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HArmonised environmental Indicators for pesticide Risk

“Occupational” indicatorsOperator, worker and bystander

SSPE-CT-2003-501997



AlterraWageningen

The Netherlands

Biologische BundesanstaltKleinmachnow

Germany

Central Science LaboratorySand Hutton

United Kingdom

Central Service for PlantProtection and Soil

ConservationHungary

Cranfield University,Silsoe,

United Kingdom

Helmholtz - Centre forEnvironmental Research

Leipzig-HalleGermany

Forschungsinstitut für

biologischen Landbau , Frick Switzerland

International Centre forPesticides and Health Risk

Prevention, Milan, Italy

Institute for Environment andSustainability

Ispra, Italy

Istituto Mario NegriMilanItaly

Joint Research Centre,European Commission,

Ispra, Italy

Laboratory of CropProtection Chemistry

GentBelgium



National EnvironmentalResearch Institute

Silkeborg

Denmark National Institute for PublicHealth and the Environment

BilthovenThe Netherlands

Norwegian Institute forAgricultural and

Environmental ResearchÅs, Norway

Università Cattolicadel Sacro Cuore

PiacenzaItalia

Université catholiquede Louvain

Louvain-la-NeuveBelgium

University of York York

United Kingdom

Veterinary and AgrochemicalResearch Centre

TervurenBelgium



This publication was funded by the EU sixth Framework Programme, within the

project HArmonised environmental Indicators for pesticides Risks, HAIR, contract

number SSPE-CT-2003-501997. The report does not represent the views of the

Commission or its services



“Occupational” indicators - Operator, worker and bystander

Authors:

Ir. Floortje Garreyn1,Ir. Bénédicte Vagenende1,Prof. Dr. Ir. Walter Steurbaut1

1 = Ghent University, Belgium

Contact persons for report on occupational indicators:Ir. Floortje Garreyn E-mail: [email protected]. Dr. Ir. Walter Steurbaut E-mail: Walter. [email protected]

Contact person for the HAIR project:Robert Luttik E-mail: [email protected]



Acknowledgements

This work has been funded by the European Union within the 6th framework (FP6, STREP:

Harmonized Environmental Indicators for Pesticide Risk, SSPE-CT-2003-501997).

Our thanks goes out to the project partners and the numerous experts that were willing to giveadvice, in particular: Dr. ir. C. Vleminckx (Scientific Institute of Public Health), Prof. Dr. J.

Willems (Superior Health Council Belgium; Ghent University, Department of Public Health,

Faculty of Medicine and Health Sciences), Dr. F. Mettruzio and Dr. S. Visentin (InternationalCentre for Pesticides and Health Risk Prevention, Luigi Sacco Hospital and University (ICPS)),

Prof. Dr. M. Trevisan (Università Cattolica del Sacro Cuore, Piacenza, Italy), M. Calliera

(University of Milano Bicocca), Prof. Dr. G. Matthews (IPARC, Imperial College, Silwood

Park), Dr. P.G. Pontal (Rhone Poulenc Agro), Prof. Dr. T. Arcury (Department of Family andCommunity Medicine, Wake Forest University School of Medicine, Winston-Salem), Dr. R.

Brown (PSD), Dr. J.J. van Hemmen (TNO Chemistry, Zeist, The Netherlands), Dr. M. Thomas(CSL), Dr. V. Flari (CSL), Dr. R. Glass (CSL), A. De Jong (Alterra Green World Research), Dr.

R. Kruijne (Alterra Green World Research), Dr. J. Deneer (Alterra Green World Research), Dr.

D.R. Johnson (ARTF), Dr. G. Herndon (U.S. EPA), Dr. C. Eiden (U.S. EPA), Dr. S.Tayadon

(U.S. EPA), Dr. D. Miller (U.S. EPA), Dr. P. Price (the LifeLine Group), Dr. R. Luttik (RIVM),

P. van Vlaardinghen (RIVM), J. Pineros (FOD Volksgezondheid, Veiligheid van deVoedselketen en Leefmilieu), V. Van Bol (FOD Volksgezondheid, Veiligheid van de

Voedselketen en Leefmilieu), L. Pussemier (VAR), B. Vagenende (Ghent University), S.

Vergucht (Ghent University) and W. Steurbaut (Ghent University).



ContentsI. INTRODUCTION........................................................ .............................................................. ................. 1

II. ACUTE INDICATORS.................................................................................. ............................................. 6

1. PESTICIDE OPERATOR .................................................... ........................................................... ................. 6 Introduction.... ...................... ...................... ...................... ...................... ...................... .................. ...................... ...... 6 Proposed Pesticide Operator Indicator .................... ....................... ...................... ....................... ..................... .......... 7

Exposure ............................................................................................................................................ 7 Toxicity............................................................................................................................................. 12 Risk Index......................................................................................................................................... 14

2. R E-ENTRY WORKER ....................................................... ........................................................... ............... 15 Introduction.... ...................... ...................... ...................... ...................... ...................... ................... ...................... ... 15 Proposed Re-Entry Worker Indicator ..................... ...................... ..................... ..................... ....................... ........... 15

Exposure .......................................................................................................................................... 15 Toxicity............................................................................................................................................. 28 Risk Index......................................................................................................................................... 28

3. GREENHOUSE WORKER .................................................. ........................................................... ............... 29

Proposed Greenhouse Worker Indicator.................. ...................... ....................... ...................... ....................... ....... 29 Exposure .......................................................................................................................................... 29 Toxicity............................................................................................................................................. 32 Risk Index......................................................................................................................................... 33

4. BYSTANDER ......................................................... ........................................................... ......................... 34 Introduction.... ...................... ...................... ...................... ...................... ...................... ................... ...................... ... 34 Proposed Bystander Indicator........................ ....................... ...................... ....................... ..................... .................. 36


5. SENSITIVE POPULATION GROUPS........................................................ ...................................................... 47 Children.................... ...................... ..................... ...................... ..................... ..................... ...................... .............. 47 Factors determining the unique vulnerability of children...................... ....................... ....................... ..................... 47 Exposure routes ...................... ...................... ...................... ...................... ..................... ..................... ...................... 48 Proposed algorithms for assessing children’s exposure to pesticides ...................... ....................... ...................... .... 49


Pregnant women .................... ...................... ..................... ...................... ..................... ..................... ...................... 55 Factors determining the vulnerability of pregnant women .................... ....................... ...................... ...................... 55 Uncertainty factor............ ...................... ...................... ...................... ...................... .................... ...................... ....... 55 Default values.................... ...................... ...................... ...................... ...................... ...................... ...................... ... 56

III. PROPOSED CHRONIC INDICATORS....... ........................................................... .......................... 57

1. PESTICIDE OPERATOR .................................................... ........................................................... ............... 57 Proposed Pesticide Operator Indicator .................... ....................... ...................... ....................... ...................... ....... 57

Exposure .......................................................................................................................................... 57

Toxicity............................................................................................................................................. 58 Risk Index......................................................................................................................................... 58

2. R E-ENTRY WORKER ....................................................... ........................................................... ............... 59 Proposed Re-Entry Worker Indicator ..................... ...................... ..................... ..................... ....................... ........... 59


3. GREENHOUSE WORKER .................................................. ........................................................... ............... 64 Proposed Greenhouse Worker Indicator.................. ...................... ....................... ...................... ....................... ....... 64


4. R ESIDENT .................................................. ........................................................... ................................... 66

Introduction.... ...................... ...................... ...................... ...................... ...................... ................... ...................... ... 66 Proposed Resident Indicator........... ....................... ...................... ....................... ..................... ....................... .......... 68 Exposure .......................................................................................................................................... 68



Toxicity............................................................................................................................................. 71 Risk Index......................................................................................................................................... 72

IV. AGGREGATE AND CUMULATIVE RISK ASSESSMENT.......................................................... 73

Introduction.... ...................... ...................... ...................... ...................... ...................... ................... ...................... ... 73 Overview of approaches to handle cumulative assessments................ ...................... ...................... ..................... .... 75 Proposed Methodology to handle cumulative assessments.................. ...................... ...................... ..................... .... 78

Overview of methodologies to handle aggregate assessments................. ...................... ...................... ..................... 81 Proposed methodology to handle aggregate assessments .................... ...................... ...................... ..................... .... 83

V. OVERALL OCCUPATIONAL AND HUMAN HEALTH RISK INDEX........................................... 84

1. OVERALL OCCUPATIONAL R ISK I NDEX ....................................................... ............................................ 84 2. OVERALL HUMAN HEALTH R ISK I NDEX...................................................... ............................................ 84 3. WEIGHTING FACTORS.................................................... ........................................................... ............... 85

VI. CALCULATION OF THE ACUTE & CHRONIC INDICATORS................................................. 86

1. PESTICIDE OPERATOR .................................................... ........................................................... ............... 86 Case I............. ...................... ..................... ...................... ..................... ...................... ...................... ...................... ... 86 Case II ...................... ...................... ...................... ...................... ...................... .................... ...................... .............. 87 Case III .................... ...................... ...................... ...................... ...................... ..................... ...................... .............. 90

2. R E-ENTRY WORKER ....................................................... ........................................................... ............... 99 3. BYSTANDER /R ESIDENT.................................................. ........................................................... ............. 107

VII. PRIORITISATION OF ACTIONS FOR REDUCING PESTICIDE IMPACT ........................... 114

1. PESTICIDE OPERATOR .................................................... ........................................................... ............. 115 Introduction.... ...................... ...................... ...................... ...................... ...................... ................... ...................... . 115 Factors influencing exposure.................... ...................... ...................... ...................... ..................... ....................... 115 Specific exposure mitigation measures.................................... ....................... ...................... ...................... ............ 116

2. R E-ENTRY WORKER ....................................................... ........................................................... ............. 121 3. BYSTANDER /R ESIDENT.................................................. ........................................................... ............. 123

Introduction.... ...................... ...................... ...................... ...................... ...................... ................... ...................... . 123 Factors influencing exposure.................... ...................... ...................... ...................... ..................... ....................... 123 Specific exposure mitigation measures.................................... ....................... ...................... ...................... ............ 128

4. QUANTITATIVE ASSESSMENT OF MITIGATION MEASURES ......................................................... ............. 132 VIII. VALIDATION ............................................................. .......................................................... ............. 134

1. PESTICIDE OPERATOR .................................................... ........................................................... ............. 137 2. R E-ENTRY WORKER ....................................................... ........................................................... ............. 138

Validation through biological monitoring .................... ...................... ....................... ...................... ....................... 138 Evaluation of indicator assumptions....... ...................... ....................... ...................... ..................... ........................ 140

3. BYSTANDER /R ESIDENT.................................................. ........................................................... ............. 143 Validation through biological monitoring .................... ...................... ....................... ...................... ....................... 143 Evaluation of indicator assumptions....... ...................... ....................... ...................... ..................... ....................... . 143

IX. CITED LITERATURE .............................................................. ........................................................ 156

X. ANNEXES......................................................................................................................................................

Annex I: EUROPOEM................................................................................................................................................. Annex II: Grouping of formulation types into categories............................................................................................. Annex III: Linking crops and application methods & categorizing them ...................... ....................... ...................... .. Annex IV: Exposure mitigation efficiency..... ....................... ...................... ....................... ....................... ................... Annex V: Dermal absorption........................................................................................................................................ Annex VI: Inhalation absorption ...................... ....................... ...................... ....................... ..................... ................... Annex VII: Agricultural Default Transfer Coefficients................................................................................................ Annex VIII: Foliar half life times................................................................................................................................. Annex IX: Estimated number of workdays a year........................................................................................................ Annex X: Spraying schemes used for validation and prioritisation............. ....................... ....................... ...................



1

I. Introduction

Annex VI of the European Council Directive 91/414EEC of 15 July 1991 provides detailed

rules (Uniform Principles) for the evaluation of information submitted by applicants and for

the authorization of plant protection products by individual member states. One of the general

principles of the evaluation process (1(b)) under Annex VI is that member states shall

“identify the hazards arising, assess their significance and make a judgement as to the likely

risks to humans, animals or the environment” (EUROPOEM II, 2002).

One of the specific principles of the evaluation process under Annex VI relates to the

assessment of the impact on human health. The assessment requires that “Member States shall

evaluate the operator exposure to active substances and/or to toxicologically relevant

compounds in the plant protection product likely to occur under the proposed conditions of

use (including in particular: dose, application method and climatic conditions). Realistic data

on exposure should be used and, if such data are not available, one should apply a suitable,validated calculation model.” The assessment also requires that “Member States shall evaluate

the possibility of exposure of other humans (bystanders or workers exposed after the

application of the plant protection product) or animals to the active substance and/or to other

toxicologically relevant compounds in the plant protection product under the proposed

conditions of use” (EUROPOEM II, 2002).

For many plant protection products and use patterns field studies of operator exposure have

not been generated. Therefore, in many cases predictive models are used to estimate likely

levels of operator exposure. It was determined that such models should be able to provide

initial estimates of likely total potential exposure, for use at the first tier stage of a tiered

approach to assessing operator exposure and risk. Within the EU various national authoritiesuse three predictive models in their authorization processes. These models are the UK-POEM

(JMP, 1986; Martin, 1990; POEM, 1992), the German model (Lundehn et al., 1992) and the

Dutch model (van Hemmen, 1992; van Golstein Brouwers et al., 1996; Snippe et al., 2002)

which were developed in isolation. Two comprehensive comparisons of the various predictive

models have been published (van Hemmen, 1993; Kangas & Sihvonen, 1996). These reviews

describe the features of the models and analyse how the differences between them affect the

outputs. Comparison of the exposure estimates for five different pesticide formulations

highlighted the need for model harmonization in Europe. In addition predictive models were

needed for assessing the exposure of re-entry workers and bystanders. Within this context,

the EUROPOEM project was established. The EUROPOEM Working Group gathered as

many high-quality, well documented exposure studies as possible in one unique and readilyavailable source. As a result of this the EUROPOEM database was developed. This database

was extended in the EUROPOEM II project. Recently a new predictive database for human

exposure has been developed, namely the AHED database (Agricultural Handlers Exposure

Database), which has a European version as well as an American one. The reviewing of

EUROPOEM II data as well as a number of industry studies including some new studies

conducted by the ECPA (European Crop Protection Agency) for inclusion in the European

version of AHED has been finalised (about 115 studies in total) (pers. comm. Pontal, 2006).

AHED is ready for use but the EU authorities have not yet reached a decision concerning the

acceptance of AHED (pers. comm. Pontal, 2006; pers. comm. van Hemmen, 2006).



2

AHED is a harmonised model underpinned by high quality generic databases of field studies

relevant to European and American exposure conditions and incorporates the best features of

all the available models. The AHETF (Agricultural Handlers Exposure Task Force), in

cooperation with the European Crop Protection Association (ECPA) developed the database

software, accessible via a web-based server, for handling the data entry, calculations and dataanalysis. A common database software tool will be used for risk assessments both in North

America and Europe. However, the data generated in Europe will be uploaded to the

European version and the AHETF data will be uploaded to the North American (AHETF)

version. Thus, the software for both the European and the American version is the same, but

only the data that are applicable to each continent are uploaded in the respective databases.

The Agricultural Handlers Exposure Database will only contain data that regulators have

deemed appropriate for use in a generic database (www.exposuretf.com). The AHED

database is a management system running under a Microsoft SQL server and will be able to

handle probabilistic assessments (in the future, not at the moment due to lack of data, pers.

comm. Pontal, 2006). The database will be able to provide percentiles, means and

distributions of exposure. AHED might rectify the deficiencies of EUROPOEM. Ideally thisdatabase could have been used in the HAIR project if a decision concerning the use of AHED

had already been taken. In order to keep the database up-to-date field studies should be

conducted in the future to fill in the data gaps and to gather data for new techniques and

formulation types. These studies should be designed in such a way that the results can easily

be incorporated in the database. Therefore a harmonized protocol for the conduct of field

studies of operator exposure to plant protection products should be established.

Outside Europe the US Pesticide Handlers Exposure Database (PHED) is currently used,

awaiting the approval of AHED. This predictive model is based on the generic PHED

database of published and unpublished studies carried out by industry and other scientists.

The initial version 1.1 (PHED, 1992) was revised in order to include studies from other

countries. A further update has been abandoned. The American version of AHED was

developed by an industry task force AHETF1, with US EPA and Health Canada’s Pesticide

Management Regulatory Agency (PMRA) participation and oversight to replace the PHED.

The defficiences of the current PHED were taken into consideration while developing AHED

and were rectified as much as possible. It has to be mentioned that the information from the

AHETF database may only be used for regulatory decisions for those members who have

sponsored the research. The American version of the proprietary exposure database will be

used by USEPA, California Department of Pesticide Regulation (CDPR), and Canada's Pest

Management Regulatory Agency (PMRA) to evaluate member companies' products

(www.exposuretf.com).

Within the framework of the HAIR project, Ghent University developed acute as well as

chronic indicators in order to assess the occupational risk to pesticides. Moreover special

attention was paid to sensitive population groups such as children and pregnant women. The

risk for applicators, workers in agriculture and bystanders/residents is assessed by the use of

risk indices. A risk index (RI) is the quotient of the estimated human exposure and a

toxicological reference dose (AOEL, Acceptable Operator Exposure Level). The global

1 The AHETF is a consortium of 18 agricultural chemical companies that formed a limited-liability company

(L.L.C.) task force. These companies are pooling their technical and financial resources to satisfy regulatory

requirements for data on exposure of agricultural workers who mix, load and/or apply pesticides.



3

process of the assessment of risks to humans exposed to pesticides is represented in diagram

form (Fig. I.1.1).

Figure I.1.1 Risk evaluation of pesticides: principle for computing the worker indicators

According to the decisions taken in the most recent HAIR project meetings (22nd

-24th

May2004, the Netherlands, 22nd – 23rd September 2004, Belgium, 14th – 15th – 16th March 2005,

UK, 22nd – 23rd September 2005, Hungary and 13th – 14th – 15th March 2006, Italy, 13th – 14th

November, 2003, Bonn) the following steps will be followed to calculate the acute and

chronic risk indicators for the occupational human health compartment:

FIRST STEP: CALCULATION OF THE BASIC RISK INDICATOR

This basic risk indicator corresponds closely to the first tier regulatory procedures approved

by the European Union. The output of the Risk indices is expected to comply with the

endpoints set in Annex VI of the European Union Directive 91/414 and the most recent

Guidance document from the European Commission. The risk indicators are expressed as risk

indices. This complies with the decisions made by the HAIR consortium at the Steering

Committees held so far. The occupational indicator work package consists of three different

indicators: the operator, the re-entry worker and the bystander/resident indicator.

Moreover, special attention was paid to sensitive population groups such as children and

pregnant women. Below a short description on the principles of the indicators is presented.

Operator Exposure Risk Assessment for different application scenarios

Tier 1: The human exposure for applicators is estimated using the human exposure model

EUROPOEM. The EUROPOEM database is a generic database of monitored operator

exposure studies relevant to plant protection products in European agriculture. Experimentalexposure studies of the applicator during mixing/loading and application of pesticides are

compiled in one database. Annex I gives additional information on the EUROPOEM (I & II)

model. We opted for this model since the EUROPOEM model is currently being revised

within the framework of AHED. It is very important that a decision concerning AHED

follows in the near future. It would be best if the European version of AHED could be used in

the HAIR Operator Indicator. We contacted Dr. Pierre Gerard Pontal, who reviewed

EUROPOEM II data as well as a number of industry studies including some new studies

conducted by ECPA (about 115 studies in total) to incorporate them into the AHED database.

This process has been finalized, but the agreement of the EU authorities is essential for

acceptance of AHED in the EU. This topic has not yet been discussed in a meeting with

experts. The UK-POEM model and the German model were not chosen since we opted for aharmonised European Model. The German model is based on published and non published

hazard identificationdata set

exposure assessment estimation of the human exposure dose (ED)

effect assessmenttoxicological reference dose (TRD)

risk characterisationRI = ED/TRD



4

studies conducted by the German phytopfarmaceutical companies. Since only German studies

were taken up in the database, this database does not seem relevant for use in risk assessment

on the European level. The UK-POEM model was also not selected for use in the HAIR

operator indicator since this model has not been updated, expecting EUROPOEM to be

accepted in Europe by the European Commission (van Hemmen et al., 2005).

Tier 2: Generation of specific/customised higher tier scenarios is only possible when relevant

data from product specific exposure studies and dermal penetration studies are available. Also

the actual mitigation provided by PPE can be incorporated at the higher tier level.

Worker Exposure Risk Assessment for different re-entry scenarios

Tier 1 & 2: The worker risk assessment is based on the generic assumption on initial

Dislodgeable Foliar Residues (DFR) and a database for transfer factors to give single

conservative point estimates (“surrogate values”) for total potential exposure, fully exploiting

the capacity of the database which is applicable to a range of re-entry scenarios common to

European conditions. If the estimated re-entry exposure is within the AOEL no further actionis required. Exposure mitigation factors (i.e. exposure reduction coefficients for PPE pertinent

to the case) are taken into account at Tier 2. Taking into account this supplementary use

specific information is a refinement of the exposure estimation and thus reduces uncertainty.

Tier 3 & 4: Generation of specific/customised higher tier scenarios is possible when relevant

additional data on dislodgeable foliar residue studies and their dissipation curves from foliar

dislodgeable residue studies under actual conditions are available. Also data on product-

specific percutaneous absorption can be taken into account when available (Tier 3). At the

highest tier (Tier 4) product specific data from biological monitoring studies or re-entry

exposure studies on the active substance under consideration and the actual re-entry

conditions should be used. This provides absolute exposure data and places the greatest

demands upon the quality and the relevance of the data required. Re-entry restrictions have to

be developed when the AOEL is exceeded. Regulatory authorities of the different Member

States have already established retricted re-entry intervals for several actives applied in

various crops.

Bystander Exposure Risk Assessment

The Bystander Working Group agreed to adopt a two tiered approach to exposure estimation.

Tier 1: A first tier bystander exposure value is estimated according to the likely level of particle drift likely to directly contaminate bystanders who are located within the range of

spray drift fallout. A series of working values for this level was generated from available

published measurements of bystander contamination by spray drift (i.e. from UK CSL studies

in EUROPOEM I). Corresponding simultaneously measured levels of spray drift fallout were

available for direct comparison.

Tier 2: Second tier estimates of likely bystander exposure are to be based upon measurements

made in the field, according to the needs for specific data. Such measurements should be

based upon study of realistic situations representative for specific cases. Measured values may

be gathered using a suitable methodology, which ideally should allow correlation with

measurements of ambient environmental contamination (e.g. spray drift deposition, airbornevapour concentrations) in the relevant situation, in order to aid interpretation of results and



5

allow controlled comparison with other similar data as far as possible. Specific/customised

higher tier scenarios take into account the presence of buffer strips and the use of low drift

nozzles amongst others.

Currently no such data are available for use in predictive modelling for the purpose of general

risk evaluation. Therefore individual field studies should be conducted in order to perform amore exact evaluation of the levels of bystander exposure to specific pesticides used under

particular conditions. Due to the possibly greater variability of bystander exposure opposed to

that of workers and operators, individual studies should be conducted following standard

procedures taking into account current and new application technologies and risk mitigation

measures.

SECOND STEP: CALCULATION OF THE ADVANCED RISK INDICATOR

In this second step refined tier information, when available, is incorporated to calculate the

advanced risk indicators. This refined tier information includes new exposure estimates in

relation to results from field trials incorporating field data for other regions in Europe, dermal

absorption studies, data on the effectiveness of personal protective equipment (PPE),

refinement of the default transfer factors used to calculate re-entry worker exposure, etc.

According to decisions made at the Steering Committee Meeting held on the 16 th of March

2005, the absence of refined tier information does not lead to re-estimated exposure values.

The risk indices will automatically adjust to the trigger values corresponding to the approved

status of the plant protection products by the regulatory authorities. Other refined tier

information, as there are label precautions or restrictions for use, is also expected to be

incorporated. Due to the different approaches of regulatory authorities in the different member

states, these refined indicators may differ. Moreover an extra level of uncertainty may be

incorporated due to lack of information regarding the degree of compliance of farmers with

label precautions and/or mitigation procedures (Good Agricultural Practices). This uncertaintycan only be quantified if farmer practises are regularly monitored. Moreover the uncertainties

concerning the missing link regarding the key effects in the field have to be highlighted (Flari

& Hart, 2006).

THIRD STEP: WEIGHTING THE RISK INDICES

By applying weight factors the actual/local parameters which may affect the likelihood of

exposure of the population, of susceptible subjects or subgroups will be taken into account.

The risk for certain regions can be weighed according to the population density in particular

regions. The risk for the applicators and workers can be weighed according to the number of

applicators/workers in particular regions. This weighting of the risk indices and its relation to

the actual usage data of plant protection products constitute a suggestion for aggregating the

risk that they pose onto workers in the field or greenhouses. The risk for sensitive population

groups contributes, as a default, for 5% to the total risk (pers. comm. van Hemmen, 2006). An

overall human health risk index can be calculated by attributing weight factors to the

consumer and applicator indicators according to the composition of the population. These

weight factors should be based on national or regional statistics, which should be available in

all the Member States.

In the first part of this report, the acute worker risk indicators are described, followed by an

outline of the chronic risk indicators. In a third phase the different worker indicators are

calculated for several case-studies.



6

II. Acute Indicators

For the calculation of the acute worker indicators, the formulas of the POCER (Pesticide

Occupational and Environmental Risk Indicator) are used (Vercruysse et al., 2002; Maraite et

al., 2005). The POCER indicator is based on the acceptance criteria formulated in Annex VI

of the European Council Directive 91/414/EC. In Annex VI, the Uniform Principles for the

evaluation and acceptance of plant protection products are set. Dependent on the availability

of specific data on the use of PPE, the area treated,… the indicator can be adapted to the real

situations of each country. When no data are available, default values will be used to calculate

the worker indicators.

The acute indicators are outlined in detail below.

1. Pesticide operator

Introduction

Pesticide handlers or operators are persons who mix, load and apply the pesticides. They are

usually considered to receive the greatest exposure because of the nature of their work, and

are therefore at highest risk for acute intoxications. The potential for development of long-

term adverse health effects depends on several factors: types of pesticides handled, frequency

of application (times per season) and exposure duration (years of application) are the most

important ones (Fenske et al., 2005). This worker population has been the subject of

significant regulatory scrutiny, and exposure databases have been developed both in NorthAmerica and Europe to better understand the extent and variability of exposure.

Exposure also depends on the type of task performed by an individual, and it is therefore

important to collect data for each. Different tasks that can be identified are mixing/loading,

application, mixing/loading/application, flagging and other activities (cleaning of equipment,

soil incorporation of an herbicide immediately after application,…). The exposure during

specific handling events can be modified by several important factors, as follows: type of

equipment used, formulation, packaging, environmental conditions, protective clothing and

personal protective equipment, hygienic behaviour, dual activities and duration of activity.

These parameters are described in detail in Fenske et al. (2005).



7

Proposed Pesticide Operator Indicator

The risk index for pesticide operators (RIoperator ) is calculated by dividing the internal exposure

(IEoperator ) by the acceptable operator exposure level (AOEL). Both the IEoperator and the AOEL

are expressed in mg/kg body weight/day.

EXPOSURE

We propose to use the following formulas to calculate the acute exposure of operators in

conformity with the POCER indicator (Vercruysse et al., 2002; Maraite et al., 2005).

treated napplicatioload mixoperator Area BW

AR IE IE IE ∗∗+= )( /

[ ]/ ( ) ( * * )mix load I I I hand hand DE IE L PPE Ab L PPE Ab= ∗ ∗ +

( ) ( * * ) ( )application I I I hand hand DE body body DE IE L PPE Ab L PPE Ab L PPE Ab = ∗ ∗ + + ∗ ∗

With:

LI, Lhand, L body (mg a.s./kg a.s.): surrogate or field data on exposure, depending on

data availability;

PPEI, PPEhand, PPE body: personal protective equipment coefficients (-);

AbI, AbDE: respectively inhalation and dermal absorption factors (%);

AR: application rate (kg/ha);

Area treated (ha/d); BW: body weight (kg);

IEmix/load: internal exposure during mixing and loading (mg a.s./kg a.s.);

IEapplication: internal exposure during application (mg a.s./kg a.s.);

IEoperator : internal exposure of the pesticide operator (mg a.s./kg bw/d).

Below, the different parameters are explained more into detail:

LI, Lhand, L body (mg a.s./kg a.s.): surrogate or field data on exposure, depending on

data availability

1. If field data on exposure are available for the different routes of exposure,these values should be used to calculate the internal exposure. These field datashould be expressed as mg a.s./kg a.s. and should be used to calculate the

indicator for real situations at particular locations (at higher tier).

2. If field data on exposure are not available for a given crop and a given activesubstance, surrogate exposure values from the EUROPOEM database are

used. The computer program should select the appropriate surrogate exposurevalues for mixing/loading and application according to the application

equipment used and the formulation type applied. It is important to mention

that it is necessary to establish a link between the active substances applied andthe formulation types of the products. This link was established by WP 5.



8

Annex I gives an overview of the surrogate exposure values used in the EUROPOEM(I & II) model. Annexes II & III present tables in which the formulation types and

application methods are linked to the crops. Using these look-up tables the correct

surrogate exposure value can be selected. Seed treatment, application of granules,dipping into a pesticide solution or pouring a pesticide solution onto plants are other

ways of pesticide application for which operator exposure is normally not assessed bythe human exposure models. In these cases the following assumptions are made. When

treated seed is used no exposure of the operator is expected, since seeding is mostly

done mechanically. Operator exposure during application of granules is only expectedduring mixing and loading, it will be estimated by assuming exposure during mixing

and loading of a water dispersible granule (WG) formulation. Operator exposure

during the use of a pesticide solution for dipping or pouring is estimated by assumingexposure during mixing and loading of a certain formulation (WG, WP (wettable

powder) or liquid). When water soluble bags are used, no exposure duringmixing/loading is assumed. For the application phase the surrogate exposure values of

liquid applications are chosen.

PPEI, PPEhand, PPE body: personal protective equipment coefficients (-)

The default factors used in EUROPOEM should be applied. These factors are given inthe table below (Table II.1.1). (see Annex IV for additional information concerning

new developments in the research topic on the reductive effect of personal protectiveequipment).

Table II.1.1: Default values for the personal protective equipment reduction coefficients

Phase Inhalation (mask) Hands (gloves) Body (overall)Mixing/Loading 0.1 0.1 -

Application 0.1 0.1 0.2

Remarks: When a tractor with closed cabin and carbon filter is used, the highest level of

PPE is chosen. When normal clothing is worn a reduction factor of 0.5 is used. The user should have the possibility to introduce other PPE factors associated

with new types of Personal Protective Equipment.

On regional and national scale, the use of PPE can be taken into account as follows:

)%1(%, PPE RI PPE RI RI noPPE k k PPE applicator −∗+∗=

RIapplicator : applicator risk index taking into account the use of PPE on

regional/national level; RIPPE,k : applicator risk index for a specific type of PPE; % PPEk : percentage of the applicators wearing a specific type of PPE; RInoPPE: applicator risk index not considering the use of PPE; % PPE: percentage of the applicators not wearing any PPE.

Usually there are no data available concerning the compliance with which different

types of PPE are worn.Therefore the operator indicator is calculated assuming that the

PPE is worn by all the individuals living or working in a particular region or country.



9

The influence of wearing a particular type of PPE on the risk for applicators is in thisway assessed. Within the framework of the Belgian Science Policy Project

‘Agriculteurs et pesticides, connaissances, attitudes et pratiques: Résultats d’une

enquête meneé en fruitculture, maraîchage et grandes culture (2002-2003)’, a surveywas conducted among the Belgian farmers concerning the sustainable use of

pesticides. One of the aspects that were studied was the use of personal protectiveequipment. Half of the farmers do not wear any protective accessories when they

handle pesticides. Of those farmers who do use protective equipment, most of them

wear gloves (49%) as the minimum. Some also wear a mask (20%), boots (6%),goggles (10%) or a coverall (17%). Of those who use gloves, only 12% replaces them

regularly (five utilisations maximum). After pesticide application, 13% of the farmersdo not wash their hands and about 80% do not wash their bodies (Marot et al., 2006).

By comparison, only 13% of the fruits growers and 11% of the vegetables growers do

not wear any protective equipment while respectively a quarter and half of thesereported that they felt unwell after spraying. Table II.1.2 gives an overview of the

survey results concerning the use of PPE.

Table II.1.2: The use of personal protective equipment during mixing, loading and applicationactivities (Marot et al., 2003)

% farmersPPEFruit growing Vegetable growing Field crops

none 13 11 50 boots 36 77 6coverall 22 37 17gloves 75 68 49

mask 57 37 20goggles 14 4 10

Although most of the farmers read the label before using a new pesticide, they do notreally follow the security advices of this label (Marot et al., 2003; Maraite et al.,2004).

AbI, AbDE: respectively inhalation and dermal absorption factors

If there are data available regarding the dermal absorption of a specific active

substance, these data should be used. For a great deal of active substances Europeanendpoints are available regarding dermal absorption. If not, the Guidance Document

on dermal absorption should be followed (see Annex V). For the inhalation absorption

factor a default value of 100% is assumed (see Annex VI).

AR: application rate (kg/ha)These data are supplied by the means of surveys (see WP5).



10

Area treated (ha/d)For the area treated per day default values are listed in Table II.1.3 below for The

Netherlands, Germany, Belgium and the United Kingdom. By means of surveys each

member state can determine the average treated area per day.

Table II.1.3: Default values for treated area per day for a few member states of the EU

CountryCropUK Germany The Netherlands Belgium

Field crops 50* 20* 10* 9**Orchards 15* 8* 6* 6**Handheld

applications1* 1* 1* 1**

* Source: EUROPOEM I (2000)

** Source: NIS (2000)

Data on farm size structures can be used to aid the selection of daily work rates.

Many countries collect this type of data. In England, an annual survey of agriculturaland horticultural holdings has been collected since 1866 (MAFF, 2000). Full censuses

are also required at intervals within the European Community to provide farmstructure information that is used to develop and monitor policies, and to assist in

planning of future agricultural activity (Hamey, 2001). Such data can be used invarious ways to estimate a typical daily work rate for a product applied in a particular

crop. A first estimate could be based on the average holding size. For field crops, theaverage arable farm size would obviously be a better, more appropriate estimate. A

further refinement would be to use data on holdings where a particular crop, e.g.

cereals is cultivated as not all the area of arable holdings will be down to cereals.There can be clear regional differences. Moreover the distribution of holding size is

clearly skewed, like farm size. These regional and size differences may be associatedwith different use patterns, equipment and application techniques which may be

relevant for exposure assessment. For example, regional climate difference may affect

pesticide use, and smaller farms may use small tractor mounted sprayers, while largefarms may be more likely to use self-propelled machines. These equipment differences

may be associated with different unit exposures, as the larger equipment may havemore technical measures to reduce exposure. Another consideration is that growers on

larger farms are more likely to use reduced doses (Thomas, 1999), and they may also

use lower spray volumes. It can be concluded that the overall mean area can be used if this is considered appropriate to represent the typical use. If regional variation is

considered important, then the mean area from the main growing regions should be

used. Having established representative crop areas estimates of maximum areas likelyto be treated in a day can be made by reference to typical work rates for different

equipment types. For example work rates can be estimated from the following formula(Baltin, 1959):



11

4 1 210 ( )w L

f s f f

T T q aq C t

Q V S S L V Qf V F

∗= + + + +

∗ ∗ ∗ ∗

Where: t: work time per hectare (s/ha); TL: time for loading (s); q: application rate (l or kg/m²); Qf : quantity loaded per fill (l or kg); Vs: speed when spraying (m/s); Vf : speed when travelling between fill site and application site (m/s); S:swath wide (m); Tw: time for one turn at the end of one spray run (s); L: average length of field (m); C: average distance between fields (m);

F: average size of fields (m²); A: average distance of fill site to fields (m).

Alternatively, some farm management manuals and other advisory literature givetypical work rate examples for selected pesticide application equipment. For example,

the Farm Management Handbook 1994/95 (Anon, 1994) gives typical work rates. Inthe Netherlands the ‘Kwantitative Informatie’ Reports give similar data

(www.ppo.wur.nl/NL). By comparing likely work rates and holding sizes, daily work

rates can be estimated. A possible further refinement of the estimated area to betreated in a workday could be to take into account information on the percentage of the

crop treated (Hamey, 2001).

However it has to be mentioned that a major limitation of the usefulness of UK

pesticide usage surveys for elucidating users’ exposure patterns is that they cannot beused to inform on use patterns of commercial contractors who apply pesticides on

more than one holding (Hamey, 2001). Studies should be performed to obtain insightin the exposure use patterns of custom applicators.

To define representative scales and patterns of use to ensure appropriate exposureassessments, information relating to the use patterns of individual operators is

required. This information should allow the assessment of: the scale of use in terms of the amount of pesticide used in a day, the duration of use, the frequency of use, the

type of application equipment used, and the application techniques employed. Thesedata should be collected in routine surveys or surveys conducted to support specific

exposure assessments. Information must support both use by individual growers andcommercial contract spray operators.The existing EUROSTAT and OECD guidelines

should be used as the basic guidance with sampling of the users at the farm level. A

separate survey should be conducted to include commercial contract pesticideapplicators where identified as relevant. As an alternative to a completely

representative survey, the use of widely used products/active substances could besampled to give a realistic worst case. Sampling strategy and validation of data are

issues that need to be resolved as well as the specific information required (Hamey,

2001).



12

The following information should be collected (Hamey, 2001): Data on the crop and area grown; Product applied; Date of application; Application rate;

Area treated; Application method; Application equipment; Handling practice; Use of PPE; Spraying machinery filling and washing practices.

BW: body weight (kg)

The default body weight is set to 70 kg.

TOXICITY

Most of the chemicals do not cause toxic or adverse effects until a certain dose has been

given. These are called threshold chemicals. The lowest dose level at which no adverse effectsare observed in the test animals is called the No Observed Adverse Effect Level (NOAEL).

The NOAEL is the starting point for the calculation of the toxicological reference dose. For the latter the terminology used may differ among regulatory agencies, but the applied

concepts are similar. In North America the term margin of safety or exposure (MOE) is used,

whereas in Europe an Acceptable Operator Exposure Level (AOEL) is applied (Franklin et

al., 2005). An AOEL is a health-based exposure limit and is established on the basis of the

toxicological properties of an active substance. The term "AOEL" under Directive91/414/EEC implies particular reference to “operators” which are represented by

mixers/loaders and applicators and “re-entry workers”. However, according to Directive97/57/EC, the AOELs established should also be used to evaluate the possible exposure of non-occupationally exposed groups (bystanders). Therefore, based on the current Community

legislation, the AOELs set for operators and workers should be established in such a way thatthey are also applicable for bystanders (EC, 2001).

The default AOEL represents the internal (absorbed) dose available for systemic distributionfrom any route of absorption and is expressed as an internal level (mg/kg bw/d). It is set on

the basis of oral studies provided that no major route-specific differences are anticipated.According to Directive 91/414/EEC, the “AOEL is based on the highest level at which no

adverse effect is observed in tests in the most sensitive relevant animal species or, if

appropriate data are available, in humans”. As a default, only one AOEL is established for anexposure period appropriate to the frequency and duration of exposure of operators (including

contractors) and re-entry workers. This is typically short-term exposure, e.g. repeatedexposure during a total of up to 3 months per year. Hence, the default AOEL will be a

systemic AOEL based on the NOAEL from an oral short-term toxicity study provided that the

critical endpoints of the substance (including reproductive/developmental toxicity andcarcinogenicity) are covered and an adequate margin of safety (MOS) for irreversible effects

is given (MOS >1000). Since targets, critical effects and NOAELs for an active substancemay differ depending on the exposure period, more than one AOEL might in principle be

established to allow for more flexibility in the risk assessment. Although the use of oral

studies for AOEL setting is preferred if there are indications that type and extent of effects of the substance is independent of the route of exposure making route-to-route extrapolation



13

possible, it might be better to use appropriate route-specific studies as a basis for AOELsetting where there are indications that toxicity is dependent on the route of exposure. For

instance, the majority of orally absorbed substances pass directly into the liver where they can

be inactivated or excreted in the bile before they reach the systemic circulation and/or thetarget organs (first pass effect). When administered dermally or by inhalation, these

substances may be distributed in an unmetabolised state and metabolic activation/inactivationmay therefore be more gradual than following oral absorption. In practice, route-specific

studies can only be considered for AOEL setting if: 1) the number and type of parameters

studied are considered adequate; 2) the number of animals examined and the animal species isadequate; and 3) the route-specific study covers all the critical effects of the substance. For

some substances, certain toxic effects, for example on the lung, only occur during inhalationexposure. In these cases (i.e. where effects are air-concentration- rather than dose-related), an

internal AOEL value cannot be established. The risk management for such substances may be

best addressed by establishing occupational exposure limit values. If a substance is mutagenicand/or carcinogenic and a threshold dose does not exist (this applies for some types of effects

such as direct interaction with DNA), it will generally not be possible to set an AOEL (EC,

2001).

In establishing an AOEL the choice of the NOAEL is very important. The NOAEL for an

effect which is relevant to humans and for which the duration, frequency and route of exposure in the test animals are relevant to human exposure should be chosen (Franklin et al.,

2005). Selection of the most appropriate NOAEL needs to be assessed on a case-by-case

basis, and requires expert judgement. The AOEL is calculated by dividing the suitable NOAEL by the uncertainty factors appropriate for the pesticide under review. A 100-fold

uncertainty factor (10 for interspecies variability x 10 for intra-individual variability) isgenerally used when considering risk for the general population, but this factor can range

upwards and downwards depending on the nature of the data and the completeness of the

database. It was recommended that the current procedure of applying 10-fold uncertaintyfactors for interspecies differences and human variability be refined (IPCS, 1994).

Subdivision of each 10-fold factor into toxicokinetic and toxicodynamic components for bothextrapolation from animals to humans (2.5 and 4.0 respectively) and for intra-human

variability (3.16 and 3.16 respectively) would allow part of the default to be replaced by

relevant, chemical-related, specific data when these were available (IPCS, 1999). However,the extent to which the uncertainty factor could be reduced under such circumstances has yet

to be determined. Although establishment of an AOEL relies heavily on expert judgement, itsderivation needs to be reported as transparently as possible. Any agreed AOEL may need to

be reassessed in the light of new data (EC, 2001).

Within the framework of HAIR, the systemic AOEL will be used as the toxicological

reference dose against which the estimated human exposure will be compared in order to

obtain an idea of the risk associated with a particular active substance. For the calculation of the acute indicators, short-term AOEL values will be applied since these AOEL values are

available in the UGent database. Acute AOELs may be applied in order to cover effects thatmay arise from a single exposure or repeated (isolated) single exposures (i.e. at intervals that

enable clearance of the active substance from the body). These AOELs can be set in line with

the procedures proposed for setting an Acute Reference Dose for consumers (EuropeanCommission, 2001; Doc. 7199/VI/99). However, since according to the current knowledge

there are no active substances with only acute exposures, the setting of acute AOELs may

only be considered on a case-by-case basis (EC, 2001). Therefore, AOELs based on short-term toxicity studies will be applied. AOELs based on long-term toxicity studies should be



14

used in the chronic risk assessments. This applies for these cases where operators or re-entryworkers are exposed for more than three months per year.

Concerning the availability of the AOEL values it has to be mentioned that only systemicAOEL values are available. The department of Crop Protection from Ghent University

compiled a database of AOEL values, but since a lot of data were delivered by the industry,the issue of confidentiality plays a role. In 2008 these data will be made public. Until then

UGent adjusted their database and replaced the confidential data by public available data. It

has to be mentioned that the confidential database is much more liable than the databasesupplied for HAIR.

If the AOEL value for a particular active substance is not available in the supplied database,

the geometric mean of the AOEL values of the substances having a similar mode of action

(thus belonging to the same chemical family) will be taken (IRAC mode of actionclassification version 5.1, September 2005, HRAC classification January 2005 & FRAC

classification December 2005).

R ISK INDEX

The risk index for pesticide operators (RIoperator ) is calculated by dividing the internal exposure(IEoperator ) by the acceptable operator exposure level (AOEL). Both the IEoperator and the AOEL

are expressed in mg/kg body weight/day.

,

,

operator acute

operator acute

IE RI

AOEL=

With:

RIoperator, acute: acute risk index for the pesticide operator (-);

IEoperator, acute: acute internal exposure of the pesticide operator (mg a.s./kg bw/d);

AOEL: Acceptable Operator Exposure Level (mg a.s./kg bw/d).



15

2. Re-entry worker

Introduction

Workers are defined as persons who are exposed to pesticides during their working activities, but are not actively involved in the actual application process. Agricultural workers are

potentially exposed to pesticide residues when they enter pesticide-treated fields to perform a

variety of hand-labour tasks, such as pruning, thinning, scouting, harvesting, bending andtying up of the crop required for the commercial production of agricultural crops. These

exposures can occur in different crops, throughout the growing season and can be of similar magnitude to exposures of operators of pesticides (Worgan and Rozario, 1995). In the case of

ornamentals, vegetables and fruits re-entry exposure is likely.

The routes of exposure during post-application activities are the same as in operator exposure,

i.e. dermal and inhalation. However the sources are different: foliage, surfaces, soil and also

dust may contribute. As a result from dermal exposure, oral exposure may occur as well. For workers this route is generally considered less important than inhalation and dermal exposure.

Since no oral data on exposure are available, this route is not considered. Only by includingdata obtained from biological monitoring techniques can this route be taken into account.

Inhalation exposure is very low compared to the dermal exposure.

Proposed Re-Entry Worker Indicator

EXPOSURE

The developed indicator assumes that during application, the foliage of a crop is covered with

pesticide residues. These residues may or may not disappear in time due to various reasons,such as uptake in the foliage or hydrolysis. What remains on the foliage (i.e. dislodgeable

foliar residue) may be transferred to clothing and skin of the worker, when activities involvingcontact with the crop, such as harvesting, are carried out. Important factors in the indicator are

the application rate, the foliage density, the time of activities after application, the transfer of residues from foliage to worker and the duration of the work. The type of activity/task also

determines the exposure.

The main activities which may lead to contact between crop and worker in the outdoor

environment are harvesting, pruning, thinning, and also inspection. Differentiation was made between crops that need to be harvested regularly or from time to time (e.g. apples, pears,

grapes, olives, etc.), and in crops that need not to be harvested (at all or hardly at all). For vegetables and ornamentals grown outdoors, conditions with respect to dermal exposure arelargely comparable to the same crops growing indoors. Therefore no further consideration is

necessary. Exposure to pesticides with field crops is highly mechanised, therefore the re-entryworker exposure will not attain a high level. This applies to crops such as cereals, maize or

sugar beet (EUROPOEM II, 2002).

In the indoor environment ornamentals and vegetables grown in greenhouses can be

distinguished. Higher crops from which the ornamentals or fruits are picked or cut and for which there is extensive physical contact with the foliage of the crop, are the most important

with regard to exposure. Roses and carnations and some pot-ornamentals are important flower

types, important vegetables are tomatoes, cucumbers and sweet peppers (EUROPOEM II,2002).



16

Table II.2.4 lists various post-application/re-entry scenarios. Exposure due to ‘sorting and packing’ was considered to be most comparable to the harvesting scenario by the

EUROPOEM Re-entry Working Group (EUROPOEM II, 2002).

a. Dermal Exposure

Dermal exposure is considered to be by far the most important exposure route during re-entry

activities. The amount of the resulting exposure for a particular activity depends on theamount of residue on foliage and on the intensity of the contact with the foliage in case of

different activities. Time of contact is also an important issue. Droplet size spectra of spray

applications also play an important role with respect to re-entry worker exposure.

The dermal exposure of the re-entry worker is estimated by a multiplication of DFR (dislodgeable foliar residue), TF (transfer factor) and T (duration of the work/re-entry). The

algorithm proposed by the Re-Entry Working Group is outlined below (EUROPOEM II,

2002):

0.001 DE DFR TF T P = ∗ ∗ ∗ ∗

0.01 AR

DE TF T P LAI

= ∗ ∗ ∗ ∗

With:

DE: dermal exposure (mg/d);

0.001/0.01: conversion factor for the units;

DFR: dislodgeable foliar residue (µg/cm²);

AR: application rate (kg a.s./ha); LAI: Leaf Area Index (m² leaf area/ m² ground area);

TF: transfer factor (cm²/person/hr);

T: duration of re-entry (hr);

P: factor for PPE (-).

Below, the different parameters are explained more into detail:

DFR: dislodgeable foliar residue (µg/cm²) and LAI: Leaf Area Index (m² leaf area/

m² ground area)

In this formula the dissipation factor of the active substance on the foliage is not

taken into account. The dissipation factors is set to one as a default sine thedissipation function is frequently unknown. The dissipation of residues on crop

foliage over time depends on the physical and chemical properties of the appliedactive substance as well as on environmental conditions. Mostly the exact nature

of dissipation over time is not known, in this case DFR 0, i.e. the residue available

directly after application (when dry) is used for calculations. The initial DFR dataare defined as the DFR samples taken between 0 and 24 hours after application.

Thus, the most conservative approach is to assume no dissipation at all. TheEUROPOEM II Re-entry Working Group made an overview of the data on initial

foliar residues. This review showed a large variation in DFR 0, even whenstandardized for application rate. There are certain factors that may potentially



17

lead to variability in DFR levels. These include (1) crosswinds that may result inhigher residues on the downwind side of the field than on the upwind side of the

field, (2) hotspots where the applicator has turned at the end of the field to start

application to a new row, (3) variability due to overlap of application betweenrows. Such spatial heterogeneity may affect the observed variability of the entire

set of DFR and worker exposure measurements (Whitmyre et al., 2005). All else being equal, a person working in an are of the field containing the high end of the

range of DFR levels has a greater probability of receiving a higher exposure per

unit time than a person working in a part of the field with lower DFR levels.

The declination of pesticide residues with time may be accounted for by using adissipation function to describe the DFR at a given time t after application. Often it

has been assumed that the residue levels on the foliage will follow a monotonically

decreasing decay curve which is exponential, namely (Whitmyre et al., 2005):

( )t

t DFR e α β − ∗=

With:

DFR t: dislodgeable foliar residue at any given time t after

application (µg/cm²);

α, β: fitted constants;

t: time after application (d);

DFR 0: the day-zero dislodgeable foliar residue;

( )

0 DFR e α = if R² > 0.85

1/ 2

(log 0.5)

( )T

β =

−

T½: half-life time (d) (see Annex VIII).

Sometimes, one encounters cases where the first order degradation equation does

not give a good yield. An alternative form that is sometimes seen is a log-log

relationship where several compartments exist (e.g. surface of leaf, interior of leaf,etc.) which are each associated with different half lives.

[ ]log( ) log( )t DFR t α β = −

Climatic factors such as humidity and temperature, as well as the physico-

chemical properties of the active ingredient, affect the rates at which the DFRsdecline for a given crop. Furthermore losses due to drift may occur outdoors,especially for low-volume and ultra-low volume spraying techniques), and losses

to soil are also likely to occur (van Hemmen et al., 1995).

AR: application rate (kg a.s./ha)

LAI: Leaf Area Index (m² leaf area/ m² ground area)

In the absence of experimentally determined DFR (Dislodgeable Foliar Residue)data, the application rate (kg a.s./ha) divided by the leaf area index (m²/m²) of the

crop (which is the one-sided surface area of the total foliage of a crop divided by

the ground surface area on which the crop is growing) provides a crude estimate of the initial foliar residue on the crop, assuming uniform distribution of residues



18

across the crop (Bates, 1990). But one should be careful since such an approachassumes that the coverage of the foliage is homogeneous. An LAI of 2 can be used

as a possible default in a first tier approach (Hamey, 2003) when no data are

available. So far there has been no report available in which a correlation betweenDFR 0 and LAI has been scientifically established. Therefore, the following

theoretical considerations have to be taken into account: in early growth stages,where the leaf canopy does not cover the ground completely, or in high crops

where the leaf canopy is not yet closed, a corresponding part of the product will

not reach the plant surface but will reach the soil or will be lost by drift. For onesided applications, a default value of 1 can be assumed and if both sides of the

leaves are treated, the theoretical LAI (m²/m²) may be increased up to 2. With progressing plant growth the LAI increases and the default LAI value may be up to

about 2 for one sided treatment and about twice the chosen LAI for two sided

treatment. If specific DFR data exist, the term (AR (kg/ha) /LAI) should bereplaced by the DFR values, since only a portion of the total initial residue

estimated using the LAI approach is actually dislodgeable to workers. Thus, DFRs

calculated using the LAI approach, probably overestimate actual starting DFR levels obtained by monitoring. The EUROPOEM II Working Group compiled allthe available information on leaf area index. In the report of the Re-entry working

group ‘Post-application exposure of workers to pesticides in agriculture’(EUROPOEM II, 2002) a review was made of the available leaf area index data.

The re-entry working group carried out a literature evaluation on leaf area index

data within the framework of the EUROPOEM II project. Additionally non- published data on LAI measurements were also included. Several databases

(EMBASE, ESBIOBASE, Agricola,…) were consulted. Only data with regard toagricultural and horticultural crops were considered, hereby giving preference to

data generated in crops grown in European countries.

For exposure determinations one should always use the DFR values that are in the

contact zone of the foliage with the workers. A conservative Default Value of 3µg/cm² per kg applied/ha can be used if no data are available for a highly

conservative assessment of the initial DFR 0 in a first tier assessment. This value

corresponds with the 90th

percentile of the distribution. This value was obtained bythe EUROPOEM II Re-entry Working Group after conducting an extensive

analysis of the available literature on initial dislodgeable foliar residue data. Thenumber of records per crop type was not large enough to assess a DFR 0 value

based on a specific crop type and application rate. Due to the small number of

records and the variety in application rates and crop types the database is useful toindicate an approximate relationship between DFR 0 values and application rate,

leaving the crop type aside.

For each task performed by a re-entry worker a different DFR should be available

if one wants to conduct refined exposure assessments. At the moment such dataare not available. If data on DFR, TF and duration of task are available for each

task performed, than the total dermal exposure can be calculated by summing thetask individual dermal exposures.



19

TF: transfer factor (cm²/person/hr)The transfer factor depends on the nature of the contact and the degree of contact

between body and foliage, and the duration of the work. The transfer of residues

from the crop foliage to the clothes or skin of the worker can be regarded as moreor less independent of the kind of product applied. Several studies regarding re-

entry scenarios have been conducted over the last two decades, primarily in theUnited States.

Zweig et al. (1985) and Nigg et al. (1984) proposed the empirical factor of 5000cm²/person/hr as a generic default for the TF for estimating dermal exposure,

based on ‘one-sided’ DFR values. However this single default value does notadequately reflect the potentially wide range of TF values across different crops

and across different work activities. Krieger et al. (1990, 1992) have presented

TF‘s varying from about 1000 cm²/hr to as high as 400.000 cm²/hr for variousworker re-entry activities involving different crops. An example listing of these TF

values are given in Table II.2.1. The data, presented in this table are based on field

studies conducted by the Worker Health and Safety Branch, California Departmentof Pesticide Regulation. Several transfer factors for more specific re-entryscenarios under American conditions are presented here (Worgan & Rozario,

1995).

Table II.2.1: Transfer factors for various re-entry worker activities (adapted from Krieger et

al. (1990))

TF (cm²/hr)Crop type Work task Standard

clothing

Plus PPE Active

21000 12000 (new nylon gloves)

- 17000 (used nylon gloves)Pole tomatoes Harvesting

- 7000 (rubber latex gloves)

Chlorotalonil

Hand harvesting 9000 -Bush tomatoesMechanical harvesting 1000 -

Chlorotalonil

Cutting - 13000 (rubber latex gloves)Lettuce

Packing - 6000 (rubber latex gloves)Folpet

- 6000 (rubber latex gloves) Captan

- 500 (rubber latex gloves) Malathion

- 2000 (rubber latex gloves) DicofolStrawberry Harvesting

- 1000 (rubber latex gloves) Naled54000 - Azinphosmethyl

Peach Harvesting24000 - Phosmet

Nectarine Harvesting 7000 - AzinphosmethylPlum Thinning 390000 - Captan

Apples Harvesting - 6000 AzinphosmethylCane-cutting 17000 -

GrapesHarvesting 18000 -

Captan

van Hemmen et al. (1995) attempted to group TF’s by activity type and contact

type, based on Krieger et al. (1990, 1992) (see Table II.2.2)

Table II.2.2: Range for Transfer factors for particular activities

Activity Details TF (cm²/hr)

Sort & selectHand exposure only, e.g.mechanical harvest

50-800

Reach & pick Hand and arm exposure e.g.tomato & strawberry

500-8000

Search, reach & pick Hand/upper body exposuree.g. tree fruit

4000-30000

Expose, search, reach & pick Whole body contact (winegrapes)

20000-140000



20

A literature evaluation was carried out by the Re-entry Woking Group on TFs. TheTFs were calculated by dividing the dermal exposure to hands or body by the

dislodgeable foliar residue sample the same day. From the relatively small

databases, indicative surrogate transfer coefficients were deduced. The rounded90th percentile was taken for the smaller databases, while the rounded 75th

percentile was taken for the larger databases. A small database may not be typicalfor a given scenario and therefore the 90th percentile was taken as surrogate

exposure value. Large databases contain more representative data and therefore the

75th

percentile is taken.

When no field data are available the following indicative TF values are proposedfor four different harvesting scenarios with bare hands:

Fruit, Vegetables and Ornamentals in greenhouses: 5000 cm²/person/hr (Schipper et al., 1999, Brouwer et al., 1992);

Vegetables in open air: 5800 cm²/person/hr (EUROPOEM II, 2002);

Ornamentals in open air: 5000 cm²/person/hr (EUROPOEM II, 2002); Fruit in open air (high crops): 30000 cm²/person/hr (Krieger et al., 1990);

20000 cm²/person/hr (EUROPOEM II, 2002);

Strawberries: 3000 cm²/person/hr (EUROPOEM II, 2002).

The TFs proposed here are based on potential exposure data (except for

strawberries and ornamentals). Therefore a generic clothing permeation factor P isused to estimate the actual dermal exposure. The number and quality of studies

which are available for inclusion in a database to predict re-entry exposure for allrelevant crop/work activity is generally poor. There is a clear need for more

studies and data need to be generated under typical EU conditions. The collection

of data on TFs is of extreme importance and further research in this field is highlyrelevant and necessary. In the Belgian registration procedure for pesticides, other

TFs are currently applied. For respectively field crops and high crops a TF of 5000cm²/person/hr and 30000 cm²/person/hr is used.

In the United States the Agricultural Re-entry task Force, which is a joint datadevelopment task force formed under FIFRA, developed a database of generic

agricultural re-entry transfer coefficients that are applicable to all crop/activityscenarios for use by its members for assessment of exposure and risk in any post-

application agricultural worker re-entry scenario. This database of generic

agricultural transfer coefficients is being used by the EPA (EnvironmentalProtection Agency), California Department of Pesticide Regulation (CDPR) and

Canada’s Pest management Regulatory Agency (PMRA) to evaluate member companies’ chemicals and enables the regulators to estimate post-application

exposure of workers. These data may however only be used to make regulatory

decisions by the members who have sponsored the research(http://www.exposuretf.com). The approach followed by the ARTF can be applied

to the European situation.



21

If monitoring data on both, exposure and DFR’s are available then the followingequation is applied to obtain a TF, normalized per hour of task duration.

ExpTF

DFR=

With: TF: transfer factor (cm²/hr);

Exp: exposure (µg/hr);

DFR: dislodgeable foliar residue (µg/cm²).

In this way chemical and activity specific transfer coefficients are obtained. When

monitoring data are not available, which usually is the case, surrogate transfer

coefficients are used. These are listed above and in Table II.2.4. It is veryimportant to mention that a harmonized approach in determining data for

dislodgeable foliar residues should be established. The Agricultural Re-entryExposure Task Force (ARTF) developed a protocol for a standardized assessment

of dislodgeable foliar residues.

T: duration of re-entry (h); (default: 8 hours a day)

Exposure duration may be up to 6 hours on a normal day of harvesting, sorting and bundling and occasionally even longer. The exposure may be over several days a

week in view of the continued presence of pesticides on the crop depending on the

stability of a particular pesticide or a mixture of pesticides. This parameter should be established for each re-entry task. Currently such data are not available.

P: factor for PPE (no PPE: 1; with PPE: 0.1)

In fruit orchards (apples and pears) the harvesting activities and the thinning aredone with bare hands. For pruning, occasionally, gloves are worn not to protectagainst pesticide exposure but against skin damage. Depending on the weather and

on the region in Europe, the clothing might vary for the same activities. When performing activities in hops, gloves are worn as a rule in order to prevent injury

since the bines have small hooked hairs with needle points. There is potential

exposure during harvesting, particularly when collecting bines for transport tostatic picking machines. Harvesting and pruning in vineyards is usually done with

bare hands and under conditions that light clothing is required. The degree of exposure largely depends on the structure of the vine. In cultures of ornamentals

all activities are done with bare hands, with possible exceptions for roses with

thorns and crop activities, which lead to greenish hands. The use of gloves is fairlycommon with all work tasks in tomatoes.

Special case: The case of possible dermal exposure to soil containing pesticide residues is based on the

concept of dermal adherence. The contribution of soil residues to the total exposure isusually expected to be less important than that of DFR. A study conducted in Florida

(Stamper et al., 1987) indicated that if soil residues were to make an equal contribution todermal exposure as DFR’s, 170 g of soil on the hands would be required.

Relevant scenarios, where exposure due to soil borne residues occurs in the absence of

contact with the treated foliage, are for example the use of compost treated with aninsecticide or the manual harvesting of root crops. Data on soil residue levels, either



22

estimated or measured, can be used with soil dermal adherence data to estimate potentialhuman exposure with the actual exposure being estimated on the fraction absorbed.

The algorithm specified below is used to estimate exposure (EUROPOEM II, 2002):

soil

Sk S C S S T SA DAConc D ρ

/∗∗∗=

A f D AD ∗=

With: D: dermal exposure (mg/d); ConcS: soil concentration of the active substance (mg/m³);

The soil concentration of the active substance can be determined by applying

the same approach as the terrestrial work package (WP 6); DAS: Dermal adherence of soil (mg/cm²);

Field studies investigating dermal exposure to soil by direct gravimetricmeasurements (Kissel et al., 1996) suggest that an appropriate hand soilloading for a worker would be 0.44 mg/cm² (geometric mean peak value for

farmers involved in hand weeding, default). A laboratory study to determinethe extent of soil adherence to hands when totally immersed in a range of dry

soil samples (Driver et al., 1996) concluded that the mean hand loading for un-

sieved soil was 0.58 mg/cm² of skin surface. Data for sieved samples suggestedthat hand loading increased when soil particle size was reduced;

SAC: Skin area contaminated (cm²);A default value of 820 cm² is to be used;

soil ρ : soil bulk density (g/cm³);

TS/Sk :Transfer of the active substance from soil to skin (-);

Data on the transfer of active substance from soil to skin are usually not

available at the moment; AD: absorbed dose (mg/d); f A: fraction absorbed (%).

As a preliminary approach it is assumed that all of the chemical in a layer of soil is bio-

available to skin. Conservative assumptions like these have to be used when no specificinformation is available. More research should be performed in order to make more

reliable exposure estimations for this route of exposure.

b. Inhalation Exposure

Inhalation exposure may occur to residual vapour and airborne aerosols during a relatively

short period after application. In case of outdoor crops, exposure will occur during the time

the spray is drying; in case of greenhouse crops, exposure will occur within a few hours after application. Outdoors, there generally is a rapid dissipation of vapour and aerosols, leading to

much lower inhalation potential than in greenhouses. Furthermore, the majority of the applied pesticides are non-volatile which implicates a low potential inhalation exposure. Thus,

inhalation exposure is in many cases less important for risk assessment than dermal exposure

especially for outdoor scenarios, with of course exceptions for aerosols and volatile pesticides

of concern.



23

The EUROPOEM Re-entry Working Group developed an algorithm for a few re-entryscenarios (EUROPOEM II, 2002). There is no generic model for inhalation exposure

available. Here, only a preliminary approach for indoor inhalation exposure is presented (see

Table II.2.3). The identified representative crop/activity re-entry scenarios relevant to Europethat may result in post-application exposure of workers to plant protection residues are listed

in Table II.2.4. Thus, this estimation procedure for inhalation exposure is only applied for greenhouse workers. For field workers, the inhalation exposure is considered negligible

because of the dilution in free air.

The algorithm proposed to estimate inhalation exposure to vapours is outlined below

T TSF AR I ∗∗=

Where: I: potential inhalation exposure (mg a.s./d inhaled); AR: application rate (kg a.s./ha); TSF: Task Specific Factor;

These factors can be used in the first tier exposure and risk assessment and have been estimated for a small set of exposure data on harvesting of ornamentals and

re-entry of greenhouses about 8-16 hours after specific applications. The indicativeTask Specific Factor values for specific indoor glasshouse scenarios are given in

Table II.2.3; T: hours working per day (hr/d);

Table II.2.3: Estimated values for indicative Task Specific Factors

Post-application scenario Re-entry time Task Specific FactorCutting ornamentals - 0.1

Sorting and bundling ornamentals - 0.01

Re-entering greenhouses after low volume mist application 8 0.03

Re-entering greenhouses after roof fogging

16 0.15

A preliminary approach to estimate inhalation exposure to dusts whenever consideredrelevant is presented hereafter. This inhalation exposure will generally be relatively low

compared to other exposures.

Inhalation exposure can be approximated by using field data on individual exposure levels to

soil dust during relevant operations. Data from California (Nieuwenhuijsen et al ., 1998) on adry-climate situation likely to give a conservative value, show a worst case total inhalation

dust exposure when cultivating using a vehicle with no cab of 98,6 mg/m³. The exposure to

respirable dust was 0.58 mg/m³ under the same conditions. A Polish study indicated personaldust levels during plant harvesting within the range of 3.3 to 19.3 mg/m³ (Molocznik &

Zagorsky, 2000).

For a chemical homogeneously distributed in soil at the rate equivalent of 1 kg/ha to a 5 cm

depth assuming the worst case exposure of 98,6 mg/m³, over an 8 hour day a 60 kg person breathing 29 litres per minute would be exposed 30 ng/(kg b.w. d). This amount is very low

indicating that the potential inhalation risk from residues in soil is typically very low.

Therefore this route of exposure will not be taken into account in this exposure assessment but

can be included when relevant and if suitable data are available.



24

Table II.2.4: Re-entry/post application scenarios relevant to Europe (Distinction is made between crops grown indoors and according to the comparability of work patterns (way of cultivation and related activities))

Crop group Crops Post-application activities LAI Common Wheat

Durum Wheat

RyeBarley (Spring & Winter)

Oats

Grain Maize

Rice

Cereals

Other cereals (Corn, Spelt, Triticale)

Inspecting/Scouting, IrrigationWeeding

Pulses Fodder PeasPulses

Pulses Fodder Field Beans

Harvesting, Inspecting, ScoutingWeeding,& Irrigation

PotatoesEarly Potatoes, Storage Potatoes, Seed

Potatoes Harvesting, Irrigation, Scouting, Planting &

Weeding

Sugar beet Sugar beet Harvesting, Scouting, Irrigation & Weeding

1**(ear2**(lat

TobaccoHarvesting, stripping, training, thinning,topping, irrigation, scouting & Planting

Hops

Training the shoots, removing superfluousground shoots (manually/mechanically),defoliation of the bottom 2 metres(manually/chemically), tilling(mechanically), stripping, thinning, topping,

irrigation, scouting and harvesting(mechanically/manually)

Cotton/Flax Nursing/Scouting, Harvesting, Irrigation &

WeedingOil seed or fibre plants (Rape, Turnip,Sunflower, Soya, Chicory (Ordinary &

Coffee), other))Scouting, Irrigation & Weeding

Aromatic, Medicinal and Culinary Plants

(Herbs: Basil, Mint)Scouting

Industrial plants

Other industrial plants Several activities

1**(ear2**(lat

Fodder Fodder Roots and Brassicas Several activities1**(ear2**(lat



25


Crop group Crops Post-application activities LAI

Fresh vegetables (Alfalfa, Artichokes,Asparagus, Broccoli, Brussels Sprouts,

Cabbage, Canola, Carrot, Cauliflower,Celery, Chinese Leaves, Chervil, Chives,

Clover, Cucumber, Fennel, Garlic, Gherkin,Kale, Lettuce, Leek, Ley, Onion, Parsley,

Radish, Rhubarb, Scorzonera, Shallot,Sorrel, Spinach, Tomato)

Pruning, harvesting, weeding, planting, (cabbage & lettuce) sorting

& packing

1** (early)2** (late)

Melons Harvesting, thinning, …

Outdoor (open field &market gardening): Fresh

Vegetables, Melons &

Strawberries

Strawberries, Blackberries, Dewberries,Currants, Gooseberries, Raspberries

Harvesting, hand pruning, pinching,training and thinning and leaf pulling

(blueberries (high bush))


Fresh Vegetables (Aubergine, Been, Beet,Cauliflower, Celery, Courgette, Cucumber,

Currant, Cutbeet, Endive, Gherkin, Lettuce,Pepper, Seed, Sweet Pepper, Tomato &Watercress)

Melons

Strawberries, Blackberries, Dewberries,Currants, Gooseberries, Raspberries

Training, thinning (one or severalweeks), pruning, harvesting (2-3 dayinterval), sorting & packing


Flowers and Ornamental Plants

Pruning, harvesting, sorting, bundling, packing, picking and planting of cuttings, thinning, pullingup grids, training of plants, visual

control for diseases, making bundlesof flowers and bagging ornamentals


Greenhouse crops(Vegetables, flowers and

permanent crops)

Permanent Crops Under Glass (Grapes)

Handling of soil containing

pesticides, handling of cuttings andinoculation activities, pruning, weedcontrol, mechanical removal of thetree and the pulling from the soil,

sorting and bundling activities of smaller trees and cuttings & plantingand potting activities




26


Crop group Crops Post-application activities

Outdoor flowers and

ornamental plants

Flowers and Ornamental Plants Pruning, harvesting, sorting & packing

Nurseries

Handling of soil containing pesticides,handling of cuttings and inoculationactivities, pruning, weed control,mechanical removal of the tree and the pulling from the soil, sorting and bundling

activities of smaller trees and cuttings & planting and potting activities

Vineyards (raisins, table grapes, other wines,other wines, quality wine)

Pruning (minor activity), foliage work,harvesting & packing

Olive plantations (oil production, table olives)Harvesting, pruning, thinning, irrigation,

scouting, sorting, packing, handling after post-harvest treatment

Citrus plantation (oranges)

Harvesting, sorting, packing, handling after post-harvest treatment, tying, pollination,training, pruning, irrigation, scouting &weeding

Fruit (temperate climate, subtropical climate)(Apple, Cherry, Kiwi, Pear, Peach, Plum) and

Nuts

Pruning, harvesting, thinning, tying, sorting,handling & packing

Permanent crops

Other permanent crops Several activities

(

Rough grazingsPermanent grassland andmeadows Pasture and meadows

No activities

Temporary grassOther green fodder (green maize, leguminous

plants)Forage plants

Other

Harvesting, Scouting, Irrigating & Weeding

Other Other No activities

NAA: Not applicableVl. p.c.: pers. comm. Vleminckx, 2006*: Source: Policy Science Advisory Council for Exposure (May 7th, 1998). Agricultural Default Transfer Coefficients (See Annex V

**: Source: EUROPOEM II Re-entry Working Group



27

Remarks: The harvest of cereals, sugar beet and maize is highly mechanised and therefore the

exposure will be considerably less than for the other scenarios. Within the framework of

this project the exposure for these crops is considered negligible. But further considerationshould be given to the inspection of crops and diseases, possibly hoeing activities. But

currently no data concerning this scenario are available. One could use a default TF factor for potential exposure of 1000 cm²/hr for hoeing and weeding activities as proposed by the

Science Advisory Council for Exposure (1998);

If the harvest and planting of potatoes or tubers and onions is done by hand, then a defaulttransfer coefficient of 10.000 cm²/hr can be applied. A default TF of 2500 cm²/hr for

sorting and packing activities of tubers can also be applied. These default values were proposed by the Science Advisory Council for Exposure (1998);

There is a relative low potential for dermal exposure regarding activities concerning

berries since after blossoming pesticides are generally not applied;

Lawns, greens and playgrounds are considered not relevant with respect to the level of re-

entry exposure. Possible exceptions may be children playing and sporting activities having

much contact between body and grass. For this scenario data should be gathered by themeans of field experiments;

The higher crops from which the ornamentals or fruits are picked or cut and for which

there is extensive physical contact with the foliage of the crop are most important. Rosesand carnations are important flower types and tomatoes, cucumbers and sweet peppers are

important vegetables in this respect;

Thinning activities are considered to involve lower or similarly intensive contact withfoliage in comparison with pruning. In addition the same body parts of the worker will be

exposed to the foliage during both types of activities. Exposure assessments for pruningwill also cover the worst case exposure assumptions for thinning;

Dermal exposure during post-application activities results primarily from sorting and

packaging (bundling). In case of sorting, workers have to handle the harvested commoditywith their hands, most often in a similar way as during harvesting but without any contact

with the foliage. Therefore, these post-harvest exposures can be considered as the mostcomparable to exposure during harvesting;

Further research is required with respect to dermal exposure resulting from particular

post-harvest activities.

c. Total Internal Exposure

The total internal exposure is calculated as the sum of the dermal exposure (DE; mg/person/d) multiplied by the dermal absorption factor (AbDE; %) and the inhalation exposure (I;

mg/person/d) multiplied by the inhalation absorption factor (AbI; %), divided by the bodyweight (BW, default: 70 kg) of the worker (Vercruysse et al., 2002).

_ ker DE I

re entry wor

DE Ab I Ab IE

BW −

∗ + ∗=

Where: DE: dermal exposure (mg/person/d); AbDE: dermal absorption factor (%/100); I: greenhouse inhalation exposure (mg/person/d); AbI: inhalation absorption factor (%/100) (default: 100%); BW : body weight (kg) (default: 70); IEre-entry worker : internal exposure (mg/kg bw/d).



28

TOXICITY

Since for most if not all of the active substances only the systemic AOEL is available, these

values are used for risk evaluation. If re-entry workers are exposed less than three months a

year, the estimated exposure should be compared with the short-term AOEL. If not, the

estimated exposure should be compared to the long-term AOEL. As mentioned before thechoice of an appropriate toxicological benchmark is very important. With respect to theintermittent exposure of pesticide workers it is recognized that both information on the

anticipated exposure scenario as well as knowledge on the effect of intermittent exposure onthe toxicity are needed. From a toxicological point of view, the setting of more than one

AOEL, covering effects that may arise after different periods of exposure, as well as thedevelopment of more robust acute and short-term studies are strongly recommended.

R ISK INDEX

For risk assessment the internal exposure is compared with the systemic AOEL according to

the European approvals process. It is assumed that the AOEL can be used as a reference doseagainst which re-entry worker exposure is assessed. Thus the risk index for re-entry workers

is calculated as follows:

_ ker,

_ ker,

re entry wor acute

re entry wor acute

IE RI

AOEL

−

− =

With:

RIre-entry_worker, acute: acute risk index for the re-entry worker (-);

IEre-entry_worker, acute: internal exposure of the re-entry worker (mg a.s./kg bw/d);


Remarks:

Agricultural re-entry exposure studies should be conducted to provide data that canultimately be based to predict worker exposure during specific re-entry activities, and

indirectly can be used to estimate transfer factors associated with a given work activity. It is clear that in general more field data on post-application (re-entry) scenarios from

which relevant data for predictive modelling can be extracted, are needed.



29

3. Greenhouse worker

Proposed Greenhouse Worker Indicator

EXPOSURE

a. Dermal Exposure

The dermal exposure for the greenhouse worker is estimated in the same way as for the re-

entry worker.


Previously the approach proposed by the Re-Entry Working Group of EUROPOEM(EUROPOEM II, 2002) to estimate the inhalation exposure of greenhouse workers was

outlined. For information, another approach is mentioned here. The inhalation exposure for

the greenhouse worker is assessed making use of the formulas proposed in the improved

computer model USES (an integral risk-decision system used by the Dutch authorities). Thismodel was developed to assess internal greenhouse concentrations and aerial pesticideconcentrations near greenhouses (Mensink, 2004).

The following formulas are proposed to estimate the gas-phase pesticide concentrations ingreenhouses:

gh

t gh H

ARC 1

105

0, ∗∗==

With: Cgh,t=0: the nominal gas-phase concentration inside the greenhouse immediately

after application. It is dependent on dosage and greenhouse volume. And it is

assumed that the applied amount is instantaneously and homogeneously distributedover the whole greenhouse volume (µg/m³);

105: a conversion factor for the units (kg to µg); AR: application rate (kg/ha); Hgh: height of the greenhouse (m).

The assumption that the whole amount of sprayed, fogged, smoked or otherwise applied

pesticides contributes to the gas-phase concentration is worst case, and less realistic in

particular for treatments with relatively larger droplets (e.g. high- and low volume

applications). Therefore a correction factor α is introduced varying from 0.02-0.71. This

factor is based on initially actually recovered amounts of pesticides in experiments. Theexperimental evidence, however, is limited as only a few experimentally based figures areavailable. Prospective experiments may reveal more realistic and suitable input values. Table

II.3.1 gives an overview of the default values for different scenarios used in the Netherlands.A first-order decrease of the pesticide concentration in the greenhouse during the first hours

after application (until re-opening) is assumed based on the combined effect of volatilisation,

ventilation and deposition. Deposition in the greenhouse may occur after volatilisation.Ventilation is mainly based on convection through chinks and cracks of the greenhouse

construction. Ventilation through open windows is not taken into account.

The following formula is proposed for estimating a more realistic pesticide concentration in

greenhouses (Mensink, 2004):



30

[ ] [ ])(

1

,,

)*(

0,

,

dep ghvent gh

xT

t gh

T ghk k T

eC C

+∗

−∗∗=

−=α

With: Cgh,T (µg/m³): the estimated actual gas-phase concentration after T seconds inside

the greenhouse; Cgh,t=0 (µg/m³): the nominal gas-phase concentration inside the greenhouse

immediately after application; α (-): empirical recovery fraction at t=0;

k gh,dep: deposition rate constant inside greenhouse (s-1

) This parameter wasexperimentally derived as an average for several active ingredients in different

greenhouses with different application techniques. The deposition rate constant

can also be determined using the following formula:

gh gh gh

dep ghW H L

ISAk

∗∗∗∗= 4

, 105,5

With: ISA (m²): Inner greenhouse surface area, including roof and floor, perpendicular

on the wind direction; Lgh (m): greenhouse length; Hgh (m): greenhouse height; Wgh (m): greenhouse width.

k gh,vent : ventilation rate constant inside greenhouse (s-1). This parameter is derived

by assuming a flow rate constant directly proportional to the wind speed;

x = dep ghvent gh k k ,, + (s-1

);

T (s): time over which the concentration is integrated.

The proposed calculation scheme is supposedly realistic worst-case taking into account that

the calculations have been based on processes (volatilisation, deposition and ventilation) and

on experimental values (ventilation and deposition rate constants, α values). However, the

assumptions in the calculation scheme cannot be sufficiently tested yet as actual

concentrations inside the greenhouse are very scarce or lacking. Comparison with measured

concentrations revealed that the scheme may be realistic for volatile pesticides, but probablynot for less volatile pesticides. This is however based on only a few measurements. The

likelihood of underestimating exposure seems small, whereas overestimating exposure to lessvolatile pesticides may still occur. To restrict the overestimation of exposure, a limit is set for

the pesticide concentrations in greenhouses, namely the saturated vapour concentration. Inthis way the approach used is more realistic for less volatile and moderately volatile pesticides. This concentration is calculated as follows:

T R

f MW P C

vap

s∗

∗∗=

With: Cs: saturated concentration of an active substance in the atmosphere

(µg a.s./ m³ air); Pvap: Vapour pressure (Pa); MW: Molecular Weight (g/mol); f: 106, conversion factor g to µg; R: gas constant (8,314 J/molK); T: Temperature (K).



31

When the calculated concentration Cgh (µg/m³) exceeds Cs (µg/m³), the value for Cs (µg/m³) isused to calculate the internal inhalation exposure I (mg/person/d).

Table II.3.1: Proposed default values

Parameter Value UnitLgh 100 mHgh 4,5 mWgh 100 mISA 450 m²k gh, dep 2,66E(-4) s-1 k gh, vent 1,67E(-4) s-1

α (highly volatile, application type, not relevant) 0.51 -

α (moderately volatile, space treatment) 0.71 -

α (moderately volatile, high-volume application) 0.02 -

α (moderately volatile, low-volume application) 0.04 -

α (slightly volatile, application type, not relevant) 0.1 -

Three volatility classes are distinguished: highly volatile (>10 mPa), moderately volatile (0.01-10 mPa)

and slightly volatile (<0.01mPa)The acute inhalation exposure for the greenhouse worker is then estimated using the formula

below:310−∗∗∗= WR IRC I gh

I : greenhouse worker inhalation exposure (mg/person/d); Cgh: concentration of an active substance in the greenhouse atmosphere (µg a.s./

m³ air); IR: inhalation rate (m³ air/ hr);

A default value of 1,25 m³/hr can be assumed for an adult. Table II.3.2 presents

alternative values for the inhalation rate recommended by the EPA ExposureFactors Handbook 1997. The PSD applies an IR of 0.82 m³/hr;

Table II.3.2: Default values for the inhalation rate for adults

Time-scale Activity/Population Value Average Value Unit SourceRest 0.4*

Sedentary activities 0.5*

Light activities 1.0*

Moderate activities 1.6*

Acute

Heavy activities 1.9*

0.55

(=13,3/24)m³/hr

Females 11.3**Chronic

Males 15.2**13.3 m³/d

Exposure Factors

Handbook (U.S.

EPA, 1997b, page 5-24)

* These values should be used to assess short-term scenarios of a few hours in duration, such as

post-application inhalation exposures following lawn treatment, foggers and crack and crevicetreatments (depending on chemical specific data, such as air measurements, vapour pressure, andpersistence).

** These values should be used to assess short-term scenarios of a few days in duration, such aspost-application inhalation exposures following crack and crevice treatments, and termiticidetreatments (depending on chemical specific data, such as air measurements, vapour pressure, andpersistence).

WR: work rate (hr/d);As a default value it is assumed that a greenhouse worker is exposed for the

duration of 8 hours a day; 10-3: correction factor for the units.



32


The total internal exposure is calculated as the sum of the dermal exposure (DE; mg/person/

/d) multiplied by the dermal absorption factor (AbDE; %) and the inhalation exposure (I;

mg/person/d) multiplied by the inhalation absorption factor (AbI; %), divided by the bodyweight (BW, default: 70 kg) of the worker (Vercruysse et al., 2002).

* DE I

greenhouse

DE Ab I Ab IE

BW

∗ +=

Where: DE: dermal exposure (mg/person/d); AbDE: dermal absorption factor (%/100); I: greenhouse inhalation exposure (mg/person/d); AbI: inhalation absorption factor (%/100) (default: 100%);

BW : body weight (kg) (default: 70); IEgreenhouse: internal exposure (mg/kg bw/d).

TOXICITY

The estimated exposure is compared to the AOEL of the considered active substance.

According to Directive 94/79/EEC, "… for volatile substances (vapour pressure > 10-2

Pascal), expert judgement is required to decide whether the short-term studies have to be performed by oral or inhalation exposure". In the case of gaseous substances, only inhalation

studies are technically feasible. Therefore, the toxicokinetic, metabolism and toxicity studies

that are required under Annexes II and III of Directive 91/414/EEC will be performed via theroute of inhalation. For converting an inhalation NOAEL (expressed as mg/l) to an internal

dose (mg/kg bw/d), the respiration rate of the test species, the duration of daily inhalationexposure in the study and the extent of respiratory absorption have to be taken into account.

The extent to which the size distribution of droplets/particles in an inhalation toxicity study is

relevant to human inhalation exposure to the active substance (as product concentrate or in-use dilution) should be considered. To calculate the NOAEL in mg/kg bw/d for a rat studywith daily inhalation exposure of 6 hours, the following assumption should be used (Lundehn

et al., 1992): NOAEL (mg/kg bw/d) = NOAEL (mg/l) x 45 l/kg bw/hr (rat respiration rate) x

6 h (daily inhalation exposure) x 1 (default respiratory absorption: 100 %) (EC, 2001).

For some substances, certain toxic effects, for example on the lung, only occur duringinhalation exposure. In these cases (i.e. where effects are air-concentration- rather than dose-

related), an internal AOEL value cannot be established. The risk management for such

substances may be best addressed by establishing occupational exposure limit values (EC,2001).



33

R ISK INDEX

For risk assessment of greenhouse workers, the internal exposure has to be compared with the

systemic AOEL. The total internal exposure and the risk index are calculated with the

following formulas (Vercruysse et al., 2002).

,

greenhouse

greenhouse acute

IE RI

AOEL=

Where:

RIgreenhouse, acute: acute risk index for the greenhouse re-entry worker (-); IEgreenhouse: internal exposure for the greenhouse re-entry worker (mg/kg bw/d); AOEL: Acceptable Operator Exposure Level (mg/kg bw/d).

Remark Concerning greenhouse scenarios there is an urgent need for more predictive modelling.

Appropriate realistic data need to be generated.



34

4. Bystander

Introduction

The term “bystander” is not formally defined under national rules and in the European

Directive 91/414 EEC on the authorization of plant protection products. In the latter it is

indicated that applicants seeking approval must submit exposure assessments for bystanders.Member States are instructed to evaluate the exposure of bystanders or workers exposed after

the application under the proposed conditions of use. In addition, it is required that waitingand re-entry intervals are such that the exposure of bystanders or workers exposed after the

application must not exceed the AOEL.

Through the evaluation of active substances at the European level a working understanding of

what is meant by “bystander” has been developed. This has been taken up by the

EUROPOEM Group of regulatory and industry scientists who proposed the followingdefinition of bystanders:

Bystanders are people who are located within or directly adjacent to the area where pesticideapplication or treatment is in process or has taken place; people whose presence is quite incidental and unrelated to work involving pesticides but whose position may put them at risk of exposure;

people who take no action to avoid or control exposure; people for whom it is assumed that no protective clothing is worn and perhaps little ordinary clothing; and for whom exposure can potentially occur during the process of application (including the preparation for application) and after the application (Anon, 1997).

In the UK the issue of bystander exposure was considered in the early 1980’s when the

Central Science Laboratory’s Application Hazards Group started making measurements of bystanders’ exposure to tracers during pesticide applications (Table II.4.1). Tracer studies are

acceptable since most pesticides are non-volatile so that potential exposure will mainly occur due to drifting spray droplets. The studies conducted involved collecting airborne tracer in the

breathing zone and tracer that would impinge on the clothing and uncovered skin of a

bystander positioned at 8 metres directly downwind from the sprayer. This distance waschosen to reflect the fact that there usually would be some distance, containing at least the

field margin and any boundary structure, between the bystander and the applicationequipment.

Table II.4.1: Data for bystander exposure (EUROPOEM II, 2002 – Report of the Bystander WorkingGroup) (estimates of bystander surface contamination by applied spray for range of application methods)

Application methodBystander contamination per single swath only (AHU 8m

data 90th percentile; percentages of the application rate persquare meter)

0.83 % (hydraulic nozzles)Arable spraying

0.97% (rotary atomizers)Orchards early 5.5 %

Orchards late 0.91%

UK bystander exposure assessments for pesticides use the average levels from these studies.The following remarks can be noted. The potential dermal exposure might be three times as

high if the sprayer is applied in sequential upwind swaths and if the bystander remains in the

same position downwind of a sprayer. But, no significant increase in airborne levels would beexpected. No reduction by clothing of the potential dermal exposure is assumed.



35

Although Directive 91/414/EEC requires all Member States to address exposure of bystanders, there appear to be few bystander data apart from the CSL (Central Science

Laboratory) data adequate for use in such assessments. Therefore other Member States follow

various approaches. Often bystander exposure is considered in a semi-quantitative manner byconsidering that bystander exposure will be less than the predicted operator exposure when

they are not wearing any PPE during spraying. A qualitative adjustment is often made asoperator exposure is based on an entire working day in close proximity to the spray and

includes exposure from direct handling of spraying equipment. Whereas bystanders will not

touch the spraying equipment and their exposure to spray drift will occur at a distance outsideof the treated area.

Within the framework of the first Concerted Action (AIR3 CT93-1370) the Expert Group

identified a series of reports available from the UK Central Science Laboratories and based on

preliminary research of these studies the Expert Group was able to establish a guidancedocument for the collection of bystander exposure data, including definitions concerning the

nature of bystanders and the likely type and duration of exposure. A likely ‘worst case’

scenario was developed for the assessment of bystander exposure. However the complexitiesinherent in assessing bystander exposure warranted greater attention to this topic. Thus, ageneric database of bystander exposure studies was developed, a number of generic bystander

exposure scenarios were defined and the tiered approach to exposure estimation was adoptedwithin the framework of the EUROPOEM II project. Nevertheless, an official model

implemented on Community level is not yet available. Also on national level, models for

assessing bystander exposure are lacking (EFSA, 2006).

The 22nd

September 2005, the Royal Commission on Environmental Pollution (RCEP) published its report on “Crop spraying and the health of residents and bystanders” (Blundell,

2005). The main focus of the RCEP report is the risk assessment that is performed for

bystander exposure to pesticides. The Advisory Committee on Pesticides (ACP) commentedon this report last December (2005). The RCEP concluded that a statutory 5 m buffer zone

should be introduced alongside residential properties and other buildings such as schools,hospitals and retirement homes where people may be adversely affected as the result of crop

spraying. This conclusion contrasts with that reached previously by the ACP. The ACP

concluded that on the basis of experimental data available to them, the risk assessment performed for bystanders provided adequate protection. Though, it was indicated that further

consideration was needed for dithianon, trifluralin and soil fumigants. Thus further confirmative work is still needed. Research involving the collection of air monitoring and bio-

monitoring data for bystanders should be undertaken and the practicality of defining standards

or limits for airborne concentrations of volatile pesticides should be considered. However,some months after the above advice had been agreed and delivered three ACP members

indicated their reservations about the confidence that could be placed that the current risk assessment provided adequate protection for bystanders. In that retrospect, one of these three

felt that he should have registered disagreement when the earlier advice was given.

One of the critical areas of uncertainty identified by the RCEP relevant to mention is the

assessment of the potential exposure for bystanders. The RCEP report highlights several

potential determinants of exposure that are not accounted for in the model currently used toestimate exposures from spray drift (e.g. use of reduced spray volumes, differences in boom

height, local air turbulence, topographical features, the height of the person exposed,…) and

expresses concern that the model does not incorporate other possible sources of exposure,such as dermal contact with contaminated surfaces by spray drift (including any subsequent



36

hand to mouth transfer in infants leading to ingestion and inhalation of vapour (Blundell,2005). The ACP commented on this aspect after reviewing data collected by the PSD

(Pesticide Science Directorate) on potential sources of bystanders’ exposure other than

directly from spray drift. Based on these data it was concluded that spray drift indeed was themain determinant of maximum possible exposure for bystanders as well as for residents. The

ACP agrees that the current model does not include all the variables that might have animpact on potential bystander exposure. Thus research is necessary to consider the extent to

which the model might underestimate exposure and the implications of any such

underestimation for risks to health. Underestimation is particularly possible for bystanderswho were very close to a spray boom as it passed. Research revealed that exposures at 1 meter

distance from a spray boom might sometimes be up to seven times higher than the average at8 metres (the distance taken in the regulatory approach). Taking this into account, together

with potentially relevant variables such as those identified by the RCEP, and also the potential

for exposure from sources other than direct contact with spray drift, the ACP assessed that thecurrent model is extremely unlikely to underestimate the maximum 24 hr exposure of any

bystander by as much of a factor 10, and that exposure above this, if it occurs at all, will be

rare. Apart from that it would also be very rare for a bystander to stand within one meter of aspraying boom. Most people would stand back and most farmers do not spray right next to amember of the public. On days when a bystander is not immediately next to the field that is

being sprayed, the potential for exposure above the one estimated by the current model iseven more reliable.

These matters indicate that more research is necessary regarding bystander and residentexposure. But overall, while there is a need for further empirical data to confirm the adequacy

of the current approach to risk assessment, there is no indication of a problem from the datathat are currently available (ACP, 2005). The ACP agrees with the RCEP that pesticide risk

assessment should be backed up by epidemiological monitoring and health surveillance.

However, how this can be achieved most effectively and efficiently is a difficult question.

In the near future, Member States and the Commission will reconsider the definition of bystanders proposed by the EUROPOEM group, along with the group’s evaluation of the

available data from CSL. It was proposed that the 90th percentile values for bystanders at 8

metres should be used in the harmonised European approach.

Proposed Bystander Indicator

EXPOSURE

Currently, there are no generally accepted models for estimating bystander exposure. There isno harmonised approach under EU rules and in fact very few appropriate data exist which can

be used for conducting bystander exposure assessments. Human health risk assessments for

bystanders are routinely performed using exposure data from tracer studies where bystanderswere monitored at 8 m distance from the application equipment. Several Member States use

drift deposition data as surrogate data and do not include any potential inhalation exposure.Therefore the exposure estimations are based on the recommendations made by the

EUROPOEM II bystander working group (EUROPOEM II – Bystander Working Group,

December 2002).



37

In general, it is assumed that exposure of bystanders mainly occurs when spray drift impingeson persons next to fields being sprayed. Bystanders, walking alongside a field which is being

treated, are exposed only for a short period of time when the sprayer moves along the person.

Repeated exposure is unlikely, since the sprayer is considered to only pass along the edge of afield for each spraying swathe. Bystander exposure can occur during the process of

application (incl. preparation) of pesticides and during the period of treatment following theapplication itself. For applications where the operator is present in the treated area (e.g. hand

held spraying) the process of application means that working period. For other applications

(e.g. space treatments) where pesticides are released into an enclosed container zone, and nooperator is present, the deposition phase of the pesticides (i.e. the operator’s own exclusion

period from first release until completion zone of the ventilation process to remove excess pesticide from the zone) should be regarded as the application process. Following completion

of the pesticide application process, but during the period of the pesticide action when the

treatment is underway direct exposure would be regarded as re-entry exposure, but indirect bystander exposure could occur due to movement of pesticide (e.g. vapour) away from the

treatment area (EUROPOEM II, 2002).

It is assumed that only ordinary clothing is worn and the total uncovered skin area amounts to0.4225 m² (= head, back & front of neck, forearms, ½ upper arms and hands) (pers. comm.

Vleminckx, 2006). Bystanders are assumed to be located at a certain distance downwind fromthe centre of the treated field (assuming that this region will contain the highest concentration

of ambient pesticide contamination). The wind direction is from the treated field straight

towards the bystander subject, which is worst case in respect of exposure to drifting spray particles. The likely access and proximity of bystander subjects to the source of potential

exposure is considered to be the main factor determining risk.

In the Guidance Document for bystander exposure data gathering (2002) several scenarios

were identified. A bystander may be located (1) within an area directly adjacent to the area being treated with pesticides or where pesticide treatment is taking place (e.g. on a footpath

next to a field), the likely worst case with little scope for assured preventive control measures;(2) within the area being treated with pesticides or where pesticide treatment is taking place

(e.g. in the same field or glasshouse). Bystanders in this situation should come under the

control of the safety regime in force for pesticide applications; (3) in some locations indirectlyconnected to the area being treated with pesticides, but with a clear scope for potential

exposure (e.g. sharing a common access). Scenario (1) is considered the most relevant one.

Bystanders participating in the work involving pesticides and thus brought into a position of

potential exposure may be expected to be managed as a part of the operational workforce andshould be considered as potentially exposed workers, thus would be protected by PPE,

engineering or operational controls.

The exposure is estimated both for the dermal and inhalation route.

a. Dermal exposure

It can be postulated that the dermal exposure is directly correlated to the amount of activesubstance applied per area, the area of the uncovered body surface contaminated and the drift

distance. Drift data are of crucial importance to the estimation of potential dermal exposure of

bystanders in the current European registration procedures. The likely amount of spray driftdepends on several factors, each of which may vary within a wide range within the bounds of



38

associated good agricultural practices for spray application (e.g. the strength of the wind, theapplication method, sprayed volume rate, applied form of spray quality, the forwards speed of

the vehicle and calibrated swath wide). The formula outlined below is applied to assess the

potential dermal exposure (pers. comm. Vleminckx, 2006). Hereby it is assumed that 100% of the drift arrives on the body of the bystander.

The following algorithm is used to calculate dermal exposure (EUROPOEM II, 2002):

EA Drift AR DE ∗∗=

Where:

DE: theoretical potential dermal exposure (mg/person/d);

AR: application rate (active substance)(mg a.s./m²);

Drift : downwind pesticide ground deposits at 8m distance from the field

(Ganzelmeier tables) (%/100);

EA: exposed area (m²/person/d) (default: 0.4225) (total uncovered area: head, back

& front of neck, forearms, ½ upper arms and hands).

The spray drift is calculated for the outdoor scenarios on the basis of drift tables published by

the BBA (BBA 2004; Rautmann & Streloke, 2001). Indoor operation e.g. with hand-heldsprayers, mist blowers or fog generators, should exclude bystanders as part of the safe

management of the work. Other persons possibly present should be regarded in the same way

as re-entry workers for risk assessment. These tables describe the percentage of applied pesticide moving beyond the borders of a treated field depending on crop type (arable crops,

fruits, grapes, hops and vegetables), the crop stage (early, late) and on the distance from thelast nozzle of the spraying equipment to the edge of the field.

Various regression functions have been derived from these drift tables (OECD, 2000;

FOCUS, 2002). The regression functions that were developed in the FOCUS group (FOCUS,2002) have been suggested for use in the aquatic indicator. For the bystander indicator thesefunctions can be simplified as risk is defined at a single distance from the edge of the treated

area rather than within a water body of defined width.

The exposure via drift for a bystander will be calculated at a particular distance from the edge

of the treated area. As proposed by the terrestrial work package, it would be prudent to retainthe functionality of the drift calculator. In this way exposure can be calculated also for other

distances. The location of the bystander relative to the pesticide related activity should always

be assessed and reported, together, if possible with the underlying reason for their being inthat position. Bystander subjects should ideally be placed at key locations, e.g. as near to the

known source of pesticide contamination as foreseeable circumstances would allow in normal practice, or no nearer to the source of contamination than assured control measures could

allow. Bystander movement patterns or activities at their static location should be described.

The following standard locations were identified by the EUROPOEM II Bystander WorkingGroup (Table II.4.2).

Table II.4.2: Standard bystander scenarios

Application Method Standard bystander locationHand-held equipment (e.g. lever operated

knapsack sprayers)

2 m downwind from the downwind edge of the swath,

midway along the length of the swath.Vehicle mounted equipment (e.g. tractor drawn

boom sprayers)

8 m downwind from the downwind edge of the swath,

midway along the length of the swath.Static equipment (e.g. dipping)

As close to the source of pesticide contamination as itwould normally be possible for a bystander to gain access.



39

According to the regression functions the mean drift deposition is calculated by a simple potential function for the crop types of arable crops, vines and vegetables. A power function is

suitable as equation function, indicating a linear run of the curve when represented with

logarithmic scales on both axes. Seed and granular treatments will always have drift of 0 %for all treatments and aerial drift loadings have been set to 33.2% for all applications. This

latter value has been calculated using the AgDrift model (SDTF, 1999) and corresponds to adistance of 3 m from the edge of the treated field.

The general functional equation for arable crops is:

B

r Drift A x f = ∗ ∗

Other crop types (fruit crops and hops) are represented by two sequential power functions

connected at the hinge distance (H):

B

r Drift A x f = ∗ ∗ (for x=0 to H) D

r Drift C x f = ∗ ∗ (for x > H)

Where:

Drift : percentage of application rate lost by drift (%) or the mean percentage driftloading at a distance x from the edge of the treated field;

A, B, C, D: previously defined regression factors (-);

x: distance from the edge of the treated field to the point of assessment (bystanders

are located at 8 metres from the treated field). (m);

H: hinge point, distance limit for each regression (m);

f r : reduction factor which considers improved spraying equipment ( f r = 0.5 in case

of 50% reduction, f r = 0.1 in case of 90% reduction).

Table II.4.3 gives an overview of the classification of crops according to crop grouping,

growth stage and application equipment.

Table II.4.3: Overview of the classification of crops according to crop grouping, growth stage andapplication equipment (Source: FOCUS (2002))

Crop Crop grouping Growth stage ApplicationCereals spring and

winter, rape, cotton,maize, tobacco, soy bean

Arable crops - Boom sprayer

Vegetables, grass,

potatoes, sugar beetsVegetables - Boom sprayer

Hops Hops - Air blast

Pome/stone fruit earlyapplications

Fruit crops Early Air blast

Pome/Stone fruit lateapplications, Citrus,

Olives

Fruit crops Late Air blast

Vines early application Vines Early Air blast

Vines late application Vines Late Air blast



40

The values for all the parameters listed above are presented in Table II.4.4. Further details can be found in the FOCUS Report Chapter 5.4 and Appendix B (2002).

Table II.4.4: Model parameters (A, B, C and D) and hinge distance (H) (Source: FOCUS (2002))

Crop grouping Growth stage Percentile A B C D HArable crops - - -Vegetables <0.5 m

- 90 2.7593 -0.9778 - - -

Vegetables >0.5 m

- 90 44.769 -1.5643 - - -

Hops - 90 58.247 -1.0042 8654.9 -2.8345 15.3Early 90 15.793 -1.6080 - - -

VinesLate 90 44.769 -1.5643 - - -Early 90 66.702 -0.7520 3867.9 -2.4183 11.4

FruitsLate 90 60.396 -1.2249 210.70 -1.7599 10.3

Aerialapplication

- 90 50.470 -0.3819 281.1 -0.9989 16.2

The use of the 90th

percentile is in accordance with proposals made on the EU-level by theFOCUS-group. Otherwise to gain an estimate of a probable realistic level of spray deposition

from a single application, mean values for drift fallout measured at 7 meters downwinddistance from a single application are 0.14% for arable crops, 8.2% for orchards sprayed in

early season and 2.6% for orchards sprayed late (with full foliage).

Intermezzo on drift-reducing equipment

All classified sprayers are listed in the list of drift-reducing equipment (www.bba.de). There

are more than 160 entries in this list. It includes field crop sprayers and air-assisted sprayers

for orchards, hops and vineyards. Some sprayers for asparagus and red/blackcurrant are alsolisted. Application rules on pesticide labels refer to this list and prescribe buffer zones

depending on the drift reduction class (Rautmann, 2001).

Field crops

Field crop sprayers can easily be equipped with air induction nozzles to reach therequirements for the drift reduction classes. Dependent on the nozzle size and the spraying

pressure, a drift reduction of 50% up to 90% is possible. Sprayers with air-assistance achievedrift reductions of 50% in crops with a minimum height of 30 cm and 75% in crops with a

minimum height of 50 cm. Band sprayers, which are used for weed control in sugar beet or maize, are listed in the 90% drift reduction class.

OrchardsIn air blast sprayers air induction nozzles lead to drift reduction. However further steps are

necessary to reach the mentioned drift reduction classes. In orchards the air-assistancetowards the field edge must be turned off in the first five rows. This can be achieved with a

cover shield on the fan outlet or a redirection metal sheet (Rautmann, 2001). The use of thesesprayers does not result in additional difficulties in comparison to standard sprayers. In

contrary to tunnel sprayers there is no restriction for the use on slopes. Some sprayers are

equipped with green detectors. They will shut of the nozzles when no leaves are in sight. Evenwith hollow cone nozzles which produce fine droplets the drift reduction is at least 50%.

Another possibility is to spray beneath a hail net. Depending on the nozzle, drift reduction isat least 50%, sometimes 75%. When using orchard sprayers with small axial fans (air flow



41

reduced to 20.000 m³/hr) and air induction nozzles are used, a drift reduction of 75% has beenfound. Some sprayers with a cross-flow fan have been tested and could be classified in the

75% and 90% reduction class. Tests with a tunnel sprayer with air induction nozzles in

orchards resulted in a drift reduction of 99%.

VineyardsIn vineyards there is a growing number of sprayers registered in the 75% and 90% reduction

classes. The first sprayer has been the tunnel sprayer, but nowadays air-assisted sprayers of

different types equipped with air induction nozzles achieve the same drift reduction, if theoutermost row is sprayed inwards only. Further tests are necessary to find solutions for

existing sprayers to improve the possibilities for drift reduction.

Hops Drift reduction in hops is quite easy. Sprayers need a shield on one side of the fan outlet andair induction nozzles to spray the outermost part of the hop garden. For the inner part the

shield must be removed. This leads to a drift reduction of 90%. Nearly all sprayers can be

adapted in this way.


Generally, in open fields inhalation exposure is negligible due to the extreme small volume of spray in droplets small enough to be inhaled into the lungs. Nevertheless, there is concern

about downwind spray drift, which may be deposited on bystanders. However tests haveshown that, in general, the exposure of unprotected bystanders is only a fraction compared

with the spray operator (Gilbert & Bell, 1988). On p 52 an algorithm is outlined for assessing

bystanders’ inhalation exposure to volatile pesticides. Specific data should be gathered toassess bystanders’ exposure to volatile chemicals by means of monitoring.

Case 1: water rate value is known

The potential bystander inhalation exposure is calculated using the formula outlined below incase the water rate value is known.

T Drift IRC I spray ∗∗∗=

Where:

I: potential inhalation exposure (mg/person/d);

Cspray: active substance concentration in the spray mist (mg/m³);

310∗=WRV

ARC spray

Where:

AR: application rate (kg a.s./ha);

WRV: water rate value (l/ha);

103: correction factor for the units.

IR: inhalation rate (m³/hr) (default value adults = 1,25 m³/hr);

Drift: percentage of application rate lost by drift (%);

T: exposure time (hr) (default: 1 min = 1/60 hr; pers. comm. Vleminckx,

2006).



42

Case 2: concentration of active substance in the spraying solution is known

The potential bystander inhalation exposure is calculated using the formula outlined below in

case the concentration of the active substance(s) in the spraying solution is known.

T IRC I spray ∗∗= Where:

I: potential inhalation exposure (mg/person/d);

IR: inhalation rate (m³/hr) (default value adults = 1,25 m³/hr);

T: exposure time (hr) (default: 1 min = 1/60 hr; pers. comm. Vleminckx,

2006);

Cspray: active substance concentration in the spray mist (mg/m³).

.).%( sa Default C spray ∗=

Where:

% (a.s.): active substance concentration in the pesticide formulation (mg/ml);

Default: - arable sprayers = 0.03 ml spray /m³ (90th)

breathed air (or per hour of spraying);- orchard sprayers = 0.06 ml spray /m³

(90th

) breathed air (or per hour of spraying).

Source: EUROPOEM II – Report of the Bystander Working Group. Inevitably, under field conditions with variations in wind speed and direction, there will be some variability inmeasurements. The adoption of a mean value has been criticised with the suggestion that

more account should be taken of the probability of more exposure under some conditions.

Bystander inhalation exposure is calculated in this case on the basis of the data collected inthe series of AHU/CSL trials listed in the EUROPOEM II Report of the Bystander Working

Group. Inhalation sampling was done by volunteers wearing respirators modified to simulatethe mechanics of nasal breathing, containing multiple layers of absorbent gauze material. The

reported results were normalised to provide potential inhalation exposure levels in terms of ml

spray per m³ of breathed air (equivalent to 1 hour of breathing). The source of spray was theairborne drift arising from the sprayed swath closest to the location of the bystander. The

results have not been correlated with respect to sprayer output volume or dose rate, because itis impossible to determine that fraction of the applied spray which would have been capable

of reaching the bystanders’ breathing zone. Therefore the data should be regarded as realisticworst case, as some sprayed swaths were close to the bystanders and others were further away. It is also reasonable to assume that the whole of a sprayed field would present a lower

potential inhalation rate than that measured for the nearest swathe. It is proposed that potential bystander exposure via the inhalation route is estimated at the 90th percentile level, i.e. 0.03

ml spray per m³ of air breathed (or per hour of spraying) for arable operations and 0.06 ml

spray per m³ of air breathed (or per hour of spraying) for orchard sprayers. The exposure predicted using these data are often orders of magnitude lower than the AOEL (EUROPOEM

II, 2002).



43

The effectiveness of bystanders’ own clothing at preventing actual dermal exposure resultingfrom the potential exposure level (i.e. surface contamination rate) has not been factored into

these recommended values. The likelihood of bystanders wearing clothing on different parts

of their body may alter depending on the season of the year, the location and the activities being performed. The most reasonable conservative estimate is to assume that bystanders are

wearing little clothing. But when wearing little clothing, it is more likely that the feeling of drifting spray droplets depositing on the exposed skin will be perceived and consequently one

will move away from the spray drift. When wearing full clothing bystanders will not be

quickly aware of spray drift contamination and will thus not move away as easily. On theother hand the clothing can absorb the contaminants and can in this way reduce the actual

dermal exposure (EUROPOEM, 2002).

Case 3: based on the operator inhalation exposure

The inhalation exposure is calculated as for the operator (only considering inhalation

exposure) but the exposure time is only 1 minute (pers. comm. Vleminckx, 2006) instead of

the total exposure time of the applicator, which can be set to 6 hours a day. The exposure timecan also be augmented to 5 or 10 min.

a treated

treated

I Area AR DED I

Area ST

∗ ∗ ∗=

∗

Where: I: bystander inhalation exposure (mg/person/d); Ia: applicator inhalation exposure (mg/kg a.s.); Areatreated : area treated per day – work rate expressed in ha per day (ha/d); AR: application rate (kg a.s./ha); DED: daily exposure duration (min/d); ST: spraying time (min/ha).

Case 4: bystanders living nearby greenhouses

For assessment of the acute residential and bystander inhalation exposure due to the

application of pesticides in greenhouses, the approach suggested for implementation in theUSES 5.0 model (a computerised and integral risk-decision system used by the Dutch

authorities) is proposed. Such calculations will be increasingly important for example in the Netherlands, as more inhabitants will be living nearby greenhouses in the future.

The model allows exposure of nearby residents to be calculated up to a distance of 20 meter.Expectations are that the module adaptations will improve the aerial pesticide calculations,

particularly within the first few hours after the application. The module uses a process-oriented approach rather than emission factors. Relevant processes that are considered are

volatilisation and deposition inside the greenhouse and outdoor ventilation of remaining

residues. The ventilation mainly occurs primarily via convection of inside air to outsidethrough chinks and cracks in the glass construction. Comparison with measured

concentrations inside the greenhouse were made and cautiously concluded these comparisonsreveal that the scheme may be realistic for volatile pesticides, but probably not for less

volatile substances. This is however based on only a few measurements. More experimental

research is needed. This module for calculating the exposure of nearby residents is based on a



44

calculation scheme developed by Alterra in consultation with the National Institute for PublicHealth and the Environment and Applied Plant Research Horticulture (Naaldwijk).

The algorithms for assessing the pesticide concentration inside a greenhouse are outlined inthis report (page 29–31). To obtain the outdoor pesticide concentration, the formula below can

be applied (Mensink, 2004):

,, ,

, ,, ,

,

gh vent gh inair T gh

gh vent gh dep gh outair T

gh gh façade

k C V

k k C

T K A u

∗ ∗ + =

∗ ∗ ∗

With:

C gh,outair,T (µg/m³): the estimated actual gas-phase concentration over T seconds

outside the greenhouse in the lee side eddy up to 20 metres;

C gh,inair,T (µg/m³): the estimated actual gas-phase concentration over T seconds

inside the greenhouse; k gh,dep: deposition rate constant inside greenhouse (s-1) This parameter was

experimentally derived as an average for several active ingredients in differentgreenhouses with different application techniques. The deposition rate constant

can also be determined using the following formula:

gh gh gh

dep ghW H L

ISAk

∗∗∗∗= 4

, 105,5

With: ISA (m²): Inner greenhouse surface area, including roof and floor, perpendicular

on the wind direction; Lgh (m): greenhouse length; Hgh (m): greenhouse height; Wgh (m): greenhouse width.

k gh,vent : ventilation rate constant inside greenhouse (s-1). This parameter is derived

by assuming a flow rate constant directly proportional to the wind speed; K gh: greenhouse construction coefficient referring also to the wind direction (-)

(default: 0.5, experimentally determined average realistic value); Agh, façade: surface area of the façade (m²) (default: 450 m² ( = 100*4.5); Vgh: volume of the greenhouse (m³) (default: 45000 m³); u: wind velocity just above the greenhouse (m/s) (default: 3 m/s; this value

corresponds with a wind speed between light air and light breeze. This value isarbitrary and it does not represent a worst-case approach. Calm air (i.e. no wind at

all) is more likely to increase the exposure in the immediate vicinity of greenhouses.);

T (s): time over which the concentration is integrated.

The outdoor concentration is thus calculated by dividing the source strength for air emission

by the wind speed, the construction factor K and the surface area of the façade perpendicular on the wind speed. The inhalation exposure is then calculated by multiplying the outdoor

concentration with the inhalation rate and the daily exposure duration.



45


The estimated exposure for bystanders is calculated with the following formulas:

tan* * DE I

bys der DE Ab I Ab IE

BW +=

Where: DE: dermal exposure (mg/person/d); AbDE: dermal absorption factor (%) (default: 10; POCER II); I: bystander inhalation exposure (mg/person/d); AbI : inhalation absorption factor (%) (default: 100); BW: body weight (kg) (default: 70).

TOXICITY

The human health risk assessments involve comparing the routes, levels and frequency of

exposure with some indicator of toxicity. For bystanders the estimated exposure is comparedwith an AOEL. Operators, particularly spray contractors, may use a pesticide repeatedly over

a period of several months. Since bystanders usually are exposed less frequently, thisapproach may be more protective for bystanders. However the aim is to protect in the worst

case, and therefore we favour its continued use.

Currently, a proposal is made to develop a Guidance Document on Community Level in

which the relevant toxicological end point(s) will be established for bystanders for

comparison with the exposure, resulting from the respective exposure assessment. When morerefined mathematical procedures for exposure assessments for bystanders will have been

developed, it should be clarified if the already existing end points may serve as referencevalues. New toxicological end points may possibly be required as a result (EFSA, 2006). The

Scientific Community on Plants has already recommended to the Commission to reconsider the adequacy of applying the AOEL to this subpopulation (Howard, 2004). Since no specific

toxicological parameter for bystanders is available, we make use of the systemic AOEL.

R ISK INDEX

The risk index for the bystander is obtained by dividing the internal exposure of the bystander

by the AOEL:

tan

tan er

bys der

bys d

IE RI

AOEL=

Where: RI bystander : acute risk index for the bystander (-); IE bystander : internal exposure (mg/kg bw/d); AOEL: Acceptable Operator Exposure Level (mg/kg bw/d).



46

Remarks:

Because of the lack of useful data for tier 2 assessments it is recognized that individual field

studies will remain necessary to be performed in order to evaluate more exactly the level of bystander exposure to specific pesticides applied under certain conditions. Research regarding

measurements of actual levels of bystander exposure arising from applications that followstandard procedures based upon current and new application technologies and risk mitigation

measures would provide useful data.

It is essential to validate the proposed approach for assessing bystander exposure against

empirical data. In case of bystanders, exposure may occur by several routes, the best methodof measuring will often be biomonitoring, provided that there is a suitable analyte. Data

regarding bystander exposure are few in most if not all European countries. Recognizing this

gap in data, biomonitoring studies should be undertaken. Such research is going on in the UK as recommended by the ACP.

Other routes of exposure, besides drift should be taken into account. Possible exposure to pesticides as vapour or as particulates which are remobilized following completion of sprayapplication has not been possible to evaluate as no useful data were found. Individual studies

are required if such routes are considered to be relevant for specific products.



47

5. Sensitive population groups

High-risk population groups can be defined as groups which are either more highly exposed

to an environmental agent or more susceptible to its effects (Ashford et al., 1990). Children of agricultural producers and re-entry workers appear to meet both of these criteria as well as

pregnant women living in agricultural areas. First of all children are discussed, followed by pregnant women.

CHILDREN

Factors determining the unique vulnerability of children

Several factors determine the unique vulnerability of toddlers, infants and children to

pesticides. Because of the differences in physiology and behaviour, exposures among children

are expected to be different than exposures among adults.

First of all there are the physiologic characteristics. These characteristics influence exposure by affecting a child’s rate of contact with exposure media or by altering the exposure-uptake

relationship that governs the internal dose resulting from exposure. Children have a larger

surface-area to body weight ratio than adults. This ratio decreases by approximately one-thirdwithin the first year of life and remains constant until approximately 17 years of age, when it

decreases to the adult value. In addition to providing more area for dermal absorption, thelarger relative surface area implies that body heat will be lost more rapidly to the

environment, requiring a higher rate of metabolism to maintain the body temperature and

additional energy requirements to sustain growth and development. The higher metabolic rate

and energy requirements imply that oxygen, water and food requirements are greater per unitof body weight than for an adult. The higher breathing rate and food consumption raterequired to meet these physiologic needs for children will result in higher relative exposures

to environmental contaminants in air and food (Hubal et al., 2000). On a body-weight basis,

the volume of air passing through the lungs of a resting infant is twice that of a resting adultunder the same conditions, and therefore, twice as much of any chemical in the atmosphere

could reach the lungs of an infant (U.S. EPA, 2002a). Another important physiologicaldifference between children and adults is the permeability of the skin, which is highest at birth

and decreases in the first year such that the permeability of the skin of a 1-year-old child is

similar to that of an adult (Bearer, 1995). In terms of risk, children may also be morevulnerable to environmental pollutants because of differences in absorption, excretion and

metabolism (U.S. EPA, 2002a). The cellular immaturity of children and the ongoing growth

processes account for the elevated risk. Compared with adults, absorption and retention of environmental chemicals is greater in early life: metabolic pathways responsible for

detoxification may differ with age so that foetuses and children may have a lower ability todetoxify exogenous agents and to repair damage. Cell proliferation in tissues of children is

higher whereas immunological surveillance is less efficient (Perera, 1997). Moreover

children’s increased sensitivity of cholinergic receptors to pesticides can be mentioned(Faustman et al., 2000). Although in some cases children can cope better with environmental

toxicants than adults. They are for example unable to metabolise toxicants to their active form(Landrigan et al., 1999). Because children have more future years of life ahead of them

compared to adults, chronic diseases that may be initiated by early exposures have theopportunity to develop over many decades.



48

Secondly behavioural characteristics and physical activities play a role. Children’s behaviour and the way that children interact with their environment may have a profound

effect on the magnitude of their exposures to contaminants. For example, children crawl

around on the floor where toxic chemicals adsorbed to dust and other particulate materialstend to reside. Factors such as hand-mouth behaviour and play patterns can be mentioned.

Exposure to pesticides is also a function of the specific physical activities in which a child isengaged, the location of these activities (outdoors, indoors, etc.) and the child’s activity level

when so engaged. Differences in duration and frequency of periods spent in particular

locations result in different exposures and risks to children that vary with age anddevelopmental stage. Other influencing factors are diet and eating habits, gender, socio-economic status and race.

Exposure routes

Children can be exposed to pesticides from multiple sources and through multiple pathways.

In addition to the standard pathways of diet , drinking water and residential pesticide use,children in agricultural communities can be exposed to pesticides used in agricultural production (Fenske et al., 2000). Children living in agricultural areas may experience higher exposure to pesticides than do other children, since concentrated formulations of pesticides

are stored and/or mixed, although not used in high concentrations near the home. The

difference in metabolite concentrations of pesticides between children from agricultural andreference families was about a four-to fivefold. These concentrations decreased with

increasing distance from farmland (Lu et al., 2000). In addition these children can play innearby fields or be exposed via consumption of contaminated breast milk from their

farmworker mother (Eskenazi et al., 1999). Children younger than 6 months of age may

receive their greatest exposures through breast milk ingestion or inhalation, whereas dermal absorption and ingestion may be the major pathway of exposure when children begin

crawling and placing their hands on dusty surfaces and increasing their hand-to-mouth behaviour. The level of exposure may continue to increase given that the normal tendency of

young children to explore their environment orally increases up to 2 years of age (Eskenazi et al., 1999). Sources of exposure can be materials carried into the home via various ‘take home’ pathways or pesticide residues that have been transferred from treated surfaces to the hands or

objects that are mouthed such as toys. Dust and tracked in soil accumulate most effectively incarpets where young children spend a significant amount of time (Lewis et al., 1999). Several

studies have also shown that agricultural workers bring contaminated clothing into the home

(Chiao-Cheng et al., 1988; Clifford & Nies, 1989). Poor hygienic practices such as these

among pesticide formulators have been associated with measurable blood levels of pesticides(chordecone or kepone) in family members (Cannon et al., 1978). Classic organo- phosphorous pesticide exposure symptoms in spouses and children of greenhouse workers

have been reported (Richter et al., 1992). Another route of exposure is the soil ingestion

pathway. Children are more likely to ingest soil than do adults as a result of behavioural patterns during childhood.



49

Proposed algorithms for assessing children’s exposure to pesticides

EXPOSURE

The following algorithms assess the level of exposure likely to result when children playing inthe garden are exposed through dermal, hand-to-mouth and object-to-mouth routes.

a. Dermal exposure

Dermal exposure resulting from direct contact with spray drift

Exposure due to direct contact with spray drift is assessed in the same way as for bystanders(see earlier). However, different default values are assumed for several parameters. Table

II.5.1 gives an overview of the proposed default values for infants, toddlers and children.

Default values for short-term as well as for long-term exposures are proposed.

When the duration of activity and the activity pattern for children are not specified, the defaultvalues for infants are to be applied, since infants have the highest daily inhalation rate among

all children groups when body weight is considered. The proposed default value equals 0.52m³/kg bw/d, assuming an inhalation rate of 4.5 m³/d and a body weight of 8.7 kg. Thisinhalation rate is based on the research performed by Layton (1993) and the body weight of

an infant proposed by The Pesticide Safety Directorate (1999). When specific information isavailable regarding age and activity level, the values summarized in Table II.5.1 can be

applied.

Table II.5.1: Default parameters needed to calculate children’s exposure

Time-scale

Parameter Population (years) Value Default Unit Source

Rest 0.3*

Sedentary activities 0.4*Light activities 1.0*

Moderate activities 1.2*

Acute Inhalation rateInfants, toddlers andchildren (<1 – 18)

Heavy activities 1.9*

0.36(=8,7/24)

m³/hr

Infants < 1 4.5**

Children (1-2) 6.8**

Children (3-5) 8.3**

Children (6-8)

males/females

10**

males 14**Children (9-11)

females 13**

males 15**Children (12-14)

females 12**males 17**

Chronic Inhalation rate

Children (15-18)females 12**

8.7 m³/d

Child-specificExposure

Factors

Handbook

(U.S.EPA,2002a)

Infants 8.7

Toddlers 14.5Acute andChronic

Body Weight

Children

males/females

43.3

15 kg PSD (1999)

Infants 0.158

Toddlers 0.263Acute and

Chronic

Exposed Area(three times

larger surface

area on a bodyweight basis) Children 0.784

0.2 m²/d pers. comm.

Vleminckx, 2006

* These values should be used to assess short-term scenarios of a few hours in duration, such as post-application inhalationexposures following lawn treatment, foggers and crack and crevice treatments (depending on chemical-specific data such as airmeasurements, vapour pressure, persistence).

** These values should be used to assess short-term scenarios of a few days in duration, such as post-application inhalationexposures following crack and crevice treatments, and termiticide treatments (depending on chemical specific data, such as airmeasurements, vapour pressure, and persistence).



50

Dermal exposure resulting from contact with a lawn contaminated by spray drift

A child’s exposure to pesticides resulting from dermal contact with a lawn contaminated byspray drift is calculated as follows:

DEDTF TTR Drift AR DE drift spray

∗∗∗∗= _

Where: DEspray_drift: Potential dermal exposure due to contact with a lawn contaminated

with spray drift when children are playing in the garden (µg/person/d); AR: application rate (µg a.s./cm²); Drift : percentage of application rate lost by drift (%/100);

Estimates of fallout from spray drift are calculated using the same algorithms as

for the bystander indicator. The distance between the edge of the field and thelocation of a neighbouring garden should be filled in by the user of the HAIR

software. As a default the distance is set to 8 m;

TTR: turf transferable residue value (%/100) (default: 5%, standard EPA value for wet hands based on data by Clothier (2000)). Clothier (2000) measured percent

transfer efficiency means of 0.156% ± 0.138%, 2.72% ± 1.12%, 4.18% ± 1.53%for the pesticides chlorpyrifos, chlorothalonil, and cyfluthrin respectively. The

results are based on single hand presses of volunteers’ hands (wetted with their own saliva) onto St. Augustine turf treated with the above mentioned pesticides.

These types of transfer efficiency data are needed to assess the hand-to-mouth

exposure pathway when using hand-to-mouth frequency data based on videotapesor other observational data. The wet values were two to three times higher than

similar hand presses performed by volunteers whose hands were dry; TF: transfer factor (cm²/hr) (default: 5200 cm²/hr, standard EPA value for this

situation); DED: daily exposure duration (hr/d) (default: 2 hr/d; standard EPA value based on

the 75th percentile for children playing on grass for ages 1-4 years and ages 5-11

years). The latter value was obtained from Tsang & Klepeis (1996) as cited on page 15-79 of the Exposure Factors Handbook (U.S. EPA, 1997b). 23% of

children aged 1-4 years played on grass more than 2 hours per day (U.S. EPA,

1997b page 15-78). In comparison, the 95th

percentile for playing outdoors is 3,5hours, the 95th percentile for time spent at home in the yard or other areas outside

the home is 5,75 hours for children ages 1-4 years (U.S. EPA, 1997b page 15-96,15-124 and 15-136).

EPA assesses dermal exposure due to spray drift only on occasion depending on the chemicalor use pattern, for the purpose of implementing a buffer zone.



51

Dermal exposure resulting from ingestion of turf residues (hand-mouthing activity)

Additional exposure resulting from ingestion of turf residues transferred from contaminatedhands to the mouth is calculated as follows:

DED N EASE TTR Drift AR DE eventsmouthhand

∗∗∗∗∗∗= _

Where: DEhand_mouth: Potential dermal exposure via the hand-to-mouth route (µg/person/d); AR: application rate (µg a.s./cm²); Drift : percentage of application rate lost by drift (%/100);


for the bystander indicator. The distance between the edge of the field and the

location of a neighbouring garden should be filled in by the user of the HAIR software. As a default the distance is set to 8 m;

TTR: turf transferable residue value (%/100) (default: 5%);

SE: saliva extraction factor (default: 50%, standard EPA default value);Camann et al. (1995) investigated the use of surgical sponges wetted with human

saliva to remove residues from hands to volunteers. Removal efficiency of 50% bysaliva was reported for the pesticides chlorpyrifos, piperonyl butoxide and

pyrethrins. Thus, for screening purposes, the value of 50% is recommended. Asaliva extraction factor of 50% allows us to more realistically model the transfer

processes from a contaminated hand in the mouth; EA: exposed area, i.e. surface area of the hands in contact with the mouth (default:

20 cm²/event, which represents the palmar surface of three fingers). The 1999

Scientific Advisory Panel (SAP) suggested that each hand-to-mouth event consistsof the insertion of 1 to 3 fingers. The same SAP also suggested the use of a palmar

surface (both hands) of 114 cm². The problem of assigning surface area values tothe palms and palmar surface of the fingers was solved by Gurunathan (1998) whosimply divided the palmar surface of the hands by 2, with each half representing

the palms and palmar surface of the fingers. Since the hand-to-mouth has beendefined by the SAP as 1 to 3 fingers (5,7 to 17,1 cm²) a screening level of 20 cm²

was selected based on the assumption that each hand-to-mouth event equals 3

fingers. A criticism of hand-to-mouth frequency data based on video tapes is that itis not clear if the counting of hand-to-mouth events is based on finger insertions or

if the hands were simply located near the mouth (Kissel et al., 1998); Nevents: Number of hand-to-mouth exposure events per hour (default: 20 events per

hour, which is the 90th percentile of observations ranging from 0 to 70 events per

hour). Reed et al. (1999) reported hourly frequencies of hand-to-mouth events in pre-school children aged 2 to 5 years based on observations using video tapes. The

data consist of 20 children at daycare centers and 10 children at home. A range of 0 to 70 events per hour were reported. The 1999 Scientific Advisory Panel (SAP)

recommended the use of the 90th percentile value of 20 events. A mean of 9,5

events was also reported by Reed et al. (1999) which is similar to the meanreported by Zartarian et al. (1997) using similar video tape techniques while

observing four farmworker children (2-4 years old); DED: daily exposure duration (hr/d) (default: 2 hr/d; standard EPA value based on

the 75th percentile for children playing on grass for ages 1-4 years and ages 5-11

years).



52

Dermal exposure resulting from ingestion of turf residues (object-to-mouth exposure)

Additional exposure resulting from ingestion of turf residues transferred from contaminatedobjects to the mouth is calculated as follows:

IgRTTR Drift AR DE mouthobject

∗∗∗= _

Where: DEobject_mouth: Potential dermal exposure via the object-to-mouth route

(µg/person/d); AR: application rate (µg a.s./cm²); Drift : percentage of application rate lost by drift (%/100);


for the bystander indicator. The distance between the edge of the field and thelocation of a neighbouring garden should be filled in by the user of the HAIR

software. As a default the distance is set to 8 m;

TTR: turf transferable residue value (%/100) (default: 20%, standard EPA valuefor object-to-mouth assessments). On the day of application, it may be assumed

that 20% of the application rate is available to be ingested. 20% dislodgeability is based on the large body of dislodgeable foliar residue data available for

agricultural crops; IgR: ingestion rate for mouthing (default: 25 cm² grass per day, standard EPA

value). The default value is the assumed ingestion rate for grass for toddlers (age 3

years) and is intended to represent the approximate area from which a child maygrasp a handful of grass.

b. Inhalation exposure

Inhalation exposure of children exposed as bystanders is assessed in the way outlined for bystanders.

One additional algorithm for assessing exposure to vapour is outlined here. This assessment

makes use of a surrogate value for residues in the air adjacent to treated crops, derived from

Californian Environmental Protection Agency studies (California EPA, 1998). In these studiesa 24 ha orange orchard was treated with chlorpyrifos using broadcast air-assisted sprayers.

During application, wind speeds ranged from 2 to 20 km/hr and the maximum temperaturewas 42 °C. Chlorpyrifos residues in air adjacent to the orchard were monitored over 72 hours.

The highest 24 hour time-weighted average residue in air was 15 µg/m³.

The algorithm applied for assessing a child’s exposure to volatile pesticides is outlined below.

DED IRC I air ∗∗=

Where: I: Potential inhalation exposure (µg/person/d); Cair : surrogate value for residues in air adjacent to treated crops (µg/m³), which

equals 15 µg/m³ (the highest 24 hour time-weighted average residue in air); IR: inhalation rate (m³/hr) (see Table II.5.1); DED: daily exposure duration (hr/d) (default: 2 hr/d).



53


The estimated total systemic exposure for children is calculated with the following formula:

BW

Ab I Ab DE

IE

I DE

child

∗+∗=

Where: IEchild: internal exposure of a child (mg/kg bw/d); DE: dermal exposure (mg/person/d) (conversion from µg to mg has to be done);

The total dermal exposure is calculated by summing up the different types of dermal exposure (spray drift, hand-to-mouth and object-to-mouth);

AbDE: dermal absorption factor (%); I: Inhalation exposure (mg/person/d) (conversion from µg to mg has to be done); AbI : inhalation absorption factor (%) (default: 100); BW: body weight (kg) (default: 15);

TOXICITY

Within the Food Quality Protection Act (1996), the U.S. EPA was instructed to incorporate an

additional ten-fold uncertainty factor into pesticide risk assessments in case of threshold

effects. The introduction of the ten-fold uncertainty factor was promoted to account for the possible inadequacies of the existing toxicological databases to provide all of the information

related to infant safety (Landrigan et al., 1999). A different margin of safety could be

incorporated only if reliable toxicological data were available. Thus, only if there is noindication of increased sensitivity in the young to the effects of the pesticide, the extra

uncertainty factor can be removed. Such factors have sometimes been incorporated by theWorld Health Organisation for ADI’s on a case by case basis. The addition of the ten fold

uncertainty factor to the ADI’s was recently proposed for evaluating acceptable pesticide

residues in infant foods, with case by case adjustments where adequate toxicological datawere available. A report of the Dutch Health Council of the Netherlands (Report N°

GZB/VVB-993063, 7th

June 2004) notes that in young animals in some circumstances evensingle exposures to some pesticides can be enough to produce an effect. The report

acknowledges that children take in pesticides from multiple sources and recommends that an

additional uncertainty factor ranging from 3 to 10 be considered to protect health during thedevelopmental period, pending a period of further research (Howard, 2004). The EPA

recently failed to apply the ten fold uncertainty factor (Agrow publications, 2003). The reason

is that if a 10-fold uncertainty factor were to be applied to the current EPA reference doses,virtually all children with detectable metabolites would exceed this level (Fenske et al., 2000).

We contacted toxicologists and asked them if an additional uncertainty factor should be

applied to assess the risk to pesticides for children. In general, it is considered that childrenand pregnant women are covered by the assessment factors which are applied. It is assumed

that the assessment factors relevant to the whole population should be used when settingtoxicological reference values (e.g. pregnant women could be operators, children could be

exposed as bystanders) (pers. comm. Brown, 2006). Thus, no additional uncertainty factor is

introduced and the systemic AOEL is applied as the toxicological reference parameter.



54

R ISK INDEX

The risk index for a child bystander is obtained by dividing the internal exposure of the

bystander by the AOEL:

AOEL IE RI child

child =

Where: RIchild: acute risk index for children (-); IEchild: internal exposure of a child (mg/kg bw/d); AOEL: Acceptable Operator Exposure Level (mg/kg bw/d).



55

PREGNANT WOMEN

For assessing systemic exposure for pregnant women, the bystander formulas are applied.

Factors determining the vulnerability of pregnant women

The toxicity of pesticides on human reproduction is largely unknown, particularly howmixtures of pesticide products may affect foetal toxicity. Research by Arbuckle et al. (2001)

showed moderate increases in risk of early abortions for preconception exposures to phenoxy-acetic acid herbicides, triazines and other herbicides. For late abortions, preconception

exposure to glyphosate, thiocarbamates, and the miscellaneous class of pesticides was

associated with elevated risks. Post-conception exposures were generally associated with latespontaneous abortions. Post-conception exposures to specific pesticides also tend to damage

the foetus or foetal placenta (Arbuckle et al., 2001). Rather than cause chromosomalanomalies.

In addition to the nature of the chemical and its target, the consequences of exposure tochemical agents depend on the timing of exposure relative to critical windows in the

development of the foetus or reproductive system. Time is as important as dose. Areproductive hazard could cause one or more health effects, depending on when the woman is

exposed. For example, exposure to harmful substances during the first three months of

pregnancy might cause a birth defect or a miscarriage. During the last six months of pregnancy, exposure to reproductive hazards could slow the growth of the foetus, affect the

development of its brain, or cause premature labour. Reproductive hazards may not affectevery pregnancy. Whether a woman or her baby is harmed depends on how much of the

hazard they are exposed to, when they are exposed, how long they are exposed, and how they

are exposed. Only a few substances are known to cause reproductive health problems. For

most pesticides examined, preconception exposure contributed more to the risk of aspontaneous abortion than exposures during the first trimester (Arbuckle et al., 2001).

The following problems may be caused by workplace exposures: menstrual cycle effects,

infertility and sub-fertility and miscarriage, stillbirths, birth defects, low birth weight and premature birth, developmental disorders and childhood cancer (NIOSH, 1999). Residential

use of pesticides increases the risk of paediatric brain tumours and birth defects.

Uncertainty factor

We contacted toxicologists and asked them if an additional uncertainty factor should be

applied to assess the risk to pesticides for pregnant women. The introduction of an additionaluncertainty factor should be considered on a case by case basis and depends upon the

completeness of the toxicological database of the concerning pesticide (Willems, pers.

omm.., 2006). The effects of active ingredients on pregnant animals are generally tested inthe regulatory guideline studies through the developmental and reproduction studies routinely

required for all active ingredients. When warranted neurotoxicity studies are also used. If a

statistically significant effect on the pregnant animals or their fetuses or offspring is observedin one or more of these studies, it is regulated on that effect to protect the developing young

and the pregnant female. When setting an AOEL, the most appropriate NOAEL is chosen.The NOAELs for the teratogenicity studies have to be compared to the NOAELs applied for

assessing the other toxicological endpoints. In case that reproductive toxicity is the most

sensitive endpoint, the AOEL is derived from this endpoint. In case of acute exposure theAOEL should be derived from a short-term study and it should be evaluated whether this



56

AOEL will prevent teratogenic effects. Moreover, considering that bystander exposure ismuch lower (at least one order of magnitude) than the operator’s exposure, no additional

uncertainty factor is required for pregnant women.

Default values

For pregnant women the same default values as for adults are assumed.



57

III. Proposed Chronic Indicators

The chronic indicators are based on the same formulas as proposed for the acute worker

indicators with the respect of a few adjustments to account for the number of treatments andthe exposure duration.


Proposed Pesticide Operator Indicator

EXPOSURE

The chronic exposure for pesticide operators (IEoperator,chronic) is calculated by multiplying the

acute internal exposure (IEoperator ) with the frequency of application (FA) and dividing the

obtained value by 365 to obtain the annual exposure estimate. The factor FA is amultiplication factor which expresses the number of treatments that were performed in oneyear on a given treated area. Annual exposure will be calculated as the sum of all daily

exposure amortized over 365 days. The averaging time can be adjusted according to the

duration of exposure. Lifetime exposure estimates will be calculated as the sum of all annualecposures amortized over 75 years (DPR, 2001). In case of intermediate-term exposure

characterized by periods of frequent exposure lasting more than 7 days but substantially lessthan one year, whether exposure is constant or intermittent during the period, it is assumed

that the average daily exposure is received on every day of an intermediate-term period.

However if the exposure is intermittent or sporadic, the exposure may be amortized over thetotal period.

We propose to use the following formulas to calculate the chronic exposure of operators.

365)( /,

FA Area

BW

AR IE IE IE treated napplicatioload mixchronicoperator ∗∗∗+=

[ ]/( ) ( * * )mix load I I I hand hand DE IE L PPE Ab L PPE Ab= ∗ ∗ +

( ) ( * * ) ( )application I I I hand hand DE body body DE IE L PPE Ab L PPE Ab L PPE Ab = ∗ ∗ + + ∗ ∗

With:

LI, Lhand, L body (mg a.s./kg a.s.): data on exposure (see p.6);

PPEI, PPEhand, PPE body: personal protective equipment coefficients (-);

AbI, AbDE: respectively inhalation and dermal absorption factors (-);

AR: application rate (kg/ha);

Area treated (ha/d);

FA: frequency of application (number of applications per year) (1/yr);

IEmix/load: internal exposure during mixing and loading (mg a.s./kg a.s.);

IEapplication: internal exposure during application (mg a.s./kg a.s.);

IEoperator : internal exposure of the pesticide operator (mg a.s./kg bw/d);

BW: body weight (kg); AOEL: Acceptable Operator Exposure Level (mg a.s./kg b.w./d).



58

Below, some more information on the FA is given. The values for the frequency of application should be available by means of surveys. The Central Science Laboratory

performed a detailed survey in which the FA for various active substances and various crops

was determined. One can estimate the frequency of application (FA) by dividing the PesticideUsage Data (kg a.s./yr) with the Application rate (kg a.s./ha). To obtain a realistic value for

the frequency of application one should apply the average application rate. Within theframework of this project the data gathered by the CSL will be used. In America, the Canada-

United States Trade Agreement (CUSTA) Working Group established a position paper on the

typical workdays for various crops. In this document default values were proposed for theestimated average number of annual workdays for a grower or an employee of a custom

applicator who apply pesticides to a specific crop. Estimates of the annual frequency of awokday exposure were derived from pesticide use patterns submitted by the registrant,

government surveys, farm advisors and grower associations. The same procedure should be

followed in Europe.

TOXICITY

An AOEL based on a long-term NOAEL should be used as toxicological reference parameter.

If there are indications that effects may occur after exposure has ceased, but did no come

apperent during short-term exposure, the NOAEL for these effects in the long-term studyshould be used for AOEL setting. In cases where exposure duration is more than three months

per year, the NOAEL from a long-term study should be considered for AOEL setting in order to cover effects arising from chronic exposure. Generally, this approach for setting an AOEL

uses the same starting point as the ADI. Although an AOEL is routinely based on the relevant

NOAEL from a particular set of short-term toxicity studies, it may sometimes be moreappropriate to use a higher NOAEL, especially when a chronic study may indicate a higher

NOAEL than a short-term toxicity study because differences in dose level selection.

R ISK INDEX

The risk index for pesticide operators (RIoperator, chronic) is calculated by dividing the chronicinternal exposure (IEoperator, chronic) by the acceptable operator exposure level (AOEL). Both the

IEoperator,chronic and the AOEL are expressed in mg/kg body weight/day.

AOEL

IE RI

chronicoperator

chronicoperator

,

, =

With:

RIoperator, chronic: chronic risk index for the pesticide operator (-);

IEoperator, chronic: internal exposure of the pesticide operator (mg a.s./kg bw/d);




59

2. Re-entry worker

Proposed Re-Entry Worker Indicator

EXPOSURE

Workers who come into contact with the crop will be contaminated by contact with pesticides

that are still available on the crop after application. Exposure during re-entry tasks, such asharvesting, bending and tying up of the crop is likely in the case of ornamentals, vegetables

and fruits. Inhalation exposure is very low compared to the dermal exposure. For outdoor re-entry scenarios inhalation exposure is neglected. Though for greenhouse scenarios the

inhalation exposure will be taken into account.

The routes of exposure during post-application activities are the same as in operator exposure,

i.e. dermal and inhalation routes. However the sources are different: foliage, surfaces, soil andalso dust may contribute.

a. Dermal Exposure

The chronic annual dermal exposure of the re-entry worker is estimated as follows:

0.001365

cum

WD DE DFR TF T P = ∗ ∗ ∗ ∗ ∗

0.01365

AR WD DE FA TF T P

LAI = ∗ ∗ ∗ ∗ ∗ ∗

With:

DE: dermal exposure (mg/d);

0.001, 0.01: conversion factors for the units;

DFR cum: cumulative dislodgeable foliar residue (µg/cm²);


LAI: leaf area index (m²/m²);

Here, I refer to page 15 till 28 for extended information;

FA: frequency of application (-);


T: duration of re-entry (hr/d); As a default it is assumed that a re-entry worker works

8 hours a day; P: factor for PPE (no PPE: 1; with PPE: 0.1);

WD: the estimated number of workdays a year (d/yr).

As mentioned before the time-window of exposure can be adjusted according to the query

made by the user of the HAIR software.



60

Below the different parameters are explained more into detail:

DFR cum: cumulative dislodgeable foliar residue (µg/cm²)

If specific DFR data are available, the DFR after consecutive applications can be

calculated as follows:

Case 1: There is no information on the decline rate

When there are no data concerning the decline rate of active substances on the

foliage, no degradation is assumed, then

0cum DFR FA DFR= ∗

Where:

DFR cum: cumulative dislodgeable foliar residue (µg/cm²);

FA: frequency of application (-);

DFR 0: initial dislodgeable foliar residue (µg/cm²).

It is clear that this case represents a worst case scenario.

Case 2: Degradation on the foliage is taken into account

Taking the degradation process and the multiple applications into account and

assuming that the dissipation function which describes the DFR at a given time t

after application follows a monotonically decreasing exponential decay curve, the

following formula can be applied to determine the cumulative DFR after consecutive

applications:

1

0

1

( )n

kt n

cum

i

DFR DFR e− −

=

= ∗

∑

Where:

DFR 0: initial dislodgeable foliar residue (µg/cm²);

n: number of applications (= FA) (-);

k: degradation factor (d-1);

The degradation factor k is the pesticide out-flux, depending on diffusion,

growth, absorption, transport, run-off, volatilisation, photo-degradation and

chemical breakdown. A literature review conducted by Seuntjes et al. (2006)

has shown that the impact of most processes is very important in the first

hours after pesticide application. In frame of the impact of dislodgeable foliar

residues, it can be concluded that the most important factor for long-term

decline is the biochemical degradation;

1/ 2

ln(2)k

T =

Where: T1/2: foliar half life (d); a lumped parameter describing the loss rate

of pesticides on the plant canopy.

Foliar half-life times were obtained from the SWAT pesticide default

database (http://www.brc.tamus.edu/swat/manual2000/pestdb/pestdflt.html).

The sources for the foliar half-life times were Willis et al. (1980) and Willis

& Mc.Dowell (1987). About 450 DT50 values (81 chemicals) for a broad

spectrum of vegetative plant materials (grass, cereals, forage crops, cotton,vegetables, tobacco, foliage of fruit trees) are presented in these papers. The



61

authors expect that many of the half-lives may be overestimates due to the

time schedule of sampling in the original studies. For most pesticides, the

foliar half-life is much less than the soil half-life due to enhanced

photodecomposition and volatilization. In the SWAT database values for

foliar half-life were available for some pesticides, but the majority of the

foliar half-life values were calculated using the following rules:

1. Foliar half-life was assumed to be less than the soil half-life by a

factor 0.5 to 0.25, depending on the vapour pressure and

sensitivity to photo-degradation;

2. Foliar half-life was adjusted downward for pesticides with vapour

pressures less than 10-5 mm Hg.;

3. The maximum foliar half-life assigned was 30days.

The foliar half-life times for several active substances according to this

database are listed in Annex IX. The EC Guidance Document on Risk

Assessment for birds and mammals Under Council Directive 91/414/EEC(2000) proposed to use a DT50 of 10 days as a default for foliar half-life

based on the bias mentioned before and on the fact that the database includes

very stable substances such as organochlorines.


Indicative TF values are listed in Table II.2.4 (see p 24-26) and more specific

information can be found on page 19 till 21 and on page 27 of this document.

If monitoring data on both, exposure and DFRs are available then the following

equation is applied to obtain a TF, normalized per hour of task duration (Franklin et al., 2005).

ExpTF

DFR=

With:

TF: transfer factor (cm²/hr);

Exp: exposure (µg/hr);

DFR: dislodgeable foliar residue (µg/cm²).

In this way chemical and activity specific transfer coefficients are obtained.

T: duration of re-entry (hr); As a default it is assumed that a re-entry worker works 8

hours a day;

P: factor for PPE (no PPE: 1; with PPE: 0.1);

WD: the estimated number of workdays a year (d/yr). This value is dependent on the

crop type. The estimated number of workdays must be divided by a factor of 365 to

obtain the annual chronic dermal exposure.



62

Special case: (EUROPOEM Re-Entry Working Group) (EUROPOEM II, 2002) The case of possible dermal exposure to soil containing pesticide residues is based on the

concept of dermal adherence. The following formula is proposed to estimate the chronic

exposure due to the contribution of soil residues:

The algorithm specified below is used to estimate exposure:

/

365

S S C S Sk

soil

Conc DA SA T WD D

ρ

∗ ∗ ∗= ∗

With:

D: dermal exposure (mg/d);

ConcS: soil concentration of the active substance (mg/m³);

The soil concentration of the active substance can be determined by applying

the same approach as the terrestrial workpackage;

DAS: Dermal adherence of soil (mg/cm²);

Field studies investigating dermal exposure to soil by direct gravimetric

measurements (Kissel et al., 1996) suggest that an appropriate hand soil

loading for a worker would be 0.44 mg/cm² (geometric mean peak value for

farmers involved in hand weeding, default). A laboratory study to determine

the extent of soil adherence to hands when totally immersed in a range of dry

soil samples (Driver et al., 1996) concluded that the mean hand loading for un-

sieved soil was 0.58 mg/cm² of skin surface. Data for sieved samples suggested

that hand loading increased when soil particle size was reduced);

SAC: Skin area contaminated (cm²);

A default value of 820 cm² is to be used;

soil ρ : soil bulk density (g/cm³);

TS/Sk :Transfer of the active substance from soil to skin (-);

Data on the transfer of active substance from soil to skin are usually not

available at the moment;

WD: number of workdays a year (d/yr).

As a preliminary approach it is assumed that the complete amount of the chemical in a

layer of soil is bioavailable to skin. Conservative assumptions like these have to be used

when no specific information is available. More research should be performed in order to

perform more reliable exposure estimations for this route of exposure.


Inhalation exposure will only be assessed for greenhouse workers. In all re-entry situations,

low volatility of the active substance (1.35 x 10-6 Pa at 20°C) removes a concern of exposure

to vapour (91/414/EEC, Point 7.1.3 Inhalation) such that an inhalation component need not to

be considered in the exposure assessment.

Inhalation exposure may occur to residual vapour and airborne aerosols during a relatively

short period after application. In case of outdoor crops, exposure will occur during the time

the spray is drying; in case of greenhouse crops, exposure will occur within a few hours after

application. Outdoors, there generally is a rapid dissipation of vapour and aerosols, leading tomuch lower inhalation potential than in greenhouses. Furthermore, the majority of the applied



63

pesticides are non-volatile which implicates a low potential inhalation exposure. Thus,

inhalation exposure is in many cases less important for risk assessment than dermal exposure

especially for outdoor scenarios, with of course exceptions for aerosols and volatile pesticides

of concern.

c. Total Internal ExposureThe total internal exposure is calculated as the sum of the dermal exposure (DE; mg/person/d)

multiplied by the dermal absorption factor (AbDE; %) and the inhalation exposure (I;

mg/person/d) multiplied by the inhalation absorption factor (AbI; %), divided by the body

weight (BW, default: 70 kg) of the worker.

_ ker, DE I

re entry wor chronic

DE Ab I Ab IE

BW −

∗ + ∗=

With:

IEre-entry_woker, chronic: internal exposure of a re-entry worker (mg/kg bw/d);

DE: dermal exposure (mg/person/d);

AbDE: dermal absorption factor (%);

I: Inhalation exposure (mg/person/d);

AbI : inhalation absorption factor (%) (default: 100);

BW: body weight (kg) (default: 15).

TOXICITY


In cases where exposure duration is more than three months per year, the NOAEL from along-term study should be considered for AOEL setting in order to cover effects arising from

chronic exposure.

R ISK INDEX


the European approvals process. It is assumed that the AOEL can be used as a reference dose

against which re-entry worker exposure is assessed. Thus the risk index for re-entry workers

is calculated as follows:

_ ker,

_ ker,



IE RI

AOEL

−

− =

With:

RIre-entry_worker, chronic: chronic risk index for the re-entry worker (-);

IEre-entry_worker, chronic: internal exposure of the re-entry worker (mg a.s./kg bw/d);




64

3. Greenhouse worker

Proposed Greenhouse Worker Indicator

EXPOSURE

a. Dermal Exposure

The dermal exposure for the greenhouse worker is estimated in the same way as for the re-

entry worker.


The EUROPOEM Re-entry Working Group developed an algorithm for a few re-entry

scenarios. There is no generic model for inhalation exposure available. Here, only a preliminary approach for indoor inhalation exposure is presented. The identified

representative crop/activity re-entry scenarios relevant to Europe that may result in post-

application exposure of workers to plant protection residues are listed in Table II.2.4. Thus,

this estimation procedure for inhalation exposure is only applied for greenhouse workers. For

field workers, the inhalation exposure is considered negligible because of the dilution in free

air.

The algorithm proposed to estimate inhalation exposure to vapours is outlined below

YED EF DEDTSF AR I ∗∗∗∗=

Where:

I: potential inhalation exposure (mg a.s./d inhaled);


TSF: Task Specific Factor;

These factors can be used in the first tier exposure and risk assessment and have

been estimated for a small set of exposure data on harvesting of ornamentals and

re-entry of greenhouses about 8-16 hours after specific applications. The indicative

Task Specific Factor values for specific indoor glasshouse scenarios are given in

Table II.2.3;

DED: daily exposure duration (hr/d); as a default a daily 8 hr exposure is assumed;

EF: number of days exposed in one year divided by 365; exposure frequency (-);The exposure frequency refers to the fraction of a year over which an exposure

occurs (e.g. 3 months/12 months = 0.25). The number of workdays a year was

estimated for several crops;

YED: yearly exposure duration, number of years exposed divided by 70 years

(average working lifetime expectancy); as a default YED is set to 40/70 (average

working life time/average life-time expectancy).



65

The chronic inhalation exposure for the greenhouse worker is estimated using the formula

below when monitoring data are availble:

YED EF DED IRC I air ∗∗∗∗∗= −310

With:

I: re-entry worker inhalation exposure (mg/person/d); C air : mean concentration of the pesticide in the greenhouse air over the period of one

year (µg a.s. /m³);

IR: chronic inhalation rate (m³ air/d); a default value of 0.28 m³/kg b.w./d can be

assumed for an adult; this equals an IR of 19.6 m³/d or 0.82 m³/hr.


For risk assessment of greenhouse workers, the internal exposure is calculated in the same

way as for re-entry workers (see p 61).

TOXICITY


In cases where exposure duration is more than three months per year, the NOAEL from a

long-term study should be considered for AOEL setting in order to cover effects arising from

chronic exposure.

R ISK INDEX



against which greenhouse worker exposure is assessed. Thus the risk index for greenhouseworkers is calculated as follows:

AOEL

IE RI

chronic greenhouse

chronic greenhouse

,

, =

With:

RIgreenhouse, chronic: chronic risk index for the greenhouse re-entry worker (-);

IEgreenhouse, chronic: internal exposure for the greenhouse re-entry worker (mg a.s./kg

bw/d);




66

4. Resident

Introduction

The term resident is in this context used for those who live near to sprayed fields (Blundell,

T., 2005). Crops can be sprayed up to the boundary of a field that can be close to

neighbouring property, residents or bystanders. Therefore spray drift (droplets as well as

vapours released at the time of a pesticide application) could lead to the direct exposure of

residents through contact with the skin or by inhalation. Other routes of exposure by which

residents may become exposed to pesticides are from vapours emitted from a treated area post

a pesticide application and from dusts contaminated with pesticide that may be emitted from

treated cropped areas particularly during harvesting operations. The exposure of residents

depends on a wide range of factors and operating conditions including the pesticides used,

wind speed and direction and boom height; the downwind distribution of pesticides both in

the air and on to non-target surfaces as well as the behaviour patterns of people in these

downwind areas relate to the potential exposure of residents and bystanders This means thatthe amount of a pesticide to which a resident may be exposed will vary over a wide range

from low to potentially high levels in the worst case. Indoor operations e.g. with hand-held

sprayers, mist blowers or fog generators, should exclude residents as a part of the safe

management of the work. In addition to any spray that may drift into residential properties,

pesticides are also used in homes and gardens, both in rural and urban areas. Apart from

potential direct exposure when using a pesticide, residents can take residues of pesticides into

their homes on clothing (especially agricultural workers) and/or on shoes by walking over

treated surfaces (Matthews, 2006). The take-home of residues of pesticides is clearly a

significant factor in children’s exposure to pesticides in rural areas (Garry, 2004), especially

where the more toxic insecticides are used in agriculture, though the uneducated use of

dispensers in a home is also a crucial source of exposure to pesticides. Lu et al. (2000) alsoreported five-fold higher concentrations of pesticide metabolites in children living in

agricultural areas compared to reference children with 0.01 µg/ml. The latter scenarios are not

taken into account within the framework of this project since the HAIR project is focussed on

risk due to the application of pesticides in agriculture.

Compared with the occupational exposure of applicators and workers following a pesticide

application in the field, post-application residential exposure to pesticides used in and around

the home is lower in level, but encompasses a wider variety of scenarios, such as age

distribution, activity patterns and product use. Typically, few data are available on residential

exposure, while a large body of data does exist for occupational exposures. In occupational

exposure assessment, a database approach is favoured, while in residential exposureassessment a mechanistic and statistical modelling approach is dominant (Matoba & Van

Veen, 2005). In the U.S. the Residential Exposure model (REx), developed by the Outdoor

Residential Task Force (ORETF), has recently become available. This model includes data for

professional lawn applicators and non-professional lawn, tree and garden applications. For the

application phase several models are available to predict residential exposures. Though, post-

application residential exposure due to the application of pesticides used in agriculture was

not considered in this model.

Currently, an exposure assessment for residents who are exposed to to the drift of alternating

products several times per year, possibly for several years, is currently carried out neither on

national nor on Community Level (EFSA, 2006).



67

In the U.K. a three-year lasting project concerning bystander and resident exposure recently

started (19th July 2006). This project is financed by DEFRA (Department of Environment

Food and Rural Affairs). It sets out to develop a computational model to predict the potential

exposure to pesticides for bystanders and residents in the countryside that can be used as a

tool in risk assessments. The work will be concerned with boom sprayers operating over

arable crops in a range of conditions relevant to the United Kingdom. Model development andvalidation will be supported by tests in controlled wind tunnel conditions with the overall

model predictions validated against full-scale field trial results. To estimate the potential

exposure of bystanders and residents, the transport of droplets and vapours during application

will be modelled using three regions, namely:

a region close to the nozzles where the interaction of air, vapour and droplet flows

associated with the operation of the nozzle and air flows arising from the natural

wind and forward motion of the sprayer combine to detrain droplets and vapours

from the spray and so provide the source for drifting spray;

a region around the spraying vehicle where the flows due to the motion of the

vehicle will be considered in the initial phases of the downwind transport of airbornedroplets and vapours;

a larger region away from the spraying vehicle where the drifting cloud of droplets

and vapours may be influenced by features in the terrain such as the presence of

hedges, buildings and slope and where the characteristics of the weather conditions

may also be important.

The construction of the models will be based on approaches identified from the existing

literature together with the results from detailed wind tunnel and field experiments aimed at

quantifying pesticide movements close to the nozzle and spraying vehicle. Where possible

and appropriate, standard modelling packages will be used particularly in the region furthest

from the spraying vehicle. The interfaces between the separate regions in the model will be

defined in terms of vertical airborne spray volume profiles as vapour and droplets with

defined size distributions and air velocity profiles. Losses as vapour from treated areas will be

determined using simplified relationships based mainly on the properties of the sprayed liquid

and the target surface to give a loss rate per unit area over a defined time period of up to 120

hours. The dispersion of this vapour cloud will then be predicted using an established

atmospheric dispersion model. The issue of potential contamination from pesticide

contamination of dusts leaving a cropped area will not be included in the initial model

development. However, it will be included in the literature reviews associated with the project

work so as to obtain an estimate of the extent of this component of potential exposure.

The results from the model will be validated by comparing predictions with measurements

made in full-scale field trials in closely monitored conditions and with active pesticide

formulations together with results in the published literature. The model will also be used to

determine the relative importance of operational factors on the risk of resident and bystander

exposure and to examine the effects of using different application technologies on the risks of

this exposure.

The work will be primarily aimed at predicting the pesticide exposure profiles of residents

and bystanders in the countryside as a tool for the regulatory risk assessment process.

(http://www2.defra.gov.uk/research/project_data/More.asp?I=PS2005&SCOPE=0&M=PSA&

V=NR%3A080).



68

Proposed Resident Indicator

EXPOSURE

Contrary to bystanders, repeated exposure is likely. It is assumed that only ordinary clothing

is worn and that the total uncovered area amounts to 0.4225 m² per person (default value – total uncovered area: head, back & front of neck, forearms, ½ upper arms and hands) for

adults. For children the exposed area equals 0.78404 m² (b.w.=43.3 kg). Residents are

assumed to be located at 50 m distance downwind from the treated field. The default drift

values are taken from the Ganzelmeier tables. The resident’s exposure will be estimated for

the dermal as well as for the inhalation route.

a. Dermal Exposure

It can be postulated that the dermal exposure is directly correlated to the amount of active

substance applied per area, the area of the uncovered body surface contaminated and the drift

distance. The number of applications is also taken into account (Franklin et al., 2005).

( * )365

RD DE AR Drift FA EA= ∗ ∗ ∗


AR: application rate (mg a.s./m²);

Drift: downwind pesticide ground deposits at 50 m distance from the field for

multiple applications (Ganzelmeier tables). A different distance can be chosen

according to the wishes of the Member States (see Table III.4.2 & Table III.4.3).

In conformity with the FOCUS-group, it was established that a reduced percentile

should be used for multiple uses in order not to exceed the 90th percentile

cumulatively. The percentiles for multiple applications, listed in the table below

(Table III.4.1), represent the exposure for one of the multiple applications, leaving

degradation processes aside. Moreover it was established that risk assessment for

multiple uses should at least amount to the Predicted Environmental Concentration

(PEC) required for the calculation of a single use. This regulation ensures in the case

of fast degrading active substances that a multiple use does not lead to a lower risk

than a single use within the assessment.

The following percentiles are used (Table III.4.1):

Table III.4.1: Percentiles used for multiple applications (Maund, 2000)

Number of applications Percentile used1 90

2 82

3 77

4 745 72

6 70

7 698 or more 67

EA: exposed area (m²/person/d) (default: 0.4225, total uncovered area: head, back &front of neck, forearms, ½ upper arms and hands);



69

FA: frequency of application;

RD: residence days (d); As a default it is assumed that a resident is exposed at the

same daily level for three months; RD = 90 days. It has to be mentioned that the

averaging time of 365 days can be adjusted according to the query perfomed by the

user of the HAIR software program.

Table III.4.2: Basic Drift values for multiple applications (Ground sediment in % of the application rate)at a distance of 50 m downwind from the edge of the sprayed area)

Drift (%) according to the percentileCrop grouping Growthstage 82 77 74 72 70 69 67

Arable Crops - 0.05 0.04 0.04 0.04 0.03 0.03 0.03

VegetablesOrnamentals &Small Fruits < 50 cm

- 0.05 0.04 0.04 0.04 0.03 0.03 0.022

Vegetables

Ornamentals &Small Fruits > 50 cm

- 0.08 0.08 0.08 0.08 0.07 0.07 0.04

Early 0.22 0.19 0.17 0.17 0.16 0.16 0.15Fruit crops

Late 0.15 0.13 0.13 0.13 0.11 0.11 0.11Early 0.02 0.02 0.02 0.02 0.02 0.02 0.009

VinesLate 0.08 0.08 0.08 0.08 0.07 0.07 0.04

Hops - 0.09 0.08 0.08 0.07 0.07 0.06 0.02

Table III.4.3: Basic Drift values for multiple applications (Ground Sediment in % of the application rateat a distance of 7 m downwind from the edge of the sprayed area)

Drift (%) according to the percentileCrop grouping Growthstage 82 77 74 72 70 69 67

Arable Crops - 0.34 0.29 0.27 0.26 0.24 0.24 0.23Vegetables

Ornamentals &Small Fruits < 50 cm

- 0.34 0.29 0.27 0.26 0.24 0.24 0.23

VegetablesOrnamentals &Small Fruits > 50 cm

- 1.89 1.80 1.75 1.72 1.67 1.65 1.63

Early 12.84 11.99 11.65 11.35 10.95 10.73 10.43Fruit crops

Late 4.66 4.06 3.79 3.67 3.55 3.49 3.39Early 0.62 0.59 0.57 0.56 0.55 0.53 0.52

VinesLate 1.89 1.80 1.75 1.72 1.67 1.65 1.63

Hops - 6.41 5.70 5.48 5.25 5.08 4.94 4.70

Source: EUROPOEM II: Bystander Working Group Report (2002)


Droplets that remain airborne are generally larger than 100 µm in size, and unless the liquid is

non-volatile they will become smaller in flight. Briand et al. (2002) evaluated spray drift from

an orchard. Compared to earlier studies, the relative high concentrations that were detected in

the gas phase indicated that evaporation in the high temperatures from small droplets allowed

them to drift. Thus, temperature and relative humidity as well as the physical properties of the

pesticide will influence the vapour and particle distribution, making it important to

differentiate between drift and post-application transfer from deposits.

In Germany, the time-weighted air concentration of pesticides downwind of a barley field dueto spray drift was highest during the first two hours after application, and then decreased.



70

Over 21 hours, up to 0.58 µg/m³ was detected at ten metres downwind. Volatile insecticides

were detected at up to 200 metres in the first two hours, but this was below the limit of

quantification (Siebers et al., 2003).

In all the Member States, there is generally a lack of data on long-term air levels of pesticides

and hence estimates of bystander exposure. Few studies have sampled air in the U.K. for pesticides, but where these have been done (Turnbull, 1995) the highest quantities observed

over 24 or 48 hours were in samples taken near to field applications. One sample was just

over 2000 pg/m³, which is 42.000 times less than the air concentration measured at the

bystander position 8m from the boom. Mean values of pesticides in air were generally less

than 400 pg/m³. Similar air quality studies elsewhere have generally detected the most volatile

pesticides, such as methyl bromide, which was used to fumigate soil (Lee et al., 2002).

Several studies conducted in California provide useful data on long-term air levels of

pesticides. These data suggest that in the 72 hours after following application maximum peak

air levels of most pesticides are very low (i.e. below 1 µg/m³ or less). At this level daily

exposure would be about 0.0003 mg/kg bw/d), which indicates that exposures of most

pesticides would be well within acceptable levels.

Case 1: monitoring data on ambient pesticide levels are available

The inhalation exposure for each active substance can then be estimated as follows:

YED EF DED IRC I air ∗∗∗∗∗= −310

With:

I: resident inhalation exposure (mg/person/d);

C air : mean concentration of the pesticide in the air over the period of one year

(µg a.s./m³); IR: chronic inhalation rate (m³ air/d); a default value of 0.28 m³/kg b.w/d can be

assumed for an adult; this equals an IR of 19.6 m³/d or 0.82 m³/hr;

DED: daily exposure duration (hr/d); as a default a daily 8 hr exposure is assumed;

EF: number of days exposed in one year divided by 365; exposure frequency (-); The

exposure frequency refers to the fraction of a year over which an exposure occurs (e.g.

3 months/12 months = 0.25). It applies only to chronic exposures which are by

definition a year or more. In California estimates of this EF were made for different

active substances based on pesticide use report (PUR) data for the agricultural sections

immediately surrounding each air monitoring site, typically within a 1,5-mile radius.

When no pesticide use was reported within this radius, which was the case for certain

pesticides, the radius was expanded to 3 miles. The exposure frequency can bedescribed by using triangular distributions when estimates of the minimum, most

likely mean, and maximum points are available (Lee et al., 2002);

YED: yearly exposure duration, number of years exposed divided by 70 years

(average lifetime expectancy); exposure duration (d); as a default YED is set to 1 in

order to be able to calculate the annual average daily concentration.

Air monitoring data on long-term pesticide concentrations are very scarce in Europe and data

on active specific exposure frequencies are also lacking. But the approach suggested here

provides the best option in order to obtain an adequate risk assessment.



71

Case 2: monitoring data on ambient pesticide levels are not available

At the moment no good models are available to predict airborne pesticide concentrations due

to spraying practices. A project financed by DEFRA is currently developing a model as a tool

for the regulatory risk assessment process in the U.K. which should be able to predict the

pesticide exposure profiles of residents and bystanders in the countryside. Within theframework of HAIR, there was not enough time to develop such a model. Moreover due to

lack of data validation of such a model is not possible without conducting full-scale field trials

in closely monitored conditions and with active pesticide formulations.

For the moment we propose to assume that residents are exposed at the same daily level for

three months (EF = 3 months or 90 days divided by respectively 12 or 365), which is far

greater than those living next door to a treated field would actually experience (Matthews et al., 2003). The daily inhalation exposure can be estimated in the same way as for bystanders.

The approach making use of the AHU/CSL data can be used to estimate the daily level of

exposure (applying the chronic, not the acute inhalation rate) (1) or one can estimate the daily

level of exposure on the basis of the operator’s exposure (2) (DED equals (8*60 = ) 480 min per day).

YED EF DED IRC I spray ∗∗∗∗= (1)

YED EF DEDST WR

ARWR I I a ∗∗∗

∗

∗∗= (2)

For the units and explanation of the parameters: see earlier.

c. Total Internal ExposureThe total internal exposure is calculated as the sum of the dermal exposure (DE; mg/person/d)

multiplied by the dermal absorption factor (AbDE; %) and the inhalation exposure (I;

mg/person/d)) multiplied by the inhalation absorption factor (AbI; %), divided by the body

weight (BW, default: 70 kg) of the resident.

r

* DE I esident

DE Ab I Ab IE

BW

+ ∗=

With:

IEresident: internal exposure of a resident (mg/kg bw/d);


AbDE: dermal absorption factor (%);

I: Inhalation exposure (mg/person/d);

AbI : inhalation absorption factor (%) (default: 100);

BW: body weight (kg) (default: 15).

TOXICITY

The long-term AOEL should be used as reference toxicological parameter.



72

R ISK INDEX



against which resident exposure is assessed. Thus the risk index for residents is calculated as

follows:

resident resident

IE RI

AOEL=

With:

RIresident: chronic risk index for the resident (-);

IEresident: internal exposure for the resident (mg a.s./kg bw/d);


Resident exposure when spraying greenhouse crops and when applications are performed with

treated seed, granules, dipping a plant in a pesticide solution or pouring a pesticides solutionto a plant is considered negligible. In these cases the RIresident equals 1E-12.

Second tier estimates of likely resident exposure are to be based upon measurements made in

the field, according to the needs for such specific data. Measurements should be based upon

study of realistic situations representative of specific cases. Measured values may be gathered

using suitable methodology, which ideally should allow correlation with measurements of

ambient environmental concentrations (e.g. airborne vapour concentrations) in the relevant

situation, in order to aid interpretation of results and allow controlled comparison with other

similar data as far as possible. The Royal Commission on Environmental Pollution has

recommended that the role of monitoring bystander and resident exposure should be part of

the Health Protection Agency under the Department of Health.



73

IV. Aggregate and Cumulative Risk Assessment

Introduction

It has been suggested that risk assessment of pesticides, primarily for food safety, should

include consideration of interactions between different pesticides and this requires some

consideration of the toxicology of mixtures, as well as exposure via other routes than food.

Historically, risk assessments were carried out on single products or active ingredients alone.

However, it has increasingly been realized that this approach may not be sufficiently

protective and not based on sound toxicological principles (WiGRAMP, 2001). Moreover, the

United States Environmental Protection Agency (U.S. EPA) issued guidelines for risk

assessment of chemical mixtures as long ago as 1986 (U.S. EPA, 1986). In the nomenclature

adopted by the US Environmental Protection Agency, ‘cumulative risk assessment’ is

defined as concurrent exposure to more than one pesticide by the same route, while

‘aggregate exposure’ refers to different routes of exposure. Risk assessments carried outusing cumulative and aggregate methods have to be considered under the US Food Quality

Protection Act (Anon, 1996).

In the EU a generally agreed framework/approach for combined risk assessment of pesticides

has not yet been established. However at European and International level there are some

activities ongoing concerning approaches for cumulative risk assessment of pesticides which

have a common mode of action. The EFSA will hold a colloquium the 28th and 29th of

November 2006 concerning this topic (http://www.efsa.europa.eu/en/science/

colloquium_series/colloquium_7.html). Also for aggregate assessments a harmonised

approach should be established. A lot of research concerning aggregate assessements has been

conducted in the U.S.

Historically, exposure assessors have focused on characterizing the highest levels of exposure

that will occur to an individual or a population over time as the result of the use of pesticides.

One approach that is used to characterize the upper-bound of exposure is to use simple models

of dose rates and a series of conservative model inputs. This approach is applied by many

federal and state agencies. It generally relies on the use of default ‘constants’ (i.e. the use of a

single value for an unknown or uncertain component of the risk assessment). Each of these

single values is generally selected to fulfil the goal of being ‘health-protective’ that is selected

to be reasonably certain that risk is not underestimated and to err on the side of overestimating

the risk. This approach has great value for screening out exposures that are of little concern. A

related approach is to back off from one or more of the ‘worst-case’ assumptions and use amixture of conservative and more reasonable estimates (U.S. EPA., 1992a). The approaches,

described above have several shortcomings that can be largely overcome by having good

quality exposure data and using probability distributions and probabilistic techniques. In

contrast to the use of a single default value for a risk parameter, a probability distribution can

reflect the relative likelihood of the different possible values of the parameter. Thus, a

probability distribution can reflect not only the largest and smallest possible values of a

parameter but also the probability of the occurrence of each of the values in its range. If

conservative default constants are used for each of several different parameters in the risk

assessments, then the conservatism in the individual components is compounded when the

components are combined in the risk characterization. Furthermore, the extent of the

overestimation cannot be readily quantified, and thus the magnitude of the overestimation of

the average risk is not identified. Distributional techniques, however, make it possible to more



74

realistically combine exposures from multiple years, subpopulations, exposure pathways and

chemicals without having to assume ‘the worst case’ for each component. By carrying all of

the information through to the end of the entire risk characterization instead of requiring

interim single-number characterizations at different stages in the risk assessment, probabilistic

techniques help avoiding the compounding of multiple conservatisms (Sielken, 2005). For

example, an individual who receives high levels of exposure from one source will notnecessarily receive high levels of exposure from a second or a third source. In fact, there are

situations in which exposures to high levels from one source will preclude exposure from a

second source. In other words there will be very few, if any, people who will actually

experience the high levels of exposure estimated by simply adding the exposures for each

pathway. Combining point estimates will overstate, sometimes significantly, the potential

exposure that the vast majority of the general population group actually receives. The degree

of overestimation however decreases as the refinement of the individual pathway exposure

estimates improve. The primary advantage of a highly conservative, deterministic approach is

that relatively fewer data, analytical resources and less time to conduct are required. But

exposure assessment approaches that focus on defining individuals who have high levels of

exposure to a single source should not be extended to evaluate exposure from multiplesources (Price et al., 2000). Moreover, the estimated upper bound for the risk obtained by

combining default constants provides no indication of the relative likelihood or frequency of

that risk or any other risk lower than the exaggerated upper bound. On the other hand, the

characterization of risk obtained by using probability functions and probabilistic techniques

provides a quantitative assessment of the relative likelihood of each of the different possible

values for the risk. Furthermore, default constants and assumptions do not explicitly address

the uncertainty and variability that are an inherent part of human risk. However, probability

distributions can explicitly include both uncertainty and variability. Finally, probability

distribution characterizations can describe the entire population (all of the people in the

exposed population) rather than a single hypothetical person or subpopulation. Individuals are

used as the building blocks for calculating doses and for developing subpopulation and

population characterizations (Sielken, 2005). The methodology for applying probabilistic

approaches for aggregate and cumulative risk assessments exists now, but is continuing to

evolve. In 1998, the ILSI Workshop on Aggregate Exposure Assessment held on February 9-

10 strongly endorsed the use of distributional analysis and probabilistic methods in estimating

and characterizing pesticide exposure and risk from all sources and multiple chemicals (ILSI,

1998).

Over the last 10 years there has been a growing recognition of the need for tools to assess

exposures to multiple chemicals from multiple sources. This need has been driven by

legislative and regulatory actions in the U.S. such as the 1996 Food Quality Protection Act(FQPA), the residual risk portions of the 1991 Clean Air Act, and the Voluntary Children’s

Chemical Exposure Program (U.S. EPA, 2000b). There has been a general recognition that

computer simulation software is a useful tool for these assessments (ILSI, 1998, 1999; U.S.

EPA, 1999b, 2000a, 2001b). A conceptual framework for the construction of simulation

software for aggregate and cumulative risk assessments has been recently published (Price et

al., 2005).



75

Overview of approaches to handle cumulative assessments

Before outlining the chosen approach to handle cumulative risk assessments, an overview is

given on the different ways in which these assessments can be conducted. First of all, it is

necessary to clarify the terminology of the toxicity of chemical mixtures. The terminologyused in this report is derived from Seed (1995) and Cassee et al. (1998, 1999). The cumulative

effects of more than one toxicant are divided into non-interactive (one toxicant has no effect

on the toxicity of the other component(s) and interactive (one toxicant does affect the

toxicity) of another. The absence of interaction may be represented in two ways additivity of dose and additivity of effect. Where two or more compounds have the same site and type of

toxic action, the usual starting assumption is that their effective doses will combine additively.

This implies that each element will contribute to the toxicity of the mixture in proportion to its

dose, expressed as a percentage of the dose of that chemical alone which would be required to

produce the given effect of the mixture. Where the components of a mixture have different

sites or different types of toxic action, they may be assumed to act independently of the dose

of the other components. The proportion of the population responding will depend on whether

there is positive, negative or no correlation between the susceptibility of individuals to the

components of the mixture. Interactions can be of two types: if the effect of the mixture is

greater than expected for the chosen additive model, the interaction is known as potentiation;

if less, than interaction is known as antagonism (WiGRAMP 2001). Table IV.1 gives an

overview of the applied terminology in this report.

Table IV.1: Terminology for the toxicity of mixtures (WiGRAMP 2001)

Concept Type of combined Effect SynonymsDose Additivity Loewe additivity, Simple similar action, Summation

Non-interactive effectsEffect Additivity Simple dissimilar action, Independent Action

Potentiation Synergism, synergy, supra-additivityInteractions

Antagonism Sub-additivity

The toxicity of mixtures can be studied in a number of ways. The different approaches will be

outlied below and consequently one approach will be proposed for use within the framework

of HAIR.

Testing mixtures

The simplest approach is to test all the mixtures of interest over the range of doses and

endpoints of interest. Tests normally used to assess the toxicity of the components should be

performed in this case. However, this approach uses a lot of resources, including animals and

would not be practicable on a large scale. The advantage of this approach is that very specific

directly applicable results are obtained (WiGRAMP 2001).

Testing standard mixtures

An alternative approach is to test a smaller set of standard mixtures in which the proportions

of the components vary systematically across the relevant range, and extrapolate the results to

the mixture or mixtures of interest. However, this approach requires subjective decisions to be

made about the similarity of the standard mixtures and the mixture of interest, possibly based

on some response model. This generic approach reduces the testing, but gives less reliable

results (WiGRAMP 2001).



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The Hazard index approach

There are two main methods used for assessing the toxicity of a mixture when it is reasonable

to assume that doses of its components combine additively. The first method is the Hazard

Index Approach. A hazard quotient is calculated for each component by dividing its dose in

the mixture by its maximum acceptable dose (AOEL in case of occupational exposure) and

adding these components together (Mumtaz et al., 1997). For a mixture (dose A, dose B) of

two components A and B with maximum acceptable doses MADA and MADB respectively,

the hazard index is expressed mathematically as:

B

B

A

A

MAD

Dose

MAD

Dose HI += (Adapted from Mumtaz et al., 1997)

The Point of Departure Index and the Margin of Exposure are varieties of this approach.

However, this relationship must be used cautiously. Problems may arise when the maximum

admissible doses are derived from NOAELs observed in different species and particularly

where NOAELs depend on different toxicological endpoints. Furthermore, the magnitude of the gaps between the LOAELs and NOAELs may skew the relationship. Although this

method is most appropriate where the components act via the same mechanism on the same

organ or organ system and assumes that no interactions occur, it can be modified to take

account of potentiation (Mumtaz & Durkin, 1992). The hazard index approach is widely used

for complex mixtures of the type found in environmental toxicology. This approach is simple

for compounds with a broad database, where the maximum admissible doses already exist.

The Toxic Equivalency Approach

The alternative toxic equivalency approach for assessing the toxicity of a mixture (when it is

reasonable to assume that doses of its components combine additively) was originallydeveloped to assess the toxicity of mixtures of dibenzo-p-dioxins and dibenzofurans. In this

approach the relative potency of components in a mixture of chemicals is calculated from a

particular test and one compound is usually accorded a toxic equivalency factor of 1. The

compound chosen to have the toxic equivalency of 1 is generally that one that has been

extensively studied. The relative potency of the other compounds is represented by a toxicity

equivalency factor (TEF) (Mumtaz & Hertzberg, 1993; Seed et al., 1995). Decisions are

necessary on which standard endpoint and which study should be used for comparison.

Simple studies can be used, including those in vitro to rank the compounds with a common

mechanism of action. The product of the amount of a compound in a mixture and the TEF is

known as the toxicity equivalent and these, for the components of a mixture, can be added

together. Again, this method is only really appropriate where the components act via the samemechanism on the same organ or organ system and it assumes that no interactions occur. In

the simple case where the risk for each component is proportional to its dose, the joint risk is

expressed as ∑ ∗= ii pd R , where pi is the potency of the component I with dose di and the

summation is over all the components in the mixture. More formal statistical methods have

also been used. This method is generally applied in case of mixture of dioxins. Moreover, it is

the most widely used approach in assessing the toxicology of mixtures. However, this

approach immediately raises a number of problems, namely the grouping of chemicals,

identifying the reference compounds and determining standardised assays. The grouping of

chemicals is comparatively easy with groups of common action such as the organophosphates,

but the precise mode of action of many pesticides is unknown. The choice of the referencecompound should be based on a high quality database and should be typical for the group of



77

compounds of interest. Concerning the determination of standardised assays, it has been

suggested that a particular assay, e.g. the 90 day study may be used in calculating the toxic

equivalent factor.

Response Additive Methods

Methods have also been developed that rely on the addition of response rather than dose as in

the Hazard Index and TEF methods. Such methods do not assume that components of a

mixture act by the same mechanism although they may act on the same target and are

frequently used in cancer risk assessment (WiGRAMP 2001).

Mechanistic Models

Attempts have been made to develop mechanistic models for both cancer and non-cancer

endpoints that can be used in risk assessment of chemical mixtures e.g. Kodell et al., 1991

and Clevenger et al., 1991.

Pharmacokinetic Modelling

A further approach is the use of pharmacokinetic modelling of the components of a mixture

itself (Krishnan et al., 1994). However, the latter is impracticable, except for specific mixtures

and cannot be applied generically to a group of compounds. This method can be applied to

humans, provided the data are there. Such data would not normally be supplied in standard

data packages (WiGRAMP 2001).

In the UK the potential for combined action in terms of human health risk assessments for

pesticides has been recently considered by a Working Group of the Committee on Toxicity of

Chemicals in Food, Consumer products and the Environment (http://www.food.gov.uk/

science/ouradvisors/toxicity). The Working Group on Risk Assessment of Mixtures of Pesticides and Similar Substances (WiGRAMP) that was set up at the request of the Food

Standards Agency (FSA) published a report in September 2002 (COT, 2002). WiGRAMP

recognised that concerning pesticides the toxic effects of different substances in combination

are not routinely addressed. Combinations and interactions are considered under specific

circumstances. The Working Group concluded that, when performing risk assessments, the

default assumptions for cumulative toxicity should be that chemicals with different toxicactions will work independently (simple additivity of effects) and those with the sameaction of toxicity will act with additivity of dose. The recommendations of WiGRAMP are

being taken forward by a combination of research work and policy development. Two of the

key outcomes will be the establishment of a scientifically based systematic framework setting

out when to perform combined risk assessments and the identification of groups of activesubstances with common mechanisms of action (http://www.pesticides.gov.uk). Approaches

to identifying common mechanism groups for pesticides are already being established in the

USA, and the International Life Sciences Institute has been developing a framework for

guiding the conduct of cumulative risk assessment, based on five key stages (U.S. EPA,

1999a; ILSI, 1999). Some common mechanism groups have already been proposed by EPA:

the organophosphates (U.S. EPA, 1998), N-methylcarbamates (U.S. EPA, 2001a) and

triazines (U.S. EPA, 2002). The EPA approach to the establishment of common mechanism

groups for organophosphates and N-methylcarbamates has been reviewed for the Science

Group of the Food Standards Agency. The Science Group has also prioritised the triazines and

four additional classes of pesticides/veterinary medicines (avermectins, canazoles, phenoxy-

herbicides, pyrethoids and natural pyrethrins) to be assessed for possible common mechanism

grouping. The IEH (2005) conducted a scoping study to identify the amount of work required



78

to establish common mechanism groups for each of the prioritised classes of pesticides. This

included an evaluation of the amount and quality of data available on each substance

belonging to each class. Also an estimate was made of the resources required to identify and

establish common mechanism groups for the prioritised classes of pesticides. While the

scientific systematic framework setting out when to perform combined risk assessments and

the identification of groups of active substances with common mechanisms of action are beingdeveloped, the current approach of ACP and PSD in evaluating combined toxicity will be

followed within the framework of the HAIR project. In what follows the PSD and ACP

approach to assessing occupational risk of two or more compounds in a pesticide product

(formulation) is outlined.

Proposed Methodology to handle cumulative assessments

Two basic assumptions have been made about how components of a mixture will act together.

These concepts on additive effects in chemical risk assessment of cumulative exposure are

simplistic but as predicted human exposures are at the bottom end of the dose-response curve

(well below the NOAELs in animal studies, making significant interactions unlikely) andassuming that that the assessment factors address the potential for simple interaction, they are

considered pragmatic and adequately protective. For compounds with similar toxicological

actions, the assumption being used by PSD is that any combined toxicity will follow the rules

of simple dose-additivity. Because compounds having the same mechanism of action act

through the same biochemical pathway, one can cumulate the doses of the individual

compounds in order to estimate the final effect. For compounds with different toxicological

actions and targets it is generally assumed that actions will be independent, with simpleadditivity of effects. In circumstances where two compounds cause toxicity to the same

organ but by different mechanisms, the assessment of potential combined toxicity is made on

an ad hoc basis. For some active substances the precise mechanism of mammalian toxicity is

not fully understood and an in depth assessment based on mechanism of action against the

pest, or common target tissues/effects might be appropriate (http://www.pesticides.gov.uk).

a. Formulated Products

Formulated products need to be evaluated on their toxicity, primarily for the acute toxicity,

irritancy and sensitisation classification and labelling, but other aspects of formulation

toxicity also need to be considered (http://www.pesticides.gov.uk).

Products containing a single active substance

A consideration of the combined toxicity of the components needs to be conducted in the lightof re-registration. If data indicate that the toxicity of the formulated product is clearly greater

than predicted, based on simple additivity of the effects of the individual components, an

explanation will be required. The calculation method used to determine the classification of a

product, which is outlined in the Dangerous Preparations Directive (1999/45/EC) and CHIP32

regulations, the default position is that the components will act with dose aditivity for acute

toxicity and irritancy, but act independently (i.e. with simple additvity of effects) for other

hazards such as skin sensitisation, carcinogenicity, mutagenicity and reproductive toxicity. If

two co-formulants or a co-formulant and the active substance are known to have the same

toxic mechanism of action or target tissue at low doses the potential for combined action

2The Chemicals (Hazard Information and Packaging for Supply) Regulations 2002, Statutory Instrument 2002

N° 1689.



79

should be addressed. It is accepted that for many co-formulants there is limited information on

the toxicity following repeated exposures. However, when the toxicity is known to be similar

to that of an active substance in the product, consideration should be given to the potential

combined toxicity other than simple additvity of effects. A related issue for co-formulants is

whether they can produce an increased systemic exposure to the active substance e.g. by

increasing absorption relative to values seen with other types of vehicles. Assessments of dermal absorption routinely consider formulation type and will take into account potential

effects of vehicles (http://www.pesticides.gov.uk).

Products containing two or more active substances

A consideration of the combined toxicity of the active substances needs to be conducted in the

light of re-registration. When a formulated product contains two or more active substances

there is a need to consider the potential for them to act other than by simple additivity of

effects, in a manner that might impact on the consumer and operator/worker/bystander risk

assessments. This concept has been part of the ACP considerations for a number of years. The

initial assessment approach follows the conclusions about the potental for interaction drawn by WiGRAMP. Most applications considered so far by the committee have been products

including active substances with different modes of action. As such the conclusion has been

reached that there is little potential for additivity of dose. A recent example has highlighted

the need for an agreed methodology to assess applications where there is a potential for

additivity of dose. When formulated products contain two or more active substances it is

necessary to determine if the mechanism of toxicity or the target tissues for the actives are

common. The effects relevant for the cumulative assessments are those that drive the critical

LOAELs/NOAELs used for setting reference doses, or are evident at dose levels in the range

of critical LOAELs/NOAELs. If the target tissue is common even though the mechanism is

different, there should be some case by case consideration of combined action. A tiered

approach was proposed for the assessment of operator risk for products containing multipleactive substances (http://www.pesticides.gov.uk).

At tier 1, the estimated exposure (usually systemic exposure) to each of the active substances

is calculated as a fraction of the AOEL agreed for that substance. If the sum of the fractions is

smaller than one, exposure of the operator is acceptable. This is the case if the proportion of

the respective reference doses taken up by the predictive exposures to each active substance

are only small, e.g. < 50% if there are two components, <33% if there are three components,

etc. Then simple dose additivity can be assumed. The risks would then still be acceptable.

This tier of the assessment can be completed quickly and easily from the information

available fairly readily in evaluation of the active substances and using the standard exposure

assessment approaches. If the sum of the fractions is > 1, a more refined assessment will berequired (http://www.pesticides.gov.uk).

At tier 2, the toxic effect of concern for the combined risk assessment must be identified for

each active substance and exposure scenario. In this case, the exposures to each active

substance represent a high proportion of the respective reference doses (e.g. 60%) and

following risk might not be acceptable if the sum of the individual contributions > 100%. A

more detailed consideration would be needed. Detailed assessments can be performed in

several ways:



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If the reference doses for the active substances are based on different

toxicological effects, effect specific reference doses should be

determined. The critical NOAEL is derived for each effect and from this

the effect specific reference dose is derived by the use of an appropriate

assessment factor. This effect specific AOEL will always be ≥ the

overall AOEL for the active substance. Estimated exposure for eachactive substance is then compared to the effect specific AOEL for each

substance and presented as a fraction (or percentage). If the sum of the

fractions is ≤ 1; exposure of the operator is acceptable. If the sum of the

factions > 1 exposure is not shown to be acceptable and further specific

data will need to be generated to address the concern. This might include

operator monitoring data, in vitro testing of the combination, or in vivo

testing of the combination. Tier 2 assessments might require some re-

evaluation of relevant studies where the earlier evaluation document does

not enable the derivation of effect specific NOAELs. Again standard

methods of estimating exposure are appropriate;

A scientifically justified case on the potential for interaction can be presented based on the study of the mechanism of toxicity of the active

substances, molecular structures, target molecules/cells or tissues;

Recommendations for use rates, timings and PPE can be made which

can lead to acceptable exposures taking the additive effects into account

(http://www.pesticides.gov.uk).

Tier 3 assessments will have to be conducted if estimated exposure is not shown to be

acceptable at tier 2. In some cases it might be necessary to perform additional studies to

investigate potential combined effects. This might include operator monitoring data, in vitro

testing of the combination or in vivo testing of the combination. The studies should focus on

the effects driving the risk assessment and use dose levels in the region of the NOAELs

/LOAELs for the individual components. On animal welfare grounds, the feasibility of an in-

vitro approach should be considered in the first instance. If in-vivo studies are required, these

should use the minimum numbers of animals necessary to resolve the issues. If one active

substance is known to be a potent inhibitor of xenobiotics metabolising enzymes (e.g. some

conazoles) the potential impact on the metabolism of other components should be considered

(http://www.pesticides.gov.uk).

Products containing synergists, agonists or herbicide safeners

In addition to the considerations described above, the following should be addressed. The

combined toxicity of the active substances and the synergists, agonists or herbicide safenersshould be evaluated. Synergists, agonists and herbicide safeners are designed to be

biologically active and modify the action of the active substance. In case of synergists and

agonists there is an increased action on the target pest, with herbicide safeners there is a

reduced action on the crop. The default assumption in performing human health risk

assessment in formulated products containing such biologically active compounds is that the

effects induced in the pest or crop could potentially apply to human exposures. The potential

impact on the human health risk assessment of co-exposure of the active substance(s) and the

synergist/agonist/safener should be assessed (http://www.pesticides.gov.uk).



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b. Tank Mixes

Concerning operator exposure it is important that tank mixes are considered. This

recommendation was reported by the WiGRAMP. Investigations on the patterns of tank

mixing have shown that the majority of tank mixing does not involve active substances with

similar toxic actions. The Medical and Toxicology Panel considered the information and

concluded that the current practices of tank mixing in the UK were not likely to give rise to

significant concerns for combined toxicity based on dose additivity. On the related aspect of

altered dermal penetration the Panel believed there might be an issue due to the potential for

increased systemic exposure when different types of formulations are combined. Operator

exposure estimates include a value for dermal penetration of the active substance from the

formulation and in-use dilution. For some active substances the degree of penetration varies

with the formulation type. It is thus possible that tank mixing two or more different types of

formulation could result in an increase in dermal penetration relative to that from a single

formulation. The greatest concern would relate to a tank mix involving a product giving a low

dermal absorption (e.g. a solid containing active substance and mainly inert components such

as kaolin), with one containing solvents or surfactants that could significantly enhance the penetration. An increase in dermal absorption by 5 fold could have a greater impact on the

risk assessment than simple dose additivity from two compounds with the same mechanism of

toxicity. PSD has commissioned a research project to investigate the effects of co-formulants

on the dermal penetration of a respective range of active substances. In considering the overall

risks associated with tank mixing, the Medical and Toxicology Panel noted that because tank

mixing reduced the number of application operations performed there could be a reduction in

the overall risks (including physical injury) (http://www.pesticides.gov.uk).

Overview of methodologies to handle aggregate assessments

Until recently most risk assessments focussed on a single pesticide, considered each routeseparately, and evaluated each separately. Aggregate assessments consider a single pesticide

but combine multiple routes (ingestion, dermal and inhalation) and multiple sources (e.g.

food, water, residence and occupation) of exposure.

In 1996, the Office of Pesticide Programs (OPP) has taken a number of steps to enhance its

risk assessment to respond to the FQPA mandate to consider aggregate exposure and risk in

making decisions about the safety of tolerances. In 1997, OPP issues ‘HED SOP 97.2 Interim

Guidance for conducting Aggregate Exposure and Risk Assessments (11/26/97)’ (U.S. EPA,

1997a). Since then the OPP has worked to develop more sophisticated methods of estimating

the combined exposure to pesticides by different routes and pathways (U.S. EPA, 2001a). The

current practices for assessing aggregate exposure and risk in the U.S. typically include a mixof deterministic or point estimate data and distributional data. US aggregate and cumulative

assessments when sufficient data are available combine the water, diet and non-dietary

pathways (e.g. residential users), but exclude occupational pathways. The latter pathways can

however be included in the aggregate assessments in the same way as the other pathways

(pers. comm. Price, 2006). In 2001 a revised approach suggests an exposure assessment on an

individual basis, culminating in a representative population of interest. In this way, the

individual’s temporal (i.e., exposures via all pathways agree in time), spatial (i.e., exposures

via all pathways agree in place/location) and demographic (i.e., exposures via all pathways

agree in age/gender/ethnicity and other demographic characteristics) characteristics are

consistent and reasonable. All ‘linkages’ of time, space and demographic characteristics

should be made using supporting data. Using this approach, a distribution of total exposure to

(many) individuals in a population of interest can be created. Distributional data analysis is



82

preferred as this tool allows an aggregate exposure assessor to more fully understand the

uncertainty and variability inherent in the data set (U.S. EPA, 2001b)

Data for these probabilistic assessments to aggregate risk can be obtained by applying

different methodologies (Price et al., 2000). In the first instance data on the simultaneous

exposures of individuals from all sources of exposure to a pesticide for each day of theindividuals’ life can be collected. However, it is difficult to obtain survey results on an

individual’s behaviour (either food consumption or activity patterns) for periods longer than

one or two days. Moreover it is difficult to collect sufficient information on a sufficient

number of individuals to allow the evaluation of different subpopulations and as the number

of potential sources increases, the number of behaviours that must be investigated in a survey

increases proportionately. Secondly, biological monitoring data can be used. This approach

proposed by the ILSI (1999) uses the individual as an integrator of exposure. The limitation of

the approach is that it requires the availability of analytical techniques for the pesticides or its

metabolites in blood or urine. It also has the disadvantage that the pesticide must allready be

in use. Thus this approach cannot be applied to new products. The most practical

methodology to handle aggregate assessments is the simulation of the doses received frommultiple sources by individuals in a population (Price et al., 2000). The exposure from each of

the pathways of exposure (e.g. drinking water ingestion, dietary consumption and herbicide

handling by workers,…) is described by an equation. Many of the components of these

equations have values that are variable (from individual to individual, from day to day or

season to season, from sample to sample) and/or uncertain. These components of the exposure

equations can be described by probability distribution functions that reflect the relative

frequency of the different values for the variable components and the relative likelihood of the

different possible values for the uncertain components. The outcome of the exposure equation

is a dose. This dose varies because of the variability of the components in the equation. These

chemical-specific doses (mg/kg bw/d) obtained for each exposure pathway and each relevant

route (ingestion, inhalation, dermal) of exposure are summed. The total chemical-specific

dose for each exposure pathway is characterized separately, and then the doses are aggregated

by summing over the multiple exposure pathways. The pathway-specific and aggregate

assessments should be performed separately for each active that is taken into consideration.

Rather than focussing on an average dose in a population, a distribution describes the relative

frequency of each dose value. The variability in the dose from individual to individual within

the population or sub-population is reflected by the distributions. The distributions indicate

the dose that is most likely to occur, the range of doses expected in a population and the

relative likelihood of the different doses in that range. Each of the individual doses in the

distribution is the best estimate of that individual dose and not an upper or lower bound on

that dose. The probability distribution of the dose is generally quite difficult to calculateanalytically but can be fairly readily approximated by using a straightforward technique

known as Monte-Carlo simulation (Mc Kone & Ryan, 1989; Mc Kone & Daniels, 1991).

Such a simulation consists of numerous iterations. In an interation in the simulation, a single

value for each component in the exposure equations is randomly sampled from its

corresponding probability distribution. These component input values are then used in the

exposure equations to calculate the exposure of an individual in the defined population. The

frequency distribution of the calculated values from numerous iterations is the simulated

exposure distribution. The simulated exposure distributions reflect exposure variability but

not the uncertainty about the exposure equations, the distributions of the components and

related assumptions (Sielken et al., 2005). This uncertainty and its quantitative impact on the

simulated exposure distribution are presented in Sielken et al. (1996). In the Monte Carloapproach, there are no inherent limitations on the complexity of the exposure equation, the



83

number of component variables, or the number of iterations (Morgan & Henrion, 1990).

Another powerful feature of the Monte Carlo approach is that it can reflect dependencies

among the components in an exposure calculation, providing for correlation among variables

and ensuring that the component values (and aggregate estimate) for an individual are

internally consistent. Distributions can be presented not only for a single day but also for

individuals or populations over time. In fact, the dimension of time may play a particularlyimportant role in aggregate exposure assessments for pesticides (Sielken et al., 2005). It is

important to keep in mind that Monte Carlo simulations are approximations. One way to

determine how much variation there is, is to re-run it several times and see how the

distribution (e.g. mean, median, standard error) varies. To determine how much iterations are

necessary, one needs to decide how precise one wants to approximate the distribution and

how much variation is acceptable. This requires careful consideration on how the distribution

will be used. Sensitivity analyses can be used to identify the parameters that contribute most

to the exposure estimates or to the variance or uncertainty in the estimates. They can be used

to assess the impact of various assumptions that are embedded in the models and, with expert

judgment, to better understand the uncertainties inherent in the models. The results of such

analysis can help in setting priorities in further data collection and refinement efforts. A rangeof well-defined statistical methods is available to evaluate variability and to conduct

sensitivity analysis in exposure modelling. The importance of routinely including these kinds

of analyses in aggregate exposure assessments was emphasized. Concerning the data quality

and adequacy, there was a general consensus that whenever possible, when a database is

marginal, it is preferable to collect more data rather than relying on estimates, surrogates or

defaults. However, it was also recognized that there are real constraints on time, cost and

practical feasibility (ILSI, 1998).

Proposed methodology to handle aggregate assessments

Within the framework of the HAIR project it is proposed to use ‘the Hazard Index’ approach

to aggregate risk for multiple sources and pathways. This method is applied by certain

agencies within the Environmental Protection Agency. The Hazard Index is an aggregation of

individual ‘Hazards Quotients’ or ‘Risk Indices’ for each route and pathway of exposure.

Within the framework of the HAIR project, route specific risk indices are not calculated, only

pathway specific reference doses are calculated because of the lack of data. Using the Hazard

Index approach the pathway specific risk indices can be combined into an overall risk index

by summing the individual risk index values. The other ways for aggregating risk follow the

aggregation methodology developd by WP 11. Important to mention is that pathways and

routes are only aggregated when they share a common toxic effect (U.S. EPA, 2001b).



84

V. Overall Occupational and Human Health Risk Index

Below, the aggregation procedure proposed to construct an overall occupational and a global

human health risk index is outlined. The risk indicators will be aggregated using weighting

factors. By applying weights the actual/local parameters which may affect the likelihood of

exposure of the whole population and of susceptible subgroups will be taken into account.

The risk for certain regions can be weighted according to the population density in these

particular regions. The risk for the applicators and workers can be weighted according to the

number of applicators/workers present in these particular regions. Probabilistic ways can also

be used to determine a risk ndex that reflects the average risk of an individual working in

agriculture.

1. Overall Occupational Risk Index

The occupational risk indices (operators, re-entry workers, bystanders, residents and sensitive population groups) are aggregated in an ‘overall occupational risk index’ using weighted

averages. The use of weighting is an opportunity that should be taken into consideration since

the impacts on operators, re-entry workers, bystanders, residents and sensitive population

groups are very likely of different significance.

∑=

∗=n

k

k k occ RI w RI 1

Where:

wk : relative importance of indicator k; RIk : risk index value taken by indicator k;

RIocc: overall occupational risk index value.

It is the task of the risk managers to identify relevant weighting factors for each of the

indicators. These weighting factors can be determined based on national statistics concerning

the composition of the population. Currently national authorities assume that 5% of the

population can be considered as sensitive or more susceptible. As a default one can assume

that children and pregnant women account for 5% of the total risk. But it has to be mentioned

that the establishment of the weights is a task that needs to be conducted by risk managers.

However, the establishment of the weights is not easy. For example, which weight should be

attributed to the bystander indicator?

2. Overall Human Health Risk Index

An overall human health risk index can be calculated by attributing a weighting factor to the

consumer and to the overall occupational risk indicator according to the composition of the

population. Obviously the consumer indicator will be attributed a weight of one, since

everyone is a consumer. The weight of the overall occupational indicator cannot be easily

established.



85

3. Weighting Factors

The establishment of the weighting factors should be conducted by the risk managers as

mentioned before. They can base their decisions on the composition of the population of a

particular country or region. Regional and national statistics should be available in all the

Member States. After requesting data at several national statistical institutions, it seemed thatdata are lacking for this specific purpose. The EUROSTAT surveys were also consulted. But

it seems that data are lacking for this particular purpose. For example the exact number of re-

entry workers is not known. Often farmers cannot be categorized into the group of the

applicators or the group of the re-entry workers. Farmers are generally both. Although

different sources provide a general indication of the number of re-entry workers in different

geographical units, it is virtually impossible to establish the exact size of the re-entry worker

population, particularly when dependents are considered. Knowing the number of re-entry

workers at risk is basic to toxicologic and epidemiologic analyses of pesticide exposure

(Arcury, T. A. et al., 2006). Estimating migrant and seasonal re-entry workers and their non-

re-entry worker household members is an extremely challenging task. Research of Larson

(2000) attempted to examine existing data and developed a reasonable approach for

estimating the number of re-entry workers. Final reports, titled “Migrant and Seasonal

Farmworker Enumeration Study Profiles” were prepared for ten states of North America. The

enumeration profiles studies of Larson for migrant and seasonal farmworkers, conducted for

several North American States provide estimates of the the following three population

subgroups:

Migrant farmworkers and seasonal farmworkers;

Non-farmworkers present in the same household as migrants farmworkers and

seasonal farmworkers;

Number of people (“children and youth”) under age 20 in six age groups.

Included in the scope of the study are individuals engaged in field and orchard agriculture;

packing and sorting procedures in food processing, horticultural specialities (including

nursery operations, greenhouse activities and crops grown under cover); and reforestation.

Concerning field agriculture and reforestation, the number of farmworkers was estimated

using a “demand for labour” process that examines the number of workers needed to perform

temporary agricultural tasks, primarily harvesting. The results estimate full-time equivalent

workers required for the task during the period of peak labor demand. Calculations prepared

for each county, are derived through a formula using four elements:

A H DFLW S

∗=∗

Where:

A: Crop acreage (ha);

H: Hours needed to perform a specific task (e.g. harvesting) for one acre of the

crop (hr/ha);

W: Work Hours per farmworker per day during maximum activity (hr/d);

S: Season length for peak work activity (d).

For nurseries/greenhouses, crops under cover and food processing, the number of

farmworkers was estimated based on employment reports. This illustrates a possible approach

for estimating the number of farmworkers. Deterrmng the number of residents and bystanders

is quite a challenge for the future.



86

VI. Calculation of the Acute & Chronic Indicators

The mode of calculating the worker indicators depends upon the data availability concerning

pesticide use and monitoring data. The indicator model should serve both the best as the worst

case of data availability. Three cases of data availability were identified.


Case I

In the best case of data availability, that will only be reached in a few member states for a

portion of or for the complete agricultural area of the country, monitoring data on exposure

are available on field level. These field data on exposure, expressed in mg a.s. per kg applied,

allow for a calculation of risk taking into account local conditions (meteorological and field

related). Usually these data are not available. Pesticide use data are available as field related

application patterns The application patterns contain all pesticides (products and active

substances) that were used to produce the crop including application time, dose rate,

application technique, formulation type, use of protective equipment for the applicators and

concentration of each active substance in a certain product if only the product dose rate is

given. The application tables can be collected within the framework of a data survey. For each

crop several application strategies exist. A single example of such an application strategy for

apple orchards is presented in Table VI.1.1.

In order to run the indicator on this level all input parameters have to be available for eachfield. For a limited number of fields the parameters could be entered by specific user input. In

this case the user must supply the necessary data for each field on the used application

equipment, the product applied, the concentration of active substance in the product,… If all

these data are available for all the fields of a farm, the calculated risk potentials can be

aggregated to a single risk potential for that farm. If the field level indicator is used to

calculate the occupational risk for a small region or even a whole country, the extent of input

data is large. Datasets have to be established in order to be able to run the indicator.

According to agricultural statistics a crop will be assigned to each field. Following the

assignment of the crop, an application strategy can be selected randomly from a field based

pesticide use survey in the considered region.

According to the mathematical notation described by WP 11, a basic risk event (for acute

exposure) can be described by the equation below. Crop was added as an additional

aggregation parameter. Between brackets the lowest aggregation level is given.

( , . ., , , _ , )

( , . ., , , _ , )

applicator a i field crop growth period acute

applicator a i field crop growth period acute

Exp RI

AOEL=

Where:

RI(…): Risk Index (-);

Exp(…): Exposure (mg a.s./kg b.w./d);

AOEL: Acceptable Operator Exposure Level (mg a.s./kg b.w./d).



87

Case II

In Case II no field data on exposure are available. Therefore the EUROPOEM surrogate

exposure values will be applied to estimate human exposure. The pesticide use data are

available as a set of crop related application patterns, which are specific for a particular region but can not be applied to a single field since no data on exposure are available on a field level

basis, only on a regional basis.

The following should also be taken into account:

Within one grid, the area of land on which a particular crop is grown is obtained as

follows. The total agricultural area within one grid can be extracted from the

CORINE geographical datasets on land use with the help of GIS procedures. In

this way values of the total agricultural area specific for every grid are acquired.

When the total agricultural area within one grid is multiplied with the fraction of a

particular crop grown in that grid, the area for a particular crop within one grid is

obtained. The fractions of each crop are supplied by regional agricultural statistics,

which should be available in all the member states. WP 3 already derived these

areas for Europe on 10 km² and 25 km².

A range of risk indices can be calculated for each crop and each crop related

application pattern. The mean is calculated to obtain a single risk index value for

each crop within a particular region. If the application method is not known, the

most dominant application method is assumed. Table III.1 links the different crops

to their most dominant application method.

The aggregation possibilities in this case are limited in space. The basic risk event (for acuteexposure) can be described as follows:

( , . ., , , _ , )

( , . ., , , _ , )

applicator a i region crop growth period acute

applicator a i region crop growth period acute

Exp RI

AOEL=

A case-study was worked out to illustrate the calculation methodology.



88

CASE-STUDY:

The input data needed in this case are listed in Table VI.1.1. An example listing is given for a spraying sch

values are also given.

Table VI.1.1: Input data, example of a spraying scheme for apple orchards

Product Formulation as FunctionDate of

applicationApplicationTechnique

Aplication Type Indoor/OutdoorApplicationDirection

N° of applications (k

Early march

Late Oktober Cuprex 50% WG cupperoxychloride FUNG

Early November

Spraying Mechanical Outdoor Upwards 3

Late MarchDodex L dodine FUNG

Early AprilSpraying Mechanical Outdoor Upwards 3

Scala L pyrimethanil FUNG Early April Spraying Mechanical Outdoor Upwards 1

Early April

Late April

Early May

Late May

Late July

Captan80 WG captan FUNG

Early August


Confidor L imidacloprid INSE Early April Spraying Mechanical Outdoor Upwards 1

Mimic L tebufenozide INSE Late April Spraying Mechanical Outdoor Upwards 1 Late April

Early MayGeyser L difenoconazool FUNG

Late May


Torque L fenbutatinoxyde INSE Early May Spraying Mechanical Outdoor Upwards 1

Early MayPirimor WG pirimicarb INSE

Late JulySpraying Mechanical Outdoor Upwards 2

Systhane L myclobutanil FUNG Early May Spraying Mechanical Outdoor Upwards 1

Insegar WG fenoxycarb INSE Late May Spraying Mechanical Outdoor Upwards 1

Late MayCandit WG kresoxim-methyl FUNG

Early JuneSpraying Mechanical Outdoor Upwards 3

Dimilin L diflubenzuron INSE Late June Spraying Mechanical Outdoor Upwards 1

Delan WG dithianon FUNGLate June/Early

JulySpraying Mechanical Outdoor Upwards 1

Topaz L penconazool FUNG Early May

Topaz L penconazool FUNG End May

Topaz L penconazool FUNG Late June


Early JulyEuparenM WG tolylfluanide FUNG

Early September Spraying Mechanical Outdoor Upwards 1

FUNG: fungicides; INSE: insecticides; *: confidential data



89

The data listed in Table VI.1.1 were used to calculate the operator indicator. The following

assumptions were made:

1. The most widespread method of applying pesticides in apple orchards is by fan

assisted axial sprayers. The applied surrogate exposure values are the ones listed in

Table VI.1.2. No field data on exposure were available. But the computer modelshould provide the possibility for users to insert their own data;

Table VI.1.2: Surrogate exposure values from the EUROPOEM database (see Annex I)

FormulationType

MixLoadInhal MixLoadDermal ApplicInhal ApplicHand ApplicDermal

WG 0.1 1 0.03 11 63

L 0.005 20 0.03 11 63

2. The average treated area per day was set to 9 ha in accordance with the default

values used in the Belgian registration procedure;

3. When the operator does not wear PPE, PPEI and PPEhand equal one, while PPE body

equals 0,5 since normal work clothing is worn;4. When the operator does wear PPE, PPEI and PPEhand equal 0.1, while PPE body

equals 0,1 since (0.5*0.2) equals 0.1;

5. The dermal absorption factor equals 0.1 and the inhalation absorption factor equals

1;

6. The body weight is set to 70 kg.

The results are listed in Table VI.1.3. The output table should present both the RI’s when PPE

is used and when no PPE is used.

Table VI.1.3: Risk Indices calculated for a spraying scheme for apple orchards

as FunctionN° of

applicationsIE applicator

AOELIncludingconfidential

values *

RI acutePPE

RI acute noPPE

RI chronicPPE

RI chronicno PPE

cupperoxychloride FUNG 3 1,73 0,05 5,89 34,56 4,84E-02 2,84E-01

dodine FUNG 2 0,73 0,19 0,57 3,83 3,12E-03 2,10E-02 pyrimethanil FUNG 1 0,36 0,6* 0,091 0,61 2,49E-04 1,67E-03

captan FUNG 8 1,04 0,125 1,41 8,29 3,09E-02 1,82E-01

imidacloprid INSE 1 0,10 0,15* 0,10 0,67 2,74E-04 1,84E-03

tebufenozide INSE 1 0,15 0,007 3,12 20,78 8,55E-03 5,69E-02difenoconazool FUNG 3 0,03 0,13* 0,035 0,24 2,88E-04 1,97E-03

fenbutatinoxyde INSE 1 0,30 0,03 1,52 10,10 4,16E-03 2,77E-02

pirimicarb INSE 2 0,19 0,14* 0,23 1,34 1,26E-03 7,34E-03myclobutanil FUNG 1 0,36 0,03 1,82 12,12 4,99E-03 3,32E-02

fenoxycarb INSE 1 0,09 0,4* 0,044 0,22 1,21E-04 6,03E-04

kresoxim-methyl FUNG 3 0,17 0,9 0,033 0,19 2,71E-04 1,56E-03

diflubenzuron INSE 1 0,36 0,02 2,73 18,18 7,48E-03 4,98E-02dithianon FUNG 1 0,60 0,03* 3,43 20,16 9,40E-03 5,52E-02

penconazool FUNG 3 0,24 0,7* 0,052 0,35 4,27E-04 2,88E-03

tolylfluanide FUNG 2 0,65 0,3* 0,37 2,16 2,03E-03 1,18E-02

FUNG: fungicides; INSE: insecticides



90

Case III

In Case III no field data on exposure are available. Therefore the EUROPOEM surrogate

exposure values will be applied to estimate human exposure. Pesticide use data are not

available in the form of pesticide application patterns. For each crop only a list of activeingredients is available including the quantity of the active ingredient used for the crop. It is

assumed that the volume of pesticides is applied to the treated area of the corresponding crop.

Information on the total area on which the crop is cultivated and the portion of the area on

which the crop is treated with each active substance can be compiled. Pesticide use data can

be derived from the EUROSTAT surveys (EC-Regulation of the European Parliament and of

the Council of Pesticide Statistics). If this regulation is enforced, pesticide use data will be

available-in this form for all the member states in the near future. When surveys are not

available, data can be derived from sales data as described by WP 5. The dose rate is either a

realistic dose rate (when the treated area of the crop is known) that is calculated by dividing

the applied volume by the treated area of the crop. When there is no information on the

treated area of the crop, the application rate is artificial and is obtained by dividing the applied

volume by the total crop area.

The basic risk event (for acute exposure) is defined as follows:

( , . ., , , , )

( , . ., , , , )

applicator a i country crop year acute

applicator a i country crop year acute

Exp RI

AOEL=

Case III will be illustrated below by means of a case study in order to demonstrate the output

of the acute operator indicator.

CASE-STUDY

Structuring the queryAssessing the acute and chronic risk for operators due to the application of the following

active substances: carbendazim, chlorpyriphos, cypermethrin, diuron, lamda-cyhalothrin,

myclobutanil, pirimicarb, tolylfluanid in apple, pear, plum and cherry orchards in the UK

during 2002.

Pesticide usage dataUsage data regarding the application of the above listed active substances were provided by

Miles Thomas (HAIR work package 5). The relevant application events were ordered per active, area treated and formulation type. Data were provided for the whole of the UK

Toxicological dataThe AOEL values and the dermal absorption percentages were taken from the UGent database

and were obtained from the following sources (in order of importance):

1. European Union;

2. CTB – The Netherlands (http://www.ctb-wageningen.nl/);

3. Pandora’s Box (Linders et al., 1994);

4. The Pesticide Manual (Tomlin, 2004);

5. Extoxnet (http://extoxnet.orst.edu/);

6. Toxnet (http://toxnet.nlm.nih.gov/);

7. Other sources (confidential data; public in 2008).



91

Assumptions taken into account1. The most widespread method of applying pesticides in orchards in the UK is by fan

assisted axial sprayers. In this way the airflow directly directs the spray droplets onto

the target crop (Blundell, 2005). Thus, the surrogate exposure values for upward

mechanical spraying were applied. These values can be found in Table IX.1.4. The

mixing/loading surrogate exposure values are specific for each formulation type andare also listed in Table IX.1.4;

2. PPEI and PPEhand equal one, while PPE body equals 0.5 (since normal work clothing

is worn) when no PPE is worn. When PPE is worn PPEI and PPEhand equal 0.1, while

PPE body equals 0.1 since (0.5*0.2) equals 0.1;

3. The average treated area per day was set to 15 ha in accordance with the default

value assumed in EUROPOEM and the body weight is set to 70 kg. The inhalation

absorption factor equals 1.

Input data: Table VI.1.4 lists the input data for the case-study.

Table VI.1.4: Input data for case-study on orchards in the UK

as Function Formulation CropApplication rate

(kg a.s./ha)N° of

applicationsAbDE

AOEL(mg/ kg bw d

Apples 0,287 3,10L

Pears 0,364 3,07

Apples 0,224 4,73WG

Pears 0,266 6,86Apples 0,246 3,04

Carbendazim FUNG

WPPears 0,256 2,75

0,01 0,02*

Apples 0,691 1,98

Pears 0,618 1,23L

Plums 0,584 1,81Apples 0,636 2,10

Pears 0,579 2,25

Chlorpyriphos INSE

WGPlums 0,733 2,58

0,01 0,01

Apples 0,031 4,68

Cherries 0,024 1,00

Pears 0,023 1,18Cypermethrin INSE L

Plums 0,031 4,65

0,42 0,5*

Apples 0,591 1,18

Cherries 0,298 1,00

Pears 0,663 1,14Diuron HERB L

Plums 0,362 1,25

0,1 0,007*

Lambda-cyhalothrin INSE L Pears 0,011 1,11 0,003 0,0025

Apples 0,057 5,42

Cherries 0,110 1,15

Pears 0,045 4,41Myclobutanil FUNG L

Plums 0,083 2,98

0,1 0,03

Apples 0,209 1,00

Cherries 0,288 1,49Pears 0,204 1,00

Pirimicarb INSE WG

Plums 0,258 1,00

0,01 0,14*

Apples 0,628 2,07Tolylfluanid FUNG WG

Pears 0,713 2,550,1 0,3*

FUNG: fungicides; INSE: insecticides; * indicates confidential values

The results are listed in Table VI.1.5. The output table should present both the RI’s when PPE

is used and when no PPE is used.



93

AggregationIn what follows different possibilities for the display of the results will be shown.

The first tier risk indices can be listed in tables as shown in Table VI.1.5. Such a Table should

list the active substance, the formulation type of the product applied, the pesticide group

category, the number of applications, the number of hectares on which the active was appliedand the RI value. Every application event is characterized by a risk index. This risk index is

either below the trigger value or above the trigger value of 1. The risk below the trigger value

is characterized as “risk below screening level”; the risk above the trigger value is

characterized as “risk above the screening level for which further evaluation is needed”.

At any level of aggregation (e.g. by crop, region or overall national level (hectares upon

which the actives are applied), time, actives, application events) these two categories can be

calculated and expressed as a percentage of the combined hectares. These aggregated results

can be presented in several ways. A few examples are given below:

Table showing the frequency in each category

Ex 1: national level (selected actives, orchards, year 2002) (TableVI.1.6)

Table VI.1.6: Table showing the frequency on national level for the two categories: requiringfurther assessment and below the screening level (absolute and relative (%))

Category of Risk Frequency %Risk below thescreening level

3,06E+03 7,84

Risk needing furtherevaluation

3,60E+04 92,16

Combined 3,91E+04 100,00

For the actives that need further evaluation, the risk index should be calculated taking

into account the use of personal protective equipment. In this case this applies to the

actives: carbendazim, myclobutanil, tolylfluanid, chlorpyrifos and diuron.

Ex 2: per crop group (selected actives applied in orchards, national level, year 2002)

(Table VI.1.7 & VI.1.8)

Table VI.1.7: Table showing the frequency per crop group for the two categories: requiring further

assessment and below the screening level (absolute)

CropFrequency no further

evaluationFrequency further

evaluationApple 1,21E+03 3,05E+04Pear 1,09E+03 3,97E+03Plum 3,69E+02 1,22E+03

Cherry 3,96E+02 3,13E+02



94

Risk below the screening level

Risk needing further evaluation

Table VI.1.8: Table showing the frequency on national level for the two categories: requiringfurther assessment and below the screening level (relative (%)).

Crop % no further evaluation % further evaluationApple 3,09 78,08Pear 2,79 10,16

Plum 0,94 3,12Cherry 1,01 0,80

In stead of listing the results along crop type, the results can also be listed along

regions or along years, along pesticide group, depending on the aggregation that is

performed by the user.

Pie or Bar Chart showing the frequency in each categoryIn stead of presenting the results in Tables, one can also make use of pie charts or bar charts

(Figure VI.1.1 & VI.1.2).

Line plots can also be used to show the time-trend evolution of the risk for applicators. A line

plot showing the evolution in frequency of the risk over the years below the screening level

and a line plot showing the evolution in frequency of the risk over the years requiring further

evaluation can be presented. A stacked line plot showing the total frequency (equals the

number of hectares treated) and a lower line showing the number of hectares below the

screening level also gives a good image of the results. It is also possible to represent the

results in a 3D-plot. In the same plot both categories of risk can be represented. Another

possibility to represent the time-trend risk associated with the use of pesticides for applicators

is by means of bar charts. Each bar consists of two parts. One part reflects the category belowthe screenineg level, while the other part reflects the category requiring further evaluation.

For risk managers further analysis of the category ‘Risk requiring further evaluation’, will be

necessary. Risk managers should be able to identify which actives are responsible for driving

the results. Therefore, tables should be generated in which the actives together with their

frequency and RI are listed (Table VI.1.9).

Figure VI.1.1: Pie Chart for Cherry

Orchards

Figure VI.1.2: Bar Chart for Cherry

Orchards

0,00E+00

1,00E+02

2,00E+02

3,00E+02

4,00E+02

5,00E+02

6,00E+02

7,00E+02

8,00E+02

Risk below the screening levelRisk needing further evaluation

Risk below the screening level



95

Table VI.1.9: List of the actives belonging to the category ‘Risk Requiring further evaluation’ (listed forapple orchards only as an example).

Active Formulation Type Frequency RICarbendazim WG 3,25E+03 1,377

Carbendazim L 2,10E+03 2,033Tolylfluanid WG 3,90E+03 2,056

Myclobutanil L 8,15E+03 2,547

Carbendazim WP 1,75E+03 6,468

Chlorpyriphos WG 1,44E+03 7,831

Chlorpyriphos L 7,49E+03 9,772Diuron L 2,42E+03 113,722

For all the risk events belonging to the category ‘Risk requiring further evaluation’, the

influence of the use of personal protective equipment (PPE) should be taken into account.

Risk Indices can be calculated for the different types of PPE and for various combinations of

PPE. Concerning the display of the results, the same options for tables and graphs can be used

as presented above, but instead of two there are three categories of risk, namely ‘risk below

the screening level’, ‘risk acceptable based on refined assessment’ and ‘risk acceptable with

mitigation’. A pie chart showing the proportion of the total frequency belonging to each of these categories can be shown. Other possibilities that can be used are the use of a 3D line

graph or a stacked line graph, with three lines showing the evolution in time for the three

categories.

Table VI.1.10 lists the results for each category of risk.

Table VI.1.10: Table showing the frequency for apple orchards for the three categories (absolute andrelative (%))

Category of risk Frequency %

Risk below the screening level 3,06E+03 7,84Risk acceptable using PPE 2,27E+04 58,08

Risk needing further evaluation 1,33E+04 34,08

Combined 3,91E+04 100,00

Chlorpyriphos and diuron are the actives that need further evaluation. Figure VI.1.3 shows the

three categories of risk. The category of risk needing further evaluation is split up according

to the degree above which the trigger value is exceeded.

Distribution of risk

0,00E+00

5,00E+03

1,00E+04

1,50E+04

2,00E+04

2,50E+04

3,00E+04

3,50E+04

4,00E+04

4,50E+04

F r e q u e n c y

1 order of magnitudeabove the trigger value

Above the trigger value, but same order of magnitudeRisk acceptable usingPPE

Risk below thescreening level

Figure VI.1.3: Distribution of risk in orchards for the selected actives



96

The frequency in each of these categories can be calculated for several consecutive years. In

this way the time-trend risk can be presented.

Another way to show the results that makes a comparison between pesticide groups and crop

groups possible makes use of the sum (RI x F) values.

Different ways of aggregation are possible. Here, pesticide group aggregation and crop

aggregation is presented. It is assumed that no PPE was worn and the aggregation is only

applied to the acute risk indicators.

1. pesticide group aggregation for orchards (apple, pear, plum and cherry)

The results are presented in Table VI.1.11.

Table VI.1.11: pesticide group aggregation

Pesticide

Group

RI medianRI 95th

percentile

Sum F Sum RI*F% of

total Risk

% of total

FrequencyFUNG 2,334 6,468 2,27E+04 6,00E+04 13 58 INSE 9,772 9,772 1,36E+04 9,90E+04 66 21 HERB 1,14E+02 1,28E+02 2,74E+03 3,10E+05 21 7

ALL 2,547 1,14E+02 3,905E+04 4,691E+05 100 100

The high median risk index of the herbicides is due to the low AOEL value of diuron, the

active substance that was selected for analysis.

2. crop group aggregation

The results are presented in Table VI.1.12 & VI.1.13.

Table VI.1.12: Crop group aggregation: Risk for all the studied active substances in apple, pear, plum andcherry orchards

Crop Group RI medianRI 95th

percentileSum F Sum RI*F

% of totalRisk

% of totalFrequency

Apple 2,55 1,14E+02 3,17E+04 4,08E+05 87 81Pear 2,33 8,74 5,06E+03 4,56E+04 10 13

Plum 3,72 9,037 1,59E+03 1,20E+04 3 4Cherry 0,04 4,948 708,70 3,29E+03 1 2

ALL 2,547 1,14E+02 3,91E+04 4,69E+05 100 100

Table VI.1.13: Crop group aggregation: Risk for the studied active substances in orchards (apple, pear,plum and cherry)

as RI medianRI 95th

percentileSum F Sum RI*F

% of totalRisk

% of totalFrequency

Carbendazim 2,033 6,742 8,58E+03 2,52E+04 5,38 21,97Chlorpyriphos 9,772 9,772 1,06E+04 9,83E+04 20,96 27,06Cypermethrin 0,273 0,352 8,66E+02 2,58E+02 0,06 2,22

Diuron 1,14E+02 1,28E+02 2,74E+03 3,10E+05 66,09 7,01

Lambda-cyhalothrin 0,206 0,206

2,55E+025,26E+01 0,01 0,65

Myclobutanil 2,547 3,721 9,32E+03 2,47E+04 5,27 23,86Pirimicarb 0,184 0,253 1,94E+03 3,90E+02 0,08 4,97

Tolylfluanid 2,056 2,334 4,79E+03 1,01E+04 2,15 12,26

TOTAL 2,547 1,14E+02 3,91E+04 4,69E+05 100 100



97

The following graphs should be presented by the computer program:

Pie charts: Each pie portion represents the proportion of a crop group or a pesticide

group in the total risk (RI x F) for applicators and the total frequency of pesticide

applications. Figures VI.1.4 and VI.1.5 give examples of such graphs.

Contribution of the pesticide groups to the total

frequency

58

21

7

FUNG (58%)

INSE(21%)

HERB (7%)

Boxplots (made by the R-program): These graphs illustrate the characteristics of

the frequency distribution in the various groups, namely the 25th, 50th, 75th and 95th

percentile as well as the maximum and the minimum.

Histograms (made by the R-program): The risk events are grouped by intervals of

one unity of log 10 of RI. Hence, from one interval to another, the risk increases10 times. Example: the interval log10=[0;1[ corresponds to an interval from RI=1

to RI=10 (10 not included) and the interval log10=[-1;0[ from 0,1 to 1 (1 not

included). The histogram for the case study is shown below. This graph was made

in Excel, but can be generated using the aggregation program, proposed by WP 11.

The first series of histograms (blue) show the proportion of the applications of the

interval in the total frequency of the crop group and the other (red) their proportion

in the total risk. The purpose of these histograms is that they help to localize the

active substances contributing the most to the total risk. Secondly, they may help

to orientate the choice of pesticide reduction actions. Indeed, in an interval with a

high proportion in the total risk (red) and a low proportion in the total frequency

(blue), the reduction of the risk could be easier because it concerns a low number of applications. It would be more difficult to decrease the risk in an interval with a

high proportion of the frequency. (Figure VI.1.6)

Figure VI.1.4: Contribution of thedifferent pesticide groups to the

total risk on an operator

Figure VI.1.5: Contribution of thedifferent pesticide groups to the

total frequency of use

Contribution of the pesticide groups to the total

risk on applicators

13

66

21 FUNG (13%)

INSE(66%)

HERB (21%)



98

Distribution of frequency and summed risk of all the active

substances taken up in the case-study for the applicator

compartment

0

0,1

0,2

0,3

0,4

0,5

0,6

0,7

0,80,9

[-1;0[ [1;0[ [1;2[ [2;3[

log 10 (RI of application)

p r o p o r t i o n

Sum(F) of interval/Sum(F) total

Sum(RI x F) of interval/Sum(RI x F)

Figure VI.1.6: Histogram: Distribution of frequency and summed risk of all the active substances takenup in the case-study for the applicator compartment

It should also be possible to graphically represent the time trend risk (Figure VI.1.6).

Riskiest active substances:The riskiest active substances can be identified by determining those substances that are

above the 95th percentile of the total risk. The riskiest active substances can also be given for a

particular crop group or a particular pesticides group.

Table VI.1.14 gives an example output table.

Table VI.1.14: The riskiest active substances (above the 95th percentile of the total risk)

a.s. crop crop group Pesticidegroup

RI RI x F % total (RI x F)

Diuron pears orchard HERB 127,60 2,93E+04 6,24

… … … … … … …HERB: herbicide



99

2. Re-entry worker

Because specific information regarding DFRs is not available, only the most conservative

indicator can be calculated. Thus, the worker risk assessment, based on the generic

assumption on initial DFR and database for TFs, gives single conservative point estimates

(“surrogate values”) for total potential exposure, fully exploiting the capacity of the databasewhich is applicable to a broad range of re-entry scenarios common to European conditions.

An example is calculated where the actual and the potential risk for workers is determined.

Table VI.2.1 lists the input parameters that are needed to calculate the re-entry worker

indicator.

CASE-STUDY

Structuring the queryAssessing the acute risk for re-entry workers in orchards (apple, pear, plum and cherry) due to

the application of all the active substances applied in orchards in the UK during 2002.


Miles Thomas (HAIR work package 5). The relevant application events were ordered per

active and area treated. Data were provided for the whole of the UK



1. European Union;2. CTB – The Netherlands (http://www.ctb-wageningen.nl/);



5. Extoxnet (http://extoxnet.orst.edu/);

6. Toxnet (http://toxnet.nlm.nih.gov/);


Assumptions taken into account

1. LAI = 4 (fruit orchard) (m²/m²);

2. T = 8 h (1 workday);3. P = 1 (without PPE) and P = 1 (with PPE) (because in this case the TF actual is

known; if this is not known, P is set to 0,1);

4. TF potential = 20.000 cm²/person/hr; TF actual = 4500 cm²/person/hr;




100

Input data: Table VI.2.1 lists the input data for this case-study.

Table VI.2.1: Input data for case-study on apple orchards in the UK


(kg a.s./ha)N° of applications AbDE (-)

AOEL(mg/ kg bw d)

L Apples 0,605 1,09L Cherries 0,709 1,00

L Pears 0,620 1,222,4-D HERB

L Plums 0,835 1,03

0,02 0,15

Amitraz AC L Pears 0,646 1,39 0,1 0,003

L Apples 0,372 1,04L Cherries 0,788 1,15

L Pears 0,611 1,03Amitrole HERB

L Plums 1,027 1,78

0,01 0,001

Asulam HERB L Apples 0,231 1,00 0,1 0,45*

L Apples 0,201 3,44Bupirimate FUNG

L Pears 0,177 6,060,1 0,15

WSB Apples 0,118 7,74WG Apples 0,824 5,06

WG Pears 1,123 4,38

WP Apples 1,103 4,94

Captan FUNG

WP Pears 1,399 4,82

0,1 0,125

L Apples 0,287 3,10

L Pears 0,364 3,07WG Apples 0,224 4,73WG Pears 0,266 6,86


Carbendazim FUNG

WP Pears 0,256 2,75

0,01 0,02*

Chlorine FUNG L Cherries 0,170 10,24 - -

L Apples 0,691 1,98L Pears 0,618 1,23

L Plums 0,584 1,81

WG Apples 0,636 2,10WG Pears 0,579 2,25

Chlorpyrifos INSE

WG Plums 0,733 2,58

0,01 0,01

L Apples 0,189 1,09

L Cherries 0,192 1,00Clofentezine ACL Pears 0,192 1,00

0,1 0,02

L Apples 0,097 1,00

L Pears 0,099 1,00Clopyralid HERBL Plums 0,180 1,00

0,1 1

L Apples 0,504 1,53

L Cherries 0,997 2,39

L Pears 0,669 1,48L Plums 0,727 1,05


WP Cherries 0,897 2,30WP Pears 0,326 5,34

Copper oxychloride FUNG

WP Plums 1,497 1,00

0,1 0,05

Copper sulphate FUNG L Apples 27,000 2,00 0,1 0,05

L Apples 0,031 4,68

L Cherries 0,024 1,00L Pears 0,023 1,18

Cypermethrin INSE

L Plums 0,031 4,65

0,42 0,5*


L Pears 0,009 1,00Deltamethrin INSEL Plums 0,020 2,00

0,1 0,0075

L Apples 0,028 1,29

L Cherries 0,048 1,00L Pears 0,026 1,43

Dicamba HERB

L Plums 0,023 1,13

0,01 1,2*

GR Apples 2,596 1,00Dichlobenil HERB

GR Cherries 0,676 1,000,1 0,002

L Apples 0,126 1,44


L Pears 0,149 1,28Dichlorprop-P HERB

L Plums 0,082 1,00

0,1 0,32*

L Apples 0,133 1,46

L Pears 0,141 1,28L Plums 0,137 1,26

Diflubenzuron INSE

WP Pears 0,072 1,00

0,1 0,02

AC: acaricides; HERB: herbicides; FUNG: fungicides; INSE: insecticides; GR: growth regulators* indicates confidential values



101



(kg a.s./ha)AbDE (-)

AOEL(mg/ kg bw d)

L Apples 0,246Dinocap FUNG

L Pears 0,4370,1 0,001

L Apples 0,112

L Cherries 0,150L Pears 0,105Diquat HERB

L Plums 0,104

0,01 0,001

L Apples 0,560Dithianon FUNG

L Pears 0,6820,003 0,03*

L Apples 0,591

L Cherries 0,298

L Pears 0,663Diuron HERB

L Plums 0,362

0,1 0,007*

L Apples 0,750

L Pears 0,777Dodine FUNG

L Plums 0,433

0,1 0,19

L Apples 0,037

L Cherries 0,059

L Pears 0,044Fenbuconazole FUNG

L Plums 0,052

0,1 0,03

WG Cherries 0,688Fenhexamid FUNG

WG Plums 0,6200,18 0,3

WG Apples 0,070WG Pears 0,069Fenoxycarb INSE

WP Pears 0,048

0,15 0,4*

Fenpyroximate AC L Apples 0,039 0,053 0,01*3

L Apples 0,130Fluroxypyr HERB

L Pears 0,0570,1 0,8

Fosetyl-aluminium FUNG WG Apples 1,885 0,1 57*

GR Cherries 0,007

GR Pears 0,007

L Apples 0,002

L Cherries 0,001

Gibberellins GR

L Pears 0,002

- -

L Apples 0,257

L Cherries 0,248

L Pears 0,232Glufosinate-ammonium HERB

L Plums 0,222

0,1 0,008*

L Apples 0,581

L Cherries 0,685L Pears 0,599

Glyphosate HERB

L Plums 0,574

0,03 0,2

Imidacloprid INSE WG Plums 0,040 0,1 0,15*

L Apples 0,134

L Cherries 0,023Isoxaben HERB

L Plums 0,014

0,1 0,066

WG Apples 0,083Kresoxim-methyl FUNG

WG Pears 0,0730,1 0,9

Lambda-cyhalothrin INSE L Pears 0,011 0,003 0,0025

L Apples 0,604

WG Apples 1,282

WG Pears 1,937MCPA FUNG

WP Apples 1,076

0,1 0,035

WP Pears 1,717

L Apples 0,380

L Cherries 0,672

L Pears 0,329

Mancozeb FUNG

L Plums 0,295

0,1 0,035

L Apples 0,080

L Cherries 0,113

L Pears 0,088Mecoprop-P HERB

L Plums 0,059

0,2 0,04

L Apples 0,099

L Cherries 0,162Methoxyfenozide INSE

L Pears 0,091

0,1 0,1

Metsulfuron-methyl HERB WG Pears 0,002 0,1 0,7

L Apples 0,057

L Cherries 0,110

L Pears 0,045Myclobutanil FUNG

L Plums 0,083

0,1 0,03

AC: acaricides; HERB: herbicides; FUNG: fungicides; INSE: insecticides; GR: growth regulators; * indicatesconfidential values



102



(kg a.s./ha)AbDE (-)

AOEL(mg/kg bw d)

L Apples 0,421Oxadiazon HERB

L Plums 1,1540,1 0,004*

L Apples 0,060


Paclobutrazol GR

L Plums 0,346

0,1 0,1*

L Apples 0,262L Cherries 0,263

L Pears 0,325Paraquat HERB

L Plums 0,254

0,005 0,0004

WSB Apples 0,006

L Apples 0,037Penconazole FUNG

L Pears 0,014

0,035 0,7*

L Apples 0,740L Cherries 0,930

L Pears 0,940Pendimethalin HERB

L Plums 0,708

0,1 0,234

WG Apples 0,209

WG Cherries 0,288

WG Pears 0,204

Pirimicarb INSE

WG Plums 0,258

0,01 0,14*

Potassium hydrogen carbonate FUNG WP Apples 1,617 - -Prohexadione-calcium GR WG Apples 0,080 0,1 0,35

L Apples 0,170

L Cherries 0,190

L Plums 0,488WP Apples 0,298

WP Cherries 0,312

WP Pears 0,317

Propyzamide HERB

WP Plums 0,288

0,19 0,08

L Apples 0,084Pyrifenox FUNG

L Pears 0,0980,1 0,02

L Apples 0,206Pyrimethanil FUNG

L Pears 0,3780,2 0,6*

L Apples 0,456Simazine HERB

L Pears 0,723

0,1 0,006

L Apples 0,121Thiacloprid INSE

L Pears 0,1240,01 0,02

WG Apples 0,014Thifensulfuron-methyl HERB

WG Pears 0,0240,1 0,07

WG Apples 1,536Thiram FUNG

WG Pears 0,844

WG Apples 0,628Tolylfluanid FUNG

WG Pears 0,713

0,1 0,02*

Triadimefon FUNG WP Apples 0,027 0,1 0,03

L Apples 0,064


Triazamate INSE

L Plums 0,070

0,1 0,004

L Apples 0,333

L Pears 0,274Triclopyr HERBL Plums 0,720

0,1 0,05

Trifluralin HERB L Cherries 0,685 0,1 0,026

Vinclozolin FUNG L Apples 0,156 0,02 0,02


The results are listed in Table VI.2.2 till VI.2.8. The output table should present both the RIs

when PPE is used and when no PPE is used. In Table VI.2.2 the risk is aggregated for

orchards and each subgroup of orchard. The aggregated risk per pesticide group is also listed.

The frequency of use for the year 2002 in the UK was calculated as described above. Only the

acute indicator results are presented.



103

Aggregation:Different ways of aggregation are possible. Here, pesticide group aggregation and crop

aggregation is presented.

1. pesticide group aggregation for orchards (apple, pear, plum and cherry)

The results are presented in Table VI.2.2.

Table VI.2.2: pesticide group aggregation

PesticideGroup

RImedian

(no PPE)

RImedian(PPE)

FRI*F

(no PPE)RI*F(PPE)

% of totalRisk

% of totalFrequency

N° activesubstances

AC 5,40 1,22 9,57E+02 5,00E+04 1,13E+04 5,72 0,58 3GR 0,34 0,08 1,25E+04 1,99E+03 4,48E+02 29,98 45,86 3

FUNG 0,82 0,18 7,59E+04 2,62E+05 5,91E+04 0,23 7,54 23

INSE 0,56 0,13 3,58E+04 7,67E+04 1,73E+04 55,31 24,37 11HERB 1,80 0,41 4,03E+04 4,84E+05 1,09E+05 8,76 21,65 24

TOTAL 0,76 0,17 1,65E+05 8,76E+05 1,97E+05 100,00 100,00 64

2. crop group aggregation

The results are presented in Table VI.2.3 & VI.2.4.

Table VI.2.3: Crop group aggregation: Risk for all the studied active substances in apple, pear, plum andcherry orchards

Crop GroupRI median(no PPE)

RImedian(PPE)

FRI*F

(no PPE)RI*F(PPE)

% of totalRisk

% of totalFrequency

Apple 0,70 0,16 1,36E+05 6,60E+05 1,48E+05 75,34 82,38

Pear 0,85 0,19 2,19E+04 1,74E+05 3,92E+04 19,91 13,23Plum 1,73 0,39 4,46E+03 2,31E+04 5,19E+03 2,64 2,70Cherry 2,10 0,47 2,79E+03 1,85E+04 4,17E+03 2,12 1,69

TOTAL 0,76 0,17 1,65E+05 8,76E+05 1,97E+05 100,00 100,00

Riskiest active substances:The riskiest active substances can be identified by determining those substances that are


particular crop group or a particular pesticides group.

Table VI.2.4 gives an example output table. Only the results for the case assuming no PPE is

worn are presented.

Table VI.2.4: The 10 riskiest active substances (above the 95th percentile of the total risk)

a.s. crop Pesticide groupRI

(no PPE)RI x F

(no PPE)Oxadiazon Apples HERB 60,19 2,01E+03

Simazine Pears HERB 68,90 4,89E+02MCPA Cherries HERB 76,77 1,66E+03

Amitraz Pears AC 123,00 1,09E+04

Dinocap Apples FUNG 140,38 1,65E+04Oxadiazon Plums HERB 164,92 3,33E+02

Dichlobenil Cherries HERB 193,22 2,02E+01Dinocap Pears FUNG 250,00 1,50E+00

Copper sulphate Apples FUNG 308,57 5,61E+02Dichlobenil Apples HERB 741,76 4,27E+02



104

Table VI.2.5 and VI.2.6 give additional information. Table VI.2.5 lists the frequency of

pesticide applications belonging to a particular risk category.

Table VI.2.5: Table showing the frequency for orchards for the two categories: requiring furtherassessment and below the screening level (absolute and relative (%))

Category of Risk Frequency %Risk below thescreening level

8,707E+04 55,00

Risk needing furtherevaluation

7,13E+04 45,00

Combined 1,58E+05 100,00

To get an idea of the distribution of the RI values, it is useful to represent the results in a

probability density graph (see Figure VI.2.1).

Distribution of the risk for a farm worker in

orchards in the UK in 2002

0,00

0,10

0,20

0,30

0,40

0,50

]-3 -2] ]-2 -1] ]-1 0] ]0 1] ]1 2] ]2 3]

log RI

p r o p o r t i o n o f f r e q u e n c y

Figure VI.2.1: Probability density graph in order to show the distribution of the RI values for re-entry

workers in orchards in 2002.

For risk managers further analysis of the category ‘Risk requiring further evaluation’will be

necessary. Risk managers should be able to identify the actives that are responsible for

driving the results. Therefore, a table should be generated in which the actives are listed

together with their frequency and risk index. For the given case-study, part of the list is

presented in Table VI.2.6.

Table VI.2.6: List of active substances belonging to the category ‘Risk Requiring Mitigation’

a.s. crop Pesticide group RI FrequencyCarbendazim Pears FUNG 1,04 1,87E+02

Myclobutanil Apples FUNG 1,08 8,15E+03Fenbuconazole Cherries FUNG 1,13 2,23E+02

Isoxaben Apples HERB 1,16 2,39E+00

Tolylfluanid Apples FUNG 1,20 3,90E+03Dodine Plums FUNG 1,30 2,19E+00

… … … … …



105

For all the risk events belonging to the category ‘Risk resuiring further evaluation’, the

influence of the use of PPE should be taken into account. Table VI.2.7 gives an overview of

the number of hectares (= frequency) belonging to each category of risk when PPE is taken

into account. It is assumed that a re-entry worker wears gloves.

Table VI.2.7: Table showing the frequency for orchards for the three categories of risk: requiringfurther assessment and below the screening level (absolute and relative (%))

Category of Risk Frequency %Risk below the screening level 8,707E+04 55,00

Risk acceptable using PPE 3,961E+04 25,02

Risk acceptable based onrefined evaluation – setting of restricted re-entry intervals

3,16E+04 19,99

Combined 1,58E+05 100,00

Table VI.2.8 lists the actives for which restricted re-entry intervals need to be set.

Table VI.2.8: List of active substances belonging to the category ‘Risk Requiring the setting of re-entryintervals’ for apple orchards.

a.s. Formulation Type Crop Pesticide group RI FrequencyCaptan WP Apples FUNG 1,1340214 5,10E+03

Clofentezine L Apples AC 1,2155432 1,72E+02

Copper oxychloride

L Apples FUNG 1,2950674 2,81E+03

Diquat L Apples HERB 1,4374466 3,27E+02

Triazamate L Apples INSE 2,0699136 2,84E+03Mancozeb L Apples FUNG 2,2173274 1,59E+01

Copper

oxychlorideWP Apples FUNG 2,7130362 6,79E+02

Mancozeb WP Apples FUNG 3,9531918 3,55E+02

Glufosinate-ammonium

L Apples HERB 4,1288703 1,90E+03

Paraquat L Apples HERB 4,2130586 1,01E+03Mancozeb WG Apples FUNG 4,7092843 2,05E+02

Amitrole L Apples HERB 4,7766551 1,56E+03

Simazine L Apples HERB 9,7657872 5,60E+01

MCPA L Apples HERB 9,7752296 3,60E+03Thiram WG Apples FUNG 9,8721443 2,43E+02

Diuron L Apples HERB 10,856529 2,42E+03

Oxadiazon L Apples HERB 13,54208 1,48E+02Dinocap L Apples FUNG 31,58639 5,22E+02

Copper sulphate L Apples FUNG 69,428571 8,08E+00

Dichlobenil GR Apples HERB 166,89561 2,56E+00

In the UK restricted re-entry intervals are generally product-specific. They would only be set

where required by the nature of the product. Although it is considered to be good practice notto re-enter the treated area until the applied product has dried (pers. communication J. Smith,

2006). Where a particular product does have specific re-entry requirements, including re-entry

intervals, this information would appear on the label.



106

Figure VI.2.2 shows the distribution of risk for a re-entry worker in orchards. The category of

risk needing further evaluation is split up according to the degree above which the trigger

value is exceeded.

Distribution of risk

0,000E+00

2,000E+04

4,000E+04

6,000E+04

8,000E+04

1,000E+05

1,200E+05

1,400E+05

1,600E+05

1,800E+05

F r e q u e n c y

2 orders of magnitude

above the trigger value

1 order of magnitude

above the trigger value

Above the trigger value -

same order of magnitude

Risk acceptable using

PPE

Risk below screening

level

Figure VI.2.2: Distribution of risk for re-entry workers in orchards



107

3. Bystander & Resident

Below, the same case-study as presented for the re-entry worker is worked out for the

bystander and for the resident.

CASE-STUDY

Structuring the queryAssessing the acute risk for bystanders and the chronic risk for residents in orchards (apple,

pear, plum and cherry) due to the application of all the active substances applied in orchards

in the UK during 2002. The same case-study could be worked out for another year(s) and in

this way the time trend risk could be graphically represented. The risk in one year can be

taken as a reference to benchmark the other risks against.


Miles Thomas (HAIR work package 5). The relevant application events were ordered per

active and area treated. Data were provided for the whole of the UK.



1. European Union;

2. CTB – The Netherlands (http://www.ctb-wageningen.nl/);



5. Extoxnet (http://extoxnet.orst.edu/);6. Toxnet (http://toxnet.nlm.nih.gov/);


Assumptions taken into account

1. Drift is calculated for the bystander as mentioned before, by applying the

following formula: B

r Drift A x f = ∗ ∗ (for x=0 to H)

The values for the different parameters are listed in Table VI.3.1.

Table VI.3.1: Input parameters for case-study on apple orchards in the UK specificallyfor bystanders

Indicator parameter value unitX 8 m

H (early) 11,4 m

A (early) 66,702 -

B (early) -0,752 -

Bystander

f r 1 -

90th perc. Drift (50 m) early 0,30 %

82th perc. Drift (50 m) early 0.22 %77th perc. Drift (50 m) early 0.19 %

74 th perc. Drift (50 m) early 0.17 %

72 th perc. Drift (50 m) early 0.17 %

70 th perc. Drift (50 m) early 0,16 %


Resident




108

For the resident, drift values are taken from Table VI.3.1 (at 50 meter distance

from the field). The above formula was not used since the values for the

different parameters were not available. Only the output Drift values were

available;

2. EA = 0,4225 m²/person/d;3. WR = 15 ha/d;

4. ST = 24 min/ha;

5. Ia (mg/kg a.s.) represents the potential inhalation exposure for operators during

application. Default values for this parameter were taken from the German

model. The value in bold was applied in this case-study;

The most widespread method of applying pesticides in apple orchards is by fan

assisted axial sprayers. The applied surrogate exposure values are the ones

listed in Table VI.3.2 (mechanical upwards spraying).

Table VI.3.2: Surrogate exposure values from the German Model

Crop Application technique Inhalation exposure Ia(mg/kg a.s.)Mechanical upwards 0,018High crop

Hand-held 0,3

Field crop Mechanical downwards 0,001


Input data

Table VI.2.1 lists the input data for this case-study. The same input data were needed as in the

previous case-study worked out for the re-entry workers.

Results

Table VI.3.3 lists the RI values for the bystander and the resident for each active substance

applied in orchards (apple, cherry, pear & plum).



109

Table VI.3.3: Output for the Bystander and Resident case-study

as Function Formulation Crop RI bystander RI resident FL Apples 0,007 8,27E-05 2,36E+03L Cherries 0,008 9,30E-05 1,13E+01

L Pears 0,007 8,94E-05 6,17E+022,4-D HERB

L Plums 0,009 1,11E-04 6,01E+01

Amitraz AC L Pears 1,816 1,58E-02 3,93E+02L Apples 0,317 5,71E-03 1,56E+03

L Cherries 0,673 1,25E-02 2,12E+01

L Pears 0,521 9,37E-03 3,64E+01Amitrole HERB

L Plums 0,877 1,70E-02 3,58E+01

Asulam HERB L Apples 0,004 2,85E-05 1,18E+01

L Apples 0,011 1,45E-04 5,10E+03Bupirimate FUNG

L Pears 0,010 1,83E-04 6,49E+01

WG Apples 0,056 9,14E-04 4,21E+03

WG Pears 0,076 1,09E-03 8,91E+02

WP Apples 0,074 1,20E-03 5,10E+03WP Pears 0,094 1,49E-03 9,15E+02

Captan FUNG

WSB Apples 0,008 1,73E-04 1,86E+02

L Apples 0,012 2,80E-04 2,10E+03L Pears 0,016 3,53E-04 1,87E+02

WG Apples 0,010 2,53E-04 3,25E+03

WG Pears 0,011 3,59E-04 7,88E+02WP Apples 0,010 2,38E-04 1,75E+03

Carbendazim FUNG

WP Pears 0,011 2,37E-04 5,05E+02Chlorine FUNG L Cherries - - 1,01E+01

L Apples 0,059 1,19E-03 7,48E+03

L Pears 0,053 1,00E-03 7,90E+02

L Plums 0,050 9,72E-04 5,41E+02WG Apples 0,054 1,12E-03 1,44E+03

WG Pears 0,049 1,05E-03 1,53E+02

Chlorpyrifos INSE

WG Plums 0,063 1,32E-03 1,64E+02L Apples 0,080 5,64E-04 1,72E+02

L Cherries 0,081 5,34E-04 2,62E+01Clofentezine AC

L Pears 0,081 5,34E-04 5,36E+01

L Apples 0,001 5,40E-06 5,88E+00

L Pears 0,001 5,49E-06 2,23E-01Clopyralid HERB

L Plums 0,002 1,00E-05 2,26E+00

L Apples 0,085 6,13E-04 2,81E+03L Cherries 0,168 1,77E-03 2,19E+02

L Pears 0,113 1,03E-03 9,06E+02

L Plums 0,123 8,41E-04 7,68E+01WP Apples 0,178 1,65E-03 6,79E+02

WP Cherries 0,151 1,54E-03 3,23E+01WP Pears 0,055 9,51E-04 4,28E+02

Copper oxychloride FUNG

WP Plums 0,253 1,66E-03 1,36E+01

Copper sulphate FUNG L Apples 4,557 4,11E-02 8,08E+00L Apples 0,002 3,18E-05 2,51E+02

L Cherries 0,002 9,65E-06 7,17E+01

L Pears 0,002 1,09E-05 4,64E+02Cypermethrin INSE

L Plums 0,002 3,09E-05 7,96E+01L Cherries 0,006 4,74E-05 1,01E+01

L Pears 0,010 6,41E-05 4,07E+01Deltamethrin INSE

L Plums 0,022 2,03E-04 1,34E+01L Apples 2,02E-05 3,90E-07 3,20E+03

L Cherries 3,44E-05 6,13E-07 9,52E+01

L Pears 1,85E-05 3,71E-07 5,08E+02Dicamba HERB

L Plums 1,61E-05 2,97E-07 3,86E+01

GR Apples - - 2,56E+00Dichlobenil HERB

GR Cherries - - 4,64E-01

L Apples 0,003 2,95E-05 4,00E+02

L Cherries 0,002 1,56E-05 1,01E+00L Pears 0,004 3,16E-05 1,46E+02

Dichlorprop-P HERB

L Plums 0,002 1,43E-05 5,61E+00

L Apples 0,056 5,08E-04 3,21E+02

L Pears 0,059 4,81E-04 2,30E+02L Plums 0,058 4,61E-04 1,73E+02

Diflubenzuron INSE

WP Pears 0,030 2,00E-04 1,48E+01

AC: acaricides; HERB: herbicides; FUNG: fungicides; INSE: insecticides; GR: growth regulators* indicates confidential values



110


as Function Formulation Crop RI bystander RI resident FL Apples 2,073 1,87E-02 5,22E+02

Dinocap FUNGL Pears 3,692 7,76E-02 2,67E-02

L Apples 0,095 1,81E-03 3,27E+02

L Cherries 0,128 2,27E-03 9,78E+00

L Pears 0,090 1,71E-03 1,61E+01Diquat HERB

L Plums 0,089 1,70E-03 4,20E+01

L Apples 0,005 2,63E-04 7,62E+03Dithianon FUNG

L Pears 0,006 3,07E-04 1,56E+03L Apples 0,713 5,36E-03 2,42E+03

L Cherries 0,359 2,36E-03 3,13E+01

L Pears 0,800 5,85E-03 2,30E+02Diuron HERB

L Plums 0,436 3,45E-03 6,16E+01

L Apples 0,033 3,31E-04 8,59E+02

L Pears 0,035 3,19E-04 1,31E+02Dodine FUNG

L Plums 0,019 1,26E-04 2,19E+00L Apples 0,010 1,06E-04 1,30E+03

L Cherries 0,017 1,26E-04 2,23E+02

L Pears 0,012 8,85E-05 5,76E+02Fenbuconazole FUNG

L Plums 0,015 1,11E-04 3,90E+02

WG Cherries 0,035 2,30E-04 2,72E+02Fenhexamid FUNG

WG Plums 0,031 2,08E-04 2,09E+02WG Apples 0,002 1,52E-05 4,28E+03

WG Pears 0,002 1,92E-05 9,88E+02Fenoxycarb INSE

WP Pears 0,002 9,37E-06 1,33E+01

Fenpyroximate AC L Apples 0,014 1,05E-04 3,12E+02

L Apples 0,001 9,00E-06 2,31E+02Fluroxypyr HERB

L Pears 0,001 3,96E-06 7,85E+01

Fosetyl-aluminium FUNG WG Apples 2,790E-04 2,52E-06 2,49E-01

GR Cherries - - 2,98E+01

GR Pears - - 1,33E+02L Apples - - 5,50E+03

L Cherries - - 3,05E+01Gibberellins GR

L Pears - - 1,14E+03L Apples 0,271 1,88E-03 1,90E+03


Glufosinate-ammonium HERB


L Cherries 0,009 8,68E-05 3,04E+02

L Pears 0,008 7,67E-05 1,55E+03Glyphosate HERB

L Plums 0,007 7,05E-05 6,51E+02

Imidacloprid INSE WG Plums 0,002 1,50E-05 1,13E+01L Apples 0,017 1,13E-04 2,39E+00

L Cherries 0,003 1,92E-05 1,06E+01Isoxaben HERB


Kresoxim-methyl FUNGWG Pears 0,001 6,17E-06 5,91E+00

Lambda-cyhalothrin INSE L Pears 0,001 5,28E-05 2,55E+02

L Apples 0,146 1,31E-03 1,59E+01WG Apples 0,309 3,15E-03 2,05E+02

WG Pears 0,467 4,18E-03 4,95E+02MCPA FUNG

WP Apples 0,259 1,92E-03 3,55E+02WP Pears 0,414 5,71E-03 7,08E+02

L Apples 0,642 5,26E-03 3,60E+03

L Cherries 1,134 7,46E-03 9,62E+01

L Pears 0,556 4,33E-03 6,51E+02

Mancozeb FUNG


L Cherries 0,048 2,83E-04 9,62E+01

L Pears 0,037 3,01E-04 6,51E+02Mecoprop-P HERB

L Plums 0,025 1,63E-04 4,42E+01

L Apples 0,008 7,07E-05 6,13E+03

L Cherries 0,014 8,97E-05 2,98E+01Methoxyfenozide INSE

L Pears 0,008 5,74E-05 6,29E+02Metsulfuron-methyl HERB WG Pears 2,920E-05 1,92E-07 1,53E+01

L Apples 0,016 2,80E-04 8,15E+03


Myclobutanil FUNG

L Plums 0,023 2,62E-04 4,54E+02




111


as Function Formulation Crop RI bystander RI resident FL Apples 0,889 5,85E-03 1,48E+02

Oxadiazon HERBL Plums 2,436 1,60E-02 8,96E+00L Apples 0,005 1,06E-04 4,79E+03

L Cherries 0,012 7,71E-05 2,46E+01

L Pears 0,004 8,09E-05 7,62E+02Paclobutrazol GR

L Plums 0,029 1,94E-04 5,25E+01L Apples 0,283 8,60E-03 1,01E+03L Cherries 0,284 8,51E-03 3,81E+01

L Pears 0,351 1,06E-02 1,82E+02Paraquat HERB

L Plums 0,274 8,84E-03 1,13E+02L Apples 1,549E-04 2,57E-06 6,48E+03

L Pears 5,803E-05 5,98E-07 1,29E+02Penconazole FUNG

WSB Apples 2,624E-05 6,31E-07 1,86E+02


L Pears 0,034 2,23E-04 2,05E+02Pendimethalin HERB


WG Cherries 0,002 3,58E-05 3,24E+02

WG Pears 0,001 2,22E-05 3,72E+02Pirimicarb INSE

WG Plums 0,002 2,80E-05 2,89E+02

Potassium hydrogen carbonate FUNG WP Apples - - 2,45E+02Prohexadione-calcium GR WG Apples 0,002 2,19E-05 1,43E+01


L Plums 0,098 5,85E-04 3,09E+01

WP Apples 0,060 3,57E-04 1,11E+03

WP Cherries 0,062 3,74E-04 7,59E+01

WP Pears 0,064 3,80E-04 1,53E+02

Propyzamide HERB

WP Plums 0,058 3,46E-04 1,29E+02

L Apples 0,035 2,44E-04 6,49E+01Pyrifenox FUNG


Pyrimethanil FUNGL Pears 0,011 1,14E-04 1,13E+01

L Apples 0,641 4,22E-03 5,60E+01Simazine HERB


Thiacloprid INSEL Pears 0,005 9,40E-05 2,02E+01

WG Apples 0,002 1,14E-05 1,24E+01Thifensulfuron-methyl HERB

WG Pears 0,003 1,87E-05 1,53E+01

WG Apples 0,648 5,25E-03 2,43E+02Thiram FUNG

WG Pears 0,356 5,14E-03 1,76E+02

WG Apples 0,018 1,65E-04 3,90E+03Tolylfluanid FUNG

WG Pears 0,020 1,97E-04 8,87E+02

Triadimefon FUNG WP Apples 0,008 5,07E-05 4,92E-01

L Apples 0,136 1,10E-03 2,84E+03L Cherries 0,148 9,72E-04 2,56E-02

L Pears 0,148 1,18E-03 2,67E-02Triazamate INSE

L Plums 0,148 9,72E-04 4,42E-02

L Apples 0,056 3,70E-04 7,19E+00

L Pears 0,046 3,05E-04 1,09E+00Triclopyr HERB

L Plums 0,122 8,00E-04 2,26E+00

Trifluralin HERB L Cherries 0,222 1,46E-03 1,01E+01

Vinclozolin FUNG L Apples 0,013 1,86E-04 1,02E+02AC: acaricides; HERB: herbicides; FUNG: fungicides; INSE: insecticides; GR: growth regulators; * indicatesconfidential values



112

AggregationThe results are listed in Table VI.3.4. Table VI.3.4 mentions a few statistical parameters

which reflect the distribution of the risk for bystanders in orchards. It is obvious that the same

graphs as presented for the other indicators can alos be made for this case-study.

Table VI.3.4: Output for the Bystander case-study

Cropapple pear plum cherry All orchards

Median RI 0,017 0,025 0,031 0,036 0,031

95th percentile 0,706 0,854 0,649 0,563 0,803

Min of RI values 2,017E-05 1,850E-05 1,610E-05 3,443E-05 1,610E-05

Max of RI values 4,557 3,692 2,436 1,134 4,557Total F (number of ha of treated area)

1,363E+05 2,189E+04 4,462E+03 2,788E+03 1,654E+05

ha with RI> 95th percentile

6,78E+02 4,25E+02 4,47E+01 9,62E+01 1,24E+03

% ha with RI> 95th

percentile 0,497 1,942 1,002 3,451 0,750% ha with RI> 1 0,389 1,941 0,201 3,452 0,641

Total RI (Sum) 13,042 11,508 4,000 6,033 34,584Sum (RI x F) 9,73E+03 2,58E+03 3,42E+02 2,75E+02 1,29E+04

Riskiest active substances:

The riskiest active substances can be identified by determining those substances that are


particular crop group or a particular pesticides group. Here the riskiest active substances are

given for orchards in general. These actives are classified by their risk index without taking

the frequency (equals the number of hectares into account).

Table VI.3.5 gives an example output table.

Table VI.3.5: The riskiest active substances (above the 95th percentile of the Risk Index values = 1,06) inorchards

a.s. cropPesticide

groupRI F RI x F

Simazine Pears HERB 1,08 3,15E+01 3,39E+01Mancozeb Pears FUNG 1,14 7,08E+02 8,04E+02

MCPA Cherries HERB 1,20 9,62E+01 1,15E+02Amitrole Plums HERB 1,38 3,58E+01 4,93E+01

Amitraz Pears AC 1,92 3,93E+02 7,56E+02Dinocap Apples FUNG 2,19 5,22E+02 1,14E+03

Oxadiazon Plums HERB 2,58 8,96E+00 2,31E+01Dinocap Pears FUNG 3,91 2,67E-02 1,04E-01

Copper sulphate

Apples FUNG 4,82 8,08E+00 3,89E+01

When the frequency would be taken into account, the following classification of active

substances would be obtained (Table VI.3.6):



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Table VI.3.6: The active substances having the highest risk when the frequency is taken into account(above the 95th percentile of the RI x F values = 5,47E+02) in orchards)

a.s. cropPesticide

groupRI F RI x F

Paraquat Apples HERB 0,60 1,01E+03 6,07E+02

Chlorpyrifos Apples INSE 0,09 7,48E+03 6,94E+02Amitraz Pears AC 1,92 3,93E+02 7,56E+02Amitrole Apples HERB 0,50 1,56E+03 7,76E+02

Mancozeb Pears FUNG 1,14 7,08E+02 8,04E+02Captan Apples FUNG 0,20 5,10E+03 1,04E+03

Dinocap Apples FUNG 2,19 5,22E+02 1,14E+03Diuron Apples HERB 0,75 2,42E+03 1,82E+03

MCPA Apples HERB 0,68 3,60E+03 2,44E+03



114

VII. Prioritisation of actions for reducing pesticide impact

In the following chapter several mitigation measures to reduce occupational exposure to

pesticides are described and quantitatively evaluated. Examples have been worked out for

standard treatment schemes for potatoes and apple orchards (see Annex X). Quantitative

assessment of the impact of reduction measures is very important for prioritizing the actions

to be implemented in reduction plans for pesticides. Such assessments are difficult to conduct

since for most reductive actions, quantitative data concerning their impact are lacking.

Reducing risk, either the hazard or the exposure, needs a systematic approach or a strategy in

which four levels of action can be distinguished on descending preference of action (Brouwer

et al., 1994). The first level of action is the reduction or elimination of the source. This can be

done by substitution of the pesticide either by less hazardous pesticides or by pesticides with

lower application rates or a combination of both. The second level is reduction of exposure by

replacement or modification of processes or equipment, by processes which result in lower exposures. Moreover, a critical review of working methods may result in improvement

regarding exposure reduction. Examples are other application techniques, e.g. more

mechanized low-volume spraying, and mechanical clod breaking in stead of manual clod

breaking during harvesting. The third level is related to the organisation of the work and the

work practice. Reducing duration of task performance will reduce exposure time. Secondly

date and frequency of application affect the total amount applied (and total exposure of the

operator). In this way the total amount of foliar residue is also affected as well as the location

of the residue in the crop. Brouwer et al. (1994) showed that after an application in the last

growth stages the increase of foliar residue in the lower zone after application is very limited,

whereas a significant decline of the residue, possibly due to degradation but certainly due to

growth dilution can be observed. This means that only pesticides which are stable and appliedin the early stage of growth will be found on the crop during harvesting. When a pesticide is

applied in the last stage of growth, however, the residue will be found in the upper zone of the

crop. The fourth level with lowest preference is personal protection, but this option often

offers the only possibility to reduce exposure. However, personal protective equipment has to

be fitted to the hazard, therefore nature and level of exposure have to be known in order to

select appropriate and comfortable equipment for performance of the task (Brouwer et al.,1994). For operators and re-entry workers priority has to be given to protection of the hands.

Protective gloves (nitrile for the operator and cotton for the harvester) may reduce actual

exposure significantly since no permeation and low penetration (<5%) has been observed in

laboratory studies (Van Kaayk & Lalleman, 1993).



115


Introduction

Pesticide operators are as mentioned before persons who mix, load and apply pesticides. Since

pesticide handlers work with the concentrated product, exposure during mixing and loading

can form an important part of the total exposure. Even if the exposure during mixing and

loading is relatively brief in comparison with the application, the preparation can contribute to

more than 50% of the total potential exposure at the time of a pesticide treatment (Vercruysse,

2000). In field crops, risk of contamination is 10 times higher during mixing/loading than

during application (Jadin & Marot et al., 2004). Operators are not only exposed to pesticides

during mixing, loading and spraying of pesticides, but also during seed treatment, application

of granules, dipping into a pesticide solution or pouring a pesticide solution onto plants

(Vercruysse & Steurbaut, 2002).

The major routes of exposure are through inhalation and dermal absorption (Lundehn et al.,

1992). The oral exposure in agriculture is of a minor importance when appropriate hygienic

measures are taken (van Hemmen, 1992). In addition, uptake through the eyes is possible

when pesticides splash up. This mainly occurs during mixing and loading activities (van

Hemmen, 1993).

Factors influencing exposure

Various factors that can effect dermal and inhalation exposure in different agricultural settingsare mentioned in the scientific literature. The most important parameters are listed below (van

Hemmen, 1992; Franklin & Worgan, 2005):

Formulation type: liquids, such as emulsifiable concentrate (EC) solutions and

aqueous suspensions are prone to splashing and occasionally spillage, resulting in

permeation of clothing and skin contact. Solids, such as wettable powders (WPs),

granules and dusts, may present a plume of dust while being loaded into application

equipment, so producing both a respiratory hazard and exposure to the face and eyes.

Some newer waterdispersable granules (WG) have been formulated to drastically

reduce this potential exposure;

Type of equipment used;

Task being performed;

Duration of activity: in addition to measuring the unit exposure for a worker on a

daily basis for a particular scenario, exposure and risk assessment requires knowledge

and characterization of the frequency and duration of exposure, both on a seasonal and

lifetime basis;

Amount of pesticide handled;

Packaging: the opening of bags, depending on type, may result in significant

exposure. The size of cans, bottles or other liquid containers may affect the potential

for spillage and splashing;

Environmental conditions: climatological factors, such as temperature and humidity,

may influence chemical volatility, perspiration rate and use of protective clothing.Wind can have a profound effect on spray drift and resultant operator exposure;



116

Personal protective equipment: protective clothing, such as chemical-resistant

gloves, coveralls and respiratory protection (masks) can dramatically reduce skin

contact and inhalation exposure;

Hygienic behaviour: worker care with regard to pesticide handling can also have

substantial impact on exposure. Proper use and maintenance of protective clothing are

very important with regard to reduced pesticide exposures.

Specific exposure mitigation measures

Once the risk factors have been identified, it may be necessary to reduce exposure levels. This

entails exploring options for reducing exposure and recalculating the risks to see if they are

within an acceptable range. To reduce the exposure risk, measures that affect above

mentioned factors can be taken. The options range from (Brouwer et al., 1994; Franklin &

Worgan, 2005):

Replacing pesticides by less toxic ones;

Using lower dose rates;

Limiting the amount of pesticides that can be sold;

Limiting the use of certain products;

Using improved lower exposure formulation types (low emission formulations (e.g.

dry flowables, micro-encapsulated products) and packaging (use of dissolvable

sachets and ‘ready to use’ packaging, these avoid opening the packaging and the

transfer of parts of the content);

Restricting the type of equipment that could be used to load and apply pesticides;

Requiring applicators to use ‘closed-cab’ systems (closed-mixing loading systems

(Such a system demands packaging that can be fitted by couplings to the sprayingtank. No separate transport of the formulation from the package to the tank is needed.

Such a system could reduce exposures associated with mixing/loading up to 90-95%

(Hall, 1990)), closed cabs for application equipment);

Requiring applicators to use personal protective equipment; Requiring appropriate hygienic behaviour of the applicators.

Regulatory agencies may also restrict the use of pesticides to trained certified applicators or

might require that registrants implement a product stewardship programme. It must be

determined whether the risk mitigation options selected are feasible and whether compliance

can be enforced. Another significant consideration is that several actions demand considerable

investments of the farmers and are unlikely to be accepted if the cost exceeds the economicsurplus (HEEPIBEE, 2006).

Control or mitigation strategies for occupational exposure are normally expressed as a

hierarchy, with engineering controls considered to be the first choice, administrative controls

the second choice and personal protection a choice of last resort (Franklin & Worgan, 2005).

This approach has a sound basis in industrial hygiene practice and is outlined explicitly in the

U.S. Occupational Safety and Health Act of 1970. For pesticide handlers, however, this

approach has not been adopted routinely. Rather, regulatory agencies worldwide have relied

heavily on chemical protective clothing to mitigate exposure, and have made the use of such

clothing a legal requirement for many compounds (U.S. EPA, 1992b; Easter & Nigg, 1992).

While this is a sensible interim strategy, it should not be considered an adequate long-termcontrol strategy for worker protection. Further efforts are needed to improve equipment



117

design, application procedures and pesticide formulations in order to reduce exposures.

Additionally, substitution of less hazardous compounds for pest control is certainly an

effective means of preventing health risks for pesticide handlers.

Following, several aspects are discussed more into detail:

Choice of the product

The product's choice determines the risk for the operator to a considerable extent. When

alternatives exist, these should be taken into consideration. To reduce their health hazard,

farmers may choose less harmful and less persistent products (with lower acute and chronic

toxicities). However in Belgium, only 7% of the fruit growers, 4% of the vegetable growers

and 5% of the field crop farmers consider user toxicity as a determinant factor for product's

choice (Marot et al., 2003; Jadin & Marot et al., 2004). Moreover, the knowledge of the

danger pictograms and of the risk sentences is very important. A survey carried out in 2003 in

the Walloon Brabant region showed that more or less one out of two farmers did not know the

significance of the pictograms present on the label (Jadin & Marot et al., 2004). Suppressionor use restrictions for the most harmful pesticides may be considered. Impacts on the

applicator's health of such measures depend on the considered pesticide.

Formulation type

WP formulations are particularly hazardous for pesticide operators. A Belgian study showed

that respiratory exposure during the handling of wettable powder (WP) formulations is 3 to 5

times greater than handling other formulations such as liquid formulations like emulsifiable

concentrates (EC) and soluble concentrates (SC) and waterdispersable granules (WG)

(University-Nebraska-Lincoln, 2005; Vercruysse & Steurbaut et al., 1999a). Moreover,

Vercruysse (2000) showed that the potential exposure of the hands during mixing and loading

coming from WP formulations is greater compared to that associated with liquid formulations.

Dermal exposure during handling of WP (wettable powder) formulations was very high: more

than tenfold the exposure of liquid formulations (SC) or granules (WG) (Vercruysse, 2000).

However, according to the University of Nebraska-Lincoln (2005), the skin is prone to

absorption of liquid formulations such as emulsifiable concentrates.

Since WP formulations give rise to an average tenfold higher exposure risk (Vercruysse,

2000) (see Figure VII.1.1), farmers should opt to reduce the use of WP formulations. Liquids

can be applied instead. However, the use of liquids leads to higher hazards due to the

possibility of splashing. Also the presence of organic solvents in some liquid formulations

(like EC) favour dermal absorption, and thus enhance the potential health risk for pesticideoperators. Wettable granules (WG) overcome the problems mentioned for liquids, moreover

they create less airborne dust than WP formulations (Vercruysse & Steurbaut et al., 1999a;

Marquart & Brouwer et al., 2003).



118

Attention has been paid to the development of ‘safe’ formulations. With respect to exposure

during mixing/loading the pre-measured single dose products, or low emission formulations

(e.g. dry flowables, micro-encapsulated products) have the advantage that weighing can be

omitted and exposure during this activity can be limited. Micro-emulsions and suspo-

emulsions tend to enable the farmer to facilitate the application of multiple active ingredients

(Brouwer et al., 2001).

Figure VII.1.1: Potential dermal exposure via the hands during mixing-loading(Jadin, Marot et al. 2004; Vercruysse 2000)

Equipment used

Since spray application has been labeled the ‘least efficient industrial process on earth’

(Rutherford, 1985), many efforts have been made to improve the efficiency and thus reduce

off-target emission to the environment. Operational efficiency has been improved by spray

volume reduction and computerized aids for calibration and delivery, whereas controlled

droplet application, electrically charged sprays, controlled release of pesticides, and dose

targeting are examples of optimization of pesticide use (Brouwer et al., 2001). In

greenhouses, the isolation of the emission during application can be achieved by enclosure of

the source in combination with automatic application techniques, e.g. low-volume misters, or

thermal vaporizers (e.g. for sulphur), however exposure during re-entry is likely to occur.

Reduction of exposure can also be achieved by the use of (semi-) automatic application

techniques (e.g. thermal pulse foggers, air assisted rotary disc misters) or remote controlled

techniques (e.g. spray tree). Because most of these techniques generate fine droplets decrease

of dermal exposure will sometimes be associated with increase of inhalation exposure.

Methner & Fenske (1994) demonstrated that directional ventilation, and training of spray

operators in greenhouses significantly decreases dermal exposure.

Farmers should be required to use spraying equipment that has a wash-hands can and pure

water supply in order to allow rinsing in case of accidents (CRP, 2004). Safety procedures

should be available, visible and explained (CRP, 2004). And farmers should be stimulated to

use ‘closed-cab’ systems or systems equipped with a filter, which significantly reduces

operator exposure (CRP, 2004; Jadin & Marot et al., 2004). Vercruysse (2000) has shown that

orchard spraying without cab leads to a fivefold higher inhalation exposure. On the other

hand, field crop spraying with a closed cab or with a semi-open cab leads to a same range of

exposure. It is better to use a cab equipped with a filter for aerosols, dusts and vapours. The

filters must also be changed regularly.



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Use of Personal Protective Equipment (PPE)

Reference to PPE may be found on the product label. It is essential to adhere to label

instructions regarding correct use of PPE. All PPE should be conform current standards

(CSIRO 2002).

In practice, protection of the applicator answers to a compromise between comfort and

protection. In Belgium for example, the use of PPE is very poorly adopted by the field crop

farmers with little less than 50% wearing no PPE. The field crop farmers score very badly

compared to vegetable and fruit growers (13% of the fruit growers and 11% of the vegetable

growers do not wear any PPE) (Marot et al., 2003; Maraite & Steurbaut et al., 2004).

GlovesWearing gloves can substantially reduce exposure with slight discomfort, since during mixing

and loading activities 80 to more than 95% of the contamination occurs via the hands

(Vercruysse, 2000). The gloves must be resistant to chemicals, leather, latex or PVC gloves

are not appropriate. Wearing nitrile rubber gloves allows a reduction of more than 99% duringmixing and loading and of 75% during application (CRP, 2004; Vercruysse, 2000). A study

performed in France with Regent 800WG® showed that the exposure ranges from 107% of

the AOEL to 61% of the AOEL when wearing gloves (Arnich & Cervantés et al., 2005).

Mask Wearing a mask is recommended during mixing/loading as well as during the application,

particularly when handling powder formulations or when spraying in orchards. If the tractor's

cab is equipped with an activated carbon filter, it is not necessary to wear a mask. It is

considered that a half mask is enough if it is equipped with filters for gas and dust and

accompanied by goggles. Masks of A2B2P3 type offer a protection up to 99,9% The

replacement of the filter must be regular (CRP, 2004; PHYTOFAR & CRP et al. 2006; Jadin& Marot et al., 2004).

GogglesSome products are corrosive or irritating. The wearing of goggles protects the applicator

against ocular damage from splashes of such products (CRP, 2004).

BootsWearing boots is also recommended. The boots must be resistant to the pesticides applied

(CRP, 2004).

CoverallThe wearing of a disposable or re-usable coverall is essential, but sometimes not very

comfortable. The coverall should comply with current standards.

The penetration of pesticides through a cotton coverall can reach more than 20%. This is quite

a lot compared to a waterproof (PVC or PA) coverall which has less than 0,5% penetration.

However, a cotton coverall offers an average reduction of potential dermal exposure during

application of 63%. Even if not totally protective, it offers satisfactory protection for

application of less hazardous pesticides. Moreover the use of a cotton coverall is associated

with less discomfort. However, since trace amounts of pesticide residues cannot be removed

from cotton coveralls, they should be replaced frequently (Fishel, 2006; CRP, 2004;

Vercruysse, 2000; Jadin & Marot et al., 2004).



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Figure VII.1.2 shows the difference in operator risk due to difference in formulation type and

PPE used.

Risk for an operator applying mancozeb in function of the PPE used and the formulation type

applied

0

10

20

30

40

50

60

70

80

SC WG WP

Formulation type

R i s k

coverall

gloves

gloves+coverall

mask

mask+gloves

mask+gloves+coverall

no PPE

Figure VII.1.2: Risk for an operator applying mancozeb in function of the PPE used andthe formulation type applied (Jadin, Marot et al., 2004)

Hygienic behaviour

All equipment, clothing, gloves, boots, goggles and masks should be thoroughly washed with

soap and water (CSIRO, 2002). Indeed, an influence of maintenance, cleaning and changing

of (protective) clothing or gloves on dermal exposure is to be expected (Marquart & Brouwer et al., 2003). After application the pesticide handler should wash his hands with water and

soap and should have a shower (PHYTOFAR & CRP et al., 2006).

As a conclusion it can be noted that improvement of farmers' behaviour with regards to

pesticide handling and wear of protective items offers prospects of significant reduction of

pesticide impact on the applicator.



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2. Re-entry worker

Agricultural workers are potentially exposed to pesticide residues when they enter pesticide

treated fields to perform a variety of manual labour tasks, such as pruning, thinning, scouting

and harvesting, required for the agricultural production of crops. These exposures can occur in

different crops throughout the growing season and can be of similar magnitude to exposuresof workers who mix, load and apply pesticides (Worgan & Rosario, 1995).

The practical options for managing exposures through the use of personal protective

equipment or engineering controls are considerably more limited for re-entry workers than for

mixer/loaders and applicators. The establishment of restricted entry intervals (REIs), which

are intended to provide sufficient time for pesticide residues to degrade to a safe level before

allowing unprotected workers to enter a field, is the primary method for managing post-

application exposures (Worgan & Franklin, 2005). Thus a REI is the minimum time (hours or

days) following application of a pesticide at which workers may re-enter agricultural fields.

REIs are established by determining the time at which the daily exposure for a given work

activity and dislodgeable foliar residue (DFR) level is equal to an established safe level for the

pesticidal active ingredient in question. Thus a suitable REI is derived on the basis of

representative dissipation studies of the active on the foliage. This determines the decrease in

exposure (thus risk) as a function of time after application. The DFR represents the potentially

available pesticide residue with which the worker may come in contact. A suitable re-entry

interval should be derived on the basis of representative dissipation studies of the a.s. on

foliage. This determines the decrease in exposure (thus risk) as a function of time after

application. Reorganising work practice, e.g. by control of the frequency of application as a

part of the Integrated Pest Management (IPM) approach, or by timing the application,

especially the final application prior to harvesting, re-entry exposure can be reduced due to

optimization of the interval between the last application and harvesting (or other cropactivities) (Brouwer et al., 2001).

Dermal exposure due to re-entry activities is strongly determined by the level of DFR. In

addition, a significant relationship between application rate and the increase in DFR has been

observed, whereas the relationship between the initial level of DFR (after application) and

actual DFR during re-entry is determined by the half-life of the pesticide. Thus, reduction of

the initial DFR and/or reduction of the actual level of DFR are key issues for exposure control

during re-entry. Reduction of the initial DFR can be achieved by the use of narrow-band

active substances that usually imply low application rates compared to wide band pesticides

primary introduced on the market. Selection of active substances with relative short half-lives

will affect the actual level of DFR during re-entry (Brouwer et al., 2001).

The formulation may also affect exposure, since for example the transfer of residues from

field strengths dust on the foliage to hands during harvesting tended to be more efficient

compared to residues of other kinds of pesticide formulations (Brouwer et al., 2001).

Automation of re-entry activities has also been effective for reduction of re-entry exposure.

The use of so-called drive-in vessels in combination with containers resulted for bulb

disinfection in significant lower exposure than manually dipping of bulbs in baskets. Another

example of automation is mechanical clod-removal which results in significant lower

exposures compared to scenarios where clod removal was performed manually (Brouwer et

al., 2001).



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3. Bystander/Resident

Introduction

Bystander exposure will mainly occur by contact with spray drift during application processes

in the field. Bystander exposure when spraying greenhouse crops and when applications are

performed with treated seeds, granules, plants dipped in a pesticide solution or when a

pesticide solution is poured onto the plant, is considered negligible (Vercruysse & Steurbaut,

2002). Although complete elimination of spray drift is very difficult, its magnitude can be

reduced significantly if factors which enhance creation of drift can be altered or eliminated.

Reducing spraying applications by applying treated seed or granules instead, can also

contribute to a lower bystander exposure.

Factors influencing exposure

In what follows a brief overview is presented of factors which may have an impact on the

exposure of bystanders.

Since bystander exposure mainly occurs due to spray drift, all the factors influencing drift will

have an impact on the former. The literature about spray drift is very abundant. In 2004 in

Belgium, more specifically in Flanders, an extensive literature review was conducted by the

“Centrum voor Landbouwkundig Onderzoek in Merelbeke”, “Departement voor

Gewasbescherming, Universiteit Gent” and “Departement voor Agrotechniek en Economie,

Katholieke Universiteit Leuven” (“Het belang van drift en haar reducerende maatregelen ter

bescherming van het milieu in Vlaanderen”) (Nuyttens et al., 2004).

A number of factors influence drift, including weather conditions, (relative wind speed, wind

direction, humidity and temperature), topography, the crop (stage of crop development and

canopy geometry and density) or area being sprayed, the application equipment (sprayer type,

nozzle type) used and the methods (formulation, direction and height of release, fan speed,

sprayer speed, spraying pressure) applied. Decisions (time and number of applications) taken

by the applicator also play a role (Meli et al., 2003).

Several inter-related factors affecting pesticide drift and deposition are listed in Table VII.3.1.

A brief discussion of each of these parameters follows in order to give an indication of

bystander exposure reduction possibilities.

Table VII.3.1: Inter-related factors affecting pesticide drift and deposition (Landers & Farooq, 2004)

Operator Care& skill

SprayingEquipment

Applicationparameters

Target Weather

skill Design application rate variety wind speedattitude Droplet size nozzle orientation canopy structure wind direction

Fan size forward speed areaAmbient

temperature

Air volumeVolatility of

spraying liquidevery row Relative humidity

Air velocity and

directionFormulation type alternate row

Atmospheric

turbulence intensityTime of application



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The factors influencing drift are usually grouped into five categories, which are briefly

discussed below:

Operator care and skill

In all discussions about spray drift risk, there seems to be universal agreement that the

competence of the people who apply chemicals is the foundation of all further risk

mitigation approaches. That competence implies an understanding of all important risk

factors affecting spray drift and suggests a responsible and constructive attitude on the part

of the operator (APVMA, 2005).

Spraying equipment characteristics (selection and proper operation)

Within this category spray droplet size is one of the most important factors. Smaller droplets

have greater potential for drifting off target (APVMA, 2005). Identification of a drift-prone

droplet size threshold is attractive but somewhat arbitrary. Some researchers have suggested

100µm as the threshold for droplets with high drift potential, others have suggested 141µm

(Spray Drift Task Force, 1997 cited by Stover et al., 2002), while still others indicate thatdroplets under 200µm are very prone to drift when wind speed exceeds 8 kilometres per hour

(Zhu et al., 1994 cited by Stover et al., 2002). As mentioned in Table VII.3.2 (BES, 2002),

studies performed in a wind tunnel indicated a strong non-linear increase in drift with

decreasing droplet size threshold (Taylor et al., 2004). HEEPIBEE (2006) indicates that the

droplet size above which drift potential becomes insignificant depends on wind speeds, but

lies in the range of 150 to 200 µm for wind speeds of 0,5 to 4 m/s. When using conventional

spraying equipment, the total volume of spray made up of droplets less than 100 µm in

diameter is relatively small, but even such small amounts may sometimes cause serious health

problems and/or damage crops in nearby fields (Ozkan et al., 1997).

Table VII.3.2: Distance covered falling 3 m in 4,8 kilometres per hour wind in function of dropletdiameter (BES, 2002)

Diameter inµm

Droplet calledTime required to fall 3

m in still airDistance covered falling 3 m in 4,8

kilometres per hour wind5 Fog 66 minutes 4,8 kilometres

100 Mist 10 seconds 125 metres

500 Light rain 1,5 seconds 2,1 metres

1000 Moderate rain 1 second 1,4 metres

Sprayer Speed can also be mentioned. A series of experiments with boom sprayers showed

an increase in spray drift with increasing speed (van de Zande & Stallinga et al. 2004). On

the whole, slower speeds are better. With a conventional boom-sprayer, there is really no

concern below 6-8 kilometres per hour (10 mph) (Nuyttens et al., 2004). Another study

showed that when the sprayer speed increased from 6 kilometres per hour to 10 kilometres

per hour, the potential drift doubled (Panneton, 2001). In case of air-blast sprayers, fan speed

also has an impact on drift. Field trials conducted in an orchard indicated that reducing fan

speed by 25%, resulted in considerably less drift, with coverage at 6.1m and 12.2m from the

target row being 16% and 0,20% respectively (Landers & Farooq, 2004).



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Spraying pressure has a controversial effect on drift. Indeed, results obtained from different

studies strongly vary. The spraying pressure influences not only droplet size but also droplet

speed. A high pressure decreases the droplet size (which increases spray drift), but also

increases the droplet speed (which decreases spray drift). When these two opposite effects are

balanced, the effect of the spraying pressure on spray drift is not very important compared to

other factors (pers. comm. Lebeau, 2006).

Nozzle type can also be mentioned. Nuyttens et al. (2004) showed that there are two main

classes of nozzles: flat fan nozzles (94.5%) and turbulence nozzles (5.5%). Turbulence

nozzles are mostly used in fruits crops. The flat fan nozzles can be divided in 3 groups:

standard flat fan nozzles (conventional nozzles), drift reducing flat fan nozzles: pre-orifice

nozzles and air induced flat fan nozzles. Among those, the standard flat fan nozzles are by far

the most popular (85.8%). Nozzles with drift reducing properties are not yet frequently used

(drift reducing flat fan nozzles: 2.7%; air induced nozzle: 6%).

The type of spraying equipment used, determines drift potential to a considerable extent.

The potential for drift is greater for aerial applications due to higher heights of spray release,higher speeds of the aircraft and greater air turbulence in the wake of the aircraft that can

shatter droplets into smaller droplets which are more prone to drift (APVMA, 2005; ARS,

2006). Traditional boom spraying has some advantages in relation to spray drift such as being

able to keep spray release height low, operating at slower speeds that do no shatter droplets.

However, under specific conditions, this technique can also lead to unacceptable amounts of

spray drift (APVMA, 2005). Drift resulting of applications of around 300 litres/ha with a

boom height of 0.5 metre varies predominantly with nozzle type, nozzle size and spraying

pressure (van de Zande & Stallinga et al., 2004). However, it seems that the potential drift of

this application method does not exceed 10% of the total applied amount (Benoît et al., 2005).

ULV (Ultra Low Volume Spraying) technology can be highly efficient. However, in some

cases ULV application can have a significantly higher drift potential than conventional low or

high volume application (CSIRO, 2002). Air assisted sprayers were designed to provide better

penetration of the crop canopy and control pests and diseases in the lower canopy. When there

is sufficient foliage to filter the droplets from the airstream, their use also reduces downwind

drift (Matthews, 1995). On the contrary, this method should not be used if there are small

plants or for a soil surface treatment (Matthews, 1995). In this case, the air jet increases the

risk of drift up to a 15-fold factor (Vancoillie, 2002; Panneton, 2001). When done properly,

air-assistance can decrease drift even when fine sprays and lower water volumes are used

(Wolf, 2004). The trend to using dwarf varieties and other changes in the planting of orchards

has enabled development of other equipment. Some sprayers now use cross-flow fans close to

the canopy. Other manufacturers have designed tunnel sprayers, in which a mobile canopy protects the tree from a crosswind during application. Spray which passes through the canopy

is impacted on the tunnel and recycled (Vancoillie, 2002). When spraying an orchard in a full-

leaf situation (LAI 1.5-2) and an average wind speed of 3 m/s with cross-flow fan sprayers,

the spray-drift deposition on the soil at 4.5-5.5 m downwind of the last tree is 6.8 % of the

application rate per surface area. Compared to this reference situation a tunnel sprayer can

achieve a reduction in spray drift on the soil surface of 85-90 % and a cross-flow fan sprayer

with reflection shields of 55% (van de Zande et al. & Michielsen, 2004; Nuyttens et al.,

2004). In Belgium, tunnel sprayers are very rarely used principally because protection against

hail hampers their passage (Nuyttens et al., 2004; Lebeau, pers. comm., 2006).



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Application parameters

In this category, the formulation type is worth mentioning. Dust formulations, very popular

during the late 1940s and 1950s, caused high drift problems. Now, their use is limited. The

least drift-prone formulations of pesticides are pellets and granules. VanDyk (1998) indicated

differences in potential drift among different formulations of a same active substance. For instance, glyphosate formulations influenced droplets size distribution and thus drift.

The height of the spray release also affects drift potential. Indeed, the amount of time that

droplets remain airborne and exposed to wind currents depends on the height of the release

(BES, 2002). Studies performed in a wind tunnel showed that doubling the boom height

increased airborne drift by a factor of three under certain conditions (Taylor et al., 2004). The

effect of sprayer boom height on spray drift was measured in the field. A drift reduction of

around 50% was found when lowering boom height from 0.70 m to 0.50 m as well as

lowering from 0.50 m to 0.30 m above crop canopy. Lowering further down will give even

more drift reduction, up to 90% but also causes stripes in the application (van de Zande &

Stallinga et al., 2004). Thus, the best is to use the lowest boom height that still offerssufficient overlap given the boom movement (Wolf, 2004). In practice, this parameter is

controlled within relatively narrow limits. Aerial applicators seek a compromise between

optimal spray placement and safety and generally maintain a release height between 1 and 3

metres. Applicators using ground boom equipment are constrained, in most cases, by nozzle

design and placement that fixes release height to a narrow range in order to achieve uniform

spray deposition (APVMA, 2005).

The correct orientation of the spray release and thus of the nozzles is crucial if pesticide is

to be targeted correctly (Landers & Farooq, 2004). For example, in orchards, the applicator

should ensure that spray droplets are contained within the canopy and not directly sprayed

into the air above the canopy (CSIRO, 2002).

The time of day of application is important mainly in the way it relates to atmospheric

conditions. Evening and night-time hours are frequently associated with stable air conditions.

Stable conditions are often referred to as “inversions”. These are conditions where very little

air mixing occurs. Because of the low dispersion conditions, pesticide droplets may remain in

the air as a concentrated cloud and drift off target but remain concentrated. This scenario can

result in a concentrated cloud of pesticide droplets landing downwind and possibly causing

damage to non-targets. Thus, spray operations should particularly not be conducted during

inversion conditions (APVMA, 2005; BES, 2002; Thistle, 2004).

The number of applications may as well play a role. Spray drift risks for some products

may be acceptable for one or for a small number of applications, but where the residue effect

is persistent, more applications may have an additive result that raises risk to an unacceptable

level (APVMA, 2005).

Target characteristics

The stage of crop development, the canopy structure, geometry and density are also

important for drift. A crop is a complex target in which thickness, shape, and foliage density

varies. Spray drift risk, particularly for ULV applications, can be substantially increased

when a crop is too small to act as an adequate “trap” to capture small spray droplets.Dormant deciduous orchards also present a higher risk situation during spray or air-blast



127

applications (APVMA, 2005). In a Belgian study performed in a semi-dwarf orchard, the

highest downwind ground deposits were measured when the trees did not have full foliage

(during blossom) (Vercruysse et al., 1999b). According to van de Zande & Stallinga et al.(2004), spraying trees without leaves increases spray drift 2 to 3 times compared to spraying

trees with full foliage. In Belgium, many orchards have planting and pruning systems that

result in a discontinuous leaf wall. Not spraying these gaps can result in a considerable driftquantity reduction (Jaeken et al., 1999). Trials in Italian vineyards indicated a considerable

influence of the canopy characteristics on the amount of drift deposit assessed on the ground

in the area adjacent to the vineyard sprayed. Vineyards featured by a narrower spacing and

compact vegetation gave lower drift than vineyards featured by wider spacing and thinner

canopy (Balsari & Marucco, 2004). According to Stover et al. (2002), variability in

deposition within the tree canopy appears to increase as tree canopy density increases.

Weather conditions

Wind speed and direction are the primary meteorological determinants of spray drift.

Though wind direction is not discussed in relation to the magnitude of drift, it is the criticalvariable as the direction of air movement determines the direction in which pesticides will

drift. The fluctuation in wind direction can also be used as an indicator of the amount of

atmospheric turbulence and, therefore, the amount of dilution of a cloud of fine droplets

(Thistle, 2004). Wind speed influences the distance over which droplets will drift, but it does

not influence droplet size to a large extent (BES, 2002). According to APVMA (2005), a

wind speed range of 3 to 15 kilometres per hour is acceptable for most situations. Research

concerning the efficiency of herbicide applications in Oxfordshire (UK) (Skinner et al.,

1997) showed that in gentle wind (10.8-13 kilometres per hour) 87-93% of the spray was

deposited on the target area, 2-3% on the soil outside the target area, 1-4% of this by drift up

to 8 metres downwind and the remainder, up to 10% was lost by volatilization or further

spray drift. Laboratory studies indicated that a wind speed as low as 4.8-8 kilometres per

hour (3-5 mph) substantially deflected droplets <200 µm in diameter. Smaller droplets were

deflected more than larger droplets (Stover et al., 2002).

Temperature and relative humidity affect the likelihood of smaller droplets impinging on

the target. At a relatively high temperature and low humidity, significant evaporation can

occur before some spray droplets reach the target, reducing the size of droplets and

increasing the influence of ambient air movement and thus increase drift. This is especially

important for droplets smaller than 70 microns. Even larger droplets evaporate to some extent

as the temperature increases or the humidity decreases (Stover et al., 2002).



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Specific exposure mitigation measures

The exposure of bystanders can be reduced during mixing/loading, application and post-

application activities (EUROPOEM, 1996). Below specific mitigation actions are prioritized.

Since spray drift is the main source of bystanders’ exposure to pesticides, mitigation measureswhich reduce spray drift are listed.

To avoid spray drift some elementary preconditions have to be fulfilled, before

implementation of specific measures. These preconditions concern the compliance with good

phytosanitary practices. In particular the following actions should be taken:

Avoid spraying when the weather conditions are not favourable (on windy

days, check wind speed and direction before spraying, check humidity and

temperature);

Reducing the volume of spray contained in small droplets (by choosing

particular nozzles, applying drift retardant chemicals);

Altering the flight paths of small spray droplets by mechanical means to

increase efficiency of deposition on the target (by adapting height and direction

of release, sprayer speed, fan speed, spraying pressure);

Choose low drift-prone formulations;

Take into account the stage of crop development.

After the implementation of the preconditions and in order to reduce drift even further,

specific measures can be taken. These basic anti-drift measures are presented here in order of

their efficiency. Several of these actions can be associated to obtain an even higher drift

reduction.

Modified equipmentSeveral recent developments have been aimed at modifying existing equipment to increase

deposition efficiency of the more effective small droplets while reducing the potential for

drift. In general, this has been accomplished by using either air-assistant technology or some

kind of shield or shroud to overcome the drift producing air currents and turbulence that occur

around the nozzle during spraying. Although air-assistant technology has been proven to be

effective in increasing deposition and thus reducing drift, this technology has currently not

widely been adopted by the pesticide applicators, because of the relatively high equipment

cost.

Management strategiesAccording to different spraying application scenarios, different management strategies can be

applied to reduce spray drift. These are listed in detail in the HEEPEBI Report (2006).

Natural and artificial shields/structures for spray interceptionMany studies have been carried out to investigate and determine the effectiveness of different

kinds of natural and artificial shields in reducing off-target movement of droplets (e.g. Ozkan

et al., 1997; Dorr et al., 1998; Van de Zande et al., 2000). Most of the studies conducted to

evaluate the effectiveness of shields indicate that most of these devices efficiently reduce off-

target spray drift by 45 to 90% (Hewitt, 2001). Several studies showed that shelter vegetation

(natural shields) is more effective than artificial shelter in reducing drift (AEI, 1987; Holland& Maber, 1991).



129

Drift retardant chemicalsDrift retardant chemicals are utility adjuvants working on the properties of the spray solution

or the spray mixture which do not directly affect the pesticide efficacy, but make the

application process easier (McMullan, 2000). DCA’s (sometimes referred to as anti-drift

agents or drift retardants) impart their effectiveness by altering the viscoelastic properties of

the spray solution (Hewitt, 2001). By altering these features, a coarser spray with a higher volume median diameter (VMD) and lower driftable fraction will be produced (McMullan,

2000). Typically, the VMD and driftable fraction, are used to characterize a DCA.

Buffer zonesTo prevent spray drift no-spray buffer zones can be required. A buffer zone is intended to

capture the major portion of driftable droplets within a treatment area to minimize risk to

adjacent protected areas (SPF, 2005; Mc Lean 2001).

Vegetation barrierThere have been a variety of research experiments on this subject, which have documented

reductions in spray drift up to 80-90%. However, there are still enormous data gaps on how toapply such a measure accurately (Ucar & Hall 2001).

In a series of field experiments spray drift was assessed when spraying a sugar beet crop and a

potato crop. Next to the crop, the field margin was planted with a 1.25 m wide strip of

different heights of Miscanthus (Elephant grass) acting as a windbreak. The height of the

windbreak had a clear effect on spray drift deposit (van de Zande & Michielsen et al., 2004).

Spray deposit at 3-4 m distance from the last nozzle decreased significantly with increasing

heights of the Miscanthus. When Miscanthus was cut to equal height as the crop height spray

drift reduction was 55% compared to spray drift on the same distance when no windbreak was

grown. With the 0.5 and 1.0 meter above crop height levels of Miscanthus spray drift was

reduced by respectively 75% and 90%. The combination of a windbreak crop higher than the

arable crop (sugar beet or potatoes) and an air-assisted field sprayer reduced spray drift with

95-99% (van de Zande & Michielsen et al., 2004). Spray drift to the soil and air next to an

orchard might also be reduced by a wind-break of trees around the orchard. In a series of

experiments the effect of a wind-break on the emission outside the orchard was evaluated.

The alder tree wind-break around the orchard resulted in significantly lower drift to the soil

and air at the places behind the wind-break. On the soil next to the orchard, the wind-break

gave an emission reduction in the range of 68 (in the growth stage before May 1st) to more

than 90% (full leaf stage) at a distance of 0-3 m behind the wind-break. The emission to the

air next to the orchard was reduced by 84 to more than 90%, in the height range of 0-4 m

above the soil surface. Results depended on the leaf density of the wind-break and the windspeed during the experiments (van de Zande & Michielsen et al., 2004).

Adjuvants

There is increasing interest in the use of adjuvants for reducing spray drift. Indeed, some

additives allow a narrowing in the droplets spectrum by decreasing the quantity of small drift-

prone droplets. A drift-reducing additive increases drop size by changing liquid properties

such as viscosity. The behaviour of an adjuvant depends in part on the tank mix partners.

However, some drift-reducing agents are sensitive to shearing by the pump and may even end

up producing smaller droplets than without the product. Thus, although drift-retardant

chemicals can be effective in reducing the number of drift-prone droplets, in most cases using

low-drift nozzles and operating sprayers at lower pressures seems to be a better and more

cost-effective approach to reduce spray drift. Therefore, according to different experts,



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adjuvants should be used as a last resort (ITV & ARVALIS et al., 2005; Woods, 2004; AG,

2004; Spanoghe & Steurbaut et al., 2002; Nuyttens & Sonck et al., 2004).

Targeting technologies

Sensing and real time control of spray application can significantly reduce the amount of

pesticide required to maintain acceptable efficacy; concurrently, non-target deposition and

spray drift can be virtually eliminated through focusing pesticide deposition exclusively on

the targets. Integration of GPS systems and on-board sensors can allow real-time mitigation of

spray drift. In addition to drift mitigation, all these technologies can potentially improve

efficacy, allow greater use of reduced-risk chemicals, increase applicator productivity and

significantly improve accountability for agrochemical use (Giles, 2004).

The use of optical sensors to actuate spray nozzles in combination with nozzles spraying

individually each row of the crop can be an effective tool in reducing spray drift. By design,

the system only sprays a detected weed or pest, and since it is not spraying all the time it is

most effective for drift control because it is reducing the amount of pesticide being applied.However, in combination with improper nozzle selection and high pressure this technology

would not be very effective (Wolf, 2004).

The behaviour of a spectrophotometric sensor system for canopy absence/presence detection

has been tested in intensive semi dwarf Belgian orchards in the full foliage growth stage. The

quantity of drift and soil deposit was reduced by 50 to 90% depending on the planting system,

on the number of sensors and on their positioning (Jaeken & Vercruysse et al., 1999).

Strategies based on the use of practical methods of dose adjustment and particularly the

approach build on the principles of Pesticide Adjustment to the Crop Environment (PACE)

has also been established to improve the control of pesticide application by achieving uniformdeposition across a wide range of different crop structures (Walklate, 2004).

The fully optimized method of dose adjustment (FO), involving spray plume adjustments to

match average tree size and suitable dose adjustments, offers the greatest potential for drift

reduction (87%) before the beginning of flowering. The tree area density method of dose

adjustment (TAD) is predicted to have a slightly lower potential for drift reduction (76%), but

avoids the necessity of adjusting the spray plume to match the size of the crop across the full

growing season. Other methods of dose adjustment, based on tree row volume (TRV) and

fruit wall area (FWA) scaling principles, are predicted to give drift reductions of 49% and

32%, respectively (Walklate, 2004).

Additional equipment that will utilize different technologies in combination with on-the-go

site-specific application practices to help reduce drift are forthcoming. Sprayers using

prescription application maps (GPS) for variable rate applications are in development (Wolf,

2004).

Each of the above technologies has seen limited adoption because of the additional cost

associated with such spraying equipment. As future application guidelines regarding increased

efficacy and spray drift minimization are established, more technologies will be developed

and adopted. These developments will require sound research to support adoption (Wolf

2004).



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Below, specific measures are listed respectively for field crops and for orchards:

In field crops/boom spraying:

The most efficient way of drift reduction will be a vegetation barrier (higher than the

crop) associated with boom air assistance. Indeed, this will allow a drift reduction up

to 95-99% (van de Zande & Michielsen et al., 2004). It is important to note that thereare some restrictions to the use of air assistance;

Specific sprayers such as band sprayers, which can be used for weed control in sugar

beets or maize, allow drift reduction ranging from 75% to 90%, depending on the

authors (Rautmann, 2001; SPF, 2005);

Then, a vegetation barrier alone also allows reductions from 55% for the same height

than the crop up to 75% for barrier 0.5 m higher than the crop and 90% for barrier 1 m

higher than the crop (van de Zande & Michielsen et al., 2004);

Using air induction nozzles is another simple and efficient means for drift mitigation.

Indeed, dependent on nozzle size and spraying pressure, a drift reduction of 50% up to

90% is possible (Rautmann, 2001; SPF, 2005);

Use of specific spraying equipment with air assistance (used alone) will also allow toreduce drift (pay attention to the use conditions). Sprayers with air assistance achieve

drift reduction of 50% in crops with a minimum height of 0.3 m and 75% in crops

with a minimum height of 0.5 m (Rautmann, 2001; SPF, 2005).

In orchards:

Specific sprayers such as tunnel sprayers can achieve a reduction in spray drift of 85-

90% and even up to 99%. (Nuyttens & Sonck et al., 2004); SPF, 2005; Rautmann,

2001);

Vegetation barrier acting as windbreak can also allow drift reduction ranging from

50% (without leaves) to 90% and more (full foliage growth stage). Results depend on

the leaf density and on the wind speed (van de Zande & Michielsen et al., 2004; SPF,

2005);

Other specific sprayers, that is to say cross-flow fan sprayers with reflection shields,

allows drift reduction of 55% (Nuyttens & Sonck et al., 2004);

Targeting technologies are other means for drift reduction. Depending on the used

technology and the planting system, reductions can vary largely. With systems of

canopy detection, the reduction achieved is at least 50% and up to 90% (SPF, 2005;

Jaeken & Vercruysse et al., 1999; Rautmann). Other more sophisticated technologies

allow a further reduction. However, these systems generally remain expensive (Wolf,

2004);

In air blast sprayers for orchards, air induction nozzles also lead to drift reduction.The reduction is about 50% (SPF 2005; Rautmann, 2001). Therefore, according to

Rautmann, further steps are necessary.



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4. Quantitative assessment of mitigation measures

The impact of several reduction measures outlined above on the risk for pesticide operators,

re-entry workers and bystanders was quantitatively assessed for two standard spraying

schemes: one for potatoes and one for apple orchards (see Annex X)

Table VII.4.1 lists the reduction of risk in terms of percentage for a few reduction measures

for the spraying scheme for potatoes.

Table VII.4.1: Results of the impact of reduction measures on the total RI value for the spraying schemeof potatoes.

% reduction compared to standardspraying schemeReduction measure

Operator Bystander

Mask 5 -

Gloves 84 -Coverall 1 -Mask +gloves + coverall 90 -

Gloves + mask 89 -

Use of Personal

Protective Equipment

Gloves + coverall 85 -

Decision support system (some fungicide treatments are left out) 17 10

Terra Nostra (some fungicide treatments are left out) 17 13.77

-75% (0,5mhigh)

- 72Drift reduction (vegetation barrier) (bystander located at 8 m from the edge of the field)

-90% (1m high) - 86

-95% - 91Drift Reduction (vegetation barrier and air assistance) (bystander is located at 8 m from the

edge of the field)-99% - 95

Buffer zone of 10 metres (bystander located at 10 metres from theedge of the field)

- 19

Alternative defoliation 49 45

Mechanical weeding 6 10

Powder formulation suppression 60 -

The impact of a decision support system (17%/10%) and the TERRA NOSTRA-label

(17%/13.77) on the total RI value is quite similar. Both of those reduction measures have an

influence on the risk for the applicator and the bystander since these measures imply a

reduction in the use of pesticides. Alternative defoliation and mechanical weeding also limit

the risk to pesticides for applicators by reducing the applied amount. These measures lead to a

decrease in risk of respectively 49% and 6%.

In case of powder formulation suppression, the WP/WS/DS/DP-formulations have been

replaced by SL or WG formulations (Curzate M replaced by Profilux, Purivel replaced by

Reglone). SL and WG formulations have a smaller impact on the applicator especially during

mixing/loading, since powder formulations lead to a considerable inhalation exposure due to

dust emission. A reduction of 60% was calculated taking into account this action.

The main reduction measure for the applicator is the use of PPE (personal protective

equipment: mask, gloves and a coverall). It is obvious that gloves provide the best protection

for pesticide applicators, while a mask (reduction of only 5%) and a coverall (reduction of

only 1%) offer a lot less protection. Especially in case of mixing/loading, gloves and a mask



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reduce the impact of splashing and dust drift. The combination of a mask, gloves and coverall

during mixing/loading and application reduces the total risk for the applicator with 90%.

When considering bystander exposure reduction, drift reduction measures offer the best

possibility, in particular vegetation barriers associated with air-assistance provide a substantial

reduction in risk. Buffer zones of 10 meter from the edge of a field provide less protection.

Table VII.4.2 lists the reduction of risk in terms of percentage for a few reduction measures

for the spraying scheme for apple orchards.

Table VII.4.2: Results of the impact of reduction measures on the total RI value for the spraying schemefor apple orchards.

% reduction compared to standard sprayingscheme

Reduction measureOperator (%)

Re-entryworker (%)

Bystander(%)

Mask 4.13 - -

Gloves 48.81 18 -Coverall 32.94 64 -

Mask +gloves + coverall 85.88 - -Gloves + mask 52.94 - -

Use of PersonalProtective Equipment

Gloves + coverall 81.75 82 -

-75% (0,5mhigh)

- - 74.92Drift reduction (vegetation barrier)(bystander located at 8 m from the edge

of the field)-90% (1m

high)- - 89.90

-95% - - 94.89Drift Reduction (vegetation barrier andair assistance) (bystander is located at 8

m from the edge of the field)

-99% - - 98.89

Buffer zone of 10 metres (bystander located at 10 metres

from the edge of the field)- - 15.43

Powder formulation suppression 36.64 - -

Table VII.4.2 learns that the wearing of PPE is a good option for reducing the risk for

operators and re-entry workers. In orchards, the use of a coverall is almost as important as the

use of gloves. This differs from the situation in field crops, where gloves provide a lot more

protection than coveralls. This can be explained by the fact that in upward application

scenarios the body exposure is much higher compared to downward exposure scenarios. In

orchards, gloves give a reduction of approximately 48%, while a coverall provides a decrease

in risk of 33%. The risk reduction of masks equal 4%, which approximates the 5% value

obtained for the field crop scenario. For re-entry workers, the wearing of gloves and a coverall

are important. In this specific scenario, namely the harvesting of apples, body exposure is

important. This explains the high reduction impact of coveralls. In other re-entry scenarios,

where hand exposure is much more important than body exposure, the use of gloves will give

the highest reduction in exposure and associated risk.

In case of powder formulation suppression, the WP -formulation has been replaced by a WG

formulation. A reduction of 37% of the applicator risk was calculated taking this action into

account.

The largest reduction in bystander exposure is achieved for the vegetation barrier associatedwith air-assistance. Comparable results are obtained as for the field crop application scenario.



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VIII. Validation

Risk per se is rather a theoretical concept than a concrete variable, and is therefore not

measurable in the real world. Hence, validation of risk indicators has to be carried out

indirectly. This can be done by different complementary approaches:

Statistic approachThe investigation of robustness and statistic reliability of the risk indicators can be studied.

One possibility is to focus on the examination of variation in temporal risk trends and the

sensitivity of trends to variability in input data. This approach provides useful information

about statistic soundness, but does not clarify the meaningfulness or plausibility of an

indicator.

Comparing indicators

If different indicators show comparable temporal risk trends this may increase their credibility, but primarily indicates that they are driven by the same variables and does not

necessarily prove their accuracy.

Plausibility testingTesting the plausibility of the indicators will be the main focus of our validation work. Three

main approaches can be applied: (1) sensitivity analysis: sensitivity of the indicators to input

variables; (2) reaction of the indicators to hypothetic scenarios (3) comparing withmonitoring data.

In principle, plausibility testing is complicated by the following main factors: First, results of

indicator calculations can only be understood and validated at a low aggregation level.Second, the assessment of sensitivity and of the reaction to assumed changes (scenarios) is

dependent on the knowledge and opinion of experts and cannot be done objectively. In

addition one has to keep in mind that ‘real’ risk can only be approximated. Hence, indicators

in general do not claim to be linearly related to ‘real’ risk, but they do allow analysing rough

trends or rankings of pesticides or crops in connection with risk.

In this chapter, plausibility testing of the indicators is conducted. The relative impact of

variable parameters is discussed briefly and the rationale behind each of the indicators’

concept on how different factors contribute to occupational exposure to pesticides is

reviewed. Where possible, indicator calculations are compared with monitoring data.



1. Pesticide Operator

Within the framework of HAIR, it was opted to use the EUROPOEM model for estimating

exposure under European conditions since in Europe this model was meant to become the

standard and the other European models would lose their value to users. Therefore existing

models will not be adapted except for specific local reasons. EUROPOEM as it is availablenow has been developed by an expert group and was extended and improved with money

made available by the European Union by adding relevant studies made available to the

expert group. However, regular updates are necessary if EUROPOEM is to remain relevant.

This specifically holds for the quality of the studies in the database, most of which are not

very recent (van Hemmen et al., 2005). Industry (the European Crop Protection Agency

(ECPA)) holds some 30 studies which have been carried out recently, but have not been

added to the current EUROPOEM database.

However it has to be mentioned that EUROPOEM II has several weaknesses as there are

several datasets that have an insufficient number of data-points, particularly for

mixing/loading and hand-held application scenarios. Moreover, many studies are old and

therefore do not follow current guidelines, do not comply with Good Laboratory Practice and

equipment was used that is no longer representative of current practices.

Following the ILSI probabilistic worker exposure assessment workshop and the EUROPOEM

Steering Committee in November 2003, it has been considered that the variability within

datasets was too high and that usual algorithms to calculate exposure do not apply. Therefore

the overall quality of the database was considered insufficient for making reliable

assessments.

Pontal (2004) mentioned the following three issues on which improvement is necessary:

The overall quality of the database has to be improved in order to base exposure

assessments on reliable data. This can be achieved by removing some borderline quality

studies, by adding new studies conducted according to modern standards (ECPA EOEM

program) and by improving the documentation of the studies especially any parameters

likely to explain variability. A more detailed description of the equipment, identification

of incidents, description of the outliers and details on analytical quality are important to

register in view of better explaining variability. Variability of worker exposure is a normal

finding in agriculture as well as in industry, however this variability should be within a

reasonable range otherwise the exposure scenario should be split into different sub

scenarios and at least partially explained by the difference in equipment, protection,incident or behaviour. An example from the EUROPOEM II database can be mentioned

within this respect. When studying the mixing/loading database for liquids, it can be

noticed that the ratio between minimum and maximum values for mixing/loading is up to

more than 20.000.000 for Potential Hand Exposure. No real explanation can be found in

the presented data to explain this variability. With such a variability, the selection of a

representative number for exposure may vary by several orders of magnitude. A guidance

has to be developed on subsetting to enable selection of data that are more appropriate to a

particular product/scenario;

The relationship between use parameters and exposure as well as scaling and mitigation

factors should be studied more extensively (statistical analysis of existing data on thenumber of operations, working time, area treated and dose rate should allow to determine



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if these factors are relevant, if other factors could be added and what the best fitted

relationship is) using the largest and most homogeneous possible database. Currently a

linear relationship is assumed. This linear relationship has however never been confirmed

when looking at the database. In any cases, when there is some relationship, this is not

likely to be linear. Moreover the relationship may be obscured by other sources of

variation such as operator’s behaviour and the study design. Some use parameters whichcould have an influence are also not taken into account. These should be studied in the

future;

A Guidance Document has to be produced to allow for a science based and consistent use

across the E.U. countries. Additional issues to be tackled are guidance for sub-setting,

rationale for selecting a given percentile, a central tendency or any other significant point,

the use of LOD/LOQ results and the use of mitigation factors. Concerning the selection of

a given percentile, no agreement exists at the time on the rationale for selecting a given

point into the distribution. The existing exposure models use different approaches to point

estimate selection, which therefore leads to discussion on a harmonised approach for

EUROPOEM. Concerning mitigation factors, existing data demonstrate that transfer of

chemicals through clothes or protective devices are more complex than a fixed percentageas presently used. Clear rules have then to be defined to incorporate them in an

assessment, especially when no actual dermal exposure data are available.

There is thus a strong need for improvement to the European (and also the North American)

database(s). The most appropriate approach would be that industry and regulatory agencies

would come together and use the recently innovated database software (AHED) for the

purpose of entering only data from studies which fit some strict criteria. The reviewing of all

the EUROPOEM II data as well as a number of industry studies including some new studies

conducted by the ECPA (European Crop Protection Agency) for inclusion in the European

version of AHED has been finalised (about 115 studies in total) (pers. comm. Pontal, 2006).

This work started in 2004 and was sponsored by the industry (Pontal, 2004). A new frame

was established to summarise the studies and evidence of all the quality criteria of the studies

was registered as a basis for a more scientifically robust selection of studies. This was

achieved in collaboration with regulatory officials. ECPA supervised in collaboration with

key EU regulatory experts, the aforementioned actions, namely the review of existing

EUROPOEM II data, the inclusion of new studies, the generation of new ones and the writing

of a Guidance Document which implied research on statistics and agreed decisions. These

actions have been finalized and have been submitted to the European Commission (pers.

comm. Pontal, 2006).

AHED is ready for use but the EU authorities have not yet reached a decision concerning theacceptance of AHED (pers. comm. Pontal, 2006; pers. comm. van Hemmen, 2006). If the

database behind AHED is considered appropriate for thorough data analysis a suitable tool is

thus available, hence leading to the best possible algorithms to be used in the exposure risk

assessment for regulatory purposes, in either a deterministic or probabilistic approach.

AHED is a harmonised model underpinned by high quality generic databases of field studies

relevant to European and American exposure conditions and incorporates the best features of

all the available models. The AHETF (Agricultural Handlers Exposure Task Force), in

cooperation with the European Crop Protection Association (ECPA) developed the database

software, accessible via a web-based server, for handling the data entry, calculations and data

analysis. A common database software tool will be used for risk assessments both in NorthAmerica and Europe. However, the data generated in Europe will be uploaded to the



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European version and the AHETF data will be uploaded to the North American (AHETF)

version. Thus, the software for both the European and the American version is the same, but

only the data that are applicable to each continent are uploaded in the respective databases.

The Agricultural Handlers Exposure Database only contains data that regulators have deemed

appropriate for use in a generic database (www.exposuretf.com). The AHED database is a

management system running under a Microsoft SQL server and will be able to handle probabilistic assessments (in the future, not at the moment due to lack of data, pers. comm.

Pontal, 2006). The database will be able to provide percentiles, means and distributions of

exposure. AHED might rectify the deficiencies of EUROPOEM. Ideally this database could

have been used in the HAIR project if a decision concerning the use of AHED had already

been taken. The possible exposure reduction potential of the newer techniques is not yet

considered in the databases. Overestimation of the exposure may be the case when using the

current databases, particularly because older studies carried out with classic techniques for

classic formulations are incorporated in the datasets. This further underlines the need for

using well designed field studies representative for these new developments. Thus, in order to

keep the database up-to-date field studies should be conducted in the future to fill in the data

gaps and to gather data for new techniques and formulation types. These studies should bedesigned in such a way that the results can easily be incorporated in the database. Therefore a

harmonized protocol for the conduct of field studies of operator exposure to plant protection

products should be followed.

The database integration and harmonization could be carried out under the auspice of the

Organization for Economic Co-operation and Development (OECD) where all relevant

countries from Europe and North America, as well as many other countries such as Australia,

are organized within the Working Group on Pesticides. The approach for integration and

harmonization could be prepared by a suitable expert group and the organizational structures

of the OECD are well suited for carrying this out. This would also promote harmonization of

pesticide-use scenarios throughout the world to the extent required (van Hemmen et al.,

2005).



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2. Re-Entry worker

Validation through biological monitoring

Following, an approach for validation of the re-entry worker indicator is outlined. The

methodology presented has been described previously in the EUROPOEM II Report of the

Re-Entry Working Group: ‘Post-application exposure of workers to pesticides in agriculture’

(EUROPOEM II, 2002) and proposes validation through biological monitoring.

The transfer factor concept for agricultural crop re-entry activities and the acceptance of its

validity are essential for the credibility and acceptance of the re-entry worker indicator

proposed for estimating post-application worker exposure. Biological monitoring data could

potentially be used as a means for validating the TF concept. Biological monitoring is

recognized for giving the most accurate estimate of the absorbed dose of a pesticide,

particularly if studies are designed and interpreted with the aid of human metabolism and pharmacokinetic data. A direct comparison of the passive dosimetry and biological

monitoring approaches to the estimation of the absorbed dose would go a long way to

providing the necessary confidence in the TF concept’s validity (EUROPOEM II, 2002).

The concurrent steps in the validation procedure are listed below (EUROPOEM II, 2002):

Choose a cluster group for which there are existing exposure data and transfer factors;

Conduct re-entry studies with concurrent passive dosimetry and biological monitoring

on a suitable surrogate compound that meets the relevant criteria for biological

monitoring. The surrogate compound should meet the following criteria: human

metabolism and pharmacokinetic data should be available (1), human in vivo dermalabsorption data should be available (2) and analytical methods to detect the principle

metabolites that can be refined to the required level of sensitivity should be available

(3). One such generic compound that meets the requirements is malathion. These

studies would give dermal exposure data, absorbed daily doses and DFR data to

enable the calculation of transfer factors;

Using dermal absorption data on the surrogate compound, the Absorbed Daily Dose

(ADD) could be calculated using the passive dosimetry approach. Following,

comparison could be drawn with the ADD calculated using biological monitoring

approaches;

A further comparison could be drawn between the calculated ADD from the generic

TF derived from the data within the same group and the ADDs from the passivedosimetry and biological monitoring approaches.

An additional advantage of the validation through biological monitoring of the TF concept is

that generic TF values based on biological monitoring data in addition to those generated

using the conventional approach are obtained. Though, one condition has to be fulfilled: the

ADDs of both approaches should be comparable. The generic TF derived on the basis of

biological monitoring data would be a whole body TF and it is recognized that this would not

meet any regulatory requirement for regional body part TFs for the purpose of mitigating

excessive exposure. However, this limitation could possibly be overcome for many

crop/activity combinations on the basis that most of the dermal exposure would involve the

hands and forearms. Another advantage is that the absorbed data could be extrapolated to



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dermal exposure values if the dermal absorption characteristics of the surrogate compound are

well understood (preferably using in vivo human skin, although there are substantial

uncertainties associated with this procedure). Using the concurrent DFR data, these

extrapolated values could be another source of TFs. The above arguments in favour of doing

biological monitoring would provide the necessary confidence in the TF data used for

assessing the acceptability of the risk of crop re-entry activity for specific actives(EUROPOEM II, 2002).

Though, some significant technical issues associated with the proposal to use biological

monitoring data can be mentioned. These are outlined below (EUROPOEM II, 2002):

The proposal has centered on doing one definitive biological monitoring study in the

belief that, if the correlation between the ADDs is found to be acceptable, then, as a

minimum, no further validation work would be needed. Doing a single study on a high

contact activity/crop combination (e.g. apple harvesting) to hopefully ensure that

measurable residues are obtained both in the passive dosimetry measurements and the

urinary metabolite analyses. But the question is whether a single study in a highcontact activity, even if a good correlation is demonstrated, is enough to ensure

confidence in the TF concept applied for activities and crops with lower potential

contact. And what about associated issues like other activities and the influence of

formulation type which also might require evaluation;

A related issue is whether a single study is likely to be wholly definitive. There will

undoubtedly be variance which might be subject to different interpretations. This

might result in a scientific need for further confirmatory validation studies;

Not only the conduct of the study involving the two types of methodology, but also the

input variables used to estimate the absorbed dose via these approaches determine the

validity of the TF concept. In the case of the passive dosimetry approach these are the

dermal absorption value used to estimate the absorbed dose from the dermal exposure

data (literature data indicate a range of 5 to 8% of the applied dose in human volunteer

studies) and the estimation method of the actual dermal exposure data. In the case of

the biomonitoring approach, the input parameters are the fraction absorbed excreted in

the urine as metabolites (90.2% from a human parental dosing), the choice of urinary

metabolites to monitor (e.g. mono- and dicarboxyllic acid, dimethyl thiophosphate),

the fraction absorbed excreted in the urine as the mono- and dicarboxyllic acid

metabolites (57% from a human dosing study), the overall calculation to estimate the

absorbed dose equivalent based upon metabolite excretion and analysis and the

sensitivity of the analytical methods for the chosen metabolites.

Biological monitoring data are often used in occupational exposure studies. However,

scientists are only beginning to understand the complexities and uncertainties involved with

the biomonitoring process (from study design, to sample collection, to chemical analysis) and

with interpreting the resulting data. An overview of concepts that should be considered when

using biomonitoring or biomonitoring data, an assessment of the current status of bio-

monitoring, and potential advancements in the field that may improve our ability to both

collect and interpret biomonitoring data are detailed by Barr et al. (2006). Issues such as the

appropriateness of biomonitoring for a given study, the sampling time frame, temporal

variability in biological measurements to non persistent chemicals, and the complex issues

surrounding data interpretation are discussed in the mentioned article. In addition,

recommendations to improve the utility of biomonitoring in farmworker studies are provided.



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Evaluation of indicator assumptions

The validation approach outlined above could not be applied by Ghent University since bio-

monitoring data were not available to us. What could be done though was an evaluation of the

basic assumptions made in the predictive model for estimating re-entry worker exposure. Thisis achieved by studying available sources in literature.

Brouwer et al. (2000) conducted a study to validate some basic assumptions made in the

predictive models for estimating worker exposure to pesticides during re-entry. Emphasis was

put on the relationship between the applied amount of active ingredient and the resulting

increase of DFR in relevant zones (crop heights), as well determining factors (i.e. application

techniques and crop density, or leaf volume index). In addition the influence of re-entry time

and crop density on transfer factors was studied. Leaf samples were collected prior to and

following the application of the pesticides abamectin, thiophanate-methyl, and methiocarb (in

seven commercial greenhouses for the cultivation of carnations) using either a high-volume

(HV) or low-volume (LV) application method. During each consecutive re-entry, both

respiratory and dermal exposure to pesticides was assessed for a period of 4 weeks, starting

from the moment of HV application. Relationships between exposure and the determinants of

exposure were analyzed using multiple linear regression analysis.

Relation DFR and Dermal & Inhalation Exposure

Schneider et al. (2002) mentioned that the potential dermal exposure levels were correlated

with the amount of dislodgeable foliar residues found on leaves. Higher dermal exposure

measurements corresponded with the increase measured in DFR. Several other studies also

indicated the direct relation between potential dermal exposure and dislodgeable foliar residue

data (Popendorf & Leffingwell, 1982; Nigg et al., 1984; Zweig et al., 1985; Krieger et al.,1992). The former indicates that the basic assumption for exposure modeling concerning the

relation between dermal exposure and DFR data seems acceptable. Vercruysse (2000) showed

on the basis of field experiments that the variation in potential dermal exposure could be

explained by the DFR and the duration of exposure for 95% with the DFR being the most

determinant factor.

Vercruysse (2000) observed that apart from the linear relation between the DFR and the

potential dermal exposure, there is also a direct correlation between the potential inhalation

exposure and the DFR. Using multi-linear regression analysis it was indicated that the

variation in potential inhalation exposure could be explained for 78% by the DFR value and

the duration of exposure. A Dutch study, conducted in apple orchards also indicated theimportance of DFR- values as the most determinant factor for potential inhalation as well as

potential dermal exposure during re-entry activities (De Cock et al., 1998).

Relation DFR and Application Rate

The HAIR re-entry worker indicator assumes that the initial DFR can be estimated by

dividing the application rate by the leaf area index. Brouwer et al. (2000) showed that the

application rate seems to be a major determinant for an increase in DFR (T-test, α = 0.01).

For the low-volume application technique, no significant relationship was observed. For the

high-volume technique and high- and low-volume techniques together, a significant

relationship was observed when using linear regression analysis. The high-volume and high-and low-volume technique showed a variation of 66% and 63% respectively. The relatively



141

limited number of data was quoted to be the reason of the not significant relationship between

DFR and application rate for low-volume applications. A linear relationship between the

average increase of DFR and application rate was observed in several studies (Brouwer et al.,

2000; Popendorf, 1992; van Hemmen et al., 1995).

Relation DFR and crop volumeBrouwer et al. (2000) found no significant contribution of the crop volume to the variation in

DFR for high-volume as well as low-volume applications. Vercruysse (2000) found that the

leaf area index contributed to less than 5% to the total variation in potential exposure.

Transfer factors’ calculation

The indicator assumes default transfer factors that were calculated as the ratio of worker

exposure to DFR. Since exposure data in re-entry studies generally appear to be lognormally

distributed, as well as the TF data, DFR data should also be lognormally distributed. If TF and

exposure values have a lognormal distribution this means that log (TF) and log (Exposure) are

normally distributed. Since the TF is calculated as the ratio of worker exposure to DFR, thenlog (TF) = log (Exposure) – log (DFR) or equivalently log (DFR) = log (Exposure) + log

(TF). Since log (DFR) can be viewed as the sum of two normal random variables, it should

also be normally distributed. Thus, from a statistical perspective at least, DFR is expected to

follow a lognormal distribution. The assumption of the lognormal distribution of DFR values

was tested by the ARTF. A study was conducted to determine if the DFR data can be better

described by either a lognormal or a normal distribution (Korpalski et al., 2005). The data

from this study indicated that the DFR data are, at least approximately, lognormally

distributed. Several types of statistical analyses of the data indicated that for all the tested

chemicals. This study also showed that geometric mean values are more appropriate for use in

calculating transfer coefficients than arithmetic mean values since the geometric mean of

lognormal random variables also has a lognormal distribution. The same is not true for the

arithmetic mean if used instead. Therefore, use of the geometric mean DFR preserves the

lognormal distribution. This is important since the calculated TF will have a lognormal

distribution only if the exposure and DFR are lognormal random variables. When the

variability of the DFR samples is small, there is little difference between a lognormal and a

normal distribution. Thus, the arithmetic mean and geometric mean DFR values are very

similar. This was true for the majority of the re-entry studies that ARTF purchased or

conducted. Practically speaking then, the use of arithmetic or geometric means will generally

have a small impact on calculated TF values. However, in cases where the variance in the

samples is high, the use of the geometric mean instead of the arithmetic mean will result in a

smaller DFR value. In these instances, the calculated TF will increase slightly (since DFR isin the denominator of the TF equation). Thus, the constant use of the geometric mean is a

conservative approach relative to the use of an arithmetic mean. That is, it would result in

higher calculated TFs, higher worker exposure estimates, and longer (more protective)

Restricted Entry Intervals.

Transfer factors: experimental TFs versus default TFs

When comparing experimentally derived transfer factors with the default values assumed, it

can be concluded that the current assumptions in the theoretical indicator model are

conservative. For example, the default value for the TF assumed by the EUROPOEM II Re-

Entry Working Group for carnations equals 5000 cm²/hr, which compares well to the EPA

data (U.S.EPA Policy Paper on Agricultural Transfer Coefficients, aug 2001c). Comparing

this default value with experimental data from Brouwer et al. (2000) (TF=2300 cm²/hr, using



142

all dermal absorption data). Calculated transfer factors for harvesting carnations using all

dermal absorption data were 2300 cm²/hr. For methiocarb a lower transfer factor of

approximately 1600 cm²/hr was observed. These transfer factors are considerably lower than

the default transfer factor of 5000 cm²/hr proposed as a default value if experimental data are

lacking. This indicates that the current assumption in the theoretical model is quite

conservative. Research conducted by Steinbach et al. (2000) which tested the exposure of workers in greenhouses containing treated chrysanthemums and pelargoniums whilst

performing various jobs, also indicated that current theoretical assumptions are worst case. A

transfer factor of 3500 cm²/hr was derived for harvesting chrysanthemums. The

recommendation was made to conduct field trials to determine specific transfer factors and

dislodgeable residues for several re-entry scenarios as been performed on a large scale by the

Agricultural Re-entry Task Force.

To yield more realistic assessments of risk potential, measurements of exposures that are task-

specific should be conducted.

First-order decay rateThe actual amount of the DFR at the time of re-entry may differ from the initial amount of

DFR, depending on the decay rate of the pesticide and the elapsed time since application

(Brouwer et al., 2000). The process of decay may in many cases be considered as a first-order

process (Willis & McDowell, 1987). Brouwer et al. (2000) showed that the application of a

model based on a first-order decay process resulted in fairly high R² and significant fit. It was

also indicated that DFR was significantly associated with re-entry time. However Nigg &

Allen (1979) indicated that not only re-entry time, but also the cumulative precipitation

influences DFR values. In conclusion, it can be said that the assumption of a first-order decay

process is acceptable.

Inhalation exposure in outdoor conditions

Several studies have shown that outdoor inhalation exposure for re-entry workers is

considered less important compared to the dermal exposure they receive (Vercruysse, 2000;

EUROPOEM II, 2002). Vercruysse (2000) compared the geometric mean of the transfer

factors for potential inhalation exposure and total potential dermal exposure obtained via field

experiments using the statistical program SPSS. The conducted T-test showed that the transfer

factors differed significantly at the 99% confidence interval (α=0.01). Based on the difference

(order four of magnitude) the potential inhalation exposure was appreciated less important

than dermal exposure. EUROPOEM II (2002) assumed the same since inhalation exposure,

which potentially may occur to residual vapour and airborne aerosols, is restricted to arelatively short period after application, e.g. for outdoor crops only during the time the spray

is drying or in greenhouses within a few hours after application. Outdoors there will be a rapid

dissipation of vapour and aerosols, leading to much lower inhalation potential than indoors.

Therefore, inhalation exposure was considered less important for re-entry workers in case of

outdoor scenarios within the framework of HAIR.



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3. Bystander/Resident

The bystander indicator is validated by comparing the indicator results to monitoring data.

However, available monitoring data concerning bystander exposure are scarce. The influence

of the different input parameters on the outcome of the indicator is also briefly discussed.

Validation through biological monitoring

In the case of bystander exposures, which may occur by several routes, the best method of

measurement will often be by biomonitoring, provided that there is a suitable analyte. Data on

concentrations of pesticides or their metabolites in humans, especially in blood or urine, can

be used in conjunction with appropriate pharmacokinetic information to provide robust

information on the total amount to which individuals are exposed. Over recent years, a

number of biomonitoring studies have been published, mainly from the United States,

including several that have investigated people living on farms where pesticides are used.These studies support the view that background exposures to pesticides in rural residents are

relatively low, and that maximum exposures on days when spraying takes place are well

below the maximum exposures incurred by operators. They also suggest that a significant

contribution to the exposures of farming families comes from pesticides brought into the

home on the hands and clothes of family members who work with pesticides (ACP, 2005).

Recently the ACP recommended that the PSD commissioned a biomonitoring study of

bystanders, and this investigation is now underway.

It must be recognised, however, that at best, biomonitoring studies of this type can only

demonstrate that exposures are generally lower than those predicted by models. If higher

exposures occurred with any frequency, they would be detected, but because of limitations of sample size, the possibility of occasional extreme outliers cannot be ruled out (ACP, 2005).

Evaluation of indicator assumptions

Spray drift module for assessing dermal exposure

Spray drift deposition is dependent on a variety of environmental, crop and application

factors. Increased wind speed (Kaul et al., 2001) and driving speed (Arvidsson, 1997) can

lead to higher drift rates. Increasing spray boom height and different nozzle types may also

have a significant effect (Elliot & Wilson, 1983). A variety of techniques are also available toreduce drift, for example using coarser nozzles, modifying the spraying angle, spraying

pressure and driving speed, or using air-assisted techniques. Such approaches can reduce

spray drift by more than 50% (Taylor et al., 1989). Clearly then, selection of an appropriate

spray drift database is very much dependent on a matter of judgement and applicability, but

this also leads to a degree of uncertainty.

For the FOCUS approach which is used within the framework of HAIR to assess dermal

exposure for bystanders, spray drift deposition is based on the German drift database

(Rautmann, 2000; Ganzelmeier et al., 1995). These data were generated from a series of

studies (at a number of locations and with a variety of crops) whose objective was to

determine the absolute level of drift in practice under a variety of conditions. However, eventhis extended database partly reflects environmental crop and application factors prevailing in



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Germany. This may become clear from comparison with other spray drift databases, such as

the Dutch database. The Dutch IMAG institute performed spray drift deposition

measurements for several crops at various sites in the Netherlands. Van de Zande et al. (2001)

compared the 90th percentile values derived from Ganzelmeier et al. (1995) and Rautmann

(2000) with 90th percentiles obtained from the Dutch database. A good correspondence

between the German and Dutch 90th

percentiles for spray drift deposition was found inorchards. However, for four arable crops Van de Zande et al. (2001) found that 90th

percentiles as estimated from the Dutch database were typically five times larger than the 90th

percentile from the German database. A preliminary analysis suggests that the difference may

be mainly caused by differences in nozzle types (less or more advanced) and in crop height,

related to spray boom height (pers. comm. Van de Zande, 2001; pers. comm. Rautmann,

2001). This comparison suggests that further refinement of drift estimates may be useful

when more specific situations need to be assessed. However, up to now, the German database

still is the most comprehensive, widely available dataset and therefore this database was

selected for inclusion in the indicator. Vercruysse (2000) conducted spray drift experiments

and compared the downwind drift deposition data with those from Ganzelmeier et al. (1995).

Not linear regression analysis showed that the drift deposition data from both studies could bewell described by a power function. Moreover, the difference in drift deposition between both

studies did not differ significantly (T-test, α=0.05), despite of the higher values from

Vercruysse (2000). De Heer et al. (1985) also obtained drift deposition values within the

same range as Ganzelmeier et al. (1995).

A realistic field study conducted by Mazzi et al. (1999) measured potential bystander

exposures in sloped vineyards in Italy. The dermal exposures obtained from this study were 2

to 10 times lower than the ones calculated using the BBA drift tables.

Currently the “Centrum voor Landbouwkundig Onderzoek in Merelbeke”, “Departement

voor Gewasbescherming, Universiteit Gent” and “Departement voor Agrotechniek en

Economie, Katholieke Universiteit Leuven” are developing and validating a Computational

Fluid Dynamics (CFD) drift-prediction model for field spraying applications. This model will

be able to predict drift for a wide range of variables such as weather conditions (wind speed

and wind direction, humidity, temperature), spray application technique (nozzle type, spray

pressure, forward driving speed of the sprayer, boom height and boom length) and

surrounding characteristics (canopy type and canopy height). The droplet diameter

distributions and velocities of sprays are used to develop the CFD model, they are obtained

by PDPA laser measurements. Field experiments are performed under a wide range of

conditions (weather, spray technique and surroundings) to validate the model

(http://www.biw.kuleuven.be/aee/vcbt/drift/).

In what follows a short overview is given of the influence of the different input parameters on

the outcome of a bystander’s exposure.



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The factors influencing dermal exposure are discussed below.

Distance from the edge of the field to the bystander’s location (= x)

The distance from the edge of the field or more exactly from the last nozzle of the spray

boom, to the bystander’s location determines the extent of spray drift deposition on the bystander. Table VIII.3.1 lists the calculated drift deposition loadings for different crops and

distances from the edge of the field. Figure VIII.3.1 gives a clear picture of the influence of

the distance from the edge of the field to the bystander’s location on drift deposition for arable

crops (e.g. potatoes) and fruit crops (early and late growth stages).

Table VIII.3.1: Drift deposition data for different distances from the last nozzle of the spray boom to thebystander.

CropDistance to field

(m)Drift deposition (%) (90th percentile)

Potential dermal exposure(mg/workday)

5 0,57 0,24

8 0,36 0,1510 0,29 0,12

Potatoes

20 0,15 0,06

5 19,88 8,40

8 13,96 5,9010 11,81 4,99

Orchards early

20 2,76 1,17

5 8,41 3,55

8 4,73 2,00

10 3,60 1,52Orchards late

20 1,08 0,46

Influence of crop type and distance from the edge of the field on drift

deposition loading

0,00

2,00

4,00

6,00

8,00

10,00

12,00

14,00

16,00

18,00

20,00

0 5 10 15 20 25

Distance from the edge of the field (m)

D r i f t d e p o s i t i o n ( % A

R )

potatoes

fruit crops early

fruit crops late

Figure VIII.3.1: Influence of crop type and distance from the edge of the field on drift deposition loading

What can be concluded from Table VIII.3.1 and Figure VIII.3.1 is that the major differences

between the downwind drift deposition data account for the differences in potential dermal

exposure of bystanders for different crops. It is also very clear that drift deposition decreases

with longer distances from the edge of the field. For fruit crops and hops the crop growth

stage also influences the drift deposition, and following dermal exposure. Indeed, when the

foliage of a crop is still under development, the filtering effect of the leaves on the spray will

be smaller than in later growth stages, resulting in a higher potential dermal exposure for

bystanders. Vercruysse (2000) also concluded in accordance with Ganzelmeier et al. (1995)that drift deposition values significantly differed according to the crop growth stage (T-test,



146

α=0.05) and this for all distances from the edge of the field. Fox et al. (1993) also found

higher drift deposition values in early growth stages compared to late.

Influence of crop height and distance from the edge of the field

Influence of crop height and distance from the edge of the fieldon drift deposition loading

0,00

0,50

1,00

1,50

2,00

2,50

3,00

3,50

4,00

0 5 10 15 20 25

Distance from the edge of the field (m)

D r i f t d e p o s i t i o n ( % A

R )

Vegetables < 0,5 m

Vegetables > 0,5 m

Figure VIII.3.2: Influence of crop height and distance from the edge of the field on drift depositionloading

In case of vegetable spray drift deposition depends on the height of the crop. Figure VIII.3.2

clearly shows that for higher vegetable crops the drift deposition loading, expressed as a

percentage of the application rate, is considerably higher at relative short distances from the

edge of the field. The difference in spray drift deposition between high and low crops

decreases with increasing distance from the field.

Exposed Area

The HAIR bystander indicator assumes a default value for this parameter of 0.4225 m² per

person (total uncovered area: head, back & front of the neck, forearms, ½ upper arms and

hands) in case of adults. For children a higher default value is assumed (see earlier).

Airborne drift module for assessing inhalation exposure

Two methods were proposed for assessing potential bystander inhalation exposure accounting

for different cases of data availability. The first method estimates a bystander’s exposure

based on the potential inhalation exposure of the operator, the daily work rate and the typical

spraying duration. The second method makes use of default values derived from field trialsconducted by the Central Science laboratory within the framework of the EUROPOEM II

project.

Both approaches were compared based on a standard treatment scheme for potatoes (Annex

X). To calculate the bystander’s exposure, the following assumptions were taken into account

for both calculation approaches:



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Method 1:− Ia = 0.008 mg a.s./kg a.s. applied (inhalation exposure for an applicator using the

EUROPOEM I surrogate exposure value)

− WR = 9 ha/d (typical value for arable crops)

− ST = 7 min/ha (typical for arable crops in Belgium) (Pineda, 1998)

− Exposure duration of one minute− AbD = 0.1

Method 2:− Inhaled amount of spray liquid equals 0.03 ml/m³ (90 th percentile value, EUROPOEM II,

2002) − Spraying volume is set to 200 l/ha (typical value for arable crops in Belgium) (Pineda,

1998) − Inhalation rate is set to 1.25 m³/hr − Exposure duration of one minute − AbD = 0.1

The results for the inhalation exposure for the active substances listed in the standard spraying

scheme are presented in Table XI.10.2-3 (see Annex X). When comparing both methods, it is

obvious that the second method is more conservative than the first one. The second approach

for calculating inhalation exposure gives rise to values which are a factor 2.73 larger than the

values calculated using approach one. This implies a significant difference between both

methods. But which impact does this have on the overall bystander risk index? Statistical

analysis conducted with S-plus version 6.1 professional showed that the RI values calculated

using both methods do not differ significantly (One-way ANOVA, α=0.05, p = 0.96) up to a

distance of 20 m. This can be explained by the fact that bystander exposure mainly occurs

through the dermal exposure route. The second method for assessing inhalation exposure is

preferred since this approach calculates bystanders’ inhalation exposure on the basis of

experimental data and is the more conservative method. Moreover inhalation data from field

experiments conducted by Vercruysse (2000) correlated very well to the CSL data. However,

data from Mazzi et al. (1999) showed 20 fold higher inhalation exposures compared to the

CSL data. Moreira et al. (1999) mentioned that a model based on the amount of airborne drift

droplets of active substance at a certain distance from the field, would probably give a better

correlation with the potential inhalation exposure of the bystander.

Both the ACP and the RCEP recommended validation of the previous approaches. They

should be supported by monitoring of representative pesticides in a range of field conditions.

Such monitoring should include measurements of concentrations in air for extended periodsafter spraying to contribute to better understanding of possible exposure other than through

immediate spray drift. The ACP suggested a stepwise approach be taken to such research,

with the aim of identifying potential worst-case scenarios (ACP, 2005).



148

The factors influencing inhalation exposure for both indicator approaches are listed below.

Method 1:Ia, ST and WR vary according to crop type and application technique. The exposure duration

is set to a default value of 1 minute.

Method 2:The inhaled amount of spray liquid varies according to crop type and application technique as

well as the spraying volume applied. The inhalation rate is set to a default value of 1.25 m³

per hour.

USES approach for assessing bystander/residential exposure nearby greenhouses

Mensink (2004) validated the module for assessing acute exposure nearby greenhouses.

Model analysis, including validation of the calculation procedure, was only possible to a

limited extent and in a theoretical way, due to a general lack of measurements under

controlled conditions. Therefore further work is required.

However, in spite of this data lack, some cautious comparisons between estimated and

measured concentrations were made. The estimations of only a few measurements inside a

greenhouse (Baas & Huygen, 1992) could be compared with the calculation scheme

outcomes. A reasonable goodness of fit was found between estimated and empirically derived

data for dichlorvos inside greenhouses. Theoretically, the initially measured concentrations

should equal the estimated concentrations, immediately after application (thus at t=0). This

seems least valid when non or slightly (e.g. fenbutatinoxide) volatile pesticides are considered

or when high-volume techniques (i.e. with the lowest α) are applied. For highly volatile

pesticides, therefore, the calculation scheme seems to yield rather realistic concentrations

inside the greenhouse, whereas in other scenarios the outcomes seem to overestimate theseactual concentrations. In conclusion, the calculation schemes for the different scenarios

probably reflect a worst-case approach, except for highly volatile pesticides. The likelihood

of underestimating exposure of nearby residents to these pesticides seems small, whereas

overestimating exposure to less volatile pesticides may still occur.

Comparing the concentrations inside the greenhouse with measured values inside the

greenhouse shows that the estimated concentrations are within 55-450% of the measured

concentrations (Mensink, 2004). The proposed calculation approach compared with the

model calculations of an earlier USES version yields an outdoor concentration of 1-67% of

the USES 4.0 estimations. It is interesting to note that the EPA estimated a concentration

inside a greenhouse one hour after using dichlorvos, with closed roof and sides - socomparable with the lack of ventilation via windows as in the USES proposal - of 9803 µg/m³

at a dosage of 3.2 kg/ha dichlorvos. This dosage would calculate a Cgh,inair,t=1h of 7536 µg/m³

according to the proposed calculation scheme: 24% lower, though the order of magnitude of

the EPA model (Mensink, 2004).

There are almost no data available about cause-effect relations between pesticide use and

emissions to open air nearby greenhouses under controlled conditions. Some older research

with methylbromide was used for preliminary validation. Its horticultural use in greenhouses

has by now been banned in several Member States (Mensink, 2004). Fumigation at 700 kg/ha

showed during the first hours after application at 20 m distance gas-phase concentrations of

5900 µg/m³ (WHO & IPCS, 1995). The proposed calculation scheme yields 34489 µg/m³,

which is six times higher. The difference may be explained by the use of gas-tight film in the



149

experiments, yielding a very small ventilation rate, probably smaller than the ventilation flow

rate of a “closed” greenhouse.

Dependent on the input parameters and their variation, and based on realistic dosages, the

outcomes of the calculations may vary substantially (up to circa five orders of magnitude,

when varying wind speed and ventilation flow rate), indicating a large range of exposureconditions. Although the outcomes had been expected to represent “realistic worst-case”

conditions - which implies that the underlying assumptions show both a “worst-case” and a

“realistic” component -, the “realism scale” is difficult to judge. Taking into account that the

calculations have been based on processes (volatilisation, deposition, ventilation) and that

various aspects of the calculation scheme have been based on experimental values

(ventilation flow rate constants, deposition rate constants, α values), the scheme is

supposedly realistic. However, the assumptions in the calculation scheme cannot be tested

sufficiently as actual measurements of both concentrations inside the greenhouse and in lee

side eddies are very scarce or lacking. As mentioned before, some comparisons with

measured concentrations inside greenhouses were made, and - cautiously concluded - these

comparisons reveal that the scheme may be realistic for volatile pesticides, but probably notfor less volatile pesticides. This is, however, based on only a few measurements on dichlorvos

and methylbromide, and less volatile pesticides as parathion-ethyl and fenbutatinoxide.

The approach presented assumes that substantial emissions occur during the first hours after

application with closed windows (conform Baas & Huygen, 1992), only acute exposure (up to

a few hours) is thus taken into account. Longer, e.g. subacute exposure (roughly up to circa

four weeks after application) may not be relevant assuming that after a few hours the larger

part of gas-phase pesticides in or around greenhouses has been dissipated. The likelihood of

subacute (or longer: semi-chronic and chronic) exposure should take into account the

pesticide emission patterns over weeks to years. Then other potential peak emissions - e.g. re-

opening ventilation windows prior to personnel re-entry - may be investigated as well. Such

an investigation will have to focus on (very) low concentrations of gas-phase pesticides,

which still is a tedious, laborious and costly affair (Mensink, 2004).

For use in risk assessment procedures, it may cautiously be concluded that if for less volatile

pesticides no toxicologically relevant exposure is indicated by the calculations, the pesticide

can be considered to be used safely, respecting nearby residents. If the calculations reveal a

substantial and possibly toxicologically relevant exposure, submitted data should be

reconsidered and a higher tier exposure assessment should be an option. Besides being a

systematic help in analysing preliminary exposure scenarios (via USES, via related scientific

research), the calculation scheme may be helpful for other purposes as well. It may e.g. behelpful in the area of triggering higher-tier studies, in this case: to obtain more reliable

exposure data and subsequent decision-making (Mensink, 2004). By using the proposed

calculation scheme the outcomes may reveal e.g. whether:

empirical higher-tier data should be required from the registrant when certain trigger

values are exceeded (e.g. higher-tier data on pesticide emissions in relation to droplet

distributions, fluxes, concentrations, inhalatory toxicity);

risk mitigating measures (energy, screens, lower dosages,…) should be required.



150

The USES approach has several limitations (Mensink, 2004):

Degradation in air has not been taken into account due to a lack of data. The pesticides

that have been studied, however, are probably resistent against photochemical degradation

during the first hours after application. In this way, the likelihood of overestimating gas-

phase pesticide concentrations within the first hour seems limited. It should be notedthough that the WHO indicated that diclorvos is rapidly degraded in air, its rate depending

on air humidity (WHO & IPCS, 1995);

The calculation scheme in the present report is not designed for use in site-specific

assessments. It is only applicable for generic assessments; When the assessment in USES is in accordance with the proposed calculation scheme of

Mensink (2004), then particular technical and physical standard conditions are the

starting points. However, these conditions can be adapted, to a certain extent;

It is recognised that certain process formulations are based on limited research and need

to be improved. Further research may reveal further improvements of the calculation

scheme;

The outcomes are based on a dosage frequency of one, whereas multiple dosagesregularly occur under horticultural conditions;

Re-volatilisation after deposition (as subsequent delivery) has not been taken into

account. Inclusion of that aspect would require temperature as input. Therefore,

underestimation of gas-phase pesticide concentrations inside greenhouses may occur;

The decrease of the gas-phase pesticide concentration in greenhouses is assumed to be due

to deposition and ventilation only. Partitioning into droplets e.g. has not been taken into

account. Therefore overestimation of gas-phase pesticide concentrations inside

greenhouses may occur.

In respons to these limitations Mensink (2004) formulated recommendations to the present

approach in anticipation of proper and useful experimental research. Such proper and useful

research should focus on technical aspects as:

The validity of the proposed calculation scheme: analytical measurements (monitoring)

both inside and outside greenhouses under controlled conditions should be included. If the

validity is not shown, the impact of various process-based parameters (e.g. rate constants)

and other parameters (e.g. K gh and α) has to be analysed. The role of temperature should

be taken into account. Prospective investigations should also take into account that

greenhouses can be expected to be largely scaled-up. Currently, there is no guarantee that

the calculation outcomes are more reliable than the outcomes of previous USES versions,

particularly in view of the poor validation status. Until better input data are available toadjust and improve the exposure assessment for horticultural workers and nearby

residents, it should be realised that from a scientific point of view, lack of data - exposure

data for residents nearby greenhouses in particular - should be reflected in relatively large

uncertainty margins;

Concerning risk assessment for residents nearby greenhouses, exposure assessment should have sufficient resolution in order to be compared with inhalatory no-effect levels

for humans. Therefore it is important to develop exposure models on equal terms with the

effects assessment. The availability of a very detailed and realistic exposure assessment

should not be undone by very rough limit values (that “do not need” such a detailed

exposure assessment). Thus, the effects assessment should be better founded. Only then,

monitoring over longer periods may reveal indications for sub-acute, semi-chronic or chronic effects. Moreover, there is also not much known about pesticide cocktails that



151

may show cumulative effects when pesticides are simultaneously applied, or about the

(potential) effects of small concentrations over a longer period of exposure (if realistic)

(Leistra et al., 2001);

Only three volatility classes have been taken into account conform Leistra et al. (2001),

whereas previous versions of USES discern five volatility classes. In this way, the scheme

could be refined; The scenario causing the least pesticide emissions is one for a moderately volatile

pesticide and not for a slightly volatile pesticide as one could intuitively expect. It should

be investigated whether this difference is real, as there seems no plausible explanation for

this difference;

The α values are based on experimental recoveries, immediately after application

(gasphase concentrations at t=0). The advantage of such correction factors is that they

refer to actual conditions, and that they are easy to substitute by more extensive data in

this field. However, as they refer to a few experiments with three different pesticides only,

their foundations are weak (no testing whether α values are statistically significant).

Besides, the effects of the temperature under the experimental conditions were not clearly

reported. It is therefore recommended that further experiments should aim at a more solid

determination of useful and reliable α values. Mass balances will be helpful to validate

these α values. This recommendation had also been postulated in (Baas & Huygen, 1992).

In conclusion, the quality of the available α values does not seem to legitimate yet their

inclusion in the USES proposal. As an example it can be mentioned that it is not clear why

the α values for dichlorvos (0.42 and 0.60, therefore 0.51 as average) were lower than

those for the less volatile parathion-ethyl (0.71), whereas the application technique was

the same (LVM) (Baas & Huygen et al., 1992). This may have been due to a larger

preference of dichlorvos to partition to condensation droplets (pers. comm. Deneer,

Alterra to RIVM, 2004). Processes may therefore be more complex than the calculation

scheme suggests. One may wonder whether such complex and simultaneously occurring processes as volatilisation, deposition and ventilation can be modelled sufficiently by such

a straightforward approach as presented by Mensink (2004). Experiments of Wang &

Deltour (1999) showed some of the complexities of modelling air movements in large

multi-span greenhouses. They found that in a 1728 m² completely closed Venlo-type

greenhouse, a surprisingly strong airflow inside the greenhouse was caused by a strong

external wind (8.33 m/s). These internal airflows were in the reverse direction as the

external wind and probably induced by buoyancy forces and forced convection due to

leakage. This may disprove that higher wind speeds cause lower lee side eddy

concentrations, as assumed by the proposed calculation scheme;

The assumption of photochemical stability of dichlorvos, parathion-ethyl and

fenbutatinoxide during at least the first hours after application should be better investigated. The inclusion of such data in (re)registration dossiers should be considered.

In recapitulation, it seems reasonable to advance the next zero hypothesis (H0): highly, or

under some application conditions (i.e. space treatment) also moderately, volatile

pesticides will show measurable gas-phase concentrations inside a greenhouse conform

the proposed calculation scheme. The estimations may deviate increasingly (estimations

increasingly exceeding measured concentrations) for scenarios with supposedly less gas-

phase emissions. Due to the nearly complete lack of measurements in ambient air nearby

greenhouses under controlled conditions, (almost) immediately after pesticide

applications, statements on validity of the scheme are not possible yet.



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IX. Conclusion

Following the switch in responsibility from DG SANCO to EFSA the required high priority

of an improved human exposure assessment was achieved. Significant progress has been

made in the area of occupational exposure assessments for pesticides. However, the currently

used approaches are based on very old exposure data, some of which were considered

inappropriate or even wrong by the EUROPOEM expert group. This holds mainly for

operator exposure. Currently a large set of new data, developed according to the recent views

on how to collect exposure data, are ‘on hold’ within industry. Industry is prepared to make

these data publicly available in an improved approach such as AHED (van Hemmen pers.

comm., 2006). The re-entry worker and bystander risk assessments are currently not carried

out in line with the proposals of EUROPOEM II and recent extensive reviews have led to the

conclusion that residential exposure assessment is strongly needed. These issues imply the

need for an intensive human exposure discussion and innovative approaches for assessing

human exposure. Therefore van Hemmen (pers. comm., 2006) proposes to install an expertgroup with national experts under the umbrella of EFSA (PPR Panel) with support of industry

experts, similar to the EUROPOEM expert group.

In recent years, there has been considerable activity in harmonizing the different approaches

and methods for assessing occupational exposure. A series of workshops designed to promote

harmonization have been pivotal to the successes to date (Henderson et al., 1993; Curry et al.,

1995; Bergeron et al., 1997, van Hemmen & Van der Jagt, 2001). However, given the rapid

evolution of the scientific discipline of occupational exposure assessments, continued

progress is very important. There are still numerous opportunities for improving the

consistency, transparency and efficiency of occupational exposure assessments through

harmonization. Norman (2005) identified several items which need to be considered as priorities for harmonization. These priorities are listed below and can be considered as good

starting points in the view of future discussions on human exposure.

First of all a common understanding of the terminology used in occupational exposure

assessments is an important prerequisite to effective harmonization. For example, inconsistent

use of terms such as exposure/dose and upper bound/worst-case is confusing and can be

impediment to effective harmonization (Norman, 2005).

Secondly a framework should be established for post-application agricultural exposure

assessments and exposure assessments for residents living in agricultural areas in order to

address data requirements. In case of post-application exposure more effort is required toreach consensus on specific inputs, including selection of ‘day zero’ default residue values

and generic transfer coefficients. EUROPOEM II has proposed a harmonized approach, but

up to now this approach has not yet officially been adopted. In addition, default dissipation

kinetics merit discussion. Some jurisdictions have assumed 10% per day, while others assume

no dissipation. Further research is also needed in order to develop approaches for estimating

inhalation exposure of agricultural workers to volatile pesticides. This research should

consider outdoor re-entry workers as well as greenhouse workers. Regarding residents living

in agricultural areas, an approach could be developed based on additional field work and the

EUROPOEM approach for bystanders (van Hemmen, 2006). Recent extensive reviews

indicated the importance of such residential exposure (and thus) risk assessments. Moreover,

since many jurisdictions waive the requirement for a quantitative exposure and risk

assessment on a case-by-case basis, citing negligible exposure, clear guidance on what



153

constitutes negligible exposure should be provided and a harmonized set of criteria for

making this determination should be established (Norman, 2005).

A third option for further harmonization is the standardization of data requirements. For

conventional pesticides an assessment of exposure is required if the proposed use pattern

indicates potential handler exposure (either occupational or residential) or agricultural re-entry exposure. The extent to which exposure assessments are required for residential re-entry

scenarios or bystanders is less uniform. For these scenarios, data requirements should be well

established and standardized. For example, subpopulations of bystanders which merit

quantitative exposure assessments (e.g. residential exposure in agricultural areas) have to be

identified (Norman, 2005). Specifically for children’s exposure assessments a lot of

improvement is required, particularly in Europe.

Moreover, methodological guidance on several topics has to be provided in order to obtain

the necessary data. Guidance is needed on: 1) how to conduct exposure studies to measure

post-application exposure & residential exposure in agricultural settings; 2) how to derive

transfer metrics for post-application exposure scenarios in agricultural settings, when thetreated surface of interest is not foliage, e.g. soil; 3) how to validate the in vitro dermal

absorption methodology; 4) how to interpret residues remaining on washed skin in in vivo

dermal absorption studies; 5) how to conduct dermal absorption studies with human

volunteers in this way that ethical issues are respected (Norman, 2005).

Concerning database development, it should be noted that updating datasets is very

important. For mixer/loader/applicator databases, such as EUROPOEM, updating is essential

so that a broader range of mixer/loader and applicator functions can be reflected and newer

trends in agricultural application technology can be captured. This need has been recognized.

For harmonized exposure assessment between North America and Europe, the use of the

AHED database, which combines the PHED and EUROPOEM database has been proposed.

Region-specific considerations could be accommodated by additional subsetting options.

Regarding the development of databases for agricultural workers, a lot of improvement is still

required. For agricultural worker assessments in the EU there is a fundamental shortage in

data. Also for bystanders and residents there is a fundamental lack of data (Norman, 2005).

Regarding modelling initiatives there is an urgent need in developing guidance regarding

model validation criteria, good modelling practices and criteria for model-based assessments

(Norman, 2005).

Current challenges in the field of data analysis and interpretation of data from occupationalexposure studies are numerous. It is important that consistent approaches to data analysis be

adopted in order to facilitate review sharing and consistent decision-making. Areas where

there is currently divergence in approaches, or no clearly outlined approach, are noted below

(Norman, 2005):

Harmonization of exposure factors (including physiologiocal (body weight,

body surface, life expectancy,…), pesticide usage (hectare treated per day,

application rates, equipment used,…) and lifestyle factors (human activity

data)). Standardization is necessary; Mathematical approach: Guidance for selecting an appropriate mathematical

approach (deterministic versus probabilistic) for a give exposure scenario andguidance regarding conduct of acceptable probabilistic assessments is needed;



154

Harmonized guidance for specific data analysis issues (i.e. corrections for

incomplete field recovery, interpretation of residues present below the limit of

detection, guidance for interpretation of outliers and atypical observations); Harmonized guidance for appropriate expressions of exposure (daily,

time-weighted average or amortized expressions of exposure).

An aspect that should also be considered in the near future is the selection of central tendency

and appropriate metrics for risk assessment addressing significant toxicity endpoints.

Regardless of the mathematical approach used, it is important that harmonized guidance exists

for metric selection. Guidance should be based on a number of considerations, including data

quality, data quantity, distribution type, duration of exposure and nature of the toxicology

endpoint. In the U.S. recent consulations on central tendency selection for the Pesticide

Handlers Exposure Database (PHED) resulted in general guidance that may be applicable

beyond PHED. This consulation resulted in the following general guidance recommendations

(Norman, 2005):

For short-term exposures, the median is the appropriate measure of central

tendency. This guidance is based on the recognition that, for the two most prevalent distribution types (log-normal and normal), the median approximates

the mean (i.e. geometric mean for log-normal distribution and arithmetic mean

for normal distributions;

For longer term exposures, the arithmetic mean is the most appropriate

measure of central tendency. This is based on the recognition that the central

limit theorem dictates that with multiple exposure events, the average of these

events will converge to the arithmetic mean of the original distribution from

which the events were drawn, and that the distribution of those averages will

follow a normal distribution, regardless of the form of the original underlying

distribution;

For risk assessments addressing significant acute toxicity endpoints, the

arithmetic mean or another reasonably high-end measure of exposure should

be selected. Such high-end measures of exposure could include an upper

percentile from a probabilistic output or a maximum. Guidance is needed on

what considerations should lead to the use of such high-end measures of

exposure. Factors to be incorporated would need to include the nature of the

toxicological endpoint, existing toxicologically based uncertainty or safety

factors, and the confidence in the exposure estimates;

Such guidance requires international debate and consensus as there can be differences among

what regulatory authorities will utilize and this can lead to divergence in country-specificasessments. Guidance is also required on the appropriate use of time-weighted average and

amortized exposure estimates as the selection of a daily versus time-weighted average versus

a lifetime daily average exposure can lead to divergent risk assessment outcomes. Guidance in

this area would be a function of several factors, including exposure duration, frequency and

interval, the toxicology endpoint, mechanism of action considerations and documented

sensitivities to specific subpopulations. Harmonized decision logic is required. An important

aspect would be addressing appropriate data analysis approaches for generating time-

weighted average or lifetime average exposures across age categories, including juvenile

categories (Norman, 2005).



155

Regarding research needs, there is a requirement to develop harmonized priorities for

research that would strengthen risk assessment methodologies for pesticides. This would

imply the development of an effective communication strategy so that funding bodies and the

research communities are informed about those priorities (Norman, 2005).

Concerning exposure mitigation, harmonization is needed regarding the degree of protection provided by personal protective equipment (clothing, gloves, respirators) and engineering

controls (mixing/loading systems, cabs,…). This should be feasible as region-specific

considerations should be minor (Norman, 2005). The Netherlands has commissioned TNO to

prepare a document based on recent data and on approaches taken on, throughout Europe and

North America, on the possibilities of harmonization of PPE efficacies as used for risk

management purposes. This report is due by the end of 2006 and is based on frequent

consultation of national authorities in the EU and North America (van Hemmen pers. comm.,

2006). Labeling of pesticides and the establishment of re-entry intervals should also be

harmonized (Norman, 2005).

Concerning occupational and residential risk assessment convergence to a commonexpression of risk is feasible and would promote harmonization. Currenly the EU applies

Acceptable Operator Exposure Levels to compare the exposure to, while the U.S. uses

Margins of Exposure. Moreover, route-specific assessments should be considered when the

toxicology database indicates the need for such assessments or when appropriate toxicology

studies exist using the dominant route of exposure. Guidance is needed on when and how to

do this. Also, consistent approaches to application of uncertainty and safety factors are

needed. In the EU, there is consensus on the use of 10 to account for interspecies differences

in toxic response to a chemical, but there are different approaches in the intraspecies factor.

These differences have not been fully resolved (Hamey, 2000). There is also a need for a

coordinated method for developing aggregate and cumulative risk assessments (Norman,

2005).

Enhancing harmonization can also be achieved by means of cooperative regulatoryactivities. In 1999, a cooperative international review team, comprised of Canada, the United

States, Australia and the European Union (Ireland), participated in a pilot project that focused

on exchange and utilization of each others’ reviews. The pilot project confirmed that the

shared approach to review being taken by regulatory agencies worldwide, leads to more

efficient regulatory systems, while still allowing each country to uphold their own rigorous

standards for health and environmental protection (Norman, 2005).

It is clear that a lot of work still needs to be done.



156

X. Cited literature

ACP (December 2005). Crop Spraying and the health of residents and bystanders. A

commentary on the report published by the Royal Commission on Environmental Pollution in

September 2005. 79p. http://www.pesticides.gov.uk/acp_temp/RCEP_Response_vfinal.pdf.

AEI, Agricultural Engineering Institute (1987). The effect of shelter on spray drift from

orchard spraying. Report N° CR358, Agricultural Engineering Institute, Hamilton, New

Zeeland, 24p.

AG. (2004). Pesticides with drift retardant less a flight risk.

http://www.agriculture.purdue.edu/aganswers/story.asp?storyID=3702.

Agrow N0 433 October 3rd 2003. EPA sued over risks to children.

Anderson, H.C., Solgaard, J. & Anderson, I. (1976). Nasal cancer and nasal mucus transport

rates in woodworkers. Acta Otolaryngol Stockholm, 82, 263-265.

Anon (1994). In: Farm management handbook 1994. Ed: Chadwick L. Edinburgh: The

Scottish Agricultural College.

Anon (1996). Food Quality Protcetion Act of 1996. Pub L 104-70.

Anon (1997). The development, maintenance and dissemination of a European predictive

operator exposure model (EUROPOEM) database. Draft final report, BIBRA International,

Carshalton (UK).

APVMA (2005). Operating principles and proposed registration requirements in relation to

spray drift risk. Australian Pesticides & Veterinary Medicines Authority.

Arbuckle, T.E., Lin, Z.Q. & Mery, L.S. (2001). An exploratory analysis of the effect of

pesticide exposure on the risk of spontaneous abortion in an Ontario Farm population.

Environmental Health Perspectives, 109, 8, 851-857.

Arnich, N., Cervantés, P. et al. (2005). Evaluation des risques pour la santé humaine liés à une

exposition au fipronil, AFSSA and AFSSE.

ARS (2006). Research Project: Development of pesticide application technologies for spray-

drift management and targeted spraying. Agricultural Research Service, United States

Department of Agriculture.

Arvidsson, T. (1997). Spray drift as influenced by meteorological and technical factors. A

methodological study. Swedish University of Agricultural Sciences. Acta Universitatis

Agriculturae Sueciae, Agraria 71, 144p.

Ashford, N.A., Spadafor, C.J., Hattis, D.B. & Caldart, C.C. (1990). Monitoring the Worker

for Exposure and Disease: Scientific, legal and ethical considerations in the use of

biomarkers. The Johns Hopkins University Press, Baltimore, MD.



157

Baas, J. & Huygen, C. (1992). Emissions of agricultural pesticides from greenhouses to open

air. TNO Report no. IMW-R 92/304 [in Dutch].

Baltin, F. (1959). The Baltin formula. Agric. Aviat., 1 (104) p 2-6.

Balsari, P. & Marucco, P. (2004). Sprayer Adjustment and Vine Canopy ParametersAffecting Spray Drift: The Italian Experience. International Conference on Pesticide

Application for Drift Management, Waikoloa, Hawai, DEIAFA – University of Turin.

Barr, D.B., Kent, T., Curwin, B., Landsittel, D., Raymer, J., Lu, C., Donnelly, K.C. &

Acquavella, J. (2006). Biomonitoring of exposure in farmworker studies. Environmental

Health Perspectives, 114 (6), 936-942.

Bates, J.A.R. (1990). The prediction of pesticide residues in crops by the optimum use of

existing data. Pure and Applied Chemistry, 62, 337-350.

BBA (2004). Aktuelle Abtrifteckwerte. Biologische Bundesanstalt für Land- undForstwirtschaft. http://www.bba.de/ap/ap_gereate/abtrift/abtrift.xls.

Bearer, C.F. (1995). How are children different from adults? Environmental Health

Perspectives, 103, 7-12.

Benoît, M. & Bonicelli, B. et al. (2005). Expertise scientifique collective: Pesticides,

agriculture et environnement. Chapitre 2: Connaissance de l'utilisation des pesticides, INRA

and CEMAGREF.

Bergeron, V., Norman, C. & Worgan, J. (1997). Workshop on Post-Application Exposure

Assessment. Final Report, Toronto, ON, Canada.

BES (2002). Pesticide Spray Drift. Bullseye Environmental Services.

Blundell, T. (2005). Crop Spraying and the health of residents and bystanders. Royal

Commission on Environmental Pollution, 184p.

Briand, O., Millet, M., Bertrand, F., Clément, M. & Seux, R. (2002). Assessing the transfer of

pesticides to the atmosphere during and after application. Development of a multiresidue

method using adsorption on Tenax and thermal desorption-GC/MS. Analytical and

Bioanalytical Chemistry, 374, 848-857.

Brouwer, D.H., De Haan, M., Peelen, S., Van de Vijver, L. & van Hemmen, J.J. (1992).

Dislodgeable foliar residues as an estimate of source strength for worker exposure to

pesticides. TNO Nutrition and food research, department of Occupational Toxicology,

Rijswijk, The Netherlands.

Brouwer, D.H., De Vreede, M., De Haan, M., Van De Vijver, L., Veerman, M. & van

Hemmen, J. (1994). Exposure to pesticides during and after application in the cultivation of

chrysanthemums in greenhouses. Health risk and risk management. Med. Fac. Landbouw.

Univ. Gent, 59/3B, 1393-1401.



158

Brouwer, D.H. & van Hemmen, J.J. (1997). Reduction of exposure to pesticides with

protective clothing: implications to risk assessment for registration purposes. Brighton Crop

Protection Conference: Weeds. Proceedings of an International Conference, Brighton, UK,

17-20 November, 1059-1065.

Brouwer, D.H., De Haan, M. & van Hemmen, J.J. (2000). Modelling re-entry exposureestimates: techniques and application rates. Worker Exposure to Agrochemicals. Eds.:

Honeycutt, R.C., CRC Press, Lewis Publishers, Baton Rouge, Fl, USA, 119-138.

Brouwer, D.H., Marquart, J. & van Hemmen, J.J. (2001). Proposal for an approach with

defaults values for the protection offered by PPE under European new or existing substance

regulations. Ann. Occup. Hyg., 45, 543-553.

California Environmental Protection Agency, Air Resources Board (1998). Report for the

application and ambient air monitoring for chlorpyrifos (and the oxon analogue) in Tulare

County during spring/summer 1996.

Camann, D.E., Majumadar, T.K. & Geno, P. 1995. Determination of Pesticide Removal

Efficiency from Human Hands Wiped with Gauze Moistened with Three Salivary Fluids.

Final Report to the U.S. EPA by ManTech under Contract 68-D5-0049. Research Triangle

Park, North Carolina: ManTech, 1995.

Cannon, S.B., Veazey, J.M., Jackson, R.S., Burse, V.W., Hayes, C., Straube, W.E.,

Landrigan, P.F. & Liddle, J.A. (1978). Epidemic Kepone Poisoning in Chemical Workers.

American Journal of Epidemiology, 107, 529-537.

Cassee, F.R., Groten, J.P., van Bladeren, P.J. & Feron, V.J. (1998). Toxicological Evaluation

and Risk Assessment of Chemical Mixtures. Crit. Rev. Toxicol., 28, 73-101.

Cassee, F.R., Sühnel, J., Groten, J.P. & Feron, V.J. (1999). Toxicology of Chemical Mixtures.

In general and applied toxicology. Edited by Ballantyne, B., Marrs, T.C. & Syversen, T.

London, Macmillian Reference Ltd, 303-320.

Chiaocheng, J.H., Reagan, B.M., Bresee, R.R., Meloan, C.E. & Kadoum, A.M. (1988).

Carbamate insecticide removal in Laundering from Cotton and Polyester Fabrics. Archives of

Environmental Contamination and. Toxicology, 17, 87-94.

Clevenger, M.A., Putzrath, R.M., Brown, S.L., Ginevan, M.E., Derosa, C.T. & Mumtaz,M.M. (1991). Risk Assessment of Mixtures: a model based on mechanisms of action and

interaction. In: Risk Analysis. Prospect and Opportunities. Edited by: Zervos, C. new York,

Plenum, 293-303.

Clifford, N.J. & Nies, A.S. (1989). Organophosphate Poisoning from Wearing a Laundered

Uniform previously contaminated with parathion, JAMA – Journal of the American Medical

Association, 262, 3035-3036.

Clothier, J.M. (2000). Dermal Transfer Efficiency of Pesticides from Turf Grass to Dry and

Wetted Palms. U.S.EPA Contract 68-D5-0049 Prepared for the U.S. EPA, National Exposure

Research Laboratory, Research Triangle Park, North Carolina.



159

COT (2002). Risk Assessment of Mixtures of Pesticides and Similar Substances. Food

Standards Agency. London. Available (October 2004) at http://www.food.gov.uk/multimedia/

pdfs/report(indexed).pdf.

CRP (2004). Guide de bonne pratique phytosanitaire - Partie générale - 2004, Comité régional

PHYTO, Ministère de la Région Wallonne, Direction du Développement et de laVulgarisation.

CSIRO (2002). Primary industries standing commitee spray drift management - Principles,

strategies and supporting information - PIS (SCARM) Report 82, CSIRO PUBLISHING.

Curry, P.B., Iyengar, S., Maloney, P.A. & Maroni, M. (Eds) (1995). Methods of pesticide

exposure assessment. NATO Committee on the Challenges of Modern Society, Plenum Press,

New York.

De Cock, J., Heederik, D., Kromhout, H., Boleij, J.S., Hoek, F., Wegh, H. & Tjoe Ny, E.

(1998). Exposure to captan in fruit growing. American Industrial Hygiene AssociationJournal, 59, 158-165.

De Heer, H., Schut, C.J., Porskamp, H.A. & Lumkes, L.M. (1985). Depositie- en

driftmetingen bij conventionele en nieuwe typen spuitmachines in een tarwe-, spruitkool- en

aardappelgewas. Gewasbescherming, 16, 185-197.

De Heer, C., Wilschut, A., Stevenson, H. & Hakkert, B.C. (1999). Guidance document on the

estimation of dermal absorption according to a tiered approach: an update. TNO report N° V

98.1237. TNO Nutrition and Food Research Institute, Zeist, the Netherlands.

Dorr, G., Woods, N., Craig, I. (1998). Buffer zones for reducing drift from the application of

pesticides. Paper N°. SEAg 98/008. International Conference on Engineering in Agriculture.

DPR – Department of Pesticide Regulation (2001). Worker Health and Safety Branch Policy

on the Estimation of Short-term, Intermediate-term, Annual and Lifetime Exposures. October

4, 2001.

Driver, J.H. Kinz, J.J. & Whitmyre, G.K. (1989). Soil adherence to human skin. Bull.

Environ. Contam. Toxicol., 43, 814-820.

Durkin, P.R., Rubin, L., Withey, J. & Meylan, W. (1995). Methods of assessing dermalabsorption with emphasis on uptake from contaminated vegetation. Toxicol. Ind. Health, 11,

63-79.

Easter, E.P. & Nigg, H.N. (1992).Pesticide protective clothing. Reviews of Environmental

Contamination and Toxicology, 128, 1-16.

EC (2001). Guidance for the setting of Acceptable Operator Exposure Levels (AOELs). Draft

Guidance Document. Commission of the European Communities. DG Sanco. 7531/VI/95

rev.6



160

EFSA (European Food Safety Authority) (2006). Consultation of Member States via the

Standing Committee on the Food Chain and Animal Health – Section: Plant Protection

Products – Legislation. Parma, 3 July 2006 REF/BB/gb/2006/1608969.

Elliott, J.G. & Wilson, B.J. (1983). The influence of weather on the efficiency and safety of

pesticide application. The drift of herbicides. BCPC Occasional Publication N° 3, BritishCrop Protection Council, Croydon, UK, 135p.

Eskenazi, B., Bradman, A. & Castorina, C. (1999). Exposure of children to organophosphate

pesticides and their potential adverse health effects. Environmental Health Perspectives, 107,

3, 409-419.

EUROPOEM (1996). The development, maintenance and dissemination of a European

Predictive Operator Exposure Model (EUROPOEM) Database, final report. BIBRA

International, Carshalton, UK, 51p.

EUROPOEM II (2002). The development, maintenance and dissemination of genericEuropean databases and predictive exposure models to plant protection products. A

EUROPOEM Operator Exposure Database. A EUROPOEM Bystander Exposure Datbase and

Harmonized Model. An evaluation of the nature and efficacy of Exposure Mitigation

Methods. A Tiered Approach to Exposure and Risk Assessment. FAIR3 CT96-1406. A

Concerted Action under area 4 of FAIR, the Fourth Framework (Agriculture and Fisheries

including Agro-Industry, Food Technology, Forestry, Aquaculture and Rural Development)

specific Community Research and Technological Development Programme. Draft Final

report. December 2002.

EU (2002). The assessment of operator, bystander and environmental exposure to pesticides.

Final Report. Contract n° SMT4-CT96-2048. 437p.

Faustman, E.M., Silbernagel, S.M., Fenske, R.A., Burbacher, T.M. & Ponce, R.A (2000).

Mechanism underlying children’s susceptibility to environmental toxicants. Environmental

Health Perspectives, 108 (6), 13-21.

Fenske, R.A., Lu, C., Simcox, N.J., Loewenherz, C., Touchstone, J., Moate, T.F., Allen, E.H.

& Kissel, J.C. (2000). Strategies for assessing children’s organophosphorus pesticide

exposures in agricultural communities. Journal of Exposure Analysis and Environmental

Epidemiology, 10, 662-671.

Fenske, R.A. & Day Jr, E.W. (2005). Assessment of Exposure for Pesticide Handlers in

Agricultural, residential and Institutional Environments. In: Occupational and Residential

Exposure Assessment for Pesticides. Eds: C.A. Franklin & J.P. Worgan. John Wiley & Sons,

Ltd. ISBN: 0-471-48989-1.

Fishel, F. (2006). Personal Protective Equipment for Working With Pesticides. From

http://muextension.missouri.edu/explore/agguides/agengin/g01917.htm.

Flari, V & Hart, A. (2006). Final delivery on the acute terrestrial indicators. WP 6 HAIR

Project.



161

FOCUS (2002). FOCUS Surface Water Scenarios in the EU Evaluation Process under

91/414/EEC. Report of the FOCUS Working Group on Surface Water Scenarios, EC

Document Reference SANCO/4802/2001-rev.2.245 p.

Fox, R.D., Reichard, D.L., Brazee, R.D., Krause, C.R. & Hall, F.R. (1993). Downwind

residues from spraying a semi-dwarf apple orchard. Transactions of the ASAE, 36, 333-340.

Franklin, C.A. & Worgan, J.P. (2005). Introduction and Overview. In: Occupational and

Residential Exposure Assessment for Pesticides. Edited by C.A. Franklin & J.P. Worgan.

John Wiley & Sons, Ltd. Chichester, U.K. p 1-10.

Ganzelmeier, H., Rautmann, D., Spangenberg, R., Streloke, M., Herrmann, M.,

Wenzelburger, H.J., Walter, H.F. (1995). Untersuchungen zur Abtrift von

Pflanzenschutzmitteln. Mitteilungen aus der Biologischen Bundesanstalt für Land- und

Forstwirtschaft Berlin-Dahlem, 304.

Garry V.F. (2004). Pesticides and children. Toxicol Appl Pharmacol, 198, 152-163.

Gerrisen-Ebben, R.M.G., Brouwer, D.H. & van Hemmen, J.J. (2006). Effective Personal

Protective Equipment (PPE). Discussion paper on the use of PPE for handling of

agrochemical, microbiological and biocidal pesticides. Draft Consultation Document.

Gilbert, A.J. & Bell, G.J. (1988). Evaluation of drift hazards arising from pesticide spray

application. Aspects of Applied Biology, 17, 363-376.

Giles, K.D. (2004). Precision Agriculture and Drift Management. International Conference on

Pesticide Application for Drift Management, Waikoloa, Hawai, Department of Biological &

Agricultural Engineering, University of California.

Gurunathan, S, Robson, M, Freeman, N, et al. (1998). Accumulation of chloropyrifos on

residential surfaces and toys accessible to children. Environmental Health Perspectives, 106

(1), 9–16.

Hakkert, B.C., Van De Sandt, J.J.M.H., Bessems, J.G.M. & De Heer, C. (2005). Dermal

absorption of pesticides. In: Occupational and Residential Exposure Assessment for

Pesticides. Edited by C.A. Franklin & J.P. Worgan. John Wiley & Sons, Ltd. Chichester, U.K.

p 318-340.

Hall, F.R. (1990). An integrated approach for improvements in application technology. In:

Save Insecticide Development in the use. Eds: Hodsjom, E. & Kuhr, R., New York, U.S.A.,

453-508.

Hamey, P.Y. (2000). Assessing Risks to Operators, Bystanders and Workers from the use of

plant protection products. In: Human and Experimental Exposure to Xenobiotics. Proceedings

of the XIth Symposium on Pesticide Chemistry, Cremona, Italy, 1-15 September 1999. Eds:

DelRe, A.A.A., Brown, C., Capri, E., Errera, G., Evans, S.P. & Trevisan, M., La Goliardica

Pavese, Pavia, Italy, 619-631.

Hamey, P.Y. (2001). The Need for Appropriate Use Information to Refine Pesticide User Exposure Assessments. Ann. Occup. Hyg, 45, 1001, p S69-S79.



162

Hamey, P. Y. (2003). Presentation to EC, EFSA & MS, 13 november 2003.

Howard, C.V. (2004). The Bystander Model. Presentation at the RCEP Meeting of 25 th

September 2004.

HEEPIBEE (2006). Health and environmental effects of pesticides and type 18 biocides.Report from the contract AP/02/05A between the Belgian Science Policy and Department of

Crop Protection Chemistry, Ghent University1; Veterinary and Agrochemical Research Centre

(VAR)2, Tervuren; Unité de Phytopathologie, Université catholique de Louvain (UCL)3 and

Environmental Consultancy & Assistance (Ecolas)4. Vergucht, S.1; de Voghel, S.2; Misson,

C.3 (until 31/01/06); Vrancken, C.3 (from 01/02/06); Callebaut, K.4; Steurbaut, W.1;

Pussemier, L.2; Marot, J.3; Maraite, H.3; Vanhaecke, P.4

Henderson, P.T.H., Brouwer, D.H., Opdam, J.J.G., Stevenson, H. & Stouten, J.T.H.J. (1993).

Risk Assessment of worker exposure to agricultural pesticides: review of a workshop. Ann.

Occup. Hyg., 37, 499-507.

Hewitt, J. (2001). Drift filtration by natural and artificial collectors: a literature review.

Holland, P.T. & Maber, J. (1991). The drift hazard from orchard spraying. Report to Ministry

of Agriculture and Fisheries, New Zeeland.

Hubal, E.A.C., Sheldon, L.S., Burke, J.M., Mc Curdy, T.R., Barry, M. R., Rigas, M.L.,

Zartarian, V.G., Freeman, N.C.G. (2000). Children’s Exposure Assessment: A review of

factors influencing Children’s exposure, and the data available to characterize and assess that

exposure. Environmental Health Perspectives, 108 (6), 475p.

IEH (Institute for Environment and Health) (2005). Establishment of Common Mechanism

Groups for Pesticides and Similar Substances. A Pilot Study to Establish Resource

Requirements. Food Standards Agency, March 2005, Ref 3/14/5a, 93p.

IPCS (1994) Assessing human health risks of chemicals: derivation of guidance values for

health-based exposure limits. Environmental Health Criteria, 170. World Health Organization,

Geneva.

ILSI (1998). Aggregate Exposure. A Report to EPA/ILSI Workshop. Edited by S. Olin. ILSI

Press, Washington, D.C., 1998.

ILSI (1999). A Framework for Cumulative Risk Assessment. An ILSI Risk Science InstituteWorkshop Report. Edited by: Mileson, B., Faustman, E., Olin, S., Ryan, S., Ference, S. &

Burke, T. ILSI Press, Washington, D.C., International Life Science Institute, available

[October 2004] at http://www.ilsi.org/file/rsiframrpt.pdf.

IPCS (1999) Principles for assessment of risk to human health from exposure to chemicals.

Environmental Health Criteria, 210. World Health Organization, Geneva.

ITV & ARVALIS et al. (2005). Amélioration de l'application des produits

phytopharmaceutiques au niveau environnement: étude des facteurs permettant de limiter la

dérive. Appel à projet ADAR.

Jadin, E. & Marot, J. et al. (2004). Phytos et santé de l'applicateur. Plein Champ 20, 6-7.



163

Jaeken, P. & Vercruysse, F. et al. (1999) Target detection technology in orchard spraying:

drift and overall pesticide reduction potential. Med. Fac. Landbouw. Univ. Gent 64(3b), 803-

812.

JMP (1986). UK Predictive Exposure Model (POEM): Estimation of Exposure and

Absorption of Pesticides by Spray Operators. UK Scientific Sub-committee on Pesticides andBritish Agrochemical Association Joint Medical Panel, Pesticide Safety Directorate, York,

UK.

Kangas, J. & Sihvonen (1996). Comparison of Predictive Models for Pesticide Operator

Exposure. TemaNord 560, Nordic Council of Ministers, Copenhagen, Denmark.

Kaul, P., Moll, E., Gebauer, S. & Neukampf, R. (2001). Modelling of direct drift of plant

protection products in field crops. Nachrichtenblatt der Deutschen Pflanzenschutzsdienst, 53

(2), 25-34.

Kissel, J., Richter, K. & Fenske, R. (1996). Effect of soil loading on dermal absorption

efficiency from ontaminated soils. J. Tox. Environ. Health, 48, 93-106.

Kissel, J.C., Shirai, J.H., Richter, K.Y. & Fenske, R.A. (1998). Investigation of dermal

contact with soil in controlled trials. J. Soil. Contam., 7(6),737–752.

Kodell, R.L., Krewski, D. & Zielinski, J.M. (1991). Additive and multiplicative relative risk

in the two stage clonal expansion model of carcinogenesis. Risk Anal., 11, 483-490.

Korpalski, S., Bruce, B., Holden, L. & Klonne, D. (2005). Dislodgeable foliar residues are

lognormally distributed for agricultural re-entry studies. Journal of Exposure Analysis and

Environmental Epidemiology, 15(2), 160-163.

Krieger, R.I., Blewett, C., Edminston, S., Fong, H.R., Meinders, D.D., O’ Connell, L.P.,

Schneider, F., Spencer, J., Thongsinthusak, T & Ross, J.H. (1990). Gauging pesticide

exposure of handlers (mixer/loader/applicators) and harvesters in California agriculture. La

Med. Lavoro, 81, 474-479.

Krieger, R.I., Ross, J.H., & Thongsinthusak, T (1992). Assessing human exposure to

pesticides. Reviews of Environmental Contamination and Toxicology, 128, 1-15.

Krishnan, K., Andersen, M.E., Clewell, H.J. & Yang, R.S.H. (1994). Physiologically based

pharmacokinetic modelling of chemical mixtures. In: Toxicology of Chemical Mixtures.Academic Press, 399p.

Landers, A. & Farooq, M. (2004). Reducing Drift and Improving Deposition in Vineyards.

International Conference on Pesticide Application for Drift Management, Waikoloa, Hawai,

Cornell University, Geneva, NY, USA.

Layton, D.W. (1993). Metabolically consistent breathing rates for use in dose assessments.

Health Physics, 64, 1, 23-36.

Landrigan, P. J., Claudio, L., Markowitz, S.B., Berkowitz, G.S., Brenner, B.L., Romero, H.,

Wetmur, J.G., Matte, T.D., Gore, A.C., Godbold, J.H. & Wolff, M.S. (1999). Pesticides and



164

Inner-City Children. Exposures, Risks and Prevention. Environmental Health Perspectives,

107, 3, 431-437.

Lee, S., McLaughlin, R., Harnly, M., Gunier, R. & Kreutzer, R. (2002). Community

exposures to airborne agriculture pesticides in California: Ranking of inhalation risks.

Environmental Health Perspectives, 110, 1175-1184.

Leistra, M., van der Staay, M., Mensink, B.J.W.G., Deneer, J.W., Meijer, R.J.M., Janssen,

P.J.C.M. & Matser, A.M. (2001). Pesticides in the air around greenhouses: how to estimate

the exposure of nearby residents and the effects. Alterra rapport nr. 296 [in Dutch].

Lewis, R.G., Fortune, C.R., Willis, R.D, Camann, D.E. & Antley, J.T. (1999). Distribution of

pesticides and polycyclic aromatic hydrocarbons in house dust as a function of particle size.

Environmental Health Perspectives, 107, 721-726.

Lu, C.S., Fenske, R.A., Simcox, N.J.& Kalman, D. (2000). Pesticide exposure of children in

agricultural community. Environmental. Research, 84, 3, 290-292.

Lundehn, J.R., Westphal, D., Kieczka, H., Krebs, B., Locher-Bolz, S., Maasfeld, W. & Pick,

E.D. (1992). Uniform Principles for Safeguarding the Health of Applicators of Plant

Protection Products. Mitteilungen aus der Biologische Bundesanstalt for Land und

Forstwirtschaft, Berlin-Dahlem, Heft 277, Kommissionsverslag Paul Parey, Berlin, Germany,

61p.

MAFF (2000). The June 2000 census – Questions and answers, 2000a.

http://www.maff.gov.uk/esg/pubs/pubs.htm.

Maraite, H., Steurbaut, W. & Debognie, P. (2004). Development of awareness tools for a

sustainable use of pesticides. Final report. Sustainable production and consumption patterns

(SPSD II). Belgian Science Policy (BSP), 105 p.

Marot, J. J. G. et al. (2003). Agriculteurs et pesticides: connaissances, attitudes et pratiques:

Résultats d'une enquête menée en fruiticulture, maraîchage et grandes cultures (2002-2003),

SPP-Politique Scientifique, Universiteit Gent, UCL, CERVA-CODA.

Marquart, J., Brouwer, D.H. et al. (2003). Determinants of Dermal Exposure Relevant for

Exposure Modelling in Regulatory Risk Assessment. Ann. Occup. Hyg., 47(8), 599-607.

Martin, A.D. (1990). A predictive model for the assessment of dermal exposure to pesticides.

In: Prediction of Percutaneous Penetration. Methods, Measurements, Modelling. Scott, R.C.;

Guy, R.H. & Hadgraft, J., IBC Technical Services Ltd, Southampton, Hampshire, UK, p 273-

278.

Matoba, Y & Van Veen, M.P (2005) Predictive Residential Models. In: Occupational and

Residential Exposure Assessment for Pesticides. Edited by C.A. Franklin & J.P. Worgan.

John Wiley & Sons, Ltd. Chichester, U.K. p 209-242.

Matthews, G.A. (1995). Application Technology. Pesticides: developments, impacts, and

controls. Cambridge, The Royal Society of Chemistry, 180.



165

Matthews, G.A. & Hamey, P (2003). Exposure of bystanders to pesticides. Pesticide Outlook.

October 2003.

Matthews, G. Pesticides: Health, Safety and the Environment (2006). Blackwell Publishing,

248p.

Maund, S. (2000). Developing scenarios for estimating exposure concentrations of plant

protection products in EU surface waters. Proceedings of the WORMM-Workshop.

Mazzi, F., Capri, E., Trevisan, M., Glass, C.R. & Wild, S.A. (1999). Potential operator,

bystander and environmental exposure in sloped vineyards. Proceedings of the XI Symposium

Pesticide Chemistry, 11-15 September 1999, Cremona, Italy, 731-736.

McKone, T.E. & Ryan, P.B. (1989). Human exposures to chemicals through food chains: An

uncertainty analysis. Environ. Sci. Technol., 23(9),1154-1163.

McKone, T.E. & Daniels, J.I. (1991). Estimating human exposures to multiple pathways from

air, water, and soil. Reg. Toxicol. Pharmacol, 13(1), 36-61.

Mc Lean, J. (2001). Pesticide Drift Management. Agriculture, Food and Rural Development,

Alberta Government, Canada.

McMullan, P.M. (2000). Utility adjuvants. Weed Technology, 14, 792-797.

Meli, S.M. & Renda, A. et al. (2003). Studies on pesticide spray drift in a Mediterranean

citrus area. Agronomie, 23, 667-672.

Mensink, B.W.J.G. (2004). Pesticide emissions from greenhouses. Proposal for the risk

assessment system USES. RIVM report 601450014.

Methner, M.M. & Fenske, R.A. (1994). Pesticide exposure during greenhouse applications.

Part 1: Dermal exposure reduction due to directional ventilation and worker training. Appl.

Occup. Environ. Hyg., 9, 560-566.

Molocznick, A. & Zagorsky, J. (2000). Exposure of female farmers to dust on family farms.

Ann. Agric. Environ. Med, 7, 43-50.

Moreira, J.F., Santos, J., Glass, R., Wild, S.A. & Sykes, D.P. (1999). Measurement of spray

drift with hand held orchard applications. Proceedings of the XI Symposium PesticideChemistry, 11-15 September 1999, Cremona, Italy, 747-754.

Morgan, M.G. & Henrion, M. (1990). Uncertainty: A Guide to dealing with uncertainty in

quantitative risk and policy analaysis. Cambridge University Press. New York, 332p.

Mumtaz, M.M. & Durkin, P.R.. (1992). A weight-of-evidence approach for assessing

interactions in chemical mixtures. Toxicol. Industr. Hlth, 8, 377-406.

Mumtaz, M.M. & Hertzberg, R.C. (1993). The status of interactions data in risk assessment of

chemical mixtures. In: Hazard Assessment of Chemicals, Vol 8. Edited by Saxena J.,

Hemisphere, Washington DC, 43-79.



166

Mumtaz, M.M., Poirier, K.A. & Colman, J.T. (1997). Risk Assessment for chemical mixtures:

fine-tuning the hazard index approach. J. Clean Technol. Environ. Toxicol. Occup. Med., 6,

189-204.

Nieuwenhuijsen, M.J.; Krusize, H. & Schenker, M.B. (1998). Exposure to dust and its particle

size distribution in California agriculture. Am. Ind. Hyg. Assoc. J., 58, 34-38.

Nigg, H.N. & Allen, J.C. (1979). A comparison of time and time-weather models for

predicting parathion disappearance under Californian conditions. Environmental Science and

Technology, 13, 231-233.

Nigg, H.N., Stamper, J.H. & Queen, R.M. (1984). The development and use of a universal

model to predict tree crop harvester pesticide exposure. American Industrial Hygiene

Association Journal, 45, 182-186.

NIOSH, National Institute for Occupational Safety and Health (1999). The effects of

workplace hazards on female reproductive health. DHHS (NIOSH) Publication N0. 99-104.

Norman, C.A. (2005). Occupational and Residential Exposure Assessment for Pesticides.

Towards a Harmonized Approach. In: Occupational and Residential Exposure Assessment for

Pesticides. Edited by C.A. Franklin & J.P. Worgan. John Wiley & Sons, Ltd. Chichester, U.K.

p 342-379.

Nuyttens, D. & Sonck, B. et al. (2004). Literatuurstudie: Het belang van drift en haar

reducerende maatregelen ter bescherming van het milieu in Vlaanderen. Departement voor

Mechanisatie - Arbeid - Gebouwen - Dierenwelzijn en Milieubeveiliging, Centrum voor

Landbouwkundig Onderzoek Merelbeke, Departement voor Gewasbescherming Universiteit

Gent, Departement voor Agrotechniek en - Economie Katholieke Universiteit Leuven,

Ministerie van de Vlaamse Gemeenschap.

OECD (2000). Report of the OECD Pesticide Aquatic Risk Indicators Expert Group.

Ozkan, H.E., Miralles, A., Sinfort, C., Zhu, H. & Fox, R.D. (1997). Shields to reduce spray

drift. Journal of Agricultural Engineering Research, 67, 311-322.

Panneton, B. (2001). Impacts des techniques de pulvérisation en agroenvironnement.

Colloque en Agroenvironnement: L’agriculture et l'environnement en harmonie.

Perera, F.P. (1997). Environment and cancer: Who are susceptible? Science, 278, 1068-1073.

Pesticide Safety Directorate (1999). Consumer exposure model. Guidance on the Estimation

of dietary intakes of pesticide residues. Ministry of Agriculture, Fisheries and Food.

PHED (1992). Notice of Availability of the Pesticide Handlers Exposure Database Version

1.1, through VERSAR, Inc., Arlington, VA, USA, Federal Register, 57 (107), 23403-23404,

June 3, Washington, DC, USA.

PHYTOFAR & CRP, et al. (2006). Lors des travaux de pulvérisation: penser à sa propre

protection. Sillon Belge 3222: 4-5.



167

Pineda, F. (1998). Evaluation du modèle EUROPOEM d’exposition des utilisateurs de

produits phytopharmaceutiques dans les conditiond agricoles belges. Rapport n°

D/1998/2505, Ministry of Public Health and Environment, 70p.

POEM (1992). UK Predictive Operator Exposure Model (POEM): A Users Guide, Pesticides

Safety Directorate, York, UK.

Pontal, P.G. (2004). Models for determining operator exposure levels. Presentation at the

AgChem Forum 2004. Hilton, Amsterdam, The Netherlands, 21st – 22nd September 2004.

Popendorf, W. & Leffingwell, J.T. (1982). Regulating OP pesticide residues for farmworker

protection. Residue Reviews, 82, 125-201.

Popendorf, W.J. (1992). Re-entry field data and conclusions. Rev. Environ. Contam. Tox.,

128, 71-117.

Price, P. S., Young, J.S. & Chaisson, C.F. (2000). Assessing Aggregate and CumulativePesticide Risks using a Probabilistic Model. August 15, 2000. http://www.thelifelinegroup

org.

Price, P. S. & Chaisson, C.F. (2005). A conceptual framework for modelling aggregate and

cumulative exposure to chemicals. Journal of Exposure Analysis and Environmental

Epidemiology, 15, 473-481.

Raheel, M. (1991). Pesticide transmission in fabrics. Effects of perspiration. Bulletin of

Environmental Contamination and Toxicology, 46, 837-844.

Rautmann, D. (2000). New basic drift values in the authorisation procedure for plant

protection products. Paper for the FOCUS-Surface Water Group, 2000, 9p.

Rautmann, D. (2001). Testing and listing of drift reduction sprayers in Germany. Biological

Research Centre for Agriculture and Forestry, Application techniques division,

Braunschweig.

Rautmann, D. & Streloke, M. (2001). Die Verzahnung der Prüfung der Pflanzenschutzgeräte

mit der Zulassung der Pflanzenschutzmittel. Nachrichtenblatt Deutscher Pflanzenschutzdienst,

53 (10), 270-273.

Reed, K, Jimenez, M, Freeman, N, Lioy, P. (1999). Quantification of children’s hand and

mouthing activities through a videotaping methodology. J. Expo. Anal. Environ. Epidemiol.,

9, 513–520.

Richter, E.D., Chuwers, P., Levy, Y., Gordon, M., Grauer, F., Marzouk, J., Levy, S., Barron,

S. & Gruener, N. (1992). Health-effects from exposure to organophosphate pesticides in

workers and residents in Israel. Israel Journal of Medical Sciences, 28, 584-598.

Rutherford, I (1985). In: Symposium on application and biology. British Crop Protection

Conference. Monograph, 28, 5-9.



168

Schipper, H.J., Brouwer, D.H., van Hemmen, J.J. (1999). Exposure assessment for re-entry

activities in high and dense vegetable crops. Field study in a cucumber crop. Mededelingen

van de Faculteit Bio-ingenieurswetenschappen, Universiteit Gent, België, 64, 783-793.

Schneider, F., Hernandez, B. & Benson, C. (2002). Pesticide Exposure of workers in

greenhouses. California Environmental Protection Agency. Department of PesticideRegulation, 1001 I Street, Sacramento, CA 95812, 27p.

SDTF (1999). AgDrift, Spray Drift Task Force Spray Model, version 1.11. Seuntjes, P., Steurbaut, W. & Vangronsveld, J. (2006). Chain Model for the Impact Analysis

of Contaminants in Primary Food Products. Financed by the Belgian Science Policy. Research

Contract n° CP/67/271.

Siebers, J., Binner, R. & Wittich, K.P. (2003). Investigation on downwind short range

transport of pesticides after application in Agricultural crops. Chemosphere, 51, 397-407.

Sielken, R.L. Jr, Bretzlaff, R.S. & Valdez-Flores (1996). Risk Characterization for Atrazinz

and Simazine. Submission by Ciba Crop Protection, Ciba-Geigy Corporation (now Syngenta

Crop Protection, Inc.), Atrazinz/Simazine: Atrazinz/Simazine Response to the United States

Environmental Protection Agency’s Position Document 1: Initiation of Special Review

(November 23, 1994); Public Docket OPP-30000-60, Sielen, Inc., Bryan, TX, USA.

Sielken, R.L. (2005). Probabilistic Approaches to Aggregate and Cumulative Risk

Assessment. In: Occupational and Residential Exposure Assessment for Pesticides. Edited by

C.A. Franklin & J.P. Worgan. John Wiley & Sons, Ltd. Chichester, U.K. p 275-316.

Skinner, J. & Lewis, K. et al. (1997). An overview of the environmental impact of agriculture

in the UK. Journal of Environmental Management, 50, 111-128.

Snippe, R.J.; van Drooge, H.L.; Schipper, H.J.; de Pater, A.J. & van Hemmen, J.J. (2002).

Pesticide Exposure Assessments for Registration Purposes. Version 2002, TNO Report V

3642, TNO, Zeist, The Netherlands.

Spanoghe, P. & Steurbaut, W. et al. (2002). The effect of adjuvants on atomisation of

pesticides. Meded. Rijksuniv. Gent Fak. Landbouwkd. Toegep. Biol. Wet. 67(2), 129-132.

SPF, S.P. (2005). Mesures de réduction de la contamination des eaux superficielles par les produits phytopharmaceutiques. Direction générale Animaux, Végétaux et Alimentation,

Service Pesticides et Engrais.

Stamper, J.H; Nigg, H.N. & Queen, R.M. (1987). Dislodgeable captan residues at Florida

strawberry farms. Chemosphere, 16, 1257-1271.

Steinbach, A.C., Siebers, J., Hoernicke, E. & Meier, U. (2000). Berechnung und Messung der

dermalen Exposition beim Wiederbetreten behandelter Zierpflanzenbestände. Calculation and

measuring of dermal exposure while re-entering ornamentals. Biologische Bundesanstalt für

Land- und Forstwirtschaft, Fachgruppe Chemische Mittelprüfung, Braunschweig und

Kleinmachnow und Institut für Pflanzenschutz im Gartenbau, Braunschweig.



169

Stover, E. & Scotto, D. et al. (2002). Spray Applications to Citrus. Overview of Factors

Influencing Spraying Efficacy and Off-target Deposition. Institute of food and agricultural

sciences, University of Florida.

Taylor, W.A., Andersen, P.G. & Cooper, S. (1989). The use of air assistance in a field crop

sprayer to reduce drift and modify drop trajectories. Brighton Crop Protection ConferenceWeeds 1989 BCPC, Farnham, 631-639.

Taylor, W.A. & Womac, A. R. et al. (2004). An Attempt to Relate Drop Size to Drift Risk.

International Conference on Pesticide Application for Drift Management, Waikoloa, Hawai.

Thistle, H.W. (2004). Meteorological Concepts in the Drift of Pesticide. International

Conference on Pesticide Application for Drift Management, Waikoloa, Hawai, Washington

State University.

Thomas, M.R. for the EUROSTAT Pesticides Statistics Task Force. (1999). Guidelines for

the collection of pesticide usage statistics within agriculture and horticulture, Organisation for Economic Cooperation and Development, Paris 1999. http://www.oecd.org/ehs.

Tsang, A.M. & Klepeis, N.E. (1996). Results tables from a detailed analysis of the National

Human Activity Pattern Survey (NHAPS) response. Draft report prepared for the U.S.

Environmental Protection Agency by Lockheed Martin, Contract No. 68-W6-001, Delivery

Order No. 13.

Turnbull, A.B. (1995). An assessment of the fate and behaviour of selected pesticides in rural

England. PhD thesis, University of Birmingham.

Ucar, T. & Hall, F. (2001). Windbreaks as a pesticide drift mitigation strategy: a review.

University-Nebraska-Lincoln (2005). Recognize pesticide hazards.

U.S. EPA (1986). Guidelines for the Health Risk Assessment of Chemical Mixtures. Fed.

Reg., 51, 34014-34025.

U.S. EPA (1992a). National Home and Garden Pesticide Use Survey. Prepared by the

Research Triangle Institute for the Office of Pesticides and Toxic Substances, Biological and

Economical Analysis Branch.

U.S. EPA (1992b). Worker Protection Standard (Final Rule). Federal Register, 57 (163),

38102-38166, August 21, Washington, DC, USA.

U.S. EPA (1997a). Memorandum from Margaret Statiskowski, health Effects Division to

Health Effects Division Staff. “HED SOP 97.2 Interim Guidance for Conducting Aggregate

Exposure and Risk Assessments (11/26/97)”, November 26, 1997. office of Pesticide

Programs, Office of Prevention, Pesticides, and Toxic Substances, Washington DC.

U.S. EPA. (1997b). Exposure Factors Handbook. http://www.epa.gov/ncea/pdfs/efh/front.pdf.



170

U.S. EPA (1998). A Common Mechanism of Action: The Organophosphate Pesticides

(Report to the Science Advisory Panel 13 February 1998), available [October 2004] at

http://www.epa.gov/scipoly/sap/1998/march/comec.htm.

U.S. EPA (1999a). Guidance for Identifying Pesticide Chemicals and other Substances that

have a Common Mechanism of Toxicity, Washington DC, Office of Pesticide Programs,Office of Prevention Pesticides and Toxic Substances, available [October 2004] at

http://www.epa.gov/fedrgstr/EPA-PEST/1999/February/Day-05/6055.pdf.

U.S. EPA (1999b). Guidance for performing aggregate exposure and risk assessments. 29

October 1999. U.S. EPA OPP, Washington D.C., 1999.

U.S. EPA (2000a). Proposed Guidance on cumulative risk assessment of pesticides chemicals

having a common mechanism of toxicity. 22 June 2000. U.S. EPA OPP, Washington D.C.,

2000.

U.S. EPA (2000b). Voluntary Children’s chemical evaluation program. Federal Register ,December 26 (65), N° 248, 81699-81718, 2000.

U.S. Environmental Protection Agency (2001a). A Common Mechanism of Toxicity

Determination for N-Methyl Carbamate Pesticides (Memorandum July 10 2001), available

[October 2004] at http://www.epa/gov/oppfead1/cb/csb_page/updates/carbamate.pdf.

U.S. EPA (2001b). General principles for performing aggregate and exposure and risk

assessments. Office of Pesticide Programs.

U.S. EPA Policy Paper on Agricultural Transfer Coefficients. (aug 2001c). Science Advisory

Council for Exposure. Policy 003.

U.S. Environmental Protection Agency (2002a). Child-Specific Exposure Factor Handbook.

National Centre for Environmental Assessment, Washington, DC; EPA/600/P-00/002B.

U.S. Environmental Protection Agency (2002b). The Grouping of a Series of Triazine

Pesticides based on a Common Mechanism of Toxicity, Washington DC, Office of Pesticide

Programs Health Effects Division, available [August 2004] at http://www.epa.gov/pesticides/

cumulative/triazines/triazinecommonmech.pdf.

Vancoillie, P. (2002). Travailler en sécurité avec les produits dangereux en agriculture ethorticulture belges, PREVENTAGRI Formation.

Van de Zande, J.C., Michielsen, J.M., Stallinga, H., de Jong, A. (2000). The effect of

windbreak height and air assistance on exposure of surface water via spray drift. Proceedings

British Crop Protection Conference – Pests and Diseases 2000, Brighton, UK, 2B-4, 91-96.

Van de Zande, J.C., Hendriks, M.M.W.B., Huijsmans, J.F.M. (2001). Spray drift when

applying agrochemicals in the Netherlands. IMAG Report, Wageningen, the Netherlands.

Van de Zande, J. C. & Stallinga, H. et al. (2004). Effect of Sprayer Speed on Spray Drift.




171

Van de Zande, J.C. & Michielsen, J.M.G.P. et al. (2004). Hedgerow Filtration and Barrier

Vegetation. International Conference on Pesticide Application for Drift Management,

Waikoloa, Hawai.

Van Dyk, J. (1998). Factors influencing drift potential. Weed Management. Iowa state

University.

Van Golstein Brouwers, Y.G.C., Marquart, J. & van Hemmen, J.J. (1996). Assessment of

Occupational Exposure to Pesticides in Agriculture, Part IV, Protocol for the Use of Generic

Exposure Data. TNO Report V 96.1358, TNO, Zeist, The Netherlands.

van Hemmen, J.J (1992). Agricultural pesticide Exposure Databases for Risk Assessment.

Reviews of Environmental Contamination and Toxicology, 126, 1-85.

van Hemmen, J.J (1993). Predictive Exposure Modelling for pesticide registration purposes.

Ann. Occup. Hyg., 37, 525-541.

van Hemmen, J.J., van Goldstein, Y.G.C. & Brouwer, D.H. (1995). Pesticide exposure and re-

entry in agriculture. In: Methods of Pesticide Exposure Assessment. Edited by Curry, P.B.,

Iyengar, S. Maloney, P.A. & Maroni, M., Plenum Press, New York, USA, 9-19.

van Hemmen, J.J. & van der Jagt, K.E. (2001). Innovative Exposure Assessment of Pesticide

Uses for Appropriate Risk Assessment. Introductory Remarks. Ann. Occup. Hyg., 45, S1-S3.

van Hemmen, J.J. & van der Jagt (2005). Generic Operator Exposure Database. In:

Occupational and Residential Exposure Assessment for Pesticides. Edited by C.A. Franklin &

J.P. Worgan. John Wiley & Sons, Ltd. Chichester, U.K. p 174-208.

Van Kaayk, J. & Lalleman, F. (1993). De effectiviteit van huidbeschermingsmiddelen bij het

werken met bestrijdingsmiddelen in de glastuinbouw. Deel II: Huidbeschermende materialen.

PML-TNO interim report. Rijswijk. The Netherlands.

Vercruysse, F. & Steurbaut, W. et al. (1999a). Exposure to pesticides in apple and pear

orchards. Proc. XI Symposium Pesticide Chemistry, Cremona, Pavese, Italy: La Goliardica.

Vercruysse, F., W. Steurbaut, et al. (1999b). Off target ground deposits from spraying a semi-

dwarf orchard. Crop Protection, 18(9), 565-570.

Vercruysse, F. (2000). Occupational exposure and risk assessment during and after

application of pesticides. Faculteit Landbouwkundige en Toegepaste Biologische

Wettenschappen, Universiteit Gent. Thesis submitted in fulfilment of the requirement for the

degree of Doctor in Applied Biological Sciences, 201p.

Vercruysse, F. & Steurbaut, W. (2002). POCER, the pesticide occupational and

environmental risk indicator. Crop Protection, 21(4), 307-315.

Walklate, P. (2004). Modelling Canopy Interactions for Drift Mitigation.

Wang, S. & Deltour, J. (1999). Lee-side ventilation-induced air movement in a large-scalemultispan greenhouse. J. Agric. Engng. Res., 74, 103-110.



172

Whitmyre et al. (2005). Development of Riks-Based Restricted Entry Intervals. In:

Occupational and Residential exposure assessment. Eds: Claire A. Franklin & John P.

Worgan. ISBN 0-471-48989-1, p 45-70.

WHO & IPCS (1995). Methyl bromide. Environmental Health Criteria, 166.

WiGRAMP (2001). Working Paper: Cumulative and Aggregate Risk Assessment of

Pesticides and Other Compounds. Working Goup on the Risk Assessment of Mixtures of

Pesticides (WiGRAMP), 29 p.

Willis, G.H. & McDowell, L.L. (1987). Pesticide persistence on foliage. Reviews of

Environmental Contamination and Toxicology, 100, 23-72.

Willis, G.H., Spencer, W.F. & McDowell, L.L. (1980). The interception of applied pesticides

by foliage and their persistence and washoff potential. In: Knisel, W.G. (ed). CREAMS: a

field-scale model for chemicals runoff and erosion from agricultural management systems.

USDA Conserv. Rep., n° 26 p 643. US Govt Print Off. 0-310-945/SEA-15.

Wolf, T.M. (2004). Nozzle Selection Guidelines for Optimum Efficacy and Least Drift.


Woods, N. (2004). Australian Developments in Spray Drift Management. International

Conference on Pesticide Application for Drift Management, Waikoloa, Hawai

, Centre for Pesticide Application & Safety, University of Queensland, Australia.

Worgan, J.P. & Rosario, S. (1995). Pesticide Exposure Assessment: Past, present and future.

In: Methods of Pesticide Exposure Assessment. Edited by Curry, P.B., Iyengar, S. Maloney,

P.A. & Maroni, M., Plenum Press, New York, USA, 1-8.

Zartarian, VG, Ferguson, A.C. & Leckie, J.O. (1997). Quantified dermal activity data from a

four-child pilot field study. J. Expo. Anal. Environ. Epimemiol., 7(4),543–553.

Zweig, G., Leffingwell, J.T. & Popendorf, W.J. (1985). The relationship between dermal

pesticide exposure by fruit harvesters and dislodgeable foliar residues. Journal of

Environmental Science and Health Part B – Pesticides Food Contaminants and Agricultural

Wastes, 20, 27-59.

Websites:http://www.bba.de

http://www.biw.kuleuven.be/aee/vcbt/drift/

http://www.brc.tamus.edu/swat/manual2000/pestdb/pestdflt.html

http://www2.defra.gov.uk/research/project_data/More.asp?I=PS2005&SCOPE=0&M=PSA&

V=NR%3A080

http://www.efsa.europa.eu/en/science/ colloquium_series/colloquium_7.html

http://europoem.csl.gov.uk

http://www.exposuretf.com

http://www.food.gov.uk/ science/ouradvisors/toxicity

http://www.frac.info/frac/index.htm

http://www.irac-online.org



173

http://www.pesticides.gov.uk/uploadedfiles/Web_Assets/PSD/CombinedToxicity20050408.p

df

http://www.plantprotection.org/hrac

http://www.ppo.wur.nl/NL



XI. Annexes

Annex I: EUROPOEM

Description of the EUROPOEM model

The EUROPOEM Database is a generic database of monitored operator exposure studies

relevant to plant protection products in European agriculture. The objective of the

EUROPOEM Database Project was to develop a generic model for use in risk assessment

procedures. The data for the database were delivered by industry, governments and academia

and have to fulfil certain quality criteria for field and laboratory measurements. The major

quality criteria are as follows: (1) good documentation, describing in detail the study design,

participants and results; (2) representativeness of the study for European situations; (3)

adequate sampling methodology applied to different workers under varying conditions of equipment and climate for a large part of the work shift (this excludes at least in part the

sampling of single workers for many times); (4) adequate chemical analysis, with appropriate

quality controls.

The EUROPOEM Database includes exposure data on mixer/loaders, applicators and

mixer/loader/applicators of pesticides. Also the exposure of bystanders is included in the

database. EUROPOEM contains up to 600 individual replicates, covering exposure with

several techniques, such as boom sprayers, air blast applications, and hand-held applications.

The EUROPOEM project has established the basis for a three-tiered harmonised risk

assessment framework. In a first tier, default exposure values, derived from all availableexposure data are used. In the second tier further data for protection factors from dermal

uptake and effectiveness of protective clothing are considered. Ultimately, assessment in the

third tier would use actual exposure data for specific products derived from dedicated field

studies, possibly involving biological monitoring.

The database allows selection of parameters for calculations, statistical analysis and exposure

summaries. The EUROPOEM Database may be used for exposure prediction, model

validation, risk analysis, comparison of application techniques, exposure study design,

determination of required need of protective measures, as well as pesticide product

authorization.

(EUROPOEM, 1996)



Spreadsheet of the EUROPOEM model



EUROPOEM I Surrogate values segregated for Applicator only

Table XI.1.2: Surrogate values segregated for applicator only

Spraying direction Application equipment Route of exposurePotential exposure nA (mg/kg

a.s.) EUROPOEM IHands 2 (40)Dermal (no hands) 0,6 (41)Dermal (body andhands)

3 (30)DownwardsVehicle-mounted

(ground boom sprayers)

Inhalation 0,008 (45)

Hands 11 (22)Dermal (no hands) 63 (22)Dermal (body andhands)

76 (22)Upwards

Vehicle-mounted

(broadcast air-assisted)< 400 l/ha

Inhalation 0,03 (18)Hands 11 (13)

Dermal (no hands) 63 (25)

Dermal (body andhands) 76 (25)Upwards

Vehicle-mounted

(broadcast air-assisted)> 400 l/haInhalation 0,03 (26)Hands 65 (indicative) (32)Dermal (no hands) 1100 (indicative) (24)

Dermal (body andhands)

1200 (indicative) (24)Upwards Hand-held (all types)

Inhalation 1 (indicative) (23)Hands 100 (indicative) (25)Dermal (no hands) 250 (indicative) (25)Dermal (body and

hands)

300 (indicative) (25)Downwards Hand-held (all types)

Inhalation 0,01 (indicative) (7)

Indicative: low confidence valuen A: number of measurements on which the surrogate exposure values are based

EUROPOEM I Surrogate values segregated for Mixer, Loader and Applicator only

Table XI.1.3: Surrogate values segregated for mixer/loader and applicator only

Formulation Type Application equipment Route of exposure Potential exposureHands 10 (indicative)Body (no hands) 15 (indicative)

Dermal (body and hands) 30 (indicative)

Vehicle-mounted (ground boom sprayers)

Inhalation 0,02 (indicative)

Hands 1350 (indicative)Body( no hands) 130 (indicative)Dermal (body and hands) 1370 (indicative)

Liquid

Hand-held (indoors)

Inhalation 0,3 (indicative)

Indicative: low confidence value



Table XI.1.4: Database used to calculate the operator exposure indicator according to EUROPOEM I

ProductFormulation

Applicationequipment

Indoor/OutdoorSprayingdirection

MixLoadInhal MixLoadDermal ApplicIn

Liquid Mechanical Outdoors Downwards 0,005 20 0,008

Liquid Mechanical Outdoors Upwards 0,005 20 0,03 Liquid Manual Outdoors Downwards 0,1 120 0,01 Liquid Manual Outdoors Upwards 0,1 120 1 Liquid Manual Indoors Downwards 0,1 1E-9 0,3 Liquid Manual Indoors Upwards 0,1 1E-9 0,3

WP Mechanical Outdoors Downwards 1 100 0,008

WP Mechanical Outdoors Upwards 1 100 0,03 WP Manual Outdoors Downwards 1 100 0,01 WP Manual Outdoors Upwards 1 100 1 WP Manual Indoors Downwards 1 1E-9 0,3 WP Manual Indoors Upwards 1 1E-9 0,3 WG Mechanical Outdoors Downwards 0,1 1 0,008

WG Mechanical Outdoors Upwards 0,1 1 0,03 WG Manual Outdoors Downwards 0,1 21* 0,01 WG Manual Outdoors Upwards 0,1 21* 1 WG Manual Indoors Downwards 0,1 1E-9 0,3 WG Manual Indoors Upwards 0,1 1E-9 0,3

Granule Mechanical Outdoors Downwards 0,1 2 1E-9

Granule Manual Outdoors Downwards 0,1 21* 1E-9

Granule Manual Indoors Downwards 0,1 21* 1E-9

* Source: German model because of absent data in the EUROPOEM database (Lundehn et al, 1992)



The EUROPOEM II Project

The EUROPOEM II Project was set up with a number of related objectives dealing with field

measurements of worker and environmental exposure to pesticides and the validation of

existing models for predictive exposure evaluation. A key element to achieving the principal

objectives was the use of validated methods of field exposure measurements which could beused by workers in a range of European countries. Therefore common protocols were

prepared during the EUROPOEM II project and followed to generate data required for the

model validation procedures (EU, 2002).

This project was undertaken to fill the data gaps for Southern European countries and Finland,

where hand-held pesticide application methods were commonly used for both outdoor and

indoor crops. Existing models such as the UK POEM (Predictive Operator Exposure Model)

and the German exposure model had few data for hand held application methods. However,

existing data indicated that levels of worker exposure were higher with such application

methods than for the application methods involving tractor mounted or tractor drawn sprayers.

The initial literature searches revealed that specifically for high volume application of pesticides to greenhouse crops with hand held equipment no suitable data were available. And

this is the most commonly used application method for pesticides in greenhouses in Southern

Europe, and was identified by members of the EUROPOEM Group as a high priority for data

generation. Phase I of this project generated over 100 data sets for the priority application

scenarios, using a common OECD guideline protocol. This protocol for potential operator

exposure assessments was included in the first annual report of the EUROPOEM Working

Group and a version published as part of a number of poster presentations and refereed

papers. The protocols were critically discussed by stakeholders and scientists before the

technical work began. For full details on the analytical methodology I refer to the EU R 20489

Report ‘The assessment of operator, bystander and environmental exposure to pesticides’

(EU, 2002). Commonly grown crops were selected for the studies, which include the worst

case conditions. i.e. tall crops such as cucumbers, green beans or melons. The majority of the

applications were done with high volume application and all were with hand-held equipment.

Potential dermal and inhalation exposure data were collected for the applicator, and where

possible associated workers such as hose men. Potential inhalation and dermal bystander

exposure data were also collected in some field studies. The Expert Group of EUROPOEM

(European Predictive Operator Exposure Model) used these datasets to supplement the

database for operator exposure. The levels of potential dermal exposure that were measured

were found to be similar to many of the existing datasets for comparable application

scenarios. The spread of the data was also as expected, with the potential dermal exposure

slightly greater for hand held methods than for the application methods involving tractor powered sprayers.

In the second phase of the EUROPOEM II project the protective factors of commonly worn

coveralls in Southern Europe were investigated. Field and laboratory studies with a range of

coveralls were carried out to quantify likely levels of penetration. Due to the warm climatic

conditions in Southern Europe impermeable coveralls are rarely worn. Therefore, the types of

coveralls worn tend to be permeable, such as cotton or cotton polyester mixtures. The results

from the conducted studies indicate that these types of coveralls can give the operator some

degree of protection during pesticide application. However the degree of protection varies

with the rate of liquid contamination of the coverall.



In the final phase a modelling exercise was performed and this appeared to be successful, with

a large number of datasets being added to the EUROPOEM database. The levels of potential

dermal exposure to pesticides for hand held applications in Southern Europe tended to fit in

with expectations based on the limited datasets available from studies conducted in Northern

Europe. The data seemed variable, but this reflected the wide range of application and

cropping conditions encountered across Southern Europe.

All the data gathered during the EUROPOEM II project assisted in the consolidation and

validation of the EUROPOEM model both by supplementing its database with field

measurements relevant to Southern European conditions and by corroborating of working

assumptions in regard of personal protective equipment and skin absorption, which mitigate

the likely exposure resulting from contamination by pesticides occurring during their use. All

the data gathered during the EUROPOEM II project should be communicated to the users’ of

the computer program.



EUROPOEM II Surrogate exposure values segregated for Mixer/Loader only

Table XI.1.5: Surrogate values segregated for mixer/loader only

Formulation typeApplicationequipment

Route of exposure Potential exposure nA (mg/kg a.s.)

Hands43,024 (11) (max);30,513 (11) (75

th)

6,2 (14) (geometric mean)

Dermal (body excl.hands)

10,715 (27) (90th);6,753 (27) (75th)


Vehicle-mounted (alltypes)

Inhalation0.385 (21) (90

th)

0,0527 (21) (75th)


Hands12,96 (2) (75th) no good data to be

considered

Dermal (body excl.

hands)

no data available

Wettable powder (WP)

Hand-held (all types)

Inhalation0,940 (3) (75

th) no good data (too small

dataset)

Hands1.319 (15) (max)0,86 (15) (75th)



4.501 (47) (90th)

1,565 (47) (75th)


Vehicle-mounted (alltypes)

Inhalation0.034 (41) (90

th)

0.034 (41) (75th)


Hands13,23 (8) (max) no good data

5,833 (8) (75

th

) no good dataDermal (body excl.hands)

6,874 (8) (max) no good data1,468 (8) (75

th) no good data

Wettable granule(WG)


Inhalation0.02089 (16) (max)

0.03062(16) (75th

)0,0037 (16) (geometric mean)

Hands19.834 (59) (75

th)

6,8 (59) (geometric mean)Dermal (body excl.hands)

1.730 (111) (75th)

0,5 (111) (geometric mean)Vehicle-mounted (all

types)

Inhalation0,002894 (70) (75

th)


Hands

519 (17) (max)

159 (17) (75

th

)59 (17) (geometric mean)


255 (32) (90th)38,33 (32) (75th)


Liquid



th)

0,022 (18) (75th)


n A: number of measurements on which the surrogate exposure values are based

It has to be noted that further well-designed field studies should be carried out to provide a

sound basis for describing exposures for these scenarios. The available datasets for granules

and wettable powders are very weak, and for mixing and loading for hand-held equipmentvirtually non existent. Data for wettable dusts are also lacking.



EUROPOEM II Surrogate values segregated for Applicator only

Table XI.1.6: Surrogate values segregated for applicator only

Spraying direction Application equipment Route of exposurePotential exposure nA (mg/kg

a.s.) EUROPOEM II

Hands 0.702 (88) (75th

)0,12 (88) (geometric mean)

Dermal (no hands)0,747 (104) (75th)

0,030 (104) (geometric mean)Downwards

Vehicle-mounted(ground boom sprayers)

Inhalation0,0286 (104) (75th)


Hands17,314 (34) (90th)

9,724 (34) (75th)


Dermal (no hands)40,66 (71) (75

th)

16 (71) (geometric mean)Upwards

Vehicle-mounted

(broadcast air-assisted)(< 1200 l/ha)


th)

0,046 (30) (75th)


Hands1341 (90th) (49)647 (75th) (49)

109 (geometric mean) (49)

Dermal (no hands)2162 (75

th) (69)

458 (geometric mean) (66)Downwards (<1,0m) Hand-held (all types)

Inhalation0,125 (75

th) (78)

0,21 (geometric mean) (48)

Hands265 (75th) (63)


Dermal (no hands)857 (75

th) (127)

251 (geometric mean) (122)Upwards (> 1,0m) Hand-held (all types)

Inhalation0,156 (75

th) (103)

0,1 (geometric mean) (93)n A: number of measurements on which the surrogate exposure values are based

EUROPOEM II Surrogate values segregated for Mixer, Loader and Applicator only

Table XI.1.7: Surrogate values segregated for mixer/loader and applicator only

Formulation Type Application equipment Route of exposure Potential exposure

Hands27,120 (90th) (23)

8,428 (75th

) (23)3,7 (geometric mean) (23)

Body (no hands)

14,520 (90th) (43)

8,486 (75th) (43)3,7 (geometric mean) (43)

Vehicle-mounted (ground boom sprayers)

Inhalation0,560 (max) (30)

0,065 (75th

) (30)0,0017 (geometric mean) (30)

Hands1346 (max) (16)55,151 (75th) (16)30 (geometric mean) (16)

Body( no hands)392 (90th) (31)

38,283 (75th) (31)


Liquid

Hand-held

Inhalation0,087 (90

th) (21)

0,0245 (75th) (21)

0,055 (geometric mean) (20)



Annex II: Grouping of formulation types into categories

Table XI.2.1: Grouping of formulation types

Formulation Type Symbol Formulation CategoryMicrogranules MG GranuleFine granules FG Granule

Fumigation granules FW GranuleTablets TB GranuleGranule GR Granule

Emusifiable granule EG WGWater dispersable granules WG WGWater soluble granules SG WGWater soluble bags WB WG

Capsule suspension CS Liquid

emulsifiable concentrate EC LiquidOil in water emulsion EW Liquid

Suspension concentrate SC LiquidSoluble concentrate SL LiquidCombi –pack (liquid-liquid) KL LiquidMicro emulsion ME Liquid

Liquid destined for use in a non-diluted form AL LiquidAquaous emulsion EW Liquid

Suspension concentrates for seed treatments FS LiquidLatex PA LiquidSuspo-emulsion SE LiquidDispersable concentrate DC Liquid

Liquids for seed treatments LS LiquidLiquid for ULV applications UL LiquidLiquid mineral oil HM Liquid

Liquid unspecified LI LiquidWettable powder WP Powder Water soluble powder SP Powder Dust powder DP Powder Powder PP Powder Wettable powder for humid treatments WS Powder

Powders for dry seed treatment DS Powder



Table XI.3.1: Crop grouping according to the location of cultivation (Indoor/Greenhouse or Outdoor/Field/Orchard) and ap

Crop group CropsIndoor (greenhouse)/Outdoor (field)

cultivationFresh Vegetables (Aubergine, Been, Beet,

Cauliflower, Celery, Courgette, Cucumber, Currant,

Cutbeet, Endive, Gherkin, Lettuce, Pepper, Seed,Sweet Pepper, Tomato & Watercress)

Melons

StrawberriesFlowers and Ornamental Plants

Greenhouse Crops (Vegetables,Melons & Strawberries;

Flowers & Ornamental Plants& Permanent Crops)

Permanent Crops Under Glass (Grapes)

Indoor

Outdoor Flowers andOrnamental Plants

Flowers and Ornamental Plants Outdoor

NurseriesVineyards (raisins, table grapes, other wines, quality

wine)

Olive plantations (oil production, table olives)Citrus plantation (oranges)

Fruit and Berry (temperate climate, subtropicalclimate) & Nuts

(Apple, Blackberry, Currants, Cherry, Dewberry,Gooseberry, Kiwi, Pear, Peach, Raspberry, Plum)

Permanent crops

Other Permanent Crops

Outdoor

Rough GrazingsPermanent grassland and

meadows Pasture and MeadowsOutdoor

Temporary GrassOther Green Fodder (Green Maize, Leguminous

Plants)Forage plants

Other

Outdoor

Other Other Outdoor



Table XI.3.2: Grouping of application equipment types into broad categories

Application Equipment Specified Application Equipment Broadcast to ground

Ground Boom Sprayer

Ground Boom Sprayer automatic boom

Ground Boom Sprayer golf cart

Ground Boom Sprayer manual BoomGround Boom Sprayer simultaneous incorporation

Ground Boom Sprayer self propelled

Ground Boom Sprayer tractor Ground Boom Sprayer trailed

Ground Boom Sprayer tractor mounted

Ground Boom Sprayer tractor simultaneous incorporationGround Boom Sprayer tractor trailed

Groundboom Sprayer

Ground Boom Sprayer Unimog

Solid Broadcast Sprayer Solid Broadcast

8 row cyclopanter Other Vehicle

8 row planter

Air-assisted Broadcast (tractor drawn Air-O-Fan airblast)

Air-assisted Broadcast (tractor drawn FMC 1087 airblast)

Air-assisted Broadcast (tractor mounted jet sprayer)

Air-assisted Broadcast (tractor drawn PTO-driven airblast)

Air-assisted Broadcast (radial sprayer)

Air-assisted Broadcast (tractor mounted vaporiser)

Broadcast air Assisted Sprayer

Broadcast Air Assisted Sprayer

Broadcast air Assisted Sprayer Tractor Mounted

Aerial

Aerial ULVAAerial

Fixed Wing Aircraft

Knapsack MotorisedHand – Held Airblast Sprayer

Knapsack Mistblower

Batch Mixing from Spray Pistol

Hand Held High Pressure LanceHand-Held Lance

Hand-Held Lance/Pistol

Hand-Held Pistol

Knapsack Sprayer Downwards

Knapsack Sprayer Pedestrian Controlled Sprayer

Pedestrian Controlled Self-Propelled Broadcast Air Assisted Sprayer

Hand –Held

Hand – Held Sprayer

Spot Gun



contamination is localised in a small area the % penetration may be high, even though most of

the coverall remains dry or free from contamination. Such areas of the coverall receiving

lesser amounts of contamination are less likely to be penetrated. This needs to be borne in

mind when considering how total coverall contamination relates to absorbed dose of pesticide

(EU, 2000).

The protective factor of coveralls which are worn by pesticide applicators and re-entry

workers needs to be considered. Several key factors affect the protective factor of coveralls,

including human behaviour, which has to be investigated in upcoming research projects (EU,

2000). The reference made in EU (2000) to respective European standards for design,

performance and selection of PPE for use with pesticides should broaden the future

applicability of the data gathered within this EU project, because the process of harmonization

across the European Union should bring about greater consistency in utilisation of PPE within

the wide range of commercial conditions under which it is used.

One of the ways of overcoming potential problems associated with the exposure of pesticide

applicators is improved levels of training. Over the last years there has been a markedimprovement in operator training in certain regions, such as Southern Spain. In other regions,

progress has not been as noticeable. Problems of lack of training result in a lack of awareness

of the hazardous nature of pesticides, and the need for simple equipment or procedures to help

mitigate the exposure to pesticides (EU, 2000).

The development of European legislation, requiring pesticide applicators to have formal

training will address the problem of pesticide applicators not adhering to basic safety

instructions on pesticide labels, e.g. wearing gloves when handling the concentrated product.

Training and instruction of the use of application equipment should also lead to the following

of good practice, and ultimately the safe use of pesticides, for both the user (operator), the

environment, and in the end the consumer of the produce.



Annex V: Dermal absorption

Tier 1

A conservative value of 100% is often used for dermal absorption. It is noteworthy that the

California Department of Pesticide Research uses a first tier default of 50% for regulatory purposes.

Tier 2

A more refined default may be justifiable taking into account a number of considerations such

as the physicochemical properties of the substance and the toxicological database. A ‘weight-

of - evidence’ approach should be used, i.e. both the physicochemical information and the

toxicological database should support the reduced default. Unfortunately clear cut-off values

for negligible, low and/or high dermal absorption of chemicals cannot be derived from data

present in the open literature (Durkin et al., 1995). De Heer et al. (1999) proposed to refine

the dermal absorption defaults based on theoretical considerations on skin permeation. It

might be expected that there is an optimum in log K ow and a maximum in molecular weight

for facilitating percutaneous absorption. The following criteria were proposed by de Heer et

al. (1999) to discriminate between chemicals with high and low dermal absorption:

10% dermal absorption is used in cases where MW>500 g/mol and log Kow is

smaller than -1 or higher than 4, or otherwise;

100% dermal absorption is maintained.

A deviation of 10% or 100% can be chosen on a case by case basis, taking into account all

data available.

Tier 3

This tier involves generation of experimental data on dermal absorption.

(Hakkert et al., 2005)

Further investigation of dermal absorption is required if potential dermal exposure data are to

be used to underpin reliable predictive modelling of the absorbed dose of pesticides. During

application activities, the dermal absorption percentage is not a constant. Moreover, dermal

absorption differs with changing formulation type.



Annex VI: Inhalation absorption

In this document it is assumed as a default that the inhalation absorption factor equals 100%.

Due to the difficulty in determining the fraction of the active substance, measured in the

breathing zone, that is really inhaled, one often assumes that the potential and actual exposureare equal. It is thus assumed that all the contamination that is really inhaled is effectively

absorbed by the lungs. This certainly leads to an overestimation since part of the particles that

are present in the air are initially too large for inhalation (Anderson et al., 1976). Moreover,

the largest part of the particles that penetrate nose and mouth are absorbed by much less

efficient mechanisms than those operative in the lungs. Part of the particles present in nose

and mouth can be swallowed and ingested in the gastro-intestinal system where chemicals are

only absorbed in a small degree or liable to degradation. Another part of the inhaled active

substance can be exhaled. According to Chester (1993), an inhalation absorption factor of

50% is still too conservative. Brouwer and van Hemmen (1997) found that for propoxur only

40% of the inhaled active substace is really absorbed.



Annex VII: Agricultural Default Transfer Coefficients

Source: Science Advisory Council for Exposure. Policy 003.

The following is a reference in case no post application exposure data are available. Its purpose is to add consistency to the choice of the default transfer coefficients under such

circumstances. It is specifically designed for agricultural workers. The generic default values

in the table are not supported quantitatively but were derived by exposure assessors based on

their best judgement from their experience with the transfer coefficients used for these crops

and agricultural activities in pesticide specific assessments.

Table XI.7.1: Crop grouping according to the potential for dermal transfer

Low dermal transfer Medium dermal transfer High dermal transferAlfalfa Beans, Bush Bananas (unbagged)

Artichokes Caneberries & bushberries Beans, PoleAsparagus Cantaloupe CornBock Choy Cranberries TomatoBroccoli CucumbersBrussels Sprouts EggplantCabbage GourdsCelery Herbs, medium-growing

Chick peas MelonCollards OkraHerbs, low-growing PeanutKale Pepper Lettuce PumpkinMustard greens Rice

Pineapple SquashSmall grains (barley, wheat, oats) StrawberriesSpinach ZucchiniSwish ChardWatercress

Table XI.7.2: Default transfer coefficients for agricultural activities

Crop group/site Activitiesr Default transfer coefficientHarvesting (hands) 2500

Scouting 1000Low dermal transfer

Irigating 1000

Harvesting (hands) 4000Scouting 4000Irigating 4000

Medium dermal transfer

Stake/Tie 4000

Harvesting (hands) 10.000Scouting 4000Irigating 4000

High dermal transfer

Stake/Tie 4000

Turfgrass Mow, maintain 1000Cut, roll, harvest 10.000

Harvest (hand), gridle, cane, tie, prune, tip, thin

15.000grapes

Irigating 4000

Tree crops (fruit & nut) All activities (Harvest (hand),summer shake, prune, rake, poleand pickup (nuts), prop

10.000



Table XI.7.2: Default transfer coefficients for agricultural activities

Crop group/site Activitiesr Default transfer coefficientCut, Harvesting (flowers) 10.000

Prune (roses) 10.000Sort & pack 2500Ornamentals indoor

Irrigating 4000

Transplant ball/burlap) 10.000Sort & pack 2500

Ornamentals outdoor (shrubs,trees)

Irrigating 4000Mushrooms Cut/Harvest/ Sort & pack 2500

Dig/harvest by hand 10.000Tubers (onions)

Sort & pack 2500Early season scouting 1000

cottonLate season scouting 4000

Hoeing, weeding 1000Till/disc negligible

Plant mechanically negligibleBuild furrows negligiblePlant by hands 10.000

All

Aligning plants (potato pieces,

sugar cane)10.000



Annex VIII: Foliar half life times

The values for the foliar half life times listed in the table below were obtained from the

SWAT pesticide database for default chemicals.



Annex IX: Estimated number of workdays a year

Several institutions were contacted in order to obtain estimates of the number of workdays

that farmworkers come into contact with the crop during the growth season. We limited the

estimations to the number of days that farmworkers are busy harvesting the crops, since theonly transfer factors available are associated with the harvesting activity. It should be noted

that the best way to obtain these data is by conducting surveys among the farmworkers and

this for the different crops. First of all crops requiring a lot of hand labour need to be

determined. This can be done on the basis of surveys. It has to be mentioned that the crops

requiring hand labour will vary among the Member States of the EU, since each Member

State has its own specific agricultural systems with varying degree of mechanisation. A

thorough review by experts from universities and delegants of the Department of Agriculture

of the estimated number of workdays is highly desirable.

Within the framework of HAIR preliminary estimations of the number of workdays are

proposed for various crops. For each crop the number of workdays a year was estimated on

the basis of expert judgment and scientific publications. It is imported to mention that surveys

should be conducted in the future to obtain more scientifically based estimates of the number

of work days a year for a farmworker in a particular crop. This parameter is of extreme

importance because it indicates the exposure duration of a farmworker.

Research from Larson (2000) determined peak hand labor season dates specific for several

North American States. These estimates were obtained from “Usual Planting and Harvesting

Dates” and information prepared by the Offices of Rural Health and Resource Development.

In the table below (Table VIII.9.1) the peak season length in work days for several crops is presented for Belgium. Data that were used to estimate the number of workdays come from

the following sources:

Dedeene, L. & De Kinder, G. (2004). Groente & Fruit Encyclopedie. Kosmos-

Z&K Uitgevers B.V., Utrecht, 405p.(1);

Dekkers, W.A. (2002). Kwantitatieve Informatie. Akkerbouw en

Vollegrondsgroenteteelt 2002. Praktijkonderzoek Plant en Omgeving, PPO-

publicatie nr 301, 317p. (2);

Ministerie van Middenstand en Landbouw (2001). Brochure: de Belgische

landbouwteelten: een overzicht. 76p. (3);

Naets, W. (2005). Verwerkingsmachines in de aspergeteelt. Eindwerk tot het

behalen van de Bachelorgraad gegradueerde in Landbouw en Biotechnologie.

Katholieke Hogeschool Kempen, 47p. (4);

Peppelman, G. & Groot, M.J. (2004) Kwantitatieve Informatie voor de Fruitteelt

2004. Praktijkonderzoek Plant & Omgeving, PPO publicatie nr 611, 154p. (5);

Schreuder, R. & van der Wekken, J.W. (2005). Kwantitatieve Informatie

Bloembollen en Bolbloemen 2005. Praktijkonderzoek Plant & Omgeving, PPO

publicatie nr 719, 215p. (6);

Vermeulen, P.C.M., Nienhuis, J.K., van der Meer, R.W. & Hendrix, A.T.M.

(2001). Beroepsbevolking in de glastuinbouw. Praktijkonderzoek Plant &

Omgeving, PPO publicatie 527,56p. (7);

Van de Voorde, S. (2004). Oogst van bloemkool voor de verse markt: traditioneelversus gemechaniseerd. Eindwerk tot het behalen van de Bachelorgraad



gegradueerde in Landbouw en Biotechnologie. Katholieke Hogeschool Kempen,

33p. (8);

van Woerden, S.C. (2006) Kwantitatieve Informatie voor de glastuinbouw 2006:

Groenten –snijbloemen – potplanten. Praktijkonderzoek Plant & Omgeving, PPO

publicatie 594, 277p. (9);

Janssens, B., Kroeze, G. & van der Voort, M. (2004). Arbeidsomstandigheden inde vollegrondsgroenteteelt. Een inventarisatie van de knelpunten en

oplossingsrichtingen rondom piekarbeid. Praktijkonderzoek Plant & Omgeving,

PPO publicatie 2.04.10, 65p. (10).



Table XI.9.1: Number of workdays a year that farmworkers perform harvesting activities in several crops.

Crop group CropsRelevant Scenario

(Yes/No) according to the assumptionsmade in POCER II

Common Wheat

Durum Wheat Rye

Barley (Spring & Winter)

Oats Grain Maize

cereals

Other cereals (Corn, Spelt & Triticale)

No (mechanical harvest)

(pers. comm. Vleminckx, 2006)

Pulses Fodder PeasPulses

Pulses Fodder Field Beans

No (one-time mechanical harvest for industry)


Early Potatoes

Storage Potatoes Potatoes

Seed Potatoes

No, only scouting and inspecting of crops(mechanical harvest)


Sugar beet Sugar beet No (pers. comm. Vleminckx, 2006,

mechanical harvest)

Tobacco Yes

Hops No (pers. comm. Vleminckx, 2006,

mechanical harvest)

Cotton/Flax No (pers. comm. Vleminckx, 2006,

mechanical harvest)Oil seed or fibre plants (Rape, Turnip,Sunflower, Soya, Chicory (Ordinary &

Coffe), other)

No (pers. comm. Vleminckx, 2006)

Aromatic, Medicinal and Culinary Plants(Herbs: Basil, Mint)

No (pers. comm. Vleminckx, 2006)

Industrial plants

Other industrial plants No (pers. comm. Vleminckx, 2006) Fodder Roots and Brassicas

Peas Fodder

Maize

No (labour extensive crop)




Crop group CropsRelevant Scenario

(Yes/No) according to thassumptions made in POCE

Asparagus Yes (source: 10)

Broccoli (summer+fall) Yes (source: 10)

Brussels Sprouts No (source: 10)

Cabbage No (source: 10)

Carrot No (source: 10)

Cauliflower Yes (source: 10)

Chinese Leaves Yes

Chichory Yes

Cucumber Yes

Gherkin Yes

Kale No (source: 10)

Spring

Lettuce Summer-fall YesLeek (fall late) Yes (source: 10)

Onion No (www.msp-onions.n

Parsley Yes

Radish Yes

Rhubarb Yes

Scorzonera No (source: 10)

Spinach No (source: 10)

Fresh vegetables

Tomato Yes

Melons Melon and Watermelon Yes

June bearing

Outdoor: Open Field &

Market Gardening

StrawberriesEver bearing

Yes (source: 10)



Table X.9.1: Number of workdays a year that farmworkers perform harvesting activities in several crops.

Crop group Crops

Relevant Scenario(Yes/No) according to th

assumptions made in POCE

IIAubergine Yes

Courgette (2 cultures a season) Yes Early (3 cultures a

season)Cucumber

Late (3 cultures a

season)

Yes

Endive (2 cultures a season) Yes Lettuce (5 cultures a season) Yes

Green Red

Yellow Pepper

Orange

Yes

Radish Yes Mooring rope

Intermediate type

Fresh Vegetables

TomatoFlesh type

Yes

Melons YesFresh Fruit

Strawberries Yes Chrysant Yes

Small flower Flowers and

Ornamental Plants RoseLarge flower

Yes

Greenhouse Crops

Permanent CropsUnder Glass

Grapes, etc. Yes

Outdoor Flowers and

Ornamental Plants

Flowers and Ornamental Plants Yes




Crop group Crops

Relevant Scenario(Yes/No) according to th

assumptions made in POCE

II Nurseries

Vineyards (raisins, table grapes, other wines, other wines, qualitywine)

Yes

Olive plantations (oil production, table olives) Yes Citrus plantation (oranges) Yes

Nuts

Apple Yes

Apricots Yes Blackberries Yes Blewberries Yes

Cherries Yes Cranberries Yes

Currants Yes Dewberries Yes Gooseberry Yes

Kiwi Yes Peach Yes

Pears Yes

Plums and Prunes Yes Raspberries Yes

Permanent crops

Orchards

Strawberries Yes

Rough GrazingsPermanent grassland and

meadows Pasture and Meadows

No (pers. comm. Vleminck

2006)Temporary GrassOther Green Fodder (Green Maize, Leguminous Plants)Forage plants

Other

No (pers. comm. Vleminck2006)

Other Other No (pers. comm. Vleminck

2006)



Annex X: Spraying schemes used for validation and prioritisation

Table XI.10.1: Treatment scheme on a sensible potato variety (14 anti-mildow treatments – moderate

Date of application

Product ActiveSubstance

Formulation Dose(kg a.s./ha)

AOEL(mg a.s./kgb.w./d)

1/meiLuxan Linuron

500linuron SC 0,5 0,009

1/mei Challenge aclonifen SC 1,2 0,0321/mei metribuzin WG 0,35 0,0131/mei

Artistflufenacet WG 0,48 0,017

4/jun Penncozeb mancozeb WG 1,5 0,035

11/jun Penncozeb mancozeb WG 1,875 0,03516/jun cymoxanil WP 0,1125 0,0316/jun

Curzate Mmancozeb WP 1,625 0,035

21/jun Penncozeb mancozeb WG 1,875 0,035

28/jun dimethomorf WG 0,1875 0,0928/jun

Acrobat extramancozeb WG 1,6675 0,035

5/jul cymoxanil WG 0,15 0,035/jul

Tanosfamoxadone WG 0,15 0,0048

13/jul Penncozeb mancozeb WG 1,875 0,03520/jul dimethomorf WG 0,1875 0,0920/jul

Acrobat extramancozeb WG 1,6675 0,035

27/jul cymoxanil WP 0,1125 0,03

27/julCurzate M

mancozeb WP 1,625 0,0352/aug Shirlan fluazinam SC 0,15 0,0024

10/aug Shirlan fluazinam SC 0,15 0,002417/aug zoxamide WG 0,1494 0,317/aug

Unikat Promancozeb WG 1,2006 0,035

23/aug zoxamide WG 0,1494 0,323/aug

Unikat Promancozeb WG 1,2006 0,035

26/augRanman

component Acyazofamide SC 0,08 0,3

3/sep Purivel metoxuron WP 1,2 0,02810/sep Reglone diquat SL 0,6 0,001



Table XI.10.2: Potential bystander’s inhalation exposure calculated using both approaches

Date of application Active SubstanceInhalation exposure

method 1 (mg/d)Inhalation exposure

method 2 (mg/d)1/mei linuron 0,00057 0,001561/mei aclonifen 0,00137 0,00375

1/mei metribuzin 0,00040 0,001091/mei flufenacet 0,00055 0,001504/jun mancozeb 0,00171 0,0046911/jun mancozeb 0,00214 0,0058616/jun cymoxanil 0,00013 0,00035

16/jun mancozeb 0,00186 0,0050821/jun mancozeb 0,00214 0,0058628/jun dimethomorf 0,00021 0,0005928/jun mancozeb 0,00191 0,005215/jul cymoxanil 0,00017 0,000475/jul famoxadone 0,00017 0,00047

13/jul mancozeb 0,00214 0,0058620/jul dimethomorf 0,00021 0,00059

20/jul mancozeb 0,00191 0,0052127/jul cymoxanil 0,00013 0,0003527/jul mancozeb 0,00186 0,005082/aug fluazinam 0,00017 0,0004710/aug fluazinam 0,00017 0,00047

17/aug zoxamide 0,00017 0,0004717/aug mancozeb 0,00137 0,0037523/aug zoxamide 0,00017 0,0004723/aug mancozeb 0,00137 0,0037526/aug cyazofamide 0,00009 0,000253/sep metoxuron 0,00137 0,00375

10/sep diquat 0,00069 0,00188



Table XI.10.3: Bystander’s risk index calculated using approach 2 (most consetvative)

Risk Index method 2Date of application Active Substance

5 m 10 m 20 m1/mei linuron SC 0,5 0,0091/mei aclonifen SC 1,2 0,032

1/mei metribuzin WG 0,35 0,0131/mei flufenacet WG 0,48 0,0174/jun mancozeb WG 1,5 0,03511/jun mancozeb WG 1,875 0,03516/jun cymoxanil WP 0,1125 0,0316/jun mancozeb WP 1,625 0,035

21/jun mancozeb WG 1,875 0,03528/jun dimethomorf WG 0,1875 0,0928/jun mancozeb WG 1,6675 0,0355/jul cymoxanil WG 0,15 0,035/jul famoxadone WG 0,15 0,004813/jul mancozeb WG 1,875 0,035

20/jul dimethomorf WG 0,1875 0,09

20/jul mancozeb WG 1,6675 0,03527/jul cymoxanil WP 0,1125 0,0327/jul mancozeb WP 1,625 0,0352/aug fluazinam SC 0,15 0,002410/aug fluazinam SC 0,15 0,0024

17/aug zoxamide WG 0,1494 0,317/aug mancozeb WG 1,2006 0,03523/aug zoxamide WG 0,1494 0,323/aug mancozeb WG 1,2006 0,03526/aug cyazofamide SC 0,08 0,33/sep metoxuron WP 1,2 0,028

10/sep diquat SL 0,6 0,001



Table XI.10.4: Treatment scheme for apple orchards

Date of application

ProductActive

SubstanceFormulation

Dose(kg a.s./ha)

AOEL(mg a.s./kg

b.w./d)3/26/2005 Dodine dodine SC 0.800 0.19

4/1/2005 Dodine dodine SC 0.800 0.194/5/2005 Dodine dodine SC 0.800 0.194/13/2005 Captan captan WG 1.760 0.125

Scala pyrimethanil SC 0.300 0.6*4/18/2005

Delan dithianon WG 0.338 0.03*4/21/2005 Captan captan WG 1.760 0.125

Calypso thiachloprid SC 0.180 0.02Captan captan WG 1.200 0.1254/28/2005Scala pyrimethanil SC 0.300 0.6*

Steward indoxacarb WG 0.075 -Captan captan WG 1.200 0.1255/2/2005Geyser difenconazool EC 0.038 0.13*Pi i i i i b WG 0 375 0 14*

hair occupational indicators tcm35-40135

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