The Stochastic Human Exposure and 

Dose Simulation Model 

for Multimedia, Multipathway Chemicals 

(SHEDS-Multimedia):Dietary Module 

SHEDS-Dietary version 1 

Draft Technical Manual 

June 16, 2010

Prepared by: Jianping Xue1, Valerie Zartarian1, and Steve Nako2

1

 US Environmental Protection Agency, Office of Research and Development, National Exposure Research 

Laboratory

2

US Environmental Protection Agency, Office of Pesticide Programs
i 

Disclaimer 

EPA's SHEDS-Dietary model is a probabilistic, population-based dietary exposure assessment 

model that simulates individual exposures to chemicals in food and drinking water over different 

time periods (e.g., daily, yearly). SHEDS-Dietary is one module (along with the separate 

SHEDS-Residential module) of EPA’s more comprehensive human exposure model, the 

Stochastic Human Exposure and Dose Simulation model for multimedia, multipathway 

chemicals (SHEDS-Multimedia), which can simulate aggregate or cumulative exposures over 

time via multiple routes of exposure (dietary & non-dietary) for different types of chemicals and 

scenarios. SHEDS-Residential and SHEDS-Dietary will be merged together in a future version of 

SHEDS-Multimedia. 

SHEDS-Dietary version 1 includes case study examples for illustrative purposes, as described in 

the the Technical Manual and User Guide. All input values used in the SHEDS-Dietary model 

for a given application should be entered or reviewed by the researcher so that the model results 

are based on appropriate data sources for the given application. 

The United States Environmental Protection Agency through its Office of Research and 

Development developed and funded the SHEDS-Dietary model with assistance from contractor 

Alion Science and Technology. SHEDS-Dietary Version 1 will undergo external peer review by 

EPA's Scientific Advisory Panel July, 2010, and should be considered draft at this time.

Acknowledgments 

We gratefully acknowledge our colleagues Andrew M. Geller in EPA’s Office of Research and 

Development, and David J. Miller and members of the HED Dietary Exposure Science Advisory 

Committee (DE SAC) in EPA’s Office of Pesticide Programs, for providing technical input and 

review of the SHEDS-Dietary model and technical manual. 
ii 

Table of Contents 

Disclaimer..................................................................................................................................... ...i 

Acknowledgments............................................................................................................................ i 

List of Figures ................................................................................................................................ iv 

List of Tables ................................................................................................................................. iv 

Acronyms and Abbreviations......................................................................................................... vi 

1 Background ............................................................................................................................... 1 

1.1 Purpose of SHEDS-Dietary .................................................................................... 1 

1.2 Overview of SHEDS-Dietary.................................................................................. 3 

1.2.1 Key Features and Model Options .............................................................. 4 

1.2.1.1 Eating Occasion Analyses............................................................ 6 

1.2.1.2 Half-life Analyses ........................................................................ 6 

1.2.1.3 Uses CSFII or NHANES/WWEIA Food Consumption Data ...... 6 

1.2.1.4 Options for Simulating Longitudinal Consumption Data............ 6 

1.2.1.5 Multi-chemical (Cumulative Exposure Assessment) Capability. 8 

1.2.1.6 Linkage to PBPK Models ............................................................ 8 

1.2.1.7 Sensitivity and Uncertainty Analyses .......................................... 8 

1.2.2 Plans and Future Research Needs.............................................................. 8 

2 SHEDS-Dietary Methodology ................................................................................................ 10 

2.1 Algorithm for Estimating Exposure to Pesticides and Other Toxicants from Food 

Consumption......................................................................................................... 12 

2.1.1 Consumption ........................................................................................... 13 

2.1.2 Residues and Process Factors.................................................................. 13

2.1.3 Sample calculation................................................................................... 14 

2.2 Drinking Water Exposure Algorithm.................................................................... 15 

2.3 Inputs..................................................................................................................... 15 

2.3.1 Food and Indirect Water Consumption Data........................................... 15 

2.3.2 Recipe Files ............................................................................................. 16 

2.3.3 Direct Water Consumption Data ............................................................. 20 

Pesticide Use (% crop treated).............................................................................. 21 

2.3.4 Process Factors ........................................................................................ 21 

2.3.5 Food Residue Data .................................................................................. 21 

2.3.6 Drinking Water Concentration Data........................................................ 22 

2.4 Methods Issues...................................................................................................... 22 

2.4.1 Issues Regarding Selection of Consumption Database ........................... 22 

2.4.2 Issues Regarding Sampling Food Residues............................................. 22 

2.4.3 Modeling Longitudinal Food Consumption ............................................ 24 

2.4.4 Considering Persisting Effects ................................................................ 27

2.4.5 Algorithm for Matching (Behavioral) Diaries: Food Consumption and 

Activity Patterns........................................................................................ 29 

2.4.6 Considering Timing and Amounts of Drinking Water Consumption ..... 30 

2.4.7 Considering Number of Person-Days (or Person-Years) to Simulate ..... 34 

2.4.8 Sensitivity Analysis Issues ...................................................................... 34 
iii 

2.4.9 Uncertainty Analysis Issues..................................................................... 34 

2.5 Output Capabilities ............................................................................................... 34 

2.6 Sensitivity Analysis Methods and Results ............................................................ 37 

2.6.1 Sensitivity Analyses Methods ................................................................. 37 

2.6.2 Sensitivity Analyses on Food Consumption ‘Outliers’ ........................... 37 

2.6.3 Drinking Water Consumption Outliers ................................................... 38 

2.7 Uncertainty Analysis Methods and Results .......................................................... 40 

2.7.1 Uncertainty Analysis Methods ................................................................ 40 

2.7.2 Uncertainty Analyses on Selection of Food Consumption Diaries ......... 41 

2.8 Quality Assurance ................................................................................................. 42 

2.8.1 Comparison SHEDS vs. DEEM (Model-to-Model Evaluation) ............. 43 

3 Applications/Case Studies to Date.......................................................................................... 48 

3.1 Arsenic (As) .......................................................................................................... 48 

3.1.1 Arsenic Uncertainty Analysis Results ..................................................... 52 

3.2 Methyl Mercury from Fish Consumption (MeHg) ............................................... 53 

3.3 Permethrin............................................................................................................. 57 

4 References............................................................................................................................... 63 

APPENDIX A. BACKGROUND INFORMATION ON THE FOOD AND WATER 

CONSUMPTION DATA USED IN SHEDS DIETARY..........................................................A-1 

APPENDIX B. BACKGROUND INFORMATION ON THE WATER CONCENTRATION 

DATA USED IN SHEDS DIETARY........................................................................................A-5 

APPENDIX C. CSFII FOOD LIST IN RAC CODE ORDER.................................................. A-6 

APPENDIX D. CSFII FOOD LIST IN ALPHABETICAL ORDER .......................................A-10 

APPENDIX E. CSFII FOOD DEFINITIONS .......................................................................A-105 

APPENDIX F. NEW FOODS IN 2005 NHANES COMPARED WITH CSFII....................A-108 

APPENDIX G: ANNOTATED SHEDS-DIETARY SAS CODE.........................................A-116 
iv 

List of Figures 

Figure 2-1. SHEDS-Dietary Methodology (modified from Xue et al. 2010) ............................... 10

Figure 2-2. Monte Carlo Simulation in Dietary Exposure Modeling ........................................... 11 

Figure 2-3. Different methods for assembling longitudinal diaries from cross-sectional data..... 24 

Figure 2-4. Hypothetical Figure to Illustrate Persisting Effect for an Individual ......................... 28 

Figure 2-5. Example SHEDS-Dietary modeled longitudinal dietary exposure profile for 1 

individual .............................................................................................................................. 29 

Figure 2-6. Total Number of Occasions of Direct Drinking Water Consumption........................ 32 

Figure 2-7. Distribution of Direct Drinking Water Consumption, By Time of Day..................... 32 

Figure 2-8. Box-and-Whisker Plot of Potato Consumption by Day for Children 1-2 Yrs. Old ... 38 

Figure 2-9. Box-Cox Plot of Infant Direct Water Consumption (mL/kg bw/day)........................ 39 

Figure 2-10. Uncertainty analysis profiles for daily dietary cis-permethrin exposure................. 41 

Figure 2-11. Comparison of SHEDS and DEEM on 99.9th percentiles of drinking water. 

Chemical ABC exposure (mg/kg/day) of 17 different scenarios. ......................................... 47 

Figure 3-1. Contribution of Inorganic Arsenic Intake by Foods................................................... 50 

Figure 3-2. Contribution of Total Arsenic Intake by Foods.......................................................... 50 

Figure 3-3. SHEDS-Dietary Exposure Model Evaluation for Arsenic Against Duplicate Food 

Data....................................................................................................................................... 51 

Figure 3-4. Total Arsenic Model Evaluation for SHEDS-Dietary linked with MENTOR PBPK 

Model and Compared Against NHANES Urinary Biomarker Data. .................................... 51 

Figure 3-5. Example SHEDS-Dietary Uncertainty Analyses........................................................ 53 

Figure 3-6. SHEDS-Dietary Methyl Mercury Exposure by Ethnicity Using 1999-2006 NHANES 

Data....................................................................................................................................... 54 

Figure 3-7. Contribution of Methyl Mercury Exposure from Different Fish Types for Asians, 

Native Americans, and Pacific Islanders Self-Reported in NHANES.................................. 55 

Figure 3-8. Contribution of Foods to Cis-Permethrin Exposure Using SHEDS-Dietary ............ 59 

Figure 3-9. Comparison of SHEDS-Dietary Estimates Against CTEPP Duplicate Diet Exposure 

Data for Cis- and Trans- Permethrin. (Note: The cloud of yellow lines illustrating 100 

modeled variability runs contains the observed data.).......................................................... 60 

Figure 3-10. SHEDS-Dietary Modeled Exposure of Cis-Permethrin with NHANES and CSFII 

for 3-5 year-olds.................................................................................................................... 61 

List of Tables 

Table 1-1 Key Features of SHEDS-Dietary.................................................................................... 4 

Table 2-1. An Example CSFII Food Diary (CSFII ID=28517-2-2: 1 yr, M, 13.6 kg). ................. 13 

Table 2-2. Example CSFII Food Consumption Diary................................................................... 16 

Table 2-3. Example Recipe File for Two Food Items................................................................... 18 

Table 2-4. Example CSFII Food Consumption Data for Two Days............................................. 19 

Table 2-5. Total Number of DWCS Diaries, By Age Group, Gender and Season ....................... 33 

Table 2-6. Potential Applications of the SHEDS-Dietary model ................................................. 35 

Table 2-7. Bootstrap uncertainty analyses for cis-permethrin ..................................................... 42 
v 

Table 2-8. A Comparison of DEEM-FCID and SHEDS-Dietary Exposure Results for Chemical 

ABC. ..................................................................................................................................... 45 

Table 2-9. Contribution to exposure by commodities for 1-2 year-olds...................................... 46 

Table 3-1. SHEDS-Dietary Methyl Mercury Exposure by Ethnicity and Age from 1999-2006 

NHANES Data...................................................................................................................... 56 

Table 3-2 Summary statistics of organic blood level by age group and ethnicity (μg/liter) from 

1999-2006 NHANES data .................................................................................................... 57 

Table 3-3. Cis-permethrin exposure by age groups from SHEDS-Dietary with CSFII Data ....... 59 

Table 3-4. SHEDS-Dietary Evaluation against CTEPP Duplicate Diet Data for Cis- and TransPermethrin with CSFII Data.................................................................................................. 60 

Table 3-5. SHEDS-Dietary Modeled Contribution of RAC to Total Food Consumption for CisPermethrin and 3-5 year-olds................................................................................................ 62 
vi 

Acronyms and Abbreviations 

ai/kg bw Active ingredient per kilogram body weight

ATUS American Time Use Survey 

CCA Copper Chromated Arsenic 

CHAD Consolidated Human Activity Database 

CRA Cumulative Risk Assessment 

CSFII USDA Continuing Survey of Food Intakes by Individuals 

CTEPP Children’s Total Exposure to Persistent Pollutants study 

D &A Diversity and Autocorrelation 

DEEM Dietary Exposure Evaluation Model 

DWCS Drinking Water Consumption Survey 

EATS Eating at America’s Table Study 

ERDEM Exposure Related Dose Estimating Model 

FCID Food Commodity Intake Database 

FDA Food and Drug Administration 

FF Food form 

FFQ Food Frequency Question 

FIFRA Federal Insecticide, Fungicide, and Rodenticide Act 

FQPQ Food Quality Protection Act 

LOD Limit of detection 

MEC Mobile Examination Centers 

NCI National Cancer Institute 

NHANES National Health and Nutrition Survey 

NMC N-methyl carbamate insecticide 

OP Organophosphorous insecticide 

OPP Office of Pesticide Programs 

PCT Percent of crop treated 

PD Persisting Dose 

PDP USDA Pesticide Data Program 

PRZM-EXAMS Pesticide Root Zone Model – Exposure Analysis Modeling System 

RAC Raw agricultural commodity 

SAP Science Advisory Panel 

SCIGROW Screening Concentration in Ground Water 

SHEDS Stochastic Human Exposure and Dose Simulation

TDS FDA Total Dietary Study 

US EPA United States Environmental Protection Agency 

USDA United States Department of Agriculture 

WWEIA NHANES what We Eat in America 
1 

1 Background 

1.1 Purpose of SHEDS-Dietary 

The EPA’s Office of Pesticide Programs (OPP) is responsible for registering all uses of 

pesticides (http://www.epa.gov/pesticides/regulating/laws.htm.). The Agency must ensure that a 

pesticide, when used according to label directions, can be used with a reasonable certainty of no 

harm to human health and without posing unreasonable risks to the environment. The Agency 

also sets tolerances (maximum pesticide residue levels) for the amount of the pesticide that can 

legally remain in or on foods when a pesticide may be used on food or feed crops. Under the 

Food Quality and Protection Act of 1996 (FQPA), “the term ‘safe’, with respect to a tolerance for 

a pesticide chemical residue, means that the Administrator has determined that there is a 

reasonable certainty that no harm will result from aggregate exposure to the pesticide chemical 

residue, including all anticipated dietary exposures and all other exposures for which there is 

reliable information.” FQPA specifies ‘all anticipated dietary exposures’ as the potential for 

concurrent exposures from ‘all other tolerances in effect for the pesticide’, and ‘all other 

exposures’ as the potential for concurrent exposures from ‘non-occupational uses’, such as lawn 

care and other residential uses of pesticides. 

Since the passage of FQPA, the Agency has conducted three types of dietary risk assessments: 

acute (1-day), chronic, and cancer. Chronic and cancer risk assessments have been based 

traditionally on deterministic calculations at the per capita level, using DEEM-FCIDTM to 

calculate exposure by combining food consumption and residue data (US EPA, FIFRA SAP 

1997, 1998). For higher tier, refined acute dietary risk assessments, OPP has generally used 

DEEM-FCID (U.S. EPA 2000a) with Monte Carlo simulations to obtain an estimate of total 

daily dietary exposure to a pesticide. To conduct several cumulative risk assessments (OP CRA, 

NMC CRA, Triazine CRA), OPP has used longitudinal aggregate exposure models (e.g., 

Calendex-FCID, CARES, Lifeline), peer-reviewed by OPP’s FIFRA SAP (U.S. EPA 1999, 

2000b, 2000c, 2000d). 

EPA’s Office of Research and Development (ORD), National Exposure Research Laboratory 

(NERL) has developed the Stochastic Human Exposure and Dose (SHEDS)-Dietary model 

version 1 (v1), a probabilistic, population-based dietary exposure assessment model that 

simulates individual exposures to chemicals in food and drinking water over different time 

periods (e.g., daily, yearly) (Xue et al., 2010). SHEDS-Dietary is a module, along with SHEDSResidential, of ORD/NERL’s more comprehensive human exposure model, SHEDS-Multimedia 

(Zartarian et al., 2008; http://www.epa.gov/heasd/products/sheds_multimedia/sheds_mm.html; 

http://www.epa.gov/scipoly/SAP/meetings/2007/081407_mtg.htm). 

The Stochastic Human Exposure and Dose Simulation model for multimedia, 

multiroute/pathway chemicals (SHEDS-Multimedia) is being developed as a state-of-science 

computer model for improving estimates of aggregate (single-chemical, multi-route/pathway) 

and cumulative (multi-chemical, multi-route/pathway) human exposure and dose. SHEDS-
2 

Multimedia is the EPA/ORD’s principal model for simulating human exposures to a variety of 

multimedia, multipathway environmental chemicals such as pesticides, metals, and persistent 

bioaccumulative toxins. 

SHEDS-Multimedia version 4 is comprised of both the dietary module, SHEDS-Dietary version 

1 (Xue, 2010) described in this technical manual and related user guide (Isaacs et al., 2010a), and 

a residential module, SHEDS-Residential version 4.0, described in a separate technical manual 

and user guide (Glen et al., 2010, Isaacs et al., 2010b). SHEDS-Residential is a physically-based, 

probabilistic model that predicts, for user-specified population cohorts, exposures incurred in the 

residential environment over time via inhaling contaminated air, touching contaminated surface 

residues, and ingesting residues from hand- or object- to-mouth activities. To do this, it 

combines information on chemical usage, human activity data (e.g., from time/activity diary 

surveys, videography studies), environmental residues and concentrations, and exposure factors 

to generate time series of exposure for simulated individuals. One-stage or two-stage Monte 

Carlo simulation is used to produce distributions of exposure for various population cohorts (e.g., 

age/gender groups) that reflect the variability and/or uncertainty in the input parameters. 

A methodology for linking the residential and dietary modules for simulated individuals (based 

on age, gender, body weight, total caloric intake/METS, race, season, weekday and region) will 

be peer reviewed by EPA’s July 20-22, 2010 FIFRA Scientific Advisory Panel. This 

methodology, described later in this manual in the section entitled, “Algorithm for Matching 

(Behavioral) Diaries: Food Consumption and Activity Patterns.” has been tested through “soft 

linking” the two modules with a permethrin pesticide case study. In the next version of SHEDSMultimedia, the dietary and residential module SAS codes will be merged, so that both types of 

exposure can be calculated for the same individual after food consumption and activity pattern 

diaries are appropriately matched. A common Graphical User Interface (GUI) will also allow the 

user to run either module separately, or to run them both together. The focus of this Technical 

Manual is the standalone SHEDS-Dietary model. 

The SHEDS-Multimedia model, including the SHEDS-Dietary and SHEDS-Residential modules, 

represent an advancement in science over existing models, given some of the key features 

described below. SHEDS-Dietary allows conducting additional analyses for pesticides; 

quantifying uncertainty in acute dietary risk assessments; and enhancing chronic and cumulative 

risk assessments. This model can be applied to other chemicals as well as pesticides, and 

therefore may be useful to other Program Offices and Agencies. 

This Technical Manual describes the algorithms, methodologies, data sources, and input and 

output options and capabilities of the SHEDS-Dietary model v1. ORD, in conjunction with OPP, 

developed this Agency state-of-the-science model to probabilistically estimate dietary exposures 

to inform regulatory risk assessments as well as address science questions for research purposes. 

ORD’s SHEDS-Dietary modeling research focused on enhancing the science of probabilistic 

dietary exposure assessments. OPP collaboration on SHEDS-Dietary model development has 

considered criteria for regulatory use: peer-reviewed / transparent (algorithms); publicly 

available (free or nominal cost); and consistent with EPA/OPP policies and guidelines. 
3 

One major purpose of the July, 2010 FIFRA SAP meeting is to review SHEDS-Dietary version 1 

and SHEDS-Residential (cumulative or aggregate) version 4 modules, and methodology for 

linking them in the next version of SHEDS-Multimedia, so they can be used for regulatory 

decision-making in EPA. Peer review of SHEDS-Multimedia, including its modules, 

methodologies, and case studies, is necessary for broad regulatory applications in EPA and 

potentially other Agencies. In 2007 the EPA FIFRA SAP reviewed the residential module of the 

SHEDS-Multimedia model (version 3), and provided peer consult of the conceptual dietary 

module (http://www.epa.gov/scipoly/SAP/meetings/2007/081407_mtg.htm). 

SHEDS-Dietary is a publicly available, transparent model that uses the SAS platform (requires a 

SAS license for version 9.1 and higher); see specific computer requirements in the User Guide, 

Isaacs et al., 2010a), which provides model adaptability and the ability to view, query, analyze, 

and update the underlying databases (e.g., food consumption, recipes, residues). It also facilitates 

food consumption data (NHANES) and recipe updates, and development of alternate exposure 

modeling assumptions (e.g., stochastic assumption on residues, by eating occasion or day). SAS 

Output Tables provide flexibility to develop alternate contribution analyses, and facilitate linkage 

with PBPK models. This flexibility contributes to various features of SHEDS-Dietary to allow 

for exposure analyses in addition to standard dietary exposure model results (i.e., exposure at the 

95th , 99th, and 99.9th percentiles of the population). 

The SHEDS-Dietary model is consistent with EPA/OPP policies and guidelines in that it 

addresses FQPA requirements for acute and longitudinal aggregate and cumulative exposure 

assessments to pesticides residues in food, drinking water, and water used in food preparation 

while fulfilling the criteria described above for regulatory-use models. 

The following sections of this manual describe the technical details of the SHEDS-Dietary model 

v1. 

1.2 Overview of SHEDS-Dietary 

SHEDS-Dietary can produce population percentiles of dietary exposure by source and age-gender 

group; quantify contribution to total exposure by food, commodity, and chemical; and be used for 

eating occasion, sensitivity, and uncertainty analyses. In general terms, it combines information 

about food and drinking water consumption data for each reported eating occasion with 

corresponding chemical residue/concentration data to estimate human dietary exposures. The 

model can use either USDA’s Continuing Survey of Food Intake by Individuals (CSFII) (1994-

96, 1998) or the NHANES/WWEIA (What We Eat in America) dietary consumption data (1999-

2006), along with EPA/USDA recipe translation files (FCID; Food Commodity Intake Database), 

and available food and water concentration data. Specifics about combining this information 

require a number of technical considerations, such as translating foods reported as eaten into raw 

agricultural commodities using recipe files, sampling residues within a day and over time, 

considering non-detects, and allocating total drinking water consumption into within-day 

drinking water events. The goals of the SHEDS-Dietary model are to use state-of-the-science 

algorithms, to enhance the science of probabilistic dietary exposure assessments by allowing 
4 

additional analyses, and to better characterize and quantify uncertainty in Agency risk 

assessments. 

 

1.2.1 Key Features and Model Options 

Some of the key features of SHEDS-Dietary are presented in Table 1-1 and described below: 

Table 1 Table 1-1 Key Features of SHEDS Key Features of SHEDS Key Features of SHEDS-Dietary Dietary Dietary 

SHEDS-Dietary Option/Feature 

Available in 

SHEDS-Dietary? 

Notes [Option linked to 2007 FIFRA SAP 

Question] 

Food Consumption Data Sources

CSFII (1994-96, 1998 Children 

supplemental) Yes 

Data used in Agency risk assessments (e.g., DEEMFCIDTM) 

NHANES (1999-2006), 

Preliminary data Yes Food recipes not available for new foods 

Modeling Longitudinal Consumption (Food, Water) Patterns

Within Day Direct DW 

Consumption: 6 Equal Amounts, 

Fixed Times (6 am, 9, 12, 3, 6, 9 

pm) Yes [Q3 FIFRA SAP 2007] 

Within Day Consumption of 

Direct DW: Bayer DW 

Consumption Survey Yes [Q3 FIFRA SAP 2007] 

2-Diary Yes 

Similar to Method used in Agency risk assessments (e.g., 

Calendex-FCIDTM) 

8-Diary Yes 

[Q2 & Q5 FIFRA SAP 2007; option available but not 

recommended; will be dropped in next update since data 

not included in NHANES] 

Diary Assembly (DA) Yes Currently based on Total Caloric Intake 

Residues (Food & Drinking Water) 

Commodity (FCID) Residues Yes 

Method used in OPP risk assessments (e.g., DEEMFCIDTM) 

Food Residue (vs. Commodity) No 

Option used to assess Arsenic (Journal article); Case study 

assigns residues to FCID commodities; Difficult to 

Incorporate in GUI 

Drinking Water Concentrations Yes Single Distributions only (e.g., DEEM-FCIDTM) 

Drinking Water Concentrations 

– Calendar Year No 

Randomly Select Year, then apply to corresponding 

Modeled Day (e.g., Calendex-FCIDTM and CARESTM use 

of 30 years of PRZM-EXAMS predicted DW 

concentrations) 

Modeling Food Residues

Select Single Residue for all 

Eating Occasions, by 

Commodity (RAC-FF) Yes 

Method used in Agency risk assessments (e.g., DEEMFCIDTM) 

Select New Residue for different 

Eating Occasions, by FoodRAC-FF Yes [Q1 FIFRA SAP 2007, 32; Option often has little effect 

for food-only analyses; may ‘add’ uncertainty] 

Correlation across commodities, 

across multiple chemicals 

(products) applied to foods, and No Minimal data to implement; (FIFRA SAP 2007, p.25) 
5 

SHEDS-Dietary Option/Feature 

Available in 

SHEDS-Dietary? 

Notes [Option linked to 2007 FIFRA SAP 

Question] 

over subsequent days 

Multiple Distributions for 

Commodity (RAC-FF) No E.g., probability of ‘Domestic’ or ‘Import’; 

Multiple Distributions for 

Commodity (RAC-FF), By 

Season No 

[Q5 FIFRA SAP 2007] E.g., linking food consumption 

with seasonal (and/or regional) residues 

Modeling Drinking Water Concentrations

Randomly select new DW 

concentration each day Yes 

Method used in Agency risk assessments (e.g., DEEMFCIDTM) 

Randomly select Year for each 

Person-iteration, then apply 

Predicted DW based on 

Calendar (365) date No 

Retain seasonal patterns (autocorrelation) in DW 

concentrations. Method used in Agency risk assessments 

(e.g., Calendex-FCIDTM, CARESTM) 

Sensitivity/Uncertainty Analyses

Sensitivity Analyses * 

Requires supplemental routine (e.g., effect of 

consumption outliers on infant DW exposures – aldicarb 

memo) 

Uncertainty Analyses * [Q4 FIFRA SAP 2007] Requires supplemental routine 

Compiling/Viewing Summary Statistics

Total Daily Exposures (99.9th) Yes Measure used in OPP assessments 

Average Daily Exposures 

(99.9th) * Need supplemental routine 

Eating Occasions (99.9th) based 

on Maximum Exposure over all 

Eating Occasions Yes Method used to characterize exposures (NMC CRA) 

Eating Occasions with 

Chemical-Specific Half-Life 

(99.9th) * 

Method used in DEEM-Based Eating Occasions analyses; 

Need supplemental routine. Supplemental program to

calculate per capita 99.9th for single-chemical, single-day 

was recently incorporated into GUI; needs QC. 

Plotting Person-Day Exposures Yes Visualize Exposure Patterns/Persisting Dose 

Contribution Analyses: Shares 

of Total Exposure, by 

Commodity Yes Used to develop risk mitigation options 

Contribution Analyses: Shares 

of Total Consumption, by Food 

(Commodity) Yes 

Output Summary Results (99.9th, 

CEC, etc.) to File (MS 

Excel/MS Word) * SAS Editor/Wizard allow users to export results 

View/Query Data (Food Diaries, 

Recipes, etc.) * SAS Editor allows users to view/query data 

New Aggregate Contribution 

Analyses * Need supplemental routine 

* not implemented in GUI but can be conducted using SAS code 
6 

1.2.1.1 Eating Occasion Analyses 

In addition to providing estimates of total daily dietary exposure, SHEDS-Dietary provides the 

Agency with a capability to conduct ‘Eating Occasions’ analyses to refine risks for pesticides 

and other chemicals; such analyses have been discussed by several Panels (US EPA FIFRA SAP 

1999, 2003, 2005). Research suggests that eating occasion analyses may refine the risk 

assessments for some compounds with short half-lives (U.S. EPA. 2007; Nako et al., 2007). Use 

of the Bayer Drinking Water Consumption Survey (DWCS) data in SHEDS-Dietary (and in the 

future, time of drinking water consumption data from NHANES) can be used to refine previous 

drinking water exposure analyses (e.g., revised OP CRA, NMC CRA; 

http://www.epa.gov/pesticides/cumulative/). Eating and drinking occasion algorithms in 

SHEDS-Dietary enhance the ability to model dietary exposures over short-term durations. 

Detailed information by eating occasion also allows conducting analyses to determine the 

contribution to exposure of different food types, chemicals, and other factors for different agegender groups. 

1.2.1.2 Half-life Analyses 

SHEDS-Dietary v1 includes longitudinal algorithms to enhance the ability to model dietary 

exposures over short-term durations - less than a day, and up to one year, and to assess the impact 

of a chemical’s half-life on the exposure results (e.g., “persisting effects” for organophosphates). 

 For example, the SHEDS-Dietary longitudinal analyses can be used to assess exposure bioindicators persisting across multiple exposure events (e.g., on cholinesterase inhibition for 

organophosphates). By including recovery half-lives, fraction of the peak effect persisting from 

one exposure event is considered when a second exposure event occurs later. 

1.2.1.3 Uses CSFII or NHANES/WWEIA Food Consumption Data 

SHEDS-Dietary can use the USDA’s CSFII 1994-96, 1998 or the NHANES/WWEIA 1999-2006 

food consumption data. The 1994-1996, 1998 CSFII data base included 5,845 food items 

consumed by respondents. The NHANES respondents reported consuming many of those same 

foods, as well as approximately 580 new foods that were not reported during the CSFII survey. 

As part of the transition to using the newer NHANES data, the Office of Pesticide Programs is 

currently planning to update the food recipe data base (FCID) to include new foods that were not 

reported by respondents in the CSFII survey. Approximately 20g (or approximately 1% of all 

food eaten by individuals) of new NHANES foods are not matched to CSFII foods (see 

Appendix F); however, our analyses have shown this does not affect results. Using this Agency 

model provides OPP a quick and economical means to assess the National Health and Nutrition 

Examination Survey (NHANES) food consumption data for modeling dietary exposures. 

1.2.1.4 Options for Simulating Longitudinal Consumption Data 

While the main focus to date in SHEDS-Dietary has been on the cross-sectional algorithms, the 

model is capable of modeling longitudinal dietary exposures to chemicals. For this purpose, 

SHEDS-Dietary requires the construction of human consumption diaries that cover the entire 

simulation period of a model run. This period is often several months, a year, or 

even longer. For a simulated individual, SHEDS-Dietary constructs a longitudinal profile of food 

consumption over a 365 day period with 3 options: a cross-sectional or 2-diary approach; an 8-
7 

diary approach, and the “D&A” approach (Glen et al., 2007) described below. Issues relating to 

longitudinal diary construction are described later in this manual. 

In both CSFII and NHANES, there are two-day dietary consumption data for the subjects. 

Therefore, we can use two-day data for the same person together with many run interactions to 

assemble the longitudinal data. For example, the residues are randomly assigned to one person 

with two-day dietary consumption data, which is counted as one interaction. The same two-day 

data will be randomly assigned with different residue concentrations. This is another interaction. 

In this way, many interactions can be generated. Then, data from the first interaction will be 

counted as day 1 and 2, the second as day 3 and 4 and so on. In this way, longitudinal data will be 

assembled. 

The 8-diary longitudinal algorithm (Xue et al., 2004) is the same approach used in the SHEDSMultimedia model, which constructs longitudinal activity profiles from human activity diaries 

drawn from EPA’s CHAD (Consolidated Human Activity Database; McCurdy et al., 2000; 

http://www.epa.gov/chadnet1). CHAD typically includes just one day (24 hours) of activities 

from each person. SHEDS-Dietary creates modeled individuals (reference population) by 

randomly drawing a person from the Census data. The food consumption diaries used by 

SHEDS-Dietary (NHANES or CSFII; see later sections) are grouped by age and gender, and for 

each of these age-gender cohorts, ‘diary pools’ are created based on Season and Day of Week 

(weekday or weekend). For each modeled individual, SHEDS-Dietary constructs a longitudinal 

profile of food consumption by randomly selecting 8 food diaries (one weekend and one 

weekday, for each of the four seasons) from the appropriate cohort-diary pools. 

NHANES/WWEIA does not provide dates, so SHEDS-Dietary randomly draws consumption 

diaries from that survey to use this approach. 

The August 2007 FIFRA SAP reviewed this approach for dietary exposure assessment, and 

found 8 diaries to be insufficient. Thus, a more detailed and desirable option for assembling 

year-long diaries is given in Glen et al., 2007. The user chooses target behavior and statistics to 

control within- and between- person variance, and day-to-day autocorrelation. Diaries are 

preferentially sampled to produce the target behavior. This method, referred to as the “D&A 

approach”, requires a few additional inputs to be designated by the user, but allows for more 

control over the properties of the assembled diaries. This diary assembly method requires the 

user to: 

1) select the diary property most relevant to exposure for the current application (e.g., Total 

Calories); 

2) specify the “D” (diversity) statistic, which relates the within-person and between-person 

variances for this diary property; and 

3) specify the 1-day lag autocorrelation “A” in this diary property. 

Guideline values for the D and A statistics for a number of diary properties have been calculated 

using the steps below for the permethrin case study, but other values can be used as more data 

become available: 

1) Calculate the total amount of major vegetables contributing to dietary permethrin 

exposure. Those vegetables are spinach, cabbage, lettuce, parsley, celery and tomato. 
8 

2) Use the total amount of those vegetables consumption (grams) per day as index to 

calculate D and A statistics. 

3) Results: D=0.27 and A=0.06 

4) Total calories consumption was used with 0.3 for D and 0.1 for A statistics, based on data 

from Lu et al. (2006a,b) (Alex Lu, personal communication), to assemble the longitudinal 

diary for one year. 

1.2.1.5 Multi-chemical (Cumulative Exposure Assessment) Capability 

There are two key differences between the single chemical and cumulative exposures in the 

SHEDS-Dietary model: 1) co-occurrence of the chemicals; and 2) addition of exposures among 

chemicals with similar mode of action. Different SAS code modules in the model are used to 

accommodate these differences even though they share a common algorithm. For pesticides, 

there is a small data set storing pesticide codes, usually in three letters or digits. Selected 

pesticides (pesticide codes) will be used to merge the residue data. Due to co-occurrence, 

pesticides measured in the same raw agricultural commodity (RAC) or food item will be stored 

in the same place labeled by the same identifier so that it will be selected as whole by Monte 

Carlo simulation. For adding exposures to obtain cumulative exposure, relative potency factors 

are used so that exposures of different pesticides can be added, weighted by the relative 

toxicities. 

1.2.1.6 Linkage to PBPK Models 

The timing of exposures throughout a simulated day becomes important as the Agency moves 

toward integrating dietary exposure models with physiologically-based pharmacokinetic models. 

 To account for eating occasions, SHEDS-Dietary preserves information from the detailed food 

diaries and corresponding exposure calculations. Linkage between SHEDS-Dietary exposure 

outputs, which preserve variability of exposures within a day, and physiologically-based 

pharmacokinetic (PBPK) models allows for refined dose and risk analyses, and evaluation of 

SHEDS-Dietary model performance against NHANES biomonitoring data (Xue et al., 2010). 

1.2.1.7 Sensitivity and Uncertainty Analyses 

SHEDS modeling research has involved developing and applying new methods for sensitivity 

and uncertainty analyses (Xue et al., 2006; Zartarian et al., 2007; 

http://www.epa.gov/heasd/products/sheds_multimedia/sheds_mm.html; 

http://www.epa.gov/scipoly/SAP/meetings/2007/081407_mtg.htm). These methods can be 

applied to different model applications for identifying key factors, outliers, and data needs. 

1.2.2 Plans and Future Research Needs 

Plans and future research needs for SHEDS-Dietary include the following: 

 Apply to other case studies with PBPK linkage, sensitivity and uncertainty analyses, 

model evaluation; 

 Expand model applications to local/community scale for different chemicals; 

 Refine longitudinal algorithms based on available data; 

 Merge dietary & residential modules (match food consumption and activity diaries); 
9 

 Analyze impact of different residue sampling: same vs. different residues within a day for 

same foods eaten by an individual; 

 Possible refinements to drinking water allocations; 

 Explore enhancements to uncertainty analyses; 

 Expand the model to local scale applications for different chemicals, seasons, regions; 

methods (including analyses of CSFII and NHANES) to examine importance of regionand season- specific dietary consumption amounts and patterns on dietary exposure 

estimates; 

 Consider other data sets for considering enhancing within-day modeling of exposures e.g. 

Child Development Supplement to Panel Study of Income Dynamics 

(http://psidonline.isr.umich.edu/CDS/time_diary_readme.html), American Time Use 

Survey (ATUS), http://www.bls.gov/tus/#overview, University of Maryland archive on 

recent and historical data sets on individual time use and activity patterns, 

http://www.webuse.umd.edu); and

 Conduct more research on sampling drinking water concentrations (e.g., randomly select 

a year then apply daily concentrations throughout the modeled calendar year). 
10 

2 SHEDS-Dietary Methodology 

The SHEDS-Dietary Module overview is illustrated in Figure 2-1, and details on the algorithms 

are given in the following sections and the annotated code in Appendix G. 

Monte 

Carlo 

sampling 

applied 

NHANES Consumption: Food consumption data from NHANES

Residue Concentration: Residue concentration data by food item or commodity from TDS

Distribution fitting: fittings of residue data into suitable statistical distribution 

Food Item: food products people in the survey consumed such pizza, raw apple

Commodity: raw agriculture commodity (RAC) 

Usage factors: Pesticide usage percentages by RAC from USDA.

Process factors: concentration or dilution factors due to processes of food from RAC into food products.

Recipe files (EPA FCID): data base for percents of various RACs for the food products.

Figure 2 Figure 2-1. SHEDS . SHEDS . SHEDS-Dietary Methodology (modified from Xue et al. 2010 Dietary Methodology (modified from Xue et al. 2010) 
11 

Normal Uniform

Consumption (g food/kg bw) x Residue (mg pesticide/gram food) = 

Exposure (mg pesticide/kg bw)

MONTE CARLO SIMULATION 

each MC trial is an iteration => simulated exposure event

a series of trials => simulated distribution of exposures

Exposure 

distribution

Exposure Outcomes

Figure 2 Figure 2-2. Monte Carlo Simulation in Dietary Exposure Model . Monte Carlo Simulation in Dietary Exposure Modeling 

Xue et al., 2010 describes the SHEDS-Dietary methodology shown in Figure 2-1. For estimating 

daily dietary exposure, detailed NHANES (or CSFII) food diaries are used by the SHEDSDietary model to simulate food ingestion exposures by separate eating occasions for a simulated 

individual (Figure 2-1). SHEDS-Dietary can use residues for food items as consumed, as well as 

residues of raw agricultural commodities (RAC). The reported food items are matched with food 

items in the FDA’s Total Diet Study (TDS) where possible (see step 1 in Figure 2-1). If TDS 

residues are available for a particular food (e.g., rice, chicken), then SHEDS-Dietary randomly 

draws a TDS residue from that corresponding residue distribution of the same food. Otherwise, 

the model applies the FCID recipe files to the NHANES or CSFII food items and randomly 

selects a residue for each of the RAC ingredients according to the recipe (see step 2 in Figure 2-

1). Note that SHEDS-Dietary version 1 does not implement the option of sampling residues from 

food items as consumed, e.g., TDS. 

Through the recipe files, the unmatched foods consumed are matched by RAC so that residues 

for those foods can be calculated. One option in SHEDS-Dietary version 1 for sampling residues 

within a day is to draw the same residue value if that RAC is found in the same foods. A second 

option is to draw different residue values for the same foods within a day. For non-detects, the 

model can assign zero or 1⁄2 LOD, depending on the chemical usage information (see details 

below). For each NHANES food diary, SHEDS-Dietary selects a residue value from an empirical 

distribution for each TDS food or RAC. While a particular commodity may be used in multiple 
12 

foods, the cooking method may differ, and thus, it will have a different food form. Process 

factors can then be applied (see step 3 in Figure 2-1). These factors account for food changes and 

related concentration changes due to dilution, drying, etc.. Each simulated individual’s exposure 

for each commodity is calculated by multiplying eating occasion consumption with 

corresponding residues. Summation of exposures from every eating occasion for one day yields 

the individual’s total daily exposure (see step 4 in Figure 2-1). Monte Carlo simulation is 

applied to generate population estimates of dietary exposure (see Figure 2-2). More details on the 

food and drinking water ingestion exposure algorithms are given below. 

2.1 Algorithm for Estimating Exposure to Pesticides and

Other Toxicants from Food Consumption 

SHEDS-Dietary v1 incorporates the data and decisions illustrated in Figure 2-1 above, and 

calculations shown in Equations 1 and 2 below, to calculate food ingestion exposure. The model 

uses food consumption diaries to simulate individuals’ food ingestion exposures by separate 

eating occasions. Reported consumption data are combined with sampled chemical residues in 

foods consumed, and concentration or dilution factors that adjust the residues for changes due to 

food processing (Figure 2-1). This section briefly introduces the equations used for calculating 

dietary exposure and their inputs, which are detailed in subsequent sections. 

Total daily exposure is calculated by summing exposures across all commodities, as depicted in 

Equation (1). Each simulated individual’s exposure for each commodity is calculated by 

multiplying the eating occasion consumption with the corresponding residues and process 

factors: 

Equation (1) – SHEDS-Dietary Equation for Estimating an Individual’s Exposure from a Single Eating 

Occasion 

Individual’s Dietary Exposure for a Single Eating Occasion [mass chemical] = amount of food 

item consumed [mass food] x concentration in the food item [mass chemical/ mass food] x 

process factors 

Equation (2) – SHEDS-Dietary Equation for Estimating an Individual’s Total Daily Exposure from All 

Eating Occasions 

Individual’s Total Daily Exposure = Σ( Individual’s Dietary Exposure for a Single Eating

Occasion) over all eating occasions in a given day.

Population estimates are obtained by applying Monte Carlo simulation as shown in Figure 2-2; 

the algorithms for computing an individual’s total daily exposure are repeated thousands of times 

to obtain a population cumulative density function (CDF). 
13 

2.1.1 Consumption 

The food consumption diaries used by SHEDS-Dietary and other dietary exposure models (such 

as those shown in Table 2-1) contain information on the timing and amounts consumed as 

reported by the survey respondents. Note that food and water consumption quantities are 

recorded in units of ounces, cups, or by count (cf. Egg, whole, Table 2-1). These units are 

converted to grams and ml for calculations. Appendix A contains details on food consumption 

data used in SHEDS-Dietary version 1. 

Table 2 Table 2-1. An Example CSFII Food . An Example CSFII Food . An Example CSFII Food Diary (CSFII ID=28517 Diary (CSFII ID=28517 Diary (CSFII ID=28517-2-2: 1 yr, M, 13.6 kg). 2: 1 yr, M, 13.6 kg). 2: 1 yr, M, 13.6 kg). 

SEQN Time of 

Day Food Description Amount (unit code) 

Consump 

tion 

(gm) 

Food 

Source 

/1 

1 7:00 AM Milk, cow's, fluid, whole 6 fl.oz 

(10205) 

183 Store 

2 Egg, whole, fried W/ LARD 2 XX 

(60919) 92 Store 

3 

10:15 AM 

White potato, home fries W/ LARD 2 C 

(10205) 388 Store 

4 Chicken, drumstick, with or without 

bone, roasted, skin eaten 

1 XX 

(61343) 52 Store 

5 

6:00 PM 

White potato, home fries W/ LARD 2 C 

(10205) 388 Store 

6 8:00 PM Milk, cow's, fluid, whole 6 fl.oz 

(10205) 183 Store 

/1 The Food Source variable is based on the question, ‘Where was the food item obtained?’ (1=store, etc.). 

2.1.2 Residues and Process Factors 

In principle, food residues as well as drinking water concentrations may also vary by eating 

occasion and/or foods consumed throughout the day. With that modeling assumption, the 

SHEDS-Dietary eating occasion approach tracks exposures throughout the simulated day based 

on the food diary data. Currently in SHEDS-Dietary, the user has 2 options for sampling 

residues consumed by an individual on multiple eating occasions within a day: (1) same residue 

sampled for same RAC and food items within a given day; (2) different residues sampled for all 

RAC and food items within in a given day. 

In SHEDS-Dietary version 1.0, empirical distributions are be used for raw agricultural 

commodities (RAC; see Appendix C). Reported food items are matched with food items (e.g., 

2% milk, raw apple) in the Total Diet Study (TDS; 

http://www.fda.gov/Food/FoodSafety/FoodContaminantsAdulteration/TotalDietStudy/default.ht

m) where possible; the model randomly draws a residue from that corresponding TDS residue 

distribution of same food. For unmatched foods, the model applies FCID recipe files to the food 
14 

items and randomly selects a residue for each of the RAC ingredients according to the recipe so 

that residues for those foods can be calculated (this approach is used for pesticides). 

The current version of SHEDS-Dietary does not include the option of matching reported foods 

(note that modifications were conducted for the arsenic case study to allow food items as 

consumed as depicted in Figure 2-1) where possible with foods reported “as eaten” (e.g., pizza 

rather than the pizza RAC tomatoes, flour, etc.) (e.g., in the FDA in the TDS), so that residues 

for those foods can be sampled; the model randomly draws a residue from that corresponding 

food residue distribution. A particular commodity may be used in multiple foods, with different 

cooking methods; thus, it will have a different food form reported. Process factors can be applied 

that account for food changes and related concentration changes due to dilution, drying, etc. 

Through recipe files (see Appendix E), unmatched foods consumed are matched by RAC so that 

residues for those foods can be calculated. SHEDS-Dietary randomly selects a residue from the 

corresponding RAC-food form distributions for those unmatched foods, according to the recipe 

amounts of those RAC. The exposure from each commodity-food form (RAC-FF) is calculated 

by multiplying that residue value with the amount consumed. 

Assignment of RAC residues for non-detects depends on the percent detected in PDP for the 

commodity and the percent of crops using that chemical. For example, if 20% of a crop is treated 

with Chemical X, and 5% of samples in PDP had detectable residues, then SHEDS-Dietary used 

the actual values for the 5%, assumes 1⁄2 LOD for 15%, and 0 for 80%. If the crop is not treated 

with Chemical X, the LOD is assigned zero. 

2.1.3 Sample calculation 

Total daily exposure is calculated in SHEDS-Dietary by summing chemical exposures across all 

commodities. Summation of chemical exposures from every commodity and every eating 

occasion for one day yields the individual’s daily total dietary exposure. 

This equivalence is illustrated with the following simple numerical example using the diary from 

a 1 yr old child (Table 2-1; CSFII ID=28517-2-2). If we assume that 47 grams of potatoes was 

consumed at 10:15 am from food, “White potato, home fries W/ LARD” and a residue of 1 ppm 

was drawn only for ‘potatoes’, then the exposure is 0.047 mg for this eating occasion. At 6:00 

pm, the same amount of the same food was consumed, therefore, 1 ppm of residue for the potato 

was used again due to the same food, then exposure for this eating occasion is also 0.047 mg: 

Exposure = 47 gms x (1/1000) x 1 (mg/kg) = 0.047 mg. 

If exposure from egg was 0.05 mg and that was the only other food to contribute any exposure 

that particular day, then total daily exposure for the subject will be the summation of 0.047,0.047 

and 0.05, i.e. 1.44 mg/day. Options for modeling food residues (i.e., randomly drawing different 

residues) are discussed in a subsequent section. A model specification could select a new residue 

for the same RAC-FF consumed through different foods (e.g., milk versus other dairy products). 

This process is repeated for many simulated individuals (for each food consumption diary, or 

simulated person-day) via Monte Carlo sampling (see Figure 2-2) to generate population 
15 

estimates of dietary exposure (Xue et al., 2010). For any particular diary, a Monte Carlo 

simulation is performed to select a residue concentration for each food commodity (raw 

agricultural commodity – food form; RAC-FF). 

2.2 Drinking Water Exposure Algorithm 

The SHEDS-Dietary drinking water exposure algorithm is similar to that for food exposure. 

Because the CSFII data does not provide information on timing and amounts of direct water 

intake throughout the day, SHEDS-Dietary currently distributes total direct water consumption 

from this database in 6 equal amounts at 6 fixed times (6am, 9am, 12pm, 3pm, 6pm, 9pm). The 

more recent NHANES 2005-2006 did collect information on timing and amounts of direct water 

intake throughout the day, so that information can also be used directly in SHEDS-Dietary in the 

future to assess timing and amounts of direct drinking water (e.g., tap, bottled) and indirect 

drinking water (e.g., infant formula, ‘kool aid’, coffee, tea, water used in cooking) intake within a 

simulated person-day (see Appendix A). Total drinking water consumed (both direct and 

indirect water consumption) is assumed to contain the same concentration, i.e., only one 

concentration value is selected in the Monte Carlo simulation for each eating occasion. SHEDSDietary randomly draws a drinking water concentration for each person-day (similar to DEEMFCID). One residue value is randomly selected and multiplied by total water intake to obtain 

drinking water exposures. In principle, drinking water concentrations may vary based on source 

(e.g., tap, bottled, other source); this is an area of future research. Currently, SHEDS-Dietary 

randomly draws drinking water concentration for any given day (no seasonality). 

2.3 Inputs 

This section summarizes the data used by SHEDS-Dietary to assess dietary exposures. 

2.3.1 Food and Indirect Water Consumption Data 

The primary sources of consumption data used in SHEDS-Dietary to model dietary exposures to 

pesticides are the food consumption diaries in the U.S. Department of Agriculture’s Continuing 

Survey of Food Intakes by Individuals (CSFII) database and in the NHANES/What We Eat in 

America (WWEIA) 1999-2006 database (see Appendices A, C, D, E). These surveys contain 

information regarding the real-time reported amount of food and water consumed by individuals, 

i.e. amounts of food and drinking water reported by individuals for each separate eating occasion. 

The CSFII food diaries contain information collected through a multiple pass 24-hour dietary 

recall instrument that was administered by trained interviewers in the respondents’ homes (Day 

1) or by phone interview (Day 2). Individuals were asked to provide food intake on 2 

nonconsecutive days (3 to 10 days apart) as well as socioeconomic and health-related 

information. A total of 20,607 individuals provided two 24 hour food diaries (total of 41,214 

diaries) during the initial survey period, 1994-1996, and through a children’s supplemental 

survey conducted in 1998 to address FQPA requirements that the USDA provide food intake data 

for a statistically adequate sample of children for use by the EPA to estimate exposure to 

pesticide residues. Table 2-2 shows an example diary from CSFII. 
16 

The NHANES/WWEIA 1999-2006 food consumption data (53,522 diaries) are also 24 hour 

recalls. The first day (Day 1) diary was collected through in-person interviews in the Mobile 

Examination Centers (MEC), while the second day 24 hour recall diary is collected by telephone, 

approximately 10 days after the in-person interview. 

Table 2 Table 2-2. Example CSFII Food Consumption Diary . Example CSFII Food Consumption Diary . Example CSFII Food Consumption Diary 

DAYCODE OCC_TIME foodname FOODAMT (grams)

1 800 MILK, COW'S, FLUID, 1% FAT 183

1 800 APPLE JUICE, W/ ADDED VITAMIN C 186

1 800 APPLE, RAW 138

1 800 CHEERIOS 30

1 800 MILK, COW'S, FLUID, 1% FAT 122

1 930 CRACKERS, CHEESE 124

1 1230 WHITE POTATO, CHIPS (INCL FLAVORED) 40.5

1 1230 PEPPER, SWEET, RED, RAW 74.5

1 1230 PEAR, RAW 166

1 1230 APPLE, RAW 69

1 1230 MILK, COW'S, FLUID, 1% FAT 122

1 1230 BREAD, POTATO 52

1 1230 TUNA SALAD 26

1 1530 APPLE JUICE, W/ ADDED VITAMIN C 186

1 1830 RICE, FRIED, W/ MEAT/POULTRY 198

1 1830 PORK, SPARERIBS, COOKED, LEAN ONLY 72

1 1830 CHICKEN PATTY/FILLET/TENDERS, BREADED, COOKED 176

1 1830 MILK, COW'S, FLUID, 1% FAT 122

1 1830 CRANBERRY JUICE DRINK W/VIT C ADDED(INCL COCKTAIL) 126.5

1 1900 ICE CREAM, REGULAR, NOT CHOCOLATE 44

2 800 MILK, COW'S, FLUID, 1% FAT 122

2 800 TEA, MADE FROM POWDERED INSTANT, PRESWEETENED 118.4

2 800 CHEERIOS 37.5

2 800 MILK, COW'S, FLUID, 1% FAT 183

2 1000 PUFFED RICE CAKE 9

2 1000 TEA, MADE FROM POWDERED INSTANT, PRESWEETENED 118.4

2 1200 CHICKEN VEGETABLE SOUP,W/RICE,MEXICAN(SOPA / CALDO DE POLLO) 242

2 1200 BREAD, ITALIAN, GRECIAN, ARMENIAN 20

2 1200 MILK, COW'S, FLUID, 1% FAT 122

2 1500 APPLE, RAW 138

2 1830 CORN DOG (FRANKFURTER/HOT DOG W/ CORNBREAD COATING) 88

2 1830 MILK, COW'S, FLUID, 1% FAT 122

2 1830 COUSCOUS, PLAIN, COOKED, FAT ADDED IN COOKING 81

2 1830 PORK & VEG (W/ CAR/DK GREEN, NO POTATO), NO SAUCE 81

Example of food consumption data for HHID=11328 and SPNUM=2 for two days

(Female and 1 years old)

2.3.2 Recipe Files 

For the purpose of assessing food tolerance, the EPA developed the Food Commodity Intake 

Database (FCID) that converts CSFII food items (e.g., apple pie, hamburger, milk and other diary 

products) into Raw Agricultural Commodities (RAC) based on likely cooking method and food 

form (FF) (Appendix E). The FCID database contains recipes or each food item reported in the 

1994-1996, 1998 CSFII diaries. These recipes (see Table 2-3) allow the model to calculate 

contributions from each food (e.g., pork and vegetables, fried rice) to aggregate exposures. FCID 
17 

recipe files break down foods into 553 RAC. Recipes are being developed by OPP for new 

NHANES/WWEIA food items (anticipated release, Fall 2010). 

The FCID commodity diaries may underestimate dietary exposures from some food items. For 

example, the FCID recipe decomposes an 8 oz. glass of whole milk (244 g) into three 

components: water (88%), fat (3.3%), and non-fat solids (8.7%). A simulation based on the food 

recipes entails randomly selecting a residue for each of the three components, and calculating 

contributions based on the corresponding weights: 214 g, 9 g, and 21 g, respectively. The 

assumption that residues are independent may lead to underestimating exposures to the extent 

that some components (e.g., water and fat) contain residues (treated), while the other components 

(non-fat solids) do not. Ideally, the user may want to directly apply the PDP data, because milk 

samples, collected from distribution centers and supermarkets, reflect foods as consumed by 

persons, and thus, do not require additional modeling assumptions regarding correlations 

(independence) across the components. The advantage of the FCID commodity diaries is that it 

facilitates developing anticipated residues for hundreds of other diary products, since the recipes 

account for different contributions from water, fat, and non-fat solids. SHEDS-Dietary version 2 

will allow the user to specify residues for both foods as eaten and/or commodities - as applied in 

Xue et al., 2010. 
18 

Table 2 Table 2-3. Example Recipe File for Two Food Items . Example Recipe File for Two Food Items . Example Recipe File for Two Food Items 

Food Items RAC CM_name CS_name FF_name Percent

Bean, lima, succulent Not specified Cooked Fresh or N/S 3.96

Bean, snap, succulent Not specified Cooked Fresh or N/S 10.45

Carrot Not specified Cooked Fresh or N/S 15.84

Corn, sweet Not specified Cooked Fresh or N/S 15.84

Pea, succulent Not specified Cooked Fresh or N/S 10.45

Pork, fat Not specified Cooked Fresh or N/S 5.995

Pork, meat Not specified Cooked Fresh or N/S 37.127

Bean, mung, seed Fried Cooked Fresh or N/S 0.486

Chicken, fat Fried Cooked Fresh or N/S 0.504

Chicken, meat Fried Cooked Fresh or N/S 6.285

Corn, field, oil Not specified Refined Not Applicable 0.201

Cottonseed, oil Not specified Refined Not Applicable 0.319

Egg, whole Fried Cooked Fresh or N/S 10.741

Olive, oil Not specified Refined Not Applicable 0.082

Onion, green Fried Cooked Fresh or N/S 2.8

Pea, succulent Fried Cooked Fresh or N/S 3.42

Peanut, oil Not specified Refined Not Applicable 0.055

Rapeseed, oil Not specified Refined Not Applicable 0.169

Rice, white Fried Cooked Fresh or N/S 24.24

Safflower, oil Not specified Refined Not Applicable 0.001

Sesame, oil Not specified Refined Not Applicable 0.001

Soybean, oil Not specified Refined Not Applicable 3.556

Soybean, seed Fried Cooked Fresh or N/S 0.956

Sunflower, oil Not specified Refined Not Applicable 0.007

Water, indirect, all sources Fried Cooked Fresh or N/S 39.31

Wheat, flour Fried Cooked Fresh or N/S 0.376

PORK & VEG (W/ CAR/DK 

GREEN, NO POTATO), NO 

SAUCE

'RICE, FRIED, W/ 

MEAT/POULTRY
19 

Table 2 Table 2-4. Example CSFII Food Consumption Data for Two Days Example CSFII Food Consumption Data for Two Days Example CSFII Food Consumption Data for Two Days 

DAYCODE OCC_TIME foodname RAC CM_name CS_name FF_name amount_gram

1 800 MILK, COW'S, FLUID, 1% FAT Milk, fat Not specified Uncooked Fresh or N/S 1.94

1 800 MILK, COW'S, FLUID, 1% FAT Milk, nonfat solids Not specified Uncooked Fresh or N/S 16.21

1 800 MILK, COW'S, FLUID, 1% FAT Milk, water Not specified Uncooked Fresh or N/S 164.85

1 800 APPLE JUICE, W/ ADDED VITAMIN C Apple, juice Not specified Uncooked Fresh or N/S 185.93

1 800 APPLE, RAW Apple, fruit with peel Not specified Uncooked Fresh or N/S 138.00

1 800 CHEERIOS Beet, sugar Not specified Refined Not Applicable 0.40

1 800 CHEERIOS Cassava Not specified Cooked Dried 0.01

1 800 CHEERIOS Corn, field, starch Not specified Cooked Dried 0.93

1 800 CHEERIOS Oat, groats/rolled oats Not specified Cooked Dried 29.94

1 800 CHEERIOS Potato, flour Not specified Cooked Dried 0.01

1 800 CHEERIOS Rice, flour Not specified Cooked Dried 0.01

1 800 CHEERIOS Sugarcane, sugar Not specified Refined Not Applicable 0.51

1 800 CHEERIOS Wheat, flour Not specified Cooked Dried 0.01

1 800 MILK, COW'S, FLUID, 1% FAT Milk, fat Not specified Uncooked Fresh or N/S 1.29

1 800 MILK, COW'S, FLUID, 1% FAT Milk, nonfat solids Not specified Uncooked Fresh or N/S 10.81

1 800 MILK, COW'S, FLUID, 1% FAT Milk, water Not specified Uncooked Fresh or N/S 109.90

1 930 CRACKERS, CHEESE Barley, flour Baked Cooked Fresh or N/S 0.45

1 930 CRACKERS, CHEESE Cottonseed, oil Not specified Refined Not Applicable 2.08

1 930 CRACKERS, CHEESE Milk, fat Baked Cooked Fresh or N/S 4.49

1 930 CRACKERS, CHEESE Milk, nonfat solids Baked Cooked Fresh or N/S 4.08

1 930 CRACKERS, CHEESE Milk, water Baked Cooked Fresh or N/S 1.15

1 930 CRACKERS, CHEESE Pepper, nonbell, dried Baked Cooked Fresh or N/S 0.27

1 930 CRACKERS, CHEESE Soybean, oil Not specified Refined Not Applicable 23.91

1 930 CRACKERS, CHEESE Wheat, flour Baked Cooked Fresh or N/S 92.66

1 1230

WHITE POTATO, CHIPS (INCL 

FLAVORED) Corn, field, oil Not specified Refined Not Applicable 1.30

1 1230

WHITE POTATO, CHIPS (INCL 

FLAVORED) Cottonseed, oil Not specified Refined Not Applicable 0.97

1 1230

WHITE POTATO, CHIPS (INCL 

FLAVORED) Potato, chips Fried Cooked Fresh or N/S 26.14

1 1230

WHITE POTATO, CHIPS (INCL 

FLAVORED) Rapeseed, oil Not specified Refined Not Applicable 0.54

1 1230

WHITE POTATO, CHIPS (INCL 

FLAVORED) Safflower, oil Not specified Refined Not Applicable 0.00

1 1230

WHITE POTATO, CHIPS (INCL 

FLAVORED) Soybean, oil Not specified Refined Not Applicable 11.02

1 1230

WHITE POTATO, CHIPS (INCL 

FLAVORED) Sunflower, oil Not specified Refined Not Applicable 0.04

1 1230 PEPPER, SWEET, RED, RAW Pepper, bell Not specified Uncooked Fresh or N/S 74.50

1 1230 PEAR, RAW Pear Not specified Uncooked Fresh or N/S 166.00

1 1230 APPLE, RAW Apple, fruit with peel Not specified Uncooked Fresh or N/S 69.00

1 1230 MILK, COW'S, FLUID, 1% FAT Milk, fat Not specified Uncooked Fresh or N/S 1.29

1 1230 MILK, COW'S, FLUID, 1% FAT Milk, nonfat solids Not specified Uncooked Fresh or N/S 10.81

1 1230 MILK, COW'S, FLUID, 1% FAT Milk, water Not specified Uncooked Fresh or N/S 109.90

1 1230 BREAD, POTATO Beet, sugar Not specified Refined Not Applicable 1.29

1 1230 BREAD, POTATO Cottonseed, oil Not specified Refined Not Applicable 0.13

1 1230 BREAD, POTATO Guar, seed Baked Cooked Fresh or N/S 0.06

1 1230 BREAD, POTATO Milk, fat Baked Cooked Fresh or N/S 0.49

1 1230 BREAD, POTATO Milk, nonfat solids Baked Cooked Fresh or N/S 2.27

1 1230 BREAD, POTATO Milk, water Baked Cooked Fresh or N/S 0.17

1 1230 BREAD, POTATO Potato, flour Baked Cooked Fresh or N/S 1.17

1 1230 BREAD, POTATO Soybean, flour Baked Cooked Fresh or N/S 0.29

1 1230 BREAD, POTATO Soybean, oil Not specified Refined Not Applicable 1.50

1 1230 BREAD, POTATO Sugarcane, sugar Not specified Refined Not Applicable 1.64

1 1230 BREAD, POTATO Wheat, flour Baked Cooked Fresh or N/S 33.30

1 1230 TUNA SALAD Beet, sugar Not specified Refined Not Applicable 0.02

1 1230 TUNA SALAD Celery Not specified Uncooked Fresh or N/S 1.90

1 1230 TUNA SALAD Coriander, leaves Not specified Cooked Canned 0.01

1 1230 TUNA SALAD Coriander, seed Not specified Cooked Canned 0.01

1 1230 TUNA SALAD Corn, field, syrup Not specified Cooked Canned 1.68

1 1230 TUNA SALAD Cucumber Not specified Cooked Canned 2.00

1 1230 TUNA SALAD Egg, whole Not specified Cooked Canned 0.16

1 1230 TUNA SALAD Egg, yolk Not specified Cooked Canned 0.11

1 1230 TUNA SALAD Fish-saltwater finfish, tuna Not specified Cooked Canned 14.08

1 1230 TUNA SALAD Ginger, dried Not specified Cooked Canned 0.01

1 1230 TUNA SALAD Herbs, other Not specified Cooked Canned 0.01

1 1230 TUNA SALAD Lemon, juice Not specified Cooked Canned 0.05

1 1230 TUNA SALAD Onion, dry bulb Not specified Uncooked Fresh or N/S 2.54

Example of food consumption data for HHID=11328 and SPNUM=2 for two days

(Female and 1 years old)
20 

Table 2-2 provides an example of real dietary consumption data for one person over two days. 

Table 2-3 shows an example recipe files for two food items consumed by the subjects (there are 

many other food items in the recipe files not shown). Through recipe files, consumption data in 

Table 2-2 can be converted into the format in Table 2-4, so that the exact amount of RAC 

consumed by the subject will be used to be assigned with residue concentrations by RAC.

2.3.3 Direct Water Consumption Data 

As discussed above, SHEDS-Dietary version 1 distributes total direct water consumption from 

the CSFII database in 6 equal amounts at 6 fixed times (6am, 9am, 12pm, 3pm, 6pm, 9pm). The 

more recent NHANES 2005-2006 did collect information on timing and amounts of direct water 

intake throughout the day, so that information can also be used directly in SHEDS-Dietary in 

future versions. 

Another option for drinking water consumption data in SHEDS-Dietary is available. For any 

modeled individual, a drinking water diary is randomly selected from the Bayer DWCS data 

based on similar socioeconomic characteristics (age, gender, season). Bayer CropScience 

sponsored a study on direct drinking water consumption entitled “Drinking Water Consumption 

Survey” (DWCS), to evaluate this issue (Barraj et.al. 2004). The objective of this study was to 

obtain a distribution of water intake for a 24-hour time period that was nationally representative 

sample of the US population. The DWCS was conducted in two waves, in August 2000 (wave 

1=- summer), and March 2001 (wave 2 = winter). The report provides the following description 

on the study design (Barraj et.al. 2004, pp.9-10): 

 “The National Product Database group (NPD) was chosen to conduct this survey 

because of its experience in tracking the consumption habits of the US population since 

1980 through its National Eating Trends (NET®

) service (NET®, 2004).” “Two 

nationally representative samples (one for each wave) were extracted from a core sample 

of 250,000 households from NPD’s Home Testing Institute (HTI) consumer panel. The 

sample for wave 1 included 3,000 households randomly selected from the core sample of 

250,000 households, while in an effort to increase the number of children in the survey, 

the sample for wave 2 included 650 households randomly selected from households with 

children less than 6 years of age in addition to 3,000 households randomly selected from 

the core sample.” “One thousand nine hundred ninety-two participants in 994 

households (33% response rate) completed the first wave of the survey, and 2,950 

participants in 1,320 households (36% response rate) completed the second wave of the 

survey.” 

Participants recorded their water consumption (time of day and amount consumed) over a oneweek (7 day) period. The following information was collected in the DWCS diaries: 

• Date and day of the week; 

• Age and gender of the household member; 

• Source of the home’s drinking water (municipal, well); 

• Time period of water consumption episode (18 hourly intervals starting at 6 am, 

and one 6 hr interval corresponding to the midnight-6 am period); 
21 

• Number of ounces of water consumed per time period (in 2-ounce bins); 

• Where the consumption episode occurred (home/work or school/other); 

• Whether the water was consumed with a meal; and 

• The type of water consumed (tap/bottled). 

A number of diaries were not used due to incomplete or missing information. The resulting 

database contained data from 4,198 individuals from 2,154 households, providing a total of 

27,282 person-day diaries (approximately 83% of the total of all participants returned diaries for 

all 7 days). 

Pesticide Use (% crop treated)

The pesticide use information, in particular the percent of crop treated (PCT) with a particular 

chemical, is used to determine how many samples were not treated and may be assumed to have 

no residues (true zero). This variable may come from either the USDA National Agricultural 

Statistics Service or proprietary data. 

2.3.4 Process Factors 

“Process factors” include concentration or dilution factors due to cooking or processing of food 

from RAC into food products. These data used in SHEDS-Dietary may come from registrant 

submission and the peer reviewed literature. 

2.3.5 Food Residue Data 

SHEDS-Dietary can use point estimates or (empirical) distributions from any source, modeled or 

measured (e.g., Field Trials, USDA/PDP, FDA/TDS; PRZM-EXAMS). Field Trial Studies are 

tests conducted by registrants to determine tolerance on Raw Agricultural Commodities. Field 

trial residues may exceed anticipated residues when the RAC: (i) includes inedible portions (e.g., 

banana and orange peel, watermelon rind, etc.), (ii) is generally cooked (e.g., pumpkin), and (iii) 

is established for feed purposes (e.g., field corn vs. cornmeal). 

The USDA Pesticide Data Program (PDP; http://www.ams.usda.gov/AMSv1.0/pdp) tests 

commodities in U.S. food supply for pesticide residues. It has tested over 85 different 

commodities: fresh/frozen/canned fruit and vegetables, fruit juices, dairy products, grains, corn 

syrup, nuts, peanut butter, honey, poultry, beef, pork, catfish. PDP has tested for more than 440 

different pesticides. Samples are collected by 12 participating States, representing about 50 

percent of the Nation's population and all regions. 

FDA’s Total Dietary Survey (TDS; FDA 1991-2004) (not used for pesticides) is a market basket 

study program that collects and analyzes ~280 foods for levels of pesticide residues, industrial 

chemicals, and toxic and nutrient elements. Foods in TDS are prepared as they would be 

consumed (table-ready) prior to analysis. 
22 

2.3.6 Drinking Water Concentration Data 

As with the food residue data, SHEDS-Dietary can use point estimates or (empirical) 

distributions of drinking water concentrations using any source, modeled or measured (Field 

Trials, PDP, FDA; PRZM-EXAMS, etc.). 

Environmental fate models that can be used to predict drinking water concentrations are PRZMEXAMS and SCIGROW (http://www.epa.gov/oppefed1/models/water/). The Agency generally 

uses environmental fate models (e.g., PRZM-EXAMS) to generate predicted drinking water 

concentrations for pesticides. 

2.4 Methods Issues 

2.4.1 Issues Regarding Selection of Consumption Database 

Since NHANES has not yet focused special attention on children, the CSFII survey continues to 

have many more food diaries for children. For example, CSFII has 2,972 infant diaries versus 

1,971 diaries in NHANES. For children aged 1-2 years old, a population of concern due to 

potentially high exposures, CSFII has 4,287 diaries vs. 2,460 diaries in NHANES. Another issue 

is whether or not to use all food diaries, or simply the two day diaries. In contrast to CSFII data, 

only one day of food intake was collected during the first four years (1999-2002) of the 

NHANES survey. Therefore, NHANES has a slightly larger total number of one day (only) 

diaries (N=22,035 subjects) as it does two day diaries (N=16627 subjects, or 33254 person-days).

Some alternative approaches for imputing values for missing data in CSFII have been explored. 

The two fields of interest are: (i) direct drinking water, and (ii) time of eating occasion. The 

modeled results appear to be relatively robust with respect to data imputations on these two 

variables. Approximately 738 diaries, or 1.8% of the total 41,214 CSFII food diaries did not 

report any information regarding direct drinking water consumption. SHEDS-Dietary (as well as 

Calendex-FCID and the other models) assume that these diaries did not consume any direct 

drinking water. DEEM-FCID uses only the 40,476 of the 41,214 CSFII food diaries that 

responded to this question (may have included people that did not consume any direct drinking 

water) when conducting drinking water risk assessments; but this subset generally has not 

affected any of the comparisons with SHEDS-Dietary (drinking water alone, or food+drinking 

water). For eating occasions, approximately 3,948 records, or 0.6% of the 598,829 food records 

in the CSFII database had missing values for the time of day question. SHEDS-Dietary replaced 

those missing values with 12:00 noon since it was the most reported frequency reported, and 

assumes zero consumption for non-reports in drinking water intake. 

2.4.2 Issues Regarding Sampling Food Residues 

SHEDS-Dietary allows Monte-Carlo simulations to be based on specific food items, as well as 

raw agricultural commodities (RAC-FF). Figures 2-1 and 2-2 illustrates this process. If residues 

are specified for particular food(s) (e.g., cheeseburger), then SHEDS-Dietary randomly draws a 

residue from that corresponding distribution and ignores any residue data assigned to the 
23 

ingredient RAC-FF (e.g., beef, tomatoes, wheat, etc.). If residues are not specified for any food 

item, then the model randomly draws residues for each of the RAC-FF ingredients. The Agency 

generally requires information at the commodity level, and so this option may be applied to a 

certain category of food items, such as milk (versus other dairy products), and meats (‘steak’). 

The current version of SHEDS-Dietary randomly draws 1 residue value for each commodity 

(RAC-FF) and applies that commodity residue to all foods, on all eating occasions. However, 

the Monte Carlo simulation can draw a new food residue for each eating occasion. The Agency 

asked the 1999 SAP, “Under what circumstances should the EPA consider using the (DEEM) 

Eating Occasion approach?” The Panel (1999) noted: 

“Dietary exposure analysis is an extremely complex process. It utilizes many pieces of 

data from different sources, each carrying its own limitations and deficiencies for the 

purpose. Therefore, a careful documentation of the database limitations and the 

uncertainties associated with the estimated exposure is essential for a proper 

interpretation of the exposure estimates.” 1 

The qualifying comments reflect a complexity in accounting for differences in eating habits 

across the population. To illustrate this point, the food consumption diary presented above 

(Table 2-1) indicates that the 1 yr old consumed the same food (‘home fries’) on two different 

eating occasions. It is likely that the child had ‘leftovers’ in the evening meal. If that is the case 

(or more home fries were prepared from the same bag of potatoes), then it would be appropriate 

to assume that the same composite residue was present on both eating occasions. On the other 

hand, if the child consumed two servings of ‘home fries’ from different fast food restaurants on 

two different eating occasions, then it may be more appropriate to randomly draw separate 

residues for each eating occasion. Such conditional modeling decisions can better made after a 

closer inspection of the food consumption data. A decision rule based on a few more variables 

(e.g., food item and primary source of food) may be helpful to determine if different residues 

should be drawn for subsequent eating occasions, and/or the same residue can be applied to all 

eating occasions. We have found that this modeling assumption often does not have a significant 

effect on the 99.9th for food-only exposure assessments – since most people consume various 

foods on only a single eating occasion as noted in the NMC CRA (USEPA FIFRA SAP 2005, 

2007); (Nako, et. al, 2007 ISEA cited earlier). 

One of ORD’s/OPP’s planned activities is to conduct a more thorough systematic review of the 

food consumption diaries. We anticipate that such analyses may help towards developing 

decision rules for selecting or not selecting a new residue. For example, if the commodity comes 

from the same food items, then the model uses the same residue. If not, then the model compares 

the foods’ sources, time of eating occasions, and if the foods were eaten at home. If those factors 

differ, then the model selects different residues, else retains the same residue. The potential for 

different decision rules also suggests the development of some type of uncertainty analyses. 

 

1 FIFRA SAP (2000) Report No. 2000-01B, May 25, 2000, Pages 33-35. 
24 

2.4.3 Modeling Longitudinal Food Consumption 

The approach for constructing longitudinal consumption profiles is currently being reevaluated, 

since the National Health and Nutrition Examination Survey (NHANES) food consumption data 

does not provide data on calendar dates (season), nor locations (region). Available food 

consumption data are cross-sectional (2days for an individual). When randomly drawing multiple 

one-day diaries from multiple individuals that are intended to represent a single individual’s 

behavior over time, the modeler faces a dilemma with optimizing inter- and intra-person 

variability (see Figure 2-3). If a small number of diaries are drawn for each individual to cover a 

long simulation period, then each diary must be re-used many times; that is, each diary must be 

used on many different dates in the simulation to represent the individual’s behavior (for dietary 

exposure, the key variable is total caloric consumption). While this creates repetitive or habitual 

behavior patterns, it also narrows the behavioral space and lessens the within-person 

consumptionvariability. Using many different one-day diaries would address these last two 

concerns by broadening the simulated individual’s behavioral space and increasing the withinperson variability. However, this approach would exacerbate other problems. In particular, any 

two persons belonging to the same cohort will draw their diaries from the same diary pools, and 

the samples will tend to converge to the same overall average behavior of the cohort. 

Figure 2 Figure 2-3. Different methods for . Different methods for . Different methods for assembling longitudinal dia assembling longitudinal diaries from cross assembling longitudinal diaries from cross- ries from crosssectional data sectional data 

1

Longitudinal Diary Assembly – Overview

0

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1019283 7465564738 29 1

1 0010 91181271 361 4515 4

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(before autocorrelation 

between days)

Option 2 method 

(diaries reordered to target 

autocorrelation)

Option 1 method

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25 

Most of the existing random-draw methods of diary selection assume that all diaries that are 

suitable (meaning they are from the correct age-gender cohort and match the chosen daytype) are 

equally likely to be chosen, and that any subsequent draws are independent of prior draws. As 

detailed in the SHEDS-Multimedia 2007 SAP documents

(http://www.epa.gov/scipoly/sap/meetings/2007/081407_mtg.htm, Glen et.al. (2007) proposed a 

new method for developing longitudinal activity profiles. This “D & A” (Diversity & 

Autocorrelation) method presented above drops both these assumptions by assigning each 

simulated person a “target behavior,” and then preferentially sampling diaries to produce the 

target behavior. The method assigns target behaviors and executes the preferential sampling 

based on the value of D (diversity) specified by the modeler. If not executed carefully, 

preferential sampling can result in behavioral biases, where some diaries are consistently drawn 

more often than others. The method contains internal rules for this sampling that ensure that over 

a large number of simulated persons, all available diaries in each diary pool will be sampled 

nearly uniformly. 

In this method, a new random draw is made for every day in the simulation. Thus, a one-year 

longitudinal diary would be comprised of potentially 365 different diaries. The D statistic affects 

the width of the diary selection probability peak around the target behavior, with a low D giving 

a broad peak and a high D giving a sharp, narrow peak. Depending on the width of this peak and 

the number of diaries in the pool, some diaries may be selected multiple times, but others may be 

selected just once or not at all. 

The longitudinal data from Lu et al., 2006a,b (Alex Lu, personal communication) were used to 

develop the D and A statistics needed to apply the Glen et al., 2007 approach for SHEDS-Dietary 

longitudinal diary construction. We need to model longitudinal food consumption in order to 

account for chemical half-lives and seasonal patterns in exposures across three primary sources: 

food, drinking water, and non-dietary exposures from residential uses. The longitudinal 

dimension does not appear to be critical for obtaining estimates of a single total daily exposure at 

per capita upper percentiles (see US EPA (2004) for some comparisons). A focus on longitudinal 

exposures may expand as the Agency continues to develop physiologically-based 

pharmacokinetic models (PBPK) for pyrethroids and other pesticides. The 2005 SAP noted that 

one-day simulation models may underestimate risks if carry-over effects from consecutive days 

of exposures are of concern.2 All three sources (food, drinking water and residential) have a 

potential for seasonal exposure patterns (positive autocorrelation). We can anticipate strong 

patterns in drinking water exposures since most people consume water daily, and both the surface 

water and ground water models generally produce drinking water concentrations that exhibit 

positive autocorrelation. Similarly, non-dietary exposures from residential uses will reflect 

seasonal patterns in product usage, as well as correlations in daily activities for a particular 

 

2 FIFRA SAP (2005), Minutes, p.10, “In particular, if one applies a 4.1-fold inter-species scaling factor to the 5.4 hr 

half-time for reversal of brain AChE inhibition in rats, one obtains a predicted half-time of 22 hr in the 70 kg human 

adult. Such a long half-time would force the risk assessment model to address carryover of inhibition from one day 

to the next. In considering this issue, the Agency should take into account cases where there is a dose dependency 

for inhibition reversal half-lives.” p.56. 
26 

person. For food, one can conceive an individual purchasing a bag of treated apples, and 

consuming one or a few apples from that bag over consecutive days. 

The relative importance of these three sources (food, drinking water, and residential) vary by 

chemical, as well as across individuals within a subpopulation. In two companion papers, Lu et. 

al. (2006a, 2006b) reported that residential uses appear to be more important for exposures to 

some synthetic pyrethroids, while dietary exposures appear to be relatively more important for 

some organophosphate pesticides. Their assessment is based on a longitudinal study of 23 

elementary school-age children, using urinary metabolites as exposure biomarkers. The 

researchers collected two spot daily urine samples, first-morning and before-bedtime voids, 

throughout a consecutive 15-day study period, which consisted of three phases. Children 

consumed their conventional diets during phase 1 (days 1-3) and phase 3 (days 9-15). During 

phase 2 (days 4-8), organic food items were substituted for most of children’s conventional diet, 

including fresh fruits and vegetables, juices, processed fruit or vegetables (e.g., salsa), and wheator corn-based items (e.g., pasta, cereal, popcorn, or chips) for 5 days. Meats and dairy products 

were not substituted. A description is provided in the paper: “Parents were asked to request 

organic foods for their children in phase 2 with the goal of exactly replacing the items the 

children would have normally eaten as part of their conventional diet. This method ensured that 

any detectable change in dietary pesticide exposure would be attributable to the organic food 

rather than a change in the diet.”3 The researchers found lower levels of two organophosphate 

pesticides during phase 2 when organic foods were provided, but no observable change in levels 

of pyrethroid insecticides. However, they did find a significant correlation between the 

homeowners self-reported use of pyrethroid products (N=7 household users) and concentration 

levels of two pyrethroid metabolites (Lu, C. et.al., 2006b). 

The literature also contains alternative methods for developing longitudinal consumption 

profiles; we will consider these in future versions of SHEDS-Dietary. For example, promising 

effort is described in three papers, authored by a team of researchers from government (NCI), 

academic and other private institutions. Dodd et.al. (2006) provide a comprehensive review of 

existing methods used to estimate long-term dietary intake using cross-sectional data. Tooze 

et.al. (2006) present a new method for estimating long-term intake of episodically consumed 

foods using food frequency questions (FFQ). A food frequency question (FFQ) is: ‘How often 

have you (respondent) consumed fish during the past 30 days?’ Tooze et.al. (2006) present a two 

stage model, with the first part (logistic regression) predicting the probability of consuming a 

particular food, and the second part (regression on log transformed consumption amount) 

predicting the amount of food consumed (>0). In the third paper, Subar et.al. (2006) apply this 

method to the Eating at America’s Table Study (EATS) data. The researchers noted that people 

that consume foods more frequently (FFQ) also tend to consume greater amounts of that food per 

occasion. Subar et.al. (2006) also provide a brief review of the development of the Food 

Propensity Questionnaire, a set of FFQ that was introduced in the 2003-2006 NHANES. 

Findings in Givens et al., 2007 suggest a longitudinal dietary survey with minimum 6 

consecutive days’ dietary consumption in each of 4 seasons would be adequate to represent an 

 

3 Lu et.al., 2006a, p.260. 
27 

individual 1 year dietary consumption pattern, and improve cross-sectional approach. “The 

majority of the Panel is convinced that given the data and analysis presented by the Agency, it is 

not sufficient to construct the longitudinal dietary consumption pattern based on the 8-day eating 

occasions.” (p. 33 of 2007 SAP). Based on comments by the 8/07 SAP, we prefer using the 

D&A methodology rather than the 8-diary approach used in the SHEDS-Multimedia residential 

module. 

We will use real data as available to evaluate these different approaches and provide the basis to 

decide which to use in future versions of SHEDS-Dietary. We anticipate that these and other 

research activities may help us to improve the current approach for modeling longitudinal 

consumption, and to develop appropriate uncertainty analyses to characterize the pesticide dietary 

exposure assessments. In the meantime, SHEDS-Dietary uses the D&A approach for modeling 

longitudinal food consumption. There are many potential covariates and measures of diversity 

across many subpopulations. The diet, health and nutrition literature contains a rich volume of 

research, indicating that food consumption patterns may vary by race, ethnicity, lifestyle 

(activities and energy requirements) and socio-economic factors. 

2.4.4 Considering Persisting Effects 

The SHEDS-Dietary Eating Occasion analysis uses the Maximum Persisting Dose (PD) in 

addition to Total Daily Exposure (for each person-day) to calculate exposure per capita various 

percentiles e.g. 99.9th. For the hypothetical case below, the Total Daily Exposure is 2 ug/kg 

(=sum of 2 exposure events), the Max PD is 1.25 μg/kg (Max point on green line) based on a 2.5 

hr half-life. The max PD reflects an approximate single bolus dose that produce same level of 

peak inhibition as the two exposures. 

Figure 2-4 depicts a hypothetical scenario in which a person obtains dietary exposures on two 

eating occasions (e.g., ate 1 slice of watermelon at noon and another at 5 pm). The red triangles 

depict the amount of exposure (1 μg ai/kg bw) obtained on each eating occasion, while the green 

line following the first exposure event depicts the Persisting Effect on cholinesterase inhibition. 

For this example, the recovery half-life is assumed to be 2.5 hours (150 minutes). Therefore, the 

persisting effect from the first exposure event (1 μg ai/kg bw) is approximately 25% of the peak 

effect (0.25=(1/2)^(300/150)) when the second exposure event occurs 5 hours (300 minutes) 

later. 

The Persisting Dose reflects the combined effect from the current exposure and the persisting 

effects from recent exposures. The persisting dose at the second eating occasion amounts to 1.25 

μg ai/kg bw (1.25=1+0.25), which is also the Maximum Persisting Dose over this person-day. 

The Maximum Persisting Dose is interpreted as an equivalent (single) bolus dose that produces 

the same peak level of inhibition as the exposure patterns from the simulated person-day. This 

refinement has different effects for different exposure profiles. For the hypothetical scenario 

above, it reduces TDE from 2 μg /kg bw to 1.25 μg /kg bw. If those exposures occurred on a 

single eating occasion (e.g., 2 slices at either noon or 5 pm), then this refinement would not affect 

that particular outcome (Max Persisting Dose=2 μg /kg bw=TDE). This analysis is based on 

several important assumptions: (i) the time to peak effect is instantaneous (for convenience), (ii) 
28 

direct drinking water consumption is allocated over 6 fixed events at fixed times, and (iii) the 

subject (person) is healthy (no carry over effects). 

 

 Figure 2 Figure 2-4. Hypothetical Figure . Hypothetical Figure . Hypothetical Figure to Illustrate Persisting Effe to Illustrate Persisting Effect for an Individual to Illustrate Persisting Effect for an Individual ct for an Individual 
29 

 

Figure 2 Figure 2-5. Example SHEDS . Example SHEDS . Example SHEDS-Dietary modeled longitudinal dietary exposure prof Dietary modeled longitudinal dietary exposure profile for 

1 individual 1 individual 

Figure 2-5 shows an example longitudinal dietary exposure profile for one individual using 

SHEDS-Dietary. The persisting dose (green line) was modeled from a cumulative data analysis 

using the chemical half lives. 

2.4.5 Algorithm for Matching (Behavioral) Diaries: Food Consumption and Activity 

Patterns 

It is a challenge to merge dietary and residential exposures because the behavioral data are from 

different sources. It is more complicated for the longitudinal data. Bins by important variables are 

used to merge the data; a balance between number of key variables and randomization has to be 

controlled. Too many variables used to form bins will reduce the randomization and too few 

variables will increase randomization but increase misclassification between the dietary and 

residential exposures. An average of 50 to 100 data points in each bin is used as a criterion to 

select the key variables to make sure that we have enough sample size in each bin for the 

randomization. Key variables are age, gender, body weight, total caloric intake/METS, race, 

season, weekday and region. The D & A method described above (see bottom of figure 2-3) uses 

total calories to turn the cross-sectional dietary exposure into longitudinal food consumption 

patterns, and uses waking time at home to turn the residential cross-sectional activity patterns 
30 

into longitudinal patterns. Then age, gender, body weight, total caloric intake/METS, race, 

season, weekday and region can be used to form the bin to match dietary and residential 

exposures. A proposed methodology for matching food consumption and activity diaries, to 

merge SHEDS-Multimedia dietary and residential modules, will be presented to the EPA FIFRA 

SAP in July, 2010. This methodology, described later in this manual in the section entitled, 

“Algorithm for Matching (Behavioral) Diaries: Food Consumption and Activity Patterns.” has 

been tested through “soft linking” the two modules with a permethrin pesticide case study. 

2.4.6 Considering Timing and Amounts of Drinking Water Consumption 

SHEDS-Dietary utilizes the CSFII or NHANES data to assess the timing and amounts of 

indirect drinking water intake (i.e., through foods, infant formula, ‘kool aid’, coffee, tea, etc.) 

within a simulated person-day. The model contains two options for allocating direct drinking 

water consumption (i.e., through tap or bottled water) throughout the day: (1) fixed approach, and 

(2) empirical using the recent NHANES data or the Bayer DWCS data described above. In the 

fixed approach, SHEDS allocates the CSFII respondents’ total direct drinking water consumption 

(mL/day) over 6 fixed occasions (6:00 am, 9:00 am, 12:00 noon, 3:00 pm, 6:00 pm, and 9:00 pm. 

Preliminary analyses revealed that there is no significant difference between these two 

approaches. 

The second option uses the Bayer DWCS data to allocate the total amount of direct drinking 

water consumed throughout the simulated person-day.4 This procedure involves the following 

steps:5 

1. Generate cohort (‘bins’) by gender, age, season 

2. For each DWCS diary, calculate the percent of Total Direct DW, by Occasion 

3. For each CSFII or NHANES diary, randomly select a Bayer DW diary from appropriate 

‘bin’ 

4. Use Total Direct DW from CSFII or NHANES and percentage of DW from DWCS data 

to calculate direct DW amount for each Eating Occasion (time of occasions also from 

DWCS) 

This second option cannot be applied for the infant subpopulation since the DWCS data did not 

include infants. Although the DWCS study did not appear to have the same level of 

sophistication as the CSFII in its sampling design, our expert view is that these data are useful to 

model the timing of direct drinking water intake for several reasons, including: (1) the marketing 

firm, the NPD group, has extensive experience at monitoring eating and drinking trends in the 

US and Canada, (2) the design of the data collection instrument, i.e. recording consumed amount 

of water for each drinking event, led to better 24 hour recall, (3) reasonable response rates 

 

4 The Panel noted: “In further development of this approach, EPA should make use of any reliable source of 

relevant empirical data on daily patterns of drinking water consumption; ideally adapted to the likely consumption 

behavior in specific regions or smaller areas of the country.” P.59 of 63, SAP 2005b. 

5 This algorithm can be modified for longitudinal models, ‘binning’ respondents (persons), rather than diaries 

(person-days) to retain the intrapersonal information contained in these 7-day drinking water diaries. 
31 

(>30%), and the relatively high percent of respondents that completed 7 day diaries (82%), and 

(4) the 7 day study period reduces the need to model intrapersonal variability over this duration. 

An alternative method being considered for using the drinking water data is a two step approach 

for each simulated person: (1) randomly draw one of the 30 years, and (2) apply the predicted 

concentration for January 1st of that selected year to calculate exposure for Day 1, and so on, with 

predicted concentration on December 31st being applied to calculate exposure for Day 365. This 

general approach, available in other aggregate models, has the advantage of retaining 

autocorrelation present in the predicted drinking water concentration data. 

Figures 2-6 and 2-7, taken from DWCS report (Figures 5 and 6, respectively), indicate that many 

respondents consume direct drinking water on multiple occasions, and at all times throughout the 

day. This provides some support for using a simple modeling assumption (e.g., equal amounts 

allocated across 5 or 6 occasions). Those distributions do not reflect variations in drinking water 

intake across individuals. The report suggests that these data may be used to model drinking 

water exposures, by eating occasion: 

“It may be possible, using the information collected by the DWCS to “allocate” the total 

daily water consumption amount reported in the CSFII into various drinking occasions. 

Specifically, if each subject in the CSFII survey was randomly matched to subjects in the 

DWCS, based on survey season, region, age, gender, and total amount of drinking water 

consumed per day, then the total amount reported by that CSFII participant can be allocated 

to the same number of drinking occasions as those reported by the matching DWCS 

participant. Similarly, the proportion of the total daily water consumption allocated to each 

of these drinking occasions can be assumed to be similar to that reported by the matching 

DWCS participant. This approach would then allow a less than 24-hour assessment of both 

food and drinking water (aggregate assessment) for a pesticide.” 6

 

 

6

 Barraj, L.M. et.al. (2004), Exponent®, Inc.; National Product Database (NPD) Group., p.17. 
32 

Figure 2 Figure 2-6. Total Number of Occasions of Direct Drinking Wat . Total Number of Occasions of Direct Drinking Water Consumption r Consumption r Consumption 

Figure 2 Figure 2-7. Distribution of Direct Drinking Water Consumptio . Distribution of Direct Drinking Water Consumption, By Time of Day , By Time of Day , By Time of Day 

Table 2-5 provides the total number of drinking water diaries in the DWCS by gender, age and 

season. Infants less than one year old were not included in this survey. The two adult bins (20-

49 yrs, 50+ yrs) contain a large number of diaries since they encompass a greater range of years. 

The DWCS contains a relatively large number of drinking water diaries for most of the children’s 

‘bins’; the one-year old female, summer bin has the fewest number of diaries (N=29). 
33 

Table 2 Table 2-5. Total Number of DWCS Diaries, By Age Group, Gende Total Number of DWCS Diaries, By Age Group, Gender and Season and Season and Season 

Season Subtotal Subtotal Age Group Gender Winter Summer Age-Season Age Group 

M 98 128 226 1 yr 

F 136 29 165 

391 

M 167 97 264 2 yrs 

F 125 73 198 

462 

M 132 81 213 3 yrs 

F 151 89 240 

453 

M 128 63 191 4 yrs 

F 149 98 247 

438 

M 141 109 250 5 yrs 

F 67 63 130 

380 

M 663 404 1,067 6-12 yrs 

F 624 457 1,081 

2,148 

M 491 322 813 13-19 yrs 

F 577 368 945 

1,758 

M 2,871 1,999 4,870 20-49 yrs 

F 4,036 2,544 6,580 

11,450 

M 1,975 1,688 3,663 50+ yrs 

F 3,332 2,717 6,049 

9,712 

M 6,666 4,891 11,557 Total 

F 9,197 6,438 15,635 

27,192 

The DWCS raw data files did not include sampling weights to make projections at the per capita 

level. The report noted that the estimated direct drinking water intakes reported by the DWCS 

respondents were slightly higher than the 1994-1998 CSFII respondents. For example, the 

overall mean intake of DWCS respondents was 37.8 oz/day (40.6 oz/day =summer, 35.7 oz/day 

=winter)7

, while the CSFII respondents reported 29.6 oz/day (32.4 oz/day =summer, 27.8 oz/day 

=winter).8,9 While no formal statistical tests were presented, the report noted that this difference 

may be due to the fact that “the DWCS provided participants with a time grid to report their 

water consumption, thus potentially helping them remember all their water consumption 

occasions, in contrast to the CSFII general 24 hour total consumption recall question.”10 

 

In future versions, we plan to utilize the reported time of water consumption data from recent 

NHANES. 

 

7 1117.9 ml/day, 1200.7 ml/day, 1055.8 ml/day respectively 

8

 785.4 ml/day, 958.2 ml/day, 822.1 ml/day respectively 

9

 Barraj, L.M. et.al. (2004), Table 7, p. 26. Figure 5 provides some estimates, by age groups. 

(p.31) 

10 Barraj, L.M. et.al. (2004), p.16. 
34 

2.4.7 Considering Number of Person-Days (or Person-Years) to Simulate 

Agency risk assessors typically specify 1,000 iterations per diary during a DEEM-FCID 

simulation, providing for about 41 million person-day simulations (=41,214 person-day diaries x 

1000 iterations/diary). Except for extremely unusual circumstances, this number of iterations has 

provided very stable results at the per capita 99.9th percentile for all subpopulations (i.e., not 

much ‘simulation’ or ‘random seed’ uncertainty). Similarly, users can specify any number of 

iterations per diary using SHEDS-Dietary (cross-sectional). The sensitivity analyses presented in 

this section were based on only 150 iterations which appeared to be sufficient to verify results 

with DEEM-FCID. We specified fewer iterations since SHEDS-Dietary (for cross-sectional 

analyses) retains all of the output from each simulated person-day (creating 4 GB in output with 

150 iterations), allowing sensitivity analyses to be conducted much more efficiently. 

ORD/NERL has not developed recommended number of person-years for SHEDS-Dietary. 

2.4.8 Sensitivity Analysis Issues 

The most difficult part of conducting sensitivity analyses is in the problem formulation: defining 

a particular issue of concern, evaluating the available data inputs, developing method(s) to assess 

how sensitive the results are to that concern, and characterizing the degree to which that analysis 

addresses that concern. More details are provided below in the section, “Sensitivity Analyses 

Methods.” 

2.4.9 Uncertainty Analysis Issues 

Uncertainty analyses may help ascertain the relative importance of the data inputs. There is 

uncertainty in estimates of a total single-day exposure from various factors, including: limited 

food consumption data (CSFII), food recipes (FCID), available residue data (e.g., PDP 

monitoring, crop translations), and processing factors. This preliminary list of factors expands 

with longitudinal measures and the use of PBPK models. To date, OPP has not utilized formal 

uncertainty analyses in its pesticide dietary exposure assessments. 

2.5 Output Capabilities 

SHEDS-Dietary can be used to estimate population distributions (and select percentiles of 

interest such as 95th, 99th, 99.9th) of aggregate or cumulative dietary exposures, and generate the 

following output results: 

 CDFs of dietary exposures for populations of interests, including food and water 

separate or combined, as well as by eating occasion; 

 Bar charts, pie charts, and summary tables showing contribution to total exposure 

(e.g., 99.9-100th), by food, commodity, or commodity-chemical (for multichemicals); 

 Summary statistics of dietary (food, drinking water, or sum) exposure by age 

group and/or gender (including females 13-49 yrs old); and 

 Eating occasion, sensitivity, and uncertainty analyses to identify key factors. 
35 

A summary of various SHEDS-Dietary outputs and analyses is given in Table 2-6. 

Table 2 Table 2-6. Potential Applications of the SHEDS- Potential Applications of the SHEDS-Dietary model Dietary model Dietary model 

Variable/Modeling Description 

User-only Analysis 

Eaters-only Report Deterministic calculation of exposures among people that consume a 

treated commodity or food 

Contribution Analysis 

Shares of Total Exposure, by 

Commodity, Food or Diaries 

Current reports provide shares of total exposures (99.9th – 100th

percentiles), by commodity or by food 

Shares of Total Exposure, by 

Chemical – Commodity, Food or 

Diaries 

For cumulative exposure assessments, SHEDS keeps track of 

residues, by chemical (i.e., not used RPF combined residue) 

Shares of Total Person-days, by 

Commodity, Food or Diaries 

(i) ‘Exceeders’ or shares of total person-days (99.9

th – 100th

percentiles), by commodity. (ii) focus on diaries: percent of 

simulations exceeding target 

Sensitivity Analysis 

Consumption ‘Outliers’ Effect of Diaries with Reported High Amounts Consumed 

Percent Samples Treated Effect of the Estimated Percent of Samples Treated (Half Level Of 

Detection (Half-LOD) used for monitoring data) 

Percent Crop Treated Effect of Annual Fluctuations in Percent Crop Treated (assuming all 

other factors constant) 

Processing Factors Effect of Estimated Processing Factors 

Uncertainty Analysis 

Uncertainty - Cohorts Effect of Different Factors for Developing ‘Cohorts’ or ‘Bins’ for 

Food Diaries 

Uncertainty – Subsamples Effect of using a Subsample of the Food diaries and Residue data on 

per capita estimates (200 person-years) 

Uncertainty – Subsamples of 

residues, by commodity Residue by commodity 

Uncertainty – Models Comparing Results Across All Models 

 

The SHEDS-Dietary module retains detailed person-level outputs for each dietary exposure 

assessment. Presently, the SHEDS-Dietary Cross-Sectional simulations retain more detailed 

information than the SHEDS-Dietary Longitudinal simulations. For the Cross-Sectional 

simulation, the SHEDS-Dietary module retains detailed information for each exposure event, 

including food diary ID, time of eating occasion, food item, FCID commodity (RAC-FF), amount 

consumed (g), residue (ppm), and exposure (ug ai). Since the CSFII and NHANES\WWEIA 

surveys contain numerous food diaries, a Cross-Sectional simulation specifying 200 iterations for 

each of the 40,214 CSFII diaries will generate 8,042,800 person-day outcomes (=40214x200), 

which may take up to 10 GB of hard disk space. To facilitate data processing, SHEDS-Dietary 

splits the outputs into multiple data tables. In the example above, the outputs from a SHEDSDietary cross-sectional simulation would be stored 200 separate tables in the SAS library 

‘Output’, and the filenames for each table will start with the prefix as provided by the User. 

The outputs from a SHEDS-Dietary longitudinal simulation are currently stored in a single table 

in the SAS library ‘OLONG’, with the filename starting with the prefix as provided by the User 
36 

followed by the suffix (long) (e.g., SAS data table for case Study #3: Olong.Cperm_long). The 

results retained in the longitudinal simulation include: person ID, date, food diary ID, total 

calorie, time of eating occasion and exposure (ug ai). The data fields and formats for the crosssectional and longitudinal simulations are provided in the SHEDS-Dietary User Guide, Appendix 

3.1 (cross-sectional simulations), and Appendix 3.2 (longitudinal simulations). Since commodity 

level details are not retained in the longitudinal simulation, the contribution analyses is limited 

compared to ‘cross-sectional’ simulations. {We will consider outputting detailed results in future 

versions; perhaps saving the results for each modeled person in a separate table (file) to ensure 

that the data tables are not too large for data processing.} 

There are several advantages and disadvantages of retaining all of the detailed data. The primary 

disadvantage is that a single simulation may take up a considerable amount of hard disk space 

(8+ GB); if users need to perform simulations for multiple chemicals, then this may pose a 

significant issue (e.g., the hard disk on the Agency’s laptops have 80 GB capacity). Another 

disadvantage is that appending data to the output tables may increase the processing time 

considerably as those tables become increasingly larger. If the user is only interested in knowing 

aggregate daily exposure at some per capita percentile (e.g., 99.9th), then processing time can be 

shortened since the model only needs to retain total daily exposure for each simulated day. 

The advantages for retaining the detailed outputs for each simulated person-day, include: (i) 

querying/viewing detailed outputs for select diaries and/or persons, (ii) perform alternative 

methods for assessing contributions (exceeders), (iii) facilitate sensitivity analyses (‘what-if’ 

scenarios), (iv) conduct sensitivity analyses (outliers), (v) perform ‘eating occasions’ analyses for 

multi-chemical assessments, (vi) pass on dietary exposures by time of day to PBPK model. 

We describe two of these advantages below. First, it is important to recognize that the user can 

query these output tables to calculate aggregate dietary exposures at various per capita 

percentiles, as provided in the “Exposure and %APAD: Summary Table”. Suppose the user 

wants to know how aggregate exposure at the per capita 99.9th percentile changes if we removed 

that pesticide’s use on lettuce. Then, the user could either zero out residues for lettuce and rerun 

the simulation, or zero out the exposures from the simulation and recalculate the 99.9th

percentile. The first option will take considerable processing time (especially in a cumulative 

setting) and add simulation uncertainty since a new set of residues is randomly selected for each 

person-day-food. The latter option is more efficient since the model simply recalculates the 

99.9th percentile with the existing results - zeroing out exposures from that particular commodity. 

 The user can submit a batch job to perform such ‘what-if’ scenarios for various combinations of 

foods (e.g., removing only lettuce, or removing only apples, or removing apples+lettuce, etc.). 

The second example, described in further below, is performing sensitivity analyses to assess 

potential uncertainties (measurement error) regarding reported food consumption. Nako and Xue 

(2006) identified several food consumption diaries that reported high drinking water intake, and 

wanted to assess the effects of those diaries upon the per capita 99.9th percentile. The user needs 

to first identify which food consumption diaries are of concern and what adjustments are to be 

made (e.g., drop diary altogether or adjust the reported consumption). Once that determination is 

made, the simulated exposures can be adjusted accordingly, and the per capita 99.9th percentile 
37 

recalculated from the adjusted outcomes. Since SHEDS-Dietary retains the outcomes, the user 

can perform such sensitivity analyses without ‘changing’ the underlying food consumption data 

base. The user needs to use supplemental SAS code (Macros) to conduct such analyses since the 

Agency had difficulty incorporating these options into the SHEDS-Dietary version 1.0 GUI. 

2.6 Sensitivity Analysis Methods and Results 

2.6.1 Sensitivity Analyses Methods 

The following sensitivity analyses have been run using SHEDS-Dietary v1: 

 key data to determine their impact, such as using CSFII vs. NHANES/WWEIA 

consumption data or PDP vs. TDS residue data; 

 different algorithms to assess the impact such as allocating drinking water consumption 

equally over 6 fixed eating occasions vs. using information from the Bayer Drinking 

Water Study (discussed below); 

 different residue sampling algorithms such as filling in non-detects with zero, half 

detection limit, detection limit); 

 outliers – impact of keeping or removing “outliers” on the key exposure output 

parameters (e.g., exposure); and 

 mitigation -- assessing impact of removing one or a group of RACs (e.g. delete one 

commodity such as a particular fruit to see the impact on the average and high exposure 

percentile). 

The sections below show results of sensitivity analyses on food and drinking water consumption 

outliers. Other analyses are illustrated in the case studies of Chapter 3. 

2.6.2 Sensitivity Analyses on Food Consumption ‘Outliers’

A component of the Agency’s risk characterization is to “Evaluate the tails of the food exposure 

distribution to verify that unusual consumption patterns are not inappropriately impacting on the 

results of the assessment.”11 Identifying ‘unusual’ consumption patterns requires inspection of 

the food diaries. If the amounts consumed are not unreasonably high, then no further analyses is 

required. As the panel noted, 

“The CSFII is designed to be representative of the population as a whole. Hence the 

“tails” of the distribution are still part of the distribution and, therefore, cannot be said 

to impact the results of the assessment inappropriately.”

12 

 

If consumption values are so unusual so as to bring into question the accuracy of the data (e.g., 

measurement or data entry error), then quantitative ‘what-if’ analyses may be appropriate. A 

question for the exposure modeler is how sensitive are exposures at the upper per capita 

 

11 EPA SAP, 2005, p.187. 

12 SAP minutes, 2005, p.36. 
38 

percentiles to one or a few such data records? The open source coding of SHEDS-Dietary 

enables the user to perform such analysis in a quick and cost-effective manner. 

Figure 2-8 presents a Box and Whisker plot of potato consumption among children ages 1 and 2 

years old. The amount of potatoes consumed by the CSFII diary highlighted earlier (Table 2-1; 

ID=28517-2-2) is about twice as high as the second highest eater in this age group. This amount 

appears to be an outlier when focusing on only ‘fried’ potato consumption, but not so much the 

case when considering potato consumption in other food forms (e.g., boiled). As absolute 

amount consumed, this amount does not appear to be implausible: a 1 yr old, 13 kg boy eating 

300 grams of home fries on two occasions. But a considerable amount of resources may be 

expended to defend that assessment, and using the SHEDS-Dietary model, we can determine that 

the per capita estimates are fairly robust to this one diary. In particular, if we either (i) removed

this ‘outlier’ from the Monte-Carlo simulations, or (ii) adjusted the amount consumed to lower 

level (e.g., second highest amount), the per capita estimates at the 99.9th percentile will not 

change considerably. 

Figure 2 Figure 2-8. Box-and-Whisker Plo Whisker Plo Whisker Plot of Potato Consumption by Day for Child t of Potato Consumption by Day for Children 1 t of Potato Consumption by Day for Children 1-2 

Yrs. Old Yrs. Old 

2.6.3 Drinking Water Consumption Outliers 

As in the case of various food items, there are some high reported drinking water consumption 

amounts in the CSFII. Figure 2-9 presents a Box-Cox transformation of drinking water 

consumption (ml/kg bw/day) for all infants in the CSFII data base. The two highest amounts are 

located in the upper right hand corner – deviating above the otherwise linear pattern established 

by the majority of the remaining reported consumptions. These two values are, respectively, 

52% and 41% higher on a ml/kg bw basis than the next (third) highest reported consumption 
39 

value. An inspection of the food diaries indicate that a set amount of formula was reportedly 

prepared and consumed by these two infants on multiple occasions throughout the day. The first 

infant diary (28892-2-1) was for a newborn (0 month old) weighing 3.2 kg, that reportedly 

consumed a total of 1,997 ml that day (1819 mL indirect, 117 direct), or about 624 ml/kg bw/day. 

 

An inspection of the CSFII diary indicated that this infant consumed a total of 8 oz of formula (6 

ounces consumed directly + 2 oz used to prepare 0.25 cup of dry rice cereal) at 8:00 am, 9:30, 11, 

1:30, 4:30, 6:00, 10 and 11:30 pm; an additional 4 oz of formula alone was prepared/consumed at 

1:00 am. The second infant-dairy (26837-3-2) was a one month old that weighed 3.6 kg, and 

consuming a total of 2,044 ml that day (1,926 ml indirect, 118 direct), or about 568 ml/kg 

bw/day. An inspection of this second diary indicate that that infant consumed 8 oz of formula on 

nine different occasions throughout the day, at 4:00 am, 6:00 am, 8:00 am, 10:00 am, 12:00 pm, 

2:00 pm, 6:00 pm, 8:00 pm and 10:00 pm. These two drinking water intake amounts appear to 

be ‘outliers’ based on the available references, and a brief review of the pediatric literature (e.g.,

U.S. EPA, 2008). -20

0 20 40 60

Box Cox Transformed Consumption, mL/kg bw

-4 -2 0 2 4

Z-score

 lambda = 0.512

Figure 2 Figure 2-9. Box-Cox Plot of Infant Direct Water Consumption (mL/kg Cox Plot of Infant Direct Water Consumption (mL/kgbw/day) bw/day) bw/day) 

Analyses of the SHEDS-Dietary simulated output for the infant subpopulation indicated that two 

food diaries constituted about 70% of all high simulated outputs in the top 0.1% of simulated 

person-days. Again, the question of concern was how sensitive are the estimates at the upper 

percentiles to the drinking water intakes reported for these two respondents. Agency staff used 
40 

(cross-sectional) SHEDS-dietary to conduct two ‘what-if’ scenarios: (1) drop these two diaries 

from the Monte Carlo simulation, and (2) reduce the reported amounts consumed by 50 percent. 

Neither of these resulted in marked changes in the estimated exposures at the per capita 99.9th

percentile. The sensitivity analyses for the potato eater and these high infant water intake 

diaries,showed similar insensitivities to these ‘outliers.’ The Agency previously noted the 

robustness of the results to residue outliers: 

“...it is often not the extreme upper tail of a residue distribution which is responsible for driving 

the 99.9th or 99th percentile exposure levels, but rather a combination of reasonable (but high 

end) consumption and reasonable (but high end) residue levels of one or two frequently 

consumed agricultural commodities.” US EPA (1999), pp. 21-22. 

While that quote referred to residue ‘outliers’, the two case studies above suggests that a similar 

level of robustness appears to hold for consumption ‘outliers’ as well. While such analyses 

cannot be performed if the consumption diaries are fixed in the code, the open source code of 

SHEDS-dietary provides agency modelers with complete access to all of the underlying data and 

algorithms. This feature enables the Agency to quantitatively address other questions that risk 

managers may have as PBPK models are used to assess dietary risks to pesticides and other 

chemicals. 

Examples of other sensitivity analyses are presented in Chapter 3. 

2.7 Uncertainty Analysis Methods and Results 

2.7.1 Uncertainty Analysis Methods 

SHEDS-Dietary has a simple bootstrapping method for conducting uncertainty analyses - 

utilizing only a subset of the consumption and residue data inputs. This proposed method is 

designed to gain some insight about ‘How much better would our estimates be if we had more

data?’, by conducting the uncertainty analyses in the other direction ‘How far off will our 

estimates go if we used only a subset of the consumption and/or residue data?’. 

The SHEDS-Dietary bootstrap procedure to conduct uncertainty analysis entails the following: 

1) Randomly draw certain percentage (e.g., 50%) of person-day from CSFII data or/and 

randomly draw certain percentage (e.g., 50%) of residue data from pollutant residue files 

by raw commodity and food form. Run 100 times for variability. 

2) Repeat step 1 many times, e.g., 200 times. 

3) Quantify variability from each run. 

4) Conduct uncertainty analyses from different runs (e.g. 200 times). 200 50th, 95th and 99th

values can be acquired respectively. The ratio of 95

th vs. 5th percentile of a given 

percentile can be used to evaluate the uncertainty. The bigger the uncertainty ratio, the 

bigger uncertainty produced by subsets of dietary and residue data. 

5) Obtain important sources contributing to the total uncertainty (e.g., structure, scenario). 
41 

To check whether there are enough data for consumption and residue data sources, and which 

data set was relatively more important for exposure, uncertainty analyses applying statistical 

bootstrapping of certain percentages of both data sets, were conducted with SHEDS-Dietary for 

permethrin, as shown in the table and figure below. 

2.7.2 Uncertainty Analyses on Selection of Food Consumption Diaries 

This section describes SHEDS-Dietary uncertainty analyses focusing on the selection of food 

consumption diaries. It includes uncertainty analyses for the permethrin application (assessing 

impact of residues vs. consumption, and sample sizes; assessing impact of number of exposure 

days before dose results stable).

Figure 2-10 shows the uncertainty for 3 CDFs for bootstrap sampling of 50% of residues and 

20%, 50%, 80% of food consumption data. The CDF of the 50% of residues and 20% of 

consumption data has the biggest uncertainty. It presents uncertainty results for daily dietary cispermethrin exposure, based on bootstrapping 200 times. The ratio of 97.5th percentile to the 2.5th

percentile (95% confidence interval) is 15.07/4.63=3.3. In the same way, we can calculate those 

ratios for other schemes of bootstraps to evaluate what are the major factors contributing the 

overall uncertainty. 

Figure 2 Figure 2-10. Uncertainty analysis profiles for daily dietar . Uncertainty analysis profiles for daily dietary cis-permethrin exposure. permethrin exposure. permethrin exposure. 
42 

Table 2-7 shows results of uncertainty analyses. The green highlighted cell (3.3) reflects the 

95th/5th percentile ratio for an uncertainty run that used subsets: 50% cis-permethrin residue by 

RAC, and 20% of NHANES dietary consumption data for 3-5 year-olds. The NHANES food 

consumption data base contains a total of 6,204 food diaries, and only a subset of those diaries 

are selected for each simulation (6204*0.2=1241). We ran this 100 times, for a sample size of 

124,100 for variability for the same subset. We then ran another 100 times for uncertainty for the 

different subsets with the same bootstrap sampling rate, yielding sample size of 12,410,000. The 

99th percentile was calculated from that simulation, and the process was repeated 100 times, 

producing one hundred estimates of aggregate exposure at the per capita 99th percentiles. From 

these 100 values we found the 97.5 th and 2.5 th percentiles; their ratios yield the uncertainty ratio, 

3.3 (15.07/4.63, see Figure 2-10). The bigger the uncertainty ratio, the bigger uncertainty 

produced by subsets of dietary and residue data. 

Table 2 Table 2-7. Bootstrap uncertainty analyses for cis . Bootstrap uncertainty analyses for cis . Bootstrap uncertainty analyses for cis-permethrin permethrin permethrin 

10 20 30 40 50 60 70 80 90

10 5.3 3.2 3.6 3.4 4.7 3.5 4.3 4.6 3.3

20 2.8 2.5 3.4 2.5 3.3 3.1 3.5 2.5 2.6

30 2.5 2.3 2.3 2.8 2.5 2.5 2.7 2.3 2.6

40 1.9 2.1 2.2 2.1 2.1 2.0 2.0 2.2 2.0

50 2.3 2.4 1.7 2.2 2.3 2.3 2.0 1.9 1.9

60 2.0 2.1 2.0 1.8 1.9 1.8 1.6 2.2 2.2

70 1.8 1.9 1.8 2.1 1.9 2.2 1.9 2.0 2.0

80 2.1 1.9 1.8 1.8 1.8 1.7 1.8 1.7 1.9

90 1.7 2.0 1.8 1.8 1.7 2.0 2.0 1.9 1.8

dietary sampling 

percent

Bootstrap sampling percentage for cis-permethrin residue data

Ratio of 97.5th v.s. 2.5th percentile of uncertainty 99th CDFs by various percentage of bootstrap 

2.8 Quality Assurance 

Three types of quality assurance have been conducted with SHEDS-Dietary. First, ORD and 

contractor Alion followed the SHEDS QAPP (US EPA, 2010) when developing the code and 

GUI. Second, ORD evaluated the model for Arsenic (Xue et al., 2010). Third, OPP provided an 

independent review, and conducted a comparison of results to the DEEM-FCID model (modelto-model evaluation), as described below. 
43 

2.8.1 Comparison SHEDS vs. DEEM (Model-to-Model Evaluation) 

This section compares SHEDS-Dietary and DEEM-FCID. Table 2-8 presents the per capita 

estimates for chemical ‘ABC’ at upper percentiles (95th, 99th, 99.9th) used by the Agency in acute 

dietary risk assessments. Figure 2-11 presents the per capita estimates for chemical ‘ABC’ at the 

99.9th percentile for nine subpopulations for 17 separate drinking water scenarios. The 

differences should reflect only simulation uncertainty (i.e., differences due to different draws of 

random numbers) since the models both rely upon the CSFII sampling design. 

The Panel previously noted the importance of respecting differences due to model uncertainty.13 

 SHEDS-Dietary was developed to evaluate the incremental effects of specific modeling 

assumptions. This tool can also help explore other issues, such as the sensitivity analyses as 

discussed above, as well as other types of analyses that may be requested as the Agency 

progresses toward using PBPK models. 

We briefly describe the DEEM-FCID model since the Agency has generally relied upon this 

model to conduct dietary risk assessments under FQPA. For each food diary, DEEM-FCID 

applies a Monte-Carlo simulation to calculate total daily exposure, as depicted by Equation 

(1).14 DEEM-FCID conducts a fixed number of ‘iterations’ to each food diary, allowing the user 

to specify the number of iterations per diary. Agency risk assessors typically run DEEM-FCID 

with 1,000 iterations per diary.15 DEEM-FCID keeps track of the total daily exposure for each 

simulated person-day, and applies the corresponding CSFII survey weights to project the 

simulated person-days to a per capita level. If the user specifies only one iteration, then the per 

capita percentiles would reflect interpersonal variability – variation in exposures across the 

subpopulation due to differences in food consumption. If multiple iterations are specified, 

DEEM-FCID treats each modeled person-day as separate (independent) simulation. The per 

capita estimates reflect both intrapersonal variability and interpersonal variability. Note that the 

purpose of these Monte-Carlo simulations is to obtain an estimate of a high-end aggregate total 

daily exposure. 

Table 2-8 presents DEEM-FCID and SHEDS-Dietary estimates (cross-sectional) of total daily 

exposure at selected percentiles for chemical ABC, for 9 subpopulation groups. Table 2-8 

suggests that these two models produce similar results across these subpopulations for this 

particular set of anticipated (food) residues. Children often have higher exposures than adults 

 

13 US EPA – FIFRA SAP Minutes 2004-04, p. 24. 

http://www.epa.gov/oscpmont/sap/meetings/2004/index.htm#april. 

14 The Monte Carlo procedure draws a residue for each RAC-FF. While a particular commodity (Potato, tuber 

w/peel) may be used in multiple foods, the cooking method may differ, and thus, it will have a different food form. 

The food form for potatoes used in ‘White potato, home fries w/Lard’ is ‘cooked-fresh-fried’ (ff=213, see legend in 

Table 1). This particular diary may have contained other foods with ‘Potato, tuber w/peel’ - some of which may 

have the same food forms, e.g., 71411000- 100701=‘White potato skins, with adhering flesh, fried, with cheese and 

bacon’, while others have different food forms, e.g., 71603010=’Potato salad’, 71101110=’Baked potato’. If the 

cooking method is the same (e.g., ‘‘Pork fat’ or ‘Lard’ used to fry eggs and home fries), then the same residue is 

applied to all those consumption amounts (‘home fries’, ‘White potato skins’, etc.). But if the food forms are 

different (e.g., ‘Potato salad’ is boiled, ff=212; ‘Baked potato’, ff=211), then a different residue is independently 

drawn and applied for those food forms in the total daily simulation. 

15 Risk assessors may increase this to 5,000 or more iterations if the results are sensitive at this level. 
44 

(mg ai/kg bw/day) at these upper per capita percentiles due to higher intakes of many foods as a 

percent of their bodyweight. 
45 

Table 2 Table 2-8. A Comparison of DEEM . A Comparison of DEEM . A Comparison of DEEM-FCID and SHEDS FCID and SHEDS FCID and SHEDS-Dietary Exposure Results for Dietary Exposure Results for 

Chemical ABC. Chemical ABC. 

DEEM-FCID results (1 simulation w/1000 iterations)

 95th Pctile 99th Pctile 99.9 Pctile 

Subpopulation (mg/kg/day) (mg/kg/day) (mg/kg/day) 

U.S. General 0.00209 0.01076 0.04873 

All Infants (< 1 yr) 0.00402 0.01661 0.05982 

Children 1-2 yrs old 0.00931 0.03261 0.12403 

Children 3-5 yrs old 0.00688 0.02717 0.10643 

Children 6-12 yrs old 0.00328 0.01515 0.06653 

Children 13-19 yrs old 0.00137 0.00762 0.03755 

Adults 20-49 yrs old 0.00130 0.00714 0.03410 

Adults 50+ yrs 0.00178 0.00879 0.03748 

Females 13-49 yrs old 0.00139 0.00792 0.03780 

SHEDS-Dietary results (150 iterations)

 95th Pctile 99th Pctile 99.9 Pctile 

Subpopulation (mg/kg/day) (mg/kg/day) (mg/kg/day) 

U.S. General 0.0021 0.0108 0.0476 

All Infants (< 1 yr) 0.0037 0.0158 0.0556 

Children 1-2 yrs old 0.0094 0.0326 0.1228 

Children 3-5 yrs old 0.0070 0.0272 0.1041 

Children 6-12 yrs old 0.0034 0.0154 0.0697 

Children 13-19 yrs old 0.0014 0.0078 0.0361 

Adults 20-49 yrs old 0.0013 0.0071 0.0323 

Adults 50+ yrs 0.0018 0.0086 0.0365 

Females 13-49 yrs old 0.0014 0.0079 0.0358 

Ratio (DEEM-FCID/SHEDS) 

 95th Pctile 99th Pctile 99.9 Pctile 

Subpopulation (mg/kg/day) (mg/kg/day) (mg/kg/day) 

U.S. General 0.99 1.00 1.02 

All Infants (< 1 yr) 1.09 1.05 1.07 

Children 1-2 yrs old 0.99 1.00 1.01 

Children 3-5 yrs old 0.97 0.99 1.02 

Children 6-12 yrs old 0.97 0.98 0.95 

Children 13-19 yrs old 0.97 0.97 1.03 

Adults 20-49 yrs old 0.99 1.00 1.05 

Adults 50+ yrs 1.01 1.02 1.02 

Females 13-49 yrs old 0.98 0.99 1.05 

In addition to comparing exposures in Table 2-8, we compared SHEDS-Dietary and DEEM 

results for contribution of exposure from major commodities. Table 2-9 shows that these results 

are also very similar. 
46 

Table 2 Table 2-9. Contribution to exposure by commodities for 1 . Contribution to exposure by commodities for 1 . Contribution to exposure by commodities for 1-2 year-olds. 

comcode Commodities DEEM SHEDS

95003590 Strawberry 34.5 40.3

12002600 Peach 13.0 8.9

95001750 Grape 10.8 13.1

11000070 Apple, fruit with peel 7.6 8.2

95003600 Strawberry, juice 7.2 6.5

12002300 Nectarine 3.7 4.5

11000100 Apple, juice 3.3 2.9

12002850 Plum 2.7 0.8

95001780 Grape, raisin 2.6 2.7

12002880 Plum, prune, juice 2.1 3.0

12002620 Peach juice 1.4 0.0

12000130 Apricot, dried 1.2 1.2

4013550 Spinach 1.1 0.3

95001760 Grape, juice 0.9 1.5

9013990 Watermelon 0.8 1.2

9023560 Squash, summer 0.8 0.1

9021350 Cucumber 0.7 0.0

11002660 Pear 0.6 0.4

Percentage

 

Figure 2-11 plots the DEEM-FCID and (cross-sectional) SHEDS-Dietary estimates of exposure 

at the per capita 99.9th for 17 different drinking water scenarios, for 9 age groups. This plot 

suggests that these two models produce similar results across many different drinking water 

scenarios, because the correlation is near 1 (line at near 45 degree angle). For any particular 

scenario, the infant subpopulation (pink) has the highest exposures (mg ai/kg bw/day) since 

infants generally have higher drinking water intakes as a percent of their bodyweight (mL/kg 

bw/day). 
47 

0

0.001

0.002

0.003

0.004

0.005

0.006

0.007

0.008

0.009

0.01

0 0.001 0.002 0.003 0.004 0.005 0.006 0.007 0.008 0.009 0.01

DEEMS

SHEDS

U. S. Population

All Infants

1-2 years

3-5 years

6-12 years

13-19 years

 20-49 years

50+ year

female 13-49 years

Figure 2 Figure 2-11. Comparison of SHEDS and DEEM on 99.9 . Comparison of SHEDS and DEEM on 99.9th percentiles of drinking water. 

Chemical ABC exposure (mg/kg/day) of 17 different s Chemical ABC exposure (mg/kg/day) of 17 different scenarios. cenarios.
48 

3 Applications/Case Studies to Date 

ORD and OPP scientists collaborated to refine, evaluate (compare with DEEM model), and apply 

the SHEDS dietary module (food and drinking water) for a number of analyses (food and 

drinking water scenarios, half-life and eating occasion sensitivity analyses, longitudinal 

simulations with half-life and eating occasion analyses, examining various ways of sampling 

residues for sensitivity analyses, analyses for contribution by crops and chemicals to identify key 

risk contributors and help assess risk mitigation scenarios) to refine OPP's risk assessments and 

inform their risk management decisions for the following: 

Aldicarb RED (2006) 

 development/testing of eating occasion analyses 

 allowed comparison to DEEM-based analyses 

 applied Bayer DWCS data (little difference) for direct water intake 

Carbaryl (2007) 

 explored longitudinal (multi-day) eating occasion analyses (DW-infants, 5+ hrs) 

N-Methyl Carbamate CRA (2007) 

 supported contention that not significantly overestimating risk by not accounting for 

recovery (food-only) 

 maximum exposure, by eating occasion, provides best case scenario for recovery 

Organophosphates CRA (2009-2011)

 updating the 2006 OP Cumulative Risk Assessment 

 SHEDS longitudinal eating occasion analysis used to consider persisting effects (carryover) on AChE inhibition using chemical-specific recovery (half-life) rates 

 SHEDS contribution analyses allowed assessing effects of mitigation options on the 

population 99.9th percentile

 

EPA/ORD scientists have also applied SHEDS-Dietary to As and MeHg case studies for research 

purposes, to answer questions about ranges of population exposures, major food contributors, 

differences in exposures for vulnerable populations, and evaluation of modeled estimates against 

duplicate food and biomarker data. 

3.1 Arsenic (As) 

Dietary exposure from food to toxic inorganic arsenic (iAs) in the general US population has not 

been well studied. This SHEDS-Dietary research quantifies dietary As exposure, and analyzes 

the major contributors to total As and iAs. Another objective was to compare model predictions 

to observed data using both duplicate diet data and (after linkage with a PBPK model) biomarker 

data. 
49 

Probabilistic exposure modeling for dietary As was conducted with the SHEDS-Dietary model, 

using NHANES/WWEIA consumption data and TDS residue data. The dose modeling was 

conducted by combining the SHEDS-Dietary model with a physiologically-based 

pharmacokinetic (PBPK) model in EOHSI’s MENTOR-3P system (Xue et al., 2010). Model 

evaluation was conducted via comparing exposure and dose modeling predictions against 

NHEXAS duplicate diet data and NHANES biomarker measurements, respectively, for the same 

individuals. 

The Xue et al., 2010 SHEDS-Dietary publication revealed that toxic inorganic As (iAs) exposure 

from food is more important than drinking water for the U.S.. The major food contributors to iAs 

exposure were vegetables, fruit juices, and fruits; rice; beer and wine; and flour, corn, and wheat 

(Figure 3-1). The major food contributor for tAS exposure is fish (contributing 60% of exposure; 

Figure 3-2). The mean modeled tAs exposure from food is 0.38 μg /kg/day, ~14 times higher 

than the mean As exposures from the drinking water. The mean iAs exposure from food is 0.05 

μg /kg/day (1.96 μg /day), ~2 times higher than the mean iAs exposures from the drinking water. 

Approximately 10% of tAs exposure from foods is the toxic iAs form. SHEDS modeled 

exposure and dose estimates matched well with the duplicate diet data and measured As 

biomarkers (Figures 3-3 and 3-4). This model evaluation effort provides more confidence in the 

exposure assessment tools used, including SHEDS-Dietary. 

Some key results are shown in Figures 3-1 to 3-4: 

 
50 

1

12%

11%

18%

5% 13%

17%

24%

Beer,Wine

Flour,Corn,Wheat

Fruit,Fruit Juice

Others

Poultry,pork,beef,egg

Rice

Vegetables

Source: Xue et al., EHP 2010

 

Figure 3 Figure 3-1. Contribution of Inorganic Arsenic Intake by Food . Contribution of Inorganic Arsenic Intake by Foods 

1

2%

60%

4%

5%

4%

5%

7% 9% 4%

Beer,Wine

Fishes

Flour,Corn,Wheat

Fruit,Fruit Juice

Others

Poultry,pork,beef,egg

Rice

Shellfish

Vegetables

Source: Xue et al., EHP 2010

 

Figure 3 Figure 3-2. Contribution of Total Arsenic Intake by Foods . Contribution of Total Arsenic Intake by Foods . Contribution of Total Arsenic Intake by Foods 
51 

0

1

2

3

0 10 20 30 40 50 60 70 80 90 100

Percentile

D ie ta ry in t a k e (u g /k g /d a y )

MODEL 5th 

MODEL 50th 

MODEL 95th 

NHEXAS

* Fill in no-detected with 1/2 LOD when As residue mean > 0

Figure 3 Figure 3-3. SHEDS . SHEDS . SHEDS-Dietary Exposure Model Evaluation for Arsenic Agai Dietary Exposure Model Evaluation for Arsenic Against Duplicate st Duplicate 

Food Data Food Data 

1

0

20

40

60

80

100

0 100 200 300 400 500

Urine Conc (ug/L)

P e rc e n tile

Mean model prediction

NHANES urine data

Comparison of total As in Urine (ug/L) from NHANES data and PBPK model

N Mean Std 50th 25th 75th 95th

PBPK model 2355 18.32 46.86 8.1 4.7 16.1 58.9

Measured conc. 2355 18.06 42.12 4.9 2.5 14.6 74.8

Source: Xue et al., EHP 2010

Figure 3 Figure 3-4. Total Arsenic Model Evaluation for SHEDS . Total Arsenic Model Evaluation for SHEDS . Total Arsenic Model Evaluation for SHEDS-Dietary linked with MENTOR Dietary linked with MENTOR 

PBPK Model and Compared Against NHANES Urinary Biom PBPK Model and Compared Against NHANES Urinary Biomarker Data. ES Urinary Biomarker Data. arker Data. 
52 

3.1.1 Arsenic Uncertainty Analysis Results 

SHEDS-Dietary has a simple bootstrapping method for conducting uncertainty analyses - 

utilizing only a subset of the consumption and residue data inputs. The SHEDS-Dietary 

bootstrap procedure applied to Arsenic involved the following steps: 

1) Randomly draw certain percentage (1/20 or 5%) of person-day from CSFII data or/and 

randomly draw certain percentage (1/4 or 25%) of residue data from pollutant residue 

files by raw commodity and food form, 

2) Perform Monte Carlo simulations (e.g., 100 iterations per diary using cross-sectional 

method) 

3) Get population based statistic from each run (e.g., aggregate exposure at per capita 99th

percentile) 

4) Repeat the steps 1-3 many times, say 200 times 

5) Conduct uncertainty analyses from different runs (e.g. will have 200 estimates of the level 

of aggregate exposure at the per capita 50th, 95th and 99th percentiles, respectively). For 

each population based statistics (e.g., per capita 99th percentile), the ratio of 95th vs. 5th

percentile can be used to evaluate the uncertainty. The bigger the uncertainty ratio, the 

bigger uncertainty produced by subsets of dietary and residue data. 

Figure 3-5 shows the uncertainty for 3 selected percentiles (see Xue et al., 2006 for details on this 

type of uncertainty analysis), and that 99th percentile has the biggest uncertainty. It presents 

uncertainty results for daily dietary arsenic exposure, based on bootstrapping 1/30 of CSFII 

diaries 200 times and 1/8 of the residues. For each percentile, such as 50th, 90th or 95th, there are 

200 values, from which the 95th and 5th percentile were acquired and its ratio was calculated. 

The ratio of the 95th to 5th percentile is 1.19 for 50th percentile; 1.93 and 3.28 for 95th and 99th

percentile respectively. We can see that there is relatively little uncertainty regarding the estimate

of the 50th percentile as compared to higher percentiles with respect to the amount of residue and 

consumption data used in the exposure assessment. For the arsenic case study, we performed 

such bootstrap procedure and calculate 95th/5th ratios for various subsets of consumption and 

residue data to evaluate the relative contributions to the overall uncertainty. 
53 

Uncertainty of daily dietary exposure of As with 1/8 for As residue and 1/30 CSFII 

bootstrap

0.1

1

10

100

0 10 20 30 40 50 60 70 80 90 100

Uncertainty Percentile

As dietary exposure (ug/kg/day)

P50

P95

P99

Figure 3 Figure 3-5. Example SHEDS . Example SHEDS . Example SHEDS-Dietary Uncertainty Analyses Dietary Uncertainty Analyses Dietary Uncertainty Analyses 

3.2 Methyl Mercury from Fish Consumption (MeHg) 

The MeHg case study examines exposures for vulnerable populations. Asians, Native 

Americans, and Pacific Islanders (A/N/P) have shown higher levels of MeHg in previous 

NHANES; reasons have not been well studied. The objectives of this research are to examine 

dietary exposures to MeHg through fish consumption in different racial/ethnic groups, and 

extend previous NHANES blood level analyses. 

Probabilistic exposure modeling for dietary MeHg was conducted with SHEDS-Dietary, using 

NHANES/WWEIA fish consumption data and FDA TDS fish residue data. MeHg exposures by 

race/ethnicity, age group, and food type were analyzed. For Asians, Native Americans, and 

Pacific Islanders, major contributors for MeHg are tuna, fresh water fish–other, seawater fish–

other. Statistical analyses of blood MeHg levels by race/ethnicity from 1999-2006 are being 

compared against previous published results for 1999-2002 data (6 times larger sample size). 

Exposure estimates for MeHg in fish can explain the high level of MeHg in blood for populations 

with higher fish consumption. 
54 

Results are shown in Figures 3-6 and 3-7, and Tables 3-1 to 3-2. For all age groups, the A/N/P 

group has higher mean dietary MeHg exposures than the general population (Figure 3-6 and 

Table 3-1). 1-2 year-olds and A/N/P have the highest ratio of SHEDS modeled MeHg exposure 

and NHANES MeHg blood levels. For A/N/P, 5 major contributors for MeHg are tuna, fresh 

water fish–other, seawater fish–other, salmon, and catfish (Figure 3-7). SHEDS exposure 

predictions correlate well with NHANES blood biomarker levels in terms of age, gender, and 

ethnicity. Percentage of MeHg blood levels higher than critical health-based concentrations is 

higher (up to 8x) for A/N/P compared to other racial/ethnic groups (Table 3-2). 

0.001

0.01

0.1

1

70 75 80 85 90 95 100

Percentile

Exposure (ug/day/kg)

Mexican American

Non-Hispanic White

Non-Hispnaic Black

Other Hispanic

Tribal,Asian,Pacific

Figure 3 Figure 3-6. SHEDS . SHEDS . SHEDS-Dietary Methyl Mercury Exposure by Ethnicity Using Dietary Methyl Mercury Exposure by Ethnicity Using1999-2006 

NHANES Data NHANES Data 
55 

Contribution of MeHg exposure from different fish types 

for Asians, Native Americans, Pacific Islanders

tuna

26%

catfish

7%

salmon

9% shell fish

6%

shrimp

5%

sword,shark

0%

Other seawater fish

22%

other freshwater fish

25%

 

Figure 3 Figure 3-7. Contribution of Methyl Mercury Exposure from Di . Contribution of Methyl Mercury Exposure from Different Fish Types for ferent Fish Types for 

Asians, Native Americans, and Pacific Islanders Self-Reported in NHANES Reported in NHANES Reported in NHANES 
56 

Table 3 Table 3-1. SHEDS . SHEDS . SHEDS-Dietary Methyl Mercury Exposure by Ethnicity and A Dietary Methyl Mercury Exposure by Ethnicity and Age from 1999 e from 1999- e from 1999-

2006 NHANES Data 2006 NHANES Data 

Ethnicity Age N Mean fold Std P95 P99 Mean fold Std P95 P99

ANP 0 < 1 170 0.1 1.7 64 0.0 4.9 0.01 1.6 6 0.00 0.53

 1 to <2 89 1.7 5.7 804 7.3 42.0 0.13 4.9 62 0.62 3.09

 2 to <3 96 0.9 1.7 307 8.2 14.9 0.07 1.9 23 0.54 1.13

 3 to <6 200 0.8 1.1 542 3.3 25.8 0.05 1.2 35 0.18 1.61

 6 to <11 285 1.7 1.6 1279 9.6 32.2 0.05 1.5 39 0.20 1.09

11 to <16 316 0.7 0.7 459 6.5 13.0 0.01 0.8 9 0.11 0.27

16 to <21 296 2.1 1.8 1565 14.8 34.2 0.03 2.1 25 0.17 0.70

21 to <50 604 3.8 1.6 2843 20.7 55.3 0.05 1.8 39 0.29 0.78

50+ 366 4.6 1.8 3896 21.9 73.0 0.06 1.8 52 0.30 0.68

REST 0 < 1 2517 0.1 69 0.0 1.3 0.01 8 0.00 0.14

 1 to <2 1704 0.3 228 1.0 8.7 0.03 21 0.10 0.77

 2 to <3 1622 0.5 419 2.1 11.5 0.04 32 0.14 0.77

 3 to <6 3153 0.7 592 3.9 21.3 0.04 31 0.22 1.08

 6 to <11 4815 1.0 856 4.0 28.3 0.03 29 0.13 1.04

11 to <16 7305 1.0 744 4.6 25.9 0.02 14 0.10 0.49

16 to <21 6721 1.1 845 5.0 25.3 0.02 11 0.08 0.38

21 to <50 13211 2.3 2895 12.8 46.7 0.03 37 0.16 0.60

50+ 11530 2.6 2101 15.9 45.9 0.03 29 0.21 0.59

ug/day ug/kg/day
57 

Table 3 Table 3-2 Summary statistics o Summary statistics o Summary statistics of organic blood level by age g f organic blood level by age group and ethnicity roup and ethnicity 

(μg/liter) from 1999- (μg/liter) from 1999-2006 NHANES data 2006 NHANES data 2006 NHANES data 

Ethnicity Age (yr) N Mean fold Std P95 P99

ANP 1 to <2 36 0.76 3.0 133 4.40 4.40

 2 to <3 42 0.59 2.2 102 2.21 4.00

 3 to <6 91 0.57 1.5 182 3.40 6.82

 6 to <11 96 1.01 2.7 277 4.40 6.80

11 to <16 94 0.69 1.5 152 3.03 4.70

16 to <21 126 1.24 2.0 244 4.90 6.71

21 to <50 270 1.69 1.6 421 5.52 6.63

50+ 108 1.70 1.4 374 5.03 5.78

REST 1 to <2 716 0.25 56 0.92 2.46

 2 to <3 726 0.27 57 1.10 2.42

 3 to <6 1570 0.38 99 1.62 3.60

 6 to <11 1336 0.38 93 1.40 3.63

11 to <16 2089 0.45 91 1.76 4.35

16 to <21 3117 0.62 116 2.32 5.20

21 to <50 6339 1.04 281 3.81 6.30

50+ 3510 1.18 253 4.00 6.00

 

This research extends and is consistent with findings from previous studies focusing on higher 

blood levels in A/N/P populations, by examining dietary exposures to MeHg from fish 

consumption. A/N/P populations are exposed to higher levels of MeHg from fish consumption 

than the general US population and other ethnicity groups. SHEDS-Dietary modeling allows 

identification of Hg intakes by age, gender, ethnicity, and type of fish. Correlations of modeled 

dietary exposure predictions with NHANES blood biomarker levels suggest that fish 

consumption is a key exposure pathway for these populations. 

3.3 Permethrin 

ORD and OPP scientists have collaborated on application of SHEDS-Dietary to estimate 

permethrin dietary exposure to support OPP’s pyrethroid cumulative risk assessment. The 

objectives of this SHEDS-Dietary application are to quantify dietary permethrin exposures in the 

U.S. population, analyze the major contributors, and compare model predictions to observed data 

using duplicate diet data from the EPA’s Children’s Total Exposure to Persistent Pollutants study 

(CTEPP). 

 
58 

CSFII 1994-1996,1998 consumption data and PDP data for residues were used. Model 

predictions were evaluated against CTEPP duplicate food data for cis- and trans-permethrin 

(matched SHEDS and CTEPP data by age and gender). A bootstrap approach was applied to 

assess uncertainty and relative importance of dietary consumption vs. residue data. SHEDSDietary was linked to PBPK models and results compared against NHANES biomonitoring data. 

Results are as follows and shown in the Tables 3-3 to 3-5 and Figures 3-8 to 3-10 below: 

 exposure: 0.44 to 2.2 μg /day; as age increases, exposure increases 

 by body weight, young children and 50+yrs. have highest exposure 

 3 most important contributors overall: spinach, lettuce, cabbage 

 for 98.5 to 99.5 %ile, lettuce more important 

 results similar for cis- and trans-permethrin 

 results similar using NHANES vs. CSFII 

 SHEDS model results and CTEPP measurement results matched well. 

Using the SHEDS-Dietary model, the mean cis-permethrin exposure for the U.S. population from 

food and drinking water ranged from 0.44 to 2.2 μg /day; 3.4E-05 to 9.9E-5 mg/kg/day. The 95th

percentiles ranged from 0.90 to 24.48 μg /day; 8.3E-6 to 6.8E-5 mg/kg/day. As Table 3-3 also 

shows, as age increases, exposure increases; normalizing by body weight, young children and 

adults over 50 years have the highest exposures. Results were similar for cis- and transpermethrin. The three most important contributors overall were spinach (48% cis-), cabbage 

(28% cis-), and lettuce (10% cis-). For the upper tails of the exposed population (98.5%ile to 

99.5 %ile), lettuce was more important (43% cis-; see Figure 3-8). 

In comparing with CTEPP measurement results, SHEDS-Dietary exposure estimates (246 paired 

comparisons) for mean, 95th, and 99th percentiles matched well: the cloud of 100 yellow

variability lines from the model contain the observed data in Figure 3-9, and the ratio of modeled 

to measured data is close to 1 in Table 3-4 for both for cis- and trans- permethrin. 

Figure 3-10 and Table 3-5 show the sensitivity of results to which consumption database is used: 

CSFII or NHANES. Table 3-5 shows that with the NHANES data, lettuce is the most consumed 

(39.7%) and greatest food contributor to dietary cis-permethrin exposure (47.5% in the % 

exposure column); using CSFII spinach was the most consumed (37.9% in the % food column) 

and greatest food contributor to dietary cis-permethrin exposure (46%). The list of key RAC is 

also different, e.g. apple juice appears in NHANES but not CSFII. Figure 3-10 illustrates the 

differences in exposure CDFs using the two different consumption databases. 

The model evaluation effort with this case study provides more confidence in SHEDS-Dietary. 

More research is needed with PBPK linkage and model evaluation. 
59 

Table 3 Table 3-3. Cis-permethrin exposure by age groups from SHEDS permethrin exposure by age groups from SHEDS permethrin exposure by age groups from SHEDS-Dietary with CSFII tary with CSFII 

Data 

age group n mean std p95 p99 mean std p95 p99

0 < 1 years 297200 6.7E-01 4.7E+02 7.7E-02 1.1E+01 7.9E-05 5.6E-02 8.3E-06 1.3E-03

1-2 years 419200 4.4E-01 3.8E+02 5.1E-01 6.0E+00 3.9E-05 3.7E-02 4.0E-05 4.7E-04

3-5 years 878200 5.2E-01 4.2E+02 7.8E-01 8.0E+00 3.0E-05 2.4E-02 4.4E-05 4.4E-04

6-12 years 417800 7.0E-01 8.9E+02 1.3E+00 1.3E+01 2.1E-05 2.7E-02 4.1E-05 3.9E-04

13-19 years 244400 1.1E+00 1.6E+03 2.4E+00 1.9E+01 1.6E-05 2.2E-02 3.8E-05 3.0E-04

20-49 years 935400 1.8E+00 2.7E+03 4.2E+00 3.8E+01 2.6E-05 4.1E-02 5.8E-05 5.3E-04

50+ years 929200 2.2E+00 2.3E+03 5.0E+00 4.7E+01 3.2E-05 3.4E-02 6.8E-05 6.6E-04

ug/day mg/kg/day

All data

spinach

48%

cabbage

10%

lettuce

28%

parsley

2%

celery

4%

tomato

2%

other

6%

98.5 to 99.5 percentiles

spinach

31%

cabbage

16%

lettuce

43%

parsley

2%

celery

1%

tomato

0%

other

7%

Contribution of foods to cis-permethrin exposure

Figure 3 Figure 3-8. Contribution of Foods to Cis . Contribution of Foods to Cis . Contribution of Foods to Cis-Permethrin Exposure Using SHEDS Permethrin Exposure Using SHEDS Permethrin Exposure Using SHEDS-Dietary Dietary Dietary 
60 

Figure 3 Figure 3-9. Comparison of SHEDS . Comparison of SHEDS . Comparison of SHEDS-Dietary Es Dietary Es Dietary Estimates Against CTEPP Duplicate Diet timates Against CTEPP Duplicate Diet 

Exposure Data for Cis- Exposure Data for Cis- and Trans and Trans and Trans- Permethrin. (Note: The cloud of yellow lines Permethrin. (Note: The cloud of yellow lines 

illustrating 100 modeled variability runs contains the observed data.) the observed data.) 

Table 3 Table 3-4. SHEDS . SHEDS . SHEDS-Dietary Evaluation against CTEPP Duplicate Diet Dat etary Evaluation against CTEPP Duplicate Diet Data for Cis for Cis for Cis- and 

Trans- Permethrin with CSFII Data Permethrin with CSFII Data Permethrin with CSFII Data 

perm mean std p5 p25 p50 p75 p95 p99

SHEDS cispermethrin 6.9E-02 6.6E-01 4.9E-07 2.6E-05 2.5E-04 9.7E-03 1.7E-01 1.3E+00

CTEPP cispermethrin 6.5E-02 3.8E-01 4.2E-04 6.8E-04 1.3E-03 5.8E-03 1.7E-01 2.6E+00

SHEDS transpermethrin 6.8E-02 7.2E-01 0.0E+00 1.8E-05 1.8E-04 8.0E-03 1.5E-01 1.2E+00

CTEPP transpermethrin 8.9E-02 3.8E-01 1.0E-03 2.2E-03 4.8E-03 2.3E-02 2.2E-01 2.0E+00

 
61 

Exposure of cis-permethrin with NHANES and CSFII (3-5 year-olds)

70

75

80

85

90

95

100

0 0.001 0.002 0.003 0.004 0.005 0.006

exposure (mg/kg/day)

percentile

CSFII

NHANES

 

Figure 3 Figure 3-10. SHEDS . SHEDS . SHEDS-Dietary Modeled Exposure of Cis Dietary Modeled Exposure of Cis Dietary Modeled Exposure of Cis-Permethrin with NHANES and Permethrin with NHANES and 

CSFII for 3- CSFII for 3-5 year-olds
62 

Table 3-5. SHEDS . SHEDS . SHEDS-Dietary Modeled Contribution of RAC to Total Food Dietary Modeled Contribution of RAC to Total Food Consumption onsumption 

for Cis- for Cis-Permethrin and 3 Permethrin and 3 Permethrin and 3-5 year-olds 

Contribution of RAC to total food consumption and cis-permethrin exposure

(3-5 year-olds)

RAC % food % exposure RAC % food % exposure

Spinach 37.9 46.0 Lettuce, head 39.7 47.5

Lettuce, head 29.0 34.5 Spinach 12.9 24.0

Cabbage 14.6 11.1 Cabbage 14.0 11.2

Endive 0.7 2.7 Endive 1.7 6.8

Lettuce, leaf 1.7 1.7 Parsley, leaves 0.3 2.7

Parsley, leaves 0.1 0.8 Lettuce, leaf 2.7 2.6

Spinach-babyfood 0.3 0.7 Pear 2.8 0.9

Brussels sprouts 0.8 0.6 Cantaloupe 4.1 0.7

Cantaloupe 3.2 0.4 Tomato 4.4 0.7

Celery 2.2 0.3 Peach 1.7 0.5

Pear 0.8 0.2 Broccoli 2.2 0.5

Peach 0.5 0.2 Watermelon 5.0 0.4

Tomato 1.5 0.2 Pepper, bell 0.7 0.3

Watermelon 2.2 0.1 Brussels sprouts 0.2 0.3

Broccoli 0.8 0.1 Celery 1.5 0.3

Pepper, bell 0.3 0.1 Apple, juice 4.2 0.3

CSFII NHANES
63 

4 References 

Barraj, L.M., Daniels, C.L. (2004) “Drinking Water Consumption Survey”, September 15, 2004, 

Sponsored by BayerCropScience, Performing Laboratory, Ex

ponent.®, pp.40. 

Dodd, K.W., Guenther, P.M., Freedman, L.S., Subar, A.F., Kipnis, V., Midthune, D., Tooze, 

J.A., Krebs-Smith, S.M. (2006) “Statistical Methods for Estimating Usual Intake of Nutrients 

and Foods: A Review of the Theory”, Journal of the American Dietetic Association, pp. 1640-

1660. 

FDA (Food and Drug Administration). (1991-2004.) Total Diet Study—Analytical Results. 

Available: 

http://www.fda.gov/Food/FoodSafety/FoodContaminantsAdulteration/TotalDietStudy/u

cm184293.htm [accessed 16 November 2009]. 

Geyh A.S., Xue J., Ozkaynak H., Spengler J.D., (2000). The Harvard Southern California 

Chronic Ozone Exposure Study: Assessing Ozone Exposure of Grade-School-Age Children in 

Two Southern California Communities. Environ Health Pepect 108:265-270. 

Givens M.L., Lu C., Bartell S.M., Pearson M.A. (2007). Estimating dietary consumption patterns 

among children: A comparison between cross-sectional and longitudinal study designs. 

Environmental Research 103: 325–330. 

Glen, G., L. Smith, K. Isaacs, T. McCurdy, and J. Langstaff. (2007). A New Method of 

Longitudinal Diary Assembly for Human Exposure Modeling, Journal of Exposure Science and 

Environmental Epidemiology, accepted article. 

Glen, G.; Smith, L.; Zartarian, V.; Stallings, C.; Isaacs, K.; Xue, J. (2007). Planned 

Methodologies for Extending SHEDS-Multimedia Model Version 3 (aggregate) to SHEDSMultimedia Model Version 4 (cumulative or aggregate), June 12, 2007, p.30. 

Isaacs K., Xue J., Stallings C., Zartarian V.G. Stochastic Human Exposure and Dose Simulation 

(SHEDS) Model for Multimedia, Multipathway Chemicals: Version 1 SHEDS-Dietary Module 

User Guide. (2010a) Prepared for the July 20-22, 2010 EPA FIFRA SAP, Crystal City, VA. 

Isaacs K., Stallings C., Zartarian V.G., Glen G. Stochastic Human Exposure and Dose 

Simulation (SHEDS) Model for Multimedia, Multipathway Chemicals: Version 4 Residential 

Module User Guide. (2010b) Prepared for the July 20-22, 2010 EPA FIFRA SAP, Crystal City, 

VA. 
64 

Lantz, J.L., Young, B.M., Jones, R.L. and Mihlan, G.J. (2006). “Aldicarb Acute Dietary Risk 

Assessment Including Food and Drinking Water”, Volume I, July 14th, 2006, Bayer CropScience. 

 

 

Lu, C., Barr, D.B., Pearson, M., Bartell, S., and Bravo, R. (2006). “A Longitudinal Approach to 

Assessing Urban and Suburban Children’s Exposure to Pyrethroid Pesticides”, Environmental 

Health Perspectives, September 2006, Volume 114, No. 9, pp. 1419-1423. 

McCurdy T, Glen G, Smith L, and Lakkadi Y. (2000). The National Exposure Research 

Laboratory’s Consolidated Human Activity Database. Journal of Exposure Analysis and 

Environmental Epidemiology 10:566-578. 

Nako, S., Xue, J. and Fort, F. (2007). ISEA Conference Panel, “Using CSFII & NHANES Data 

to Assess Within-day Dietary (food) Exposures to Pesticides.” 

Subar, A.F., Dodd, K.W., Guenther, P.M., Kipnis, V., Midthune, D., McDowell, M., Tooze, J.A., 

Freeedman, L.S., Krebs-Smith, S.M. (2006). “The Food Propensity Questionnaire: Concept, 

Development, and Validation for Use as a Covariate in a Model to Estimate Usual Food Intake”, 

Journal of the American Dietetic Association, pp. 1556-1563. 

Tooze, J.A., Midthune, D., Dodd, K.W., Freedman, L.S., Krebs-Smith, S.M., Subar, A.F., 

Guenther, P.M., Carroll, R.J., Kipnis, V. (2006). “A New Statistical Method for Estimating the 

Usual Intake of Episodically Consumed Foods with Application to Their Distribution”, Journal 

of the American Dietetic Association, pp. 1575-1587. 

US Department of Agriculture (not dated), Agricultural Marketing Service, “Documentation: 

Supplemental Children’s Survey (1998) to the 1994-96 Continuing Service of Intakes by 

Individuals”, http://www.ars.usda.gov/Services/docs.htm. 

US EPA FIFRA Science Advisory Panel (1998). SAP Meeting Comments, March 1998. “A Set 

of Scientific Issues Being Considered by the Agency in Connection with Policy for Review of 

Monte Carlo Analyses for Dietary and Residential Exposure Scenarios.” 

http://www.epa.gov/scipoly/sap

US EPA FIFRA Science Advisory Panel (2000), SAP Report No. 2000-01B, May 25, 2000. 

Session II – A Set of Scientific Issues Being Considered by the Environmental Protection Agency 

Regarding: Dietary Exposure Evaluation Model (DEEM) and MaxLIP (Maximum Likelihood 

Imputation Procedure) Pesticide Residue Decompositing Procedures and Software. 

http://www.epa.gov/scipoly/sap 
65 

US EPA FIFRA Science Advisory Panel (2004), SAP Minutes No. 2004-04, “A Model 

Comparison: Dietary and Aggregate Exposure in Calendex, CARES, and Lifeline”, 

http://www.epa.gov/scipoly/sap

US EPA FIFRA Science Advisory Panel (2005), SAP Minutes No. 2005-04, “A Set of Scientific 

Issues Being Considered by the Environmental Protection Agency Regarding: Prelminary NMethyl Carbamate Cumulative Risk Assesment”, August 23-26, 2005. 

US EPA (1997). Exposure Factors Handbook, Volume I. General Factors. 

US EPA (1999). Time Sensitive Reversibility of Aldicarb Induced Cholinesterase Inhibition as a 

Factor in Acute Dietary Risk Assessment, FIFRA SAP, February 23-24, 1999. 

http://www.epa.gov/scipoly/sap 

US EPA (1999). “The LifelineTM Project to Model Aggregate Exposures to Pesticides”, 

Background papers and Panel comments may be found at 

http://www.epa.gov/oscpmont/sap/meetings/1999/092199_mtg.htm

US EPA (2000a). “Dietary Exposure Evaluation Model”, background papers and Panel 

comments may be found at: 

http://www.epa.gov/oscpmont/sap/meetings/2000/february/final_sap_document_feb_1_2000.pdf

US EPA (2000b) “Aggregate and Cumulative Assessments Using LifelineTM” 

U.S. EPA (2000c) “Models - Residential Exposures - REx Model,”(2000d) “Models - 

CALENDEXTM”, Background papers and Panel comments may be found at: 

http://www.epa.gov/oscpmont/sap/meetings/2000/092600_mtg.htm, 

US EPA (2000). Food Consumption Intake by Individuals. Documentation (1/11/200) and raw 

data files are available on the FCID CD-ROM. See US EPA (2004) website. 

US EPA (2000). Office of Water, ‘Estimated Per Capita Water Ingestion in the United States, 

Based on Data Collected by the United States Department of Agriculture’s 1994-96 Continuing 

Survey of Intakes by Individuals, EPA 822-R-00-08. Available in the U.S. EPA FCID CD-ROM 

US EPA (2000). “Available Information on Assessing Exposure from Pesticides, A User’s 

Guide”, Office of Pesticide Programs, See web link: http://www.epa.gov/fedrgstr/EPAPEST/2000/July/Day-12/6061.pdf. 

US EPA (2003). “Physiologically-Based Pharmacokinetic/ Pharmacodynamic Modeling: 

Preliminary Evaluation and Case Study for the N-Methyl Carbamate Pesticides”, FIFRA SAP, 

November 10th, 2003. 
66 

US EPA (2004). “A Model Comparison: Dietary and Aggregate Exposure in Calendex, CARES, 

and Lifeline”, April 29 - 30, 2004. 

US EPA (2005). “A Set of Scientific Issues Being Considered by the Environmental Protection 

Agency Regarding: Prelminary N-Methyl Carbamate Cumulative Risk Assesment”, August 23-

26, 2005. 

US EPA (2007). “An Update on the Development of the SHEDS-Dietary Model, July 25th, 2007, 

Papers and Panel comments may be found at 

http://www.epa.gov/oscpmont/sap/meetings/2007/081407_mtg.htm 

U.S. EPA. Child-Specific Exposure Factors Handbook (Final Report) (2008). U.S. 

Environmental Protection Agency, Washington, DC, EPA/600/R-06/096F, 2008. 

US EPA (2010). Level I Quality Assurance Project Plan (QAPP) for the Stochastic Human 

Exposure and Dose Simulation Model for Multimedia, Multipathway Chemicals (SHEDSMultimedia), National Exposure Research Laboratory, Human Exposure & Atmospheric 

Sciences Division, Prepared by Alion Science and Technology under Work Assignment #3-12 

Contract No. EP-D-05-065. 

Xue J, Zartarian V, Özkaynak H, Dang W, Glen G, Smith L, and Stallings C. (2006). A 

probabilistic arsenic exposure assessment for children who contact chromated copper arsenate 

(CCA)-treated playsets and decks: Part 2: Sensitivity and uncertainty analyses. Risk Analysis 26: 

533-541. 

Xue J. (2007). SHEDS-dietary SAS code. 

Xue J, McCurdy T, Spengler J, and Özkaynak H. (2004). Intra- and inter-individual activity 

considerations in human exposure modeling. J. Expos. Anal. Environ. Epidem. 14: 221-233. 

Xue J., Zartarian V., Wang S-W., Liu S.V., Georgopolous P. (2010). Probabilistic Modeling of 

Dietary Arsenic Exposure and Dose and Evaluation with 2003-2004 NHANES Data. Environ 

Health Perspect 118(3): 345-350. 

Zartarian V.G. , Glen G., Smith L., Xue J. (2008). SHEDS-Multimedia Model version 3 

Technical Manual, EPA 600/R-08/118, September 2008,

http://www.epa.gov/heasd/products/sheds_multimedia/sheds_mm.html.