# 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 Scien tific

Advisory Panel Meetingi 

## 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...........................................

................................................... ....... 451

PREFACE Over the past ten years, the Agency has requested t he Panel to review several probabilistic dietary exposure software models. 1 These have included DEEM-FCID TM , Calendex-FCID, CARES TM

, LifeLine TM , and an earlier (specialized) version of SHEDS (SH EDS- Wood) designed to assess exposures and doses of wood preservatives. SHEDS-W ood was presented to the Panel on August 28, 2002 and December 3-5, 2003, and focused on childre n’s dermal and non-dietary ingestion exposure to chromated copper arsenate (CCA) on treated decks an d play sets.

In August 2007, SHEDS-Multimedia version 3 underwen t 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 conceptu al 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 usabil ity and potential improvements to the SHEDSMultimedia residential module, as well as approache s to assessing dietary exposures, which would be

coded into SAS and SHEDS in a future version. At t his 2007 SAP, the Panel stated that it was generall y “impressed with the current version of the SHEDS mo del and future plans for the model...” (SAP 2007, p.10), that “the model algorithms appear to be tech nically correct... [and that] the code is clear eno ugh that the algorithms can be easily followed” (SAP 20 07, p.10). In addition, the Panel stated that “th e GUI is helpful and straightforward, but ...could be i mproved 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 numb er of suggestions on the organization, clarity, completen ess, and usefulness of the model manuals.

Since that time, Agency staff from the Office of Pe sticide 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 P anel’s 2007 suggestions. These efforts were geared toward producing a SHEDS-Multimedia Version 4.0 mod el, 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-c hemical) exposure assessment.

To date, this collaborative effort between ORD and OPP has produced separate SHEDS-Dietary and SHEDS-Residential exposure assessment software prog rams, each of which address a number of the 2007 Panels’ ideas and concerns relating to user in terface, algorithms, usability, utility, and documentation. The current (2010) SAP is being req uested to review each of these two models, which

are considered as modules of SHEDS-Multimedia versi on 4, and a proposed approach to merge them (i.e., calculate dietary and residential exposure f or the same simulated individual after food consump tion and activity pattern diaries are appropriately matc hed) 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 r esponse to them. It is divided into two sections, with the first relating to the dietary module and t he second relating to the residential module. Thes e sections highlight research and development on the SHEDS-Multimedia dietary and residential modules that has occurred since the 2007 SAP; it is hoped t hat this document provides some background and context with respect to the Agency’s continuing act ivities and efforts aimed at advancing SHEDS in particular, and exposure science in general, and th at it will assist the Panel in its current delibera tions.

1 Documentation for these models are listed in the A pril 2004 FIFRA SAP, Model Comparison. See website for meeting minutes and documents: http://www.epa.gov/scipoly/s ap/meetings/index.htm.

2 See http://www.epa.gov/scipoly/sap/meetings/2007/081407 _mtg.htm for meeting materials and background documents2

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 pertain ed to food residues, food consumption patterns; dir ect drinking water consumption patterns and the Bayer D rinking 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 mo tivated by the anticipated transition from using CSFII to using NHANES/WWEIA food consumption data i n assessing dietary exposures to pesticides. The CSFII data were collected in 1994-1996, and 199 8. 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 CSF II. The Agency’s Office of Pesticide Programs has been developing food recipes for these new NHAN ES/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 r ecipes 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 expo sure 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 top ics 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 t hose features into this version of the SHEDS-Dietar y GUI. The table in the Appendix to this document sum marizes the status of a variety of features/options that are available in SHEDS-Dietary and how they re late to a number of 2007 FIFRA SAP questions, and the Panel may wish to refer to this as it revie ws the 2007 SAP comments.

The following sections introduce, by topic area, th e questions asked of the Panel in its 2007 review o f the (planned) dietary aspects of SHEDS, followed by both a relevant excerpt of the Panel’s responses t o those questions, as well as EPA’s 2010 correspondin g 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 assis t 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 bac kground document, the timing information

available in CSFII can be used to model food and in direct water intake throughout the day. With the ability to incorporate the timing of eating occ asions in dietary exposure assessments, it is possible to assign either the same residue or a dif ferent 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 appropr iate (e.g., hash browns at home for breakfast and fried potatoes away from home for dinner). Ple ase comment on developing simple decision rules – as described in the document – for assignin g 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 dia gram or flowchart to describe decision rules for assigning residues to commodities on multiple eatin g occasions. (p. 31)

EPA 2010 Response : The Agency acknowledges that omission in the 2007 document. The default approach (e.g., DEEM-FCID TM ) for dealing with residues is to select a single r esidue for each commodity (RAC-FF) and apply that value to tot al daily consumption of that particular commodity (RAC-FF). The Agency considered modifyin g that decision rule based on the information contained in several data fields, inclu ding: (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 20 07 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 dieta ry exposures expressed on an eatingoccasion basis and conducting some preliminary revi ews of the food consumption data (see below), the Agency is generally comfortable with th e default assumption of selecting a single residue for each modeled person-day for each commod ity (RAC-FF). The SHEDS-Dietary module follows that particular assumption. The cur rent version of SHEDS-Dietary includes an option in which separate residues are randomly sele cted 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 eac h distinct FoodRAC-FF-TimeOfDay

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

The example food diary in the 2007 paper (taken fro m the 1994-96/1998 CSFII) is reproduced below to illustrate the SHEDS-Dietary algorithm. T he food diary indicates that the child consumed ‘home fries’ (food item=71403000) on two e ating 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 S HEDS-Dietary will select a new residue for ‘Pork Fat’ consumed via Home Fries at the 6:00 pm e ating occasion. If that option is not selected, the SHEDS-Dietary Module selects a new re sidue 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 tha n ‘Home Fries’. [Note: DEEM-FCID will select the same residue for all 3 eating occasions since t he 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 Residue14

2007 Panel Comment: The Panel expressed several concerns regarding the use of such options: “Such level of refinement may not have added value especi ally 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 us e 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 ass essment. 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 ap propriate to select a new residue for each apple that the second person ate, rather than apply ing 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 resid ue 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 app les.

If a user had different types of residue data for d ifferent commodities, then one might want to apply a different algorithm based on the type of da ta 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 res idue for each apple consumed, but would draw only one residue for multiple oranges that som eone might have consumed on any given day. Implementing such an option would require ano ther 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 re sidue 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 uncert ainties in the exposure analysis. For example, the only way to determine whether the same food forms e aten 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 co mplex) 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 wi th “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 fo od 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 pr epared using a different bag of potatoes. Since the person consumed the same food on both occ asions, the SHEDS-Dietary model has the option to use – or not use -- the same residues fo r all four ingredients, including the fried potatoes (RAC-FF=0132990-213) to calculate total da ily 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 ne w residue) may have indeed been used to prepare the second meal. Alternatively, if SHEDS-D ietary drew a new residue for each eating event in which the same Food-RAC-FF was consumed, t hen the model would underestimate the probability that such a person obtained high residu es on both eating occasions if he indeed ate “leftovers”. Whichever decision rule is implemente d, some explicit modeling assumption will be made - even if the user is unaware of that assumpti on and resulting effects.5

2007 Panel Comment: “For this issue, the first consideration is whethe r dietary exposure constitutes a major contributor to an overall multi-media exposur e that exceeds a regulatory threshold. When dietar y exposure is the focus for refinement, high contribu ting commodities are identified in the initial assessment and the impact of link-day residue for t hese commodities can then be assessed. .... Once the potential high contributing commodity-residue pairs are identified, the possibilities for repeated eat ing occasions could then be further investigated. Anot her 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 i n dietary exposure assessments for commodities that do not contribute significantly to an exposure estimate

The Agency has performed some analyses of food cons umption 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) ar e more likely to be consumed on multiple occasions throughout the day. For example, we foun d 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 di d so on a single eating occasion. The Agency believes that such food consumption patterns help e xplain why recovery between exposure events (‘eating occasions’ analyses) often has limi ted effects upon the per capita 99.9 th 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 foo d 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 applicatio n, and transparency in operation. ... a simple set o f

rules in this case can be somewhat akin to a tier a pproach 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 99.9 th percentile since a large fraction of consumers obt ained 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 l ies in risk assessment, it is unlikely that there will be significant gain in cha nging 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 99.9 th percentile exposure for food-only exposure assessments, perhaps due to food consumption patter ns and reasons noted above.

2007 Panel Comment: Finally, the Panel recommended that the Agency rev iew several data sources that, including Child Development Supplement to Pan el Study of Income Dynamics (PSID) and American Time Use Survey (ATUS).6

EPA 2010 Response: The Agency has some familiarity with the PSID and A TUS 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 (activi ty patterns) and may use these data to update the CHAD data base (residential scenarios), but bel ieves those data contain limited information on within-day food consumption patterns. The Agenc y plans to incorporate the CDC NHANESWWEIA food consumption data into its dietary exposu re models (more on that below - Q5) before exploring other food consumption data source s.

B. Food Consumption Patterns

Question 3-2: Please comment on the 8-record approa ch in SHEDS-dietary and the selection of age group, gender, season and day-type from which t o 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 con sumption 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 co nstruct the longitudinal dietary consumption patter n 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. Specifical ly, the NHANES/WWEIA survey does not disclose the specific dates on which food consumpti on 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 N OT real since the NHANES/WWEIA publically available datasets do not contain date i nformation . 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 t he Agency is dropping the 8-record option for use with the NHANES/WWEIA survey data, t hat 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 f ood 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 o ver 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 grou p of persons. The Agency appreciates these data, and plans to consider them to develop and eva luate 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 w ithout concurrent good prediction of median. Such a situation assumes that the tail was accurately esti mated 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 wi ll focus on both the overall distribution including median and upper tail.

While modeled results of the upper tail are often r obust to simulation uncertainty, the Agency hopes to get good predictions of the median since t hose are more amenable to use in model evaluation. As noted above, the Agency was provide d food consumption data patterns for a small group of persons. These data contain more di aries 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 pat terns for actual persons, although not at the

extreme tails.

2007 Panel Comment: “The Panel recommends that the Agency should condu ct analysis similar to those already performed with subsets of CHAD databa se, 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 calor ic intake to develop longitudinal (multi-day) consumption patterns using the food consumption dia ries in the corresponding age and sex cohort for the modeled person (See SHEDS-Dietary Te chnical Manual.)

2007 Panel Comment: “...the Panel generally agreed that significant val ue would likely be added to the SHEDS model development by collecting longitudi nal dietary consumption, as well as individual behavior/activity data, particularly the activities at the microenvironment level from a small group o f study participants stratified by age and gender. ... Other Panel members have suggested the possibility of nesting a separate study within the proposed Nat ional 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 val ue 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 sci entists 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 yo ur comments any statistical concerns or issues associated with the design and conduct of th e DWCS study. [ Clarification to Charge Question 3. EPA recognizes that is has provided eac h 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 present ed 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 model ed day. However, the Panel noted: “The FIFRA SAP (2005) suggested allocating (direct) water amount t o five events – with three occasions during meals8

(25% of total per occasion) and two occasions in be tween meals (12.5% of total per occasion)”; not 6 fixed events as stipulated in the Agency’s Eating O ccasion analyses.

EPA 2010 Response : The Eating Occasions analyses currently employed by SHEDS-Dietary (and the DEEM-based approach) allocate direct drink ing water consumption in 6 equal amounts, and at fixed times: 6 am, 9 am, 12 pm, 3 pm, 6 pm a nd 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 t imes for two drinking water events between those eating occasions for all reported food diarie s would require additional and likely complex programming (and decision rules).

2007 Panel Comment: In attempting to answer this question the Panel ra ised 8 questions of its own (resorted and grouped):

1. “Do DWCS drinking water patterns differ very much f rom 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 (Figu re 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 consu mption patterns. Children generally consume more water per kilogram of bodyweight than adults, and as a result, infants and children often have the highest total d ietary (food+water) exposures when expressed on a body weight basis at the per capita 99.9 th percentile across all age groups.

The observation that drinking water consumption pat terns 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 se cond option that allows the total direct water intake as reported by that (CSFII or NHANES/W WEIA) respondent to be allocated over the day, in proportion to the empirical distributio ns reported in the Bayer DWCS diaries. For a modeled person, the ‘drinking water bins’ were base d 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 implementat ion 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 patt ern types that are internally homogeneous and externally varied, allowing individuals to be selec ted 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 statistica l analyses (e.g., cluster or principal components) on the Bayer DWCS data cou ld help identify different ‘types’ of drinking water profiles. Reducing the number of th e reported drinking water patterns into a few ‘types’ would simplify modeling drinking water expo sures and discussing those predicted outcomes. The Agency has not conducted such analys es to date.

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

.9

The Agency notes that the exposure model would stil l have to assign each modeled person to one of those ‘types’. The food diaries (CSFII or NHANE S/WWEIA) and drinking water diaries (Bayer DWCS) reflect behavioral patterns of differe nt 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. Th is 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 appar ent only when the time of day for each behavioral activity (time spent playing on turf) fr om those corresponding diaries.

The Agency has not conducted further statistical an alyses of the Bayer DWCS data as suggested by the Panel. The Agency notes that the NHANES/WWE IA survey recently began collecting information on the precise timing and amounts of di rect drinking water consumption, and those food diaries will not require modeling within-day w ater consumption using either method (fixed approach nor Bayer DWCS data).

2007 Panel Comment:

5. Can the DWCS database be properly weighted to produ ce population relevant information? This question was voiced by a number of Panel members wh o were concerned about a number of the sampling aspects of the study. In particular, ther e is the potential for bias in study results becaus e of the low response rate and high fraction of incomple te diaries. An attempt should be made to compute sampling weights to better match study resu lts (the demographics of study responders) with the (demographics of the) US population. In partic ular, the Panel wondered which age, race, ethnicity, geographical or other groups were not ad equately 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 weighte d to some degree. The documentation to the Bayer DWCS indicates that the sponsors and primary investigator employed the services of the National Product Datab ase Group (NPD), a marketing firm that maintained a National Home Testing (NHT) panel of 2 50,000 households. The document stated:

The NPD group was chosen to conduct this survey bec ause of its experience in tracking the consumption habits of the US population since 1980 through its Nationa l Eating Trends (NET  , 2004) service.” “The NET maintains a diary panel of 2,000 households to trac k consumption habits of the US population.

“Two nationally represented samples (one for each wave) were extracted” from the NHT panel. “The samp le for wave 1 included 3,000 households...randomly selec ted from the core sample of 250,000 households, whi le

in an effort to increase the number of children in the survey, the sample for wave 2 included 650 hous eholds randomly selected from households with children les s than 6 years of age in addition to 3,000 househol ds randomly selected from the core sample.” (p.9)

The Bayer DWCS data did not include sampling weight s (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 respondent s. While there might be some bias with respect to low response rate and/or under-sampled g roups, 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 NHA NES/WWEIA survey (2005/2006 onward).

2007 Panel Comment:

6. Will allocation of direct drinking water consumptio n via an empirical use of the Bayer DWCS data, say via some form of bootstrap sampling methodology , produce results that are markedly different10

from a modified fixed drinking water pattern? Prel iminary simulation results seem to suggest that it does not (p. 28).

EPA 2010 Response: The Agency has found that the Bayer DWCS option ten ds to produce slightly higher estimates at the per capita 99.9 th percentile than the fixed (6 event) approach. For example, (Nako and Xue, 2006) compared the two meth ods for several drinking water scenarios for a particular chemical. The Bayer DWCS option p rovided slightly higher estimates than did the fixed approach for the US population and most a ge groups (within 10%). As noted in the document, the Bayer DWCS did not collect data for i nfants, 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 hig h intake of water (ml/kg bw); however, many of the high consumption diaries reflect indirect wa ter consumption (infant formula) for which eating occasion data (time of day and amounts consu med) are available.

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

Percentile a

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 (Eatin g Occasion) results as provided in this table are s imilar to the Cross-Sectional SHEDS-Dietary Eating Occasion resul ts, but that procedure does not allow empirical use of the Bayer DWCS data. They are presented here for comparison p urposes.

2007 Panel Comment:

7. Is it true that all water consumed in a day must ha ve 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, filte red, 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. Anothe r situation mentioned is where the water used in cooking food is the contamination pathway. It is p ossible that factors related to drinking water source, municipal versus private, ground versus sur face, chlorinated versus not, may influence consumption rates. Individuals may not be fully aw are of many of these factors, but they will know11

where the water they pay for comes from and this ma y also affect consumption. Finally we should not lose sight of the information available on resi due data in drinking water as available in the Offi ce 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.), a nd water from different sources may contain different concentrations. The CSFII survey asked t he following questions regarding this issue:

• “What is the main source of the water used for coo king 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? {sam e 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 direc t water consumption reported in the food diary:

• “How much of this plain drinking water came from yo ur 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 foun tain, bottled water, or something else?” (RT25, D1_H2O_A)

The Agency used responses to some of those question s 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-FCID TM , CARES TM , Lifeline TM ) allow users to assign different drinking water concentrations based on those four d ifferent sources; however, the Agency’s risk

assessments have not

used that option to refine drinking water exposure s. There are no plans to incorporate such an option into the SHEDS-Dietary m odel.

2007 Panel Comment:

8. Because exposure from drinking water sources is a v ery small fraction of total exposure, is there a need to further model drinking water in the SHEDS m odel? “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 cons umption would not noticeably change model results” – this view was also mentioned at a previo us SAP; “other Panel members felt that in some exposure scenarios the contribution from drinking w ater could represent a large fraction of total exposure and in those cases different water allocat ion models could produce quite different results” (p.36)

EPA 2010 Response: The Agency has found that drinking water contributi ons 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 99.9 th percentile.

When relatively high predicted drinking water conce ntration values are included in the dietary assessment and water is the primary contributor, in fants (newborns) often have the highest total dietary (food+water) exposures. Otherwise, childre n (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 total12

daily water intake through infant formula. Since i nfant 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 opt ion focuses on allocating only the ‘direct’ portion of total drinking water intake ove r the particular day. The Bayer DWCS survey did not collect information on infants, but that in formation 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 devo ted to refining the SHEDS-Dietary drinking water exposure algorithm. The focus on refining di etary 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 tho se 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 p esticide use, percent of crop treated and labeled use rates). A risk characterization of tho se parameters may better address uncertainties regarding such predicted drinking water exposures t han 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 th ink that the issue related to exposure from drinkin g water sources are likely to be less in the future a nd would like to see drinking water’s impact examin ed in more detail. Issues such as water sources, use in cooking, temporal changes in contamination level s, methods of disinfection, etc., all seem to need fur ther 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 drink ing 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 a nd NHANES/WWEIA data (1999-2004) at a scientific conference (Xue, J. Nako, S. and Zarta rian, V. 2007; International Society of Exposure Analysis). Those analyses provided severa l findings, including: (i) no significant differences of Indirect DW consumption, but (ii) Di rect 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 use d in the reanalysis for drinking water contribution when needed.” (p.37)

EPA 2010 Response: The Agency has not used the PDP data to model drink ing water exposures to pesticides. Except for a few exceptions, the Ag ency 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 concen trations), and the number of samples collected were not designed to capture annual peak concentrat ions). 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 exp osures may change when certain model inputs are modified. These modifications to the mo del inputs are typically performed “one at a time” to permit isolation of the effect. In a typi cal 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 num ber of Monte Carlo iterations such that exposure estimates are stable with respect to the r andom seed.

The Agency has not conducted formal quantitative un certainty analyses. The Agency presented a simple bootstrapping procedure for conducting uncer tainty analyses, utilizing only a subset of the consumption and residue data inputs. That procedu re was designed to provide some insight into the question, “How much better would our exposure e stimates be if we had more data?” by conducting the uncertainty analysis in the other di rection. (a) Please comment on the scientific soundness and utility of the proposed bootstrap unc ertainty approach. (b) Can the Panel recommend alternative approaches – and how they mig ht be interpreted and used –for conducting uncertainty analyses of dietary exposure estimates?

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

percentile, is actually a measure of distributional spread. The median of this measure was proposed a s one way of quantifying the potential impact on mode l results of inadequacies in the diet or residue da ta. An alternative measure suggested is the median of s ome important upper percentile; say the 99 th

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

EPA 2010 Response: The Agency believes that some Panel members may hav e misinterpreted the ratio (95 th /5 th ), and would like to clarify how that ratio was cal culated. 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 dietar y exposure at some per capita percentile (99 th ) is computed from those outcomes. This procedure is repeated 200 times with subsets of residue and/or consumption data by the bootstrap method, pr oviding 200 values of estimated exposure at the per capita 99 th percentile. The 5 th and 95 th percentile of those 200 values of 99 th percentile are calculated, and the ratio is based on those two values. If the 99 th percentile over those 200 replicated samplings ranged from 2 ug/kg bw-day (5 th ) to 4 ug/kg bw-day (95 th ), then the ratio is 2 (=4/2). Note that the GUI for the SHEDS-Dietary does not al low the user to conduct the uncertainty analyses method presented in the 2007 a nalyses 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 v alue of enlarging the database. It will be useful for showing the uncerta inty 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 anal yses approaches, including: (i) Measuring impact of changes to p1 an d p2, (ii) post hoc power analysis, (iii) inter- an d intra-run variability and (iv) alternate indices.” (pp. 38-40)

EPA 2010 Response: The Agency has not yet been able to carefully consi der the Panel’s recommendations or conduct/evaluate uncertainty ana lyses 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 publicati ons, 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 dietar y 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 u sed in addressing this issue.

2007 Panel Comment: “Statistical methods alone cannot resolve the unc ertainties 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 sea son-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. Us ing bootstrap methods, the percentiles of the exposure estimates can be computed for each region, each season, and region and season combination.

The 50 th , 95 th , and 99 th percentiles can be compared to evaluate the impact of regional and/or seasonal differences in diaries, consumption, and exposure o n the exposure estimates.” (p.41)

EPA 2010 Response: The Agency conducted limited analyses on food consu mption patterns based on the CSFII food consumption survey informat ion, and provided some of those results to ORD/NCEA to update the Agency’s draft Exposure Fact ors Handbook (EFH 2009). For example, there are differences in consumption by r egion: e.g., consumption of okra is more prevalent among people living in the South (1.2 % o n any given day) than Midwest (0.4%); and by season: e.g., more people eat pumpkins during t he 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 com modity consumption differs regionally, pointing out that diaries might be different from region to region, but commoditie s are likely to be from national or international sources . Exposure analyses that were presented at the previous SAP meetings indicated that seasonal and r egional variations do not generally contribute significantly to the high end dietary exposures whe n a large number of commodities are included in the dietary exposure estimation. However, without seas onal- and regional- specific analyses, this conclusion is not apparent in light of the intuitio n that seasonal and regional variations of consumpt ion likely exist.” (p.40-41)

EPA 2010 Response: The Agency recognizes two aspects of this issue: (i ) calculation of per capita 99.9 th percentile, by region and/or season, and (ii) mode ling 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 per centages 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 w ebsite: http://cfpub.epa.gov/ncea/cfm/recordisplay. cfm?deid=20563)15

national-level residues or ‘binning’ residues by re gion and/or season. With regards to calculating population-based statistics, the Agency notes that current aggregate exposure models (e.g., DEEM-FCID TM ) can calculate aggregate exposure at the per capit a 99.9 th percentile by region or season. The user can also perform simila r calculations using the SHEDS-Dietary module (by region, season or both) since SHEDS reta ins 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 s urvey). The 99.9 th percentile exposures for the highest season or region will generally be higher than the overall 99.9 th percentile since it is based on a subset of all outcomes; however, the Agency has focused on the overall per capita

99.9 th 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 1 3-49 years old for reproductive and developmental toxicity endpoints.

The second aspect concerns whether the aggregate ex posure model bins and selects residues based on source (origin and/or season). Such a ref inement could produce slightly different effects if there is correlation between consumption and residues. For example, if high consumption and high residues occur in particular s easons (binge eaters at pick-your-own farm), then having one distribution for all residues may u nderestimate the occurrence of such exposure events. On the other hand, if low consumption ofte n 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 oc cur. The model currently used by the Agency (DEEM-FCID TM ) does not permit such refinements; we are consider ing plans for developing such an option in the SHEDS-Dietary modu le.

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 dat a among the Agencies.

EPA 2010 Response: The Agency has developed strong working relationshi ps 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 longit udinal patterns...If possible, the further study that the Agency will conduct should be reviewed by a separat e SAP prior to the initiation of data collection.”

EPA 2010 Response: The Agency will consider review of science and ethi cal 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 t o 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 sugge stions regarding this aspect of the model were made. Other questions asked of the Panel at that time reflected various specialized issues such as t he fugacity approach and Sobol’s method for sensitivit y analysis.

The purpose of this section of this background docu ment is to recap and summarize for the current Pane l the earlier 2007 comments with respect to the resid ential 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 correspondin g 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 cu rrent 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 ru nning 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-Residen tial.”

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 fo r SAS 9.1 (or higher)

in both manuals.

2007 Panel Comment: “Panel members also noted a bug in the Installatio n Process: the batch file, “multimedia.bat” in the installation folder, contai ns incorrect information on the installation folder location, and hence does not run. It should be fixe d with the correct folder location.” (p.15)

EPA 2010 Response: This is not a bug. The manual makes it explicit th at 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 fi le is now named ‘SHEDS.bat’.

2007 Panel Comment: “Cross-platform compatibility was one issue that t he Panel felt should be addressed in future iterations of the program. The refore, it may benefit users of other operating sys tems if the installation package is also provided as a z ip archive (i.e., the users can then extract the fo lder 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 ope rating 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 m ajor development effort, especially for the GUI, that would require significant QA and resources.

2007 Panel Comment: “It appears that the uninstall program simply remo ves the underlying SHEDS program and all the simulations the user may have d efined and run until that point. This should be clearly noted, and the user should be provided deta iled information and prompted about this step.” (p. 16)

EPA 2010 Response: Uninstalling is a function of the installation wiza rd 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 p reviously created. A warning box is in Section 3.2, “Installation under MS Windows” and boldface t ype 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 n ote extreme or illogical combinations, or a script generator to run designed experiments on the model. ”

EPA 2010 Response: Checks on illogical combinations would be impractic al to do comprehensively given available resources, so no ma jor additional checks or review opportunities have been added. After considering t his suggestion, however, verification of the legitimacy of inputs was improved slightly. For exa mple, checks have been added that variables representing fractions or probabilities do not exce ed one. Also, checks to prevent negative values have also been implemented.

Regarding a script generator, the user cannot submi t multiple variability runs simultaneously via the GUI; however, design of experiments may be assi sted 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 submit ted 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 co de 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 Tec hnical Manual.

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

distributions.”

EPA 2010 Response: This was considered for v. 4, but given available r esources, data, and the number of inputs that would be required for this op tion, and considering relative importance of this option compared to other priorities, autocorre lation 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 varia ble between diaries for each simulated individual, as detailed in the SHEDS-Residential ve rsion 4 Technical Manual, section 2.5.1.

2007 Panel Comment: “It is important that the developers anticipate fu ture data sets, especially longitudinal, and try to keep the code scalable.”18

EPA 2010 Response: We agree with the Panel's comments, and have implem ented flexibility and scalability in the SHEDS SAS code and input dat a. 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 grap hical user interface (GUI) was designed to be user-friendly to exposure modelers and risk asse ssors. Please comment on the organization and usability of the GUI, any difficulties you encounte red, which features and output capabilities were most useful, and whether any additional options wou ld be helpful. Please also comment or offer suggestions for improving the GUI/model interface.

2007 Panel Comment: “The Panel identified three major issues that shou ld be addressed in future versions of the software. First, the Panel felt th at there was need for a substantive case study to g uide novice users, preferably using realistic data.

A novice user, without appropriate data sets, will have significant difficulty or be overwhelmed by th e number of inputs to provide, even for a simple case . This highlights the need for an example case tha t

will also allow users to make minor customizations and not have to develop a case from scratch.” (p. 1 6)

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 defau lt 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 t o adjustment by advanced users from within the progra m.” (p. 16)

EPA 2010 Response: SHEDS developers used what we feel are best availab le approaches, and provide several options to the user in some cases ( e.g., dermal method, concentration generation). The longitudinal diary assembly and s ensitivity options have been expanded in

version 4. These are discussed in the SHEDS-Residen tial Technical manual, sections 2.5 and 5.5, respectively. In addition, the interface now provi des 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 c an make additional changes. We minimized hard-wired inputs and documented them clearly in th e 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 simulat e (on the order of 10 200 : higher numbers are reset to 10), without checking or reporting disk space or pr ocessing time requirements.” (p. 16-17)

EPA 2010 Response: Both the Technical Manual and User Guides now provi de 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 app ears in Section 1.3 “SHEDS-Multimedia

Version 4 Overview” of the Technical Manual. An adv isory on storage requirements appears in19

both Section 1.3 of the Technical Manual and Sectio n 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 quic k 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 convention s 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 Er rors” button), the application gave very cryptic messages such as: “de leted”, “library not found”, etc., and then finally reported “no errors found.” The error messages are likely due to outputs of some underlying operation s that do not impact the specific case, so they shoul d 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 specif y different folders for storing simulations (this w ill often be dictated by disk space constraints, etc.) and allow a user to organize simulations in a hierarchical manner. A major advantage of this appr oach is that the previous simulations are saved eve n 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 “Applicati on Scenarios” versus “Run Files” can be confusing;” (p. 17)

EPA 2010 Response: Wording has been changed in both the User’s Guide a nd 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 e ach menu item which provides descriptive informatio n while being unobtrusive; (p. 17)

EPA 2010 Response: Help text is now available in the Version 4 GUI. I t 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 err or message is displayed (saying that the run alread y 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. Howev er, 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 righ t 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 V ersion 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 res ponsibility on the user to make sure that the input s are logical. The user can, for example, assume an expon ential distribution for the value of a parameter, w ith no suggestion that it has to be between 0 and 1. Th e Panel also felt that there should be a more convenient way to see a summary of the inputs to fa cilitate checking that all values are logical.” (p. 16)

EPA 2010 Response: In the version 4 GUI, the user has the option of ex porting all inputs for a given run to Excel for printing. Checks have been a dded 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 fema le subpopulations, and then changed the inquiry to include both sexes, onl y to have the program lock up with no ability to return to previous screens. Since there was no opt ion to cancel, the user needed to restart SAS. The user should be allowed to return to previous select ions or the screen revised to include a running out put of the selections made. For example, it would be u seful 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 tra ced to a nonfunctioning “Cancel” button. This has been remed ied.

2007 Panel Comment: “...Panel members expressed concerns about the quali ty 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 pro vide 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 S AS, 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 a nd Files” refers the user to this appendix.

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

EPA 2010 Response: Both the Technical Manual and User Guide now provid e 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 app ears in Section 1.3 “SHEDS-Multimedia

Version 4 Overview” of the Technical Manual. The us er 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 c omment, 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 prerequis ite 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 ru n time expected in the documentation. ...

• “The SAS log window will accumulate numerous unesse ntial 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 no w clearly stated in the documentation up front, as suggested. A run progre ss indicator is now displayed on the screen. In addition, text has been revised in Section 5.5.8 “S ave 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 t he

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

2007 Panel Comment:

• “The issue of truncation of distributions is import ant for the user to be able to review. The program should warn the user when inappropriate val ues 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 Figu re 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 n ow designated Figure 2.1. It was not intended as a detailed flowchart, but rathe r as an overview of the program’s operation. Text

has been revised to alert the user that not all scr eens are represented in the figure and therefore th is 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 info rmation 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, t his comment would

seem to be less relevant than at the time of the la st SAP. However, the point has been investigated with SAS. Vista Professional is supported by SAS, b ut 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 develop ed 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 assu rance has not been conducted for Version 4 within this operating system. SAS does support Wind ows 7 Professional and Home Premium (but only ‘Premium’ for the home).

2007 Panel Comment:

• “The minimum requirements for running SHEDS, as des cribed 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 act ual requirements for running the model are unlikely to be met by most of the commonly used com puters. Specifying more realistic requirements for a “reasonable performance” or just specifying the requirements as “a computer that runs SAS (version x)” are two possible alterna tives.” (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 t o 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 Man uals and a substantive glossary.” (p. 18)

EPA 2010 Response: Sections have been numbered for the SHEDS-Residenti al version 4 User’s Guide and Technical Manual. However, other topics h ad higher priority than cross-referencing and hyperlinking.

2007 Panel Comment:

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

EPA 2010 Response: This comment was taken into account in developing t he table and figure captions for the Version 4 User’s Guide and Technic al Manual.

2007 Panel Comment:

• “The User Guide also should contain some of the tec hnical 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 appendi x.

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 (Integrat ed 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 h ow to orient new

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

SHEDS-Residential.”

2007 Panel Comment:

• “Specify Application Exposure Scenarios” does not i nclude the option to add new exposure scenarios relevant to pesticide application. The m odel should allow assessment of non-standard application methods.” (p. 18)

EPA 2010 Response: This can be done with the SHEDS-Residential SAS cod e. 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 descr iptions of specific model components are scientifically sound and whether the algorithms des cribed in the Technical Manual represent the state of the science for performing exposure assess ments. Please also comment or offer suggestions for improving or modifying these algorithms or othe r aspects of the model construct.

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

. The larger overlying issue is the quality and the t ransparency 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 la rgely invisible to the user and not clearly identif iable without careful examination of the underlying SAS c ode. While it is not necessary to present this dat a 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 a nd 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 Mul timedia.,” that presents an overview of the capabilities of the model, its underlying proce sses, distributions, and configuration options; these are expanded on in the User Guide and the Tec hnical Manual.

2007 Panel Comment: “The Panel’s greatest concern was about dermal expo sure analysis. SHEDS appears to use both the transfer efficiency (TE) an d transfer coefficient (TC) approaches in its assessments. The Panel noted that, at present, the documentation appears to favor the use of the TE o ver 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 sphe re is weak. The SHEDS dermal protocol is regarded as s tate-of–the-practice, not-state-of–the-science.” (p

. 19)

EPA 2010 Response: The Panel’s issue appears to be with whether the tr ansfer 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 a s 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 tha t internally the model utilizes a user-supplied TC, or calculates a transfer coefficient as a funct ion of TE, as part of estimating dermal exposure. The dermal exposure algorithm has been m odified in version 4 to incorporate reduced transfer of chemical to the skin as the amount of p esticide on the skin increases. With respect to absorption of the chemical, the model moves the pes ticide 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 mere ly represents observed similarities), and cannot incorporate different types of surfaces or those wi th 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 c aution 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 tra nsfer 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 agai nst any violations of conservation of mass.

2007 Panel Comment: “The Panel noted that the paragraph at the bottom o f page 43 is incorrect in its

assertions about the rate of dermal transfer. Derm al 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 expos ure/absorption terminology in the SHEDS documentation. We believe that the clarified terminology regarding exposure and dose in version 4 addresses these conc erns. 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 ve rsion 4. A new feature in version 4 is that the

chemical now moves into the stratum corneum first a nd later is absorbed into the blood.

2007 Panel Comment: “For example, Fenske & Lu (1994) showed apparent ra pid uptake of chlorpyrifos at low skin loadings. Failure to accou nt for dependence of absorption efficiency on skin loading is a significant weakness of current practi ce.” (p. 19)

EPA 2010 Response: SHEDS includes a simple PK model, but allows for ex porting to PBPK models that can include more realistic absorption r ates. As noted above, the dermal exposure algorithm has been modified in version 4 to incorpo rate reduced transfer of chemical to the skin as the amount of pesticide on the skin increases an d 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 fr om the dietary exposure analysis default groups. Operationally, this may n ot 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 b asis of the SHEDS specific age cohort system. The same should be presented for dietary and water comp onents when they are added to the next version of25

SHEDS. For practicality, the Agency may want to co nsider 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 opti ons 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 Samplin g screen.

Outputs: The user has the option to select a max and a min a ge 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 s elections.

Dietary GUI Inputs: (U.S. Population, 0,1-2,3-5,6-12,13-19,20-49,50+ ye ars, 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 P anel noted that techniques of partial correlation coefficients could be used to allow for multiple co rrelations.” (p. 19)

EPA 2010 Response: We recognize the validity of the comment, but at th is point there does not

seem to be much of a demand nor available databases for addressing complex multivariate dependencies. Allowing pair wise correlations of i nputs in version 4 is a significant enhancement from existing exposure models.

2007 Panel Comment: “The system also could be augmented by allowing def ault 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 m ore uncertainty into the model. In the input data set (via SAS not GUI), the user can ente r/construct functional relationships to address complex correlations among multiple variables.

2007 Panel Comment: “The Agency is encouraged to consider providing def ault shapes of continuous probability distributions for user specified input parameters. The Agency scientists have invested substantial effort in reviewing these data and appe ar to be in a good position for suggesting default distribution types, while still allowing the user t o deviate from them. Having defaults has the advan tage of more easily comparing different runs by differen t 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 p aragraphs 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 ones elf 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 associ ated files are provided as the demonstration26

input files, as noted in Section 2.2 of the User’s Guide. The Technical Manual also contains default suggestions for non-chemical specific input s 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 construct ive comments. Estimated absorbed dose is an outcome many would like to see as part of a SHEDS r un. 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 wit h 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 in tended to provide a quick screening level of blood and urine doses. Ver sion 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 PBP K modeling. One case study, dietary arsenic, is included in the materials for Panel rev iew. The linkage with SHEDS and PBPK modeling with a permethrin case study will be prese nted to the July 2010 SAP.

2007 Panel Comment: “Verifying the precision and accuracy of SHEDS pred ictions is not described in the materials provided to the Panel for either vers ion 3 or 4 of the model, except for the proposed sensitivity and uncertainty analyses. The Panel fe lt that before implementing SHEDS or other models i n risk assessment, it is critical to understand how t he model predicted values compare to the reasonable range of exposures that can be measured in the gene ral 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 th e simulations. Evaluating predictions from SHEDS using exposure measurements is one exercise that wi ll inform the Agency about the accuracy and robustness of the model. Extremely wide or extreme ly narrow distributions of predicted exposures compared to observed values are suggestive of probl ems 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, link ing 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 i ts 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 Us er Guide presented a typical diary of one weekday and one weekend day as part of the example case stu dy.” (p. 20)

EPA 2010 Response: The text of Section 2.5 “Generating Longitudinal Ac tivity Patterns for a Simulated Individual” and Figure 2-5 describe how t he model constructs longitudinal activity diaries and follows simulated individuals through t hem. The version 4 Technical Manual now contains an Appendix D “Summary of the CHAD Databas e” that includes summary information by individual study, diary counts by age/gender coh ort, 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 im provement.27

2007 Panel Comment: “The Panel had a number of suggestions on the organ ization, clarity, completeness, and usefulness of the Technical Manua l. 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 intro ductory section of

the User Guide “Considerations for New Users of SHE DS-Residential” attempts to better orient users to the model’s scope.

2007 Panel Comment: “The most important issue identified by the Panel w as 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 st andard data set that could be used to orient variou s model clients; users, reviewers, Agency risk manage rs, and stakeholders are all important audiences, a nd 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 Gu ides 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 sp ecific inputs, based on available literature data, in the SHEDS-Residential package and Technica l 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 ful ler description of the fixed inputs and decisions embedded in the model is needed: particular datase ts (e.g., CHAD, the contacts data base for dermal exposure) need fuller explication to achieve user u nderstanding and model transparency. Because the manual lacks a description of the strength of the u nderlying 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 qu alifiers 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 d ata 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 ve rsion 4, and documented them clearly in the Technical Manual. D ata sets used in SHEDS runs are CHAD, U.S. Census, and NHANES as assumed underlying datab ases. 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 indi vidual study, diary counts by age/gender cohort, an example diary excerpt, and complete list ings of all CHAD activities and locations. In version 4 the GUI allows sub-setting of the CHAD da tabase. 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. C ensus 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 re ference guide and should

inform on technical issues associated with installi ng 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 int erpretation 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 exten sions 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 vari able names and definitions are provided in Appendix B “Selected SA S Variable Nomenclature” and Appendix F “Description of SHEDS Multimedia Input Files.” Macr o 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 repr esentative run times and disk space requirements for a set of population and uncertainty run sizes. This can help in planning f or a simulation beforehand.” (p. 21)

EPA 2010 Response: Both the Technical Manual and User Guides now provi de 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 app ears in Section 1.3 “SHEDS-Multimedia

Version 4 Overview” of the Technical Manual. An adv isory on storage requirements appears in

both Section 1.3 of the Technical Manual and Sectio n 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 representativ e activity patterns for an individual: temperature, season, and employment status. Some of the criteri a for selecting activity diaries (e.g., diaries tha t have non-zero sleep time) can be strengthened. For examp le, selecting a diary with “sleep > 0” is reasonabl e for a short-term simulation. However, repetition of such a diary for an extended period will likely re sult in unrealistic scenarios.” (p. 21)

EPA 2010 Response: The version 4 Residential Technical Manual now cont ains an Appendix D “Summary of the CHAD Database” that includes summar y 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 selecti ng diaries may be altered by editing the “Diary QA rules” input file.

2007 Panel Comment: “The Technical Manual should also list the intermed iate files generated, provide relevant details so that users can write scripts fo r assessing model correctness.” (p. 21)

EPA 2010 Response : This would require significant effort because inter mediate 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 desire d intermediate variables of interest, if

interested.

2007 Panel Comment: “Information on restarting a simulation from an int errupted portion should also be provided.” (p. 21)

EPA 2010 Response: Development of this “restart” feature has been init iated but is not yet completed.

2007 Panel Comment: “The Technical Manual should also provide details o n weighting population based on the distribution in a simulated population region (e.g., a county or a state), i.e., sufficie nt29

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 defaul t population file may be replaced with one tailored to the specific appli cation desired, or alternatively specific inputs may be so tailored.

2007 Panel Comment:

• “The Quality Assurance section should be moved to t he 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 t he table and figure captions for the version 4 User’s Guide and Technic al 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 Techn ical Manual now contain the detailed probability distribution descriptions in an appendix.

2007 Panel Comment:

• “Numbering chapters and subchapters is strongly rec ommended to be used instead of the current, unnumbered heading-based organization.” (p. 21)

EPA 2010 Response: Sections have been numbered for the version 4 Resid ential User’s Guide and

Residential Technical Manual.

2007 Panel Comment:

• “Formulae should be presented using mathematical no tation, in contrast to the textual expansion of terms (e.g. F = ma versus Force = Mass x Acceler ation), especially for complex equations. If common explanation is thought to be necessary, an i nformal presentation could be added.” (p. 21)

EPA 2010 Response: We appreciate the panel’s desire for standard, brie f mathematical notation. However, we chose more expansive notation in the in terest of clarity for the broad spectrum of users envisioned for SHEDS. The notation used also prov ides a more direct connection with the SAS

code itself.

2007 Panel Comment:

• “The usage of some terms in the manuals differs sli ghtly from their definition in statistical literature, e.g., interchangeable usage of the term s “variability distribution,” “probability distribution,” and “probability vector.” These inco nsistencies should be addressed.” (p. 21-22)

EPA 2010 Response: Statistical terminology was reviewed and some chang es made, particularly in the wording applying to discrete variables. A subse ction on discrete variables has been added to Appendix E “Probability Density Functions,” a footn ote has been added to the first page of the30

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 distr ibution,” “probability distribution,” and “probability vector.” Because of the way the code h andles them and one enters these values into the software, Bernoulli variables and multinomial varia bles are now discussed separately.

2007 Panel Comment:

• “The manual addresses at several instances the issu es of accuracy, e.g., when sampling from a population and generating the population variabilit y and then comparing the center of the distribution of an exposure endpoint with the extre mes of the distribution, e.g., percentiles. A formal definition of accuracy would clarify the dis cussion of these issues. When addressing the characterization of the variability distribution th e Panel noted that the population variability as an

outcome of SHEDS is not different from the variabil ity of endpoints investigated in an empirical study (e.g., a large epidemiological study). In bot h cases principles of statistical data analysis have to be applied. The document should refer to su ch 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 v ariability and uncertainty results, confidence intervals can be generated for model output results.

2007 Panel Comment:

• “The description of the probability vectors needs t o 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 pe t or lawn.” (p. 22)

EPA 2010 Response: Because of the way the code handles them and one in puts them, Bernoulli

variables and multinomial variables are discussed s eparately. 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 A ppendix with mathematical formulae for the distributions described in a formal mathematical ty pe, 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 pr obability distributions now reside in Appendix E of the Technical Manual and ha ve been augmented with a subsection on

discrete distributions.

2007 Panel Comment:

• “On page 24 there are instances where no dust is co ntacted. The basis for this decision is unclear: is it for model internal consistency or b ased 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 Versio n 4. When dust concentrations are provided, they are converted to surface loadings to be combin ed with other forms of the chemical. This point is explained in Section 1.6.2 “Dust and soil ingest ion 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 a ddresses 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 ind icates that to contrast explicitly exposures betwee n two different regions or seasons, it would be bette r to conduct separate model runs with inputs specific to those given regions or seasons. Current ly, seasonally-varying chemical usage inputs are supported. In addition, the text points out that a dvanced 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 sta te 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 informa tion, though the user may employ such data at his or her discretion. Regarding updating over tim e, 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 r esidents of multi-family dwellings have less control over their pesticide applications, the mode l 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 ru n for different subpopulations of interest to the user, with sub-po pulation specific inputs. We did not make any code changes in SHEDS per this recommendation becau se 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 approache s 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 vari ous procedures of SHEDS.” (p. 22)

EPA 2010 Response: The SAS manual style is for more advanced users tha n the anticipated end

user of SHEDS.32

Question 1-3: a) Please comment on whether the anno tated code is sufficiently clear such that the algorithms can be followed and understood. b) Pleas e also comment on whether these algorithms are technically correct and consistent with the des criptions 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, an d the actual code. This would allow a user to check the underlying code for a given operation wit hout reading the code file that contains multiple modules. This can be accomplished by providing hype rlinks 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 utilit y 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 ha d 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 su ch as washprob, absr_gr, absr_gm, bioavm, and bioavr, with subtle changes in variable names for d ifferent quantities – this can potentially lead to difficult to notice errors. The code can be improve d 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 con sistent naming scheme. For example, the current

version of the SHEDS code uses variables such as wa shprob, has_lawn_p, timeofday_indoor, and reentry_indoor, all denoting probabilities.” (p. 23)

EPA 2010 Response: Variable naming has been improved in Version 4. th ese variables are listed in tables throughout the Technical Manual an d 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 majo r versions of SHEDS available to interested researc hers so that they can use simple text differences betwee n different versions of the SHEDS code or individua l modules.” (p. 23)

EPA 2010 Response: The differences between versions 3 and 4 are highli ghted in Section 1.6 “Changes from version 3 to version 4” in the Reside ntial 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 ar e 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 c onsistency. 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 th e 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 shou ld provide details on the intermediate variables in the SHEDS simulations, an d 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 a re eventually overwritten for space considerations. As noted ear lier, advanced SAS users may alter the code to retain any intermediate variables desired. A new fe ature in version 4 is the ability to resubmit an earlier run using the same random numbers as previo usly. The user may now request output for selected persons from the earlier run. The interme diate variables for these persons may be saved without overwriting the values for other persons.

2007 Panel Comment: “... some suggestions for improving algorithms as we ll as for enhancing the consistency between the Technical Manual and code w ere 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 correlati ons from runs on multiple machines has been addressed by using two levels of randomization invo lving different streams, so whenever the initial seeds for runs are different, the streams d o not overlap.

2007 Panel Comment: “A minor comment from the Panel members includes d ocumenting the descriptions of probability distributions used in t he Technical Manual and User Guide, because the distributions are often parameterized in different ways, and it is important to clearly describe how t hese distributions have been parameterized in SHEDS.” (p . 24)

EPA 2010 Response: Both the Version 4 Residential User Guide and Techn ical Manual now contain the detailed probability distribution descr iptions in an appendix.

F. Multiple Chemicals

Question 2-1: Please comment on the technical aspec ts 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 metaboli tes, programming issues, scenarios, database representativeness and absorption and bioavailabili ty.

“The logic of extending the application of SHEDS fr om single to multiple chemical assessments is plausible and warranted. However the nature of suc h proposed work would be complex, time and resource consuming, and the process itself may pose significant challenges. The Panel expressed severa l concerns about the planned methodology.

“...Overall, the concept of going from aggregate to c umulative as described is useful. One important

feature that is not mentioned in the document is th e 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 t he focus shifts to model structure and outputs, and the34

Panel focused its comments on mass tracking, produc t co-occurrence, pesticide metabolites, programming issues, scenarios, and absorption and b ioavailability.” (p. 24)

EPA 2010 Response: The importance of input from risk managers and Agen cy scientists about the chemicals that are subject to cumulative assess ments and the process used to make these determinations is not directly relevant to the SHED S structure or manuals and does not need to

be discussed in them.

2007 Panel Comment: “The process of tracking mass is unclear in Versio n 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 tou ching and subsequent washing of the hand, and allow s 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 do cument, the definition of “carrier” in SHEDS is somewhat overlapping with “media.” For example, ai r (either indoor or outdoor, or in the treated or untreated rooms) is considered a carrier and a medi um in some places in the manual. The manual is als o unclear as to why “residue” itself would be conside red as a carrier. Residue has to be associated wit h or

adhered to the carrier before it can reach the simu lated individuals in any of the media. As stated i n the documentation, “When a person comes into contact wi th a contaminated MEDIUM, a certain amount of

one of more carriers will be transferred onto or in to the body” (page 7, SHEDS Version 4 Planning document, June 12, 2007 version). Care must be tak en 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 vers ion 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 woul d outweigh the effort needed and the computational difficulties that would arise. Our e xperience indicates that the adjustments needed to the environmental concentrations are insignifica nt. Terminology has been revised in the

version 4 Technical Manual. The terms “carrier" an d "residue" are no longer used. The model code now computes the chemical mass in the microenv ironments 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 s oil) 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 chang e of soil mass would not affect the model outputs. Since each diary is being considered as an independ ent 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 intende d 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 “C ontact Duration and Concentration averaging.”35

2007 Panel Comment: “As stated by the Agency, the run-time and the sto rage space for the model output may significantly be hampered by the propose d 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 chemica ls 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 ingred ients that cause the same health concern. So inste ad of n=10 as proposed for use in the algorithms in ve rsion 4, the actual sample size might be no more th an 3.” (p. 25)

EPA 2010 Response: While we agree that is useful to have an upper boun d on the number of chemicals, we cannot in the model development proce ss rule out the ability of the model to address several chemicals simultaneously. We recog nize 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 inspecti on. The document was unclear, but during the presentation the Agency indicated that it will cons ider addressing both multiple products that contain multiple chemicals and multiple chemicals with a co mmon mode of action that are not necessarily present in a single product. The Agency should exp lore using different data sources for assessing multiple chemical co-occurrences: for example, pes ticide 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 f ood 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 bre vity of the documents describing these model extensions. The version 4 R esidential Technical Manual provides a complete explanation of the revised model. The Pane l’s concern with respect to co-occurrence

between different scenarios is not an issue in vers ion 4 – users may specify intentional cooccurrence or leave the probabilities as independen t. 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-o ccurrence” and 3.4.3 “Co-occurrence Factors” in the Technical Manual. When multiple ch emicals of interest are present in the same residential model run, SHEDS automatically calculat es co-occurring exposures based on the user-specified co-occurrence pattern mentioned abov e. Regarding the dietary module, SHEDS accounts for co-occurrence by preserving the struct ure of PDP residue data, which captures cooccurrence by analyzing each sample for multiple ch emicals.

2007 Panel Comment: “The Panel noted that the ability to model compoun d metabolites should be included in the multiple chemical model, but this w ill 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 balan ce. In the absence of full accounting for metaboli tes, 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 co mpound.” (p. 25)

EPA 2010 Response: The Panel has recommended that SHEDS does not need an improved PK model (for example, that may include metabolism) bu t rather focus on linking with existing dose models for this purpose.36

2007 Panel Comment: “The current document states that using a single e fficiency for washing removal of soil/dust from the skin regardless of compound i s an advantage of the multiple chemical model. Uniform washing efficiency may be reasonable for so il or dust-borne chemicals. However, a single removal efficiency is also apparently assumed for " residues" (i.e., for free chemical on the skin). S ince chemical washing efficiency is likely to be tied to the physical-chemical properties of a given chemic al, this could be a disadvantage. Relatively lipophili c compounds are less likely to be efficiently remov ed by washing than relatively water soluble compounds. ” (p. 25-26)

EPA 2010 Response: As noted in the response to the Panel’s earlier com ment regarding dermal absorption, we believe that this recommendation ari ses from unclear terminology. In SHEDS,

residues are not free chemical on the skin. We ass ume that the amount of “free chemical” is negligible in practice and that all chemical will b e bound in some matrix or carrier. Terminology has been improved in the Technical Manual. Chemica l-specific washing removal rates can be specified, if desired, using the SAS data set edito r.

2007 Panel Comment: “While this move from aggregate to cumulative asse ssment provides an opportunity to use an “object oriented” programming approach for encapsulation of variables, it is als o possible that users can be overwhelmed by the numbe r of parameters that they have to provide. Therefore, the Panel envisions that GUI modificatio ns 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 hav e one underlying code base and different interfaces .” (p. 26)

EPA 2010 Response: A true object-oriented approach is not compatible w ith the SAS programming language. As noted earlier, variable n aming 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 da ta 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 requiremen ts of such a system may be large due to the increase in the number of chemi cals to be simultaneously modeled. The temporal resolution of the simulation will potentially be di ctated by the fastest simulation time and the lowes t time scale to be modeled.” (p. 26)

EPA 2010 Response: If a large number of chemicals are to be run simult aneously, then this is a potentially serious issue. The version 4 code loop s 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 algorit hm. 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 i ncluded in the written plan is scenarios for home applicator exposure. This was p resented at the meeting as one potential added component for version 4, and includes both professi onal and home applicators, and the Panel felt that this was an important addition that needs careful c onsideration.” (p. 26)

EPA 2010 Response: Handler exposure by home applicators is available i n 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 ab sorption efficiency and bioavailability for non-dermal pathways in the Tech nical Manual is unclear, and therefore the state of science cannot be evaluated because equations and m odel inputs are not available. The Panel cautions that, like dermal absorption, GI and lung bioavaila bility estimations and assumptions are dependent upon factors such as medium, particle size, materia l thickness on exposure surfaces, and other factors that need careful attention in a multi-pathway, mul ti-media, and multi-chemical model.” (p. 26)

EPA 2010 Response: Terminology has been revised for Version 4. Regard ing the Panel’s caution with respect to absorption, the focus of SH EDS 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 wit h a permethrin case study, will be presented at the 2010 July SAP meeting.

G. Fugacity

Question 2-2: Please comment on the technical aspec ts and usefulness of the planned methodology for incorporating a fugacity-based source-to-concen tration module into SHEDS-Multimedia

version 4. Does the Panel recommend additional effo rts with the fugacity module (e.g., modeling more realistic multi-room dwellings) given availabl e 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 optio n would provide more useful results than approaches currently used in the model. The Panel expressed a desire to see example calcul ations and sensitivity analyses that illustrate the performance of the fug acity-based approach.”

EPA 2010 Response: A fugacity model can be useful to help guide the de termination of some version 4 inputs. Such a model has been developed f or use with SHEDS, and was presented to the August 2007 SAP. However, this is currently e xternal to SHEDS version 4, not a module

within it. Future research could include sensitivi ty analyses that illustrate the performance of the fugacity-based approach. Depending on those result s, the Agency may consider incorporating the fugacity module as an integral part of the SHED S code.

2007 Panel Comment: “The Panel notes that EPA proposes to evaluate the fugacity module using chemical transport data from a 1-house study. Whil e this is a reasonable place to start, the Panel encourages more extensive testing including applica tion 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 sensi tivity analyses as recommended by the Panel.

2007 Panel Comment: “The Panel expressed less unanimity with respect t o the actual likely utility of the fugacity option and expressed a desire to see e xample calculations that might better illustrate th e performance of this option. Only through sensitivi ty analysis can the question of value be determined

. In particular, the effect of time scale being simul ated should be examined. The Panel also asked for clarification regarding whether the fugacity approa ch will be limited to indoor environments.” (p. 26- 27)

EPA 2010 Response: See above. The Agency will conduct additional analy ses on the utility and performance of the fugacity model before considerin g making it an integral part of SHEDS.

2007 Panel Comment: “With respect to the second part of the charge que stion, the Panel agrees that simulation of a multi-room house is certainly feasi ble mathematically. Mixed opinions were expressed38

regarding the value of moving beyond a two-zone (tr eated/untreated) model. Clearly there is some poin t at which lack of data necessary to describe more co mplicated systems will prevent derivation of added benefit by increasing complexity. The Panel therefo re suggested that EPA start with a simple (two-zone ) version and more thoroughly investigate and documen t 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, b ut 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 en vironmental compartments via absorption by occupants. It was suggested that EPA compare predi cted mass absorbed to assumed mass applied for various scenarios to provide more information on th is topic. Although the term did not arise explicit ly in the discussion, in effect the Panel suggested th at EPA include routine computation of Intake Fracti on (Bennett et al., 2002a,b) in SHEDS output.” (p. 27)

EPA 2010 Response: As noted above, SHEDS-Residential version 4 now per forms the calculations regarding the fraction of chemical mas s that is transferred to the simulated individual. However, the environmental concentrati ons are not adjusted for this transfer, for two reasons. First, most houses have multiple individu als 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 to tal removal, and SHEDS already reduces the amount of chemical present on an hourly basis in th e decay/dispersion calculations. The second reason is the modularity of SHEDS; the program assu mes 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 aspec ts, 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 a ssemblage of diaries that better simulates reality and provides more accurate estimates of exp osures 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 coe fficient, 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 cur rent activity database (CHAD) may not contain enough diaries to allow random draw s using the new longitudinal diary assembly

method and several members raised concerns about th e representativeness of the US population in the CHAD dataset. Since a significant number of CHAD d iaries were excluded for various reasons, further proposed decisions that reduce an already small dat aset might be problematic for accurately determinin g the within-subject variation. Furthermore, as stat ed in the documentation, two of the most important pieces of information, chemical usage patterns and contact possibilities, are not included in the diar y 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 po ols and the representativeness of CHAD are equally applicable t o 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 cou ld be restricted to the NHAPS study (approximately 9300 diaries), which is nationally r epresentative. 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 se ts. But again, these issues apply to the underlying database, not the assembly method. As n oted 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 w ithin-subject variation. However, this applies to all diary assembly methods and the new m ethod 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 a dditional 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 autocorrel ation function (currently set at 1-day) would be necessary to eliminate repetitive a ctivities from the longitudinal diaries that are li kely 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 pestici de use, such as frequency of gardening activity, rathe r than examples related to air pollution exposure assessment.” (p. 28)

EPA 2010 Response: One of the advantages of introducing autocorrelatio n 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 m odeled. 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 scenario s 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 exposur es, 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 Secti on 5.5.4 “Diary Assembly Method” of the User’s Guide and a case study was co nducted.

2007 Panel Comment: “The Panel believes that the concept behind the ne w longitudinal diary assembly method has scientific merits, and the technical asp ects of the proposed method based on the D-statisti c and 1-day lag autocorrelation is sound. However, t he application of this method in the interest of predicting aggregate human exposures to environment al chemicals may be somewhat limited. This is particularly true for activities that may lead to p otential exposures that are either difficult or imp lausible to track as continuous “key variables.” For exampl e, it is useful to use the D-statistic for tracking how long a person spent outdoors for studying human exp osure to air pollutants (like the “Southern California Ozone Exposure Study” cited by the Agenc y) because the time spent outdoors is correlated with the amount of ozone being inhaled by the subje cts. However, it might not be as feasible to implement the D-statistic approach in compiling lon gitudinal exposure data sets, such as the frequency of hand contact on a contaminated surface, when mul tiple factors need to be considered before the40

exposure scenario can be confirmed. Such a limitat ion is inherited from the concept of incorporating the within and between-subject variation in order to be tter control the longitudinal properties of the fin al 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 t his proposed method for predicting longitudinal exposures in which inhalation exposure is not the p redominate pathway. In other words, unless the longitudinal diaries do not encompass a great numbe r of human micro-activities (such as potential exposures other than inhalation) in which time spen t in the area where the contaminant is present is t he only and the ideal key variable, the incorporation of this new longitudinal diary assembly method in SHEDS version 4 may not add significant value. Bas ed on Glen et al. (2007), it appears that there is also a possibility to choose more than one key vari able for creating a longitudinal diary and therefor e 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 t he work detailed in Glen et al. (2007) and the fact that the method has been incorporated in anoth er SHEDS model, the effort to include it in Version 4 was manageable. As a point of clarificat ion, the Glen et al. reference suggested combining diary variables into a single key variabl e as opposed to having multiple key variables.

2007 Panel Comment: “One Panel member wanted to see how the longitudi nal 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 p otential 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 C HAD diaries collected by the Office of Research and Development. EPA has also recently obt ained longitudinal dietary data from Dr. Alex Lu that was mentioned at the August 2007 SAP m eeting. We plan to examine the D&A

statistics from these datasets.

I. Sobol

Question 2-4: Please comment on the technical aspec ts and usefulness of the planned methodology for utilizing Sobol’s method for sensitivity analys is 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 m ethod, and wondered whether the new sensitivity analysis method is needed. Sev eral Panel members expressed concern that the required number of simulations for sensitivity anal ysis 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

95 th percentile for population exposures). It appears that the proposed use of Sobol’s method will allow the computation of “local sensitivity,” without add ressing 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 r equired 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 analys is 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 si ngle population metric (e.g., the 95 th percentile for population exposures).

“...Many of the Panel members were not convinced abou t the utility of the Sobol’s method for SHEDS, and were also perplexed by the complexity of the me thod.

“...Since Sobol’s method has not been used with a lar ge 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 deter ministic, with random parameters provided in one module) appear to be large, further reducing the us efulness of incorporating this methodology in

SHEDS.

“However, some Panel members felt that since the So bol 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 altern atives 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 mater ial provided. There seems to be a misunderstanding of the method with respect to seve ral 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 comment s. These brief comments are intended to clarify, but do not fully explain the method.

• Sobol’s method does not target the sensitivity of a ny particular population summary statistic (such as the 95 th percentile). Rather, it attempts to answer the qu estion of why individuals differ in their ultimate exposure. Briefly, it quantifies the relationship between input settings and exposure through random variation of selected subse ts of the inputs. Because the entire parameter space is explored, Sobol’s method is a gl obal 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 indivi duals 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 c hoice and a larger number of individuals provides a better characterization of sensitivity.

• The panel is correct that Sobol’s method does not a ddress uncertainty explicitly. Sobol’s method can address both variability and uncertainty by fol ding 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 t o control the random number seeds to allow reproducibility of the model runs. As noted earlie r, this capability was not present in v.3 but this42

was added in v.4. This is the key requirement in t erms of implementing Sobol’s method for the SHEDS code, and this is what was meant by making th e 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 vari ance decomposition approaches, but otherwise are very distinct. Both were explored for use in S HEDS, 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 avail able 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 han dling variables that must be re-sampled through time. In addition, Sobol’s method handles d iscrete inputs better.

• The Panel also suggested the use of ANOVA for sensi tivity analysis. In addition, at least one Panel member thought that Sobol’s method was a rein vention of experimental design. It was not the intention to suggest that Sobol’s method replac e ANOVA. Quite to the contrary, we feel that Sobol’s analysis can be a useful complement to help ing design an effective ANOVA approach. Though Sobol’s method does not provide a statistica l significance test, it is extremely helpful in indicating which combinations of inputs are negligi ble and which inputs have important interactions associated with them. Thus, results f rom Sobol’s analysis can provide very valuable input to the design of, for example, a fractional f actorial ANOVA.

• Sobol’s method has been added as an option for sens itivity analyses. This is described in the SHEDS-Residential version 4 Technical Manual, secti on 6.5, and also in the User Guide section 5.11.5.2. Sobol’s method is an option to the user t hrough 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 G UI for SHEDS-Multimedia version 4 that are

listed items in Section 5 of the above-referenced b ackground 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 associat ed 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 dev elopers should do everything that is simple and does not involve a major reorgan ization of the code. At the same time, a lot of eff ort should not be placed into options that will not mak e much difference in the overall conclusions drawn from the model. Most Panel members thought that PB PK modules should not be a high priority and should remain independent modules from the SHEDS mo del output.”

EPA 2010 Response: Version 4 incorporates exposure to multiple chemica ls and addresses key comments from the 2007 SAP; it does not contain a d etailed PBPK module.

2007 Panel Comment: “The Panel felt that the Agency should get more ex perience with the application of this model before getting into a major reorganiz ation of the code. It is important that the develo pers anticipate future data sets, especially longitudina l, and try to keep the code scalable. Autocorrelati on can have an important effect on the tails of the exposu re distributions. Anything that helps the user understand uncertainty and variability is important . Several Panel members emphasized that improved graphics were important for wide model comprehensio n and acceptance. They also felt it was useful to43

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 gene rator to run designed experiments on the model. Thi s is similar to sensitivity analysis but could be mor e general. The user could ask a series of question s about what factors and interactions are of interest , and then plan and implement a fractional factoria l or response surface design to test hypotheses. Prior to adding additional functionality (modules, featur es, or capabilities), sensitivity analysis with the spe cific modeling assumptions should be conducted. Th is 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 expos ure 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 r e-parameterize distribution mean, variance and shape so they can conveniently m ove between distributions without having to refer to statistics texts (e.g., Johnson, et al., 1994, 1 995, and 2005) for the parameter definitions. Expe rience has shown, however, that unless a distribution has an extreme right tail, the choice of distribution d oes not make much difference.” (p. 30)

EPA 2010 Response: The version 4 GUI reports summary statistics such a s 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 b y EPA practice and will want to model non-compliant application, i.e., not follo wing the pesticide label. It is important that the model provide an “application programming interface” defi nition that describes the format and the type of intermediate data, in a simple and easy to use mann er. This will lead to the following advantages: a) users and developers can independently create input /output analysis modules, provided there is adequat e documentation for developing such tools; b) this wi ll overcome current issues with the quality of plot s and graphs; and c) provide facility to input empiri cal distributions for specifying uncertainty and variability; d) plus provide sufficient number of e xamples, 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 pan el requests is available. The user may create a non-compliant scen ario, 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 h ave the API capabilities discussed above.

2007 Panel Comment: “The Panel felt that it would be useful if SHEDS p rovided better contextspecific help files, the ability to calculate margi ns of exposure (MOEs), and other options for select ing 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, an d 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 diffe ring exposure averaging times and the interpretation of population variability. We will explore other options for selecting and matching44

CHAD and food consumption diaries, and seek the Jul y 2010 SAP’s input on linking dietary and

residential modules.45APPENDIXSHEDS-Dietary Option/FeatureAvailable inSHEDSDietary?Notes [Option linked to 2007 FIFRA SAP Question]Food Consumption Data SourcesCSFII (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 foodsModeling Longitudinal Consumption (Food, Water) PatternsWithin 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 DWConsumption SurveyYes [Q3 FIFRA SAP 2007]2-DiaryYes Similar to Method used in Agency risk assessments (e.g., Calendex-FCIDTM)8-DiaryYes[Q2 & Q5 FIFRA SAP 2007; option available but not recommended; will be dropped innext update since data not included in NHANES]Diary Assembly (DA)Yes Currently based on Total Caloric IntakeResidues (Food & Drinking Water)Commodity (FCID) Residues Yes Method used in OPP risk assessments (e.g., DEEM-FCIDTM)Food Residue (vs. Commodity) NoOption used to assess Arsenic (Journal article); Case study assigns residues to FCIDcommodities; Difficult to Incorporate in GUIDrinking Water Concentrations Yes Single Distributions only (e.g., DEEM-FCIDTM)Drinking Water Concentrations – Calendar Year NoRandomly Select Year, then apply to corresponding Modeled Day (e.g., CalendexFCIDTMand CARESTMuse of 30 years of PRZM-EXAMS predicted DW concentrations)Modeling Food ResiduesSelect Single Residue for all Eating Occasions, byCommodity (RAC-FF)Yes Method used in Agency risk assessments (e.g., DEEM-FCIDTM)Select New Residue for different Eating Occasions,byFood-RAC-FFYes[Q1 FIFRA SAP 2007, 32; Option often has little effect for food-onlyanalyses; may‘add’ uncertainty]Correlation across commodities, across multiplechemicals (products) applied to foods, and oversubsequent daysNo 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), BySeasonNo[Q5 FIFRA SAP 2007] E.g., linking food consumption with seasonal (and/or regional)residuesModeling Drinking Water ConcentrationsRandomly select new DW concentration each day Yes Method used in Agency risk assessments (e.g., DEEM-FCIDTM)Randomly select Year for each Person-iteration, thenapply Predicted DW based on Calendar (365) date NoRetain seasonal patterns (autocorrelation) in DW concentrations. Method used in Agencyrisk assessments (e.g., Calendex-FCIDTM, CARESTM)Sensitivity/Uncertainty AnalysesSensitivity Analyses*Requires supplemental routine (e.g., effect of consumption outliers on infant DWexposures – aldicarb memo)Uncertainty Analyses* [Q4 FIFRA SAP 2007] Requires supplemental routine46SHEDS-Dietary Option/FeatureAvailable inSHEDSDietary?Notes [Option linked to 2007 FIFRA SAP Question]Compiling/Viewing Summary StatisticsTotal Daily Exposures (99.9th) Yes Measure used in OPP assessmentsAverage Daily Exposures (99.9th) * Need supplemental routineEating Occasions (99.9th) based on Maximum Exposureover all Eating OccasionsYes Method used to characterize exposures (NMC CRA)Eating Occasions with Chemical-Specific Half-Life(99.9th)*Method used in DEEM-Based Eating Occasions analyses; Need supplemental routine.Supplemental program to calculate per capita 99.9thfor single-chemical, single-day wasrecently incorporated into GUI; needs QC.Plotting Person-Day Exposures Yes Visualize Exposure Patterns/Persisting DoseContribution Analyses: Shares of Total Exposure, byCommodityYes Used to develop risk mitigation optionsContribution Analyses: Shares of Total Consumption,byFood (Commodity)YesOutput Summary Results (99.9th, CEC, etc.) to File (MSExcel/MS Word)* SAS Editor/Wizard allow users to export resultsView/Query Data (Food Diaries, Recipes, etc.) * SASEditor allows users to view/query dataNew Aggregate Contribution Analyses * Need supplemental routine* not implemented in GUI but can be conducted usingSAS code