pesticide residues in food—acute dietary exposure

Pest Management Science Pest Manag Sci 60:311–339 (online: 2004)DOI: 10.1002/ps.865

Pesticide residues in food—acute dietaryexposure†

Denis Hamilton,1∗ Arpad Ambrus,2 Roland Dieterle,3 Allan Felsot,4 Caroline Harris,5

Barbara Petersen,6 Ken Racke,7 Sue-Sun Wong,8 Roberto Gonzalez,9 Keiji Tanaka,10

Mike Earl,11 Graham Roberts12 and Raj Bhula13

1Department of Primary Industries, Brisbane, Australia; 2International Atomic Energy Agency, Vienna, Austria; 3Syngenta Crop ProtectionAG, Basel, Switzerland; 4Food and Environmental Quality Laboratory, Washington State University, USA; 5Exponent International Ltd,Harrogate, UK; 6Exponent, Inc, Washington DC, USA; 7Dow AgroSciences, Indianapolis, USA; 8TACTRI, Taichung Hsien, Taiwan;9University of Chile, Santiago, Chile; 10Sankyo Co, Ltd, Shiga-ken, Japan; 11Syngenta, UK; 12State Chemistry Laboratory, Vic, Australia;13Australian Pesticides & Veterinary Medicines Authority, Canberra, Australia

Abstract: Consumer risk assessment is a crucial step in the regulatory approval of pesticide use on foodcrops. Recently, an additional hurdle has been added to the formal consumer risk assessment processwith the introduction of short-term intake or exposure assessment and a comparable short-term toxicityreference, the acute reference dose. Exposure to residues during one meal or over one day is important forshort-term or acute intake. Exposure in the short term can be substantially higher than average becausethe consumption of a food on a single occasion can be very large compared with typical long-term or meanconsumption and the food may have a much larger residue than average. Furthermore, the residue levelin a single unit of a fruit or vegetable may be higher by a factor (defined as the variability factor, which wehave shown to be typically ×3 for the 97.5th percentile unit) than the average residue in the lot. Availablemarketplace data and supervised residue trial data are examined in an investigation of the variabilityof residues in units of fruit and vegetables. A method is described for estimating the 97.5th percentilevalue from sets of unit residue data. Variability appears to be generally independent of the pesticide,the crop, crop unit size and the residue level. The deposition of pesticide on the individual unit duringapplication is probably the most significant factor. The diets used in the calculations ideally come fromindividual and household surveys with enough consumers of each specific food to determine large portionsizes. The diets should distinguish the different forms of a food consumed, eg canned, frozen or fresh,because the residue levels associated with the different forms may be quite different. Dietary intakes maybe calculated by a deterministic method or a probabilistic method. In the deterministic method the intakeis estimated with the assumptions of large portion consumption of a ‘high residue’ food (high residue inthe sense that the pesticide was used at the highest recommended label rate, the crop was harvested at thesmallest interval after treatment and the residue in the edible portion was the highest found in any of thesupervised trials in line with these use conditions). The deterministic calculation also includes a variabilityfactor for those foods consumed as units (eg apples, carrots) to allow for the elevated residue in somesingle units which may not be seen in composited samples. In the probabilistic method the distributionof dietary consumption and the distribution of possible residues are combined in repeated probabilisticcalculations to yield a distribution of possible residue intakes. Additional information such as percentagecommodity treated and combination of residues from multiple commodities may be incorporated intoprobabilistic calculations. The IUPAC Advisory Committee on Crop Protection Chemistry has made 11recommendations relating to acute dietary exposure. 2004 Society of Chemical Industry

Keywords: pesticide residues; consumer risk assessment; dietary exposure; probabilistic; deterministic; residuevariability

∗ Correspondence to: Denis Hamilton, Animal and Plant Health Service, Department of Primary Industries, 80 Ann St, GPO Box 46, BrisbaneQld 4000, AustraliaE-mail: [email protected]†This report was prepared by members of the Advisory Committee on Crop Protection Chemistry, Division of Chemistry and the Environmentof the International Union of Pure and Applied Chemistry (IUPAC)Contract/grant sponsor: Division of Chemistry and the Environment, International Union of Pure and Applied Chemistry; contract/grantnumber: 1999-009-1-600(Received 16 April 2003; revised version received 11 November 2003; accepted 18 December 2003)

2004 Society of Chemical Industry. Pest Manag Sci 1526–498X/2004/$30.00 311

DJ Hamilton et al

1 INTRODUCTIONPesticide residues in food ready for consumption usu-ally result from direct application of that pesticide tothe crop producing that food commodity. Consumerrisk assessment is a crucial element in the approval,registration or licensing of pesticide uses on food crops.The ingestion of a pesticide does not result in adversehealth consequences unless the intake∗ is excessive.It is necessary to understand how much is consumedin order to conduct an assessment of potential risk.The aim of the risk assessment is to compare thedietary intake of the pesticide residue by the consumerwith a measure of levels that are acceptable. Estimatesof acceptable levels of intake ADIs (acceptable dailyintakes) and acute RfDs (reference doses) are derivedfrom a range of toxicology studies.

Until recently, attention has focused on estimatingchronic intake because long-term effects have been ofmore concern since, for many compounds, animalsare more sensitive to repeated doses for most of theirlifetime than they are to single doses or repeated dosesover a shorter period of time. Also some early syntheticpesticides such as DDT exhibited virtually no signs ofacute or short-term toxicity. Considerable progressand refinement of methodologies in recent years havemade chronic assessments more realistic. Total dietstudies, which rely on measurement of residue levels infood purchased in the marketplace and then preparedfor consumption, have confirmed that chronic intakesof residues are below the ADIs.1,2

At the same time, it has been recognized that manyof the widely used pesticides can have adverse effectswhen a single (albeit high) dose is ingested. Pesticidessuch as the organophosphates and carbamates havebeen shown to cause effects after a single high dose,3

and it is therefore legitimate to question the effects ofshort-term pesticide residue intakes. A specific residueintake for a single meal or over one day can be muchhigher than the average lifetime daily intake becausethe consumer might eat a larger than average portion ofa specific food on one occasion and that specific foodmay have a residue level higher than average. Lifetimeexposure can also legitimately take account of ‘non-consuming’ days as part of producing an ‘average’ dietfor long-term consumer risk assessment.

The methodology of acute dietary intake assessmentaims to answer the question of the magnitude ofexposure to pesticide residues in food from a singlemeal during a single day or, at most, on a few days.The estimated exposure is then evaluated to ensure thesafety of that exposure for the consumer by comparingit to an appropriate toxicological endpoint that mustbe derived from studies in which the test animals wereexposed for only a short period of time.

Chronic exposure can be estimated relativelyeasily from the median residue in foods andthe appropriate amounts of each food consumed.

* Note that ‘intake’ and ‘dietary exposure’ are used interchangeablyand are intended to have the same meaning.

Unfortunately, estimating acute exposure tends to bemore complicated since some consumers will consumeone food in rather high amounts and yet others willconsume high amounts of a second, third or fourthfood.

This paper will outline the background andhistory of acute risk assessment and will explain thespecial requirements and appropriate use of dietaryinformation, residue data and assessments in theevaluation of short-term risks from pesticide residuesin food.

2 BACKGROUND AND HISTORYAlthough extremely rare, single exposures to somepesticide residues have been toxic. It is welldocumented that organophosphates and carbamates,if the dose is sufficient, can depress cholinesteraseactivity in animals and humans and, at still higherdoses, can produce clinical symptoms. As a result ofmisuse by growers, some consumers became ill fromeating watermelons and cucumbers that had beentreated with aldicarb in the USA4 and from cucumbersgrown hydroponically in Ireland.5

Subsequently, the US EPA developed a newsoftware tool, the Dietary Residue Evaluation System(DRES). One of the important features of DRES wasthe ability to estimate acute exposures to pesticides.DRES initially included worst-case exposure estimatesas well as a simple model for estimating more realisticexposures.6

The Codex Committee on Pesticide Residues(CCPR) in the early 1990s expressed reservationsabout some MRLs (maximum residue limits) thathad been proposed for acutely toxic pesticides.7 TheCCPR felt that the ADI was not an appropriatetoxicological standard for assessing short-term risks,and requested the JMPR (Joint FAO/WHO Meetingon Pesticide Residues) to examine the specific cases ofmonocrotophos and aldicarb and to consider the moregeneral question of dietary risk assessment of acutelytoxic pesticides when the Guidelines for PredictingDietary Intake of Pesticide Residues8 were revised.

The JMPR proposed to use the term ‘acute referencedose’ or ‘acute RfD’ as an acceptable daily intake forsingle exposures. The 1995 JMPR went on to estimateacute RfDs for monocrotophos and aldicarb.9 The1998 JMPR10 explained further its basis for estimatingacute RfDs and stated criteria for cases where an acuteRfD is not necessary.

The revised guidelines11 were published in 1997 byWHO and contained chapters on risk assessment ofacute hazards and predicting dietary intake of acutelytoxic pesticide residues.

A Joint FAO/WHO Consultation on ‘Food Con-sumption and exposure assessment of chemicals’12

in Geneva in 1997 recommended procedures forshort-term dietary intake estimates for use at theinternational level for pesticide residues. The Con-sultation recognized new research conducted in the

312 Pest Manag Sci 60:311–339 (online: 2004)

Pesticide residues in food—acute dietary exposure

UK where it was found that standard sampling pro-tocols used to collect samples to determine the levelsof pesticides in foods could underestimate residues insome individual units of foods. The standard samplingprograms recommended collection of multiple itemsfrom different parts of the plant and field. A com-posite sample, consisting of a number of individualitems, was formed, and this was then analyzed. Workcarried out in the UK indicated that residue levelsvaried considerably between individual units withina composite sample, and therefore all of the residuecould be present in only a sub-set of the items, per-haps theoretically even in only a single item. In thiscase, the consumer eating that item would encounterhigher than anticipated residues. Where a single itemcontains substantially more residues than the averageresidue, the possibility exists for exposures exceedingan acceptable level. Since 1994, the UK has publishedwork on carrots,13–15 apples, pears, oranges, peaches,nectarines, tomatoes, bananas,16 kiwi fruits, celery,plums17 and potatoes18 all showing a similar degree ofvariability between individual units.

The UK Pesticides Safety Directorate organizedan international conference19 on Pesticide ResiduesVariability and Acute Dietary Risk Assessmentin 1998 where, for the first time, all aspects(toxicology, intakes, sampling and residue variability)were considered together.

The Netherlands government hosted an ad hocexpert working group20 in 1999 prior to the CCPRmeeting to develop specific guidance for the JMPRin the evaluation of residue data for acute dietaryrisk assessment at the international level. Therecommendations of that workshop were used by the1999 JMPR, but with additions based on the practicalcases to be dealt with.21

3 VARIABILITY OF RESIDUES IN UNITS OFFRUIT AND VEGETABLESThe concentration of a residue or contaminant in abatch, lot or consignment of a food commodity hasbeen treated historically as if it were reasonably evenlydistributed through the batch. However, we recognizethat some variation exists and the sampling proceduresrecommended by Codex to take a representativesample from a lot result in a sampling uncertaintyof about 30% for medium-size crops.22

Some commodities are consumed as single unitsand for short-term consumer risk assessment it is thepossible residue in single units that is more importantthan the average residue in the lot, which is representedby the residue in a representative composite sample.Many fruits and vegetables are consumed as singleunits in a meal, or at least as part of a single unit,eg apples, peaches, oranges, pineapples, tomatoes,potatoes, carrots and lettuce. A bunch of grapes mayalso be treated as a single unit for the purposes ofshort-term intake.

Where multiple units of a food commodity areconsumed in a single meal or in one day the residue

level in the composite representative sample is morelikely to represent the residue in the serving. Berryfruits, cherries, peas and beans are examples.

The unit-to-unit variability of residues in some fruitsand vegetables is then an issue for estimating short-term consumer intake of residues.

The concept of a variability factor was introducedto describe the relationship between the residue in ahigh-residue unit and the typical or average residue inthe whole batch. The concept was refined to a moreprecise definition: residue in the 97.5th percentile unitdivided by the mean residue for the lot (see Section 11,Calculation of intake). An FAO/WHO consultation12

recommended that the 97.5th percentile residue value(in the edible portion) should be used in acute dietaryexposure assessment.

3.1 The 97.5th percentile value: estimation fromdata setsObtaining variability factors from monitoring data andunit variability experiments is not always straight-forward because the monitoring and experimentsmay have had other purposes or the practicalitiesof generating the data may complicate extraction ofthe data. It is worthwhile examining the details ofsampling calculations with a view to extracting asmuch information as possible from the available data.

If concentration values for all the items in apopulation are placed in sequence, the 97.5thpercentile value may be conceptually visualized asthe one that divides the top 2.5% of the values fromthe remainder.

When we sample from that population, how canwe best estimate the 97.5th percentile value? Weshould distinguish between the 97.5th percentile inthe population to be sampled and the 97.5th percentilein the n samples that we take from that population.The 97.5th percentile sample in our n samples (97.5thpercentile sample = 1 + 0.975 × n) may not be a goodestimate of the 97.5th percentile value in the largepopulation, especially when n is small. In generalterms:

Probability that a single sample is less than the selectedpercentile (sp) = sp/100,

Probability that all n samples are less than sp =(sp/100)n,

Probability that at least one sample in n exceeds sp isP≥1 and

P≥1 = 1 − (sp/100)n (1)

The number of samples n is 119 when eqn (1) issolved for 95% assurance (P≥1 = 0.95) that at least onesample exceeds the sp (97.5th percentile). A similarcalculation shows that there is a 70% chance that atleast one sample exceeds the 99th percentile value forn = 119. Correspondingly:

Probability that a single sample exceeds sp = 1− sp/100

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DJ Hamilton et al

Probability that exactly one sample in n exceeds sp =(1 − sp/100) ×n C1 × (probability that all remain-ing samples are less than sp).

Probability that exactly one sample in n exceeds sp isP=1

P=1 = (1 − sp/100) ×n C1 × (sp/100)(n−1) (2)

Similarly, probability that exactly r samples in n exceedsp is P=r

P=r = (1 − sp/100)r ×n Cr × (sp/100)(n−r) (3)

Equations (2) and (3) may now be used to examinethe contributions to the 95% assurance that at leastone sample exceeds the 97.5th percentile value.

P=1 (exactly 1 in 119 exceeds 97.5th percentile value)= 0.15

P=2 (exactly 2 in 119 exceed 97.5th percentile value)= 0.23






In the situation where exactly r samples exceedthe 97.5th percentile value the rth highest samplemay be taken as the best estimate of that value; it isthe lowest of the r samples that exceed the value.In the above example with 119 samples our bestoverall estimate of the 97.5th percentile in the popula-tion being sampled is 0.15 × highest sample + 0.23 ×second highest sample + 0.23 × third highest sample+ 0.17 × fourth highest sample, etc.

In this case the probability distribution is truncatedby 0.05 (95% probability accounted for), so the sumshould be divided by 0.95 to compensate. In general,the probability calculated from eqn (1) should be usedas the divisor. However, accuracy is compromisedwhen this probability is much below 0.95.

This approach will be used in the interpretation ofdata sets in the following sections.

This binomial-type calculation may also be usedfor estimating confidence intervals on the variabilityfactors obtained from unit data when sample numbersare sufficiently large. The sample values at thecumulative probabilities of 0.025 and 0.975 providethe 95% confidence interval around the estimate asdescribed in the previous paragraphs. For example, inFig 3 the cumulative probabilities of 0.025 and 0.975occur at sample values of 2.60 and 3.47, respectively,in the 97.5th percentile distribution, so that the 95%confidence limit range for the variability factor is2.60–3.47.

3.2 Marketplace samples—distribution of unitresidues3.2.1 Marketplace samples—UKThe UK in 199716 provided a briefing note to theCodex Committee on Pesticide Residues on thesubject of unit-to-unit variation of pesticide residuesin fruit and vegetables. Annex 1 of that documentprovided a summary of residues found in compositesamples and the corresponding residues in individualunits of fruit or vegetables from the same lot. Thedata related to marketplace samples from a variety ofsources containing a variety of pesticide residues. Intotal, 719 batches of fruit and vegetables were screenedfor residues. For the purposes of data analysis wehave selected only those cases where 95% or higherof the individual units had detectable residues fromthis study and other studies subsequently publishedwhich fit these criteria.17,18 It is difficult to deal with‘non-detects’ here because the ratio of high residuesunit to mean residue might just reflect a mixture oftreated and untreated units in the one box where manyof the units have no residue. Data are summarizedin Table 1. Residues quoted as <LOQ were takenas 0 in the statistical calculations (the conservativeassumption in this situation), but because there werevery few, the assumption did not influence the results.

For 100 samples the probability that at least onesample exceeds the 97.5th percentile value is 0.92. Theexample in Table 2 shows the method of calculatingthe 97.5th percentile value from the unit data and theprobabilities. Figure 1 shows the considerable overlapof values contributing to the 97.5th and 99th percentilevalues, which suggests inadequate resolution between97.5th and 99th percentiles for a 100-unit sample.

This calculation method was used for the 97.5thpercentile values in Table 1 for all commoditiesexcept for plums, where the data including the 97.5thpercentile values were received in summary form. Thedata sets include: apples from Argentina, France, UKand USA; bananas from Jamaica; kiwifruit from Chile,Greece, and New Zealand; oranges from Cyprus; pearsfrom France; plums from Italy, Portugal and Spain;potatoes from UK and Jersey; and tomatoes fromSpain.

The relationship between C975 (97.5th percentileunit concentration) and Cmean (mean concentrationof units) was examined for the data in Table 1. Therelationship is shown in the form of a log–log plot inFig 2 with the points from the various commoditiescontributing to:

y = 0.945x + 1.0226.

and the straight line is a good fit for the 26 data sets(R2 = 0.95). In another form:

ln(C975) = 0.945 × ln(Cmean) + 1.0226 (4)

C975 = C0.945mean × exp(1.0226)

C975 = 2.8 × C0.945mean .



Table 1. Summary information on unit sample testing from fruits and vegetables purchased in the marketplace and where residues were detected

in 95% or more of the units tested and where approximately 100 units were tested16

Commodity SourceMean unit

wt (g) Pesticide

Unitsamples> LOQ

Unitsamplestested

Mean ofunits

(mg kg−1)

Compositesample

(mg kg−1)Max unit(mg kg−1)

97.5thpercentile

(C975)

Apple Argentina 127 Phosalone 100 100 0.607 0.77 2.70 2.06Apple France 156 Phosalone 100 100 0.482 0.65 1.8 1.27Apple UK 130 Carbaryl 99 100 0.977 1.11 2.73 2.31Apple UK 130 Carbaryl 100 100 1.055 0.94 2.26 2.12Apple UK 103 Chlorpyrifos 100 100 0.085 0.15 0.47 0.416Apple UK 84 Chlorpyrifos 108 110 0.151 0.13 1.35 0.82Apple USA 166 Carbaryl 108 108 1.41 1.12 3.89 3.25Apple USA 166 Diphenylamine 108 108 0.473 0.51 1.82 1.45Apple USA 166 Thiabendazole 108 108 1.021 0.80 2.97 2.73Banana Jamaica 141 Chlorpyrifos 100 100 0.0085 0.009 0.091 0.048Kiwifruit Chile 75 Phosmet 98 100 0.071 0.08 0.43 0.35Kiwifruit Greece 83 Parathion-methyl 100 100 0.0091 0.02 0.019 0.017Kiwifruit Greece 82 Parathion-methyl 99 100 0.014 0.02 0.026 0.024Kiwifruit NZ 80 Diazinon 97 100 0.011 0.01 0.035 0.029Orange Cyprus 171 Imazalil 100 100 1.73 3.2 3.62 3.32Pear France 152 Phosalone 100 100 0.526 0.43 1.55 1.38Plums Italy 87 Phosalone 100 100 0.38 0.12 1.86 1.50Plums Portugal 71 Acephate 98 100 0.13 0.14 0.52 0.40Plums Spain 42 Acephate 100 100 0.24 0.38 1.11 0.64Plums Spain 52 Acephate 95 100 0.04 0.03 0.12 0.10Plums Spain 72 Fenitrothion 99 100 0.03 0.03 0.13 0.08Plums Spain 42 Methamidophos 98 100 0.04 0.06 0.18 0.12Plums Spain 52 Pirimiphos-methyl 99 100 0.03 0.05 0.25 0.18Potato (main) UK 175 Aldicarb 99 100 0.039 0.05 0.16 0.12Potato (new) Jersey 38 Aldicarb 100 100 0.146 0.19 0.67 0.48Tomato Spain 42 Methamidophos 95 100 0.061 0.08 0.67 0.41

Apples

0.00

0.05

0.10

0.15

0.20

0.25

0.30

0.35

0.40

2.70 1.97 1.79 1.67 1.64 1.61 1.53 1.53 1.26 1.17

Residue (mg/kg) in individual apples.

Pro

babi

lity

99th percentile

97.5th percentile

Figure 1. Contributions of the highest values from a set of 100 unit residues (phosalone) of a market sample of apples to calculated 97.5th and99th percentile values. Note the considerable overlap of the contributions for a 100-unit sample (probabilities that at least 1 unit exceeds 97.5th and99th percentile values are 0.92 and 0.63 respectively).

The variability factor v = C975 ÷ Cmean = 2.8 ×C(0.945−1)

mean = 2.8 × C(−0.055)mean

ie v = 2.8 × C(−0.055)mean (5)

The exponent for the Cmean is close to zero, suggestingthat, for this dataset arising from marketplace samples,the variability factor is independent of concentration

over the range examined (0.01–3 mg kg−1). Theequation provides an estimate of the variability factorof 2.8 for the Table 1 data.

This derivation has assumed that the variabilityfactor is not dependent on the nature of the pesticideor the commodity or method of pesticide application.However, the York Conference considered that theeffect of application conditions was one of the


DJ Hamilton et al

Table 2. Calculation of the 97.5th percentile residue value for single

apple residue data (one market sample, 100 single apples analyzed)

from UK

97.5th percentile contributions

Residues (mg kg−1)in descendingrank order Probability

Contribution(= residue

value × probability)

P≥1 = 0.922.70 P=1 = 0.20 0.551.97 P=2 = 0.26 0.511.79 P=3 = 0.22 0.391.67 P=4 = 0.14 0.231.64 P=5 = 0.07 0.111.61 P=6 = 0.03 0.041.53 P=7 = 0.01 0.011.53 P=8 = 0.00 0.00

Total 1.84

Total (÷P≥1) 2.0 mg kg−1

most significant factors influencing the distribution ofresidues (and therefore the magnitude of the variabilityfactor).19 The data in Table 1 were from marketsamples, where application conditions were unknown,and were likely to contain samples which had beenobtained as a result of compositing crops from differentsources.

3.2.2 Apple samples—UK marketplaceThe data from the apple samples in Table 1 (fromArgentina, France, UK and USA) were combinedand considered together. Each residue level wasdivided by the mean of its market sample and theresulting 934 residue ÷ mean values were listed in rankorder. Probability contributions were calculated for

UK market samples

-5.0

-4.0

-3.0

-2.0

-1.0

0.0

1.0

2.0

-5.0 -4.0 -3.0 -2.0 -1.0 0.0 1.0

ln (mean)

ln (

97.5

th p

erce

ntile

)

applebananakiwifruitorangepearplumpotatotomato

y = 0.9451x + 1.0226R2 = 0.9487

Figure 2. Relationship between the 97.5th percentile unit residue andthe mean residue in a log-log plot for marketplace samples from UKmonitoring (Table 1 data).

n = 934. The 97.5th and 99th percentile values weresubstantially separated and there was no truncation(Fig 3). The estimated 97.5th and 99th percentilevalues were 2.9 and 4.1, respectively. The variabilityfactor was 2.9 (chosen as the 97.5th percentile).

3.2.3 Kiwifruit samples—UK marketplaceThe data from the four kiwifruit market samples inTable 1 (from Chile, Greece and New Zealand) werecombined in the same way as described for apples.The estimated 97.5th and 99th percentile valuesfor residue ÷ mean were 3.4 and 4.4, respectively(n = 400).

3.2.4 Celery samples—UK marketplaceResidue data from seven marketplace samples ofcelery (40 units, ie head bunches, analyzed from eachsample) from the UK marketplace, but originating

Apples

0.00

0.02

0.04

0.06

0.08

0.10

0.12

0.14

8.98

5.41

4.46

4.24

4.19

3.69

3.59

3.47

3.28

3.20

2.95

2.79

2.75

2.71

2.67

2.64

2.61

2.58

2.52

2.49

2.38

Residue/mean values, descending rank order

Pro

babi

lity

99th percentile

97.5th percentile

Figure 3. Probability contributions to 97.5th and 99th percentile values, of the 41 highest values (residue ÷ mean values) from 934 values forindividual apples from nine market samples. (Note that only every second value is shown in the x axis labelling).



Table 3. Summary information on unit sample testing from celery head bunches purchased in the marketplace where 40 units were tested (UK

data)17

SourceMean unit

wt (g) PesticideUnit samples

>LOQ

Mean ofunits

(mg kg−1)

Compositesample

(mg kg−1)Max unit

(mg kg−1)

Spain 717 Chlorpyrifos 40 1.221 1.78 5.29Spain 582 Chlorpyrifos 40 0.665 0.37 1.86Spain 546 Chlorpyrifos 40 0.355 0.29 0.97UK 714 Disulfoton 40 0.089 0.12 0.26UK 721 Heptenophos 40 0.090 0.07 0.20UK 692 Heptenophos 39 0.029 0.06 0.070UK 692 Tolclofos-methyl 40 0.202 0.16 0.29

Table 4. Calculation of variability factor from residue÷mean values

for single head bunch residue data (Seven market samples of 40-unit

data, for 280 residue÷mean values) on celery from UK

97.5th percentile contributions

Residue ÷ meanvalues in descendingrank order Probability

Contribution(residue ÷ mean

value × probability)

P≥1 = 0.9994.33 P=1 = 0.01 0.032.93 P=2 = 0.02 0.062.80 P=3 = 0.05 0.142.73 P=4 = 0.09 0.252.51 P=5 = 0.13 0.322.43 P=6 = 0.15 0.372.38 P=7 = 0.15 0.362.36 P=8 = 0.13 0.312.30 P=9 = 0.10 0.242.18 P=10 = 0.07 0.162.11 P=11 = 0.04 0.092.08 P=12 = 0.03 0.052.00 P=13 = 0.01 0.031.95 P=14 = 0.01 0.011.94 P=15 = 0.00 0.00

Total = 2.42

Total (÷P≥1) = 2.4

from UK and Spain, are summarized in Table 3.Each residue level was divided by the mean of itsmarketplace sample and the 280 residue÷mean valueswere combined for calculation as already describedfor apples. The estimated 97.5th percentile value forresidue÷mean was 2.4 (Table 4).

3.2.5 Marketplace samples—USAThe USDA in 199923 conducted a special survey toexamine residue levels in single servings of apples. Thepurpose was to provide information on the distributionof residues in composite samples and to providedata for use in probabilistic models for acute dietaryrisk assessment. The survey focused on azinphos-methyl and chlorpyrifos residues in apples from 3–5 lb(approximately 1.4–2.3 kg) survey samples. Each of10 washed apples from the sample was cut into eightsegments. Four alternating segments from each applewere combined with those from the other apples as

the composite sample. If the residue level in thecomposite exceeded LOQ values (0.02 mg kg−1 forazinphos-methyl and 0.01 mg kg−1 for chlorpyrifos)the remaining halves of the 10 apples were analyzedindividually as the single servings.

The project resulted in residue data for 120 samples.For azinphos-methyl there were 78 samples wherethe residues in all 10 apples exceeded the LOQ.The individual residue of each apple in the 10 wasdivided by the mean of the 10, which provided 780residue÷mean values. The 97.5th and 99th percentilevalues were 2.7 and 3.5, respectively.

For chlorpyrifos there were 34 samples where theresidues in all 10 apples exceeded the LOQ, providing340 residue ÷ mean values. The 97.5th and 99thpercentile values were 3.5 and 4.5, respectively.

3.3 Field trial samples—distribution of unitresidues3.3.1 Tomatoes in HungaryTomato fields treated with mancozeb and zineb weresampled with systematic collection of fruits that werehighly exposed to pesticide, partly covered with leavesand fully covered with leaves. Altogether 90 primarysamples were collected from each field 3 h afterspraying.24 Residue data are summarized in Table 5.The highest residue levels in a single tomato were 2.4and 3.6 times the mean residue.

3.3.2 Kiwifruit in NZChlorpyrifos, diazinon, permethrin, pirimiphos-methyl and vinclozolin residues were determined witha multi-residue procedure for all of the 209 individ-ual kiwifruits on four canes of a T-bar-trained blockwhich had received a standard full-season spray pro-gramme. The fruits were taken at harvest.25 Residuedata are summarized in Table 5. Estimated variabilityfactors (C975 ÷ mean) were: chlorpyrifos 3.3, diazi-non 2.5, permethrin 3.6, pirimiphos-methyl 4.4 andvinclozolin 2.9.

3.3.3 Apples in HungaryIn a trial specifically designed to obtain informationon unit-to-unit variability of residues and validatefield sampling procedures,26 chlorpyrifos-methyl wasapplied in a commercial apple orchard at the maximum


DJ Hamilton et al

Table 5. Summaries of single-unit residue data resulting from supervised trials on tomatoes, kiwi fruit and applesa

Crop PesticideApplication

method

Unitsamples>LOQ

Unitsamplestested

Max unit(mg kg−1)

97.5thpercentile

value(mg kg−1)

Mean ofunits

(mg kg−1)Variability factor= C975 ÷ Cmean Ref

Tomato Mancozeb Foliar 90 90 9.4 3.92 2.4a 24Tomato Zineb Foliar 90 90 2.9 0.81 3.6a 24Kiwi fruit Chlorpyrifos Foliar 189 209 0.72 0.57 0.17 3.3 25Kiwi fruit Diazinon Foliar 196 209 0.14 0.11 0.046 2.5 25Kiwi fruit Permethrin Foliar 184 209 0.21 0.18 0.050 3.6 25Kiwi fruit Pirimiphos-methyl Foliar 202 209 1.09 0.67 0.15 4.4 25Kiwi fruit Vinclozolin Foliar 209 209 2.64 2.24 0.76 2.9 25Apple Chlorpyrifos-methyl Foliar (day 0) 319 319 1.05 0.68 0.212 3.2b 26Apple Chlorpyrifos-methyl Foliar (day 14) 319 320 0.106 0.082 0.027 3.0c 26

a Residue levels <LOQ were assumed as zero in the calculation of means, leading to slightly higher calculated variability factors than if <LOQ valueswere assumed equal to LOQ.b max ÷ mean is used because 97.5th percentile value was not available; max ÷ mean will produce a larger v than if the 97.5th percentile value wasused.c C99 = 0.84; C99 ÷ mean = 3.9.d C99 = 0.10; C99 ÷ mean = 3.7.

registered rate in Hungary. The experimental plotincluded five rows with 149 trees each planted in6 × 3 m2 blocks. The individual apple fruits weretaken from the outer high (7%), inner high (11%),outer middle (21%), inner middle (28%), outer low(16%) and inner low (18%) segments of the treesproportional to the average abundance of fruit in theexperimental site, which was determined by countingthe fruits found in the above segments of 14 treesselected randomly. Residue data are summarized inTable 5. Estimated variability factors (C975 ÷ mean)were 3.2 and 3.0 at day 0 and day 14, respectively.

3.3.4 Potatoes in USASeveral studies have been conducted to determinethe level and magnitude of the residues following theapplication of a granular systemic pesticide (aldicarb)to potatoes in USA and some other countries during1990–1993. In these trials large numbers (typically80–300 per site) of individual potato tubers, takenfrom the uniformly treated single sites, were analyzedin order to obtain information on the within-field andon the field-to-field variation of residues dependingon the mode of application, irrigation method andclimatic conditions.27 Summary data are published,but are insufficient for estimating variability factors bythe method described in this paper.

3.3.5 Carrots in UKThe UK, beginning in 1993, analyzed individualcarrots and composite samples from batches ofcarrots taken from commercially grown crops ofknown treatment history subjected to treatmentwith organophosphorus insecticides.13–15 Mostly 10individual carrots (sometimes 9) were analyzed. Whenresults were grouped according to pesticide, threegroups had more than 100 results: triazophos 458results from 45 batches, quinalphos 110 results from

11 batches and chlorfenvinphos with 104 results from11 batches.

The data were treated in the same way as describedfor celery (Section 3.2.4). Each residue was dividedby the mean residue in its batch to provide theresidue ÷ mean values. Probability contributions werecalculated for n = 458 for triazophos, n = 110 forquinalphos and n = 104 for chlorfenvinphos. Thevariability factors for triazophos, quinalphos andchlorfenvinphos residues in carrots were 2.7, 3.0 and2.5, respectively (see Tables 8 and 9).

3.3.6 Apples and lettuce in EuropeRecent experiments in Europe on apples and lettucehave been designed to determine the variability ofresidues from unit to unit (Brennecke H-R andAnderson C, Bayer CropScience Monheim/Germany,2000, pers comm). In each experiment 30 single unitsof the crop were harvested and analyzed separately.

Apples in Germany (11 trees of Jonagold with thickfoliage and 11 trees of Golden Delicious with thinfoliage) were treated twice with an insecticide in anSC formulation by tractor-mounted sprayer at a 28-day interval with a spray volume of 1000 litre ha−1 andharvested 14 days after the second treatment. Apples inUK (15 trees of Golden Delicious) were treated threetimes at 14-day intervals by knapsack sprayer with anSC formulation and a spray volume of 1000 litre ha−1

and harvested 14 days after the final treatment. ThreeJonagold trees in Belgium were treated under the sameconditions as described for UK except that the sprayvolume was 1500 litre ha−1. Data were converted toresidue ÷ mean for each group of 30 and the 97.5thpercentile value was calculated as 2.7 for the combined120 samples (see Table 8).

Head lettuce (var Sensai) in France were treated onsix occasions at 7-day intervals with a mixture of threefungicides in an SC formulation by knapsack sprayerwith a spray volume of 600 litre ha−1 and harvested



Table 6. Summary of unit residue data for apple trials in Switzerland where the fungicide was applied in accordance with the registered usea

Commodity

Nominal sprayvolumeb

(litre10 000 m−3 ha−1)Mean unit

wt (g)Numberof fruits

Max residue(mg kg−1)

Mean residue(mg kg−1)

97.5th percentilevalues

(mg kg−1)Variability factor= C975 ÷ Cmean

Apples 200 165 126 0.190 0.0494 0.139 2.80Apples 400 150 36 0.110 0.0407 0.109 2.67Apples 800 154 36 0.102 0.0454 0.099 2.17Apples 1000 144 126 0.150 0.0486 0.114 2.36

a Apple variety: Golden Delicious. Application: axial airblast sprayer.b The overall objective of the tree row volume (TRV) concept is to adapt spray volume and dosage to the size or volume of the crop inorder to achieve equal deposits per unit leaf surface (ie cm2). The crop volume can be calculated using the following formula: TRV =[total foliage height (m) × average tree width (m) × 10 000]/row distance (m) Application rate (kg ha−1) = v/2 + [(v/20 000) × TRV]; v = registeredrate (kg ha−1)

7 days after the final treatment. Data were convertedto residue ÷ mean for each group of 30 and the 97.5thpercentile value was calculated as 1.7 for the combined90 samples (see Table 9).

3.3.7 Apples in SwitzerlandField trials in Switzerland on apples were designedto determine the unit-to-unit variability of residues(Dieterle RM, Gasser A, Kaethner M and WohlhauserR, Syngenta Crop Protection AG, Basel/Switzerland,2001, pers comm). Different spray volumes wereapplied but with constant application rate, calculatedaccording to the Tree Row Volume concept.28 A trialplot consisted of five adjacent rows of approximately30 spindle trees each; the plot length was 50 m, thewidth 21 m. The trees were treated with a fungicidefrom both sides by use of a commercial tractor-pulledaxial airblast sprayer (two applications, 14-day sprayinterval). Nineteen days after the final application,single apples were collected from different, pre-definedpositions within six or seven trees from the middle row(1/3 in the bottom, middle, and top, each; 1/3 inner,middle, outer position; left and right side). The residuedata are summarized in Table 6.

All single fruits contained residues above the limitof quantification. The mean residues were of the sameorder of magnitude and independent of the sprayvolume. The variability factors were between 2.2 and2.8. The position of the apples influenced residuelevels; the average residues in the apples of the bottomand middle third of the foliage height tended to beapproximately three times higher than those in theones of the top third (only calculated for trials with126 units collected). No significant differences couldbe found between the inner, middle and outer applesof the spindle trees.

A variability factor of 2.5 was calculated for thecombined 324 values, combined as unit residue ÷mean for each set.

3.3.8 Peaches in USAIn an experiment in the USA an orchard with 120peach trees was treated with an organophospho-rus insecticide.29 Twenty-five trees were randomlyselected and eight fruits were taken from each tree,

10 days after treatment. For each fruit the tree numberand its position on the tree were recorded.

From these fruits 20 groups of 10 peaches werecreated by taking random samples of 10 peaches.Each individual fruit and in addition the combinedextract aliquots for each group of 10 peaches wereanalyzed, resulting in 200 individual and 20 compositesample results (range of residues in composites0.06–0.34 mg kg−1). These data were used to assigndistributions for the residue levels in individualpeaches using, first, the observed values in the 200peaches analyzed and second by calculation usinga log-normal distribution derived from the observedresidues in the 20 composite samples. Observed andcalculated values were in good agreement (Table 7).

3.3.9 Apples in Australia, post-harvestDuring the 2000 apple harvest in Victoria, Australia,Roberts et al30 conducted an experiment to determinethe unit-to-unit variability of residues on Lady

Table 7. Percentiles of the observed residue concentration in the 200

individual peaches and the percentiles in the log-normal

single-serving residue concentration distribution imputed from the

sample of composite residue concentrations29

Percentiles ofthe populationof 200

200 peachesin rank order.

Observedresidue

Calculatedresidue

concentrationby the imputed

individualpeaches

Peachnumber

concentration(mg kg−1)

distribution(mg kg−1)

5 11 0.006 0.01210 21 0.010 0.01720 41 0.016 0.02930 61 0.025 0.04140 81 0.042 0.05750 101 0.067 0.07560 120 0.096 0.10070 140 0.148 0.13880 160 0.216 0.19590 180 0.404 0.32595 190 0.508 0.49897.5 195 0.767 0.76399 198 0.877 1.077Mean residue in 200 peaches 0.245 0.250Standard deviation 0.300 0.332


DJ Hamilton et al

William apples simultaneously post-harvest treatedwith diphenylamine, iprodione and carbendazim.

Consistent with normal practice, the apples werecontained in field bins (1200 mm square × 760 mmhigh) constructed of wooden boards with narrow gapsbetween them, open at the top and with a pallet baseto facilitate movement by forklift machinery. Eachbin contained approximately 300 kg of apples. Fortreatment, vertical stacks of three bins were placedon a conveyor system and passed through consec-utive combination overhead and side-jet drenchingsystems. The side-jets were aligned with openings cre-ated between the bins when they were stacked ontheir pallet bases. The first drenching system deliveredchlorinated water to remove dirt and orchard debris,and the second a solution containing diphenylamine(2.48 g litre−1), carbendazim (0.25 g litre−1) and ipro-dione (0.5 g litre−1). Bins moved along the conveyorat a rate so that they remained under each drench for40 s, while 2000 litres of the respective solutions weredelivered. The mixed fungicide solution was toppedup by the addition of further active ingredient after 51bins (17 × 3 tiered stacks) were treated.

After the bins had been allowed to drain, fifteenindividual fruits were taken from each of Bins 1–3(first three-tier stack treated), Bins 25–27 (middlestack treated) and Bins 49–51 (last stack treated beforedrench replenished). Five apples were sampled fromthe top, centre and bottom layers of each bin. Thisyielded a total of 135 apples to be individually analyzedfor residues of the three fungicides.

The residue concentration ranges, mean concentra-tions and v (defined as the 97.5th percentile residueconcentration found in an individual fruit, divided bythe sample mean) were:

Diphenylamine: range 2.4–33 mg kg−1, mean8.51 mg kg−1, v = 2.49

Iprodione: range 0.36–6.4 mg kg−1, mean2.06 mg kg−1, v = 2.78

Carbendazim: range 0.15–12 mg kg−1, mean1.22 mg kg−1, v = 7.17

The different v value for carbendazim indicates thatfactors other than the method of treatment contributeto variability. The difference was not due to analyticalvariability. Data from recovery experiments conductedwith each sample batch (n > 20 for each compound),covering the range of concentrations found in samples,produced coefficients of variation for diphenylamine,iprodione and carbendazim of 13.0, 15.2 and 9.6%,respectively.

It appeared that carbendazim exhibited differentphysical and chemical interaction with the applesurface compared with the other two compounds. Ingeneral, the residue concentrations in apples sampledfrom the bottom layers of bins were higher than forapples sampled from the other layers. In the caseof carbendazim, residue concentrations in fruit fromthe bottom layer were about 2.7 times higher thanconcentrations found in apples from the other two

layers, whereas for iprodione and diphenylamine theresidue levels in the bottom layer were approximately40% higher.

3.3.10 Strawberries in DenmarkChristensen et al31 measured residues of tolylfluanid,fenhexamid and pyrimethanil in 36 individual straw-berries from the same plot of a field trial. Values forresidue ÷ mean were calculated for the three com-pounds and then were combined for calculation of the97.5th percentile value (2.9) on the 108 values (seeTable 9).

3.3.11 Grapes and head-lettuce in Germany andFranceKaethner32 generated single-unit residue data ontable- and wine-grapes and head lettuce33 in fieldtrials at two sites in Germany and two in southernFrance in 2000 and 2001. The trials were designedspecifically to examine single-unit variability in twodissimilar crops (grapes and lettuce). Products wereapplied by commercial equipment to grapes and bybackpack sprayer to lettuce once in each trial as asingle tank-mix. Lettuce were treated with a mixtureof anilinopyrimidine, triazole, pyrethroid, organophos-phate, carbamate and dicarboximide products, whilegrapes were treated with the same products but exclud-ing the carbamate.

In the lettuce trials 120 treated single-unit samplesfrom each site were taken 3 days after treatment foranalysis. Only those heads meeting commercial qualitystandards were taken and outer leaves were discardedaccording to the local practice. For each set of 120units, residue÷mean values were calculated and thevalues for each pesticide were combined across sitesto produce six sets of 480 data points. The 97.5thand 99th percentile values were calculated for each ofthe six sets, producing ranges of 1.4–1.7 and 1.7–2.0respectively (see Table 8).

In the grape trials, 120 treated samples (bunchesof grapes) from each site were taken 7 days aftertreatment for analysis. In Germany wine grapes(Kerner and Riesling) were used in the trials whilein France table grapes (Muscat de Hamburg) werechosen. Only those bunches meeting commercialquality standards were picked for analysis but, as adeparture from commercial practice, unripe, dry oroverripe berries were not trimmed from the bunches.For each set of 120 units, residue÷mean values werecalculated and then the values for each pesticidewere combined across sites to produce five sets of480 data points. The 97.5th and 99th percentilevalues were calculated for each of the five sets,producing ranges of 2.0–2.5 and 2.3–4.4 respectively(see Table 8).

The active ingredients were applied in each trial as asingle tank mix. There was generally good correlationamong the residue levels of the different actives,demonstrating that the level of residue on a singleunit was largely a result of what was applied to it.



3.4 Summary of variability factors frommarketplace monitoring and field trialsEstimates of variability factors are summarized inTable 8 and Table 9. In Table 8, where numbers ofunit analyses are large enough to provide reasonableassurance on the 97.5th percentile values and toprovide some resolution from higher percentile values,the mean variability factors are 3.0 from marketplacesamples and 2.6 from the supervised trials. In thesupervised trials the one value that seems inconsistentis carbendazim on apples with a variability factor of7.2. Carbendazim was applied post-harvest to applessimultaneously with iprodione and diphenylamine,

but apparently separated and concentrated in thebottom layer of apples in the bins during drainageand drying.30

The estimated variability factors from the supervisedtrials in Table 9 are consistent with those fromTable 8. The variability factors from two of themarketplace samples, 5.3 and 6.4, are higher thanexpected from the data in Table 8. In both cases themean residues were low, 0.0085 mg kg−1 chlorpyrifosin bananas and 0.061 mg kg−1 methamidophos intomatoes, and more than one source for each lotcannot be ruled out.

The majority of the variability factors fall intothe 2.0–3.0 range. A variability factor of 3 should

Table 8. Estimated 97.5th and 99th percentile values for residue÷mean (single units) in cases where the number of unit analyses (n) is large enough

to provide 95% assurance that at least one value exceeds the stated percentile; the 97.5th percentile for residue÷mean represents the variability

factor

Residue ÷ mean estimatedpercentilea

Commodity Pesticide n 97.5th 99th Ref

Market placeApples Azinphos-methyl 780 2.7 (2.4–3.0) 3.5 (2.9–4.4) 23Apples Carbaryl, thiabendazole,

phosalone, diphenylamine,chlorpyrifos

934 2.9 (2.6–3.5) 4.1 (3.4–5.1) 16

Apples Chlorpyrifos 340 3.5 (3.0–4.2) 4.5 (3.4–5.7) 23Celery, bunch Chlorpyrifos, disulfoton,

heptenophos,tolclofos-methyl

280 2.4 (2.0–2.8) 17

Kiwi fruit Phosmet, parathion-methyl,diazinon

400 3.4 (2.4–4.1) 4.4 (3.4–5.9) 17

Potatoes Aldicarb 200 2.9 (2.2–4.3) 18

Supervised trialsApples Carbendazim 135 7.2 30Apples Chlorpyrifos-methyl (day 0) 319 3.2 (2.4–3.7) 3.9 26Apples Chlorpyrifos-methyl (day 14) 320 3.0 (2.3–3.7) 3.7 26Apples Diphenylamine 135 2.5 30Apples Iprodione 135 2.8 30Apples Not stated 120 2.7 b

Apples Not stated 324 2.5 (2.3–2.9) 3.0 c

Carrots Triazophos 458 2.7 (2.5–3.1) 3.4 (2.7–4.9) 13,14,15Grapes, bunch Anilinopyrimidine 480 2.0 (1.8–2.4) 2.4 (2.0–2.7) 32Grapes, bunch Dicarboximide 480 2.5 (2.1–3.8) 4.4 (2.4–5.6) 32Grapes, bunch Organophosphate 480 2.0 (1.9–2.2) 2.5 (2.1–3.1) 32Grapes, bunch Pyrethroid 480 2.0 (1.9–2.3) 2.3 (2.1–2.7) 32Grapes, bunch Triazole 480 2.1 (1.9–2.2) 2.3 (2.1–3.0) 32Kiwi fruit Chlorpyrifos 209 3.3 (2.8–3.9) 25Kiwi fruit Diazinon 209 2.5 (2.0–3.1 25Kiwi fruit Permethrin 209 3.6 (2.5–4.0) 25Kiwi fruit Pirimiphos-methyl 209 4.4 (3.7–6.4) 25Kiwi fruit Vinclozolin 209 2.9 (2.6–3.4) 25Lettuce Anilinopyrimidine 480 1.6 (1.5–1.9) 2.0 (1.7–2.1) 33Lettuce Carbamate 480 1.7 (1.5–1.9) 1.9 (1.7–2.1) 33Lettuce Dicarboximide 480 1.6 (1.5–1.7) 1.9 (1.6–2.4) 33Lettuce Organophosphate 480 1.4 (1.3–1.6) 1.8 (1.5–2.6) 33Lettuce Pyrethroid 480 1.6 (1.5–1.7) 1.8 (1.6–2.1) 33Lettuce Triazole 480 1.5 (1.5–1.6) 1.7 (1.6–1.9) 33

a 95% confidence limits shown in parentheses. Ranges for 95% confidence limits are shown where n is large enough for the calculation.b Brennecke and Anderson, 2000, pers comm.c Dieterle R et al, 2001, pers comm.


DJ Hamilton et al

Table 9. Estimated 97.5th percentile values for residue÷mean (single units) in cases where the number of unit analyses (n) is marginally adequate

to provide the estimate

Commodity Pesticide nResidue ÷ mean,97.5th percentile P≥1 = 97.5 Ref

Market placeBananas Chlorpyrifos 100 5.3 0.92 16Oranges Imazalil 100 1.9 0.92 16Pears Phosalone 100 2.6 0.92 16Tomatoes Methamidophos 100 6.4 0.92 16

Supervised trialsCarrots Chlorfenvinphos 104 2.5 0.93 13,14,15Carrots Quinalphos 110 3.0 0.94 13,14,15Lettuce Not stated 90 1.7 0.90 a

Strawberries Tolylfluanid, pyrimethanil, 109 2.9 0.94 31Fenhexamid

a Brennecke and Anderson, 2000, pers comm.

be adopted as a default value in deterministiccalculations.

4 CAUSES OF VARIABILITYFew sets of data exist that can be used to examine thecauses of variability. The York Conference consideredprimarily monitoring data based on samples from retailsources to determine what possible parameters couldaffect the variability factor;19 clearly, for these data, asource of variability could be the different applicationregimes to which individual units within the lots mayhave been subjected.

The effect of the following factors on residue levelsin an individual unit were considered:

(i) the deposition of the pesticide on the individualunit;

(ii) degradation of the pesticide;(iii) change in weight of the unit, ie growth dilution

effects.

The effect of application conditions was thought tobe one of the most significant factors influencing thedistribution of residues, and therefore the magnitudeof the variability factor. At the time of applicationor delivery, the ‘agro-climatic and environmentalconditions’ may also significantly influence thevariability factor. Existing spray-deposition data couldbe useful in confirming the significance of depositionon individual units and providing supplementaryinformation on experimental data on unit-to-unitvariability of residues. The position of apples on a treeduring treatment by axial airblast sprayer did influenceresidue levels (Dieterle RM, Gasser A, KaethnerM and Wohlhauser R, Syngenta Crop ProtectionAG, Basel/Switzerland, 2001, pers comm). Averageresidues in apples in the bottom and middle foliageheight tended to be approximately three times higherthan in apples from the top third.

In studies where residues and their metaboliteshad been measured, metabolite levels did not bearany relationship to parent levels. Furthermore, very

similar variability of residues of various pesticides inor on the same fruits was observed, although the meanresidues were significantly different. On the basis ofthese observations, it was agreed that the physico-chemical properties of some active substances did notappear to influence the variability factor.

However, the post-harvest treatment of apples witha tank-mix of diphenylamine, iprodione and carben-dazim resulted in a substantially higher variabilityfactor (7.2) for carbendazim than for the other two(2.5 and 2.8), suggesting that the chemical and phys-ical properties of a pesticide in combination with thesurface of the commodity may be influential. In thiscase the application conditions were the same and canbe ruled out as the cause of the differences.30 Appleposition in a bin during draining after post-harvesttreatment did influence residue levels. Residue levelsin apples from the bottom layers of bins were higherthan in apples from other layers.

Studies on a wide range of crops (from field trialsand retail samples) demonstrated that there was nocorrelation between crop unit size and residue levelsin the majority of cases. These variables were thereforeconsidered unlikely to have a significant influence onthe variability factor.

The effect of pre-harvest interval (PHI) was notconsidered significant in influencing the variabilityfactor, particularly where the crop unit size does notchange significantly in the period under consideration.

A number of other factors, such as soil conditionsat time of application, may also have an influence.Inherent soil factors gave rise to the same order of CVas was seen in crops. Further studies on the soil werenot considered necessary, as these factors were morelikely to influence the magnitude of the residues ratherthan their distribution.

In conclusion, the Conference was agreed thatdeposition was likely to have the most significance,and growth dilution effects would have the leastsignificance.

However, it is worthwhile re-examining the variabil-ity from a slightly different perspective. The question



‘What are the causes of the variability?’ conveys anunderlying assumption that variability should be min-imal in the absence of specific causes. But variability isthe norm and is recognized in field and glasshouse tri-als with plant nutrients, trace elements, contaminantsand pesticide residues, where the design takes intoaccount the anticipated ‘biological variability.’ Repli-cation and specified sampling protocols are introducedto help distinguish cause-and-effect relationships fromthe inherent biological variability.

In the case of the variability factor, useful questionscould be: Why are we observing the variability factorsthat we do and why should they not be higher orlower? Is there a minimum variability factor that couldnot reliably be improved upon even with very carefulattention to all the known factors?

One possible explanation of the available variabilitydata is that the suggested causes are generally lessthan the inherent ‘natural variability’, and we onlysee the influence of the suggested causes in occasionalcases. This explanation is consistent with the prevailingopinion that unit-to-unit variability is essentiallyindependent of crop, PHI and pesticide, but thatmethod of application is a significant factor and mayresult in extra variability.

The variability factor is now taken as the residueconcentration in the 97.5th percentile unit valuedivided by the mean of the unit concentration.Conceptually, the variability factor is the residue levelin the high residue unit divided by the average orbulk residue in the lot from which it came. Usually,the measured residue concentration in a compositesample is the best estimate of the average residue inthe lot.

The evidence presented in this paper suggests that,in most situations, the variability factor is typicallyaround 2–3. Observations of apparently highervariability factors (typically up to 6, but sometimeshigher) are likely to be observations of higher-than-97.5th percentile values. The difficulty is that, whenonly 100–120 single-unit analyses are available, the97.5th percentile values is not well resolved fromthe 99th percentile (eg Fig 1). It is necessary toaggregate data, eg from different pesticides or siteson the one crop to produce a population of 300–500or more, which allows reasonable resolution of the97.5th percentile value from the 99th (eg Fig 3).

The variation between composite samples in somecases is substantial, and if a composite sample residueis taken as equivalent to the average residue in the lotit may produce an apparently high (or low) variabilityfactor. In the peach trials summarized in Table 7, theresidue range for 20 composite samples (each of 10peaches) from the same crop was 0.06–0.34 mg kg−1.

5 DIETSIn most situations the estimation of exposure willrequire information about what foods are consumed,by whom and in what combinations. The most

appropriate food consumption data for acute exposureassessment will be determined by a combination ofthe available data about consumption, the level ofdetail about the foods eaten, and access to the originaldataset. Of equal importance in choosing the bestdata for an assessment will be the available residuedata and the intended use of the exposure assessment(‘worst-case screen’ versus ‘refined assessment’). Theavailability of data is always a practical constraint. Inaddition, not all data are directly useful for estimatingexposure—either because of the way the data werecollected or because of the information that is availablefrom the survey. For example, many survey results areonly expressed in terms of nutrient intakes. None theless there are many available types of data. Thereare a number of specific questions to consider whenselecting food consumption data to estimate intake:

— Are the data relevant to assess current dietarypractices or have consumption practices changedsubstantially since the data were collected?

— Are data available for the subgroups of thepopulation that have the highest potential forexposure (eg children, special ethnic groups,geographical regions)?

— Are data available for the foods that are likely tocontain pesticide residues?

— Do the data provide estimates of the quantity ofeach food that was consumed?

Generally, the more detail that is available aboutthe foods that were consumed, the more reliable theresulting estimates of exposure to a pesticide will be.

Some sub-populations may consume foods thatcome from a unique source, and special steps arerequired to assess these groups’ intake accurately.Children’s diets may be quite different from thoseof the rest of the population, and different tech-niques may be required to capture their expo-sure—particularly if the national survey does notinclude information for foods obtained away fromhome, or the child is not with its parent for a largepart of the day. Another example of a special sub-population is subsistence fishermen who consumelocally caught fish from waters in which the pesticideis present at higher levels than typical.

5.1 Types of food consumption dataThere are four broad categories of food consumptiondata: food supply surveys (market disappearance),household or community inventories, household fooduse and individual food intake surveys

Surveys that measure actual consumption ratherthan estimating the available supply of food are thebest source of information to use in estimating acuteexposure. In particular, surveys that measure theconsumption of individuals or households providegood data. However, such surveys are often notavailable and it will be necessary to rely on other data.The characteristics of commonly available types of


DJ Hamilton et al

survey are described below along with an evaluation ofstrengths and weaknesses for estimating acute intake.

5.1.1 Food supply surveysFood supply surveys, also called Food Balance Sheetsor disappearance data, describe a country’s foodsupply during a specified time period. They can beused to estimate average annual exposure but theyare not typically useful for estimating acute or short-term exposure. For example, the FAO Food BalanceSheets34 measure the amount of food produced andremaining for each calendar year. It is not possibleto estimate ‘high consumers’ diets or to estimate thediets of subgroups. FAO Balance sheets can be usedto identify countries with higher or lower consumptionof certain foods. Since the FAO Balance Sheets areavailable for almost all countries, it is possible toidentify groups of countries with similar diets. Oftenadditional data will be available for at least one countryin the group.

5.1.2 Household surveysHousehold surveys generally can be categorizedas (a) household or community inventories, or(b) household or individual food use. Householdinventories are accounts of what foods are availablein the household.35,36 Ideally, data will be availablefor a single day in order to make reliable estimates ofacute exposure. More typically, the data will be fora somewhat longer period (week or month). In thosecases, adjustments must be made for a single day’sexposure.

Even where data are available for a short periodof time, the data may vary in precision with thetype of data collected. Questionnaires may or maynot ask about forms of the food (ie canned, frozenand fresh), source (ie grown, purchased or providedthrough a food programme), cost or preparation.Quantities of foods may be inventoried as purchased,as grown, with inedible parts included or removed, ascooked or as raw. Such data are available from manycountries including Germany, UK, Hungary, Poland,Greece, Belgium, Ireland, Luxembourg, Norway andSpain.35,36

Although household food use data have been usedfor a variety of purposes, including intake assessment,serious limitations associated with such data shouldbe noted. Food waste is often not taken into account.Food purchased and consumed outside the householdmay or may not be considered. Users of a food withina household cannot be distinguished, and individualvariation cannot be determined. Intakes by sub-populations based on age, gender, health status andother variables for individuals can only be estimatedbased on standard proportions or equivalents for ageand gender categories.

5.1.3 Individual consumption studiesIndividual consumption studies provide data on thediets of specific individuals. Methods for assessing

food consumption of individuals may be retrospective(eg 24-h or other short-term recalls) and/or prospective(eg food diaries, food records or duplicate portions).The most commonly used studies are those usingrecall or records. These methods are used to estimateintake for individuals or for the family. When dataare collected for a family, additional adjustments mustbe made to estimate the amounts consumed by eachindividual.

The recall method (eg 24-h recall) is used to collectinformation on foods consumed in the past. The unitof observation is the individual or the household. Thesubject is asked to recall what foods and beverageshe or she or the household consumed during aspecific period, usually the preceding 24 h. Since thismethod depends on memory, foods are quantifiedwith the aid of pictures, household measures or foodmodels. Recalls have been used successfully withindividuals as young as 6 years of age, and interviewer-administered recalls are usually the method employedfor populations with limited literacy.

The main disadvantage of the recall methodis the potential for error due to faulty memoryof respondents. Items that were consumed maybe forgotten, or the respondent may recall itemsconsumed that actually were not consumed duringthe time investigated. To aid recall memories, theinterviewer may probe for certain foods or beveragesthat are frequently forgotten, but this probing has alsobeen shown to introduce potential bias by encouragingreporting of items not actually consumed.

Food record or diary methods are similar to therecall method except that the subject is asked to keepa record of foods and beverages as they are consumedduring a specific period. Quantities of foods andbeverages consumed are entered in the record usuallyafter weighing, measuring, or recording package sizes.Food models, photographs or other devices may beused by the consumer to assist in estimating portions.

An FAO/WHO Consultation12 chose the 97.5thpercentile eaters only consumption to represent thehighest single day, single commodity consumption.

6 CONDUCTING EXPOSURE ASSESSMENTSIn February 1997, FAO/WHO12 convened a jointconsultation to develop procedures for estimatingdietary intake at the international level. One of themajor outcomes of the conference was a schemefor evaluating intake which proceeds from screeningtechniques at the international level to refined intakeanalyses at the national level.

The purpose of an assessment will play a critical rolein determining the most desirable methodology. Theoptimum method when the assessment is designed tobe conservative (as is often the case for regulatorydecision-making applications) is different from theoptimum method when the analysis is designed tobe as realistic as possible (as in scientific hypothesistesting).



Screening methods and model diet methodstypically make conservative assumptions that sacrificeaccuracy for speed and simplicity. In the caseof the evaluation of toxic effects, when screeningdemonstrates that intakes are acceptable, actualintakes will also be acceptable because actual intakeswill be lower than the ‘worst-case’ estimate. Therefore,it can be assumed that there is no need to expendresources to collect better data or to apply moresophisticated techniques in search of greater accuracy.In contrast, a research project that is attemptingto evaluate the cause-and-effect relationship of achemical and a disease would require more accurateintake assessments. The length of dosing that isrequired to elicit a specified biological effect should beused to define the key intake assessment parameters.That is, the biological effects that are the result of asingle or at most few doses will be compared to dietaryintake on a single day.

Other considerations include any breakdown prod-ucts of toxicological significance, and the metabolicpathways in plant and animal systems. Potentialbiological effects must be carefully considered inplanning an intake assessment. Factors of interestinclude (1) dose-response relationships, (2) the lengthof intake required to produce an adverse effect,(3) potentially sensitive populations and (4) variabilityand uncertainty factors.

Diets in many countries are highly processed.Therefore, for most assessments it will be critical toinclude estimates of the residues in the products asthey are consumed.37

For acutely toxic chemicals, it is desirable to evaluatethe exposure for those consumers who received thehighest exposure, either because they ate more food orbecause their food had higher residues at a single mealor during a single day.

The diet of a single individual will contribute varyingamounts to intake, since foods will contain differentamounts of each chemical on different days. Moreover,the diets of individuals vary, both between individualsand in the same individual from day to day. Thus,an assessment of the potential exposure to a chemicalwith one or more uses needs to take into considerationthe variability in the factors affecting the exposurelevel.

6.1 Deterministic estimates (point-estimates) ofexposureIt is tempting simply to estimate the highest possibleamount of food that could be consumed and thenmultiply that value by the highest possible residueconcentration. This can be a reasonable approachfor estimating possible exposures from a single food.However the chance of such an exposure occurringis very low, given that the estimate is based on ahigh consumer eating a food containing high residues.Nevertheless, point estimates are simple and quick toperform and, if they do not exceed the appropriatetoxicological standard, the risk to consumers can

be considered acceptable. Summing point estimatesacross foods will lead to an estimate of foodconsumption that is extremely large and unrealistic.Therefore, the resulting estimate of exposure topesticides is correspondingly large and unrealistic.

The potential for extreme and unrealistic estimatesis compounded exponentially when estimates are madeof exposure for multiple sources of a chemical, egwhen the pesticide could be present on more thanone food. The uncertainty introduced by using single-point estimates to represent these factors (whether anaverage or upper percentile exposure level) increaseswith the number of variables used to derive theseestimates. An approach is needed to accurately deriveand combine exposure distributions from varioususes with information about product use. The bestapproach will take into account the probability thatexposures from more than one source may occur on asingle day while not overstating the actual exposure.

6.2 Distributional (probabilistic) estimates ofexposureIt is possible with today’s computing technologyto do realistic simulations of potential exposuresfrom multiple sources and even from multiplecompounds. Statistical methods for conducting thesetypes of analyses have been developed, tested,improved and validated for a wide variety of differentapplications—ranging from nuclear reactor safety tospace travel. Virtually all of the approaches involvedistributional models that utilize the full range ofpotential observations and combine these to simulatepotential exposure for the consumer, environment orworker. These models are described generically asdistributional models of exposure, but are mostlyreferred to as Monte Carlo models. A publicationon the occasion of the 50th anniversary of theinitial publication of a Monte Carlo model wasdevoted entirely to various aspects of Monte Carlotechniques.38 It must be noted that Monte Carlomodels can be conducted in a wide variety of differentways using widely different data, assumptions andalgorithms. Not all assessments conducted usingMonte Carlo techniques are equally valid.

6.2.1 Overview of Monte Carlo analysistechnique—the theoryThe primary goal of all Monte Carlo analyses shouldbe to present the best possible characterization ofthe full distribution of exposures for the populationunder consideration. A Monte Carlo simulation ideallyshould:

— calculate exposure and present the results asdistributions of exposure;

— incorporate the time frame of the exposure;— permit different estimates of exposure for different

populations including age, ethnic, geographicaland seasonal sub-populations;

— identify uncertainty and variability;


DJ Hamilton et al

— utilize the best available data; and— evaluate the impact of various assumptions.

The most appropriate data and models will dependon the specific toxic effects of concern. For example,if the toxicological effect being modelled is an acuteeffect, then the model should focus on estimating peakshort-term exposures.

Many sources of data and a variety of methods canbe used to determine the distribution of intake usingMonte Carlo analysis procedures.39 Regardless of themethods and data, all procedures estimate intake usingthe following formula:

exposure = residue concentration

× consumption of food × proportion absorbed

A Monte Carlo analysis can also be described asfollows. The analysis begins by selecting an individualfrom the population. That individual’s intake issimulated, including his or her exposure from eachsource. The intakes from each of the sources arethen combined to provide a profile for that individual.Usually each individual’s intake is computed for asingle day, but intakes for multiple days may alsobe calculated for each individual. It is importantto note that intakes from multiple sources must betemporally, spatially and demographically specific;that is, they are calculated for the same individual atthe same time, in the same place and under the samedemographic conditions. The simulation is repeatedfor the individuals (n) in the sub-population and theresults are presented as a frequency distribution ofdaily doses (or doses averaged over the specified timeinterval) for the sub-population.

A Monte Carlo procedure used by the NationalAcademy of Sciences40 for estimating total dietaryintake for all foods is as follows:

(1) The consumption of food 1 by individual 1 ateating occasion 1 of the survey period is multipliedby a randomly selected residue value from theresidue distribution for food 1.

(2) Step 1 is repeated for all foods identified in theassessment, which were consumed by individual1 at different eating occasions on each day of thesurvey.

(3) An estimate of the total daily exposure for allpertinent foods for person 1 on each day isobtained by summing the exposure estimates forall the foods. (Steps 1 to 3 constitute an iteration.)

(4) Steps 1 to 3 are repeated a large number of times,still using the consumption data for person 1 onday 1.

(5) The exposure estimates from Step 4 for person1 on day 1 are stored as frequencies in exposureintervals.

(6) Steps 1 to 5 are repeated for person 1 onsubsequent days of the survey period.

(7) Steps 1 to 6 are repeated for all individuals in thesub-population.

(8) The frequency distribution of the exposureestimates for all individuals on all days is usedto derive the percentile estimates.

It should be noted that each consumption record,including those for the extreme consumers, is usedrepeatedly in the Monte Carlo assessment. In addition,since all residue data files were randomly sampled alarge number of times for each consumption record,it is virtually certain that all residue data points,including extreme data points, were used in the MonteCarlo assessment.

6.2.2 Model input parametersFor each parameter of a Monte Carlo modelingscenario, the most desirable data are those measureddirectly on the population being evaluated.

The model needs to be flexible enough toaccommodate a variety of types of input, ranging fromempirical data sets to parametric or mixed empiricaland parametric inputs (eg parametric estimation ofsome values in an otherwise empirical data set) and topermit professional judgment.

The models and analyses must be transparent.The algorithms must be clearly stated along withassumptions. The criteria for assessing the validityof a Monte Carlo method are summarized in Fig 4and described in more detail in the sections below.

6.2.3 Data and model outputsThree types of data generally needed are: (1) consump-tion information, (2) residue concentrations in thefoods consumed and (3) proportion of each food con-taining the compound being evaluated. The modelparameters and algorithms used in the Monte Carloassessment should be selected to provide the followingfeatures:

— accuracy and precision— ability to conduct sensitivity analyses— ability to highlight sources of exposure.

The model outputs consist of estimates of thedistributions of daily exposure from multiple sources.

Validity of the Monte Carlo Model depends upon:

The validity of the distributions of values used to represent the input variables

The ability of the model to predict the real world relationship between the inputand output variables

Other factors that affect the output variable

Whether adjustments were made for potential correlations

The method used to sample from the input distributions

The number of iterations

Number of observations needed to obtain reliable estimates of consumptionand residue distributions

Representative data

When using Monte Carlo simulations with modeled data:

Testing assumptions about distribution shapes and parameters

Deciding on what percentile to truncate the distribution

Figure 4. Monte Carlo method.



6.2.4 CorrelationsWhere correlations between data parameters areknown to exist, they should be taken into accountin the model. Intra-individual correlations include theselection of foods on a given day and on sequentialdays. There are known correlations between intakeson several days. There can also be inter-individualcorrelations. For example, there would be correlationbetween measurements on individuals in the samehousehold.

6.2.5 Sensitivity analysesMonte Carlo simulations are approximations. Sensi-tivity analyses can be used to identify the parametersthat contribute most to the exposure estimates, or tothe variance or uncertainty in the estimates. Sensitivityanalyses should be used to assess the impact of variousmodel assumptions and to determine whether thereare any systematic model-rules that are consistentlybiasing the results.

Sensitivity analyses may also be needed where thereare unusually large values (suspect ‘outliers’) in thedataset. It may also be appropriate to conduct teststo determine whether the suspect outlier is sufficientlyextreme to warrant its exclusion from the analysiseither based on biological plausibility or mathematicalcriteria. Extreme values may strongly influence theestimates of exposure for at least the upper percentilesof exposure.

6.2.6 Data quality, reliability, sources of error and‘non-detects’Reliability, or reproducibility, is the ability of a methodto produce the same or similar estimate on two or moredifferent occasions,41,42 whether or not the estimate isaccurate.

Analytical data, particularly from monitoring stud-ies, will include many samples without detectableresidues. These samples may be true zeros, or theymay contain concentrations of the chemical which arebelow the analytical capability of the methods used.The treatment of these samples can have a significanteffect on the average or median value for a distribu-tion. These samples will have smaller effects on theupper percentile estimates of exposure.

The quality of the exposure assessment is mostimportantly a function of the source, quantity andquality of the data used in the assessment. Data whichare more representative of the actual situation willprovide better estimates of exposure.

Monte Carlo techniques will generally require moredata than have been historically available for exposureestimation. This is particularly the case for estimatesof the upper percentiles of the exposure distribution.Minimum criteria need to be established for datafor exposure estimates. Surrogate data and defaults(in lieu of data) may need to be used, but whensuch defaults are used, criteria need to be establishedto ensure the best possible estimate. The types ofsurrogate data might include data for a chemical with

similar physicochemical properties or similar patternsof distribution in the environment.

Another problem frequently encountered is inade-quate data for sub-populations with predicted extremeexposures, eg children and infants. Where data aremissing, it may be necessary to utilize a conservativedefault estimate for that parameter. The impact of thevalue chosen for such defaults should be tested.

Expert judgment will be used in most assessments.Consistent application of expert judgment is critical.

Monte Carlo type assessments will be extremelydifficult to conduct in a scientifically reasonable wayon an international basis, mainly because of a lackof suitable and robust consumption data. Most often,they will be conducted for national populations orfor sub-groups within national populations. At theinternational level, the analyst will then consider therange of results from a series of national Monte Carloassessments in order to decide whether they cover asuitable geographical or consumer range.

6.2.7 National intake assessmentFor some compounds, biomarkers have been identifiedthat are present in either blood or urine. In theory, itmay be possible to use biomarkers to estimate directlydietary exposure. However, such data are virtuallynever available for consumers and, in any event, itis necessary to have calibration data that relate theamount of exposure that led to the concentrations ofbiomarkers that are found.

Indirect exposure is estimated from the concentra-tions of the chemical in foods along with the frequencyof occurrence and the amounts of each food that areconsumed by the population being evaluated.

In general, data from large national surveys usingrecall or record methods provide the most readilyavailable food consumption data for estimating intakeof pesticides from food. The surveys work best whenmost of these foods containing the pesticide to beevaluated are consumed on a regular basis. It ismore difficult to estimate exposure from a food thatis infrequently consumed because it is difficult tocapture intake of infrequently consumed foods usingshort-term recalls or records.

Some pesticides concentrate in farm animal liversand other foods that may be consumed infrequently.However, they may be eaten in significant quantitieson certain occasions and thus be of concern for acuteexposure assessments. If an infrequently consumedfood, such as liver, is a major source of the chemicalunder investigation, intake estimates for that chemicalmay be low. Intake assessments for those chemicalswill be most accurate when based on surveys in whichthe frequency of consumption is estimated. In thiscase, it may be better to use estimates of large portionsize. If the chemical is found in only a few foods, intakeassessments for that chemical will be most accurate ifthe data used are from surveys that captured veryspecific information on foods consumed even when


DJ Hamilton et al

the survey did not capture information about the totaldiet.

6.2.8 Model and software validation and sensitivityanalysesThere is a critical need to test and validate distributionmodels against actual exposure and dose data.Such a validation has been conducted for thepesticide chlorpyrifos with good agreement betweenbiomonitoring conducted by Hill et al43 and anaggregate exposure computed using Monte Carlomodelling and the available experimental data.44

All computer-based models should be tested toensure that the algorithms are being computed asintended. This can be accomplished by conductingsimulations outside the software for comparisonpurposes. Furthermore, it may be valuable to conductassessments using different approaches to determinewhether similar conclusions are reached.

One of the advantages of distributional models isthat it is possible to identify the major contributorsto the estimates of exposure. Validation efforts canthen focus on those parts of the estimate. Surveymethodology has also been validated by the useof biological markers associated with dietary intake.Possible sources of biological markers include urine,faeces, blood, breast milk and tissue samples, but themost easily accessible, and therefore most commonlyused, is urine. Nitrogen content of urine has beenused to verify protein intake. For example, if proteinintake calculated from the reported food intake isin agreement with protein intake calculated fromnitrogen excretion, it is assumed that intake of othernutrients is valid.45

6.2.9 ConclusionMonte Carlo techniques provide powerful tools thatwill take advantage of the best available data inorder to provide realistic estimates of exposure. Theresults are only as good as the input data, algorithmsand assumptions. The impact of assumptions shouldalways be tested carefully. The basis for the resultsmust be fully documented and the data used in themodel must be made available to allow independentverification of outputs if required.

7 USE OF SUPERVISED RESIDUE TRIALS DATASupervised residue trials are defined by FAO46 asscientific studies in which pesticides are applied tocrops or animals according to specified conditionsintended to reflect commercial practice, after whichharvested crops or tissues of slaughtered animalsare analyzed for pesticide residues. Usually specifiedconditions are those which approximate existing orproposed GAP (Good Agricultural Practices).

A set of supervised trials should sufficiently coverthe variety of conditions likely to occur in practice,and is normally planned to cover the geographicaland climate ranges for the crop within a country

for a national registration system. The trials wouldalso cover agricultural practices that might producedifferent residue levels, eg aerial application orglasshouse production.

Supervised trials provide the essential data thatrelate a use pattern (or GAP) to MRLs. Other veryuseful information may also be obtained from thetrials, such as the data used for estimating chronic andacute intake of the residue.

National authorities evaluate the residue data as acomponent of the registration process, and, in setting anational MRL, the authority requires trials conductedat the maximum allowed by the proposed label,ie at maximum application rate, minimum intervalsbetween treatments and minimum interval betweenfinal treatment and harvest. This is referred to asthe critical GAP; the supervised trials establish therelationship between critical GAP and national MRLs.

At the international level, Codex MRLs are setas standards for food commodities in trade. Unlikenational systems, MRL evaluation is not part of aregistration system, so the available data are evaluatedagainst established national GAPs, and MRLs are setwhere sufficient supervised trial data are available. Theuse of supervised trial data for intake estimates is bestillustrated by examples.

Dimethoate use on pome fruits in Europe in 16 trialsproduced the following residues (one residue valueper trial): 0.01, 0.03, <0.05 (5 values). 0.06, 0.07,0.08, 0.10, 0.14, 0.15, 0.16, 0.26 and 0.30 mg kg−1.For pome fruits, the commodity of trade and theedible portion are the same, so we can use thecommodity data directly for the intake calculations.The recommended MRL is 0.5 mg kg−1. The medianis 0.065 mg kg−1, denoted the supervised trials medianresidue or STMR. The highest residue (HR) is0.30 mg kg−1. The HR is used in the calculationsdescribed in Section 11, Calculation of intake.

Chlorpyrifos use on oranges, mandarins and lemonsin Spain, South Africa and USA in 14 trialsproduced the following residues (one residue valueper trial) in the whole fruit: 0.05, 0.10, 0.12,0.14, 0.15, 0.19, 0.21, 0.26, 0.33, 0.41, 0.55,0.66, 0.99 and 1.2 mg kg−1 (median 0.235 mg kg−1).An additional study showed a threefold reductionin residue concentration in orange pulp comparedwith the whole fruit. The recommended MRLis 2 mg kg−1 based on residues in whole fruit.The STMR and HR are based on residues inthe edible portion: STMR = median 0.235 × 1/3 =0.08 mg kg−1; and HR = highest residue 1.2 × 1/3 =0.4 mg kg−1.

8 FOOD PROCESSING47

Experimental data on the fate of pesticide residuesduring industrial processing of food are routinelyprovided to government authorities by pesticideregistrants. Data are also provided in some cases on theeffects of consumer preparation of the food. Hydrolysis



studies provide information on likely breakdownproducts during cooking and storage. A knowledgeof hydrolysis properties and physical properties suchas volatility and solubility will assist in predictingbehaviour during processing.

Food processing studies are designed to measurechanges in residue levels when raw commodity isconverted to processed commodity. Changes aresimply expressed as processing factors (residue levelin processed commodity ÷ residue level in rawcommodity). For the purposes of dietary intakeevaluation, residue levels in the processed commodity,STMR-P and HR-P, are obtained by multiplying theSTMR and HR respectively of the raw commodityby the processing factor. The STMR and HR arederived from the supervised residue trials as describedin Section 7.

Food processing usually, but with some exceptions,results in a decline of residue levels. The drying offruits will, for many pesticides, result in an increasedlevel of residues in the dried commodity. However, thereduced consumption compared with that of the freshfruit will tend to compensate for the higher level. Othersituations where an increase in residues may occur arethe concentration of lipophilic residues in oils or grainprotectants in the bran of grain. However, commercialfood processing generally operates on a large scale, andcommodities such as fruit juices, vegetable oils, sugarand flour are subject to bulking and blending in theprocess, which will reduce residue levels by dilution.Canned fruits and vegetables would not be subject toso much dilution and the contents of a specific canare likely to have come from a single farm. The singleunit of fruit can be maintained through the processand into a can for pineapples.

Food processing may cause a change in the natureof the residue for some pesticides. Hydrolysis is themost likely reaction during cooking, so compoundssubject to hydrolysis are likely to be converted duringprocessing.48 Cooking happens in many processessuch as canning, juicing and baking. The parentpesticide may be broken down by hydrolysis to aless toxic compound, eg captan is readily convertedto tetrahydrophthalimide during cooking.49 However,the ethylenebisdithiocarbamates are converted toethylenethiourea (ETU) on cooking.50 ETU has alower ADI than the parent dithiocarbamates, so is ofchronic toxicity concern. It has not yet been assessedin terms of a need for an acute RfD. The fact that acrop has been bulked prior to processing will generallymean that a consumer is unlikely to encounter avery high residue in isolation as may happen withthe consumption of a single unit of fruit or vegetables.However, processing should still be taken into accountduring acute risk assessments to ensure that estimatesare as realistic as possible.

9 ACUTE REFERENCE DOSEThe acute reference dose (acute RfD) of a chemical isthe estimate of the amount of a substance in food or

drinking-water, expressed on a body weight basis, thatcan be ingested over a short period of time, usuallyduring one occasion or one day, without appreciablehealth risk to the consumer on the basis of all theknown facts at the time of the evaluation.11 The 2002JMPR changed the wording to ‘. . ..that can be ingestedin a period of 24 h or less. . ..’51

The nature of the toxic end-point must also beconsidered. For example, an acute RfD based onteratogenic effects is not relevant to risk assessmentfor a sub-population such as children unless maternaleffects also occur at that level.

The York Conference considered a number ofquestions which have arisen frequently in discussionson acute RfDs and short-term dietary exposure andmade recommendations on how these should beapproached.19

Up to 2002, JMPR has assigned acute RfDs to 39compounds. On the basis of their toxicology, JMPRhas decided for some pesticides that a short-termdietary intake of residues is unlikely to present a riskto consumers and it is unnecessary to establish acuteRfDs (25 compounds in Table 10) or to estimateIESTIs (international estimated short-term intakes).Values for acute RfDs are listed in Table 10. Theacute RfD would not be set at a level lower thanthe ADI.

The 2000 JMPR52 explained that the case for settingan acute RfD should be considered for all compounds;it listed a number of toxicological alerts that wouldsuggest the need to establish an acute RfD. Whenthere is no toxicological alert for acute effects, reasonsshould still be given if the decision is not to establishan acute RfD.

The duration of dosing in the animal studies that wasrequired to elicit a specified biological effect shouldbe used to define the key parameters for the intakeassessment. That is, the biological effects that are theresult of a single or, at most, a few doses should becompared to dietary intakes for a single meal or asingle day.

Early examinations of data suggested that range-finding studies may play a useful role in determiningsuch parameters; however this has not been foundto be the case. Also it is not always possible tomatch exactly the period of dosing in the animalstudies to equivalent consumer exposures. Workshould continue towards the development of standardprotocols for toxicological tests aimed at establishingacute RfDs for pesticides.

10 RESIDUE DEFINITIONThe residue in food resulting from the use of apesticide in crop or animal production is often thesame compound as the parent pesticide, but this isnot necessarily the case. The nature of the residue infood depends on the fate of the parent pesticide in theenvironment (hydrolysis, photolysis, soil metabolism),plant metabolism and animal metabolism. Important


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metabolites and degradation products are includedin residue definitions depending on their toxicity,contribution to the total residue and ease of analysis.

If the parent pesticide is reasonably stable, the parentitself will be the major part of the residue detected infood. In this case the residue definition will be theparent pesticide itself.

Where a metabolite or degradation product is asignificant part of the residue and where it exhibitstoxicity higher or even slightly lower than that of theparent it should be included in the residue definitionfor risk assessment.

Table 10. Acute RfDs reported by JMPR

Pesticide Acute RfD (mg kg−1 bw) Year

Aldicarb 0.003 1995Amitraz 0.01 1998Bentazone Unnecessary 1999Bitertanol Unnecessary 1998Buprofezin Unnecessary 1999Captan Unnecessary 2000Carbaryl 0.2 2001Carbofuran 0.009 2002Chlormequat 0.05 1999Chlorpropham 0.03 2000Chlorpyrifos 0.1 1999Chlorpyrifos-methyl Unnecessary 2001Clethodim Unnecessary 19992,4-D Unnecessary 2001DDT Unnecessary 2000Deltamethrin 0.05 2000Diazinon 0.03 2001Dicloran Unnecessary 1998Diflubenzuron Unnecessary 2001Dimethipin 0.02 1999Dinocap 0.008 1998Dinocapb 0.03 2000Diphenylamine Unnecessary 1998Disulfoton 0.003 1996Dodine 0.2 2000Endosulfan 0.02 1998Esfenvalerate 0.02 2002Ethoprophos 0.05 1999Ethoxyquin Unnecessary 1998Fenamiphos 0.0008 1997Fenitrothion 0.04 2000Fenpropimorph 1 2001Fenthion 0.01 1997Fipronil 0.003 1997Fipronila 0.003 2000Flutolanil Unnecessary 2002Glufosinate-ammonium Unnecessary 1999Imazalil Unnecessary 2000Imidacloprid 0.4 2001Kresoxim-methyl Unnecessary 1998Methidathion 0.01 1997Methiocarb 0.02 1998Methomyl 0.02 2001Methoprene and S-methoprene Unnecessary 2001Mevinphos 0.003 1996Monocrotophos 0.002 1995N-Acetyl glufosinate Unnecessary 1999

Table 10. Continued

Pesticide Acute RfD (mg kg−1 bw) Year

Oxamyl 0.05 20022-Phenylphenol Unnecessary 1999Parathion 0.01 1995Parathion-methyl 0.03 1995Permethrin Unnecessary 1999Phosalone 0.3 2001Phosmet 0.02 1998Piperonyl butoxide Unnecessary 2001Prochloraz 0.1 2001Propargite Unnecessary 1999Propylene thiourea 0.003 1999Pyrethrins 0.2 1999Pyriproxyfen Unnecessary 1999Spinosad Unnecessary 2001Tebufenozide 0.05 2001Thiodicarb 0.04 2000Thiophanate-methyl Unnecessary 1998Tolylfluanid 0.5 2002

a Value applies to fipronil and fipronil-desulfinyl, alone or incombination.b Value for dinocap applies to the general population, with theexception of women of child-bearing age

The Joint FAO/WHO Meeting on PesticideResidues recognized the divergent needs of enforce-ment residue definitions and risk assessment residuedefinitions and now proposes separate residue defini-tions for some pesticides. JMPR defines the residues(for estimation of dietary intake) as the combinationof the pesticide and/or its metabolites, impurities anddegradation products to which the STMR applies.46

(The STMR is used in the chronic risk assessment.)The FAO Manual46 explains further that the residuedefinition for estimation of dietary intake depends onthe results of toxicology studies and its general suit-ability for estimating dietary intake of the residues forcomparison with the ADI. The FAO Manual lists eightfactors to be considered for a residue definition; thefirst two relate to the risk assessment—compositionof the residues found in animal and plant metabolismstudies and the toxicological properties of metabolitesand degradation products.

The risk assessment residue definition was initiallyconsidered only in terms of chronic risk assessment.However, it seems likely that the residue definition foracute and chronic risk assessment will almost alwaysbe the same, although an exception cannot entirely beruled out. For example, theoretically there could beone component of the residue largely responsible for achronic effect and a different component responsiblefor an acute effect. To date, no such compounds havebeen identified.

11 CALCULATION OF INTAKE11.1 International (JMPR) deterministicmethodsAt the international level we should use those factorsthat generally apply, such as residue levels in edible



portions and effects of processing and cooking onresidue levels, but not those factors that are limited tospecific situations and can only be used at the nationallevel, such as food consumption of population sub-groups within a country or localized food preparationpractices.

The 1997 consultation12 examined two cases: case1 where the residue in a composite sample reflects theresidue level in a meal-sized portion of the commodity,and case 2 where the meal-sized portion as a singlefruit or vegetable piece might have a higher residuethan the composite. Case 2 is further divided into case2a and case 2b where the unit size is, respectively,less than or greater than the large-portion size. The1998 conference19 added a third case for a processedcommodity where bulking and blending occur.

The 1999 JMPR53 summarized the methods forcalculating short-term intake of residues and reportedthe results of such calculations for the first time.The methods use the data from supervised residuetrials that are evaluated principally for the purpose ofsetting MRLs, but also to provide the residue levels forcalculation of the international estimated daily intake,the measure of long-term or chronic intake. The 1999JMPR explained the calculations for Cases 1, 2 and 3and the choice of data on diets and residues to be usedin those calculations.

Case 1In the first case, composite sampling data reflect theresidue level in a meal-sized portion of the food(commodity unit weight is below 25 g). The intakeis simply the large portion size divided by body weight(bw) and multiplied by the highest residue in the edibleportion found in the supervised trials at maximumregistered use and supporting the MRL:

IESTI = LP × HRbw

Case 2In Case 2 composite residue data do not reflect theresidue level in a meal-sized portion of the food(raw commodity unit weight exceeds 25 g). Differentcalculations are needed depending whether the unitweight (edible portion) is less than or greater than thelarge portion size.

Case 2aIn Case 2a unit weight of raw commodity (edibleportion) is less than large-portion weight. Theassumption is that the first unit consumed isthe elevated residue one, ie the residue level =composite residue × variability factor. The remainderof the large portion then consists of other units fromthat same lot with a residue level equivalent to that ofthe composite sample, the HR.

IESTI = U × HR × v + (LP-U) × HRbw

Note: It was assumed originally that the commodityoffered for sale to the consumer would come frommixed lots and then the remainder of the large portionafter the elevated residue unit could be assumed tohave a residue equivalent to the STMR. However, it islikely that the units supplied together have originatedfrom a single lot and, in that case, the second partof the equation, which accounts for consumptionof the second and subsequent units, making up theremainder of the large portion, would have a residuelevel equivalent to the HR.52

Case 2bIn Case 2b the unit weight of raw commodity (edibleportion) exceeds the large-portion weight, so theresidue level in the large portion equals the residuelevel in the elevated residue unit, ie the residuelevel = composite residue × variability factor:

IESTI = LP × HR × vbw

Case 3Case 3 is used for processed commodities, wherebulking or blending means that the STMR-Prepresents the likely highest residue. Such examplesare fruit juices, vegetable oils, sugar and flour,where processing is on a large scale and where rawcommodities from a number of farms contribute tothe final product. Generally, processing leads to areduction in residue levels, although there are someexceptions such as the concentration of lipophilicresidues in oils or grain protectants in the bran ofcereal grains.

IESTI = LP × STMR-Pbw

In the above Cases,

IESTI: international estimated short term intake(mg kg−1 bw).

LP: large-portion (97.5th percentile of eaters) con-sumption per day for the food.

HR: highest residue in composite sample of edibleportion (mg kg−1) found in the supervised trial datasupporting the MRL and STMR. An alternative tothe HR was the MRL with an adjustment for residuein the edible portion when the commodity had aninedible portion. The HR has some advantages:

(1) it is an intermediate stage of the IESTIcalculation and rounding to the MRL could have asubstantial effect on the calculation in some cases,(2) residue data on edible portion are available frommany trials and can be used directly for dietaryintake calculations,(3) the MRL residue definition is designed forenforcement purposes and may be inappropri-ate for dietary risk assessment, which shouldinclude toxicologically significant components ofthe residue, and


DJ Hamilton et al

(4) use of the MRL for dietary intake estimatesmay influence the setting of the MRL and maygive people the impression that recommending adifferent MRL results in a different intake, which isnot the case if GAP is unchanged. The MRL has theadvantage that it is easier to explain, but on balancethe HR advantages will lead to a more consistentresult.

bw: body weight provided by the country andconsumer population from which the large portion(LP) was used.

U: unit weight provided by the country in the regionwhere the trials gave the highest residue supportingthe MRL. Unit weights consist of two components,the weight of a typical commodity unit in tradeand the percentage edible portion. The unit weight(edible portion) is used in Case 2 calculations, butthe unit weight (25 g) of the commodity unit intrade determines the border line between Case 1and Case 2. Only a few countries had provided unitweight data to WHO for use by the 1999 JMPR,which decided to choose the unit weight from acountry in the region with the relevant supervisedresidue trials.

v: variability factor. Residue in the 97.5th percentilesingle unit of a lot divided by the mean residuefor the lot for samples taken from controlled trials.The 1999 JMPR used a variability factor of 7 formost items, a value of 5 for large items (>250 g)and a value of 10 for granular soil treatments forcommodities with unit weights <250 g and >25 g.

STMR: supervised trials median residue (mg kg−1),representing typical residue in edible portionresulting from the maximum permitted pesticideuse pattern.

STMR-P: STMR for processed commodity, calcu-lated from STMR for the raw commodity and theprocessing factor.

11.2 National methodsThe UK and the EU have adopted methodologysimilar to that used by the JMPR for calculating acutepoint estimates of intake.54 Parameters such as portionsizes, body weights, etc are taken directly from UKsurveys.55–57 The UK Case 3 is designed for thosesituations where data on individual units are available,which is a different situation from the JMPR Case 3for processed commodities.Both the US EPA and the California Department ofPesticide Regulation (DPR) employ a tiered approachfor acute dietary intake assessment that includesprobabilistic (ie Monte Carlo as generally described inSection 6.2) methodology at the higher tiers.58,59 Thedietary exposure evaluation model (DEEM) employedfor these assessments is a commercially availablesoftware package.60 The DEEM model combines foodconsumption data from a series of US Departmentof Agriculture (USDA) surveys and residue datafor a given pesticide from MRLs, field residues,monitoring data or market basket surveys. In addition,

processing and cooking effects on residue levels canbe accommodated. Conceptually, dietary exposure iscalculated based upon a simple formula:

Ei = (Ri × Ci × Pi)

Et = �Ei

In this formula Ei stands for the exposure fromthe pesticide on food in mg kg−1 bw day−1, Ri is thepesticide residue on food i in mg pesticide g−1 food,Ci is the daily consumption of food i in grams per kgbodyweight for a single day, and Pi is the probabilityof consuming a given pesticide residue on food i ina given day. Et is the total daily exposure to a givenpesticide summed across all food commodities.61

The EPA/DPR dietary exposure assessmentapproach involves four tiers of refinement, movingfrom use of the most conservative inputs to the mostrealistic upon higher tiers of refinement. The firsttwo tiers involve a deterministic approach and do notutilize probabilistic methodology. For a Tier 1 assess-ment, the residue Ri is assumed to be a residue levelequivalent to the MRL for all commodities (single-serving and blended), and the probability Pi is 1 (ie100% likelihood that the food commodity will con-tain a residue level equivalent to the MRL). A Tier2 assessment is similar, but employs a level equiva-lent to the MRL only for non-blended, single-servingcommodities; average field residue values are utilizedfor blended commodities. Tier 3 is the first level ofassessment that can be considered truly probabilis-tic. Rather than fixing a single value as the residueRi on a given commodity, the full distribution ofresidue data from USDA or FDA monitoring pro-grams (preferred) or field trials (if monitoring dataare not available or adequate) are employed. In addi-tion, percentage crop treated (ie market share) is usedas either an adjustment factor for the residue con-centration (blended commodities) or the probabilityof encountering a treated item (non-blended com-modities). The Monte Carlo approach described inSection 6.2 is employed for Tier 3 to create condi-tional, joint probability functions from the individualpesticide residue and food consumption information.The output is a distribution of daily exposures whichrepresents the range of potential exposures acrossthe population to the pesticide and the associatedprobabilities. The Tier 4 approach is similar in manyrespects, but also employs data from advanced stud-ies such as market basket surveys, residue declinestudies, and cooking residue and degradation stud-ies. It should be noted that for tiers employing fieldresidue, monitoring or market basket residue data,decompositing methods may be used to transformvalues derived from composite samples into simu-lated distributions of possible residue concentrationsin individual units.59



12 COMPARISON OF PROBABILISTIC ANDDETERMINISTIC METHODSA number of questions relating to the calculationof consumer exposure are equally applicable to bothprobabilistic and deterministic methods of analysis.The CCPR discussed the feasibility of the use ofprobabilistic models in international fora at its meetingin May 200262 but did not consider that sufficient datawere available to carry out such modelling routinely atan international level. However, it was noted that thecurrent acute dietary models are only based on a verylimited number of data sets.

Historically, the 97.5th percentile has been chosenas the ‘upper’ percentile for deterministic acuteintake assessments, ie the 97.5th percentile of eatersfor the specific food and the 97.5th percentileresidue level, which, in combination, amounts tothe 99.94th percentile for residue intake (100 × (1 −(1 − 0.975) × (1 − 0.975))). Probabilistic models cancalculate percentiles in excess of this providing thatthe input data are reliable.

An objection by some to the use of probabilisticmodels is that they ‘artificially manipulate down’exposure estimates. Examples show that wherecomparisons are made between deterministic andprobabilistic calculation outputs, the calculated intakeat the 97.5th percentile will often be lower forthe probabilistic model, but this is a function ofthe combinations of consumption, residue level andbody weight not always being at extreme values. Forexample, in the UK, acute intakes of carbaryl fromapples, nectarines and peaches, and triazophos inapples or carrots were calculated deterministically.Intakes by toddlers of carbaryl from apples gavean intake equivalent to 102% of the acute RfD(0.04 mg kg−1 bw), while acute intakes of triazophosfrom apples or carrots were 175 and 294% of theacute RfD (0.001 mg kg−1 bw), respectively. Whenthese intakes were modelled probabilistically, takingaccount of the distribution of consumption patternsand body weights, occurrence and levels of residuesin batches of crop and distribution of residueswithin the batch, the maximum intake of carbaryl(from apples, nectarines and peaches combined) was0.058 mg kg−1 bw and the probability of exceedingthe acute RfD was 0.001–0.006%. When intakes oftriazophos residues were modeled probabilistically, themaximum intake was 0.019 mg kg−1 bw (from applesand carrots) and the probability of exceeding the acuteRfD was 1.7–3.0%.63

Comparison of the outputs of deterministic andprobabilistic dietary exposure assessments can bedifficult because most deterministic approaches focuson only a single commodity at a time, whereasmost probabilistic approaches estimate exposure fora population across all dietary items. Some insight canbe gained from the results of a published comparisonof the use of the DEEM model in deterministic andprobabilistic modes.64 Different permutations of thevarious tiers of the US EPA assessment approach

Table 11. Chlorpyrifos acute dietary DEEM exposure estimates

(mg kg−1 bw day−1)a

Modelling parametersUS

population ChildrenMonteCarlo?

MRL residue levels, 100%crop treated

0.1166 0.2648 No

Highest field trial residues,100% crop treated

0.0516 0.1177 No

Field trial residue distribution,actual % crop treated

0.0243 0.0412 Yes

Residue monitoring data,actual % detection

0.0005 0.0009 Yes

a Included 204 food items; results listed for the 99.9th centile.64

were employed to examine how the use of additionalinformation from residue monitoring, market basketsurvey and processing studies affected predictedexposure to the common insecticide chlorpyrifos forthe entire US population as well as children (1–6 yearsold). As shown in Table 11, deterministic estimatesbased on residue levels equivalent to MRLs or fieldresidue levels can yield predicted exposures more thantwo orders of magnitude greater than those resultingfrom Monte Carlo estimates using distributions offield residue data or monitoring data.

Comparisons may also be made by examin-ing single-commodity, deterministic assessments andprobabilistic, multi-commodity assessments. Table 12summarizes several recent acute dietary intakeevaluations58,64–66 for the common insecticide chlor-pyrifos, and a couple of noteworthy observationsmay be made regarding the various estimates. First,there are differences in predicted acute exposure forspecific commodities among the various determinis-tic, single-commodity evaluations. Although most ofthese differences are rather small, examination of theUK side-by-side evaluations indicates that reliance onmonitoring results versus MRLs results in a lowerestimate of exposure. Second, it appears that, in anumber of instances, the deterministic approaches arepredicting greater exposure on each individual fooditem than are estimated via probabilistic approachesfor total dietary intake from all food items. The factthat deterministic estimates are generally based onresidue levels equivalent to MRLs or field trial data(generally higher residue values and 100% detection),whereas the probabilistic ones are based on actualmonitoring data (generally lower residue values andactual detection frequency), may explain some of thediscrepancy in observed results. However, even theuse of monitoring data, as in the UK, appears to resultin higher estimates via the deterministic approach thanwith probabilistic estimates.

The above comparisons provide some initialobservations but, in light of the concomitant useof deterministic and probabilistic acute dietaryapproaches by various regulatory authorities andadvisory bodies, additional and more exhaustive


DJ Hamilton et al

Table 12. Comparison of deterministic acute dietary intake estimates of chlorpyrifos on individual items with probabilistic estimates of total dietary

intake of chlorpyrifos for the general population (mg kg−1 bw day−1)

FAOa UKb UKb Californiac

Residue data source Field trial MRL Monitoring MRL USAd

Deterministic estimateApple 0.0293 0.0061 0.0057 0.0066 NAe

Banana 0.0009 0.0210 0.0008 0.0001 NABroccoli 0.0405 0.0004 NA 0.0048 NAGrapes 0.0082 0.0068 0.0014 NA NACitrus (grapefruit) 0.0110 0.0049 NA 0.0035 NAStrawberries 0.0001 0.0006 0.0003 0.0001 NATomatoes 0.0057 0.0044 0.0005 0.0012 NAProbabilistic estimate

Monitoring/MRL MonitoringTotal diet NA NA NA 0.0037 0.0009

a IESTI.65

b NESTI.66

C DEEM deterministic evaluation for 95th centile consumption. For probabilistic, MRL used if monitoring data unavailable for a particular commodity;95th centile consumption assumed.58

d DEEM evaluation at 99th centile consumption; monitoring data includes market basket values for some commodities.64

e NA = not available.

comparisons of the two approaches would appearwarranted.

13 COMBINATIONS OF PESTICIDES WITH ACOMMON MODE OF ACTION13.1 Establishing a common mode of actionAlthough knowledge of pesticide toxicological modeof action has long been employed to group pesticidesinto classes and sub-classes, dietary intake assessmentrequiring consideration of simultaneous exposure topesticides with a common mechanism of toxicity hasonly recently received renewed official attention.40

Several regulatory authorities (eg Canada, UK,USA) have established pesticide review programmeswhich are to consider the potential for cumulativedietary intake of pesticides which act via a commonmechanism of toxicity. The most notable of theseprogrammes was occasioned in the USA followingpassage of the Food Quality Protection Act of 1996,which required that the process for establishmentof tolerances (USA term synonymous with MRLs)for a given pesticide must take into account thepotential for exposure to other pesticides or chemicalswhich may act with the same mechanism oftoxicity. More recently, an independent working groupestablished by the UK government has publisheda report recommending that additional research beconducted to develop cumulative exposure assessmentmethodology, and that authorization of pesticides byregulatory authorities should include more formalanalysis of all routes of potential exposure to thatpesticide and other chemicals with the potential forcombined toxic action or interaction.67

A challenging aspect of a cumulative approach todietary intake assessment relates to the methodologyfor establishing whether two or more pesticides havethe potential in man to act via a common toxicological

mechanism of action. In the past, most groupingof pesticides has been on the basis of commonchemical moiety (eg organophosphorus insecticides,dinitroaniline herbicides) or pesticidal mode ofaction (eg ALS-inhibiting herbicides, chitin-synthesis-inhibiting insecticides) and has served the interestsof structure-activity-based research or resistance-management programs. An expert group organizedby the International Life Sciences Institute (ILSI)68

to consider relevant criteria agreed that two ormore pesticides may act via a common mechanismof toxicity if they: cause the same critical effect,act on the same molecular target at the sametarget tissue, and act by the same biochemicalmechanism of action, possibly sharing a common toxicintermediate.

The ILSI group was unable to agree, however,whether or not all three criteria should be fulfilledin order for compounds to share a common mech-anism of toxicity. The US EPA has established afive-step ‘weight-of-evidence’ process for identifyingpesticides and other substances that cause a commontoxic effect by a common mechanism,69 but so far it hasbeen used only to confirm previous groupings basedon insecticidal mode of action (eg cholinesterase-inhibiting organophosphorus and carbamate insecti-cides). In reviewing the EPA’s approach, a recentScience Advisory Panel concluded that grouping ofcholinesterase-inhibiting organophosphorus and car-bamate insecticides may not be sufficient and that pos-sibly all pesticides which exert neurotoxic effects (egneonicotinoids) should be considered for grouping.70

The problem with establishing commonality of mech-anism of toxicity is in part a result of the regulatoryevaluation process itself, which requires submissionof toxicological studies most keenly targeted at estab-lishing LOELs and NOELs (lowest observable andno observable effect levels) rather than elucidating



specific biochemical and physiological mechanisms oftoxicity in any comparative fashion.

13.2 Cumulative dietary intake assessmentOnce a grouping of pesticides with a commonmechanism of toxicity is identified, a cumulativedietary intake assessment may be considered. Thefirst practical methodology for conducting suchan assessment has been developed71 and recentlyimplemented by the US EPA on a test-case basisfor the organophosphorus (OP) insecticides.72 Theproposed EPA approach is a rather complex one, andinvolves probabilistic risk assessment of a group ofpesticides for all possible routes of exposure (dietary +non-dietary). Once a cumulative mechanism oftoxicity has been established, the EPA approachinvolves four primary steps:

(1) Determination of realistic exposure scenariosleading to co-occurrence of pesticide residues (egdietary, drinking water, household).

(2) Combination of residues from multiple pesticidesinto a single dose for each exposure scenario. Thisstep involves establishing a common benchmarkby which the toxicity of all pesticides with thecommon mechanism of toxicity can be describedand the differential potency of the variousmembers of the class expressed.

(3) Determination of the magnitude of pesticideresidue exposure for all scenarios. This stepinvolves a probabilistic exposure assessment usingthe DEEM model.

(4) Characterization of risk by comparison of expo-sure magnitude with a predetermined benchmark.

The EPA model includes outputs for individualand combined routes of exposure (eg dietary, non-dietary) for one-day (ie acute) and one-week exposureperiods. For the OP insecticides, residue data fordietary intake were drawn from the US Department ofAgriculture’s Pesticide Data Program. As for all EPAdietary assessments, the DEEM model was employedto combine data on distributions of pesticide residuesand food consumption patterns. A detailed discussionof the methodology and results of the OP test case isbeyond the scope of this report, and can be found in theEPA documentation.72 It is interesting to note that,after years of development, scientific debate, publichearings and legal proceedings, EPA’s cumulative

OP assessment does not appear to flag concernsthat require any significant further regulatory actionregarding dietary intake and indeed confirms the highdegree of safety of the US food supply.73

Although a number of regulatory and advisorybodies are now routinely conducting acute dietaryintake assessment, the methodology for completionof cumulative dietary intake assessments for a groupof pesticides which act via a common mechanism oftoxicity is still in the developmental stage. Only the USEPA has so far advanced a proposed methodology andtest case, and a first discussion of the internationalapplicability of such an approach resulted in fewpromising conclusions.74 It may be some time beforethe broader applicability of such an approach oralternative approaches is well established, and itremains to be seen whether other countries will havethe information and resources necessary for such data-intensive methodologies.

13.3 Co-occurrence of pesticide residuesAn important consideration for cumulative dietaryintake assessment of pesticides with a commonmechanism of toxicity relates to the frequency ofco-occurrence of pesticide residues in single-servingcommodities. The source of such information wouldmost appropriately be from monitoring studies,and a few programmes document frequency of co-occurrence as well as individual detection information.Such monitoring data are most frequently availablefor composite samples in which several single-servingitems have been combined. In rare instances data onsingle-serving commodities have also been generated.As anticipated, there is more frequent detection ofpesticide residues in composite samples and thelikelihood of co-occurrence of pesticide residues isalso more prevalent in these samples (Table 13).75

Australian States use targeted residue monitoringprograms in the administration of control-of-use leg-islation. Roberts compiled organophosphorus residueco-occurrence data from three States (Queensland,Victoria and Western Australia) in a report onthe potential for cumulative exposure (Roberts G,1999, Cumulative exposure—OP insecticides, sum-mary of Australian data, State Chemistry Laboratory,Victoria, unpublished document). Co-occurrence oforganophosphorus insecticide residues was found in43 of 900 fruit samples and 12 of 870 vegetable sam-ples. Sample size was generally 1–2 kg and limits of

Table 13. Residue monitoring frequency of multiple pesticide detections in single-serving and composite samples (USDA)76

% of samples in category

Commodity 0 1 2 3 4 5 or more

Pear (single-serving) 32.3 47.4 19.5 0.9 0.0 0.0Pear (composite)a 6.0 21.7 36.4 18.1 10.1 7.7Tomato (composite)a 38.5 27.9 13.5 9.3 5.7 5.1Strawberry (composite)a 9.4 19.5 24.6 18.4 12.5 15.5

a Composite sample of approximately 2.3 kg.


DJ Hamilton et al

Co-occurrence of organophosphorus residues in fruit and vegetables

in composite monitoring samples

0.001

0.01

0.1

1

0.01 0.1 1 10

Residue A, mg/kg

Res

idue

B,

mg/

kg

FruitVegetablesVegetables,

residue A >MRL

Figure 5. Co-occurrence of organophosphorus residues in fruit andvegetables—Australian targeted monitoring.

reporting were mostly 0.01–0.02 mg kg−1. In 54 sam-ples two organophosphorus residues were detectedand in only one sample were three residues detected(apples: chlorpyrifos 0.09 mg kg−1, azinphos-methyl0.03 mg kg−1 and parathion 0.01 mg kg−1). Residuesof one or both residues tended to be low; in 43 ofthe 55 samples the level of the lower residue wasbelow 0.1 mg kg−1. In only one sample the level of thelower residue exceeded 0.3 mg kg−1. The distributionof residues is summarized in a scatterplot of the lowerresidue versus higher residue (Fig 5).

13.4 Cumulative intake assessment at theinternational levelGiven the complexities involved in conducting acutedietary intake assessment for pesticides with a commonmechanism of toxicity, and the early stage of methoddevelopment at the national level, it does notappear appropriate at present to adopt this typeof assessment at the international level. Followingadvances in national methodologies dealing withestablishment of appropriate toxicological endpointsand probabilistic methods to account for the frequencyof co-occurrence of pesticide residues, further progressat the international level will be possible. A necessaryprerequisite to the adoption of such a cumulativeassessment would appear to be adaptation of theprobabilistic dietary intake assessment methodologyat the international level.74

14 DATA REQUIREMENTS AT REGISTRATIONThe residue data requirements for acute dietary intakeestimation are essentially the same as for chronic intakeestimation.

Residue data will be required on the edible portionas well as on the commodity of trade as needed forMRL setting. Data will be required for both residuedefinitions, if they are different, for MRLs and for riskassessment.

Research is currently under way to characterize unitvariability on a generic basis, ie the prevailing opinionis that unit-to-unit variability is essentially independent

of crop, crop unit size, nature of the pesticide, residuelevels and, possibly, some methods of application. Ifthis is the case, there will be no special requirementsfor a set of supervised trials to provide extra dataon unit variability. However, there may be particulartypes of uses or crops that are inherently more variablethan others and where more unit variability data arerequired, eg soil treatments and leafy vegetables havebeen suggested as possibilities.

If a trial does show lesser unit variability thanthe accepted generic variability it may be difficult toconvert that lesser variability into commercial practiceunless there is a good explanation of the mechanismand how that mechanism will apply on farm. A goodexample is the use of aldicarb granules as a furrowtreatment of the soil in potato production. Whenthe granules were applied by a gravity mechanismit was found that more granules were applied fora given length of furrow near the ends of the rowwhere the machinery slowed to turn. When a positivedisplacement applicator was used the granules wereapplied more evenly and the residue variation inindividual potatoes was reduced.27

In trials where replicate samples are taken fromthe same treated plot or where samples are takenfrom replicate sub-plots subject to the same sprayingoperation, careful data recording may assist in usingthe variance between replicates to calculate thevariance between units. If the same number of fruitunits (n) are included in each replicate that is choppedand mixed to provide the analytical sample, thevariance between replicates can be attributed to thevariance of samples each of n units. Variance ofsamples each of a single unit can then be calculated.However, without a clear explanation of how thereduced variance was achieved, registration authoritiesmay be reluctant to depart from the accepted genericvariability in the risk assessment.

In reality, acute risk assessment should have littleeffect on trade. Acute consumer exposure should betaken into account during the risk-assessment processused to determine that the expected residues fromapproved uses give rise to consumer exposure thatis acceptable. If estimated exposure is unacceptable,then the GAP under consideration should be redefinedto give a practice which is both efficacious and safeto consumers. These questions should generally beresolved during registration.

15 FUTUREConservative assumptions of residue variability andextreme diets will be modified in the light of newdata. It is already clear that individual units may havesignificantly higher residue levels than the average ina treated lot and individual units of some fruits andvegetables are consumed as such. We might concludethat the opportunity for further refinement withbigger changes probably rests with the toxicologicalassessment rather than the residue assessment.



In the meantime registration of a pesticide use willnot go ahead where there is evidence that the usemight result in residues in individual units that, oncalculation, would cause exceedance of the acute RfDfor a single-unit consumer.

Improved application technology may reduce thevariability factor in some situations, but generally notbelow a value of 3.

Specially designed toxicology tests may be neededwhere an acute RfD is required, but adequate no-observed-adverse-effect levels (NOAELs) for short-term effects cannot be established from the availablestudies.76 Current estimates, to some degree, haverelied on studies designed for other purposes. Workshould continue towards the development of standardprotocols for toxicological tests aimed at establishingacute RfDs for pesticides.

15.1 Research needs(1) Screening models are needed to estimate acute

dietary exposures on an international basis.Existing screening methods need to be validated.

(2) Approaches are needed to ‘bridge’ from data inone country or population to other populationswith similar diets.

16 CONCLUSIONS AND RECOMMENDATIONSDietary intake in the short-term can be much higherthan chronic intake expressed on a daily basis becauseon a particular day a consumer may eat much more ofa food item than average, and the residue level mightbe much higher than average.

Residue levels in individual units of fruit andvegetables can be significantly higher than the averageof all the units.

Deposition of pesticide on the individual unit duringapplication is likely to have the most influence onresidue variability. Crop unit size, residue level andpre-harvest interval do not generally have a significantinfluence. The physico-chemical properties of someactive substances also tend not to influence thevariability factor, but an exception should be noted forthe post-harvest treatment of apples with carbendazim.Possibly most of the suggested causes of variabilityare not observed to have an effect except whenthey exceed the inherent ‘natural variability’, whichsuggests a generic nature to the variability and a basisfor extrapolating data across crops and pesticides.

Single point calculations and probabilistic modelingfor estimating short-term intake require furtherdevelopment. The two approaches are complementaryand together provide important information to riskmanagers on the potential high-level short-termexposure and the probability of such an exposureoccurring.

A pesticide acute reference dose (acute RfD) derivedfrom toxicology studies designed for assessment ofchronic risk is likely to be more conservative thannecessary for assessment of acute risk.

Registration will not proceed for some pesticideuses where the estimated short-term intake of residuesexceeds the acute RfD.

At the national level it is possible to use detaileddata on residues and dietary habits to produce a bestestimate of short-term intake. The sources of exposuremust be defined along with the determination ofmagnitude and frequency of exposure. Models willneed to take into account variations in concentrationsof the chemical, variations in food consumptionpatterns and day-to-day and season-to-season changesin the exposed individual’s diet and food preparationmethods.

Similarly, concentration levels vary depending onwhen and where they are measured, when and wherethe chemical was used, the extent of commercial foodprocessing, etc. Since pesticide usage may be seasonal,a calendar-based approach to derive the probabilitydistribution of potential exposures is most suitable.

The results from a Monte Carlo assessment are onlyas valid as the input parameters, data and assumptions.

The Monte Carlo model allows the assessment ofmajor contributing factors to the overall exposure.Sensitivity analyses can be performed to show whichfactors contribute most to the variability in theexposure distributions and where additional data maybe needed to better define the exposure estimates.

At the international level the assessment must take abroader perspective and cannot use some details thatare available at the national level such as percentagecrop treated and diets of sub-populations within acountry.

The IUPAC Advisory Committee on Crop Protec-tion Chemistry has eleven recommendations relatingto acute dietary exposure (Fig 6).

The IUPAC Advisory Committee on Crop Protection Chemistry makes thefollowing recommendations:

1. Pesticide residues in food should be assessed for short-term consumer risk as well as for chronic risk.

2. Studies designed to find a no-observed-adverse-effect-level (NOAEL) for acute effects should match the time period for toxicity testing with the anticipated acute exposure time.

3. The diets used in the exposure estimates should be valid for the population being assessed. The source of the dietary information and the methods for obtaining the information should be traceable by documentary reference.

4. Account should be taken of seasonal consumption, e.g. heavy consumption of a fruit or vegetable is more likely when it is in season. Dietary surveys not covering the in-season period for a fruit or vegetable will not accurately reflect the high consumption end of the distribution.

5. The 97.5th percentile daily consumption of a food for eaters of that food in a given population should be used to represent the daily large-portion size for deterministic intake assessment.

6. Residue data for exposure estimation should be expressed on the edible portion as prepared for consumption.

7. The residue definition for acute exposure assessment should generally be the same as for chronic exposure assessment.

8. Deterministic intake assessments should deal with only one food at a time. For a realistic estimate of short-term dietary exposure through multiple foods, a probabilistic approach is recommended.

9. For deterministic acute intake assessment the equations developed by the Geneva Consultation (FAO/WHO) in 1997, with subsequent amendments by the JMPR (1999, 2000), are recommended.

10. The variability factor for use in deterministic assessments should be defined as the residue level in the 97.5th percentile single unit of a commodity population divided by the mean residue of that population.

11.A variability factor of 3 should be adopted to give a "likely high-unit residue level" in Case 2 deterministic calculations. In Case 2 the residue level in a composite sample of the food may not reflect the residue level in a meal-sized portion.

Figure 6. Recommendations of the IUPAC Advisory Committee onCrop Protection Chemistry.


DJ Hamilton et al

ACKNOWLEDGEMENTSPreparation of this report was supported by fundingfor Project 1999-009-1-600, Pesticide Residues inFood: Acute Dietary Exposure, from the Division ofChemistry and the Environment of the InternationalUnion of Pure and Applied Chemistry. Members ofthe Advisory Committee on Crop Protection whoreviewed the project report and contributed to theconsensus recommendations included HA Kuiper,J Linders, RD Wauchope, E Carazo, PT Holland,A Katayama, B Rubin, J Unsworth, Y-H Kim andGR Stephenson.

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