦 (𝑒𝑞𝑢𝑎𝑡𝑖𝑜𝑛 4)




BPwhole body Total surface area of the body (m2)

Lcn/BPbody-ch Proportion of body part affected according to crop height (m2 of BP)

Surface contacts with the crop

𝐷𝑐𝑤𝑐𝑟𝑜𝑝 =𝐷 ∗ 𝐶𝑓𝑐𝑟𝑜𝑝 ∗ 𝐵𝑃𝑤ℎ𝑜𝑙𝑒 𝑏𝑜𝑑𝑦 ∗ 𝑙𝑐𝑛

𝐵𝑃𝑏𝑜𝑑𝑦−𝑐ℎ (𝑒𝑞𝑢𝑎𝑡𝑖𝑜𝑛 5)

𝐷 = 𝐸∗𝐶𝑐𝑜𝑖∗𝐴𝑟0.99∗𝐻1.49∗103


𝐶𝑓𝑐𝑟𝑜𝑝 = �𝐶𝑓𝑠𝑚𝑢𝑑𝑔𝑒 ∗ 0.5(𝐹𝑟−1)𝐹𝑟

𝐾=1


D Average of PPP deposited per unit of leaf area (µg/m2)

E Spray efficiency (100 %)


Ar Application rate (L/m2)

H Crop Height (m)

Fr Frequency of contact according to row space (Table 5)

Cfcrop Corrected transfer efficiency for maximum amount of contacts (maximum:

0.22 with Cf = 0.112; Fgsmudge= 6) (Appendix H)

Cfsmudge Transfer efficiency for the smudging of smooth surfaces (triangular

distribution with lower value 0.019, mode 0.112 and upper 0.45 (Appendix

H)

106


𝐷𝑐𝑤𝑏𝑎𝑐𝑘𝑝𝑎𝑐𝑘 =𝑆𝑐𝑏𝑎𝑐𝑘𝑝𝑎𝑐𝑘 ∗ 𝐶𝑐𝑜𝑖 ∗ 𝐶𝑓𝑏𝑎𝑐𝑘𝑝𝑎𝑐𝑘 ∗ 𝐵𝑃𝑏𝑜𝑑𝑦 ∗ 𝑙𝑐𝑛

𝐵𝑃𝑏𝑎𝑐𝑘 (𝑒𝑞𝑢𝑎𝑡𝑖𝑜𝑛 6)


𝐹𝑚&𝑙

𝐾=1




0.45; Fgrasp = 6)



Lcn/BPback Affected surface area of the back (m2 of BP)

Scenario 2: Backpack – Up- and Downward spraying


Dph = Ddhapp + Dchcrop + Dchgun + Dchbackpack

Dph Potential whole body dermal exposure a.s (µg)


Dchcrop Potential dermal hand exposure via surface contacts with the crop (µg)

Dchgun Potential dermal hand exposure via surface contacts with the gun (µg)

Dchbackpack Potential dermal hand exposure via surface contacts with the backpack (µg)

107

Deposition








Surface contact with the crop

𝐷𝑐ℎ𝑐𝑟𝑜𝑝 = 𝐷 ∗ 𝐶𝑓𝑐𝑟𝑜𝑝 ∗ 𝐵𝑃ℎ𝑎𝑛𝑑𝑠

𝐷 = 𝐸∗𝐶𝑐𝑜𝑖∗𝐴𝑟0.99∗𝐻1.49∗103



𝐾=1



E Spray efficiency factor



H Crop Height (m)

Cfcrop Corrected transfer efficiency for maximum amount of contacts (maximum: 0.22 with

Cf = 0.112; Fgsmudge= 6) (Appendix H)

Cfsmudge Transfer efficiency for the smudging of smooth surfaces (triangular distribution with

lower value 0.019, mode 0.112 and upper 0.45 (Appendix H)


108




𝑆𝑐𝑔𝑢𝑛 = 𝐶𝑎𝑖𝑟 ∗ 𝑡 ∗ 𝑣𝑑𝑒𝑝 (𝑒𝑞𝑢𝑎𝑡𝑖𝑜𝑛 2.1)

𝐶𝑓𝑚𝑎𝑥 = �𝐶𝑓𝑔𝑟𝑎𝑠𝑝 ∗ 0.5(𝐹−1)𝐹=6

𝐾=1



Cfmax Corrected transfer efficiency for maximum amount of contacts (maximum: 0.89 with

Cf = 0.45; Fgrasp= 6) (Appendix H)

Cfgrasp Transfer efficiency for grasping of smooth surfaces (triangular distribution with lower

value 0.23, mode 0.45 and upper 0.68)



𝐷𝑐ℎ𝑏𝑎𝑐𝑘𝑝𝑎𝑐𝑘 =𝑆𝑐𝑏𝑎𝑐𝑘𝑝𝑎𝑐𝑘_𝑇𝑜𝑡 ∗ 𝐶𝑓𝑏𝑎𝑐𝑘𝑝𝑎𝑐𝑘 ∗ 𝐵𝑃ℎ𝑎𝑛𝑑𝑠 ∗ 𝑙𝑐𝑛


𝑆𝑐𝑏𝑎𝑐𝑘𝑝𝑎𝑐𝑘_𝑇𝑜𝑡 = (𝑆𝑐𝑏𝑎𝑐𝑘𝑝𝑎𝑐𝑘 ∗ 𝐶𝑐𝑜𝑖) + (𝐶𝑎𝑖𝑟 ∗ 𝑡 ∗ 𝑣𝑑𝑒𝑝) (𝑒𝑞𝑢𝑎𝑡𝑖𝑜𝑛 3.1)


𝐹𝑚&𝑙

𝐾=1







109




Dpw = Ddwapp + Dcwcrop + Dcwbackpack

Dpw Potential whole body dermal exposure a.s (µg)


Dcwcrop Potential dermal whole body exposure via surface contacts with the crop (µg)

Dcwbackpack Potential dermal whole body exposure via surface contacts with the backpack (µg)

Deposition









𝐷 = 𝐸∗𝐶𝑐𝑜𝑖∗𝐴𝑟0.99∗𝐻1.49∗103



𝐾=1


110





H Crop Height (m)






Lcn/BPbody-ch Affected surface area of the back (m2 of BP)

Surface contacts with the backpack

𝐷𝑐𝑤𝑏𝑎𝑐𝑘𝑝𝑎𝑐𝑘 = 𝑆𝑐𝑏𝑎𝑐𝑘𝑝𝑎𝑐𝑘∗𝐶𝑐𝑜𝑖∗𝐶𝑓𝑏𝑎𝑐𝑘𝑝𝑎𝑐𝑘∗𝐵𝑃𝑏𝑜𝑑𝑦∗𝑙𝑐𝑛𝐵𝑃𝑏𝑎𝑐𝑘

(𝑒𝑞𝑢𝑎𝑡𝑖𝑜𝑛 6)


𝐹𝑚&𝑙

𝐾=1









Lcn/BPback Affected surface area of the back (m2 of BP)

111

Scenario 3: Gun and Hose – Downward spraying


Dph = Ddhapp + Dchgun

Dph Potential hands dermal exposure a.s (µg)



Deposition











𝑆𝑐𝑔𝑢𝑛 = 𝐶𝑎𝑖𝑟 ∗ 𝑡 ∗ 𝑣𝑑𝑒𝑝 (𝑒𝑞𝑢𝑎𝑡𝑖𝑜𝑛 2.1)

𝐶𝑓𝑚𝑎𝑥 = �𝐶𝑓𝑔𝑟𝑎𝑠𝑝 ∗ 0.5(𝐹−1)𝐹=6

𝐾=1


112








Dpw = Ddapp + Dccrop



Dcwcrop Potential dermal whole body exposure via surface contacts with the crop

(µg)

Deposition









113

𝐷 = 𝐸∗𝐶𝑐𝑜𝑖∗𝐴𝑟0.99∗𝐻1.49∗103



𝐾=1






H Crop Height (m)








Scenario 4: Gun and Hose – Up- and Downward spraying

I. Hand exposure model

Dph = Ddhapp + Dchgun + Dchcrop

Dph Potential hands dermal exposure a.s (µg)


Dchcrop Potential dermal hand exposure via surface contacts with the crop (µg)


114

Deposition









𝐷𝑐ℎ𝑐𝑟𝑜𝑝 = 𝐷 ∗ 𝐶𝑓𝑐𝑟𝑜𝑝 ∗ 𝐵𝑃ℎ𝑎𝑛𝑑𝑠

𝐷 = 𝐸∗𝐶𝑐𝑜𝑖∗𝐴𝑟0.99∗𝐻1.49∗103



𝐾=1






H Crop Height (m)






115











Dpw =Ddwapp + Dcwapp



Dcwcrop Potential dermal whole body exposure via surface contacts with the crop (µg)

Deposition






116




𝐷 = 𝐸∗𝐶𝑐𝑜𝑖∗𝐴𝑟0.99∗𝐻1.49∗103



𝐾=1






H Crop Height (m)








(c) Model testing

Please refer to the type of plots/graphs and explanation under item 3.2.6 (f)

117

The whole model was run with a dataset of more than 600 data entries in which some inputs were

imputed. There were at least 120 data entries per scenario. Imputation was done by empirical

sampling from existing values in the data and 2,000 iterations were used.

Mean, median, 2.5th percentile and 97.5th percentiles were calculated and plotted against the true

values that we have from data and plot the 95% confidence intervals, medians and means of the

output against the true values (all on log10 scale) for comparison

Additionally the suitability of considering four different scenarios based on the spraying direction

and the equipment used was tested. Figures 1 and 2 prove that the variation in exposure decreases

by considering the above mentioned scenarios.

As shown in figures 3 to 10, the whole model tends to underestimate exposure for hand exposure

as well as for whole body exposure, nevertheless it can be due to the imputed data used. The model

provides a high variability and broad range in exposure

Scenario 1: Backpack Downward Spraying

Testing of the hands exposure model in plotted in Figure 3 and whole body is plotted in Figure 4

Scenario 2: Backpack Up and Downward Spraying


Scenario 3: Gun and Hose Downward Spraying


Scenario 4: Gun and Hose Up and Downward Spraying


(d) Comment to Gun and Hose Up and Downwards spraying

Within the Browse Reserve fund Report Field trials conducted in Greece data on operator exposures for hand held application of a gun and hose up and downward scenario were collected.

118

These data are considered to mimic worst case scenario’s. In these experiments the transfer from crop to operator was measured during spraying processes. This was done by first spraying pesticide A and later spraying pesticide B while measuring pesticide A. Crops were very dense and high with narrow row spacings. Comparisons of our model estimations of crop transfer and the experimental data collected in the reserve fund projecte are displayed in the table Operator Model output (% of total exposure

contribution by the crop transfer route) Reserve fund output (worst case) (% of measured pesticide A transferred from crop during application of pesticide B directly after application of pesticide A)

Total Body 0 – 11 % 10 – 16 % Hands 0 – 22.6 % 9 – 21 % (e) Tables

Table 1: Hand Exposure Models, Scenarios and Pathways included

Model Scenario Pathway

Hands Exposure Backpack Downward

Spraying

Potential dermal hand exposure to droplets via deposition (Ddhapp) Potential dermal hand exposure via surface contacts with the gun (Ddhgun)

Potential dermal hand exposure via surface contacts with the backpack (Ddhbackpack)

Backpack Up and

Downward Spraying

Potential dermal hand exposure to droplets via deposition (Ddhapp) Potential dermal hand exposure via surface contacts with the crop (Ddhcrop)

Potential dermal hand exposure via surface contacts with the gun (Ddhgun)

Potential dermal hand exposure via surface contacts with the backpack (Ddhbackpack)

Gun and Hose

Downward Spraying

Potential dermal hand exposure to droplets via deposition (Ddhapp) Potential dermal hand exposure via surface contacts with the gun (Ddhgun)

Gun and Hose Up and

Downward Spraying

Potential dermal hand exposure to droplets via deposition (Ddhapp) Potential dermal hand exposure via surface contacts with the crop (Ddhcrop)

Potential dermal hand exposure via surface contacts with the gun (Ddhgun)

Table 2: Whole Body Exposure Models, Scenarios and Pathways included

Model Scenario Pathway

Whole Body (excluding

hands) Exposure Backpack Downward

Spraying Potential dermal whole body exposure to droplets via deposition (Ddwapp) Potential dermal whole body exposure via surface contacts with the crop (Dcwcrop ) Potential dermal whole body exposure via surface contacts with the backpack

(Dcwbackpack ) Backpack Up and

Downward Spraying Potential dermal whole body exposure to droplets via deposition (Ddwapp) Potential dermal whole body exposure via surface contacts with the crop (Dcwcrop ) Potential dermal whole body exposure via surface contacts with the backpack

(Dcwbackpack )

119

Gun and Hose

Downward Spraying Potential dermal whole body exposure to droplets via deposition (Ddwapp) Potential dermal whole body exposure via surface contacts with the crop (Dcwcrop )

Gun and Hose Up and

Downward Spraying Potential dermal whole body exposure to droplets via deposition (Ddwapp) Potential dermal whole body exposure via surface contacts with the crop (Dcwcrop )

Table 3: Handheld application model inputs

abbr User input Model Inputs Lower Upper Type Based on

t Total application

time of spraying

activity (s)

Application Time 0 1500 Selected value

Cc.o.i Concentration of

a.s. in spray volume

(g/l)

Concentration of a.s. in

spray volume (g/l)


Cair n/a Concentration of A.I. in

air (µg/m3) according to

inhalation model

Fixed

Fmlp n/a Number of M&L periods

(estimated with volume

applied and tank size)


Fr n/a Frequency of contact

according to row space

1 6 Uniform

distribution

according to row

space

Table 6

H Crop height (m) Crop height (m) Selected value

Ar Application Rate,

(L/ha)

Application Rate, (L/ha) Selected value

E n/a Spray deposition

Efficiency factor

0.6 1 Default is 1 (L. R. Ahuja,

(2000);

Zabkiewicz,

(2007)).

120


drow Row space (m) Row space (m) 0.1 >1.5 Selected Value (Frenich et al.,

2002),

(Machera,

2003)

Cfcrop n/a Corrected transfer

efficiency for maximum

amount of contacts

(maximum: 0.22 with Cf

= 0.112; Fgsmudge= 6)

- - Fixed Appendix H

Cfsmudge n/a Transfer efficiency for

the smudging of smooth

surfaces

0.019 0.45 triangular

distribution with

lower value

0.019, mode

0.112 and upper

0.45

Appendix H

BPhands Gender (male,

female)

Surface area of hands

(m2)

- - lognormal

distributions

(Males: log(SA) ~

Normal (-2.2319,

0.109922)

Females: log(SA)

~ Normal(-

2.4313, 0.10362))

Appendix I

BPwholeb

ody

Gender (male,

female)

Surface area of the

whole body (m2)

- - Lognormal

distributions

(Males:

Appendix I

Lcn/BPb

ody-ch

n/a Proportion of body part

affected according to

crop height

- - Table 5

121


lcn/

BPhands_t

op

n/a Affected surface area of

top of hands

- - Table 5

* Number of contacts with the treated crop is linked to the row space (See table 6)

Table 4. Droplet setting velocity variables

Symbol Description Units Multiplier Effect Range

V Droplet Setting Velocity m∙s-1 0.078 – 0.303

ø Droplet Diameter m - 5∙10-5 - 1∙10-4

δg Droplet density Kg∙m-3 1,000

δa Air Density Kg∙m-3 1.275

ἠ Dynamic viscosity of the air kg∙m-1∙s-1 1.78 ∙10-5

g Gravitational constant m∙s-2 9.81

Table 5. Body surface area affected. Default values No Input Variable User

input Inputs Multiplier Effect

Range Type of distribution

Based on

1 Total surface area of hands, m2 (BPhands)

No Male 0,107 0.09 – 0.131 Mean 0.107 (5% 0.09, 95% 0.131)

US EPA (1985)

Female 0.089 0.076 – 0.106

Mean 0.076 (5% 0.076, 95% 0.106)

US EPA (1985)

2 Total surface area of the body, m2

(BPwhole body)

No Male 1.95 1.62 – 2.41 Mean 1.95 (5% 1.62, 95% 2.41)

US EPA (1985)

Female 1.76 1.40 – 2.24 Mean 1.76 (5% 1.40, 95% 2.24)

US EPA (1985)

3 Total surface area of the back, m2 No 0.155 US EPA 2000

4 Lcn/Bphands_top No 0.5 ?

5 Lcn/Bphands-palms No 0.5

6 Lcn/BPback No 1

7 Icn/BPbody-ch Crop height(m)

US EPA 1996

0-0.19 Male 0.066

Female 0.066

122

0.2-0.59 Male 0.2

Female 0.19

0.6-0.99 Male 0.4

Female 0.39

1-1.29 Backpack model

Male 0.66

Female 0.64

1-1.29 gun and hose model

Male 0.86

Female 0.81

>1.3 Backpack model

Male 0.74

Female 0.68

>1.3 gun and hose model

Male 0.94

Female 0.86

Table 6 Frequency of contacts according to row space

No Input Variable User input Number of contacts

Row Space (m)

Base on

8 Number of Contacts according to Row space

no 1-2 >1.5 (Brouwer et al., 2001) (Cohen Hubal et al., 2005) 3-4 0.91 – 1.49

5-6 <90

Table 7. Overview of induction bowl contamination after emptying a full container

Mathers et al 2000 Glass et al 2009 Glass et al 2009

Container

size

Induction bowl

contamination

(L)

SD Induction bowl

contamination

(L)

Induction bowl

contamination

(L)

Induction bowl

contamination

(L)

Induction bowl

contamination

(L)

Surface area

induction bowl

(m2)

Average surfac

contamination

(L∙m-2)

1 1.00E-06 6.00E-06 0,25 4,00E-06

5 3.10E-06 1.13E-

05

3.10E-06 7.00E-06 0,25 1,22E-05

10 5.8E-06 8.70E-

06

3.80E-05 1.01E-04 0,25 8,76E-05

20 5.00E-05 7.00E-

05

5.10E-05 8.60E-05 2.20E-04 3.80E-04 0,25 4,28E-04

123

Table 8. Default values for Transfer Coefficients.

Cf Description / specifics Reference N median AM SD min max

Cfgrasp

Donor Surface = Spray tank Surface to glove, Smudge, >12 hours, Dry, Mass/mass, Liquid

Ramwell et al., 2010 Decontamination of agricultural sprayers, HSE report RR792

6 48.5 47.8 23.2 21 79

Cfsmudge Donor Surface= treated crop to hands, >12 hours, Wet and Dry, Mass/mass.

16 8 11.21 12.7 1,9 45.5

(f) Figures

Figure 1. Comparison of geometric means against true values for all hand PDE models. Red

represents the Backpack Downwards scenario, Blue represents the Gun and Hose

Downwards scenario 2, Black is the Gun and Hose Up and Downwards scenario 3 and green

is Backpack Up and Downwards scenario 4.

0.001 0.01 0.1 1 10 100 1000 10000 100000 1e+06 1e+070.001

0.01

0.1

1

10

100

1000

10000

100000

1e+06

1e+07

log10 (Mean(Model outputs(µg/hands)))

log 10

(Dat

a(µg

/han

ds))

124

Figure 2. Comparison of geometric means against true values for all whole body excluding hand PDE

models. Red represents the Backpack Downwards scenario, Blue represents the Gun and

Hose Downwards scenario 2, Black is the Gun and Hose Up and Downwards scenario 3 and

green is Backpack Up and Downwards scenario 4.

Figure 3. Hands exposure. Plot of 95% CIs (grey lines), geometric means (red dots) and medians (black dashed lines) for the log10 (model outputs) with the log10 (measured potential dermal exposure (hands)*) (blue diamonds). (Backpack Downwards scenario). * Including data sets which have model inputs that were imputed

1 10 100 1000 10000 100000 1e+06 1e+07 1e+08 1e+091

10

100

1000

10000

100000

1e+06

1e+07

1e+08

1e+09

log10 (Mean(Model outputs(µg/body)))

log 10

(Dat

a(µg

/bod

y))

0 20 40 60 80 100 120 140 1600.1

1

10

100

1000

10000

100000

1e+06

1e+07

Data index

log 10

(Mod

el o

utpu

ts (µ

g/ha

nds)

)

125

Figure 4. Whole body (excluding hands) exposure. Plot of 95% CIs (grey lines), geometric means (red

dots) and medians (black dashed lines) for the log10 (model outputs) with the log10 (measured potential dermal exposure (whole body excluding hands)*) (blue diamonds). (Backpack Downwards scenario).* Including data sets which have model inputs that were imputed

Figure 5. Hands exposure. Plot of 95% CIs (grey lines), geometric means (red dots) and medians (black dashed lines) for the log10 (model outputs) with the log10 (measured potential dermal exposure (hands)*) (blue diamonds). (Backpack up and downwards scenario). * Including data sets which have model inputs that were imputed

0 20 40 60 80 100 120 140 160 1801

10

100

1000

10000

100000

1e+06

1e+07

1e+08

Data index

log 10

(Mod

el o

utpu

ts (µ

g/bo

dy))

0 10 20 30 40 50 60 70 80 90 1000.001

0.01

0.1

1

10

100

1000

10000

100000

1e+06

Data index

log 10

(Mod

el o

utpu

ts (µ

g/ha

nds)

)

126

Figure 6. Whole body (excluding hands) exposure. Plot of 95% CIs (grey lines), geometric means (red

dots) and medians (black dashed lines) for the log10 (model outputs) with the log10 (measured potential dermal exposure (whole body excluding hands)*) (blue diamonds). (Backpack up and downwards scenario). * Including data sets which have model inputs that were imputed

0 20 40 60 80 100 1201

10

100

1000

10000

100000

1e+06

1e+07

Data index

log 10

(Mod

el o

utpu

ts (µ

g/bo

dy))

127

Figure 7. Hand exposure. Plot of 95% CIs (grey lines), geometric means (red dots) and medians (black

dashed lines) for the log10 (model outputs) with the log10 (measured potential dermal exposure (hands)*) (blue diamonds). (Gun and hose downwards scenario). *Including data sets which have model inputs that were imputed

Figure 8. Whole body (excluding hand) exposure. Plot of 95% CIs (grey lines), geometric means (red dots) and medians (black dashed lines) for the log10 (model outputs) with the log10 (measured potential dermal exposure (whole body excluding hands)*) (blue diamonds). (Gun and hose downwards scenario). *Including data sets which have model inputs that were imputed

0 10 20 30 40 50 60 70 80 90 100

10

100

1000

10000

100000

Data index

log 10

(Mod

el o

utpu

ts (µ

g/ha

nds)

)

0 10 20 30 40 50 60 70 8010

100

1000

10000

100000

1e+06

1e+07

Data index

log 10

(Mod

el o

utpu

ts (µ

g/bo

dy))

128

Figure 9. Hand exposure. Plot of 95% CIs (grey lines), geometric means (red dots) and medians (black

dashed lines) for the log10 (model outputs) with the log10 (measured potential dermal exposure (hands)*). (Gun and hose up and downwards scenario). * Including data sets which have model inputs that were imputed) (blue diamonds)

Figure 10. Whole body (excluding hands) exposure. Plot of 95% CIs (grey lines), geometric means (red dots) and medians (black dashed lines) for the log10 (model outputs) with the log10 (measured potential dermal exposure (whole body excluding hands)*) (Gun and hose up and downwards scenario) * including data sets which have model inputs that were imputed) (blue diamonds)

0 20 40 60 80 100 120 1400.1

1

10

100

1000

10000

100000

Data index

log 10

(Mod

el o

utpu

ts (µ

g/ha

nds)

)

0 20 40 60 80 100 120 140 16010

100

1000

10000

100000

1e+06

1e+07

1e+08

1e+09

Data index

log 10

(Mod

el o

utpu

ts (µ

g/bo

dy))

129

3.3 Aggregated exposure

3.3.1 External exposure

As presented in the previous section, potential inhalation and dermal exposure models were

developed for each scenario. The ingestion estimates are based on input from the dermal model and

default estimates of hand-to-mouth events. For the inhalation and dermal exposure models, a

distinction is made between potential and actual exposure estimates. For dermal exposure, model

estimates distinguish between body surface areas of males and females (Appendix I).

Potential exposure

The ‘potential’ exposure models predict an operator’s exposure to PPP without taking into account

(protective) clothing or respiratory protective equipment. The potential exposure level therefore

reflects the exposure levels typically measured during field studies, e.g. an inhalation sample in the

breathing zone of the operator (and not taking account of the effect of respirators), or the amount

of PPP sampled on the exterior of the body (on gloves and clothing, but also on the skin if

unprotected). These models can therefore be calibrated and validated using potential exposure

data.

Actual exposure

The estimates of the potential exposure models are subsequently used for ‘actual’ exposure

estimates. An actual estimate represents the predicted PPP exposure considering the reduction in

exposure due to skin or respiratory protection. In some instances, the actual and potential exposure

will be equal because no protection was used. For ingestion exposure the potential exposure is equal

to the actual exposure. However, the effect of respiratory equipment (gloves, respirators) needs to

be considered to estimate the (default) frequency of hand to mouth contacts. The ingestion

exposure will also be estimated separately for each scenario (since hand contamination levels may

vary).

The actual estimates cannot always be validated because of a lack of data. Data on actual exposures

are generally limited to measurements taken inside respirators, or skin samples taken underneath

clothing or PPE. Extensive literature reviews and analyses of data on the effectiveness of PPE are

applied as default reduction factors in the models (see separate report from reserve fund proposal

on this). 130

For dermal exposure, the BROWSE models distinguish between hand exposure and body exposure.

However, to include the use of PPE and work wear as risk management measures, the models

distinguish between hand, body and head exposure. For this purpose, an estimate is made to

differentiate between body and head exposure. Based on a literature review of body part

distributions (Appendix F), the following distributions of head exposure compared to the total body

exposures are presented in Table 21.

3.3.1.1 Inhalation exposure (Ia)

The actual inhalation exposure (Ia) is estimated using the following equation:

Ia = Ipde * Prpe

Where,

Ipde potential inhalation exposure to a.s. in droplet spray in µg/m3

Prpe protection factor of respiratory protective equipment (RPE) (Table 20)

3.3.1.2 Dermal exposure (Da)

The actual dermal exposure (Da) is estimated using the following equation:

Da = (Dph * Pppe) + (Dpw * Pppe)

Where,

Dph Potential dermal exposure to a.s. in µg on the hands

Dpw Potential dermal exposure to a.s. in µg on the whole body (excl. hands)

Pppe Protection factor of personal protective equipment (PPE) (Table 20)

3.3.1.3 Ingestion exposure (Ina)

Ingestion of pesticides may occur as a result of hand-to-mouth contacts in the workplace, also

referred to as inadvertent ingestion exposure. It has been estimated that 16% of the working

population in the UK may be exposed to hazardous materials by the ingestion route (Cherrie et al,

2006).

To estimate ingestion exposure, a similar approach was used as described by Gorman NG et al

(2012). The actual ingestion exposure (Ina) is estimated as follows:

131

Ina = Dph * Am/Ah * SE * (N*t)

Ina ingestion exposure, µg (per scenario)

Dph potential dermal exposure to a.s. in µg on both hands (per scenario)

Am area of one hand making contact with the mouth [cm2] (fixed 75cm2) (14% from

EPA, Michaud)

Ah area of both hands [cm2] (EPA’s value for male hands of 1070cm2)

Am/Ah 0.07

SE skin to mouth transfer factor (0.43); see Table 23

N number of hand-to-mouth contacts (events/hr); Table 22

t exposure time of scenario in hrs (minutes/60; roundup=1). So N*t ≥1 roundup

More information of each parameter is presented in Table 22. Evidence for the input parameters of

ingestion exposure was obtained from various sources. The potential dermal exposure to a.s. on

both hands (Dph) for a given per scenario is considered the mass balance input. The relative

(contaminated) hand surface area (Am) that may contact the peri-oral or oral cavity is estimated

using an EPA default value (14%), assuming that only the fingertips of one hands are involved in a

hand-to-mouth contact event. A hand-to-mouth transfer factor (0.43) was calculated using EPA and

IOM data (Table 23). The latter value is derived for hand-to-mouth (oral cavity) transfer, but does

not include the transfer efficiency from the peri-oral area to the oral cavity Since hand-to-mouth

contacts may primarily involve contacts with the peri-oral region (and much less contacts directly

with the mouth), the value of 0.43 may be too conservative. A value of 10% (0.1) may also be

considered for the transfer efficiency into the mouth (Cherrie et al, 2006).

To derive values for the frequency of hand-to-mouth contacts, an observational study from Gorman

NG & colleagues (under review) was used. They observed workers’ hand-to-mouth behaviors in

different industries, worksites and tasks. The majority of the contacts occurred between the hands

and peri-oral area (lips and within 2cm of the lips). Interestingly, the hand-to-mouth contact

frequencies were significantly higher among workers in the high risk perception group. The high risk

group is workers who are aware that they are using highly hazardous or toxic substances. This

suggests that workers working with substances they consider hazardous may not necessarily reduce

their hand-to-mouth contact behavior. It was found that the hand-to-mouth contact frequency

(contacts/hr) was significantly higher between-tasks than during tasks, and significantly higher in

132

industrial worksites (AM=7.6; 6.5-26) compared with laboratories (AM=0.5; 0-4). Among workers

who used personal protective equipment (PPE), the contact frequencies (contacts/hr) were lower

when gloves (AM=1.2) and respirators (AM=0.1) were worn, than when they were not worn (gloves;

AM=4.8; respirators; AM=5.3).

To derive default values for the ‘number of hand-to-mouth contacts’ (N), we assumed that the

operator exposure scenarios may be more comparable to the ‘laboratory/research’ work sectors.

The reason for this assumption is that this sector adopted more stringent ‘best practices’ and

respirator use compared with the ‘industrial sector’. Since the WP1 models assess task-based

exposures, the ‘between tasks’ hand-to-mouth contacts are excluded. We decided to distinguish

between the presence and absence of respirators or glove use. Since the effect of the combined use

of both gloves and respirators were not presented in the study, a hand-to-mouth contact frequency

(N) was derived for ‘use of gloves/respirators’. Considering the overall outcome of the study

described earlier, it was decided to assign a uniform distribution for when gloves/respirators are

worn (0.1; 1.0), and when not worn (0.5; 5.0).

Existing PPP models do not take account of ingestion exposure for operators. Observational

evidence is available that indicates that ingestion exposure is possible through hand-to-mouth

contacts in different industries (Gorman Ng et al, 2013). However, limited evidence is available from

epidemiological studies to substantiate actual ingestion exposure, e.g. using biomonitoring. With

that in mind, and the evidence currently available, we feel that it is appropriate to include an

ingestion estimate in the WP1 operator models. Considering the fact that the ingestion exposure

route is new and not commonly used for regulatory purposes in the agricultural sector, the ingestion

estimate is by default not activated in the software. However, it is up to the software user to

activate the ingestion module and interpret the ingestion exposure predicted by the model.

3.3.2 Internal exposure

Absorption values together with the bodyweight are applied to convert the external (actual)

exposure to the internal exposure. This is done per exposure route in order to compare the exposure

with the AOEL (given in mg/kg bw). For inhalation and ingestion exposure a default absorption of

100% is used. The absorption value used for dermal exposure is substance-specific and also specific

for pure and diluted formulations (e.g. different for ML and boom spraying scenarios).

133

The EFSA guidelines are used to take account of the variations. The software therefore allows users

to enter their own values.

(g) Tables

Table 20: Personal protective equipment (PPE) and work wear reduction factors

PPE or work wear item Protection factor (by which exposure in absence of protection should be multiplied)

Specific exposure value affected

Type or source of value

Hand exposure Protective (chemical resistant) gloves

Liquids 10% Solids 5%

Dermal exposure – hands only

Constant-EFSA

Gloves unspecified 3.95% M/L Liquids 0.85% Solids 4.52%


75th percentile- BROWSE

Gloves unspecified 0.00-97.07% M/L Liquids 0.00-19.17% Solids 0.00-56.93%


Distribution- BROWSE

Latex/PE/Vinyl/PVC gloves

5.51% M/L Liquids: 0.18%



Latex/PE/Vinyl/PVC gloves

0.00-70.85% M/L Liquids: 0.00-4.05%



Nitrile gloves 3.14% M/L Liquids 0.85% Solids 4.15%



Nitrile gloves 0.00-97.07% M/L Liquids 0.00-19.17% Solids 0.00-56.93%



Body exposure Long sleeved shirt and trousers (Working clothing)

10%

Dermal exposure – body only

Constant-EFSA

Unspecified work clothing

7.2% Dermal exposure – body only


Unspecified work clothing

0.01-71.31% Dermal exposure – body only


Protective coverall 10%


Constant-EFSA

Protective coverall certified

5%


Constant-EFSA

134

PPE or work wear item Protection factor

(by which exposure in absence of protection should be multiplied)

Specific exposure value affected

Type or source of value

Cotton coverall 9.60% Dermal exposure – body only


Cotton coverall 0.01-50.07% Dermal exposure – body only


Polyester-cotton coverall



Polyester-cotton coverall



Polyester-cotton coverall for high-intensity crops (hand-held spraying only)



Polyester-cotton coverall for high-intensity crops (hand-held spraying only)



Uncertified rain suit 0.2% Dermal exposure – body only

AM- BROWSE (indicative value)

Certified PPE 0.1% Dermal exposure – body only

AM- BROWSE (indicative value)

Head exposure Hood and visor 5% Dermal exposure –

head only Constant-EFSA

Hood 50% Dermal exposure – head only

Constant-EFSA

RPE mask type

Filter type

Half and full face masks

FP1, P1 and similar

80% Dermal exposure – head only

Constant-EFSA

FFP2, P2 and similar

80% Dermal exposure – head only

Constant-EFSA

Inhalation exposure RPE mask type

Filter type

Half and full face masks

FP1, P1 and similar

25% Inhalation exposure Constant-EFSA

FFP2, P2 and similar

10% Inhalation exposure Constant-EFSA

135

Table 21: Distribution of percentage of dermal head exposure compared to dermal total exposure*

Scenario Distribution of % head exposure compared to total dermal exposure (head, body, hands)

Boom spraying 0-6% Orchard spraying 0-17% Hand held spraying high level 0-12% Hand held spraying low level 0-1% Mixing and loading 0-1% *the upper percentage is currently proposed and programmed in the software

Table 22: Ingestion exposure - main user and model inputs

No User inputs Model inputs Lower Upper Based on 1 n/a Potential dermal exposure to a.s. in µg on

both hands (Boom spraying) (Dph_bs)

Full distribution

- BS hand model output

2 n/a Potential dermal exposure to a.s. in µg on both hands (Mixing & loading) (Dph_ml)

Full distribution

- M&L hand model output

3 n/a Area of one hand making contact with the mouth (cm2) (Am)

Fixed 75 cm2

- (14% from EPA, Michaud)

4 n/a Area of both hands [cm2] (Ah) Fixed 1070 cm2

- Appendix I

5 n/a Skin to mouth transfer factor (SE) Fixed 0.43 - EPA, IOM data (Table 23)

6 PPE inputs (relevant for both BS and ML)

Number of hand-to-mouth contacts# (events/hr) (i) if gloves or respirators are used

(events/scenario) if gloves or respirators are not used (events/scenario)

uniform 0.1 0.5

uniform 1.0 5.0

Cherrie et al 2007; Gorman Ng (in press)

7 Exposure duration (per scenario)

Exposure time of scenario in hrs (minutes/60; roundup=1). So N*t ≥1 roundup

Scenario-specific

- -

*different values may be proposed for the frequency of hand-to-mouth contacts to distinguish between PPE use and no PPE use #assuming primarily peri-oral contact

Table 23: Analysis outputs to derive a skin-to-mouth transfer factor (SE)

IOM EPA Combined N 12 27 39 AM 0.14 0.48 0.38 SD 0.31 0.13 0.26 GM - * 0.46 - * Min -0.14 0.22 -0.14 50th percentile 0.05 0.50 0.43~

136

IOM EPA Combined 75th percentile 0.22 0.59 0.57 90th percentile 0.33 0.62 0.61 95th percentile 0.64 0.66 0.67 99th percentile 0.93 0.70 0.89 Max 1.00 0.71 1.00 ~ The 50th percentile was used for the skin to mouth transfer factor (0.43) * Due to negative values

3.4 Quality assurance and testing procedures

(h) Exposure data

For the inclusion or non-inclusion of exposure data collected from literature a decision tree was used

(see Figure 1) for uptake of these data in the exposure database used for modeling purposes. Studies

should contain information on inhalation, dermal (potential and/or actual) or biomonitoring

exposure data, preferably at the level of individual measurements (raw data), but at least with

summary statistics with regard to measured exposure levels (minimum, maximum and AM/GM,

SD/GSD).

Figure 19: Decision tree acceptance exposure data

For included studies, the study design is adequately described (with general information like year of

study, location, target persons, site of application, indoor/outdoor, environmental conditions, etc.),

including the methods used to measure exposure (whole body, patch techniques, etc.) and the

sampling devices/material used. In principle, only studies in which personal measurements are

performed are included (i.e. stationary measurements will only be used for scenarios where no

personal measurements are available for operators. The guidelines (e.g. GLP) under which the study

Exposure data B,R,O,W?

Adequate description study design and

monitoring methods

Analysing methodology

adequate described

Contextual information on

exposure determinants

available

Inclusion in database

No inclusion

No inclusion

No inclusion

137

is carried out are described. The number of study subjects measured, test sites etc. are clearly

described. All the conditions of the study are reported so a decision on representativeness and

relevance to the models to be developed can be made.

As a minimum the study reports for included studies contain an outline or reference to description

of the analytical method. The analytical method used should be a validated method.

Ideally, the report should state if the data have been corrected for either for field or laboratory

recoveries. Also how data <LOD or <LOQ are being handled is described properly.

Although the data from the EUROPOEM database was not checked (see paragraph 6.2), based on

the information available in the executive summaries of the studies and information from persons

involved in the EUROPOEM project itself, the analytical quality of the study and the

representativeness of the study were combined in an overall quality score, which has been

documented in the database, together with the underlying remarks about the analytical quality,

representativeness and other remarks. This information was taken into account when selecting data

for the use during the model development, for which a tiered approach was used, depending on the

availability of data for certain scenarios. The inclusion of data from studies was discussed within the

project team.

(i) Development of the exposure database

All the exposure data that are entered in the database by a project member have been checked on

good entry by another person from the project team and errors have been corrected. The starting

point of the BROWSE exposure database was the EUROPOEM database, consisting of already

entered data, including quality check. The data from the original EUROPOEM database have not

been checked again, in part because it was known that this was already done extensively within the

EUROPOEM project, and in part because we had no access to the a large part of the original study

reports.

138

(j) Model development

For the development of the mechanistic models all relevant literature has been reviewed and

summarized. This information was used as input for model parameters and the resulting model

algorithms and inputs were developed in the statistical package R (by FERA statistician). The

programmed models and model outputs were checked by TNO. All the relevant contextual

information in the database was translated and coded according to relevant model parameters

required to test the models. In case of missing information, empirical sampling was performed to

obtain imputed values. All model runs and testing was critically evaluated, checked and re-run if

required to exclude errors in the models.

(k) Software

The programmed model in R was checked by FERA and directly used to develop the software. All

code was peer reviewed within the software team and then tested by partners to ensure it

performed as intended, before release to the BROWSE Advisory Panel and stakeholders for further

testing.

139

4 Comparison with existing models

The comparison with existing models is describes in “Operator exposure: Comparison of BROWSE

model with the existing models: Annex to the Final WP1 Technical Report” by Charistou et al, 2014.

140

5 Model outcome interpretation and level of conservatism

5.1 Exposure outcomes

Potential exposure estimates of the WP1 models are expressed differently depending on the route

of exposure, e.g. inhalation (µg/m3), dermal (µg/body part) and ingestion (µg). These values are

converted to mg/kg bodyweight for each exposure route, distinguishing between potential and

actual exposure after taking account of the effect of PPE and/or work clothing. Subsequently these

estimates are translated to the absorbed amount (mg/kg body weight) and the proportion of the

AOEL or AAOEL. The model outputs can therefore relate to longer-term or acute estimates related to

the AOEL or AAOEL (75th and 95th percentiles respectively).

5.2 Routes and sources of exposure included

Where appropriate, the different routes of exposure (inhalation, dermal and ingestion) are included

in the respective models. The dermal exposure pathways that are often neglected in exposure

modeling, like bulk/splashes, are also included if relevant, for instance in the M&L dermal model.

Also, model estimates always include both hand and body exposures. The models only estimates

primary exposure (direct exposure due to work performed by the operator him/herself), and thus

not from possible secondary sources of exposure (e.g. other application sources in the vicinity).

5.3 Exposure scenarios included

The operator exposure models are developed for the following scenarios:

• Boom spraying

• Mixing & loading (liquids and solids)

• Orchard spraying

• Hand-held spraying

Depending on input parameters like vehicle sprayer type, mixing and loading method, sprayed area,

tank volume, the presence of a cabin, wearing PPE etc. these scenarios are further subdivided in

order to make a fit-for purpose exposure assessment for reasonable worst-case and typical

scenarios.

141

5.4 Population

The models are considered relevant for both males and females and all age groups, although no

explicit account was made to include differences between these population groups. An exception is

where a distinction is made between body surface areas of males and females (in the dermal

models). Although most of the available information on operator exposure, especially the exposure

data, is based on studies with males, the mechanistic models with their underlying parameters are

considered also applicable for estimating exposure of female operators, although some difference in

actual working practices may exist. The models intend to cover operators that perform their work

according to Good Agricultural Practice (GAP). Misuse during handling of PPPs is not taken into

account.

5.5 Representativeness of model outcomes versus “real world”

Model inputs are mostly obtained from experimental studies or evidence from literature. These

model inputs (distributions) represent a range (with lower and upper values) of the best available

data on ‘actual’ conditions in practice. Model parameter distributions are sometimes based on

small-sized populations (or number of operators), limited experimental data (and conditions of use)

and may not always include differences found between field studies or workplace settings. This will

obviously increase the level of uncertainty in the model parameter inputs and model outputs.

5.6 Level of uncertainty and variability

All the WP1 models are probabilistic and dependent on iterations of (distributions) of model input

parameters. In turn, the models are capable of producing an exposure distribution. The median,

75th percentile, and 95th percentile are presented to the user in the software. Variability and

uncertainty were quantified using distributions for a large number of internal model parameters (see

WP1 Technical Report), and in some of the options for user inputs (body weight, breathing rate and

PPE). Therefore the percentiles output by the model represent the effect of variability and

uncertainty in all these factors on the exposure estimate. This output variance has also been

referred to in this report when carrying out sensitivity analysis (SA). One of the outputs from SA is a

table in which the relative contributions to the total output variation can be quantified and ranked

to show their relative impact on the model output.

142

5.7 Model comparison as indication for level of conservatism

An indication of the level of conservatism can be seen from the model testing as presented in this

report. The models produce exposure estimates that are in the overall range of the available

exposure data. However, these estimates do not always represent the required level of conservatism

expected for regulatory risk assessment. On the other hand, a considerable part of the data used for

testing is relatively old, and therefore the exposure levels in the available test set may be higher and

thus less representative of current agricultural practices and conditions. Based on these

considerations, and the fact that only a limited amount of recent exposure data was available during

model development, it is assumed that the level of conservatism of the models is applicable for

current agricultural practices and conditions.

5.8 Overall level of conservatism

The level of conservatism in the models and final distributions of model inputs and defaults is

reviewed (in collaboration with other work packages) to ensure an acceptable level of conservatism.

143

6 Conclusion

(a) Rationale for the modeling approach chosen

The aim of WP1 was to develop a single, new and improved modeling framework for operator

exposure, to integrate all available exposure data and take explicit account of key factors and

mechanisms influencing exposure. To meet this objective, it was decided to use a mechanistic and

probabilistic modeling approach. Unfortunately WP1 was not able to get access to new operator

exposure data. However, an advantage of mechanistic models is that exposure data is not required

as direct input for model development, and that the model inputs can be improved and updated on

a regular basis using new evidence. WP1 was also fortunate to get access to experimental data that

could provide indicative input distributions in some of the models.

For the most part, the data that were collated in the BROWSE database and used for testing /

checking of the WP1 operator models mostly consisted of existing and old data (often dating back to

the 1990s and earlier). This data (mostly EUROPOEM) has previously been found outdated, of low

quality and not representative in the rapidly (technological) advancing agricultural sector. It is also

often referred to as low quality because of high variability seen in the data itself. Whether this is

true or not, it is clear that the high variability in operator exposure data is also evident in new data.

An example is the modeling approach described in the development of a new (empirical) agricultural

operator exposure model (AOEM) (BfR, 2003; Großkopf et al, 2013). The AOEM is based on new

empirical data in which the determinants influencing exposure are unraveled and explicitly taken

into account. The latter publication emphasizes the importance of more detailed modeling

approaches that are required for operator exposure models.

(b) Status of models

The models presented in this report have not been thoroughly validated. In order to do so, new and

good quality data is required. However, outputs of the respective models were tested / checked with

(mostly outdated) datasets extracted from the BROWSE database. As complete datasets were not

always available for testing, imputed datasets were also used.

Compared to existing models, the BROWSE operator models include more model parameters and

distinguish between more exposure routes or exposure types (e.g. both hand and whole body,

ingestion).

In general, the models produce exposure estimates that are in the overall range of the available

exposure data. However, when plotting the confidence intervals for each model output, it is clear 144

that the model outputs do not always capture all the variance in the highly variable exposure data.

Suppose the data used for testing can be considered acceptable, these model outputs may under- or

over-estimate exposure. However, it is evident from the model testing plots that imputed data

dominate the outliers in the data versus model comparisons. As imputed data is commonly (and

often exclusively) used in the model testing, it was not possible to estimate the actual variance

explained by the respective models. It is, however, evident that the whole body dermal models

explain more variance than the hand models. Due to the fact that few ‘true datasets’ were available

for testing, the effect of between-study variances etc. were not investigated. Also, no conclusions

can be drawn from the testing results in terms of the selected (sub-) scenarios, e.g. whether it is

acceptable to develop a single model for mixing & loading for all applications (field sprayers,

broadcast sprayers, hand-held, aerial).

(c) Use of models in regulatory framework

With the development of the operator exposure models and the integration into the software, the

aim is to contribute to the implementation of Regulation (EC) 1107/2009 and Dir. 2009/128/EC on

the Sustainable Use of PPPs. Much effort was put into the integration of current agricultural

practices in the EU from publications and survey data (from the BROWSE and EFSA survey data, e.g.

Glass et al, 2012). With the implementation of the models in a user-friendly tool with sufficient

flexibility for the user to make use of reasonable worst case defaults given in the software or the use

of own values, the tool can be used to perform risk assessments in the framework of Regulation (EC)

1107/2009. Next to this the tool can identify risk indicators (for example the effect of the use of a

cabin on exposure, drift-reducing nozzles, etc.) meeting the requirements of Dir. 2009/128/EC on

the Sustainable Use of PPPs.

145

7 Recommendations for future development and refinement

In order to keep the models up to date and refine them where necessary after thorough user

feedback, the following steps can be considered.

• A thorough analysis of all the available dermal exposure data is proposed to determine the

differences in dermal sampling techniques (e.g. interception and removal techniques) and the

effect on the variability found in exposure data. This might give insight into the effect of different

dermal sampling techniques on dermal exposure, and a possible introduction of a correction

factor

• In case new, more recent, exposure data becomes available, this can be used to check and if

necessary refine the models. For example recently the AOEM data became available so it would

be good for the future to use this data to validation and refinement of the current models.

• More extensive final sensitivity analyses of the algorithms can be performed. This could include

different categorical scenario inputs.

• An additional validation of the models could be performed using new data that has not been

used for model development.

146

(a) Disclaimer

The model comparisons described in a separate annex (see reference in chapter 4) and calculations

included in the report are performed with software developed by FERA which is at this moment still

under development. Therefore no rights can be derived from the results presented in this report.

147

8 References

Abdelbagi, H., & Adams, A. (1987). Influence of droplet size, air-assistance and electrostatic charge upon the distribution of ultra-low-volume sprays on tomatoes. Crop Protection, 6, 226–233. Retrieved from http://www.sciencedirect.com/science/article/pii/0261219487900433

Berger-Preiss, E., Boehncke, A., Könnecker, G., Mangelsdorf, I., Holthenrich, D., & Koch, W. (2005). Inhalational and dermal exposures during spray application of biocides. International Journal of Hygiene and Environmental Health, 208(5), 357–72. doi:10.1016/j.ijheh.2005.04.006

Brouwer, D., Semple, S., Marquart, J., & Cherrie, J. (2001). A dermal model for spray painters. Part I: Subjective exposure modelling of spray paint deposition. Annals of Occupational …, 45(1), 15–23. Retrieved from http://annhyg.oxfordjournals.org/content/45/1/15.short

BROWSE. (2011). Prioritisation of scenarios and overall model objectives for operator, worker, bystander & resident exposure modelling and identification of data required from WP4.

Cohen Hubal, E. a, Suggs, J. C., Nishioka, M. G., & Ivancic, W. a. (2005). Characterizing residue transfer efficiencies using a fluorescent imaging technique. Journal of Exposure Analysis and Environmental Epidemiology, 15(3), 261–70. doi:10.1038/sj.jea.7500400

Dosemeci, M. (2002). A Quantitative Approach for Estimating Exposure to Pesticides in the Agricultural Health Study. Annals of Occupational Hygiene, 46(2), 245–260. doi:10.1093/annhyg/mef011

FAO. (2001a). Guidelines on minimun requirements for agricultural pesticide application equipment. Volume one.

FAO. (2001b). Guidelines on standards for agricultural pesticide application equipment and related test procedures, vol. 1.

FAO. (2013). International Code of Conduct on the Distribution and Use of Pesticides Annotated list of Technical Guidelines for the implementation of the International Code of Conduct on the Distribution and Use of Pesticides, (January).

Gil, E., Escolà, a., Rosell, J. R., Planas, S., & Val, L. (2007). Variable rate application of plant protection products in vineyard using ultrasonic sensors. Crop Protection, 26(8), 1287–1297. doi:10.1016/j.cropro.2006.11.003

Hughes, E. a, Flores, A. P., Ramos, L. M., Zalts, A., Richard Glass, C., & Montserrat, J. M. (2008). Potential dermal exposure to deltamethrin and risk assessment for manual sprayers: influence of crop type. The Science of the Total Environment, 391(1), 34–40. doi:10.1016/j.scitotenv.2007.09.034

Juste, F., Sanchez, S., Ibanez, R., Val, L., & Garcia, C. (1990). Measurement of spray deposition and efficiency of pesticide application in citrus orchards. Journal of Agricultural …, (August 1988),

148

187–196. Retrieved from http://www.sciencedirect.com/science/article/pii/S0021863405801258

Leonas, K. K., Yu, X. K., & Hall, D. (1992). Deposition Patterns on Garments during Application of Lawn and Garden Chemicals: A Comparison of Six Equipment Types, 234, 230–234.

Machera, K. (2003). Determination of Potential Dermal and Inhalation Operator Exposure to Malathion in Greenhouses with the Whole Body Dosimetry Method. Annals of Occupational Hygiene, 47(1), 61–70. doi:10.1093/annhyg/mef097

Marquart, J. (2003). Determinants of Dermal Exposure Relevant for Exposure Modelling in Regulatory Risk Assessment. Annals of Occupational Hygiene, 47(8), 599–607. doi:10.1093/annhyg/meg096

Matuo, T., & Matuo, Y. K. (1998). Efficiency of Safety Measures Applied to a Manual Knapsack Sprayer for Paraquat Application to Maize ( Zea mays L .), 701, 698–701.

Nuyttens, D., Braekman, P., Windey, S., & Sonck, B. (2009). Potential dermal pesticide exposure affected by greenhouse spray application technique. Pest Management Science, 65(7), 781–90. doi:10.1002/ps.1755

Rincón, V., Páez, F., & Sánchez-Hermosilla, J. (2009). Estimation of Leaf-Area Index in greenhouse tomato crop to determine the volume rate to spray. cirg.ageng2012.org. Retrieved from http://www.cirg.ageng2012.org/images/fotosg/tabla_137_C0402.pdf

Sánchez-Hermosilla, J., Rincón, V. J., Páez, F., Agüera, F., & Carvajal, F. (2011). Field evaluation of a self-propelled sprayer and effects of the application rate on spray deposition and losses to the ground in greenhouse tomato crops. Pest Management Science, 67(8), 942–947. doi:10.1002/ps.2135

Sanjrani, W. (1990). Effect of application volume and method on spray operator contamination by insecticide during cotton spraying, 9(October).

Smith, D., & Askew, S. (2000). Droplet size and leaf morphology effects on pesticide spray deposition. Transactions of the …, 43(2), 255–259. Retrieved from http://www.prairieswine.com/pdf/2987.pdf

Uk, S. (1977). Tracing insecticide spray droplets by sizes on natural surfaces. The state of the art and its value. Pesticide Science, 8(5), 501–509. doi:10.1002/ps.2780080512

Van Zyl, S. a., Brink, J.-C., Calitz, F. J., & Fourie, P. H. (2010). Effects of adjuvants on deposition efficiency of fenhexamid sprays applied to Chardonnay grapevine foliage. Crop Protection, 29(8), 843–852. doi:10.1016/j.cropro.2010.04.017

Wicke, H., Ba, G., & Frie, R. (1999). Comparison of spray operator exposure during orchard spraying with hand-held equipment fitted with standard and air injector nozzles, 18, 0–7.

149

Zabkiewicz, J. a. (2007). Spray formulation efficacy—holistic and futuristic perspectives. Crop

Protection, 26(3), 312–319. doi:10.1016/j.cropro.2005.08.019

Abdelbagi, H., & Adams, A. (1987). Influence of droplet size, air-assistance and electrostatic charge upon the distribution of ultra-low-volume sprays on tomatoes. Crop Protection, 6, 226–233. Retrieved from http://www.sciencedirect.com/science/article/pii/0261219487900433

ASAE S525. Agricultural cabs – Engineering Control of Environmental Air Quality. 2003.

Balsari P, Marucco P, Oggero G. 2006. External contamination of sprayers in vineyards. Aspects of

Applied Biology. 77: 1-6

Balsari P, Marucco P. 2003. Sprayer cleaning: The importance of the sprayer adjustment on the

external contamination. ASAE Meeting Presentation, Paper number 031097.

Berger-Preiss, E., Boehncke, A., Könnecker, G., Mangelsdorf, I., Holthenrich, D., & Koch, W. (2005).

Inhalational and dermal exposures during spray application of biocides. International journal of

hygiene and environmental health, 208(5), 357–72. doi:10.1016/j.ijheh.2005.04.006

Bundesinstitut für Risikobewertung (BfR). Joint Development of a new Agricultural Operator

Exposure Model. Project report, Berlin 2013. ISBN3-943963-03-8

CEN (2004) prEN 15051 Workplace atmospheres- measurement of dustiness of bulk materials-

Requirements and test methods. CEN/TC 137 N330 Comité Européen de Normalisation, Brussels,

Belgium.

Brouwer, D., Semple, S., Marquart, J., & Cherrie, J. (2001). A dermal model for spray painters. Part I:

Subjective exposure modelling of spray paint deposition. Annals of Occupational …, 45(1), 15–23.

Retrieved from http://annhyg.oxfordjournals.org/content/45/1/15.short

Cherrie JW, Semple S, Christopher Y, Saleem A, Hughson GW, Philips A. 2006. How important is

inadvertent ingestion of hazardous substances at work? Ann. Occ. Hyg. 50 (7): 693-704

150

CropLife International. Catalogue of pesticide formulation types and international coding system.

CropLife International, Technical monograph No. 2, 6th Edition, revision May 2008, 2008.

Christopher Y. 2008. Inadvertent ingestion exposure to hazardous substances in the workplace. PhD

Thesis. Aberdeen, UK: University of Aberdeen.

Department for Environment Food and Rural Affairs (DEFRA). SID5 (Rev. 07/10). 2011. Field spray

drift studies to mature winter cereal crops with modern application practices to inform policy on

setting of buffer zones in the UK. Research Project Final Report. Project Code: PS2017

Dosemeci, M. (2002). A Quantitative Approach for Estimating Exposure to Pesticides in the

Agricultural Health Study. Annals of Occupational Hygiene, 46(2), 245–260.

doi:10.1093/annhyg/mef011

US EPA. Human Health Evaluation Manual. Part E: Supplemental Guidance for Dermal Risk

Assessment, RAG Vol. 1, 1999.

Fransman W, van Tongeren M, Cherrie JW, Tischer M, Schneider T, Schinkel J, Kromhout H, Warren

N, Goede H, Tielemans E. Advances Reach Tool (ART): Development of the mechanistic model. Ann.

Occup. Hyg. 2011; 55 (9): 957-979.

Gerritsen-Ebben MG, Brouwer DH, van Hemmen JJ. Effective Personal Protective Equipment (PPE).

Default setting of PPE for registration purposes of agrochemical and biocidal pesticides. TNO report

V7333. Zeist, 2007

Gil, E., Escolà, a., Rosell, J. R., Planas, S., & Val, L. (2007). Variable rate application of plant protection

products in vineyard using ultrasonic sensors. Crop Protection, 26(8), 1287–1297.

doi:10.1016/j.cropro.2006.11.003

Gilbert AJ, Wild SA, Mathers JJ, Glass CR. Measurement of spillage and contamination arising from

the progressive emptying of a 10 litre container and evaluation of the influence of pack size on hand

contamination. York: Central Science laboratory; 1999. Report nr FD 98/91.

151

Gilbert, AJ, C R Glass, R Lewis, JJ Mathers, F Mazzi, S A Wild. 2000. Determination of operator and

environmental contamination with simulated pouring studies into an induction bowl. Project

Number PA1701, Report Number FD 99/61.

Glass CR, Mathers JJ, Lewis RJ, Harrington PM, Gilbert AJ, Smith S. Understanding exposure to

agricultural pesticide concentrates. York: Central Science Laboratory; 2009?. Report nr HSE Contract

Ref: 4030/R51.193; DEFRA Contract Ref: PA1722.

Glass R, Garthwaite D, Pote A, et al. Collection and assessment of data relevant for non-dietary

cumulative exposure to pesticides and proposal for conceptual approaches for non-dietary

cumulative exposure assessment. External scientific report CFT/EFSA/PPR/2010/04. Supporting

publications 2012:EN-346.

Gorman Ng M, Semple S, Cherrie JW, Christopher Y, Northage C, Tielemans E, Veroughstraete V, van

Tongeren M. 2012. The relationship between inadvertent ingestion and dermal exposure pathways:

A new integrated conceptual model and a database of dermal and oral transfer efficiencies. Ann.

Occ. Hyg. Published Online First: 23 July 2012. doi:10.1093/annhyg/mes041

Großkopf C, Mielke H, Westphal D, Erdtmann-Vourliotis M, Hamey P, Bouneb F, Rautmann D,

Stauber F, Wicke W, Maasfeld W, Salazar JD, Chester G, Martin S. A new model for the prediction of

agricultural operator exposure during professional application of plant protection products in

outdoor crops. Journal of Consumer Protection and Food Safety: Published online: DOI

10.1007/s00003-013-0836-x

Hubal, Cohen, Suggs, J. C., Nishioka, M. G., & Ivancic, W. a. (2005). Characterizing residue transfer

efficiencies using a fluorescent imaging technique. Journal of exposure analysis and environmental

epidemiology, 15(3), 261–70. doi:10.1038/sj.jea.7500400

Hughes, E. a, Flores, A. P., Ramos, L. M., Zalts, A., Richard Glass, C., & Montserrat, J. M. (2008).

Potential dermal exposure to deltamethrin and risk assessment for manual sprayers: influence of

crop type. The Science of the total environment, 391(1), 34–40. doi:10.1016/j.scitotenv.2007.09.034

152

Institute of Occupational Medicine (IOM). Database of Dermal and Oral Transfer Efficiencies.

(http://www.iom-world.org/research/research-expertise/exposure-assessment/database-of-dermal-

and-oral-transfer-efficiencies/)

Juste, F., Sanchez, S., Ibanez, R., Val, L., & Garcia, C. (1990). Measurement of spray deposition and

efficiency of pesticide application in citrus orchards. Journal of Agricultural …, (August 1988), 187–

196. Retrieved from http://www.sciencedirect.com/science/article/pii/S0021863405801258

Kennedy, M. C., Anderson, C. W., Conti S., and O’Hagan. (2006). Case Studies in Gaussian Process

Modelling of Computer Codes. Reliability Engineering and System Safety. 91, 1301—1309.

Lebailly P, Bouchart V, Baldi I, Lecluse Y, Heutte N, Gislard A, Malas JP. 2009. Exposure to pesticides in open-

field farming in France. Ann Occ Hyg. 53(1): 69-81

Leonas, K. K., Yu, X. K., & Hall, D. (1992). Deposition Patterns on Garments during Application of

Lawn and Garden Chemicals: A Comparison of Six Equipment Types, 234, 230–234.

Lloyd GA, Bell GJ. Hydraulic nozzles: Comparative Spray Drift study. Ministry of Agriculture, Fisheries and

Food (MAFF). United Kingdom. SC7704, 1988

Machera, K. (2003). Determination of Potential Dermal and Inhalation Operator Exposure to

Malathion in Greenhouses with the Whole Body Dosimetry Method. Annals of Occupational

Hygiene, 47(1), 61–70. doi:10.1093/annhyg/mef097

Marquart J, Lansink C, Engel R, van Hemmen J. 1999. Effectiveness of local exhaust ventilation

during dumping of powders from bags. TNO report v99.267, Zeist, The Netherlands

Marquart, J. (2003). Determinants of Dermal Exposure Relevant for Exposure Modelling in

Regulatory Risk Assessment. Annals of Occupational Hygiene, 47(8), 599–607.

doi:10.1093/annhyg/meg096

153

http://www.iom-world.org/research/research-expertise/exposure-assessment/database-of-dermal-and-oral-transfer-efficiencies/

http://www.iom-world.org/research/research-expertise/exposure-assessment/database-of-dermal-and-oral-transfer-efficiencies/

http://www.sciencedirect.com/science/article/pii/S0021863405801258

Mathers JJ, Glass CR, Gilbert AJ, Wild SA. Determination of operator and environmental

contamination with simulated pouring studies into an induction bowl. York: Central Science

Laboratory; 2000. Report nr FD 99/61.

Matuo, T., & Matuo, Y. K. (1998). Efficiency of Safety Measures Applied to a Manual Knapsack

Sprayer for Paraquat Application to Maize ( Zea mays L .), 701, 698–701.

Michielsen JMGP, van de Zande JC, Wenneker H, Stallinga H, Van Velde P. 2012.

External loading of an orchard sprayer with agrochemicals during application. Aspects of Applied

Biology. 114: 151-157

Nuyttens, D., Braekman, P., Windey, S., & Sonck, B. (2009). Potential dermal pesticide exposure

affected by greenhouse spray application technique. Pest management science, 65(7), 781–90.

doi:10.1002/ps.1755

Ramwell CT, Johnson PD, Boxall ABA, Rimmer DA. 2004. Pesticide residues on the external surfaces

of field-crop sprayers: environmental impact. Pest Management 60:795–802 (SciencePest Manag Sci

(online: 2004)

Ramwell CT, Johnson PD, Boxall ABA, Rimmer DA. 2005. Pesticide Residues on the External Surfaces

of Field Crop Sprayers: Occupational Exposure. Ann. Occup. Hyg., Vol. 49, No. 4, pp. 345–350

Rincón, V., Páez, F., & Sánchez-Hermosilla, J. (2009). Estimation of Leaf-Area Index in greenhouse

tomato crop to determine the volume rate to spray. cirg.ageng2012.org. Retrieved from

http://www.cirg.ageng2012.org/images/fotosg/tabla_137_C0402.pdf

Sánchez-Hermosilla, J., Rincón, V. J., Páez, F., Agüera, F., & Carvajal, F. (2011). Field evaluation of a

self-propelled sprayer and effects of the application rate on spray deposition and losses to the

ground in greenhouse tomato crops. Pest Management Science, 67(8), 942–947.

doi:10.1002/ps.2135

154

Sanjrani, W. (1990). Effect of application volume and method on spray operator contamination by

insecticide during cotton spraying, 9(October).

Schinkel J, Warren N, Fransman W, van Tongeren M, McDonnell P, Voogd E, Cherrie JW, Tischer M,

Kromhout H, Tielemans E. Calibration of the ART mechanistic model. J. Environ. Monit. 2011; 13:

1374–82.

Schneider T, Vermeulen R, Brouwer DH, Cherrie JW, Kromhout H, Fogh CL. Conceptual model for

assessment of dermal exposure. Occup Environ Med 1999;56:765–773

Smith, D., & Askew, S. (2000). Droplet size and leaf morphology effects on pesticide spray

deposition. Transactions of the …, 43(2), 255–259. Retrieved from

http://www.prairieswine.com/pdf/2987.pdf

Wild SA, Mathers JJ, Glass CR. 2000. The potential for operator hand contamination to pesticides

during the mixing and loading procedure. Aspects of Applied biology, 57: 179-183

Tielemans E, Schneider T, Goede H, Tischer M, Warren N, Kromhout H, van Tongeren M, van

Hemmen JJ, Cherrie JW. Conceptual model for assessment of inhalation exposure: defining

modifying factors. Ann. Occup. Hyg. 2008; 52 (7): 577-586.

Van de Zande JC. Inventarisatie externe verontreiniging spuitapparatuur. Plant Research International BV,

Wageningen. Nota 470. 2007.

Van Zyl, S. a., Brink, J.-C., Calitz, F. J., & Fourie, P. H. (2010). Effects of adjuvants on deposition efficiency of

fenhexamid sprays applied to Chardonnay grapevine foliage. Crop Protection, 29(8), 843–852.

doi:10.1016/j.cropro.2010.04.017

Uk, S. (1977). Tracing insecticide spray droplets by sizes on natural surfaces. The state of the art and its

value. Pesticide Science, 8(5), 501–509. doi:10.1002/ps.2780080512

155

United States Environmental Protection Agency (USEPA). Occupational and residential exposure test

guidelines. United States Environmental Protection Agency (US-EPA); 1996. Report nr OPPTS 875.1100.

United States Environmental Protection Agency (USEPA). Human Health Evaluation Manual. Part E:

Supplemental Guidance for Dermal Risk Assessment, RAG Vol. 1, 1999.

Veldhof RJG, Lurvink MWM, de Vreede SAF, Gijsbers JHJ, Tjoe Nij E, Brouwer DH.

2006. Dermal exposure to dust during manual transfer of powder; the effect of amount

handled and dustiness. TNO report V5816, Zeist, The Netherlands

Wicke, H., Ba, G., & Frie, R. (1999). Comparison of spray operator exposure during orchard spraying

with hand-held equipment fitted with standard and air injector nozzles, 18, 0–7

Zabkiewicz, J. a. (2007). Spray formulation efficacy—holistic and futuristic perspectives. Crop

Protection, 26(3), 312–319. doi:10.1016/j.cropro.2005.08.019

156

BROWSE - Fera Science · 2014-11-13 · BROWSE . Bystanders, Residents, Operators and WorkerS...

Documents

Transcript of BROWSE - Fera Science · 2014-11-13 · BROWSE . Bystanders, Residents, Operators and WorkerS...