UNITED STATES ENVIRONMENTAL PROTECTION AGENCY WASHINGTON, D.C

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OFFICE OF CHEMICAL SAFETY AND POLLUTION PREVENTION MEMORANDUM
SUBJECT:
FROM:
THRU:

TO: Upper Bound**Es~imate** ofthe Likelihood ofInsecticide Residues onFood Resulting from Treatment in Food Handling Establishments (DP 413125) Jonathan Becker, Senior Science Advisor 

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# ~~ Biological Analysis Branch 

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AmetJones, Chief, Biological Analysis Branch Yu-Ting Guilaran, Director Wynne Miller, DeputyDivision Director Biological and Economic Analysis Division (7503P Dana Vogel, Acting Division Director Christina Swartz, Chief, Risk Assessment Branch II Health Effects Divison Office ofPesticide Programs (7509P) 

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Peer Review Panel Date: June 4, 2014

1SUMMARY BEAD recommends that the value of4.65% be used for the likelihood ofa commodityhaving a residue resulting from non-fumigantpesticide treatment in food handling establishments. This value is an upper bound estimate ofthe likelihood that any given commodity would have a residue resulting from treatment in food handling establishments. BEAD is reasonably certain that this level will not be exceeded and is appropriately conservative for use in a dietary risk assessment. Federal Register language is provided inAppendix 1.
INTRODUCTION This document describes the method developed by the Biological and Economic Analysis Division (BEAD) with the Health Effects Division to estimate the likelihood ofa commodity having a residue resulting from non-fumigant pesticide treatment in food handling establishments (FHEs). This method should be used for all non-fumigant active ingredients (e.g., liquids and aerosols used as space sprays or as crack and crevice sprays). Fumigants used in FHEs (e.g., flour mills) may be assessed using a previously developed method (Cook, et al., 2009). BEAD is reasonably certain that the recommended value is unlikely to be exceeded. This value is not chemical specific, and as such, may be used for any dietary assessment. For the purposes ofthis report, food handling establishments are divided into the following segments:
• Food Processors- includes manufacturers ofbaked goods, canned goods, processed foods, meats, spices, and beverages. • Food Warehouses- includes food warehouses, distribution centers, and food ingredient warehouses. • Commercial Kitchens- includes institutional facilities (schools, prisons, government buildings and arenas), lodging and hospitality establishments (hotels and motels), and healthcare facilities (hospitals, nursing homes, and convalescent centers). • Restaurants- includes both full-service and fast-food types. • Grocery Stores

- includes any retail grocery stores. Survey data on pest control practices in retail grocery stores are not available. BEAD estimated that the contribution of grocery stores to the recommended value is very small. This is discussed further in the characterization sectionbelow. A generalized schema showing the major relationships betweenthese FHE segments is provided in Figure 1. This schema is simplified and only includes those channels oftrade that involve the food handling establishments described above. Commodities move from the producerto either a food processor orto a warehouse facility ofa packer/shipper/distributer. Once processing is completed, the food products are also transported

2to warehouse facilities ofdistributors. From there, foods are distributed to commercial kitchens, restaurants and grocery stores, and from there to the consumers. METHODS AND DATA The proportion offood thatreaches the consumer containing potential residues acquired during transitthrough food handling establishments is the sum ofthe food likely to be exposedin every upstream segment. For example, a food item purchased from a grocery store couldhave residues as a result ofthe food handling establishmentbeing treated during while the food item was in the channel oftrade (i.e., from the processorto the food warehouse to the grocery store). This means, for the purpose ofestimation, the proportion offood passing through each segment and the proportion ofcommoditythat mightbe exposed at each segment must be determined. Table 1provides the notation and definitions used in this estimation. Table 1. Notation and definitions.

Variable Definition Percent ofcommodities transiting a given FHE segment. "x" is a placeholderfor %Cx each FHE segment
- FoodProcessor (%Cp), Food Warehouse (%Cw), Commercial Kitchens 

### IRestaurants (%CcKJR), and Grocery Stores (%Co). Percent ofcommodity with potential residues. "x" is a placeholder for each FHE %Rx segment- Food Processor (%Rp), Food Warehouse (%Rw), Commercial Kitchens 

### I Restaurants (%RcKJR), and Grocery Stores (%Ro). Likelihood oftreatment in a FHE segment. "x" is a placeholder for each FHE Px(T) segment- FoodProcessor (Pp(T)), FoodWarehouse (Pw(T)), Commercial Kitchens 

### I Restaurants (PcKJR(T)), and Grocery Stores (Po(T)). %Form**R~lative** percent ofpestcontrol expenditures spent on forms ofpesticide products (liquids and aerosols) that couldresult inresidues on commodities. %Fac Percent offacilities in a FHE segmentthatuse pest control. Kline (2008) reports that all facilities in every FHE segmentuse some pestcontrol measure. Po(T) Single day probability for treatment in a FHE segment. This is equivalentto the 1 

### I (number ofdays betweentreatments). %Sx Percent offood obtainedbythe consumerfrom a grocery store (%So) or from a non-grocery store source (%ScKJR).


### Commodities PassingThrough Each FHE Segment (%Cx) Division of Commodities between FoodProcessors andShipperI Warehouses IDistributers As is shown in Figure 1, once produced, commodities, are divided into two streams
- one stream going to food processors and the other stream to warehouses, shippers, and distribution centers. The determination ofthe relative amounts in each ofthese two paths is complicatedbythe definition of"processed" and the metric usedto quantify it.

3The definition ofprocessed food range from washed and packaged fruits and vegetables (e.g., bagged spinach) to "ready-to-eat" foods that require minimal preparation (e.g., granolabars) (Denny 2014, Foodinsight 2010, Jacob 2014). Forthe purpose ofthis analysis processed foods are considered to be "packaged foods" such as processed frozen and canned food, ready-to-eat meals, snacks and candy, bakery goods, dairy products and condiments. Fresh foods include fruits, vegetables, eggs, nuts, beans, meat and seafood (Fairfield 2010). Estimates ofthe amount ofprocessed food consumed have been based ontwo metrics

-- total calories consumed per capita, and total pounds consumed per capita. Values for both metrics are based on studies conducted by USDA's Economic Research Service. The NY Coalition for Healthy School Food (2009) reports that approximately 69% ofthe calories consumed in the United States come from processed foods. Fairfield (2010) reports that in 2009 the annual per capita consumption inthe United States is 787 pounds ofpackaged foods and 602 pounds of fresh foods. Forthis analysis BEAD has selected the per capita consumption measured in pounds as the most appropriate metric for this analysis. BEAD assumes that the percent ofthe commodities going to food processors (%Cp) is equal to the percentage ofthe US per capita consumption (in pounds) ofpackaged foods [787lb I (787 

### + 602) = 56.6%]. Division of Commodities between Commercial Kitchens/Restaurants andGroceryStores As shown in Figure 1, from warehouses, commodities are distributed to consumers through eating establishments and through grocery stores. BEAD estimates the proportion ofproduce passing through these FHEs according to the origin offood items. The source and location offood items consumed among the General US Population were estimated using data from the last 3 cycles (2003104, 2005106, 2007108) ofthe National Health and Nutrition Examination Survey (NHANES), What We Eat in America (WWEIA) survey (CDC 2014). These data are used inthe US EPA's DEEM FCID model (Ver 3.18). Further information on the NHANES WWEIA surveys, the DEEM Model, and the Food Commodity Intake Database (FDIC 2014) database can be found at the following web site (http:llwww.epa.govlpesticideslscienceldeem/). A total of27,638 respondents provided food consumption data overthese 3 WWEIA cycles. The combined 2003-08 WWEIA database contains 55,277 food diaries. For each food consumption diary, WWEIA respondents are asked to list all food items that they consumed over the previous 24 hour period (24-hourrecall), the time ofday the item was consumed, the primary source ofthat food item and whether itwas consumed athome or elsewhere. Ofthe 798,170 food items reportedly consumed among these respondents, approximately 71% (= 568,008 

### I 798,170) were obtained from a store, and the remaining 29% were obtained elsewhere (restaurants, schools, etc.). 4Residues on Commodities in Each Segment (Px(T)) The proportion ofcommodities with residues as it leaves each segment is a function ofthe likelihood ofa treatment that might leave residues occurring on a given day plus any upstream residues: %Rx = %UntreatedCx x Px(T) 

### + %Rx-l. This assumes that the typical daily throughput would be present inthe FHE (i.e., there would be no clearing ofcommodity priorto treatment). Further, this assumes only previously unexposed commodity will be exposed at every stage (%UntreatedCx = 1 - %Rx-I) and that residues accumulate as the commodities move through the FHEs. The probability oftreatment potentially producing residues on a given day in a FHE segment is equal to thepercent ofthe facilities in the FHE segmentusing pest control products multiplied by the daily probability oftreatment and the percent ofthe pest control products in a form that can result in residues on commodities (i.e., liquids and aerosols). Px(T) = 

## *Po(T) 

## * % Formx
Pesticide Usage in FoodHandlingEstablishments The estimates developed in this document are based on a 2008 market survey ofpest control in food handing establishments (Kline 2008). Table 2 provides background information about the number ofestablishments, the number ofsurveys conducted, and the approximate pest control purchases in each FHE segment. This survey also showed that all FHE facilities use some type ofpest control product (% Facx = 1 00). Table 2. Segments, Surveys and Pest Control Purchases FHE Segment Number of Number ofSurveys Pest Control Purchases Establishments Conducted* (approximate) Food Processors 45,368 100 $ 11,200,000 Food Warehouses 2,010 79 $ 14,950,000 Commercial 231,700 211 $ 29,900,000 Kitchens Restaurants 237,600 companies at 300 $ 68,500,000 391,000 locations



### .. *In additiOn to the segment surveys, another 60 pest management professiOnals servmg this industry were also surveyed. Grocery stores were not included in this survey. See characterization and uncertainty section.

5Pesticide Formulations Used in FoodHandlingEstablishments Kline (2008) reports the amount ofpest control productexpenditures bythe form ofthe product. BEAD combinedthese data into two groups for each FHE segment and calculatedthe relative percentages. Forms that are unlikely to produce residues on food (e.g., rodenttraps, bait stations, gel baits) were grouped into one category and forms thatmayproduce residues (e.g., aerosols, surface sprays) were grouped into another category. Table 3 provides the relative percent ofpest control expenditures byproductform.
Table 3. Percent ofPest Control Expenditures by Product Form Form Food Food Commercial Restaurants Processors Warehouses Kitchens Non-Liquid orNon-Aerosol* 64.2 61.8 53.3 53.9 Liquids and Aerosols(% Form) 35.8 38.1 46.7 46.1 *Non-hqmd or non-aerosol forms mclude baits, glue boards Itraps, blocks Ipellets, granules, dusts, powders, and other forms not specified. Probability o(Pesticide Treatment in FoodHandling Establishments Kline (2008) reports the pestcontrol treatment schedule and the percent offacilities in each category for each FHE segment. The single dayprobability for treatmentis the inverse oftime in days betweentreatments. Forexample, ifa given facility is treated monthlythe single day probability ofa pest controltreatment occurring is 3.3% (1/30 days =3.33% perday). Table 4 provides an example ofthis calculation for the restaurant segment. The single day treatmentprobability for this segment is equal to the percent offacilities in eachtreatment interval multiplied bythe single dayprobability ofthattreatment interval, summed across all treatment intervals. Table 4. Example- Single Day Probability ofTreatment for the FHE restaurants segment. Treatment Schedule Restaurants Interval Days in Single Day Percent ofFacilities in Category Probability Interval Probability the Treatment Interval (weighted by% (1 I#Days) Facilities in Interval) Monthly 30 3.33% 65.0% 2.17% Every 2 weeks 14 7.14% 20.0% 1.43% Weekly 7 14.29% 8.0% 1.14% Every2 or 3 months 75 1.33% 3.0% 0.04% Other* 21.06 4.75% 4.0% 0.19% Totals 100% 4.97% *Number ofdays IS calculated as a weighted average number ofdays across all FHE segments. Kline (2008) reports that all FHE facilities intheir survey receive some sort ofpest control. This, along withthe calculated values shown in Tables 3 and 4, allows forthe calculation of
6Equation 1for each FHE segment. The percent offacilities in each segmentthat use pest control products multiplied by the segment single day probability oftreatment multiplied by the percent ofexpenditures onproducts that may produce residues yields the single day probability of treatment withproduct forms that may result inpotential residues. The values for Px(T) are shown in Table 5. Table 5
. Single Day Probability ofTreatment with Product Forms Resulting in Potential Residues FHE Segment Percent of Segment Percent ofExpenditures Single Day Probability Facilities in Single Day on Products That Could ofTreatment with Segment Using Probability Result in Potential Product Forms

Pest Control ofTreatment Residues Resulting in Potential

Products [Po(T)] (%Form) Residues (% Fac)* [Px(T)] Food Processors 100% 9.68% 35.8% 3.47% Food Warehouses 100% 5.23% 38.1% 1.99% Comm. Kitchens 100% 6.46% 46.7% 3.02% Restaurants 100% 4.97% 46.1% 2.29% Unweighted Average 2.65% ofRestaurants & Comm. Kitchens
..
. . 

## *Kline (2008) reports that all fac1hties in every FI-lE segment use some pest control measure. 

## ** Restaurants account for a much larger share by volume ofcommodities based on number of establishments (Table 2) while commercial kitchens have a higher single day probability of treatment. Datato calculate this is lacking, but using an unweighted average likely overstates the probability oftreatment. Residues on Commodities Consumed The next step is to mathematically accumulate the treatmentprobabilities as the food travels through the FHE segments from the producer to the consumer. This requires estimates ofthe relative amount offood moving through each segment (%Cx) and determining the probability of a treated commodity entering a given FHE segment with potential residues, the probability of treatment while in that segment [Px(T)], and the resulting residues in the food leaving the segment (%Rx). This canbe expressed using the formulae below for each FHE segment. The results from these calculations are given in Table 6. Forprocessors %Rp 

### =Pp(T) 

### * %Cp Forwarehouses %Rw 

### = {%Rp 

### + [(1- %Rp) 

## *Pw(T)]} x %Cw For commercial kitchens and restaurants %RcKJR 

### = {%Rw 

### + [(1- %Rw) 

## *PcKJR(T)]} x %CcKJR
7For grocery stores %Ra = {[%Rw 

### + [(1- %Rw) 

## *Pa(T)] x %Co Table 6. Calculated values for the potential offinding residues inion commodities/food for each HIE segment. All values are in decimal percents. Packer I Commercial Kitchens Grocery Calculated Values Processor Warehouse I & Restaurants Stores Distributer Combined Entering Segment with Residues 0.00 0.0196* 0.0391 0.0391 Untreated Amount 1.00 0.9804 0.9609 0.9609 Probability ofTreatment in 0.0347 0.0199 0.0265 0.000 Segment Newly Treated Amount 0.0347 0.0195 0.0255 0.000 Leaving Segment 0.0347 0.0391 0.0647 0.0391



### .. *Thisvalue IS based on 43.4% ofcommodities entermg the segment from producers (with zero residues) and 56.6% ofcommodities entering the segment from food processors (with 0.0347 residues

- see Table 5). The final step is to use the calculated values, combined with the source ofthe food consumed to determine the potential for consuming food with residues from FHE treatments, using the following equation:%Consumer (%RcKJR 

## *%ScKJR) 

### + (%Ra 

## *%Sa) The values used for these calculations, as well as the results, are provide in Table 7. Table 7. Percent oftotal food consumed with potential residues from treatment in food handling establishments. Source Percent Residue Probability Food items not from grocery store 29% 0.0647 0.0187 Food items from grocery store 71% 0.0391 0.0278 Total 100% 0.0465

CHARACTERIZATION AND RECOMMENDATIONS BEAD recommends that the value of4.65% be used for the likelihood ofa commodity having a residue resulting from non-fumigant pesticide treatment in food handling establishments. This value is an upper-bound estimate ofthe likelihood that any given commodity would have a residue resulting from treatment infood handling establishments. BEAD is reasonably certain that this level will not be exceeded and is appropriately conservative for use in a dietary risk assessment.

8The characterization ofthis value as anupper bound estimate ofthe actual probability of consuming a food with residues from FHE treatment is based on a consideration ofthe following:
• The recommended value is based onthe assumption that only a single active ingredient is used in all ofthe food handling establishments for all liquid and aerosol pest control applications. The Kline (2008) survey shows that approximately 50 liquid or aerosol products containing well over a dozen active ingredients are used in FHEs. The assumption ofa single active ingredient being used in all applications results in a very large over-estimation ofthe likelihood ofpotential residues. • Assumes that the facilities take no precautions, such as clearing processing lines, when making a pesticide treatment. • Assumes that the likelihood oftreatment accumulates as the commodities travel through FHEs, thus maximizing the estimate ofthe total proportion offood exposed. Note that the magnitude ofresidues included in the dietary assessment is estimated orobserved in a separate process and may reflect accumulated residues. • Assumes that commodity is exposed to residues no matter what packaging is used. Many products in commercial kitchens, restaurants, and warehouses are packaged in containers that limit orprevent direct contact with an insecticide. • Assumes that the facility is always treated according to the treatment schedule. Refrigerated and frozen products may not be treated as often as non-refrigerated food (i.e. temperatures below 50° F reduce insect feeding and development). • Assumes thatthe site oftreatment (e.g. directly on food handling surfaces versus cracks and crevices) has no effect ofthe probability ofresidues on commodities. There remain several areas ofuncertainty in the analysis. BEAD is reasonably certain that the effect ofthe following uncertainties would have a very small effect onthe estimates:

• Some pest control treatments may account for a larger percentage ofexpenditures than others. Currently, BEAD does not have sufficient information to account for this. • Grocery stores are not included in this definition offood handling establishments. There are no available survey data for pesticide used in this category. The contribution to the estimate is expected to be very small based onthe following observations: 1) residence time offood in most grocery store is relatively short (stores are restocked frequently because oflimited storage space and because ofthe need for "fresh" foods such as bread), 2) most fresh fruits and vegetables are refrigerated which**substan~ially** reduces the probability ofa pest problem occurring that would be treated with sprays or aerosols, and 3) most ofthe remaining food is packaged in containers thatprevents contaminationby residues (e.g., canned) or is in a form that would not need treatment (.e.g., frozen).

9• A small amount offood does not pass through FHEs (e.g., farmer markets) and therefore would not have residues from FHE treatments. The recommended value assumes all food passes through food handling establishments. This assumption results in a very small over estimation.

10Figure 1. Simplified FlowofCommodities through Food Handling Establishments.

56.6% of 1

- Producers 

# I 43.4% of production 

# + production 

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# ------..**~-~-~-~-~·_;_;_'o_o;_:_;_e~h-o-u-se_s_/_D_i_st-ri_b_u-to_r_s**__. 

# !

Commercial Kitchens and Restaurants Stores PG(T) =0.00 %RG =0.0391 PcK/R(T) =0.0265 %RcK/R =0.0647**:'"""''"'""""' ''''''''"'** **'' ''** **'' ''"'''** **'"'' '"""""""""""""""'** **'"""""'''' '''** **'' ''** **''u~** 29% of Food items

not from grocery stores !
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Consumer 4.65 %oftotal fool consumed**~ith** potential FHE residues r·······················-·-·..**···-~- --··** **················ ·······--~**  71 %of Food items from grocery stores 

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11REFERENCES
CDC. 2014.NHANES WWEIA survey. This survey can be accessed through the following site: http://www.cdc.gov/nchs/nhanes/about nhanes.htm
. Cook, C., J. Becker, and E. Rim. 2009. Revised Assessment ofPercent Commodity Treated Values used in the Registrant's DietaryExposure Assessment for Fluoride. (DP# 361041). Biological and Economic Analysis Division memo to K. Davis, V. Eagle, Registration Division and M. Doherty, C. Swartz, Health Effects Division, Office of Pesticide Programs, US Environmental Protection Agency. Denny, S. 2014. Avoiding Processed Foods? Surprise! This is Processed Too! Academy of Nutrition and Dietetics. Retrieved 1April 2014 from http://www.eatright.org/Public/content.aspx?id=6442471055 Fairfield, H. 2010. Factory Food. NewYork Times (based on work conductedby M. Gehlhar, USDA Economic Research Service). FCID, 2014. What We Eat in America- Food Commodity Intake Database, 2003-2008 (WWEIA-FCID 2003-08). Available at http://fcid.foodrisk.org/. FDIC. 2014. Food Commodity Intake Database. The FCID database canbe accessed at the following site: http://fcid.foodrisk.org/faq/. Foodinsight. 2010. What is a Processed Food? You Might Be Surprised! Handout 1of'S. International Food Information Council Foundation. Available at: www.foodinsight.org Jacob, A. 2014. Processed Food Definition. DemandMedia, Hearst Communications. Retrieved 1April 2014 from http:/ /healthyeating.sfgate.com/processed-food-definition2074.html Kline Market Research. 2008. Pest Control in Food-Handling Establishments 2008: U.S. MarketAnalysis and Opportunities. Kline Market Research. New York Coalition for Healthy School Food, 2009. U.S. Food Consumption as a% of Calories. Based on USDA Economic Research Service Publication EIB33. USEPA. 2014. DEEM-FCID model. This model can be downloaded at the following site: http:/ /www.epa.gov/oppOOOO 1/science/deem/

12APPENDIX 1. F:gDERAL REGISTER LANGUAGE EPA estimates the percent ofcommodities treated inFood Handling Establishments (FHE) for new uses ofactive ingredients based on the best available information. This includes survey information onpesticide usage related to the number offacilities being treated, product forms used (e.g., liquids and aerosols), and treatment"schedule by FHE segments (e.g., warehouse, food processor, distributor, and restaurant). EPA also incorporated the best available information related to the transfer ofcommodities between various segments offood handing establishments and the percent offood consumed by location, either in the home or outside the home. All information currently available has been considered and EPA has concluded that for any active ingredient, including [Chemical], there is at most a 4.65% likelihood that a food commodity could containpotential residues resulting from one or more treatments while in the food handling establishmentchannel oftrade. Similarto estimates ofagricultural use, this estimate should be reconsidered in 5 years.

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