AUGUST 2007 FIFRA SCIENTIFIC ADVISORY 

PANEL RECOMMENDATIONS FOR SHEDSDIETARY AND SHEDS-RESIDENTIAL 

MODULES (SUMMARIZED) 

AND EPA RESPONSES 

Background Material for July 20-22 2010 FIFRA Scientific 

Advisory Panel Meeting
i 

TABLE OF CONTENTS 

PREFACE...................................................................................................... 1 

I. DIETARY EXPOSURE ............................................................................ 2 

A. Eating Occasion Analyses: Food Residues........................................... 2

B. Food Consumption Patterns.................................................................. 6

C. Drinking Water Patterns....................................................................... 7

D. Sensitivity and Uncertainty Analyses.................................................. 13

E. NHANES: Seasonal and Regional Effects ......................................... 14

II. RESIDENTIAL EXPOSURE ............................................................... 16 

A. Code....................................................................................................... 16

B. Graphical User Interface (GUI) .......................................................... 18

C. User Guide ............................................................................................ 21

D. Technical Manual................................................................................ 26

F. Multiple Chemicals .............................................................................. 33

G. Fugacity ................................................................................................ 37

H. New Longitudinal Diary Assembly Method ....................................... 38

I. Sobol....................................................................................................... 40

J. Other Changes....................................................................................... 42

APPENDIX..................................................................................................... 45 
1

PREFACE

Over the past ten years, the Agency has requested the Panel to review several probabilistic dietary 

exposure software models.1

 These have included DEEM-FCIDTM, Calendex-FCID, CARESTM, 

LifeLineTM, and an earlier (specialized) version of SHEDS (SHEDS- Wood) designed to assess 

exposures and doses of wood preservatives. SHEDS-Wood was presented to the Panel on August 28, 

2002 and December 3-5, 2003, and focused on children’s dermal and non-dietary ingestion exposure to 

chromated copper arsenate (CCA) on treated decks and play sets. 

In August 2007, SHEDS-Multimedia version 3 underwent review by FIFRA SAP.2 This earlier version 

of SHEDS was an aggregate (single chemical) version that was able to assess residential postapplication exposures. During that SAP, a conceptual approach to a planned dietary module for 

SHEDS-Multimedia was also presented to and reviewed by the Panel. At that time, the Agency directed 

a number of questions to the Panel regarding usability and potential improvements to the SHEDSMultimedia residential module, as well as approaches to assessing dietary exposures, which would be 

coded into SAS and SHEDS in a future version. At this 2007 SAP, the Panel stated that it was generally 

“impressed with the current version of the SHEDS model and future plans for the model...” (SAP 2007, 

p.10), that “the model algorithms appear to be technically correct... [and that] the code is clear enough 

that the algorithms can be easily followed” (SAP 2007, p.10). In addition, the Panel stated that “the 

GUI is helpful and straightforward, but ...could be improved by adding context specific information that

is clearly available at all times” (SAP 2007, p.10). As part of its report, the Panel did have a number of 

suggestions on the organization, clarity, completeness, and usefulness of the model manuals. 

Since that time, Agency staff from the Office of Pesticide Program (OPP) and the Office of Research 

and Development (ORD)’s National Exposure Research Laboratory (NERL) have continued to 

collaborate to develop and code the dietary module for the SHEDS-Multimedia software and to improve 

and update the residential module considering the Panel’s 2007 suggestions. These efforts were geared

toward producing a SHEDS-Multimedia Version 4.0 model, with both a residential and dietary module, 

that could be used to conduct an aggregate (single chemical) exposure assessment for a pesticide or 

other multimedia chemical, or a cumulative (multi-chemical) exposure assessment. 

To date, this collaborative effort between ORD and OPP has produced separate SHEDS-Dietary and 

SHEDS-Residential exposure assessment software programs, each of which address a number of the 

2007 Panels’ ideas and concerns relating to user interface, algorithms, usability, utility, and 

documentation. The current (2010) SAP is being requested to review each of these two models, which 

are considered as modules of SHEDS-Multimedia version 4, and a proposed approach to merge them 

(i.e., calculate dietary and residential exposure for the same simulated individual after food consumption 

and activity pattern diaries are appropriately matched) for the next iteration of SHEDS-Multimedia. 

The purpose of this background document is to recap and summarize for the current (2010) Panel the 

earlier 2007 comments and to provide the Agency’s response to them. It is divided into two sections, 

with the first relating to the dietary module and the second relating to the residential module. These 

sections highlight research and development on the SHEDS-Multimedia dietary and residential modules 

that has occurred since the 2007 SAP; it is hoped that this document provides some background and 

context with respect to the Agency’s continuing activities and efforts aimed at advancing SHEDS in 

particular, and exposure science in general, and that it will assist the Panel in its current deliberations. 

 

1

 Documentation for these models are listed in the April 2004 FIFRA SAP, Model Comparison. See website for meeting 

minutes and documents: http://www.epa.gov/scipoly/sap/meetings/index.htm. 

2

 See http://www.epa.gov/scipoly/sap/meetings/2007/081407_mtg.htm for meeting materials and background documents 
2

I. DIETARY EXPOSURE 

The Agency’s charge to the 2007 FIFRA SAP included five questions (3-1 to 3-5) regarding modeling 

dietary exposures to pesticides. Questions pertained to food residues, food consumption patterns; direct 

drinking water consumption patterns and the Bayer Drinking Water Consumption Survey; uncertainty 

analysis to better characterize exposure estimates; and aspects related to regional and seasonal 

differences in consumption. 

Several questions asked of the Panel were partly motivated by the anticipated transition from using 

CSFII to using NHANES/WWEIA food consumption data in assessing dietary exposures to pesticides. 

The CSFII data were collected in 1994-1996, and 1998. The 1998 data were from a supplemental 

survey, conducted in large part to provide EPA with additional data for children. The 

NHANES/WWEIA database spans 1999-2006 and includes 53, 522 diaries; this is more up-to-date and 

includes approximately 12,000 more diaries than CSFII. The Agency’s Office of Pesticide Programs 

has been developing food recipes for these new NHANES/WWEIA foods, so that the current dietary 

exposure models can be updated to include this more current food consumption survey data. The 

Agency anticipates a public release of those food recipes in summer 2010. SHEDS-Dietary v. 1.0 does 

not have those new recipes, but used instead a very rough form of these in anticipation of this 

information being received at a later date. OPP is working to have the NHANES/WWEIA data 

incorporated into all of the available dietary exposure models, including SHEDS-Dietary. The Agency 

hopes to conduct dietary exposure assessments using the newer these food consumption data in the near 

future, but a timeframe has not been developed for that transition. 

Finally, the Agency has continued work on other topics noted in the dietary methods paper presented to

the 2007 SAP, including Commodity-Specific (Eaters-only) Analyses, and some new contribution 

analyses. The Agency was not able to incorporate those features into this version of the SHEDS-Dietary 

GUI. The table in the Appendix to this document summarizes the status of a variety of features/options

that are available in SHEDS-Dietary and how they relate to a number of 2007 FIFRA SAP questions, 

and the Panel may wish to refer to this as it reviews the 2007 SAP comments. 

The following sections introduce, by topic area, the questions asked of the Panel in its 2007 review of 

the (planned) dietary aspects of SHEDS, followed by both a relevant excerpt of the Panel’s responses to 

those questions, as well as EPA’s 2010 corresponding responses that indicate how the 2007 SAP 

comments were – or sometimes were not – able to be incorporated into this version of SHEDS-Dietary. 

We believe that this background material will assist the Panel in its current review of SHEDS-Dietary 

v.1.0. 

A. Eating Occasion Analyses: Food Residues 

Question 3-1: As described in the SHEDS dietary background document, the timing information 

available in CSFII can be used to model food and indirect water intake throughout the day. With 

the ability to incorporate the timing of eating occasions in dietary exposure assessments, it is 

possible to assign either the same residue or a different residue to foods consumed on multiple 

eating occasions. In certain instances, the former seems logical (e.g., consumption of leftovers) 

while in other instances the latter appears appropriate (e.g., hash browns at home for breakfast 

and fried potatoes away from home for dinner). Please comment on developing simple decision 

rules – as described in the document – for assigning residue to commodities eaten on multiple 

eating occasions. 
3

2007 Panel Comment: The Panel noted that the Agency’s document did not include a clear list of rules 

for review, and suggested developing a decision diagram or flowchart to describe decision rules for 

assigning residues to commodities on multiple eating occasions. (p. 31) 

EPA 2010 Response: The Agency acknowledges that omission in the 2007 document. The 

default approach (e.g., DEEM-FCIDTM) for dealing with residues is to select a single residue for 

each commodity (RAC-FF) and apply that value to total daily consumption of that particular 

commodity (RAC-FF). The Agency considered modifying that decision rule based on the 

information contained in several data fields, including: (1) food item, (2) commodity (RAC-FF), 

(3) time of eating occasion, (3) source of food (e.g., store, restaurant, fast food, school, etc), and

(4) if food was eaten at home or elsewhere. The 2007 background paper did not elaborate what 

data might be used nor depict a flowchart depicting how those responses would affect whether or 

not a new residue would be selected. 

After having gained some experience assessing dietary exposures expressed on an eatingoccasion basis and conducting some preliminary reviews of the food consumption data (see 

below), the Agency is generally comfortable with the default assumption of selecting a single 

residue for each modeled person-day for each commodity (RAC-FF). The SHEDS-Dietary 

module follows that particular assumption. The current version of SHEDS-Dietary includes an 

option in which separate residues are randomly selected when a person consumes a commodity 

(RAC-FF) via different food items. The two options available are as follows: 

• Option not selected: For each new person-day, select a residue for each distinct FoodRAC-FF-TimeOfDay 

• Option Selected: For each new person-day, select a residue for each distinct Food-RACFF 

The example food diary in the 2007 paper (taken from the 1994-96/1998 CSFII) is reproduced 

below to illustrate the SHEDS-Dietary algorithm. The food diary indicates that the child 

consumed ‘home fries’ (food item=71403000) on two eating occasions: at 10:15 am and 6:00 

pm, and fried eggs at 10:15 am; both foods contain the same commodity ‘Pork Fat’. If the 

option “Same food-same residue” is selected, then SHEDS-Dietary will select a new residue for 

‘Pork Fat’ consumed via Home Fries at the 6:00 pm eating occasion. If that option is not 

selected, the SHEDS-Dietary Module selects a new residue for each of the three eating events if 

the option is not selected. In either case, SHEDS-Dietary will select a new residue for the ‘Pork 

Fat’ in Fried Eggs since it is a different food than ‘Home Fries’. [Note: DEEM-FCID will select 

the same residue for all 3 eating occasions since the Food-RAC-FF combination is the same at all 

three eating occasions.] 

CSFII ID=28517-2-2: 1 yr, M, 13.6 kg 

SHEDS-Dietary 

Option (“same food-same residue”) 

Time of 

Day 

Food (COM) Commodity 

(RAC-FF) 

Not 

Selected 

Selected 

Home Fries 

(7140300) 

Pork Fat 

(25002930-213) 

Residue1 Residue1 

10:15 

am Fried Eggs 

(3110500) 

Pork Fat 

(25002930-213) 

Residue2 Residue2 

6:00 pm Home Fries 

(7140300) 

Pork Fat 

(25002930-213) 

Residue3 Residue1 
4

2007 Panel Comment: The Panel expressed several concerns regarding the use of such options: “Such 

level of refinement may not have added value especially when resolution in the food intake diary and 

residue data (each item in composite data does not contain the same level of residue) is lacking.” (p.32) 

EPA 2010 Response: The Agency agrees that it would be preferable to use consumption and 

residue data having similar resolution. We do not know what type of data the users might have 

available for use in any given dietary exposure assessment. Suppose for example, that there are 

two persons, one eating a single apple, and another eating four apples in a single day. If single 

serving residue data were used, then it would be appropriate to select a new residue for each 

apple that the second person ate, rather than applying a single residue to all four apples 

consumed that day. If composite residue data were used, then a single value from that composite 

distribution might better reflect the average residue for those four apples, although the model 

might underestimate exposures for the first person to whatever extent a composite residue may 

underestimate residue concentrations for single apples. 

If a user had different types of residue data for different commodities, then one might want to 

apply a different algorithm based on the type of data being used. For example, if someone had 

concentration data representing single apples, and concentration data representing composited 

samples for oranges, then one might draw a new residue for each apple consumed, but would 

draw only one residue for multiple oranges that someone might have consumed on any given 

day. Implementing such an option would require another layer of programming. Since 

composite data are generally used by OPP in dietary exposure assessments, the SHEDS-Dietary 

model retains the option to select only a single residue value for each food item (e.g., raw 

uncooked apple) consumed on any given day. 

2007 Panel Comment: “In addition, the Panel was concerned that making further assumptions without 

proper support data may introduce additional uncertainties in the exposure analysis. For example, the

only way to determine whether the same food forms eaten at lunch and dinner are separate dishes or the

same dish as leftovers for dinner may only be mere speculation.” (p.32) 

EPA 2010 Response: The Agency agrees that decision rules (simple or complex) may be based 

on limited or no information, but believes that: (i) decision rules can be beneficial in assessing 

uncertainty in the data and modeling algorithm(s); and (ii) that the information contained in the 

CSFII/NHANES surveys (food item) allow for educated guesses with respect to some of those 

uncertainties. In any case, the ability to model with “same food – same residue” option or not can 

be seen as a form of a sensitivity analysis to help evaluate how these assumption may affects the 

exposure and risk estimates. In the case of the food diary highlighted above, we do not know 

whether the ‘home fries’ that the person ate on the second occasion was a leftover from the 

morning consumption, or if a brand new batch was prepared using a different bag of potatoes. 

Since the person consumed the same food on both occasions, the SHEDS-Dietary model has the 

option to use – or not use -- the same residues for all four ingredients, including the fried 

potatoes (RAC-FF=0132990-213) to calculate total daily exposures for that person for that 

particular day. Such an assumption to use the same residue values may overestimate exposure to 

the extent that a different bag of potatoes (and new residue) may have indeed been used to 

prepare the second meal. Alternatively, if SHEDS-Dietary drew a new residue for each eating 

event in which the same Food-RAC-FF was consumed, then the model would underestimate the 

probability that such a person obtained high residues on both eating occasions if he indeed ate 

“leftovers”. Whichever decision rule is implemented, some explicit modeling assumption will be 

made - even if the user is unaware of that assumption and resulting effects. 

 
5

2007 Panel Comment: “For this issue, the first consideration is whether dietary exposure constitutes a 

major contributor to an overall multi-media exposure that exceeds a regulatory threshold. When dietary 

exposure is the focus for refinement, high contributing commodities are identified in the initial 

assessment and the impact of link-day residue for these commodities can then be assessed. .... Once the

potential high contributing commodity-residue pairs are identified, the possibilities for repeated eating 

occasions could then be further investigated. Another generic decision process may be to evaluate how

often people eat the same foods within a day or on different days based directly on food intake diary,

e.g., CSFII data.” (p.32) 

EPA 2010 Response: The Agency agrees that there is little value added to regulatory decisionmakers by examining different residue assumptions in dietary exposure assessments for 

commodities that do not contribute significantly to an exposure estimate

The Agency has performed some analyses of food consumption patterns for selected fruits and 

vegetables and presented some of those findings to the research community (Nako, et. al., panel 

discussion at ISEA Conference, 2007). Two patterns that we observed were: (i) that people often 

consume fresh fruits and vegetables (e.g., apples, grapes, potatoes) on a single eating occasion, 

and (ii) fruit juices (apple juice, grape juice) are more likely to be consumed on multiple 

occasions throughout the day. For example, we found that over 90% (618/669) of children 1 to 2 

years old eating fresh uncooked apples did so on a single eating occasion, while only 58% 

(1129/1957) of the children drinking apple juice did so on a single eating occasion. The Agency 

believes that such food consumption patterns help explain why recovery between exposure 

events (‘eating occasions’ analyses) often has limited effects upon the per capita 99.9th percentile 

for food-only exposure assessments. Accounting for recovery between eating occasions 

(exposure events) is important if a person obtains significant exposures on multiple occasions 

throughout the day, but concentration levels in food items that are often consumed on multiple 

eating occasions (e.g., apple juice, orange juice, bread, etc.), are often lower than those found on 

fresh fruits (apples, oranges) and vegetables. 

2007 Panel Comment: “....the Panel was in general agreement that simple rules have the advantage of 

complex rules for clarity, uniformity of application, and transparency in operation. ... a simple set of 

rules in this case can be somewhat akin to a tier approach that is informed by a series of stepwise 

refinement analysis, progressing toward increasing complexity only when supported by data and the 

need for fine-tuning the analysis.” (p.32) 

EPA 2010 Response: The Agency agrees that such refinements should only be explored when 

necessary. The Agency has found minimal effects on food-only analyses upon aggregate 

exposure at the per capita 99.9th percentile since a large fraction of consumers obtained much of 

their total daily exposures from a single food on a single eating occasion (e.g., NMC CRA). 

2007 Panel Comment: “Since the upper tail is where the main interest lies in risk assessment, it is 

unlikely that there will be significant gain in changing into random draws of residues for multiple 

[within-day] eating occasions” (p.31) 

EPA 2010 Response: The Agency has found that accounting for intra-day recovery often has 

modest effects (10%-20%) on the per capita 99.9th percentile exposure for food-only exposure 

assessments, perhaps due to food consumption patterns and reasons noted above. 

2007 Panel Comment: Finally, the Panel recommended that the Agency review several data sources 

that, including Child Development Supplement to Panel Study of Income Dynamics (PSID) and 

American Time Use Survey (ATUS). 
6

EPA 2010 Response: The Agency has some familiarity with the PSID and ATUS surveys, but 

has not analyzed those data due to limited time and resources. The Agency is aware that those 

surveys contain additional data on time use (activity patterns) and may use these data to update 

the CHAD data base (residential scenarios), but believes those data contain limited information 

on within-day food consumption patterns. The Agency plans to incorporate the CDC NHANESWWEIA food consumption data into its dietary exposure models (more on that below - Q5) 

before exploring other food consumption data sources. 

B. Food Consumption Patterns 

Question 3-2: Please comment on the 8-record approach in SHEDS-dietary and the selection of 

age group, gender, season and day-type from which to create the “diary pools”. What other 

approaches does the Panel recommend? Can the Panel suggest any “bounding approaches” that 

may – based on knowledge of actual eating patterns – provide upper and lower limits for 

longitudinal exposure estimates (e.g., yearlong consumption of the same diary throughout the year 

vs. random daily selection of CSFII diaries). 

2007 Panel Comment: “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) 

EPA 2010 Response: The Agency plans to drop the 8-record approach for some of the reasons 

cited by the Panel, as well as limited data in the CDC NHANES/WWEIA survey which are 

restricted to two non-consecutive days. Specifically, the NHANES/WWEIA survey does not 

disclose the specific dates on which food consumption data (24 hr recall) were collected; 

therefore, this option cannot be implemented using the NHANES/WWEIA data. 

The NHANES/WWEIA data in the current SHEDS-Dietary module contains a DATE field that 

was randomly generated – those Calendar dates are NOT real since the NHANES/WWEIA 

publically available datasets do not contain date information. These date values were created 

since it was difficult to modify the GUI such that users could use the 8-record approach for the 

CSFII data, but not the NHANES/WWEIA data. Since the Agency is dropping the 8-record 

option for use with the NHANES/WWEIA survey data, that Calendar date field will be deleted 

in future versions of the SHEDS-Dietary module. 

2007 Panel Comment: The Panel noted that recent findings (Givens et al., 2007 Env. Research), 

suggests that a minimum of 6 consecutive days’ of food consumption in each of 4 seasons would be 

adequate to represent an individual 1 year dietary consumption pattern. (p.33) 

EPA 2010 Response: The Agency agrees that more information per person would be most 

beneficial for modeling food consumption patterns over medium term durations (1 year). One of 

the Panel members that also co-authored the Givens et al. paper recently provided the Agency 

with longitudinal consumption data for a small group of persons. The Agency appreciates these 

data, and plans to consider them to develop and evaluate methods for generating longitudinal 

consumption patterns. 

2007 Panel Comment: “Some Panel members suggested that the Agency pay attention to the tails, as 

well as the median of the distribution in terms of consumption patterns. The Agency should treat with

skepticism apparent good prediction of upper tail without concurrent good prediction of median. Such a 

situation assumes that the tail was accurately estimated in the absence of accurate prediction of the 

distribution or sources of variance.” (p.33-34) 
7

EPA 2010 Response: The Agency agrees. Efforts to develop and evaluate longitudinal 

consumption patterns for the SHEDS-Dietary model will focus on both the overall distribution 

including median and upper tail. 

While modeled results of the upper tail are often robust to simulation uncertainty, the Agency 

hopes to get good predictions of the median since those are more amenable to use in model 

evaluation. As noted above, the Agency was provided food consumption data patterns for a 

small group of persons. These data contain more diaries per person (~40/person/year) than either 

the CSFII or NHANES/WWEIA surveys, perhaps allowing for evaluation of modeled food 

consumption patterns against actual consumption patterns for actual persons, although not at the 

extreme tails. 

2007 Panel Comment: “The Panel recommends that the Agency should conduct analysis similar to 

those already performed with subsets of CHAD database, and see if the D-statistic may be useful for 

longitudinal implementation.”(p.33) 

EPA 2010 Response: The Agency has pursued this suggestion. The SHEDS-Dietary module 

contains this option (D&A method) using total caloric intake to develop longitudinal (multi-day) 

consumption patterns using the food consumption diaries in the corresponding age and sex 

cohort for the modeled person (See SHEDS-Dietary Technical Manual.) 

2007 Panel Comment: “...the Panel generally agreed that significant value would likely be added to 

the SHEDS model development by collecting longitudinal dietary consumption, as well as individual 

behavior/activity data, particularly the activities at the microenvironment level from a small group of 

study participants stratified by age and gender. ... Other Panel members have suggested the possibility 

of nesting a separate study within the proposed National Children’s Study, and that this option should be 

considered by the Agency.” (p.34) 

EPA 2010 Response: The Agency agrees with the general message that new and better data, 

including longitudinal data, can add tremendous value to the development of aggregate exposure 

models. Agency scientists (ORD and OPP) have also been and will continue to be involved with 

the NCS study. The Agency also recognizes that scientists in other Agencies (e.g., NIH), and 

abroad (e.g., EFSA) have addressed longitudinal and other exposure assessment issues (e.g., Q4 

Uncertainty Analyses), and aims to keep informed of those efforts through scientific meetings 

and publications. 

C. Drinking Water Patterns 

Question 3-3: Please comment on the advantages and disadvantages of providing an option to use 

the Bayer DWCS data in SHEDS. Please include in your comments any statistical concerns or 

issues associated with the design and conduct of the DWCS study. [Clarification to Charge 

Question 3. EPA recognizes that is has provided each of the Panel members with only background 

information on the Bayer DWCS....and has not provided Panel members with the Bayer submission 

itself....EPA will use the thoughts and ideas presented by the Panel in its discussion of this topic and, 

if warranted at a later date, may present the study and our analysis to the Panel, along with our 

proposed use of the study as an available option.] 

2007 Panel Comment: The Panel appeared to consider the fixed option as a reasonable method to 

allocate total ‘direct’ water intake over the modeled day. However, the Panel noted: “The FIFRA SAP 

(2005) suggested allocating (direct) water amount to five events – with three occasions during meals 
8

(25% of total per occasion) and two occasions in between meals (12.5% of total per occasion)”; not 6 

fixed events as stipulated in the Agency’s Eating Occasion analyses. 

EPA 2010 Response: The Eating Occasions analyses currently employed by SHEDS-Dietary 

(and the DEEM-based approach) allocate direct drinking water consumption in 6 equal amounts, 

and at fixed times: 6 am, 9 am, 12 pm, 3 pm, 6 pm and 9 pm.3 Our decision to use fixed times 

vs. the specific meals as recommended by the Panel was made to facilitate programming this 

option. Specifically, not everyone reported eating three meals (breakfast, lunch, dinner). Trying 

to determine ‘three’ primary eating occasions and times for two drinking water events between 

those eating occasions for all reported food diaries would require additional and likely complex 

programming (and decision rules). 

2007 Panel Comment: In attempting to answer this question the Panel raised 8 questions of its own 

(resorted and grouped): 

1. “Do DWCS drinking water patterns differ very much from the current implementation? The limited 

analysis made available to the Panel indicated that most individuals have 5 or fewer drinking water 

events during the day with a modal value of 3 (Figure 4). Distribution is very uniform throughout 

the 16 hours measured (Figure 5). This would seem to suggest that the approach currently 

implemented in SHEDS is representative and there is little here to support implementing the 2005 

FIFRA SAP recommended allocation.” 

EPA 2010 Response: Drinking water patterns are as varied as food consumption patterns. 

Children generally consume more water per kilogram of bodyweight than adults, and as a result, 

infants and children often have the highest total dietary (food+water) exposures when expressed 

on a body weight basis at the per capita 99.9th percentile across all age groups. 

The observation that drinking water consumption patterns vary considerably across individuals 

suggests that using a single distribution (i.e., 5 or 6 fixed events) may not capture such 

variability. The SHEDS-Dietary model includes a second option that allows the total direct 

water intake as reported by that (CSFII or NHANES/WWEIA) respondent to be allocated over 

the day, in proportion to the empirical distributions reported in the Bayer DWCS diaries. For a 

modeled person, the ‘drinking water bins’ were based on similar age, gender and season 

(information ‘synthesized’ in NHANES), as noted in the Technical Manual. 

2007 Panel Comment:

2. Can a minor modification of the current implementation move the drinking water pattern closer to 

the DWCS without having to fully implement the DWCS into the simulation? (p. 35) 

3. Can individuals be grouped into drinking water pattern types that are internally homogeneous and 

externally varied, allowing individuals to be selected to a drinking water pattern “type”? (p. 35) 

4. Can “typical” drinking water patterns be discerned from the data that would lead to pattern types that

would be easier to model and interpret? (p. 36) 

 EPA 2010 Response: The Agency concurs that various types of statistical analyses (e.g., cluster 

or principal components) on the Bayer DWCS data could help identify different ‘types’ of 

drinking water profiles. Reducing the number of the reported drinking water patterns into a few 

‘types’ would simplify modeling drinking water exposures and discussing those predicted 

outcomes. The Agency has not conducted such analyses to date. 

 

3

 The use of 6 vs. the 5 events as recommended by the Panel was an oversight on the part of the Agency. 
9

The Agency notes that the exposure model would still have to assign each modeled person to one 

of those ‘types’. The food diaries (CSFII or NHANES/WWEIA) and drinking water diaries 

(Bayer DWCS) reflect behavioral patterns of different people on different days, and merging the 

information introduces new issues – e.g., a person may have been asleep in one diary at a given 

time and awake at that time in the other diary. This is a similar problem to using time activity 

data (CHAD) and food consumption data (NHANES/WWEIA) to combine potential exposures 

from residential and food uses. The issue is apparent only when the time of day for each 

behavioral activity (time spent playing on turf) from those corresponding diaries. 

The Agency has not conducted further statistical analyses of the Bayer DWCS data as suggested 

by the Panel. The Agency notes that the NHANES/WWEIA survey recently began collecting 

information on the precise timing and amounts of direct drinking water consumption, and those 

food diaries will not require modeling within-day water consumption using either method (fixed 

approach nor Bayer DWCS data). 

2007 Panel Comment:

5. Can the DWCS database be properly weighted to produce population relevant information? This 

question was voiced by a number of Panel members who were concerned about a number of the 

sampling aspects of the study. In particular, there is the potential for bias in study results because of 

the low response rate and high fraction of incomplete diaries. An attempt should be made to 

compute sampling weights to better match study results (the demographics of study responders) with 

the (demographics of the) US population. In particular, the Panel wondered which age, race, 

ethnicity, geographical or other groups were not adequately sampled or whether some of these 

groups are not represented in the dataset at all. (p. 36) 

EPA 2010 Response: Yes, the Bayer DWCS sampling results can be weighted to some degree. 

The documentation to the Bayer DWCS indicates that the sponsors and primary investigator 

employed the services of the National Product Database Group (NPD), a marketing firm that 

maintained a National Home Testing (NHT) panel of 250,000 households. The document stated: 

The NPD group 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, 2004) service.” “The NET 

maintains a diary panel of 2,000 households to track consumption habits of the US population. 

 “Two nationally represented samples (one for each wave) were extracted” from the NHT 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.” (p.9) 

The Bayer DWCS data did not include sampling weights (i.e., oversampling households with 

children), but did include various demographic data, including census region, household income, 

head of household education, race, date (DD-MM-YY), as well as age and gender of subjects 

which could be used to weight the sample respondents. While there might be some bias with 

respect to low response rate and/or under-sampled groups, the direction of such bias is unclear. 

The Agency does not anticipate using the Bayer DWCS data once the timing of direct drinking 

water consumption data becomes available in the NHANES/WWEIA survey (2005/2006 

onward). 

2007 Panel Comment:

6. Will allocation of direct drinking water consumption via an empirical use of the Bayer DWCS data, 

say via some form of bootstrap sampling methodology, produce results that are markedly different 
10

from a modified fixed drinking water pattern? Preliminary simulation results seem to suggest that it 

does not (p. 28). 

EPA 2010 Response: The Agency has found that the Bayer DWCS option tends to produce 

slightly higher estimates at the per capita 99.9th percentile than the fixed (6 event) approach. For

example, (Nako and Xue, 2006) compared the two methods for several drinking water scenarios 

for a particular chemical. The Bayer DWCS option provided slightly higher estimates than did 

the fixed approach for the US population and most age groups (within 10%). As noted in the 

document, the Bayer DWCS did not collect data for infants, and therefore, the option does not 

affect estimated exposures for infants. The infant subpopulation often produces the highest 

drinking water exposure due to their relatively high intake of water (ml/kg bw); however, many 

of the high consumption diaries reflect indirect water consumption (infant formula) for which 

eating occasion data (time of day and amounts consumed) are available. 

SHEDS-Dietary (and DEEM-Based EO) Estimated Exposure at Per Capita 99.9th 

Percentilea

Total Daily Exposure 

SHEDS-Dietary 

(DEEM-Based EO) 

6 Fixed Events 

(2 hr half-life) 

SHEDS-Dietary EO 

(DEEM-Based EO) 

Bayer DWCS 

(2 hr half-life) 

SHEDS-Dietary EO 

Food 

Only 

Food+ 

Water 

Food 

Only 

Food+ 

Water Food Only 

Food+ 

Water 

US Population 37% 

(35%) 

116% 

(119%) 

35% 

(34%) 

55% 

(58%) 35% 66% 

Infants 41% 

(42%) 

278% 

(285%) 

41% 

(41%) 

139% 

(147%) - - 

1-2 yrs 82% 

(77%) 

144% 

(145%) 

77% 

(72%) 

91% 

(95%) 77% 100% 

3-5 yrs 60% 

(64%) 

131% 

(135%) 

57% 

(60%) 

71% 

(77%) 57% 101% 

6-12 yrs 45% 

(50%) 

84% 

(87%) 

43% 

(46%) 

46% 

(48%) 43% 62% 

13-19 yrs 32% 

(30%) 

88% 

(91%) 

31% 

(28%) 

44% 

(46%) 31% 53% 

20-49 yrs 31% 

(30%) 

91% 

(94%) 

30% 

(29%) 

52% 

(54%) 30% 64% 

50Plus 33% 

(35%) 

70% 

(72%) 

32% 

(34%) 

45% 

(47%) 32% 46% 

Females 13-49 yrs 31% 

(30%) 

89% 

(92%) 

30% 

(29%) 

50% 

(50%) 30% 55% 

a

From Nako and Xue (2006). The DEEM-Based EO (Eating Occasion) results as provided in this table are similar to the 

Cross-Sectional SHEDS-Dietary Eating Occasion results, but that procedure does not allow empirical use of the Bayer 

DWCS data. They are presented here for comparison purposes. 

2007 Panel Comment:

7. Is it true that all water consumed in a day must have the same concentration? “A note in Section 3.5 

suggests that all water consumed in one day has the same concentration, regardless of source. This 

may have been true in the past but can this really be the case when many individuals drink water 

from multiple sources each day (bottled, tap, filtered, etc)? One situation mentioned was the 

scenario where contaminants in the water occur only at one specific time in the year (a pulse) with 

concentrations the rest of year being zero. Another situation mentioned is where the water used in 

cooking food is the contamination pathway. It is possible that factors related to drinking water 

source, municipal versus private, ground versus surface, chlorinated versus not, may influence 

consumption rates. Individuals may not be fully aware of many of these factors, but they will know 
11

where the water they pay for comes from and this may also affect consumption. Finally we should 

not lose sight of the information available on residue data in drinking water as available in the Office 

of Drinking Water.” (p.36) 

EPA 2010 Response: People often drink water from different sources on any given day (e.g., 

tap: home, office/school; bottled; travel, etc.), and water from different sources may contain 

different concentrations. The CSFII survey asked the following questions regarding this issue: 

• “What is the main source of the water used for cooking in the home? {Community 

water supply, Well or cistern, Spring (HH or public), Bottled water, Other, Don’t 

know}” (RT15, H20_COOK) 

• “What is the main source of the water used in your home for preparing beverages 

such as coffee, tea, juices, and baby formula? {same responses as above}” (RT15, 

H20_BEVR) 

• “What is the main source of plain drinking water in your home?” (RT15, H20_DRNK 

The next two questions refer to the amount of direct water consumption reported in the 

food diary: 

• “How much of this plain drinking water came from your home? Would you say all, 

most, some, or none?” (RT25, D1_H2O_) 

• “What was the main source of plain drinking water that did not come from your 

home? Was it tap water, water from a drinking fountain, bottled water, or something 

else?” (RT25, D1_H2O_A) 

The Agency used responses to some of those questions to decompose both direct and indirect 

drinking water by four sources; tap, bottled, other, source not specified; indirect drinking water is 

further decomposed over 19 different food forms (e.g., uncooked, cooked-boiled, etc.). The 

documentation for those allocations is contained in the FCID food recipes. Other aggregate 

exposure models (e.g., DEEM-FCIDTM, CARESTM, LifelineTM) allow users to assign different 

drinking water concentrations based on those four different sources; however, the Agency’s risk 

assessments have not used that option to refine drinking water exposures. There are no plans to 

incorporate such an option into the SHEDS-Dietary model. 

2007 Panel Comment:

8. Because exposure from drinking water sources is a very small fraction of total exposure, is there a 

need to further model drinking water in the SHEDS model? “A number of Panel members felt that 

because of its low contribution to total exposure (e.g., 1-2 orders of magnitude less than food 

exposures), further modeling of drinking water consumption would not noticeably change model 

results” – this view was also mentioned at a previous SAP; “other Panel members felt that in some 

exposure scenarios the contribution from drinking water could represent a large fraction of total 

exposure and in those cases different water allocation models could produce quite different results” 

(p.36) 

EPA 2010 Response: The Agency has found that drinking water contributions can often ‘drive’ 

total dietary (food+water) exposures, and therefore, the eating occasions’ analyses may have a 

significant effect upon aggregate exposures at the per capita 99.9th percentile. 

When relatively high predicted drinking water concentration values are included in the dietary 

assessment and water is the primary contributor, infants (newborns) often have the highest total 

dietary (food+water) exposures. Otherwise, children (1 to <3 years old or 3 to <6 years old) 

often have the highest total dietary exposures when fresh fruits and vegetables are the primary 

contributors. The infants having the highest water intake generally obtained much of their total 
12

daily water intake through infant formula. Since infant formula is a food item, the time and 

amounts consumed throughout the day (24 hr recall) are provided in the CSFII and 

NHANES/WWEIA surveys. The Bayer DWCS and Fixed option focuses on allocating only the 

‘direct’ portion of total drinking water intake over the particular day. The Bayer DWCS survey 

did not collect information on infants, but that information is not critical for most of the high-end 

exposures since the CSFII (and NHANES/WWEIA) survey captured the timing and amounts of 

their indirect water (infant formula) intake. 

Relatively little time and resources have been devoted to refining the SHEDS-Dietary drinking 

water exposure algorithm. The focus on refining dietary exposures from food is largely due to 

the developers’ experience that there is relatively less uncertainty in food residue inputs. The 

predicted drinking water concentrations used in those analyses are often derived from 

environmental fate models using conservative inputs (e.g. prevalence of land use, percent of 

watershed planted to crop, scope and magnitude of pesticide use, percent of crop treated and 

labeled use rates). A risk characterization of those parameters may better address uncertainties 

regarding such predicted drinking water exposures than other refinements to the drinking water 

exposure algorithm (e.g., by source, etc.). 

2007 Panel Comment: “Previous SAP consultations on this topic remarked at the lack of adequate 

information on drinking water consumption patterns to properly analyze the fraction of exposures from 

this source. A good portion of the Panel do not think that the issue related to exposure from drinking 

water sources are likely to be less in the future and would like to see drinking water’s impact examined 

in more detail. Issues such as water sources, use in cooking, temporal changes in contamination levels, 

methods of disinfection, etc., all seem to need further discussion and examination.” (p.36-37) 

EPA 2010 Response: The Agency concurs that more data should be sought, and further 

examination of those data should be made when drinking water exposures pose concerns. We 

note that we do not expect drinking water exposures to be a concern for pyrethroids.

 

2007 Panel Comment: “Water consumption patterns in the US seem to have changed since CSFII data 

were collected and, due to the wide use of bottled water, water is now a consumer product.” (p.37) 

EPA 2010 Response: The Agency agrees. The Agency presented some preliminary analyses 

comparing drinking water consumption in the CSFII and NHANES/WWEIA data (1999-2004) at 

a scientific conference (Xue, J. Nako, S. and Zartarian, V. 2007; International Society of 

Exposure Analysis). Those analyses provided several findings, including: (i) no significant 

differences of Indirect DW consumption, but (ii) Direct drinking water consumption rates is ~20 

- 30% higher in the NHANES/WWEIA data. 

2007 Panel Comment: “The current PDP data on finished water can be used in the reanalysis for 

drinking water contribution when needed.” (p.37) 

EPA 2010 Response: The Agency has not used the PDP data to model drinking water exposures 

to pesticides. Except for a few exceptions, the Agency has used predicted drinking water 

concentrations from environmental fate models, such as PRZM-EXAMS and SCIGROW to 

assess drinking water exposures. There are various science and policy reasons for this practice 

(e.g., the community water systems sampled may not reflect the most conservative sources for 

specific pesticides (i.e., highest predicted concentrations), and the number of samples collected 

were not designed to capture annual peak concentrations). The Agency may consider using the 

PDP data in future work on pyrethroids to evaluate the SHEDS-Dietary model. 
13

D. Sensitivity and Uncertainty Analyses 

Question 3-4: Sensitivity and contribution analyses are a routine part of OPP risk assessments. 

These analyses help inform the risk manager how exposures may change when certain model 

inputs are modified. These modifications to the model inputs are typically performed “one at a 

time” to permit isolation of the effect. In a typical risk assessment, all the dietary consumption 

data (i.e., reported CSFII diaries) are used along with the best available pesticide residue data. 

OPP risk assessors specify a sufficiently large number of Monte Carlo iterations such that 

exposure estimates are stable with respect to the random seed. 

The Agency has not conducted formal quantitative uncertainty analyses. The Agency presented a 

simple bootstrapping procedure for conducting uncertainty analyses, utilizing only a subset of the 

consumption and residue data inputs. That procedure was designed to provide some insight into 

the question, “How much better would our exposure estimates be if we had more data?” by 

conducting the uncertainty analysis in the other direction. (a) Please comment on the scientific 

soundness and utility of the proposed bootstrap uncertainty approach. (b) Can the Panel 

recommend alternative approaches – and how they might be interpreted and used –for conducting 

uncertainty analyses of dietary exposure estimates?

2007 Panel Comment: “One index of uncertainty suggested, the ratio of the 95th percentile to the 5th

percentile, is actually a measure of distributional spread. The median of this measure was proposed as 

one way of quantifying the potential impact on model results of inadequacies in the diet or residue data. 

An alternative measure suggested is the median of some important upper percentile; say the 99th

percentile, across the set of simulated response distributions.” (pp. 37-38). 

EPA 2010 Response: The Agency believes that some Panel members may have misinterpreted 

the ratio (95th/5th), and would like to clarify how that ratio was calculated. First, the uncertainty 

analyses presented in the document dated 7/25/07 is based on the cross-sectional option SHEDSDietary model (not the D&A or 8-Diary longitudinal method). One hundred iterations were 

performed on all food diaries, and the total dietary exposure at some per capita percentile (99th) 

is computed from those outcomes. This procedure is repeated 200 times with subsets of residue 

and/or consumption data by the bootstrap method, providing 200 values of estimated exposure at 

the per capita 99th percentile. The 5th and 95th percentile of those 200 values of 99th percentile 

are calculated, and the ratio is based on those two values. If the 99th percentile over those 200 

replicated samplings ranged from 2 ug/kg bw-day (5th) to 4 ug/kg bw-day (95th), then the ratio is 

2 (=4/2). Note that the GUI for the SHEDS-Dietary does not allow the user to conduct the 

uncertainty analyses method presented in the 2007 analyses at this time, although it can be done 

by programming in the underlying SAS code and will be done in the next version. 

2007 Panel Comment: “...the proposed method will not indicate the true value of enlarging the 

database. It will be useful for showing the uncertainty associated with re-sampling from a finite set of 

consumption and residue inputs.” (p.38) 

EPA 2010 Response: The Agency concurs. 

2007 Panel Comment: “Some Panel members had suggestions for other analyses approaches, 

including: (i) Measuring impact of changes to p1 and p2, (ii) post hoc power analysis, (iii) inter- and 

intra-run variability and (iv) alternate indices.” (pp. 38-40) 

EPA 2010 Response: The Agency has not yet been able to carefully consider the Panel’s 

recommendations or conduct/evaluate uncertainty analyses in the SHEDS-Dietary module. 
14

2007 Panel Comment: “One Panel member indicated that the figures used in the report are transposed 

with respect to what is typically seen in publications, in this case with the probability axis on the 

horizontal and amount on the vertical axis. From a communication standpoint it would be preferable to

make that change.” (p.38) 

EPA 2010 Response: The Agency has incorporated the Panel’s suggestion regarding axes 

configuration when presenting the results for some outputs, and will consider this 

recommendation further in future versions of SHEDS. 

E. NHANES: Seasonal and Regional Effects 

Question 3-5: Please suggest statistical or other methods that might be used to determine the 

extent to which region- and season- specific dietary consumption amounts and patterns might be 

important in developing dietary exposure estimates. Please consider in your response whether 

and how quantitative uncertainty methods could be used in addressing this issue. 

2007 Panel Comment: “Statistical methods alone cannot resolve the uncertainties that arise due to 

regional and seasonal variations. The issue of the regional and seasonal differences can be addressed

through analyzing the population and region- or season-stratified datasets, such as data from, for 

example, CSFII. For example, the CSFII dataset has the information on season and region, and CSFII 

was designed to represent the whole population. Using bootstrap methods, the percentiles of the 

exposure estimates can be computed for each region, each season, and region and season combination. 

The 50th, 95th, and 99th percentiles can be compared to evaluate the impact of regional and/or seasonal 

differences in diaries, consumption, and exposure on the exposure estimates.” (p.41) 

EPA 2010 Response: The Agency conducted limited analyses on food consumption patterns 

based on the CSFII food consumption survey information, and provided some of those results to 

ORD/NCEA to update the Agency’s draft Exposure Factors Handbook (EFH 2009). For 

example, there are differences in consumption by region: e.g., consumption of okra is more 

prevalent among people living in the South (1.2 % on any given day) than Midwest (0.4%); and 

by season: e.g., more people eat pumpkins during the fall (2.8% on any given day) than during 

the spring (0.3%)4

. 

2007 Panel Comment: “One Panel member raised a question of whether commodity consumption 

differs regionally, pointing out that diaries might be different from region to region, but commodities are 

likely to be from national or international sources. Exposure analyses that were presented at the 

previous SAP meetings indicated that seasonal and regional variations do not generally contribute 

significantly to the high end dietary exposures when a large number of commodities are included in the

dietary exposure estimation. However, without seasonal- and regional- specific analyses, this 

conclusion is not apparent in light of the intuition that seasonal and regional variations of consumption 

likely exist.” (p.40-41) 

EPA 2010 Response: The Agency recognizes two aspects of this issue: (i) calculation of per 

capita 99.9th percentile, by region and/or season, and (ii) modeling dietary exposures based on 

 

4

 These values are somewhat different than the 2.6% in the South and 0.4% in the Midwest for okra and the 4.9% in the fall 

and 0.4% in the spring for pumpkins reported in the EFH. The EFH reports two-day averages and the percentages there 

reflect percent of population reporting consumption of the commodity of interest on at least one of the two reporting days. 

(The draft 2009 EFH is available at the following website: http://cfpub.epa.gov/ncea/cfm/recordisplay.cfm?deid=20563) 
15

national-level residues or ‘binning’ residues by region and/or season. With regards to 

calculating population-based statistics, the Agency notes that current aggregate exposure models 

(e.g., DEEM-FCIDTM) can calculate aggregate exposure at the per capita 99.9th percentile by 

region or season. The user can also perform similar calculations using the SHEDS-Dietary 

module (by region, season or both) since SHEDS retains all simulated outcomes and the 

demographics of the corresponding persons (at least for CSFII since seasonal and regional 

information is not available for the NHANES/WWEIA survey). The 99.9th percentile exposures 

for the highest season or region will generally be higher than the overall 99.9th percentile since it 

is based on a subset of all outcomes; however, the Agency has focused on the overall per capita 

99.9th percentile, for specific age groups (infants, 1-2 yrs olds, 3-5 yrs olds, 6-12 yrs old, 13-19 

yrs old, 20-49 yrs old, 50+ yrs old), and females 13-49 years old for reproductive and 

developmental toxicity endpoints. 

The second aspect concerns whether the aggregate exposure model bins and selects residues 

based on source (origin and/or season). Such a refinement could produce slightly different 

effects if there is correlation between consumption and residues. For example, if high 

consumption and high residues occur in particular seasons (binge eaters at pick-your-own farm), 

then having one distribution for all residues may underestimate the occurrence of such exposure 

events. On the other hand, if low consumption often occurs with high residues (e.g., less 

purchases and consumption of higher priced imports during off-season), then assuming nationallevel residues may generate exposures that never occur. The model currently used by the 

Agency (DEEM-FCIDTM) does not permit such refinements; we are considering plans for 

developing such an option in the SHEDS-Dietary module. 

2007 Panel Comment: “Panel strongly recommends that the Agency should be involved in the study 

design stage for the future NHANES to address those missing data/information issues. The Panel 

supports a better and stronger sharing of these data among the Agencies. 

EPA 2010 Response: The Agency has developed strong working relationships with staff and 

managers at CDC and USDA regarding the NHANES study (both food consumption as well as 

biomonitoring data). 

2007 Panel Comment: The Agency is encouraged to continue looking into collecting more updated 

dietary survey data, especially for building longitudinal patterns...If possible, the further study that the 

Agency will conduct should be reviewed by a separate SAP prior to the initiation of data collection.” 

EPA 2010 Response: The Agency will consider review of science and ethical issues (protocol, 

etc.) by a separate SAP if resources are available for future data collection efforts. 
16

II. RESIDENTIAL EXPOSURE 

The Agency’s charge to the 2007 FIFRA SAP included eight questions regarding the aggregate 

residential module of SHEDS. Questions pertained to model code, approaches, and algorithms; the 

graphical user interface (GUI); documentation; and diary assembly methods. The Panel was asked to 

“try out” and test the software and many good suggestions regarding this aspect of the model were 

made. Other questions asked of the Panel at that time reflected various specialized issues such as the 

fugacity approach and Sobol’s method for sensitivity analysis. 

The purpose of this section of this background document is to recap and summarize for the current Panel 

the earlier 2007 comments with respect to the residential exposures assessed by SHEDS v.3 and to 

provide the Agency’s 2010 response. The following sections introduce, by topic area, the questions 

asked of the Panel in its 2007 review, followed by both a relevant excerpt of the Panel’s responses to

those questions, as well as EPA’s 2010 corresponding responses indicating how the comments were, if 

possible, able to be incorporated into this version of SHEDS-Residential. We believe that this 

background material will assist the Panel in its current 2010 review of SHEDS-Residential v.4.0. 

A. Code 

Question 1-1 a) Were Panel members able to load the software on to their computers? What, if 

any, difficulties were encountered in loading or running the software? 

2007 Panel Comment: “The Panel recommends...that the prerequisite of SAS version 9.1.3 needs to be 

clearly stated in the introduction and installation instructions up front.” (p.15) 

EPA 2010 Response: The SHEDS-Residential version 4.0 Technical Manual and User Guide 

both state that SAS version 9.1 or higher is needed. In the Technical Manual, the notice appears 

in sections 1.1 and 1.3 of the Introduction. In the User Guide, the notification appears in section 

1.1, “Considerations for New Users of SHEDS-Residential.” 

2007 Panel Comment: “...the SHEDS installation program should check for SAS and abort installation 

if SAS is not installed.” (p.15) 

EPA 2010 Response: As noted above, the user is informed of the need for SAS 9.1 (or higher) 

in both manuals. 

2007 Panel Comment: “Panel members also noted a bug in the Installation Process: the batch file, 

“multimedia.bat” in the installation folder, contains incorrect information on the installation folder

location, and hence does not run. It should be fixed with the correct folder location.” (p.15) 

EPA 2010 Response: This is not a bug. The manual makes it explicit that you need this 

directory and that it may need to be renamed. The appropriate text is now in Section 7.1 of the 

user’s guide, “Installing Batch Capability.” The file is now named ‘SHEDS.bat’. 

2007 Panel Comment: “Cross-platform compatibility was one issue that the Panel felt should be 

addressed in future iterations of the program. Therefore, it may benefit users of other operating systems 

if the installation package is also provided as a zip archive (i.e., the users can then extract the folder and 

change the SAS invocation based on the requirements of their operating system). This facilitates, with a 

minimal amount of effort, use of SHEDS on other operating systems. Such a package could be provided 

through EPA’s ftp servers.” (p.15-16) 
17

EPA 2010 Response: This was not done for version 4. This would be a major development 

effort, especially for the GUI, that would require significant QA and resources.

2007 Panel Comment: “It appears that the uninstall program simply removes the underlying SHEDS 

program and all the simulations the user may have defined and run until that point. This should be 

clearly noted, and the user should be provided detailed information and prompted about this step.” (p.

16) 

EPA 2010 Response: Uninstalling is a function of the installation wizard and Windows, and 

because of this, no prompt message has been added. However, in the User Guide, two warnings 

appear about the loss of files that may have been previously created. A warning box is in Section 

3.2, “Installation under MS Windows” and boldface type appears in Section 3.2.5 “Removing 

SHEDS-Residential.” 

2007 Panel Comment: “They also felt it was useful to add utilities to make the model more accessible 

and useful, e.g., code to review input values and note extreme or illogical combinations, or a script 

generator to run designed experiments on the model.” 

EPA 2010 Response: Checks on illogical combinations would be impractical to do 

comprehensively given available resources, so no major additional checks or review 

opportunities have been added. After considering this suggestion, however, verification of the 

legitimacy of inputs was improved slightly. For example, checks have been added that variables 

representing fractions or probabilities do not exceed one. Also, checks to prevent negative 

values have also been implemented. 

Regarding a script generator, the user cannot submit multiple variability runs simultaneously via 

the GUI; however, design of experiments may be assisted in that the user can now submit 

sensitivity and uncertainty runs through the GUI. Multiple variability runs could be prepared in 

advance via the GUI, and then simultaneously submitted in batch mode (for example, to run over 

a weekend). See Section 7 "SHEDS-Residential: Batch Mode" of the User's Guide. 

2007 Panel Comment: “... the naming of the variables within the SHEDS code can be significantly 

improved.” 

EPA 2010 Response: Variable naming has been improved for version 4.0, as detailed in 

Appendix B of the SHEDS-Residential version 4.0 Technical Manual. 

2007 Panel Comment: “Autocorrelation can have an important effect on the tails of the exposure 

distributions.” 

EPA 2010 Response: This was considered for v. 4, but given available resources, data, and the 

number of inputs that would be required for this option, and considering relative importance of 

this option compared to other priorities, autocorrelation between events was not implemented. If 

the D&A method is chosen for the longitudinal diary method in v.4, however, then the user can 

implement autocorrelation on the key exposure variable between diaries for each simulated 

individual, as detailed in the SHEDS-Residential version 4 Technical Manual, section 2.5.1.

 

2007 Panel Comment: “It is important that the developers anticipate future data sets, especially 

longitudinal, and try to keep the code scalable.” 
18

EPA 2010 Response: We agree with the Panel's comments, and have implemented flexibility 

and scalability in the SHEDS SAS code and input data. However, we are still exploring options 

of these aspects for longitudinal analyses because we are refining the methodology. 

B. Graphical User Interface (GUI) 

Question 1-1 b) The SHEDS-Multimedia version 3 graphical user interface (GUI) was designed to 

be user-friendly to exposure modelers and risk assessors. Please comment on the organization and 

usability of the GUI, any difficulties you encountered, which features and output capabilities were 

most useful, and whether any additional options would be helpful. Please also comment or offer 

suggestions for improving the GUI/model interface. 

2007 Panel Comment: “The Panel identified three major issues that should be addressed in future 

versions of the software. First, the Panel felt that there was need for a substantive case study to guide 

novice users, preferably using realistic data. 

A novice user, without appropriate data sets, will have significant difficulty or be overwhelmed by the 

number of inputs to provide, even for a simple case. This highlights the need for an example case that 

will also allow users to make minor customizations and not have to develop a case from scratch.” (p. 16) 

EPA 2010 Response: Both dietary and residential case study simulations were conducted with 

version 4, and are provided in the Technical Manual and User Guides. The case studies in the 

User Guides (dietary and residential) were designed to lead a novice user through the GUI 

screens. Also, the Technical manual contains default suggestions for non-chemical specific 

inputs in Appendix G, and provides some guidance on running the model with fewer relevant 

inputs by disabling user-selected pathways.

2007 Panel Comment: “Second, Panel members noted that there were many hard-wired approaches in 

the model, and these need to be clearly identified in the program and the manual and preferably open to 

adjustment by advanced users from within the program.” (p. 16) 

EPA 2010 Response: SHEDS developers used what we feel are best available approaches, and 

provide several options to the user in some cases (e.g., dermal method, concentration 

generation). The longitudinal diary assembly and sensitivity options have been expanded in 

version 4. These are discussed in the SHEDS-Residential Technical manual, sections 2.5 and 5.5, 

respectively. In addition, the interface now provides a utility for sub-setting the CHAD database 

(see Section 5.4 "Specify Population and Sampling" of the User's Guide). Because the SAS code 

and GUI are available to the user, advanced users can make additional changes. We minimized 

hard-wired inputs and documented them clearly in the Technical Manual (see beginning of 

section 2).

2007 Panel Comment: “Error checking and reporting need to be improved within the program in terms 

of the number of simulations that can or should be run. At present the system allows the input of an 

unreasonably large number of individuals to simulate (on the order of 10200: higher numbers are reset to 

10), without checking or reporting disk space or processing time requirements.” (p. 16-17) 

EPA 2010 Response: Both the Technical Manual and User Guides now provide estimates of run 

time based on the number of simulated individuals. The notice appears within the first three 

paragraphs of the User’s Guide. The information appears in Section 1.3 “SHEDS-Multimedia 

Version 4 Overview” of the Technical Manual. An advisory on storage requirements appears in 
19

both Section 1.3 of the Technical Manual and Section 3.1 “Requirements” in the User’s Guide. 

The user is now also provided an on screen estimate of the run time.

2007 Panel Comment: “Similarly the specification of run names needs to be made more systematic. 

When a run name is not specified, instead of a quick error message, the application goes to the next 

screen and provides a cryptic error message. Later, a blank case is created. It is probably better to have a 

default such as “untitled” following the conventions used in many applications.” (p. 17) 

EPA 2010 Response: This has been implemented for Version 4.

2007 Panel Comment: “When checking for errors (using the “Check for Errors” button), the 

application gave very cryptic messages such as: “deleted”, “library not found”, etc., and then finally

reported “no errors found.” The error messages are likely due to outputs of some underlying operations 

that do not impact the specific case, so they should be appropriately filtered out.” (p. 17) 

EPA 2010 Response: No change was implemented in this regard, as other revisions had greater 

priority. 

2007 Panel Comment: “Additional major features that would be desirable for SHEDS are 1) “the 

ability to specify alternative locations or folders for storing simulations;” (p. 17) 

EPA 2010 Response: This has been implemented in Version 4.

2007 Panel Comment: 

2) “the application should allow the user to specify different folders for storing simulations (this will 

often be dictated by disk space constraints, etc.) and allow a user to organize simulations in a 

hierarchical manner. A major advantage of this approach is that the previous simulations are saved even 

when SHEDS is uninstalled;” (p. 17) 

EPA 2010 Response: This has been implemented in Version 4.

2007 Panel Comment: 

3) “the model documentation should clarify what is meant by some terminology within the program, for 

example: the use of terminology such as “Application Scenarios” versus “Run Files” can be confusing;”

(p. 17) 

EPA 2010 Response: Wording has been changed in both the User’s Guide and Technical 

Manual to improve clarity. Terminology revision was a particular focus in the transition to 

version 4. 

2007 Panel Comment:

4) “the interface should include a “tool tip” for each menu item which provides descriptive information 

while being unobtrusive; (p. 17) 

EPA 2010 Response: Help text is now available in the Version 4 GUI. It appears in the SAS 

Help Bar at the bottom of the SAS screen.

2007 Panel Comment: 

5) “the application should include functional help buttons.” (p. 17) 

EPA 2010 Response: Help buttons are now available in the Version 4 GUI. 
20

2007 Panel Comment: “Desirable additional minor features noted by the Panel include having the “Run 

Name” shown on the title bar of the pop-up windows at all stages. This is even more important in the 

context of the following software bug noted by one Panel member: when the user asks to overwrite an 

existing run with the one he/she is editing, an error message is displayed (saying that the run already 

exists), and the application loads up the existing old run. The user is not provided an opportunity to force 

the change, and has to cancel the operation. However, upon canceling the operation, the old run 

becomes the “current run”, and any further changes made will be reflected in that run. Having the run

name displayed prominently will alert the user right away.” (p. 17) 

EPA 2010 Response: The number of allowable characters for the run name has been limited to 

32. The run name now appears on each title bar in Version 4.

2007 Panel Comment: “The GUI is helpful and straightforward, but most Panel members felt it could 

be improved by adding context specific information clearly available at all times. One of the major 

issues with the program is that it places great responsibility on the user to make sure that the inputs are 

logical. The user can, for example, assume an exponential distribution for the value of a parameter, with 

no suggestion that it has to be between 0 and 1. The Panel also felt that there should be a more 

convenient way to see a summary of the inputs to facilitate checking that all values are logical.” (p. 16) 

EPA 2010 Response: In the version 4 GUI, the user has the option of exporting all inputs for a 

given run to Excel for printing. Checks have been added that variables representing fractions or 

probabilities do not exceed one. Checks to prevent negative values have also been implemented. 

2007 Panel Comment: “One Panel member wanted to focus on modeling female subpopulations, and 

then changed the inquiry to include both sexes, only to have the program lock up with no ability to 

return to previous screens. Since there was no option to cancel, the user needed to restart SAS. The

user should be allowed to return to previous selections or the screen revised to include a running output 

of the selections made. For example, it would be useful to be able to return to the “specify exposure

scenario details” screen easily from submenus.” (p. 16) 

EPA 2010 Response: The problem encountered by the panel member was traced to a 

nonfunctioning “Cancel” button. This has been remedied.

2007 Panel Comment: “...Panel members expressed concerns about the quality of the graphics and 

plots generated by the program. Although this is a problem associated with SAS itself, providing 

systems and/or advice to the user on methods to provide reasonable quality graphics and plots is 

important for promoting SHEDS usage.” (p. 16) 

EPA 2010 Response: The option to export output files into CSV format, which can then be 

imported into other graphical packages outside of SAS, exists within SAS, and the procedure for 

using it is described in Section A.5 “Exporting SAS Datasets” in Appendix A of the User’s 

Guide. Text in Section 5.11.1 “Additional Outputs and Files” refers the user to this appendix.

2007 Panel Comment: “The Panel noted a need for clearer instructions on run times...” 

EPA 2010 Response: Both the Technical Manual and User Guide now provide estimates of run 

time based on the number of simulated individuals. The notice appears within the first three 

paragraphs of the User’s Guide. The information appears in Section 1.3 “SHEDS-Multimedia 

Version 4 Overview” of the Technical Manual. The user is now also provided an on screen 

estimate of the run time.
21

2007 Panel Comment: “...it is important that units of input variables be displayed because specification 

of units fosters better user understanding. Units of input variables should be clearly stated in all cases 

because specification of units is important to user understanding.” 

EPA 2010 Response: In reviewing SHEDS-Multimedia in response to this comment, some 

instances were found where units did not appear on the screen. This has been remedied. 

C. User Guide 

Question 1-1 c) Please comment on the organization, clarity, completeness, and usefulness of the 

User Guide document and provide any suggestions for improvement. 

2007 Panel Comment: “The Panel recommends, however, that the prerequisite of SAS version 9.1.3 

needs to be clearly stated in the introduction and installation instructions up front. Furthermore, it would 

be helpful to provide users with some idea as to run time expected in the documentation. ...

• “The SAS log window will accumulate numerous unessential messages, so it might be useful to 

have a separate SHEDS window to show progress of a simulation.” (p. 17) 

EPA 2010 Response: The prerequisite of SAS version 9.1 or higher is now clearly stated in the 

documentation up front, as suggested. A run progress indicator is now displayed on the screen. In 

addition, text has been revised in Section 5.5.8 “Save the Log File” in the User’s Guide. The revised 

wording clarifies that when the log screen is saved to an external file, this “turns off” the log on the 

screen and allows the user to check the status of the run via accessing the external file. 

2007 Panel Comment:

• “The issue of truncation of distributions is important for the user to be able to review. The 

program should warn the user when inappropriate values are selected.” (p. 18)

EPA 2010 Response: In the GUI, a plot of the specified distribution is available for many inputs. 

Truncated values are "stacked up" at the truncation point. It is visually clear when this becomes 

excessive. Additional options for handling inputs that are sampled “out of bounds” have been added 

to version 4; these are discussed in Section 3.1.4.1 “Truncation of Distributions” in the Technical 

Manual. 

2007 Panel Comment: 

• “The overview of the SHEDS interface, shown in Figure 1 of the User Guide, is inconsistent 

with the flow of the program” (p. 18) 

EPA 2010 Response: This figure has been revised for Version 4 and is now designated Figure 2.1. It 

was not intended as a detailed flowchart, but rather as an overview of the program’s operation. Text 

has been revised to alert the user that not all screens are represented in the figure and therefore this 

figure is not meant to lead one through individual screens as they are encountered in the interface.

 

. 2007 Panel Comment:

• “The installation documentation should provide information relevant to Windows Vista, 

especially given that in a time frame of one or two years, a significant portion of the user 

community may be using Vista.” (p. 18) 
22

EPA 2010 Response: With Windows Vista being superseded by Windows 7, this comment would 

seem to be less relevant than at the time of the last SAP. However, the point has been investigated 

with SAS. Vista Professional is supported by SAS, but Vista Home is not. SAS did indicate that 

some problems have been reported when using SAS on the latter version. As noted in the User’s 

Guide, SHEDS-Residential version 4 has been developed and quality assured under the Windows 

XP environment. Consultation with SAS directly has indicated that the model and interface should 

operate correctly under Windows 7, but quality assurance has not been conducted for Version 4 

within this operating system. SAS does support Windows 7 Professional and Home Premium (but 

only ‘Premium’ for the home).

 

2007 Panel Comment:

• “The minimum requirements for running SHEDS, as described in the User Guide, appear 

unrealistically low (e.g., the actual requirements for Windows XP and SAS may actually exceed 

the requirements described in the manual). The actual requirements for running the model are 

unlikely to be met by most of the commonly used computers. Specifying more realistic 

requirements for a “reasonable performance” or just specifying the requirements as “a computer 

that runs SAS (version x)” are two possible alternatives.” (p. 18) 

EPA 2010 Response: We reviewed the minimum requirements. We disagree that commonly used 

computers cannot run the model, and most 2007 Panel members had no problems running SHEDS. 

No change was made to the documentation in regard to specific requirements. However, as noted 

elsewhere in the responses, the requirement of SAS 9.1 or higher and the estimate of run time and 

storage requirements have been made more prominent.

2007 Panel Comment:

• “Sections should be numbered, and additional cross-referencing should be included, including 

hyperlinks between the User Guide and Technical Manuals and a substantive glossary.” (p. 18) 

EPA 2010 Response: Sections have been numbered for the SHEDS-Residential version 4 User’s 

Guide and Technical Manual. However, other topics had higher priority than cross-referencing and 

hyperlinking.

2007 Panel Comment:

• “The captions for figures and tables in the User Guide should be more descriptive.” (p. 18) 

EPA 2010 Response: This comment was taken into account in developing the table and figure 

captions for the Version 4 User’s Guide and Technical Manual.

2007 Panel Comment:

• “The User Guide also should contain some of the technical details of the probability distributions 

(can be directly copied from the Technical Manual). As written, the manual expects users to 

understand the type of distribution they are using.” (p. 18) 

EPA 2010 Response: Both the version 4 User Guide and Technical Manual now contain the detailed 

probability distribution descriptions in an appendix. 

2007 Panel Comment:

• “Other models developed by EPA and revised over the years have addressed many user issues in 

a readable manner. A review of the IEUBK (Integrated Exposure Uptake Biokinetic Model for 

Lead) interface and User Guide will address some of the challenges faced by new users.” (p. 18) 
23

EPA 2010 Response: The IEUBK documentation was examined for ideas on how to orient new 

users. The User Guide now contains a new initial section, “Considerations for New Users of 

SHEDS-Residential.”

2007 Panel Comment: 

• “Specify Application Exposure Scenarios” does not include the option to add new exposure 

scenarios relevant to pesticide application. The model should allow assessment of non-standard 

application methods.” (p. 18) 

EPA 2010 Response: This can be done with the SHEDS-Residential SAS code. Also, the GUI 

now allows the creation of a user-defined scenario and/or chemical, as explained in Sections 

5.7.1.1 and 5.6.1, respectively, of the User Guide.

Question 1-2 a) Please comment on whether the descriptions of specific model components are 

scientifically sound and whether the algorithms described in the Technical Manual represent the 

state of the science for performing exposure assessments. Please also comment or offer suggestions 

for improving or modifying these algorithms or other aspects of the model construct. 

2007 Panel Comment: “In general the model is scientifically sound and appears to represent the stateof-the-science, with the notable exception of the dermal exposure analysis section, as discussed below. 

The larger overlying issue is the quality and the transparency of “hard-wired” approaches within the 

model. The representation of some of these issues, e.g., hand to mouth contact, are in existing EPA 

documents or peer-reviewed publications, but are largely invisible to the user and not clearly identifiable 

without careful examination of the underlying SAS code. While it is not necessary to present this data 

in the Technical Manual, several of the suggestions about the transparency of processes and equations 

noted by the Panel are important for understanding the functioning and reasonableness of the model. 

Equations, units, and key decision points should be clearly stated and, if needed, hyperlinked to 

important decision variables.” (p. 18-19) 

EPA 2010 Response: The equations, assumptions, hard-wired approaches and input, and units 

are explicitly stated in the Technical Manual. The user’s guide now contains a new initial 

section, “Considerations for New Users of SHEDS Multimedia.,” that presents an overview of 

the capabilities of the model, its underlying processes, distributions, and configuration options; 

these are expanded on in the User Guide and the Technical Manual.

2007 Panel Comment: “The Panel’s greatest concern was about dermal exposure analysis. SHEDS 

appears to use both the transfer efficiency (TE) and transfer coefficient (TC) approaches in its 

assessments. The Panel noted that, at present, the documentation appears to favor the use of the TE over 

TC approach.” (p. 19) 

EPA 2010 Response: Version 4 supports both the TE and TC methods. The current 

documentation does not favor either method. 

2007 Panel Comment: “The Panel urges use of a scientifically defensible approach to dermal exposure 

estimation, and notes that the current state-of–the-practice of dermal modeling in the regulatory sphere 

is weak. The SHEDS dermal protocol is regarded as state-of–the-practice, not-state-of–the-science.” (p. 

19) 

EPA 2010 Response: The Panel’s issue appears to be with whether the transfer efficiency or 

transfer coefficient approach (Cohen Hubal et al., 2000, EHP, 108(6): 475-486) is preferable. 
24

There is no consensus in the scientific community as to which approach is best to use. As 

before, SHEDS-Residential version 4 allows the user the choice of either the TE or TC approach. 

However, the Technical Manual explicitly states that internally the model utilizes a user-supplied 

TC, or calculates a transfer coefficient as a function of TE, as part of estimating dermal 

exposure. The dermal exposure algorithm has been modified in version 4 to incorporate reduced 

transfer of chemical to the skin as the amount of pesticide on the skin increases. With respect to 

absorption of the chemical, the model moves the pesticide into the stratum corneum and thence 

to the blood, as opposed to directly into the blood. These aspects are discussed in Sections 

2.7.2.1-2.7.2.4 of the Technical Manual.

2007 Panel Comment: “The Panel noted that the TE approach is misnamed, as it does not represent 

efficiency, has no inherent internal logic (it merely represents observed similarities), and cannot 

incorporate different types of surfaces or those with varying degrees of contamination. As an example, 

one Panel member noted that one oft-cited use of a TE approach (Gurunathan et al., 1998) violates 

conservation of mass. Therefore, the Panel urges caution in use of this approach, as it needs to be 

anchored with and compared to other relevant data, such as biomonitoring.” (p. 19) 

EPA 2010 Response: The issue is more with what value to select for transfer efficiency, not with 

the SHEDS dermal equation. The SHEDS user has the responsibility to select the appropriate 

study/values. Version 4 allows different TE or TC values for different surfaces. A variable 

"chemfrac" is provided in version 4 as a check against any violations of conservation of mass.

2007 Panel Comment: “The Panel noted that the paragraph at the bottom of page 43 is incorrect in its 

assertions about the rate of dermal transfer. Dermal absorption (at least in the sense of binding to 

stratum corneum as opposed to systemic absorption) is not necessarily slow relative to diary events in

the context of residential exposure scenarios.” (p. 19) 

EPA 2010 Response: There appears to be confusion over the dermal exposure/absorption 

terminology in the SHEDS documentation. We believe that the clarified terminology regarding 

exposure and dose in version 4 addresses these concerns. Specifically, we now define dermal 

exposure at the external skin surface. Section 2.7.2 of the SHEDS-Residential Technical Manual 

explicitly discusses dermal exposure and dose in version 4. A new feature in version 4 is that the 

chemical now moves into the stratum corneum first and later is absorbed into the blood.

2007 Panel Comment: “For example, Fenske & Lu (1994) showed apparent rapid uptake of 

chlorpyrifos at low skin loadings. Failure to account for dependence of absorption efficiency on skin 

loading is a significant weakness of current practice.” (p. 19) 

EPA 2010 Response: SHEDS includes a simple PK model, but allows for exporting to PBPK 

models that can include more realistic absorption rates. As noted above, the dermal exposure 

algorithm has been modified in version 4 to incorporate reduced transfer of chemical to the skin 

as the amount of pesticide on the skin increases and with respect to absorption of chemical into 

the stratum corneum as opposed to directly into the blood. These aspects are discussed in 

Sections 2.7.2.1-2.7.2.4 of the Technical Manual.

2007 Panel Comment: “The age grouping in SHEDS is slightly different from the dietary exposure 

analysis default groups. Operationally, this may not be a major problem as long as it is possible for the 

user to conduct model runs for a custom population of choice. However, since the apparent differences

in age cohort pooling has underlying emphasis that is specific to certain key parameters of interest, it 

would help to include a brief explanation for the basis of the SHEDS specific age cohort system. The 

same should be presented for dietary and water components when they are added to the next version of 
25

SHEDS. For practicality, the Agency may want to consider consolidating the two different age cohort 

systems, and informing the user of the rationale.” (p. 19) 

EPA 2010 Response: The SHEDS version 4 GUI provides the following options for age groups: 

Residential GUI

Inputs: 

The user has the option to use either the "EPA Age Groups" (1,2,3-5,6-10,11-15,16-20,21-29,30-

49,50-69,70+ years) or "Optional Age Groups" (0,1-2,3-5,6-12,13-19,20-49,50+ years). This is 

selected via a button on the Population and Sampling screen. 

 

Outputs: 

The user has the option to select a max and a min age for displaying outputs (tables and graphs) 

in the GUI. Thus, output age groups are completely at the discretion of the user; any summary 

statistics are calculated on the fly based on the selections. 

 

Dietary GUI

Inputs: 

(U.S. Population, 0,1-2,3-5,6-12,13-19,20-49,50+ years, Females 13-49 years) 

 

Outputs: 

Same as Inputs.

2007 Panel Comment: “The current model construct allows only pair-wise correlations although more 

complex multivariate dependencies may exist. The Panel noted that techniques of partial correlation 

coefficients could be used to allow for multiple correlations.” (p. 19) 

EPA 2010 Response: We recognize the validity of the comment, but at this point there does not 

seem to be much of a demand nor available databases for addressing complex multivariate 

dependencies. Allowing pair wise correlations of inputs in version 4 is a significant 

enhancement from existing exposure models. 

2007 Panel Comment: “The system also could be augmented by allowing default correlations such as 

low, medium, or high correlations.” (p. 19) 

EPA 2010 Response: Allowing default low, medium, or high correlations with subjective 

assignment of correlation numbers would introduce more uncertainty into the model. In the 

input data set (via SAS not GUI), the user can enter/construct functional relationships to address 

complex correlations among multiple variables.

2007 Panel Comment: “The Agency is encouraged to consider providing default shapes of continuous 

probability distributions for user specified input parameters. The Agency scientists have invested 

substantial effort in reviewing these data and appear to be in a good position for suggesting default 

distribution types, while still allowing the user to deviate from them. Having defaults has the advantage 

of more easily comparing different runs by different users, making the deviation from defaults more 

readily identifiable and more transparent.” (p. 20)

EPA 2010 Response: In the version 4 User Guide, the second and third paragraphs of Section 

1.3 “Configuring SHEDS to the Scenarios of Interest” inform the user that the distributions in the 

demonstration files may be used to familiarize oneself with the model and its operation, but that 

runs must be customized by the user for a specific application. As noted above, a case study for 

permethrin was conducted with version 4. The associated files are provided as the demonstration 
26

input files, as noted in Section 2.2 of the User’s Guide. The Technical Manual also contains 

default suggestions for non-chemical specific inputs in Appendix G. 

2007 Panel Comment: “The description of embedding a simple PK model on SHEDS version 3 was not 

sufficient to allow Panel members to make constructive comments. Estimated absorbed dose is an 

outcome many would like to see as part of a SHEDS run. Panel members were aware that the Agency 

has separate groups working on various PBPK models. Instead of developing their own simple PBPK 

model specifically for SHEDS, the Panel recommended that the SHEDS development staff keep abreast 

of developments by these other PBPK model teams with the idea of integrating SHEDS with these 

PBPK models at some future point.” (p. 20) 

EPA 2010 Response: Version 4 retains a simple PK dose model that is intended to provide a 

quick screening level of blood and urine doses. Version 4 also contains the capability to output 

exposures to a more comprehensive PBPK dose model. The developers of SHEDS have followed 

the Panel recommendations in keeping abreast of PBPK modeling. One case study, dietary 

arsenic, is included in the materials for Panel review. The linkage with SHEDS and PBPK 

modeling with a permethrin case study will be presented to the July 2010 SAP.

2007 Panel Comment: “Verifying the precision and accuracy of SHEDS predictions is not described in 

the materials provided to the Panel for either version 3 or 4 of the model, except for the proposed 

sensitivity and uncertainty analyses. The Panel felt that before implementing SHEDS or other models in 

risk assessment, it is critical to understand how the model predicted values compare to the reasonable

range of exposures that can be measured in the general population, or in important population subgroups. SHEDS offers a very flexible platform for users to input variables and parameters and other 

characteristics that will affect the outcomes of the simulations. Evaluating predictions from SHEDS 

using exposure measurements is one exercise that will inform the Agency about the accuracy and 

robustness of the model. Extremely wide or extremely narrow distributions of predicted exposures 

compared to observed values are suggestive of problems with the model that could lead to questions of 

its ultimate utility in describing a situation.” (p. 20) 

EPA 2010 Response: EPA has conducted a case study for permethrin, linking SHEDS with 

EPA/ORD’s PBPK model, and comparing results against the NHANES biomarker data. Part of 

this case study has included evaluating SHEDS and its individual modules where possible, using 

available measurements (e.g., duplicate diet intake data). These results are being presented to the 

July 20-22, 2010 SAP.

2007 Panel Comment: “Given the centrality of the CHAD diaries and human activities for developing 

model runs, it would be extremely helpful if the User Guide presented a typical diary of one weekday 

and one weekend day as part of the example case study.” (p. 20) 

EPA 2010 Response: The text of Section 2.5 “Generating Longitudinal Activity Patterns for a 

Simulated Individual” and Figure 2-5 describe how the model constructs longitudinal activity 

diaries and follows simulated individuals through them. The version 4 Technical Manual now 

contains an Appendix D “Summary of the CHAD Database” that includes summary information 

by individual study, diary counts by age/gender cohort, an example diary excerpt, and complete 

listings of all CHAD activities and locations. The user may use the GUI to select or deselect 

particular studies from the CHAD database.

D. Technical Manual 

Question 1-2 b) Please comment on the organization, clarity, completeness and usefulness of the 

Technical Manual and provide any suggestions for improvement. 
27

2007 Panel Comment: “The Panel had a number of suggestions on the organization, clarity, 

completeness, and usefulness of the Technical Manual. In general the Panel felt that the audience and

scope should be more clearly defined sooner in the manual.” (p. 20) 

EPA 2010 Response: We agree with these recommendations. The new introductory section of 

the User Guide “Considerations for New Users of SHEDS-Residential” attempts to better orient 

users to the model’s scope.

2007 Panel Comment: “The most important issue identified by the Panel was the need for a 

comprehensive case study that would illustrate the strengths and limitations of the model. A second 

issue identified by the Panel was the need for a standard data set that could be used to orient various 

model clients; users, reviewers, Agency risk managers, and stakeholders are all important audiences, and 

a comprehensive example is important for explaining the utility of the model as well as serving as a 

benchmark for evaluating future changes to SHEDS.” (p. 20) 

EPA 2010 Response: We included illustrative case studies for both the dietary and residential 

modules in the version 4 documentation. The User Guides provide step-by-step instructions to 

walk through these case studies using provided data and settings. Regarding a standard data set, 

we have provided a default file for non-chemical specific inputs, based on available literature 

data, in the SHEDS-Residential package and Technical Manual Appendix G.

2007 Panel Comment: “As part of these improvements, the manual needs an early statement as to what 

the user can and cannot change in the model. A fuller description of the fixed inputs and decisions 

embedded in the model is needed: particular datasets (e.g., CHAD, the contacts data base for dermal 

exposure) need fuller explication to achieve user understanding and model transparency. Because the 

manual lacks a description of the strength of the underlying datasets, risk assessors have no way of 

estimating confidence in the output distributions. As the Agency does with weight of evidence 

descriptors in toxicology studies and confidence qualifiers in IRIS, this would allow the assessor to 

understand whether exposure estimates are based on studies or diaries that capture the characteristics of 

the exposure of the population or individuals being evaluated. It also provides an understanding of data 

gaps and can be used to prioritize resources, both human and financial.” (p. 20-21) 

EPA 2010 Response: We minimized hard-wired approaches and inputs in version 4, and 

documented them clearly in the Technical Manual. Data sets used in SHEDS runs are CHAD, 

U.S. Census, and NHANES as assumed underlying databases. Other data are supplied by the 

user. The Version 4 Technical Manual now contains an Appendix D “Summary of the CHAD 

Database” that includes summary information by individual study, diary counts by age/gender 

cohort, an example diary excerpt, and complete listings of all CHAD activities and locations. In 

version 4 the GUI allows sub-setting of the CHAD database. In addition, a note has been added 

in Section 3.8.1 of the Technical Manual explaining that NHANES was designed as a nationally 

representative study. No elaboration on the U. S. Census has been added. The user may alter or 

replace any of these databases using SAS.

2007 Panel Comment: “The Technical Manual has to serve as a detailed reference guide and should 

inform on technical issues associated with installing and running the program. It should address the 

limits of the model, ways to avoid common mistakes and errors, the process of obtaining results, 

modification and extensions of the program, and interpretation of results.” (p. 21) 

EPA 2010 Response: Most of this information is in the User Guide. We do not discuss 

interpretation of results or modification and extensions of the program in the SHEDS manuals.
28

2007 Panel Comment: “As presently constructed it [The Technical Manual] has no central logic in 

terms of variable names and definitions: the Panel noted that it would be useful to have a central 

definitions list that consolidates this information in one location.” (p. 21) 

EPA 2010 Response: In the revised version 4 Technical Manual, SAS variable names and 

definitions are provided in Appendix B “Selected SAS Variable Nomenclature” and Appendix F 

“Description of SHEDS Multimedia Input Files.” Macro names and definitions are provided in 

Appendix C “The SHEDS Code Residential Modules and their Functions.” In addition, some 

naming information is also contained in Appendix A “Terminology Used in SHEDSResidential.”

2007 Panel Comment: “The document should provide a table detailing representative run times and 

disk space requirements for a set of population and uncertainty run sizes. This can help in planning for a 

simulation beforehand.” (p. 21) 

EPA 2010 Response: Both the Technical Manual and User Guides now provide estimates of run 

time based on the number of simulated individuals. The notice appears within the first three 

paragraphs of the User’s Guide. The information appears in Section 1.3 “SHEDS-Multimedia 

Version 4 Overview” of the Technical Manual. An advisory on storage requirements appears in 

both Section 1.3 of the Technical Manual and Section 3.1 “Requirements” in the User’s Guide. 

The user is now also provided an on screen estimate of the run time through the GUI.

2007 Panel Comment: “In general, the Panel felt that the description of CHAD data could be expanded 

and improved. Although several criteria are listed in the manual, the following factors should also be

considered in the random selection of representative activity patterns for an individual: temperature,

season, and employment status. Some of the criteria for selecting activity diaries (e.g., diaries that have 

non-zero sleep time) can be strengthened. For example, selecting a diary with “sleep > 0” is reasonable 

for a short-term simulation. However, repetition of such a diary for an extended period will likely result 

in unrealistic scenarios.” (p. 21) 

EPA 2010 Response: The version 4 Residential Technical Manual now contains an Appendix D 

“Summary of the CHAD Database” that includes summary information by individual study, 

diary counts by age/gender cohort, an example diary excerpt, and complete listings of all CHAD 

activities and locations. The criteria for selecting diaries may be altered by editing the “Diary 

QA rules” input file.

2007 Panel Comment: “The Technical Manual should also list the intermediate files generated, provide 

relevant details so that users can write scripts for assessing model correctness.” (p. 21) 

EPA 2010 Response: This would require significant effort because intermediate variables are 

overwritten, and this is not a high priority to the Agency. An advanced user could directly 

modify the SAS code to output the particular desired intermediate variables of interest, if 

interested.

2007 Panel Comment: “Information on restarting a simulation from an interrupted portion should also 

be provided.” (p. 21) 

EPA 2010 Response: Development of this “restart” feature has been initiated but is not yet 

completed.

2007 Panel Comment: “The Technical Manual should also provide details on weighting population 

based on the distribution in a simulated population region (e.g., a county or a state), i.e., sufficient 
29

documentation should be provided for sampling from “study specific” population characteristics.” (p. 

21) 

EPA 2010 Response: No change in this regard has been made. The default population file may 

be replaced with one tailored to the specific application desired, or alternatively specific inputs 

may be so tailored.

2007 Panel Comment:

• “The Quality Assurance section should be moved to the end of the Technical Manual.” (p. 21) 

EPA 2010 Response: Quality Assurance is now discussed in Section 8 of the version 4 Technical 

Manual. 

2007 Panel Comment:

• “The captions for the figures and tables should be made more descriptive.” (p. 21) 

EPA 2010 Response: This comment was taken into account in developing the table and figure 

captions for the version 4 User’s Guide and Technical Manual.

2007 Panel Comment:

• “The descriptions of the probability distributions should be moved to an Appendix.” (p. 21) 

EPA 2010 Response: Both the version 4 Residential User Guide and Technical Manual now contain 

the detailed probability distribution descriptions in an appendix.

 

2007 Panel Comment:

• “Numbering chapters and subchapters is strongly recommended to be used instead of the current, 

unnumbered heading-based organization.” (p. 21) 

EPA 2010 Response: Sections have been numbered for the version 4 Residential User’s Guide and 

Residential Technical Manual.

2007 Panel Comment:

• “Formulae should be presented using mathematical notation, in contrast to the textual expansion 

of terms (e.g. F = ma versus Force = Mass x Acceleration), especially for complex equations. If 

common explanation is thought to be necessary, an informal presentation could be added.” (p. 

21) 

EPA 2010 Response: We appreciate the panel’s desire for standard, brief mathematical notation. 

However, we chose more expansive notation in the interest of clarity for the broad spectrum of users 

envisioned for SHEDS. The notation used also provides a more direct connection with the SAS 

code itself.

2007 Panel Comment:

• “The usage of some terms in the manuals differs slightly from their definition in statistical 

literature, e.g., interchangeable usage of the terms “variability distribution,” “probability 

distribution,” and “probability vector.” These inconsistencies should be addressed.” (p. 21-22) 

EPA 2010 Response: Statistical terminology was reviewed and some changes made, particularly in 

the wording applying to discrete variables. A subsection on discrete variables has been added to 

Appendix E “Probability Density Functions,” a footnote has been added to the first page of the 
30

Introduction, and text has been added to section 3.1.5 “Probability vectors.” Better distinctions are 

now made in the use of the terms “variability distribution,” “probability distribution,” and 

“probability vector.” Because of the way the code handles them and one enters these values into the 

software, Bernoulli variables and multinomial variables are now discussed separately. 

2007 Panel Comment:

• “The manual addresses at several instances the issues of accuracy, e.g., when sampling from a 

population and generating the population variability and then comparing the center of the 

distribution of an exposure endpoint with the extremes of the distribution, e.g., percentiles. A 

formal definition of accuracy would clarify the discussion of these issues. When addressing the 

characterization of the variability distribution the Panel noted that the population variability as an

outcome of SHEDS is not different from the variability of endpoints investigated in an empirical 

study (e.g., a large epidemiological study). In both cases principles of statistical data analysis 

have to be applied. The document should refer to such general principles where possible. 

Similarly one may require that the exposure modeler set up a study design in a sort of study 

protocol before starting SHEDS for sensitivity and/or uncertainty analyses. This would then 

resolve some of the fallacies discussed on page 76. It might be reasonable, to combine the two 

issues identified in separate locations within the manual (on pages 47 and 75).” (p. 22) 

EPA 2010 Response: Detailed results are kept in model runs; for both variability and uncertainty 

results, confidence intervals can be generated for model output results.

2007 Panel Comment:

• “The description of the probability vectors needs to be revised so that it covers also a variable 

with only two discrete outcomes; i.e., a Bernoulli variable with one single parameter p, the event 

probability, e.g., to parameterize existence of pet or lawn.” (p. 22) 

EPA 2010 Response: Because of the way the code handles them and one inputs them, Bernoulli 

variables and multinomial variables are discussed separately. Bernoulli variables are called 

"Binomial" variables in SHEDS version 4. 

2007 Panel Comment:

• “The associated text on page 49 would benefit from more formal probabilistic and statistical 

terminology. If the authors think that would be too formal, that part could be moved to an 

appendix.” (p. 22) 

EPA 2010 Response: We prefer to leave the existing description in the text. 

2007 Panel Comment:

• “Pages 51-54 on the distributions should go to an Appendix with mathematical formulae for the 

distributions described in a formal mathematical type, referring to the statistical literature, e.g., 

that of Johnson, et al. (1994, 1995, and 2004).” (p. 22) 

EPA 2010 Response: As noted above, the detailed descriptions of the probability distributions now 

reside in Appendix E of the Technical Manual and have been augmented with a subsection on 

discrete distributions. 

2007 Panel Comment:

• “On page 24 there are instances where no dust is contacted. The basis for this decision is 

unclear: is it for model internal consistency or based on empirical data?” (p. 22) 
31

EPA 2010 Response: As a simplification measure, the separate modeling of dust contact has been 

eliminated from the exposure calculations in Version 4. When dust concentrations are provided, 

they are converted to surface loadings to be combined with other forms of the chemical. This point 

is explained in Section 1.6.2 “Dust and soil ingestion in version 4” and Section 2.6 “Contact 

Duration and Concentration averaging.”

2007 Panel Comment:

• “The manual needs to clarify if and how the model addresses region-based seasonal events, such 

as spring-time applications, in calculation of the annual exposures.” (p. 22) 

EPA 2010 Response: A paragraph is included at the end of Section 1.3 “Configuring SHEDS to the 

Scenarios of Interest” in the User’s Guide that indicates that to contrast explicitly exposures between 

two different regions or seasons, it would be better to conduct separate model runs with inputs 

specific to those given regions or seasons. Currently, seasonally-varying chemical usage inputs are 

supported. In addition, the text points out that advanced SAS users can conduct a single run that 

incorporates seasonal-variation in other inputs. 

2007 Panel Comment:

• “There is insufficient description to determine how the calculation or estimation of application 

rates is determined. The manual should clearly state if this is proprietary information, and if so 

can they be updated over time with empirical data? Otherwise, the information should be 

presented in detail.” (p. 22) 

EPA 2010 Response: Application rates, amounts, usage frequencies, and locations are userspecified. A sentence has been added to Section 2.4.3 “Chemical usage patterns (applications)” to 

indicate that SHEDS contains no proprietary information, though the user may employ such data at 

his or her discretion. Regarding updating over time, the user can do another model run with revised 

application rates and usage information.

2007 Panel Comment:

• “Because this is a residential model, and because residents of multi-family dwellings have less 

control over their pesticide applications, the model should consider a multi family housing 

designation as a person level variable and address whether it is correlated with other 

microenvironments or parameters.” (p. 22) 

EPA 2010 Response: The user has the option of conducting a separate run for different 

subpopulations of interest to the user, with sub-population specific inputs. We did not make any 

code changes in SHEDS per this recommendation because it raises questions of adequate diary pools 

and whether code changes would need to be made for other special sub-populations.

2007 Panel Comment:

• “The Technical Manual and the User Guide provide a wealth of information and many details on 

SHEDS' construction and structure, on the approaches used and on their programming. One 

Panel member noted that the structural description of SAS procedures as given in SAS Manuals 

could be a template for the description of the various procedures of SHEDS.” (p. 22)

EPA 2010 Response: The SAS manual style is for more advanced users than the anticipated end 

user of SHEDS.
32

Question 1-3: a) Please comment on whether the annotated code is sufficiently clear such that the 

algorithms can be followed and understood. b) Please also comment on whether these algorithms 

are technically correct and consistent with the descriptions provided in the Technical Manual. 

2007 Panel Comment: “In terms of code readability, it is desirable to have adequate cross-referencing 

between the Users’ Guide, Technical Manual, GUI, and the actual code. This would allow a user to 

check the underlying code for a given operation without reading the code file that contains multiple 

modules. This can be accomplished by providing hyperlinks in the electronic versions of the documents,

and by providing the annotated code separately (e.g., in the form that can be easily used in a “code 

browser,” or in the form of HTML with the comments as “tool tips”). Furthermore, separating the utility 

modules from the core modules in the current SHEDS code is relatively straightforward and 

recommended as this will enhance code readability.” (p. 23) 

EPA 2010 Response: Given available time and resources, other topics had higher priority than 

cross-referencing and hyperlinking. 

2007 Panel Comment: “The naming of the variables within the SHEDS code can be significantly 

improved. Currently, the code uses cryptic names such as washprob, absr_gr, absr_gm, bioavm, and 

bioavr, with subtle changes in variable names for different quantities – this can potentially lead to 

difficult to notice errors. The code can be improved by providing more informative names, and through 

the use of a “nested” or hierarchy of variables (e.g., through the use of objects/classes in SAS). The

variable naming can also be improved by using a consistent naming scheme. For example, the current 

version of the SHEDS code uses variables such as washprob, has_lawn_p, timeofday_indoor, and reentry_indoor, all denoting probabilities.” (p. 23) 

EPA 2010 Response: Variable naming has been improved in Version 4. these variables are 

listed in tables throughout the Technical Manual and in Appendix B.

2007 Panel Comment: “The code changes across different versions should be adequately documented. 

In fact, it is desirable to have an archive of major versions of SHEDS available to interested researchers 

so that they can use simple text differences between different versions of the SHEDS code or individual 

modules.” (p. 23) 

EPA 2010 Response: The differences between versions 3 and 4 are highlighted in Section 1.6 

“Changes from version 3 to version 4” in the Residential Technical Manual. 

2007 Panel Comment: “The technical documentation and code comments do not mention issues 

regarding the backward compatibility of the current version with earlier versions, which is very 

important.” (p. 23) 

EPA 2010 Response: The input and output files from earlier versions are not compatible with 

version 4. Many of the variables used in version 3 are still used in version 4, although in several 

cases the variables have been re-named to improve consistency. Also, the exposure and dose 

boundaries have changed between versions 3 and 4. A list of the correspondences in outputs 

between the versions is provided in Table 7-1 in the Residential Technical Manual. 

2007 Panel Comment: “Additionally, a bug tracking/reporting system is also desirable for users and 

developers.” (p. 23) 

EPA 2010 Response: We will discuss designating a person(s) for ongoing SHEDS Technical 

Support via email and/or phone.
33

2007 Panel Comment: “The technical documentation or code comments should provide details on the 

intermediate variables in the SHEDS simulations, and on how a user can enable these to be stored and 

analyzed. Experienced users can then easily obtain summary statistics, improved visualizations, and 

other desired outputs.” (p. 23) 

EPA 2010 Response: In general, intermediate variables are dropped or are eventually 

overwritten for space considerations. As noted earlier, advanced SAS users may alter the code to 

retain any intermediate variables desired. A new feature in version 4 is the ability to resubmit an 

earlier run using the same random numbers as previously. The user may now request output for 

selected persons from the earlier run. The intermediate variables for these persons may be saved 

without overwriting the values for other persons. 

2007 Panel Comment: “... some suggestions for improving algorithms as well as for enhancing the 

consistency between the Technical Manual and code were made [by the Panel]. These include providing 

reproducibility of simulation results (by reusing a random “seed” if it is provided by the user), 

addressing or acknowledging issues associated with running stochastic programs on multiple machines 

and associated potential correlations in the random number generation process.” (p. 24) 

EPA 2010 Response: Reproducibility by controlling the random seed has been implemented in 

version 4. The Panel’s concern regarding correlations from runs on multiple machines has been 

addressed by using two levels of randomization involving different streams, so whenever the 

initial seeds for runs are different, the streams do not overlap. 

2007 Panel Comment: “A minor comment from the Panel members includes documenting the 

descriptions of probability distributions used in the Technical Manual and User Guide, because the 

distributions are often parameterized in different ways, and it is important to clearly describe how these 

distributions have been parameterized in SHEDS.” (p. 24) 

EPA 2010 Response: Both the Version 4 Residential User Guide and Technical Manual now 

contain the detailed probability distribution descriptions in an appendix. 

F. Multiple Chemicals 

Question 2-1: Please comment on the technical aspects and usefulness of the planned methodology 

for extending SHEDS-Multimedia version 3 to address multiple chemicals in version 4. 

2007 Panel Comment: “The Panel focused its comments [on extending the application of SHEDS from 

single to multiple chemical assessments] on issues associated with tracking of mass of media and 

chemical, product co-occurrence, pesticide metabolites, programming issues, scenarios, database 

representativeness and absorption and bioavailability. 

“The logic of extending the application of SHEDS from single to multiple chemical assessments is 

plausible and warranted. However the nature of such proposed work would be complex, time and 

resource consuming, and the process itself may pose significant challenges. The Panel expressed several 

concerns about the planned methodology. 

“...Overall, the concept of going from aggregate to cumulative as described is useful. One important 

feature that is not mentioned in the document is the importance of input from risk managers and Agency

scientists about the chemicals that are subject to cumulative assessments and the process used to make

these determinations. Once this decision is made the focus shifts to model structure and outputs, and the 
34

Panel focused its comments on mass tracking, product co-occurrence, pesticide metabolites, 

programming issues, scenarios, and absorption and bioavailability.” (p. 24) 

EPA 2010 Response: The importance of input from risk managers and Agency scientists about 

the chemicals that are subject to cumulative assessments and the process used to make these 

determinations is not directly relevant to the SHEDS structure or manuals and does not need to 

be discussed in them. 

2007 Panel Comment: “The process of tracking mass is unclear in Version 3, and many Panel members 

felt that future versions of the model should track mass in media because, for example, it makes it 

possible to track the mass that is lost through touching and subsequent washing of the hand, and allows 

tracking of mass in specific media over time. The Agency is encouraged to explore the feasibility of this 

mass balance step. The Panel noted that, in the document, the definition of “carrier” in SHEDS is 

somewhat overlapping with “media.” For example, air (either indoor or outdoor, or in the treated or 

untreated rooms) is considered a carrier and a medium in some places in the manual. The manual is also 

unclear as to why “residue” itself would be considered as a carrier. Residue has to be associated with or 

adhered to the carrier before it can reach the simulated individuals in any of the media. As stated in the 

documentation, “When a person comes into contact with a contaminated MEDIUM, a certain amount of 

one of more carriers will be transferred onto or into the body” (page 7, SHEDS Version 4 Planning 

document, June 12, 2007 version). Care must be taken to correctly define and use the terms "carrier",

"media" and "residue."” (p. 24-25) 

EPA 2010 Response: We are tracking mass of chemicals contacted in version 4 via loading and 

removal processes; however, we do not feel that the benefit obtained by a mass balance approach 

to tracking chemical mass in the various media would outweigh the effort needed and the 

computational difficulties that would arise. Our experience indicates that the adjustments needed 

to the environmental concentrations are insignificant. Terminology has been revised in the 

version 4 Technical Manual. The terms “carrier" and "residue" are no longer used. The model 

code now computes the chemical mass in the microenvironments and the amount transferred to 

the simulated individual, and reports the ratio as the "chemfrac" variable. 

2007 Panel Comment: “A scenario (a child playing with uncontaminated soil) provided by the Agency 

further complicated this matter. As stated in the documentation, by taking into account such activity, the 

mass of the carrier, which is soil, will change on the hands, the body, and possibly in the GI tract. 

However, if the soil is not contaminated, the change of soil mass would not affect the model outputs. 

Since each diary is being considered as an independent event (with some autocorrelation), even if this

child might come in contact with contaminated soil on a different occasion, the mass of the soil that will 

be transferred this time would not affect the mass of soil that is uncontaminated.” (p. 25) 

EPA 2010 Response: The example cited was hypothetical and only intended to apply within the 

context of a “carrier” implementation for version 4, and assumed that all contact would be 

modeled to determine total "carrier" exposure (for example, a mixture of contaminated and 

uncontaminated soil). However, as a simplification measure, dust and soil exposure have been 

revised for the exposure calculations in version 4. This point is explained in Section 1.6.2 “Dust 

and soil ingestion in version 4” and Section 2.6 “Contact Duration and Concentration 

averaging.”
35

2007 Panel Comment: “As stated by the Agency, the run-time and the storage space for the model 

output may significantly be hampered by the proposed approach, if all the chemicals in the formulation

are included in developing the variables. However, if the intent of developing SHEDS version 4 is to 

cumulatively assess exposures to a group of chemicals that has the same toxicological endpoint, the 

concern about resources may not be warranted. One Panel member noted that it is uncommon to have a 

single product containing more than 2 active ingredients that cause the same health concern. So instead 

of n=10 as proposed for use in the algorithms in version 4, the actual sample size might be no more than 

3.” (p. 25) 

EPA 2010 Response: While we agree that is useful to have an upper bound on the number of 

chemicals, we cannot in the model development process rule out the ability of the model to 

address several chemicals simultaneously. We recognize that there may be a need for more than 

10 chemicals within a single simulation.

2007 Panel Comment: “The document available for the Panel to review is brief and lacks detail on a 

number of issues. The Panel noted that the current product-related co-occurrence system may be 

problematic if there are substantial future changes in product formulation. This also implies that 

intentional product co-occurrence is more important than unintentional “random” product cooccurrence, an assumption that needs close inspection. The document was unclear, but during the 

presentation the Agency indicated that it will consider addressing both multiple products that contain

multiple chemicals and multiple chemicals with a common mode of action that are not necessarily 

present in a single product. The Agency should explore using different data sources for assessing 

multiple chemical co-occurrences: for example, pesticide use data could potentially be useful for 

assessing multiple chemicals for different purposes (e.g., garden versus indoor versus lawn). In 

addition, the considerations for co-occurrence for residue in food is another complex issue to be 

explored (e.g., number of pesticides found in one food commodity versus multiple chemicals in a salad)

that is not fully explicated in the document.” (p. 25) 

EPA 2010 Response: We appreciate the Panel’s concern regarding the brevity of the documents 

describing these model extensions. The version 4 Residential Technical Manual provides a 

complete explanation of the revised model. The Panel’s concern with respect to co-occurrence 

between different scenarios is not an issue in version 4 – users may specify intentional cooccurrence or leave the probabilities as independent. This is described in Section 2.4.3 

“Chemical usage patterns (applications)” with more specific discussion in Sections 2.4.3.1 

“Usage frequency” and 2.4.3.2 “Usage dates and co-occurrence” and 3.4.3 “Co-occurrence 

Factors” in the Technical Manual. When multiple chemicals of interest are present in the same 

residential model run, SHEDS automatically calculates co-occurring exposures based on the 

user-specified co-occurrence pattern mentioned above. Regarding the dietary module, SHEDS 

accounts for co-occurrence by preserving the structure of PDP residue data, which captures cooccurrence by analyzing each sample for multiple chemicals. 

2007 Panel Comment: “The Panel noted that the ability to model compound metabolites should be 

included in the multiple chemical model, but this will require development of a module to quantify 

metabolite production. One Panel member noted that one important lesson from the Agency’s CTEPP 

(Children's Total Exposure to Persistent Pesticides and Other Persistent Organic Pollutants) Study is that 

tracking of metabolites is necessary for mass balance. In the absence of full accounting for metabolites, 

evaluation of model performance will be limited to a small number of compounds, such as PCP and 2,4-

D, for which the urinary biomarker is the parent compound.” (p. 25) 

EPA 2010 Response: The Panel has recommended that SHEDS does not need an improved PK 

model (for example, that may include metabolism) but rather focus on linking with existing dose 

models for this purpose.
36

2007 Panel Comment: “The current document states that using a single efficiency for washing removal 

of soil/dust from the skin regardless of compound is an advantage of the multiple chemical model. 

Uniform washing efficiency may be reasonable for soil or dust-borne chemicals. However, a single 

removal efficiency is also apparently assumed for "residues" (i.e., for free chemical on the skin). Since 

chemical washing efficiency is likely to be tied to the physical-chemical properties of a given chemical, 

this could be a disadvantage. Relatively lipophilic compounds are less likely to be efficiently removed 

by washing than relatively water soluble compounds.” (p. 25-26) 

EPA 2010 Response: As noted in the response to the Panel’s earlier comment regarding dermal 

absorption, we believe that this recommendation arises from unclear terminology. In SHEDS, 

residues are not free chemical on the skin. We assume that the amount of “free chemical” is 

negligible in practice and that all chemical will be bound in some matrix or carrier. Terminology 

has been improved in the Technical Manual. Chemical-specific washing removal rates can be 

specified, if desired, using the SAS data set editor. 

2007 Panel Comment: “While this move from aggregate to cumulative assessment provides an 

opportunity to use an “object oriented” programming approach for encapsulation of variables, it is also 

possible that users can be overwhelmed by the number of parameters that they have to provide. 

Therefore, the Panel envisions that GUI modifications may be substantial (e.g., moving from pesticidespecific interface to general multimedia interface) even though the underlying code changes may be 

relatively minor. Therefore, it is desirable to have one underlying code base and different interfaces.” (p. 

26) 

EPA 2010 Response: A true object-oriented approach is not compatible with the SAS 

programming language. As noted earlier, variable naming has been improved in version 4. Also, 

the GUI allows users to retrieve and edit chemical-specific input data defined in an earlier run. 

In addition, the user can load chemical-specific data for permethrin into a new run. In the future, 

data for other chemicals will be available in SHEDS.

2007 Panel Comment: “One Panel member noted that the memory requirements of such a system may 

be large due to the increase in the number of chemicals to be simultaneously modeled. The temporal 

resolution of the simulation will potentially be dictated by the fastest simulation time and the lowest 

time scale to be modeled.” (p. 26) 

EPA 2010 Response: If a large number of chemicals are to be run simultaneously, then this is a 

potentially serious issue. The version 4 code loops over chemicals and overwrites temporary 

variables and files specific to each chemical, thus reducing storage needs. While the quantity of 

output grows with the number of chemicals, this is limited by the space on the hard drive, not by 

memory. Changing the temporal resolution from the current CHAD diary event level would 

require substantial changes in the exposure algorithm. So far, there is no indication that the 

temporal resolution must be altered to permit runs with many chemicals.

2007 Panel Comment: “An additional feature that is desirable but not included in the written plan is 

scenarios for home applicator exposure. This was presented at the meeting as one potential added 

component for version 4, and includes both professional and home applicators, and the Panel felt that 

this was an important addition that needs careful consideration.” (p. 26) 

EPA 2010 Response: Handler exposure by home applicators is available in version 4. However, 

this is designed to simulate exposure of a resident who applies pesticides, not a pest control 

professional.
37

2007 Panel Comment: “One Panel member noted that the description of absorption efficiency and 

bioavailability for non-dermal pathways in the Technical Manual is unclear, and therefore the state of

science cannot be evaluated because equations and model inputs are not available. The Panel cautions 

that, like dermal absorption, GI and lung bioavailability estimations and assumptions are dependent 

upon factors such as medium, particle size, material thickness on exposure surfaces, and other factors

that need careful attention in a multi-pathway, multi-media, and multi-chemical model.” (p. 26) 

EPA 2010 Response: Terminology has been revised for Version 4. Regarding the Panel’s 

caution with respect to absorption, the focus of SHEDS is on the exposure to the chemical(s). As 

recommended by the Panel and noted elsewhere, SHEDS will be linked to a more sophisticated 

dose model. This linkage with ORD’s PBPK model, illustrated with a permethrin case study, will 

be presented at the 2010 July SAP meeting. 

 

G. Fugacity 

Question 2-2: Please comment on the technical aspects and usefulness of the planned methodology 

for incorporating a fugacity-based source-to-concentration module into SHEDS-Multimedia 

version 4. Does the Panel recommend additional efforts with the fugacity module (e.g., modeling 

more realistic multi-room dwellings) given available information? 

2007 Panel Comment: “The Panel believes that the fugacity approach is well suited to treatment of 

multiple chemicals and is therefore compatible with other development goals for SHEDS, but the Panel 

had less unanimity about whether the fugacity option would provide more useful results than approaches

currently used in the model. The Panel expressed a desire to see example calculations and sensitivity 

analyses that illustrate the performance of the fugacity-based approach.” 

EPA 2010 Response: A fugacity model can be useful to help guide the determination of some 

version 4 inputs. Such a model has been developed for use with SHEDS, and was presented to 

the August 2007 SAP. However, this is currently external to SHEDS version 4, not a module 

within it. Future research could include sensitivity analyses that illustrate the performance of the 

fugacity-based approach. Depending on those results, the Agency may consider incorporating 

the fugacity module as an integral part of the SHEDS code. 

2007 Panel Comment: “The Panel notes that EPA proposes to evaluate the fugacity module using 

chemical transport data from a 1-house study. While this is a reasonable place to start, the Panel 

encourages more extensive testing including application to cases in which both environmental data and 

biomonitoring data are available.” (p. 27) 

EPA 2010 Response: There are other existing sources of input data that we plan to assess, in 

addition to the 1-house study, for conducting sensitivity analyses as recommended by the Panel. 

2007 Panel Comment: “The Panel expressed less unanimity with respect to the actual likely utility of 

the fugacity option and expressed a desire to see example calculations that might better illustrate the 

performance of this option. Only through sensitivity analysis can the question of value be determined. 

In particular, the effect of time scale being simulated should be examined. The Panel also asked for 

clarification regarding whether the fugacity approach will be limited to indoor environments.” (p. 26-27) 

EPA 2010 Response: See above. The Agency will conduct additional analyses on the utility and 

performance of the fugacity model before considering making it an integral part of SHEDS. 

2007 Panel Comment: “With respect to the second part of the charge question, the Panel agrees that 

simulation of a multi-room house is certainly feasible mathematically. Mixed opinions were expressed 
38

regarding the value of moving beyond a two-zone (treated/untreated) model. Clearly there is some point 

at which lack of data necessary to describe more complicated systems will prevent derivation of added 

benefit by increasing complexity. The Panel therefore suggested that EPA start with a simple (two-zone) 

version and more thoroughly investigate and document performance at that level before considering 

multi-room systems.” (p. 27) 

EPA 2010 Response: We agree that a 2-zone house is adequate for now, but could in the future 

consider multi-room residences. 

2007 Panel Comment: “Under this charge question, there was discussion of whether SHEDS should 

account for removal of contaminant residues from environmental compartments via absorption by 

occupants. It was suggested that EPA compare predicted mass absorbed to assumed mass applied for 

various scenarios to provide more information on this topic. Although the term did not arise explicitly 

in the discussion, in effect the Panel suggested that EPA include routine computation of Intake Fraction 

(Bennett et al., 2002a,b) in SHEDS output.” (p. 27) 

EPA 2010 Response: As noted above, SHEDS-Residential version 4 now performs the 

calculations regarding the fraction of chemical mass that is transferred to the simulated 

individual. However, the environmental concentrations are not adjusted for this transfer, for two 

reasons. First, most houses have multiple individuals in them and the removal of chemical by all 

persons should be considered, as should removal by pets or by cleaning. The simulated 

individual will generally be a small part of the total removal, and SHEDS already reduces the 

amount of chemical present on an hourly basis in the decay/dispersion calculations. The second 

reason is the modularity of SHEDS; the program assumes the activity diary and concentrations 

are independent and it would complicate the program to have feedback between the diary module 

and concentration module. 

H. New Longitudinal Diary Assembly Method 

Question 2-3: Please comment on the technical aspects, potential utility, and added value of the 

planned methodology for longitudinal diary assembly in SHEDS-Multimedia version 4. Does the 

Panel believe that this new method will create an assemblage of diaries that better simulates 

reality and provides more accurate estimates of exposures related to within-individual timeactivity patterns? Please suggest procedures and/or longitudinal data which could be used to select 

factors (the “D” factor intra-class correlation coefficient, and the 1-day lag autocorrelation) or 

refine/evaluate this method in SHEDS. 

2007 Panel Comment: “The Panel believes that it is likely that the current activity database (CHAD) 

may not contain enough diaries to allow random draws using the new longitudinal diary assembly 

method and several members raised concerns about the representativeness of the US population in the 

CHAD dataset. Since a significant number of CHAD diaries were excluded for various reasons, further 

proposed decisions that reduce an already small dataset might be problematic for accurately determining 

the within-subject variation. Furthermore, as stated in the documentation, two of the most important 

pieces of information, chemical usage patterns and contact possibilities, are not included in the diary 

database. These concerns dampened the enthusiasm of the Panel for implementing this new 

longitudinal diary assembly method in the upcoming revision of SHEDS.” (p. 28)
39

EPA 2010 Response: The Panel’s concerns with respect to small diary pools and the 

representativeness of CHAD are equally applicable to any diary assembly method, and are 

related to the user’s intended objective. Ideally, the user should customize the diary database to 

match the target population. For example, CHAD could be restricted to the NHAPS study 

(approximately 9300 diaries), which is nationally representative. For another example, the diary 

database was restricted to 1 to 6 year-old children with a minimum amount of outdoor time for a 

study of exposure to chemicals on decks and play sets. But again, these issues apply to the 

underlying database, not the assembly method. As noted earlier, improvements in the 

description of CHAD and how to better customize it to the user’s application will be explored.

Small diary pools do make it problematic to model within-subject variation. However, this 

applies to all diary assembly methods and the new method is better at characterizing withinperson variation. 

It is true that the main drivers of dermal exposure are not included on the diaries. However, the 

method is simple to implement and requires little additional input by the user. The new method 

is now available as an option in version 4. 

2007 Panel Comment: “The Panel concurs that including a lag autocorrelation function (currently set at 

1-day) would be necessary to eliminate repetitive activities from the longitudinal diaries that are likely 

unrealistic (such as pumping gas into an individual’s car on successive days). However, the Panel 

suggests that the Agency’s efforts should focus on behaviors relevant to potential residential pesticide 

use, such as frequency of gardening activity, rather than examples related to air pollution exposure 

assessment.” (p. 28) 

EPA 2010 Response: One of the advantages of introducing autocorrelation in the diary assembly 

is that it allows the user to determine the degree of repetitiveness present for the key exposure 

variable. The Panel is correct in noting that the key variable used in the new method must be 

relevant to the specific pesticide exposure being modeled. Some suggestions for key variables 

are listed in Section 5.5.4 “Diary Assembly Method” of the User’s Guide. 

2007 Panel Comment: “The use of realistic and well documented scenarios in the model is, in the 

Panel’s judgment, as high or higher in priority as further refinement of the diary assembly and 

autocorrelation methods. For the explicit purpose of estimating the individual and population exposures, 

it may be more constructive for SHEDS 4 to suggest defaults for key variables and allow for deviation 

from these defaults as needed by informed users to display the strengths and limitations of the future

versions of the model.” 

EPA 2010 Response: As noted earlier, key variables are listed in Section 5.5.4 “Diary Assembly 

Method” of the User’s Guide and a case study was conducted.

2007 Panel Comment: “The Panel believes that the concept behind the new longitudinal diary assembly 

method has scientific merits, and the technical aspects of the proposed method based on the D-statistic 

and 1-day lag autocorrelation is sound. However, the application of this method in the interest of 

predicting aggregate human exposures to environmental chemicals may be somewhat limited. This is 

particularly true for activities that may lead to potential exposures that are either difficult or implausible 

to track as continuous “key variables.” For example, it is useful to use the D-statistic for tracking how 

long a person spent outdoors for studying human exposure to air pollutants (like the “Southern 

California Ozone Exposure Study” cited by the Agency) because the time spent outdoors is correlated 

with the amount of ozone being inhaled by the subjects. However, it might not be as feasible to 

implement the D-statistic approach in compiling longitudinal exposure data sets, such as the frequency

of hand contact on a contaminated surface, when multiple factors need to be considered before the 
40

exposure scenario can be confirmed. Such a limitation is inherited from the concept of incorporating the 

within and between-subject variation in order to better control the longitudinal properties of the final 

assembled diary. The key variable(s) also needs to be ranked in order to generate the x-score. Those

theoretical requirements constrain the utility of this proposed method for predicting longitudinal 

exposures in which inhalation exposure is not the predominate pathway. In other words, unless the 

longitudinal diaries do not encompass a great number of human micro-activities (such as potential 

exposures other than inhalation) in which time spent in the area where the contaminant is present is the 

only and the ideal key variable, the incorporation of this new longitudinal diary assembly method in 

SHEDS version 4 may not add significant value. Based on Glen et al. (2007), it appears that there is 

also a possibility to choose more than one key variable for creating a longitudinal diary and therefore the 

Agency is encouraged to explore this possibility.” 

EPA 2010 Response: We agree that there may be situations for which the new method does not 

provide a significant benefit over existing methods. However, SHEDS is a general model 

designed to be applicable to multiple chemicals and multiple pathways. For some of these 

pathways, the variables in the CHAD database may be very relevant. Therefore, we felt that it is 

useful to add the new method as an option. Given the work detailed in Glen et al. (2007) and the 

fact that the method has been incorporated in another SHEDS model, the effort to include it in 

Version 4 was manageable. As a point of clarification, the Glen et al. reference suggested 

combining diary variables into a single key variable as opposed to having multiple key variables. 

2007 Panel Comment: “One Panel member wanted to see how the longitudinal diary development 

using the D-statistic and the autocorrelation would impact exposure prediction. As presented by the 

Agency, there are several data sets that have the potential to be used for this purpose, and the Panel

recommends that the Agency contact the PI of those studies to obtain permission to access their data 

sets.” (p. 28-29) 

EPA 2010 Response: Exploration of some of these longitudinal datasets is currently underway. 

For example, EPA is analyzing 10 new longitudinal CHAD diaries collected by the Office of 

Research and Development. EPA has also recently obtained longitudinal dietary data from Dr. 

Alex Lu that was mentioned at the August 2007 SAP meeting. We plan to examine the D&A 

statistics from these datasets. 

I. Sobol 

Question 2-4: Please comment on the technical aspects and usefulness of the planned methodology 

for utilizing Sobol’s method for sensitivity analysis in SHEDS-Multimedia version 4, and whether 

Sobol’s method would be a useful supplement to the existing sensitivity analysis methods used for 

the SHEDS-Multimedia version 3 model. 

2007 Panel Comment: “The Panel was divided on the utility of Sobol’s method, and wondered whether 

the new sensitivity analysis method is needed. Several Panel members expressed concern that the 

required number of simulations for sensitivity analysis appeared to be very low. The Agency’s view 

that a single sensitivity run can be considered to represent multiple “individual-days” of simulations is 

not correct, because a single variability run will ultimately produce a single population metric (e.g., the 

95th percentile for population exposures). It appears that the proposed use of Sobol’s method will allow

the computation of “local sensitivity,” without addressing the corresponding uncertainty. Global 

sensitivity analysis, on the other hand, allows the estimation of sensitivity as well as uncertainty, and 

thus would be a preferred approach. 
41

“Several Panel members expressed concern that the required number of simulations (2N + 2) for 

sensitivity analysis involving N parameters appears to be very low. For example, if a single parameter

has 10 levels of values, doing a sensitivity analysis with just 4 simulations (2*1+2) is impractical. The 

Agency’s view that a single sensitivity run can be considered to represent multiple “individual-days” of 

simulations is not correct in this case, because a single variability run will ultimately produce a single 

population metric (e.g., the 95th percentile for population exposures). 

“...Many of the Panel members were not convinced about the utility of the Sobol’s method for SHEDS, 

and were also perplexed by the complexity of the method. 

“...Since Sobol’s method has not been used with a large scale probabilistic model before, the Panel 

urges caution in using the method. Furthermore, the number of changes to the model structure and code 

(e.g., by making a majority of the SHEDS code deterministic, with random parameters provided in one 

module) appear to be large, further reducing the usefulness of incorporating this methodology in 

SHEDS. 

“However, some Panel members felt that since the Sobol method is an accepted method for sensitivity 

analysis it should be included, since in general it is a good idea to provide new techniques as alternatives 

to variability and uncertainty analysis.” (p. 29) 

EPA 2010 Response: We appreciate the Panel’s difficulty in being able to evaluate a complex and 

unfamiliar method given the limited amount of material provided. There seems to be a 

misunderstanding of the method with respect to several points. More extensive explication of the 

approach is certainly desirable. A manuscript is in preparation on this, and a poster was presented 

on the topic at the 2007 ISEA conference (Glen G., Isaacs, K. Smith, L. Adapting Sobol’s method of 

sensitivity analysis to stochastic models. The 17th Annual Conference of the International Society of 

Exposure Analysis. Durham, NC. October 14-18, 2007.). With respect to several specific points 

raised by the Panel, we offer the following comments. These brief comments are intended to clarify, 

but do not fully explain the method. 

• Sobol’s method does not target the sensitivity of any particular population summary statistic 

(such as the 95th percentile). Rather, it attempts to answer the question of why individuals differ 

in their ultimate exposure. Briefly, it quantifies the relationship between input settings and 

exposure through random variation of selected subsets of the inputs. Because the entire 

parameter space is explored, Sobol’s method is a global sensitivity analysis method. 

• The number of runs in Sobol’s method is adequate to address sensitivity as described above. 

There may be confusion between the number of individuals simulated and the 2N+2 runs. The 

latter represents the number of selected subsets of inputs that are used. As with a variability run 

in SHEDS, the number of individuals is the user’s choice and a larger number of individuals 

provides a better characterization of sensitivity. 

• The panel is correct that Sobol’s method does not address uncertainty explicitly. Sobol’s method 

can address both variability and uncertainty by folding the two into a single distribution for each 

input. However, it is envisioned that the current SHEDS 2-stage approach will be maintained 

for uncertainty analysis. 

• As the Panel noted earlier, it is quite desirable to control the random number seeds to allow 

reproducibility of the model runs. As noted earlier, this capability was not present in v.3 but this 
42

was added in v.4. This is the key requirement in terms of implementing Sobol’s method for the 

SHEDS code, and this is what was meant by making the SHEDS code deterministic. 

• In their comments, the Panel suggested that Sobol’s method is a variant of the FAST sensitivity 

analysis method. This is incorrect. Both are variance decomposition approaches, but otherwise 

are very distinct. Both were explored for use in SHEDS, but FAST had limitations that rendered 

it inapplicable to SHEDS. Briefly, FAST requires a number of incommensurate frequencies 

equal to the number of inputs. The number of available frequencies is limited, and the number of 

runs required to calculate the sensitivity quickly becomes infeasible. Another major limitation 

was that FAST provided no reasonable method for handling variables that must be re-sampled 

through time. In addition, Sobol’s method handles discrete inputs better. 

• The Panel also suggested the use of ANOVA for sensitivity analysis. In addition, at least one 

Panel member thought that Sobol’s method was a reinvention of experimental design. It was not 

the intention to suggest that Sobol’s method replace ANOVA. Quite to the contrary, we feel that 

Sobol’s analysis can be a useful complement to helping design an effective ANOVA approach. 

Though Sobol’s method does not provide a statistical significance test, it is extremely helpful in 

indicating which combinations of inputs are negligible and which inputs have important 

interactions associated with them. Thus, results from Sobol’s analysis can provide very valuable 

input to the design of, for example, a fractional factorial ANOVA. 

• Sobol’s method has been added as an option for sensitivity analyses. This is described in the 

SHEDS-Residential version 4 Technical Manual, section 6.5, and also in the User Guide section 

5.11.5.2. Sobol’s method is an option to the user through the version 4 GUI. 

J. Other Changes 

Question 2-5 a) Please comment on (and prioritize, as appropriate) the technical aspects and 

usefulness of planned changes to the SAS code and GUI for SHEDS-Multimedia version 4 that are 

listed items in Section 5 of the above-referenced background document. b) Please comment on any 

additional modules, features, or capabilities that the Panel feels should also be high priorities for 

the next version of SHEDS including issues associated with the code, user interface/user 

friendliness, input, and output/output display. Are there modules, features, or capabilities of other 

human exposure models that should be considered for inclusion in SHEDS-Multimedia version 4 

(e.g. simulation of individuals; longitudinal diary assembly? 

2007 Panel Comment: “As a first suggestion the Panel felt that the developers should do everything 

that is simple and does not involve a major reorganization of the code. At the same time, a lot of effort 

should not be placed into options that will not make much difference in the overall conclusions drawn 

from the model. Most Panel members thought that PBPK modules should not be a high priority and 

should remain independent modules from the SHEDS model output.” 

EPA 2010 Response: Version 4 incorporates exposure to multiple chemicals and addresses key 

comments from the 2007 SAP; it does not contain a detailed PBPK module. 

2007 Panel Comment: “The Panel felt that the Agency should get more experience with the application 

of this model before getting into a major reorganization of the code. It is important that the developers 

anticipate future data sets, especially longitudinal, and try to keep the code scalable. Autocorrelation can 

have an important effect on the tails of the exposure distributions. Anything that helps the user 

understand uncertainty and variability is important. Several Panel members emphasized that improved 

graphics were important for wide model comprehension and acceptance. They also felt it was useful to 
43

add utilities to make the model more accessible and useful, e.g., code to review input values and note

extreme or illogical combinations, or a script generator to run designed experiments on the model. This 

is similar to sensitivity analysis but could be more general. The user could ask a series of questions 

about what factors and interactions are of interest, and then plan and implement a fractional factorial or 

response surface design to test hypotheses. Prior to adding additional functionality (modules, features, 

or capabilities), sensitivity analysis with the specific modeling assumptions should be conducted. This 

involves defining the population exposure metrics, and performing sensitivity analysis. It is possible that 

some proposed features can be discarded after this analysis, based on lack of sensitivity of the exposure 

metric to the new feature.” (p. 30) 

EPA 2010 Response: These summary comments have been addressed above. 

2007 Panel Comment: “The Panel liked the idea of users being able to re-parameterize distribution 

mean, variance and shape so they can conveniently move between distributions without having to refer 

to statistics texts (e.g., Johnson, et al., 1994, 1995, and 2005) for the parameter definitions. Experience 

has shown, however, that unless a distribution has an extreme right tail, the choice of distribution does 

not make much difference.” (p. 30) 

EPA 2010 Response: The version 4 GUI reports summary statistics such as the mean when 

plotting distributions, but does not automatically parameterize different forms to match their 

moments. 

2007 Panel Comment: “Some users of the model will not be constrained by EPA practice and will want 

to model non-compliant application, i.e., not following the pesticide label. It is important that the model 

provide an “application programming interface” definition that describes the format and the type of 

intermediate data, in a simple and easy to use manner. This will lead to the following advantages: a)

users and developers can independently create input/output analysis modules, provided there is adequate 

documentation for developing such tools; b) this will overcome current issues with the quality of plots 

and graphs; and c) provide facility to input empirical distributions for specifying uncertainty and 

variability; d) plus provide sufficient number of examples, so the novice and expert users can utilize the 

system to the fullest extent.” (p. 30-31) 

EPA 2010 Response: With the changes to version 4, much of what the panel requests is 

available. The user may create a non-compliant scenario, if desired. This is accomplished simply 

via the choice of inputs. Also see Section 5.7.1.1 “Create User-Defined Scenario” in the User 

Guide. The output capabilities have been enhanced in version 4 so that the user may conduct 

their own analyses of model outputs. 

The SAS language packages the input and output data in a form that can be manipulated by other 

SAS programs. Essentially, SAS data sets already have the API capabilities discussed above. 

2007 Panel Comment: “The Panel felt that it would be useful if SHEDS provided better contextspecific help files, the ability to calculate margins of exposure (MOEs), and other options for selecting 

and matching CHAD and food consumption diaries (e.g., by age, gender, season, weekday, region, race, 

METS/caloric intake).” (p. 31) 

EPA 2010 Response: Help buttons on now provided throughout the GUI, and illustrative case 

studies are presented in the manuals. MOEs are now available in version 4. EPA is currently 

finalizing guidance to address such issues as differing exposure averaging times and the 

interpretation of population variability. We will explore other options for selecting and matching 
44

CHAD and food consumption diaries, and seek the July 2010 SAP’s input on linking dietary and 

residential modules.
APPENDIX 

SHEDS-Dietary Option/Feature 

Available in 

SHEDSDietary? Notes [Option linked to 2007 FIFRA SAP Q

Food Consumption Data Sources

CSFII (1994-96, 1998 Children supplemental) Yes Data used in Agency risk assessments (e.g., DEEM-FCIDTM) 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., Calend8-Diary Yes 

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

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., DEEM-FCIDTM) Food Residue (vs. Commodity) No 

Option used to assess Arsenic (Journal article); Case study assigncommodities; 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 DaFCIDTM and CARESTM use of 30 years of PRZM-EXAMS predi

Modeling Food Residues

Select Single Residue for all Eating Occasions, by Commodity (RAC-FF) Yes Method used in Agency risk assessments (e.g., DEEM-FCIDTM) Select New Residue for different Eating Occasions, by Food-RAC-FF Yes 

[Q1 FIFRA SAP 2007, 32; Option often has little effect for food

‘add’ uncertainty] 

Correlation across commodities, across multiple chemicals (products) applied to foods, and over subsequent days No Minimal data to implement; (FIFRA SAP 2007, p.25) 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 sea

residues 

Modeling Drinking Water Concentrations

Randomly select new DW concentration each day Yes Method used in Agency risk assessments (e.g., DEEM-FCIDTM) Randomly select Year for each Person-iteration, then apply Predicted DW based on Calendar (365) date No

Retain seasonal patterns (autocorrelation) in DW concentrations. risk assessments (e.g., Calendex-FCIDTM, CARESTM) 

Sensitivity/Uncertainty Analyses

Sensitivity Analyses * 

Requires supplemental routine (e.g., effect of consumption outlieexposures – aldicarb memo) 

Uncertainty Analyses * [Q4 FIFRA SAP 2007] Requires supplemental routine 
SHEDS-Dietary Option/Feature 

Available in 

SHEDSDietary? Notes [Option linked to 2007 FIFRA SAP Q

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 program to calculate per capita 99.9th for single-ch

recently incorporated into GUI; needs QC. 

Plotting Person-Day Exposures Yes Visualize Exposure Patterns/Persisting Dose Contribution Analyses: Shares of Total Exposure, byCommodity 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