Report No

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Title 

## Updated (2010) Aldicarb Acut e Dietary Risk Assessment Including Food and Drinking Water

Data Requirement 

## Not Applicable

Author(s) 

## Iain D. Kelly Jennifer L. Lantz Bruce M. Young Gary J. Mihlan Russell L. Jones Ann M. Blacker Tom Gould

Report Completed On



## July 27, 2010

Submitted By 

## Bayer CropScience Product Safety Management 2 T. W. Alexander Dr. Research Triangle Park, NC 27709 USA

Report Number



## US0092

Total Number of Pages 39 

### Confidential Business Information removed on August 19, 2010Report No

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## STATEMENT OF NO DATA CONFIDENTIALITY CLAIMS

No claim of confidentiality is made for any informa tion contained in this study on the basis of its falling within the scope of FIFRA § l0(d)(l)(A), (B), or (C).

The information contained herein is the property of Bayer CropScience and although subject to release to nonmultinationals pursuant to FIFRA S ection 10, such information is considered trade secret for all other purposes.

Company: Bayer CropScience Company

Agent: Iain D. Kelly Product Safety Manager Bayer CropScience

Date: 27JUL2010

The above statement supersedes all other statements of confidentiality that may occur elsewhere in this report.Report No

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## GOOD LABORATORY PRACTICE COMPLIANCE STATEMENT

This report does not meet the requirements for EPA FIFRA Good Laboratory Practice Standards, 40 CFR Part 160, and differs in the following way:

(1) Good Laboratory Practice Standards are not required.

Submitter: 27JUL2010 Iain D. Kelly Date Product Safety Manager Bayer CropScienceReport No

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## TABLE OF CONTENTS

TITLE PAGE ..................................................................................................................... .................... 1 STATEMENT OF NO DATA CONFIDENTIALITY CLAIMS .......................................................... 2 GOOD LABORATORY PRACTICE COMPLIANCE STATEMENT ................................................ 3 TABLE OF CONTENTS .............................................................................................................. ......... 4 SUMMARY ........................................................................................................................ ................... 6

1. INTRODUCTION.......................................................................................................................... 8

2. OVERVIEW OF CURRENT AND PR EVIOUS ASSESSMENTS INPUTS ............................... 9

3. SELECTION OF THE FQPA SAFETY FACTOR ..................................................................... 11

3.1 Determination of the FQPA Safety Factor from the PND 11, CCA Study ........................ 11 3.2 Relevance and Age Dependence of the FQPA Safety Factor............................................. 13 4. RESIDUE DATA...................................................................................................................

...... 14

4.1 Analytical Limit of Detection............................................................................................. 15 4.2 Oranges............................................................................................................................... 16 4.3 Orange Juice ...................................................................................................................

.... 17 4.4 Grapefruit ........................................................................................................................... 18 4.5 Sweet Potatoes.................................................................................................................

... 19 4.6 Potatoes .............................................................................................................................. 20 5. PERCENT CROP TREATED...................................................................................................... 22

6. DRINKING WATER.................................................................................................................

.. 24

6.1 Discussion of EPA Modeling Approach ............................................................................ 26 6.2 Parameterization of the Model ........................................................................................... 26 6.3 Calibration of the Model with Monitoring Data................................................................. 27 7. DIETARY (FOOD AND DRINKING WATER) ANALYSIS MODELS .................................. 27

7.1 Within Day Dietary (Food and Drinking Water) Assessment............................................ 27 7.2 Half-life Determination ...................................................................................................... 28 7.3 Event Based Exposure Distribution.................................................................................... 28 8. RESULTS AND DISCUSSION .................................................................................................. 29

8.1 Daily Dietary Food Assessment Results ............................................................................ 29 8.2 Aggregate (Food and Drinking Water) Assessment........................................................... 30 8.3 Contribution Analysis and Impact of 1⁄2 L OD Value on Dietary Food Only Assessments 31 8.3.1 Consumption Data ............................................................................................. 31 8.3.2 Residue Assignment........................................................................................... 34 8.3.3 Dietary Assessment Results Using Refined 1⁄2 LOD Values.............................. 35 8.4 Commodity Specific (Eat er Only) Assessment.................................................................. 36 9. CONCLUSION ............................................................................................................................ 36

10. REFERENCES............................................................................................................................. 37Report No

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## TABLE OF CONTENTS (continued)

Tables
Table 1 Comparison Dietary Assessment by HED, 2006 Versus BCS, 2006........................... 8 Table 2 Data Changes in Dietary Ri sk Assessment between 2006 and 2010 ......................... 10 Table 3 USDA PDP Data Used in Aldicarb Dietary Assessment ........................................... 14 Table 4 Orange PDP Data ....................................................................................................... 16 Table 5 Orange Juice PDP Data .............................................................................................. 17 Table 6 Grapefruit PDP Data .................................................................................................. 18 Table 7 Sweet Potatoes PDP Data........................................................................................... 19 Table 8 Potatoes Fresh PDP Data............................................................................................ 20 Table 9 Potatoes Frozen PDP Data ......................................................................................... 21 Table 10 Current Perc ent Crop Treated Data ............................................................................ 22 Table 11 Changes in Percent Crop Treated Value in Dietary Risk Assessments...................... 23 Table 12 Trend of TEMIK ® Use on Florida Citrus.................................................................. 23 Table 13 Percentile Di stribution Comparison ........................................................................... 25 Table 14 Daily Dietary Food Risk Asse ssment Results for Various Scenarios ........................ 30 Table 15 Comparison of Daily vs. Within Day Exposure and Risk Determination.................. 30 Table 16 CSFII Records for Extreme Grap efruit Juice Consumption in Infants ...................... 32 Table 17 CSFII Records for High Orange Juice Consumption in Children 1-2 yrs old............ 33 Table 18 Daily Dietary Assessment Results Using Refined 1⁄2 LOD Values ............................ 36 Table 19 Commodity Specific (Eater Only) Assessment for Orange Juice and Indirect Water 36


## TABLE OF CONTENTS (continued)

Figures

Figure 1 Mean and 95% confidence intervals for RBC AChE in adult rats ............................. 12 Figure 2 Mean and 95% confidence intervals for RBC AChE in PND11 rats ......................... 12 Figure 3 Distribution of Aldicarb Residues in Oranges............................................................ 16 Figure 4 Distribution of Aldicarb Residues in Orange Juice.................................................... 17 Figure 5 Distribution of Aldicarb Residues in Grapefruit ........................................................ 18 Figure 6 Distribution of Aldicarb Residues in Sweet Potatoes................................................. 19 Figure 7 Distribution of Aldicarb Residues in Potatoes Fresh ................................................. 20 Figure 8 Distribution of Aldicarb Residues in Potatoes Frozen ............................................... 21 Figure 9 Percent Crop Treated For Use of Aldicarb in Florida Citrus ..................................... 24 Figure 10 Comparison of Ground Water Residue Data from Modeling and Monitoring........... 26 Figure 11 Hypothetical Exposure Profile ................................................................................... 29 Figure 12 Contribution Analysis at the 99.9 th percentile for Infants and Children 1-2 yrs......... 31 Figure 13 Grapefruit Juice Consumption by Infants (< 1 yr old) ............................................... 32 Figure 14 Orange Juice Consumption by Children 1-2 yrs. old ................................................. 33 Figure 15 Orange Juice Resi due Distribution............................................................................. 35Report No

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## SUMMARY

Updated (2010) Aldicarb Acut e Dietary Risk Assessment In cluding Food and Drinking Water

Summary Aldicarb is the sole active ingredient in Temik ® brand Aldicarb Pesticide, sold and used almost exclusively as a 15% granule. It was first register ed in 1970. The active ingredient aldicarb has been extensively tested to define both toxicological properties and pharmacokinetic behavior. Aldicarb residue levels have been monito red in food and drinking water for approximately 40 years, resulting in aldicarb having one of the most extensive monito ring data set of all registered insecticides. The combination of robust toxicology and residue data sets enables conduct of highly refined risk
assessments.

As part of the 2007 Registration Eligibility Deci sion (RED), EPA relied on a Health Effects Division (HED) 2006 risk assessment demonstrating that aldica rb uses met the required standard of reasonable certainty of no harm if subsequently implemente d label changes were applied. Bayer CropScience (BCS) also conducted a risk assessment for consider ation which pointed out that the HED assessment was still conservative and did not incorporate a ll the knowledge and data available on aldicarb.

Since the 2006 assessments, new data has become availa ble that is pertinent to the risk assessment for aldicarb, including a Comparative Cholinesterase Assay (CCA) for postnatal day 11 pups which modifies the FQPA Safety Factor. Both HED and BCS have conducted a risk assessment based on current data. Using all available data and fully accounting for the rapid reversibility of aldicarb effects, BCS has demonstrated based on an aggr egate (food plus water) assessment for currently registered uses consume 60% and 50% of the acute Population Adjusted Dose (%aPAD) 99.9 th percentile for infants and children (1-2 years), resp ectively, thus meeting the standard of reasonable certainty of no harm. While the HED assessment indicates potential concerns, this document discusses credible scientific refinements that can be incorporated into the HED assessment to address these concerns. The following refinements are discussed and justified in the report:

1. Using an FQPA safety factor for infants of 3.9 based on the most robust analysis of statistical data. 2. Using an FQPA safety factor of 2 for children (> 1 years) based on a previous CCA study to more accurately reflect the comparative ontogeny of rats versus humans. 3. Using a percent crop treated of 20% in oranges rather than the maximum due to a change in cultivation practices. 4. Using appropriate distribution of water detects based on extensive monitoring rather than relying on highly inflated modeled values that are not predictive of field studies. 5. Constructing exposure profiles by using all even t based exposures within each day rather than using the worst case event from each day. 6. Adjusting for treated non-detects in PDP monitoring data by considering only analytes that would be reasonably expected to be present.Report No

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Furthermore, although not part of the formal sa fety assessment, BCS have conducted a Commodity Specific Analysis for orange juice and water, the consumption drivers for infants and children, using worst case assumptions and demonstrated acceptable Margins of Exposure.

Bayer CropScience is confident that by applying scien tifically credible dietary exposure refinements, it can conclude that there is reasonable certain ty of no harm to any age or population subgroup exposed to aldicarb derived residues in food and dr inking water based on currently registered uses.Report No

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1. INTRODUCTION

In September 2007, EPA issued the Registration Eligibility Decision (RED) for aldicarb [1] which incorporated the Health Effects Division’s dietary risk assessment completed in 2006 [2]. While HED’s assessment met the required standard of reasonable certainty of no harm, Bayer CropScience (BCS) submitted a risk assessment [3] for consider ation which pointed out that the HED assessment was still conservative and did not incorporate all the knowledge and data available on aldicarb. A comparison of HED’s and BCS’s risk assessment at that time is shown in Table 1.

Table 1 Comparison Dietary Assessment by HED, 2006 Versus BCS, 2006

BCS, 2006 HED, 2006 Population

Subgroup Scenario a Dose Metric b Exposure (mg/kg) %aPAD c Exposure (mg/kg) %aPAD e Food Alone Daily 2.37E-04 36 2.68E-04 41 Food Alone Within Day 6.03E-05 10 2.68E-04 41 Food + Water Daily 4.29E-04 66 1.09E-03 168 All Infants (< 1 yr) Food + Water Within Day ND d ND d 5.72E-04 88 Food Alone Daily 4.78E-04 74 5.09E-04 78 Food Alone Within Day 1.08E-04 16 4.68E-04 72 Food + Water Daily 4.94E-04 76 6.36E-04 98 Children

1-2 yrs Food + Water Within Day ND d ND d 5.20E-04 80

a Food Alone based on current registration for both HED and BCS assessments Food + Water based on groundwater monitoring data from South Carolina (BCS, 2006) Food + Water based on Georgia Peanut/Cotton model data with 500 ft setback (HED, 2006)
b Daily used daily consumption x residue value. Within Day used 1.7 hr half-life using all minute exposure data (BCS, 2006) Within Day used 2 hr half-life using maximum daily value (HED, 2006)

c aPAD = 0.00065 mg/kg for both HED and BCS assessments, although BCS [2] proposed a value of 0.013 mg/kg.
d Not determined.

e HED, 2006 used aPAD = 0.00065 mg/kg.

The most notable refinements that were not incorporated into the HED assessment were:

1) Incorporation of aldicarb monitoring data, par ticularly BCS’s extensive ground water monitoring study conducted in 2005 (see Section 6). 2) Constructing exposure profiles by using all even t based exposures within each day rather than using the worst case event from each day (see Section 7).

As part of the RED, BCS was required to con duct a Comparative Cholinesterase Assay (CCA) for PND 11 (postnatal day) pups for consideration in de termining the FQPA SF. In February 2010, BCS completed and submitted the CCA dose-response study for PND 11 pups versus adults [4] and calculated an FQPA SF of 3.9 based on inhibition of RBC cholinesterase for both sexes combined [5]. Furthermore, since the 2006 assessments, there is ne w data available (percent crop treated values and PDP residue data) that is pertinent to the risk assessment for aldicarb. Additionally BCS have enhanced the CARES dietary aggregate model to fully account for the rate of reversibility of cholinesterase inhibition in conducting a population based risk assessment.Report No

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Both HED and BCS have conducted new aggregat e dietary assessments using the data that has become available since the 2006 assessments. This document lays out the BCS 2010 risk assessment with a justification of the inputs selected. Areas of differences between the previous assessments and the HED 2010 [6] and this BCS 2010 assessments are discussed.

By credibly incorporating all available data, BCS have calculated that current aldicarb use utilizes 60% and 50% of the aPAD for infants and children 1-2 years, respectively, the most sensitive subpopulations.

2. OVERVIEW OF CURRENT AND PREVIOUS ASSESSMENTS INPUTS

HED and BCS have reassessed the dietary risk for al dicarb based on the most current data. Both of these assessments incorporated an FQPA safety factor based on the new CCA study, updated food monitoring data (USDA Pesticide Data Program) a nd recent percent crop treated values. However, the approach to accounting for drinking water re sidues varies significantly between EPA and BCS. Due to a re-interpretation by the Environmental Fa te and Effects Division (EFED) of data that was available in 2006, a significant change has been made to the 2010 EPA assessment which has further exaggerated the already extremely conservative appr oach to incorporating drinking water into the aggregate assessment. This resu lts in an extremely biased EPA assessment in which refined food residue values are aggregated with unrealistic water values from modeled estimates. BCS uses conservative distributions of drinking water resi due values based on extensive monitoring data.

EPA informed BCS in 2009 of the pending change in its water assessment. BCS voiced objections at that time and informed EPA that we could not reproduce its assessment and asked for pertinent information. Despite several requests, this has not been provided. BCS reminds the Agency that FQPA states that the best available data shoul d be used for aggregate assessments. No other carbamate has the extensive amount of relevant mon itoring data that is available for aldicarb. Additionally, aldicarb has a Health Advisory Level (HAL) for water which is protective of vulnerable subpopulations. Under FQPA’s mandate, the monitoring data and existence of the HAL should be considered in the risk assessment.

Table 2 summarizes the differences between HED’ s 2006 assessment, HED’s 2010 assessment, and BCS’s 2010 assessment. Each input parameter is discussed further in the sections following.Report No

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Table 2 Data Changes in Dietary Risk Assessment between 2006 and 2010

Section for further
comments Input EPA 2006 EPA 2010 BCS 2010

3 FQPA SF 2.0 (brain for PND17 male pups) 4.8 (RBC for PND11 male pups) 3.9 for infants (combined sexes) 2.0 for Children 1-2 and above. 4 Residue data PDP data: 1997 potato single serve. 2000 citrus market basket. PDP data: >2000 PDP composite for potato and citrus PDP data: >2000 PDP composite for potato and citrus

Percent crop treated (except oranges) Doanes data 2001-2005 Agrotrak data 2005-2009. Rounded up beyond maximum values. Agrotrak data 2005-2009 using maximum values.

Potato fresh vs processed percent crop treated Based on percent of market that goes to fresh vs. processed Not used. Growers could not reliably differentiate fresh vs. processed at field level. Not used. Growers could not reliably differentiate fresh vs. processed at field level. 5

Orange percent crop treated 12% 35%. Rounded up beyond 2007 peak value 20%. Based on current disease control regimens None Processing GLP study values GLP study values GLP study values

6 Drinking Water Modeled data –typical use
rates Modeled data – maximum use rates Monitoring data for surface and groundwater 7 Model DEEM-FCID SHEDS DEEM-FCID SHEDS CARES DMM, WMM Within day, event based exposure distribution Based on maximum event in a day (daily
exposure distribution) Based on maximum event in a day (daily exposure distribution) Based on all events in the day (event exposure distribution)Report No

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3. SELECTION OF THE FQPA SAFETY FACTOR

3.1 Determination of the FQPA Safety Factor from the PND 11, CCA Study

In their assessment, EPA used an FQPA SF of 4.8 based on the ratio of the benchmark dose (BMD10) values for RBC from males only. BCS estimated an FQPA SF of 3.9 based on combined data from male and female pups, which w as statistically more robust. BCS offers the following, further comments on the difference. In its documents, EPA’s decision to conduct separate BMD analyses for males and females appe ars to be based on two main considerations:

1. The p-value for likelihood ratio test for male and female adult RBC which was significant for the BMDS exponential model (5) (p-value = 0. 0180) with a non-constant variance model. 2. The BMD10 values for RBC cholinesterase in male and female pups of 0.00477 and 0.00731 mg/kg, respectively.

These considerations taken in isolation ignore all of the other statistical analyses performed by Sielken [7] as well as EPA [8] which support the lack of any difference in response between the sexes. Further, examination of the dose-res ponse curves from the most recent CCA study as well as the wealth of cholinesterase data available for aldicarb supports the lack of any biological or toxicological difference in the response between males and females at the expected exposure levels.

In EPA’s Data Evaluation Record (DER) for the most recent CCA study, no significant TRT*SEX interactions were noted for PND11 RBC and brain cholinesterase results and adult RBC cholinesterase results and thus, for these pa rameters, EPA performed pairwise comparisons of treated versus control for both sexes combined. Similarly, Sielken’s analysis using a modelindependent two-sided t-test showed no signifi cant differences between the sexes at any of the doses tested for pup RBC and brain or adult RBC. Only adult brain data showed a significant TRT*SEX interaction in EPA’s analysis, a finding consistent with Sielken’s evaluation which showed a statistical difference in adult brain at the highest dose tested of 0.3 mg/kg using a model-independent two-sided t-test analysis. Based upon the number of t-tests performed (26) and a 0.05 level of significance, the expected number of times the hypothesis that males and females are the same would be rejected (when in fact they are the same) is 26×0.05=1.3. Thus the finding of a single difference was probably found by chance due to conducting multiple ttests. Thus, both EPA’s and Sielken’s analyses demonstrate that the response in RBC and brain is similar for males and females within each age group.

For adult RBC acetylcholinesterase activity, comparison of BMDS models fit individually to male and female data versus models fit to the combined sexes, the likelihood ratio test was significant with a non-constant variance model (Sielken, 2010 - Table 8) but was not significant with a constant variance model (Sielken, 2010 – Table 7). However Model 5 fit to the combined sexes was found to adequately describe the data as the likelihood ratio test was not significant with both the constant and non-constant variance models (Sielken, 2010 – Tables 9 and 10). For adult brain acetylcholinesterase activity, comparis on of BMDS models fit individually to male and female data versus models fit to the combin ed sexes, the likelihood ratio test was significant with a constant variance model but was not signi ficant with a non-constant variance model. For PND11 pup RBC and brain acetylcholinesterase activity, comparison of BMDS models fit individually to male and female data versus models fit to the combined sexes, the likelihood ratio test was not significant regardless of the variance model used. The lack of consistency betweenReport No

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the adult models suggests that the statistical differences are likely to be an artifact of the model parameters rather than a true difference in the biological response. Thus, the overall conclusion from the results of (Sielken, 2010 -Tables 5 to 8) is that there are no consistent differences between males and females. The lack of a biol ogical difference in res ponse between the sexes is supported by a comparison of the dose respons e data for RBC and the overlap of the 95% confidence intervals ( Figure 1 and Figure 2).

Figure 1 Mean and 95% confidence interv als for RBC AChE in adult rats

0 500 1,000 1,500 2,000 2,500 3,000

0 0.05 0.1 0.15 0.2 0.25 0.3AChE

Dose (mg/kg) 

### RBC ‐ Adult ‐ Male & Female

Females

Males

Figure 2 Mean and 95% confidence intervals for RBC AChE in PND11 rats

0 1,000 2,000 3,000 4,000 5,000 6,000

0 0.02 0.04 0.06 0.08AChE

Dose (mg/kg) 

### RBC ‐ PND11 ‐ Male & Female

Females

Males

Examining the dose-response, particularly in the linear part of the response range, the adult RBC data appear to be more disparate than the PND11 RBC data. However, the BMD10 values for each sex within these age groups would imply that pups have a more disparate response compared to adults. The difference in BMD10 va lues for PND11 pups is further surprising when considering the similarity in the percent inhi bition for RBC observed at the two lowest doseReport No

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levels tested (10.5 and 9% in males and females, respectively, at 0.005 mg/kg and 28.4 and 34% in males and females, respectively, at 0.01 mg/kg).

RBC BMD 10 (mg/kg) Source Adult female 0.0242 CEB

Adult male 0.0228 CEB Adult combined sexes 0.0223 Sielken PND11 female 0.00731 CEB

PND11 male 0.00477 CEB PND11 combined sexes 0.00573 Sielken

EPA states, “ In general the BMDs tended to be sim ilar between sexes within a compartment and age group ... with the exception PND11 pup RBC ChEI , which showed males to be more sensitive than females.” However, none of the tests, including those ran by EPA, show any statistically significant difference in RBC acetylcholinesterase activity between male and female pups to support this conclusion and a plot of the RBC data for pups (Figure 2) supports the lack of a substantial difference between males and females.

In addition to the CCA studies, prior analyses of adult rats and human adu lts support a conclusion that there is no sex difference in the cholinesterase response to aldicarb.

EPA’s analysis of the human volunteer aldicarb oral dosing study in the n-methyl carbamate cumulative risk assessment concluded that th e BMD does not differ between sexes, and the recovery half life is homogeneous across doses and sexes (U.S. EPA. Revised N-Methyl Carbamate Cumulative Risk Assessment. U.S. Environmental Protection Agency. Office of Pesticide Programs. September 24, 2007).

EPA’s analysis of the adult rat aldicarb oral gavage study from the n-methyl carbamate cumulative risk assessment concluded that th e BMD does not differ between sexes, and the recovery half-life is homogeneous across dose groups and sexes. EPA concluded that it does not appear likely that the female-male differences found are solely due to interactions due to differences in different studies. In this BMD, half-life and confidence interval, there was no evidence that BMD differed between the sexes (U.S. EPA, 2007).

Therefore, a preponderance of evidence evaluate d by BCS and EPA supports the conclusion of a lack of any sex difference in the dose response for aldicarb. Combining the sexes is appropriate for calculation of the BMD values and forms more robust and more powerful dose-response data sets for modeling purposes.

Based on the recent CCA study, the appropriate FQPA SF for infants is 3.9 using the ratio of the BMD10 for the combined sexes RBC data.

3.2 Relevance and Age Dependence of the FQPA Safety Factor

For carbamates, the calculation of the factor for pr otection of infants and children, i.e. the FQPA SF, is based on a BMD analysis of cholinesterase results using a benchmark response of 10%. This level provides a conservative endpoint and is within the range of variability for baseline RBC cholinesterase which in humans has been repor ted to show fluctuations as high as 13 to 25% in the same unexposed individual [9]. This con servative endpoint of 10% for RBC is consideredReport No

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to be a biological marker for exposure not an a dverse effect, thus providing an added level of safety inherently incorporated in to the risk assessment for carbamates. RBC inhibition at 10% is well below levels at which any clinical signs are seen.

The use of a conservative endpoint is further co mpounded by the requirement for direct dosing of neonates in the CCA study. However, bolus dos ing does not reflect the normal consumption process, particularly for a newborn and underest imates the impact of rapid reversibility. Furthermore, the CCA study is conducted at dos ing levels higher than actual exposure levels. This is likely to have a more pronounced effect on newborns as their lower levels of metabolism enzymes may begin to saturate while adult levels do not. At actual dietary exposure levels, it is probable that neither infant nor adult metabolizing enzymes experience saturation and differences in relative sensitivity would be lower. The limitations of the CCA study are discussed in detail in the BCS 2006 assessment. There is a large data base of developmental studies, including the developmental neurotoxicity (DNT) study with aldicarb, which provides a reliable endpoint, under realistic exposure c onditions for newborns.

As part of the RED, BCS was required to conduc t a CCA study for PND 11 (postnatal day) pups for consideration in determining the FQPA SF. Prior to this study, a PND 17 study conducted by EPA was available from which an FQPA SF of 2 was assessed based on inhibition of brain cholinesterase. In general development term s, a PND 11 rat shows similar developmental (ontogenic) characteristics to a newborn human (0-1 year) whereas a PND 17 rat is ontogenically similar to a human toddler (>1 year) [10],[11] ,[12]. In February 2010, BCS completed and submitted the CCA dose-response study for PND 11 pups versus adults and calculated an FQPA SF for infants of 3.9 based on inhibition of RBC cholinesterase for both sexes combined.

Based on the available studies and the ontogenic re lationships between rat and human, an FQPA SF for infants (0-1 year) of 3.9 is appropriate a nd protective while an FQPA SF for toddlers (>1 year) of 2.0 is appropriate and protective.

4. RESIDUE DATA

The HED, 2006 and BCS, 2006 assessments used 1997 data from USDA PDP when a special single serving potato study was conducted. For citrus commodities, data from th e 2000 Carbamate Market Basket Survey (CMBS) [13] had been used (also single serving data). These data are over 10 years old and use patterns and market share for aldicarb in potatoes and citrus have since changed. It is appropriate, therefore, to use newer compos ite data. Since 2000 potatoes and many citrus commodities have been included in the annual PDP monitoring survey. Table 3 lists commodities and y ears. Both the HED, 2010 and the BCS, 2010 ass essments, use the more recent residue data from the USDA Pesticide Data Program [14] as shown in Table 3.

Table 3 USDA PDP Data Used in Aldicarb Dietary Assessment

Commodity Years of PDP Data Total No . of Samples Total No. of detects Potatoes 2000-2002, 2008 2216 57 Frozen Potatoes 2006-2007 1544 93 Sweet Potatoes 2003-2004, 2008 1657 17 Oranges 2000-2001, 2004-2005 2972 9 Orange Juice 2004-2006 1487 46 Grapefruit 2005-2006 430 4Report No

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4.1 Analytical Limit of Detection

Aldicarb residues of concern for dietary analysis consist of the parent, metabolite aldicarb sulfoxide and metabolite aldicarb sulfone. In th is assessment, metabolite residue values measured in oranges, orange juice, grapefruits, potatoes, and sweet potatoes were converted to parent aldicarb equivalents and the total residue is repor ted in parent equivalents. For non-detectable parent or metabolites the average 1⁄2LOD (Limit of Detection) value for a given year was used, then converted to aldicarb equivalents before summi ng to give a total residue. The conversion to aldicarb equivalent residues is based on the following equation:

Total residue (as aldicarb) = (aldicarb sulfone x 0.86) + (aldicarb) + (aldicarb sulfoxide x 0.92)

It has been standard practice to combine averag e 1⁄2 LOD values for each of the three analytes to account for “treated non-detects” in aldicarb ass essments. For samples where one (usually sulfoxide) or two metabolites (sulfoxide and su lfone) are detected 1⁄2 LOD for each of the metabolites not detected in the sample are adde d to the sample value. Although the parent aldicarb is rarely seen in any monitoring samples, this practice was considered to conservatively account for potential residues that might exist below the limit of detection. For certain commodities, the PDP data shows that the Limit of Detection (LOD) has decreased in more recent years for aldicarb and metabolites (0.02 pp m to 0.001 ppm) as analytical methodologies have improved. Likewise, there was a correlated increase in the number of detected residues due to the lower limit of detections. As the sens itivity of the assessment to citrus inputs increases because of the smaller risk cup, the appropria te handling of 1⁄2 LODs for non-detects becomes particularly important. If inappr opriately applied, the standard procedure anomalously results in samples with non-detects having higher residues than samples with detects. For example, this can occur, if a sample from a lab with a low limit of detection has a detect for sulfoxide to which is added an average (from multiple labs over an extended period of time) of 1⁄2 LOD for both aldicarb and aldicarb sulfone that is higher than the lab’s detection limit. It is, however, not practical to correct for each lab individually. However, in some cases it must be recognized that this anomaly can significantly distort the risk as sessment. Examination of the distribution of aldicarb equivalent values shows the drastic imp act this has on commod ities, such as, oranges ( Figure 3), orange juice ( Figure 4), sweet potatoes ( Figure 6) and fresh potatoes ( Figure 7). Several scenarios for dietary risk assessment were conducted to show the effects of using the 1⁄2 LODs versus true zeroes for “treated non-detect s in analytes” according to percent crop treated values per EPA policy [15] (see Section 8.3).Report No

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4.2 Oranges

Data from the 2000-2001, 2004-2005 USDA PDP pr ogram were used to represent aldicarb residues on oranges, limes, and lemons. The datase t consists of 2972 samples with 9 detects. Table 4 summarizes all the values for oranges. The 1⁄2 LOD for total aldicarb equivalents was 0.01 492 mg/kg. Figure 1 illustrates the distribution of all the total residues as aldicarb equivalents for oranges used in the dietary assessment.

Table 4 Orange PDP Data

Average LOD Aldicarb Aldicarb Sulfoxide Aldicarb Sulfone

Year Detects Number Samples Detect Rate Aldicarb Aldicarb Sulfoxide Aldicarb Sulfone 1⁄2LOD a Comb. Min Max N Min Max N Min Max N 2005 2 741 0.3 0.0091 0.00854 0.00971 0.0127 . . 0 0.008 0.025 2 . . 0 2004 0 742 0 0.01058 0.01493 0.01556 0.0189 . . 0 . . 0 . . 0 2001 4 745 0.5 0.012 0.00989 0.00817 0.0141 . . 0 0.005 0.017 4 . . 0 2000 3 744 0.4 0.012 0.00994 0.0082 0.0141 . . 0 0.005 0.005 3 . . 0

a These values are expressed as aldicarb equivalents.

Figure 3 Distribution of Aldicarb Residues in Oranges

586 2377

0 500 1000 1500 2000 2500

0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 Aldicarb Equivalence (mg/kg)Frequency (count) Zeros

NonDetects = Average

1/2 LOD Orange Residues Non-Detects = 2963 Detects = 9 % Crop Treated = 20%Report No

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4.3 Orange Juice

Data from the 2004 to 2006 USDA PDP program were used to represent aldicarb residues on orange juice, grapefruit juice, lemon juice, and lime juice. The dataset consists of 1487 samples with 46 detects. Table 5 summarizes all the values for orange juice. The 1⁄2 LOD for total aldicarb equi valents was 0.01122 mg/kg. Figure 2 illustrates the distribution of all the total residues as aldicarb equivalents for or anges used in the dietary assessment.

Table 5 Orange Juice PDP Data

Average LOD Aldicarb Aldicarb Sulfoxide Aldicarb Sulfone

Year Detects Number Samples Detect Rate Aldicarb Aldicarb Sulfoxide Aldicarb Sulfone 1⁄2LOD a Comb. Min Max N Min Max N Min Max N 2006 37 557 6.6 0.00496 0.00429 0.00545 0.0068 . . 0 0.002 0.005 37 . . 0 2005 9 744 1.2 0.00913 0.00855 0.00971 0.01267 . . 0 0.008 0.008 9 . . 0 2004 0 186 0 0.01058 0.01494 0.01319 0.01783 . . 0 . . 0 . . 0

a These values are expressed as aldicarb equivalents.

Figure 4 Distribution of Aldicarb Residues in Orange Juice

9 252

22 33 1189

0 200 400 600 800 1000 1200 1400

0 0.002 0.004 0.006 0.008 0.01 0.012 0.014 0.016 0.018 Aldicarb Equivalence (mg/kg)Frequency (count) Orange Juice Residues Non-Detects = 1441 Detects = 46 % Crop Treated = 20%

Non-Detects

= Average

1/2 LOD ZerosReport No

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4.4 Grapefruit

Data from the 2005 to 2006 USDA PDP program were used to represent aldicarb residues on grapefruit. The original data for aldicarb, aldicarb sulfone, and aldicarb sulfoxide residues on grapefruit consisted of 1462 samples. However, in some cases there were no analyses for all three analytes. Excluding blanks, the dataset consisted of 430 samples with 4 detects. Table 6

su mmarizes all the values for grap efruit. The 1⁄2 LOD for total aldicarb equivalents was 0.0106 mg/kg. Figure 3 illustrates the distribution of a ll the total residues as aldicarb equivalents for grapefruit used in the dietary assessment.

Table 6 Grapefruit PDP Data

Average LOD Aldicarb Aldicarb Sulfoxide Aldicarb Sulfone

Year Detects Number Samples Detect Rate Aldicarb Aldicarb Sulfoxide Aldicarb Sulfone 1⁄2LOD a Comb. Min Max N Min Max N Min Max N 2006 4 216 1.9 0.00707 0.005 0.009 0.01062 . . 0 0.008 0.063 4 . . 0 2005 0 214 0 0.007 0.005 0.009 0.01058 . . 0 . . 0 . . 0

a These values are expressed as aldicarb equivalents.

Figure 5 Distribution of Aldicarb Residues in Grapefruit

206 220

2 1 1 0 50 100 150 200 250

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 Aldicarb Equivalence (mg/kg)Frequency (count) Grapefruit Residues Non-Detects = 426 Detects = 4 % Crop Treated = 52% Zeros Non-Detects = Average

1/2 LODReport No

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4.5 Sweet Potatoes

Data from the 2003 to 2004 and 2008 USDA PDP program were used to represent aldicarb residues on sweet potatoes. The dataset cons ists of 1657 samples with 17 detects. Table 7

su mmarizes all the values for orange juice. Th e 1⁄2 LOD for total aldicarb equivalents was 0.0103 mg/kg. Figure 3 illustrates the distribution of a ll the total residues as aldicarb equivalents for oranges used in the dietary assessment.

Table 7 Sweet Potatoes PDP Data

Average LOD Aldicarb Aldicarb Sulfoxide Aldicarb Sulfone

Year Detects Number Samples Detect Rate Aldicarb Aldicarb Sulfoxide Aldicarb Sulfone 1⁄2LOD a Comb. Min Max N Min Max N Min Max N 2008 1 184 0.5 0.00912 0.00853 0.00971 0.01266 . . 0 0.041 0.041 1 0.027 0.027 1 2004 8 739 1.1 0.00912 0.00259 0.00405 0.00749 . . 0 0.0027 0.077 8 0.003 0.06 7 2003 8 734 1.1 0.0103 0.00897 0.00751 0.01251 . . 0 0.0027 0.083 8 0.003 0.023 4

a These values are expressed as aldicarb equivalents.

Figure 6 Distribution of Aldicarb Residues in Sweet Potatoes

66 1574

0 400 800 1200 1600 2000

0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 Aldicarb Equivalence (mg/kg)Frequency (count) Zeros

Non-Detects

= Average

1/2 LOD Sweet Potatoes Residues Non-Detects = 1640 Detects = 17 % Crop Treated = 5%Report No

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4.6 Potatoes

Data from the 2000 to 2002 and 2008 USDA PDP program were used to represent aldicarb residues on fresh potatoes. The dataset consists of 2216 samples with 57 detects. Table 8

su mmarizes all the values for fresh potato data. The 1⁄2 LOD for total aldicarb equivalents was 0.0313 mg/kg. Figure 5 illustrates the distribution of all the total residues as aldicarb equivalents for fresh potatoes used in the dietary assessment.

Table 8 Potatoes Fresh PDP Data

Average LOD Aldicarb Aldicarb Sulfoxide Aldicarb Sulfone

Year Detects Number Samples Detect Rate Aldicarb Aldicarb Sulfoxide Aldicarb Sulfone 1⁄2LOD a Comb. Min Max N Min Max N Min Max N 2008 30 744 4 0.00357 0.00299 0.00415 0.00494 . . 0 0.002 0.34 30 0.002 0.062 14 2002 12 370 3.2 0.01766 0.03444 0.03268 0.03873 . . 0 0.045 0.186 12 0.035 0.062 2 2001 12 733 1.6 0.01771 0.03449 0.03502 0.03978 . . 0 0.045 0.43 12 0.035 0.11 9 2000 3 369 0.8 0.0177 0.05274 0.05949 0.05869 . . 0 0.045 0.32 3 0.035 0.125 2

a These values are expressed as aldicarb equivalents.

Figure 7 Distribution of Aldicarb Residues in Potatoes Fresh

0 500 1000 1500 2000 2500

0 0.1 0.2 0.3 0.4 0.5 0.6 Aldicarb Equivalence (mg/kg)Frequency (count) Potato Fresh Residues Non-Detects = 2159 Detects = 57 % Crop Treated = 11%

Non-Detects

= Average 1/2 LOD Zeros

187 1972Report No

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Data from the 2006 to 2007 USDA PDP program were used to represent aldicarb residues on frozen potatoes. The dataset consists of 1544 samples with 93 detects. Table 9 summarizes all the values for frozen potatoes. The 1⁄2 LOD for total aldicarb equivalents was 0.004 mg/kg. Figure 6 illustrates the distribution of all the total residu es as aldicarb equivalents for frozen potatoes used in the dietary assessment.

Table 9 Potatoes Frozen PDP Data

Average LOD Aldicarb Aldicarb Sulfoxide Aldicarb Sulfone

Year Detects Number Samples Detect Rate Aldicarb Aldicarb Sulfoxide Aldicarb Sulfone 1⁄2LOD a Comb. Min Max N Min Max N Min Max N 2007 32 800 4 0.00282 0.00222 0.00343 0.00391 . . 0 0.002 0.021 31 0.002 0.004 14 2006 61 744 8.2 0.00277 0.00284 0.00332 0.00412 . . 0 0.002 0.039 60 0.002 0.02 23

a These values are expressed as aldicarb equivalents.

Figure 8 Distribution of Aldicarb Residues in Potatoes Frozen

0 200 400 600 800 1000 1200 1400 1600

0 0.01 0.02 0.03 0.04 0.05 0.06 Aldicarb Equivalence (mg/kg)Frequency (count) Potato Frozen Residues Non-Detects = 1451 Detects = 93 % Crop Treated = 11% Zeros

Non-Detects

= Average 1/2 LOD 1374

77Report No

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5. PERCENT CROP TREATED

In the 2007 assessment, EPA and BCS used values from Dfr Kynetec Agrotrak® (previously known as Doane’s Market Research) from 2000-2004 or 2 005. The newer assessment uses data from 2005- 2009 Agrotrak and NASS data ( Table 10). EPA policy is to take the maximum value from the last 5
y ears of data that is available [16]. This resu lts in 11% for potatoes, 31% for oranges and 52% for grapefruit. The EPA analysis rounds these values up to 15%, 35% and 55% respectively. BCS used the actual values. Changes in percent crop treated us ed in dietary risk assessments are summarized in Table 11.

Table 10 Current Percent Crop Treated Data

Source Commodity Percent Crop Treated Potatoes 2005 2006 2007 2008 2009 Insecticide 7.3 8.7 5 7.5 5.4 Nematicide 1.6 2.4 3.5 0.8 1.1 Total 9 11.1 8.5 8.3 6.5 Oranges 2005 2006 2007 2008 2009 Insecticide 1 8.8 24 20.6 14.3 Nematicide 1.9 2.8 6.8 9.6 9.8 Total 2.9 11.6 30.8 30.2 24.1 Grapefruit 2005 2006 2007 2008 2009 Insecticide 27.8 36.8 46.2 35.1 38.5 Nematicide 5.3 4.1 6 6 11.3 Agrotrak

Total 33.1 40.9 52.2 41.1 49.8 Fall Potatoes 1999 2001 2003 2005 Total 5 9 7 9 Oranges excluding Temples 1995 1995 1999 2001 2003 Total 4 8 8 10 7 Grapefruit All 1997 1999 2001 2003 NASS

Total 7 14 8 20Report No

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Table 11 Changes in Percent Crop Treated Va lue in Dietary Risk Assessments

Commodity EPA/BCS 2006 BCS 2008 EPA 2010 Maximum Agrotrak

Potatoes 5 (Fresh) 22 (Processed) 9 15 11.1 Oranges 12 12 35 30.8 Grapefruit 25 25 55 52.2

Prior to this assessment both BCS and EPA were applying the ratio of potatoes that went to fresh and processed markets to the percent crop treated values based on fresh and processed markets. Historically a smaller amount of potatoes are grown for the fresh market versus the processed market (i.e., frozen French fries, canned potatoes, etc.). While this is still the case, growers have indicated that it is not possible to fully segregate fresh and pr ocessed potatoes at the time of planting. The final destination will vary by market demand. Therefor e, although conservative, one value of percent crop treated is now applied to fresh and processed potatoes.

Additionally BCS ran a scenario which includes oranges at 20% crop treated. When Asian citrus psyllid disease became a serious issue in 2006-20 07, many growers began using aldicarb as a preventative treatment in their citrus groves for the disease. This spike is reflected in the 2007-2008 Agrotrak data for oranges. However, since then growers have switched fro m preventative treatments for Asian citrus psyllid to mainte nance of other trees when Asian citr us psyllid is seen in a grove. The decline in percent crop treat ed can be seen in 2009 ( Table 12, Figure 9). BCS marketing projects that in 2010 and beyond percent of crop tr eated values will return to 2006 leve ls (12% crop treated). Therefore scenarios were run which include orang es at 20% crop treated as a conservative value for future years.

Table 12 Trend of TEMIK ® Use on Florida CitrusReport No

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Figure 9 Percent Crop Treated For Use of Aldicarb in Florida Citrus

6. DRIN KING WATER

A refined drinking water assessment for both surface water and ground water was conducted by Bayer CropScience [3]. The surface water assessme nt used water monitoring data from a 3 year study of raw and finished water in 38 different co mmunity water systems in aldicarb use areas in the United States [17]. The data from surface water in Florida and North Carolina were converted to 365 day time series using a step function to fill in missing values. The ground water assessment used potable well monitoring data [18]. Samples were analyzed from 1673 drinking water wells in nine major use areas, which were within 1000 feet of fields treated at least once with TEMIK ® Brand 15G Aldicarb pesticide. The ground water data we re used in the CARES drinking water model by incorporating the probability of occurrence from the results of sampling by regions. The 365 day time series was held constant at the level detected from the ground water monitoring.

Results from the drinking water assessment by Baye r CropScience in 2006 showed that the worst case ground water scenarios were more vulnerable than surface water scenarios when aggregated with the food exposures for all populations. Note that the distributions proposed for use for representing drinking water exposure are not merely a distribution of residues in rural wells in high use areas but instead represent the most vulnerable subset of these rural wells in high use areas. In the 2006 BCS report, the ground water monitoring data from South Carolina was the most sen sitive to infants and children 1-2 yrs and has been used in the current risk assessment.

In EPA drinking water assessments, the Agency con tinues to use PRZM modeling data rather than use the extensive water monitoring data that provides a more realistic representation of the potential for drinking water exposure. In the 2006 assessment EPA determined a distribution of residues for use in the dietary risk assessments based on inappropriately parameterized PRZM modeling (see Section 6.2) and typical use rates. As shown in ( Figure 10) the blue line representing the PRZM

m odeling does not show any correlation with monito ring data (brown and green line) until beyond theReport No

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99 th percentile. EPA stated that their average conc entrations were equal to the maximum of the monitoring data but as a probabilis tic assessment depends on distributi ons of all probable exposures the modeling and monitoring data do not show appropriate correlation.

Comparison of the modeled estimates and monitoring data are described in Figure 10 and Table 13. In the 2010 assessment, the distribution has been fu rther distorted by using maximum label rates (black line in Figure 10). EPA used a comparison of labe l application rates that showed a 4-fold difference be tween the typical application rate and maximum application rate, therefore; the Agency multiplied the PRZM data from a “typical rate” by 4. 04 to cover “maximum application rates”. The percentile distribution of all the PRZM data co mpared to actual measured ground water samples illustrates the conservatism in these data ( Figure 10). There are approximately 7% of the modeled

sa mples exceeding the Health Advisory Level of 10 ppb. The maximum modeled residue was 13 ppb while the highest residue ever observed in groun d water monitoring (7,946 measured samples) was only 4.4 ppb. Less then 5% of the modeled values matched measured values in ground water monitoring, the minimum modeling value was 2.6 ppb. The Agency used these overly conservative water residues in the DEEM-FCID exposure model which will randomly assign a residue value from the distribution to each day of consumption for a 365 day period for each individual in a population.

Table 13 Percentile Distribution Comparison

Concentration ( μ g/L)

Scenario Data Source 50th %ile 75th %ile 80th %ile 90th %ile 95th %ile 99th %ile Maximum PRZM Model

Maximum Application 6.4 8.4 8.8 9.6 10.3 12.1 12.9 GA Coastal Plain

Peanuts/Cotton PRZM Model Typical Application 1.9 2.4 2.5 2.8 3.0 3.5 3.7 Florida State Potable Well Monitoring Monitoring (6,273 measured samples) 0.50 0.50 0.50 0.50 0.50 0.50 4.4

BCS Potable Well Monitoring, 2005 Monitoring (1,673 measured samples) 0.012 0.012 0.012 0.012 0.1 0.7 2.9Report No

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Figure 10 Comparison of Ground Water Residue Data from Modeling and Monitoring a

0 2 4 6 8 10 12 14

0 10 20 30 40 50 60 70 80 90 100
PercentileAldicarb Residue (ppb) EPA PRZM Model (Maximum Rate) EPA PRZM Model (Typical Rate) FL Ground Water Monitoring (6,273 samples) BCS Ground Water Monitoring (1,673 samples)

EPA PRZM Model (Typical Rate) EPA PRZM Model (Maximum Rate) 13 ppb

10 ppb

3.7 ppb 4.4 ppb

2.9 ppb

Florida GW Monitoring BayerCropScience GW Monitoring EPA randomly samples value from this distribution each day in drinking water exposure model. Health Advisory Level

a Note that this does not reflect all available monitoring data. It was the most appropriate data set for risk assessment but is

consistent with a much larger database.

6.1 Discussion of EPA Modeling Approach

The reason that the modeled distributions of residues do not reflect the monitoring data is a function of the parameters used in the model rath er than the model itself. The modeling approach of EPA in calculating the potential movement of residues reflects a reasonable, conservative approach to estimating potential concentrations in potable wells. However, several of the parameters used in the modeling are beyond realistic worst case. The use of several exaggerated values for input parameters results in a highly distorted distribution.

6.2 Parameterization of the Model

The values of four parameters used by EPA are ove rly conservative: degradation rates in surface soils, degradation rates in subsurface soils, degr adation rates in ground water, and the speed of ground water flow. One of the parameters used by EPA is not always conservative enough is water table depth.
Degradation Rate in Surface Soils. There are five field dissipation studies in these four states (AL, FL, GA, and SC) with half-lives of 0.3-0.6 months. Therefore, the longest half-life of 18 days should have been used, while EPA used 55 days [19].
Degradation Rate in Subsoils. The extensive set of field studies conducted with aldicarb show no decrease in degradation rate in subsoils compar ed to the degradation rate in surface soils. A relevant exception is when subsoils are composed of acidic, coarse sand subsoils (coarse sand isReport No

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an important distinction since degradation rates do not slow down in acidic, finer textured sand subsoils). Therefore, in these four states, the degr adation rate in subsoils is the same as in surface soils, except on the Florida ridge. EPA assumed that below 25 cm degradation varied linearly to 1 m and below 1 m a degradation rate of 500 days was assumed [19].
Degradation Rate in Ground Water. The degradation rate on the Florida ridge determined in field studies was 270 days. The degradation rate in flatwoods soils was much shorter and residues did not persist from year to year. Also field data has shown that degradation rates are faster in anaerobic conditions compared to aerobi c conditions and as a result aldicarb degradation rates increase with increased depth into the water table. EPA assumed a degradation rate of 500 days [20].
Speed of Ground Water Flow. EPA has assumed a value of 0.5 feet per day. This is a reasonable value for the Florida ridge, but not fo r the flatwoods areas of Florida, or the flat coastal plains.
Water Table Depth. The ground water depth of 30 feet assumed by EPA is generally deeper than in areas in which residues have been found in Al, FL, GA, and SC, especially off the Florida ridge. However, this does not significantly in fluence model results since EPA assumed minimal degradation below 1 m.

6.3 Calibration of the Model with Monitoring Data

A major failing of EPA’s parameterization of the mo del is that it does not predict the pattern of residues in more detailed monitoring studies that could be used for calibration. Combined soil and ground water monitoring studi es were conducted at one location in South Carolina (Edisto) [21] and two in Florida outside the Florida ri dge (Oviedo [22] and Fort Pierce [23]) and one ground water monitoring study without soil monitoring (Indiantown). Modeling using the EPA compound specific values for aldicarb combined with the site-specific environmental characteristics would not adequately represent the residues patterns observed at these experimental sites.

7. DIETARY (FOOD AND DRINKING WATER) ANALYSIS MODELS

A description of the model used for the dietary risk assessments are described in this section. The Cumulative Aggregate Risk Evaluation System® (C ARES®) was used to conduct dietary exposure based on the inputs previously described above. Daily exposure assessments were conducted with the CARES Production Version 3.0 (Build 1.2.4, date), while the within day exposure assessments were conducted with the CARES Evaluation Version 4.0 (Build 1.1.9, 02/16/2010). This evaluation version was developed and verified by Bayer Cr opScience to conduct both food and water within day exposure assessments.

7.1 Within Day Dietary (Food and Drinking Water) Assessment

A basic principle of human risk assessment is th at an individual’s exposure should be matched with relevant toxicological doses in terms of route, duration, and effect. This requirement is of particular importance when assessing aggregate or cumulative risk to populations or potentially sensitive sub-populations. Accumulation of common toxicity is not likely to occur if exposures and duration of effects are separated in time (a nd thus near-simultaneous exposures are necessaryReport No

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for cumulative effect to be seen) and if the comm on mechanism effect is reversible and recovery is rapid [24]

Due to the rapid reversibility of ChE inhibition following exposure to aldicarb, the previous dietary (food) assessment developed the capab ility to conduct a within-day minute based exposure assessment. The CARES-Dietary Minu te Module (DMM) version (Build 7.8.9, 3/14/2006) was developed to use the time of ea ting event from the CSFII consumption database and incorporate reversibility in the exposure calculations. Recently a CARES-Water Minute Module (WMM) was developed to conduct a within -day exposure assessment for drinking water. The time of eating events for indirect water were taken from the CSFII consumption database. In order to establish the time of direct water consum ption, there are two options: 1.) Use the Bayer Drinking Water Consumption Survey (BDWCS) [25] or 2.) Divide the daily direct water consumption based on user defined fixed interval. The daily sequence of consumption events from the BDWCS is assigned to a CARES individual and repeated for 365 days.

BCS incorporated EPA’s method which is to di vide the direct water consumption by fixed equally spaced intervals. For example, drinking water consumption starting at 6:00AM and ending at 9:00PM with 6 events would resulting 6 events spaced 3 hours apart (6:00AM, 9:00AM, 12:00PM, 3:00PM, 6:00PM, and 9:00PM). The direct drinking water consumption data from the FCID consumption data base would be divided by 6.

7.2 Half-life Determination

The inhibition of RBC ChE by aldicarb is rapidl y reversible with an estimated half-life ( t 1/2 ) of 1.7 hours [26] based on the first-order elimina tion coefficient using Ch E inhibition from a human volunteer study [27]. A lag interval was used in the present assessment as 1 hour since the first observation of the repeated observations obtai ned from each volunteer used to estimate the benchmark dose was obtained at 1 hour and is the same as used for the food within day
assessments.

7.3 Event Based Exposure Distribution

Dietary assessments have historically been conducted on either a chronic (one year to lifetime) or acute (one day) exposure time frame. Acute effects are summed for a day and each daily exposure value for the individuals in the po pulation is placed in a probabilistic exposure distribution. In effect the distribution refl ects the probability of a day long exposure occurring which is above the endpoint of concern. Regulat ory decisions are then made for the exposure value at the 99.9 th percentile, considered to be very conservative and protective. The daily exposure metric, however, is somewhat arbitrary, as multiple exposures may occur during a day. Until recently the capability for calcu lating exposure in increments less than a day has not been available in probabilistic dietary exposure models and thus a dose metric based on daily exposure has been used as the de facto standard.

With aldicarb and other carbamates the half-life of cholinesterase inhibition is very short. Thus the time step of one day is not the appropriate time step for the exposure distribution. Since separate and distinct exposures could occur at each meal eaten or each occasion of water consumption (an eating or drinking event) then the proper time step would be less than a day and in this case would be an eating or drinking event. Events are typically two or more hours apart, but if they are not, the additive exposure of overlapping events would be captured by the decayReport No

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factor (BCS uses the 1.7 hours derived from the appropriate study) incorporated in the minute by minute exposure distribution generated by the DMM or WMM. ( Figure 11). Each eating / drinkin g event for the individuals in a population are then the basis for the exposure distribution and the exposure distribution reflects the probability of an exposure occurring above the level of concern for an event (meal or drinking water consumed). Thus route, effect, and duration have been properly matched to the toxicologi cal effect which is being considered.

EPA generates exposures at each eating occasion in a similar manner to BCS, but then chose to construct the exposure distribution by selecti ng the worst case event from each day. BCS constructs the exposure distribution by us ing all of the events from each day ( Figure 11).

Figure 11 Hypothetical Exposure Profile

01234567 Time (day)Exposure Maximum Daily (EPA) Maximum Event Based (BCS) Minute Based Exposure Profile Exposure Events

8. RESULTS AND DISCUSSION

8.1 Daily Dietary Food Assessment Results

Table 14 shows the results from daily dietary (fo od only) assessments and explores the impact of removing various food uses from the baseline diet ary scenario. The baseline dietary scenario was conducted using all crops in the current regist ration based on the maximum percent crop treated values of 11%, 51%, and 31% for potatoes, grape fruits, and oranges, respectively. Based on differential FQPA safety factors based on ontogenic considerations of 3.9X for infants and 2X for children the %aPAD at the 99.9 th percentile was 146, 135, and 112 for infants, children 1-2, and children 3-5, respectively. The removal of all potato uses from the baseline assessment and assigning 20% crop treated to citrus resulted in a decrease of the %aPAD utilization to 85 and 94 for infants and children 1-2 yrs. old, respectively for a daily assessment.Report No

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Table 14 Daily Dietary Food Risk Assessment Results for Various Scenarios

Scenario Population a Exposure @ 99.9 th %ile %aPAD b
Infants 4.85E-04 146 Child 1-2 8.80E-04 135 Current Registration with maximum percent crop treated Potato (11%); Grapefruit (51%); Orange (31%) Child 3-5 7.30E-04 112

Infants 3.44E-04 103 Current Registration with maximum percent crop treated No Potatoes Child 1-2 7.05E-04 109

Infants 2.82E-04 85 Current Registration with maximum percent crop treated No Potatoes; Orange = 20%CT Child 1-2 6.13E-04 94

a Population size for runs: Infants (5000); Children 1-2 (3367); and Children 3-5 (4985)
b Infants based on FQPA safety factor = 3.9X. Children 1-2 yrs. old and children 3-5 yrs. old based on FQPA safety factor = 2X.

The residue and consumption inputs are further discussed in Section 8.3.

8.2 Aggregate (Food and Drinking Water) Assessment

When the within-day methodology to account for aldicarb reversibility is appropriately applied to the daily analysis scenarios that include all current registrations, an acceptable risk is obtained for the most sensitive populations of Infants (< 1 yr) and Children 1-2 yrs (Table 15). This de monstrates that a reasonable certainty of no harm results from the currently registered aldicarb uses when all available and sound scientific data is used.

Table 15 Comparison of Daily vs. Within Da y Exposure and Risk Determination

Daily c Within Day c

Population Subgroup a Scenario b Exposure @ 99.9 th %ile %aPAD d Exposure @ 99.9 th %ile %aPAD d Food Only 4.45E-04 134 2.06E-04 62 Water Only 3.26E-04 98 3.51E-05 11 Infants (< 1 yr old) Food + Water 5.29E-04 159 1.94E-04 58 Food Only 8.51E-04 131 3.55E-04 55 Water Only 1.32E-04 20 2.69E-05 4 Children 1-2 yrs. old Food + Water 8.52E-04 131 3.07E-04 47

a Daily used population size = 1,000. Within Day used population size = 200 for infants and 100 for children 1-2 yrs. old.
b Food Only = current registration with %CT = 11, 52, and 20 for potatoes, grapefruit, and citrus, respectively. Water Only = ground water monitoring data from South Carolina region.

c Daily used daily consumption x residue value. Within Day incorporates elimination rate = 1⁄2 life based on 1.7 hrs or β 3 = -0.90 h -1

. Within Day water uses 6 equally spaced events 3 hrs apart starting at 6:00AM to 9:00PM.
d Infants based on FQPA safety factor = 3.9X. Children 1-2 yrs. old based on FQPA safety factor = 2X.Report No

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8.3 Contribution Analysis and Impact of 1⁄2 L OD Value on Dietary Food Only Assessments

Comparisons of the contribution analysis for di etary assessment with and without potatoes for infants and children 1-2 yrs old revealed speci fic commodities that are driving the exposure ( Figure 12). For infants, the contribution analysis at the 99.9 th percentile showed that potatoes and grapefruit juice contributed to 49% and 40% of the exposure, respectively. Removing potatoes increased the contribution of grapefru it juice and orange juice to 55% and 26%, respectively. It seemed unusual that grapefruit ju ice would be a commodity that is driving (40 to 55%) the dietary risk assessment for infants. For ch ildren 1-2 yrs, the contribution analysis at the 99.9 th percentile showed that potatoes and orange ju ice contributed 45% and 43% of the exposure, respectively. Removal of potatoes resulted in or ange juice as the only driver of exposure at 99.7%. The assumption that children 1-2 yrs old would have high consump tion of orange juice seemed reasonable, however; the drastic shift from 43% to 99.7% seemed to be unusual. A detailed analysis of the consumption and residue inputs at the high end was performed to determine if there were any confounding factors leading to overestimation of exposure.

Figure 12 Contribution Analysis at the 99.9 th percentile for Infants and Children 1-2 yrs.

7 26 55

5 49 1 4 40

0 10 20 30 40 50 60 70 80 Potato, tuber,

w/peel Potato, tuber, w/o peelOrange, juicebabyfood Orange, juice Grapefruit, juice

% Contribution Current Registration with Potatoes Current Registration without Potatoes Infants (< 1 yr)

12 45 43

99.7

0 20 40 60 80 100 Potato, tuber,

w/peel Potato, tuber, w/o peel Orange, juice

% Contribution Current Registration with Potatoes Current Registration without Potatoes Children 1-2 yrs. old

8.3.1 Consumption Data

Analysis of grapefruit juice consumption data (all food forms) for infants from the FCID database showed two extreme va lues of 60.2 and 69.8 g/kg ( Figure 4). The third highest

consum ption value for grapefruit juice in infants was 1.4 g/kg which represents the 94 th percentile. The percentile distributions s how little difference between daily and eating occasion consumptions meaning that individuals typically consume all grapefruit juice in single event (i.e. a glass of juice).Report No

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Figure 13 Grapefruit Juice Consumption by Infants (< 1 yr old)

1.4 g/kg 60.2 g/kg

69.9 g/kg

0 10 20 30 40 50 60 70

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
PercentileConsumption (g/kg) Daily By Event Grapefruit Juice Consumption by Infants (< 1yr) based on CSFII/FCID

Further analysis for these individuals linked to the CSFII reveal the food and amount of food consumed during the day ( Table 16). The maximum consumption of 69.8 g/kg is from an infan t (8 month old) consuming 2003 gr ams of grapefruit juice drink that is made up of 30% grapefruit juice food form (2003 g * 0.30 = 600.9 g / 8.6 kg = 69.8 g/kg). The second highest consumption 60.2 g/kg is from an infant (8 month old) consuming 494 grams of unsweetened grapefruit juice canned that is made up of 100% grapefruit juice food form (494 g / 8.2 kg = 60.2 g/kg). Assuming a density of 1 g/mL equivalent to water, consumption would be e quivalent to an infant (19 lbs.) consuming 0.5 L and 2 L of juice in a single serving.

Table 16 CSFII Records for Extreme Grape fruit Juice Consumption in Infants

Eating Time Food Code Food Consumed (g) Description 800 11111000 61 MILK, COW'S, FLUID, WHOLE 800 11710603 244 ENFAMIL W/ IRON, INFANT FORMULA, PREPARED FROM POWDER 800 57348000 20 FROSTED CORN FLAKES, NFS 800 92530810 2003 GRAPEFRUIT JUICE DRINK W/ VITAMIN C ADDED 1200 11710603 244 ENFAMIL W/ IRON, INFANT FORMULA, PREPARED FROM POWDER 1600 11710603 244 ENFAMIL W/ IRON, INFANT FORMULA, PREPARED FROM POWDER 1730 25210110 28.5 FRANKFURTER, WIENER OR HOT DOG, NFS 1730 41201040 31.63 BAKED BEAN W/ PORK & SWEET SAUCE 2000 11710603 244 ENFAMIL W/ IRON, INFANT FORMULA, PREPARED FROM POWDER FCID/CSFII Household ID = 18411; Person Number = 2; Day = 2 8 month old infant weighing 8.6 kg. Eating Time Food Code Food Consumed (g) Description 800 57802000 15 HIGH PROTEIN CEREAL, BABY, DRY, INSTANT 800 61201220 494 GRAPEFRUIT JUICE, CANNED, BOTTLED, CARTON, UNSWEET 800 64104010 46.5 APPLE JUICE 800 64104050 496 APPLE JUICE, W/ ADDED VITAMIN C 1300 11710603 61 ENFAMIL W/ IRON, INFANT FORMULA, PREPARED FROM POWDER 1900 53220000 32 COOKIE, FRUIT-FILLED FCID/CSFII Household ID = 25011; Person Number = 2; Day = 2 8 month old infant weighing 8.2 kg.Report No

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Analysis of orange juice consumption data (all food forms) for children 1-2 yrs old from the FCID database showed two maximum daily values of 119.7 and 107.7 g/kg and a maximum event value of 75 g/kg ( Figure 14). The percentile distributions shows a slight difference between daily and eating occasi on consumptions meaning that some individuals consume all orange juice in multiple events (i.e. a glass of juice with each meal).

Figure 14 Orange Juice Consumption by Children 1-2 yrs. old

75.5 g/kg 107.7 g/kg 119.7 g/kg

0 20 40 60 80 100 120 140

0% 10% 20% 30% 40% 50% 60% 70% 80% 90% 100%
PercentileConsumption (g/kg) Daily By Event Orange Juice Consumption by Children 1-2 yrs. old based on CSFII/FCID

Further analysis for individuals linked to the CSFII reveal the food and amount of food consumed during the day ( Table 17). Both the maximum daily values (119.7 and 107.7 g/kg) were fr om the same individual (CSFII Household ID = 48112; Person = 1) on two different survey days. This child consumed orange juice 3 and 4 times during the day with the maximum consumption per event at 47.8 g/kg. The maximum consumption by event of 75.5 g/kg is from 1 yr old child consuming 996 grams of unsweetened, canned orange juice that is made up of 100% ora nge juice food form (996 g / 13.2 kg = 75.5 g/kg). Assuming a density of 1 g/mL equi valent to water, consumption would be equivalent to an infant (29 lbs.) consuming 1 L of juice in a single serving.

Table 17 CSFII Records for High Orange Juice Consumption in Children 1-2 yrs old

Eating Time Food Code Food Consumed (g) Description 1000 11112110 244 MILK, COW'S, FLUID, 2% FAT 1000 25221410 10.6 PORK SAUSAGE, FRESH, BULK, PATTY OR LINK, COOKED 1000 31105000 20 EGGS, WHOLE, FRIED (INCL SCRAMBLED, NO MILK ADDED) 1400 54202010 12 CRACKERS, SALTINE, LOW SODIUM 1400 58403010 120.5 CHICKEN NOODLE SOUP (INCLUDE CHICKE N & STARS SOUP) 1400 61210220 996 ORANGE JUICE, CANNED/BOTTLED/CARTON, UNSWEETENED 1900 54202010 9 CRACKERS, SALTINE, LOW SODIUM 1900 58403010 120.5 CHICKEN NOODLE SOUP (INCLUDE CHICKE N & STARS SOUP) 2100 13150000 48.25 SHE RBET, ALL FLAVORS FCID/CSFII Household ID = 25654; Person Number = 1; Day = 1 1 yr. old child weighing 13.2 kg.Report No

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These extreme consumption values for grapefruit juice and orange juice reflect why these commodities are driving the risk assessment for in fants and children 1-2 yrs, respectively.

8.3.2 Residue Assignment

As previously discussed in the Residue Secti on, the residue data from PDP was analyzed with a multi-residue analytical method that detects aldicarb, aldicarb sulfoxide, and aldicarb sulfone. As per EPA policy, all an alytes are stoichiometrically converted to aldicarb and summed to represent aldicarb equivalents. If an analyte was not detected, then the 1⁄2 LOD value is used in the determ ination of aldicarb equivalents. This is summarized in the following equation:

Total residue (as aldicarb) = (aldicarb sulfone x 0.86) + (aldicarb) + (aldicarb sulfoxide x 0.92)

The presence of aldicarb parent has almost never been detected in PDP samples. Reviewing the last 10 years of PDP data from 1998 to 2008 encompassing more than 75,000 samples on various commodities there have only been 1 detect of aldicarb parent: sweet corn in 2003 at 0.033 ppm (there is no tolerance on sweet corn). The current PDP data for citrus (oranges, orange juice, a nd grapefruit) reveals no detects for aldicarb parent or aldicarb sulfone. The approach of summing all three 1⁄2 LOD values results in assumed values where in some cases the 1⁄2 LOD value in citrus is greater than many of the detected values ( Figure 15). For citrus PDP data, a m ore realistic approach would be to use only the aldicarb sulfoxide data convert ed to aldicarb equivalents to examine the sensitivity of the analysis to this parameter.

Accounting solely for aldicarb sulfoxide non-de tects in juice is consistent with the metabolic profile. In a 1996 metabolism st udy [28], lemon trees were treated with radiolabeled [14C]-aldicarb. The aldicarb was soil applied and the fruit were harvested 153 days later. Aldicarb was found to be metabolized readily upon application to soil and uptake by lemon plants. Very low levels of radiocarbon were detected in the RAC components (peel, pulp and juice). These materials were quantitatively extracted with acetone and aqueous acetone suggesting minimal (if any) incorporation into plant components. The main metabolic pathway pr oceeded via sulfone intermediates to yield toxicologically non-relevant metabolites arising from oxidative and hydrolytic processes previously described for plant metabolism of aldicarb in the literature. No aldicarb, per se, was detected in the RAC indicating complete metabolism of the parent. Traces (<0.005 ppm) of aldicarb sulfone and sulfoxide were also detected. A supplemental study looked specifically for aldicarb residues in the lemon foliage, but found none [29]. Traces of aldicarb sulfoxide and aldicarb sulfone were detected in the peel and pulp, but not in the juice.

A further consideration in not including aldicar b sulfone detects in the citrus residues is that is has been shown to be 20-fold less potent as a cholinesterase inhibitor than aldicarb [30].Report No

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Figure 15 Orange Juice Residue Distribution



### 9

22 33 33

2 2 9

0 10 20 30 40 50 60

0

.0

0

0 0 .0

0

2 0 .0

0

4 0.006 0 .0

0

8 0 .0

1 0 0 . 01

2 0.014 0.016 0 .0

1 8



### Aldicarb Equivalence (mg/kg)Frequency (Count) 1/2 LOD for all analytes 1/2 LOD for sulfoxide only 

### 1440 1440 1500

8.3.3 Dietary Assessment Results Using Refined 1⁄2 LOD Values

The baseline daily dietary (food) assessment using 1⁄2 LOD from all three analytes with revised percent crop treated for citrus (20%) shows a %aPAD at the 99.9 th percentile of 144% and 131% for infants and children 1-2 yrs old, respectively ( Table 18). Revising all citrus data to include only the aldi carb sulfoxide values decreased the %aPAD for food only to 91% and 98% for infants and children 1-2 yrs. old. Removing potatoes and sweet potatoes further reduced the exposure resulting in %aPAD of 26% and 33% for infants and children 1-2 yrs. The aggregate food and drinking water using worse case water monitoring data from South Carolin a resulted in acceptable %aPAD for both sensitive populations in a daily assessment. These results are conservative in that they do not reflect the incorporation of reversibility into the analysis.Report No

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Table 18 Daily Dietary Assessment Results Using Refined 1⁄2 LOD Values

Food Only Including potatoes Citrus 20% 1⁄2 LOD all analytes Food Only Including potatoes Citrus 20% 1⁄2 LOD sulfoxide Food Only No Potatoes Citrus 20% 1⁄2 LOD sulfoxide Food + Water No Potatoes Citrus 20% 1⁄2 LOD sulfoxide Population Exposure %aPAD Exposure %aPAD Exposure %aPAD Exposure %aPAD Infants (< 1 yr old) 4.79E-04 144 3.04E-04 91 8.80E-05 26 3.30E-04 99 Children (1-2 yr old) 8.50E-04 131 6.35E-04 98 2.12E-04 33 2.37E-04 37

a Infants based on FQPA safety factor = 3.9X. Children 1-2 yrs. old based on FQPA safety factor = 2X.

8.4 Commodity Specific (Eater Only) Assessment

Bayer CropScience conducted a commodity specifi c analysis for orange juice and indirect drinking water similar to the EPA 2010 dietary assessment (p. 23) [10]. Residue values were taken from worse case PDP monitoring data for or ange juice and highest measured residue from ground water monitoring study in Florida for indir ect drinking water. Margins of exposure were estimated for orange juice by children 1-2 yrs ol d and indirect drinking water by infants. The margins of exposure were greater than 39 for in fants and 20 for children 1-2 yrs indicating these levels were not a concern ( Table 19).

Table 19 Commodity Specific (Eater Only) Assessment for Orange Juice and Indirect Water

Indirect Water Consumption by Infant Consumption a (g/kg BW/event) Residue b (ppm) Exposure (mg/kg) NOAEL (mg/kg) MOE 50th percentile of Consumption 15.8 0.0044 6.95E-05 0.013 187 90th percentile of Consumption 29.2 0.0044 1.28E-04 0.013 101

Orange Juice Consumption by Child 1-2 yrs. Consumption c (g/kg BW/day) Residue d (ppm) Exposure (mg/kg) NOAEL (mg/kg) MOE 50th percentile of Consumptio n 14.1 0.00736 1.04E-04 0.013 125 90th percentile of Consumptio n 31.4 0.00736 2.31E-04 0.013 56

a Indirect water consumption based on CSFII/FCID data pe r eating occasion.
b Highest ground water residue measured in Florida potable well monitoring program.

c Orange juice consumption based on food form uncooked, fresh, not specified.
d Highest orange juice detect in PDP 2006 of 0.008 ppm alicarb sulfoxide converted to aldicarb equivalence (x 0.86).

9. CONCLUSION

Based on all available data, BCS consider that this assessment demonstrates that current registered uses of TEMIK ® meet the EPA safety standard of “reasonable certainty of no harm”.Report No

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10. REFERENCES

1. USEPA, 2007. Reregistraion Eligibility Deci sion (RED) for Aldicarb, List A Case Number 0140. EPA, Washington D.C. September 2007. p. 191.

2. USEPA, 2006. Aldicarb. List A Reregist ration Case No. 0140/Chemical ID No. 098301. Revised Dietary Exposure Analyses for the HE D Human Health Risk Assessment. DP Barcode No. D299882. EPA, Washington, D.C. November 1, 2006. p. 98.

3 Lantz, J.; B. M. Young; R. L. Jones; G. J. Mihlan. 2006. Aldicarb Acute Dietary Risk Assessment Including Food and Drinking Wa ter. Report Number: G201350. Bayer CropScience, RTP, NC. July 14, 2006. p. 219. MRID #46891402.

4 Beck, M. 2009. A Comparative Dose Study of the Effects of Aldicarb on Red Blood Cell and Brain Cholinesterase in Adult and Juvenile Ra ts following oral (gavage) administration. Study Number: WIL-21210. WIL Research Laboratories, Ashland, OH. November 19, 2009. p. 134. MRID #47994305.

5 Blacker, A. 2010. Application of the FQPA Factor for Aldicarb. Report Number: G202153. Bayer CropScience, RTP, NC. February 25, 2010. p. 4. MRID #47994301.

6 USEPA, 2010. Aldicarb. Acute Probabilistic Aggregate Dietary (Food and Drinking Water) Exposure and Risk Assessment Incorporating Re vised FQPA Factor. PC Code: 098301. DP Barcode: 299874. EPA, Washington, D.C. July 15, 2010. p. 24

7 Sielken R. L. 2010. Benchmark Doses for Brain and RBC Acetylcholinesterase in Adult and Pups Exposed to Aldicarb. Sielken & Associ ates Consulting, Inc., Bryan, TX 77802, February 18, 2010. p. 68. MRID #47994306.

8 USEPA, 2010. Statistical Review of Sielke n, R. (2010) Benchmark Doses for Brain and RBC Acetylcholinesterase in Adult and Pups Exposed to Aldicarb. Project Number: G202152 Unpublished study prepared by Sielken, Inc. DP Barcode: 379831. EPA, Washington, D.C. p. 42.

9 Hayes, W. 1982. Pesticide studies in man. Baltimore, MD: Williams & Wilkins.

10 Benjamins, J. and McKhann, G. Eds. 1981. Development, regeneration, and aging of the brain. Basic Neurochemistry. Boston, Brown and Co.

11 Davision, A.N. and Dobbing, J. 1966. My elination as a vulnerable period in brain development. Br. Med. Bull. 22 (1): 40-44.

12 Dobbing, J. and Smart, J.L. 1974. Vulner ability of developing brain and behavior. Br. Med. Bull. 30 (2): 164-168.

13 Carringer, S.J. 2000. Carbamate Insecticide Market Basket Survey. The Carringer, Inc., Apex, NC 27502. Study No. TCI-99-001. Unpublished.

14 USDP Pesticide Data Program (PDP). ( http://www.ams.usda.gov/AMSv1.0/pdp )Report No

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15 USEPA, 2000. Assigning Values to Non-de tected/Non-Quantified Residues in Human Health Food Exposure Assessment. Office of Pesticide Programs, EPA, Washington, D.C. March 23, 2000. p. 33.

16 Doucoure, C. 2007. Presentation: Percent Crop Treated (PC) and Projected Percent Crop Treated (PPCT): BEAD’s Approach at the Dietary Assessment Working Group. Biological and Economic Analysis Division (BEAD). May 15, 2007.

17 Movassaghi, S, S. Daussin, J. P. Hanzas, Jr . 2005. Surface Water Monitoring for Residues of Aldicarb In High Use Areas in the United States. Bayer CropScience Study No. 00T30605, Report No. B004894. 1367 pages. MRID #46466401.

18 Jones, R. L., and T. S. Ramanarayanan. 2006. TEMIK® brand15G Aldicarb Pesticide: Summary of Five Retrospective Ground Wate r Monitoring Studies conducted for Aldicarb and Its Metabolites in the Pacific Northwest, California, Texas, Mississippi Delta and the Southeast Regions of the United States in 2005. Bayer CropScience Study No. RAAVY010. MRID # 46793701.

19 Jones, R. L. and T. L. Estes. 1995. Summary of Aldicarb Monitoring and Research Programs in the United States. Journal of Contaminant Hydrology 18:107-140.

20 Jones and Estes, 1995, listed above and Carsel, R. F., R. L. Jones, J. L. Hansen, R. L. Lamb, and M. P. Anderson. 1988. A Simulation Procedure for Groundwater Quality Assessments of Pesticides. Journal of Contaminant Hydrology 2:125-138.

21 Hegg, R. O., W. H. Shelley, R. L. Jone s, and R. R. Romine. 1988. Movement and Degradation of Aldicarb Residues in South Carolina Loamy Sand Soil. Agriculture, Ecosystems, and Environment

22 Jones, R. L., A. G. Hornsby, and P. S. C. Rao. 1988. Degradation and Movement of Aldicarb Residues in Florida Citrus Soils. Pesticide Science 23:307-325. Some of the ground water monitoring data is included in: Hornsby, A. G., P. S. C. Rao, W. B. Wheeler, P. Nkedi-kizza, and R. L. Jones. 1983. Fate of Aldicarb in Florida Citrus Soils: 1. Field and Laboratory Studies. paper presented at Char acterization and Monitoring of the Vadose (Unsaturated) Zone, Las Vegas, December 8-10, pp. 936-958.

23 Jones, R. L., J. L. Hansen, R. R. Romine, and T. E. Marquardt. 1986. Unsaturated Zone Studies on the Degradation and Movement of Aldicarb and Aldoxycarb Residues. Environmental Toxicology and Chemistry 5:361-372.

24 Kelly, I.D., Lantz, J.L., Mihlan, G.J. and Young, B.M. 2006. CARES-compatible Dietary Minute Module (DMM) Version 2.2 Build 7.8.9 (3/14/2006) Technical Manual. Bayer CropScience Report Number G201504. MRID #46796602

25 Barraj, L., Scrafford, C., Lantz, J., Daniels, C., and Mihlan, G. 2008. Within-day drinking water consumption patterns: Results from a dri nking water consumption survey. J. Expo. Sci. Env. Epid. Advance online publication, 14 May 2008; doi:10.1038/jes.2008.28 pp. 1-14.Report No

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26 Williams , R. L. , Mihlan , G. J. , and Tobia , A. J. ( 2008 ) . Modeling cholinesterase activity for human dietary risk assessment of carbamates insecticides. Risk Anal. 28 ( 4 ) , 1069 – 1079 .

27 Wyld, P.J., Watson, C.E., Nimmo W.S., Watson, N. 1992. A Safety and Tolerability Study of Aldicarb at Various Dose Levels in Healthy Male and Female Volunteers. Inveresk Clinical Research Report No. 7786. ICR Project No. 003237. MRID # 42373001.

28 Hathcock, T.; Kimmel, E.C., Ruzo, L.O. 199 6. A Metabolism Study with [l4C]-Aldicarb in Citrus (Lemon Trees). PTRL Report No. 478W-1. PTRL West, Inc., Richmond, CA. January 10, 1996. p. 234. MRID #43902401.

29 Wells-Knecht, M. C.; Andrawes, N. R. 1996 . (14C)-Aldicarb metobolism in citrus (lemon trees). Supplemental Study: Metabolism in citrus foliage (Supplemental report to MRID No. 43902401). Report No. C046227. Rhone-Poulenc Ag Company, RTP, NC. March 11, 1996. p. 286. MRID #43970001.

30 Blacker, A.; I.D. Kelly, J.L Lantz, G.J. Mihlan, R. L. Jones, B.M. Young. 2010. Aldicarb: Toxicity, Exposure and Risks to Humans In: R. Kreiger Ed. Hayes Handbook of Pesticide Toxicology, Elsevier Inc.