# Revised N -Methyl
Carbamate Cumulative

Risk Assessment

U.S. Environmental Protection Agency Office of Pesticide Programs September 24, 2007

# --

______________________ ___________________ Revised N -Methyl Carbamate
Cumulative Risk Assessment

Technical Executive Summary

With the passage of the FQPA (1996), EPA was requir ed to consider availab l e information concerning the cumulative effe cts on human health resulting from exposur e to multiple chemicals that have a common me chanism of toxicity. In 2001, the Agency

identified the N -methyl carbamate (NMC) pestici des as a group whic h shares a common mechanism and publishe d a preliminary Cumulative Risk Assessment (CRA)

in 2005. A cumulative risk assessment incor porates exposures from multiple pathways (i.e., food, drinking water, and residential/non-occupatio nal exposure to pesticides in air, or on soil, grass, and indoor surfaces) fo r those chemicals with a common mechanism
of tox i city.

Since the release of the preliminary NMC CRA, the Agency has incorporated new hazard and exposure data, a ssigned FQPA safety factors, evaluated comments from the public, and addres sed comments by the FIFRA Scientific Advisory Panel (SAP). In addition, since 2005, the Agency ’ s Office of Pesticide Programs (OPP) has completed

several additional risk assessments for indivi dual NMC pesticides (aldic arb, carbaryl, carbofuran, methomyl and propoxur) and, where necessary, established mitigation measures to be implemented to reduce exposure to these pesticides. These mitigation
measures are reflected in this revised NMC CRA.

The methodology used in this revised CRA is similar to that used in the preliminary CRA and s upported by the SAP (USEPA, 20 05a,b). Throughout the development of this CRA, EPA has reli ed on the SAP to peer-review guidance documents, methods, approache s, and pilot analyses to ensure that appropriate

methods and sound science wer e applied. In addition to the SAP reviews, EPA has sought and considered public comments on these approaches as it developed these

cumulative assessment methods.

Backgrou nd:

A CRA begins with the identif ication of a group of c hemicals, calle d a Common

Mechanism Group (C MG), which induces a common toxic effect by a common

mechanism of toxic i ty. Pesticides are determi ned to have a "common mechanism of

toxic i ty" if they act the same way in the body--t hat is, the same toxic effect occurs in the same organ or tissue by essentially the sa me sequenc e of major biochemic al events. The NMCs were established as a CMG by E PA in 2001 (USEPA, 2 0 01a) base d on the

shared structural characteristics and simila rities and their shared ability to inhibit acetylcholinesterase (AChE) by carbamylat ion of the serine hydroxyl group lo cated in the active site of the enzyme. When AChE is inhibited, acetylcholine accumulates and cholinergic toxic i ty results due to conti nuous stimulation of c holiner gic receptors throughout the central and peripheral nervous systems that innervate virtually every

ES - Page 2 of 277

### organ in the body. An important aspect of NMC toxicity is the rapid nature of the onset and recovery of effects; following maxima l inhibition of cholinesterase (typically between 15 and 45 minutes), recovery occu rs rapidly (minutes to hours).

Once a CMG is identified, it is import ant to determine which chemicals from that group should be included in the quantification of cumula tive risk. The group of pesticides which is inc l uded in the quantific ation of cumulative risk -- and consequently incorporated into the CRA -- is termed the Cumulative Assessment Group ( C AG). In

determinin g the specific NMC pesticides to be inclu ded in the CAG, EPA considered risk mitigation decision s and expo sure pot ential. EPA id entified three exp osur e pathways of interest for these pesticides: food, drin king water, and residential/nonoccupational. Each of these pathways wa s initially evaluated separately, and -- in performing this portion of the analysis -- EPA determined which of the NMCs were appropriate to include for each given pathway

. The cumulative ass e ssment of potential exposure to NMCs in food includes those whic h are currently registered in the U.S. or have import tolerances. The drinking water exposure pathway includes NMC pesticides with registered uses in the U. S. that can potentially reac h water bodies or ground water (i.e., outdoor uses). The re vised NMC CRA evaluates the resident ial expos ure pathway for three pesticides registered in the U.S. (carbaryl, methiocarb and propoxur) for home

use, The current assessment refl ects the most up-to-date and best available
information for these chemicals.

There are many steps involved in quantit atively assessing the potential human health risk associated with expos ure to the NMC pesticides. The complex s e ries of

evaluations involve hazard and dose-respon se analyses; assessment s of food, drinking water, resi dential/non-occupational exposur es; combi n ing expos ures to produce a cumulative risk estimate; and risk characterization. Thes e steps are described more fully in OPP’s Cumula tive Guidan ce (U SEPA, 2002a). The approac h to each of these component s and their results is briefly explained below:

 Selection of an index c hemical to use as the point of reference to standardize the toxic potencies of each NMC, determination of the relative toxic contribution of each NMC, and establishm ent of a val ue to estima te potential risk for the group (i.e. point of departure);

 Evaluation of inter-species differences (i.e., extrapolation of rat responses to human responses) intra-species varability; a nd the potential sensitivity to infants and children;

 Estimation of the risks associated wit h all pertinent pathways of exposure (i.e., food, drinking water, resi dential) in a manner that is bot h realistic and reflective of variability due to differences in location, time, and demographic characteristics of exposed gr oups;

 Identification of the significant cont ributors to risk; and

ES - Page 3 of 277

###  Characteriz a tion of the confidence in the results and the uncertainties

associated with the assessment.

Hazard an d Dose-Response Assessment:

EPA used t he relative potency factor (R PF) method to determine the combin ed risk associated with expos ure to NMCs. Br iefly, the R P F approach uses an index chemical as the point of reference for comp aring the toxic i ty of the NMC pesticides.

RPFs are calculated as the rati o of the toxic potency of a giv en chemical to that of the

index chem ical and ar e used to convert exposu res of all chemicals in the group into exposure equivalents of the index chemical

. Because of its high quality dose response data for all routes of expos ure, as well as high quality time-to-recovery data, EPA selected oxamyl as the index chemical for standardiz i ng the toxic potencies and calculating relative potency fact ors for each NMC pesticide.

Toxic potencies for the NMCs were determi ned using brain AChE inhib i tion measured at peak inhibition foll owing gav age expos ures in rats. B r ain AChE inhib i tion
is a direct measure of the mechanism of toxi city and thus does not have the uncertainty associated with using blood meas urements of cholinest e rase inhibition which serve as surrogates for cholines terase inhibition in the peripheral nervous system. Furthermore, relative toxic potencies derived from brai n data were shown in the preliminary

assessment to be similar to those derived from red blood cell data but showed less variability, and thus less uncertainty, w hen comparing potency across the NMCs.

The Agenc y used an exponential dose-time-response model to develop

benchmark dose estimates at a level estimated to result in 10% brain cholinest e rase inhibition (i. e ., a benchmark dose or BMD 10 

### ) to estimate relative potency. The Agency has also calculated the half-life to recovery for brain AChE inhibi tion. The Agency has
used the lower confidence limit on the BMD 10 

### (i.e., BMD L 10 

### ) to develop points of departure (PoD) from the oral, dermal, and inhalation routes for oxamyl, the index

chemical. A PoD is a point estimate on t he index chemical’s dos e-r e sponse curve from

which risks associated with the exposure le v e ls anticipated in the human population are extrapolate d

. EPA compares estimated exp o su res with the route-specific PoD values to calculate Margins of Expos ure (MOE) and to estimate potential ris k to humans.

The Agenc y has used available c o mparative cholinester a se studies in juvenile (post-natal day 11 and/or 17) and adult rats to evaluate the FQPA 10X safety factor.

These studies are available for six NMCs. For these NMCs, the Agency calculated the

BMD 10 

### in pups and adults—the ratio of these benchmark doses provides the chemicalspecific FQPA factor. For the remaining NM Cs without comparative data, a 10X factor was applied. For the i n ter-species extrapolat ion factors, there ar e studies wit h human subjects with three N M Cs (aldicarb, me thomy and oxamyl) that were determined by EPA, after considering the advice of the Hum an Studies Revie w Bo ard, to be ethically
and scientif ically acceptable for use in this risk assessment. These studies were used

to derive the chemical- specific int e r-spec ies extrapolation factor for these three

chemicals. For the remaining NMCs, the standard 10X factor was assigned for interES - Page 4 of 277

### species extrapolation. Since eac h NM C has been assigned its own inter-species and FQPA safety factors, the Agency has mat hematically applied the value of these factors directly to the RPF for each NM C. In addition, the Agency has used the
standard 10X factor for intra-s pecies extrapolation for all t he NMCs. Thus, to account for intra-species extrapolatio n, the target MOE for t he revised NMC CRA is 10.

Exposure Assessment:

An important aspect of the exposure analys es is to develop expos u re scenarios resulting from the uses for each NMC. Three key pathways of expos ure to NMC pesticides -- food, drinking water, and resi dential and other non-occupational settings --

were inclu d ed in this assessment. The factors EPA con s idered in the analys is of exposure by each of these three pathw ays included duration, frequency, and seasonality of exposure. Evaluat ion of chemical use profiles allows for the identification of exposur e scenarios that may overlap, co-occur, or vary between chemicals, as well
as for the identification of populations of concern.

All of the hazard data, ex posure data, and expos ure scenarios must be combined in a manner designed to produce reasonable and r ealistic estimates of exposures likely to be encountered by the public in location and time of year. As w a s done in previous CRAs, EPA used Calendex TM 

### software to integrate vari ous pathways while simultaneously incorporating the time dimensions of the data. Calendex TM 

### provides a focused, detailed prof ile of potential exposur es to individuals acros s a calendar year.
LifeLine TM 

### software was also used to evaluate exposur es through the food pathway and these results are presented and discussed in Appendix C. Expos ures through residential uses and in drinking water are incorporated into cumulative expos ure assessments on a regi onal bas is. EPA cond uc ted regio nal assess ments for drinking water and joined thes e with generic re sidential exposur e scenarios generally representative of regions in the Southern U.S. These regi onal ass e ssments reflect the highest potential exposure scenarios for the U.S. and account for differing agronomic

uses and reflect the differences in climat e, soil conditions, and pest pressures across the country. Exposures that are represent ed in these generic residential exposure scenarios are not expected to be exceeded in any region in the U.S. Exposure to NMC pesticide residues in foods is considered to be uniform across the nation (i.e., there are no signific a nt differences in food exposure due to time of year or geographic location). The assumption of nationally unif o rm food ex pos ure is based on the understanding that, to a large extent, food is distributed nati onally and food consumpt ion is independent of geographic region and season. The single national estima te of food expos u re was combined with region-specific exposures from residential uses and drinking water in three regions that represent t he highest potential for exposure.

ES - Page 5 of 277

### Table ES -1. Summary In formati on Regarding the NMC Pesticides and the Uses, and Pathways Included in the revised NMC Cumulative Risk Assessment

Pesticide Pesticide Uses Pesticide Pathway s Food Drin kin g Wat er Re side ntial

Car b a r y l Ag Crops X X

Lawn X

Gard en X

Orna mental s X

Fruit Tre e s X

Pet Collar X

Golfer Expo sure X

Aldic a rb Ag Crops X X

Oxamyl Ag Crops X X

Formet anate HCl Ag Cro ps X X

Methomyl Ag Crops X X

Carbofu r an Ag Cro p s X X

Propoxu r Food Use s X

Pet Collars X

Methiocarb Ag Crops X

Orna mental X

Thiodic a rb Ag Crops X X

Pirimic a rb Ag Crops X



### The approach for each pathway of expos ur e and results for the revised NMC CRA are explained below:

Food:

The food component of the revised NMC CRA is considered to be highly refined and to provide reasonable estima tes of the distribution of exposures across the U.S. The expos ure estimates for food are based on residue monitoring data from the USDA's Pesticide Data Program (PDP) supplement ed qualitatively with in formation from the Food and Drug Administration’s (FDA) Pestici de Residue Monitoring Program and Total Diet Study. The PDP data provide a very rel i able estimate of pesti cide residues in the major children's foods and account -- directly or indirectly through the use of commodity surrogates -- for approximately 93 % of food consumption by ch ildren. These data also provide dir e ct measures of co-occurrence of NMC pesticides in the same sam p le. PDP

ES - Page 6 of 277

### samples with non-detectable residues were treated in this assessment as "zero"

values. Sensitivity analyses hav e determined that this approach does not significantly underestimate exposures at the upper percent iles for the NMCs (i.e., those percentiles which are of the greatest r egulatory importance). For th ose foods not monitored in PDP, similar commodities that are measured by PDP served as surrogate data sources. This approach is considered to be reasonable and generally sound given that it is based on the concept that families of commodities with similar cultural practices and insect pests are likely to have similar pesticide us e patterns and residue levels. Additionally, these surrogated commodities account for less than 1% of children’s diets.

The reliability of the food component of th is assessment is also supported by the use of the food consumption dat a from the USDA's Continuing Survey of Food Intakes by Individuals, (CSFII 1994-1996/ 1998). The CSFII surveyed more than 20,000

indiv i duals over two non-consec utive days and provides a detailed r epresentation of the food consumption patterns of the U.S. public across all age groups, during all times of the year, and across all 50 states. Thus , EPA has con f idence that the consumption
estimates for food are well-established and consequently support reasonable risk estimates for the U.S. population. The NM C CRA focus e s on the following age groups:

children 1- 2 years old; children 3-5 years old; adults 20-49 years old; and adults 50+ years old. These age groups were selected since they provide a broad representation of potential exposures and because they include age groups that are commonly shown to be the most highly expos ed in s i ngle-chem ical assess ments. Details regarding estimated exposures of other age groups are presented in the appendic e s to this report.

During the period sinc e the issuance of t he preliminary NMC CRA in August 2005, the Agency has further improved and refined its assessment of the cumulative risks associated with the NMC pesticides. These refinements include inco rporating the most recent food residue data by including pesti cide residue data through 2006 from USDA’s PDP Progr am and updating the assessment to reflect i ndividual risk mitigation measures and other use pattern changes for i ndividual NMC pesticides. Specifically, during this period, the Agency has imposed risk reduction measures on some of the major contributors to carbamate cumulative risk. The risk estimates presented in the revised NMC CRA reflect the risk mitigation measures taken on individual car b amates since FQPA was sign ed into law in Augus t 1 996. In general, EPA’s risk estimates contained in this CRA reflect mitigation m easures that E PA determined to be warranted
based on its assessment of t he single chemical’s risks. Since the preliminary assessment, the Agency has received a request for voluntary cancellation for methomyl on grapes and strawberries, has determined that carbofuran is ineligible for reregistration, and will implement certain label restrictions for aldicarb that will increase drinking water well set-backs in the southeastern coasta l plains when certain criteria are triggered. Therefore, these uses (and expos u res) are not included and the aldicarb

label restrictions have been accounted for in the revised NMC CRA.

In evaluating expos ure through the remainin g uses on food, OPP conclud e s that a few uses of NMC pesticides on food crops generally pla y a larger role in contributing to the cumulative risks of the NMC p e sticides. These include use of aldicarb on potato;

ES - Page 7 of 277

### carbaryl on peach and strawberry; and methomyl on cantaloupe, watermelon, peach, spinach, and strawberry. Howev e r, eval uation of the total ri sk from exposure to

NMCs in foods indic a ted that the cumulative MOEs from exposure to NMCs do not raise

a concern. Specifically, the target MOE of 10 is reached at the 99.848 th 

### and 99.870 th


### percentiles of exposur e for the most hi ghly expos ed age groups, children 1-2 and

children 3- 5 years old, respectively. Thes e percentiles are not c onsidered meaningfully

different from the 99.9 th 

### percentile. Associated MOEs range from 7.9 for the most exposed subgroup (children 1-2) to 42 for adults 20-49.

When developing any risk assessment, assumptions must be made in areas

where data are not available. In the revised NMC CRA, the Agency has made health protective assumptions in its baseline analy s is, particularly with r egard to the years of PDP data which are used (for which it used all years of PDP data except in cases where use patterns have change d or will ch ange),the use of a 10x inter-species extrapolation factor for t hose NMCs without human data, and summing expos u res over a 24-hour period. In an effort to char acterize and understand the MOEs esti mated in this assessment, four sensitivity analyses we re performed by the Agency to evaluate the degree to which key areas of the ri sk assessment may under- or over-esti m ate

cumulative risk. The sensitivity analyse s demonstrate that t he Agency has not underestimated exposures through food and associ ated risks to any significant degree since these sensitivity analy ses result in only sm all changes in the percentile at which the target MOE of 10 is reached. The results of the sensitivity analys es using the most recent PDP data and inter-species factors of 3x instead of the st andard 10x for certain pesticides provide added certaint y that risks are below the Agency ’ s level of concern.

Residential:

Applications of NMC pesticides in and around homes, schools, offices, and other public areas may resul t in potential exposur e via the oral (due to hand-to-mouth activity by children), dermal, and inhalation rout es. There are three NMC pesticides with currently registered residential us es consider ed as part of the revised NMC CRA in the residential/non-occupational exposure pathw ay asses sment. The residential uses

considered in this assessment include the ca rbaryl uses on lawns, golf courses, fruit trees, and vegetable and ornamental gardens; the methiocarb snail and slug bait use; and the carbaryl and propoxur pet collar uses. In addition to the uses listed above, the preliminary NMC CRA also included an assessm ent of i ndoor spray uses of propoxur (crack and crevice). S i nce the prelimin ary assessment, the Agency has received a request for voluntary cancellati on of all propoxur indoor spra y uses that may result in non-occupational exposure for children. Theref ore, these uses are not included in the
revised NMC CRA.

Another notable change since is suance of the preliminary NMC CRA is the revision of the methodology used to assess children’s hand-to-mouth expos ure. The non-dietary ingestion pathway was the leas t refined of the resident ial expos u re pathways modeled in the prelim inary NMC CRA. The refined methodology used in this

revised assessment is based on recommendat ions from the August 2005 FIFRA SAP,

ES - Page 8 of 277

### and addres ses limitations in the non-diet ary oral pathway by modifying the probabilistic hand-to-mouth algori thm. This modified algor ithm is a product of a

collaborativ e effort between OPP scient ists and the developers of the SHEDS (Stochastic Human Exposure and Dose Simulation) and CARES (Cumulative and Aggregate Exposure System) models.

For the resi dential/non-occupational ex posure pathway, several reliab l e data sources were used to define how pestici des are used, how quick ly the residues dissipate, how people may come i n to cont act with pesticides (e.g., via dermal or inhalation expos ure), and the length of time people migh t be expos ed based on certain activities (e.g., playing on a treated lawn). As with the drinking wat e r assessment (see below), the residential exposure assessment considers seasonal applications and timing as well as regional differences. In the case of regional differences, the revised

NMC CRA considered the Sout heast Region of the United St ates. Due to longer periods of pesticide use, this assessment prov ides a worst case es timate of ex posure.

The results of the residential risk assessment indicate that remaining residential uses of NMCs -- as borne out by the analyse s here -- are below OPP’s lev e l of concern for all subpopulations.

Drinking Water:

The drinking water assessment fo cuses on areas where combined NMC exposure is likely to be among the highest within eac h r egion as a result of total NMC usage and

vulnerab ilit y of drinking water sources. This analysis is based on a probabilistic modeling approach that consi ders the full range of drin king water consumption and concentration data and not single high-end e sti mates. EPA estim a ted NMC exp o sures in drinking water to individuals in t he CRA for both ground water and surface water sources of drinking water in each of thr ee regions. The regional drinking water exposure assessments represent exposures from vulnerable drinking water sources resulting from typical NMC usage and reflect seasonal variations as well as region al variations in cropping and NMC pesticide us e. Each regional assessment focuses on areas where combined NMC exposure is lik ely to be among the highest within the region as a result of total NMC usage, adjusted for relati ve potencies, and vulnerability of the drinking water sources. For ground wa ter, private wells ext ending through high ly permeable soil and vadose zone materials into shallow, acidic ground water are expected to be most vulnerable. For surface water, dri n king water reservoirs in small, predominantly agricultural watersheds are lik ely to be most vulnerable. The cooccurrence of NMC residues in water is esti mated primarily from modeling since sufficient monitoring data are not available to be the sole basis for the assessment.

Howev e r, monitoring data are used to co rroborate the modeling results and have helped confirm locations of potentially vulnerable drink i ng water sources.

In most of the country, NMC residues in dr ink i ng water sources are at levels that are not likely to contribute substantially to the multi-pathway cumulative exposure. Estimated NMC exposures from surface water sources of drinking water resulted in

ES - Page 9 of 277

### MOEs well above 10. For most ground water sources of drinking water, NMC exposures were similarly low. Shallo w private wells ext e nding through highly permeable soils into s hallow, acidic ground water represent what the Agency believes to be the most vulnerable drinking wate r sources for the NMCs based on available monitoring, current use patterns, and k nown soil and hy drologic conditions. Those
instances where NMC concentrations resu lted in MOE s of less than 10 are being addressed with mitigation measur es in the single-chemical assess ments – an increase
in the well setback distance fr om 300 feet to 500 feet for al dicarb us e on peanuts in the southern portion of the Coastal Plain and a not ice of intent to cancel all dom estic
carbofuran uses. With these mitigation m easures, NMC exposures from drinking water result in MOEs greater than 10.

Combined Pathw a y (Cumulative) Assessment:

EPA als o evaluated total MOEs for all thr ee pathways (food + water + residential) simultaneously. Evaluating expos ures is si gnificantly more complex when the analyses address the simultaneous exposures to more than one pesticide and when dist ributional inputs deriv ed from data from surveys and m onitoring studies – as opposed to default assumptions or point estimates – are used. The detailed mult i-pathway graphical outputs presented in Part III of this r eport reflect individual and c o mbined pathway MOEs at multiple percentiles of expos ure overthe course of an entire year and allow indepth analysis of interactions of data sets to evaluate potential risk concerns and identify the sources of exposu res. The graphical outputs improve the ability to i n terpret the complete risk picture. Based on the si multaneous evaluation of all three exposure pathways and their associated routes using the Calendex software, the MOEs at the

99.9 th 

### percentile are approximat ely 8 or greater for all populations. Generally, exposures through the food pathway dominat e total MOEs, with residential e x pos ures less throughout most of the year. Alt hough still substa ntially less than expos ures through food, dermal expos ures from lawn us es of carbaryl dominate the residential pathway. Exposures through drinking wate r exposures are smaller than exposures through both the food and resi dential pathways with MOEs exceeding 15 for all

scenarios.

Conclusion:

The Agenc y has developed a highly refined and complex cumulative risk

assessment for the N M Cs that represents the state of t he science regarding existing hazard and exposure data and t he models and approac hes used. Interpretati on of the risk estimates presented in this revis ed NM C CRA depends upon the synthesis and processing of a vast body of data on hazar d and expos ures. No si ngle value in the assessment should be used to independently arri ve at the interpretation of the risk

estimates or results. E PA continues to have confidence -- as demonstrated by this

assessment -- in the overall sa fety of our food supply.

Sensitiv ity analyses performed by the Agency were designed to evaluate the degree to which key areas of the risk a ssessment may or may not under- or overES - Page 10 of 277

### estimate the cumulative ri sk in an effort to characterize and unders t and the MOEs

estimated in this assessment. The sens itivity analyses demonstr ate that the Agency has not under-estimated exposur e s and associat ed risks. Also, the Agency has elected
to use 10% inhibition in brain AChE as t he response level for the RPFs and PoDs. The 10% response level is health protective in that no functi onal or behavioral effects have

been noted at or below this level in adult or juvenile animals. Thus the 10% response level provides a point where functional or be havioral neurotoxic ity is not expec ted.

The Agenc y has undertaken extensive risk mitigation and risk reduction efforts over the last several years for many NM Cs through the single-che m ical aggr egate risk

assessments and notes that the ri sk mitigation efforts of the past several years have signific antly reduced risk from exposures to the NMCs through food and drink i ng water
and from residential us e in the U.S. Based on these efforts, the cumulative risks from food, water, and residential exposure to NM Cs do not excee d the Agency’s level of

concern. Taking into account these reduc tions and acknowledging that several key assumptions are designed to minimize the pot ential for this cumulative assessment to underestimate exposure and risk, the Agency conc ludes that -- based on the results of

the revised NMC CRA -- ther e is a reasonab le certainty that no harm will result from exposure to the NMC pesticides covered by this assessment, taking into account the
cumulative effects o f such residues. A ccordingly, the pesticide tolerances for the NMCs covered by this risk assessment are considered to be “safe” as defined in FFDCA section 408(b)(2)(A) and to be reasse ssed for purposes of FFDCA section 408(g).

ES - Page 11 of 277

# LIST OF ACRONYMS

AChE Acetycholinesterase

BMD Benchmark dose (or BM D 10 

### )

BMDL Lower limit on the benchmark dose (or BMDL 10 

### )
CAG Cumulative Assessment Group
CARES Cumulative and Aggregate Risk Evaluation System
CELs Comparativ e Effect Levels
CFSAN Center for Food Safety and Applied Nutrition
CGCM Center for Golf Course Management
CHAD Consolidat ed Human Activity Database
ChE Cholinester a se CMG Common Mechanism Group
CNS Central Ner v ous System
CRA Cumulative Risk Assessment
CSFII USDA Continuing Survey of Food Intake by Individuals
CWS Community Water Sys t ems DEEM-FCI D Dietary Exposure Evaluation Model

DFR Dislodgeable Foliar Residue

EFED Environmental Fate and Effects Divis ion

EFH Exposure F a ctors Handbook

EPA Environmental Protection Agency
FCID Food Com m odity Intake Databas e FDA Food and Drug Administration

FIFRA Federal Insecticide, Fungicide, Rode nticide Act
FQPA Food Quality Protection Act

FR Federal Register

GoF Goodness of Fit
HCl Hydrochloride

HED Health Effects Division
HSRB Human Studies Rev i ew Board

IRED Interim Re-registration Eligibility Decis i on LCO Lawn Care Operator

LN Lognormal

LOAEL Lowest Observable Adverse Effect Level
LOC Level of Concern

LOD Limit of Detection
LOQ Limit of Quantification
MBS Market Basket Study

MOE Margin of Expos ure MRID Master Record Identification Number

N A SS National Agricultural Statistics Survey
NHANES National Health and Nutriti on and Examination Survey
NHANES III Third National Health and Nutritio n Examination Survey
NAWQA USGS National Wate r-Quality Assessment Program

NHE XAS National Human Exposure Assessment Survey
NHGPUS National Home and Garden Pesticide Use Survey

Page 12 of 277

### NMC N-Methyl Carbamate
NMC CRA N-Methyl Carbamate Cumu lative Risk Assessment

NOAELs No-Observed-Adverse-Effect-Levels

OPs Organophosphorus Pesticides
OP CRA Organophosphorus Pesticide Cu mulative Risk Assessment

OPP EPA’s Offic e of Pesticide Programs

ORETF Outdoor Residential Exposure T a sk Force

ORD Office of Research and Development

PBPK Physiologic ally Based Pharmacokinetic
PCA Percent Crop Area
PCO Pest Control Operator
PCRA Prelimin ary Cumulativ e Risk Ass e ssment

PDP USDA’s Pesticide Dat a Program

PoD Point of Departure

PK Pharmacokinetic
PNS Peripheral Nervous System
PRZM-EXAMS Pesticide Root Zone Model- Expos ure Analysis Modeling System
RBC Red Blood Cell

RED Re-registration Elig ib ility Decision

REJV Residential Expos ure Joint Venture

RPF Relative Potency Factor

RTU Ready-to-Use
SAP Scientific Advisory Panel
SHEDS Stochastic Human Exposure and Dose Simulation
SLN Special Local Need
SOP Standard Operating Procedure
TC Transfer Coefficient
TDS Total Diet Study

TTR Turf Transferable Res i dues

UE Unit Expos ure USDA United States Departm ent of Agriculture
USE PA United States Environmental Protection Agency

WOE Weight of the Evidenc e

Page 13 of 277

### TABL E OF CONTENT S

TECHNI C AL E X EC UTI V E SU M M A R Y

.. .................................................................................................. 2

I. NM C C U M U LATIVE UP D A TE

... ....................................................................................................... 21

A. I NTRODUCT I ON

... ............................................................................................................................ . 21

Back ground

... .................................................................................................................. ................................. 21

B. H AZARD R ELA TIV E P OT ENCY F ACTORS

... ....................................................................................... 27

1. Intr oduc tion

... ................................................................................................................ ............ 27

2. Endpoints and Toxi col ogy St udies

... .......................................................................................... 28

3. Determi nation of T o xic P o tency

... ............................................................................................. 33

a. Em pirica l Model i ng: Dose-T im e Response Model and B e nchm ark Dose Estim ation

... ............................ 34

i. D o s e -Tim e Res p ons e M odel

... .................................................................................................... .......... 34

ii. S tatis ti cal M e tho dolog y

... ..................................................................................................... ................. 37

b. Res u lts : Bench m ark D o s e and P o tenc y Es tim ati o n

... ............................................................................. ... 39

c. Res u lts : H a lf Li fe T i m e to Re co ver y

... ........................................................................................ .............. 44

4. Selection of Re lative Potency Fa ctor s: Br ain C h E Inhibition

... ............................................... 45

5. Selection of t h e I nde x

Che m ical ( O

x a myl)

... .............................................................................. 46

a. Candidat e s for t h e Index Chem i cal

... ........................................................................................... ............... 46

b. D es cription of th e O x am yl D a tab as e

... .......................................................................................... ............. 46

6. Relative Pote ncy Fact ors f o r the Revise d Cu mul a tive Risk Assessme n t of t h e N-Methyl

Ca rbam a tes 48

7. Intr a-s p ecies V a riability, Inter-spe cies Extra pol ation, and FQPA 10X Safety Factor s

............ 49

a. Inter-s pec i es Ext r apola tion F a cto r in the re vis e d N M C CRA

..................................................................... 49

b. F Q P A S afet y F a ctor

............................................................................................................. ...................... 50

i. Backgr ound

..................................................................................................................... ...................... 50

8. Inc o r por ation of U n cert a inty/Extrapolati on Fa ctor s and t h e Tar g et M a r

g i n of Ex pos ure

....... 54

9. Dose A dditiv ity

................................................................................................................ ........... 55

10

. Sum m ary

........................................................................................................................ ............ 56

C. C UMU LAT IV E R ISK FR OM P ES TICID E S IN F OOD S

............................................................................. 58

1. Food Cons um ption Dat a

.......................................................................................................... .. 58

2. Pesticide Re sidue Dat a

......................................................................................................... ..... 60

a. U S D A

- P D P

....................................................................................................................... ......................... 60

b. N M C M a rket B a s k et S u rve y

....................................................................................................... ............... 61

c. F D A

- CFS A N P e s ticide Res i du e M onitori ng P r og ram

............................................................................... 61

d. F D A

- CFS A N Total D i et S t ud y

..................................................................................................... ............. 62

3. NMC Pesticides I n cluded in t h e F o od Risk A ssessme n t

............................................................ 63

4. Food Commodities Include d i n the F o od Ris k Assess me nt

........................................................ 67

5. Method of Esti mation of C u m u lative F o od R i sk

........................................................................ 70

a. O v erview of S i n g le-Chem i c a l D i etar y Ris k A s s es s m ent P r oces s

............................................................... 70

b. NMC Food Residue Dat a base

...................................................................................................... .............. 71

c. Manipulat ion of Residue Da ta for Exposure Assessm ent

........................................................................... 73

d. A s s u m p tions

.................................................................................................................... ........................... 74

6. Estimation of Acute Ex posur e Usi n g DE EM -FCI DTM Software

................................................ 77

7. Results

........................................................................................................................ ................ 77

8. Sum m ary

........................................................................................................................ ............ 81

D. R ESID EN TIA L NM C C U M UL AT I VE R IS K

.......................................................................................... 83

1. Intr oduc tion

................................................................................................................... ............ 83

2. Scope of Re gional Assess me nts

.................................................................................................. 84

3. Residential Sce nari o s

.......................................................................................................... ....... 85

a. Law n Car e

...................................................................................................................... ............................ 85

b. V egetab l e G arde n s

.............................................................................................................. ........................ 86

c. O r nam entals

.................................................................................................................... ........................... 86

d

. F r uit Tr ees

.................................................................................................................... .............................. 87

e. P et Coll ars

.................................................................................................................... .............................. 87

f. G o lf Cours e

.................................................................................................................... ............................ 88

4. Exposure R out es/Scen arios C onsi d ere d

.................................................................................... 88



### Page 14 of 277a. Oral Rout e of E xpos ure

......................................................................................................... ..................... 90

i. M odeling the N on-D i etar y Inges tion P athw a y

..................................................................................... . 91

b. Derm al Route of Expos ure

....................................................................................................... .................. 94

c. Inhala tion Rou t e of Expos ur e

................................................................................................... .................. 94

5. Dat a Sources

................................................................................................................... ........... 95

 pes ticid e us e da t a;

............................................................................................................ ..................... 95

 res i due conc entr ation and dis s i pa tion/de ca y da ta;

.............................................................................. ... 95

 exposure con t a c t fac t or d ata

. .................................................................................................. ............... 95

a. P es ticide U s e D ata

............................................................................................................. ......................... 95

i. Broadcas t L a w n S cenarios

....................................................................................................... ............. 96

ii. P e t Coll ar S c ena r ios

........................................................................................................... ................... 96

b. Res i due Con cen t r ation D ata

..................................................................................................... .................. 96

c. Exposure Fac t or (Conta ct) Data

................................................................................................. ................ 97

6. Exposure S cenarios

............................................................................................................. ....... 97

a. Law n Car e Expo s u re S cena r ios

................................................................................................... ............... 97

i. Lawn Appli c a t or Expos ure

....................................................................................................... ............. 97

ii. Lawn Post-Appl ica ti on Derm a l Exposure

.......................................................................................... ... 99

iii. Lawn Non-Diet a r y Hand- to-Mou th Exposur e

.................................................................................... 102

b. Vegetab l e Garde n E xposure S cen arios

............................................................................................ ......... 103

i. A pplica t or Expo s u re

............................................................................................................ ................ 103

ii. Post-Application De rm al E xposure

............................................................................................... ...... 105

c. O r nam e ntal P l an ts and S h rubs Ex pos ure S cena r ios

................................................................................ .. 105

i. A pplica t or Expo s u re

............................................................................................................ ................ 106

ii. Post-Application De rm al E xposure

............................................................................................... ...... 106

d. Fruit Tr ee Expos ure Scen arios

.................................................................................................. ................ 107

i. A pplica t or Expo s u re

............................................................................................................ ................ 107

ii. Post-Application De rm al E xposure

............................................................................................... ...... 108

e. O r nam e ntal G a r d en - S n a il and S l ug Bait S cen ar ios

.............................................................................. .. 108

i. A pplica t or Expo s u re

............................................................................................................ ................ 109

ii. Post-Applicatio n Expos ure

...................................................................................................... ............ 109

f. P e t Coll ar S c ena r ios

........................................................................................................... ...................... 110

i. A pplica t or Expo s u re

............................................................................................................ ................ 110

ii. Post-Application De rm al E xposure

............................................................................................... ...... 110

iii. Oral (Hand- to- M outh) Po st-Applic atio n Exposu r e

............................................................................ 111

g. G o lf Cours e S c e n ario

........................................................................................................... .................... 112

i. Post-Application De rm al E xposure

............................................................................................... ...... 112

7. Risk

........................................................................................................................... ............... 11 3

8. Sum m ary

........................................................................................................................ .......... 11 3

E. C UMU LAT IV E R ISK FR OM NM C P EST I CID E S IN D RINKING W ATER

............................................... 12 9

1. Intr oduc tion

................................................................................................................... .......... 12 9

2. Problem For m ulation

............................................................................................................ .. 13 0

a. Drinking W a t e r Exposure Ne eds for the NMC Cu m u lativ e Assess m ent

.................................................. 130

b. Nature of NMC Exposure in Dri nking W a ter Sou r ces

............................................................................. 131

c. S u mm ar y

........................................................................................................................ .......................... 136

3. Conce p tual M odel

............................................................................................................... ..... 13 6

a. Regional S c re en ing A pproach fo r V u lner ab le S ources of D r inkin g W ater

.............................................. 137

b. Conceptu al M o d e l for S u rfac e W a ter S ources of D r inking W at er

............................................................ 138

c. Conceptu al Mod e l for Vulner abl e Gr ound W a t e r Sources of Drin king W a ter

......................................... 140

4. Analysis Plan

.................................................................................................................. ......... 14 2

a. N M C P rope rties

................................................................................................................. ....................... 142

b. Identif y i ng Reg i onal E xposure S cen ar ios

........................................................................................ ......... 143

c. Regional U s age

................................................................................................................. ........................ 145

d

. Surface W a t er E xpos ure Asse ssm ent

.............................................................................................. ......... 146

e. Ground W a ter E xposur e Assess m ent

............................................................................................... ........ 148

5. NMC Conce n tr ations in Surface Wa ter Sour ces of Dr inki ng W a ter

........................................ 14 9

a. Individual N M C Lev e ls in S u rfa c e W at er

......................................................................................... ....... 149

b. Cum u lative N M C Lev e ls in S u rf ace W ater

......................................................................................... ..... 151

c. Spatial Ex tent of

NMC E xposure s in Su rfac e W a t er

............................................................................... . 152

d. S e ns itivit y An al ys is : Carbof ur a n in S u rfa ce W at er

............................................................................. .... 154

6. NMC Conce n tr ations in Gr ound W a te r Sour ces of Dr inki ng W a ter

....................................... 15 6

a. Individual N M C Lev e ls in G r oun d W ater

.......................................................................................... ...... 157



### Page 15 of 277b. Cum u lative NMC Lev e ls in Gro und W ater

.......................................................................................... ......................... 158

c. S p atial Ex tent of

H i gh N M C Exp o s u res in G r oun d W ater

........................................................................... ................. 160

d. Sensitivit y An al y s is: Car bofur a n in Gr ound W at er

.............................................................................. ........................ 162

7. Sum m ary

........................................................................................................................ .......... 16 6

F. T HE M UL TI -P ATH W AY C UM U L AT IVE A SSESS MEN T

...................................................................... 16 7

1. Basic C once p t

s

................................................................................................................. ........ 16 7

2. Framing the P o pulati on-Base d Ass e ssme n t

............................................................................. 16 8

3. Inter p reting th e O u tputs

....................................................................................................... .... 17 0

4. Attributes of the Revised N -M ethyl Car b am ate Cum u la tive Risk Assess me nt

......................... 17 0

a. Children

, 1-2 ye ars , G e org ia Coa s tal P l ain G r oun d W ater

....................................................................... 172

b. Children 3-5 y e a r s, Georgi a Coas tal Plain Ground W a ter

........................................................................ 173

c. A dults , 20-49 ye ars , G e org ia Coa s tal P l ain G r oun d W ater

...................................................................... 174

d. Adults, 50+ y e ar s, Georgi a Coast a l Pl ain Ground W a ter

......................................................................... 174

e. Children

, 1-2 y e ars, Flor id a C itr us Ground W a te r

............................................................................... ..... 175

f. Children 3-5 y e a rs, Flor ida Ci trus Ground W a ter

................................................................................ ..... 175

g. Adults, 20-49 y e ars, Flor id a C itr us Ground W a te r

............................................................................... .... 176

h. Adults, 50+ y e ar s, Flor ida Cit rus Ground W a ter

................................................................................. ..... 177

i. Children

, 1-2 y e ars, North Caro l i n a Coast a l Plai n s Ground W a ter

.......................................................... 177 j. Children 3-5 y e a r s, North C a rolin a Coast a l Plains Ground W a ter

........................................................... 178

k. Adults, 20-49 y e ars, North Caro l i n a Coast a l Plai n s Ground W a ter

.......................................................... 179

l. Adults, 50+ y e ar s, North C a rolin a Coast a l Plains Ground W a ter

............................................................. 179

G. R ISK C HARAC T E RIZA TI ON

............................................................................................................. 18 1

1. Intr oduc tion

................................................................................................................... .......... 18 1

2. Haz a r d

and D o se- Res ponse Assessme n t

.................................................................................. 18 1

a. Acetyl cholin es te ras e Inhib ition : Data Qua lit y & Com m on Effect

........................................................... 182

b. Dose-Response Anal y s is

......................................................................................................... ................. 183

c. S e lec ting the In d ex Chem ic al

................................................................................................... ................ 184

d. As s u m ption of Do s e-Additivit y

.................................................................................................. .............. 184

3. Food Assess me nt

................................................................................................................ ...... 18 5

a. Cons um pti on D ata

............................................................................................................... ..................... 185

b. P D P M onitoring D a ta in the A s s es s m ent

.......................................................................................... ........ 186

c. D a ta Trans l a tion from P D P

...................................................................................................... ................ 187

d. O t her S ources of Res i due D at a

.................................................................................................. ............... 187

e. Im pact of Regul ator y A c tions

................................................................................................... ................ 188

f. Im pact of A s s u m p tions : S e ns it i v it y A n a l y s es

................................................................................... ...... 189

i. Lim it of Det ect i on in PDP: Use of ‘ze r o’ assum ption for non-de te ctab le r esidues

............................ 190

ii. U s e of Re cent P D P D ata O n l y

.................................................................................................... ........ 192

iii. Summ ing food e xposur es over 2 4 hours

........................................................................................... . 193

iv. Cons ideration of

Chem ica l S p e c i f ic A d jus tm e nt F actor A pproach f

o r the Int e r-s pec ies U n cer ta int y F actor

........... 197

g. M odel O u tputs & D i s cus s i on

..................................................................................................... .............. 200

4. Residential Ass e ssme n t

......................................................................................................... ... 20 3

a. P es ticide U s e D ata

............................................................................................................. ....................... 204

b. Pesticide Residu e and Exposure Cont act Dat a

.................................................................................... ..... 204

i. Derm al E xposur e

................................................................................................................ ................. 204

ii. N on-dietar y ing e s tion

.......................................................................................................... ................ 207

5. Drinki ng Wate r Assess me nt

..................................................................................................... 21 3

a. Ground W a ter E xposur e

.......................................................................................................... ................. 213

i. Depth to gr ound wate r

.......................................................................................................... ............... 214

ii. S etback dis tan ce s betw een th e w ell and the tr eat e d fie l d

.................................................................... 215

iii. H y draul i c condu ctivi t y of th e soil /v adose zone

................................................................................. 216

iv. Soil/vadose zone a nd ground wa t er pH

........................................................................................... ... 216

v. O t her fa ctors

.................................................................................................................. ...................... 216

b. Surface W a t er E xposur e

......................................................................................................... .................. 217

c. U s age Info rm ati o n

.............................................................................................................. ...................... 218

d. Tim i ng of Exposur e

............................................................................................................. ..................... 219

6. FQPA 10X Factor for the Pr otec tion of Inf a nts and C h ildr e n

................................................. 22 0

7. Physiol ogic a lly-Based Ph ar mac o kinetic M odels

..................................................................... 22 1

8. Concl u sions

.................................................................................................................... .......... 22 2

H. R EFER ENCES

............................................................................................................................... ... 22 7



### Page 16 of 277II. APPE NDI CE S

..................................................................................................................... ............ 24 8

A. S UMMAR Y OF R ISK M IT IGA T I ON FOR I NDIV I DUAL N-M ETH YL C ARBAMATES

.............................. 24 8

B. H AZARD

............................................................................................................................... ......... 25 4

1. Dat a Spr eads heets of the NM C Pe sticides

............................................................................... 25 4

2. Dose -Res pons e m odeling of t

h e NMC pe sticides

..................................................................... 25 5

3. N-M-C a rbamate Do se Res ponse

.............................................................................................. 25 6

4. R-pack age s with file name s

..................................................................................................... . 25 7

5. Sum m ary AC hE pr otoc ol ev aluations, mixtur e experi m ents , m o tor activity me asureme n ts

.... 25 8

6. Physiol ogic a lly-Based Phar mac o ki netic M odeling f o r t h e NM C CRA

.................................... 25 9

C. F OOD

............................................................................................................................... .............. 26 0

1. The Sour ces of Residue Inputs for t h e As sess me nt of the Cumul a tive Dietary Expos u re t o NMethyl Car b m a te Pes ticides on F o ods

............................................................................................ .......................... 26 0

2. S u mma r y o f

PDP Residu e Ana l yses o f

N-Meth yl

Ca rb ama t e Pesticid es o n Food Commod ities
Incl ude d i n Re vised NMC CR A

.................................................................................................... ............................. 26 1

3. Processi ng F a ctors Used to Estimate Resi dues o f

N-Met h

y l Ca rbama t e Pesticid es in Foo d

For m s* 26 2

4. Translation of Residue Sourc e D a ta t o FC I D F o od Forms

..................................................... 26 3

5. Sum m ary of Re sidue Distribut i on I n puts to D EEM-FC I D for the Revise d N M

C CRA

............ 26 4

6. An al ysi s of C

h emi c al s a n d F

o o d s i n t h e Up per P o rt i o n of t h e Revi se d N M

C

C

RA. Exp o su re Distribution f o r Childre n 1- 2 Years Ol d

........................................................................................ ........................... 26 5

7. Co- O ccurrenc e of N- Methyl Carbamate Pe sticides on P D

P Samples, 199 4- 2006

................. 26 6

8. Co mp arison o f D E

EM- F C I D versio n 2.03 an d Lifelin e versio n 4.30 Exposu r e an d Risk

Estimates t h rough t h e F o od Pathw a y Only

........................................................................................ ....................... 26 7

D. R ESID EN TIA L

............................................................................................................................... .. 26 8

1. Residential Pe sticide Use In puts from REJ V Sur vey Dat a

....................................................... 26 8

2. Residential Ex pos ure Sce n ari o s A p

pendix

............................................................................... 26 9

E. D RINKING W A TER

......................................................................................................................... 27 0

1. Sum m ary of Surface W a ter M onitori ng Dat a for NM C Pestic ides

.......................................... 27 0

E-2 S UMMARY OF G ROUND W ATER M ON ITO R ING D AT A FO R NM C P ESTIC I DES

........................................... 27 1

E-3 D RINKING W ATER T REATMENT E FF ECTS ON N- METH YL C ARBAMATE P ESTIC I DE S

................................. 27 2

E-4 N- METHYL C ARBAMATE U SAGE E ST IMA TES

........................................................................................... 27 3

E-5 C HEMICAL -S PECIFIC F AT E AN D T R A N SPO R T P ROPER T I E S U SED F OR T H E W ATER E XP OSURE M OD EL S

.. 27 4

E-6 NM C S URFACE W ATER E XPOSUR E A SSE SSME NT M ET H ODS

................................................................... 27 5

E-7 NM C G ROUND W ATER E XPOSURE A SSESSM ENT M E T HODS

................................................................... 27 6

III. MULTI -P ATHWA Y GR A P HS

...................................................................................................... 27 7



### Page 17 of 277

## Tables

Table ES -1. Summary In formati on Regarding the NMC Pesticides and the Uses, and Pathways Included in the revised NMC Cumulative Risk Assessment

.......................... 6 Table I.A-1. Summary In formation Regarding the NMC Pe sticides and the Uses, Routes

Table I.B-2. List of toxicity studies used in the Revised N -Methyl Carbamate Risk

Table I.B-3. Oral BM D 10 

### s and BMDL 10 

### s from rat brain and RBC ChE inhibition for the

Table I.B-4. Dermal B M D 10 

### s, BMDL 10 

### s, and potency estimates from rat and rabbit brain

Table I.B-5. Inhalation BMD 10 

### s, BMDL 10 

### s, and potenc y estimates from rat brain and

RBC ChE inhibition for the N -methyl carbamates with resi dential/non-occupational uses 44

Table I.B-7. Oral, dermal, and inhalation brain BMD 10 

### s and BMDL 10 

### s for ox amyl, the

Table I.B-9. Inter-species uncertainty factors and corresponding rat and human

Table I.B-10. Dose-response and recovery hal f-life estimates in juvenile and adult rats

Table I.B-12. Adjusted oral relative poten cy factors for children and adults based on

Table I.B-13. Adjusted dermal relative potency factors for children and adults based on

Table I.B-14. Adjusted inhalation relative potency factors for children and adults based

Table I.C-3. Summary of Probabilistic Analys is of Distribution of the Cumulativ e Dietary

Table I.D-2. Lognormal Distributions of Unit Exposures Used for Carbaryl Lawn Care

Table I.D-3. Lognormal Distributions of Unit Exposures Used for Carbaryl Garden, Fruit and Pathways Included in the NM C Cumulative Risk Asse ssment

............................... 22

Table I.B-1. Test guidelines/studies that con t ain evaluations for ChE activity.

............ 30

Assessm ent.

................................................................................................................. 32

N

-methyl ca rbamates

.................................................................................................... 41

and RBC ChE inhibition for the N -methyl carbamates with residential/non-occ u pational

uses 1



### ............................................................................................................................. 43

Table I.B-6. Half-life for time to recovery for adult rats from oral studies for brain ChE

inhibition for the N -methyl carbamates 1



### ........................................................................ 45

index c hemical

.............................................................................................................. 48

Table I.B-8. Relative potency factors fo r oral , dermal, and inhalation routes 1



### .............. 48

BMD 10 

### s and BMDL 10 

### s

................................................................................................... 50

from co mparative chol inesterase studi es

...................................................................... 53
Table I.B-11. FQPA s a fety fa ctors for the revised NMC CRA 1



### ...................................... 53

inter-species and FQPA s pecific fa ctors

....................................................................... 54

inter-species and FQPA s pecific fa ctors

....................................................................... 55

on inter-species and FQPA specific factors

.................................................................. 55
Table I.C-1. N -methyl Carbamates a 

### and RPFs

............................................................ 63

Table I.C-2. Crop T r anslations for Pesticide M onitoring Data

...................................... 69

Exposures and Risk from Use of N -Methyl Carbamate Chemicals on Food Crops a



### ..... 77
Table I.C-4. Relative Exposure Contribution from Foods for Children 1 to 2 Years Old (At 99.8 th 

### Percentile of Ex posure and Above)

............................................................... 78 Table I.D-1. Specific Ex posure Routes and Path ways/Scenar ios

................................. 89

Scenario s

...................................................................................................................... 98

Tree, and Ornament a l Scenar ios

................................................................................ 104

Table I.D-4. Summa ry of NMC R e sidental Ex pos ure Scenar ios

................................ 115 Table I.E-1. NMC use patterns and availa bility of national monitoring data

................ 131

Page 18 of 277

### Table I.E-2. Summary of carbamate det ections in the USGS NAWQA study, 1991­

Table I.E-4. Regional drin king water exposure sites and dominant NMC pesticide uses 144

Table I.E-5. Estimated peak concentrations for NMC residues for the regional surface

Table I.E-6. Percentile concentrations for es timated NMC cumulative distributions in the surface water scenario sites (30-year per iod), adjusted for relati ve potency, interTable I.E-7. Estimated peak concentrations for carbofuran residue s for the regional

Table I.E-8. Percentile concentrations for es timated NMC cumulative distributions with

and without carbofuran in the surface water sc enario sites (30-year period), adjusted for

Table I.E-9. Estimated concentrations for NMC residues for the regiona l ground water

Table I.E-10. Percentile concentrations for estimated NMC cumulative distributions in the ground water scenario sites adjusted for relative potency and using safety factors

Table I.E-11. Estimated concentrations for NMC and carbofuran for regional ground

Table I.E-12. Percentile concentrations for estimated NMC cumulative distributions in the ground water scenario sites adjusted for relative potency and using safety factors

Table I.G-1

. Sensitivity Analyses for NM C C u mulative Food Assessment: Limit of

Table I.G-2

. Sensitivity Analyses NMC Cu mulative Food Assessment: Recent PDP

Table I.G-3

. Sensitivity Analyses NMC Cu mulative Food Assessment: 24 Hour Food

Table I.G-4

. Sensitivity Analyses NMC Cu mulative Food Assessment: CSAF Approach

Table I.G-7

. Input Parameters Us ed in the Ex posure Models: Bias, Assumptions, 2001 (provisional data publis hed by USGS in 2003)

................................................... 133

Table I.E-3. Summa ry of N -me thyl carbamate fate and transport properties

............. 142

water drinking water scenar ios (30-year period)

......................................................... 150

species and FQPA safety factors for children

............................................................. 152

surface water drinking water scenarios (30-y ear per iod)

............................................ 154

relative potency, inter- species and FQPA safety factor s for children

.......................... 155

scenarios (25-year period) fo r gro und water at 30 feet

............................................... 157

for child ren

.................................................................................................................. 159

water scenarios (25-year period) for ground water at 30 feet

..................................... 162

for child ren

.................................................................................................................. 165

Detection Assumption a



### ................................................................................................ 191

Data Assu mption

......................................................................................................... 193

Summati on

.................................................................................................................. 197

for In ter-sp e cies UF

.................................................................................................... 199

Table I.G-5

. Summa ry of NMC C u mulati ve Food Assessment: Ba seline Estimates

. . 200

Table I.G-6

. Summa ry of NMC C u mulati ve Food Assessment: Se nsitivity Analyses. 201

Uncertainties, and Str engths

....................................................................................... 209

Page 19 of 277

## Table of Figures

Figure I.B-1. Plot of B M D 10 

### s and the 95% confidence limits for rat brain ChE inhibition

for the N

- m e thyl ca rbamates

......................................................................................... 41

Figure I.B-3. Plot of brain ChE meas ured in a seven chemical mixt ure of N -methyl

Figure I.C-1. Relative Contribution of Crop/ Chemical Pairs to Top 0.2 Percentile of

Figure I.C-2. Relative Contribution of Crop/ Chemical Pairs to Top 0.2 Percentile of

Figure I.E-1. NMC C R A regions for drinking water exposure assessment showing high

Figure I.E-2. Conceptual model for surface wa ter sources of drinking water illustrating

Figure I.E-3. Depiction of general ground wa ter scenario concept used for estimating

Figure I.E-4. Location of surface water intake s (blue dots) in relation to relative NMC pesticide usage (high use areas in dark orange) in the southeastern/south-central U.S. 153 Figure I.E-5. Cumulative margins of exposur e for drinking water from private wells in

Figure I.E-6. Extent of high leaching pot ential soils in NMC use areas in the

Figure I.E-7. Cumulative margins of exposure for drinking water from private wells in

Figure I.F-1. Three-dimensional plot of the total MOE by day of the year and percentile

Figure I.G-1. Number of Eating Events Cont ributing to Exposures at 99.8+ Percentile

Figure I.G-2. Number of Eating Events Cont ributing to Exposures at 99.8+ Percentile Figure I.B-2. Plot of BMD 10 

### s and the 95% confi dence limits for rat RB C ChE inhibition

for the N -methyl carbamates 1



### ....................................................................................... 42

carbam ates

................................................................................................................... 55

Cumulative Distributio n for Child ren 1-2

....................................................................... 79

Cumulative Distributio n for Child ren 3-5

....................................................................... 79 Figure I.D-1. Pesticide Cumu lative Assessm ent Regions

............................................ 85

NMC use areas and regional drin king water ex pos ure sites

....................................... 137

how multiple NMC uses are proportioned in t he wate rshed

........................................ 139

pesticide concentrations in drinki ng water

.................................................................. 141

high leaching potential soils in the southern coastal plain (GA Peanuts), infan t s

....... 160

Southeastern U.S

. ....................................................................................................... 162

high leaching potentia l scenarios, childr en 1-2 year s old

............................................ 165

of ex posure

................................................................................................................. 169

for Childre n 1-2

........................................................................................................... 195

for Childre n 3-5

........................................................................................................... 195

Page 20 of 277

# I. NMC Cumulative Up date

A. Introduction

Ba ck ground

The Food Quality Protection Act (FQ PA) of 1996 signif icantly amended the Federal Insecticide, Fungicide, and Rodent ic ide Act (FIFRA) and the Federal F ood, Drug, and Cosmetic Act (FFDCA ). One of the major changes imposed by FQPA was to require EPA to consider the cumulative e ffects of chemicals with a common mechanism of toxicity in its tolerance reassessment decisions.

In 2001, EPA conclu d ed that the N -methyl carbamate (NMC) pesticides share a common mechanism of toxicity. This common mechanis m group (C MG) was

established based on the shared structural characteristics and similarity and their shared ability to inhibit acetyl cholinesterase (AChE) by carbamylation of the serine hydroxy l group located in the active site of the enzyme (USEPA, 20 01a). For this group of pesticides, recovery typically occurs rapidly (minutes to hour s) following maximal

inhibition of cholinester a se (ChE). In a February 4, 2004 Federal Register notice, EPA

announc ed the members of t he Common Assessment Group (C AG) (FR Vol.69, No.23,

p. 5340-5344). These ten carbamates all disp lay ChE- inhibiting ac tivity, have current active registrations, and are expected to co ntribute to the carbamate cumulative risk through quantitatively meaningful exposure scenarios. The ten members of the CAG

for the N -methyl carbamates and those chemicals whic h are included in the quantitative

cumulative risk assessment are listed in ive risk assessment.

Section 1.A - Page 21 of 277

### Table I.A. Summary Information Regarding the NMC Pesticides and the Uses, Routes and Pathways Included in the NMC Cumulative Risk As sessment

Pesticide Pesticide

Us es Pesticide Pathway s Pesticid e Ro utes

Food Drin kin g Wate r Re side ntial Oral Der m al Inhalation

Ag Crops X X X

Lawn X X X X

Garden X X X

Orna mental s X X X

Fruit Trees X X X
Car b a r y l Pet Collar X X X

Golfer Exposu r e X X

Aldic a rb Ag Crops X X X Oxamyl Ag Crops X X X

Formet anate
HCl Ag Crops X X X

Methomyl Ag Crops X X X

Carbofuran Ag Crops X X X

Propoxu r Food Use s X X

Pet Collar X X X

Methiocarb Ag Crops X X

Orna mental X X X

Thiodic a rb Ag Crops X X X

Pirimic a rb Ag Crops X X



### To meet the requirements of FQ PA, EPA d e velop ed methodolo g ies for conducting cumulative risk assessments. As part of this process, EPA

consulted with the FIFRA Sc ientific Advisor y Panel (SAP) to obtain expert review, advice, and recommendations at eac h major step in the development of the underly ing method olog ies for cumu lative risk assessments. EPA held

numerous ex ternal peer-review meetings with the SAP and asked for co mme nt on many issues, including its approac hes to grouping c hemicals based on a common mechanism of toxicity; Office of Pesticide Program’s (OPP) guidance for conducting cumulat i ve risk asse ssment; methods and approac hes for doseresponse and exposur e assessment; and probabilistic exp o sure models for combining food, drinking water, and resi dent ial expos ure pathways

. In addition, the Agency also held numerous meeti ngs wit h the FQP A Federal Advisory Committees TRAC (Tolerance Reasse ssment Advisory Committee ) and CARAT (Committee to Advise on Rea sses sment and Transition), which wer e established under the Federal Adviso ry Committee Act (FACA). Various stakeholders including public inter e st groups, state agricultural agencies, pesticide industry representatives , grow ers, United States Department of Agriculture (USDA), and others were represented on these committees. In addition, numerous public technical br iefings on each component of the cumulative methodology were held. In short, the Agency sought and receive d

Section I.A – Page 22 of 277

### advice, comments, and recommendat ions on the methodologies and framework that were to guide the im plementation of FQPA and tolerance

reassessment.

Based in part on the above cons ultations, OPP developed and published guidance on conducting cumulative risk assessments (“Guidance on

Cumulative Risk Assessment of Pesticide Chemicals T hat Have a Common

Mechanism of Toxicity”) which is available on EPA’s website at http://www.epa.gov/pe sticides/trac/science/cumulative_ guida nce.p d f

. This guida nce h as been reviewe d by the FIFRA SAP and d escribes key principles for conducting these risk assessments. One such principle is the need to

consider the time frame of both the expos ure (e.g., When does exposure

occur? What is the exposure duration?) and the toxic effect (e .g., What are the time-to-peak effects and the time to re covery? How quickly is the effect reversed?). Both should be adequately cons idered so that an indiv i dual's exposure is matched with relevant and appropr iate toxic o logical values in terms of duration and timing. Inhibition of ChE caused by the N -methyl carbamates is followed by rapid recovery within minutes to hours. This rapid recovery is a unique characteristic of this group of pesticides and was considered and characterized as part of the risk a ssessment. Cumulative risk assessments should also account for temporal aspects of exposure, such as those related to the time of year during which applications resulting in exposures are likely to occur, the frequency of applic ation, and the period of r eapplication. Moreover, these assessments must appropria tely consider age-dependent and demographic factors and patterns. T he Agency’s approach to each of these challenges in the cumulative haz ard, expos ure, and risk assessment is described throughout the document.

This cumulative assessment is int ended to identify major sources of risk that could potentially accrue due to the use of a variet y of pesticides which act through a common mechanism of toxic i ty. R egulatory decision making is bas ed on the many detailed aspects of t he single-chemical aggregate risk

assessment. Because of the requirement th at many data sets be combined into a single as sessment, reducing the im pact and likelihood of compounding conservative assumptions and over-estimat i on bias bec omes very i m portant in constructing the cumu lative risk assessm ent. As a result, OPP has chosen to

work with those data that most cl osel y reflect likely exposures and not to incorporate those data that are inherently conservative by their nature (e.g.,

field trial data which in corporate maximum applicat ion rates and minimum preharvest intervals). These principles are fully described and laid out in the aforementioned guidance document.

EPA previo usly releas ed the “Estim ation of Cumulative Risk from N

methyl Carbamate Pesticides: Prelimin ary Assessment” in August 2005. During the period since the issuance of the pre liminary cumulative risk assessment, the Agency has been working to further impr ove and refine its assessment of the cumulative risks associated with the NMC pesticides. These refinements

Section I.A – Page 23 of 277

### include changes to: incorporate the most recent food residue data by including pesticide residue data through 2006 from USDA’s Pestic ide Data Program; reflect the Agency’s review of new toxic i ty data in juvenile animals; and to incorporate human data for certai n NMC pesticides. In addition, the Agency has updated the assessment to re fl ect individual risk mitigation measures and other use pattern changes for individual NMC pesticides since the preliminary NMC CRA was issued in August 2005. Specifically, during this period, the Agency im posed risk reduction measures on some of the major

contributors to carbamate cumulative risk, as discuss ed below. The risk estimates presented in the revised NMC CRA reflect the risk mitigation

measures taken on individual car bamat es since FQPA was signe d into law in August 1996. A table summarizing these mitigation measures is provided in Appendix II.A. In general, EPA’s risk estima tes reflect ri sk mitigation measures

that EPA determined to be warranted based on its assessment of the single

chemical’s risks. For all of the risk mitigat ion measures that ar e reflected in this document, EPA has c o mmenced the proces ses necessary to impl ement its selected risk mitigation, but may not ye t have completed these processes. Having already determined that risk miti gation is warranted for the individual chemical, EPA has c hosen to exclude it from this assessment to avoid any confusion that yet further mitigation might be warranted solely on that basis,
either for the indiv i dual chemical or fo r other NMC chemical s. Rather, where the risks are adequately addressed by previ ously identified risk mitigation, it was considered to be unnecessary to confirm that here. To the extent that any risk mitigation measures are not subs equently implemented as envisioned in this assessment, the revised NM C CRA will be revised as necessary. The following summarizes the ma jo r mitigation actions that the Agency has recently or will be taking with respect to registration of uses which have bee n excluded
from the revised NMC CRA :

Carbofuran

. In July 2006, the Agency issued its proposed decision to

cancel all domestic uses of Carbof uran; only four import tolerances, (coffee, bananas, sugarcane, and rice) w ould remain. A Federal Register (FR) notice announcing this decision and soliciting public comments was published on August 30, 2006.

Any cancellation hearing for EPA’s proposed decision o n carbofuran would be scheduled to commence in 2008, which is after the issuance of

this document. If all remaining uses of carbofuran are not cancelled after

conclus i on of a cancellation hearing, this assessment will be revise d as

necessary.

Methom y l

. The methomyl registrant submitted a letter requesting the voluntary cancellation of the stra wberry use on January 4, 2007. A Federal Register notice announc ing the re ceipt of this request to delete the methomyl strawberry use pub lished on April 25, 2007 (72 FR 20541) (FRL-8125- 6). The public comment period for this notice closes on October 22, 2007.

Section I.A – Page 24 of 277

### A letter requesting voluntary cance llation of the use of methomyl on

grapes was received from the regi strant on S eptember 14, 2007. A Federal Register notic e announc ing the receipt of this request will be published in October 2007. Based on t hese voluntary cancellations, the use of methomyl on grape and strawberry has been excluded from the N

methyl carbamate cumu lative risk assessment.

Propoxur. In February 2007, the propoxur registrant submitted a letter requesting the voluntary cancellation of the all ind oor spray uses that may result in non-occupational expos ure fo r children. A Federal Regis t er notice announcing this voluntary c ancellation and solic iting public comments was publis hed on April 25, 2007 (72 FR 20541) (FRL-8125-6). In July 2007, the Agency issued it s Final Use Termination Order for Propo xur Residential Spray Use (EPA Reg i stration Number 432-1288) for the use of propoxur, when formulated into a product that can be used as a spray on residential indoor use si tes. The Agency has evaluated N -me t h y l
Carbamate cumulative risks in a manner that excludesthese crack and crevice-type residential uses so as to reflect the Agency’s final termination

order.

Aldic arb. In September 2007, EPA complet ed the Aldicarb Reregistration Elig ib iility Decisio n (RE D ). The A gency identif i ed potential human h e alth
risks of con cern associated with the cu rrent registered us es of aldicarb

from d r inking water expos ure, and potential environm ental risks of concern to birds, mammals and fish. To reduce these potential exposures
and to address current risks of concer n, EPA -- in agreement with the technical registrant of al dic a rb -- will implem ent cert ain label restrictions. To address groundwat er contamination conc erns, the A gency will increase drinking water well set-backs for a pplic ations to peanuts in the southeastern coastal plains when cert ain criteria are tri ggered. In

additio n , to reduce environmental concerns, the Agency will imp l e m ent application rate reducti ons and restri ctions, state limitations, label

amendmen ts, and cancellation of cert ain commodities. EPA is als o requiring data to confi rm the decisi ons presented in the Aldic a rb RED which and will seek public comment on the decisions in the RED in
October 2007.

The current document is pres ented in three major parts:

• Part I: Revised NMC Cumulative risk assessment

• Parts II and III: Appendices wh ic h provide background material, additional graphs, and more technical and/ or extens ive details surrounding the analyses contained in Part I

Section I.A – Page 25 of 277

### Part I is divided into eight chapter s. Chapter A is this general introduction. The following chapter (I.B), presents the Hazard Assessment with specific dis cussion of the Relativ e Potency Factor approach and empirical dose-response and time course modeling us ed to estimate relative potency. The next three chapters (C, D, and E) focus on each of the major expos u re pathways (food, residential, and drinking wat e r, respecti vely), including a discussion of assumptions, data inputs, and interrelationships of expos ure da ta. Each of these pathway s has unique issues relating to availability of data, scale, and interpretation of results. Results of each aspect of the assessment are discussed in these chapters with particular attention given to how they reflect potential exposures to the populat ion and wh at might be inferred with regard to signific ant exposure pathways/scenarios. Chapter F of the document examin es the results of combining estimates of ri sk from all sources of expos ure, in a multi-pathway, probabilistic cumulative assessment, and further discusses the interpretation of the outputs with respec t to the most significant pathways and

scenarios. The results in this chapter were generated by the DEEM/Calende x software. Chapter G of th is document is a ri sk characterization, which further

discusses and charac terizes the inputs to the assessment as well as the resulting model expos ure estimates. Chapter H of this document provides

references for the material cited in Parts A through G.

Section I.A – Page 26 of 277

# B. Hazard Relative Potency Facto r s

1. Introducti on

OPP designated the NMC pesticides as a common mechanism group (USEPA, 2001a) based on the shared structur al characteristics and similarities and their shared ability to inhibit AChE by c a rbamylation of the serine hydroxyl group locat ed in the active site of the enzyme. Following maximal inhibition of cholinesterase, recovery typically occurs rapidly (minut es to hours). Pharmacokinetic data are only available for one NM C (i.e., carbaryl), Consequently, a multi-chemical, multi-pathway phy siol o g ica lly based phar macokinetic (PBPK) model cannot be developed at this time for the NMC cumulative risk assessment (Appendix II.B.6). Therefor e, the 2007 revised cumulative risk assessment relies on the relative poten cy factor (RPF) method for quantifying chemical potency. In t he RPF approach, the toxic pot ency of each chemical is

determined. A member of the cumulative assessment group (CAG) is selected

as the index chemical which is us ed as the point of reference for standardiz i ng the cholinesterase inhibiting potency of the other chemical members of the

CAG. In th e case of the NMC CRA, ox amyl is used as the index chemical.

The FIFRA SAP supp orted the scientif ic ap proach employed in the NMC cumulative hazard in the February and Augu st 2005 meetings. EPA has

considered the comments collected from the SAP as well as the registrants’ error-only comment phase in July 2005 in the developm ent of the current

revised NMC CRA. The revised NMC CRA incorporates additional data available since the 2005 pre liminary CRA in addition to uncertainty factors for the inter- and intra-species factor and FQPA 10X safety factor. Specifically, the Agency has included comparat ive cholinesterase data in juvenile ( p ost-natal day 11 [PND 11] and PND17) and adult rats for six chemicals as well as cholinesterase inhibition and recovery data from human subjects for three

chemcials. The comparative cholineste rase data are used here to inform the
FQPA 10X factors while the cholinestera se data in human subjects has been used to form the inter-species factor in the revised CRA. It is noted that carbofuran has been ruled ineligible for reregistration and is under going the process of cancellation. However, for completeness and because tolerances for bananas, coffee, ri ce and sugarcane wil l continue for import purposes, the hazard chapter includes RPFs and uncertain ty factor information for carbofur an.

This cumulative hazard assessment repr esents the collaborative efforts of

scientists from OPP and EPA’s National Health and Env i ronmental Effects Research Laboratory (NHE ERL) and National Center for Computational Toxicology (NCCT). The purpose of this hazard chapter is to descri be EPA’s approach for:

Section I.B – Page 27 of 277

###  Determination of the relative cho linesterase inhibiting potency and half-life to recovery used for each N -methyl carbamate in the CAG;

 Selection of the index chemical used as the point of reference to

standardiz e the potency of each N -methyl carbamate;

 Establishm ent of a baseline or refe rence value (i.e., poi nts of departure) used to estimate potential risk for t he group for each route of interest; and

 Identification of the i n tr a-species, inter-species, and FQPA 10X safety

factors use d in this cumulative risk assessment.



## 2. Endpoi nts and To x i cology Studie s

When using the RPF method and before th e cumulative risk of exposure

to the NMC s can be quantified, the rela tive toxic potency of each NMC must

first be determined. The determination of relative toxic potency is calculated using a unif o rm basis of comparison, by using, to the extent possible, a common tissue, species, and sex for a ll the expos ure routes of interest (USEPA, 2002a). NMCs exert their neurot oxic ity by carbamylating the enzyme acetylcholinesterase (AChE) in both the central (brain) and peripheral nervous systems. Since cholinesterase (ChE) in hibition is the critical event in NMC

toxic i ty, ChE inhibition provides the common endpoint for the revised NMC

CRA. The available ChE activity measur es provide a more uniform measure of

toxic i ty compared to behavioral measur es for performing cumulative risk

assessment. Behavior a l measures are o ften limited in terms of the scope of effects assessed and by the lack of standardization of la boratory equipment among laboratories. Moreover, behaviora l changes in animal studies usually

occur at si milar or higher doses compared to doses needed to inhibit cholinesterase activity. In order to evaluate the concordance between ChE

inhibition a nd behavio ral endpoin t s, EPA has performed a series of doseresponse and time course studies with seven NMCs where RB C and brain ChE, along with clinical signs (‘tox’ score ) and motor activity, were measured (Appendix II.B.5; McD aniel et al ., 2007; Padilla et al ., 2007).

There are laboratory animal data on NMCs for cholinest e rase activity in plasma, red blood cell (RBC), whole bl ood, and brain (whole brain and brain sections). Measures of ChE inhibiti on in the peripheral nervous system (PNS) are very limited for C h E inhibiting pestici des, in general. As a matter of science policy, blood cholinesterase data (plasm a and RBC) are considere d appropriate surrogate measures of potent ial effects on PNS acetylcholinesterase activity, and of potential effects on the central nervous system (CNS) when brain ChE data are lacking (USEPA, 2000a)

. Furt hermore, when RBC ChE data are of adequate quality, as is the case for the NMC s, RBC ChE data are preferred over plasma ChE data. AChE is the target enzyme for this common

mechanism group and is the primary form of ChE found in RBCs.

Section I.B – Page 28 of 277

### Butylcholinesterase (BChE) is t he primary form found in plasma.

Inhibition of BChE is c onsidered a measure of exposur e, but has not been shown to be of toxicological significanc e. Some studies with NMCs provided

whole blood ChE. Whole blood ChE r epresents a mixture of plasma and RBC ChE, and thus may not provide a uni form endpoint for comparison across

chemicals. Consequently, whole blood ChE data were not used in this

assessment. In the ca se of brain ChE inhibition, dat a are available for each NMC with whole brain (or half brain). In some studies, brains were dissected into different brain areas (e.g., cer ebellum). Because the brain diss ections provided ar e not standardized across the studies and br ain section data are not available for each NMC, these data do not represent a uniform basis of comparison. RBC and brain (namely w hole, half) ChE inhibition were considered potential endpoint s for extrapolating risk to humans in the revised
NMC CRA. As described in Section B.4 belo w , the A g ency is using brain ChE data as the basis for RPFs and points of departure (PoD) in this

assessment.

Humans may be exposed to the NMCs through food and drinking water and in and around residences, schools, commerc ial buildings, etc. Therefore, the potency of NMCs needs to be det ermined for the oral, dermal, and inhalation routes of exposure. Under FIFR A, toxicity studies in various species (e.g., dog, mouse, rat, and rabbit) are subm itted to OPP. For the N M Cs, toxicity studies in the rat provi de the most extensive and robust database of ChE

inhibition data. Thus, the focus of this analysis was on ChE activit y data derived from male and female (non-pregnan t) rats. EPA used rabbit studies for pesticides with residentia l/non-occupational exposur e potential when dermal

toxic i ty data in rats were not available.

Toxicologic a l characteristics of the NMCs involve maximal ChE in hibition followed by the rapid recovery, typically in minutes to hours. As such, the critical duration of exposure for this common mechanism group is acute ChE

inhibition m easured at the peak time of ef fect. Characterizing chemical specific recovery is critical for characte rizing overlapping expos ures and thus

cumulative risk. EPA has compiled data fr om several different kinds of studies:

1. oral (gavage) studies quantifying the relationship between maximum

inhibition from single or multiple administered dose(s) in adult rats;

2. oral (gavage) studies quantifying the in vivo recovery time course, u sually

at several doses, and beginning at or ar ound the time of maximum inhibition (which had typically been determined in pr eliminary studies) in adult rats;

3. comparative cholinesterase assa y (CCA) studies quantifying the ChE

sensitivity of juvenile rats compared to adult rats; comparing dose- response and time to recovery in ju venile (PND11 and/or PND 17) and adult rats; and/or

Section I.B – Page 29 of 277

### 4. oral double blind ascending st udies quantifying the dose-response and recovery time course of ChE in humans; and,

5. inhalation and dermal studies fo r those pesticides with residential

exposure.

Data included in the revised NMC CRA were extracted from studies submitted by pesticide registrants and from dose-response and time course studies performed by EPA’s NHE ERL. Table I.B-1 provides the list of various types of studies inc l uded in the analysis. Appendix II.B.1 contains the electronic spreadsheet s of br ain and RBC ChE data used.

Table I.B-1. Test guidelines/studies that contain evaluat ions for ChE activity.

Stud y T y pe Guideline T y pe

Oral

Acute oral toxicity study in rat OPPTS 870.1000

Acute neu rot o xicity in rat OPPTS 870.6200 a

Subch r o n ic n eurotoxi c ity in rat OPPTS 870.6200 b

Develo pment al neurotoxici t y oral in rat OPPTS 870.6300

Chroni c oral toxicity in rat OPPTS 870.4100

Ran ge findin g oral toxicity study in rat Not appli c a b le

Other/Spe cial Studies Not appli c a b le

Derm al

21/28-Day dermal toxicity in rat or rab b it OPPTS 870.3200

Inhalation

Acute inhal ation in rat OPPTS 870.1200

Chroni c inhal ation in rat OPPTS 870.4100



### In toxicolo g y studies submitted to EPA for pesticide registration,

measurements of cholinesterase inhibi tion are typically performed using some

variation of the Ellman s pectrophotometric method (Ellman et a l., 1961). Under standard conditions, this method usually inv o lves extensive sample dilution, prolonged incubation, and te mperatures around 37°C; all of which promote reversal of the enzyme inhibition. If precauti ons are not taken to prevent

Section I.B – Page 30 of 277

### recovery using this method, then repor ted cholinesterase activities can

underestimate actual cholinesterase i nhibitio n (Winteringham and Fowler, 1966;

Williams and Casterlin e, 1969; Nostrandt et al ., 1993; Hunter et al ., 1997) whic h could have an impact on the relative potenc y estimates. A radiometric method such as that reported by Johnson and Russell (1975) provides the most appropriate method for measuring cho linester a se inhibition due to NMC exposure because factors which promot e reversibility are minimized. The dilution is minimized (1:30 vs. more t han 1:1000 dilution for the standard Ellman method), and incubation time may be mo re rapid for the radiometric method (one to three minutes compared to 10 mi nutes or greater). Furthermore, the radiometric method may be conducted at lower temperatures. The Ellman
method can be modified to minimize c onditions promoting reactivation.

Reduc ing the tissue dilution, shortening the time, and lowering the temperature of the assay all limit the amount of spontaneous decarbamalyation of the inhibited enzyme (Nostrandt et al ., 1993). Although modifications to the Ellman method are not standardized, when per formed with the appropriate care, the modified Ellman method can provide reliable cholinesterase data.

To aid in the characterization of t he cholinesterase data provided by the

studies sub m itted for registration, scientists from EPA’s NHEERL h a ve systematically evaluat ed cholinesterase i nhibition following acute exposures of

adult rats to seven N- methyl carbamates (carbar yl, carbofuran, formetanate HCl, methomyl, methiocarb, oxamyl and prop oxur) using both the standard Ellman and radiometric techniques. This work has been published in the
scientific lit erature (Padilla et al ., 2007); the data from t hese exper i ments are also included in Appendix II.B.1. EPA’ s issue paper presented to the FIFRA
SAP in Fe b r uary, 2005 provided g r aphica l co mparisons of the data from selected registration studies and EPA’s radiometric exper iments. These graphical comparisons s howed good concor dance between the registration

data and EPA’s radio m etric exper iments. In the current revised cumulative risk assessment, these data have been analyzed st atistically (see section I.B.3). Overall, the results provided by the EPA radiometric studies provid e similar benchmark dose estimates to the registration studies.

The laborat ory protocols or standar d operating procedures (SOPs) for some registration studies have been provided by the pesticide registrants. E PA has received protocols or SOPs for studies for nine of the ten NMCs. Methiocarb is the only chemical the A gency has not received a protocol or SOP

for measuri ng cholines terase activity. T he protocols available indic a te that the experimental conditions am ong laboratories vary but th at dilutions are generally limited to approximately 1:20 and that sampl e s are frozen immediately. Although information regarding the time of sample handling is more limited, the available information suggests that reas onable precaut ions were taken in these

studies to reduce reac tivation prior to analysis. The Agency considers the methods used to evaluate ChE activity in the laboratory to be a critical component of the hazard assessment fo r the NMCs and will continue to

Section I.B – Page 31 of 277

### evaluate SOPs as new studies are submitted in the future. A summary of the information provided in these prot ocols can be fo und in Appendix II.B.5.

A summary of the studies and en dpoints inc l uded in the revised
cumulative risk assessment for th e NMCs are provided in Table Table I.B-2. This table includes studies recently submitted, such as comparative

cholinesterase studies and human studies reviewed and found to be ethically

conducted and scientif ically valid by the Human Studies Review Board (HSRB)

in 2006.

Table I.B-2. List of toxicity studies used in the Revised N -Methyl Carbamate Risk

Assessment.

Oral Der m al Inhalation

Chemi c al Study ID ChE Inhibitio n Data Study ID ChE Inhibitio n Data Study ID ChE

Inhibition

Data

4344 2305 1 Brain, RBC

4344 2302 2 RBC

4507 9705 RBC

4382 9601 Brain, RBC

4382 9602 Brain, RBC

4506 8601 3

4515 0701

4661 8001 Brain Aldic a rb

4237 3001 Huma n RB C No re sid ential use s , thus d ata are not n eede d

4384 5202 Brain, RBC

4384 5203 Brain, RBC

4412 2601 Brain, RBC

4439 3701 Brain, RBC

4700 7001/

4714 3001 Brain, RBC (NHEERL

CCA )

NHEE R L Padilla et al.,

2007 Brain, RBC Car b a r y l

NHEE R L

4714 3001 Brain, RBC

comp arative 4563 0601 (471 519 02) Brain, RBC (In vitro

derm al penetration ) Inhalation d a ta are not

available

4567 5701 RBC

4668 8912 -1 4 CCA Brai n

4714 3703 -0 5 CCA Brain, RBC

Mos e r CCA Brain, RBC Carbofu r an

Padilla et al.,

2007 Brain, RBC No re sid ential use s , thus d ata are not n eede d

4661 8901 CCA Brain, RBC Formet anate Padilla et al.,

2007 Brain, RBC No re sid ential use s , thus d ata are not n eede d

4092 2301 Brain, RBC Methiocarb Padilla et al.,

2007 Brain, RBC 4177 1701 Brain Data are not available

Methomyl 4447 2001 Brain, RBC No re sid ential use s , thus d a ta are not n eede d



### Section I.B – Page 32 of 277Oral Der m al Inhalation

4448 7501 Brain, RBC

4664 6401 CCA Brain, RBC
Padilla et al.,

2007 Brain, RBC

4472 1401 Huma n RB C

4425 4401 Brain, RBC

4447 2001 Brain, RBC 4082 7601

4442 0301 Brain

4661 5301 CCA Brain, RBC
Padilla et al.,

2007 Brain, RBC 4475 1201 Brain, RBC 4515 5801 Brain, RBC Oxamyl

4491 2301 Huma n RB C

4448 5301 Brain, RBC

4423 3103 RBC Pirimica rb

0011 3638 Brain, RBC No re sid ential use s , thus d ata are not n eede d

Propoxu r Padilla et al.,

2007 Brain, RBC 4106 6001 Brain,
RBC 4264 8001 Brain, RBC

4513 8702 RBC Thiodi ca rb 4513 8703 Brain, RBC No re sid ential use s , thus d ata are not n eede d

1 Brain and RB C data for pa rent o nly used in the a nal y s is; 2 Brain d a ta at 24 hou rs n o t used in the an aly s is; 3 MRIDs listed here ar e referenced in th e Aldicarb oral rat brain ChE anal y s is in Appendix II.B.2 as: 1) 46618 001 as Moser-1; 2) 450686 01 as Moser-2; a nd 3) 451507 01 as Moser-3.



## 3. Determi n ation of Toxic Pot e ncy

As described in the guidanc e document for cumulative risk assessment (USEPA, 2002a), dose-response modeling is preferred over the use of NOAEL/LOAELs (i.e., no- or lowest- observed-adverse-effect-levels) for determining relative toxi city potency. NOA E Ls and LOAELs do not necessarily

reflect the relations hip between dose and response for a given chemical, nor do they reflect a uniform response ac ross di fferent chemicals. In the present analys is, benchmark dose (BMD) modeling has been used to determine the toxic poten cy of the NMCs. EPA’s draft BMD guida nce (USEPA, 2 000d) suggests that the central estimate on the BMD provides an appropriate measure for comparing chemical potency and that the lower lim it on the central estimate (i.e., BMDL) provides an appropr iate measure for extrapolating risk. The 10% response level is gener ally at or near the limit of sensitivity for discerning a statistically signific a nt decrease in ChE activity across the blood

and brain c o mpartments and is a response level close to the background Ch E. As part of EPA’s Revised Cumulati ve Risk Assessment for the OPs, EPA performed a power analysis of brain ChE data availa ble for more than 30 OPs (USEPA, 2002b). The results of the analys is indicated that most studies can reliably detect 10% brain ChE inhibition. Furthermore, in studies submitted to EPA for pesticide registration, clinical signs and behav ioral effects have not

been shown in studies with below 10% ChE i nhibition. In this cumulative risk

Section I.B – Page 33 of 277

### assessment, the central estimate of the BMD 10 

### was selected as the response level for developing RPFs. The lower limit on the BMD 10 

### (i.e.,

BMDL 10 

### ) was selected for the points of depar ture (PoDs). A PoD is a point

estimate on the index chemical’s dose-response curve that is used to extrapolate risk to the exposu re levels anticipated in the human population.

The following section describes the empiric a l dose-response modeling performed for the NMC s. BMD 10 

### and BMDL 10 

### estimates for the NM Cs are provided in Tables 1.B-3 thru 5. Half-life time to re covery for each of the NMC s is provided in Table 1. B-6. Detail ed information about the empiric a l modeling
for each ch emical can be found in Appendix II.B.2.

a. Empirical Modeli ng: Dose-Time Response Model and Benchm ark

Dose Estimation

i. Dose-Time Response Model

Several features of the dose-time response for the N- methyl carbamates were to be captured in an empirical model:

 The rapid decline of ChE activity with increa sing dose, perhaps after a

“shoulder” at the low-dose end of the dose-response curve;

 A potential minimum level belo w which ChE activity will not drop, regardless of dose;

 The rapid decline of ChE activity after dosing to a minimum level which depends upon dose, then returns to the background level over a period of minutes to hours, at a rate that may also depend upon dose;

 Lack of early time points in most of the time course studies to accurately

estimate th e time of m a ximum effec t, but instead start collecting data around a previous ly estimated time of maximum effect.

The model described is the result of multiplying a dose-response model for inhibition that is closely relate d to the model that was successful at characterizing OP dose-response curves (U SEPA, 2002b) and a time-course

model for inhibition. Tr ansformations of parameters w e re used to enforce constraints, since the statistical softw are used for estimating model parameters does not incorporate bounded est i mation (for example, to require that half-life estimates remain posit ive).

The model for inhibition, before para meters were transformed to enforce constraints, is

Section I.B – Page 34 of 277

### ⎛

lo g ⎛ 1

− − R P ⎞ ⎜ ⎛ d ⎟ ⎞ γ 

### ⎞

() = g d R P D ( ; , , R 

### , γ ) = 

# ( 1

− P 

# ) ⎜ ⎜ 1

− e ⎜ ⎝ 1

− P ⎟ ⎠ ⎝ D R ⎠ 

### ⎟ ⎟ gd ⎝ ⎠


### (Eq. 1)

where:

 d is administered dose, and is part of the data set;

 P is the minimum fraction of background Ch E activity, and is
constrained to fall between 0 an d 1;

 R is the inhibition fracti on associat ed with the desired benchmark dose (that i s, the benchmark dose is the dose expected to yield

100× R % inhibition at the time of maxi mum effect), and is set to 0.10 in this analysis;
 D R 

### is the benchmark dose, constr ained to be greater than 0.0;

 γ is a shape parameter to allow a shoulder at the low-dose end of the dose-response curve, and is c onstrained to be greater than 0.0.

Two different time course models were used. One time course model is the difference of two expo nential functions, scaled so that the maximum is always 1:

ln 2 t ln 2 t 

### ⎛

− ( )

− ( ) 

### ⎞

R A 

### h t



# ( )

= h t T T (;

, ) = C e ⎜ T



### − e T 

### ⎟

A R 0 

### ⎜ ⎟ ⎝ ⎠


### (Eq. 2)

where:

 T A 

### is the half-life of the process that results in an increase in inhibition, and
 T R



### is the half-life of the process that results in a decrease in inhibition (recovery or reactivation).

The maxim u m of h(t) occurs at:

TT 

# ( ln 

# ( T 

# )

− ln 

# ( T 

# ) 

# )

T *



### = R A R A


### ln 2 T

− T 

# ( ) (

R A 

# )

(Eq. 3)

Section I.B – Page 35 of 277

### so 

# ⎛

− ln *



### − 2 * 

# ⎞ ( )

2 T ln 

## ( ) T


# C 0 

# = 1 ⎜
e T R



# − e T A 

# ⎟ ⎜ ⎟ ⎝ ⎠

With this sc aling, h(t) is symmetr ic in the two parameters ( that is, h(t; a

, b )

= h(t ; b

, a ) ), which complicates statistical estimation unless a constraint is added to keep T R



### > T A



### . Also, many data sets require that T* be specified (not estimated from the data), because the des igns were inadequate for estimating

T*

. For these reasons, it is convenient to reparameterize the model in terms of

T* and α = T R



### / T A 

### and make sure α is constrained to be greater than 1.0.

The design of most of the time-c ourse datasets considered in this
assessment did not allow clean estimation of both T * 

### and α , and the reparameterization sometimes increased the difficulty of estimation. Thus, an alternative, much simpler, time-cours e model was used in all but one of the dose-time studies (aldicarb, brain ChE). In this simpler model, ChE activity is taken to be described by an exponential rec o very time-course, beginning at a

time δ after dosing. This gives t he following recovery function:

ln ( 2 ) ( t

− δ )

− 

### h t

( ) = e T R



### (Eq. 4)

where:

T

R



### is the half-life of recovery



### δ is the difference in time between dosing and the first ChE

measurement.

In this model, the only parameter to be estimated is T R



### .

Multiplying g(d) and h(t) together gives a function for ChE inhibition as a

function of dose and time

. Thus, Equation 5

f(t, d) = A×(1 – g(d)×h(t))

is a model for ChE activity as a function of dose and time, where A gives the background (that is, control) l e vel of ChE activity.

Section I.B – Page 36 of 277

### There were no time-course data for any of the dermal and inhalation data sets, so the above model wa s simplified for those sets, either by setting the time course parameters to a fi xed value, or by fi tting a linear model to the natural logarithm of ChE activity , which is equivalent to an exponential dose-response model when the variance is proportional to the square of the mean ChE activity level (that is, the coe fficient of variati o n is constant across doses).

The following transformations were used to ensure that parameters remained in their permitted range:

 lA = ln(A) , to force A > 0

 lD = ln(D R 

### ) , to force D R 

### > 0

 tz = -ln((1 – R – P)/P) , to force 0 < P < 1 – R

 lg = ln( 

### γ ), to force 

### γ > 1

 lTr = ln(T R



### ) , to force re covery half-life > 0 (in simplified time-course model)

 ldT = ln( 

### α

) , to force T R



### > T A



###  lT ma x 

### = ln(T ma x 

### ) , to fo rc e T ma x 

### > 0.

ii. Statistical Methodology

The statistical model fit to the dose or dose-ti me response data depended

on whether the experimental design in volv ed repeated measures (some RBC studies only) or not. The most ge neral model fit to the C h E activity data was (for the simplified time course model), for individual j in study i , with sex s(j) at

time t ik 

### :

When there was more than one study,

Section I.B – Page 37 of 277

### that is, the log BMD was taken to be normally distributed around a mean that possibly differed between sexes.

When there were repeated observations on a subject, the logarith m of

indiv i dual animals bac kground ChE activity levels were assumed to be normally

distributed about a mean that vari ed between sexes, studies, and, when there were controls at all times, among times (this latter allows for the possib ility of variation among analytic batches, if samp les from the same time post dosing were analy zed as a batch).

When recovery time-course data were av ailable, the recovery half-life was allowed to differ among the doses for wh ich recovery data were available. Often for a chemical, some datasets were just dose response studies
conducted around the time of maximu m inhibition, and others included a recovery phase, with samples taken every few hours or more frequently. In this case, the range of doses in all the studi es together was grouped so that one

dose with a time-course was included in eac h group. This allowed the estimate of recovery half-life to change with dose when the right data were available.

Howev e r, often a chemical had recovery ti me course data for only a single dose level, so only a single recovery half-life could be estimated.

The process of estimating parameters proceeded in three steps. First, initial values for the parameters w e re arrived at using the R function getInitialV a l ues (inclu d ed in the lib rary DRUtils). This function provides a graphical interface that allo ws the user to quickly arrive at reasonable estimates for the parameters, and allows a few iterations of an opti m ization algorithm to improve those initial estimates, using ordinary least squares as an objective
function. Based on these initial estima tes, the degree to which it would be possible to uniquely es timate the model parameters was determined, by analyz ing the condition number of the ma trix of gradient of the model with respect to the model parameters, and of t he matrix of (unsca led) variances and covariances of the paramet ers, evaluated at the dat a points (times, doses, sexes) in all the data sets. At this poi nt, it was often possible to simplify the model by noticing that it was impossibl e to determine a unique value for, for example tz , because doses did not go high enough for inhibition to approach it s maximum value, or the maximu m level of inhibition was 100%.

The next step was to determine an appropriate model for the error

variance. The options considered were either; a constant variance, a constant

Section I.B – Page 38 of 277

### variance that differed among studies and sexes, or a variance that was proportional to a power of the mean ChE activity level, and whose constant of proportionality varied among studies and bet ween sexes. This was determined by fitting either a cell mean model (with i ndicator functi ons identify i ng individual dose X time X sex X study groups) or, mo re commonly, fitting the full nonlinear dose-time model using generaliz ed nonlinear least squares (Pinh e iro and Bates, 2000). In either case, likelihood ratio tests were used to identify the variance model to use (Pinheiro and Bates, 2000).

Using that variance model, a full vers ion of the dose-time course model was fit to the data, and contra sts used to determine whether lD needed to differ

among sexes. Pinheiro and Bates (2000) note that likelihood ratio tests for

fixed effects in mixed effects models tend to reject the null hypothesis enthusiastically, wher eas using c ontrasts to test parameter values comes close

to the nomi nal type I error rates.

Finally, a simplified model was fit to the data, and the re sulting parameter
estimates used to determine the values of lD and lTr and their stand a rd errors

. BMDs were calculated as exp( lD ), and BMD L s were calculated by exponentiating the lower end of a two- sided 90% confidence interval for lD

.

All statistic al analys es used the stat istical software environment R (version 2.0.1, patched version of 2005-01-26; R Development Core Team, 2004) and its associated packages. Appendix II.B.3 c ontains the computer code used in EPA’s analyses.

b. Results: Benchmar k Dose an d Potenc y Estimation

Results of the empiric a l dose-res ponse modeling are provided below. Detailed descriptions of the analysis and results of empirical dose-response modeling for each chemical are provided in Appendix II.B.2.

The oral BMD 10 

### s for the NMCs range acros s se veral orders of magnitude with aldicarb and pirimicarb representi ng the most and l east potent pesticides, respectively, for both brain and RBC ChE inhibition. The numbe r of studies available for analysis varies among the chem icals (Table 1. B-2). At least two studies containing RBC and whole brain Ch E inhibition in male and female rat were available for eight of ten NMCs (aldicar b, carbaryl, carbofuran, formetanate HCL, oxamyl, me thomyl, pirmicarb, and thiodicarb). At present time, the only RBC and whole br ain ChE data for methi o carb and propoxur are from EPA’s NHEERL dose-respo n se and time course studies in male rats (Padilla et al ., 2007).

For those chemicals that have data in male and female adult rats, EPA analyzed both sexes. When male and female data provided statistically similar

BMD 10 

### s, the data were combined and analyzed jointly. This joint analys is

Section I.B – Page 39 of 277

### provides a more robust analysis using all the available data. In cases

where the BMD estimates were statis tically different, s e x specific BMD 10 

### s are presented ( Table I.B-3, and Table 1.-5, below). As mentioned above, only male data are available for two NMCs (me thiocarb, propoxur). Reliable BMD 10 

### estimates for RBC ChE inhibition from pirimicarb could not be calculated due to a lack of response even at the highest doses tested (110 mg/kg).

ChE inhibition measured using both radiometric and modified Ellman techniques are availab l e for aldicarb, carbaryl , carbofuran, formetanate HCl, methomyl, and oxamyl. RBC and brai n ChE data from the two methods provided statistically similar BMD 10 

### estimates for all of the chemicals and were combined in the analysis to provide a mo re robust potency estimate. As shown in Table I.B-3, for ca rbaryl, both methods provided similar BMD 10 

### estimates for

RBC Ch E. However, for brain ChE in males, the BMD 10 

### estimated from EPA’s radiometric study is larger than that es timated from the studi es using modified

Ellmans. Four registration studies we re inclu ded in the analys is (MRID nos. 43845202, 43845203, 44122601, 44393701). For all four studies, S p ragueDawley rats were administered via gavage with an aqueous vehic l e of 0.5% (w/v) carboxymethyl-c e llulose (high visco sit y)/0.1% (w/v) Tw een 80 (10mL/kg). EPA’s experiments involved Long Evans rats dosed via gavage wit h corn oil (1 mL/kg) as the administ r ation vehic l e. Gi ven that each of the carbaryl studies provided v a lid and acceptable ChE data, there is no scientific support for removing any studies from the analysi s. Thus, the Agency has decided to

include all t he available brain ChE data in the carbaryl BMD 10 

### estimate used for potency determination (i.e., registration combined with Padilla dat a of 1.6 mg/kg).

Section I.B – Page 40 of 277

### Table I.B-3. Oral BM D 10 

### s and BMDL 10 

### s from rat brain and RBC ChE inhibition for the

N

-methyl carbamates

Chemi c al Brain RBC

BMD 10 (mg/k g) BMDL 10 (mg/k g) BMD 10 (mg/k g) BMDL 10 (mg/k g)

Aldic a rb F= 0.05

M= 0.06 F= 0.03

M= 0.03 0.03 0.02

Car ba r y l Regi stratio n F= 1.60 Regi stratio n M=

1.21

NHEE R L M=5.46

Combi ned M = 1.58

Mo s e r = 2

. 63 Regi stratio n F= 1.35 Regi stratio n M=

0.99

NHEE R L M= 4.15

Combi ned M = 1.11

Mo s e r = 2

. 03 Reg. =5.59

Mo s e r = 0

. 96 Reg. = 3.4 1

Mo s e r = 0

. 73

Carbofu r an 2 0.10 0.0873 0.03 0.01

Formet anate
HCl 2 0.11 0.06 0.09 0.03

Methiocarb 2 1.31 0.56 3.18 0.81

Methomyl 0.36 0.2677 0.20 0.11

Oxamyl 0.24 0.18 0.28 0.16

Pirimica rb 11.96 6.98 NA NA

Propoxu r 2 2.09 0.83 1.54 0.28

Thiodi ca rb 0.27 0.23 1.39 0.90

1 BMD estimates are present ed as a single estimat e w hen the r e ar e no differences b e tween sexes an d bet w een the radiomet ric and modified Ellman methods, unle ss other w i se noted.

2 BMD estimates are for m ale onl y NA: No relations hip bet w een RB C Ch E activity a n d pirimicarb dose.



### Figure I.B-1. Plot of B M D 10 

### s and the 95% confidence limits fo r rat brain ChE inhibition

for the N -methyl carbamates

Section I.B – Page 41 of 277b r a c i d

l A n a r u f o b r a C e t a n a t e m

r o F l y m a x O b r a c i d o
i h T l y m o h t e M b r a c o
i h t e M l y r a b r a C r u x o p o r P b r a c i m

i r i P

b r a c i d

l A n a r u f o b r a C e t a n a t e m

r o F l y m o h t e M l y m a x O b r a c i d o
i h T r u x o p o r P b r a c o
i h t e M l y r a b r a C b r a c i m

i r i P Ra t Bra i n ChE Inhibition

10 0BMD10(95%confidencelimits) 10

1

0.1

0. 0 1

Chemica l Name 

### Figure I.B-2. Plot of BMD 10 

### s and the 95% confi dence limits for rat RBC ChE inhib i tion
for the N -methyl carbamates 1

R a t RB C Ch E In hi bi t i o n

10 0BMD10(95%confidence limits) 10

1

0.1

0. 0 1

Chem ical Nam e

1 BMD 10/ BMDL 10 for RBC ChE w e r e not dev el ope d for pirmic a rb; no dos e-re spons e relati on ship

w a s obs erved up to hig hest d o se tested (1 1 0 mg/kg).



### Potency estimates (B MDs) used for calculating dermal and inhalation RPFs are provided in Tables 1.B- 4 and 1.B-5. Dermal and i nhalation RPFs are needed for carbaryl, methiocarb, and propoxur as these have residential uses. Sufficient dose-response data were ava ilable for carbaryl to calculate BMD 10 

### estimates for RBC and brain ChE via t he dermal route. As for the dermal

Section I.B – Page 42 of 277

### studies wit h methiocarb and propoxur, no ChE inhibition was observed up to the highest doses tested. The highest doses in the methiocarb and propoxur studies have been used to es timate dermal relative potency.

Table I.B-4. Dermal B M D 10 

### s, BMDL 10 

### s, and potency estimates from rat and

rabbit brain and RBC ChE inhibition for the N -methyl carbamates with residential/non-occupational uses 1

Chemi c al Brain RBC

BMD 10 (mg/k g) BMDL 10 (mg/k g) BMD 10 (mg/k g) BMDL 10 (mg/k g)

Car b a r y l 2 49.35 3 30.56 F= 86.1 8

M= 59.04 F= 60.5 5

M= 46.91

Methiocarb 4 375 5

Propoxu r 4 1000 5

1 See Table I.B.7 for brain BMD 10 s and BMDL 10 s for oxam y l ; 2 Data from rat studies; 3 Comparative In v i tro dermal penet ration data w e re N O T used to refine t he brain BMD; 4 Data from rabbit studies; 5 Dermal endpoint is based on the highest do se tested in the dermal stud y ; N o ChE inhibition was observed at a n y dose.



### Rat inhalation data with propoxur were available to estimate a BMD 10 

### for

brain ChE. Inhalation studies wit h carbar yl and methiocarb are not available at

this time. Howev e r, dose-response and time-course data via the inhalation route were requested for car baryl as part of the carbar yl IRED. Route specific studies are preferred since they account fo r route specific kinetic characteristics which may impact chemical potency. In the absence of inhalat ion studies, oral data are being used in the revised cumu lative risk assessment to e stimate inhalation relative potency for carbaryl and methiocarb. This introduces uncertainty regarding the estimation of cumulative risk for the inhalation pathway. Howev e r, given that these chemicals do not have a port of entry effect, are expected to be rapidly absor bed, and do not require activation, ChE

measured from oral studies are not ex pected to substantially underestimate potency. (Note: Data from dermal and inhalation studies with oxam yl are not provided here becaus e oxamyl does not have residential uses. See Section

II.B.5 for selection of index chemical [ox a myl]).

Section I.B – Page 43 of 277

### Table I.B-5. Inhalation BMD 10 

### s, BMDL 10 

### s, and potenc y estimates from rat brain

and RBC ChE inhibition for the N -methyl carbamates with residential/non-occ upational

uses

Chemi c al Brain RBC

BMD 10 BMDL 10 BMD 10 BMDL 10

Car b a r y l 1 1.58 mg/kg 1.11 mg/kg 5.59 mg/kg 3.41 mg/kg

Methiocarb 1 1.31 mg/kg 0.56 mg/kg 3.18 mg/kg 0.81 mg/kg

Propoxu r 2 F= 0.00 95 m g /L

M= 0.016 m g /L (co n verte d to 4.54 mg/k g for RP F

cal c ulatio n) F= 0.00 76 m g /L

M= 0.011 m g /L NA NA

1 No inh a lati on studies ar e ava ilab l e for carb a r y l a nd meth ioc a rb; potenc y e s timates are from oral

studies

2 Inhalati on BM Ds and BMD L s for propo xur w e re diffe re nt be t w e en se xes, therefor e are d i spla ye d

separ atel y. No app arent d o se- r espo nse for R B C inh a lati on f o r prop o x ur a n d therefore no BMD.



### c. Results: Half Li fe Time to Recover y

Half-lives for time to r e covery fro m oral studies in adult rats are provided in

Table I.B-6. Since brain ChE is the fo cus of this revised assessment and the preliminary assessment indicated sim ilar recovery for brain and RBC ChE, Table I.B-6 provides only brain half-lif e estimates. For most o f the NMCs, recovery half-life estimates for brain AChE inhibition range from <1 hour up to 4

hours for adults. Recovery half-lives increased with dos e for brain AChE in carbaryl studies. No signific ant sex differences were noted in brain AChE recovery half lives. At higher dos es of carbaryl, recovery half-life for oral exposure was estimated to approximatel y 12 hours. However, at lower doses
more relevant for risk assessment purposes, the half-life for carbaryl

cholinesterase inhibition was estimated at 1 to 2 hours.

For those NMCs whic h have data in male and female adult rats, the Agency analyzed both sexes. When male a nd female data provided statistically

similar BMD 10 

### s, the data were combined and analyzed jointly. This joint analys is provides a more robust analys is us ing all the available dat a. However, female data are not available for methiocarb and propoxur while in vivo recovery ti me course data were not sufficiently robust to estimate brain
cholinesterase half-liv es for pirimicarb and thiodicarb. Overall, the half-life to recovery data support the use of acut e, single day exposures in the NMC

cumulative risk assessment.

Section I.B – Page 44 of 277

### Table I.B-6. Half-life for time to recovery for adult rats from oral studies for brain

ChE inhibition for the N -methyl carbamates 1

Brain

Chemi c al Re cov ery H al f -Life Estimat e (hr s ) Lower & Up p er Co nfident

Inter v als (hrs )

Aldica rb 1.52 1.16-1.9 9

Carba r yl 1.83 1.23-2.7 2

NHEE R L 1.6 5 NHEE R L 1.0 4-2.62 Carbofu r an Regi stra nt 0.68 Regi stra nt0.5 4-0.86

Formet anate HCL 4.26 3.32-5.4 60

Methiocarb 2 2.77 1.91-4.0 1

Regi stra nt (F) 0.67 Regi stra nt (F) 0.55-0.98 Methomyl Regi stra nt (M ) 1.05 Regi stra nt (M ) 0.91-1.23

NHEE R L (M ) 0.70 NHEE R L (M ) 0.50-0.9 8

(F) 0.9 3 (F) 0.7 8-1.1 1 Oxamyl (M) 0.7 0 (M) 0.5 8-0.8 5 6

Pirimica rb NA 3 NA

Propoxu r 2 2.69 1.02-7.0 4

Thiodi ca rb NA NA

1 Recover y h a lf- life estimates a r e prese n ted a s a singl e esti mate w h e n the r e are no d i fferences

bet w e en se xes and bet w e e n radi ometric an d modifi e d Ellm an metho d s, u n less oth e r w is e noted; 2 Half- life

estimates are f o r males o n l y ; 3 NA: insufficient time course d a ta to estimate brain ch oli nest e rase h a lf-life.



## 4. Selection of Relative Poten c y Factor s : Brain C h E Inhi bition

A key component of cumulative hazard assessment is to select an endpoint pertinent to the common mechanis m of toxicit y that can be used to quantify cumulative risk. EPA is quantifyi ng cumulative risk to the NMCs usin g RPFs and PoDs from brain ChE data

. As mentioned a bove, in cases where male and female rats provide sim ilar BMD 10 

### estimates, EPA has d e velop ed potency estimates jointly (methomyl, pirimi carb and thiodicarb). At the present time, only male data are available for methiocarb, and propoxur. For NMCs where the female and male data provided stat istically different results (aldicarb, carbaryl), the male BMD 10 

### has been used to calculate relative potency factors since it was the more health pr otective (i.e., lower) value.

Section I.B – Page 45 of 277

### As shown in Table I.B-3, BMD 10 

### estimates of brain ChE inhibition

were gener ally similar to those for RBC ChE data. For nine of the ten NMCs (including the most potent NMCs), br ain ChE is equally sensitive or more

sensitive c o mpared to RBC ChE inhibiti on. Thus, brain ChE inhibit i on data provides a health protective endpoint for esti mating cumulative risk on both the central and peripheral nervous system. Compared to BMD 10 

### estimates based
on RBC Ch E, BMD 10 

### estimates based on brain ChE hav e tighter confidence

intervals and therefor e will confer less uncertainty on cumulative risk estimates. Moreover, brain ChE inhibition repres ents a direct measure of the common

mechanism of toxic i ty as opposed to using surrogate measures (e.g., blood measures).



## 5. Selection of the I n dex Chemi cal (Oxamy l )

OPP’s cumulative risk assessment guidance document (USEPA, 2002a)

states that the i ndex chemical should be selected based on the availability of high quality dose-response data, preferably in each route of interest, for the common mechanism endpoint and that it ac ts toxicologically similar to other members of the common mechanism gr oup. High qualit y dose-response data
allows the calculation of PoDs for oral, dermal, and inhalation exposures with

confidence. Because the PoDs for the index chemical ar e used to extrapolate risk to the exposure levels anticipated in the human population, any error or uncertainty in an inde x chemical’s PoD value will be carried forward in the

cumulative risk estimates.

a. Candidates for the Index Chemical

When sele cting the index chemic al, EPA ev aluated the availab i lity of quality oral, dermal, and inhalation studies for all ten NMCs. Dermal toxic i ty studies that provided RBC and whole brai n data were available for 4/10 NMCs (carbaryl, methiocarb, oxamyl , propoxur). Inhalation st udies were available for only propoxur and oxamyl. At present ti me, the only NMCs with studies in all three routes of interest are oxamyl and propoxur. As shown in Table I.B-2, the oxamyl dat abase of oral studies is more robust than propo xur. Moreover, the oxamyl der mal study in rabbits provides more robust dose-response data compared to the propoxur rabbit derma l study (Tables 1.B-4 and 1.B-7). Consequently, oxamyl has been selected as the index chemical for the revised

cumulative risk assessment of the NMCs.

b. Description of the Oxam y l Dat a base

Oxamyl has a robust oral database that includes 6 acute oral studies (4 registration, 1 NHEERL, 1 human). Radiometric ChE data are available from EPA’s NHEERL dose-response and time course studies. A comparative cholinesterase study with juvenile (PND 11) and adult rats is also available (46615301)

. Doses in oral rat studies ranged from 0.005 to 15.3 mg/kg and

Section I.B – Page 46 of 277

### thus provide a broad dose-response range. RBC ChE was measured at the time of peak effect in all six studies

. Whole (or half) brain ChE data at peak

inhibition ar e available from the five rodent studies. High quality ChE recovery

data in adults and PND11 rats are also ava ilable. As shown in Table I.B-3, the

brain BMD 10 

### s for male and female rats are similar. For both sexes, the
confidence limits on the BMD 10 

### s also are narrow. Thus, the BMDL 10 

### s provide robust values for extrapol ating cumulative risk.

A double-blind, ascending, single oral dose, human study is also available for oxamyl (MRID 44912301). Mutliple RB C ChE sampling events provided the progression of ChE inhibition, maximum inhibition, as well as enzy m e recovery for each volunteer. The human study was examined by the HSRB in April 2006

and deemed scientific ally robust and ethica lly sound for use in risk assessment (HSRB Final Report, June 2006).

Two dermal studies were available for oxamyl, both in t he rabbit. Oxamyl exhibited a robust dose-response relati onship for assessing cholinesterase activity with RBC and brain. The effect of sex on dose was not significant in either study or compartment

. RBC and brain (half-brain) ChE activities for both studies were measured once, at the end of the study. The dermal brain and
RBC Ch E BMD 10 

### s are 34.91 and 64.01 mg/kg, respectively.

An acute (single day, 4 hours) i nhalation toxicology study (MRID 45155801) is available for oxamyl. Brain and RBC ChE inhibition were

measured at the end of t he study. The BMD analyses indicate a robust doseresponse relations hip for assessing ChE activity with RBC and brain. ChE

inhibition was similar for both RBC and brai n compartments in both sexes. The
inhalation brain and RBC ChE BMD 10 

### s are 0.005 mg/L and 0.002 mg/L, respectively.

A detailed description of the benchm ark dose analysis for dermal and inhalation studies in oxamyl can be found in Appendix II.B.2. Table I.B-7 provides the brain BMD 10 

### s and BMDL 10 

### s for oxamyl. As the index chemical, it

is used to calculate RPFs and PoDs:

 Oxamyl brain BMD 10 

### s for oral, dermal, and i nhalation routes have been used to calculate the oral, dermal, and inhalation RPFs for the revi sed

cumulative risk assessment.

 Oxamyl brain BMDL 10 

### s for oral, dermal, and inhalation routes have been used as the oral, dermal, and inhalation Po Ds in the revised cumulative risk

assessment.

Section I.B – Page 47 of 277

### Table I.B-7. Oral, dermal, and inhalation brain BMD 10 

### s and BMDL 10 

### s for oxamyl,

the index chemical

Endpoint Oral Der m al Inhalation

BMD 10 0.24 mg/kg 34.91 mg/kg 0.0047 mg/L

BMDL 10 0.18 mg/kg 17.05 mg/kg 0.0038 mg/L (c onverted to 0.66 mg/k g)



## 6. Relati ve Pot e ncy Factors f o r the Revised Cumulative Risk

Assessm ent of t h e N -Met hy l Carbam ates

RPFs were calculated from endpoints fo r brai n ChE inhibition provided in

Tables 1.B- 3, 1.B-4, 1.B-5, and 1.B-7. An RPF is the ratio of the B M D 10 

### of oxamyl divided by the BMD 10 

### (or appropriate value) for each NMC

. RPFs are

listed in Table I.B-8.

Table I.B-8. Relative potency factors fo r oral , dermal, and inhalation routes 1

Chemi c al Oral RP F Der m al RPF Inhalation RPF

Aldica rb 4

Aldica rb sulfo ne ( A ld o xyc ar b) 1 3.44

Aldicarb sulfoxide 1 3.68

Carba r yl 0.15 0.71 0.51

Carbofu r an 2.4

3 & 5-hyrdox ycarb o furan 2 2.4

Formet anate HCL 2.18

Methiocarb 0.18 0.09 0.62

Methomyl 0.67

Oxamyl 1.00 1.00 1.00

Pirimica rb 0.02

Propoxu r 0.11 0.03 0.18

Thiodi ca rb 0.89

1 Aldicarb su lfo ne an d sulfo x id e w e re n o t model ed b a sed o n metabo lite-s pecific d a ta. Instead the y

w e re ca lcul ate d base d on mo lecul a r w e ight convers i ons fro m aldic a rb ass u min g equ ip otent to aldic a rb. 2

Carb ofuran an d 3 and 5-h y dr oxyc arb o furan a ssume d to be equi pote n t to carbofur an.



### Section I.B – Page 48 of 277

## 7. Intra-species Variability

, In ter-species Extrapolation, and
FQ PA 1 0 X Saf e ty Fa ctors

Typically, EPA applies standard 10X fact ors to account for inter-species extrapolation and intra-spec ies variability. The FQ PA (1996) also mandates that a 10X safety factor be applied to protect for infant s and children unless there is sufficient data to support remo val of the 10X. For the revised NMC CRA, the standard 10X intra-species factor i

s applied to each of the ten Nmethyl carbam ates

. The inter-species and FQPA 10X factors applied in the

NMC CRA are described below.

a. Inter-species Extrapo l ation Factor in the revised NMC CRA

The rat provides the basis for the RPFs and PoDs in the cumulative risk

assessment for the N M Cs. As such, a consideration of inter-species extrapolation is neces sary (i.e., animal to human). EPA typically applies a 10X

factor to account for di fferences in animals and humans

. In the revised NMC CRA, the Agency has retained the 10X in ter-species factor for those seven

NMCs with no reliable human cholinestera se data. Oral studies wit h adult human subjects with measur ements of peak RBC ChE inhibition and recovery

data are available for aldic a rb (MRI D 42373001), methomyl (MRID 44721401), and oxamyl (MRID 44912301) and provide the basis for refinement of the interspecies factor for these specific NM Cs. These three human studies were evaluated by the HSRB in April, 2006 (HSRB Final Report, June 2006). The

Board conc luded the human intentional dos ing studies were ethical and scientifically robust and appropriate for us e by the Agency for purposes of risk

assessment. It is note d t hat the carbofuran human oral study was presented to the HSRB in May 2006; however, the Board concluded t hat it was not scientifically robust and not useful for risk assessment (HSRB Final Report, July 2006). The revised NMC CRA does not include ChE data from the carbofuran human study. For the acceptable human studies, the RBC ChE da ta were
modeled in a consistent fashion with the rat data to cal c ulate human RBC

BMD 10 

### s and BMDL 10 

### s

. The Agency then used the RBC BMD 10 

### ratios for ra ts and humans for the pesticide-spec ific inter-species factor. A comparison of

RBC Ch E BMD 10 

### s and half-life estimates sugges ts humans are approximately

2-5 times more sensitive than rats with half-life estimates similar between rats and humans (1-2 hours). The oral BMD 10 

### s and BMDL 10 

### s generated from the rat and human ChE data for aldicarb, met homyl, and oxam yl along with the corresponding inter-species factors are provided below in Table I.B-9.

Section I.B – Page 49 of 277

### Table I.B-9. Inter-species uncertain ty factors and corresponding rat and human

BMD 10 

### s and BMDL 10 

### s

Chemi c al Rat Huma n Interspe c ie s UF Brain RBC RBC

BMD 10 (mg/k g) BMDL 10 (mg/k g ) 1⁄2

life ( h rs ) BMD 10 (mg/k g ) BMDL 10 (mg/k g ) 1⁄2

life ( h rs ) BMD 10 (mg/k g ) BMDL 10 (mg/k g ) 1⁄2

life ( h rs )

Aldic a rb F=0.0 48

M=0.05 6 F=0.0 35

M=0.03 5 1.5 0.031 0.020 1.1 0.016 0.013 1.7 2X

Methomyl 0.486 0.331 1.0 0.204 0.112 0.8 0.040 0.028 1.6 5X

Oxamyl F=0.1 45

M=0.18 5 F=0.1 11

M=0.14 3 0.9 0.278 0.158 0.8 0.083 0.068 2.4 3X

BMD estimates are prese n ted as a sing le esti mate w h e n the r e are no d i fferences b e t w e e n sexes. Huma n RBC d a ta obtai ne d from MRID 423 7 300 1 (ald icar b) , MRID 44721 4 01 (methom yl ), MRID 4491 23 01 (o xam y l )

. Rat brai n an d RBC ChE d a ta obtain ed for al dicar b from MRIDs 434 42 30 2, 4344 23 05, 4 382 96 01, 43 82 960 2,

450 68 601; for methom yl from MRIDs 4447 2 001, 44 48 75 01

, 46646 40 1, Pa dill a et al. 20 07 ; and for oxam yl from

MRIDs 442 544 01, 444 72 00 1, Padi lla et al. 2 007.



### b. FQPA Safet y Factor

i. Backgrou nd

The FQPA (1996) instructs EPA, in ma king its “reasonable certainty of no harm” finding, that in “the ca se of threshold effects, an additional tenfold margin of safet y for the pesticide chemical residue and other sources of exposure shall be applied for infants and children to take into account potential pre- and post-natal toxicit y an d comp leteness of data w i th respect to exposure and toxicity to infants and children .” Section 408 (b)(2)(C) further states that “the Administrator may use a diffe rent margin of safe ty for the pesticide c hemical res i due only if, on the bas is of reliable data, such margin will

be safe for infants and children.”

The FQPA requires that the Agency consi der issues related to toxic i ty and

exposure. The text contained in this c hapter only considers potential sens itivit y of infants and children with respect to to xicit y. The risk characterization chapter (I.G) contains a more complete discussion of issues related to exposures from food, water, and in/around the home that could contribute to increased exposure to infants and children relati ve to adults. However, the Agency believes that there are quality data and sci entifically supportable methods to account for specific exposure and behavioral patterns of children. Because
characteristics of children are dire ctly accounted for in the expos ure assessment and the A gency’s methods are not expected to underestimate exposure to NMCs, evaluating the potential for increased tox i city of juveniles is the key component in determining the m agnitude of the FQPA factors in the

revised NMC CRA.

Section I.B – Page 50 of 277

### As described in detail in OPP’ s cumulative risk assessment guidance, determination of relative to xic pot ency should be calculated using a uniform basis of comparison, by using, to the extent possible, a common response derived from the comparable measurement methodology , species, and sex for all the exp o sure routes of interest (USEPA 2 001a, 2002 a). For the NMCs, estimates of relative potency are required for 10 pesticides. Toxic o logy studies in the adult rat provide the most extensive cholinesterase activity data for all routes, compartments, and both se xes and as a result provide the basis
for the RPFs and PoDs in the NMC CRA. Since adult rat data have been used

to derive the RPFs an d PoDs, EPA has retained the 1 0 X FQPA safety factor

unless reliable data are available addressi ng the sensitivity of the young such

that EPA can determine that a different sa fety factor value is protective of

infants and children. Consistent with the mode of action for NMCs (i.e.,

neurotoxicit y mediated through the inhibiti on of AChE via carbamylation of the active site), the comparative cholines terase assays in juvenile and adults provide the most relevant data for evaluat ing potential sensitiv ity to infants and

children to NMCs.

The Agenc y has compared the sensit ivity of NOAELs (No-ObservableAdverse- Effect-Level), LOAELs (Lowes t-Observable-Adverse-Effect-Level), and BMDs from developmental neurotoxic ity studies (DNTs) and comparative

cholinesterase studies for OPs as well as NMCs. The Agency has three developmental neurotoxicity studies fo r the NMCs (aldicarb, carbaryl and carbofuran). For every OP and NMC evaluated, the comparative cholinesterase assays (CCA) provide a more sensitive (i.e., lower) endpoint than the respective DNT. In the case of NMCs, the CCA studies are 10-100

fold more sensitive than the DNT studies

. Thus, use of AChE inhibition as the endpoint for evaluating the FQP A 10X safety factor is expected to be protective

of functional and behavioral effects.

The Agenc y has focused its evaluation of the FQPA 10X safety factor on post-natal ex posure to juvenile rats. In a detailed analysis provided in the OP CRA (USEPA 2006), the Agency showed that following in utero ex posure to OPs, dams exhibit larger am ounts of ChE inhibition compared to fetuses. In other words, protecting against inhibi tion in the pregnant dams is believ ed to protect against pup AChE inhibition in utero

. In contras t to this in utero exposure, pups have been shown to be more sensitive than adults in post-natal

studies. Thus, data from post-natal expos ur es in juvenile and adult rats provide

the most robust toxicit y data for dete rmining the magnitude of the FQPA safety

factor for the NMC CRA. The CCA studies provide sens itive and reliable results, and have been identified for use in the cumulative risk assessment as the most appropriate studi es for developing the chemical-specific factor to address the potential susceptibility of inf ants and childr e n to the effects of NMC

exposure. Comparativ e ChE data are available and can be used to derive a

chemical-s pecific factor for use in the cumu lative risk assessm ent to reflect th e

Section I.B – Page 51 of 277

### differential sensitivity of children and infants compared to adults. For those NMCs without such data, the FQ PA 10X safety factor is retained.

As described in detail below, the Agency has used a dose response modeling approach for evaluat ing quantitatively the rela tive sensitivity between juvenile and adult rats. In this approach, a BMD was calculated for juvenile and

adult brain ChE data. The ratio of the ju venile and adult BMDs from the specific CCA study was calculated—this ratio has been used mathematically as the data-derived, chemical-specific FQPA safety factor. This approach is similar to
that used in the OP C R A but different from (although not inconsistent with) approaches used in the single chemical agg regate risk assessments. In single chemical, aggregate risk assessm ents, the mathematical calculations are more simple and straightforward as only one acti ve ingredient is included. As such, in single chemical risk assessments, when availa ble, data from young or juvenile animals can be (and have been) us ed directly as the PoD. When the data from the young are used directly in deriving a PoD and the PoD is

established based on the most sensitive e ffects, the FQPA safety factor can b e reduced or removed so long as ther e are no residual concerns regarding potential pr e- and post-natal toxicity or concerns regarding the completeness of the toxicity or exposur e databases. In the revised NMC CRA, the data-derived FQPA safety factor is used to adjust the che m ical specific RPF to account for the potential increased s ensitivit y of the young.

The Agenc y has relied primarily on CCA studies in juvenile and adult animals to evaluate the potential sensitiv ity of young animals to cholinesterase

inhibition. Brain cholinesterase inhibiti on is the focus of this analysis as brain

cholinesterase inhibition has been sele cted as the endpoint for derivation for

RPFs and PoDs in the NMC CRA. For each indiv i dual NMC, the magnitude of the FQPA 10X safety factor was bas ed on the ChE dose-response data comparing relative sensitivity of adul t and juvenile animals. The Agency has also evaluated the recovery data in t he young to evaluate the extent to which the young recover from NMC inhibition in comparison to adults (e.g., faster, slower, or similar to adults). If the Agency were to evaluate NMC exposure at shorter intervals than 24 hours (Chapter C), then the Agency would need to account for the half-life to recovery in young animals. The Agenc y has four CCA studies generated by registrants: ca rbofuran, formetanate, methomyl, and oxamyl. In addition, NHEERL has prov ided comparative sensitivit y data for

aldic a rb and carbaryl. The brain BMD 10 

### estimates for PND 11 pups span an order of magnitude an d are generally 2-3 times lower than adult rats. The halflife estimates for brain inhibition in PND11 r a ts range from approximately 30 minutes to almost 10 hours compared to 1 to 4 hours in adults. Table I.B-10 displays the BMD 10 

### values of both juvenile (PND11 or PND17) and adult rats specifically from the av ailable CCA studies. It is noted that the adult BMD 10 

### estimates from the CC A study may be different than the more robust BMD 10 

### estimate based on the combined adult data. BMDL 10 

### and half-life values for pups are provided for information purposes only.

Section I.B – Page 52 of 277

### Table I.B-10. Dose-response and recovery hal f-life estimates in juvenile and adult rats from comparative cholinesterase studies

PND1 1 Brain Adult Brain

Chemi c al BMD 10 (mg/k g) BMDL 10 (mg/k g) Half-Life (hr s .) Adult BMD (mg/k g)

Aldic a rb 1 0.017 0.016 NA 2 0.033

Carba r yl 1.459 1.135 5.4 3 2.627

Carbofu r an 0.039 0.030 3.0 0.109

Formet anate 0.188 0.098 9.5 0.382

Methomyl 0.104 0.070 0.4 0.317

Oxamyl 0.051 0.025 1.5 0.177.

1 T he juven ile r a t data for ald i carb is bas ed o n a pub lis hed a c ute oral n eur o t oxic it y stu d y in PND17 rats (MRID450 68 60 1)), 2 T i me-course data in j u ve nile r a ts not availa bl e for ald i c a rb; 3 T he recovery half-lif e estimate for

carbar yl is bas ed on N H EER L data from PN D17 p ups.



### The resulting FQPA safety fac t or fo r each N M C is the ratio of the BMD 10 

### for adult/pup. Those NMCs without comparativ e cholinesterase data retain the 10X FQPA safety fac t or. Since the FQPA sa fety factor i s specific to protecting children and developed from juvenile rat data, it may be applied in the NMC CRA to scenarios spec ific to children’s expos ure. The FQPA safety factor is therefore not applied to RPFs for adults. The resulting chemical s pecific FQPA safety fac t ors for these six NMCs are listed in Table I.B-11.

Table I.B-11. FQPA s a fety fa ctors for the revised NMC CRA 1

Chemi c al FQPA Safety Fac t or

Aldica rb 2.0

Carba r yl 1.8

Carbofu r an 2.75

Formet anate 2.03

Methomyl 3.05

Methiocarb 10

Oxamyl 3.48

Pirimica rb 10

Propoxu r 10

Thiodi ca rb 10

1 T hose NMCs w i t h o u t juve nil e pup d a ta reta in the 10 X F Q PA safet y facto r



### Section I.B – Page 53 of 277

## 8. Inc o rpora t ion of Uncertai n ty /Extra pol a t ion Fact ors a nd t h e Tar g et M a rgin of E x posur e In general, when performing a cumulative risk assessment using a RPF approach, like that done for the NMCs, uncertainty and extrapolation factors can be incorporated into the risk asse ssment in two different ways: 1) adjustment of the chemical-s pecific RPF or 2) incorp oration into the target Margin of Expos ure. Both ways are used in the NMC CRA.

Adjustment of the Chemical Spec ific RPF : In cases where the uncertainty or extrapolation factor varies among the chemicals, the chemical- specific RPF is adjusted (i.e., multiplied) by the uncer tainty or extrapolation factor. In the case of the NMCs, the FQPA and in ter-species factors vary among the

chemicals. As such, the Agency has multiplied the FQPA safety and interspecies factors by the RPFs to generate adjusted RPFs for each NMC ( Table I.B-11).

Incorporation into the Target Margin of Exposure (MOE): There may be assessments where the magnitude of an unc ertainty or extrapolation factor is

the same for each member of the common mechanism group. In these assessments, the target MOE identified addr esses the total magnitude of the uncertainty or extrapolation factor(s). Th is is the situation for the intra-species

factor in the NMC CRA wher e the standard 10-fold factor has been applied. As discussed above, both the FQP A safety and inter-species extrapolation factors are accounted for in the adj usted RPFs for the NMC CRA. As such , the target MOE for the NMC CRA is 10 accounting for intra-species variability.

Table I.B-12. Adjusted oral relative pot ency factors for children and adults based on inter-species and FQPA specific factors

Chemi c al Oral RP F Inter-s p ec ies

Facto r FQPA Facto r Children Only Adjusted

RPF
Children Adjusted RP F

Adult

Aldica rb 4 2 2 16 8

Aldica rb sulfo ne ( A ld o xyc ar b) 3.44 2 2 13.8 6.9

Aldica rb sulfo x ide 3.68 2 2 14.7 7.4

Carba r yl 0.15 10 1.8 2.7 1.5

Carbofu r an 2.4 10 2.75 66 24 5-hyd r oxyca r bofura n 2.4 10 2.75 66 24

Formet anate HCL 2.18 10 2.03 44 22

Methiocarb 0.18 10 10 18 1.8 Methomyl 0.67 5 3.05 10 3.3 Oxamyl 1 3 3.48 10 3

Pirimica rb 0.02 10 10 2 0.2 Propoxu r 0.11 10 10 11 1.1

Thiodi ca rb 0.89 10 10 89 8.9



### Section I.B – Page 54 of 277

### Table I.B-13. Adjusted dermal relati ve potency factors for children and adults based on inter-species and FQPA specific factors

Chemi c al

Carba r yl Der m al

RPF

0.71 Inter-s p ec ies

Facto r

10 FQPA

Facto r Children Only

1.8 Adjusted RP F
Children

13 Adjusted RP F

Adults

7.1

Methiocarb 0.09 10 10 9 0.9 Oxamyl 1.00 3 3.48 10 3 Propoxu r 0.03 10 10 3 0.3



### Table I.B-14. Adjusted inhalation relative potency factors for children and adults based on inter-species and FQPA spec ific factors

Chemi c al Inhalation

RPF Interspe c ie s Facto r FQPA Facto r Children Only Adjusted RP F
Children Adjusted RP F

Adults

Carba r yl 0.511 10 1.8 9 5.1

Methiocarb 0.619 10 10 62 6.2 Oxamyl 1.00 3 3.48 10 3 Propoxu r 0.178 10 10 18 1.8



## 9. Dose Additivity

A key assumption of the RPF method is dose additivity. While there are a
few interaction studies of N- methyl carbamate and OP pesticides in the literature (e.g., Gupta and Dettbarn, 1993; Takahashi et al ., 1987), no studies conducted using mixtures of more than two N- methyl carbamates and which use low dose levels (i.e., t hat do not produce lethality or profound toxicity) have

been ide n tified. To fill this data need, NHEERL scientis ts have conducted a mixture study using seven N- methyl carbamates (carbaryl, carbofuran, formetanate HCl, methiocarb, methomyl, oxamyl, and propoxur) (Padilla et a l

., 2006). In the mixture study, a dose-addi tive experimental design was used and the proportion of the car bamates in the mixture was based on their potency using the individual-chemical benchma rk dose values as the point of comparison. Five different dosage levels of the mixture were given, predicted to produce <5%, 10%, 25%, 45% or 60% br ain ChE inhibition. Each NMC was given alone at a previously tested dosage to confirm the original dose-response data (7 single-chemic al experim ental group s). The effects on mo tor activity and
RBC and brain ChE were measured. As can be seen from Figure I.B-3 below, increasing dosages of the mixture produced increasi ng decrements in brain
ChE activit y. Moreover, the dose-additive model predicted t he degree of ChE
inhibition within the 95% confidence limit s of each predicted value. Additivity
was also measured in the RBC ChE and mo tor activity evaluations (manuscript in preparation).

Figure I.B-3. Plot of brain ChE meas ured in a seven chemical mixt ure of N -methyl

carbamates

Section I.B – Page 55 of 277Cholinesterase activity(%control) 120

110

100

90

80

70

60

50

40

30

20 

### E x pect ed V al ues ( + 95% conf i d ence l i m i t s)

A

c u t a l D a ta ( + sem )



# D o s e

- A ddi t i v e D e s i gn

Equi pot ent M i xt ur e Co n t r o l

0 2 4 6 8 1 0 1 2 1 4 1 6



### D o se of M i x t ur e ( m g / kg)



## 10. Summary

This chapt er has described the applic ation of the RPF method in the

revised cumulative hazard assessment for the NMCs. Whole brain ChE is a sensitive, health protective endpoint repr esenting the target tissue. The brain data provide the most appropr iate dataset for extrapolating cumulative risk to

this common mechanis m group. Potency for the NMCs varies over several orders of magnitude. Analys is of recove ry data for brain ChE in adults suggests that half-life time to recovery ranges from less than an hour up to 4 hours is chemical dependant, and for some chem icals, is dose dependant

. For some NMCs, recovery of ChE activity in pups may be longer. Overall, the analysis of recovery data supports the Agency’s assu mption that at the low concentrations found in the environment, the appropriate duration of expos ure for the NMC

cumulative risk assessment is acute ex posur e. Oxamyl has been selected as

the index chemical based on the avail ability of high quality dose response data for the oral, dermal, and inhalation routes. B M DL 10 

### estimates of brain ChE from oral, dermal, and inhalation studies with oxamyl represent the PoDs for the

NMCs cumulative risk assessment. BMD 10 

### estimates of brain ChE from oral, dermal, and inhalation studies were us ed to develop RPFs for the NMCs.

The Agenc y has, when availa ble, utilized eth i cally and scientifica lly valid
human studies for refi nement of the inter-s pecies factor as well as comparative sensitivity rat data for refinement of the FQP A safety fa ctor. These uncertainty

Section I.B – Page 56 of 277

### factors apply to the oral, dermal, and inhalation RPFs, which result in adjusted RPFs for individual NMCs, spec ifically for adults and children. In instances where there is no human study or comparative sensitivity data for particular NMCs, the inter-species factor and/or FQPA safety factor remain(s) unchanged (i.e., 10x) and is/are used to adjust the RPF accordingly. As a result, the target MOE for the NMC CRA is 10 which ac counts for the intraspecies 10x factor which is the same fo r all of the NMCs in the revised NMC

CRA.

Section I.B – Page 57 of 277

# C. Cumulative Risk from Pesticides in Foods

This chapt er discusses the cumulative risk associated with the food exposure pathway. As with previ ous cumulative assessments released by OPP, the data for this pathw ay are developed from two primary sources: dietary consumption data collected by USDA’s Continuing Survey of Food Intakes by Indivi duals (CSFII) and pesticide residue monitoring data collect ed by the USDA Pestic ide Data Program (PDP).

As described further i n Chapter I.B of this document, oxamyl serv es as the index chemical and the residue va lues for the other NMC pesticides were converted to oxamyl equival ents using the RPF approach. The exposure estimates presented in this chapter, therefor e, are expressed in terms of the index chemical oxamyl.

The purpos e of this chapter is se veral-fold: (i ) to describe and characterize the food consumption, pesticide residue, and other data sources used to develop the cumula tive risk assessment for the food pathway; (ii) to describe how and the extent to which the PDP pesticide

residue dat a on approximately 80 f ood commodities, including those most commonly consumed by children, was extended/trans lated to other foods in order to produce a more co mplete data set on pesticide residues that more nearly appro x imated th e total diet; (iii) to describe the methods used to convert pesticide residue dat a from the PDP dat a program i n to index-chemical (i.e., oxamyl) eq uivalents in order to conduct a cumulative assessment; (iv) to describe how this cumulative residue data

set was combined wit h USDA’s food consumption data and food processing data to produce a distri bution of estimated cumulative exposures to the NMC group of pestic ides; and (v) to provide infor m ation with respect to those crop-commodity combinations whic h contribute to exposure at the upper-ends of the ex posure distribution. This chapter

does not describe the extensive sens itivity analyses that were performed nor does it provide inf o rmation on or discuss risk characterization. These are important -- indeed critical -- components of any risk assessment and are presented in C hapter G of this document.



## 1. Food Consumption Data

Data on food consumption are a necessary component for estimating pesticide expos ure through t he diet. For the revised NMC CRA, food consumption data were obtained from the USDA CSFII 1994­ 96/1998 (USDA, 2007). The CSFII 1998 incorporated a supplem ental children’s survey conducted in 1998 in whic h the food consumption of an additional 5,559 childr en (birth through 9 yea r s old) were surveyed. The
CSFII 1994-96/1998 is a nationally repr esentative stratifi ed, multi-stage area probability sample with a target populat ion consisting of nonSection I.C – Page 58 of 277

### institutionalized individuals in all 50 states and Washington, DC. CSFII

1994-96/1998 data are deriv ed from informati on provided by 20,607

indiv i duals who participated in the su rvey. Individuals who took part in the survey were asked to provide two non-consecutive days of dietary data through the administration of in -person, 24-hour dietary recalls spaced 3–10 days apart. The USDA CS FII consumption survey data are

included as an integral component of the DEEM-FCIDTM software used

to conduct this cumulative risk assessment. 1



### As in previous cumula tive risk assessments produced b y EPA, separate assessments were conduct ed on the various sub-populations as represented in the CSFII 1994- 96/1998. The current assessment reports on the U.S. general popula tion and the following standard age

groups:

‘ Infants less than 1 year old

‘ Children 1-2 years old

‘ Children 3-5 years old

‘ Children 6-12 years old

‘ Youths 13-19 years old

‘ Adults 20-49 years old

‘ Adults 50+ years old

‘ Females 13-49 years old

1 It is impo rta n t to note th at the CSFII food di ary i n formatio n is expre s sed i n terms of food a s c o ns ume d (e.g., pizza, apple pie, lasa gna, etc.) while OPP’s

asse ssm ents are currentl y condu cted in terms of food com m odities (e.g., tomatoes

, milk, wh eat flour, be ef, apples, etc. ). OPP use s st anda rd reci p e s to convert foods “as

con s um ed ” as rep orted in CSFII diaries into food commoditie s for use in OPP dietary

risk asse ssm ents. The reci pe inform atio n use d to bre ak do wn the f ood s wa s de veloped jointly by EPA and USDA and i s one module in the EPA’s F o od Commodity Intake (FCI D) data b ase which i s available upo n reque st. T hus, whil e thi s NM C CRA refers t o “food” consumption from CSFII, the Agency’ s cal c ul ations are performed in functionally equivalent “f ood com m od ity” term s fro m FCI D

. While often di stinct, “foo d” a nd “fo od

comm odity” will be used i n terchange ably in this document.



### Section I.C – Page 59 of 277

## 2. Pesticide Residue Data

There are multiple sources of pesticide residue data (on food)

available to EPA. However the sa mpling des ign and ext ent, intent, and representativeness of these various s ources of pesticide residue data

differ. USDA PDP provides the most comprehensive s a mpling design,

and the most extensiv e and intensive sampling procedures for pesticide

residues of the various data sources av aila b l e to EPA. Additio nally , the intent of PDP’s sampling design is to provide statistically representative samples of food commodities eaten by the U.S. population specific ally for the purpose of performing dietary risk assessments for pesticides. As such, PDP serves as the only source of pesti cide residue data used in quantitative manner in the dietary portion of this revised NMC CRA. The other sources of pesticide residue data are used in a qualitative manner to ensure that EPA is not signific antly under estimating pesticide exposure through food. The vari ous sources of pesticide residue data

are discussed below.

a. USDA-PDP

As with the preliminar y NMC CRA, this revised NMC CRA als o relies exclusively on the PDP prog ram for residue data that are used quantitatively. The USDA PDP is a cooperative effort by federal and state agencies to obtain statistically -reliable data on pesticide residues in food (USDA, 2007a). Federal funds suppor t sampling, testing, and datareporting activities conducted by parti cipatin g states. The participating states include California, Colorado , Florida, Maryland, Michigan, Minnesota, Montana, New York, Oh io, Texa s, Washington, and

Wisconsin. In addition, 13 of their neighboring states are in the direct
distribution networks of the PDP participating stat es. Together, the se states represent over 50% of the nation’s population and all 4 cens us regions of the U.S. These states also represent the major commercial production areas of fruit and vegetables in the U.S.

The PDP s a mple collection procedures are specifically designed to produce dietary exposure estimates t hat cl osely appr oximate real world

exposures. Samples are collected by USDA at terminal markets and warehouses immediately before these commodities are shipped to supermarkets and other retail establis hments. Both domestically produced and imported foods are subject to sampling. Sampling is

conducted in accordance with a stat istically-based plan designed and put in place by USDA’s Na tional Agric ult ural Statistics Service (NASS) to be representative of the U. S. food supply. Samples are prepared by the analytical laboratory as if for consum ption (i.e., they are washed, peeled, and/or cored, as appropriate) and thus are more reflective of actual exposures than data typically av ailable fr om field trials or FDA monitoring

Section I.C – Page 60 of 277

### programs. Thus, measur ements simulate as closely as possible dinnerplate expos ures to consumers.

The program focuses on high-cons umption foods for children and reflects foods typically available throughout the year. A complete description of the PDP progr am and all data through 2005 2 

### are available online (USDA, 2007b)

. The PDP dat a are available in downloadable
electronic format from this site and can be easily transferred, imported, analyzed, and summarized. Appendix II.C.1 l i sts all of th e FCID food

commodities for which PDP residue dat a were used to estimate dietary exposure to NMC pesticides. The PDP residue data on NMC pesticides

included in the revised NMC CRA are summarized in Appendix II.C.2.

b. NMC Market Basket Survey

The Carbamate Market Basket Survey Task Force sponsored a market basket survey (MBS) of NMC pesticide residues and their

toxico log i c a lly releva n t metabolites in single-serving samples of fresh fruits and vegetables c o llected in 1999-2000 (Carringer, 2000). The NMC pesticides analy zed in the MBS were aldic a rb, carbaryl, carbofuran, methomyl, oxamyl, and th iodicar b

. The food commodities sampled in the MBS were apple, banana, broccoli, grape, head lettuce, orange, peach, and tomato. However, not al l NMC pesticides were analyzed in each type of commodity. For instance, aldic a rb was analyzed in only one commodity, or anges; and carbaryl was the only NMC analy zed in all eight food commodi ties. Of the top ten pesticidecommodity combinations contributing to the exp o sure of young children as determined by this NMC CRA, only two (for children 1-2) and three (for children 3-5) of these signific ant contributors were sampled in the
MBS. Comparing the residue dat a fr om the PDP progr am with the MBS, the top pesticide-commodity contri butors common to both MBS and PDP have similar ranges of pesticide c oncentrations and frequencies of

detects. Additionally, PDP has c o lle cted more recent data on the same commodities sampled in the MBS. Fi nally, all of the pesticide-commodity

combinatio ns analyze d in the MBS are data for which EPA already has adequate PDP data for the purpose of dietary risk assessment. Consequently, the MBS dat a have not been used directly in the revised
NMC CRA.

c. FDA-CF SAN Pesticide R esidue Monitoring Program

2 Although the 2006 PDP d a ta are not cu rre ntly available for do wnl oad from the

PDP web s ite, the new data are expe cted to be publish ed and p ubli c ly available in 2007.

Due to an a c t ive interage n c y MOU b e tween US DA PDP and USE PA OPP, the 2006

PDP data on NMC p esti c id es were give n prio rity in PDP’s QA/Q C pro c e ss a nd relea s ed early to OPP upon requ est.



### Section I.C – Page 61 of 277

### The Food and Drug Administration ’s (FDA’s) Center for Food Safety and Applied Nutrition (CF SAN) Pesticide Residue Monitoring Program is design ed p r imarily for enforcement of EPA pesticide tolerances on imported foods and domes tic foods shipped in interstate commerce (USFDA, 2007a). In this monitoring program, domestic samples are generally collected close to the point of production in the distribution system. Import samples ar e collected at the point of entry

into U.S. commerce. The emphasis in sample collectio n is on the agricultural commodity which is analyzed as the unwashed, whole (unpeeled), raw commodity. Process ed foods are also included in the

program. Because the emphasis of this progr am is not on dietary exposure, it is being used in the cu rrent assessment mo stly as a se miquantitative check on the potential for residues and as support for data

from other sources. The program has extens iv e data available on eggs and fish, which support t he judgment that NMC residues are negligible
on these foods as consumed. Thus, the FDA data were used in a qualitative manner in this revised NM C CRA to support the decision to assign residue values of zero to the NMC residues on eggs and fish effectively removing these food co mmodities from the assessment. Appendix II.C.1 indicates the foods for which such decisions were supported by this program.

d. FDA-CF SAN Total Diet Stud y

The FDA’s CFSAN Total Diet Study (TDS) has provided data on

concentrations of contaminants in a wide range of foods for about 46 years (USFDA, 2007b)

. Foods are purchased at retail (from grocery

stores and fast-food rest aurants), generally 4 time s a year, prepared and cooked for consumption, and analyzed by highly sensitiv e multi-residue

methods. Between 1991 and 2004, there have been 48 market baskets

collected. For each market basket, three samples of approximately 280

different foods are collected and co mposited, and the composite samples are analyz ed for – among other things – NMC pesticide residues. A disadvantage of these data is that only one composite sample of each food is analyzed in eac h market basket. For this reason, these data have been used primarily as semi- quantitative support for judgments on

residues in foods.

In previous cumulative risk assessments performed by EPA, conservative estimates of pestici de residue values for some highly

consumed foods such as beef were based on the TDS data. However, beef, poultry, and pork are now sampled under the PDP program, being most recently sampled in 2002, 2005, and 2006, respectively. PDP data

on these commodities serve as the primary source of residues of pesticides used by EPA in dietar y risk assessments, replacing the corresponding FDA T D S data previous ly used. Both programs have

Section I.C – Page 62 of 277

### found very few detects of low c oncentration NMC residues in these commodities supporting the previous unders t anding that beef, poultry, and pork are negligible contributors to dietary exposures to the NMC pesticides. The TDS data also inc l udes samples of fish and eggs. The analytical results for these samp les confirm those of the FDA

surveillanc e monitoring program, nam ely, that fish and eggs contain negligible c oncentrations of NMC pesticides.



## 3. NMC Pesticides Included in th e F ood Risk Assessmen t

The N -methyl carbamate Cumulative Assessment Group (CAG) consists of 10 NMC pesticides. T hese 10 pesticides (along with their ChE—inhibiting metabolites and thei r associated RPFs) are listed in

Table I.C-1.

Tab l e I.C-1. N -methyl Carbamates a 

### and RPFs

Chemi c al RPF (O ral )

Car b a r y l b 0.15

Aldica rb 4

Aldic a r b sulfone ( A ldoxycarb) 3.44

Aldica rb sulfo x ide 3.68

Oxamyl (Index Ch emi c al) b 1

Formet anate hydro c hlo r ide 2.18

Methomyl 0.67

Carbofu r an 2.4
3- H y dr oxycarbofur an

Propoxu r 0.11

Methiocarb 0.18

Thiodi ca rb 0.89

Pirimica rb 0.02

a See “Carba mat e Cumualtive Assessment Group: Availabil i ty ” (US EPA, 2004a).

b The car bar y l m etabolite, 1-naph thol and the o x a m y l metabolite, o x am y l

oxime w e re anal y z e d b y PDP, bu t not included in the dietar y assessment

since these com pounds are n o t kno w n to inhibit a c et y l cholinesterase.



### Section I.C – Page 63 of 277

### The above listed pesticides and me tabolites were used as a “starting point” in determining which pesticides (and associated

metabolites ) would be considered in the food pathway of the NMC CRA. During the period sinc e the issuance of the prelimi nary NMC CRA in August 2005, the Agency identified, and in some cases imposed, risk
reduction measures on some of the major contributors to carbamate

cumulative risk. The risk estimates presented in this revised NMC CRA reflect the risk mitigation measures identified for or taken on indiv i dual carbamates since FQPA was signed into law in Augus t 1996. These mitigation measures generally reflect determinations of risk based on the single-chemical risk assessments. For all of the risk mi tigation measures
that are reflected in this doc ument, EPA has either commenced the processes necessary to implement it s selected risk mitigation or intends to commence the appropriate processes in the near future. Having already det ermined that the identifi ed risk mitigations are warranted for

the individu al chemicals regardle ss of the cumulative assessment, EPA has chosen to reflect that mitigati on in this assessment. Consequently pesticide-cr op combinations that hav e been (or are being) cancelled or not considered eligible for reregistration 3 

### , do not have food uses, or have had signific ant and substant ial label modific a tions were excluded from

the 1994 to 2006 PDP residue dat a. As a practical matter, EPA determined that it would serve no pur pose to include suc h uses in the

cumulative assessment. Other than by adding a new is sue that mi ght delay action, adding these uses would not likely have any impact on the timing or substance of any cancellation decision relating to such u ses. And given that the purpose of toler ance reassessment is to determine whether regulatory action should be in itiated to modify or revoke tolerances that the Agency finds do not meet the safety standard of section 408, there seems to be little value in including us es in the assessment that will disappear regardle ss of their impact on cumulative risk, The critical issue for dete rmi ning whether regulatory action will

have to be initiated un der section 408 is whe t her the uses that will

remain result in unacc eptable die t ar y risk. EPA recog n izes, however, that to the extent that any risk mi tigation measures are not subsequently implement ed as envis ioned in this revised NMC CRA, the cumulative assessment will have to be revised as necessary.

Table in Appendix II.C.2 provides a summary of the PDP samples including the total number of labor atory analyses completed for the NMC pesticides and metabolites on each food commodity in the database.

3 Although re sidue s re sultin g from uses t hat no longe r exist were removed from

this revise d NMC CRA, EP A contin ue s t o in cor porate violative re sid ues in its cum u lative

risk a s se ssm ents to refle c t potential exp osu re to resi due not con s i s tent with reg i stere d

label uses. The s e viol ative re sidu e s rep r e s ent pesti cide co nce n tration s in PDP sampl es eith er above an alrea dy existing tolera nce level (po ssi bl y due to agri c ultural

pra c tice s not con s i s tent wit h label inst ru ctio n s ) o r in a commo dity for whi c h n o tolera nce has b een e s t ablished.



### Section I.C – Page 64 of 277

### PDP analytical data for the above pesticides (and their associated

metabolites 4 

### , where applicable) are being us ed in the NMC CRA assessment for the food pathway. A dditional details regarding thes e pesticides and their inclusion in t he assessment NMC CRA food pathway assessment are provided below.

Aldicarb: Aldic a rb or its sulfox ide and/or sulfone metabolites
have been detected in more than 1% of the PDP samples of the following c o mmodities : potato, sw eet potato, grapefruit, and orange juice. It has been detected in les s than 1% of the samples of the following c o mmodities : green bean, cantaloupe, grape, orange, sweet corn, and poultry liver. PDP reports each of these compounds (all of which are acetylcholinesterase inhi bitors) in terms of the parent or specific metabolite per se

. The metabolites have higher molecular weights than the parent, but the R P F for aldicarb is based on the concentration of the parent chemical

. Therefore, the metabolite RPFs were adjusted to account for the higher molecular weig ht of the

metabolites compared to the parent chemical. The conversion of the metabolite concentrations to parent c oncentrations is important since the majority of detectable aldic a rb re sidues found in PDP c o mmodities are the metabolites.

Carbar y l : Carbaryl has been detected in more than 1% of the PDP samples of the following commodi ties: apple, apple juice, apple sauce, peach, strawberry, pear, pear juice, grape, grape juice, green bean, orange, nectarine, cantaloupe, carrot, celery, cherry, cranberry, cucumbers, eggplant, grapefruit, orange, orange juice, pineapple, plum, prune, pork fat, pork meat, chicken mea t, raisin, rice, sp inach, sweet bell pepper, sweet pea, tomato (canned) , and asparagus. It has been
detected in less than 1% of the sa mples of the follo wing commodities: banana, broccoli, cauliflower, heavy cr eam, lettuce, milk, sweet potato, tomato (fresh), watermelon, wheat, and winter squash. Significant label changes, primarily lengthened pre- harvest intervals and reduced application rates, occurred for apple, peach, and strawberry in the late 1990’s (Lantz and Young, 2006). These label changes have resulted in

lower resid ue levels fo r these thr ee crops making only the 2000 and later

PDP data relevant. Thus, for the revised NMC CRA, only PDP data for periods dur ing 2000-2006 were used for apple, peach, and strawberry; whereas, the full PDP dataset (1 994-2006) was used for all other commodities with carbaryl detects.

Carbofuran: Carbofuran has been detected in more than 1% of the PDP samples of the following comm odities: cucumb er , kale greens, sweet bell pepper, and wheat. It has been detected in less than 1% of the samples of the following comm odities: cantaloupe, grape, grape

4 The carbaryl metab o lite, 1-na phthol a nd the oxam yl metabolite, oxamyl oxim e,

were analyzed by P D P, but not in cluded in th e dieta r y a s sessment si n c e th ese

comp oun ds a r e not known to inhibit ACh E

.



### Section I.C – Page 65 of 277

### juice, green bean, spinach, watermelos, and winter squash. EPA has

determined that all domestic carb ofuran uses are ineligible for reregistration and this chemical is undergoing cancellation (USEPA, 2006e). Thus, the revi sed NMC CRA inc l udes only carbofuran uses on the following imported commodities for which tolerances are being

retained: bananas, rice, sugarcane, and coffee 5



### . Formetanate HCl: Formetanate has been detected in more than 1% of the PDP samples of nectarines and pears. It has been detected in less than 1% of the samples of apples and oranges. Formetanate is analyzed by PDP using a single-residue method and not all PDP commodities have been analyzed using this method. The laboratory performing this analysis ceased partici pating in the PD P program after

2001. Thus, PDP data are only avai lable for formetanate on orange, pear, nectarine and apple through 2001. Late season uses on oranges, nectarines, and apples were cancelled and field trial dat a conducted by the registrant using only early-seas on applications demonstrate that no detectable residues are expected (U SEPA, 2 006f). Thus, formetanate residues on oranges, nectarines and apples are assumed to be negligible and were not considered in the revised NMC CRA. Only formetanate use on pear has been inclu ded in the assessment, and then only using data from 1997 and 1998.

Methiocarb: Methiocarb has been detected in less than 1% of

the PDP sa mples of cauliflo wer, c herries, cucumbers, and sweet bell

peppers. Methiocarb has not been detected on any other PDP

commodities.

Methomy l : Methomyl has been detected in more than 1% of the PDP samples of the following commodi ties: apple, asparagus, broccoli, cantaloupe, cauliflower, celery, cu cumbers, eggplant, grape, green bean, kale greens, lettuce, nectarine, peac h, spinach, strawberry, summer squash, sweet bell pepper, and watermelo n. It has been detected in less than 1% of the samples of the following commodi ties: carrot, orange, pear, tomato, and winter squash. PD P data for all crops except grape and strawberry were i n cluded in the revised NMC CRA. A voluntary cancellation request has been received by the Agency for methomyl use on grapes, and the strawberry use was voluntarily canceled by the registrant early in 2007. T hus methomyl residues on grapes and strawberries were not incorporated into the assessment. In an effor t to better reflect NMC residues on foods as eaten, the PDP lettuce samples

were divided into leaf and head wher e information regarding the lettuce variety was available. Separating the lettuce varieties ensured that l e af lettuce residues, whic h tend to be higher than head let t uce residues,

5 Sugarcan e a nd coffee a r e not assume d to contain residue s of carb ofuran d ue to the extensi v e processin g , purificatio n

, and refinem ent to which the com m oditi es are subj ecte d.



### Section I.C – Page 66 of 277

### were not inappropriately assigned to foods that contain head lettuce as an ingredient, as per the FCID recipe files.

Oxam y l : Oxamyl (parent) has been de tected in more than 1% of the PDP samples of the following commodities: cantaloupe, celery, cucumber, eggplant, lettuce, pear, pot ato, summer squash, sweet bell pepper, tomato and watermelon. It has been deteced in less than 1% of the samples of the following comm odities: apples, green bean, orange, spinach, and winter squash. An oxam yl metabolite, oxamyl oxime, also detected in the PDP program, was not considered in the assessment

since it does not inhibit acetylcholinesterase.

Pirimicarb : Pirimicarb has been detected on less than 1% of the PDP samples of peach and sweet bell pepper. Pirimicarb has not been detected on any other PDP commodities.

Propoxur: Propoxur has not been detected on any PDP

commodities.

Thiodicarb : Thiodicarb has been detected on less than 1% of PDP samples of pear. Thiodicarb has not been found on any other PDP

commodities. 6



## 4. Food C o mmoditie s Incl ude d in the Food Ri sk Assessm e nt

The univer se of foods included in the cumulative dietary exposure assessment is defined by the USDA CSFII 1994-96/1998. The CSFII food diary information is ex pressed in terms of food as consum ed (e.g., pizza, apple pie, lasagna, etc.). These foods as reported in CSFII diaries

are converted to food commodities (e.g., tomatoes, milk, wheat flour, beef, apples, etc.) using standard reci pes. The recipe information used to break down the foods was develope d jointly by EPA and USDA and is one module in the EPA’s Food Commodity Intake (FCID) database. Table in Appendix II.C.1 lists all of the FCID food commodities (translated from CSFII f oods) in decreasing order of their relative per capita cons umption by children 1-2 years old and childr en 3-5 years old while table in Appendix II.C.5 con t ai ns a complete listing of the FCID

food commodities and food forms (e.g. “Cooked; Fresh or N/S; Cook Meth N/S”) in the DEEM-FCIDTM so ftware that were included in this

6 Although the PDP analytical method s u s ually convert thiodica rb re sidu es to methomyl, the majority of methomyl re sidue s dete c te d in PDP com modities a r e

assume d to be the re sult of methomyl use ra the r than thiodicarb u s e. The basi s for

this assum p tion is that the numbe r of re gist ered u s e s for thiodica rb is much less than
those fo r methomyl and pe sticid e usage inform atio n in dicate s very low thiodi ca rb use on food crop s for whi c h it is registe r e d

. Since meth o myl resid u e s resulting in th e highe st expo sures a r e fro m food crop s for whi c h thio dica rb is n o t registe r ed, the assumptio n that all PDP methomyl re sid ues a r e du e to methomyl u s e (rathe r tha n thiodicarb u s e) is not expe cted to sig n if icantly und ere s timate dieta r y exposure to

thiodicarb.



### Section I.C – Page 67 of 277

### assessment. This table also in cludes summary information on the

residue dist ributions that were pre pared from the revi sed NMC CRA food

residue dat abase as input for each food form. The actual DEEM-F CIDTM input files and associat ed residue files will be made available on the internet or upon request via CD- ROM for any interested party.

PDP has an extensiv e monitoring program that focuses on food commodities commonly consumed by children and includes a variety of fruits, vegetables, meat/poultry/pork products, dairy products, and grains. In all, 80 food commodities monitored by PDP are included in the revised
NMC CRA. Food processing factor s that refl ect reduction or concentration of NMC pesticides in pr ocessed foods were applied t o food commodities or specific pesticide-co mmodity pairs in the PDP program
to extend these data for use on c ooked and processed food/food forms in the analy sis. Through the 80 foods commodities dir e ctly analy zed by

PDP , the revised NM C CRA acc ounts for approximately 93% of the foods consumed by children 1-2 years of age.

As mentioned previously, the PDP residue data were further

extended to other commodities identif ied as reasonable for translation of pesticide residue data per Agency policy. That is, residue data on commodities which were analyzed by PDP were translated to similar food commodities with registered uses which were not analyzed by PDP. In this way, residues on foods acc ounting for an additional 1% of the per capita cons umption of children 1- 2 years of age were estimated using

these translated PDP data. For example, cant aloupe melon residues are translated to honeydew melon and peac h residues are translated to apricot. Translations were made using HED SOP 99.3 (USEPA, 1999b)

as summarized in Table I.C-2.

Section I.C – Page 68 of 277

### Table I.C-2. Crop Translations for Pesticide Monitoring Data

Comm odity

Analyzed Commodity trans lated to... Comm ents

Potato Subgroup 1

- C

Carrot Subgroup 1

- A or 1-C

Hea d Lettuce Cab bag e, Chi nese ca bba g e Nap a (tight heade d varieti e s),

Brussel s spro uts, radi cchio All have a head morphol og y best

rep r e s ente d by lettuce. All are in Subgroup 5

- A except radi cchio (4 -A).

Broccoli Cauliflower, Chin ese bro c coli,
Chin ese ca bb age bo k choy

, Chin ese must ard, ko hlrabi Broccoli b etter rep r e s ent s these

headi ng, thickly stemme d and/or m o re bran chi ng col e cro ps tha n spin ach

doe s.
Spinach Subgroup 4

- A, Subgroup 5-B and Subg ro u p 4-B (ex c ept

cele ry and fe nnel unl ess a st ron g ca se c an be mad e ) Cele ry and fe nnel typically are exclud ed si nce resi due s m ay be highe r in these cro p s due to the who r led, overlap p ing p e tioles whi c h may retain

spr a y re sidu e s

. Gree n Bean Subgroup s 6-A and 6-B

Soybean Subgroup 6

- C

Tomato or

bell pep per Grou p 8 All are fruiting vegetable s

.

Cu cumb er Subgroup 9

- B All are cu cu rb it vegetables; resid ue s in

melon an d pu mpkin exp ect ed to be

lowe r be cau s e of removal of rind Cantal oupe o r Winter squ ash Subgroup 9

- A and pump k in

Oran ge Grou p 10 Fruit will be p eeled b efore analysi s by

PDP.

Apple or Pea r Grou p 11 All are pome fruits.

Peach Grou p 12, except cherrie s (sweet an d tart) All are ston e fruits.

Grap e Kiwifruit Based o n sim ilar cultu r al p r actices.

Whe a t Grou p 15, except corn, ri ce, or wild ri ce All are small grain crop s or closely

related the r et o,

Milk Meat Metaboli s m study must ind icate that

resi due s in m eat, fat, and meat-byproduct s will l ikely be equal to or lower

than re sidu es in milk. If de rmal u s e is

allowed on b eef cattle, then it must be permitted a n d used o n dairy cattle as

well.



### PDP has not analyzed eggs or fish , but surveillance monitoring
data from FDA include ext ens ive analysis of eggs and fish and indic a te that NMC residues would not be expect ed to occur in signific a nt amounts
on these two foods. C onsequently, residues in the revised NMC CRA

were assumed to be zero. These foods account for about 2% of the per capita cons umption of ch ildren 1- 2 years old.

Section I.C – Page 69 of 277

### PDP has analyzed high fructo se corn syrup and found no NMC residues but has not analyzed any other sugar or syrup sources. The FDA TDS has analyzed refined sugar and maple sugar and found no

NMC residues in 46 market baskets surveys (FDA, 2007b). Knowledge of the highly refined nature of sugar s and syrups supported by the limited

residue dat a mentioned above is the basis for assuming that negligible residues of NMC pesticides occur in sugars and syrups. Therefore,

residues were assumed to be zero for those foods derived from

sugarcane, sugar beet, and maple. These foods, in total, account for about 2% of the per capita consum ption by children 1-2 years old.

In summary, food forms, not in cluded in the current assessment, account for only slightly more than 2% of the per capita consumption by

children 1- 2 years of age and the revised NMC CRA accounts for and incorporates almost 98% of the per c apita food consumption by 1-2 year

old children. No one single food form excluded from the assessment accounts for a significant portion of t he consumption. Most of the foods

that are not included in the asse ssment are considered very minor consumption items and include such commodities as mango, sunflower seed, peppermint, and pomegranate.



## 5. Met h od of Estimati on of Cumulative Food Risk

The cumulative dietary exposure was estimated using the Dietary Exposure Evaluation M odel-Food Commodity Index Database (DEEMFCIDTM) software (Exponent, 2007). Es timation of dietary exposur e was accomplished by combining distributi ons of pesticide c oncentrations on
foods from USDA PDP with distribut ions of food consumption from

USDA CSF II. The pri m ary advantage of using distribut ions of pesticide concentrations and consumption values to assess cumulative exposure

is that distri butions of exposure values are obtained that represent a
distribution of realistic scenario s of exposure that describe both probabilities and magnitudes of multi-chemical cumulative exposur e through the food pathway.

a. Overview of Single-Chem ical Dietar y Ri sk Asses sment
Process
The dietary exposure models curr ently used by the Agency for single-chemical assessments rel y upon the food consumption data provided by CSFII consumption survey respondents. For any particular respondent ’s reported consumption, a Monte-Carlo simulation is performed i n which a series of random ly-drawn residue concentrations is selected for each food commodity. The exposure from each food commodity is calculated by multiply ing each randomly-s elected residue value by the amount consumed, and the total daily exposure is calculated by summing expos ures wit hin each indiv i dual across all

Section I.C – Page 70 of 277

### commodities reported consumed by that individual, as depicted below in Equation (1).

n



## Dietary Exposure = 

# ∑ Consumptio n i 

## × Unit Conversion × Residue i 

## (1)

(mg ai/kg bwt) i 1 (gm food/kg bwt) (1 kg food/1000 gm food) (mg ai/kg food)

=



### w here n = number of unique foods (or food commoditie s) consumed



### This repeated sampling of pesti cide residues is performed 5,000

times for each indiv i dual’s repor ted food consumption and produc es a distribution of 5,000 potent ial exposure estimates for each indiv i dual respondent

. The exposure software keeps track of the total daily exposures for each simulated person-day and applies the corresponding survey weights to project the simulat ed pers on-days to a per capita level.
It is from th is distribution of total da ily expos u res that the expos ure at any given perc entile (e.g., 95 th 

### , 99 th 

### , or 99.9 th 

### ) can be estimated.

b. NMC Food Residue Database

Equation (1) above is, in principle, fairly basic: it is the task of performing and keeping account of these necessary calculations -- particularly for a multi-chemical, mu lti-pathway cumulative assessment -- that can be cumbersome, compl e x, and tedious. The residue dat a used in this assessment consist of nearly 790,000 records of analytical data and sample information. The processing factors account for several

thousand additional records of informa tion. Calculations and algor ithms are complic ated by the fact that they must be done in such a manner that
the risk assessor can “work backward” from the cumulative assessment results to identify contributors -- and t heir relative contributions -- to the

overall cum u lative risk, and such contributors must be able to be identified on a crop, pest icide, or crop-pesticide combination basis.
Because of these issues, all the data manipulations performed as part of

this cumulative assessment were conducted outside of the DEEMFCIDTM exposure software using rela tional database techniques in MS

Access. The databas e used to c onduct these cumulative residue calculations consists of, among ot her things, four major data tables 7, 8 

### as follows:

7 By maintaini ng all of the calcul ation in p u ts in se parate tables in th e databa se, it is possible t o modify inpu ts or pe rform sen s itivity analyse s by sim p ly repla c ing or addin g data to the approp riate table. For ex ample, a spe c ific chem ical can be o m itted from the entire pro c e ss by assigni ng it a val ue of zero in the RPF table. Specific

chemi c al -co mmodity com b ination s can be sele ctivel y omitted by enterin g a ze ro value for that pair in the pro c essi ng facto r table. Specific fo od com m oditi es can be eli m inated
from the asse ssment by re moving the e n tries from th e transl a tion table.



### Section I.C – Page 71 of 277

### 1) Residue Data : contai ns essentially all of PDP sample and analyses data for NMC pesticide s for the years 1994-2006. The table in Appendix II.C.2 contains summary i n formation of PDP

residue dat a on the NMC pesticides.

2) Processing Factors : contains all relevant processing factors for specific food form/chemical combinations. The table in Appendix II.C.3 is ex tracted from these data.

3) Relativ e Potency Factors : contai ns the relative potency
factors for all chemicals of interes t. This table also contains the

chemical-s pecific FQPA safety factor and inter-species uncertainty factor, which are also used to adjust the relative potencies of the

NMC residues.

4) Bridging (Translations ) : provides bridging links or

translations between PDP commodity codes, such as AP (for

“ AP ple”) and all corresponding DEEM-FCIDTM food forms, such as Apple, fruit with peel; Uncooked; Fresh or N/S; Cook Meth N/S

. This table also translates surr ogate PDP commodities for other food forms, e.g., orange residue da ta are assigned to lemon food

forms as described in the tabl e found in Appendix II.C.4.

These four tables are linked through common fields, including pesticide c odes and commodity codes. Calculation queries are coded into the MS Access database so that all the pertinent PDP samples records can be extracted, each calculation outlined above can be performed, and the results can be so rted and output in various formats for further analys is. A cumulative re sidue calculation query performs the cumulative calculations (described in the next section), extracting the various parameters needed from the four tables described above. The calculation is performed on all of the food samples that are of interest and the results are compiled in text files containing the cumulative distributions for each food commodity of interest. Each text file contains a header with sample information (number of values, number of detects, number of zeros, average of residues ) and all cumulative residue values for a single food form, sorted in de scending order. This permits the complete history of each cumulati ve residue value in the exposure assessment to be traced back to its origins. In this way, all of the sample collection and analytic al information associat ed with a given PDP s ample

and all arit hmetic adjustments used to produce a cumulative residue estimate can be traced to permit sens itivity analyses or food commodity contribution analyses to be performed.

8 The NM C food re sidu e da tabase is ba s ed on the sa me desi gn a s the one used for the OP CRA (USEPA, 2006a).



### Section I.C – Page 72 of 277

### c. Manipulation of Residue Data for Exposu r e Assessment
Equation (1) above describes the fundamental algorithm used to estimate exposure from dietary source s. In the case of a cumulative assessment in which it is importa nt to accou n t for m u ltiple pesticides within a food commodity, the formula is modified to account for these multiple chemicals. As seen in Equation (2) below, the residues are expressed in (i.e., converted to) index-chemic al equivalents and Equation (1) re-cast as follows:

n 

## Dietary Exposure = Consumptio n × Unit Conversion × Residue ( 2 )
IE 

# ∑

i (1 kg food/1000 gm food) IE i (mg ai/kg bwt) i = 1 (gm food/kg bwt) (mg ai/kg food)



### where n = number of unique foods (or food commoditie s) consumed



### Two changes in terms are evident in this equation whic h reflects a multi-chemical (cumulative) approach: the term “Residue i 

### ” is replaced

with “Residue IE, 

### ” (for index-equivalent residu e, or residue expressed in terms of the index chemical oxamyl ) and the term “Dietary Exposur e” is replaced with the term “Dietary Exposure IE 

### ” (for index equivalent dietary exposure). More spec ifically: residues (and the resulting estimated dietary exposures) are represented in this new equation in terms of the

index chemical (for the NMCs: oxamyl). Th is re-expr e ssion of residues

in terms of index chem ical equival ents is a fundamental principle of cumulative risk assessment and is used throughout this and all of OPP’s

cumulative assessments. Such re-exp ression of residues in terms of the

common index chemic al is per formed through the use of the Relative Potency Factor (RPF) 9 

### described (and derived) in Chapter B of this

document. More specifically: the c oncentration of each pesticide in a given PDP food commodity sample is adjus ted by multiplyin g that concentration by a RPF to obtain t he equivalent residue expressed in
terms of the index chemical. Th is new calculated residue is the Index Equivalent Residue (Residue IE 

### ) appearing in the above equation and the dietary exposure e s timate resulting from combinin g Resid u e IE 

### and consumption is the Index Equivalent Exposure (Dietary Exposur e IE 

### )

The following two-step procedure pr ovides additional detail with respect to how this calculation is performed for an individual PDP sample. This process is repeated for each and every PDP sample

included in the food assessment.

9 The RPF s are also a d ju sted to accou n t for the additional sen s itivity of children

comp ared to adults a nd hu mans com pared to rats b a sed on the che m ical -spe cific
FQPA and int e r-sp eci e s u n c ertai n ty factors, re spe c tively. If additional toxicity data to quantify these safety facto r s a r e not ava il able or la cki ng, a default value of 10 is

ass i gned to eac h of thes e s a fety fac t ors. Refer to Sec t ion B for details regarding the

cal c ulatio n of chemi c al -spe cific RPF s

, F Q PA safety factors, and in ter-spe c ie s uncertainty fac t ors



### Section I.C – Page 73 of 277

### Step 1: For each pesticide in the cumulative assessment group, an Index Equivalent Residue (Residue IE 

### ) is calculated for every residue in a particular PDP sample by multiplying the residue value by the chemical-specific processing factor (PF i 

### ) for the food form of interest and the chemical-spec ific Relative Potency Factor (RPF i 

### ):



## Residue
IE i 

## = Residue × PF i 

## × RPF i 

## ( 3 )

where i indicates an individual pesticide in the cumulative assessment group



### Step 2: The cumulative Residue IE 

### for an individual PDP sample is then calculated by summing the indiv i dual Resid ue IE i 

### of all the pesticides in the cumulative a ssessment group found in that sample:

n 

## Cumulative Residue
IE 

## = 

# ∑ Residue
IE i 

## ( 4 )

i = 1



### where n = 

### the number pesticides in the cumulative assessment group



### The above- described procedure is critical in maintaining sample-bysample integrity. By summing re sidues expr essed in index-chem ic al equivalent concentrations on a sample-by-sample basis, capturing the co-occurrence of residues on the same sample is assured, and the ability to appropriately account for certai n pesticides to be used (or not be used) on the same commodity is concomitantly enhanced.

These distributions of cumulative residues (expressed in terms of

index chem ical equivalents) are treated as distribut ions of representative residues and linked to all appropr iate food forms. Finally, as descri bed previously, these cumulative resi dues -- now expressed in terms of index-chemical equiv a lents -- are comb ined with a distribution of daily food consumption values via a probabi listic, Monte Carlo simulation using the DEEM-FCIDTM software. T he probabilistic co mbination of food consumption distributio ns and food residue distributions produces distributions of estimated exposu r e s for OPP’s standard age groups (Infants < 1, children 1- 2, children 3-5, childr en 6-12, youths 13-19, adults 20-49, adults 50+ years old, and females 13-49 years old). This process has been described in public doc uments and proceedings of the

FIFRA Scientific Advis o ry P anel (FIFRA SAP, 2000a).

d. Assumptions

The input residue dat a were drawn from the PDP data base. The PDP progr am tests different commodities for various pesticides in 10 states throughout the U.S. The residue dat a from 1994 to 2006 were

Section I.C – Page 74 of 277

### used in this assessment unless otherwise not ed. The assumptions in this revised NMC CRA, which are summarized below, are essentially identical to those used in the preliminary NMC CRA.

1) Although PDP has conducted si n g le-unit sampling for limited crops (e.g., individual apple and pear samples) since 1998, only the

residue dat a from composite samples (e.g., 10 pounds of apples or pears) were utilized in this assessment. Since a single composite sample can contain several indiv i dual servings of some foods, it is implic itly assumed that all these single servin gs have residues no more or less than the composite residue (a verage value). For this revised NMC CRA, it is assumed that resi dues reported on composite homogenat es adequat ely reflect the residues in any given single serving contained in that homogenate. Ther efore, no attempt was made to “decomposite” residue values to simula te residues that might be present in the single servings contained in the PDP composite sample.

2) PDP generally uses multi-resi due methods to simultaneously analyze food commodity samples fo r several pesticides in single sample 10



### . H o wever, occasionally, for va rious reasons, not every sample is analyzed for every single pestic i de. In instances wher e a pesticide is not analyzed in a sample, the pestici de is assumed to have a residue of

zero. Although not every single pest icide is analyzed on every sample, PDP attempts to analyze for pesti cides that are registered on the food commodity of interest. For each pesticide, generally only a small percentage (less than 10%) of the samples of a si ngle commodity is not analyzed for all residues.

3) All residue analyses ar e subjec t to the limitations of the sensitivity of the analytical methods. Many of the sa mples analyz ed are reported as being below the limit of reliable detection of the analytical

method. It is usual practice in Agency single chemical assessments to

assume that residues in non-detectabl e samples are present at one-half the limit of detection (LOD ) of the analytical method in samples that were potentially harvested from treated fi elds. Thus, for purposes of estimating residues in samples r eported as less than the LOD, a proportion of the samples equal to t he estimated percent crop treated is assigned a residue lev e l of one-half LOD and the remai n ing samples,
which are assumed to come fr om untreated crops, are assigned a
residue value of zero. This pr ocedure becomes problematic for a

cumulative assessment. It is not enough to simply estimate the percent crop treated for each of the pesti cides in the cumulative assessment; it is also important to consider the potent ial for co-occurrence of residues of multiple res i dues on the same crop. In the case of the NMC pesticides,

10 The table in Appendix II.C.7 contains su mmary information with respect to the

co-occu r ren c e of NMC pe sticide re sid u e s in the PDP comm odity sample s.



### Section I.C – Page 75 of 277

### we assess ed the impact of incorpor ating one-half LOD values for nondetects in the cumulative assessm ent. The food portion of the N M C assessment was conducted using the two extreme default assumptions:

all non-det ects = 0, and all non- detects = 1⁄2LOD for the chemical most frequently detected in each PDP co mmodity. The most prevalent

detected chemical was selected because it is reasonable to assume that

chemical would als o have the great est number of residues below the

LOD. The result of thi s comparis on confirmed that t he assumption of

zero values for all non-detects did not signific antly impact on the results at the higher end of the cumulative ex pos ure distributions. For additional information regarding this sensitiv ity analyses, refe r to the Risk
Characteriz a tion chapter.

4) The sample-by-sample me thod of summing residues relies on the PDP sampling procedures to adequately capture the temporal and geographic variations in agricultura l practices and pesticide use. This procedure recogniz e s that the PDP sa mpling protocols are designed in such a way as to reflect the foods av ailable to the public for consumption in different regions of t he country throughout the year.

5) This asses sment uses PDP re sidue data collec ted over a 13 year period, (1994-2006) to maximize the number of food commodities in

the assessment and to minimize the s ensitivity of expos ure estimates to year-to-year variations in pesti cide usage (e.g., atypical pesticide residues in a commodity due to unus ual pest pressures). However, including pesticide residues over an ex tended period of time introduces an issue related to temporal co rrespondenc e of pesticide residues in

various food commodities. Since PDP cannot sample every commodity every year, OPP relies upon residues in food samples collected in different years. In so me cases, the residues in one food may be only one or two years older than residues in another food. In other cases, the food residues may have been sampled several years apart. Temporal correspondence of pesticide residues ma y be important to consider since acute dietary assessment consider foods eaten over relatively short time period, such as 24 hours. For example, it is not readily obvious if it is appropriate to model 24-hour di etary exposu r e based on pineapples grown in 2002 and cranberries grown in 2006.

6) In chemical-specific diet ary exposure assessments, OPP routinely translates residue data fr om one food commodity to related ones if the pesticide use patterns are similar on these commodities

. For example, data on cantaloupe are often used as surrogate data for honeydew and other melons. For a cumulative assessment, in w h ich a grower has a choice of several chemicals from the cumulative assessment group, these translations of data become more difficult. In the revised NMC CRA, translations of the residue data were made using

Section I.C – Page 76 of 277

### the surrogation scheme in HED SO P 99.3 (U SEPA, 1999b) to ensure representation of the ma ximum number of commodities possible. The cross walk between crops is pres ented in the table in Appendix II.C.4.



## 6. Estimation of Acute Exposu re Using DEEM- F CIDTM Software

Residue distribution files were entered in the DEEM-FCIDTM software for a Monte Carlo analy sis. The Monte Carlo analys is was conducted by an iterative process of multiplication of residue concentrations on foods, express ed in index chemical e quivalents, by one-day consumption of these foods, as reported by all individuals in
CSFII. This process used all indivi duals reporting in the consumption survey for both days of the survey and the exp o sures were calculated as mg/kg body wt/day.

DEEM-FCI DTM uses public ly available USDA/EPA recip e s for conversion of foods (e.g., lasagna) r eported on an “as eaten” basis in the survey to the recipes’ component co mmodities (e.g., tomatoes, wheat, beef, milk, etc.) for which residue data are available. The use of DEEMFCIDTM for dietary expo sure analysis has been described previously in public, technical briefings on pestici de risk assessments for pesticides in the re-registration process as well as to the FIFRA Scien t ific Advisory

Pane l (SAP). The detailed functi oning of DEEM-FCIDTM has also been described in previous SAP prese n tations (FIFRA SAP, 2000a).



## 7. Results

Table I.C-3 summarizes the DEEM-FCIDTM-generated estimated dietary (food only) exposures from the revised NMC CRA.

Table I.C-3. Summary of Pr obabilistic Analys is of Distribution of the Cumulativ e Dietary Exposures and Risk from Use of N -Methyl Carbamate Chemicals on Food Crops a

Population

U.S. Population

All infants < 1 yrs
Child ren 1

- 2 yrs
Child ren 3

- 5 yrs
Child ren 6

- 12 yrs
Youth 13-19 yrs
Adults 20 -49 yrs
Adults 50 + yrs
Female s 13 -4 9 yrs 95 th Percentile

Exposur e (m g/kg) MOE

0.0004 404 0.0023 79 0.0115 15 >99.9

0. 0005 342 0.0024 74 0.0106 16 >99.9

0.0013 141 0.0051 35 0.0229 7.9 99.848

0.0010 185 0.0044 40 0.0209 8.6 99.870

0.0006 323 0.0028 63 0.0145 12 >99.9

0.0003 576 0.0017 106 0.0098 18 >99.9

0.0001 1278 0.0008 236 0.0042 42 >99.9

0.0002 1035 0.0009 193 0.0044 40 >99.9

0.0004 505 0.0019 97 0.0101 17 >99.9 99 th Percentile

Exposure (m g/kg) MOE 99.9 th Percentile

Exposure (m g/kg) MOE Percen tile

at w h ich

MOE=10

a Exposure is i n mg/kg/day of oxamyl equivalent re sid ues.



### Section I.C – Page 77 of 277

### Exposures and MOEs (Margins of Expos ures) are presented for the U.S. General population and the following sub-populat ions: infants < 1 years, children 1-2 years, children 3-5 years, children 6-12 years, youth 13-19 years, adults 20-49 years, adults 50+ years and females 13-49

years. In addition, the percent ile at which the MOE equals 10 is provided. The summary results are pr ovided for three percentiles in the estimated distribution of exposur es: the 95 th 

### percentile, 99 th 

### percentile, and 99.9th percentiles of ex posur e. The exp o sure values are expressed in terms of index-chemical equiv a lent s. MOEs range from 7.9 (chi ldren 1-2 years) to 42 (adults 20-49 years) at the 99.9 th 

### percentile of exposure. For children 1-2 years, an MOE of 10 is reached at the 99.848 th


### percentile; for children 3-5 years, this MOE is reached at the 99.870 th


### percentile.

Table I.C-4, Figure I.C-1 (for children 1- 2 years old) and Figure 1.C2 (for children 3-5 years old) prov ide additional information with respect to the contributors (in terms of crops, pesticides, and crop/pesticide pairs) to ex posure. Appendix II.C.6 provides additional detailed information regarding the relative cont ribution of all crop/pesticide pairs for children 1-2 years old, the highest expos ed sub-population.

Table I.C-4. Relative Exposur e Contribution from Foods for Children 1 to 2 Years Old (At 99.8 th 

### Percentile of Expos ure and Above)

Food Percen t

Potato 28.4%

Peach 14.4%

Strawb er ry 11.1%
Spinach 10.4%

Wate rmelo n 6.7%

Pear 5.3%

Cu cumb er 3.4%

Cantal oupe 3.2% Grap e 3.0%
Bean, sna p 2.4%

Ne ctarin e 2.2%

Oran ge 1.7% Apple 1.6%
Lettuce, he ad 1.3%

Total 94.8% a

a No single remai n ing commodit y contributes more than 1%

to the total e x po sure.



### Section I.C – Page 78 of 277

### Figure I.C-1. Relative Contribution of Cr op/Chemical Pairs to Top 0.2 Percentile

of Cumulative Distribution for Children 1-2

Sum of H X F 30%

25%

P A RE NT P E S T I CID E

20% T h iodic a r b

Pirim i c a r b

Ox a m y l

Met h o m y l 15% Met h io ca rb

F o r m e t a nat e hy d r oc hlo r ide

C ar b of ur an
10% Ca rba r y l

Aldic a rb

5%

0%

F ood



### Figure I.C-2. Relative Contribution of Cr op/Chemical Pairs to Top 0.2 Percentile

of Cumulative Distribution for Children 3-5

Sum of H X F 30 %

25 %

PAR ENT P EST I C IDE

20 % Th i o d i c a r b

P i ri m i c a rb

Oxam y l

15 % Me thom y l

Me thi o car b

F o r m et a n a t e h y d r oc hl or id e

C ar b of u r an

10 % Ca rba r y l

Aldic a r b

5%

0%PotatoStrawberryPeachWatermelonCantaloupeCucumberSpinachNectarinePearLettuce, headGrapeAppleBean, snapLettuce,leafCabbageOrangeCherryPotatoPeachStrawberrySpinachWatermelonPearCucumberCantaloupeGrapeBean, snapNectarineOrangeAppleLettuce,headApricotHoneydewmelonCabbageCherryPepper,bellLettuce,leaf

Food



### Section I.C – Page 79 of 277

### In evaluating expos ure through fo od, OPP concludes tha t a few uses of NMC pesticides on food crops gener ally play a larger role in the
results of the assessment. These incl ude aldic a rb on potato; carbaryl on peach and strawberry; and methomyl on cantaloupe, watermelon, peach, spinach, and strawberry. However, evaluation of the total risk from exposure to NMCs in foods indic a te s that the cumula tive MOEs from exposure to NMCs do not raise a concern.

In addition to exposur e estimates generated by DEEM-FCIDTM software, the Agency has conduc ted a similar assessment using LifeLine software which is also public ly availabl e. Similar ex posure estimates to those generated by DEEM-FCIDTM were obtained with this software. It is important to recogniz e that expo sure estimates between DEEM-FCIDTM and LifeLine are not expected to be i dentical since there are important design differences relating to differ ent reference populations, binning methodologies by which each model groups individuals, reference population bodyweight s, and model (or sampling) weight s. Never-theless, expos ure estimates agree to withi n sev e ral percent. The operation of this software and the exposur e es timates produced by LifeLine are described in Appendix II.C.8.

Finally, it is important to not e that the exposure estimates and associated MOEs presented in Tabl e I.C-3 represent the Agency’s

“baseline” assessment described in th is on exposure through the food pathway. There are a number of assump tions that are reflected in this

baseline as sessment that might affe ct expos ure and MOE estimates that are importa nt to consider in ev aluating risks associated with this pathway. Specifically :

 The baseline assessment presented here represents virtually all available PDP data (1994-2006) 11 

### and thus represents an average exposure over this ti me period. The Agency has performed an equiv a lent asses sment using just the most recent years of PDP (2002-2006), the result of which are discussed in Chapter G. To the extent that more recent PDP pesticide residues more accurately reflect current (and future) conditions, exposure estimates conducted with only this more recent data mi ght be more
reflective of the current (and future) risk situati on.

 The baseline assessment presented here assumes that PDP samples with non-detectable residues do not contain

11 Although ad ditional PDP data are avail able for 19 92 and 19 93, the s e data

rep r e s ent the first years of the prog ram whi c h are limi t ed in scope, compl e tene ss, and

rep r e s entativ ene ss.



### Section I.C – Page 80 of 277

### residues (i.e., residues are zero). In reality, residues may be present and – to the extent t hey are – exposures presented in Table I.C-3 may be underestima tes of actual exposure.

 For the baseline asses sment, carbofuran was assumed to be present only on commodities with import tolerances (banana, rice, coffee, and sugarc ane) since the Agency has determined that all domestic uses are ineligible for reregistration (USEPA, 2006e). Pesticide r e sidue concentrations on uses that are being cancelled were assumed to be zero to represent future carbofuran use.

 The DEEM-FCIDTM model does not separate eating events by time and instead sums all eating (and thus exposure)

events that occur over a 24-hour time frame. For this Revised NMC CRA, no account is made for the potential reversal of acetylcholinestera se inhibition that would be expected to occur if two or more exposure events are separated in time to such a degree that substantial recovery
of AChE inhibition occ u rs. Such an assumption would be expected to over-estimate risk to the extent that some recovery of AChE activity would occur between expos ure occasions.

To account for and evaluate the effe ct of the above-list ed factors on the baseline expos ure and risk esti mates, the Agency has performed additional analyses which represent “extensions” (sensitivity analy ses)

to this baseline assess ment and permit the A gency to better evaluate how these assumptions and polic y choices might affect its exposure and

risk estimates. These activities and their results are more fully described

in the Risk Characteriz a tion chapt er of this document.



## 8. Summary

The cumulative dietary exposure due to the use of NMC pesticides on food crops was assessed using residue monitoring data collect ed by

PDP. Oxamyl was selected as the index chemical and the residue

values for the other NMC chemicals were converted to i n dex chemical equivalents using the Relativ e Poten cy Factor method. Residue data

were collec t ed on approximately 80 food commodities monitored by PDP between the years of 1994 and 2006. Food processing factors were applied to specific chemical-commodity pairs to extend these data for use on more food forms (e.g. boil ed, baked, fried, etc.). When appropriate, the PDP residue dat a were further extended to similar commodities which were not sampled in PDP. Food consumption data

Section I.C – Page 81 of 277

### were obtained from the USDA Continuing Survey of Food Intakes by Individuals (CSFII), 1994-96/1998.

The residue data were compiled as distributions of cumulative residues of index chemical equiv a lent s that were, after adjustment for processing, summed on a sample-by-sample basis. These residue

distributions were combined with a di stribution of daily food consumption values via a probabilis t ic procedure to produce a distribution of potential exposures for the general U.S. popul ation and various sub-populations. The estimated exposures (expressed in oxa m yl-equivalents) are shown

in Table I.C-3. An analysis of the rela tive exposure contribution from

foods for children 1 to 2 years at or above the 99.8 th 

### percentile of exposure is presented in Table I.C-4.

The results of the baseline a sses sment indicate that all subpopulations, except children 1-2 and 3-5 years of age, exceed a n MOE of 10 at 99.9 th 

### percentile of exposure. Howev e r, an MOE of 10 was

reached for children 1-2 and 3-5 at the 99.848 th 

### and 99.870 th 

### percentiles of exposur e, respectively. EPA conclud e s that the cumulative MOEs from exposure to NMC s in foods do not raise a concern. (Refer to Chapter G on Risk Characterization fo r a complete discussion of the rationale for this conclusion.)

Section I.C – Page 82 of 277

# D. Residential NMC Cumulative Risk

1. Introducti on

The Office of Pesticide Program s (OPP) e m ployed a calendarbased model (CalendexTM) to address the temporal aspects of the residential use of pesticides. A ca lendar-based approach provides the ability to estimate daily exposures fr om multi p le sources over time to an

indiv i dual and is in keeping with two key tenets of aggregate risk

assessment: 1) that expos ures -- when aggr egated -- be internally consistent and realistic; and 2) that appropriate temporal and geo g raphic linkages or correlations/associat ions between expos ure scenarios be

maintained. The CalendexTM softw are allows OPP to delineate the critical timing aspects of seasonal uses of N M C insecticides that result in exposure to pesticides during the y ear. Calendex also enables OPP to identify potential risks caused by co-occurrence of exposures fro m multiple routes and pathways (e .g., near si multaneous same-day exposures through drinking water and residential uses). This includes the exposure from home lawn an d garden treatments and pesticides used on golf courses.

In the revised NMC CRA, the te mporal aspects of residential pesticide applic ations were evaluat ed by relying on information from a variety of sources including regis t er ed labels, survey data, and public ly available information provided by Stat e Cooperative Ext e nsion Services. These information resources were comprehensively used to identify information such as frequency of applications and the seasonal

appearanc e of target pests. OPP al so relied on a national pestic i de usage diar y survey delineating day of application of registered pesticide products. This longitudinal survey also captures incidence of cooccurrence of residential uses of the same pesticide or similar pesticides on the same day. The survey was conducted by the National Family Organization on behalf of the Resi dential Exposure Joint Venture (REJV). A dditional details regar ding all use information used in the revised NMC CRA is presented in Appendix II.D.1.

In addition to the use practice and timing information described above, information regarding residues , exposure and standard exposure factors (such as breathing rates and acti vity duration) are required. In nearly all c a ses, the residential expos ure scenarios in this assess ment were developed using proprietary resi due and expos ure data. Exposure factors such as breathing rates and durations of time spent outdoors were taken from various sources including Agency’s Exposure Factors Handbook (USEPA, 1997a). For the majority of residential uses

Section I.D – Page 83 of 277

### considered in this assessment, the full range of expos u re values – expressed as uniform, log-normal, em pirical, or cumulative distributions – are used, where appropriate , rather than relying on point

estimates. While the dietary and drinking water assessments address only the oral exposur e r oute, the residential a ssessment considers the dermal and inhalation exposure routes as well as the oral route, w h ich is based on the mouthing behav ior of young children.



## 2. Sc ope of Re gi onal Assessm e nt s

Three NMC pesticides in this cumulative assessment have residential uses: carbaryl, methi o carb , and propoxur. More specifically,

the residential uses included in this assessment are:



### • carbaryl on turfgrass (residential lawns and golf courses);



### • carbaryl on fruit trees, v egetable and flower gardens, and ornamental trees and shrubs;



### • carbaryl impregnated pet collars;



### • propoxur impregnated pet collars;



### • methiocarb use in ornamental gardens as a snail and slug bait.

All other N -methyl carbamate residential uses are considered minor contributors to exposure and theref ore were not included in this

assessment. 12 

### Additionally, the Agency recently received voluntary cancellation of all propoxur indoor spray uses that may result in nonoccupational exposur e for children (USEPA, 2007c). Therefore, the

12 For example, propoxu r is registe r ed fo r several re sid ential uses in cludi ng; outdoo r use a s a cra ck a nd crevice and spot sp ray, a nd indo or u s e s as a contai neri z ed bait, paste, shelf pape r, or strip. For th e outdoo r cra ck a nd crevice and spot sp ray

use s , appli c at ions a r e typically made alo ng win d o w sil ls or in pave m ent cracks; to ant

hills an d wa sp nest s

. Additionally, the l abels for the shelf pape r, pa ste, and st rip prod uct s re strict use to ina c ce ssi ble area s. For insta n c e, the pa ste prod uct s are packa ged in a pre

- filled di spo s a ble syri nge. Applicat ions a r e ma d e by pushi ng the syring e plun g e r into cra c ks and crevice s in counte r s, t able s , shelvin g , drawers, u nder

sin ks, an d around pi pe s, stoves, and el ec tri c al boxe s . The prop oxur sh elf pape r prod uct s are use d in se we rs, cabinet s, or sto r ag e areas a r ou nd g arba ge, und e r sin ks,

in basement s, or other secl uded a r ea s where in se cts con gre gate. The co ntaine rized bait, paste, st rip, and shelf pape r produ cts, in addition to the outdoo r spot u s e s , a r e expecte d to result in very low expo su re and therefore are not incl u ded in this

asse ssm ent. Specifically, sin c e the shel f paper, pa ste and strip s a r e use d only to

inacce ssible area s; chil dre n’s de rmal o r hand -to-m out h exposure i s not expected

. Becau s e the bait is pa ckag ed in a chil d-proof contain e r, use in a n d aroun d the h o me also i s expe cted to re sult in negligible ex pos ure. Addi tionally, the use of ca rba r yl for oyster be ds i n Wa shin gton State was a s se ss ed in the carb aryl RE D. Since the oyster

bed u s e is re stri cted to on e area of the cou n try, and sin c e the a s sessment of th is use

indicated low exposure a n d risk, this use is co nsi dered to be a minor contrib u tor to

overall ri sk a nd therefo r e i s not incl ude d in the revised NM C CRA

.



### Section I.D – Page 84 of 277

### propoxur indoor crack and crevice scenar io, included in the preliminary NMC CRA, has been removed from this assessment.

In this revised NMC CRA assess m ent, only the Southeast region of the United States is considered ( see Figure I.D-1 below). While insect growth may slow during the winter m onths i n the South, unlike other regions of the country, there is no period of dormancy. Since the growing season is longer in the Sout h and the associated pest pressures are therefore greater , this assessment provides a worst case estimate of

exposure.

Figure I.D-1. Pesticide Cumulative Assessment Regions



## 3. Residenti a l Scenarios

The Residential Scenarios addressed in this document represent critical NMC uses that have the pot ential for signific ant exposure when

considered in a cumulative assessmen t. A brief description of each of the use scenarios covered in this assessment is provided below.

a. Law n Care
Carbaryl (adult applicator and adult and child postapplication exposur es)

Section I.D – Page 85 of 277

### Carbaryl may be applied by homeowners or professional lawn care operators (LCO). Granular, dust, and sprayable applic ations can be made by consumers using push-type spreaders, ready-to-use (RTU) shaker cans, and hose-end sprayers respectively. OPP has recently amended the use pattern of carb aryl (see Table II.A). The label changes restrict broadcast lawn applic ation to granular formulations. However, spot treatments with the liquid formu lations are permitted. Liquid products will be packa ged in read y-to-dispense containe rs that treat areas of no more than 1000 squar e feet. The current assessment incorporates the recent label c hanges for the use of carbaryl on

residential lawns.

Dermal and inhalation exposure was asses s ed for homeowners loading, and applying carbar yl to residential lawns. This assessment also considered dermal post-application exposur e for adults and childr en contacting treated lawns. Additionally , oral non-dietary exposure (handto-mouth) was considered for toddlers transferring treated-turf residues

from their hands to their mouths

. Post-applic ation expos ure was assessed for the granular broadc ast use of carbaryl but not for the liquid spot treatment uses.

b. Vegetable Gardens
Carbaryl (adult applicator and adult and teenagers postapplication exposur es)

Dust, liquid, and granular formulati ons of carbaryl may be applied to garden vegetables using RT U shak er cans, handwands, trigger pump sprayers or hose-end sprayers. (Note that recent label changes require all home garden products formulated as either a dust or a granular to be packaged in ready-to-dispense containers (see Table II.A). Dermal and inhalation expos ure was assessed fo r homeowners mixing, loading, and apply i ng carbaryl to vegetable gar den plants based on data for the liquid

and dust formulations. The use of liquid and dust data for granular applications is conservative and resu lts in higher estimated expos ure. Post-applic ation dermal exposur e al so was considered for adults and teenagers re-entering treated gardens to harvest vegetables or perform maintenance tasks (such as weeding).

c. Ornamentals
Carbaryl (adult applicator and adult and teenager p o stapplication exposur es)

Carbaryl may be applied as a dust to ornamental plants using a

RTU shaker can. Note that rece nt label changes requir e all home garden products formulated as eit her a dust or a granular to be packaged in ready-to-dispens e contai ners. Carbaryl may also be

Section I.D – Page 86 of 277

### sprayed on ornamentals (flowers, trees and shrubs) using a small handwand or hose-end sprayer. T he current assessment evaluated exposure for homeowners applying liqu i d formulations of carbaryl via the handwand sprayer since chemical-specific applic ator data suggests that the handwand sprayer resulted in sim ilar yet higher exposure than the

hose-end s p rayer. The data used to assess this scenario account for homeowners applying sprays below t he waist as well as overhead. Dermal and inhalation exposure was asses sed for homeowners mixing, loading, and applying carbaryl to ornamental garden plants. Postapplication dermal exposure also was considered for adults and teenagers performing ornamental gar den maintenance tasks (such as pruning).

M

e t h iocar b (adult applicator ex posure)

Methiocarb may be applied to so il areas in and around ornamentals for the control of snails and slugs. This product is formulated as bait applied as a broadcas t application ov er plan t foliage or to the soil surrounding ornamental plants. Exposur e from this use is expected to

be minimal. Therefore, post-applicat ion exposure was not evaluated for

this scenario.

d. Fruit Tree s Carbaryl (adult applicator and adult and teenager p o stapplication exposur es)

Carbaryl may be sprayed on fruit trees using a handwand or hoseend sprayer. The current assessm ent considers dermal and inhalation exposure for handwand applications only. Chemical specific applicator

data for this use indic a te greater exposure resulting from handwand applications than from hose-end sprayers , and therefore is considered to

be worst case. Post-applic ation dermal exposure was assessed for adults and teenagers harvesting fruit and performing fruit tree maintenance tasks (such as pruning).

e. Pet Collars
Carbaryl (adult and child post-application exposur es) Propoxur (adult and child pos t-application exposur es)

Carbaryl and propoxur are formul a ted as impregnated pet collars. Post-applic ation dermal exposur e was considered for adults and children contacting (hugging, petting) treated pet s. Oral non-diet ary exposure also was assessed for toddlers contacting treated pets and transferring

residues from their hands to their mouths.

Section I.D – Page 87 of 277

### f. Golf Course
Carbaryl (adult and teenager p o st-application exposures)

Carbaryl is also used on golf cour se turf. Golf course workers may apply liquid or granular formulations of carbaryl as a broadcast application to fairways, greens and tees

. Post-application exposur e was assessed for adults and teenagers playing rounds of golf on courses treated with the sprayable formulations of carbaryl.



## 4. Exposur e Routes/S cenarios Consi d er ed

The routes of exposur e consider ed in this cumulative assessment varied depending on certain applicat ion and post-applic ation expos ure activities that were determined to be age group-specific. Since

cumulative risk assessments do n o t in clude occupational risks, applicator exposure is not assessed for the golf course scenario. However, EPA does perform separate occupational ri sk assessments for such exposure

scenarios. The specific exposure routes and pathways/scenarios a r e summarized and described in additiona l detail below in Table I.D-1:

Section I.D – Page 88 of 277

### Table I.D-1. Specific Expos ure Routes and Pathways/Sc enarios

Scena rio Population Applicato r Post Application

Oral Der m al Inhalation Oral Der m al Inhalation

Adults X X X

Lawn/Turf Children

1-2 X X

Children

3-5 X X

Adults X X X

Home Gar de n Youth

13-1 7 X

Children

1-2

Children

3-5

Adults X X X X

Indoor (c& c) Children

1-2 X X X

Children

3-5 X X X

Adults X

Pet Collars Children

1-2 X X

Children

3-5 X X

Adults X X X

Orna mental Plants and Youth

13- 17 X

Tree s Children

1-2

Children

3-5

Adults X X X

Fruit Tre es Youth

13-1 7 X

Children

1-2

Children

3-5

Golf Cou r se Adults X

Youth

13-1 7 X



### Section I.D – Page 89 of 277

### a. Oral Route of Exposure
Toddler ingestion via hand-to-mouth activity was the only oral route of exposur e consider ed in the resident ial portion of this assessment. Specifically , oral hand-to-mouth in gestion was considered only for

children 1- 2 and 3-5 years old for the lawn care and pet collar scenarios. OPP acknowledges that there are very limited data on expos u re to young children; in general, howev er, children ages six and older no longer exhibit mouthing behavior to the degree seen in younger ch ildren (Freeman et al, 2001). In addition ,OPP recognizes that non-dietary exposure may occur not only from hand- to-mouth activities but also from activities such as ingestion of soil and mouthing of grass. However, the latter two pathways were not considered in this assess ment because these types of non-dietary expos ures are expected to be small

contributors to overall risk from residential NMC products 13



### .

13 Typically, in singl e ch emical human h e alth ris k ass e ss ment for pestic ides

, hand -to-m out h exposure i s significantly highe r than o bject-to

- mo uth (i.e., grass inge stion) ex posure. Ho wever, in the carb a r yl RED, the gra ss in g estion M OE for the

lawn care sce nario i s 150 while the h a n d -to- m outh MOEs range d from 71 to 720.

Curre n tly, the Agency doe s not have a me thodol ogy to assess g r ass ingestio n exposure fo r gran ula r lawn produ cts. As a con s ervati ve assessme nt for gran ula r turf prod uct s , the Agency u s ed stand ard a ssumpti on s ba sed on the u s e of surrogate l i quid
turf transfe ra ble (TT R ) d a ta. Specifical l y , the quantitative assessment of object-tomouth expo sure in the carbaryl asse ssment relie s u pon the a s su mption that re sidu es

on gra s s will be between 3 and 5% of the appli c ation rate (HED P o licy 1 2 : Re vis ed

Re side ntial SOP Assum p ti ons, 2/20 01 ). This a s sump tion is derive d from liquid DFR
turf studie s fo r multiple che m ical s usi ng the disl odge a ble foliar resi due (DF R ) m ethod.
The use of this method h a d been con s id ered by inve stigators in the past, but is n o longe r used d ue to its high betwe en-i nve stigat or va ria b ility. In addition the metho d had

been u s e d for measu r in g turf that is unifo rmly sprayed resulting in m o st gra s s lea v es being conta c t ed by the sprays. The DF R me thod inv o lves sha k ing grass leave s in a

dislo dging sol u tion for 20 m i nutes. The method itself is likely to be more a exh a u s tive
estimate of transfe r than m a y be expect ed wh en child ren’ s obje c ts come in cont act with
turf treated wi th granul ar fo rms that a r e wate red

- in. Since all carb aryl liquid b r o adcast lawn p r od uct s have be en can c ell ed, the quantitative assessme nt for toddle r s g r ass inge stion is hi ghly con s e r vative. Further , beca u se the carba r yl ha nd-to -mouth MOEs

for the lawn a s sessme nt were of concer n whe n gra n u l ar turf produ cts were not

watered

- in, the Agency will modify all gra nular tu rf pro duct lab e ls to requi re watering-in after appli c ati on. The targ et pests fo r the gran ula r formulation a r e soil in se cts,

con s e que ntly, it is important that the pesti cid e rea c h the soil for ad equate contro l rather

than rem a in o n the gra ss. The Agen cy belie ve s that wateri ng -in, a s demo nst rat ed by

the carba r yl handp re ss d a ta, will signifi cantly lower th e resi due s of carba r yl on treated

gra ss. Howe ver, the curre nt methodolo gy used to a s se ss g r a ss in gestio n doe s not

accou n t for the redu ction of resid u e s follo win g irri gation. For all of the above not ed

rea s on s, the Agency beli e ves that gra s s inge stion ex posure re sulti ng from the u s e of carbaryl granular lawn pro duct s will result in minimal exposure and therefore was not

inclu ded in th e revise d NM C CRA.



### Section I.D – Page 90 of 277

### i. M o deling the Non-Dietary Ingestion Pathway

In the preliminary N -methyl carbamate cumulative risk assessment (preliminar y NMC CRA), the non-die tary oral exposure pathway produced the lowest Margins of Ex posure (MOEs), and would therefore be of greatest concern to the Agency. These low MOEs were mainly du e to the incorporation of micro-activity data into our macro activity models (defined as human exposure models bas ed on daily time step). The non-dietary ingestion pathway was the least refined of the resident ial exposure pathways modeled in the pr eliminary NMC CRA. This section highlights modifications made to the methodology used to assess this pathway. This refined methodology is based on comments and input

from the FIFRA Scient ific Advisor y Panel, and the SHEDS and CARES developers.

The Calendex model used in the preliminar y and the revised NM C CRAs is a macro activity model. S pecifically , this model simulates exposures by randomly drawing age-appr opriate values for each of the various exposure factors (e.g., exposure duration, frequency of hand to mouth events, surface area of h and mouthed per event, etc.) then multiplying these values together per the OP P Standard Operating

Procedures (SOPs) for Residentia l Exposure Assessment algorithm (USEPA, 1997b). The distributions fo r many of these exposure factors were obtained from micro-activity data. For example, t he distribution for frequency of hand-to-mouth event s was based on data from

observational studies in whic h all hand contacts were recorded as handto-mouth events, regardless of the fraction of hand mouthed. For the fraction of hand mouthed, no adjustment was made for the duration of

time the hand remaine d in the mo uth. As the August 2005 SAP pa nel discussed, utiliz ing su ch micro-activi ty data with macro activity models poses many challenges. For example, if two variables are negatively correlated (e.g., more frequent mouthing is as sociated with smaller areas of hand mouthed), then “modeling the pr oduct of two joi n tly distributed variables as independent draws will overestimate the variances... or overestimate exposur e at the high end.” Similarly, “fixing the residue on a child’s hands (and/or other exposure factors) for a two hour play period...will yield ‘grea t er variab ilit y in the modeled d i stribution of exposures than a run that updates the residue concentration hourly during the exp o sure”. (FIFRA SAP, 2005b).

As recommended by the SAP, the A gency is exploring the use of

SHEDS-Multimedia for fu ture probabilistic risk assessments. In the revised NMC CRA, Agency staff addr essed shortcomings in the nondietary oral pathway by modify ing the probabilistic ha nd-to-mouth algorithm as discussed below. This m odified algorithm is a product of a

Section I.D – Page 91 of 277

### collaborativ e effort between OPP scient ists, and the developers of the SHEDS-Multimedia ( D r. Valerie Za rtarian and Dr. Jianping Xue) and CARES models (Dr. Jeffrey Driver and Dr. Muhilan Pandian).

The refined algorithm for estimati ng daily hand-to-mouth ingestion
in the revised NMC Calend e x asse ssment is similar to the SHEDSMultimedia and CARES Mass Balance al gorithms in the manner in which it addresses residue replenishmen t between hand-to-mouth contacts. Specifically , the new algorithm establis hes a maximum amount of residue which can be on the hand, or a maximum dermal hand loading. The amount of non-dietary oral inges tion incr eases with the exposu re duration, the frequency of hand-to -mouth events per hour, and the surface area mouthed per event, wh ile the hand loading serves as an

upper cons traint on oral ingestion between replenishm ent events.

New Hand-to-Mouth Algorithm

The following equation is the algor ithm used in the Revised NMC

CRA:



# Exposure = HR * SA mouthed

_ fraction 

# * ( N

_ replen ) * [ 1

− ( 1

− SE )] N

_ events 

# 2

HR/2 = initial residues available on one hand [mg/day]
SA mouthed fraction = surface area of hand mouthed [unitless]. N_Replen = number of repleni shment intervals per day. This is equivalent to the values used fo r Duration [hrs/day] in the preliminary NMC CRA since t he residues ar e assumed to be "replenis h ed" every hour of the exposure duration
SE = saliva extraction factor (i.e ., mouthing removal efficiency) [unitless]; N_Eve nts = number of hand-to-mout h contacts events per replenis h ment interval;

Each of these terms is discussed in detail below.

Hand Loading

Hand loading, or HR/2, is bas ed on loading concepts used in
SHEDS and CARES. The initial am ount of residue available on the hand is determined as a percentage of total dermal exp o sure. The EPA Child Specific Exposure Factors Handbook indi c a tes that the surface area of the hands is approxim ately 6% of the total surface area of the body for children age 4 and under; 5% for ch ildren age 5 and over (USEPA,

Section I.D – Page 92 of 277

### 2002c, table 8-3). The algorithm a ssumes mouthing one hand at a time; therefore, hand loading resi dues are divided by 2.

Frequency of mouthing events and fraction of surface area

mouthed

The distributions for frequency of mouthing events (N_Events)

and the fraction of surface area of hand mouthed (SA mouthed fraction), are based on observational studies (s ee scenario-specific discussions in

section I.D.6 for more details on t he information sources used for these parameters). This algorithm allows for only one draw (per simulated day), therefore, the interpretation is that this behavior continues at the same rate for the entire expos ure durat ion selected. In reality, a highend mouthing frequency recorded over a short time inte rval (e.g., one hour) may not be likely to continue at the same intensity over a longer time period (e.g., 6 or 8 hours).

Saliva extraction factor (i.e., mouthing removal efficiency )

The saliva extraction factor (SE) i ndicates the fraction of residues that is removed during each mouthi ng event. The greater the removal efficiency (i.e.,SE), the less residue remains on the hand after each mouthing event. For example, if t he initial loading is 1000 ug on the thumb (part of hand mouthed), and the removal efficiency is 20%, then after one event, 200 ug is removed and 800 ug remains on the thumb; after two events, an additional 160 ug is removed (=800x.2), or a total of 360 ug (=1000x(1-(1-.2) 2 

### )) is removed and ingested, and 640 ug (=1000x((1-.2) 2 

### )) remains on the thumb.

The SE term is used in combination with the mouthing frequency

term (N_Events) in the above equation as:

(1-SE) (N_E v e nts )



### This portion of the new hand-to -mouth algorithm expresses the percent of i n itial loading remain ing on hand after N hand-to-mouth events or, equivalently, the percent of init ial loadin g mouthed that is inges t ed
after N hand-to-mouth events.

Replenish m ent Interval

In the preliminary NMC CRA, t he non-dietary ingestion algorithm assumed complete replenishment between each hand- to-mouth event. This new algorithm is more comp arable to the SHEDS-Multimedia and the CARES Mass Balance algorit hms in addressing the replenishment. The new algorithm assu mes that replenishment occurs only once every

Section I.D – Page 93 of 277

### fix ed time interval (SHEDS uses the Consolidated Human Activity Database (CHAD) time interval; CAR ES us es a daily time interval).

Within eac h replenishment interval, a fraction of the remaining residues are removed and ingested during each hand-to-mouth event. The total
amount of residues removed and i ngested increases with the SE, the number of events per interval, and with the number of replenishment intervals per day (or exposure duration).

In this new algorithm, the hand is fully replenished with residues from a contaminated surface (e.g., the lawn, pet or hard flooring), on an hourly basis. The replenishment frequency (hrs/day) and the mouthing frequency per replenishment interval (events/hr) are expressed in the same time unit (hr). T herefore, the same distribution, total exposur e duration (hrs/day) was used in the pr eliminar y NMC CRA. This algorithm may be generalized to model replenis hment at different (fixed) time

intervals. 14



### b. Dermal Route of Exposure

The dermal route was assessed for adults applying consumer pesticide pr oducts to lawns, gardens, fruit trees, and ornamental plants. For both childr en and adults, post-applic ation dermal exposure was assessed for the la wn and pet collar scenarios. The dermal route was also assessed for adults and teenagers reentering treated vegetable and or namental garden to perform maintenance (weeding, pruning) and harvesting activi ties. Similarly, exposure was assessed for adults and teenagers involv ed in fruit tree cultivation. Dermal pos t-application expos ure also was assessed for adults and teens playing golf on treated courses.

c. Inhalation Route of Exposure

The inhalat i on route of expos ure was considered for adult populations only. Specifically, in halation exposure was assessed for

14 For example, if it is assum ed that the ha nd is fully repl enished eve r y half hour,

then both vari able s (N_Rep len and N_Events) would be co nstructe d usin g 30 mi nutes
as the unit for duration. Similarly, if one repleni sh me nt per day (N_Re p len = 1 ) is

assume d, this ne w algo rithm red u ces t o the CARES Mass Balan c e equatio n. This is

demon strated by replaci ng the numbe r o f repleni shm e nt intervals p er day (N_ R e p len = 1 / day), and the total mouthi ng events pe r repleni shm ent interval (events/d a y) with the equiv a lent term s (N_E vents = F R EQEvents/h r x Duratio n h r s/day )

in the followin g equatio n: 

# HR Freq events 

### * Duration hrs 

# Exposure = 2 * SA mouthed

_ fraction 

# * 1 * 

# [ 1

− ( 1

− SE ) 

# ]

hr day



### Section I.D – Page 94 of 277

### adults applying pesticide formulations to lawns, vegetable gardens, ornamental plants and fruit trees.



## 5. Data Sour ces

Three basic types of data were considered in this assessment:

 pesticide use data;

 residue concentration and dissipation/decay data;

 exposure contact factor data.

These data are described in more detail below.

a. Pesticide Use Data

The probab ilistic mode ls require residential p e sticide us e inputs to aggregate expos ure from mult iple use scenarios. The percent of households apply i ng the various products and the ti ming of those applications directly impact esti mate s of aggregate expos ure. The REJV data can be used to generate empirically-based estimates to address

those needs. Appendix II.D.1 pro vides further details regarding the

REJV data. However, the REJV did not collect informati on on the purpose of use (pest treated), areas tr eated, or applicati on rates. Since these factors may impact timing and frequency of application, REJ V data was used in combination with professional judgment, product label information and pest pressure information from the Cooperative State

Extens ion Services. The revised NMC CRA consider ed the Southeast Region of the United States for two reasons ; 1) the growing seas on is longer in the South and the associ ated pest pressures are therefore greater, and 2) drinking water concentra tions are highest in this region of the country. The residential and gr oundwat er assessments are based on

the most hi ghly expos ed localized ar eas within the southeastern region
of the United States. Specifically , the drinking water expos ure for Georgia was combined with residential expos ure in Florida Pest pressure
data for Florida are assumed to addre ss pest pressure for other areas of the country where NMC water concent rations are high (such as Georgia and North Carolina). Due to longer per iods of pesticide use coupled with higher concentrations of NMC in ground wat e r, this assessment provides a worst case estimate of exposur e.

The revised NMC CRA focuses on post-application exposures for children, including the broadcast la wn and the pet collar scenarios. Examples of how pesticide use data were incorporated into these

scenarios are discussed below.

Section I.D – Page 95 of 277

### i. Broadcast Lawn Scenarios

Current label revisions for carbar yl lawn care products restrict broadcast applications to granular formulations. Therefore, postapplication exposure for children was asses sed only for granular applications of carbaryl to lawns. The major turf pests treated with carbaryl are grubs, mole crickets, caterpillars, cinch bugs, scales, ticks, and a variety of spiders, and ants. However, pests that would mostly likely be treated with granul ar applications are mole crickets and white grubs. The other pests listed are more likely to be treated with spray applications. Therefore, this assessment focused on timing of applications for residential lawns treated for white grubs and mole

crickets. In formation fr om the Univer sity of Fl orida Cooperative State

Extens ion Service indicates that grubs actively feed in Florida from April through October, depending on s pecies and weather conditions. Additionally , tawny mol e crickets be come active in March, and granular applications are typically made in August and September. For these reasons, the broadcas t lawn assessm ent considered the season of use to be early spring through fall. O PP assumed two applications to the lawn per year (based on REJV).

ii. Pet Collar Scenarios

Propoxur and carbaryl product labels indicat e that pet collars are effective for 180 days and 120 days, re specti vely. Additionally, season of use is considered to be year-ro und since flea lifecycle information shows that in humid climates, fleas may be active year-round. Therefore, the pet colla r assessment assumed that pet collars would be used year-round in southern climates.

b. Residue Concentration Data

Residue concentration dat a and associated pesticide decay/dissipation parameters were used to define the sources and magnitude of exposur e resulting from human contact with transferable

residues. In many cases, chemical-specific data were used to assess homeowner applicator and post-applic ati on expos ure resulting from the registered uses of carbaryl. Fo r the lawn and garden scenarios, data
from the Outdoor Resi dential Pes t icide Use and Usage Survey and National Gardening Association Surv ey (Johnson et al, 1999) submitted by the Outdoor Residential Exposure Task Force (OR E TF) were also

used. Surrogate data were used to determine expos ure resulting from the ornamental garden use of methio carb. Appendix II.D.2 contains a summary of all residue data used in the revised NMC CRA, as well as

the derivation of various distributional param eters.

Section I.D – Page 96 of 277

### c. Exposure Factor (Contact) Data

Exposure factors such as the am ount of time spent in an area, frequency of hand-to-mouth contacts, si ze of area treated, and location of residue source (lawn, garden, or indoor surface) are critical for estimating ex posures to a given subs tance. Appendix II.D.2 contains a summary of exposure factors used in the revised NMC CRA, as well as

the derivation of various distributi onal param eters. Unless otherwise noted, all distributions were truncated at the 99th percentile in order to

avoid a dist ribution which contained va lues that were well beyond those

deemed reasonable.



## 6. Exposur e Scenari o s

This asses sment considered a va riety of exposure scenarios for consumer applicator and post-applic ati on expos ures. Each of these is

described in additional detail below. Si nce it is difficult to determine typical rates for homeowner pr oducts, OPP used the maximum application rate, as allowed by curr ently registered labels, to assess exposure for all scenarios. (8 lbs ai /A was used for lawns and fruit trees; and 2 lbs ai/A was used for veget able gardens and ornamentals.) Table I.D-4 summarizes the selected inputs for al l scenarios.

a. Law n Care Exposure Scenarios

i. Lawn Applicator Exposure
Only carbaryl has registered lawn care uses. Applicator exposure

was asses sed for homeowners mixing, loading, and applying a variety of carbaryl products to their lawns. T here are two formul a tions of carbaryl

that are available for lawn us e: granular and liquid sprayable
formulations. OPP has amended the use pattern of carbaryl and the

current cumulative assessment inco rporates these changes. The label changes restrict broadcast lawn applic ation to granular formulations.

Howev e r, spot treatment s of the liquid formulati on are permitted. All liquid products must be packaged in ready-to-dispense containers. Such formulations will limit spot treatment s to areas of less than 1000 sq uare feet.

Total exposure is calculated as the product of the unit exposure (UE) (either dermal or inhalation), t he application rate, and the lawn size.

Unit Exposures: Both dermal and inhalation expos ure routes

were considered. OR ETF studies we re used for the granular broadcast and liquid s pot treatment scenarios.

Section I.D – Page 97 of 277

### The ORET F submitted a report (Klon ne, 1999) in whic h a variety of products were used on turf. In these studies , both homeowners and lawn care operators (LCOs) we re monitored following broadcast applications to turf. Al l of the data submitted in this report were completed in a series of studies.

The two studies that monitored homeowner exposure resulting from granular spreader (Klonne, 1999/OM A003 Study) and hose-end sprayer (Klonne, 1999/OMA004 Study) applications were used in this

assessment. Volunteers participating in these expos ures studies were adult non-professionals who use pes ticides on their own gardens and

lawns. Many of the volunteers sele cted as subjects in these studies were members of garden clubs. All volunte e rs made their applications without specific instructi on from the study investi gators. Unit expos ures estimated from these studies cover various clothing sc enarios that range
from weari ng short pants and shor t-sleeved shirts, to long pants and long- sleev ed shirts.

All dermal and inhalation unit ex posure were normalized and

expresse d as milligra ms exposu r e per pound of active ingredient handled (mg/lb ai) (referred to as unit ex posures, or UE). The lognormal
distributions of the UEs for the la wn applicat or scenarios are shown in

Table I.D-2.

Table I.D-2. Lognormal Distributions of Unit Exposures Used for Carbaryl Lawn

Care Scenarios

Applicatio n

Method Exposu r e Ro ute Unit Exposu r e Distri bution

(mg/lb ai) LN(μ, σ ) Comm ents

Gran ula r Rot ary Sprea der Dermal 0.81, 0.57 This di strib ution wa s u s ed for the bro ad c a s t

lawn scena rio Inhalation 0.0013, 0.00 1 3

Ho se-end Sp rayer on Turf Dermal 8.4, 26 This di strib ution wa s u s ed for the lawn

SPOT treatm ent scena rio ONLY Inhalation 0.022, 0.040

NOT E S: LN(μ, σ ) repres ents a log norm a l distri butio n w i t h μ = arithm etic mean a nd σ = arithmetic standar d dev ia tion.

F o r la w n sce na rios, informati o n w a s der ived f r om carbar yl -s pecific d a ta an d studies co nd ucted b y the O R ET F.

A more detailed ex plan ation of the statistical ana ly sis of this data is prov ided in Appe ndix II.D.2.



### Application Rates : For all scenarios assessed, OPP used the maximum application rate to assess ex pos ure (8 lbs ai/A was used for the lawn care scenario).

Section I.D – Page 98 of 277

### Area Treated : An important variable for estimating home-owner applicator exposure is the size of the lawn. OPP considered the average and median lawn sizes reported i n a journal article by Vinlov e and Torla (1995). The means and medians were ~13,000 ft 2



### . H o wever, the authors noted problem s interpreting the data since it is based primarily

on low inc o me houses and consis ts of adjustments of the lot size by the house's foundation (footprint) only. The data do not consider other

structures such as decks or othe r green space such as gardens, which can reportedly reduce the lot size by up to 50%. Similar lawn siz e s were noted in the ORETF survey (Johnson et al, 1999) with similar problems encountered with respect to confounding variables suc h as decks and other green spaces. For this assessment, OPP used a uniform distribution for lawn size bounded by 1000 ft 2 

### and 20,000 ft 2



### . The lower end of this range cons iders smaller lawns for residences such as town

houses. The upper bound of 20,000 ft 2 

### (~ 1⁄2 acre) appears reasonable given the type of applic ation equi pment assumed to be used by residential applicators. Information from the ORETF survey also

indic a tes that many pesticide users ma ke spot treatments of insecticides. Similarly for spot treatments, O PP assumed a uniform distribution for treated area bounded by 100 ft 2 

### and 1000 ft 2



### .

ii. Lawn Post-Application Dermal Exposure
The fate of pesticides appli ed to turf, and subsequent human contact, is a key variable for assessing post

- application dermal exposure and can be an important exposur e pathw ay to consider as part of a

cumulative assessment. This exposure pathway was evaluated here in the revised NMC CRA by using data fr om a number of available st udies (described in more detail below). Briefly, post-application dermal exposure (mg pesticide) is calc ulated by multiply ing the residue concentration on the lawn (mg/cm 2 

### ) by the transfer coefficient (cm 2 

### /hour)

derived from literature and other studies and the time spent on the lawn (hours/day). For this assessment, t he transfer coefficient and the ti me spent on lawns were represented by a distribution of values while the

residue concentration on the lawn was represented by a time seri es of concentration values (which account ed for residue degr adation ov er time and incorporated the relevant half

- lives or decay coefficients). Due to the label revisions, post-applicati on exposur e was considered for the granular broadcast treatments only.

Residue Data: There are chemical-specific turf transferable residue (TTR) data for granular formulati ons of carbaryl (Krolski, 2005). This study was designed to determine transferable residues of carbaryl from both irrigated and non-irrigated turf treated with SEVIN® 2G (see Appendix II.D.2 for stu d y summary). Measured carbaryl residues rapidly

declined ov er the first 4 hours and then le veled off or rose slightly after

12 hours. By the 24-hour sampling interval, the residues declined to

Section I.D – Page 99 of 277

### approximately 10 percent of the corresponding 0-ti me residue value and then steadily dropped to below 1.0 perc ent of the corresponding 0-time residue values by 3- to 5-days after treatment.

In order to provide a conservati ve estimate o f ex posure for the revised NMC CRA, only the samples in the above study from the nonirrigated site in Florida were used. This assessment assumes an initial concentration of 0.00021 mg/cm 2



### . Dissipation is based on a 0.8 day halflife, with residues set to zero 14 days after applic ation. Although the carbaryl TTR studies show residues below 1% of the initial residues by 3- to 5-days after application, to pr ovide a conservative estimate of exposure, it is assumed that carb aryl residues on the lawn would be available for up to 2 weeks after application.

Transfer Coefficients (TC): The transfer coefficients used in this assessment were developed by dividing the hourly dermal expos ure ( μ g/hr) obtained from a set of activi ties by the measurement commonly
referred to as turf trans ferable residues (TT R ) ( μ g/ cm 2 

### ). Since none of the dermal exposure studies used to estimate hourly exposure permitted direct calculation of the TTR, the trans fer co efficients for this asses sment were developed by assuming a transfer efficiency of at l east 0.5% for granular formulations. This was done for two reasons:

� to make use of availa ble der mal expos ure measurements

which are not influenced the methodolo g y used to estimate TTR, and

� to make use of the availa ble residue dis s ipation dat a for

which there are no corresponding dermal exposure

measurements

The 0.5% value is within the r ange of efficiency for the existing

chemical s pecific TTR data described abov e. To account for the additional uncertainty of assuming a ce rtain transfer effi ciency to develop the transfer coefficients, TTR data hav ing transfer efficiencies lower than 0.5% (for granular applic ations) were adjusted upwards to make up the difference in efficiency. If the tr ansfer efficiency of the TTR data wa s higher than 0.5% for granular form ulations, it was not adjusted.

For a more detailed discussion of the relationship of transfer coefficients and TTRs please refer to t he “Overview of Issues Relat ed to the Standard Operatin g Procedures for Residential Exposure Assessment” presented to the FIFRA Scientific Advisor y Panel on September 21, 1999.

Section I.D – Page 100 of 277

### Transfer Coefficients used to assess children’s exposure to

treated turf :

One study was used to assess child ren’s dermal ex posure resulting from granular applications to resident ial turf (Vaccaro, 1996). In this study, a granular formulation of chlo rpyrifos was applied, after which

seven adult s performed pre-choreographed activities intended to mimic a typical child’s behav ior.

The subjects performed these acti vities for a period of four hours beginning after the turf had dried. Turf had been treated earlier with a granular form of chlorpyrifos and exposu re was estimated in the study by monitoring the amount of a chlorpyr ifos metabolite – 3,4,5,6-TCP –

excreted over the followin g period of 6 days. This method directly

measures internal dos e and was used to back-calculat e a generic “to the skin” transfer coefficient by using chemical s pecific dermal absorption

data for chl o rpyrifos (Nolan et al., 1984). These data were further adjusted to account for differences in surface area of adults and children.

The study data discus s ed above was fit to a lognormal distribution

with an arithmetic mean of 1970 cm 2 

### /hr with a standard deviation of 1426

cm 2 

### /hr. The lognormal distribution was truncated at the calculated 99th percentile of the distri bution (i.e. 7224 cm 2 

### /hr for the granular application)

in order to avoid a dist ribution whic h containe d values that were wellbeyond those that are deemed reasonab le. (See Appendix II.D.2 fo r study summary and details of the distributional analys is.)

Transfer Coefficients used to assess adul t exposure to
treated tur f:

The Vaccar o study data discussed above were also used to assess exposure to adults following granular applications.

The revised NMC CRA used a distribut ion of values for the transfer coefficient characterized by a logno rmal distribution wit h an arithmetic
mean of 5376 cm 2 

### /hr and a standard deviation of 4717 cm 2 

### /hr for the granular application. The lognorm a l distribution was truncated at the calculated 99th percentile of the distribution (i.e., 23436 cm 2 

### /hr ) for the granular application. (See Append ix II.D.2 fo r study su mmary and
details of the dist ributional analysis.)

Duration: Another important vari able for addressing postapplication exposure from home lawn treatment is the duration of ti me spent on lawns. In this revised NM C CRA, cumulative distributions of durations on lawns of up to two hours were used to address adult exposure on lawns. These data are presented in Table 15-64 of the

Section I.D – Page 101 of 277

### Exposure F a ctor’s Handbook (EF H ) (USEPA, 1997a); however, OPP notes that the percentiles above t he 95th have the same values (121 minutes). A similar cumulative di stribution was given for children ages

one to five. In order to be protec ti ve of children and to address the uncertainty in the upper percentiles of the expos ure fa ctor data, OPP selected an empirical distribution (whi ch was expressed as a cumulative distribution function) from EPA’s EFH (Table 15-80) with a bound of 3.5

hours for children. This distribution represents the amount of time spent outdoors rather than just on lawns. Th is adjustment allows for additional time that children may spend out doors (such as parks and schools) where there is potential for addi tional contac t with treated turf.

iii. Lawn Non-Dietary Hand-to- M outh Exposure

The assess ment also incorporates exposure resulting from toddler hand-to-mouth activity on lawns. T he revised NMC CRA incorporates a new algorithm to esti mate hand-to-m outh expos ure. Details of this algorithm can be found in Section I.D.4.i of this document.

Initial Hand Loading: Hand loading (HR/2) is based on loading concepts used in SHEDS and CARES. The initial amount of residue available on the hand is determined as a percentage of total dermal

exp o sure. The EPA Child Sp ecific EF H (USEPA, 2002 c) indicates that

the surface area of the hands is approximately 6% of the total surface area of the body for children age 4 and under; 5% for children age 5 and

over. The algorithm assumes mout hing one hand at a time, therefore, hand loading residues are divided by 2.

Frequency of Mouthing Behavi or: For the revised NMC CRA, the frequency of hand-to-mouth events is based on Xue et al, 2007. The estimates of mouthing frequency were deriv ed from several expos u re studies and videotaping studies. Stat istical analysis indicated that a

Weibull dist ribution best fit the data

. For the lawn care scenario, handto-mouth events per hour were based on outdoor frequencies as defined by a Weibull distribution (mean = 6 events/hour, standard deviation = 8).

OPP believ e s that the meta-analysi s cited above provides the best

available data to assess child ren’s hand-to-mouth exposures.

Surface Area of Hand Mouthed: The revised NMC CRA relied on Zartarian’s (2003) analysis of surf ace area of hand mouthed. The analys is used the Leckie, et al., 2000 data to determine the fraction of

the hand mouthed. The fraction of hand mouthed values were fit with a beta distribution (mean = 0.13 events/ hour, standard deviation = 0.06).

Saliva Extraction Factor: To address the removal of residues from the hands by saliva during mout hing events, several studies were

considered. The removal efficiency of residues on hands by saliva and

Section I.D – Page 102 of 277

### other substances (e.g., ethanol) suggests a range of removal

efficiencies (Geno et al., 1995; Fenske and Lu 1994; Wester and Maibach 1989). Based on the above st udies , a uniform distribution of
20% to 50% was used in this assessment for saliva extraction factors.

Duration: The time spent on the lawn was estimated as a cumulative distribution ranging from 0 hours to 3.5 hours. To be protective of childrens ’ exposure and to address the uncertainty of the upper percentiles of the expos ure factor data, OPP selected a

cumulative distribution from EF H (USEPA, 1997a) Table 15-80 with a
bound of 3.5 hours for children 1 to 5 years old. This distribution represents the amount of time spent outdoors. This allows for the time that children spend out doors not only at home but also in parks and near

schools.

Assessing exposure through the non- dietary ingestion pathway is difficult due, in part, to issues ass ociated wit h meas urement of the

above-disc ussed varia b les as well as issues associated with the utility of using childr en’s hand-t o -mouth frequencies based on indoor activities for outdoor exposure scenarios. There ar e also differences in mouthing behavior based on active and quiet play with increased mouthing likely to be during activities of quiet play. Li mited data evaluated by Groot et al., 1998 suggests that children aged six to 12 months can experience longer durations of mouthing activ i ti es (exceeding 160 m i nutes per day)

than childr en 18 to 36 months (up to 30 minutes per day). Howev e r, children in this age group are not likely to be engaged in post-applic ation

lawn activ i ties OPP is modeling that would result in higher estimated

exposure. Additional data for very young children (under the age of two) are needed to delineate the frequ ency differences between hand-tomouth events for children engaged in active and quiet play.

b. Vegetable Garden Exposure Scenarios

Carbaryl has registered uses in home vegetable gardens. This assessment includes scenarios for applic ations of carbaryl using dust formulations (hand/shake), ready-to- use trigger sprayers, and hose-end

sprayers. While there are other po ssible application methods for use on these sites, these application met hods were selected based on use and exposure considerations.

i. Applicator Exposure
Dermal and inhalation exposures for homeowners applying carbaryl to their vegetable gardens were calc ulated in a manner similar to that used to assess applic ator exposure for t he lawn care scenario. Both are the product of the unit exposure (mg/l b ai handled), application rate (lbs

ai/ft 2 

### ), and area treated (ft 2 

### ).

Section I.D – Page 103 of 277

### Unit Exposure: Dermal and inhalation unit exposures were derived from chemical-specific data (Mester, 1998a) for dust (shake/pour), trigger pump spraye r, and liquid hose-end sprayer applications to vegetable gardens. The UE for all garden scenarios are based on lognormal distribution as listed in Table I.D-3.

Application Rate: A n applic ation rate of 2 lbs ai/A was used for al l liquid veget able garden scenarios ( hose-end sprayers and trigger pump sprayers) even though trigger pump sprayer rates are considerably

lower. This assessment conservatively uses the 2 lb ai/A application rate for all liquid scenarios. Due to recent mitigation, the maximum application rate for dust formulations is 0.5 lbs ai/container (see Table II.A)

. The ex posure assessment fo r dust for m ulation applied to home gardens as sumes use of one entir e container per treatment.

Area Treated: For vegetable gar dens, the area treated was entered as a lognormal di stribution (mean = 4600 ft 2 

### , standard dev iation
=1500 ft 2 

### , and maxim u m = 8000 ft 2 

### ); these dimensions are based on data from the National Gardening A ssociation Survey (Johnson et al, 1999). In these asses sments, it is assumed that the entire garden is

treated. H o me gardens consist of m any types of vegetables which all may not be treated since they tend to have different pest pressures (e.g. squash vine borer and corn earworm ma y not appear at the same time).

Table I.D-3. Lognormal Distributions of Unit Exposures Used for Carbaryl Garden, Fruit Tree, and Ornamental Scenarios
Application

Metho d Exposur e Rou t e Unit Exposure

Distribu tion(mg/lb ai) C o mments

Ho se-E nd

Spray er Dermal (51, 58 ) This di strib u tion used for th e vegetable g a rden sce nari o ONLY Inhalation (0.002 4, 0.00 15) Liquid Hand wan d Dermal (74, 64 ) This di strib u tion also used for the

orna mental a nd fruit tree scen ario s Inhalation (0.009, 0.01 0 )

Du st
Shake r /Powd e r Dermal (250, 33 0) This di strib ution wa s u s ed for vegetable g a rden an d orn a m ental

scena rio s

. This di stributio n wa s also u s e d for the SPOT treatment

on lawns. Inhalation (2.9, 9.5)

RTU T r igg e r Pump Sprayer Dermal (86, 110 ) This di strib ution wa s u s ed for the vegetable g a rden an d orn a m ental

scena rio s

. Inhalation (0.10, 0.14)

NOT ES : LN(μ, σ ) represents a log norma l dis t ributio n w i th μ = mean and σ = standard dev iatio n Studies for g a rden, fruit tree, and or nam ent a l app licati ons a r e carbar yl-s pe cific.

A more detailed ex plan ation of the statistical ana ly sis of this data is prov ided in Appe ndix II.D.2.



### Section I.D – Page 104 of 277

### ii. Post-Application Dermal Exposure

Post-applic ation expos ure for adults and teenagers harvesting vegetables or performing post-applic ation gardens maintenance were assessed using a range of transfer coefficients to account for the diversity of activities. Post-applic ation expos ure was estimated as the product of dislodgeable residue concentration (mg/ cm 2 

### ) a transfer coefficient (cm 2 

### /hour), and time spent in the activity (hours).

Residue Data: Chemical-spec ific dislodgeable foliar residue dataon sunflowers (Klonne et al, 1999) were used to assess dermal postapplication exposure. Although OPP has additional information regarding c a rbaryl specific DFR da ta on cabbage (Klonne et al, 2000a) ,

the sunflower DFR data were used since the residues detected in the sunflower study were higher than those detected in the cabbage study. Statistical analysis of the carbaryl sunflower DFR data was performed.

The initial residue concentrations and t he half-life were determined to be 0.0061 mg/cm 2 

### and 5 days, respectively.

Transfer Coefficient: For the vegetable gar den scenar i o, transfer coefficients were characterized by a uniform distribution ranging from

180 to 1000 cm 2 

### /hour, to re flect a range of g a rdening tasks for a variety of crops of differing heights and fo liage development. The TCs used in

this assessment were derived from studies on chrysanthemum pinching (Rotondaro, 2000) and tomato harvesti ng (USEPA, 2000e). All transfer

coefficients are based on indiv i duals wearing short sleeved shirts and short pants. A reduction factor w a s applied to account for body weights

and surface area differences between adults and teenagers.

Duration: The time spent harvesting or performing post-application maintenance activities was repres ented by a uniform distribution ranging from 0.17 hour/day to 1 hour /day. These estimates of ti me spent in the garden performing post application activities (as well as the frequency of applications) were based on the ORETF survey (Johnson et al, 1999).

c. Ornamental Plants a nd Shrubs Exposure Scenarios

Carbaryl also has registered uses on ornamental plants and

shrubs. This assessment incl udes scenarios for the RTU dust formulations, RTU trigger pump sprayers, and liqu i d hand wand uses on ornamental plants. While there are other possible application methods for use on this site, these methods were selected based on use and exposure considerations.

Section I.D – Page 105 of 277

### i. Applicator Exposure

Dermal and inhalation exposur es for homeowners treating ornamental plants were estimated as the product of the Unit Exposure (mg/lb ai handled), application rate (lbs ai/ft 2 

### ), and area treated (ft 2 

### ).

Unit Exposure: Dermal and inhalation unit exposures were derived from chemical-specific dat a for carbaryl used on ornamental plants (Mester, 1998a; Merricks, 1998). The UE for all garden scenarios are based on lognorm a l distribution as listed in Table I.D.3.

Application Rate: A n applic ation rate of 2 lbs ai/A was used for al l liquid veget able garden scenarios ( hose-end sprayers and trigger pump sprayers) even though trigger pump sprayer rates are considerably

lower. This assessment conservative ly uses the 2 lbs ai/A applic ation rate for all liquid scenarios. Due to recent mi tigation, the maximum application rate for dust formulations is 0.5 lb ai/container. The exp o sure assessment for dust formulation appli ed to ornamental gardens assumes use of one entire container per treatment.

Area Treated: The area treated was entered as a uniform

distribution of 500 to 2000 ft 2 

### ; these dimensions are based on data from the National Gardening Associati on Survey (Johnson et al,1999) and professional judgment. The ornamental bed size was determined by estimating the perimeter of 2200 ft 2 

### house. It is assumed that the majority of ornamental beds are lo cated around the per imeter of the

house.

ii. Post-Application Dermal Exposure

Post-applic ation expos ure for adults and teenagers performing ornamental garden activities were a ssessed using a range of transfer

coefficients to account for the divers ity of activities. Post-application exposure was estimated as t he product of dislodgeable r e sidue concentration (mg/ cm 2 

### ), transfer coefficient (cm 2 

### /hour), and time spent in the activity (hours).

Residue Data: Chemical-spec ific dislodgeable foliar residue dataon sunflowers (Klonne et al, 1999) were used to assess dermal postapplication exposure from harve sting or performing maintenance activities in ornamental gardens. Although OPP has additional information regarding carbaryl spec ific DFR data on cabbage (Klonne et al, 2000a), the sunflower DFR data were used since the residues detected in the sunflower study were higher than those detected in the cabbage study. A statistical analysis of this data was performed and the

Section I.D – Page 106 of 277

### initial conc entration was estimated to be 0.0061 mg/ cm 2 

### . Residue dissipation is based on the half-life of 5 days. The half-l i fe used in this

assessment was determined from the stat istical analys is of the carbaryl

sunflower DFR data. A more detaile d explanation of the statistical analysis of this data is provided in Appendix II.D.2.

Transfer Coefficient: For the ornamental garden scenario, a
uniform distribution of transfer coe fficients, ranging from 99 to 550

cm 2 

### /hour, was used to reflect a range of gardening tasks. The TC s used
in this assessment were derived from studies that evaluated chrysanthemum pinching (Rotondaro, 2000) and nursery stock pruning (Klonne et al, 2000b). All transfer coefficients are based on indiv i duals wearing short-sleeved shirts and shor t pants. A reduction factor w a s applied to account for body weig hts and surface area differences between adults and teenagers.

Duration: The time spent harvesting or performing postapplication maintenance activiti es was represented by a uniform distribution ranging from 0.17 hour/day to 1 hour/day. These estimates of time spent in the garden performi ng post-application activities (as well as the frequency of applic ations) were based on the ORETF surv ey (Johnson et al, 1999).

d. Fruit Tree Exposure Scenarios

Carbaryl also has registered uses on fruit trees. This assessment addresses exposure for homeowners appl ying sprayable formulations of carbaryl via handwands. While t here are other possible application methods for use on these sites, this method was selected based on use and expos ure considerations.

i. Applicator Exposure

As described for the l a wn applic ator scenario, expos ure is the product of the unit exposure (mg/l b ai handled), application rate (lbs

ai/ft 2 

### ), and area treated (ft 2 

### ).

Unit Exposure: The dermal and inhalat ion unit exposures were derived from chemical-specific data for liquid handwand applications to fruit trees (Merricks, 1 998). These unit exposures are b a sed on study data in which applic ations were made with handwands , spraying below

the waist as well as overhead. The UEs for fruit tree scenario are based on lognorm a l distribution as listed in Table I.D.3.

Section I.D – Page 107 of 277

### Application Rate: For all scenarios assessed, OPP used the maximum application rate to assess ex pos ure (8 lbs ai/A was used for the fruit tree scenario).

Area Treated: For fru i t trees, mo st of which are of the d w arf variety and therefore occupy relatively small areas, the area treated was entered as a uniform distribution (minimum 500 ft 2 

### , maximum 1000 ft 2 

### ).

The maxim u m value in this distributi on was based on label restriction for applications made at the maximum rate (8lbs ai/A) for fruit tree applications.

ii. Post-Application Dermal Exposure

Dermal pos t-application expos ur e for adults and teenagers harvesting or pruning fruit trees w a s assessed using TCs from an apple pruning study. Post- applic ation ex pos ure was estimated as the product of dislodgeable residue concentration (mg/ cm 2 

### ), transfer coefficient (cm 2 

### /hour), and time spent in the activity (hours).

Residue Data: Chemical spec ific dislodgeab le foliar residue data on olive trees (Klonne et al, 2000c) were used to assess dermal postapplication exposure for this scenario. Statistical analys is of this data was performed and the initial residue c oncentrations were determi ned to be 0.0035 mg/ cm 2



### . Residue dissipation is based on the half-life of 7 days (as determined by the stati stical analy s is of the carbaryl oliv e DFR data).

Transfer Coefficient: For the fruit tree scenari o, the distribution of

transfer coefficient was characteriz ed as lognormal, with a mean of 940

cm 2 

### /hour and a standard deviation of 260 cm 2 

### /hour. The TCs were based on an apple pruning study. All transfer coefficients are based on

indiv i duals wearing short-sleeved sh irts and short pants. A reduction factor was applied to account fo r body weights and surface area differences between adults and teenagers.

Duration: The time spent harvesting or performing postapplication maintenance activiti es was represented by a uniform distribution ranging from 0.17 hour/day to 1 hour/day. These estimates of time spent in the garden performi ng post application activities (as well as the frequency of applic ations) were based on the ORETF surv ey (Johnson et al, 1999).

e. Ornamental Garden - Sn ail and Slug Bait Scenarios

This asses sment includes the bait use of methiocarb in ornamental gardens. Applic ator exposure is calculated as the product of the unit exposure (mg/lb ai handled), applic ation rate (l bs ai/ft 2 

### ), and area treated (ft 2 

### ).

Section I.D – Page 108 of 277

### i. Applicator Exposure

Unit Exposure: The dermal and inhalation UEs for the methiocarb snail and slug bait scenario were based on study data for disulfoton applications to residential shrubs and flower beds (Merricks, 2001). The surrogate data consist of dermal a nd inhalation measurements of

indiv i duals using granular produc ts. Specific ally, the field study was conducted in Vero Beach, Florida. A total of 15 volunteers were monitored using pass i ve dosimetry (hand/forearm wash solutions and personal air monitors). Application of the product was made by pouring the granules into the measuring cup/ lid attached to the product package, and then distributing the granules ont o the soil around the base of a

shrub or onto a flower bed. The granules were then soil-incorporated with a garden rake. E a ch voluntee r applied granular disulfoton around shrubs while wearing gloves and t hen again without gloves. Exposure data from the 15 replicates who did not wear gloves were reported

. A lognormal distribution with a mean of 0.23 mg/lb ai, a standard dev iation of 5.8 mg/lb ai, and maximum value of 3.4 mg/lb ai (representing the estimated 99th percentile of the l ognormal distribution) was used to assess dermal expos ure. A single poi nt estimate of 0.00001 mg/lb ai (1/2 LOQ) was used for the inhalation UE since all measured values for

inhalation were non-detects.

Application Rate: The applic ation rate used in this assessment is based on the maximum label applicat ion rate of 0.2 lbs ai/1000 ft 2



### .

Area Treated: The area treated was entered as a uniform

distribution of 10 to 2000 ft 2 

### ; these dimensions ar e based on data from

the 

### National Gardening Association Survey (Johnson et al, 1999) and professional judgment. The low value of 10 sq ft was based on the label direction for treating small areas. T he high value for ornamental bed size was determined by estimating the perimeter of 2200 ft 2 

### house. It is assumed that the majority of orna mental beds are located around the perimeter of the house.

ii. Post-Application Exposure

Since this product is formulated as a bait and is applied as a broadcast application over plant fo liage or to the soil surrounding ornamental plants, post- application expos ure is expected to be minimal in comparis on to the post-applic at ion exposure assessed for the ornamental use of carbaryl. Theref ore, post-application exposure was
not evaluated for the methiocarb snail and s l ug bait scenario.

Section I.D – Page 109 of 277

### f. Pet Collar Scenarios

The revised NMC CRA also considered exposures through the use of flea collar products fo r carbaryl and propoxur. These assessments rely on A gency default assu mptions for pet fur transferable

residues. The dermal contact facto r(s) for post-applicat ion exposure is based on a shampoo and groomer exposur e study for carbaryl (each groomer shampooed, brus hed and groomed 8 dogs) (Mester,1998b). Each groomer shampooed the dogs, picked them up w e t to be placed in crates until all the dogs were sham pooed. The dogs were then dried and groomed. These activ i ties are lik ely to result in higher contact factors

than intermittent contact wit h a pet wearing a collar.

i. Applicator Exposure

Applicator exposure was not directly considered in this assessment since it is expected to be minimal when compared to the post-applic ation expos ure.

ii. Post-Application Dermal Exposure

Post-applic ation dermal exposur e scenarios were considered for

both adults and childr en while post-app lication non-diet ary oral exposure scenarios (oral hand-to-mouth) w e re assumed to apply only to children ages 1-5 years old. These data, as described below, were used to assess the pet collar uses of both carbaryl and propoxu r. Frequency, timing, and probability of collar treatments are also incor porated in the
revised NMC CRA.

Dermal pos t-application expos ure (to adults and children) was calculated as the product of residue concentration (mg/ cm 2 

### ), the transfer coefficient (in cm 2 

### /hour), and the duration of expos ure (hours/day). A further description of each of these terms is presented below:

Residue Concentration: The fur residue concentration for the pet

collar assessment is based on the am ount of active ingredient in the respective pet collars and Agenc y default assumptions from the OPP Standard Operating Procedures (SOP s) for Residential Expos ure Assessment (USEPA, 1997b). The resi due values for carbaryl and propoxur are 0.00012 and 0.000069 mg/ cm 2 

### , respectively. Residues

were assumed to be available on a daily basis since pet collar products are designed to emit residues thr oughout their active period (120 days for carbaryl and 180 days for propoxur).

Transfer Coefficient: The transfer coefficients used in the dermal post-applic ation expos ure assessment were derived from a groomer

Section I.D – Page 110 of 277

### exposure study (Mester, 1998b) in which sixteen different veterinary personnel t r eated/handled eight dogs each, over a two to five hour time period. In this assessment, t he transfer coefficients for adults and children were derived assuming an av erage transfer efficiency of 2.97% from the previous OP pet fur resi due transfer efficiencies. For the revised NMC CRA, the data were used directly to generate an empirical
distribution for the derma l transfer coefficient. The selected TCs ranged

from 180 to 4700 cm 2 

### /hour for adults and from 66 to 1800 cm 2 

### /hour for

children. These empirical distributions were used for both pet collar

scenarios.

Duration: The time spent in this activity was assumed to follow a triangular distribution with a mi nimum value of 0.03 hours and a maximum value of 1.03 hours per day (F reeman et al, 2001). As part of this study, macroactivity and microac tivity data were collected via questionnaires and videotaping of 19 child ren (aged 3 to 12) for a four hour period. The videotapes from the observational portion of thi s study were analy zed to determine frequency of contacts for several mouthing behaviors, as well as duration of time each child spent in various

locations ar ound the home. The results of this study include severa l
measurements for the duration of time the observed children spent with their pets.

In this assessment, the duration of exposur e is assumed to be

continuous contact rather than th e intermittent contact normally associated with pet care (e.g. walking, feeding). Furthermore, dog collar residues are likely to be localiz ed around the neck, and therefore, contact with other areas of the pet will result in little to no exp o sur e

. OPP is attempting to draw the dist inction bet w een direct contact with a treated pet and the time s pent with a pet where ther e is limited contact. For example, time spent with pets in and around the house or sleeping in the same bed may not result in direct contact for the entire duration. The pet collar scenario assessed in the revised NMC CRA uses pet fur

residues transferred to indiv i duals at a rate found dur ing a study of shampooing and grooming, for a durat ion of approximately 1 hour. Use of these data to represent residenti al exposur e to pets is likely to

encompass all other potential exposure scenarios involv ing direct or indirect contact with treated pets.

iii. Oral (Hand-to- M outh) Post-Application Exposure

Post-applic ation expos ure through the oral (hand-to-mouth) route was also assessed for children ages 1-5 using the approach detailed in

Section I.D.4.i of this document.

Section I.D – Page 111 of 277

### The approach taken to assess hand-to-mouth expos ure is the

same as that used to assess the lawn scen a r io oral non -dietary

exposure. The initial hand loading values were determi ned as a portion of total dermal expos ure variable to the hands. Additionally, contact factors, fraction of surface area of hand mouthed (Zartarian, 2003), saliva extraction factor (Geno et al ., 1995; Fenske and Lu 1994; Wester and Maibach 1989)) are the same as those used in the lawn care

assessment. The estimates of m outhing frequency wer e derived from several exposure studies and videotaping studies. For the pet collar scenario, hand-to-mouth events pe r hour were based on indoor frequencies as defined by a Weibull distribution (mean = 13 events/hour, standard deviation = 18). The distribution for frequency of hand-tomouth events for indoor exposur es, (provided by ORD’s Dr. Jiaping Xue), was calculated based on the same methodology for outdoor exposures in the lawn care assessmen t. OP P believes that this an alysis provides the best available data to assess children’s hand-to-mouth

exposures. The duration of exposu re values used for the non-dietary exposure assessment are the same as those used in the dermal postapplication assessment for the pet collar scenarios presented above.

.

g. Golf Course Scenari o

i. Post-Application Dermal Exposure

Carbaryl is also used on golf courses. The current assessment addresses dermal post-application expos ure for adults and teens playing rounds of golf on treated courses. Post-application exposure was estimated as the product of tu rf-transferable residue (mg/ cm 2 

### ), tra n sfer coefficient (cm 2 

### /hour), and time spent in the activity (hours).

The percent of the population play ing golf and the percent of golf

courses that are treated with car baryl was also considered and incorporated into the assessment. The 1992 Golf Course Operations: Cost of Doing Busines s/Profitability survey conducted by the Center for Golf Course Management (CGC M) was us ed to establish the percent of

indiv i duals playing golf

. The CGC M survey reported t hat an average of 12% of the population plays golf. To determine the likelihood of playing golf on a treated golf course, percent of golf courses treated data provided by Doane’s GolfTrak (1 998-1999) were used. These data

indic a ted up to 25% of golf course s are treated with carbaryl, depending upon the region of use.

Residue Data: Since liquid broadcast applications to golf course turf are permitted, the liquid TTR data (Mester, 1999) w e re used to assess post-application expos ure for the golf course scenario. Statistical analys is of these data was perform ed and an initial concentration of

Section I.D – Page 112 of 277

### 0.00065 mg/ cm 2 

### was calculated and used in this assessment. Dissipation is based on a 3.6 day half-life. For details of the statisti cal analys is see Appendix II.D.2.

Transfer Coefficients: The surrogate data used to derive transfer

coefficients were based on two measurem ents of four individuals playing golf on two golf courses treated with ch lorothalonil (Ballee, 1990), and the exposure of golfers (four volunteer s) to fl urprimidol (Moran et al

, 1987). For both studies, an assumed transfer efficiency of 1% was used to calculate the transfer coefficients, since the studies were conducted using spray able formulations. Based on these two studies, a lognormal
distribution with a mean of 480 cm 2 

### /hour and a standard deviation of 160

cm 2 

### /hour was used to represent the transfe r coefficient. Th is distribution

was truncated at the calculated 99th percentile value of 960 cm 2 

### /hour.

All transfer coefficients are based on indiv i duals wearing short-sleeved shirts and short pants. A reduction factor was applied to account for body weight and surface area differences for adults and teenagers.

Duration: The expos ure duration for i ndividuals play ing golf was assumed to be a uniform distribution bounded at the low end by two hours and at the upper end at four hours. The four-hour value was obtained from the CGCM survey.



## 7. Risk

For all subpopulations , the MOEs are greater than 10 w hen all use s are considered, at t he 95th, 99th, and 99.9 th 

### percentiles. Graphs for all percentiles of regulatio n and all subpopulations are described in more detail in the cumulative chapter (F ), of this do cument. (See Appendix III for detailed graphs.)



## 8. Summary

This asses sment relied upon the best available data from all

sources that could be identified. S ources included chemical specific and task force-generated data, as well as data from the scientific literature.

The revised NMC CRA assessm ent was performed for the Southeast region of the United States. While insect growth may slow during the winter months in the S outh, unlike other regions of the country, there is no period of dorman cy. Since the growing seas on is longer in the South and the associ ated pest pressures are therefore greater, this assessment provides a worst case estimate of expos ure. The residential asses sment of th e revised NMC CRA includes the carbaryl lawn and golf course us e, the carbaryl vegetable and ornamental garden use, the methiocarb snail and slug bait use, the

Section I.D – Page 113 of 277

### carbaryl fruit tree use, and the carbar yl and propoxur pet collar uses. All MOEs are greater than 10 and therefore not of concern.

Section I.D – Page 114 of 277Table I.D-4.Summary of NMC Residental Exposure ScenariosLawn Care ScenarioApplicatorExposureUnit ExposureBoth dermaland inhalationexposureroutes wereconsidered.ORETF studies were used for the granularbroadcast and liquid spottreatment scenarios.The ORETF (Outdoor Residential Exposure TaskForce)submitteda report (Klonne, 1999) inwhich a varietyof productswere used onturf.In these studies, bothhomeownersand lawn care operators (LCOs)weremonitored followingbroadcast applications to turf.All ofthe data submitted in this report were completed in a series ofstudies.The two studiesthatmonitored homeowner exposure resulting from granular spreader (Klonne, 1999/OMA003 Study) and hose-end sprayer (Klonne,1999/OMA004Study) applicationswereusedin this assessment.Volunteers participating inthese exposures studieswere adultnon-professionals whouse pesticides ontheir own gardens and lawns.Many of the volunteers selected assubjects in thesestudiesweremembers of gardenclubs.All volunteers made their applicationswithout specificinstruction fromthe studyinvestigators.Unit exposures estimatedfrom thesestudiescover various clothing scenariosthat range fromwearingshort pantsand short-sleeved shirts, to long pantsand long-sleeved shirts.All dermal and inhalationunit exposurewere normalized andexpressed asmilligramsexposure per pound of active ingredient handled (mg/lbai) (referred to as unit exposures, or UE). Thelognormal distributionsof the UEs for the lawnapplicator scenarios areshown in Table I.D.2.Application RateFor all scenarios assessed, OPP used the maximum application rateto assess exposure (8 lbs ai/A was used for the lawn carescenario).Area TreatedAn importantvariable for estimating home-owner applicatorexposure is thesize of the lawn.OPP considered the average andmedian lawnsizes reportedin a journal article by Vinlove andTorla(1995).Themeans and medians were~13,000 ft2.However,the authors noted problemsinterpretingthedata since it is basedprimarily on low income houses and consists of adjustments ofthe lot size bythe house's foundation (footprint) only.The data donot considerotherstructures such as decks orother greenspacesuch asgardens, whichcan reportedly reduce the lot sizebyup to 50%.Similar lawn sizeswere noted in ORETFstudy(Johnson et al, 1999) withsimilar problems encountered with respect to confounding variables such asdecks and other greenspaces.For this assessment, OPP useda uniform distribution for lawn size bounded by 1000 ft2 and 20,000ft2.The lower endofthis range considers smaller lawns for residencessuch astown houses.The upper bound of20,000 ft2 (~ 1⁄2 acre) appearsreasonable given the type of applicationequipment assumed to beused by residential applicators.Information from theORETFsurvey also indicates that many pesticideusers makespot treatments of insecticides.Similarly for spot treatments, OPP assumeda uniform distribution for treated area bounded by 100 ft2 and 1000 ft2.Dermal Post-Application ExposureResidueThere arechemical-specific turf transferable residue (TTR) data for granular formulationsof carbaryl (Krolski, 2005).This studywasdesigned to determinetransferable residuesof carbaryl from both irrigated and non-irrigated turf treated with SEVIN® 2G (seeAppendix II.D.2 for study summary).Measuredcarbaryl residuesrapidly declined over the first4 hours and then leveled off orroseSection I.D – Page 115 of 277slightly after 12 hours.By the 24-hoursampling interval, the residues declinedto approximately 10 percentof the corresponding 0­time residuevalue and thensteadily dropped to below1.0 percentof the corresponding 0-timeresidue values by 3- to 5-days aftertreatment. In order to provide a conservative estimate ofexposurefor the revised NMCCRA,only the samples fromthe nonirrigated site in Florida were used. Thisassessment assumes an initial concentration of 0.00021 mg/ cm2.Dissipation is based on a0.8 day half-life, with residuesset to zero 14 days after application.Although the carbaryl TTR studiesshowresidues below1% ofthe initial residues by 3- to 5-days after application, to provide aconservative estimate of exposure, it is assumed that carbarylresidues on the lawn would be available for up to 2 weeks after application.TCTransferCoefficients used to assesschildren’sexposure to treatedturf:One study was used to assess children’s dermal exposure resultingfrom granularapplicationsto residentialturf (Vaccaro, 1996).In this study, a granularformulation of chlorpyrifoswas applied, after which seven adultsperformed pre-choreographedactivitiesintended to mimic a typicalchild’sbehavior.The subjects performedthese activities for a periodof four hours beginningafter theturf had dried.Turf had been treated earlierwith a granularformofchlorpyrifosand exposurewas estimated in the studybymonitoring the amount of achlorpyrifosmetabolite– 3,4,5, 6-TCP– excretedover the followingperiod of 6days.Thismethoddirectly measuresinternaldose andwas used to back-calculate ageneric“to theskin” transfer coefficientby usingchemicalspecific dermal absorption data for chlorpyrifos (Nolanet al, 1984),Thesedatawere further adjusted to account for differencesinsurface areaof adults and children.Thestudy data discussed above was fit to a lognormal distributionwith an arithmeticmean of1970 cm2/hrwith a standard deviation of1426 cm2/hr.The lognormal distribution was truncated at the calculated99thpercentile ofthe distribution(i.e. 7224 cm2/hr for the granularapplication)in order to avoid adistribution whichcontained values thatwere wellbeyond those that are deemed reasonable. (see Appendix II.D.2 for studysummary and details of the distributional analysis).TransferCoefficients usedto assess adult exposure to treated turf:The Vaccarostudy data discussed above were also used to assess exposure to adults following granularapplications.Therevised NMCCRA used adistribution ofvalues for the transfercoefficient characterized by alognormal distribution withanarithmeticmean, of 5376 cm2/hr and a standard deviation of 4717 cm2/hr for the granularapplication.The lognormal distributionwas truncated at the calculated 99th percentile of thedistribution (i.e. 23436cm2/hr for the granular application.See AppendixII.D.2 for study summary and detailsof the distributional analysis).DurationAnother important variable for addressing post-application exposure from home lawn treatment is the duration of time spent onlawns.In this revisedNMC CRA, cumulative distributions of durations on lawns ofup to two hourswere used to address adultexposure on lawns. Thesedata are presented in Table 15-64of the EPA’s Exposure Factor’sHandbook (EFH)(USEPA, 1997a);however, OPP notes that the percentiles above the 95th have the same values(121 minutes).A similar cumulative distribution wasgiven for children agesone to five.In order to be protectiveof children and to address the uncertainty in the upper percentiles ofthe exposurefactor data, OPP selectedan empirical distribution(which was expressed asa cumulative distribution function) fromEFH Table 15-80with a bound of 3.5 hoursfor children (USEPA, 1997a).Thisdistribution represents the amount of time spentoutdoorsrather than just on lawns.This adjustment allowsfor additional time thatchildren may spend outdoors (such asparksandschools)where there is potential for additional contactwith treated turf.Oral non-dietary Post-Application ExposureInitial HandLoadingsHand loading(HR/2)is based on loadingconcepts used in SHEDSand CARES.The initial amount of residue availableon thehand isdetermined as a percentage of total dermal exposure.The EPA EFH indicates that the surface area of the hands isapproximately 6% of the total surface area of the bodyfor children age 4 and under; 5% for children age 5and over(USEPA,2002c).Thealgorithmassumes mouthing one handat a time,therefore, hand loading residues are dividedby 2.Section I.D – Page 116 of 277Frequency ofmouthing eventsFor the revised NMCCRAassessment, the frequencyof hand-to-mouth events isbased onXue et al, 2007.The estimates ofmouthing frequency were derived fromseveral exposure studiesand videotapingstudies.Statistical analysis indicated that aWeibull distribution best fit the data.For the lawncarescenario, hand-to-mouthevents per hour were based on outdoorfrequencies asdefined by a Weibull distribution(mean = 6 events/hour, standard deviation =8).OPP believes that the metaanalysiscitedabove provides the best available datato assess children’s hand-to-mouth exposures.Surface AreaMouthedThe revisedNMCCRA relied on Zartarian’s (2003) analysisof surface area of hand mouthed.The analysisused the Leckie, et al,2000 data to determine thefraction of the hand mouthed.The fraction of hand mouthed valueswere fit witha beta distribution(mean = 0.13events/hour, standard deviation = 0.06).Saliva ExtractionFactorTo address the removal of residuesfromthe hands by saliva duringmouthing events, several studieswere considered.Theremoval efficiency of residues onhands by saliva andothersubstances(e.g., ethanol) suggests a range ofremoval efficiencies(Geno et al., 1995; Fenskeand Lu 1994;Westerand Maibach 1989).Based onthe above studies, a uniform distribution of 20% to50% was usedin this assessmentfor saliva extraction factors.DurationThe time spent on the lawn was estimated as acumulative distribution ranging from 0 hours to3.5 hours.To be protective ofchildrens’ exposure and to address the uncertainty of the upper percentiles of the exposure factor data, OPP selecteda cumulativedistribution from EFH (USEPA, 1997a)Table 15-80 with a boundof 3.5 hoursfor children 1 to 5 years old.This distributionrepresents the amount of time spent outdoors. Thisallows for the time that children spend outdoors not only at home but also inparks and near schools.Vegetable Garden ScenarioDermal and Inhalation Applicator ExposureUnit ExposureDermal and inhalation unitexposureswere derived from chemical-specific data (Mester, 1998a) for dust (shake/pour), trigger pumpsprayer, and liquid hose-endsprayer applicationsto vegetable gardens.The UE for all garden scenariosarebased on lognormaldistribution aslisted in Table I.D.3.Application RateAn application rate of 2 lbs ai/A was usedfor all liquidvegetable gardenscenarios(hose-end sprayers and trigger pump sprayers)even though trigger pump sprayer rates are considerably lower.This assessmentconservatively usesthe 2 lb ai/A application ratefor all liquid scenarios.Due to recent mitigation, the maximum application ratefor dust formulationsis 0.5lbs ai/container(seeTable I.A).The exposure assessment fordust formulation appliedto home gardens assumes use of one entire container pertreatment.Area TreatedFor vegetablegardens, thearea treatedwas enteredas a lognormal distribution(mean= 4600 ft2, standard deviation =1500 ft2, andTaximum = 8000 ft2); these dimensionsare based ondata from the National Gardening Association Survey(Johnson et al, 1999).In these assessments, it is assumed thatthe entire garden istreated.Home gardensconsist of many types of vegetables whichallmay not be treated since they tend to have different pest pressures (e.g. squash vine borerand corn earworm may notappear atthe same time).Dermal Post-Application ExposureResidueChemical-specific dislodgeable foliarresidue data on sunflowers(Klonne et al, 1999)were used to assessdermal post-applicationexposure.Although OPP has additional information regarding carbaryl specific DFR data oncabbage (Klonne et al, 2000a), thesunflower DFR data wereused since the residues detected in thesunflower study were higher than thosedetected in the cabbagestudy.Statistical analysisof the carbarylsunflower DFRdata was performed.Theinitial residue concentrations and thehalf-lifewere determined to be 0.0061 mg/ cm2and 5 days, respectively.Section I.D – Page 117 of 277TCFor the vegetable garden scenario, transfer coefficients werecharacterized by auniform distribution ranging from 180 to1000cm2/hour, to reflect a rangeof gardeningtasks for a variety ofcropsof differing heights and foliage development. The TCs usedinthis assessment were derived from studies onchrysanthemum pinching(Rotondaro, 2000)and tomato harvesting(USEPA, 2000e).All transfercoefficients arebased on individualswearing short-sleeved shirtsand short pants.A reductionfactorwas applied toaccount for body weights and surface area differencesbetween adults and teenagers.DurationThe time spent harvesting or performingpost-application maintenance activitieswas represented by a uniform distribution rangingfrom 0.17 hour/day to 1 hour/day.These estimates oftime spent in the gardenperforming post application activities (as wellasthefrequency of applications)were based onthe ORETFsurvey (Johnson et al, 1999).OrnamentalPlants and Shrub ScenarioDermal and Inhalation Applicator ExposureUnit ExposureDermal and inhalation unitexposureswere derived from chemical-specific data for carbaryl used on ornamental plants (Mester,1998a; Merricks, 1998).The UE for all garden scenarios are basedon lognormal distribution aslisted in Table I.D.3.Application RateAn application rate of 2 lbs ai/A was usedfor all liquidvegetable gardenscenarios(hose-end sprayers and trigger pump sprayers)even though trigger pump sprayer rates are considerably lower.This assessmentconservatively usesthe 2 lb ai/A application ratefor all liquid scenarios.Due to recent mitigation, the maximum application ratefor dust formulationsis 0.5lbs ai/container(seeTable II.A).The exposure assessment for dust formulation appliedto ornamentalgardens assumes use ofone entire container pertreatment.Area TreatedThe area treated was entered as a uniform distributionof 500 to 2000 ft2;these dimensionsarebasedon data from the NationalGardeningAssociation Survey (Johnsonet al, 1999) and professional judgment.The ornamental bed size was determinedbyestimating the perimeter of2200 ft2house.It is assumed that the majority of ornamental bedsare located around the perimeter ofthe house.Dermal Post-Application ExposureResidueChemical-specific dislodgeable foliarresidue data on sunflowers(Klonne et al, 1999)were used to assessdermal post-applicationexposure fromharvestingor performingmaintenanceactivitiesin ornamental gardens.Although OPP has additional informationregarding carbaryl specificDFR data oncabbage (Klonne et al, 2000a), the sunflower DFR data were used since the residuesdetected in the sunflower study were higher than those detected inthe cabbage study.A statistical analysisof this datawasperformed andthe initial concentrationwas estimatedto be 0.0061mg/ cm2.Residue dissipation is based on the half-life of 5 days.The half-life used in thisassessment was determined from the statistical analysisof the carbaryl sunflower DFR data.Amoredetailed explanation of thestatistical analysis of thisdata is provided in Appendix II.D.2.TCFor the ornamental garden scenario, a uniform distribution of transfercoefficients, ranging from 99 to 550cm2/hour, was used toreflect a range of gardeningtasks.TheTCs used in this assessment were derived from studies that evaluated chrysanthemumpinching (Rotondaro, 2000)and nurserystockpruning(Klonne et al, 2000b).All transfer coefficients are based on individualswearingshort-sleeved shirtsandshort pants.A reduction factorwas applied toaccount for body weights and surface areadifferencesbetween adultsand teenagers.Section I.D – Page 118 of 277DurationThe time spent harvesting or performingpost-application maintenance activitieswas represented by a uniform distribution rangingfrom 0.17 hour/day to 1 hour/day.These estimates oftime spent in the gardenperforming post-application activities (as wellasthefrequency of applications)were based onthe ORETFsurvey (Johnson et al, 1999).Fruit TreeScenarioDermal and Inhalation Applicator ExposureUnit ExposureThe dermal and inhalationunit exposureswere derived from chemical-specific data for liquidhandwand applications to fruit trees(Merricks, 1998). These unit exposuresare based onstudy data inwhich applicationswere made with handwands, spraying belowthe waist aswell asoverhead.The UEs for fruit treescenarioare based on lognormal distribution as listedin Table I.D.3.Application RateFor all scenarios assessed, OPP used the maximum application rateto assess exposure (8 lbs ai/A was used for the fruit treescenario).Area TreatedFor fruit trees, most of which are of the dwarf variety and thereforeoccupy relatively smallareas, the area treatedwas entered asauniform distribution (minimum 500 ft2, maximum 1000 ft2).The maximum value in this distribution was based on label restriction forapplicationsmade at themaximum rate(8lbs ai/A) for fruit tree applications.Dermal Post-Application ExposureResidueChemicalspecific dislodgeable foliarresidue data on olive trees(Klonne et al, 2000c) were used to assessdermal post-applicationexposure for this scenario.Statistical analysis of thisdata was performed and the initial residue concentrationswere determined tobe 0.0035 mg/ cm2.Residue dissipationis based on the half-life of 7 days (as determinedby the statisticalanalysisof the carbarylolive DFR data).TCFor the fruit tree scenario, the distribution of transfer coefficient was characterized as lognormal, with a mean of 940 cm2/hour and astandard deviation of 260 cm2/hour. TheTCs were based on an apple pruningstudy.All transfer coefficients are based onindividuals wearing short-sleeved shirtsand short pants.A reduction factorwas applied to account for body weights andsurfacearea differences between adults and teenagers.DurationThe time spent harvesting or performingpost-application maintenance activitieswas represented by a uniform distribution rangingfrom 0.17 hour/day to 1 hour/day.These estimates oftime spent in the gardenperforming post-application activities (as wellasthefrequency of applications)were based onthe ORETFsurvey (Johnson et al, 1999).OrnamentalGarden - Snail and Slug Bait ScenarioDermal and InhalationApplicatorExposureUnit ExposureThe dermal and inhalationUEs for the methiocarbsnail and slugbait scenariowere based on study data for disulfoton applicationsto residentialshrubsand flowerbeds (Merricks, 2001).The surrogate data consist of dermaland inhalationmeasurements ofindividuals using granularproducts. Specifically, the field study wasconducted inVero Beach, Florida.A total of 15 volunteerswere monitored using passive dosimetry (hand/forearm washsolutionsand personal air monitors).Application of the product wasmade by pouring the granules into the measuring cup/lid attachedto the productpackage, andthen distributing the granules ontoSection I.D – Page 119 of 277the soil around the base of a shrub oronto a flower bed.Thegranules were thensoil-incorporated with a garden rake.Eachvolunteer applied granulardisulfoton around shrubswhile wearing gloves and then again without gloves.Exposure datafrom the15 replicateswho did not wear gloveswere reported.A lognormaldistribution with a mean of 0.23 mg/lb ai, a standard deviation of5.8 mg/lb ai, and maximum value of 3.4 mg/lb ai (representing the estimated 99th percentileofthe lognormal distribution) wasusedto assess dermal exposure.A single point estimate of0.00001mg/lb ai (1/2 LOQ)was used for the inhalation UE since allmeasured values for inhalation were non-detects.Application RateThe application rate used inthis assessment is based onthe maximum label application rateof 0.2 lbs ai/1000 ft2.Area TreatedThe area treated was entered as a uniform distributionof 10 to 2000ft2; these dimensionsare based on datafrom theNationalGardeningAssociationSurvey(Johnsonetal,1999)and professional judgment.Thelow value of 10 sq ft was based on thelabeldirection for treating small areas.The high value for ornamentalbed size was determinedby estimating theperimeter of 2200ft2house.It is assumed that the majority of ornamentalbeds are located around the perimeter ofthe house.Pet Collar ScenariosDermal Post-Application ExposureResidueThe fur residue concentration for the petcollar assessment is based on the amount of active ingredient in the respectivepet collarsand Agency default assumptions from theOPP SOPs for Residential ExposureAssessment (USEPA, 1997b).The residue valuesfor carbaryl and propoxurare 0.00012and 0.000069mg/ cm2, respectively. Residueswere assumed to be available ona dailybasissince pet collar products are designed to emit residuesthroughout their active period (120 days forcarbaryl and 180 days forpropoxur).TCThe transfercoefficients used in the dermal post-application exposure assessment was derived from a groomer exposure study(Mester, 1998b) in which sixteen different veterinarypersonnel treated/handledeight dogs each, over a two to five hour time period.In this assessment, the transfercoefficients for adults andchildren were derived assuminganaverage transfer efficiency of 2.97%from the previous OP pet fur residue transfer efficiencies.For the revisedNMCCRA, the datawere useddirectly to generate anempirical distribution for the dermal transfercoefficient.The selected TCs ranged from 180 to 4700cm2/hour for adultsand from 66to 1800 cm2/hour for children.These empirical distributionswereused for bothpet collar scenarios.DurationThe time spent in this activity was assumed to follow a triangulardistribution withminimum value of 0.03 hours, and a maximumvalue of 1.03 hours per day(Freeman etal, 2001).As part of this study, macroactivity and microactivity data were collectedviaquestionnaires and videotaping of 19 children(aged 3to 12) for afour hour period. The videotapes from the observational portionof this study were analyzedto determinefrequency ofcontactsforseveral mouthing behaviors, as well asduration of time eachchildspent in various locations around the home.Theresults ofthis study include several measurements for the durationof timethe observedchildrenspentwith their pets.Oral non-dietary Post-Application ExposureInitial HandLoadingsHand loading(HR/2)is based on loadingconcepts used in SHEDSand CARES.The initial amount of residue availableon thehand isdetermined as a percentage of total dermal exposure.The EPA Exposure Factors Handbook indicatesthat the surfacearea of the hands is approximately 6%of the total surface area of the bodyfor children age4 and under;5% for children age5 andover (USEPA, 2002c).The algorithm assumesmouthing one hand at a time, therefore, hand loading residues are divided by 2.Section I.D – Page 120 of 277Frequency ofmouthing eventsThe estimates of mouthingfrequency were derived from severalexposure studies and observational studies.For the pet collarscenario, hand-to-mouth events per hourwere based on indoorfrequenciesasdefined by aWeibull distribution (mean= 13events/hour, standard deviation = 18). The distribution for frequency of hand-to-mouth events for indoor exposures, provided byORD’sDr. Jiaping Xue, was calculated using thesamemethodology used to determinethe frequency of mouthing events foroutdoor exposures thatwas used in thelawncare assessment.Surface AreaMouthedThe revisedNMCCRA relied on Zartarian’s (2003) analysisof surface area of hand mouthed.The analysisused the Leckie, et al,2000 data to determine thefraction of the hand mouthed.The fraction of hand mouthed valueswere fit witha beta distribution(mean = 0.13events/hour, standard deviation = 0.06).Saliva ExtractionFactorTo address the removal of residuesfromthe hands by saliva duringmouthing events, several studieswere considered.Theremoval efficiency of residues onhands by saliva andothersubstances(e.g., ethanol) suggests a range ofremoval efficiencies(Geno et al., 1995; Fenskeand Lu 1994;Westerand Maibach 1989).Based onthe above studies, a uniform distribution of 20% to50% was usedin this assessmentfor saliva extraction factors.DurationThe durationof exposure valuesused for the non-dietary exposure assessment are thesameas those usedin the dermal post-application assessment forthe petcollar scenariospresentedabove.Golf Course ScenarioDermal Post-Application ExposureResidueSince liquid broadcast applicationsto golf course turf are permitted, the liquid TTR data (Mester, 1999) usedto assess postapplication exposure for the golf course scenario.Statisticalanalysis of this datawas performed andan initial concentration of0.00065 mg/ cm2was calculated and used in this assessment.Dissipation is based ona 3.6 day half-life.For detailsof thestatistical analysis see Appendix II.D.2.TCThe surrogate data used to derive transfer coefficients were basedon two measurements of four individuals playing golf on twogolfcourses treated with chlorothalonil (Ballee,1990), and the exposure of golfers(four volunteers) to flurprimidol (Moran etal,1987).For bothstudies, an assumed transfer efficiency of 1% was usedtocalculate the transfer coefficients, since the studieswereconducted using sprayableformulations.Based on these two studies, a lognormal distributionwith a meanof 480 cm2/hour and astandard deviation of 160 cm2/hour wasusedto represent the transfer coefficient.This distribution was truncated at the calculated99th percentile value of 960 cm2/hour.All transfercoefficients arebased on individualswearing short sleeved shirtsand shortpants.A reduction factorwas applied to account for body weight and surface area differencesfor adults and teenagers.DurationThe exposureduration for individuals playing golf was assumedto be a uniform distribution bounded at the low end by twohoursand at the upper end at four hours.The four-hour value was obtained fromthe CGCM survey.ApplicatorExposureSection I.D – Page 121 of 277Unit ExposureBoth dermaland inhalationexposureroutes wereconsidered.ORETF studies were used for the granularbroadcast and liquid spottreatment scenarios.The ORETF (Outdoor Residential Exposure TaskForce)submitteda report (Klonne, 1999) inwhich a varietyof productswere used onturf.In these studies, bothhomeownersand lawn care operators (LCOs)weremonitored followingbroadcast applications to turf.All ofthe data submitted in this report were completed in a series ofstudies.The two studiesthatmonitored homeowner exposure resulting from granular spreader (Klonne, 1999/OMA003 Study) and hose-end sprayer (Klonne,1999/OMA004Study) applicationswereusedin this assessment.Volunteers participating inthese exposures studieswere adultnon-professionals whouse pesticides ontheir own gardens and lawns.Many of the volunteers selected assubjects in thesestudiesweremembers of gardenclubs.All volunteers made their applicationswithout specificinstruction fromthe studyinvestigators.Unit exposures estimatedfrom thesestudiescover various clothing scenariosthat range fromwearingshort pantsand short-sleeved shirts, to long pantsand long- sleeved shirts.All dermal and inhalationunit exposurewere normalized andexpressed asmilligramsexposure per pound of active ingredient handled (mg/lbai) (referred to as unit exposures, or UE).Thelognormal distributionsof the UEs for the lawnapplicator scenarios areshown in Table I.D.2.Application RateFor all scenarios assessed, OPP used the maximum application rateto assess exposure (8 lbs ai/A was used for the lawn carescenario).Area TreatedAn importantvariable for estimating home-owner applicatorexposure is thesize of the lawn.OPP considered the average andmedian lawnsizes reportedin a journal article by Vinlove and Torla(1995).Themeans and medians were~13,000 ft2.However,the authors noted problemsinterpretingthedata since it is basedprimarily on low income houses and consists of adjustments ofthe lot size bythe house's foundation (footprint) only.The data donot considerotherstructures such as decks orother greenspacesuch asgardens, whichcan reportedly reduce the lot sizebyup to 50%.Similar lawn sizeswere noted in ORETFstudy(Johnson et al, 1999) withsimilar problems encountered with respect to confounding variables such asdecks and other greenspaces.For this assessment, OPP useda uniform distribution for lawn size bounded by 1000 ft2and 20,000ft2.The lower end ofthis range considers smaller lawns for residencessuch astown houses.The upper bound of20,000 ft2(~ 1⁄2 acre) appearsreasonable given the type of applicationequipment assumed to beused by residential applicators.Information from theORETFsurvey also indicates that many pesticideusers makespot treatments of insecticides.Similarly for spot treatments, OPP assumeda uniform distribution for treated area bounded by 100 ft2and 1000 ft2.Dermal Post-Application ExposureResidueThere arechemical-specific turf transferable residue (TTR) data for granular formulationsof carbaryl (Krolski, 2005).This studywasdesigned to determinetransferable residuesof carbaryl from both irrigated and non-irrigated turf treated with SEVIN® 2G (seeAppendix II.D.2 for study summary).Measuredcarbaryl residuesrapidly declined over the first4 hours and then leveled off orroseslightly after 12 hours.By the 24-hoursampling interval, the residues declinedto approximately 10 percentof the corresponding 0­time residuevalue and thensteadily dropped to below1.0 percentof the corresponding 0-timeresidue values by 3- to 5-days aftertreatment. In order to provide a conservative estimate ofexposurefor the revised NMCCRA,only the samples fromthe nonirrigated site in Florida were used.Thisassessment assumes an initial concentration of 0.00021 mg/ cm2.Dissipation isbased ona 0.8 day half-life, with residues set to zero 14 days after application.Although the carbaryl TTR studiesshow residuesbelow 1%of the initial residuesby 3- to 5-days after application,to provide a conservative estimate of exposure, it is assumed that carbarylresidues on the lawn would be available for up to 2 weeks after application.Section I.D – Page 122 of 277TCTransferCoefficients used to assesschildren’sexposure to treatedturf:One study was used to assess children’s dermal exposure resultingfrom granularapplicationsto residentialturf (Vaccaro, 1996).In this study, a granularformulation of chlorpyrifoswas applied, after which seven adultsperformed pre-choreographedactivitiesintended to mimic a typicalchild’sbehavior.The subjects performedthese activities for a periodof four hours beginningafter theturf had dried.Turf had been treated earlierwith a granularformofchlorpyrifosand exposurewas estimated in the studybymonitoring the amount of achlorpyrifosmetabolite– 3,4,5, 6-TCP– excretedover the followingperiod of 6days.Thismethoddirectly measuresinternaldose andwas used to back-calculate ageneric“to theskin” transfer coefficientby usingchemicalspecific dermal absorption data for chlorpyrifos (Nolanet al, 1984).Thesedatawere further adjusted to account for differencesinsurface areaof adults and children.Thestudy data discussed above was fit to a lognormal distributionwith an arithmeticmean of1970 cm2/hrwith a standard deviation of1426 cm2/hr.The lognormal distribution was truncated at the calculated99thpercentile ofthe distribution(i.e. 7224 cm2/hr for the granularapplication)in order to avoid adistribution whichcontained values thatwere wellbeyond those that are deemed reasonable. (See Appendix II.D.2 for studysummaryand details of the distributional analysis.)TransferCoefficients usedto assess adult exposure to treated turf:The Vaccarostudy data discussed above were also used to assess exposure to adults following granularapplications.Therevised NMCCRA used adistribution ofvalues for the transfercoefficient characterized by alognormal distribution withanarithmeticmean, of 5376 cm2/hr and a standard deviation of 4717 cm2/hr for the granularapplication.The lognormal distributionwas truncated at the calculated 99th percentile of thedistribution (i.e. 23436cm2/hr for the granular application).(See AppendixII.D.2 for study summary and detailsof the distributional analysis.)DurationAnother important variable for addressing post-application exposure from home lawn treatment is the duration of time spent onlawns.In this revisedNMC CRA, cumulative distributions of durations on lawns ofup to two hourswere used to address adultexposure on lawns.These data are presented in Table 15-64 of the EPA’s Exposure Factor’sHandbook(EFH)(USEPA, 1997a);however, OPP notes that the percentiles above the 95th have the same values(121 minutes).A similar cumulative distribution wasgiven for children agesone to five.In order to be protectiveof children and to address the uncertainty in the upper percentiles ofthe exposurefactor data, OPP selectedan empirical distribution(which was expressed asa cumulative distribution function) fromEFH Table 15-80with a bound of 3.5 hours for children (USEPA, 1997a). Thisdistribution represents the amount of time spentoutdoorsrather than just on lawns.This adjustment allowsfor additional time thatchildren may spend outdoors (such asparksandschools)where there is potential for additional contactwith treated turf.Oral non-dietary Post-Application ExposureInitial HandLoadingsHand loading(HR/2)is based on loadingconcepts used in SHEDSand CARES.The initial amount of residue availableon thehand isdetermined as a percentage of total dermal exposure.The EPA EFH indicates that the surface area of the hands isapproximately 6% of the total surface area of the bodyfor children age 4 and under; 5% for children age 5and over(USEPA,2002c).Thealgorithmassumes mouthing one handat a time,therefore, hand loading residues are dividedby 2.Frequency ofmouthing eventsFor the revised NMCCRAassessment, the frequencyof hand-to-mouth events isbased onXue et al, 2007.The estimates ofmouthing frequency were derived fromseveral exposure studiesand videotapingstudies.Statistical analysis indicated that aWeibull distribution best fit the data.For the lawncarescenario, hand-to-mouthevents per hour were based on outdoorfrequencies asdefined by a Weibull distribution(mean = 6 events/hour, standard deviation =8).OPP believes that the metaanalysiscitedabove provides the best available datato assess children’s hand-to-mouth exposures.Section I.D – Page 123 of 277Surface AreaMouthedThe revisedNMCCRA relied on Zartarian’s (2003) analysisof surface area of hand mouthed.The analysisused the Leckie, et al,2000 data to determine thefraction of the hand mouthed.The fraction of hand mouthed valueswere fit witha beta distribution(mean = 0.13events/hour, standard deviation = 0.06).Saliva ExtractionFactorTo address the removal of residuesfromthe hands by saliva duringmouthing events, several studieswere considered.Theremoval efficiency of residues onhands by saliva andothersubstances(e.g., ethanol) suggests a range ofremoval efficiencies(Geno et al., 1995; Fenskeand Lu 1994;Westerand Maibach 1989).Based onthe above studies, a uniform distribution of 20% to50% was usedin this assessmentfor saliva extraction factors.DurationThe time spent on the lawn was estimated as acumulative distribution ranging from 0 hours to3.5 hours.To be protective ofchildrens’ exposure and to address the uncertainty of the upper percentiles of the exposure factor data, OPP selecteda cumulativedistribution from EFH (USEPA, 1997a)Table 15-80 with a boundof 3.5 hoursfor children 1 to 5 years old.This distributionrepresents the amount of time spent outdoors.This allows forthe time that children spend outdoors not only at home but alsoinparks and near schools.Vegetable Garden ScenarioDermal and Inhalation Applicator ExposureUnit ExposureDermal and inhalation unitexposureswere derived from chemical-specific data (Mester, 1998a) for dust (shake/pour), trigger pumpsprayer, and liquid hose-endsprayer applicationsto vegetable gardens.The UE for all garden scenariosarebased on lognormaldistribution aslisted in Table I.D.3.Application RateAn application rate of 2 lbs ai/A was usedfor all liquidvegetable gardenscenarios(hose-end sprayers and trigger pump sprayers)even though trigger pump sprayer rates are considerably lower.This assessmentconservatively usesthe 2 lb ai/A application ratefor all liquid scenarios.Due to recent mitigation, the maximum application ratefor dust formulationsis 0.5lbs ai/container.Theexposure assessment for dust formulation applied to homegardensassumes useof one entirecontainer pertreatment.Area TreatedFor vegetablegardens, thearea treatedwas enteredas a lognormal distribution(mean= 4600 ft2, standard deviation =1500 ft2, andmaximum = 8000 ft2); these dimensionsare based ondata from the National Gardening Association Survey(Johnson et al, 1999).In these assessments, it is assumed thatthe entire garden istreated.Home gardensconsist of many types of vegetables whichallmay not be treated since they tend to have different pest pressures (e.g. squash vine borerand corn earworm may notappear atthe same time).Dermal Post-Application ExposureResidueChemical-specific dislodgeable foliarresidue data on sunflowers(Klonne et al, 1999)were used to assessdermal post-applicationexposure.Although OPP has additional information regarding carbaryl specific DFR data oncabbage (Klonne et al, 2000a), thesunflower DFR data wereused since the residues detected in thesunflower study were higher than thosedetected in the cabbagestudy.Statistical analysisof the carbarylsunflowerDFR datawasperformed.Theinitial residue concentrations and thehalf-lifewere determined to be 0.0061 mg/ cm2and 5 days, respectively.TCFor the vegetable garden scenario, transfer coefficients werecharacterized by auniform distribution ranging from 180 to1000cm2/hour, to reflect a rangeof gardeningtasks for a variety ofcropsof differing heights and foliage development. The TCs usedinthis assessment were derived from studies onchrysanthemum pinching(Rotondaro, 2000)and tomato harvesting(USEPA, 2000e).All transfercoefficients arebased on individualswearing short-sleeved shirtsand short pants.A reductionfactorwas applied toaccount for body weights and surface area differencesbetween adults and teenagers.Section I.D – Page 124 of 277DurationThe time spent harvesting or performingpost-application maintenance activitieswas represented by a uniform distribution rangingfrom 0.17 hour/day to 1 hour/day.These estimates oftime spent in the gardenperforming post application activities (as wellasthefrequency of applications)were based onthe ORETFsurvey (Johnson et al, 1999).OrnamentalPlants and Shrub ScenarioDermal and Inhalation Applicator ExposureUnit ExposureDermal and inhalation unitexposureswere derived from chemical-specific data for carbaryl used on ornamental plants (Mester,1998a; Merricks, 1998).The UE for all garden scenarios are basedon lognormal distribution aslisted in Table I.D.3.Application RateAn application rate of 2 lbs ai/A was usedfor all liquidvegetable gardenscenarios(hose-end sprayers and trigger pump sprayers)even though trigger pump sprayer rates are considerably lower.This assessmentconservatively usesthe 2 lb ai/A application ratefor all liquid scenarios.Due to recent mitigation, the maximum application ratefor dust formulationsis 0.5lbs ai/container.Theexposure assessment for dust formulation applied to ornamentalgardens assumes use of oneentire container per treatment.Area TreatedThe area treated was entered as a uniform distributionof 500 to 2000 ft2;these dimensionsarebasedon data from the NationalGardeningAssociation Survey (Johnsonet al, 1999) and professional judgment.The ornamental bed size was determined byestimating the perimeter of2200 ft2house.It is assumed that the majority of ornamental bedsare located around the perimeter ofthe house.Dermal Post-Application ExposureResidueChemical-specific dislodgeable foliarresidue data on sunflowers(Klonne et al, 1999)were used to assessdermal post-applicationexposure fromharvestingor performingmaintenanceactivitiesin ornamental gardens.Although OPP has additional informationregarding carbaryl specificDFR data oncabbage (Klonne et al, 2000a), the sunflower DFR data were used since the residuesdetected in the sunflower study were higher than those detected inthe cabbage study.A statistical analysisof this datawasperformed andthe initial concentrationwas estimatedto be 0.0061mg/ cm2. Residue dissipation is based onthe half-life of 5 days.The half-life used in thisassessment was determined from the statistical analysisof the carbaryl sunflower DFR data. A moredetailed explanation of thestatistical analysis of thisdata is provided in Appendix II.D.2.TCFor the ornamental garden scenario, a uniform distribution of transfercoefficients, ranging from 99 to 550cm2/hour, was used toreflect a range of gardeningtasks.TheTCs used in this assessment were derived from studies that evaluated chrysanthemumpinching (Rotondaro, 2000)and nurserystockpruning(Klonne et al, 2000b).All transfer coefficients are based on individualswearingshort-sleeved shirtsandshort pants.A reduction factorwas applied toaccount for body weights and surface areadifferencesbetween adultsand teenagers.DurationThe time spent harvesting or performingpost-application maintenance activitieswas represented by a uniform distribution rangingfrom 0.17 hour/day to 1 hour/day.These estimates oftime spent in the gardenperforming post application activities (as wellasthefrequency of applications)were based onthe ORETFsurvey (Johnson et al, 1999).Fruit TreeScenarioSection I.D – Page 125 of 277Dermal and Inhalation Applicator ExposureUnit ExposureThe dermal and inhalationunit exposureswere derived from chemical-specific data for liquidhandwand applications to fruit trees(Merricks, 1998). These unit exposuresare based onstudy data inwhich applicationswere made with handwands, spraying belowthe waist aswell asoverhead.The UEs for fruit treescenarioare based on lognormal distribution as listedin Table I.D.3.Application RateFor all scenarios assessed, OPP used the maximum application rateto assess exposure (8 lbs ai/A was used for the fruit treescenario).Area TreatedFor fruit trees, most of which are of the dwarf variety and thereforeoccupy relatively smallareas, the area treatedwas entered asauniform distribution (minimum 500 ft2, maximum 1000 ft2).The maximum value in this distribution was based on label restriction forapplicationsmade at themaximum rate(8lbs ai/A) for fruit tree applications.Dermal Post-Application ExposureResidueChemicalspecific dislodgeable foliarresidue data on olive trees(Klonne et al, 2000c) were used to assessdermal post-applicationexposure for this scenario.Statistical analysis of thisdata was performed and the initial residue concentrationswere determined tobe 0.0035 mg/ cm2.Residue dissipationis based on the half-life of 7 days (as determinedby the statisticalanalysisof the carbarylolive DFR data).TCFor the fruit tree scenario, the distribution of transfer coefficient was characterized as lognormal, with a mean of 940 cm2/hour and astandard deviation of 260 cm2/hour. TheTCs were based on an apple pruningstudy.All transfer coefficients are based onindividuals wearing short-sleeved shirtsand short pants.A reduction factorwas applied to account for body weights andsurfacearea differences between adults and teenagers.DurationThe time spent harvesting or performingpost-application maintenance activitieswas represented by a uniform distribution rangingfrom 0.17 hour/day to 1 hour/day.These estimates oftime spent in the gardenperforming post application activities (as wellasthefrequency of applications)were based onthe ORETFsurvey (Johnson et al, 1999).OrnamentalGarden - Snail and Slug Bait ScenarioDermal and InhalationApplicatorExposureUnit ExposureThe dermal and inhalationUEs for the methiocarbsnail and slugbait scenariowere based on study data for disulfoton applicationsto residentialshrubsand flowerbeds (Merricks, 2001).The surrogate data consist of dermaland inhalationmeasurements ofindividuals using granularproducts. Specifically, the field study wasconducted inVero Beach, Florida.A total of 15 volunteerswere monitored using passive dosimetry (hand/forearm washsolutionsand personal air monitors).Application of the product wasmade by pouring the granules into the measuring cup/lid attachedto the productpackage, andthen distributing the granules ontothe soil around the base of a shrub oronto a flower bed.Thegranules were thensoil-incorporated with a garden rake.Eachvolunteer applied granulardisulfoton around shrubswhile wearing gloves and then again without gloves. Exposure datafrom the15replicateswho did not weargloveswerereported.A lognormaldistributionwith a mean of 0.23mg/lb ai, a standard deviationof 5.8mg/lb ai, and maximum value of 3.4 mg/lb ai (representing the estimated 99th percentile of the lognormal distribution)was usedtoassess dermal exposure.A single point estimate of 0.00001 mg/lbai (1/2 LOQ)was used fortheinhalationUE since all measuredSection I.D – Page 126 of 277values for inhalation werenon-detects.Application RateThe application rate used inthis assessment is based onthe maximum label application rateof 0.2 lbs ai/1000 ft2.Area TreatedThe area treated was entered as a uniform distributionof 10 to 2000ft2; these dimensionsare based on datafrom theNationalGardeningAssociationSurvey(Johnsonetal,1999)and professional judgment.Thelow value of 10 sq ft was based on thelabeldirection for treating small areas.The high value for ornamentalbed size was determinedby estimating theperimeter of 2200ft2house.It is assumed that the majority of ornamentalbeds are located around the perimeter ofthe house.Pet Collar ScenariosDermal Post-Application ExposureResidueThe fur residue concentration for the petcollar assessment is based on the amount of active ingredient in the respectivepet collarsand Agency default assumptions from theOPP SOPs for Residential ExposureAssessment (USEPA, 1997b).The residue valuesfor carbaryl and propoxurare 0.00012and 0.000069mg/ cm2, respectively.Residueswere assumed to be available ona dailybasissince pet collar products are designed to emit residuesthroughout their active period (120 days forcarbaryl and 180 days forpropoxur).TCThe transfercoefficients used in the dermal post-application exposure assessment was derived from a groomer exposure study(Mester, 1998b) in which sixteen different veterinarypersonnel treated/handledeight dogs each, over a two to five hour time period.In this assessment, the transfercoefficients for adults andchildren were derived assuminganaverage transfer efficiency of 2.97%from the previous OP pet fur residue transfer efficiencies.For the revisedNMCCRA, the datawere useddirectly to generate anempirical distribution for the dermal transfercoefficient.The selected TCs ranged from 180 to 4700cm2/hour for adultsand from 66to 1800 cm2/hour for children.These empirical distributionswereused for bothpet collar scenarios.DurationThe time spent in this activity was assumed to follow a triangulardistribution withminimum value of 0.03 hours, and a maximumvalue of 1.03 hours per day(Freeman etal, 2001).As part of this study, macroactivity and microactivity data were collectedviaquestionnaires and videotaping of 19 children(aged 3to 12) fora four hour period.The videotapes from the observational portionof this study were analyzedto determinefrequency ofcontactsforseveral mouthing behaviors, as well asduration of time eachchildspent in various locations around the home.Theresults ofthis study include several measurements for the durationof timethe observedchildrenspentwith their pets.Oral non-dietary Post-Application ExposureInitial HandLoadingsHand loading(HR/2)is based on loadingconcepts used in SHEDSand CARES.The initial amount of residue availableon thehand isdetermined as a percentage of total dermal exposure.The EPA Exposure Factors Handbook indicatesthat the surfacearea of the hands is approximately 6%of the total surface area of the bodyfor children age4 and under;5% for children age5 andover (USEPA, 2002c).The algorithm assumesmouthing one hand at a time, therefore, hand loading residues are divided by 2.Frequency ofmouthing eventsThe estimates of mouthingfrequency were derived from severalexposure studies and observational studies.For the pet collarscenario, hand-to-mouth events per hourwere based on indoorfrequenciesasdefined by aWeibull distribution (mean= 13events/hour, standard deviation = 18). The distribution for frequency of hand-to-mouth events for indoor exposures, provided byORD’sDr. Jiaping Xue, was calculated using thesamemethodology used to determinethe frequency of mouthing events forSection I.D – Page 127 of 277outdoor exposures thatwas used in thelawncare assessment.Surface AreaMouthedThe revisedNMCCRA relied on Zartarian’s (2003) analysisof surface area of hand mouthed.The analysisused the Leckie, et al,2000 data to determine thefraction of the hand mouthed.The fraction of hand mouthed valueswere fit witha beta distribution(mean = 0.13events/hour, standard deviation = 0.06).Saliva ExtractionFactorTo address the removal of residuesfromthe hands by saliva duringmouthing events, several studieswere considered.Theremoval efficiency of residues onhands by saliva andothersubstances(e.g., ethanol) suggests a range ofremoval efficiencies(Geno et al., 1995; Fenskeand Lu 1994;Westerand Maibach 1989).Based onthe above studies, a uniform distribution of 20% to50% was usedin this assessmentfor saliva extraction factors.DurationThe durationof exposure valuesused for the non-dietary exposure assessment are thesameas those usedin the dermal post-application assessment forthe petcollar scenariospresentedabove.Golf Course ScenarioDermal Post-Application ExposureResidueSince liquid broadcast applicationsto golf course turf are permitted, the liquid TTR data (Mester, 1999) usedto assess postapplication exposure for the golf course scenario.Statisticalanalysis of this datawas performed andan initial concentration of0.00065 mg/ cm2was calculated and used in this assessment.Dissipation is based ona 3.6 day half-life. For detailsof thestatistical analysis see Appendix II.D.2.TCThe surrogate data used to derive transfer coefficients were basedon two measurements of four individuals playing golf on twogolfcourses treated with chlorothalonil (Ballee,1990), and the exposure of golfers(four volunteers) to flurprimidol (Moran etal,1987).For bothstudies, an assumed transfer efficiency of 1% was usedtocalculate the transfer coefficients, since the studieswereconducted using sprayableformulations.Based on these two studies, a lognormal distributionwith a meanof 480 cm2/hour and astandard deviation of 160 cm2/hour wasusedto represent the transfer coefficient.This distribution was truncated at the calculated99th percentile value of 960 cm2/hour.All transfercoefficients arebased on individualswearing short sleeved shirtsand shortpants.A reduction factorwas applied to account for body weight and surface area differencesfor adults and teenagers.DurationThe exposureduration for individuals playing golf was assumedto be a uniform distribution bounded at the low end by twohoursand at the upper end at four hours.The four-hour value was obtained fromthe CGCM survey.Section I.D – Page 128 of 277

# E. Cumulative Risk from NMC Pesticides in Drinking

Water

1. Introducti on

The Food Quality Protection Ac t (FQPA) of 1996 requir e s the Agency to assess the risks from di fferent pesticides having a common

mechanism of action, focusing on t he likelihood that a person will be concurrently exposed to multiple pest icides from multiple sources (food, drinking water, and residential uses). Ideally, data to support the drinking water portion of this expo sure assessment would consist of information on multiple pesticides and their transformation products, collected from sufficient drinking wa ter sources throughout the U.S. and at a sufficient frequency to reflect the range in spatial and temporal patterns of pesticide occurrence in water. The great diversity of geographic -, climatic-, and time-dependent factors that affect the levels of pesticide residues in water create s unique challenges in characterizing drinking water ex posure. EPA’s Offi ce of Pesticide Pr ograms (OPP) must rely on both available moni t o ring data and modeling to develop
sufficient data for use in the exposure assessment.

The Agenc y used the same methods for esti mating surface water exposure in the revised NMC CRA as it did in the OP CRA (US E PA, 2002b; FIFRA SAP, 2002) because of sim ilarities in use (both NMC s and OPs are insecticides), hazard endpoints (ChE inhibition occurring in the acute- or short-term), and expos ure requirements (esti m ates of peak concentrations and time-series distributions).

Unlike the OP pesticides for which surface water is the likely source of concern for drinking water, the NMCs also are likely to reach groundwater sources of drinking water. The Agenc y presented a conceptual model for ground water exposure and a pl an for evaluating the capability of three ground water models to es timate NMC concentrations to the

FIFRA SAP in Febru a ry, 2005. Based on feedback from the SAP (FIFRA SAP, 2005a), EPA followed up with a revised conceptual model and analys is plan using one of the m odels evaluated in the preliminary assessment (FIFRA SAP, 2005b)

.

The revised NMC CRA follows th e same approach as outlined in the preliminary assessment (U SEPA, 2005 b), and includes several additional ground water exposure scenar ios to further capture the range of high leac hing potent ial areas in high NMC use areas, as well as a more representative assumption for the depth to groundwater. Based on analys is of additional monitoring data, the A gency has been better able to identify the specific conditions t hat are likely to result i n high NMC

Section I.E – Page 129 of 277

### exposure in private drinking water wells and to better characterize the spatial extent of potentia l high NMC exposure areas.

In addition, the reported cumulative NMC exposures in this section

reflect the revised relat i ve potenc y

, inter-species, and FQPA safety

factors documented in the Hazard chapter (I.B) of this revised

assessment.



## 2. Probl em Form u lati on

Pesticide c oncentrations found in drinking water are not random, but are in large part determined by the amount, method, timing and location of pesticide applic ation, t he physical characteristics of the

watersheds and/or aquifers in which the community water supplies (CWS) or wells are located, and other environmental factors, such as rainfall, which can cause the pesticide to move from the l o cation where it was applied.

a. Drinking Water Exposure Needs for the NMC Cumulative

Assessment

For the NMC group, the toxicity e ndpoint of concern results from

short-ter m ex posure (acute effec t s)

. To ade quately characterize th e potential im pacts of pesticide residues in drinking water

, the estimated
residue concentrations need to reflect a sufficient frequency in time to capture peak concentrations. Because pesticide loads in surface water tend to move in relatively quick pulses in flowing water, frequent sampling is necessary to reliably capture peak concentrations for surface water sources of drinking water. Pesticide c oncentrations in groun d water are generally the result of l onger-term processes and less frequent sampling c an sufficiently characteri ze peak ground water concentrations.

The drinking water exposure assessment needs to account for the potential for any or all of the NMC residues included in the cumulative assessment group ( Table I.E-1) to occur together in drinking water sources. To realistically estimate exposures, the asse ssment must take into account those factors (crop uses, pest pressures, ti ming of application, etc.) that determine w hether more than one NMC pesticide can occur together in time and place. Although multiple NMC pesticides may be registered for use on the same crop, they may not necessarily be

used at the same time. Monito ring data could provide real-time
estimates of co-occurrence but needs to account for all of the potential NMCs used in the monitoring area, be of sufficient frequency to capture short-term peaks in pesticide exposure , parti cularly in s u rface water, and span sufficient years to capture the impact of variabilit y in use and weather patterns on pesticide transport.

Section I.E – Page 130 of 277

### Table I.E-1. NMC use patterns and availa bility of national monitoring data

Pesticide Use pa ttern l i kely to result in

w a te r expo s ure? Av ailabilit y of nation al w ater monitoring d a ta?

Aldica rb (in c l uding sulfoxide, sulfone degradate s ) Yes (a gri c ultu ral uses) Yes: NAWQA

, Rese rvoir

monitori ng; st ate monitori n g ; regi st ra nt st u d ies

Carba r yl Yes (a gri c ultu ral and re side ntial

use s ) Yes: NAW QA

, Rese rv oir

monitori ng; re gistra nt studi es Carbofu r an (i nclu ding 3-hyd r oxyca r bofura n) Yes 1 (ag r i c ult ural u s e s ) Yes: NAW QA

, Rese rv oir

monitori ng; st ate monitori n g Formet anate HCl Yes (a gri c ultu ral uses) No

Methiocarb Ins i gnific ant impac t from limited use S ome limited NAWQA mon itoring Methomyl Yes (a gri c ultu ral us es ) Yes: NAW QA

, Rese rv oir

monitori ng Oxamyl Yes (a gri c ultu ral us es ) Yes: NAW QA

, Rese rv oir

monitori ng Pirimica rb Insignifi cant i m pact fro m limited use N o Propoxu r No (in doo r u s es; voluntarily

can c ell ed) Some limited NAWQA mon itoring

Thiodi ca rb (in c ludi ng methomyl de grad ate) Yes (a gri c ultu ral uses) No

1 EPA propose d to cancel all domestic us es in 20 06 IRED, i n w h ic h case e x p o sur e w o u l d not be

likel y



### b. Nature of NMC Expo sure in Drinking Water Sources

This section briefly summarize s the nature of expected NMC exposure in drinking water sour ces based on indiv i dual chemical assessments, available water moni toring dat a , and published literature on the potential impact of conventi onal drink i ng water treatment processes on NMCs in water.

Re-registration eligibility documents (REDs), interim R E Ds (IREDs), drinking water assessments, or ecological risk assessments are
available for all of the NM C pesticides in Table I.E.1 except for piri micarb (USEPA, 1997c. 1997d, 1999f, 2 003a, 2005 c, 2005d, 2005e, 2006c, 2006d, 2007b, 2007d, 2007e).

Seven NM C pesticides – aldicarb (including its sulfoxide and

sulfone de gradates), carbaryl, car bofuran, formetanate HCl, methomyl, oxamyl, and thiodicar b – have the potential to reach surface and/or ground wat e r sources of drinking water based on use and chemical fate and transport properti es. Propoxur has been detected in a few, predominantly non-agricultural monito ring sit es in the US Geologic al Survey (USGS) National Wate r Quality Assessment (N AWQA) monitoring program (A ppendix II.E.1). However, the current indoor uses are not expected to contribute to t he NMC cumulative lo ad in drinking

water sources. When the Agency gathered usage infor m ation on the NMC pesticides for the regional cumu lative drinking water exposures,

Section I.E – Page 131 of 277

### usage of methiocarb and pirimicarb we re of such a limited extent that they did not factor into the NMC cumu lative ex posure fo r drinking water (see Appendix II.E.4). These pesticides were not used in areas of high

combined NMC use identified in t he regional assessments described

below.

The indiv i dual chemic al asse ssments indicat e that the NMC pesticides are likely to reach surface water sources of drinking wat e r via runoff or sediment transport, and have been detected in monitoring

studies. Two carbamates – aldic a rb and carbofuran – are likely to reach and persist in ground water sources of drinking water, especially in shallow, acidic aquifers. Three ot her carbamates – carbaryl, methomyl, and oxamyl – may also reach ground water, but are not li kely to persist.

The most extensiv e source of nati onal water monitoring data for pesticides is the USGS NAWQA progr am, w h ich inc l udes seven of the carbamates in its list of pesticides ( Table I.E-2). The NAWQA program

focuses on ambient water rather on than on drinking wat e r sources, is not specifically targeted to pesticide use areas, and is sampled at a frequency (generally weekly or bi-w eekly during the use season) not sufficient to provide reliable estimate s of peak pesticide concentrations in

surface water. However, the pr ogram provides a good understand i ng on a national level of the expected occurr ence of pesticides in flowing water bodies that may be representative of drinking water sources. The monitoring data are better indicators of the nature of occurrence of pesticides with wides pread use rather than of pesticides that are limited to a few crops or pests. USGS (2004) provides a detailed description of the pesticide monitoring com ponent of the NAWQA program.

A summary of the first cycle of NAWQA monitoring from 1991 to

2001 indicates that the seven NMC pesticides were not frequently detected in the NAWQA study units ( Table I.E-2). Carbaryl and carbofuran were the most frequently detected NMC pesticides in streams and ground water, reflecting the broader use patterns of these particular

insecticides. In most i n stances, maximum reported detections of the NMC pesticides were in the single parts per billion or sub- parts per billion

range.

As expected, co-occurrence of NMC pesticides in monitored water samples reflects use patterns and overa ll intensity of us e. Carbaryl and

carbofuran are the most common NMCs occurring together in the NAWQA sampling; up to three different NMCs have been detected in the same surface water samples in the NAWQA study units. Although less commonly observed, more than one ca rbamate was also detected in a small number of ground water samples. More detailed summaries of the

Section I.E – Page 132 of 277

### USGS NAWQA monitoring data can be found in Appendices II.E.1 (surface w a ter monitoring) and II.E.2 (ground water monitoring).

Table I.E-2. Summary of carbamate detec tions in the USGS NAWQA study, 1991­ 2001 (provisional data published by USGS in 2003)

Pesticide Agricultural Land Use Mixed Land Use Urban L and Use

%

dete ct Max ug/L 95th
%i l e %

dete ct Max ug/L 95th
%i l e %

dete ct Max ug/L 95th
%i l e Surfac e Wa ter Monitorin g (Martin et a l, 2003)

Aldica rb 0.2% 0.5 nd 0% Nd nd 0% Nd nd

Carba r yl 9.2% 5.2 nd 15. 4% 0.5 nd 43.8% 5.2 0.3

Carbofu r an 9.6% 7.0 0.04 3.3% 0.7 nd 2.1% 0.1 nd

Methiocarb 0.1% 0.1 nd 0% Nd nd 0% Nd nd Methomyl 1.6% 0.7 nd 0.3% 0.3 nd 0% Nd nd Oxamyl 0.8% 0.2 nd 0% Nd nd 0% Nd nd Propoxu r 0.2% 0.1 nd 0. 2% 0.2 nd 0.2% 0.3 nd

Ground Wa ter Monitorin g (Koplin an d Martin, 2003 )

Aldic a rb (incl.d eg r adat es) 0.3% 1.8 nd 0.1% 0.1 nd 0% Nd nd

Carba r yl 0.4% 0.02 nd 0. 8% 0.5 nd 1.6% 0.03 nd

Carbofu r an 1.6% 1.3 nd 0. 4% 0.2 nd 0.7% 0.09 nd

Methiocarb 0% nd nd 0.1% 0.03 nd 0% Nd nd Methomyl 0.1% 0.04 nd 0. 1% 0.1 nd 0.2% 0.4 nd Oxamyl 0.8% 2.1 nd 0. 1% 0.03 nd 0.2% 0.3 nd Propoxu r 0.1% 0.06 nd 0. 1% 0.06 nd 0.2% 0.3 nd



### NAWQA and other surface-water m onitoring programs show that pesticide c oncentrations in surface water are highly variable in location

and in time. This is particularly true for insecticides, such as the NMCs, where usage is often in response to specific pest pressures which are likely to be concentrated in some ar eas but not in others and in some years but not necessarily every year. In addition to variable use patterns, N M C concentrations in surface water are influenced by local soil, hydrology, and weather patterns and by the timing of rainfall events in relation to pesticide use.

In 2001, The U.S. Department of Agriculture (USDA) expanded its Pesticide Data Program (PDP) to in clude drinking water

. Over the following three years, between 21 and 36 surface water systems were monitored, the majority of which were located in California and New

York. Sampling frequency varied from site to site, but was no more frequent than two times a month. Tr eated (fi n ished) wat e r samples were

collected at different locations at di fferent times; several NMC chemicals were included in the analyses. Se veral NMC compounds (carbaryl, carbofuran, and oxam yl) were found in finis hed drinking water at a
number of sites. Concentrations found were low, none exceeding 80 ppt. The magnitude of these detections can be interpreted as a minimum exposure level at these sites, but cannot be interpreted to be representative of overall ex pos ure. Given the site to site variability in

Section I.E – Page 133 of 277

### factors associated wit h pesticide exposure, limited frequency of sampling, and limited number of si tes, the study cannot be used to represent national exposure to pestici des in finished drinking water. Monitoring is most representative of sites sampled in California and New

York. Mo re information on the results can be found in Appendix II.E.1.

Aldic a rb is the NMC that has been the focus of the most extensive monitoring in water resources. While aldic a rb has not been detected frequently or in high concentrations in ground water in the NAWQA program, extensiv e targeted monitori ng by others (registrant, state and local governments, un iversities – see Appendix II.E.2) s h ows that, under certain conditions, aldicarb residues (parent and degradates) can occur in ground water and private wells at concentrations as high as sev e ral tens to several hundred parts per billion ( 

### μ g/l)

.

The frequency and magnitude of detection of aldicarb residues in ground wat e r is dependent not only on pesticide use but also on the leaching potential of the overlyi ng soil and vadose zone and, for the

sulfoxide and sulfone transformation product s, the pH of the soil and ground wat e r. For example, while no aldicar b residues were detected in ground-wat e r monitoring conducted by the USGS in the Biscayne a n d surficial aquifers of southern Flor ida (McPherson et al, 2000), roughly one-third of the monitoring wells loca ted under citrus groves in the central ridge of Florida had detections of aldicarb residues (USGS, 2006). Monitoring data for private wells collec t ed by the state of Florida

showed few detections of aldicarb re sidues (<2% of wells sampled).

Howev e r, the detections, as high as 47 

### μ g/l were clustered along the central ridge in the vicinity of citrus groves (FLDEP, 2005).

Label changes for aldicarb now restrict use from certain areas (such as th e northeastern states and Wisconsin) and add well-setbacks in other areas. In addition, total aldica rb residues (primarily the sulfoxide and sulfone transformation products) can persist in ground water for years or decades after use. Tw enty years after aldicarb use on Long Island, NY, was halted

, aldicarb residues ar e still the most frequently detected pesticide compounds in ground water in Suffolk County (Suffolk County Dept. of Health Services, 2000).

A recent survey of private we lls by Bayer CropScience (MRIDs 46793701, 46793702, 46793703, 467937 04, 46793705, 46793706)

found aldic a rb residues – predominantly sulf oxide and s u lfone

metabolites – in 10 percent of the wells sampled, with the greatest frequencies of detecti ons in the Sout heastern US (16%, with a maximum detect of 2.9 ug/L) and the Mississippi Delta (9%, with a maximum detect of 2.6 ug/L) regions. Aldicarb det ections sho w ed a regional pattern, with this highest frequency of detects i n Alabama (22%) and South Caro lina

Section I.E – Page 134 of 277

### (21%) in the Southeas t region and s outheastern Missouri/northeastern Arkansas (23%) in the Mississippi Delt a region. Because the detections come from a single sample, the concentrations should be compared against median concentrations from a distri bution of ground water concentrations over time (USEPA, 2007a).

Carbofuran was another NMC for wh ich extensive monitoring has occurred; however, the extent of ground wat e r monitoring has decreased signific antly in the last decade, so current i m pacts are not as well

documented. Several inferences can be drawn from the body of

available data on carbofuran. Ta rgeted ground water monitoring studies show a clear pattern of carbofuran movement into ground water, with maximum detections in the same range as that reported for aldicarb. Because transport to ground wat e r ty pically takes longer than transport to surface water, measured concent rations of carbofuran in ground water, especially in deep groundwa ter, may represent usage that occurred years before the samples we re collected. As with aldicarb, carbofuran will also persist in ground water for long periods of time after

use has been discontinued. This is particularly true for acidic grou nd
water because carbofuran is stable to hydrolysis (the major route of degradation in ground water) at pH values of 6.0 or less.

EPA’ s review of available labor atory studies and monitoring data

indic a tes that conventional water treatment processes such as coagulation, sedimentation, and convent ional filtration will not reliably

remove or transform the NMCs in drinking water sources (Appendix

II.E.3 ). This is further substant iated by USDA PDP monitoring that

shows detections of NMC residues in finished/treated w a ter samples (Appendix II.E.1). Lime softening and ac tivated carbon filtration can be effective in removing the NMC pestici des. With the exception of parent

aldic a rb, lime softening processe s will break down NMC pesticides through alk a line-cataly zed hydroly sis. Sorption on activated carbon by granular ac tivated carbon (GAC) or powdered activated carbon (PAC) appears to be at least partially effect ive in removing NMCs from drinking water (percent removal ranges from 20 to 38% for aldic a rb and oxamyl to 60 to 80% for carbofuran, carbaryl, and methiocarb). Other treatment methods, such as chlorination, ch loramination, chlorine dioxide, and potassium permanganate, are only effective i n oxidiz ing NMC compounds containing a methylthio group (CH 3 

### -S-), e.g., me thiocarb and

aldic a rb. These compounds are expe cted to oxidize to sulfoxide and sulfone carbamates that hydrolyze r apidly at alkaline pH values. The Agency estimates that lime softening is used on 7% or less of community ground wat e r systems serving populatio ns of 10,000 or less and les s than a third of systems serving more than 10,000 people (USEPA, 2001b). Carbon filtration is used on less than half of the large

Section I.E – Page 135 of 277

### community water syste m s, with de creasing percentages of smaller systems using GAC or PAC (USEPA, 2001b)

.

c. Summary

The goal of the NMC drinking wat e r exposur e assessment is to provide estimates of di stributions of NMC residues (conc entrations in drinking water) that account for:



### • daily and seasonal variations in residues ov er time associated wit h time of app lication(s) and runoff/leaching events (surface water concentrations are expected to be more vari able in time than ground water concentrations)



### • year-to-year variations related to weather pa tterns, pest pressures, and

use



### • variability in residues from place to place, resulting from the source and nature of drinking water and from t he regional / local factors (soil, geology, hydrology, climate, crops, pes t pressures, usage) that affect the vulnerability of those sources



### • the potential for co-occurrence of more than one NMC in location and time only when this is likely to happen



## 3. Conceptual Model

Risk is a function of both hazard and expos ure, and estimation of the exposure portion for drinking wa ter requires data on concentrations of the pesti cides in the drinking water and consumption of drinking water for different demographic populations. Drinking water is locally derived and concentrations of pesticides in source water fluctuate over time and location for a variety of reasons. Pe sticide r e sidues in water fluctuate daily, seas onally, and yearly as a resu lt of the timing of the pesticide application, the vulnerability of th e water supply to pesticide loading through runoff, spray drift and/or leac hing, and changes in the weather. Concentrations are also affected by the method of application, the location and characteristics of the sites where a pesticide is used, the climate, and the type and degr ee of pest pressure.

While monitoring data provide a pi cture of the occurrence of NMC pesticides in drinking water resulti ng from variable use in selected locations, monitoring data alone ar e not sufficient for the NMC cumulative drinking water exposur e assessment, due to the spatial and temporal scale of monitoring net wor ks. This section describes the approach used to estimate cumulative NMC residues in drinking water using models, to ensure that peak c oncentrations from registered uses are addressed and can be incorporat ed into the aggregate dietary risk

Section I.E – Page 136 of 277

### Section I.E – Page 137 of 277 O

P

P

s

e l

e

c t

e d assessment. The model estimates are evaluated against available monitoring data.

Based on the needs of the probabi listic cumulative exposure
assessment and the information from monitoring data, OPP designed a drinking water assessment that provi des multiple years of daily residue concentrations from drinking water sources i n regions where high NMC

use coincides with vulnerable drin king water sources. While the

available m onitoring studies were not designed to provide data for a

multi-chemical drinkin g water exp o sure assessment and were not of sufficient frequency to provide a time series for use in a probabilistic exposure assessment, the data w e re us ed to make sure that es timated exposures from modeling were with in the same range or that any discrepanc ies could be explained.

a. Regional Screening Approach for Vulnerable Sources of Drinking Water

Drinkin g water expos ure will var y locally as a result of pesticide use, agricultural practices, the natu re and vulnerability of drinking water sources, and weather patterns. T hus, the water exposure assessment focused on specific geographic ar eas of relatively high NMC use in a manner that would be realistically prot ective of all NMC use areas. To facilitate the regional screening approach, the Agency adapted a
modification of the USDA Farm Re source Region map (Heimlich, 2000) as a framework for identifying one or more locations which represent an area of the greatest concern for drinki ng water exposure in each region ( Figure I.E- 1 ). In this way, the Agency chos e a set of locations to represent vulnerable drinking wa t e r sources throughout the U.S.

Figure I.E-1. NMC C R A regions for drinking water exposure assessment showing high NMC use areas and regi onal drinking water exposure sites

### Locations where NMC residues in drinking water sources are likely to be of greatest concern based on:



### • Relativel y high NMC use: both total NMC use by county and

relative potency-adjusted NMC us e were considered; for ground water sources, EPA also looke d at the areas with the highest

aldic a rb and carbofuran uses;



### • Nature and source of drinking w a ter : EP A used the USGS

report on w a ter use in the U.S. (U SGS, 1998, 1999) to i dentify the drinking water sources (public surface water, public gro u nd water, domestic private) by county and information on surface water

intake locat i ons to identify the dominant drinking water sources in high NMC use counties;



### • Vulnerabilit y of the drinking w a ter sources : vulnerab ility of

surface water sources was based on t he relative runoff potential of the watershed area around surface water intakes; vulne r ability of ground wat e r sources was based on the leaching potent ial of the overlying s o ils and vadose zone.

For each region, the Agency used the estimated NMC cumulative

distribution from the vul nerable water source to represent the drinking water portion of the dietary exposure estim a te for the en tire region. If

NMC levels in water from these vulnerable sites are not major

contributors to the tota l regional cumulative exposure, then the Agency can reasonably conclude that drin king water exposures will not be a

concern in other less vulnerable areas in that region. If drinking water exposure from one or more of these vulnerable sites is a significant

contributor to the total cumulative exposure, then additional evaluations may be necessary to characterize t he extent of the potential exposure.

For the cu mulative assessment, t he Agency considered exposure from both surface- and gr ound-water sources of drin king water. In both cases, the Agency simulat ed potential exposure to sensitive populations in a geographic sense. Surface-w a te r sources of water consisted of

source water from small reservoi rs in predominantly agricultural

watersheds with high NMC use. Gr ound-water sources of water w e re shallow private wells located in highly permeable soils in high NMC use

areas.

b. Conceptu al Model for Su rface Water Sources of Drinking

Water

EPA id entified region a l surface water expos ure scenarios where

high NMC use areas coin cided with potentially vul nerable surface water

sources. The Agency bases its drin king water exposure assessment for

Section I.E – Page 138 of 277

### surface-water sources on a small reservoir in an agricultural watershed. An analys is of available monitoring data indic a tes that such reservoirs are likely to be among the most vu lnerable surface drinking water sources (FIFRA SAP, 1998; US EPA, 1999c, 2000b). The NMC CRA focused on watershed-scale impacts fr om multiple NMC uses occurring in multiple fields in a watershed ( F i gure I.E-2).

Co-occurrence of NMC pesticides in surface water sources of drinking water is based on the amount and timing of pesticide us e in the

watershed. EPA used county- or multi-county level pesticide use information, based on agricultural chemical use surveys (Append ix

II.E.4 ), to identify the p o tential fo r co-occurring NMC uses in the same

location. The potential for co-occu rrence of NMC residues in water at

the same ti me depends upon application timing, pesticide persistence and transport characteristics. The relative proportions of each NMC used in the watershed are based on the amount applied in a given year (a function of the rate and frequency of application, combined with the crop area treated); pesticide fate and tr ansport properties that affect the amount of pesticide available at t he surface for runoff; the runoff susceptibility of the soil; and t he ti ming, amount, and frequency of

rainfall.

Figure I.E-2. Conceptual model for surf ace water sources of drinking water illustrating how multiple NMC us es are proportioned in the watershed

No n-C a rbam at e

t r e ated A g 72.4% 

### La n d us e i n

w atershed

M odel ed

W a t ers hed

C a rbam ate use

In w a ter s he d



### Section I.E – Page 139 of 277

### For the watershed approach, OP P estimated pesticide concentrations over time (30-ye ar simulation ) for each crop-NMC

combination. The temporal dist ributions allowed the Agency to
determine the likelihood of co-occurr ence of the NMCs in water over

time. The Agency us ed regional crop areas based on USDA Ag Census data (USDA, 2002, as described in USEPA, 2000b) and acre treatments to adjust the estimated daily concentra tions for each of the NMCs for the portion of the watershed that is treat ed by that particular NMC. These crop-adjusted concentrations are conv erted to a concentration equivalent for the index chemical (in this ca se, oxamyl) and combined into a single set of daily NMC cumulative conc ent rations (spanning multiple years) for each region.

For exposure from surface water sources of drinking wat e r, the Agency us ed estimated concentrations derived for the source water from a reservoir, assuming no treatment effe cts. As noted, available studies

indic a te that conventional drinking water treatment processes will not fully remove NMC residues from wate r. At the same time, the Agency has no reason to expect that t he standard drinking wat e r treatment process will result in more toxic transformation products (see Appendix

II.E.3 ).

c. Conceptu al Model for Vuln erable Ground Water S ources of Drinking Water
The potential for pesticide movement to ground water sources of drinking water depends on such factors as hydrologic properties of the overlying s o il and vadose zone that affect downward movement of water

and chemic als, travel ti me th rough the unsaturated zone to ground water, aquifer properti es (conductivity, porosity, depth, type, location of recharge area), the leaching potential of the pestici de (persistence and mobility), and the type of well dra wing water for drinking purposes (Focazzio et al, 2002). These fact ors vary geograph ic ally and cause certain wells in one region to be more vulnerable than those in another region. EPA based its ground wa ter exposure assessment on private
rural wells which draw water from a shallow, unconfine d aquifer (also known as a water table aquifer). In general, such drinking water sources tend to be more vulnerable than public water supply wells and prov ide estimates of drinking water expos ure representative of people living in agricultural areas and relying on s hallow wells for drinking water.

Figure I.E-3 illustrates the conceptual mode l used to estimate pesticide transport to private wells. The pesticide is applie d to the soil surface or plant canopy and precip itation or irrigation moves the pesticide through the vadose zone into a saturated zone. Simultaneously, physical and chemical properties of the indiv i dual NMCs determine the degree to which they are degraded or sorbed to soils.

Section I.E – Page 140 of 277

### Wat e r T a b l e

La t e r a l G r ou ndw a t e r Mo v e m e n t De pt h to

Wa t e r Ta bl e 

### EPA assu med a depth to the top of a s hallow, surficial aquifer of 9.1 m (30 ft) in thi s revised assessment. While such information is not readily available, s ources ranging from USGS NAWQA (Berndt et al, 1998; McPherson et al, 2000) and ground water atlases (USGS, 1990) to FL water management districts, suggest that 30- to 50-feet is representative of the depth to shallow ground wate r supplying private wells. The concentration in the well is the average saturated pore water concentration across a one-meter lengt h of the screen, extending down from the top of the water table.

Degradation occurs at different rates through the soil profile. Generally, faster degradation from micr obial processes occurs in the top of the profil e and decreases with dept h. The model assumes that NMC degradation resulting from aerobi c met abolism occurs in the top 25 cm, with rates declining linearly to 1 me ter. Below a meter, only abiotic processes (in this case, hydrolysis) are simulated.

For some p e sticides, well setbacks ( Figure I.E-3) are specified by

state or federal labels. For such case s, the additional travel time for a pesticide to reach a dri n king water well and the degradation that occurs during that time is taken into consi deration by modeling lateral plug flow movement toward the well (s ee Appendix II.E.7 for details).

Figure I.E-3. Depiction of general ground water scenario concept used for estimating pesticide c oncentrations in drinking water

10 0 c m 

### S e t ba ck dis t a n c e

1 00 c m P es t i ci de A ppl i ca t ion A r ea 

### Pot ab l e Wel l

Wat e r T a b l e

La t e r a l G r ou ndw a t e r Mo v e m e n t 10 0 c m 

### S e t ba ck dis t a n c e

1 00 c m P es t i ci de A ppl i ca t ion A r ea 

### Pot ab l e Wel l

De pt h to

Wa t e r Ta bl e Bi o d eg r adat i o n Zon e

Ab i o t i c D eg r adat i on



### Section I.E – Page 141 of 277

### Pesticide f a te properties (persi stence under acid hydrolysis) and

available m onitoring data (Suffolk Co. Dept. of Health, 2000; USGS, 2006) indic a te that several NMC residues are likely to persist in acidic ground wat e rs. In addition, cumulative expos u re in ground water is likely to reflect past as well as current uses, particularly for deep wells.

Available monitoring data, prim a r ily from th e USGS NAWQA program, confirm that more than one NMC residue may occur together in ground wat e r (Appendix II.E.2). Co-o ccurrence in ground water can result when more than one NMC pesti cide is used at different times on the same crop, on different crops in rotation on the same fields, or on different crops grown on adjacent fields



## 4. Analy s is Plan

This section provides a brief descr iption of the methods of analysis EPA used in generatin g the cumulative NMC concentrations in drin king water sources for use in the cumulative dietary exposure assessment. The dietary baseline analysis ass u mes that all carbof uran uses other than import tolerances are removed. The impacts of currently registered

domestic uses of carbofuran on drin king wat e r sources were modeled in this assessment, but results are summarized separately in a sensitivity analys is.

a. NMC Prop erties
The predicted persistence and movement of each NMC pesticide in

the environment are bas ed on environmental fate and transport studies submitted by the registr ants as a requirement of registration and/or reregistration. Inputs for the water models are based on environmental fate data reviewed by EPA and described in the individual c hemical

assessments. Table I.E-3 summarizes the dominant persistence and mobility ch aracteristics of the NMCs inclu d e d in the drinking water

ex posure assessment. Appendix II.E.5 provides detailed chemical inputs used in the water exposure models. For aldicarb and its sulfoxide and sulfone degradates included in t he common assessment group, the Agency us ed half-life values for the combined aldic a rb residues (parent plus degradates) and the sorption val ue for the most mobile of the degradates

.

Table I.E-3. Summa ry of N -methyl carbamate fate and transport properties

Pesticide Persiste nce / Degrad atio n Path w ay Mobilit y / Sorption

Aldica rb, incl uding
sulfoxide a nd sulfone degradates (USEPA,

2006 c, 200 6d ) Field : Aerobi c soil meta bol ism (5 5 d half

- life); pH-dep end en t hydrolysis fo r deg rad ates (2-3 d @ pH9 for sulfo x ide; 60-63 d @ pH7, 6 d @p H8, 1 da @ p H 9 for sulfone )

Wa ter : Aero b i c aqu atic me tabolism (12 d halflife); pH-dep e ndent hydrolysis fo r deg rad a tes Kd = 0.12 mL /g (Koc = 10 mL /g)



### Section I.E – Page 142 of 277Pesticide Persiste nce / Degrad atio n Path w ay Mobilit y / Sorption Carbaryl (US EPA,

2003 a, 2007 b ) Field : Aerobi c soil meta bol ism (1 2 d half

- life); hydrolysi s (1 2 d @ pH7, 0

. 1 d @ pH9 )

Wa ter : Aero b i c aqu atic me tabolism (30 d halflife); pH-dep e ndent hydrolysis Koc = 1 96 m L/g

Carbofuran (USEPA,

2005 c, 200 5d ) Field : Hydrol ysis (2 8 d @ pH7, 9 d @ p H 7.5, 3 d @ pH8, <1 d @ pH9); ae ro bic soil metab olism (321 d h alf-lif e)

Wa ter : pH-de pend ent hydrolysis Koc = 36 mL/g

Formet anate HCl (USEPA, 2005e) Field : Aerobi c soil meta bol ism (6 d h a lf-l ife); hydrolysi s (2 4 d @ pH7,9)

Wa ter : Aero b i c aqu atic me tabolism (13 d halflife); hydrolysi s Koc = 3 40 m L/g

Methomyl (USEPA,

1997 c, 200 7d ) Field : Aerobi c soil meta bol ism (7 9 d half

- life);

alkali ne hydrolysis (30 d @ pH9)

Wa ter : Aero b i c aqu atic me tabolism (7 d half-life); hydrolysi s Koc = 24 mL/g

Oxamyl (USE PA, 1999f, 200 7e ) Field : Hydrol ysis (7 d @ p H 7, 0.1 d @ pH9 ) ;

aero b ic soil metaboli s m (20 d half-life )

Wa ter : Hydro lysis; aerobi c aquati c meta bolism (40 d ) ; anae robic a quati c metaboli s m (7 d) Koc = 6 mL/g

Thiodi ca rb, d egra de s to methomyl (USEPA,

1997 d) Field : Aerobi c soil meta bol ism (2 d h a lf-l ife); hydrolysi s (3 2 d @ pH7, 0

. 5 d @ pH9 )

Wa ter : Aero b i c aqu atic me tabolism (3 d half-life);

anae robi c aq uatic metab ol i sm (<1 d ) ; pHdepe ndent hy drolysi s Koc = 4 85 m L/g



### Most of the NMC pesticides exhibi t pH-dependent hydrolysis (Table I.E.3). They tend to be stable or degr ade slowly under acidic cond itions. As the pH increases to neutral and al kaline conditions, they hydrolyze more rapidly. Unless otherwise indi cated, EPA used the hydrolysis rates for acidic conditions in modeling. These rates are generally slower and would result in likely overestimates of actual exposures in neutral to

alkaline conditions. Where the Agen cy had information that showed that the dominant pH of the soil/v adose zone/groundwater system was neutral to alkaline, hydrolysis rates ( appropriate to the pH) were used.

b. Identify ing Regional Exposure Scenarios
The selection of specific locati ons for regional drinking water assessments involves several steps. Fi rst, the Agency id entified the high NMC usage areas within each region. The Agency us ed data collected by Thelin and Gianess i (2000) for c ounty-level estimates of NMC usage.

EPA eva l u ated the relative high NMC use areas several different ways:



### • Summing total pounds of each NMC by county to calculate both total pounds of NMC pesticide per county and total pounds per

acre;

Section I.E – Page 143 of 277

### • Adjusting the county-level esti mates of pounds of each NMC by their respective relative potency factors to identify areas of greatest use of the most potent of the NMCs;



### • In addition, for ground water expo sure, identifying the aldicarb and carbofuran use areas, which we re driving NMC exposure, to

determine if additional scenarios we re needed and for sensitivity analys is.

Ne xt, EPA identified the types of dr inking wa ter sources in each high usage area. The Agency us ed a spatial dataset that describes water use for all the counties in the continental US (USGS, 1999) to

determine the dominant source of dr inking water – (1) public sup p ly served by surface water, (2) public s upply served by ground water, or (3) domestic self-supplied drinking water (primarily private wells). For surface water exposur es, the Agency overlaid the public surface water supply data, along with locations of drinking water intakes (based on SDWIS data), with the NMC use maps to identify counties in whic h high
NMC use coincided with surface water sources of drinking water. For ground wat e r exposur es, the Agency focused on private wells, which are generally shallower than public supply we lls and typically have no water treatment applied. The Agency over laid the domestic drinking wat e r supply data with NMC use to identif y those counties where high NMC use coincided with populations drinking from pr ivate wells.

The final step in choos ing regional locations for modeling was to

assess the vulnerab ilit y of drinking water sources within the high NMC usage areas. For surface water s ources of dri n king water

, OPP compared relative vulnerabilities of the areas based on average annual runoff, average 2-month runoff (beginni ng of the growing season), a nd average soil loss, as developed by the USDA Natural Resources Conservation Service (Kellogg et al, 1997). The regional drinking water

scenario sites are shown in Figur e I.E-1 and summarized in Table I.E-4

.

Table I.E-4. Regional drinking wa ter exposure sites and dominant NMC pesticide uses

Region Exposur e sc enario sites (1) Dominan t us e crops NMC p estici d es used (2)

Southea st NC coa s tal pl ain (SW,

GW ), easte rn GA (SW),

south ern GA (G W) Cotton, peanuts

, tobacco, pe cans A l dica rb, ca rb ary l

, carbofu r an, methomyl, oxamyl

Florid a South FL (S W), Central FL (G W, SW)

, North ea s ter n FL (G W) Citrus , sweet c o rn,

sug a rcan e, cucum b e r

, pepp er, potat o A l dica rb, ca rb ary l

, carbofu r an, methomyl, oxamyl, thiodicarb

Mid-south North e a s t LA (S W) Cotton, co rn, sorghu m Aldica rb, ca rb ofuran, oxamyl, thiodicarb

North / no rth South cent ral PA (SW), Apples, corn, Carba r yl, carbofura n

,



### Section I.E – Page 144 of 277Region Exposur e sc enario sites (1) Dominan t us e crops NMC p estici d es used (2)

central Central IL (SW),

Delma r v a (G W) ( 3 ) peac h es , sweet c o rn, alfalfa, pump k in, potato, bean s,

cu cur b it s formetan ate HCl, methomyl, oxamyl,

thiodicarb

Lower Mid w e s t Southern tip of TX (SW) Grap efruit, co tton, vegetable s Aldica rb, ca rb ofuran,

formetan ate HCl, methomyl, oxamyl

N o r t he rn G r ea t

Plains Red Riv er Va lley (SW) Potatoes , sugar beets, wheat A l dica rb, ca rb ary l

, carbofu r an, o x amyl

North w e s t Centr a l WA ( S W, GW) Potatoes, ap ples,

c h er r i e s , b ean s

, carrot s, onion s A l dica rb, ca rb ary l

, carbofu r an, formetan ate HCL, methomyl, oxamyl

Southwe s t CA Cent ral Valley (SW) Citru s , stone fruit

trees , c o tton, melons

, grap es, tomat oes, various c o le, root, tuber vegeta b l es A l dica rb, ca rb ary l

, carbofu r an, formetan ate HCl, methomyl, oxamyl

(1) SW = surface w a t e r scen a rio site; GW = ground w a t e r scen a rio site (2) EPA propos ed cance lin g all d o mestic uses o f ca rbofuran; c a rbofur an mo d e l resu lts are p r esente d

as separ ate se nsitivit y an al ysi s

. (3) T he Delmarva scenar io w a s u s ed for the ca r bofura n sens iti v it y a nal ys is on l y

.



### For potentially vulnerable ground wate r sources of drinking water, EPA relied on a variety of sources, in cludin g Nolan et al (2002), USGS NAWQA reports and Ground Water At lases, USDA/NRCS county soil datasets (SSURGO), and other state/ local inf ormation. EPA also us ed monitoring data from Florida (FL DEP, 2005; USGS, 2006) to identify specific site, soil, and hydrologic proper ties that might serve as indicators of potential high NMC exposure c onditions. Regional gr ound water exposure scenario sites are listed in.

c. Regional Usage
The regional exposur e areas of i n terest consist of multi

- county areas that encompass the vulnerable drink i ng water source in high NMC

use areas. EPA collec ted information on the target crops, estimated NMC usage, and timing of applic ati on for these multi-county areas.

The drinking water exposure assessments require infor m ation on crop use, pounds applied, applicat i on rate, number of applications, percent of crop treated, and application timing. Much of this information is not easily available or does not exist at the geographic scale needed for the exposure assessment. As a result, EPA used t he best availa ble information to provide the regional es timates for the NM C pesticide-crop combinations that actually occur in scenario areas. Because countylevel pestic i de usage data is based on surveys and is uneven in quality,

EPA create d county clusters that su rrounded the initial scenario are a s

Section I.E – Page 145 of 277

### shown in Figure I.E-1. EPA also used multip le data sources and multiple years of data to improve t he robustness of the use data.

For each regiona l scen a rio site, EPA used USDA Natio n al Agricultural Statistics Service (USD A NASS) and Doane’s databas es to estimate usage (acres planted, to tal pounds used, percent of crop treated, application rate, and number of applications) for each NMC and crop reported in the use cluster. Usage was averaged for the years 1998 through 2002. The Agency ident ifi ed those NMC-crop uses that
accounted for at least 95% of the to tal NMC usage in the scenario area.

For the crop/chemical combinatio ns identified in a given area, USDA crop profiles, typical planti ng/harvesting dates and various other

sources were used to i dentify most likely windows of application for each

use. Typically, all the NMC pestici des discussed here target multiple pests or pests that can occur mu lti p le times during a giv en crop’s growing season, so applic ations often occur over a broad time period. EPA systematically selected the beginni ng of the most active window for the initial applic ation date of each NMC. Where multiple applic atio ns were identif ied, the Agency spread t hose evenly over the most active

window. Details of the methods and resulting regional usage infor m ation can be found in Appendix II.E.4.

d. Surface Water Exposure Assessment

The Agenc y estimated the daily drinking wat e r exposur e from surface water sources using the simulation models PRZ M (Pesticide Root Zone Model) and EXAMS (EXpos ure Analysis Mo deling System). With PRZM/EXAMS modeling for a drin king water reservoir, the Agency

can:



### • Account for potential co-occurrenc e of NMC residues by modeling all uses in a region/area, as de scribed in the conceptual model



### • Combine daily time series over multiple years (using 30 years of recorded weather data) to account for year-to-year variations in weather and to separate peak concent rations that are not likely to occur together in time



### • Estimate peak concentrations (o n a daily time step); adjustments to pesticide use inputs (“typic al” rates, frequencies) reflect estimated concentrations in a “typical” year



### • Model vulnerable surface water sources in regions to reflect spatial variations in crops, use, weather, soils, and hydrology

Section I.E – Page 146 of 277

### • Adjust for crop area, acres treat ed in order to prevent doublecounting ov erlapping uses

A detaile d description of the models is available from the EPA OPP

Water Models web sit e (USEPA, 2007f).

The model estimates daily pestic i de concentrations in surface water sources of drinking water (a reservoi r) using local soil, site, hydrology,

and weather characteristics al ong with pesticide applic ation and agricultural management practices, and pesticide environmental fate and transport properties. The input parameters are specific for each NMCcrop scenario in each region. For example, in the eastern North C a rolina exposure site representing the Sout heast region of the US, the cotton, peanut, and tobacco scenarios consist of properties for soils on which the crops are grown in the coastal pl ain of North Carolina. The weather
data used in the simulations come fr om 30 years of weather collec t ed at
a NOAA weather stati on in Raleigh/ Durham, just west of the scenario

area. Appendix II.E.6 provides details on the site-specific inputs for the surface water exposur e.

The cumulative assessment focuses on the likelihood of concurrent exposure to multiple pesticides from food, w a ter, and residential us e. This involv es using av erage applic ation rates, average numbers of applications, and estimates of acres tr eated to adjust concentrations, and specific application windows so that only those NMC pesticides that have overlapping use periods may potentiall y occur together in water. The implications of these assump tions are discussed in the risk characterization chapter.

PRZM is a field-scale model, while the cumulative water
assessment focuses on watershed-scale impacts (i.e., the contributions of multiple NMC uses on multiple crops occurring in multiple fields in a watershed). The Agency used PRZM to model multiple fields in a watershed. While this approach provides a more realistic depiction of multiple chemical usage in a watershed, it provides no spatial cont ext for

those fields

. It also assumes that the runoff from each of those fields goes into the reservoir.

To adapt PRZM for this wate rshed approach

, EPA adjusted the estimated pesticide c oncentrations generated for each crop-NMC combination to account for the port ion of the watershed treated by a particular NMC pesticide:



### • The NMC-crop combination was modeled with PRZM/EXAMS, using the region-spec ific usage, applic ation timing, soil, site,

Section I.E – Page 147 of 277

### and weather data. The result is a time-series of daily pesticide concentrations in a reservoir spanning a 30- year period.



### • Each daily concentration is adjusted by the fraction of the watershed occupied by the crop being modeled. The fraction is calculated by dividing the acre s of crop grow n in the multicounty region by the total acres in that region (percent crop area).



### • The daily c oncentrations are t hen adjusted by the fraction of acres of the crop treated by the particular pesticide. The fraction is calculated by dividi ng the acres of crop treated by the total crop acres in the mult i-county regio n (percent crop treated).

The resulting concentrations for each crop-NMC combination mus t be converted to a concentration equiva lent for an index chemical. The

concentrations were normalized to an index equivalent by multiplying each of the daily conc entrations by the relative potency and FQPA safety and inter-species factors for the respective NMC pesticide. The normalized outputs for each crop-NM C combination were summed dayby-day to give a daily time series of total NMC residues in water over 30

years.

e. Ground Water Exposure Assessment

EPA used t he Pesticid e Root Zone Model (PRZM) to estimate NMC concentrations in ground water sources of drinking wat e r. Specifically, the ground water exposure assessment must account for:



### • Variations in Residues Over Time : Pesticide residues in ground water are likely to fluctuate less drastically than residues in surface water; however, the di etary exposure estimates require a concentration
time series for co -occu rrence in time.



### • Variations in Residues Over Location : As with the surface water assessment, EPA focused on regional groun d water sources of drinking water that are expected to be among the most vulnerable to NMC contamination based on soil, geol ogy, hydrology, climate, crops, and usage.



### • Co-occurr ence : USGS NAWQA monitoring shows that co-occurrence of NMC residues, though infrequent, does occur in ground water. Therefore, EPA estim a ted ground wate r concentration s for multiple NMC pesticides in ground water, based on regional usage data.

Section I.E – Page 148 of 277

### The model simulates pesticide movement through the soil and underlying vadose zone to a saturat ed zone, representing the surficial aquifer, at a depth of 30 feet (see Figure I.E-3). The saturated zone can be created in PRZM by setting the field capacity input parameter equal to the porosity. Output concentrations are the average from the top of the saturated zone to a depth 1 meter below the water table.

Because c o -occurrence of NMC residues in ground wat e r is likely to be more localized than for surface water, E PA considered cooccurrence based on the potential for more than one NMC to be used at

different ti mes on the same crop or on different crops in rotation on the

same fields. This resulted in less crop-chemical combinations than for the surface water scenarios, whic h encompassed a larger area of contribution (watershed rather th an fields). The Agenc y modeled multiple NMC uses on a crop at the same ratio of pounds used as that reported in the usage su mmary for the region.

As with surface water, the c oncentrations for each crop-NMC

combination was converted to a c oncentration equivalent for an indexchemical based on the relative potency and FQPA safety and interspecies factors. The adjusted concentrations were summed day-by-day

for a cumul a tive time series of NMC residues in ground water

distribution.

Appendix II.E.7 provides details on groundwater scenario development, model inputs, and compar isons with monitoring. It also provides site-specific inputs fo r the ground water model scenarios.



## 5. NM C C o nce n trations in Surf ac e Wat e r Sourc e s of Drinki ng Water

The Agenc y estimated drinking wate r concentrations for individual

and cumulative NMC load for each of the regional surface water scenario

sites listed in Table I.E-4. Details and results of these expos ure estimates c an be found in Appendix II.E.6.

a. Individual NMC Levels in Surface Water
Estimated peak concentrations of t he individual NMC pesticides in each of the regional s u rface water sc enario sites were in the sub-parts per billion range ( Table I.E-5), except for aldicarb, whic h had estimated peaks as high as a single part per billion in the NC coastal plain

scenario. Aldic a rb had the highest estimated peak concentrations in 4 scenarios (NC coastal plain, GA coastal plain, central FL, northeast LA), methomyl in 5 scenarios (south FL, south-central PA, central WA, CA Central Valley, south TX tip), and car baryl in 2 scenarios (central IL, Red

River Valle y, MN/ND).

Section I.E – Page 149 of 277

### Table I.E-5. Estimated peak concentrati ons for NMC residues for the regional surface water drinking water scenarios (30-year period)

Valley) Region /
Scenario Pesticide Crops Conc entrati on, ug/l (ppb)

Max. 99 th % i le 95 th % i le

SE: NC Aldica rb cotton, pea nu t, tobacco 1.26 0.28 0.06

coa s tal plai n Carba r yl cotton, pea nu t, cucum ber,

tobacco 0.04 0.02 0.007

Methomyl pean ut, tobacco 0.03 0.004 0.001 Oxamyl pean ut 0.23 0.008 0.002

SE: GA Aldica rb cotton, pea nu t, pecan 0.37 0.09 0.02

coa s tal plai n Carba r yl pecan 0.05 0.01 0.004

Florid a Aldica rb oran ge s, gra pefruit 0.02 0.004 0.001 (South) Carba r yl oran ge s, gra pefruit 0.01 0.002 0.001
Methomyl sweet co rn, p eppe r, cu cum ber 0.63 0.18 0.08 Oxamyl pepp er, oran ges, cucumb er 0.14 0.02 0.006

Thiodi ca rb sweet co rn 0.06 0.007 0.003

Florid a (Ce n tral ) Aldica rb oran ge s, gra pefruit 0.46 0.07 0.01

Carba r yl oran ge s, gra pefruit 0.11 0.03 0.009 Oxamyl oran ge s 0.05 0.008 0.002

Midsouth (no r thea st LA) Aldica rb Cotton 0.70 0.14 0.02 Methomyl thiodicarb d e g rad a te (cotton) 0.34 0.05 0.009 Oxamyl cotton 0.19 0.01 0.001

Thiodi ca rb cotton 0.08 0.003 0.001

N o r t he as t/

Central (so uth ce ntral PA) Carba r yl apple, pe ach, swe e t co rn 0.02 0.003 0.001

Formet anate apple 0.001 <0.00 1 <0.00 1 Methomyl apple, pe ach, potat o, swe e t corn 0.07 0.02 0.006 Oxamyl apple 0.003 <0.00 1 <0.00 1

Thiodic a rb s w eet c o rn < 0 .001 < 0 .001 < 0 .001

N o r t he as t/

Central (IL) Carba r yl corn, sweet corn 0.04 0.01 0.003 Methomyl lima bean s 0.02 0.002 0.001

Lower

Midwest (South TX tip) Aldica rb Grap efruit, co tton 0.07 0.02 0.005

Formet anate Grap efruit 0.04 0.008 0.001 Methomyl Onion s , cu cu mber, spina c h 0.21 0.03 0.01 Oxamyl Cotton, ca rrots, onion s,

cu cumb er, ca ntaloup e, watermelo n , pepp ers 0.19 0.07 0.02

North er n Great Plain s (Re d Riv e r Valley) Aldica rb Potatoes, sug ar beet s 0.004 0.001 <0.00 1

Carba r yl Spring whe a t 0.10 0.02 0.007 Oxamyl Potatoes 0.003 0.001 <0.00 1

N o r t hw es t Aldica rb Beans, potat oes 0.03 0.01 0.002 (Ce ntral WA) Carba r yl Apples, Che r ries 0.04 0.004 0.003

Formet anate Apples <0.00 1 <0.00 1 <0.00 1 Methomyl Beans, swe e t corn 0.09 0.01 0.006 Oxamyl Carrot s, onio n s, potatoe s 0.02 0.005 0.003

Southwe s t (CA C entral Aldica rb Cotton, bea n s /pea s, potat oes 0.08 0.02 0.001

Carba r yl Apples, canta loupe, cotton, 0.02 0.005 0.002



### Section I.E – Page 150 of 277Conc entrati on, ug/l (ppb) Region /
Scenario Pesticide Crops Max. 99 th % i le 95 th % i le

necta rine, o r a nge s, pea che s

, pista c hio s , pl ums Formet anate Grap efruit, le mons, ne cta r i ne,

oran ge s, plu m s, tange rine s 0.03 0.004 0.001

Methomyl Alfalfa, asparagu s, bro c col i,

cantal oup e, carrots, ga rlic, lettuce, ne cta r ine, onio n s,

oran ge s, pea che s , potatoe s,

s u gar beets

, tomatoes

, water m elo n 0.40 0.08 0.03

Oxamyl Cantal oupe, cotton, ga rlic,

oran ge s, pea che s , tomato es 0.02 0.01 0.005



### A comparison with available surface water monitoring data (Appendix II.E.I) indicates that t he estimated peak NMC residues are

similar to or less than the maximu m reported detections from NAWQA, with a couple of except ions wher e reported NAWQA detections were greater.



### • Estimated maximum aldic a rb residues (parent, sulfone, sulfoxide) are similar to maximum reported detections in the southeast (NC, GA, FL). Maximum and upper percentile estimates for total aldicarb residues for

the Mid-South were less than the maximum detection reported in the region (Mississippi Embayment study uni t) for aldicarb sulfoxide, but greater than the 99 th 

### percentile of detections. However, even if if the modeled peak was adjusted by the difference (2.5X), the resulting cumulative NMC exp o s ures would still be be low drink i n g water levels
of concern.



### • Estimated carbaryl concentration s were less than reported detections in NAWQA, primarily because the NM C cumulative scenarios wer e not focused on the highest carbaryl use ar eas. Howev e r, the impact of this on overall NMC cumulative exposures is not expected to be significant because carbaryl has a lower adj usted RPF than do the other NMC pesticides estimated to dominate expos ure in source drinking water.



### • Estimated methomyl and oxamyl concentrations were similar to maximum reported detections in NAWQA.

Overall, the estimated NMC conc entrations in surface water are similar to or less than reported peak concentrations from monitori ng data in the same or similar regions.

b. Cumulative NMC Levels in Surface Water
The highes t estimated cumulative NMC concentrations in surface water sources of drinking water occu rred in the southern United States

Section I.E – Page 151 of 277

### ( Table I.E-6), with the highest esti mated peak following the trend:

Eastern NC > northeastern LA > south FL > central F L = central IL >
southeast GA. The concentrations in Table I.E-6 reflect adjustments for

indiv i dual chemical FQPA safety and inter-species factors in addition to relative potencies. Thus, the concentrations cannot be directly compared to measured values in the environment.

Table I.E-6. Percentile concentrati ons for e stimated NMC cumulative

distributions in the surface water scenar io sites (30-year period), adjusted for relative potency, inter-species an d FQPA safety factors for children

Region/Site Percentile concentration in ug/L (oxam y l equiv alents, adjusted for uncer tainty factor s) Major contributor s 1

Max 99 th 95 th 90 th 75 th 50 th

Florid a / South 8.5 2.5 1.0 1.0 0.2 0.03 Methomyl, thiodicarb, oxamyl

Lower Mid w e s t / TX 2.8 1.0 0.4 0.3 0.1 0.03 Aldica rb, formetanate, methomyl, oxamyl

Midso u th / LA 12.6 2.9 0.5 0.2 0.03 0.002 Aldica rb

Southea st / NC 20.2 4.4 1.0 0.4 0.02 <0.00 1 Aldica rb

Southwe s t / CA 4.3 1.0 0.4 0.2 0.1 0.03 Aldica rb, formetanate, methomyl

Florid a / Cent ral 7.3 1.2 0.2 0.09 0.02 0.004 Aldica rb

North e a s t-ce ntral / IL 0.2 0.04 0.01 0.007 0.002 <0.00 1 Carba r yl, methomyl

Southea st / GA 5.9 1.5 0.3 0.09 0.02 <0.00 1 Aldica rb

North e a s t-ce ntral / PA 0.7 0. 2 0.06 0.04 0.02 0.006 Methomyl

North w e s t / WA 1.3 0.3 0.1 0. 07 0.03 0.007 Aldica rb, methomyl, oxamyl

N. Great Plains / MNND 0.3 0.06 0.02 0.01 0.001 <0.00 1 Carba r yl

1 Major contri butors, after adju s ting con c entration s for relative pote n cy and F Q PA safety and interspe c ie s f a ct o rs.



### c. Spatial Extent of NMC Exposur es in Surface Water
Many surface water sources of dri n king water in the southern portions of the US occur where total NMC pesticide use is relatively low ( Figure I.E-4). The watersheds that ar e most vulnerable to runoff in the high NMC use areas tend to occur in areas where ground water is the
dominant source of dri n king water

. For these southern region al scenarios (Southeast, Florida, Mid-sout h, Lower Midwest), the esti mated exposures represent a few drinking wa ter intakes locate d in relative ly high NMC use watersheds.

Section I.E – Page 152 of 277

### Figure I.E-4. Location of surface water inta kes (blue dots) in relation to relative NMC pesticide usage (high use areas in dark orange) in the southeastern/southcentral U.S.

The south Florida scenario repr esents a handful of community water syste m s (CWS) around the Evergl ades Agricultural Area (EAA). Dominant uses are sugarcane, vegetables ( sweet corn, pepper, cucumber), and citrus. Drainage canal s from sugarcane fields and other agricultural areas in and around the EAA are not used directly for drinking water, but eventually feed wa ter bodies used in southern Florida for drinking water supply. Three community water syste m s (CWS) draw

from the southern end of Lake Okeechobee. The city of West Palm Beach draws water from Clear Lake, which is fed in part by drainage

water from the EAA.

The North Carolina and Georgia surf ace water sites represent high NMC use areas within the coastal plai n from southeastern Virginia to

southeastern Alabama. The domi nant NMC uses in the region are on cotton, peanuts, and tobacco. A few CW S occur in this area; many are located to the west, with watersheds draining relatively low NMC use

areas.

Section I.E – Page 153 of 277

### The high-use region around nort heastern Louisiana and west central Mississippi has few surface water intakes, but represents the

most vulnerable area in the Mid-sout h region in terms of NMC usage and runoff vulnerability. Transport of pesticides in surface water is complicated by levees on the Missi ssippi Riv er and a system of drainage

canals. Consequently, the surface wate r assessment scenario is lik ely to be health-protective for other CWS in lower NMC-use areas in the region.

The scenario for the southern Te xas tip represents a number of

small CWS intakes that draw fr om channels in a highly-irrigated agricultural area.

d. Sensitivity Analy s is: Carbofuran in Surfa ce Water
Including carbofuran in regional surface water modeling would impact modeled surface water exposures, as carbofuran was a major

contributor to cumulative NMC conc entrations in a number of regions. The estimated peak concentrations of the carbofuran were in the same range as other individu al NMC pe st icides (the sub-parts per billio n range). Table I.E-7 summarize s the estimated carbofuran concentrations in the regional scenar ios. In comparison to the N M C

concentrations in Table I.E-7, carbofuran had the highest estimated peak concentrations in 4 scenarios (south FL, south-central PA, central IL, south TX tip); when carbofuran was removed the NMC that replaced it was methomyl (3 scenarios) or carbar yl (1 sc enario), with the max i mum estimated concentration differing by less than 0.2 ppb.

Table I.E-7. Estimated peak concentra tions for carbofuran residues for the regional surface water drinking wa ter scenarios (30-year period)

Region / Sce nario Crops 1 Conc entrati on, ug/l (ppb)

Max. 99 th % i le 95 th % i le

SE: NC coa s t al plain Toba cco 0.002 0.001 <0.00 01

Florid a (Sout h) sug arcan e, sweet corn, cu cumb er 0.82 0.18 0.08

Midso u th (n ortheast LA) cotton, co rn, sorghu m 0.33 0.15 0.07

N o r t he as t/ C e n t r a l (s ou th central PA) alfalfa, corn, pump k in, swe e t corn 0.09 0.02 0.008

North e a s t/ Central (IL ) alfalfa, corn, sweet co rn 0.11 0.04 0.01

Lower Mid w e s t (South TX tip) Cotton, co rn 0.35 0.17 0.07

N o r t he rn G r ea t Pla i n s (R ed

R i ver Valley) Potatoes, sug ar beet s, sunf lowe rs 0.008 0.003 0.001

North w e s t (Central WA) Potatoes 0.04 0.02 0.005

Southwe s t (CA Central Vall ey) Alfa lfa, cotton, grape s 0.08 0.04 0.02

1 The estimated exposures de picted here repr esen t current uses for carbofuran. Ho w e ve r, the Age n c y has proposed cancellation

of all domestic carbofur an uses.



### A comparison with available surface water monitoring data (Appendix II.E.I) indicates that t he estimated peak NMC residues are

similar to or less than the maximu m reported detections from NAWQA,

Section I.E – Page 154 of 277

### with a couple of except ions wher e reported NAWQA detections were greater. While estimated peak ca rbofuran concentrations reported

above wer e similar to or greater t han reported detections in most U S GS NAWQA units, they w e re well belo w the maximum reported detections (3-32 ug/l) from Zollner Creek in the Willamette Valley st udy unit. The maximum detections reported for Zollner Creek are of the same magnitude as the upper percentile of estimated ground water concentrations for carbofuran (see the sensitivity analys is for carbofuran in ground water below), which resulted in MOEs of less than 10. While peak concentrations of carbofuran in surface water are not expected to remain elevated for as long as thos e estimated in ground water, peak exposures of the same magnitude found in the monitoring might result in an MOE of less than 10. The Agency’ s analy sis of potential carbofuran exposures in drinking water in the single chemical ass e ssment includes scenarios with a use intensity similar to Zollner Creek but with greater

rainfall.

In the remaining NMC scenarios ( Table I.E-8), the percentile

concentrations increas e, in some regions by almost an order of magnitude when currently r egistered carbofuran uses are incorporated.

Howev e r, it did not result in NMC c oncentrations that resulted in MOEs

of less than 10.

Table I.E-8. Percentile concentrati ons for e stimated NMC cumulative

distributions with and without carbofuran in the surface water scenario sites (30­ year period), adjusted for relative poten cy, inter-species and FQPA safety factors

for children

Region/Site Percentile concentration in ug/L (oxam y l equiv alents, adjusted for uncer tainty factor s) Major contributor s 1 Max 99 th 95 th 90 th 75 th 50 th

Florid a / South

with carbo f uran

without ca rbofu r an 54.5

8.5 12.1

2.5 5.3

1.0 2.6

1.0 0.8

0.2 0.3

0.03 Carbofu r an Methomyl, thiodicarb, oxamyl

Lower Mid w e s t / TX

with carbo f uran

without ca rbofu r an 24.5

2.8 11.7

1.0 5.0

0.4 3.0

0.3 1.1

0.1 0.2

0.03 Carbofu r an Aldica rb, formetanate, methomyl, oxamyl

Mids outh / LA

with c a rbofuran

without ca rbofu r an 21.9

12.6 7.5

2.9 2.4

0.5 1.1

0.2 0.3

0.03 0. 03

0.002 Aldica rb, ca rb ofuran

Aldica rb

Southea st / NC 20.2 4.4 1.0 0.4 0.02 <0.00 1 Aldica rb

Southwe s t / CA

with carbo f uran

without ca rbofu r an 7.4

4.3 3.2

1.0 1.5

0.4 0.9

0.2 0.4

0.1 0.2

0.03 Aldica rb, formetanate, methomyl, ca rbofu r an Aldica rb, formetanate, methomyl

Florid a / Cent ral 7.3 1.2 0.2 0.09 0.02 0.004 Aldica rb

Northeas t-c e ntral / IL

with carbo f uran without ca rbofu r an 7.2

0.2 2.5

0.04 0.9

0. 01 0.5

0.007 0.1

0.002 0.02

<0.00 1 Carbofu r an Carba r yl, methomyl



### Section I.E – Page 155 of 277Percentile concentration in ug/L (oxam y l equiv alents, adjusted for uncer tainty factor s) Region/Site Max 99 th 95 th 90 th 75 th 50 th Major contributor s 1

Southea st / GA 5.9 1.5 0.3 0.09 0.02 <0.00 1 Aldica rb

N o r t he as t-c e n t r a l / PA

with c a rbofuran 5.7 1.7 0.6 0.3 0. 1 0.03 Carbofu r an without ca rbofu r an 0.7 0.2 0.06 0.04 0.02 0.006 Methomyl

Northwes t / WA

with carbo f uran 3.6 1.3 0.4 0.3 0.2 0.03 Aldica rb, ca rb ofuran, methomyl, oxamyl

without ca rbofu r an 1.3 0.3 0.1 0.07 0.03 0.007 Aldic a rb, methomyl, oxamyl

N. Great Plains / MNND

with carbo f uran 0.6 0.3 0. 1 0.07 0.03 0.005 Carbofu r an, carba r yl

without ca rbofu r an 0.3 0. 06 0.02 0.01 0.001 <0.00 1 Carba r yl

1 Major contribut ors, after adjustin g c oncentrations for relative poten cy and F Q PA saf e t y and inte r-spe cies factors.



## 6. NM C C o nce n trations in Ground W a te r Sourc e s of Drinki ng Water

Individual NMC risk assessments and monitoring data indic a te that

aldic a rb (primarily its sulfoxi de and sulfone degradates) and carbofuran are the two NMC pesticides most lik ely to reach and persist in ground water sources of drinking water, especia lly in shallo w, acidic aq uif e rs. Three other NMC pesticides – carbar yl, methomyl, and oxamyl – may also reach ground wat e r, but ar e not as likely to persist.

High NMC use areas occurred in counties where substantial portions of the populat ion obtained thei r drinking water from private wells along the southeastern Coastal Plain, in Florida, and the Delmarv a Peninsula. EPA also include d a sc enario in c entral Washington to represent potential exposures in the western US. Based on the conceptual model, the Agency focused on private wells drawing from the surficial aquifer in these regions. Details and results of these exposure

estimates c an be found in Appendix II.E.7.

Section I.E – Page 156 of 277

### a. Individual NMC Levels in Ground Water
The Agenc y estimated drinking wate r concentrations for individual NMC pesticides and for the cumulative NM C load bas ed on high leaching potential scenarios repres enting specific high NMC uses in Florida, the southeastern coastal pl ain and the northwest (Tables I.E-4, I.E-9). Each ground water scenario reflects shallow wells in high leaching potential soils and vadose zones with ground water at a depth of 30 feet, with the e xception of the northwestern scenario, which represents deeper groundwat er. The estimated concentrations in Table I.E-9 reflect typical application ra tes for the NMC pesticides (Appendix

II.E.4 ) and well setback distances s pecified on the ex isting label for

aldic a rb.

Table I.E-9. Estimated concentrations fo r NMC residues for t he regional ground water scenarios (25-year period) for ground water at 30 feet

pesticid e setb ack

distan ce NMC Well Crop (s) Max

imum 99th
%i l e 95th
%i l e Conc entrati ons, ug/l

90th
%i l e 75th
%i l e 50th
%i l e

Florida Cen t ral Ridge, ac idic ground w a te r

Aldica rb 1000 ft Citru s 3.0 2.8 2.6 2.5 2.0 1.7 Oxamyl 0 ft Citru s 0.7 0.7 0.5 0.4 0.3 0.2

Northea s te r n FL, neutral ground w a t e r

Aldica rb 300 ft Potatoes 1.7e-0 5 1.3e-0 5 8.0e-0 6 5.7e-0 6 2.7e-0 6 9.9e-0 7

Souther n Co astal Plain - south e rn G A , acidic ground w a ter

Aldic a rb 300 ft 1 Peanuts 6.5 6.0 5.1 4.8 4.1 3.1

500 ft 1 Peanuts 3.7 3.4 2.9 2.7 2.4 1.8

Easter n Coa s tal Plain - easte rn NC, acidic ground w a ter

Aldica rb 300 ft Peanuts 1.3 1.2 1.1 1.0 0.8 0.6 Oxamyl 0 ft Peanuts 0.01 0. 01 0.01 0.01 <0.01 <0.01

North w e s t e r n US - Cen t r a l WA, alkaline ground w a ter, 15 -fo ot w e ll dep t h

Aldica rb 300 ft Potatoes 0.001 0.001 <0.00 1 <0.00 1 <0.00 1 <0.00 1

1 T he current la bel for al dicar b specifies a 3 0 0 -foot set back bet w e en the fie ld of app lic atio n and dr inki ng w a t e r w e lls for use on p e a nuts. A propos ed setback of 500 feet, bas e d on the IRED (U SEPA, 2006 d), w a s mo de led t o determi ne the impact on expos ure that w o ul d be e x pe cted from this modific a tion to the lab e l. Aldi carb use o n pe anuts is

inclu d e d in the dietar y b a sel i n e assessme n t.



### This comparison of model estimates with monitoring relies heavily

on aldic a rb monitoring data, as it is the most extens ive. Estimated
concentrations of total aldicarb residues (the parent plus its sulfoxide and sulfone transformation products) are comparable to existing monitoring

data from a number of studies:



### • Estimated concentrations with no setback distance between the well and field of application are similar to recent in-field monitoring

concentrations from w e lls in and around citrus groves in the FL Central Ridge (Lake Wales Ridge) condu cted by USGS and the Florida Department of Agriculture (USGS, 2006).

Section I.E – Page 157 of 277

### • Estimated aldic a rb concentrations with no well setback s were also similar to monitoring detections from the earl y 1990’s from private wells in Florida (FL DEP, 2005). Those detections do not reflect subsequent label changes and required well setba cks. While aldic a rb residues in recent years have been below the analyt ical limits of detection in the FL monitoring program, estimate d concentrations modeled with a
1000-foot well setback are also bel ow those limits of detection.



### • A 2006 study by Bayer CropScience of private wells in selected

aldic a rb use areas (excluding FL) found detections of aldicarb residues in 10% of the wells, with detec tions as high as 2.9 ug/l (USEPA, 2007a). Because the single samples represent a snapshot of aldicarb concentrations over time, they are best compared to the median concentrations of the estimated expo sures. The highest detections in the monitoring study (2.6-2.9 ug/l range) are similar to the median

concentrations estimated for the southern coastal plain scenario represented by peanut use in GA

.

Additional information on the monitori ng studies for aldicarb is in Appendix II.E.2; more detail on moni toring-modeling comparisons can be found in Appendix II.E.7.

b. Cumulative NMC Levels in Ground Water
The cumulative NMC residues in the high-exposure ground water scenarios ( Table I.E-10) represent the combi ned contributions of the

indiv i dual NMC residues listed in Table I.E-9 weight ed for relative potency and uncertainty factors and c onverted to oxamyl equivalents. For this NMC assessment, high-exposu re conditions refer to shal low wells (30 ft to ground water) extending through soils with a high leaching potential rating (according to USDA NRCS ratings) into an unconfined aquifer. Except where noted, the gr ound wat e r is acidic, an environment that favors persistence of the NMC chemicals.

The greatest estimated cumulative NMC concentrations in ground water sources of drinking water occurr ed in the southern portions of the coastal plain (representing the coasta l plain provinces of SC, GA, AL, and northern FL). Concentrations estimated in the Washington scenarios were substantially lower than those estimated in other areas.

Because the concentrations in T abl e I.E.10 reflect adju stments for

indiv i dual chemical FQPA safety and unc ertainty factors in addition to relative potencies, they cannot be dire ctly compared to measured values

in the environment.

Section I.E – Page 158 of 277

### Table I.E-10. Percentile concentrations for estimated NMC cumulative distributions in the ground water scenario sites adjusted for relative potency and using safety factors for children

Percentile concentration in ug/L (oxam y l equiv alents, adjusted for uncer tainty factor s) Region/Site

Max 99th 95th 90th 75th 50th Major RPF/UF-adj

contribu tor ( s)

NMC Cumul a tiv e Distributions incor porating F Q PA fac t ors for children 1

Southern Co astal Plain / GA, 3 00-ft

set b a c k 104.7 96.9 82.8 76.8 66.1 49. 6 Aldica rb (p ea nuts)

GA, 500-ft se tback 59.6 55.2 47.1 43. 7 37.6 28.2 Aldica rb (p ea nuts)

FL Cent ral Ri dge,

1000 -ft setba ck 48.1 45.5 41.6 39.7 33.0 27. 6 Aldica rb (citru s)

Eastern Co astal Plain / NC, 300-ft s e tback 21.6 20.0 17.5 15.7 13.5 10. 3 Aldica rb (p ea nuts)

North e a s t FL, neutral 2.7E­ 2.0E­ 4.2E­ 1.6E­ 1.3E-04 9.2E-05 Aldica rb, (pot atoes) GW 04 04 05 05

NMC Cumul a tiv e Distributions incor porating F Q PA fac t ors for adults 1

Southern Co astal Plain / GA, 3 00-ft 52.3 48.5 41.4 38.4 33.0 24. 8 Aldica rb (p ea nuts)

set b a c k
GA, 500-ft se tback 29.8 27.6 23.6 21. 9 18.8 14.1 Aldica rb (p ea nuts)

FL Cent ral Ri dge,

1000 -ft setba ck 23.9 22.6 20.7 19.8 16.4 13. 8 Aldica rb (citru s)

Eastern Co astal Plain / NC, 300-ft s e tback 10.8 10.0 8.8 7.8 6.8 5. 2 Aldica rb (p ea nuts)

North e a s t FL, neutral 1.3E­ 1.0E­ 2.1E­ 7.9E­ 6.4E-05 4.6E-05 Aldica rb, (pot atoes) GW 04 04 05 06

1 Concentrations h a ve bee n adju s ted for relative pote n cy and inte r-spe c ie s facto r s in ad dition to children

- or adult-spe c ific FQPA factors.



### Figure I.E-5 illustrates the differenc e in expo sure as a result of

different well setback s in the s outhern coastal plain scenario. The cumulative margin of expos ure (MOE ) for dri n king water exposure for

infants is less than 10 when the set back distance between the field and

the well is 300 feet. Taking into acc ount a larger (500-foot) well setback distance, the resulting drinking wate r exposure is greater than an MOE of

10. The drinking water exposure conc erns are being addressed in the single chemical assessment for aldi c a rb. For other scenarios modeled (representing high leaching potential ) the cumulative expos ures for drinking water are greater than an MOE of 10 (see Figure I.E-7 ).

Section I.E – Page 159 of 277

### Figure I.E-5. Cumulative margins of ex posure for drinking water from private wells in high leaching potential soils in the southern coastal plain (GA Peanuts),

infants

C u m u la tiv e M O E s fo r D r in k i n g W a te r E x p o s u r e s in a D a ily A n a l y s is - In fa n t s

1

10

10 0

100 0

1 000 0

10 000 0 1 1 5 2 9 4 3 5 7 7 1 8 5 9 9 1 1 3 1 2 7 1 4 1 1 5 5 1 6 8 9 1 2 3 5 6 8 9 0 2 3 5 6 9 1 3 1 7 2 1 2 5 2 9 2 3 2 7 2 1 2 5 3 9 3 3 3 7 3 1 3 5 J u l i an D aysMOEsatthe 99.9thPercentile

GA C o a s ta l P l a i n 3 0 0 ft W e l l S e tb a c k I n f a n t s

GA C o a s ta l P l a i n 5 0 0 ft W e l l S e tb a c k I n f a n t s



### c. Spatial Extent of High NMC Exposures in Ground Water

The ground water exposure assessment focused on areas where combined NMC exposure is likely to be among the highest within the region as a result of total NMC us age, adjusted for relati ve potencies,

and vulner ability of the drinking wate r sources. Based on the fate and transport characteristics of the NMC pesticides, interpretations of existing monitoring data, and results of ex posure modeling, the following conditions are likely to result in el evated concentrations of NMC residues in ground water:



### • Shallo w wells . Concentrations will vary with varying de pths to ground water and well depths (the depth from which the well draws water). Higher concentrations would be expect ed in more shallow wells while

lower conc entrations would be likely in deeper wells.



### • High leaching potential soils as classified by the USDA Natural Resources Conservation Service (USDA NRCS, 2003), with similar l y permeable conditions extending th rough the vadose zone to ground

Section I.E – Page 160 of 277

### water. Such soils are well-drained, highly permeable, and have a low organic matter content.



### • Acidic soil and ground w a ter , which favor the persistence of the NMC chemicals, particularly the su lfoxide and sulfone transformation products of aldicarb.

In most of the country, NMC residues in drink i ng water sources are

at levels that are not li kely to cont ribute substantially to the multi-pathway cumulative exposure. Ho wev e r, some areas of t he Delmarva Peninsula, the southeastern coastal plain, and Florida (primarily along the central ridge) and the southeastern coastal plain hav e conditions that could
result in elevated NMC concentrations in ground water. These areas represent what the Agen cy believes to be the most vulnerable private well drinking water sources for th e NMCs based on available monitoring, current use patterns, and known s o il and hydrologic conditions. In those vulnerable areas, which represent a re latively small area of the country, the estimated ground water residues are reasonable estimates of drinking water exposure for residents who get their drinking water from private wells that draw water from shallow de pths in unconfined aquifers.

Figure I.E-6 illustrates the spatial extent of high leach i ng potential soils (shown in red) in the NMC use areas (shown in gr een) in the southeastern US. Although count y-level soil information was not

available for the entire r egion at the time of the spatial assessment, such
soils can be identified. While the map includ es acidic, high leac hing potential soils, it does not reflect depth or pH of the ground water

, or the relative permeability of the underlying vados e zone and aquifer. Information on the location or depth of private drinking water wells

available is also not available.

Anticipated exposures in other parts of the country are expected to

be lower than surface water estimates in other regions of the country. In the north and north-central regions, to tal NMC use, particularly aldicarb, is relatively low. Aldic a rb is no longer labeled for use in a number of

northern and northeastern states because of a hi story of ground water

contamination. In t he mid-south, drinking wa ter comes predominantly from a public ground water supply drawing from deep, protected aquifers. NMC contamination is not expected, except in an area around southeastern Missouri and northeastern Arkansas, whic h had several detections in the Bayer CropScience monitoring study (USEPA, 2007a).

In the Great Plains and Lower Midw est, anticipated exposure is expected
to be lower than surface water estimates because of low rainfall and deeper aquifers than in t he southeast and Florida.

Section I.E – Page 161 of 277

### Figure I.E-6. Extent of high leaching pot ential soils in NMC use areas in the

Southeastern U.S.

d. Sensitivity Anal y s is: Carbofuran in Ground Water

Carbofuran was modeled in two NMC scenarios: one representing use on potatoes in Northeastern Flor ida, and the ot her representing

cucurbits in the Delmarva Penninsul a in the North/North central region ( Table I.E-11). The D e lmarva scenar io was not included in the NMC assessment, but was developed specific a lly for the sensitivity analy sis for carbofuran to represent a high-l eaching potential area for cucurbit

use.

Table I.E-11. Estimated concentrati ons for NMC and carbofuran for regional ground wat e r scenarios (25-year peri od) for ground wat e r at 30 feet

Section I.E – Page 162 of 277

### Section I.E – Page 163 of 277 Conc entrati ons, ug/l NMC pesticid e Well

setb ack
distan ce Crop (s) Maximum 99th
%i l e 95th
%i l e 90th
%i l e 75th
%i l e 50th
%i l e

Northea s te r n FL, neutral ground w a t e r

Aldica rb 300 ft Potatoes 1.7e-0 5 1.3e-0 5 8.0e-0 6 5.7e-0 6 2.7e-0 6 9.9e-0 7

Carbofu r an 1 0 ft Potatoes 2.7e-1 1 1.7e-1 1 9.3e-1 2 4.8e-1 2 1.5e-1 2 1.5e-1 3

Delmarv a Pe ninsula, acid i c ground w a ter

0 ft Cu cur b it s, high typ. rate 38.5 36.4 28.8 25.6 20.6 15.5

Carbofu r an 1

0 ft Cu cur b it s, lo w typ. rate 10.2 9.6 7.6 6.8 5.4 4.1

1 The Agen cy has p r op osed a can c ellatio n of all dome s tic carbofura n use s . The e s timated exp o su re s depi cted he re represent cu rre nt use s for carb ofuran.



### Carbofuran concentrations estimat ed in Northern Florida were six orders of magnitude lower than th ose estimated for aldicarb, and

therefore w ould not be a si gnificant contributor to th e NMC in that region.

Howev e r, carbofuran concentrati ons estimated on the Delmarva Peninsula are the highest estimated fo r any NMC. Estimated carbofuran concentrations in private wells in t he Delmarv a Penins ula are in line with monitoring data summarized in the 2006 Car bofuran IRED and suitable for human health exposure asse ssments (A ppendix II.E.7).

The greatest estimated cumulative NMC concentrations in ground water sources of drinking water occurred in the Delmarv a Penins ula for carbofuran-driven NMC exposures (

### Table I.E-12). The Delmarva Penins ula (carbofuran on cucurbits) would result in cumulative exposures for drinking water with a MOE of less than 10 ( Figure I.E-7). These drinking wate r exposure concerns are being addressed through cancellation of a ll domestic carbofuran uses. All other estimated expos ures from hi gh leac hin g potential ground wat e r sites result in MOEs great er than the target of 10.

Section I.E – Page 164 of 2771 1 5 2 9 4 3 5 7 7 1 8 5 9 9 1 1 3 1 2 7 1 4 1 1 5 5 1 6 9 1 8 3 1 9 7 2 1 1 2 2 5 2 3 9 2 5 3 2 6 7 2 8 1 2 9 5 3 0 9 3 2 3 3 3 7 3 5 1 3 6 5 

### Table I.E-12. Percentile concentrations for estimated NMC cumulative distributions in the ground water scenario sites adjusted for relative potency and using safety factors for children

Percentile concentration in ug/L (oxam y l Major RPF/UF-adj equiv alents, adjusted for uncer tainty factor s) Region/Site contribu tor ( s) Max 99th 95th 90th 75th 50th

NMC Cumul a tiv e Distributions incor porating F Q PA fac t ors for children (1)

Delma r va / high-e nd Carbofu r an 2540 2400 1898 1689 1357 1024 typic a l rate ( c uc ur b i t)

Delmarva / low-end Carbofu r an 670.6 633.8 501.2 445.9 358.3 270.5 typic a l rate ( c uc ur b i t)

North e a s t FL, neutral 2.7E­ 2.0E­ 4.2E­ 1.6E­ Aldica rb, ca rb ofuran 1.3E-04 9.2E-05 GW 04 04 05 05 (potatoe s)

NMC Cumul a tiv e Distributions incor porating F Q PA fac t ors for adults (1)

Delma r va / high-e nd Carbofu r an 923.7 873.0 690.3 614.2 493.5 372.6 typic a l rate ( c uc ur b i t)

Delmarva / low-end Carbofu r an 243.9 230.5 182.2 162.2 130.3 98.4 typic a l rate ( c uc ur b i t)

North e a s t FL, neutral 1.3E­ 1.0E­ 2.1E­ 7.9E­ Aldica rb, ca rb ofuran 6.4E-05 4.6E-05 GW 04 04 05 06 (potatoe s)

(1) Con c e n tratio ns have b een adjuste d for relative poten cy and inter-speci e s fa ctors in additio n to

child ren

- or a dult-sp ecific FQPA factors.



### . Figure I.E-7. Cumulative margins of ex posure for drinking water from private wells in high leaching potential sc enarios, children 1-2 years old

C u m ul at i ve M O E s f o r D r i n ki n g W a t er E x po su r es i n a D ai l y A n al ys i s - C h i l d r en 1- 2

J u l i an D a ys

1

10

10 0

10 00

10 00 0

10 00 00MOEsatthe99.9thPercentile

D el m a r v a C u cu r bi t s ( l ow

- en d t y pi cal r at e f or

car b of u r an ) C h i l d r en 1- 2

G A C oa s t a l P l a i n 30 0 f t W e l l S et b ac k C h i l d r en 1- 2

G A C oa s t a l P l a i n 50 0 f t W e l l S et b ac k C h i l d r en 1- 2

F L C i t r u s 10 00

- f t W e l l S e t b ac k C h i l d r en 1

- 2

N C C oa s t a l P l a i n 30 0 f t W e l l S et b ac k C h i l d r en 1- 2



### Section I.E – Page 165 of 277

## 7. Summary

The drinking water assessment focuses on areas where combined NMC exposure is likely to be among t he highest within each region as a result of total NMC usage and vulnerabi lity of drinking water sources. This analys is is based on a probabi listic modeling approach that considers the full rang e of data and not a single high-e nd estimate. EPA estimated NMC exposures in drinking wa ter to individuals in the CRA for both ground water and surface water source s of drinking water by region. The regional drinking water ex posure assessments are intended to represent expos ures from vulnerable drinking water sources resulting from typical NMC usage and reflect seasonal variations as well as regional variations in cropping and NMC use. Each regional asses sment focuses on areas where combined NMC exposure is lik ely to be among the highest within the region as a resu lt of total NMC usage, adjusted for relative potencies, and vulnerabilit y of the drinking water sources. For ground wat e r, private wells extendi ng through highly permeable soil and vadose zone materials into shallow, acidic ground water are expected to

be most vulnerable. For surface wa ter, drinking water reservoirs in small, predominantly agricultural watersheds are likely to be most

vulnerable. The co-occurrence of NM C residues in wate r is primarily estimated from modeling. Monitoring data are not available consist ently enough to be the sole basis for the assessment. However, monitoring

data are used to corroborate the modeling results and have helped confirm locations of potentially v u lnerable drinking wat e r sources.

In most of the country, NMC residues in drink i ng water sources are

at levels that are not li kely to cont ribute substantially to the multi-pathway

cumulative ex posure. Estimated NMC exposures from surface water sources of drinking water resulted in MOEs w e ll in ex cess of 10. Fo r most ground water sources of dri n king water

, NMC exp osures were similarly low. Private wells in high ly permeable soils that draw water at shallow depths in acidic, unconfined aquifers represent what the Agency

believes to be the most vulnerable dr inking water sources for the NMCs based on available monitoring, current use patterns, and known soil and hydrologic conditions. Those in stances wher e NMC concentrations

resulted in MOEs of less than 10 ar e being addressed with mitigation measures in the single chemical assessments – an increase in the well
setback distance from 300 feet to 500 feet for aldicarb us e on peanuts in the southern portion of the Coastal Pl ain and cancellation of all domestic

carbofuran uses. With these mitigat ion measures, NMC exposures from drinking water result in MOEs that are greater than 10.

Section I.E – Page 166 of 277

# F. The Multi-Pathway Cumulative Assessment

The previous chapters of this document have described the development of the pr imary components of the risk assessment. They describe a highly complex proces s of combining multiple data sets to generate a description of the pote n ti al risks from NMC pesticides by each of the pathways described. OPP has had to generate new methods for each component of the assessment in order to produce an assessment, which presents as realis tically a s possible the potential exposure to NMC pesticides. The purpos e of this chapter is to explain the concepts used to accumulate risk fr om each pathway into a total risk estimate, s u mmarize some of the major revised findings, and to provide a basis for understanding t he graphical temporal expos u re profiles that are provided in the Appendic e s.



## 1. Basic Concept s

The definition of cumulative ri sk developed as a result of the passage of FQPA requires OPP to conduct a risk assessment for a group of pesticides wit h a common mechani s m of toxicit y that is multipathway, multi-route, and multi-chem ical in s cope and nature. As described in Chapter I.B of this re vised cumulative assessment for the NMCs, the RPF method was used to address the issue of combining toxic responses from NMCs with vary ing propensities to inhibit acetyl

cholinesterase. Exposure to eac h NMC was normalized to equivalent exposure to the index compound, oxam yl. The toxic i ty data currently available for conducting this analy sis are estimates of response by routespecific dosing, and do not support estimati ng deliv ered dose to the target tissue. OPP decided to address this problem by comparing routespecific exposures to route-specific points of departure (PoD) to produce unitless margins of exposure for each route. Thus, each expos ure route

is associat ed with an MOE for that r oute. A total (or combined) MOE was calculated by taking the inverse of the MOE for eac h route, adding these together, and then taking the invers e of that sum. This process was used to produce a distribution of daily estimates of MOEs for the subpopulation of concern t hat reflects regional and seas onal variation 15


### in the patterns of exposure that ar e likely to occur throughout the US across the year. OPP used a probabi listic assessment procedure to capture the full range of exposur e possi bilities from all sources analyzed. The intent was to produce an estimate of risk that is as realistic as possible. The NMC cumulative risk assessment is not a high end risk assessment for the specific situati on, e.g., geographic location. OPP

15 Note that se aso nal variati on wa s only consi dered for the resi dentia l and

drin king wate r pathways. No sea s on al vari ation was con s id ere d for the food pat hway.



### Section I.F – Page 167 of 277

### believes it reflects the full range of likely exposures and exposure possib ilities for consideration in a regulatory context an d avoids developing exposure estimates based upon combination of exposure scenarios and assumptions that are not reasonable or are unlikely to cooccur in practice. This method has been standard practice for developing total MOE estimate s for aggregate and cumulative

assessments and is further de scri bed in OPP’s 2001 Aggregate guidance document http://www.epa.gov/pesticides/trac/science/aggregate.pdf



## 2. Frami ng t h e P o pul ation- Ba s ed As se s sme nt

OPP used the above-described methodologies to develop a series of daily exposure distributions and array them as a distribution across

time. The distribution of daily exposures and resulting MOEs are developed such that the exposur e s fr om NMCs in foods, drinking water

and from residential us es are all calculated simultaneously for each hypothetical indiv i dual in the s ubpopulation. OPP used the Calendex software to develop the distributio ns and resulting MOEs. Calendex permits incorporation of time-course information with regard to resi dential uses of pesticides and exposures th rough drinking water

, but does not permit specific allowance for regi onal va riab ility. As described in Chapter

I.E of this document, OPP address ed this issue by focusing on and developing separate assessments for r egional locations that represent what is likely to be the most vul nerable drinking water sources in high
NMC use areas. Based on a comparison of estimated drinking water exposures from surface- and ground-wa ter sources in three regions as part of the prelimi nary NMC CRA , OPP selected drinkin g water exposures representing what have been determined to be the three most

vulnerable areas –southern coastal pla i ns (Georgia), Florida central ridge, and Eastern coastal plains (North Carolina) regions – for the multipathway as sessment. NMC exposures in drinking water from the remaining parts of the country are expected to be substantially lower
than from these three sites.

To generate a daily distribution of exposure for the subpopulation of interest, a consumption record is selected from the CSFII that corresponds to the age group of inte rest. Calende x uses this consumption record to estimate NM C exposure from food commodity by randomly assigning a residue value fo r each food commonly inc l uded. After multiplying eac h amount of food commodity consumed by its selected residue value, the total expos ur e for this individual from food is

summed. At the same time, all appropriate residential s cenarios that may be encountered for the calendar day 1 (January 1) are reviewed. A probability-based decision is made as to whether or not that scenario will

be encount ered (e.g., a lawn treatment; not l i kely in January). If the

Section I.F – Page 168 of 277

### scenario is assigned a "yes" answer, t hen the appropriate values defining the exposure are selected from t he many distributions of input parameters for residential exposur e scenarios

. Dermal, oral and inhalation expos ures are calculated for all selected residential scenarios. A drinking water value taken from t he estimated distribution of water residues for January 1 is selected and paired with the water consumption reported in the CSFII consumption re cord. These values are used to calculate expos ure from drinking water fo r that date. All of the exposures are converted to route-specific MOEs to defi ne the total exposure to the hypothetical indiv i dual on January 1. The process is repeated for each consumption record for the age group in the CSFII one hundred times to build a distribution of expos ures for January 1. This process is repeated for January 2, January 3 and so fo rth across the same year.

The 365 daily exposur e distributions are arrayed together in order to provide a profile of possible exposures by each route and in total

as MOEs. A hypothetical example of such a distribution of distributions is presented in Figure I.F-1

. In this figure, each daily distribution is arrayed on the yz plane of the plot. Day 365 can be clearly seen on the right side of the plot. This distribu tion of total risk is expressed as a

cumulative distribution function of MO Es versus percentile of expos ure. Percentile of exposur e refers to t hat portion of the population that has less than or equal exposure. For ex ample, 80 % of t he population has

an exposur e level that is equal to or less than the 80th percentile.

Figure I.F-1. Three-dimensional plot of the total MOE by day of the year and percentile of exposur e

Section I.F – Page 169 of 277

## 3. Interpreti ng t h e O u tput s

The results of the final assessm ent are presented in graphical form in the appendices. They reflec t year-long slices across the 3­ dimensional plot in Figu re I.F-1

. In that plo t, dark lines can be seen
across the total MOE surface. Fo r instance, the top line in the 3­ dimensional plot represents the 99.9 th 

### percentile of exposure for the population. A slice through the surfac e parallel to the xy plane at the 99 th 

### percentile would look like the plot presented in Figure I.F.2. This plot presents the potential total MOE for the population exposed to NMCs by the exposure scenarios included in th is assessment. In addition, the contributions from various pathways and routes of exposure are arrayed separately to assist the risk manager in identifying contri butors to risk for

further eval uation. Other percentiles of exposure may also be of interest.

OPP will use the changes in graphi cal presentations of data such as these to evaluate the signific a nc e of various sources of exposure, considering the percentile at whic h the exposure becomes significant

and the duration over which the ex pos ure route and source remain

dominant in the risk assessment results.



## 4. Attribute s of the R e vise d N -Meth y l Carb amate Cu mu lative

Risk Assessmen t

The current revised assessment focuses on estimating the potential risk from expos ure to ten NMC pesti cides in food and drinking wat e r and
from residential uses. The assess m ent is limited in geographic scop e to
the Southern area of t he U.S. This limitation was placed on the

assessment to ensure that the wa ter and resi dential com ponents of the

assessment would reflect what a coher ent set of pesticide uses are likely

to exist. Understanding the likelihood of co-occurrence of pesticide uses is critical to developing a reasonable esti mate of total cumu lative risk. In
the absence of direct measures of co-occurrence, overlapping exposures must be extrapolated from use data

. The residential and groundwat er assessments are based on the most highly expos ed loc a lized areas within the southeastern region of the United States.

As indicated previously in this report, Table I.B.7 for the food and residential component s of the cumula tive risk assessment, a PoD was used for the oral component of the total cumulative risk assessment.

The estimated BMDL 10 

### (0.18 mg/kg body wt/day) for brain AChE inhibition by the index compound (oxamy l) was used. The inhalatio n and dermal components of the assessm ent were compared to BMDL 10 

### s of

0.66 and 17.05 mg/kg body wt/day, respectively.

Section I.F – Page 170 of 277

### Integrated cumulated risk asse ssments were conducted for the following age groups: Children 1-2 years, Children 3-5 years, Adults 20­ 49 years, and Adults 50+ years of age. These four groups were chosen to emphasize the effects of di fferences in behavior and food consumption patterns on estimating the risk from exposure to pesti cides.

The assess ments reflect the same assumptions about use scenarios, timing of exposures, and exposur e s to pesticides in food and water as used in the previous pathway specific assessments. An entire year of exposure is simulated. Three diffe rent water scenarios from the south

were matched with a residential scenar io that used application timing patterns that are characteristic of the South. Three water scenarios simulated ground water sources i n Geor gia, Florida, an d North Carolina.

The food component of the cumulati ve risk assessment contains as many commodities as could r easonably be extrapolated from the available PDP and FDA monitoring data. This component of the assessment is regarded as highly re fined and reflective of exposures likely to be encountered by the U. S. population. Because data on residential exposure are more limit ed, the residential component of the assessment was also designed to refl ect some overestimation bias to
ensure that risk from these sources of expos ure were not likely to be

underestimated. The water component s of the assessment focused on

what OPP believes are the most vu lnerab le drinking water sources. While the estimated drinking wa ter concentrations are reasonable reflections of actual exposures in t hose particular areas, the rest of the country is expected to have substantially lower combined (or “cumulative”) NMC residue leve ls in its drinking water.

As discuss ed earlier, exposure estima tes are specific to the regions discussed; they take into account region-specific water and residential use practices and cannot – as a general matter – be necessarily extrapolated to different regions. The Florida central ridge groundwater scenario is specific to an area in Florida in which the use of NMC pesticides, particularly aldic a rb, is high, soils are highly permeable, the depth to groundwater is shallow, and the soils and water are acidic. These conditions are favorable to pot entially high levels of NMC residues in drinking water sources. The North Carolina and Georgia coastal plain groundwat er scenarios represent another area where high NMC use (dominantly aldicarb), highly per m eable soils, shallow ground wat e r and acidic conditions are likely to favor potentially high combined NMC

levels. Further description of these sites, conditions, and characteristics
that led OPP to select these sites as high-end with respect to ground
water concentrations for this cum u lati ve ass e ssment is in Chapter I.D of

this document. OPP notes that dr inking water concentrations from a

combinatio n of multiple NMCs in the much of the rest of the U.S. w ould be expected to be substantially lower than estimated for this

Section I.F – Page 171 of 277

### assessment. It is possible, howev er, that the concentration of a single NMC in drinking water could be higher than that of the concentration of that single chemical's contributio n to this cumulative exposure

assessment.

Estimates of cumulative risk fro m 10 NMCs associated with exposure through foods, drinking water, and residential uses are presented in Appendic es III.F.1-24 for Children 1-2 years old, Children 3­ 5 years old, Adults 20-49 years old and for A dults 50+. The contributions of each of the major routes of ex posure and the likely sources of those exposures are discussed in previ ous chapters of this revised

assessment. Graphic a l presentati ons are provided for the 95 th 

### , 97.5 th



### , 99.5 th 

### , 99 th 

### , and 99.9 th 

### percentiles but written characterizations are

limited to the 95 th 

### , 99 th 

### , and the 99.9 th 

### because these percentiles capture the range of exposure which has traditi onally been of most interest to the Agency. As described in the re sidential chapter of this document, exposures through the inhalation route were not assessed and are considered to be very small. Thus, th is exposure route i s not specifically discussed in each of the following descriptions.

a. Children, 1-2 y ears, Geor gia Coastal Plain Ground Water

The results of the total cumu lative assessment for Children 1-2 years using the BMDL 10 

### of th e index chemical (ox a myl) for the PoD are presented in Appendix II.B.2

. Temporal Ex posure Profile Plots for Georgia Coastal Plain G r ound Water appear in Figure

III.F.2.

95 th 

### Percen tile – The significant source of pesticide risk from exposure to pesticides at this perc entile of exposure is through both the drinking water pathway wit h MOEs ranging from 117 to approximately 125 and the food component wit h an MOE that is generally near 142 across the year (Figure III.F.1). Dermal exposure that is associated with residential use does not occur at this percentile becaus e typically only a small percentage of the population uses such products.

99 th 

### Percen tile – The daily total MOEs ranged from 26 to 28. At this percentile, MOEs associated wit h food pathways were generally approximately 35 and comprise the majo r source for tota l expos ure. The daily MOE values from drin king water sources ranged from 64

to 70. MOEs from oral non-diet ar y ingestion which are associated with residential use (i.e., hand-to-mout h) were higher than drinking water exposure and generally th e MOEs for oral non-dietary exposure pathway ranged from ca. 296 to greater than 350 (Figure

Section I.F – Page 172 of 277

### III.F.1). MOEs associated wit h the dermal route are generally greater than 110 but as low as 99.

99.9 th 

### Percentile – At the 99.9 th 

### percentile, the total cumulative risk (all pathways) was as low as ca . 7 for this age group and nearly all

of the esti mated exposure came through the oral route that included significant contributions from the food pathway (Figure III.F.1). Oral non-diet ary expos ure (hand-to-mouth) resulted in MOEs remaining cons istent thro ugh the year between ca 76 and

90. Dermal MOEs go down to 24 with an average MOE of 27.

b. Children 3-5 y ears, Geor gia Coastal Plain Ground Water

The results of the total cumu lative assessment for Children, 3-5 years old using the estimated BMDL 10 

### of the index chemical (ox a myl) for the PoD are present ed in Appendix II.B.2. Temporal Exposure Profile Plot s for Geor gia Coastal Plain Ground Water appear in Figure III.3

.

95 th 

### Percen tile – Total MOEs at this percentile range from 90 to 94 throughout the year. The signific a nt contributor to total cumulative

ex posure comes throu gh the drin king water pathway (Figure III.F .3 ) with a range of MOEs of ca. 170 to ca. 180. The next most signific ant contributor to total cumulative exposure is through the food pathway; this pathway has fairly stable MOEs of slightly greater than 180.

99 th 

### Percen tile – At this percentile the to tal (cumulative) exposure

resulted in MOEs from 32 to 34 MOEs associated with food were generally near 41. MOEs from drinking wat e r sources generally remained in the 93 to 102 range. MOEs associated wit h the dermal route appear for the first time here and average ca. 160. Exposures from oral non-dietary ingestion (i.e., hand-to-mouth) was less than expos ure from drinking water and food and MOEs for this source generally ranged from ca . 422 to greater than 480 (Figure III.F.3)

. .

99.9 th 

### Percentile – At the 99.9 th 

### percentile, the total MOE (all pathways) was in the 7 to 9 range and this was nearly all contributed through food exposur e (Fi gure III.F.3). Drinking water MOEs average 53. MOEs varied for oral non-dietary expos ure around 115. MOEs from derma l expos ures generally ranged

between ca. 35 and 40.

Section I.F – Page 173 of 277

### c. Adults, 20-49 y e ars, Geor gia Coastal Plain Ground Water

The results of the total cumu lative assessment for Adults, 20-49 years using the BMDL 10 

### for the PoD are presented in Appendix II.B.2. Temporal Ex posu re Profile Plots for Georgia Coastal Plain Ground Water appear in Figure III.F.6.

95 th 

### Percen tile – Total MOEs at this percentile are in the 290 to 340 range with contributions from drinking water dominant and persistent throughout the year; exposures through the food pathway contribute a relatively sm all amount compared to total

ex posure, with MOEs for food ab ove 1400 (Figure III.F .6). Dermal MOEs were all greater than 4,000.

99 th 

### Percen tile – Total MOEs are general ly in the 100 to 150 range at this perc entile. Ex posure from drinking water results i n MOEs between 220 and 240 (Figure III.F. 6). MOEs associated with food are generally 230. Dermal expos ures are associated wit h MOEs of approximately 280 to greater than 2,300.

99.9 th 

### Percentile –Tot al MOEs at this percentile are generally in the 30-43 range, with exposure from food domin ant throughout the year (Figures III.F.1-9). M O Es a ssociat ed with drinking water are generally about 115. Dermal exposu res are associated with MOEs of approximately 50 to greater than 500.

d. Adults, 50+ y ears, Geor gia Coastal Plain Ground Water

The results of the total cumu lative assessment for Adults, 50+ years using the BMDL 10 

### for the PoD are presented in Appe ndix

II.B.2

. Te mporal Ex posure Profile Plots for Georgia Coastal Plain Ground Water appear in Figure III.F.7.

95 th 

### Percen tile – Total MOEs at this percentile are in the 220 to 260 range with contributions from dr inking water and food contributing all year long a relatively similar exposure resulting in MOEs above 400 and dermal MOEs were all greater than 4,000 (Figure III.F.7).

99 th 

### Percen tile – Total MOEs are generally in the 71 to 90 range at this percentile, with food signific a ntly contributing throughout the entire year with food MOEs of ca. 100 (Fi gure III.F.7). MOEs associated with drinking water are generally about 230. Dermal exposures are associated with MOEs of approximately 280 to greater than 2,300.

Section I.F – Page 174 of 277

### 99.9 th 

### Percentile –Tot al MOEs at this percentile are generally in the 15 to 20 range, with expos ure fr om food dominant throughout the year (Figure III.F.7). MOEs from exposure through the food pathway were in ca. 20. Drinking water ex posure resulted in MOE s of approximately 110. Dermal ex pos ures are associated with MOEs of generally ca. 56 to 543.

e. Children, 1-2 y ears, Florida Citrus Ground Water

The results of the total cumu lative assessment for Children, 1-2 years using the estimated BMDL 10 

### of the index chem ical (ox a myl) for the PoD are present ed in Appendix II.B.2. Temporal Exposure Profile Plot s for Florida Citrus Ground Water appear in Figure III.F .9.

95 th 

### Percen tile – Total MOEs at the 95 th 

### percentile rang ed from 67 to 74 (Figure III.F.9). One source of pesticide risk from exposur e to pesticides at this percentile of exposure is through the drinking water pathway with total MOEs ranging from 121 to 143. The food component of the assessment was stable across time with an MOE that is generally near 140 across the year.

99 th 

### Percen tile – At this percentile , the daily MOE values from drinking water sources were ca. 75. Total MOEs averaged about

27. Expos ures from oral non-diet ary ingestio n which are

associated with residential us e (i.e., hand-to-mouth) were somewhat lower than drinking water exposure and generally the
MOEs for oral non-diet ary expos ure pathway ranged from ca. 290 to greater than 350 (Figure III.F.9 ). MOEs associated with food

were gener ally around 35. MO Es associated with the dermal pathway are as low as ca. 99.

99.9 th 

### Percentile – At the 99.9 th 

### percentile, the total cumulative risk (all pathways) generally was in t he 6-8 range for this age group and nearly all of the estimated exposure came through the food pathway (Figure III.F .9 ). Drin king water ex posures resulted in
MOEs of about 40. Oral non-di et ary exposure (hand-to-mouth) resulted in MOEs remaining cons istent through the year between

76 and 90. Dermal MOEs generally ra nged between ca. 25 and 30.

f. Children 3-5 y ears, Florida Citrus Ground Water

The results of the total cumu lative assessment for children, 3-5 years old using the estimated BMDL 10 

### of the index chemical (ox a myl) for the PoD are present ed in Appendix II.B.2. Temporal

Section I.F – Page 175 of 277

### Exposure Profile Plot s for Florida Citrus Ground Water appear in Figure III.F .10.

95 th 

### Percen tile – Total MOEs at this percentile are approximately 95 throughout the year. The signific a nt contributor to total cumulative exposure comes through the dr inking water and food pathways (Figure III.F.10) with a MOE of ca. 185 each

99 th 

### Percen tile – At this percentile , t he MOE from food sources generally remained in the 40 ran ge and are essentially equivalent to total (cumulative) expos ure since the food pathway predominat ed. MOEs associat ed with drinking water were generally near 100. E x pos ures from oral non-dietary ingestion (i.e., hand-to-mouth) was less than exposu re from drinking water and

food and MOEs for this source generally ranged from ca. 420 to greater than 480 (Figure III.F.10). MOEs associated with the dermal route appear for the first ti me here and always exceed ca.

140

99.9 th 

### Percentile – At the 99.9 th 

### percentile, the total MOE (all pathways) was in the 7 to 10 ran ge for this age group and this was nearly all c ontributed by food (Figur e III.F.10). Dermal is next in importance with MOEs as low as 35. Drinking water MOEs averaged around 60. MOEs vari ed for exposure through oral nondietary exposure (hand-to-mouth) around 110. MOEs for dermal exposures generally ranged bet ween 35 and 40.

g. Adults, 20-49 y e ars, Florida Citrus Ground Water

The results of the total cumu lative assessment for Adults, 20-49 years using the BMDL 10 

### for the PoD are presented in Appendix II.B.2. Temporal Ex posure Profile Plots for Fl orida Citrus Ground Water appear in Figure III.F.14.

95 th 

### Percen tile – Total MOEs at this percentile are in the 420 to 500 range with contributions from drinking water dominant and persistent throughout the year ; exposures through the food pathway contribute a relatively sm all amount compared to total

ex posure, with MOEs for food ab ove 1300 (Figure III.F .14). Dermal MOEs were all greater than 4,000.

99 th 

### Percen tile – Total MOEs are general ly in the 110 to 160 range at this percentile, with exposu re from food and drinking water dominating during the entire year. Drinking water MOEs were consistently about 250. MOEs a ssociated with food are generally

Section I.F – Page 176 of 277

### about 260 (Figure III.F .14). Dermal exposur e s are associated wit h MOEs of approximately 300 to greater than 2,000.

99.9 th 

### Percentile –Tot al MOEs at this percentile are generally in the 30 to 44 range, with expos ure fr om food which was dominant throughout the year. Drinking wa ter resulted in MOEs of 125 (Figure III.F.14). Dermal expos ur es are associated wit h MOEs of generally ca. 50 to greater than 500.

h. Adults, 50+ y ears, Florida Citrus Ground Water

The results of the total cumu lative assessment for Adults, 50+ years using the BMDL 10 

### for the PoD are presented in Appe ndix

II.B.2

. Te mporal Ex posure Profile Plots for Florida Citrus Ground Water appear in Figure III.F.15.

95 th 

### Percen tile – Total MOEs at this percentile are ca. 340 with contributions from drinking water a ll year long of MOEs around 500 and food contributing a relatively small amount of exposure resulting in MOEs above 1000. De rmal MOE s were all greater than 4,000 (Figure III.F.15).

99 th 

### Percen tile – Total MOEs were generally around 140 at this percentile, with expos ure from fo od dominat ing during t he entire year. MOEs associated with drinking water are generally about 300 (Figure III.F.15). Dermal expos ur es are associated wit h MOEs of approximately 330 to greater than 2,000.

99.9 th 

### Percentile –Tot al MOEs at this percentile are generally in the 30 to 40 range, with expos ure fr om food dominant throughout the year (Figure III.F.15). MOEs fr om exposure through the drinking water pathway were in ca. 160. Dermal exposures are associated with MOEs of generally ca. 80 to 550.

i. Children, 1-2 y ears, Nort h Carolina Coastal Plains Ground

Water

The results of the total cumulati ve assessment for Children, 1-2 years using the estimated BMDL 10 

### of the index chemical (oxamyl) for the PoD are presented in Appe ndix II.B.2. Temporal Exposure
Profile Plot s for North Carolina Coastal Plain Ground Water appear in Figure III.F.18.

95 th 

### Percen tile – The significant source of pesticide risk from exposure to pesticides at this perc entile of exposure is through the food pathway with total MOEs ranging from 88 to 95 (Figure

Section I.F – Page 177 of 277

### III.F.18). The food component of the assessment was stable

across time with an MOE that is generally near 140 across the

year. Drinking water expos ure resulted in MOEs as low as ca. 300. Dermal exposures (associated with residential use) do not occur at this percentile becaus e typically only a small percentage of the population uses such products.

99 th 

### Percen tile – MOEs associated with food were gener ally around 35 and comprise the major source for total exposure. Exposures from oral non-dietary ingesti on which are associated with residential use (i.e., hand-to-mout h) were somewhat lower than drinking water exposure and genera lly the MOEs for oral nondietary exposure pathway ranged from ca. 290 to greater than 350 (Figure III.F.18). At this percent ile, the daily MOE values from drinking water sources ranged from 166 to greater than 186. MOEs associated with the dermal pathway are generally greater than 100.

99.9 th 

### Percentile – At the 99.9 th 

### percentile, the total cumulative risk (all pathways) generally was about 8 for this age group and was nearly all of the estimated expos ure came through the oral route comprised mostly of food pathway s (Figure III.F.18). Dri n king water and oral non-dietary exposu re (hand-to-mouth) is next in terms of magnitude of contributi on with MOEs from about 70 to

100. Dermal MOEs ranged between 24 and 30.

j. Children 3-5 y ears, North Carolina Coastal Plains Ground

Water

The results of the total cumu lative assessment for Children, 3-5 years old using the estimated BMDL 10 

### of the index chemical (oxamyl) for the PoD are present ed in Appendix III.F.19 Temporal Exposure Profile Plot s for Nort h Carolina Coastal Plain Ground Water appear in Figure III.F.19.

95 th 

### Percen tile – Total MOEs at this percentile are approximately 110 to 120 throughout the year. The si gnific a nt contributor to total cumulative exposure comes th rough the food pathway (Figure III.F.19) with MOEs of ca. 185. The next most significant

contributor to total cumulative exposure is through the drinking water pathway; this pathway has MOEs of greater than 400. Dermal exposures do not occur at this percentile because typically only a small percentage of the population uses such products.

99 th 

### Percen tile – The total (cumulative) exposure was ca. 35 with most of the exposure through exposure to food. At this percentile,
the MOE from food sources generally remained between 39 and

Section I.F – Page 178 of 277

### 42. The drinking wat e r pathway averaged in MOEs of ca. 250.

MOEs for oral non-diet ary ingesti on (i.e., hand-to-mouth) generally ranged from ca. 420 to greater than 480 (Figure III.F.19). As with Children 1-2 years old dermal expos ures do not occur at this percentile.

99.9 th 

### Percentile – At the 99.9 th 

### percentile, the total MOE (all pathways) was in the 7 to 9 range for this age group and this was nearly all c ontributed by expos ure through the oral route (drinking water, oral non-dietary and food pat hways all contributed to these MOEs) (Figure III.F.19). Or al non-dietary expos ure (hand-tomouth) resulted in MOEs generally in the 110 to 120 range throughout the year. MOEs vari ed for exposure through food

around 9. MOEs for dermal exposures were above 30.

k. Adults, 20-49 y e ars, North Carolina Coastal Plains Ground

Water

The results of the total cumu lative assessment for Adults, 20-49 years using the BMDL 10 

### for the PoD are presented in Appendix II.B.2. Temporal Ex po sure Profile Plots for N o rth
Carolina Coastal Plain Ground Wa ter appear in Figure III.F.22.

95 th 

### Percen tile – Total MOEs at this percentile are around 600 with

contributions from food a ma jor contributor and persistent throughout the year with MOEs of around 1200; exposur es through the drinking water pathway cont ribute a similar amount compared to total exposure, with MOEs for drinking wat e r above 1100 (Figure III.F.22). D e rmal MOE s we re all greater than 4,000

99 th 

### Percen tile – Total MOEs are general ly in the 120 to 210 range at this percentile, with exposure from food dominating d u ring the entire year (Figure III.F.22). MOEs associat ed with food are generally about 230. Drinking wate r MOEs ranged from ca. 560 to greater than 600. Dermal expos ur es are associated wit h MOEs of approximately 280 to greater than 2,300.

99.9 th 

### Percentile –Tot al MOEs at this percentile are generally in the 30-50 range, with exposure from food domin ant almost throughout the year (Fi gure III.F.22). Drinking water MOEs average ca. 300. Dermal exposures are associated wit h MOEs of generally ca. 50 to

500.

l. Adults, 50+ y ears, North Caro lina Coastal Plains Ground Water

Section I.F – Page 179 of 277

### The results of the total cumu lative assessment for Adults, 50+ years using the BMDL 10 

### for the PoD are presented in Appe ndix

II.B.2

. Te mporal Ex posure Profile Plots for North Carolina Coastal Plain Ground Water appear in Figure III.F.23.

95 th 

### Percen tile – Total MOEs at this percentile are in the 480 to 630 range with contributions from food all year lo ng. Drinking water contributed a relatively similar amount of exposure as food, both resulting in MOEs above 1000. De rmal MOE s were all greater than 4,000 (Figure III.F.23).

99 th 

### Percen tile – Total MOEs are general ly in the 130 to 180 range at this percentile, with exposure from food dominating during the entire year. MOEs associated wit h drinking water range from 650 to greater than 700 (Figure III.F .23). Dermal exposures are
associated with MOEs of approximately 300 to greater than 2,300.

99.9 th 

### Percentile –Tot al MOEs at this percentile are generally in the 40 range, with expos ure from food dominant throughout the year (Figure III.F.23). MOEs associat ed with drinking water range from 300 to greater than 400. Derma l exposures are associated with MOEs of generally ca. 80 to 550

Section I.F – Page 180 of 277

# G. Risk Characterization

1. Introducti on

Risk characterization is the inte rpretation phase of the assessment

process. This chapter characterizes t he risks identified as part of the revised

NMC CRA. The intent is to note and discu s s uncertainties and strengths in the hazard and exposure element s of risk estimates and to assess quantitatively (when poss i ble) or qualitativ ely the potential impact of those uncertainties on

the risk esti mates.

Proper and appropriate risk characterizati on is particularly important for an assessment as complex as the NMC CRA. Many types of data derived from a variety of sources have been combined to produce es timates of risk from exposure to multiple NMCs in food, drin king water, and from residential use. The outputs of the assessm ent should be ev aluated in a variety of ways. Potential biases in input parameters, the direction of the bias, and the uncertainty surrounding the inputs and t he expos ure model must be considered with regard to their potent ial impact on the result s of the assessment.

Sensitiv ity analyses ar e important as is a description of how changes in input assumptions might – or might not—affect the assessment.

This revised NMC CRA reflects the impacts on the risks from implementing the risk miti gation measures from the single chemic al

assessments. The current document presents the estimates of risks associated with expos ures to NMCs in food, drinking water, and from residential uses. For the multi-pathway assessment, food, drin king water, and resident ial expos u res are cumulated as a set of temporal or time-series plots of MOEs over a period of 365 days. Contributions from vari ous pathways and routes of expos ure are arrayed separately. The assessment pr esents and discusses results for the 1-2 year old, 3-5 year old, 20-49 year old, and 50+ year old age groups for the three geographic regions of interest

. While the results for infants, 6

- 12 year old children, 13-19 year old youths, and fe males 13-49 are not discussed in this chapter, these analys es were performed and are presented in Part III.F of this assessment with the other four age groups.

No single value in the assessment should be used to independently arrive at the interpretation of the results. As discussed below, interpretation of the assessment depends upon the synthesis of a vast body of information about the input data and the processing of that dat a to determine whether estimated risk
is below OPP’s lev e l of concern.



## 2. Hazar d and Dose- R espon se Assessmen t

The hazard and dose-response as sessment is presented in detail in Chapter I.B. That chapter outlines t he steps in developing the dose-response

Section I.G - Page 181 of 277

### relationships for each pesti cide based on inhibition of acetylcholinesterase via carbamylation of the active site. It includes a description of all of the data used in the dose-response analyses. Reasons fo r the selection of oxamyl as the index chemical for the N M C CRA are also

discussed. In addition, Chapter I.B de scribes the dose-time-response model used to develop the dose response curves and half-life to recovery estimates that provided the basis for developing the RPF for each chemical, the PoDs for

the index chemical for each route of exposure (i.e., oral, dermal, and inhalation), the FQPA 10X fact ors, and the inter-species extr apolation factors.

a. Acet y l cholinesterase Inhibition: Data Quality & Common Effect

The first step in deciding that a cu mulative risk assessment was needed

was the determination that the N M Cs we re toxic by a common mechanism, i.e., AChE inhibition via carbamyl ation of the active site of the enzyme. Once a common mechanism was identified, the nex t step in the process was to select an appropriate method for co mbining t he risks from exposures to several pesticides from more than one source/route. Data describing the inhibition of AChE in RBCs and brain has been gener ated for each registered NMC.

Studies de velope d for registration along with studies performed by EPA’s

NHEERL have been used in this risk assessment. OP P has elected to use the
brain AChE data from adult rats as the basis for developing RPFs and PoDs for

use in the assessment. Brain AChE inhi bitio n was selected as it reflects a response in a target tissue of concern t hat is relevant to humans. Although
RBC cholinesterase inhibition data do refl ect exposure to NMCs and, therefore, the potential for adverse effe cts, brain AChE inhibition is an indicat i on of direct effects upon the target ti ssue itself. Error due to the extrapolation between the response in a surrogate tissue (i.e., red blood cell and plasma) and a target tissue itself (brain) is eliminated. In addition, the data for t he brain compartment have very narrow confidence limits when compared to those from the blood, suggesting that there i s much less variabi lity in this compartment across the

data base. As described in Chapt er I.B , human studies on RBC ChE are

available fo r aldicarb, oxamyl, an d methomyl. EPA has determined, after considering the advice of the HSRB, t hat these studies are ethically and scientifically acceptable and appropriate fo r use in this risk assessment. For these three chemicals, inter-species fa ctors were deriv ed by comparing this
ChE data from human RBCs with simila r RBC ChE data from rats : this comparison was used to replace t he standard 10x inter-species uncertainty

factor for th e thr ee NMC pesticides.

Due to the nature of A C hE inhibiti on with NMCs (i.e., rapid time-to-peak effect followed by recovery), accura te measurements of inhibition can be challenging in the laboratory. The Agency acknowledges this and also the importance of data quality on estimates of potency. As part of the cumulative

risk assessment and in an effort to aid in the evaluation of data submitted for registration, ORD performed a series of dose-response studies where data on
AChE inhibition derived from a radiom etric method were compared with data

Section I.G - Page 182 of 277

### derived from a standard Ellman method. When evaluating the toxicity of NMCs, data from the radiometric studies are considered the highest quality. The Agenc y has also compared result s from modified Ellman assays from studies submitted for registration with the radiometric data and has requested
and reviewed the SOPs for AChE measur ements for NMCs. The review of these SOPs is provided in Appendix II.B.5

. Overall, the A gency has concluded

that the available databas e of AChE studies is high quality and appropriate for deriving relative potencies. The Agency is c onfident that the assessment, as performed, is scientific a lly and statistically sound and based upon a reliable

data set.

A BMD 10 

### was selected as the basis for comparison of the dose-response

curves for the NMCs. OPP's goal in se lecting a point of comparison was to choose a point where the magnitude of t he response (cholinesterase inhibition)

was reliab l y distinguis hable from backgroun d but still be protective of behavioral

and functional effects. As described in detail in Chapter I.B, the response level of 10% from control provides this bal ance. A power analysis has shown that 10% inhibition can be reliably detected in the majority o f tox i city studies

submitted to EPA. The Agency b e liev e s that the BMD 10 

### is protective of

functional and behavioral effects as none ha ve been observed at levels at or
below 10% inhibition.

b. Dose-Response Anal y s is

This asses sment uses the RPF approach. Briefly, the R P F approach uses an index chemical as the point of reference for standardizing the common

toxic i ty of the chemical members of t he cumulative ass e ssment group. RPFs (i.e., the ratios of the toxic potenc y of a given chemical to that of the index chemical) are then used to convert exposures of all chemicals in the cumulative assessment group into exposure equivalent s of the inde x chemica l

. The RPF approach u t ilizes dose -response information to provide an estimate of each NMC's potency for the common toxic i ty, and thus allows for t he quantification of exposure as it relates to the joint ri sk of the cumulative assessment group.

OPP selected the RPF approach based upon t he relatively rich toxic i ty data

base on cholinesterase inhibition availabl e for the NMCs. Although a biologica l or pharmacokinetic modeling appr oach would have advantages in determining the cumulative risk for these NMCs, t he input parameters for such an approac h
are not available for most of the NMCs

. Thus, OPP has applied sim p le dose addition and has used an empirical curve fitti ng model to determine RPFs and

PoDs. OPP, in collaboration with ORD, has used an exponential model to describe the dose-time-response curves for each NMC. This dose-timeresponse model has been used to fit cholines terase activity data from different

studies to derive a BMD in addition to esti mation of half-life to recovery. Use of the exponential model to describe cho linester a se data has been subjected to

extens ive p ublic comment and peer re view by the SAP (FIFRA SAP 2001b, 2002, 2005a, 2005b). OPP belie v e s that the model fitting procedure used in

Section I.G - Page 183 of 277

### this assessment provides reliable esti mates of relative potency and PoDs

for all route s.

c. Selecting the Index Chemical

OPP selected oxamyl as the index chemical for the NMC CRA; this selection was supported by the FIFRA SAP (2005b). C e rtainty in the PoDs for risk extrapolation is considered to be of great importance in cumulative risk assessment as the PoDs impact the overall uncertainty in the entire risk

assessment. Oxamyl has sufficient data for cholinesterase inhibition to support modeling of a BMD 10 

### and BMDL 10 

### in adult and juvenile rats and human subjects. Moreover, oxamyl studies in adult rats are available for all three routes of exposure (oral, dermal, inhalation) in addition to half-life to recovery in rats and humans. The hi gh quality dose response data for oxamyl permit reliable estimates of PoDs for all routes.

d. Assumption of Dose-Additivity

The cumulative risk assessment for t he NMCs is based on the assumption of dose-additivity. Dose-additivit y is the Agency's assumption when evaluating the joint risk of chemicals that are to xicologic a lly similar and act at the same target site (USEPA, 20 01a). The SAP (FIFRA SAP, 20 01a) indica ted that

substantial reliance would have to be placed on what is known about the

mechanism of toxic i ty because it is very difficult to prove dose-addit i vity at human exposure levels. They further pointed out that studies available on

indiv i dual chemicals were usually not designed to address the issue of doseadditiv i ty.

Although there are a few interaction studies of N- methyl carbamate and OP pesticides in the literature (e.g ., Gupta and Dettbarn, 1993; Takahashi et al

., 1987), the Agency did not identify any st udies conducted using mixtures of

more than two N- methyl carbamates and which use low dose levels (i.e., that do not produce lethality or profound toxic i ty). NHEERL scientists have conducted a mixture study using seven N- methyl carbamates (carbaryl, carbofuran, formetanate HCl, methioca rb, methomyl, oxamyl, and propoxur) (Padilla et al., 2006). In the mixture study, a dose-additive experimental design was used and the proportion of the NMCs in the mixture was based on their potency using the indiv i dual-chemical benc hm ark dose values as the point of comparison. In general, increasing dosage s of the mixture pr oduced increasing decrements in brain ChE activity. Mor eover, the dose-additive model predicted the degree of ChE inhibition (RBC and br ain) and changes in motor activity
within the 95% confidence limits of eac h predicted value (Padilla et al, 2006; Manuscript in preparation).

The NMCs all act on the same target site– namely, the inhibition of acetylcholinesterase by carbamylation in nerve tissue, which elicit s a variety of

Section I.G - Page 184 of 277

### cholinergic effects. Dose addition is regarded as a reasonable and appropriate approach for estimati ng the cu mulative risk associated with joint exposure to the NMC comm on mechanism group.



## 3. F ood Assessmen t

The food component of the NMC cumulative risk assessment is based primarily upon two ext ensive, reliable data sets: 1) USDA's Continuing Survey of Food Intakes by Individuals, 19 94-1996 and 1998 (CSFII) and 2) USDA's Pesticide Data Program (PDP). The CS FII provides a detailed representation of the food consumption patterns of the U.S. publ ic across all age groups, during all times of the y ear, and across the U

. S. The P D P data provide a very reliable sample of pesticide residues in the major children's foods, including fruits, vegetables, milk, grains and beef/ poultry/pork. The PD P pr ogram utiliz es multi-residue analytic al methods such t hat co-occurrence of several pesticides in individual samples is captured, alle viating much of the uncertain ty that otherwise might exist about co-occurrence in foods that are monitored in the

program. These two sour ces of data – CSF II consumption data and PDP petisticide residue dat a -- provide a firm foundation upon which to assemble other data to develop the NMC cumula tive risk assessment and are discussed

in more detail be low.

a. Consumpt ion Data

As with the previous cumulative ri sk assessments performed by OPP, this

revised NMC assessment is based on diet ary consumption data obtained from

the USDA’s CSFII 1994-96/98. This is an extensiv e, two-phase (1994-1996, and then 1998) survey and includes more than 20,000 individuals sampled over four years. The CSFII 1998 provided an additional 5,559 c onsumption diaries for children ages newborn through nine years of age, which supplem ented the 4,253 children sampled in the CSFII 1994- 96. This additional, supplemental children’s survey was specifically r equested of USDA by OPP in order to improve the means to assess exposures to children. In each year of the survey, approximately 5,500 participants in 62 geographical areas across the country were interviewed on their dietar y consumption over two separate (nonconsecutiv e) days. The survey was designed to provide a nationally representative sample of non- institutionaliz ed persons residing in the U.S. USDA also provides sampling weights, whic h allo w the survey results to be projected to the U.S. population.

The sampling procedure was designed to account for inter-individual variability in indiv i dual consumption patte rns (e.g., types and amounts of foods eaten) due to differences in age, gender , ethnicity, regional location, and

socioeconomic status. Also, survey re spondents are interviewed on different days of the week throughout the year to account for seasonal and within week variability in consumption patterns. A num ber of other aspects of the survey are

also controlled in order to maximize the prospect that the results are representative not only of the entire U.S. populati on, but also particular

Section I.G - Page 185 of 277

### subgroups, including those for which OPP generates ac ute dietary food exposure distributions.

While the USDA food consumption su rveys are designed to be generally representative of the U.S. population, it is clear that some factors that can

influenc e dietary choices are not addressed in the survey design. For example, the CSFII surveys do not purport to be r epresentative of people in institutional living arrangements (colleges, nursing homes, etc.) or of different religions or

health status. Specific subpopulations su ch as vegetarians, those on restricted diets, or those on specialized diets were not specifically surveyed. In addition, smaller specialized subpopulations such as Native Americans or subsistenc e fishermen are not specifically targeted 16



### . Overall, however, the dietary

information which OPP used for the cumulative assessment of N M C pesticides is the highest quality data available and is representative of many subgroups in the U.S. population. T he consumption data available from the CSFII 1994­ 96/1998 provide a reasonable basis for esti mating NMC food exposure for the subpopulations surveyed.

b. PDP Monitoring Data in the Assessment

USDA PDP data are used for all of the measured pesticide residues in the

revised NMC CRA. PDP samples fruits, vegetables, juices, meats, grains, and dairy products at central distribution c enters and warehouses immediately prior to distribution to supermarkets or groc ery stores. The samples are washed an d inedible portions (e.g., cores, peels, et c.) are removed prior to analysis. PDP data, thus, closely reflect residues in foods, as consumed. OPP has applied factors, where available, to the PDP data to account for cooking and processing that might further reduce residues (e .g., cooked potatoes, canned beans). Thus, pesticide residue data used dire ctly from PDP or adjusted with processing factors accurately represent pesticide concentrations to which

consumers are expos ed.

The use of PDP as a source of re sidue data has a number of inherent benefits that minimize the need to incorpor ate conservative assumptions in the assessment and produces more realisti c exposure estimates. The PDP sampling design and procedures provide OPP with a nationally representative sample of selected food commodities ava ilable to the U.S. population in groce r y stores. OP P assumes a uniform distribut ion of these food commodities across

the U.S. While the ass u mp tion of nationa lly uniform di stribution of foods does not reflect highly localized consumption events that may be occu r as a result of

indiv i duals obtaining foods at road si de stands or local farmers markets and consuming it closer to the time of harve st than the foods availab l e in larger grocery stores, it is bel ieved that only a small percentage of food consumed is actually obt ained from thes e sources and would be affected by this assumption.

16 Although po pulation s that rely prima r ily on fish co nsumption were not spe c ifical ly targeted in CSFII, available resi due data indicate th at fish consum ption is not a major source of pesti cide exp osu re for currently registe r ed pe st icid es in gene ral, no r NM Cs in p a rticula r

. 

### Section I.G - Page 186 of 277

### In addition, as noted above, PDP uses multi-residue methods and so provides a direct measure of the co-occu rrence of multiple pesticid e s in each sample analyzed. Thus, the PDP data inhe rently reflect existing use patterns of pesticides. Given the size, scope, quality, and breadth of the PDP sampling program, these data provide the best available information regarding pesticide residues in the U.S. food supply.

c. Data Translation from PDP

Not all food commodities treated wit h NMC pesticides are monitored in

PDP. OPP has developed a procedure by which commodities that are sampled by PDP serve as surrogate data sources fo r some commodities that are not. This approach is outlined in OPP/HED SO P 99.3 (USEPA, 1999b)

. It is based upon the concept that families of commoditie s with simila r cultural practices and insect pests are likely to have similar insectic ide use patterns. Although this approach is generally sound, it introduces uncertainty with regar d to how similar the use patterns for a given pesticide ar e to those for even closely related

commodities.

For example, the same pesticide may be applied to several crops belonging t o the same crop group (or fa mily) on a similar time schedule.

Howev e r, the application rates and/or t he number of treatments may differ between the treated crops. Such issues should be taken into consideration when conducting sens itivity analy ses of the results of the risk assessment. When such translations are done in a cumulative risk assessment, the assumption of similar agricultural pr actices is inherently applied to all the pesticides in the cumulative asse ssment group and the co-occurrence of pesticides on the surrogate commodity is extended to the commodities to which the residues are being trans lated if the translated co mmodity is a registered

use. Whether any such potentially inappr opriate translations overestimate or underestimate exposu r e will differ from one commodity to another. Howev e r, the impact of any such potential translation errors on total exposur e is not likely to be significant since the commodities for which PDP data were translated represent less than 1% of the mean per capita consumption by small children.

d. Other Sources of Residue Data

The PDP program provides pesticide resi due data for a variety of fruits, vegetables, juices, meats, grains, and dairy products. Never-the-less, PDP data -- and its associated surrogate data for translated co mmodities -- still do
not cover all food commodities of in terest. For example, PDP does not currently include data for seafood and eggs; for these commodities , FDA's Total Diet Study and FDA Monitoring data were reviewed. No NMC residues were found in seafood or eggs in either of these programs. Thus, the analytical results suggest that eggs and seafood cont ain negligible resi dues of NMCs and OPP used a zero to represent NMC pesticide concentrations in these

Section I.G - Page 187 of 277

### commodities. OPP considers this fa ctor neutral with regard to the i m pact
on the results of the assessment.

Appro x imately 3% of the foods cons umed b y children 1

- 2 years of age still

remains unaccounted for after considerin g the PDP dat a, the FDA Total Diet Study data, and the FDA surveillance moni toring data. This includes sugar, molasses, and various oils and syrups wh ic h were assigned a residue value of

zero. Evidence suggests that these commodities would have de min i mis pesticide residues. These produc ts are hi ghly processed commodities that are

unlik ely to retain any significant residues following the int ensive commercial processing they undergo. PDP has samp led high fructose corn syrup during

1998 and 1999 and did not find residues of any pesticide. PDP has also sampled field corn during the 2006-2007 period and only found one sample containing an NMC pesticide (carbaryl) at a very low (ppb) concentration. The limited data from the Total Diet Study found no residues in pancak e syrup or

sugar. OP P believ e s that the assumption of zero residues for highly processed

commodities such as those described abov e will not result in a significant under-estimation of expos ure to NMC pesticides from food for chi l dren or any other subpopulation.

No data are available for commodities such as dried beans, spices, seeds, nuts, and low consumption specialty crops (such as avocado, kiwi, raspberry, and mango). OPP believes t hat the omission of various low cons umption and specia lty foods from the assessment will not signific antly under-estimate exposure to NMC pesticides from food fo r children or any other subpopulation.

e. Impact of Regulatory Actions

There has been significant mititgati on measures implemented on many NMCs as a result of the indiv i dual chem ical decisions, including canceling uses

, lengthening pre-harvest intervals, and reducing the number and/or application

rates. As a result, duri ng the period si nce the issuance of the preliminary NMC CRA in August 2005, the Agency has ident ified, and in some cases imposed,

risk reduction measures on some of t he major contributor s to carbamate cumulative risk, as discussed below. The risk estimates presented in the revised NMC CRA reflect the risk mitigati on measures identified for or taken on

indiv i dual carbamates since FQPA was si gned into law in August 1996. A table summarizing these mitigation measures is provided in Appendix II.A. These mitigation measures generally reflect determinations o f risk based on EPA’s assessment of the single chemical’s ri sks. For all of th e risk mitigation
measures that are refl ected in this document, EPA has either commenced the processes necessary to implement its selected risk mitigation or intends to commence the appropriate processes in the near future. Having already
determined that the identif ied risk mitigation is warr anted for the individual

chemical re gardless of the cumulative assessment, EPA has chosen to reflect that mitigation in this assessment, and c onsequently has excluded uses that are

Section I.G - Page 188 of 277

### slated for cancellation from this assessment. 17 

### More specifically: in
cases for w h ich agreements have been sig ned or voluntary cancellations are being implemented or have been requested by the pesticide registrant, the uses

have been exc l uded from the assessment. Examples include use of methomyl on strawberries and grapes. In additi on, EPA has exc l uded all uses where specific mitigation measures such as cancellation have been identified and proposed b a sed on sin g le-chemic al risk assessments. For example, EPA has

exc l uded all carbofuran uses from the cumulative ass e ssment except the few import tolerances that the Agency is not proposing to revoke. (See Chapter I.A for additional details regarding the specif ic cancellation actions that have taken place.)

For other pesticides, pre-harvest i n tervals have been extended or application rates have been reduc ed. Fo r example, pesticide use labels for commodities such as apples, peac hes , pears, potatoes, nectarines, oranges,
and strawberries were modified at various ti mes in the past several years to
reduce residues of a number of N M Cs such as aldicarb, carbaryl, and
formetanate HCl. To reflect the impact of these risk mitigation me asures, EPA used residue data only from years a fter the measures had been implemented (i.e. the more recent years that reflect the changes in pesticide us e practices). PDP data from prior years – that reflect older, disc ontinued use patterns – were

not included. To the ext ent that the impacts of risk mitigation measures on

residue lev e l are not yet apparent in t he available PDP monitoring data, the

PDP residu e data will not reflect the ex p e cted decrease in expos ure and the assessment likely overestimates ri sk on this basis.

f. Impact of Assumptions : Sensitivity Analy s es

The following section describes sens itivity analyses conducted on the
cumulative food assessment. Thes e analys es focus on four areas:



### • Limit of detection in PDP and th e use of ‘zero’ assumption for non-detectable residues;



### • Comparison of expos ure esti mates using all available PDP data deemed appropriate for the CRA with those using only the PDP data from recent years;

17 As a prac tical matter, EPA determined that it would s e rve no purpos e to inc lude

su ch u s e s in the cum u lative asse ssm en t. Ot her than by adding a n ew is su e that might delay action, ad din g these u s e s woul d not like ly have any impact on the timing or sub s tan c e of any

can c ell a tion d e ci sion relatin g to such use s

. Given that the purpo se of toleran c e

rea s sessm en t is to determi ne wh ether re gulator y ac tion s h ould be initiated to modify or

revoke tolera nce s that the Agency find s do not meet the safety sta ndar d of se ction 408, there

seem s to be l ittle value in including uses in t he assessment that will di sappear regardless of their impa ct o n cumul a tive risk; the criti c al is sue for d e termini ng whether reg u la tory action wil l

have to be ini t iated unde r section 4 08 is wheth e r the u s e s that will remain result in unacceptabl e dietary risk. EPA recogni zes, how ever, that to the e x tent that any risk mitigation me asu r e s are no t subsequ entl y implem ente d as envi s ion ed in this a s sessment, the

N-m e thyl Carbamate cumulative assessment will hav e to be revised as necessary.



### Section I.G - Page 189 of 277

### • Summation of exposure over 24 hours instead of smaller

time increments or individual eat ing events that would better account for rapid reversibility on NMCs; and



### • Consideration of a chemical -specific adjustment factor approach for the inter-species extrapolation f a ctor

Each of these sensitivity analyses is discussed below.

i. Limit of Detection in PDP: Use of ‘zero’ a ssumption for nondetectable residues

One of the important attributes of a cumulative risk assessment is that its scope and complexity, unless carefully considered, can potentially lead to

inflated estimates of risk due to co mpounding conservatisms which would reduce the interpretability and ultimately the utility of the assessments. When
little or no information is available to in form potential sources of exposure, a single chemical assessment ma y incor porate conservative assumptions to reflect reasonable worst case exposure estimates. In cumulative risk assessments, the incorporat ion of such conservative assumptions would imply multiple simultaneous reasonable worst case expos ure estimates for each

indiv i dual chemical. As such, some of the conservative assumptions appropriately used in the single chemical ris k assessments are not appropriate
or reasonable for use in a cu mulative risk assessment.

One exam ple is the way in which assumptions for single chemical versus cumulative (multiple) chemical as se ssments differ for PDP sample s with no

detectable residues. For any analytical method, there is a minimum concentration at which a compound must be present in order fo r it to be reliably detected in the sample. This mi nimum level is referred to as the analytical method’s limit of detection (LOD). With re spect to a specific pesticide, a sample having no detectable residue (i.e. a residue below the LOD) is referred to as a

non-detect. A non-detect does not necessa rily imply that no residues are present; instead, a non-detect simply indi cates that residues, if present, are present at concentrations less than the LOD. In si ngle chemical assessments,

certain non-detects are assumed to be present at one-half the LOD of the analytical method (more specifically: t hose non-detects for which percent crop treated information suggests were actually tr eated with the pesticide of interest). In contrast, all PDP samples with non-detectable residues are assumed to be
"zero" for this and other cumulative risk assessments. Althoug h the result of replacing all non-detec table re sidues with "zero" values might intuitively suggest
a bias toward under-estimation of ri sk, OPP has conducted a sensitivity analys is and determined that this assumption has little impact on the upper end of the exposure distribution for the NM Cs covered by this assessment and upon which EPA has based its regulatory decis ions

. The most highly exposed

indiv i duals tend to be associated with relativ e ly high consumption of high

Section I.G - Page 190 of 277

### residue values, not residues below PD P’s limits of detections. The
details of this sensitivit y analysis are provided below.

The sensitivity analysis is presented in Table I.G-1 and i ndicates that assuming residues of one-half LOD for a ll non-detects of the most frequently detected NMC pesticide on each commodity increased the estimated exposur e by 4.2% for both groups of children ages 1-2 and 3-5 years at the 99.9 th


### percentile.

Table I.G-1. Sensitivity Analyses for NM C C u mulative Food Assessment: Limit of Detection Assumption a

Age Gro up Estimated Exposure

at the 99.9 th

Percentile (mg/kg oxam yl

equivalent s)

0.0229 MOE at the 99.9 th

Percentile Percentile at whi c h Targ et of 10 is

R e ac he d

99.848 th 7.9 Baseline CRA Child ren 1

- 2

Child ren 3

- 5 0.0209 8.6 99.870 th

LOD of PDP: Repl ace 0s with
one-half LO D Child ren 1

- 2 0.0238 7.6 99.833 th

Child ren 3

- 5 0.0217 8.3 99.859 th

a T he sensitivit y anal ys es perfo rmed her e in th e CRA w i t h th e LOD differ from those t hat are t y pica ll y pe rformed

for LOD values in the sing le c hemic al asses s ments in t w o w a ys: (1) rather than assum i ng that ever y c hemic al in

the assessme n t group is pres ent on the n on- detect sampl e s

, onl y th e most freque ntl y dete c ted chemic al on eac h

commodit y is a ssumed to be prese n t at one- half the LOD. T he rational e b ehi nd this a ppr oach is that the most

commonl y dete c ted pestici de on eac h comm odit y is lik el y to be the most w i del y us ed p e sti c ide o n that pa rticular

commodit y a n d thus most likel y to be pr ese n t at level lo w e r than the PD P L O D; and (2) in contrast to single

chemic al asse ssments for w h ich on l y tre a ted commoditi es a r e assig ned on e-ha lf LOD val ues, all n on- de tect sampl e s of a particul a r commodit y are g i ve n a valu e of one- half the LOD ra ther than o n l y some of the no n-detects

in the cumulative assessment.



### As can be seen from the baseline analysis using zero residues for all PDP non-detect data, the calculat ed MOEs at the 99.9th per centile of exposure are

7.9 and 8.6 for children 1-2 and 3-5, re specti vely, with MOEs reaching the target of 10 at the 99.848 th 

### and 99.870 th 

### percentiles of expos ures 18



### , respectively. After replac ing the zero values with one-half LOD values in the manner described above, the ca lculated MOEs at the 99.9 th 

### percentile of exposure decrease to 7.6 and 8.3 fo r children ages 1-2 and 3-5 years, respectively, with MOEs reaching the target of 10 at the 99.833 th 

### and 99.859 th


### percentiles of exposur es, respectively. Th is indicates that the use of zero for PDP non-detect data has only a mini mal effect on estimated high-end

exposures. The results of this s ensitivity analysis ar e not unexpec ted:

18 In this table --and all sub s eque nt tables which provid e summ ary in formation reg a rdin g the Agency’ s sensitivity analyses -- EPA has elected to express ex posures to three signifi cant digits an d percentile s at wh ich the targ et MOE of 10 is rea c he d to 5 digits. The Agen cy fully recogni ze s th at its exposu r e asse ssm en t tools are in sufficient to produ ce this lev e l of preci s io n, but has cho s en to displ ay the results of its sen s it ivity analysi s to this le vel in ord e r to more effectively illustrate the cha nge s that occur in the CRA when a s sum ptions o r othe r input

para m eters a r e modifie d

. The re ade r should n o te that any percei v ed differen c es in expo su re or risk at this lev e l are well be yond the abili ty of the Agency to mea s u r e or d e tect.


### Section I.G - Page 191 of 277

### generally, the LODs for PDP data are very low (the average LOD for the entire data base is about 0.01 ppm) and the vast majority of exposures at the upper percentiles are derived from detecta ble residues in a single commodity rather than from multip le commodities having one-half LOD residue values. Therefore, it seems reas onable that the effect of assumptions related to

estimation of values below the LOD woul d not signific antly influence exposure estimates at the highest percentiles of ex pos ure and this sensitivity analysis has

demonstrated that the manner in which non-detects ar e handled in the dietary assessment does not signific antly impact exposure estimates of the most highly exposed children.

ii. Use of Recent PDP Data Only

As described above, PDP data provi de a critical component of the

cumulative food risk assessment as this database provides reliable residue dat a for commonly consumed commodities sampl ed near their point of sale. As such, the PDP data provide a realistic es timate of pesti cide residues actually consumed by the public. Except in cases where the Agency has mitigated exposures through modification of pestici de labels, has cancelled uses, and/or has revoked tolerances, essentially a ll available PDP data (1994-2006) have

been used in the revised NMC CRA. 19 

### The practice of incorporating all

available PDP data is consistent with previous prelimin ary, revised, and updated cumulative risk assessments conduc ted by the Agency, including the OP CRA (USEPA, 2006a). Use of all re levant data takes advantage of more samples, and thus more data, for use in the Monte Carlo simulations. Moreover, i n corporation of a wide r ange of years of PDP data may better capture the typical and ever-present trans ient shifts in pesticide use practices due to temporal variations in weather and/or pest pressures. Any of these changes may ultimately result in hi gher or lower pesticide residues or an increased or decreased frequency of resi dues found in food. On the other hand, it is possible that PDP data that are older than five years may not best represent current agricultural practice s and expected dietary expos ures. For example, changes in the pesticide market resulting in less expens ive or more effective alternatives and/or gradual decline or shifts in use due to pesticide resistance may make older PDP pesticide data less representative of current and (expec ted) future conditions than more recent data (e.g., within 5 years).

To evaluat e the degree to which changes in pesticide use practices over time may o r may not h a ve affecte d the estimated expos ures and risks, OPP performed a sensitivit y analysis in which only the most recent PDP data (2002­ 2006) were used. Specifically, OPP ran a second, supplemental analys is which used only the last five years of P D P data (except for a few commodities lik e frozen green beans, grape juice, and fresh ch erries that were not sampled in any year from 2002 to 2006) instead of using all PDP data (1994-2006) that

defines the baseline. T he purpos e of this analysis was to determine if the elimination of earlier (p re-2002) PDP data -- which migh t be considered less

19 F o r reaso n s d e scrib ed in th e food cha p ter, PDP data from 1 992 a nd 1 9 9 3 w e re n o t used. 

### Section I.G - Page 192 of 277

### typical of current use patterns and prac tices -- would significantly affect the exposure and risk estimates. The det ails and results of this sensitivity analys is are provided in Table I.G-2.

Table I.G-2

. Sensitivity Analyses NMC Cu mulative Food Assessment: Recent PDP Data Assumption

Age Gro up Estimated Exposure

at the 99.9th

Percentile (mg/kg oxam yl

equivalent s) MOE at the 99.9th

Percentile Percentile at whi c h Targ et of 10 is

R e ac he d

Baseline CRA Child ren 1

- 2 0.0229 7.9 99.848th

Child ren 3

- 5 0.0209 8.6 99.870th

Rec ent PDP Data Only Child ren 1

- 2 0.0185 9.8 99.895th

Child ren 3

- 5 0.0170 10.6 N/A



### As can be seen in this table, the ca lculated MOEs at the 99.9th percentile of exposur e are 7.9 and 8.6 for children ages 1-2 and 3-5 years, respectively, using all relevant PDP data (1994-2006). MOEs reach the target of 10 at the

99.848 th 

### and 99.870 th 

### percentiles of exposur es fo r children ages 1-2 and 3-5 years, respectively. When using onl y the most recent PDP data (2002-2006)

, the calculat ed MOEs at the 99.9 th 

### percentile of exposur e are 9.8 and 10.6 for children ages 1-2 and 3-5 y ears, respectively , with the MOE for children 1-2 reaching the target of 10 at the 99.895 th 

### percentile of expos ure. The results of this sensitivity analysis suggests that EPA has not significantly und e restimated

– and may have overestimated -- exposures by using all years of P D P instead of only the most recent years. To the ex tent that t he most recent years of P D P data are more representative of pres ent and future-expected expos ures, an MOE of 10 was reac hed at the 99.895 th 

### percentile for children 1-2 and were greater than 10 for chi l dren 3-5.

iii. Summing food exposures over 24 hours

Another key principle in cumulative risk assessment is the proper matching of duration of the toxic effect (W hat is the time to peak effect? What is the time to recovery?) and the dur ation of exposure (When do expos u res occur? How long or how often do expos ures last? Do exposures overlap?). As described in Chapter I.B, the nature of NMC toxicity is rapid onset (typically 30 min to 1 hour) followed by rapid recovery (one-half life to recovery is typically approx. 2 hours). Conceptually, a robus t mul t i-chemical, multi-pathway PBPK
or PBPK/PD model would be id e a l to a ccount for the timing of environmental exposure(s) and the ti ming for A C hE inhibi tion and recovery. However, at this time, such a model is not availab l e. In lieu of such a model, EPA has used the RPF method to quantify chemical potency. In this assessment, each NMC was

converted into units of the index c hem ical, oxamyl. The probabilistic exposure models (DEEM-Calendex, Li feline, SHEDS) used in this assessment sum

Section I.G - Page 193 of 277

### exposures to oxamyl equivalents ov er 24 hour periods. These models do not allow the typical user to separate exposures into time steps smaller than 24 hours or to separate expos ures by ex pos ure events (i.e., breakfast, lunch). Due to the rapid recovery associat ed with NMC toxicity , 24 hour expos ure periods may or may not, a priori , be appropriate. More spec ifically, to the exte nt that a day’s eating occasions leading to high total daily expos ure are close together in time or occur from a si ngl e eating event, the approach used in this revised assessment which sums eating ev ents over a 24 hour period provides

reasonable estimates of risk from food. Under this assumption, mi nimal AChE recovery would occur between eating o ccas i ons (i.e., expos ure events) since exposure events are assum ed to occur close together. Conversely , if eating occasions leading to high total daily ex posures are widely separated in time (within one day), then subst antial AChE recovery would occur between eating

occasions and the estimated risks in t he cumulative risk assessment could be

overstated.

In the absence of a fully develope d PBPK m odel and in the absence of probabilistic exposure models that can evaluate expos ure durations shorter than 24 hours, OPP began an ex amination of the exposu re patterns for food records from the high end of exposure distribution with the case study presented to the SAP in February, 2005 and follo wed this work in a presentatio n of the pre liminary NMC CRA to the SAP in August 2005. OPP acknowledged this aspect of the limitations in the currently availab l e probabilistic food exposure models and thes e early exercises were an attempt at determining the degree to which hi gh-end food expos ures in the NMC CRA could be attributed to specific eating occa sions (within a day) that may (i) occur closely spa ced in time, (ii) occur widely sep a rated by time, or (iii) come from single eating events. This was done by look ing at actual individual eating
occasions as recorded in the USDA’s CS FII. The records in the CSFII capture detailed information not only about the i dentity and amount of foods consumed,

but also about the timi ng of each eat ing occasion within a 24-hour period; it was these consumption diary records that were examined for those survey respondent s at the upper end (99.8 th 

### to 100 th 

### percentile) of the exp o sure distribution. To the extent that a da y’s eating occasions leading to high total daily expos u re might be found close together in time or to occur from a single eating event such that minimal AChE recov e ry occurs between eating occasions (i.e., exposure events), the “24 hour sum” approach, whic h sums eating events over a 24-hour period, woul d provide reasonable estimates of ri sk
from food. To the extent that eating occasions leading to high total daily exposures are widely separated in time su ch that substantial AChE recovery occurs between eating occasions, t he estimated risks under any 24 hour sum approach may be overstated and a more sophisticated approach – one that accounts for intra-day eating patterns and the recovery of AChE between exposure events -- ma y be more appropriate.

The updated analyses described here as part of this revised NMC confirms EPA’s prior analys is that daily exp o sures to NMC pesticides in the upper extremes of the dist ribution (99.8+ percentile) for expos ures from food

Section I.G - Page 194 of 277

### mainly involve single eating events. Specifically, OPP found that a large fraction (~80%) of daily records for children 1-2 and children 3-5 years old contributing to the upper tail of the f ood exposure distribution represent single eating occasions. Less than about 3% of these upper-end diary records result from exposures that are divided among thr ee or more eating occasions. (Figures I.G-1& I.G-2 )

.

Perso n

- D ay s Ca te gori z ed b y N u m b er of E a t i ng E v ent s

43 52 R e cord s f o r C h i l d r en 1- 2 at t h e 99.8t h Perc ent i l e o f Exp o su re an d A b o v e

<1 % 2%

17%

Numbe r of

Ea tin g E v e n ts

1

2

3

4

81 %



### Figure I.G-1. Number of Eating Events Cont ributing to Exposures at 99.8+ Percentile

for Children 1-2

Person -Day s Catego rize d b y Nu mber of Eatin g Ev e nts

9333 Recor d s f o r Chil dr en 3-5 at the 99.8t h Per centi le of Exposure and A bov e

1% 2%

16%

Num b e r of

Ea ti n g E v e n ts

1

2

3

4

81%



### Figure I.G-2. Number of Eating Events Cont ributing to Exposures at 99.8+ Percentile
for Children 3-5

These pie charts represent exposures from the 99.8 th 

### to 100 th 

### percentile of exposure distribution, use PDP da ta through 2006, and exc l ude eat i ng events

that contribute small amounts (<10%) of total daily expo sure. Similar pie charts

Section I.G - Page 195 of 277

### representin g PDP data through 2004 were presented to the SAP in February 2005 as part of the NMC Case Study and August 2005 as part of the preliminary NMC CRA. At these SAP meetings, EPA conclud ed a nd the SAP agreed that since most food exposure to NMCs occurred in a single eating event, a more sophisticated, tempor al-based approach which evaluated food exposure patterns throughout t he day was not warranted.

As part of the risk characterization phase of this revised NMC CRA, the Agency has further refined its assessment of within-day consumption of food and used a second approach to characterizi ng the effect of eating occasions with respect to the timi ng of exposure ev ents. In this second appr oach, OPP worked collaboratively with ORD’s Nati onal Expos u re Re search Lab oratory (NERL) to use the SHEDS (Stoc hasis tic Human Expos ure Dose Simulation) model to evaluate the impact of rapid AChE recovery on estimates of food

exposure. Specifically , the O RD SHEDS model was us ed to bound the maximum extent to which accounting fo r NMC reversibility could potentially reduce estimates of expos ure. The SHEDS model provides the user with greater ability to perform “what if” analys is, particularl y with respect to dietary consumption data whic h is hard-coded in DEEM and t he other dietary exposur e models. This greater ability to specify t he use of specific dietary records affords

the risk assessor the a b ility to select spec ific eating occasions to include in the

assessment. With respect to investi gating the effect of NMC half-lives on exposure estimates, this capabilit y pe rmitted the Agency to exclud e -- from each individual CSFII food consumption reco rd -- all eating events resulting in NMC exposures except the eating event resulting in the largest exposure to

each individual

. Here, all other eating events we re assumed to re sult in zero exposure and only the eating event asso ciated with eac h indiv i dua l ’s maximum exposure was retained. This is equival ent to assuming a n infinitesimal half-life for all NMCs (i.e., complete and in stantaneous recovery) and provides a bounding estimate that demonstrates t he maximum theoretical effect that quantitative incorporation of half-lives could have on exposure estimates . The results of this bounding ex ercise ar e shown below in Table I.G-3.

Section I.G - Page 196 of 277

### Table I.G-3

. Sensitivity Analyses NMC Cumulative Food Assessment: 24 Hour

Food Summation

Age Gro up Es timated Exposu r e at the

99.9 th Percentile (mg/kg oxam yl

equivalent s) MOE at the 99.9 th

Percentile Percentile at whi c h Targ et of 10 is

R e ac he d

Child ren 1

- 2 0.0229 7.9 99.848 th Baseline CRA
Child ren 3

- 5 0.0209 8.6 99.870 th

Child ren 1

- 2

C h ild re n 3- 5 80% of individual s at the 99.8 th percentil e of exposu r e and above

were due to e x posu r e s fro m one eating event

Child ren 1

- 2 0.0220 8.2 99.86 th a Summing Exposu r e ove r 24 Ho urs

Child ren 3

- 5 0.0196 9.2 99.88 th a

a These pe rcentiles are repo rted t o onl y fou r significant digits: these are pr oduced b y the ORD S H E D S model and o nly repo rted to

tw o decimal places



### This bounding exercise reduces exposur e at the 99.9th percentile by no more than about 3-9% depending on age gr oup. For 1-2 and 3-5 year old children, exposure estimates are reduced by 6% and 7%, respectively. MOEs would be expected to change in a similar (proportionate) manner.

As described above, two approaches have been used to evaluate the extent to which the Agency’s 24-h our approach to food risk assessment

overestimates risk from the NMC s. The results of both approaches indicate that

the cumulative risk to NMCs is in deed not substantively overestimated using the current exposure models and the 24-hour approac h. This is due to the fact that exposure to NMCs occurs predominant ly through single eating events and not from multiple events that occur thr oughout the day. Based on these analyses, the Agency concludes that the current expos ure assessment methods used in the revised NMC CRA provide realistic and high confidence estimates of risk to the NMCs through food.

iv. Consideration of Chemical Specific Adjustment Factor Approach for the Inter-species Uncertainty Factor

The Agenc y has applied a 10X factor fo r inter-species extrapolation in the
NMC cumulative risk assessment for thos e N M Cs without human studies. This 10X factor is consistent with typical A gency practice for most single chemical

and cumulative risk assessments. The fo llowing text describes a sensitivity analys is employing an alternative appr oach used by the WH O for performing inter-species extrapolation.

In 2005, the WHO published its guidanc e for deriving chemical s p ecific adjustment factors (C SAFs) (WHO, 2005)

. This CSAF guidance d e scribes approaches for use of kinetic and mec hanistic data to refine inter-species and

Section I.G - Page 197 of 277

### intra-species extrapolation factors. The International Pr ogramme on Chemical Safety (IPSC) guidance is based in large part on analyses by Renwick (1993) and Renwick and Lazarus (1998) which describe the use of

toxicok i netic and toxic odynamic data as a means of replacing the traditional 10­ fold safety factors for human sensit ivity and animal-to-human extrapolation. Althoug h EPA does n ot yet have guidance for developing CSAFs, the Agency has used these concepts in a few risk assessments. One such example

includes the recent risk assessment for dimethyl arsenic acid (DMA) where the

available in vivo and in vitro mode of action and metabolism data supported toxicodynamic equivalence between ra ts and humans and thus the interspecies factor was reduced to 3X for DMA.

Understanding mode of action is an important component of deriving CSAFs in that it provides the foundat ion for understanding what toxic o kinetic and/or toxicodynamic characteristics are cr iti cal for evaluating inter

- or intraspecies extrapolation. In the case of NMCs, the mode of action is well

understood in both animals and humans. Specifically, in rodents and humans,

NMCs cause neurotoxic ity via the inhibiti on of AChE by carbamylation of the serine hydroxy l group located in the ac tive site of the enzyme leading to accumulation of acetylcholine and ultimate ly clinical signs. As par t of the risk

characterization for the revised NMC CRA, the Agency has considered the extent to which available data suppor t a CSAF-type approach for those NMCs

without human toxicit y studi es. Regarding toxicok i netics, unlike many OPs, NMCs do not require activation; the parent compound is an active AChE

inhibitor. A l though some metabolites of NMCs have been shown to be active AChE inhibitiors, none have been show n to be more potent than the parent active ingredient. Thus, metabolism is considered to be a detoxific ation

process. A s such, species differences in tissue dosimetry are likely correlated with differences in body weight to the 3⁄4 power and are also consis tent with a
3X factor to account fo r inter-species differences in toxic o kinetics (USEPA, 2006).

Regarding toxicodynamic characterist ics, as noted abov e, the mode of action of NMCs is applicable to animals and humans such that inhibition of AChE leads to clinical signs of neurotox ic ity. The AChE enzyme in humans and

rats has si milar function and structure. (See reviews by Radic and Taylor, 2006 and Sultatos, 2006.) The half-life to re covery values for rats and humans provided in Table I.B-9 range from appr oximately 1 to 2 hours and demonstrate the similarity of the half-lives of the two species. Based on this information, given a similar dose or concentration at t he target site, i t is likely that human and rat AChE would respond sim ilarl y. It may be possible to use in vitro studies using hum an and rat tissues and human and rat AChE to test this hypothesis. In other words, it may be possible to use in vitro studies to demonstrate toxicodynamic equivalence between rats and humans. If these data were available and they showed toxic odynam ic equivalence, the Agency could reduce the inter-species factor for those NMCs without human toxicity studies to

3X. Because of the lack of these in vitro studies, the Agency does not believe it appropriate at this time to further refi ne the standard 10X factor for inter-species

Section I.G - Page 198 of 277

### extrapolation. Instead, the Agency has used the CSAF approach as a sensitivity analys is in its risk characterization.

The Agenc y also notes that in vivo studies with human subjects are available for three NMCs (aldicar b, ox amyl, methomyl). These studies were

determined by EPA, after considering t he advice of the Human Studies Rev i e w Board, to be ethically and scientif ical ly acceptable for use in this risk

assessment. The Agency has developed BMD estimates in rats and humans
for RBC AChE inhibition. This analysis has shown that the ratio of the BMDs for rat/human ranges from 2 to 5 for these NMCs. Th is range would tend to support the CSAF approach described her e to reduce the standard 10X interspecies factor to 3X as part of the sensit ivity analys is in the risk characterization
for those N M Cs without human data and which make meaningful contribution s to the cumulative risk.

In this sensitivity analy sis, the 10X in ter-species factor was reduced to 3X based on the assumption of toxic ody namic equivalence for carbaryl

, carbofuran, and formetanate HCl. These th ree NMCs were selected as they

contribute to the overall cumulativ e MO Es. The results of this sensitivity analys is are shown below in Table I.G-4.

Table I.G-4

. Sensitivity Analyses NMC Cumu lative Food Assessment: CSAF Approach

for In ter-sp e cies UF

Age Gro up Estimated Exposure

at the 99.9 th

Percentile (mg/kg oxam yl

equivalent s) Percentile at whi c h Targ et of 10 is

R e ac he d MOE at the 99.9 th

Percentile

Baseline CRA Child ren 1

- 2 0.0229 7.9 99.848 th

Child ren 3

- 5 0.0209 8.6 99.870 th

CSAF Approac h for

Inter-s p ec ies UF Child ren 1

- 2 0.0183 9.8 99.896 th

Child ren 3

- 5 0.0171 10.5 N/A



### Using the standard 10X inter-species factor for all NMCs without human data, the (baseline) MOEs at the 99.9th perc entile of exposure reach the target

of 10 at the 99.848th and 99.870th percentiles of expos ures for chi l dren 1-2 and 3-5 years, respectively as shown in T able I.G-4. However, when considering analternative approach to the inter-species factor which assumes a 3X interspecies factor for carbaryl, carbof uran, and formetanate HCl, the MOEs at 99.9th percentile of exposure increase to 9.8 and 10.5 for children 1-2 and 3-5 years, respectively, with the exposu re for the younger age group reaching the target MOE of 10 at the 99.896th percent ile of exposur es. [Note: because of the lack of key in vitro studies to confirm the a ssumption of toxicody namic equivalenc e, the Agency did not believ e it appropriate at this time to use a 3X factor for inter-species ex trapolation in the baseline ass e ssment. Instead, the

Section I.G - Page 199 of 277

### Agency has used the CSAF approach as a sensitivity analysis in its risk characterization.]

g. Model Outputs & Discussion

The food component of the NMC cumulative risk assessment was conducted using the DEEM software. Th is software program evaluates the full range of dietary exposures and permits a detailed evaluation of the source of exposures with regard to which foods and pesticides are the predominant sources of the exposure. The results of the food por tion of the revised NMC cumulative risk assessment using baselin e assumptions as wel as the various sensitivity analyses ar e summari zed in Tables I.G-1 through 4 with detailed discussion of methods and approaches pr esented in Ch apters I.C and I.G.

Table I.G-5 below presents a sum m ar y of the NMC Cumulative food

assessment baseline MOE estimates and t he percentiles at which the target
MOE of 10 is reached.

Table I.G-5

. Summa ry of NMC C u mulati ve Food Assessment: Ba seline Estimates

Age Gro up Percentile s of Exposure MOE at the Selecte d

Percentile s Percentile at whi c h Targ et of 10 is

R e ac he d

B a selin e cu m u lat i v e ris k as se ssm ent

95 141

99 35 Child ren 1

- 2

99.9 7.9 99.848 th

95 185

99 40 Child ren 3

- 5

99.9 8.6 99.870 th

95 1278

99 236 Adults 20 -49

99.9 42 N/A

95 1035

99 193 Adults 50 +

99.9 40 N/A



### The results are presented in the form of MOEs for children 1-2 and 3-5 years of age and for adults 20-49 and 50+ years of age. MOEs at the 95 th 

### , 99 th



### , 99.5 th



### , and 99.9 th 

### percentiles of expos ure are pr esented for each age group. Children 1-2 and 3-5 years old are consistently the most highly exposed subgroups in the analy sis.

Due to the complex nature of cumulati ve risk assessments, it is imp o rtant not to use any single point or any MOE es timate alone or in isolation. Each
estimated MOE is derived from a combinatio n of data from multiple sources that describe multiple areas of exposure or hazard. Each dataset and assumption
used to derive the MOEs includes its ow n variability and uncertainty. Moreover,

Section I.G - Page 200 of 277

### some datasets contain more or less precision than others. As such, MOEs and the percentile of regulation are not “bright lines.” The Agency has focused its sensitivity analyse s on MOEs of 10 and the 99.9 th 

### percentiles of exposure as points of reference, not as required thresholds, values, or cutpoints.

When developing any risk assessment, assumptions must be made in

areas where data are not ava ilable; this is th e also case for the NM C CRA. In the NMC CRA, the Agency has made health protective assumptions in its baseline analysis, particularly regardi ng whic h years of PDP data are most appropriate for use in the CRA and the inter-species extrapolation f a ctors for

those NMCs without human data. T he sensitivity analy s es shown and discussed here are designed to evaluate t he degree to which key areas of the

risk assessment under- or over-estimate t he cumulative risk in an effort to

characterize and understand the MOEs presented in this assessment. As described in detail above, the Agency has conducted four sensitivity analyses

on the food assessment. The results of these sensitivit y analyses were discussed earlier in this chapter and are summarized in Table I.G-6.

Table I.G-6

. Summa ry of NMC C u mulative Food Assessment: Se nsitivity Analyses.

Sensitivity Analysis

1. Baseline CRA Age
Grou p

Children

1-2 Estimated Exposure at

the 99.9 th Percentile (mg/kg oxam yl

equivalent s)

0.0229 MOE at the 99.9 th Percentile at whi c h Targ et of 10 is Re ached

99.848 th Percentile

7.9

Children

3-5 0.0209 8.6 99.870 th

2. LOD of PDP: Repla c e 0s with o ne-h a lf LOD Children

1-2 0.0238 7.6 99.833 th

Children

3-5 0.0217 8.3 99.859 th

3. Rec e nt PDP Data Only (200 2-2006 ) Children

1-2 0.0185 9.8 99.895 th

Children

3-5 0.0170 10.6 N/A

4. Summing Exposu r e over 24 Hou r s Children

1-2 80% of individual s at the 99.8 th percentil e of exposu r e and above

were due to e x posu r e s fro m one eating event Children

3-5

Children

1-2 0.0220 8.2 99.86 th a

Children

3-5 0.0196 9.2 99.88 th a

5. CSAF Approa ch for Children 0

. 0 1 83 98 99

. 896 th



### Section I.G - Page 201 of 2771-2

Inter-s p ec ies UF Children

3-5 0.0171 10.5 N/A

Children

1-2 0.0160 11.3 N/A Re cent PDP Data only & CSAF Approac h for Interspe c ie s UF Children

3-5 0.0147 12.3 N/A

a These p e rce n tiles are re p o rted to only four si gnifican t digits sin c e they are p r od uce d by the ORD SHEDS

model an d on l y reporte d to two de cimal p l ace s



### As can be seen, these four sensitivit y analy ses result in only minimal changes to the estimated cumulative ex pos ure to the NMCs through the food pathway and did not result in meaningful changes in the associated MOEs. These results support the Agency ’ s assumptions and findings that:

 residues below the LOD can be assumed to be “zero” for the cumulative risk assessment without underestimating to any substantial degree the cumulative risk to these pesticides. For childr en 1-2 and 3-5 years, MOEs reach the target of 10 at the 99.833 th 

### and 99.859 th 

### percentiles of expos ure when the one-half LOD assumption is applied. Thes e percentiles at which MOEs of 10 are reached are not meaningfully differ ent from those under the Agency’s

baseline as sumption.

 using only the most recent PDP data (as opposed to all relevant PDP data

from 1994-2006 20 

### ) does not result i n substantive changes in expos ure estimates or associated MOEs. For ch ildren 1-2 and 3-5 ye ars, MOEs reach the target of 10 at the 99.895 th 

### and >99.9 th 

### percentiles of exposure respectively, when only the most recent PDP data is used. These percentiles
at which MOEs of 10 are reached are not meaningfully different from those under the Agency’s baseline ass u mption.

 the cumulative estimates of food ex posure provided by summing expos u re over 24 hours do not substantively overesti mate cumulative ex posure to food. Food exposure to NMCs most often o ccurs at one eating event such that incorporating recovery between eating events is not necessary.

 using the CSAF approach for the inter-species UF does not result i n substantive changes in exposure esti mates or associated MOEs. For

children 1- 2 and 3-5 years, MOEs reac h the target of 10 at the 99.896 th 

### and

>99.9 th 

### percentiles of exposure, respectively , when using the CSAF approach to adjust the inter-species UF from 10x to 3x for carbaryl, carbofuran, and

formetanate. These percentiles at wh ich a MOE of 10 is reached are not meaningfully different fr om those under the Agency’s baseline ass u mption.

20 The Agency has u s ed all years of PDP ex cept in ca ses where use pattern s have been

cha nge d in the baseline a n a lysis. Specif ic ally, use p atterns h ave ch ange d for ca rbaryl, methomyl, formetante HCl, and carb ofuran.


### Section I.G - Page 202 of 277

### In addition, Table I.G-6 shows the co mbined impact of using only the recent PDP data and the CSAF approach for the inter-species factors on the

cumulative food estimates. When eval uating these two aspects simultaneously, MOEs for all age groups, including child ren 1-2 and 3-5 years increase to 11 or greater.

The food risk assessment is considered highly refined and is designed to provide realistic estimates of exposure to NMCs. Som e assumptions used in single chemical risk assessments have been removed in the CRA to prevent compounding conservative assumptions when assessing the combined risk to the 10 NMC pesticides comprising the cu mulative assessment group. Even with the highly refined nature of this cu mulative risk assessment, there are sti ll conservative assumptions used in the bas eline estimates of cumulative risk. The Agenc y has evaluated the effect of a number of these assumptions in its sensitivity analyses c onducted for the food assessment and found that they result in only minimal changes to the es timated cumulative expos u re to the NMCs through the food pathway and did not result in meaningful changes in the

associated MOEs. Further, the Agency has a high level of confidence that the

cumulative risks are n o t under- o r over-e sti m ated based on these results.



## 4. Residenti a l Assessment

The residential component of t he revised NMC cumulative risk assessment is a probabilistic assessmen t, which applies distributional analys is to residential exposur e assessments. In addition to inc o rporating distributional analys is, the assessment also factors in the seasonal and regional aspects of pesticide use. Three types of data are used in the residential assessment:



### • Pesticide use,



### • Pesticide r e sidue diss ipation, and



### • Exposure contact/human exposur e factors.

Pesticide use data are used to determi ne the percent of households using a pesticide, the timing of the pesti ci de treatments, and frequency and duration of exposur e. In the revised NMC CRA, all pesticide us e data were based on pest pressures in the Southeast region of the U.S. Whi l e insect growth may slow during the winter months in the Sout h, unlike other regions in the country, there is no period of dorman cy. Residential exposure to pesticides is greatest in this region due to the longer periods of pest pressure. Consequently, this

assessment as a whole is assumed to provide a worst case estimate of

exposure.

Section I.G - Page 203 of 277

### Pesticide r esidue diss ipation dat a addr ess the fate of the pesticides once applied to an environment (e.g., lawns). Expos ure contact data are exposure-specific metrics that relate human expos ure to pesticide residues.

Humans come in contact with the residues by contacting the product directly or by contacting the residues left after t he pesticide applic ations are made. Distributions of human exposure factors, such as the body weight assumption used in this assessment, come fr om t he Agency’s Exposure Factors Handbook (USEPA, 1997a). The exposure factors taken from the A gency’s Expos u re Factors Handbook have been previous ly c haracterized and are used throughout the Agency

.

a. Pesticide Use Data

Accurate pesticide us e data, includi ng information on regional site/pest markets, ti ming of application and t he percent of households using NMC products, are key to the residential risk assessment. In the absence of that specific pesticide use information, OPP developed residential exposure scenarios based on timing aspec ts found in survey data from the Residential Exposure Joint Venture (REJV), regi onal Cooperative Extension Service publications, and Doane's GolfTrak. While the REJV data contains a complete 12-month pesticide us e diary for 1,217 hous ehold-user s, use of these NMCs by

homeowners is a relati vely infrequent event, l eading to relatively high uncertainty around the various pesticide use estimates. Additionally , the REJV did not collect information on the purpose of use (pest treated), areas treated, or application rates. T herefore, R E JV data was used in combination with professional judgement, and product label and pest pressure information from the Cooper ative State Extens ion Services to estimate applic ation frequency and timing. Doane's GolfTrak was used to identify the percent of golf courses treated with pesticides. OPP believes this is a robust data source.

b. Pesticide Residue and Exposure Contact Data

i. Dermal Exposure

Applicator Exposure

Dermal exposure to pesticides may occur during applicat ion and post-applic ation activities. Exam ples of application activities that might result in pes ticide expo sure include, but are not limited to, spraying liquid pes ticide formulat ions on ornamental plants, or apply i ng granular formulations to residential turfgrass.

The applic ation of pesticides is one of the more straightforward activity patterns to measure because it represents easily

defined activities. As a result, dermal expos ure contact data used to assess expos ures during applic ation of consumer-oriented pesticides are the most robust in formation used in the residential

Section I.G - Page 204 of 277

### portion of this assess ment. Recent data generated by ORETF have been used to assess the use of hose-end sprayers (lawn care products), rotary granular spreaders (lawn care products), and hand-pump sprayers (home gardens and orchards) and hand-held dusters (home veget able gardens). Another study, submitted by a registrant, was also used to assess residential applicator exposure using granular shaker cans. All studies meet or exceed current Agency guide li ne requirements (in particular regarding the number of monitori ng units) and can be extrapolated

to include c l othing scenarios rangi ng from short-sleeved shirts and short pants to long-sleeved shirts and long pants. OPP has high

confidence in these data.

Post-Applic ation Exposure

Examples of post-application acti vities that might result in pesticide expos ure inc l ude, but are not limited to, weeding and harvesting home gardens, mowing and playing on lawns, and playing golf

. There are several post-applicat ion dermal exposure

scenarios addressed in this a ssessment. These are: postapplication dermal exposure result ing from lawn care products, garden and home orchard products , pet collar products, and contact with treated golf courses.

Like the applicator scenarios, the post-applic ation garde n and home orchard exposure scenario s are easily defined. For harvesting vegetables or weeding, t here is a subst antial amount of data on farm worker expos ures. These contact values have the potential to overestimate residential exposur e , since they are based on activity patterns of indiv i duals whose pay is based lar g ely on their productivity. Professional harvesters are likely to be more efficient than most home gardener s, and therefore exposed to a greater amount of treated surface. Because home gardens consist of a wide variety of plants, the use of a uniform distribution of values represents activities as di verse as hoeing and harvesting. These values may overestimate early season activities that consist predominantly of potential expos ure to smal l plants.

There are a variety of dislodgeab le foliar residue data available for carbaryl. Dislodge able residue data for sunflowers were used to assess the vegetab le and ornamental garden scenarios, while oliv e tree dislodgeable data were used to assess
the fruit tree scenario. These dislodgeab le data were scaled in

accordanc e with the label applic ati on rates for the carbaryl home garden and orchard products. Chem ical-specific residue data for crops with foliage similar to thos e typically found in hom e gardens and orchards are expected to adequat ely represent residues foun d on home-grown ornamentals, fruit, and vegetables.

Section I.G - Page 205 of 277

### Dermal exposure from post-applic ation contact with lawn

chemicals is varied. C ontact data, representative of the range of

human activities on lawns has been difficult to model. 21 

### Dermal contact exposure values were id entified for a dults who performed scripted activities (Vac caro, 1996). Rates of transfer in the studies with surrogate compounds were similar to those observed in the

chemical s pecific diss ipation data available to OPP.

Granular turf transfe rable residue (TTR) data are availa ble for carbaryl , the only chemical re gistered for residential lawn use

considered in this assessment. These residue data were used to calculate post-applicat ion dermal exposure for the broadcast use of carbaryl on residential lawns. Be c ause the current mitigation for carbaryl lawn products limits br oadcast applications to granular formulations only, these data ar e expected to adequately estimate dermal exposures resulting from broad cast applications to the lawn.

The current assessment also addresses dermal postapplication exposure for adults and t eens playing rounds of golf on

treated courses. Carbaryl liquid T T R data were used to assess risk
for this sce nario. Because golf course turf is intensively maintained (generally watered and mowed ever y day), these residue data are

assumed to overestimate residues on treated golf course turf. The exposure contact factors used to estimate post-applicat ion dermal exposure are based on a few measur ements from two studies that assessed golfer expos ure. The expos ure duration for individuals playing golf was assumed to be two to four hours per day based on information obtained from a 1992 survey conducted by the Center for Golf Course Management. Thes e assumptions are expected to adequately estimate potential exposure for golfers.

The revised NMC CRA also considered exposures through the use of flea collar products fo r carbaryl and propoxur. Estimates of exposur e for these scenarios were developed using an approach similar to the one taken with the turf care products. The dermal

21 The August 2005 SAP recomm ended performing a sensitivity analys is to com pare exposure estimate s tha t result when distrib ution s are ent e r e d b o th with and (then) with out truncation of

distrib u tion s that extend (at least theoretically) to infinity. More spe c ifically, the prelimina r y NM C CRA that was presented to the SAP in Septembe r 2005 truncated any l ognormally distributed input paramet er at a high e nd perce ntile (99 th ) sin c e value s asso cia t ed with high er pe rcentile s

were co nsi d e r ed to be un realisti c. As par t of this revis ed NMC CRA, OPP performed a

sen s itivity analysis to eval uate the impa ct of truncatio n on the re sid ential scen ari o that was
found to have the greate s t influen ce on t he NM C CR A result s. Specifically, OPP utilized the full (untrun c ated ) lognormal di stributio n for the tran sf er co efficient para m eter to esti mate derm a l exposure to treated la wn s. This full (unt rucate d) di stri bution extend s to infinity implying no limit to how hig h the tran sfer co efficient ca n be. The re sul t s of this sen s itivity analysis (not pre s e n ted

here ) indi cate that truncatio n at the 99 th percentile in o u r ba selin e a s sessme nt had no si gnificant

effect on the result s of the NMC CRA.


### Section I.G - Page 206 of 277

### contact factor(s) for post-app lication exposur e is based on a shampoo and groomer expos ure study for carbaryl in which each groomer shampooed the dogs, pick ed them up wet, and placed

them into crates. The dogs were then dried and groomed. These activities are likely to result in higher contact factors than intermittent contact with a pet wear ing a collar and thus provide a conservative estimate of exposur e.

ii. Non-dietary ingestion

Toddler ingestion via hand-to -mouth activity was the only oral route of exposure considered in the residential port i on of this

assessment. Specific ally, oral hand-to-mouth ingestion was considered only for children 1-2 and 3-5 years old for the lawn care and pet collar scenarios.

In the preliminary NMC CRA, t he non-dietary oral exposure pathway produced the lowest Margins of Exposure (MOEs), and

would therefore be of greatest concern to the Agency. These low MOEs were mainly du e to the inco rporation of micro-activity data into our macro activity models which are based on daily average activities (and thus use a daily time step). The non-dietary ingestion pathway was the least re fined of the resident ial expos ure pathways modeled in the prelimi nary NMC CRA. In the revised NMC CRA, OPP has modified the met hodology used to assess this pathway. This refined methodology is based on comments and input from the FIFRA Scientific Advisory Panel, and the SHEDS and CARES developers.

The Calendex model used in t he preliminar y and the revised NMC CRA is a macro activity m odel. Specifically, this model simulates expos ures by randomly drawing values for each of the various exposure factors (e.g., exposure duration, frequency of hand to mouth events, surface area of hand mouthed per event) then multiplying these values t ogether per the OPP Residential Standard Operating Procedures al gorithm (USEPA, 1997b). The distributions for many of these ex posure factors were obtained from micro-activity data. For example, the distribution for frequency of

hand-to-mouth events was based on data from observational

studies in which all hand contac ts were recorded as hand-to-mouth events, regardless of the fraction of hand mouthed. For the fraction of hand mouthed, no adjus tment was made for the duration of time

the hand remained in the mouth. Ut iliz ing su ch micro-activity data with macro activity models poses many challenges. For example, if two variables are negatively co rrelated (e.g., more frequent mouthing is associated with sma ller areas of hand mouthed), then “modeling the product of two jointl y distributed variables as indep ende nt draws will overestimate the vari ances...or

Section I.G - Page 207 of 277

### overestimate exposur e at the hi gh end.” Similarly, “fixing the

residue on a child’s hands (and/or other exposure factors) for a two hour play p e riod...will yield ‘gr eater variabilit y in the modeled distribution of exposur es than a run that updates the residue concentration hourly during the expos ure.” (FIFRA SAP, 2005b).

The new algorithm establis hes a maximum amount of residue that can be on the hand, or a ma ximum dermal loading. The amount of non-dietary or al ingestion increas es with the exposure duration, the frequency of hand-to -mouth events per hour, and the surface area mouthed per event, wh ile the hand loading serves as an upper constraint on oral i ngestion between replenis h ment

events. This approac h is a refinem ent of the approach used in the preliminary NMC CRA and is bette r suited for assessing children’s hand-to-mouth exposure in a probabilistic model.

The MOEs for all residential scenarios assessed in the NMC

CRA were derived from a combi nation of data from multiple

sources. The data sources used in the NMC residential assessment rely upon the best availa ble data. However, each data set introduces possible uncertain ties in the outcome of the exposure assessment. Post-applic ation exposures from the lawn and pet uses are considered to be t he most significant source of uncertainty in the residential risk assessment. A summary of these

uncertainties and their directi on and magnit ude, is presented in

Table I.H.1. The assumptions m ade in this assessment provide a reasonable, high-end estimate of cumulative exposure to NMC residential products. OPP is c onfident that the resident ial assessment is sufficiently conser vative and will not underestimate exposure or risk.

Section I.G - Page 208 of 277Table I.G-7.Input Parameters Usedin the Exposure Models: Bias, Assumptions, Uncertainties, and StrengthsModelInput ParameterExposureBiasAssumptions,Uncertainties, or Strengthsand Other CommentsExposure Model forResidential PathwayHuman Activity Pattern+ = upward~ = neutral- = downwardLawn ExposureUnit Exposure: push-type rotaryspreader (mgexposure peramount of active ingredientapplied)+Assumptions/UncertaintiesThis unit exposure value isbased on30replicatesconsisting ofindividuals using a push-type rotaryspreader.A number of clothingscenariosarepossible to begenerated from these data.Inthisassessment short-sleevedshirt and short pants wereassumed.Thismay overestimate exposure aslarge portion of exposure is tothe lower legs. Although asurrogate compound wasused, exposureis believed to be more influenced by the type of equipment usedratherbeingchemical specific.OPP has high confidence in thesedata.A lognormal distribution was used for the Unit Exposure (UE).Assumed gloves are not worn.Survey data do indicate that someresidential handlers use gloves and thusthis may overestimateexposure for these residentialhandlers.However, becauseconsumers are unlikely to use, remove and care for PPE in themanner of professionals, it is unclearwhat impact this may have onactual use.The surrogate compound (dacthal) usedin the exposure study maybe dustier thanthe granularformulations of the NMC compoundsassessed. This factor increases confidence that thisvariablewill notunderestimate exposure.Area treated (square feet)- to ~Assumptions/UncertaintiesA difficult variable to estimate.However, the assumption isreasonable given the application equipment used.Although mayunderestimate areas that have largerlawns (Midwest), margins ofexposure arelarge.Section I.G - Page 209 of 277ModelInput ParameterExposureBiasAssumptions,Uncertainties, or Strengthsand Other CommentsExposure Model forResidential PathwayHuman Activity Pattern+ = upward~ = neutral- = downwardDermal Contact Transfer~ to +Adults: activities performedwithtank topsandshort pants, lognormaldistributions may be reflective of studydesign ratherthan actualactivities (choreographed)Children: Based abovescripted activities study and adjusts transferfactors for differencedin body weight and surface areabetweenadults and children.Assumes all adults and children living inhouseholdsbeing treatedwith lawn care productsare exposed (enter treatedarea).Turf Residues: dermal andhand-to-mouth~Chemicalspecific data for granularformulation of carbaryl.Frequency of hand-to-mouthevents and surface area ofhand mouthed~Based onanalysis of the best available observationaldata (Xue et al,2007).Duration on lawn+For children, the value usedactually measured timespent outdoorsand not just time spent on lawns.Doesnot account for surveyresponses of individuals that didnot playon lawns or go outside.Home Garden,FruitTrees, and OrnamentalPlantsApplicator: Hose-End Sprayer,Dust ShakerCan, TriggerPump Sprayer, Handwand~ to+All UE data for thesescenarios arechemical-specific.In thisassessment short-sleevedshirt and short pants wereassumed.Thismay overestimate exposure as large portion of exposure is to the lowerlegs and upper arms. Although asurrogate compound was used,exposure isbelieved to bemore influenced by the type of equipmentusedratherbeing chemicalspecific.OPP has highconfidence in thesedata.A lognormal distribution was used.Assumed gloves are not worn.Survey data do indicate that someresidential handSection I.G - Page 210 of 277ModelInput ParameterExposureBiasAssumptions,Uncertainties, or Strengthsand Other CommentsExposure Model forResidential PathwayHuman Activity Pattern+ = upward~ = neutral- = downwardArea treated: ornamentals~ to +Assumes all plants are treated around the perimeterof an averagesized house.Area treated: vegetables~A lognormal distribution ofa well studiedvariable.Area treated: fruit trees+Assumes all fruit trees aretreated with the maximumlabeled applicationrate.Little data to determineactual areaoccupied by home orchard.Postapplication:vegetables/fruits~ to+Contact valuesrepresent a wide range of activities.All plants areassumed to be treated.Postapplication: fruit trees~ to+Based onolive dfr study data.Plant residues~Basedonchemicalspecific DFRdata.Ornamental Snail/SlugBaitApplicator: Granular~ to +This unit exposure is based on 15 replicates.Used study assessingexposurewhile treating shrubswhich had higher unit exposuresthan forflowers.A lognormal distribution was used.Pet CollarsPostapplication~ to +Dermal contact value fromstudiesin which there wassubstantialcontact.Assumptionsfor pet fur residues are based on the OPP’s SOPs forResidential Exposure Assessment.Based onanalysis the best available observational data (Xue et al,2007).Duration~Based ondata from the Freeman et al, 2001 (fewreplicates).GolfPost-application:DermalContact Transfer~The surrogate data used to derive transfer coefficients were basedontwo measurements of fourindividuals playing golf ontwo golf coursestreated withchlorothalonil (Ballee, 1990), and the exposure of golfers(four volunteers) to flurprimidol (Moran etal., 1987).Section I.G - Page 211 of 277ModelInput ParameterExposureBiasAssumptions,Uncertainties, or Strengthsand Other CommentsExposure Model forResidential PathwayHuman Activity Pattern+ = upward~ = neutral- = downwardDuration~Estimate based on 1992 Golf Course Management Report, describingamount of time spent golfing.TurfResidues: dermal~Chemicalspecific data for liquid formulation of carbaryl.Section I.G - Page 212 of 277

## 5. Drinki ng Water Assessm e nt

The regional drinking water expo sure assessments are intended to represent expos ures from vulnerable drinking water sources resulting from typical NMC pesticide usage. Each regional asses sment focuses on areas where combined NMC expos ure is likely to be among the highest wit hin the region as a result of total NMC usage, adjusted for relative potencies, and vulnerabilit y of the drinking water sources. The estimated drinking water concentra tions for each scenario are not

national numbers but are reasonable for people living in those vulnerable

areas. For ground wat e r, shallow priv ate wells in highly permeable soil

and vados e zone materials with acidic (pH < 7) soil and ground water are expected to be most vulnerable. For surface water, dri n king water reservoirs in small, predominantly agr icultural watersheds are likely to be

most vulnerable.

Because the selection process took into account the relative potencies of the NMC pesticides, the sites used for the i n itial drink i ng water exposure estimates are biased to ward the areas in which the more toxic NMC pesticides are used. Sinc e the purpose of the assessment is to identify the impact from multiple NMCs occurring in water in the same area, the area(s) selected for t he assessment do not necessarily represent the highest exposure of a single chemical, but rather the highest multiple NMC exposure within the region. Sinc e pesticide use may vary from year to year and cropping and usage patterns may change, some areas in other parts of the region may have greater water exposure in a given year.

a. Ground Water Exposure

Based on monitoring studies, pesticide fate and transport properties, and model projections, t he Agency believes that the highest

overall cum u lative NM C concentrations in drinking water sources will be

associated with:



### • Shallo w wells: Concentrations will vary with varying depths to ground water and well depths

. Higher conc entrations would be expected in

more shallow wells while lower c oncentrations would be likely in deeper wells.



### • High leaching potential soils and vadose z one: Such soils are welldrained, highly permeable, and have low organic matter content.

Section I.G – Page 213 of 277

### • Acidic soil and ground w a ter: Which favor the persistence of the NMC chemicals, whic h degrade more rapidly in neutral to alkaline pH

conditions.

These conditions hav e been ide n tified in some areas of the Delmarva Peninsula, the southeaste rn coastal plain, and Florida (primarily along the central ridge). Estimated NMC exposures in other NMC use areas underlain by less pe rmeable soils or with neutral to alkaline soil and ground water are ex pected to be considerably lower.

While area s of potential concern are illu strated in Figur e I.E.7, it does not take into account localized conditions such as type and depth of well,

local variations in soil/ vadose zone permeability, or acid ity/alkalinity of the ground water. It also can not a ccount for the locations of private wells or the population that may be drawing their drinking water from

such vulner able wells.

The estimated NMC concentrations for these vulnerable wells are comparable to available monitori ng data under similar conditions, particularly for aldicarb and carbof uran, which are the dominant NMC pesticides in ground water. High c oncentrations of these pesticides (including both the parent pesticide and degradation products) have

been found in wells ac ross the country where the pesticide use coincided with highly permeable soils, shallow ground water, and acidic conditions (summarized in Chapt er I.E and Appen dices II.E.2 and II .E.7). These detections led to voluntary label changes that restricted the use of those pesticides in some regions or plac ed conditio n s under which the pesticides could be us ed in some soils (for example, the well setback

distances added to the aldicarb label s for certain soil and groundwater conditions).

Actual NMC concentrations in pr ivate wells may vary from the

estimated concentrations as a result of a number of fact ors. Important conditions that may affect NMC resi due lev e ls in drinking water from private wells including depth to gr ound water

, distance between the well and the field of application (setback distance), amount of NMC pesticide applied over time, and soil/vados e z one properties that affect the

downward movement of wa ter and pesticides.

i. Depth to ground water

EPA set the water table at 30 feet to represent a shallo w private well, based on a number of sources (B erndt et al, 1998; McPherson et al, 2000; USGS, 1990; FL water management di stricts; monitoring study by Bayer CropScience reviewed by USEPA, 2007a). In the Bayer CropScience study, w h ich surveyed well s in selected aldicarb use areas, 37% of the 800 wells sampled in the s outheast (exclusiv e of Florida) had

Section I.G – Page 214 of 277

### reported groundwater depths <50 feet below the surface, with a total of
21% of the wells with reported depths <25 feet below the surface (however, the depth to groundwat er was unknown for 45% of the sampled wells). Othe r facto r s, s u ch as type of well construction,

presence o f casing, and depth to we ll screen will a l so inf l uence the
concentration of NMC residues found in the water. With deeper wells,
travel time between the soil surf ace and ground water will increas e, allowing more time for degradation in transit and lower concentratio n s.

EPA eva l u ated the potential impa ct of varying well de pth on estimated NMC concentrations in gr oundwater in Appendix II.E.7. In the central ridge of Florida, estimated concentrations of total aldicarb residues and oxamyl ranged from an or der of magnitude gr eater for wells

at 15 feet to a factor of 2-to-4 time s lower for wells at 50 feet. A similar range in concentrations was found for to tal aldicarb concentrations in the southern coastal plain (GA) scenario.

ii. Setback distances b e tween the well and the treated field

For aldicar b , EPA simulated a setback distance betwee n the well and treated field, based on the label spec ific ations: 1000 feet for ci trus in Florida; 300-500 feet for other uses in the other regional scenarios. The Agency evaluated the impacts of the assumed label setbacks in Appendix II.E.7. The conceptual model accounted for s e tback distances by increasing the travel time betw een the treated field and the well. The
effect of the setback is based on the assumption of first-order degradation of aldicarb by hydrolysis dur ing the extra travel time from the field of application to the well.

For aldicar b , a 300-foot setba ck distance reduced estimated concentrations by a factor of 2 wh ile a 1000-foot setba ck distance

reduced es timated concentrations by a factor of 20. Actual reductions will vary, depending on the direction and velocity of lateral groundwater

flow in the field. The Agency does not have any monitoring data in similarly vulnerable areas with which to judge estimated concentrations. While the Bayer CropScience monitori ng study showed some differences in frequenc ies of detection based on so il leac hing potent ial, the study only identified distance between the well and the field, not the area of application.

The well setbacks only apply for certain high- leaching soils where groundwat er is within 25 feet of the surface and the well is not cased. For other soils, no setback is specified.

Section I.G – Page 215 of 277

### iii. Hydraulic conductivity of the s o il/vadose zone

The soils in the central ridge of Florida have very high saturated hydraulic c onductivities. Less permeable soils and soils without substantial macropore flow are lik ely to result in lower than predicte d

concentrations becaus e of the longer transport time.

iv. Soil/vadose zone and ground water pH

All of the NMC pesticides, except for the parent aldicar b , are susceptible to pH-dependent hydrolysis. Under acidic conditions (low pH), these chemicals persist; under alka line conditions (high pH), they degrade rapidly. The estimated concentrations reflect acidic conditions.

Where soils and water are neutral to alkaline, the concentrations are expected to be lower than those estimated for the preliminary

assessment. In general, the soils and ground water in the ground water
scenario locations in the NMC CRA – the Central Ridge of Florida, the southeastern Coastal Plain, and the De lmarv a Penins ula – are expected

to be acidic.

v. Other factors

Label changes for aldicarb, a major contributor to the NMC residue levels in ground water

, made in the mi d- to l a te-1990’s were inten ded to
reduce the amount of total aldic a rb residues reaching gr ound water in

vulnerable areas. These included well setbacks and some water management changes. While the Agen cy addressed well setbacks in the conceptual model, it did not ex plic itly account for recommended water management changes on the label. As noted in Chapter I.E, while the private well monitoring data from FL DEP, which analyzed water from the tap rather than from the well, indic ate a reduction in total aldicar b residues detected in later years, in terpretation of these results has been

confounded because the state of Flori da has also been placing car bon filters on the taps of thos e homes with aldicarb detections in well water.

The ground water exposure repres ents private drinking water wells

. The Agenc y assumed in this assessm ent that, in general, public water supplies su pplied by ground wate r will typically draw from deeper aquifers and/or aquifers that have a relatively impermeable layer between the surface and the water suppl y. Such supplies are expected to be much less vulnerable to pestici de contamination. Public water supplies have a higher probability of being treated, although conventional treatments processes are likely to resu lt in little or no reduction of NMC

residues in water. However, where lime softening, wh ic h will accelerate pH-dependent hydrolysis for all but par ent aldicarb, or activated carbon

Section I.G – Page 216 of 277

### filtration is used, some reduction in NMC residues bet ween untreated and treated water may occur (Appendix II.E.3).

b. Surface Water Exposure

The Agenc y does not expect cumulative NM C residues in surface water sources of drinking water to reach levels that will contribute substantially to the cumulative ex posure. Estimated NMC levels in drinking water surface water sources from the coastal plain of North Carolina were greater than predicted fo r any of the other regional surface water exposure sites. When the drinking wat e r component was
combined with the food and residential ex po sure routes in the cumulative assessment, the highest seasonal expos ures from surface water sources of drinking water were approximat ely an order of magnitude less than
those estimated for food or for the to tal NMC exposure from all routes. For most of the year, predicted exposures from drinking water were

much lower

.

For the surface water sources of drinking wat e r, OPP used PRZM/EXAMS to predict pesticide conc entrations in a small reservoir. This modeling approac h makes certain assumptions regarding the nature of the drinking water source, the wa tershed, and year-to-year variability.

The reservoir used for the expos ure assessment is based on the specific geometry (wat ershed and reservoir size) of an actual reservoir (Shipman City) in the Midwestern US. As such, it is more representative of potential transport to similar drin king water sources in high rainfall

areas such as the Midwest and Eastern U.S. than in the west.

PRZM is not a basin-scale model, but a field-scale model whic h estimates edge-of-field pesticide loads in runoff. It does not explicitly

account for the relative contributions of each field to the reservoir. OPP used a cumulative adjustment factor (a combination of the regional percentage of the total watershed ar ea in crops with carbamate uses and the percentage of acres treated by each carbamate on each crop) to adjust the resulting res e rvoir conc entrations calculated by EXAMS (see USEPA, 2000b, for assumptions in volved in apply i ng percent crop area factors for drinking water assessments).

PRZM does not account for location in the watershed: all fields are assumed to be uniformly distributed within the watershed, with runoff going directly into the reservoir. Each crop use simulated in PRZ M
assumes that the entire area of the watershed planted in the crop consists of a single soil. In eac h of the regions, OPP used data from

local soils on which th e crops are grown. When possible, the soil
selected for each scenario was a benchm ark soil that was prone to runoff

Section I.G – Page 217 of 277

### (classified as hydrologic group "C" or "D" soils). While an assessment using a single soil assumes that each part of the watershed will be equally vulnerable to runoff, area s of higher and lower runoff vulnerability

will exist in an actual watershed.

Because the applic ation rates, fr equencies, and timing are held constant, the PRZM/ EXAMS simulations o ver multiple years evaluate the impact of the variabil ity in precipitation o n the amount of pesticide

that reaches surface w a ter. Becaus e weather data spanning 30 years is

available fo r many locations ac ross the country, PRZM/ EXAMS ca n account for pesticide runoff from a wide ran ge of weather patterns not otherwise possible wit h monitoring studies that span relatively few years. The age of the weather data (1961 to 1990) limits OPP's ability to compare of the modeling output to more recent monitoring data.

Weather data files for PRZM are available for weather stations across the country. The weather station nearest to the county or

counties us ed for the simulations was chosen for the cumulative

assessment. To the extent that precipitation in these counties over the period of record might have been great er or l e ss than that recorded at the nearest weather station, runoff fo r that area may have been over- or underestimated by PRZM.

c. Usage Information

Typical application rates and frequencies for each NMC pesticide on each crop were generated by taki ng the average (spanning multiple years) of agricultural c hemical us age surveys. This assumes that all applications were made at this ty pical or average rate and that frequencies of applications were const ant year to year. Using thes e typical application rates and frequencies may underestimate water concentrations in years when pes t pressure i s higher than in our reported years and may overestimate in years when lower amounts of pesti cide

are used. The usage data were general ly not sufficient to conduct a probabilistic assessment over a distribution of act ual applic ation rates.

The Agenc y used typical applicat ion rates and acres treated for the

NMC assessment because of a low lik elihood that all of the NMC pesticides will be used at maximum ra tes on all of the crop acreage at

the same ti me. In the case of citrus, which resulted in the highest
estimated NMC residues in drink i ng water for this assessment (for private wells along the central ri dge), the maximum label rate for

aldic a rb, the major contributor to total NMC residues, is 4.95 lb ai/A, while the typical rate used was 3.9 lb ai/A. Given that estimated ground water residues are expected to be proportional to the applic ation rate, the total NMC residues for private wells in the central ridge of FL would

Section I.G – Page 218 of 277

### be no more than 20 percent greater than that used in the expos ure assessment. In the earlier OP cumu lative risk assessment, the Ag ency compared cumulative OP concentrati ons in surface water estimated using the average application rates wit h those estimated using maximum

label rates. Estimated peak exposures assuming maximum applic ation rates for all pesticides ranged from no difference for the Florida region to 2 to 4 times greater in the Sout heast and Mid-south Regions (USEPA, 2002b).

The typical application rates and per cent acres treated are derived
from state-l e vel data and assume unifo rm use practices across the state. In actuality, an uneven distribution of application rates and percent acres treated is expected in response to di ffering pest pressures. Thus, this assumption will un dere stimate areas where pest pressures may dictate a higher perc entage of acres treated in a given year; similarly, it will overestimate areas where low pest pressures will require fewer acre

treatments.

d. Timing of Exposure

OPP used crop profiles and other relative crop production publications to establish a window for the applic ation date of the pesticide on a particular crop. This window doesn’t nec essarily reflect the range over which a pesticide will be applied in a par ticular year, but captures the year-to-year variation in the application dates over ti me. Thus, in any given year, the timing of application may be clustered within a shorter time-frame than suggest ed by the application window.

Howev e r, because of weather and other environmental factors, the timing of intensive pest pressure a nd/or pesticide applic ation may vary

across the window. Thus, while the time series estimated in the drinking water exposures show a definite time period of peak exposures for surface water sources, the actual time of that peak may vary by several weeks, depending on the size of the win dow of application. Because of the interaction of processes in subs urface transport, there is a damping

effect in the concentrations observed in shallow groundwater. A sl ight seasonal pattern in ground water residue lev e ls is evident in the ground water estimates, but the seasonal pa tterns in concentrations in ground water are less affected by timing of application.

The date of application can have an effect on the predicted concentrations genera t ed by PRZM/EXAM S for surface water expo sure, depending on how near in time the pes ticide applic ation coincides with rainfall events in any given year. OPP evaluated the impact of varying the dates of application across th e applic ation window on the OP cumulative distribution (USEPA, 2002b). The impact of varying dates of application was most evident at the extremes in the distributions. The ratio in maximum concentrations between the lowest and highest

Section I.G – Page 219 of 277

### estimates was a factor of 5 to 6. For 99th and lower percentiles, the differences were not as dramatic, with the ratio between lowest and highest values generally two or less. This analysis only looked at the
cumulative OP distribution and did not evaluate variations in indiv i dual chemical distributions. This analys is has not been conducted for the

NMC cumulative.

In the absence of data to show ot herwise, OPP assumed that all of the pesticide applied on a particula r crop is done on the same date. While this may be an unreasonable assu mp tion for a large watershed, it

is not unrealistic for the size of the watershed or fields overlying s hallow aquifers supplying priv ate wells used in this assessment. This assumption may result in higher p eaks for surface water, but similar overall average concentrations than if applic ations are spread out over

time. The resulting est i mate of exposure may result in a small

overestimation bias in the results that will be greater in large than in

small watersheds. Little change is expected for ground water.



## 6. FQ PA 1 0 X Fa ctor f o r the Pr otection of Infant s a nd Chil dren

The FQPA (1996) instructs EPA, in making its “reasonable certainty of no harm” finding, that in “t he case of thre shold effects, an additional

tenfold ma rgin of safet y for the pesticide c hemical res i due and ot her sources of exposure shall be applied fo r infants and children to take into

account potential pre- and post-natal toxicity an d completeness of

data w i th respect to exposure and toxicity to infants and children .” Section 408 (b)(2)(C) fu rther states that “the Administrator may use a different margin of safety for the pes ticide c hemical res i due only if, on the basis of reliable data, such margin will be safe for infants and childr en.” The following discussion synthesizes in formation discussed in previous

sections of this characterization a nd information from the hazard, food, water, and residential chapters of the NMC CRA to inform FQPA 10X

factor for infants and children. Over all, the Agency bel ieves that there are quality data and scientif ically supportable met hods to account for specific exposure and behavioral patterns of children. Because
characteristics of children are dire ctly accounted for in the expos ure assessment and the A gency’s methods are not expected to underestimate exposure to NMCs, eval uating the potential for increased

toxic i ty to juveniles is the key co mponent in determining the magnitude of
the FQPA factors in the CRA.

The previous sections of this ri sk characterization describe the data sources and models used to generate t he food, drinking water, and residential exposure assessments. Overall, there is a high degree of confidence in the exposure data and methodologies used when assessing cumulative risk to childr en from food, drinking water and

Section I.G – Page 220 of 277

### residential exposure. The cumula tive expos u re assessments are considered to be protective of ch ildren and do not understate risk. As such, the Agency has focused its evaluation of the FQP A 10X safety factor on post-natal expos ure to juvenile rats.

Comparativ e cholinesterase studi es with post-natal exposures have been shown to provide more sensitive results than DNT studies or from studies using in utero only expos ures. Thus, comparati ve cholines terase

studies hav e been identified for use in the cumulative risk assessment as the most appropriate studies for devel oping the chemical-specific factor to address the potential susceptibilit y of infants and children to the

effects o f N M C ex posure. Data fr om comparative cholinesterase studies
have been used to refi ne the FQPA safety factor for aldi carb, carbaryl, carbofuran, formetanate, methomyl , and oxamyl. For those NMCs (methiocarb, pirimicarb , propoxur , thiodicarb) without such data, the FQPA 10X safety factor is retai ned. The Agency believ e s that the refined FQPA factors are protecti ve of infants and childr en in that high quality data from sensitive populat i ons were used. Moreover, the methods used to perform dose-response have been peer-reviewed multiple times. These methods prov ide a quality statistical fit to the

toxic i ty data in juvenile and adult s.



## 7. Phy s iologically -Based Pharmaco kinetic Mo dels

PBPK models, which describe the time course disposition of chemicals and their metabolites, coul d help assess cumulative risk and to evaluate the relationship betw een variable environmental expos ures and dynam ic biologica l processes. Appropriate PBPK models cou l d quantify the cumulative toxicity that can result from mu ltiple exposures (multiple chemicals and multiple pathways) and from exposure to multiple chemicals wit h a common mec hanis m or mode of action. While

these models are exc e llent tools, numerous input parameters are necessary for each chemical. Organ-specific thermodynamic parameters (such as ti ssue to blood equi librium partition coefficients) are required for each pesticide entering the body and for each of its

metabolites

. Additionally, values for all of the metabolic rates governing all the biotransformation steps for eac h pesticide would be necessary as
would information on cholinesterase inhibition and potential mixture
effects.

Exploratory PBPK mo dels have b een develo ped for some NMCs. Appendix II.B.6 includ es a description and results from EPA’s wor k on such a model for carbaryl. Because PBPK modelin g techniques offer good promise, continued development and testing of the models is necessary and should be pursued despite the current l i mitations with respect to the amount of input info rmation required. Pharmacokinetic

Section I.G – Page 221 of 277

### studies ( in vivo and in vitro experiments to determine key values for pharmacokinetic parameters and the ti me course disposition of the compounds in the body) need to be performed with many compounds to determine the key parameters of use in PBPK modeling. It is anticipated

that data and methods will contin ue to improve and evolve as more experience is gained in this ar ea. Although a biological or pharmacokinetic modeling approach would pr ovide anot her means to
determine the cumulative risk for t hese NMCs, the input parameters for such an approach are not available. Therefore, OPP ha s applied simple dose addition and used an empirical curve fitting model (i.e., the exponential model) to determine RPFs and PoDs.



## 8. Concl u si ons

With the passage of the FQPA (1996), the A gency is required to consider the cumulative risk of pesticides that share a common

mechanism of action. The Agenc y designated the NMCs as a common

mechanism group in 2001 and published its preliminary CRA in 2005. Since that time, the Agency has incorporated new hazar d and exposure data, assigned uncertainty and safety factors, evaluated comments from the public, addressed comments by the SAP, and made appropriate adjustments due to risk mitigation ac tions. The NMC CRA is a highly complex, highly refined risk assessment that uses data from multi p le sources and multiple models. Because of this complexity, no single

value in the assessment should be us ed to independently arrive at the interpretation of the re sults. Inste ad, it is necessary to consider the

results as a whole in or der to appropriately int e rpret the results and

arrive at conclusions.

This NMC CRA assessment refle cts the com p leted risk mitigation measures that have been propos ed or comp leted as a result of the single chemical assessments as of September , 2007. Since the publication of the preliminary CRA in 2005, many us es of NMCs have been voluntarily cancelled, have had voluntary cancel lation requests submitted or have been determined to be ineligible for re -registration. Specifically, the registrations of methomyl on stra wberries is undergoing voluntary cancellation, and the registrant has requested that methomyl use on grapes be cancelled. In addition, carbofuran was determined to be ineligible for reregistration and EPA has initia ted the process to cancel all domestic uses and to revoke most tolerances; this cancellation impacts risk to the food and drinking water pat hway. Residential indoor spray uses of propoxur that may result i n non-occupational exposure for children have been voluntarily cancelle d. The registrant of aldicarb has agreed to an increase in the well setba ck distance from 300 feet to 500 feet for aldicarb use on peanuts in t he southern portion of the Coastal

Plain. Each of these risk mitigat ion measures provides substantive

Section I.G – Page 222 of 277

### reductions in the exposure to NMCs and improvements to the cumulative

risk estimates.

Consistent with the mode of acti on of NMCs, the revised NMC CRA focuses on acute, single day exposure s. It presents the estimates of cumulative risks associated with expos ures to NMCs in food, drinking

water and from residential uses. Contributions from various pathways and routes of exposur e are arrayed s eparately in a set of temporal or time-series plots of MOEs over a period of 365 days so that the reader can assess and evaluate -- on a pathway and/or route- s pecific basis -- the signific ant contributors to risk. This practice permits expressio n of the full range of values for each param eter and facilitat es interpretation of the complete risk picture. OP P is confident that t he results reasonably represent expos ures and risks fro m f ood, wa ter, and residential use to the U.S. population.

The food component of the NMC cumulative risk assessment is based primarily upon two extens ive, reli able data sets: 1) USDA's CSFII 1994-96/98 and 2) USDA's PDP. The CSFII provides a detailed representation of the food consumpti on patterns of the U.S. public across all age groups, during all times of the year, and across the US. The PDP data provide a consistent and reliable sample of pesticide residues in the major children's foods, including fruits, vegetables, dairy products, meats, and grains. The data from PDP are collected so as to closely
reflect resi dues in foods, as consumed and are statistically representative of the U. S. food supply. The PDP program utilizes multiresidue analytical methods such t hat co-occurrence of pesticides in

indiv i dual samples is captured. These two sources of data provide a firm foundation upon which to assemble other data to develop the NMC

cumulative risk assessmen t. Oxamyl serves as the index chemical and
the residue values for the other N M C pesticides were converted to oxamyl equivalents using the RP F approach. After adjustment for processing, these index equivalen t residues were then compiled as
distributions of cumulative resi dues by summi ng them on a sample-bysample bas is. These cumulative residue distributions were combined with distributions of daily food cons umption values via a probabilis t ic procedure to produce a distribution of potential exposur es for the general

U.S. population and sub-popul ations using the DEEM-FCID software. The primary advantage of using distri bution s of pesticide concentrations and consumption values to assess cumulative expos ure is that distributions of exposure values are obtained that represent a distribution of realistic scenarios of exposure that describe both probabilities and magnitudes of multi-chemical cu mulative exposure through the food pathway.

Section I.G – Page 223 of 277

### The drinking water assessment focuses on areas where combined NMC exposure is likely to be among t he highest within each region as a result of total NMC usage and vulnerabi lity of drinking water sources. This analys is is based on a probabi listic modeling approach that considers the full rang e of data and not a single high-e nd estimate. EPA estimated NMC exposures in drinking wa ter to individuals in the CRA for both ground water and surface water source s of drinking water by region. The regional drinking water exposu re assessments rep r esent exposures
from vulnerable drink i ng water sour ces resulting from typical NMC usage

and reflect seasonal variations as well as regional variations in cropping
and NMC use. For the majority of the U.S., NMC residues in drink i ng water sources are at l e vels that are not likely to contribute substantially

to the multi

- pathway cumulative ex pos ure. Estimated NMC exposures

from surface water sources of dri n king water resulted in MOEs well in

excess of 10. For most ground wate r sources of drinking water, N M C exposures are expect ed to be similarl y low. Private wells e x tend ing through highly permeable soils and drawing from shallow depths in acidic, unc onfined aquifers (also known as water table aquifers) represent what the Agen cy believes to be the most vulnerable drinking
water sources for the NMCs based on available monit o ring, current use patterns, and known s o il and hydrologic conditions. Those instanc es where NMC concentrations resulted in MOE s of less than 10 are being addressed with mitigation measures in the single chemical assess ments

– an increase in the well setback di stance from 300 feet to 500 feet for

aldic a rb use on peanuts in the sout hern portion of the C o astal Plain and
notice of intent to cancel all dom estic carbofuran uses. With these mitigation measures, NMC exposures fr om drinking water result in MOEs that are greater than 10.

There are three NMC chemicals wit h currently registered resident ial uses considered as part of the revi sed NMC CRA in the residential/ nonoccupational exposur e pathway assessment. The residential uses

considered in this assessment incl ude the carbaryl lawn and golf course uses, the carbaryl vegetable and ornam ental garden use, the methiocarb snail bait use, the carbaryl fruit tree use, and the carbaryl and propoxur pet collar uses. Several reliable data sources were used to define how pesticides are used, how quick ly t he residues dissipate, how people may come into contact with pesticides (e.g., via dermal or inhalation exposure), and the length of ti me people might be exposed based on certain activities (e.g., playing on a treated lawn). As with the drinking water assessment, the residential exposur e assessment considers seasonal applic ations and timing as well as regional differences. In the case of regional differences, the revised NMC CRA foc u sed on the Southeast Region of the Un ited States for two r easons ; 1) the growing season is longer in the South and the associated pest pressures are therefore greater, and 2) drinking water concentrations are highest in this

Section I.G – Page 224 of 277

### region of the country. The reside ntial and gr oundwater assessments are based on the most highly expos ed localized areas within the southeastern region of the United States

. Specifically, the drinking water exposure for Georgia was combined wit h residential exposure in Florida. Pest pressure data for Florida are assumed to address pest pressure for other areas of the country where estimated NMC water concentrations are the highest (such as Georgia and No rth Carolina). Due to longer periods of pesticide use coupled with hi gher concentrations of NMCs in ground wat e r, this assessment provides a reasonable worst case estimate of exposure. The result s of the residential risk assessment

indic a te that remaining uses of N M Cs in a residentia l setting– as borne out by the analyses here -- are belo w OPP’s level of concern for all subpopulations.

EPA als o evaluated total (combined) MOEs for all three pathways (e.g., multi-pathway, which is the sum of food + water + residential) simultaneously. The multi- pathway MOEs at the 99.9 th 

### percentile are approximately 8 and 9 for children 1- 2 and children 3-5 years of age, respectively, for the si ngle day resu lts from Cale nde x. At the 99.9 th


### percentile of exposur e, the food pathway is the most si gnificant

contributor. These multi-pathway results are consistent with – and essentially dominated by -- the fo od results found in Table I.G-5. Because the expos ure through food is the dominant exposure pathway for the revised NMC CRA, the tota l MOEs derived fr om the multipathway as sessment are virtually ident ical to the MOEs from exposure through the food pathway.

The sensitivity analys es shown and discuss ed here are designed to evaluate the degree to which key areas of the risk assessment may or may not under- or over-e stimate the cumulative risk in an effort to

characterize and understand the MOEs esti mated in this assessment. When developing any risk assessment, assumptions must be made in

areas where data are not av ailable; this is th e also case for the NM Cs. In the revised NMC CRA, the Agen cy has made health protective assumptions in its baseline analy sis, particularly with regard to the years of PDP data which are used (for wh ich it us ed all years of PDP data except in cases where use patterns have been proposed or changed) and the use of inter-species extrapolati on factors for those NMCs without human data. The sensitivity analyses shown here demonstrate that the Agency has not under-estimated expo sures and associated risks since many of the modified scenarios resu lt in only small changes in the percentile at which an MOE of 10 is reached. 22

22 More spe c ifically, the com p lex nature of this CRA ha s been st resse d , and the ma ny, varied

data so urce s use d to devel op qua ntitative estimate s o f exposur e ha ve been de scribed. Althou gh

there is a hi g h level of con f idence in this assess me nt, it is importan t to recog n ize the limits on the

pre c isi on of the estimate s gene rated by the asse ssm ent. For example, althoug h the percenti l es



### Section I.G – Page 225 of 277

### The Agenc y has developed a highly refined and complex

cumulative risk assessment for th e NMCs that represents the state of the science regarding exist i ng hazard and expos ure data and the models and approaches used. The Agenc y notes that the risk mi tigation efforts of the past several years have signifi cantly reduced risk from NMCs in the food, drinking water and fr om residential use in the US. Taking into account these reductions and acknowledging that several key assumptions are designed to minimi ze the potential to underestimate exposure and risk, the Agency conc ludes that, based on the results of the revised NMC CRA, there is a reasonable certainty that no harm will result from exposure to the NMC pes ticides covered by this assessment, taking into account the cumulative effe cts of such residues. Accordingly, the pesticide tolerances for the NM Cs covered by this risk assessment

are considered to be “safe” as defi ned in FF DCA section 408(b)(2)(A), and to be reassessed for purposes of FFDCA section 408(g).

have bee n re ported o u t to 5 signifi cant figure s (e.g., 99.854), the A gen cy doe s n o t believe tha t the

asse ssm ent has the p o we r to discrimin a te betwe en diffe rent tenth s or hu nd redt hs of pe rce ntiles of

exposure. Put another wa y, there is no meanin g ful di fference between the expo sure re ceive d by

the 99.9th an d the 99.8th p e rcent ile of a sub gro up, mu ch le ss b etwe en the 99.85t h and 99.8 6th percentile s. The mag n itud e of actual ex posur e exp e ri enced by a p a rtic ul ar p e rcentage of the popul ation is l ikely to be close to, but somewhat below, EPA's esti mate for that percentile. Actual exposure fo r, say, the 99.9th percentile may fall within the values estimated fo r the 99.85th a nd the 99.95th percentiles. But EPA does not believe that the assessment reliably predi cts the

pre c ise difference in expo sure levels fo r peopl e fa llin g at different points o n the distrib u tion when the points a r e sepa rated by no more tha n 0.1% difference. Therefore, for ri sk manag ement

purp o ses, it is also app rop r iate to con s i dere the p e rcentile at whi c h the estimat ed MOE for a

sub g ro up rea c he s 10. If th at percentile i s not m eani n g fully differen t from the 99.9th percentile , as is the ca se h ere, it can be rega rd ed a s an additio nal con s id eratio n to suppo rt the con c lu sio n that

there is rea s o nable certai nty no harm will resu lt fro m cumulative exposure to NM Cs.



### Section I.G – Page 226 of 277

# H. References

“1992 Golf Course Operations : Cost of Doing Business / Profitabilit y.” The Center for Golf Course Management. Librar y of Congress GV975. G56 1992.

Anderson, S. 2002. The Toxicokinetics of Peripheral Cholinesterase Inhibition from Orally Administered Carbofuran in Adult Male and Female CD Rats: Final Report: Lab Project N u mber: 65C-08124: A2001-5379: ATM-0405. Unpublis hed study prepared by Toxic o kinetics Laborat ory, Center for Life Sciences and Toxicology, RTI. 192 p. MRID 45675701.

Austin, E. 2002. 4 week repeated-dose de rmal toxic i ty study with carbaryl technical in rats: Final report: Lab project number: COV A NCE 6224-268. Unpublished study prepared by Cov ance Laboratories Inc. 161p. MRID

45630601.

Ballee D. 1990. "A Golfer Exposur e Study with Chloroth alonil Used for GolfCourse Maintenance, Lab. Proj . No. 1148-85-0059.” Unpublis hed study submitted by Ricerca, Inc. MRID 42433811.

Barnett, J

. 2005. Oral (Gavage) Acute Relative Sensitiv ity Study of Formetanate

HCl in Neo natal and Adult Rats: Final Report. Project Number: WJI00007, WJI00005, WJI00006. Unpublished study prepared by Charles River Laboratories. 648 p. MRID 46618901.

Berndt, M.P., Hatzell, H.H., Crandall, C.A., Turtora, M., Pittman, J.R., and Oaksford, E.T. 1998. Water Q ual ity in the Georgia-Flori da Coastal Plain, Georgia and Florida, 1992-96: U.S. Geological Survey Circular 1151, on line at URL: http://water.usgs.gov/pubs/circ1151 , updated April 2, 1998.

Beyrouty, P. 2000. A time of peak behavior al and neur ochem ical effects study of a single or al administr ation of thiodicarb technical in rats: Lab Project No. 97507. Unpublished study prepared by ClinTria ls BioResearch Ltd. 115 p. MRID

45138702.

Beyrouty, P. 2000. An acute study of the potential effe cts of orally administered thiodicarb technical on behavior, neuroc hem istry, and neuromorphology in rats: Lab Project No. 97508. Unpublis hed study prepared by ClinTrials BioRes earch Ltd. 457 p. MRID 45138703.

Blaier, S.C., and Baxter, S.J. 2000. The occurrence and distribution of several agricultural pesticides in Delawar e ’s s hallow ground water: Delaware Geologic a l Survey Report of Invest igations No. 61, 23 p.

Section I.H – Page 227 of 277

### Boesten, J. 2004. Influence of Disper sion Length on Leaching Calculated wit h PEARL, PELMO, and PRZM for FOCUS groundwater scenarios. Pest Management Science, 60, 971-980.

Boman, B., N. Morris, M. Wade. 2002. Water and Enviro nmental Considerations for the Design and Dev e lopment of Citrus Groves in Florida, Circular 1419, Institute of Food and Agricultur al Sciences, University of Florida, Gainesville, FL.

Brock, W. 1988. Repeated dose dermal to xicity: 21-day study with IN D1410-196 in rabbits: Haskell Lab Report No. 523-88: Medical Re search No. 8472-001. Unpublished study prepared by Haskell Labora tory for Toxicology and Industrial Medicine. 299 p. MRID 40827601.

Brooks, W., et al. 1995. An Acute Study of the Time Course of Cholinesterase Inhibition by Orally A d ministered Ca rbaryl, Technical Grade, in the Rat. MR ID

43845203.

Brooks, W. and Broxup, B. 1995. A Time of Peak Effect Study of a Single Orally

Administer ed Dose of Carbaryl, Techni cal Grade, in Rats: Lab Project Number: 97388. Unpublished s t udy prepared by Bio-Re search Labs, Ltd. 215 p MRID

43845202.

Brooks, W., Broxup, B. 1994. An acute study of the time course of cholinesterase inhibition by aldicarb techni cal, aldicarb sulfoxide, and aldicarb sulfo n e in the rat: Lab Project No 97352. Unpublished study by Bio-Research Lab Ltd. 358 p.MR ID

43442305.

Burke, C., Anderson P, Dugger, D., Cowle s, J., 2006, Surface Water Monitoring Program for Pesticides in Salmonid-B earing Streams, 2003-2005. A Cooperat ive Study by the Washington State Depar tments of Ecology and Agriculture, http://www.ecy.wa.gov/pubs/0603 036.pdf

Carringer, S.J. 2000. Carbamate Insect icide Market Basket Survey. The Carringers, Inc., Apex, NC 27502. Study No. TCI-99-001.

Clothier JM. 1999a. “D ermal Transfer Effici ency of Pesticides from Carpets to Dry and Wetted Palms.” Prepared for U.S. Environmental Protection Agency

, National Exposure Res earch Laborato ry, Research Triangle Park, NC.

Clothier JM. 1999b. “D ermal Transfer Effici ency of Pesticides from New, Viny l Sheet Flooring to Dry and Wetted Palm s.” Prepared for U.S. Environmental Protection Agency, National Exposure Re search Laboratory, Research Triangle Park, NC.

Section I.H – Page 228 of 277

### Costa, J.L. and Prunt y, L. 2005. Solute Transport in fine sandy loam soil under
different flow rates. Ag ricultural and Water Management.

Crandall, C.A., and M.P. Bernd t. 1996. Water Quality of Su rficial Aquifers in the Georgia–Florida Coast a l Plain, Water-R esources Investi g ations Report 95-4269,

U.S. Geological Survey, Tallahas see, FL.

Dies i ng, L, Flucke, W. 1989. Subc hronic dermal toxicity study in rabbits: BOQ 5812315 (propoxur): Study No. T 8030050/T 5030543. Unpublished study prepared by Bayer AG. 269 p. MRID 41066001.

Doane and GolfTrak. DOANE Marketing Research, Inc. GolfTrak, 1998-1999.

Donohue, J.C. 2001. Ground-Water Qua lity in Georgia for 2000 Georgia Department of Natural Resources , Envir onmental Protection Divis ion, Georgia Geologic Survey, Circular 12P, Atlanta, GA.

Ellman GL, Courtney KD, Andres V, Jr., and Featherstone RM. 1961. “A New and Rapid Colorimetric Determination of Ac etylcholinesterase Activity.” Biochem

Pharmacol. 7:88-95.

Exponent. 2007. Durango So ftware, Dietary Expos ure Evaluation Model-Food Commodity Index Database (DEEM-FCID). http://www.durangosoftware.com/software/deem.html

Fenske RA, and Lu C. 1994. “Determination of Handwash Removal Efficiency:Incomplete Removal of the Pesticide Chlorpy r ifos from Skin by

Standard Handwas h Techniques.” Am Ind Hyg Assoc J. 55:425-432.

Fenske RA, Wong SM, Leffingwell JT, and Spear RC. 1986. "A Video Imaging Technique for Assessing Dermal Exposure II. Fluorescent Tracer T e sting." Am Ind Hyg Assoc. 47: 771-775.

FIFRA Scie nce Adviso ry Panel (SAP). 2005a . “Final report on N-Methyl

Carbamate Cumulativ e Risk Ass e ssm ent: P ilot Cumulative Analysis.” http://www.epa.gov/scipoly/s ap/20 05/february/minutes.pdf

FIFRA Scie nce Adviso ry Panel (SAP). 2005b . “Final report on Preliminary NMethyl Carbamate Cumulative Risk Assessment.” http://www.epa.gov/scipoly/ sap/20 05/august/minutes.pdf

FIFRA Scie nce Adviso ry Panel ( SAP). 2002. “Organophosphate Pe sticides:

Prelimin ary OP Cumulative Risk Assessment.” Final re port: http://www.epa.gov/scipol y/sap/20 02/inde x.htm

Section I.H – Page 229 of 277

### FIFRA Scie nce Adviso ry Panel (SAP). 2001a. “End Point Selectio n and Determination of Relative Potency in Cumulative Hazard Assessment: A Pilot Study of Organophosphorus Pesticide C hemicals.” Report from the FIFRA Scientific Advisory Panel Meeting of S eptember 27, 2000. FIFR A Scientific Advisory Panel, Office of Science Coordi nation and Policy, Office o f Prevention,
Pesticides and Toxic Subst ances, U.S. Environment al Protection Agency. Washingto n , DC. SAP Report 2000-0X. Availa ble: http://www.epa.gov/scipol y/sap/20 00/September/

FIFRA Scie nce Adviso ry Panel (SAP). 2001b. “Preliminary Cumula tive Hazard and Dose Respons e Assessment for Organophosphorus Pesticides: Determination of Relative Potency and Poin ts of Departure for Cholinesterase

Inhibition.” Report from the FIFRA Scientific Advisory Panel Meeting of September 5-6, 2001 (Report dated Septem ber 11, 2001). FIFRA Scientific Advisory Panel, Office of Science Coordi nation and Policy, Office o f Prevention,
Pesticides and Toxic Subst ances, U.S. Environment al Protection Agency. Washington, DC. Available: http://www.epa.gov/scipo ly/sap/2000 /September/

FIFRA Scie nce Adviso ry Panel (SAP), 2000a . Dietary Expos ure Evaluatio n Model (DEEM) and MaxLIP (Maximum Likelihood Imputation Procedure). http://www.epa.gov/oscpmont/sap/ meetings/2000/inde x.htm#022 900

FIFRA Scie nce Adviso ry Panel (SAP). 2000b . “Case Study of the E stimation of Risk From 24 Organophosphate Pest icides. ” Final report: http://www.epa.gov/oscpmont/sap/m eetings/2000/dece m ber/finalreport.pdf

FIFRA Scie ntific Advis o ry Panel (SAP). 1998 . FIFRA SAP Report on Proposed

Methods for Basin-Scale Estimation of Pesticide Conc entrations in Flowing
Water and Reservoirs for Tolerance Reassessment. http://www.epa.gov/scipoly/s ap/19 98/july/finaljul.htm

Florida Department of Environmental Protection (FL DEP). 2005. Ground water monitoring data for aldicarb. Communica tion with Andrew Priest, FL DEP.

Florida Department of Environmental Protection (FL DEP). 2004. GIS land use coverages for South Florida Water Management District (1995), Southwest Florida Water Management District (1999), and St. John’s River Water Management District (2000). Downloaded in 2004 from the FL DEP GIS data

web site at ftp://ftp.dep.state.fl.us/pub/gis/data/

Focazio MJ, Reilly TE, Rupert MG, and Helse l DR. 2002. “Assessing Groundwater Vulnerability to Contaminat ion: Providing Scientifically Defen s ible
Information for Decision Ma kers. US Geological Survey Survey Circular 1224.

Available: http://water.usgs.gov/pubs/circ/2002/circ1224/

Section I.H – Page 230 of 277

### Formoli TA. 1996. “Estimation of Exposure of Persons in California to Pesticide Products that Contain Propetamphos.” HS-1731, Califor nia Environmental Protection Agency.

Freeman, N. C. G., Jimenez, M., Reed, K. J., Gurunathan, S., Edwards, R. D., & Lioy, P. J. 2001. “Quantitativ e Analysis of Children’s Microactivity Patterns: The

Minnesota Children’s Pest icide Expos ure Study”. Journal of Exposure Analysis

and Environm ental Epidemiology

, 11 (6), 501–509.

Freeseman, P.I., R.L. Jones, R.E. Lee, U. B. Handihalli, and D. R. Wyatt. 2006. A Retrospective Groundwater Monitoring Study for Aldicarb and Its Metabolites in the Pacific Northwest. Bayer CropS cience St udy No. RAAVY0 05. MRID

46793702.

Geno PW, Camann DE, Harding, HJ, V illalo bos K, Lewis RG. 1995. "Handwipe Sampling and Analys is Procedure for t he Measurement of Dermal Contact with

Pesticides. " Arch Environ Contam Toxicol. 30:132-138.

Griffiths, D., Conning, D. 1995. First revision to ninety- day oral toxicity of PP062­ albino rats: Lab Project No. PR0106: CTL/ R/237. Unpublished study prepared by
Central Toxic o logy La b. 331 p. MRID 44233103.

Groot ME, Lekkerkert MC, and Steenbekke r s LPA. 1998. "Mouthing Behaviour ofYoung Children–An Observational St udy." Agricultural University Wageningen,Household and Cons umer Studies, Wageningen, Netherlands.

Gupta RC and Dettbarn W-D. 1993. “Role of Carboxylesterases in the Prevention and Potentiation of N-methylcarbamate To xicity.” Chem-Biol Interactions. 87:295­

303.

Heimlich, R. 2000. Farm Resource Region s. USDA Economic Res earch Service

No. 760. Available: http://www.econ.ag.gov/whatsnew/issues/regio n s

Hoberman, A. 2007. Acute Oral (Gav age) Dose Range-Finding Study of

Cholinester a se Depres sion from C a rbofuran Technical in Juvenile (Day 11) Rats: Final Report. Project Number: KZH00140, A2006/61 35. Unpublis hed study prepared by Charles River Labora tories. 184 p. MRID 47143703.

Hoberman, A. 2007. Acute Oral (Gavage) Ti me Course Study of C holinesterase Depression from Carbofuran Technica l in Adult and Juvenile (Day 11 Postpartum) Rats: Final Report. Project Number: KZH00141, A2006/6136. Unpublished study prepared by Charle s Rive r Laboratories. 326 p. MRID

47143704.

Hoberman, A. 2007. Cholinesterase Depr ession in Juvenile (Day 11) and Adult Rats Following Acute Oral (Gavage) Dose of Carbofuran Technic a l: Final Report.

Section I.H – Page 231 of 277

### Project Number: KZH00142, A2007/ 6137. Unpublished study prepared by Charles River Laboratories. 374 p. MRID 47143705.

Horner, S. 1996. Pirimicarb: Acute neurot oxicity study in rats: Lab Project No. CTL/P/5232. Unpublis hed study prepared by Central Toxicology Lab. 712 p.

MRID 44485301.

Hornsby, A.G., Rao, P

. S.C., and Jones, R.L. 1990. Fate of Aldicarb in the

Unsaturated Zone Beneath a Citrus Grove. Water Resources Research, 26(10),

2287-2302.

Human Studies Rev i ew Board Final Report . July 2006. Final Report of the May 2-3 2006 Meeting of the Human Studi es Review Board with the USEPA. http://www.epa.gov/osa/hsrb/files/meet ing-materials/may-2-3-2006-publicmeeting/final7706.pdf

Human Studies Rev i ew Board Final Re port. June 2006. Final Report of the April 4-6 2006 Meeting of the Human Studies Revi ew Board with USEPA. http://www.epa.gov/osa/hsrb/files/meet ing-materials/apr-4-6-2006-publicmeeting/april2006m tgfinalreport62606.pdf

Hunter DL, Chen D, Marshall RS, Padilla S, Setzer W, Moser VC, Lowit A. 2005. “Comparison Between Ellman and R adiometric Metho d s for Asse ssing
Cholinester a se (ChE) Inhibition in Rats Treated with N-methyl Carbamate

Insecticides.” Presented at the 44th Annual Meeting of the Soci ety of Toxicology;

New Orlea n s, LA.

Hunter DL, Marshall RS, and Padilla S. 1997. “Automated Instrument Analysis of Cholinesterase Activity in Tissues from Carbamate-tr eated Animals: A Cautionary Note.” Toxicol Methods. 7:43-53. IPCS World Health Organization. 2005. Chemical Specific Adjustment factors for Interspecies Differences and Human Vari ability: Guidance Document for use

of Data in Dose/Conc entration-Response Assessment.

Jacoby, H., C. Hoheis e l, J. Karrie, S. Lees, L. Davies-Hilliard, P. Hannon, R. Bingham, E. Behl, D. Wells , and E. Waldman, 1992. Pe sticides in groundwat er database: a compilation of monitori ng studies: 1971-1991 National Summary.

EPA 734-1 2 -92-001.

Johnson CD and Rus sell RL. 1975. “A Rapid, Simple Radiometric Assay for

Cholinester a se, Suitable for Multi p le Determinations.” Anal Bioc hem. 64:229-238.

Johnson D, Thompson R, Butterfield B. 1999. "Outdoor Resident ial Pesticide Use and Usage Survey and National Gardening Association Survey." Unpublished study prepared by DOANE Marketing Res ear ch, Inc., and Gallup Organization,

Section I.H – Page 232 of 277

### Inc. Submitted by the Outdoor Re sidential Exposure T a sk Force. MRID

44972202.

Jones, R.L., and T.S. Ramanarayanan. 2006. TEMIK® brand 15G Aldicarb Pesticida: Summary of Five Retros pective Ground Water Monitori ng Studies

conducted for Aldicarb and Its Metabolites in the Pacific Northwest, California, Texas, Mississippi Delta and the Southeast R egions of the United States in 2005. Bayer CropScience St udy No. RAAVY0 10. MRID 48793 701.

Jones RL, Hornsby AG, Rao PSC. 1987. Mo vement and Degradation of Aldicarb

Residues in the Saturated Zone Under Citrus Groves on the Florida Ridge.

Journal of Contaminant Hydrology, 1(3), 265-285.

Kellogg RL, Goss DW, et al. 1997. "Potentia l Priority Watersheds for Protection of Water Quality from Nonpo int Sources Related to Agriculture.: U S DA Natur a l
Resources Conservation Service. State of the Land website. http://www.nhq.nrcs.usda.gov/l an d/pubs/wqp ost2.html.

Kellogg, R.L. S. Wallace, K. Alt, and D.W. Goss. 1997. "P otential Priority
Watersheds for Protection of Water Qua lity from Nonpoint Sources Related to Agriculture.” USDA Natural Resources Conservation Service. State of the Land

website. http://www.nhq.nrcs.usda.gov /land/pubs/wqpo st2.html

Klon ne DR, Fuller R, and Willard T. 2001. “D etermination of Dermal and Inhalation Exposure to Reentry Worker s D u ring Hand Pruning in Apples.” Unpublished report prepared by American Ag ricultural Services, Inc. MRID

45480302.

Klonne DR, Fuller R, and Belcher T. 2000a. “Determination of Dermal and Inhalation Exposure T o Reentry Worker s D u ring Weeding In Cabbage, Study Number: ARF037.” Unpublished study submi tted by Agricultural Reentry Task Force. Performing Laboratories: Field - Ex c e l Research Services a n d Analytic al -

Maxim Tec hnologies. MRID 45191701.

Klonne DR, Fuller R, and Belcher T. 2000b. “Determination of Dermal and Inhalation Exposure to Reentry Worker s D u ring Pruning in Nursery Stock.” Unpublished report prepared by Horizon Laboratories, Inc. MRID 45469501.

Klonne DR, Fuller R, Honeycutt R. 2000c. “Determination of Dermal and Inhalation Exposure T o Reentry Workers D u ring Pruning of Olive Trees, Study Number: ARF033.” Unpublished study submi tted by Agricultural Reentry Task Force; Performing Laboratories: Field - HERAC, Inc. and A nalytical - Morse

Laboratories. MRID 45175102.

Klonne DR, Bruce E, A r tz S, Howell C. 1999. “Determination of Dermal and Inhalation Exposure T o Reentry Workers D u ring Scout ing in Sunflower, Study

Section I.H – Page 233 of 277

### Number: ARF022” Unpublished s t udy submi tted by EPA Agricultural Reentry Task Force; Performing Laboratories: Fiel d – ABC Laboratories and Analytical -

Maxim Tec hnologies. MRID 45005909.

Klonne DR. 1999. "Integrated Report For Evaluation of Potential Exposures To

Homeowners and Professional Lawnca re Operators Mixing, Loading, and Apply i ng Granular and Liquid Pes t icides to Residential Lawns." Unpublis hed study submitted by Outdoor Resident ial Exposure Task Force. MRID 449722 01.

Koplin DW, and Martin JD. 2003. “Pes ticides in Ground Water: Summary Statistics, Prelimin ary Results from Cycle I of the National Water Quality Assessment Program (NAWQA). ” 1992-2001 (provisional data). http://ca.water.usgs.gov/pns p/pestgw/Pest-GW_2001_T ext.html

Krolski, ME. 2000. "S EVIN 2G - Determi nation of Transferable Res i dues on Turf". Bayer Study Number CA25TF01; Bay e r PSI/Report Number RACAX01 5

. September 30, 2000. MRID 46673901.

Lantz, J.; Y oung, B. 2006. Revised Ca rbaryl Dietary Ris k Assessment (Food Only) Incorporating Updated Res i due Data. Project Number: G201683. Unpublished study prepared by Bayer CropScience LP. 30p. MRID 47094501. Larson SJ, Crawford CG, and Gilliom RJ. 2004. “Develop ment and Applic ation o f Watershed Regressions for Pesticides (WARP) for Estimating Atrazine

Concentration Distributions in Stream s.” U.S

. Geologic a l Survey WaterResources Investigations Report 03-4047, 68 p.

Lee, R.E., R.L. Jones, D.R. Wyatt, and U.B. Handihalli. 2006. A Retrospective Groundwater Monitoring Study for Aldi carb and Its Metabolites in the Southeastern United States. Bayer CropS cience Study No. RAAVY009. MRID

46793706.

Lenz, M, R.L. Jones, D.R. Wyatt, U. B. Handihalli, and R.E. Lee. 2006. A Retrospective Groundwater Monitoring Study for Aldicarb and Its Metabolites in California. Bayer CropScience St udy No. RAAVY0 06. MRID 46793 703.

Malley, L. 2005. Oxamyl (DPX-D1410) Techni cal (98% w/w): Relative Sensitivit y of Preweanling Rat Pups and Adult Rats to Inhibition and Recovery of

Cholinester a se Activity. Projec t Number: DUPONT/16755, 969, 970. Unpublished study prepared by DuPont Cr op Protection. 70 p. MRID 46615301.

Malley, L. 2005. Methomyl (DPX-X 1179) Technical: Comparison of

Cholinester a se Activity in Adult and Prewean ling Rats: Revision 1. Project Number: DUPONT/15433. Unpu blished study prepared by E. I. du Pont de Nemours and Company, In c. 73 p. MRID 46646401.

Section I.H – Page 234 of 277

### Malley, L. 1999. Oxamyl technical: 21-day repeated dos e dermal toxic i ty study in rabbits: Lab Project N o . 1599: 12190: 695. Unpublished study pre p ared by E.I. du Pont de Nemours and Co. 130 p. MRID 44751201.

Malley, L. 1997. Acute oral neurotoxicity study of oxamyl technical in rats: Lab project number: HLR 1118-96: 10730-001: 1118-96. U npublished study prepared by Du Pont Haskell Lab for Toxic o l ogy and Industrial Medicine. 629p. MRID

44254401.

Malley, L. 1997. Acute oral neurotoxicity study of oxamyl technical in rats: Supplement No. 1: Lab project number: 11268-001: 10730-001: 1118-96. Unpublished study pre pared by E.I. du Pont d e Nemours and Co. 67p. MRID

44420301.

Malley, L. 1997. Reversibility study with carbamate insecticides in rats: Lab project number:11377: HL -1997-00641. Unpublished s t ud y prepared by Du Pont, Haskell Laboratory for Toxic o logy and Industrial Medicine. 126 p. MRID

44472001.

Martin JD, Crawford CG, and Larson SJ. 2003. “Pesticides in Streams: Summary Statistics; Prelimin ary Results from Cycle I of the National Water Quality Assessment Program (NAWQA), 1992-2001 (provisional data). http://ca.water.usgs.gov/pnsp/pestsw/Pest-SW_2001_T ext.html

McDanie l K.L., Padilla S., Marshal l R.S ., Phillips P.M., P odhorniak L., Qian Y., Moser V.C. 2007. Comparison of ac ute neurobehavior a l and cholinesterase inhibitory effects of N -methyl carbamates in rats. Toxicol. Sci . 98, 552-560.

McFarlane, P.; Freestone, S. 1999. A Rando mised Double Blind Ascending Oral Dose Study with Oxamyl: Lab Projec t Number: ICR 012765: 12245: HLO-1998­

01505. Un published s t udy prepared by Invere sk Clin ical Research. 624 p. MRID

44912301. McFarlane, P.; Sanderson, J.; Freestone, S. 1998. A Randomis ed Double Blind Ascending Oral Dose Study with Methomyl to Establish a No Adverse Effect Level: Lab Project Number: HLO-1998- 00969: 11739: ICR 0124 56. Unpublis hed study prepared by Inveresk Clin ical Resear ch. 468 p. Relates to L00003 06.

MRID 44721401.

McPherson, B.F., Miller, R.L., Haag, K. H., and Bradner, A. 2000. Water Quality in Southern Florida Florida,1996–9 8: U.S. Geolog ical Su rvey Circular 1207, 32 p.,

on-line at http://pubs.water.usgs.gov/circ1207/

Merricks L. 2001. "Determination of De rmal (Hand and Forearm) a nd Inhalation Exposure to Disulfoton Resulting from Re sidential Application of Bayer Advanced

Section I.H – Page 235 of 277

### Garden 2-in-1 Systematic Rose and Flower Care to Shrubs and Flower Beds." Lab Project No. 4201. Unpublished study pr epared by Agrisearch Inc. 178 p.

MRID 45333401.

Merricks L. 1998. “Carbaryl Mixer / Loader /Applicator Exposure Study during Application of RP-2 Liquid (21%) to Fruit Trees and Ornamental Plants, Agrisearch Study No.1518.” Unpublished study submitted by Rhone Poulenc Ag Company. MRID 44518501.

Mester TC. 1999. “Carbaryl: Determination of Transferable Res i dues From Turf Treated With Dragon® Sevin® Liquid.” Unpublis hed study submitted by Bayer Corporation; Performi ng Laboratory: ABC Laboratories

. MRID 45114301.

Mester TC. 1998a. “Carbaryl Mixer/Loader /Applicator Exposure Study during Application of RP-2 Liquid (21%) Sevin® Ready to Use Insect Spray or Sevin® 10 Dust to Home Garden Vegetables, Agris earch Study No. 1519.” Unpublis hed study submitted by Rhone Poul enc Ag Company. MRID 44459801.

Mester TC. 1998b. "Dermal Exposure and Inhalation Exposure to Carbaryl by Commercial Pet Groomers During Applic at ions of AdamsTM Carbaryl Shampoo." Unpublished study submitted by P f iz er Animal Health. MRID 44658401.

Mikles, K. 1998. Methomyl Technical (D PX-X1179-512): Acute oral neurotoxicity study in rats: Lab project number: 11300: HL-1998-01080. Unpublished study prepared by Du Pont Haskell Laboratory for Toxic o logy and Industrial Medicine. 386 p. MRID 44487501.

Moran JW, Saund ers DG, Carter JE, and Emery KAB. 1987. "Exp osure of Workers and Golfers to Flurprimidol fr om use of Cutlass 50W on Golf Course Turf, Lab P r oj. I.D. AAC8606.” Un published s t udy submitted by Lilly Research

Laboratories. MRID 40184414.

Moser, G. 2007. Report of Cholinesterase Comparativ e Sensitivit y Study of Carbaryl. Unpublished study prepared by U.S. Environm ental Protection Agency. 39 p. MRID 47143001.

Moser, G. 2006. Report on Cholinestera se Comparativ e Sensitivit y Study of Carbaryl. Unpublished study prepared by U.S. Environmental Protection Agency. 35 p. MRID 47007001.

Moser VC, Phillips PM, McDaniel KL, Marshall RS, Padilla S, Lowit A. 2005. “Comparison of Acute Neurobehavioral Effects of N-methyl Carbamate

Insecticides.” Presented at the 44th Annual Meeting of the Soci ety of Toxicology;

New Orlea n s, LA.

Section I.H – Page 236 of 277

### Moser, V.C., Phillips, P.M., McDanie l , K.L ., Marshall, R.S., Pad illa, S., Lowit, A.

2005. Comparison of acute neurobehavior a l effects of N- methyl carbamate

insecticides. Presented at the 44 th 

### Annual Meeting of the Society of Toxicology; New Orleans, Louisiana.

Moser VC. 2005. Unpublis hed study prepared by US EPA, National Health and

Environmental Effects Re search Lab. MRID 46618001.

Moser, VC 1999. Aldic a rb replication st udy. Unpublishe d study prepared by US EPA, Natio nal Hea l th and Enviro nmental Effects Research Lab 403 p. MRID

45150701.

Moser VC 1999. Comparison of aldic a rb and methamidophos neurotoxic ity at different ages in the rat: Behavioral and bi oc hemical parameters. Toxicol. Appl.

Pharmacol. 157:94-106. MRID 45068601.

Mosier, D.G., R.L. Jones, U.B. Handihalli, and R.E. Lee. 2006. A Retrospective Groundwater Monitoring Study for Aldicarb and Its Metabolites in T e xas. Bayer CropScien ce Study No. RAAVY0 07. MRID 4679370 4.

Nolan BT, Hitt KJ, and Ruddy BC. 2002. “M ap of the Probabilit y that Nitrate

Exc eeds 4 mg/L in Shallow Ground Waters of the United States, B a sed on the LR Model.” Accessed April 13, 2004. URL:http://water.usgs.gov/lookup/ getspatial?gwrisk

Nolan RJ, Rick, DL, Freshour NL, Saunders JH. 1984. “Chlorpyrifo s: Pharmacokinetics in Human Volunteers. ” Toxic o l Appl Pharmocol 73:8-15.

North Carolina Div ision of Water R e s ources (NC DWR). 2007. North Carolina Aquifers. NC DWR, DENR, Ralei gh, NC. Last modified 6/13/2007. http://www.ncwater.org/Educatio n_and_ Te c hnica l_Ass i stance/Ground_Water/Aq

uiferCharacteristics/

Nostrandt AC, Duncan JA, and Padilla S. 1993. “A Modified Spec trophotometric Method Appropriate for Measuring Choli nest e rase Activity in Tissues from Carbaryl-treated Anim als.” Fundam Appl Toxicol. 21:196-203.

Obreza, T. A. and Collins, M. E. 2002. Common Soils Used for Citrus Production in Florida, BuSL-193. Institute of Food and Agricultural Sciences, University of Florida, Gainesville, F L (also available at http://edis.ifas.ufl.edu/SS403 ).

O’Neil, AJ 2000. Cholinesterase inhi bition determined in rats exposed to inhalation atmospheres of oxamyl tec hnical (96.9%). The Haskell Lab of Toxicology & Industrial Medicine, EI du Pont de Nemours Co. DuPont Study No. 4383 (6/27/2000) Unpub lis hed. MRID 45155801.

Section I.H – Page 237 of 277

### Padilla S., Marshall R.S., Hunter D.L ., and Lowit A. 2007. Time course of

cholinesterase inhibition in adult rats treated acutely with ca rbaryl, carbofuran, formetanate, methomyl, methiocarb, oxamyl, or propoxur. Toxicology and Applied Pharmacology, 219; 202-209.

Padilla S., Setzer W., Marshall R.S., Hunt er D., Phillips P., McDaniel K., Moser V.C., Lowit A. 2006. A dose-response study of the toxic i ty of a mixture of 7 Nmethyl carbamates in adult, male rats. Presented at the 45 th 

### Annual Meetingof the Society of Toxciology ; San Diego, Califor nia.

Padilla S, Marshall RS, Hunter DL, Phillip s P, McDaniel K, Moser VC, Lowit, A. 2005. “Time Course and Dose Response A ssessment of Cholines terase (ChE)

Inhibition in Adult Rats Treated Acutel y with Carbaryl, Methomyl, Methiocarb, Oxamyl, or Propoxur.” Pres ented at the 44th A nnual Meeting of the Society of Toxicology; New Orleans, LA.

Paramasiv a m, S. Alva, A.K., and Graham , W.D. 1999. Transpor t of Bromide in

an Entisol and its Dis sipation in a Surficial Aquifer. J. Environ Sci Health A34(3),

585-604.

Parkinson, G. 1978. Pirimicarb metabolite R 34885: Subacute oral toxicity to rats: Report No. CTL/P/402. (Unpublis hed st udy. March 2, 1979 under 10182-7; prepared by Imperial Chemical Industries, Ltd., Eng., s ubmitted by ICI Americas, Inc., Wilmington, DE; CDL :097848-F) MRID 00113638.

Parson, L.R. and Morgan, K.T. 2004. M anagement of Microsprin kler Systems for Florida Citrus. HS-958, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, F L

.

Pauluhn, J. 1992. BOQ 5812315: Study for chr onic inhalation toxicity in the rats: Lab Project No. 21848. Unpublis hed study prepared by Bayer AG. 4358 p. MRID

42648001.

Pinheiro J, Bates D. 2000. Mixed Effect s Models in S and S-Plus. Springer, NY.

Procter, B. 1989. A 21-Day Dermal Toxicity Study of Mesurol Tec hnical in Albino Rabbits: Lab Project N u mber: 51925. Unpublised Study prepared by BioResearch Laboratories Ltd. 190 p. MRID 41771701.

Procter, B. 1988. A 21-day dermal toxic i ty study of mesurol technic a l in alb i no rabbits: Bio-Research Laboratory 51901. Unpublished Mobay Report No. 98369 prepared by Bio-Research Laborator ies, Ltd. 259 p. MRID 40922301.

R Development Core Team. 2004. R: A Language and Environment for Statistical Computing. R Foundation for St atistical Computing. Vienna. ISBN: 3­

900051-07- 0. http://ww w .R-project.org

Section I.H – Page 238 of 277

### Renwick AG. 1993. D a ta-derived safety factors for the evaluation of food

additives and environmental contaminants. Food Addi tives and Contaminants ,

10: 275–305.

Renwick AG, Lazarus NR. 1998. Hu man variability and noncancer risk assessment — An analysis of the defaul t uncertainty factor. Regulatory Toxicology and Pharmacology , 27(1 Pt 2): 3–20.

Robinson K. Broxup, B. 1997. A Developm ental Neur otoxic ity Study of Orally

Administer ed Carbaryl. Technical Grade in the Rat: Lab Project N u mber 97391. Unpublished study prepared by ClinTrai ls BioResearch Ltd. 1826 p. MRID

44393701.

Robinson, K and B. Broxup 1996. A 13 Week Study of the Potentia l Effects of Orally Administered Carbaryl, Technica l Grade, on Behavior, Neurochemistry and Neuromorphology in Rats. Bio-Res earc h Laboratories Ltd., Senneville,
Quebec. Laboratory Project I.D. 97390, September 24, 1996. Unpublished

MRID 44122601.

Robinson, K., Brooks, W., Broxup, B. 1995. A 13-week study of the potential effects of orally administered aldic a rb technical on behavior, neurochemistry and neuromorphology in rats: Lab Project No . 97234. Unpublis hed study prepared by

Bio-Res earch Labs, Ltd. 825 p. MRID 43829602.

Robinson, K., Brooks, W., Broxup, B. 1994. A time of peak behavioral effects study of a single oral admin istration of aldicarb, technical grade, in rats: Lab Project No. 97236. Unpublished s t udy prepared by Bio-Research Lab Ltd. 278 p.

MRID 43442302.

Rodic, Z. and Taylor, P. 2006. Structure and function of cholines terase. In Toxicology of Organophosphate and Ca rbamate Compounds. Pp. 161-186.

Rotondaro A. 2000. “Dete rmination of Dermal and In halation Exposure to Reentry Workers During Chrysanthemum Pinching in a Greenhous e .” Unpublished study prepared by Paragon Research Services. MRID 45344501.

Sieczka, J.B., and R.E. Thornton, 1993. Commercial potato production in North

America. The Potato Association of Americ a, University of Maine, Orono, Mai ne, 46 p.

Smajstra, A.G. and H a rman, D.Z

. 2002. Irri gated Acreage in Florida: A Summary through 1998. CIR 1220 , Institute of Food and Agricult ural Scienc es, University of Florida, Gainesville, FL.

Section I.H – Page 239 of 277

### Suffolk County Dept. of Health Serv ices. 2000. “Water Quality Monitoring Program to Detect Pesticide Contam ination in Groundwaters of Nassau and Suffolk Counties, NY.” December, 2000.

Sultatos, L. 2006. Interactions of organophosphorus and ca rbamate compounds

with cholinesterases. In Toxico logy of Organophosphate and Carbamate Compounds. Pp. 209-218.

Takahashi H, Kato A, Yamashita E, Naito Y, Tsuda S, and Shirasu Y. 1987. “Potentiations of N-methylcarbamat e Toxic i ties by Organophosphorus Insecticides in Male Mice.” Fundam Appl Toxicol. 8:139-146.

Tesoriero, A.J., T.B. S p ruill, and J

. L. Eimers. 2004. Geochemistry of shallow ground wat e r in coastal plain env ironment s in the southeastern United States: implication s for aquifer susceptibility. Appl. Geochem. 19 (2004): 1471-1482.

Thelin, G.P., and L.P. Gianessi. 200 0. Method for Estimating Pesticide Use for County

Areas of the Contermino us United States. US Ge ological Sur v ey Open-File Report 00­

250. Sacramento, California, 2000. http://ca.wat er.usgs.gov/pnsp/rep/ofr 00250/



### Tilden, R., and G. Wiegand. 1998. GPS/ GIS Used in Pr oject to Analyze Residential Drinking Water Wells for Al dicarb and Nitrates in Putnam and St. Johns Counties. Abstract. FL Dept. of Environmental Protection. GIS Conference

in Tallahas see, FL, October 14-15, 1998.

Tyl, R.; Marr, M.; Myers, C. 2005. Acut e Range-Finding Study of Carbofuran Technical (CAS No. 1563-66-2) Administer ed by Gavage to Post-natal Day 11 Male and Female CD (Sprague-Dawley) Rat Pups: Final Report. Project Number: 65C/09728/000/003, A2005/5983, RT05/FMC/1. Unpublished study prepared by Research Triangle Institut e. 107 p. MRID 46688912.

Tyl, R.; Marr, M.; Myers, C. 2005. Acute Time-C ourse Study of Carbofuran

Technical Administer ed by Gavage to Adult and Post-natal Day 11 Male and Female CD (Sprague-Dawley) Rats : Final Report. Project Number: 65C/09728/000/004, A2005/5982, 954. Unpublis hed study prepared by Research Triangle Institute. 136 p. MRID 46688913.

Tyl, R.; Marr, M.; Myers, C. 2005. Acute Dos e -R espons e Study of Carbofuran

Technical Administer ed by Gavage to Adult and Post-natal Day 11 Male and Female CD (Sprague-Dawley) Rats : Final Report. Project Number: 65C/09728/000/005, A2005/5981, RT05/FMC3 . Unpublished study prepared by Research Triangle Institut e. 151 p. MRID 46688914.

Tyl, R. Anderson, S. 2000. The toxic o kinetics of peripheral cholinest e rase inhibition from orally administered aldica rb in adult male CD rats: Final Report: Lab Project No. 65C-07532-100: 65C-075 32-200: RTI-726. Unpublished study prepared by Research Triangle In stitute. 108 p. MRID 45079705.

Section I.H – Page 240 of 277

### U.S. Department of Agriculture. 2007a. Agric u ltural Res earch Service, Continuing Survey of Food Intakes by Individuals 1994- 1996, 1998. http://www.ars.usda.gov/Ser vice s/docs.htm?docid=1 4 392

U.S. Department of Agriculture. 2007b. Agri c u ltural Marketing Service, Pesticide Data Program. http://www.ams.usda.gov/science/pdp

U.S. Department of Agriculture. 2005. Pesticide Data Program-- An nual Summary Calendar Year 2005, November 2006, 31p, appendices A – P (drinking water is Appendix J), http://www.ams.usda.gov /scienc e/pdp/download.htm

, accessed 4/19/2007.

U.S. Department of Agriculture. 2004. Pesticide Data Program-- An nual Summary Calendar Year 2004, Feb 2006, 24p, appendices A – L (drinking water is Appendix F), http://www.ams.usda.gov/science/pdp/ Summary2004.pdf

U.S. Department of Agriculture. 2003. Pesticide Data Program-- An nual Summary Calendar Year 2003, June 2005, 22p, appendices A – N (drinking water is Appendix J), http://www.ams.usda.gov/scienc e/pdp/Summary2003.pdf

U.S. Department of Agriculture. 2002. Pesticide Data Program-- An nual Summary Calendar Year 2002, Feb 2004, 23p, appendices A – M (drinking water is Appendix I), http://www.ams.usda.gov /science/pdp/ Summary2002.pdf

U.S. Department of Agriculture. 2001. Pesticide Data Program-- An nual Summary Calendar Year 2001, Feb 2003, 21p, appendices A – M (drinking water is Appendix I), http://www.ams.usda.gov /science/pdp/ Summary2001.pdf

U.S. Depart m ent of Agriculture Natu ral Resources Conservation Service (USDA NRCS). 2005. County Soil Surve y Data (SSURGO) for Hardee, Highland, and Polk counties, FL.

Downloaded 2005. http:// soildatamart.nrcs.usda.g ov/



### U.S. Department of Agriculture Natura l Resources Conservation Service (USDA NRCS). 2003. Florida NRCS Field Office Technical Guide. Section II. Water Quantity and Quality. Av ailable from the electronic FOTG site at http://efotg.nrcs.usda.gov/treemenuFS.aspx

U.S. Department of Agricult ure. 2002b. Ag Census, 2002, http://www.nass.usda.gov/census /c ensus02/volume1 /us/us2app xa .pdf

U.S. Depart m ent of Agriculture Soil Surve y Divis i on Staff (USDA SSDS). 1993. Soil

Surve y Man ual. USDA Handbook No. 18. Issue d October 1993. USDA, Washington,

DC. http://soils.usda. gov/technical/manual/



### U.S. Environmental Prot ection Agency. 2007a. Review of “A Retrospective Groundwater Monitoring Study for Aldi carb and Its Metabolites in the

Section I.H – Page 241 of 277

### Southeastern United States (MRID 46793706); Mississippi De lta (MRID 46793705); Texas (MRID 46793704); Calif ornia (MRID 46793703);and Pacific Northwest (MRID 46793702). DP 299866. July 24, 2007.

U.S. Environmental Protec tion Agency. 2007b. Revised carbaryl drinking water assessment including time-series si mulations . DP334714. May 2, 2007.

U.S. Environmental Protection Ag ency. 2007c. Memorandum from Peter

Caulk i ns to Karen Shearer. “Final Use Termination Order for Propoxur Residential Spray Use”. July 31, 2007.

U.S. Environmental Prot ection Agency. 2007d. Refined Methomyl Surface Water Drinking Water Assessment with Time Series and Regional PCA. DP339492. May 16, 2007.

U.S. Environmental Protec tion Agency. 2007e. Tier II Drinking Water Exposur e Assessment for the S e ction 3 New Use Re gistration of Oxamyl on Sugar Beet s. DP337180. Jul. 16, 2007.

U.S. Environmental Prot ection Agency. 2007f. Pesticides: Science and Policy.

Water Mod e ls. http://w ww.epa.gov/oppefed1/models/water/ Last updated Sep. 11, 2007.

U.S. Environmental Protection Ag ency. 2006a. Updated Organophosphorus

Cumulative Risk Assessment. Office of Pesticide Progra m s, US Environmental Protection Agency, Washington, DC. July 31, 2006. http://www.epa.gov/pe sticides/cu m ulative/20 06-op/

U.S. Environmental Protection Ag ency. 2006b. DRAFT Harmonizat i on in Interspecies Extrapolation: Use of BW3/4 as Default Method in Derivation of the

Oral RfD. Risk Asses sment Forum Technical Panel.

U.S. Environmental Protec tion Agency. 2006c. Reregistra tion Eligibility Science Chapter for Aldicarb. Revised Environmental Fate and Effects Chapter.

DP332265. Sep. 27, 2006.

U.S. Environmental Prot ection Agency. 2006d. Revised drinking water exposure

assessment for total aldicarb residues. DP333309. Oct. 23, 2006.

U.S. Environmental Protec tion Agency. 2006e. Carbofuran Interim Reregistration Elig ib ility Decisio n . Interim Risk Manageme n t, Reregistration, and Tolerance

Reassess ment Decision Chapter. Aug. 3, 2006. http://www.epa.gov/op p srrd1/ REDs/carbofuran_ired.pd f

U.S. Environmental Pr otection Agency. 2006f. Fo rmetanate Hydrochloride Interim Reregistration Elig ib ility Decisio n . Summary of Risk Asses sment Chapter. March, 2006.

Section I.H – Page 242 of 277

### U.S. Environmental Prot ection Agency. 2005a. “Estimati on of Cumulative Exposure from N-Methyl Carbamate Pestic ides: A Case Study of 1 0 N-Methyl
Carbamates” Februar y 18. Available: http://www.epa.gov/oscpmont/sap/2005/#feb

U.S. Environmental Protec tion Agency. 2005b. “Preliminary N-Methyl Carbama t e Cumulative Risk Assessment.” Available: http://www.epa.gov/oscpmon t/sap/2005/ind e x.htm#au gust

U.S. Environmental Pr otection Agency. 2005c. Carbofuran Drinking Water Estimated Concentrations. DP306789. Jan. 27, 2005.

U.S. Environmental Protec tion Agency. 2005d. Reregistra tion Eligibility Science Chapter For Carbofuran Environmental Fate And Effects Chapter. DP306788. Jan. 31, 2005.

U.S. Environmental Protec tion Agency. 2005e. Reregistra tion Eligibility Science Chapter for Formetanate HCl

. Revised Environmental Fate and Effe cts Chapter.

DP317676. May 31, 2005.

U.S. Environmental Protection Ag ency. 2004a. “Carbamate Cumulative Assessment Group; Availability”; February 4, 2004. Available: http://www.epa.gov/fedrgstr/EPA-PEST /200 4/February/ Day-04/p2 157.htm

U.S. Environmental Prot ection Agency. 2004b. “A Model Comparison: Dietary and Aggregate Expos u re in Calendex, CARES, and Life line”; April 29 - 30, 2004.

Available: http://www.epa.gov/EPA-PEST /2 004/March/Day-24/p6 570.htm

U.S. Environmental Pr otection Agency. 2003a. Environmental Fate and Ecological Risk Asses sment for the Re-registration of Carbaryl.

U.S. Environmental Protec tion Agency. 2003b. Final Report of Carbaryl EEC’s for Drinking Water, additiona l simulations, D288455, 6/30/2003.

U.S. Environmental Prot ection Agency. 2002a. “Guidance on Cumulative Risk

Assessment of Pesticide Chemic als T hat Have a Common Mechanism of Toxicity”; January 14, 2002. Available: http://www.epa.gov/pe sticides/trac/science/cumulative_ guida nce.p d f

U.S. Environmental Protection Ag ency. 2002b. Revised Organophosphorous

Pesticide Cumulative Risk Asses sment; June 10, 2002. Office of P e sticide Programs, U.S. Envi ronmental Protection Agency. Wa shington, D.C. Available: http://www.epa.gov/pe sticides/cu m ulative/rra-op/

Section I.H – Page 243 of 277

### U.S. Environmental Protec tion Agency. 2002c. “Child-Specific Exposure Factors Handbook (Interim Report)”. Office of Research and Development, National
Center for Environmental Assess ment, U.S. Environmental Protection Agency

. Washingto n , D.C. EPA-600-P- 00 -002B, 2002. Availab l e: http://oaspub.epa.gov /eims/eim scomm.getfil e?p_download_id=36528

U.S. Environmental Pr otection Agency. 2001a. Memorandum from Marcia Mulkey to Lois Rossi. “Implementati on of the Determinations of a Common

Mechanism of Toxicity for N_Methyl Carbamate Pesticides and for Certain

Chloroac etanilide Pes t icides.” July 12, 2001. http://www.epa.gov/op p fead1/cb /csb_page/ updates/carbamate.pdf

U.S. Environmental Pr otection Agency. 2001b. FR Notice OPP-00731: “The Incorporation of Water Treatment Effects on Pesticide Removal and Transformation in Food Quality Prot ection Act (F QPA) Drinking Water Assessments.” November 21, 2001. http://www.epa.gov/op p fead1/trac /science/water_treatment.pdf

U.S. Environmental Protection Ag ency. 2000a. “The Use of Data on

Cholinester a se Inhibition for Ri sk Assessments of Organophosphorous and

Carbamate Pesticides ”; A ugust 18, 2000. Available: http://www.epa.gov/pe sticides/trac/science/cholin.pdf

U.S. Environmental Prot ection Agency. 2000b. Drinking Water Screening Lev el
Assessment. Part A: Guidance for Use of the Index Reservoir in Drinking Water Exposure Assessments; Part B: Applying a Percent Crop Area Adjustment to Tier

2 Surface Water Mod e l Estimates for Pesticide Drink i ng Water Exposure
Estimates. USEPA Office of Pesticide Pr ogra m s (OPP) Pestici de Science Policy. http://www.epa.gov/op p fead1/trac/science/reservoir.pdf

U.S. Environmental Protec tion Agency. 2000c. “Cumulative Risk: A Case Study

of the Esti mation of Risk from 24 Organophosphate Pesticides“ November 9.

Available: http://www.epa.gov/scipoly/sap/20 00/decemb e r/sap_casestudy2.pdf

U.S. Environmental Prot ection Agency. 2000d. “Benchmark Dose Technica l Guidance Document” Draft report. Risk Asse ssment Forum, Office of Research and Development, U.S

. Environmental Protection Agen cy. Washington, DC.

EPA/630/R-00/001.

U.S. Environmental Prot ection Agency. 2000e. Science Advisory Council on Exposure. Policy 3.1. Health Effects Divisio n, Office of Pesticide Programs, U

. S. EPA, Arlin g t on, VA, August 7, 2000.

U.S. Environmental Protection Ag ency. 1999a. “Guidance for Identifying

Pesticide Chemicals and Ot her S ubstances that Have a Common Mechanism of

Section I.H – Page 244 of 277

### Toxicity”; January 29, 1999. Available: http://www.ep a.gov/fedrgstr/EPAPEST/19 9 9 / February/Day-05/60 55.pdf

U.S. Environmental Prot ection Agency. 1999b. Memo randum from Margaret

Stasikowsk i, Health Effects Division to Staff. “Translation of Monitoring Data. HED Standard Operati ng Proc edure 99.3 (3/26/99);” Ma rch 26, 1999. Office of Pesticide Programs, Office of Prevention, Pestici des, and Toxic Substances, Washingto n , DC.

U.S. Environmental Prot ection Agency. 1999c. Estimati ng the Drinking Water Component of a Dietary Ex posur e Asse ssment. USEPA Office of Pesticide Programs (OPP) Pestic ide Scienc e Policy. http://www.e pa.gov/fedrgstr/EPAPEST/19 9 9 / November/Day-10/6044.pdf

U.S. Environmental Protecti on Agency. 1999d. Overview of Issues Related to the Standard Operating Procedures for Resident ial Expos ure Assessment. Presented to the EPA Scientific Advisory Pa nel for the meeting on September 21, 1999. U.S. EPA, Arlington, VA.

U.S. Environmental Protec tion Agency. 1999e. General Pr inciples for Performi ng Aggregate Exposure and Risk As sessments (10/29/99), see http://www.epa.gov/pe sticides /trac/science/aggregate.pdf)

U.S. Environmental Protec tion Agency. 1999f. Reregistra tion Eligibility Science Chapter for Oxamyl. Environmental Fate and Effe cts Chapter. DP224135. Nov

. 9, 1999.

U.S. Environmental Pr otection Agency. 1997a. Ex posur e Factors Handbook (EFH). Offi ce of Research and Developmen t. EPA/600/002Fac.

U.S. Environmental Prot ection Agency. 1997b. Standar d Operating Procedures

for Residen tial Exposu r e A ssessment. Office of Pestic ide Programs, U.S. EPA, Arlington, VA.

U.S. Environmental Protec tion Agency. 1997c. Reregistra tion Eligibility Science Chapter for Methomyl. Environmental Fate and Effe cts Chapter. DP233622. Feb. 4, 1997.

U.S. Environmental Protec tion Agency. 1997d. Reregistra tion Eligibility Science Chapter for Thiodicarb. Environmental Fate and Effects Chapter. Jun. 26, 1997.

U.S. Environmental Protec tion Agency. 1993. Wellhead Protection: A Guide for Communities, EPA/62 5/R-93/002 , U.S. Environmental Protection Agency, Washingto n , DC.

U.S. Environmental Protection Ag ency. 1992. National Home and Garden Pesticide Use Survey, March 1992. Prepar ed by Research Triangle Institute.

Section I.H – Page 245 of 277

### U.S. Environmental Pr otection Agency. 1990. Nonoccupational Pes t icide Exposure Study (NOPES). Final Report. Wash ington, D.C. Office of Research and Development, U.S

. En vironmental Agency.

U.S. Food and Drug Administration. 2007a. Center for Food Safety and Applied Nutrition, Pesticide Re sidue Monit o ring Program. http://vm.cfsan.fda.gov/~lrd/pestadd.html

U.S. Food and Drug Administration. 2007b. Center for Food Safety and Applied Nutrition, Total Diet Study. http://vm.cfsan.fda. gov/~lrd/pestadd.html

U.S. Geological Survey. 2006. Lake Wales Ridge Ground-Water Monitoring Study. USGS Water Resources Investigatio ns Report in preparation. Available: http://fisc.er.usgs.gov/Lake_Wales_Ridge/ Page last modified April 11, 2006.

U.S. Geological Survey. 2004. The National Water Qual ity Assessment (NAWQA) Pesticide National Sy nthesis Project (PNSP). http://ca.water.usgs.go v/pnsp/

U.S. Geological Survey. 1999. Digital dat a sets describing county water use for 1995 in the conterminous US. USGS Open File Report 99-78. http://water.usgs.gov/lookup/ getspatial?ofr99-78_cowu95

U.S. Geological Survey. 1998. Water Use in the United States: USGS Circular No. 1200, accessed July 21, 1998, at URL http://water.usgs.gov/public/watuse/

U.S. Geological Survey. 1997. Ground Wa ter Atlas Of The United States: Delaware, Maryland, New Jersey, North Ca rolina, Pennsylvania, Virginia, West Virginia. HA 730-L. USGS, Office of Gr ound Water, Reston, VA. Last modified

5/15/2005. http://capp.water.usgs.gov/gwa/ch_l/L-text3.html

U.S. Geological Survey. 1990. Ground Wa ter Atlas of the United States: Alabama, Florida, Georgi a, and South Carolina. HA 730-G. USGS, Office of Ground Water, Reston, VA. Last modified 5/06/2001. http://capp.water.usgs.gov/gwa/ch_g/index.html

Vaccaro, J.R. Beard, K.K. Max e y, S.W

. M u rphy, P.G. My ers, C.R. Nolan, R.J. Ormand, J.R. & Timchalk, C. Chlorp yrifos: Exposure to adults and Children upon Re-entry to Domestic Lawns, followin g Treatment with a Chlorpyrifos-based

Granular Insecticide”. Laborat ory Project Study ID HEH2 .1-1-182(134). October 14, 1996. MRID 44167101.

Vinlove FK and Torla R. 1995. “Comprehens ive Estimation of U.S. Home Lawn Area.” Journal of Turfgr ass Management 1(1):83-97.

Section I.H – Page 246 of 277

### Weiler, M. 1995. Development al neurotoxicit y study with aldicarb in rats: Final Report: Lab Project N o . HWI 6224-213. Un publis hed study prepared by Hazleton Wisconsin, Inc. 891 p. MRID 43829601.

Wester RC, and Maibach HI. 1989. "De rmal Decontaminatio n and Percutaneous Absorption. " In: Percutaneous Absorption. 2nd ed. R. Bronaugh and H.I. Maibach, editors. New York : Marcel Dekker, pp 335-342.

WHO (Worl d Health Organization). 2005. Ch emical-specific Adjustment Factors for Inter-species Differences and Human Va riablity: Gui d ance Doc u ment for Use

of Data in dose/Concentration-Res ponse As sessment (IPCS harmonization project document, No. 2). WHO Docu ment Production Services, Geneva,

Switzerla n d

.

Williams CH and Casterline JL , Jr. 1969. “A Compariso n of Two Methods for Measurement of Erythro cyte Cholinesterase Inhibition After Carbamate

Administration to Rats.” Food Cos Toxic o l. 7:149-151.

Winteringham FPW and Fo wler KS. 1966. “Substrate and Dilution Effects on the Inhibition of Acetylcholinesterase by Carbamates.” Biochem J. 101:127-134.

Wyatt, D.R., R.L. Jones, U.B. Handiha lli, and R.E. Lee. 2006. A Retrospective Groundwater Monitoring Study for Aldica rb and Its Metabolites in Mississippi Delta Reg i on. Bayer CropScien ce St udy No. RAAVY0 0 8. MRID 4679370 5.

Wyld, P.; Watson, C.; Nimmo, W.; et al. 1992. A Safety and Tolerability Study of

Aldic a rb at Various Dose Levels in Healthy Male and Female Volunteers: Lab Project Number: 003237. Unpublished st udy prepared by Inveresk Clinica l Research. 372 p. MRID 42373001.

Xue, J, Zartarian, V, Moya , J, Freeman, N, B eamer, P, Black, K, Tulve, N, & Shalat, S. 2007. A Meta-Analysis of Children’s Hand-to-Mouth Frequency Data

for Estimati ng Nondietary Ingestion Exposur e. Risk Analysis, Vol. 27,

. 411-420.

Yasuda, H., Berndtsson R. , Bahri, A. Ji nno, K. (1994) Plot-S cale Transport in a Semiarid Agricultural Soil. So il Sci. Soc. Am. J. 58:1052-1060.

Zartarian VG, Xue J, Driver J, P andi an M, Johnson T. 2003. “Availab l e Microactivity Data and their Applicab ilit y to Aggregate Expos u r e Modelin g.” Presented at Society for Risk Analysis Annua l Meeting 2003: Sess ion M23.5.

Section I.H – Page 247 of 277II.AppendicesA.Summaryof Risk Mitigation for Individual N-Methyl Carbamates23Decision DocumentUse SiteMitigationResidential Uses RemainingALDICARBAlfalfa grown for seedCancelledCoffeeCancelledOrnamentalsCancelledPecansCancelledSorghumCancelledSugarcaneCancelledTobaccoCancelledCottonSoybeanRate ReductionsDry BeansSoybeanSugar BeetSweet PotatoGeographic UseRestrictions09/2007 REDPeanuts500 foot wellsetback andapplication restrictionsNone23This summary only captures those mitigation measures likelyto impact thecumulative riskassessment.It does not includespecific mitigation measures intended toprotect workers (e.g.,REIs, PPE) or to address ecological risks (e.g., spray drift measures,buffer zones for non-target plants).Section II.A.1 - Page 248 of 277Decision DocumentUse SiteMitigationResidential Uses RemainingCARBARYL06/2003 IREDWheatCancelled - tolerance retainedLiquid lawn use is limited tospot treatment only (<1000 ft.);packagedin pint-size ready-touse (RTU) hose-endsprayersFor garden/ornamental dustproductsused onvegetables/ornamentals, allend-use products are to bepackagedin RTU shakercancontainers with≤0.05 lbai/container.Granularproductsare availablefor lawncare (drop orrotaryspreader); for home-garden usegranularproductspackagedinRTUcontainersonly.Broadcast applications of liquidformulationstoturf EXCEPTsod farms, golf courses,commercial landscape areasand cemeteriesCancelledApplicationsby hand, spoon,and belly grinderCancelledPet usesEXCEPT pet collarsCancelledCornGranularandBait FormulationsProhibitedGrain sorghumAlfalfaRiceSunflowersAsparagusRate ReductionsCitrusField CornStone FruitSection II.A.1 - Page 249 of 277Decision DocumentUse SiteMitigationResidential Uses RemainingCARBOFURAN08/2006 IREDAlfalfaNot eligible for reregistration -Import tolerancesretained forbanana, coffee, sugarcaneand riceNoneArtichokeBananaBarleyCoffeeCornCottonCurcubitsGrapesOatsPepperPlantainPotatoSorghumSoybeanSugar BeetSugarcaneSunflowerWheatSpinach for seedCancelled with a 4 year phase outChili PeppersSunflowersArtichokeCucurbits- granularformulationSection II.A.1 - Page 250 of 277Decision DocumentUse SiteMitigationResidential Uses RemainingFORMETANATE HYDROCHLORIDE03/2006 IREDPlumsCancelledNonePrunesCancelledPome FruitApplication RestrictionsStone FruitCitrusOrchard crops- aerialapplicationProhibitedMETHIOCARB03/1994 REDAll food usesCancelledOrnamentalwoody shrubsandvines, household/ dwellingsoutdoor premisesGranularformulaPackage sizerestrictionsMETHOMYL12/1998 REDGrapes*Cancelled in 2007NoneStrawberries**BroccoliReduction in Max. Seasonal RatesCabbageCauliflowerCeleryChinese CabbageCorn, SweetLettuce, HeadTomatoPeachesReduced Max. Single ApplicationRateCommercial sod farmsOXAMYL12/2000 IREDSeed piece dip (yams)CancelledNoneSoybeanSoil broadcast treatment forcottonAll CropsRate ReductionsPIRIMICARB- No Mitigation NecessaryPROPOXURSection II.A.1 - Page 251 of 277Decision DocumentUse SiteMitigationResidential Uses Remaining09/1997 REDIndoorResidential UsesforCrack andCreviceTreatments***Cancelled in 2007Indoor: pastes,tape/strip/patches, baits, andshelf paperOutdoor uses:Structuralperimeter applications, spottreatments to wasp nests andant hill, insecticidal tape forboat mooringlinesTHIODICARB12/1998 REDColecropsReduced Single and SeasonalApplication RatesNone* Voluntary cancellation requestreceivedSeptember 14, 2007.** FR Noticeannouncing receipt of voluntary cancellation published on April 25, 2007 witha 180 day comment periodwhich closes 10/22/07.*** Use deletion finalized onSeptember 10, 2007.Section II.A.1 - Page 252 of 277Section II.A.1 - Page 253 of 277

# B. Hazard

1. Data Spr e adsheet s of the NMC Pestici d es

A CD contatining the data in this append ix may be obtained by contacting the
Office of Pesticide Pr ograms Public Docket

Docket # EPA-HQ-OPP-2007-09 35

One Potomac Yard 12777 S. Crystal Drive

Room S-4400 Arlington, Virginia. 22202

703-305-5805

Monday through Friday, 8:30 am – 4:00 pm

II.B.1 - Pag e 254 of 277

## 2. Dose- Response m o deling of the NMC pesticides

A CD contatining the data in this append ix may be obtained by contacting the
Office of Pesticide Pr ograms Public Docket

Docket # EPA-HQ-OPP-2007-09 35

One Potomac Yard 12777 S. Crystal Drive

Room S-4400 Arlington, Virginia. 22202

703-305-5805

Monday through Friday, 8:30 am – 4:00 pm

II.B.2 - Pag e 255 of 277

## 3. N-M-Carbamat e Dose Response

A CD contatining the data in this appendix may be obtained by contacting the
Office of Pesticide Pr ograms Public Docket

Docket # EPA-HQ-OPP-2007-09 35

One Potomac Yard 12777 S. Crystal Drive

Room S-4400 Arlington, Virginia. 22202

703-305-5805

Monday through Friday, 8:30 am – 4:00 pm

II.B.3 - Pag e 256 of 277

## 4. R-packages wit h file names

A CD contatining the data in this append ix may be obtained by contacting the
Office of Pesticide Pr ograms Public Docket

Docket # EPA-HQ-OPP-2007-09 35

One Potomac Yard 12777 S. Crystal Drive

Room S-4400 Arlington, Virginia. 22202

703-305-5805

Monday through Friday, 8:30 am – 4:00 pm

II.B.4 - Pag e 257 of 277

## 5. Summ ar y AChE protocol evaluations, mixtur e experiments, motor activity measurements

A CD contatining the data in this appendix may be obtained by contacting
the Office of Pesticide Programs Public Doc ket

Docket # EPA-HQ-OPP-2007-09 35

One Potomac Yard 12777 S. Crystal Drive

Room S-4400 Arlington, Virginia. 22202

703-305-5805

Monday through Friday, 8:30 am – 4:00 pm

Section II.B.5 - Page 2 58 of 277

## 6. Phy s iologically -Based Phar macokinetic Modeling for the
NMC CRA

A CD contatining the data in this appendix may be obtained by contacting
the Office of Pesticide Programs Public Doc ket

Docket # EPA-HQ-OPP-2007-09 35

One Potomac Yard 12777 S. Crystal Drive

Room S-4400 Arlington, Virginia. 22202

703-305-5805

Monday through Friday, 8:30 am – 4:00 pm

Section II.B.6 - Page 2 59 of 277

# C. Food

1. The Sour ces of Residue Inpu ts for the Assessment of the
Cumul a tive Di etar y Exposure to N-Methy l Carbm a te
Pesticides on Foods

A CD contatining the data in this append ix may be obtained by contacting
the Office of Pesticide Programs Public Doc ket

Docket # EPA-HQ-OPP-2007-09 35

One Potomac Yard 12777 S. Crystal Drive

Room S-4400 Arlington, Virginia. 22202

703-305-5805

Monday through Friday, 8:30 am – 4:00 pm

Section II.C.1 - Page 260 of 277

## 2. Summary of PDP Residue Analy ses of N-Met h yl Carbam ate
Pesticides on Food Commodit ies Incl uded i n Revised NMC
CRA

A CD contatining the data in this appendix may be obtained by contacting
the Office of Pesticide Programs Public Doc ket

Docket # EPA-HQ-OPP-2007-09 35

One Potomac Yard 12777 S. Crystal Drive

Room S-4400 Arlington, Virginia. 22202

703-305-5805

Monday through Friday, 8:30 am – 4:00 pm

Section II.C.2 - Page 261 of 277

## 3. Proc es si ng Fa ctor s Us e d to Es timat e Resi due s of N- Me thy l
Carbam at e Pesticides in Food Forms*

A CD contatining the data in this appendix may be obtained by contacting
the Office of Pesticide Programs Public Doc ket

Docket # EPA-HQ-OPP-2007-09 35

One Potomac Yard 12777 S. Crystal Drive

Room S-4400 Arlington, Virginia. 22202

703-305-5805

Monday through Friday, 8:30 am – 4:00 pm

Section II.C.3 - Page 262 of 277

## 4. Tra n slati on of Re s i due S our ce D a ta t o FCID Food Form s

A CD contatining the data in this appendix may be obtained by contacting
the Office of Pesticide Programs Public Doc ket

Docket # EPA-HQ-OPP-2007-09 35

One Potomac Yard 12777 S. Crystal Drive

Room S-4400 Arlington, Virginia. 22202

703-305-5805

Monday through Friday, 8:30 am – 4:00 pm

Section II.C.4 - Page 263 of 277

## 5. Summary of Residue Distribu tion Inputs to DEEM- F CID for

the Revi sed NMC CRA

A CD contatining the data in this appendix may be obtained by contacting
the Office of Pesticide Programs Public Doc ket

Docket # EPA-HQ-OPP-2007-09 35

One Potomac Yard 12777 S. Crystal Drive

Room S-4400 Arlington, Virginia. 22202

703-305-5805

Monday through Friday, 8:30 am – 4:00 pm

Section II.C.5 - Page 264 of 277

## 6. Analy s is of Chemi c als and Foods in the U p per Portion of

the Revi sed NMC CRA. Expos ure Distr i bution for Childre n 1-2 Years Old

A CD contatining the data in this appendix may be obtained by contacting
the Office of Pesticide Programs Public Doc ket

Docket # EPA-HQ-OPP-2007-09 35

One Potomac Yard 12777 S. Crystal Drive

Room S-4400 Arlington, Virginia. 22202

703-305-5805

Monday through Friday, 8:30 am – 4:00 pm

Section II.C.6 - Page 265 of 277

## 7. Co- Oc cur renc e of N-Met h y l Carbam at e Pesticides on PDP
Sam p les, 1994-2006

A CD contatining the data in this appendix may be obtained by contacting
the Office of Pesticide Programs Public Doc ket

Docket # EPA-HQ-OPP-2007-09 35

One Potomac Yard 12777 S. Crystal Drive

Room S-4400 Arlington, Virginia. 22202

703-305-5805

Monday through Friday, 8:30 am – 4:00 pm

Section II.C.7 - Page 266 of 277

## 8. Comparis on of DE EM

- F CID versi on 2.03 a n d Lif e line vers ion

4.30 Exposure and Risk Es timates thr o ugh the Food

Pat h way Only

A CD contatining the data in this appendix may be obtained by contacting
the Office of Pesticide Programs Public Doc ket

Docket # EPA-HQ-OPP-2007-09 35

One Potomac Yard 12777 S. Crystal Drive

Room S-4400 Arlington, Virginia. 22202

703-305-5805

Monday through Friday, 8:30 am – 4:00 pm

Section II.C.8 - Page 267 of 277

# D. Residential

1. Residenti a l Pestici d e Use In put s from REJ V S u rvey Data

A CD contatining the data in this appendix may be obtained by contacting
the Office of Pesticide Programs Public Doc ket

Docket # EPA-HQ-OPP-2007-09 35

One Potomac Yard 12777 S. Crystal Drive

Room S-4400 Arlington, Virginia. 22202

703-305-5805

Monday through Friday, 8:30 am – 4:00 pm

Section II.D.1 - Page 268 of 277

## 2. Re sidenti a l Ex pos u re Sce n arios A p pe ndi x

A CD contatining the data in this appendix may be obtained by contacting
the Office of Pesticide Programs Public Doc ket

Docket # EPA-HQ-OPP-2007-09 35

One Potomac Yard 12777 S. Crystal Drive

Room S-4400 Arlington, Virginia. 22202

703-305-5805

Monday through Friday, 8:30 am – 4:00 pm

Section II.D.2 - Page 269 of 277

# E. Drinking Water

1. Summ ar y of Surface Wa ter Monitoring Data for NMC

Pesticides

A CD contatining the data in this appendix may be obtained by contacting
the Office of Pesticide Programs Public Doc ket

Docket # EPA-HQ-OPP-2007-09 35

One Potomac Yard 12777 S. Crystal Drive

Room S-4400 Arlington, Virginia. 22202

703-305-5805

Monday through Friday, 8:30 am – 4:00 pm

Section II.E.1 - Page 2 70 of 277

# E-2 Summary of Ground Water Monitoring Data for NMC

Pesticid es

A CD contatining the data in this appendix may be obtained by contacting
the Office of Pesticide Programs Public Doc ket

Docket # EPA-HQ-OPP-2007-09 35

One Potomac Yard 12777 S. Crystal Drive

Room S-4400 Arlington, Virginia. 22202

703-305-5805

Monday through Friday, 8:30 am – 4:00 pm

Section II.E.2 - Page 2 71 of 277

# E-3 Drinking Water Treatment Effects on N-methyl
Carbamate Pest icides

A CD contatining the data in this append ix may be obtained by contacting the
Office of Pesticide Pr ograms Public Docket

Docket # EPA-HQ-OPP-2007-09 35

One Potomac Yard 12777 S. Crystal Drive

Room S-4400 Arlington, Virginia. 22202

703-305-5805

Monday through Friday, 8:30 am – 4:00 pm

II.E.3 - Pag e 272 of 277

# E-4 N-methyl Carbamate Usage Estimates

T

A CD contatining the data in this appendix may be obtained by contacting
the Office of Pesticide Programs Public Doc ket

Docket # EPA-HQ-OPP-2007-09 35

One Potomac Yard 12777 S. Crystal Drive

Room S-4400 Arlington, Virginia. 22202

703-305-5805

Monday through Friday, 8:30 am – 4:00 pm

II.E.4 - Pag e 273 of 277

# E-5 Chemical-S pecific Fate an d Transport Properties Used For the Water Exposure Models

A CD contatining the data in this appendix may be obtained by contacting
the Office of Pesticide Programs Public Doc ket

Docket # EPA-HQ-OPP-2007-09 35

One Potomac Yard 12777 S. Crystal Drive

Room S-4400 Arlington, Virginia. 22202

703-305-5805

Monday through Friday, 8:30 am – 4:00 pm

Page 274 of 277

# E-6 NMC Surface Water Exposure Assessmen t
Methods

A CD contatining the data in this appendix may be obtained by contacting
the Office of Pesticide Programs Public Doc ket

Docket # EPA-HQ-OPP-2007-09 35

One Potomac Yard 12777 S. Crystal Drive

Room S-4400 Arlington, Virginia. 22202

703-305-5805

Monday through Friday, 8:30 am – 4:00 pm

II.E.6 - Pag e 275 of 277

# E-7 NMC Ground Water Exposure Assessment Methods

A CD contatining the data in this appendix may be obtained by contacting
the Office of Pesticide Programs Public Doc ket

Docket # EPA-HQ-OPP-2007-09 35

One Potomac Yard 12777 S. Crystal Drive

Room S-4400 Arlington, Virginia. 22202

703-305-5805

Monday through Friday, 8:30 am – 4:00 pm

II.E.7 - Pag e 276 of 277

# III. Multi-Pathway Graphs

A CD contatining the data in this section ma y be obtaine d by contacting the Office of Pesticide Programs P ublic Dock et

Docket # EPA-HQ-OPP-2007-09 35

One Potomac Yard 12777 S. Crystal Drive

Room S-4400 Arlington, Virginia. 22202

703-305-5805

Monday through Friday, 8:30 am – 4:00 pm

III - Page 277 of 277