Slide 1 of 168 A Model Comparison:

Dietary ( Food and Water) Exposure in DEEM/ Calendex, CARES, and LifeLine F. Bart Suhre, David Miller, Steve Nako U. S. EPA Health Effects Division Office of Pesticide Programs A Model Comparison:

Dietary ( Food and Water) Exposure in DEEM/ Calendex, CARES, and LifeLine F. Bart Suhre, David Miller, Steve Nako U. S. EPA Health Effects Division Office of Pesticide Programs FIFRA SAP Meeting April 29 30, 2004 FIFRA SAP Meeting April 29 30, 2004 Slide 2 of 168 Model Names Model Names  CARES

Cumulative Aggregate Risk Evaluation System  DEEM

Dietary Exposure Evaluation Model  SHEDS

Stochastic Human Exposure and Dose Simulator  LifeLine and Calendex

Not acronyms Slide 3 of 168 SHEDS SHEDS  SHEDS Wood is a probabilistic model which aggregates oral ( incidental hand to mouth), dermal and inhalation exposure routes  SHEDS Wood was developed by EPA/ ORD and used by OPP to assess Chromium and Arsenic exposure to children from CCA treated wooden play sets and decks

FIFRA SAPs: Aug 2002, and Dec. 2003 Slide 4 of 168 Why wasn't SHEDS considered in this Model Comparison ?

Why wasn't SHEDS considered in this Model Comparison ?

 Development of SHEDS Pesticide, an aggregate model for oral ( dietary and incidental hand to mouth), dermal and inhalation exposure routes was delayed as a result of ORD resources being channeled to the CCA risk assessment  SHEDS Pesticides was recently received by OPP and we plan to include it in future model comparisons Slide 5 of 168 Acknowledgements Acknowledgements  David Miller, EPA/ OPP  Steve Nako, EPA/ OPP  Sheila Piper, EPA/ OPP  Alan Dixon, EPA/ OPP  William Smith, EPA/ OPP  OPP/ HED, DeSAC members

Dietary Exposure Science Advisory Council  Muhilan Pandian, Infoscientific. com Slide 6 of 168 Materials Provided to SAP Materials Provided to SAP  Document entitled:

A Model Comparison: Dietary ( Food and Water) Exposure in DEEM/ Calendex, CARES, and LifeLine. March 31, 2004. Slide 7 of 168 Materials Provided to SAP Materials Provided to SAP  Reference material from past SAP Sessions on DEEM, Calendex,

CARES, and LifeLine  Users Manuals and Technical manuals for DEEM, Calendex,

CARES, and LifeLine Slide 8 of 168 Materials Provided to SAP Materials Provided to SAP  SAS Programs to approximate predictive models ( DEEM/ Calendex, CARES, and LifeLine) estimates  SAS approximations of predictive models ( DEEM/ Calendex,

CARES, and LifeLine) estimates Slide 9 of 168 Materials Provided to SAP Materials Provided to SAP  Questions on:

The impact model design has on estimating dietary exposure

Methodology for testing model design features on exposure estimates Slide 10 of 168 Presentation Outline Presentation Outline  Background

F. Bart Suhre  Goals, objectives and focus of current model comparisons in OPP

F. Bart Suhre  Future model comparisons in OPP

F. Bart Suhre  Probabilistic models and past SAP Reviews

David Miller Slide 11 of 168 Presentation Outline Presentation Outline  Side by Side comparison of dietary results  DEEM and Lifeline

David Miller  Approximating Consumption Distributions for Probabilistic Models using SAS Simulations

Steve Nako  Case Study   DEEM, CARES, and LifeLine Dietary ( food and water)

exposure estimates using a common dataset

David Miller Slide 12 of 168 Presentation Outline Presentation Outline  Summary ( very brief)

F. Bart Suhre Slide 13 of 168 Background ` Legislative Authority'

Background ` Legislative Authority'

 FIFRA and FFDCA authorize EPA to regulate pesticides  In 1996, FQPA amended FIFRA and FFDCA and directed EPA to consider " aggregate exposure" in its decision making Slide 14 of 168 Background ` Guidance ' Background ` Guidance '

 EPA developed Guidance Documents:

Interim Guidance for Aggregate Assessments ( 1997)

Probabilistic Guidance ( 1998)

Aggregate Guidance ( 1999)

Cumulative Guidance ( 2000) Slide 15 of 168 Background ` Models' Background ` Models'

 EPA encouraged development of probabilistic models that permit time based integration of residential and dietary ( food and water) exposures to pesticides  Models were developed and presented to FIFRA SAP by EPA and various model development teams Slide 16 of 168 Background ` Model Implementation'

Background ` Model Implementation'

 Three Basic Criteria:

1) conform to EPA guidance 2) peer review 3) available to all Slide 17 of 168 Goals of EPA Current Model Comparisons Goals of EPA Current Model Comparisons  Better understand design features of probabilistic models currently available for estimating pesticide exposure  Develop methodology to test the impact model design features have on dietary exposure estimates Slide 18 of 168 Objectives of EPA Current Model Comparisons Objectives of EPA Current Model Comparisons  Compare dietary ( food and water)

exposure estimates derived from DEEM/ Calendex, CARES and LifeLine using common data sets  Investigate specific reasons why these exposure estimates might differ Slide 19 of 168 Focus of Today's SAP Focus of Today's SAP  Describe design features of DEEM/ Calendex, CARES, and LifeLine with emphasis on the dietary exposure pathway  Present dietary results from these 3 models  Demonstrate why dietary exposure estimates may differ for each of the three models Slide 20 of 168 Future Model Comparisons in OPP Future Model Comparisons in OPP  Using the dataset discussed today:

We plan to investigate model design features for SHEDS Pesticide and compare its 1 day dietary exposure estimates to the other models already tested

We plan to investigate the impact model design has on estimating exposure for periods > 1 day ( e. g. 7, 14, and 30 days)

for all available pesticide models Slide 21 of 168 Future Model Comparisons in OPP Future Model Comparisons in OPP  We plan to more fully investigate the drinking water model design features and compare exposure estimates for each model using a common dataset  We plan to investigate modeldesign features for key residential pathways and compare residential exposure estimates for each model using a common dataset Slide 22 of 168 Future Model Comparisons in OPP Future Model Comparisons in OPP  We plan to investigate and compare aggregate exposure estimates over all pathways ( food,

water, and residential) for all available pesticide models using a common dataset Slide 23 of 168 Future Model Comparisons in OPP Future Model Comparisons in OPP  We will be performing model comparisons on a routine basis as a result of stakeholders using different models to support pesticide regulatory activity Slide 24 of 168 Future Model Comparisons in OPP Future Model Comparisons in OPP  We will continue to work closely with model development teams to improve their software  Future development priorities:

Standardized formats for data entry

More sophisticated output reports to assess model sensitivity and exposure contributors

Link exposure outputs to Physically Based Pharmacokenetic ( PBPK)

models Slide 25 of 168 Probabilistic models and past SAP Reviews Side by Side comparison of dietary results  DEEM and Lifeline Probabilistic models and past SAP Reviews Side by Side comparison of dietary results  DEEM and Lifeline David J. Miller David J. Miller  Background  Goals, objectives and focus of current model comparisons in OPP  Future model comparisons in OPP  Probabilistic models and past SAP Reviews  Side by Side comparison of dietary results  DEEM and Lifeline  Approximating Consumption Distributions for Probabilistic Models using SAS Simulations  Case Study  Summary Presentation Roadmap Slide 26 of 168  Background  Goals, objectives and focus of current model comparisons in OPP  Future model comparisons in OPP  Probabilistic models and past SAP Reviews  Side by Side comparison of dietary results  DEEM and Lifeline  Approximating Consumption Distributions for Probabilistic Models using SAS Simulations  Case Study  Summary Presentation Roadmap Slide 27 of 168 Slide 28 of 168 Software Models Software Models  A number of software models are now available which allow probabilistic aggregation of exposures through multiple pathways and routes

Lifeline

CARES

DEEM/ Calendex

SHEDS Slide 29 of 168 Past SAP Reviews Past SAP Reviews  DEEM/ Calendex

September, 2000: DEEM

February 2000: Calendex  CARES

April 2002  Lifeline

September 1999: Design Concepts and Theory

September 2000: A Case Study.

March 2001: Review of Lifeline v. 1.0  SHEDS Wood

August, 2002

December, 2003: CCA Wood Preservatives Slide 30 of 168 Introduction and Purpose Introduction and Purpose  This presentation will provide a brief introduction and review of three models being considered in this SAP  Purpose is to review general background information on each of the models and how they operate in order to provide overall context for subsequent presentations involving more detailed model investigations Slide 31 of 168 Presentation Outline Presentation Outline For each model:

Brief Developmental History

Overview/ Framework of Model  Base Reference Population  " Binning"

 Model ( Sampling) Weights  Body Weights Slide 32 of 168 Version 2.0 Version 2.0 Slide 33 of 168 Brief Developmental History Brief Developmental History  Purpose: to create publicly accessible risk assessment software for a wide range of anticipated users  Developed by Hampshire Research during 1998   2000 under Cooperative Agreement with EPA and USDA  Transferred to The Lifeline Group in December, 2000  Made publicly available on Dec. 15, 2000  Currently in Version 2.0

Released October, 2002  Website:

www. thelifelinegroup. org Slide 34 of 168 The LifeLine   Model The LifeLine   Model  Probabilistic ( Monte Carlo) model of aggregate exposure to pesticides that occurs to each member of a simulated population of individuals  Models each potentially exposed individual within a population as an individual

Attempts to define each simulation of an individual using " transition rules" Slide 35 of 168 The LifeLine   Model The LifeLine   Model  Uses data from well known surveys to generate and evaluate specific daily exposures for individuals

Uses available databases to address each component of simulation

Combined datasets allow model to define aggregate exposure for each day of an individual's life

Birth

Growth and aging

Movement from home to home and region to region

Extent of pesticide use

Daily activity patterns and daily dietary patterns Slide 36 of 168 Transition Rules Transition Rules  Used to model changes in values of exposure factors over time  Ensure that selections from multiple databases are consistent and scientifically defensible

Ensure consistency with prior values of a factor

Ensure consistency with values of other factors

Determine when a value should change and when should remain the same  Accounting for Correlations

Record based modeling

Contingent modeling Slide 37 of 168 Transition Rules Transition Rules  Characteristics of individuals generated by Lifeline are:

Fixed Characteristics

Include sex, race/ ethnicity, birth date, body type,

maternal SES

Characteristics that very slowly over time

Long term trends/ progressions ( weight, height,

residence location)

Episodic/ non periodic State changes ( room sizes,

pest pressures, etc)

Cyclic ( periodic) state changes ( weekday vs.

weekend, dietary residues, residential pesticide use etc.

Characteristics that vary day to day

Ephemeral ( activity patterns, consumption, residues) Conception and Birth Childhood Retirement Career Home ownership Child raising Modeling A Person's Life Source: HRI SAP Presentation Slide 38 of 168 Conception and Birth Childhood Retirement Career Home ownership Child raising Product A Product B Product C Source: Modified from HRI SAP Presentation Modeling A Person's Lifetime Exposures To One Active Ingredient Slide 39 of 168 Source: HRI SAP Presentation Slide 40 of 168 Repeating the Process Yields a Model of the Exposed Population Slide 41 of 168 Basic Steps in LL Analysis Basic Steps in LL Analysis  STEP 1: Creating and Modeling Representative Individuals

Assigning fixed and initial characteristics

Modeling individual growth  STEP 2: Assigning Physiological and Residential Characteristics  STEP 3: Assigning Exposure Related Behaviors

Residential

Food/ Water Slide 42 of 168 STEP 1: Creating and Modeling Representative Individuals STEP 1: Creating and Modeling Representative Individuals  Assigning Fixed and Initial Characteristics

Begin with a national survey of fixed and initial characteristics

Natality Database

Serves as Lifeline's " Reference Population"

Determines Lifeline's Model ( Sampling) Weight

Two option for Mortality

Based on standard life tables ( NCHS Vital Statistics of the U. S.)

Mortality fixed after at 85 years Slide 43 of 168 STEP 1: Creating and Modeling Representative Individuals STEP 1: Creating and Modeling Representative Individuals  Modeling Individual Growth

LifeLine uses a anthropometric model of the patterns of change in physiology based on NHANES III data

Each year the physiology of the individual is updated Slide 44 of 168 STEP 2: Assigning Physiological and Residential Characteristics STEP 2: Assigning Physiological and Residential Characteristics  Once the general description of a residence is determined:

AHS database of specific homes is used to define the residential characteristics

American Gardening Survey ( AGS) and National Home and Garden Pesticide Use Survey ( NHGPUS) used to evaluate other characteristics  US census tracks mobility in a series of multiple and partially overlapping databases

Each year the mobility of an individual is modeled in binary decision Slide 45 of 168 STEP 3: Assigning Exposure Related Behaviors STEP 3: Assigning Exposure Related Behaviors  Once the residence, ethnicity, age, gender and race are defined for an individual at any given point in time, these data can be used to guide the selection of data on

Activities/ locations ( NHAPS)

Pesticide use ( NHGPUS)

Daily diets ( CSFII)

 These predictive factors are usually determined by selecting records from individuals that match the modeled individual

" Binning" Lifeline Binning Lifeline Binning ° Identify key parameters related to exposure and determine natural groupins based observed patterns Slide 46 of 168 Slide 47 of 168 Lifeline Dietary Assessment and CSFII Lifeline Dietary Assessment and CSFII  Lifeline uses USDA's CSFII ( Continuing Survey of Food Intake by Individuals) as a base data set for the dietary component of its assessment

Nationally Representative/ Statistically

Based  CSFII reports foods " as eaten" on an individual basis.

200 g apple pie  not apples, wheat,

soybean oil, etc. Slide 48 of 168 CSFII CSFII  Lifeline uses residue data on individual components of food to build residue distributions on foods " as eaten" using recipe file ( aka 100 g file)

Distribution of residues on apple pie " built" from residue distribution in apples, wheat, sugar, etc. Frequency Chart .000 .031 .062 .092 .123 0 30.75 61.5 92.25 123 0.00 0.33 0.65 0.97 1.30 1,000 Trials 1,000 Displayed Forecast: Apple Residues Frequency Chart .000 .021 .042 .063 .084 0 21 42 63 84 0.00 0.01 0.03 0.04 0.05 1,000 Trials 978 Displayed Forecast: wheat residues Frequency Chart .000 .007 .015 .022 .029 0 7.25 14.5 21.75 29 0.00 0.00 0.01 0.01 0.01 1,000 Trials 983 Displayed Forecast: sugar residues Forecast: Residues in Apple Pie 0 10 20 30 40 50 0.00 0.03 0.05 0.08 0.10 Frequency Estimating Residue Distribution in Apple Pie Slide 49 of 168 Slide 50 of 168 Lifeline Dietary Consumption Lifeline Dietary Consumption  Dietary Bins created to reflect groupings based on explicitly identified eating patterns/ similarities

age x season  Each individual's daily food consumption in LL is drawn from a similar individual ( age x season) from the CSFII

Daily eating patterns " built up" for each day in an individual's lifetime

Uses matched individual's intake ( grams food) direction

No " normalization" for bodyweight Slide 51 of 168 Exposure through Tap Water Exposure through Tap Water  Water Consumption data obtained from CSFII

Direct and indirect water

Tap, bottled, Other, Missing  Tapwater concentrations entered by user

Matched to each residence through AHS by

Census region

Type of water supply ( public or private system,

well, or other)

Setting ( urban vs. rural)

Season Slide 52 of 168 Exposure through Tap Water Exposure through Tap Water  Census Region ( 4)

Northeast, South, West, Midwest  System Type ( 3)

Public or Private Water system, Well,

Other  Season ( 4)

Spring, Summer, Fall, Winter  Setting ( 2)

Rural, Urban ° User can enter up to 96 different water concentration distributions ° Each distribution entered as cumulative distribution in Lifeline Slide 53 of 168 Exposure through Tap Water Exposure through Tap Water  Each house is then randomly assigned a percentile

Corresponding water concentration value for that region, water supply type, season and setting is selected  Upper percentile households remain in upper percentile and vice versa  Same 4 season specific residue values repeated each year that the individual resides in the home Slide 54 of 168 Characterizing Exposure Characterizing Exposure  On each day of an individual's life the model has defined:

Physiology ( weight, skin surface area, inhalation rates)

Residence ( region, setting, room sizes, air exchange rates, type of floor surface, and presence of a lawn, pets, or garden)

Daily activities, room locations, and dietary consumption

Pesticide residue levels in foods, tapwater, on surfaces, and in air  Using this data the model calculates route specific exposures and doses  These routes specific doses are use to estimate the the total aggregate ( systemic dose) Slide 55 of 168 Lifeline Outputs Lifeline Outputs  Lifeline determines exposure history for each individual on each day of a person's exposure simulation period  Lifeline saves only a portion of the data collected for each individual. For each season, Lifeline saves

Seasonal average

Maximum day

Random Day  These are saved for each routespecific and source specific exposure Seasonal Data and Report Data 0 0.2 0.4 0.6 0.8 1 1.2 Days in Spring Exposure Value 1 Day Mean1 Max 1 Slide 56 of 168 Cumulative and Aggregate Risk Evaluation System Cumulative and Aggregate Risk Evaluation System Version 2.0 SRS 040401 CARES Slide 57 of 168 Slide 58 of 168 Brief Developmental History Brief Developmental History  Purpose: CARES is a software tool for performing Aggregate and Cumulative Exposure/ Risk Assessments  Developed under contract with member companies of Crop Life America  First made publicly available on April, 2002  Transferred to ILSI's Risk Science Institute in January, 2004  Currently in Version 2.0

Released January, 2004

QA testing  Website:

http:// www. infoscientific. com/ software_ main. htm# cares CARES Reference Population CARES Reference Population  CARES uses a Population Generator to develop a Reference Population ( RP)

Selected as probability sample of 100,000 individuals of Census/ 5% PUMS Stratified so as to support analysis of upper percentiles of exposure for subgroups of interest

Race/ Ethnicity and Age/ Gender RP appropriately re weighted to be similar to U. S. Census Reference Population:

5% PUMS Population:

Total 1990 Census: 241,000,000 12,000,000 100,000 ( 0.04% of Census ) Slide 59 of 168 Slide 60 of 168 CARES Reference Population CARES Reference Population  U. S. Census/ PUMS data contains many descriptive variables of interest for each person in RP. However, PUMS data is insufficient to completely model an individual's exposure

No data on

Dietary consumption

Human activity patterns

Pesticide Use patterns

Etc.

 Necessary to match individuals in Census with individuals in other databases

  but it is not possible to make an " exact"

match Slide 61 of 168 CARES Reference Population CARES Reference Population  Matching between individuals in RP and those in other ancillary databases ( e. g., CSFII, NHAPS,

etc.) done based on distance or dissimilarity measures on critical parameters ( e. g., region, age,

gender, etc.) Slide 62 of 168 CARES Reference Population CARES Reference Population  General Matching strategy:

Determine how similar each sampled Census individual person is to each person in the the auxilliary database of interest, and match based on this similarity measure

Similarity measure calculated using relevant characteristics that each database has in common ( Gower Dissimilarity Index)

Certain parameters receive higher priority in matching than others, depending on characteristic of interest. For example, for food consumption

Always exactly match on gender

Match very closely on age for infants, children

Match less closely on age for youth

No match for adults (> 19 y. o.) Slide 63 of 168 Dietary Matching Dietary Matching

For initial RP CSFII match, match is determined based on characteristics important in determining food consumption

Age/ gender

Race/ ethnicity

Region

CSFII data used to determine body weight,

nursing/ pregnancy status, and one/ two days food consumption

Combine data from PUMS and CSFII to create an individual that is both realistic and more " complete" Slide 64 of 168 Dietary Profile Dietary Profile  CARES next creates a 365 day consumption profile

Begins with 1 or 2 days present in CSFII

Uses statistical matching ( Gower Dissimilarity Index) to match similar individuals and develop consumption diaries as " fill in" for remaining days of year

Priority matching on: gender, pregnancy status, age in months for infants, age in years for youth, etc.

To preserve seasonal nature of consumption data, attempt to match +/ 7 days Slide 65 of 168 Slide 66 of 168 Residential Exposures Residential Exposures  Residential Factors are assigned to each individual based on U. S. Census, AHS, etc.

E. g, homeowner status, urban vs. rural, housing type, presence of lawn or garden, pets, # rooms,

AERs, etc.

 A product use profile is constructed to assign product use through event allocator

Considers product use surveys, label information,

PPIS, NHGPUS, ORETF, REJV databases  Exposures for each time step ( e. g. calendar day) and activity/ pathway are calculated

Initial media concentrations, degradation rates,

transfer coefficients, activity patterns/ duration,

absorption, etc. Slide 67 of 168 CARES Outputs CARES Outputs  Broad Range/ Selection of Available Output Options

Total daily exposures, by person day  Rank order from high to low

Total daily exposures, by person day and food Slide 68 of 168 Slide 69 of 168 CARES Output CARES Output  Contribution Analysis

Allows the assessor to evaluate those commodities which are significant contributors to the highend exposures

Useful for risk management purposes for determining which commodities which, when removed from the assessment, would result in the largest decreases in high end exposures CARES: CEC Report CARES: CEC Report Slide 70 of 168 Slide 71 of 168 Dietary Exposure Evaluation Model Dietary Exposure Evaluation Model Slide 72 of 168 Brief Developmental History Brief Developmental History  Purpose: DEEM and DEEM/ Calendex are software tools for performing Aggregate and Cumulative Exposure/ Risk Assessments  Developed by Durango Software and originally licensed under Novigen Sciences,

Inc. Licensing now being done by Exponent,

Inc.

 Currently in Version 8

Released March, 2000  Website:

http:// www. exponent. com/ practices/ foodchemical/ Slide 73 of 168 DEEM DEEM  DEEM's base population is derived from USDA's CSFII

Limited to individuals with two ( both) days of consumption  DEEM uses MC techniques to combine crop residue concentrations with each individual's reported daily food consumption  Result is a distribution of 1 day acute dietary exposures across a population Slide 74 of 168 DEEM DEEM  Calendex is an available " add in"

to DEEM

Permits aggregation and cumulation of water and residential exposures which incorporate temporal sequence of events Slide 75 of 168 DEEM Outputs DEEM Outputs  Provides Exposure and associated risk estimates for each subpopulation of interest at each percentile of interest  Provides Commodity Exposure Contribution Analysis which permits evaluation of those commodities which are significant contributors to the highend exposures DEEM Output DEEM Output Slide 76 of 168 Slide 77 of 168 Side by side comparisons of exposure estimates Side by side comparisons of exposure estimates David J. Miller David J. Miller  Background  Goals, objectives and focus of current model comparisons in OPP  Future model comparisons in OPP  Probabilistic models and past SAP Reviews  Side by Side comparison of dietary results  DEEM and Lifeline  Approximating Consumption Distributions for Probabilistic Models using SAS Simulations  Case Study  Summary Presentation Roadmap Slide 78 of 168  Background  Goals, objectives and focus of current model comparisons in OPP  Future model comparisons in OPP  Probabilistic models and past SAP Reviews  Side by Side comparison of dietary results  DEEM and Lifeline  Approximating Consumption Distributions for Probabilistic Models using SAS Simulations  Case Study  Summary Presentation Roadmap Slide 79 of 168 Slide 80 of 168 DEEM vs. Lifeline Comparisons DEEM vs. Lifeline Comparisons  OPP has conducted comparisons among models to serve as QA checks

DEEM vs. Lifeline Comparison

As part of standard procedures, OPP has run as part each review both DEEM and Lifeline in parallel to compare results

Represent actual assessments performed in OPP

Standard 1000 ppm single commodity file

1000 ppm input residue distribution for single commodity will produce distribution equivalent to consumption

Resulting predicted " exposures" correspond exactly to consumption values Slide 81 of 168 DEEM vs. Lifeline Comparisons DEEM vs. Lifeline Comparisons  DEEM vs. Lifeline Comparison

Generally, OPP has found close agreement between DEEM and Lifeline results at percentiles of interest

Typically, differences have been  Less than 3% at 95th percentile  Less than 10% at 99th percentile  Less than 20% at 99.9th percentile " Exposure" Comparison   1000 ppm Fresh Tomato " Exposure" Comparison   1000 ppm Fresh Tomato 1.13 1.33 1.32 95 2.30 2.47 2.90 99 5.35 6.69 6.26 99.9 Adults 20 49 1.17 1.13 1.11 95 3.58 3.57 4.02 99 9.10 10.82 10.45 99.9 Children 3 5 DEEM CARES Lifeline % ile AGE GROUP Slide 82 of 168 Slide 83 of 168 Approximating Consumption Distributions for Probabilistic Models using SAS Simulations Approximating Consumption Distributions for Probabilistic Models using SAS Simulations Steve Nako Steve Nako  Background  Goals, objectives and focus of current model comparisons in OPP  Future model comparisons in OPP  Probabilistic models and past SAP Reviews  Side by Side comparison of dietary results  DEEM and Lifeline  Approximating Consumption Distributions for Probabilistic Models using SAS Simulations  Case Study  Summary Presentation Roadmap Slide 84 of 168  Background  Goals, objectives and focus of current model comparisons in OPP  Future model comparisons in OPP  Probabilistic models and past SAP Reviews  Side by Side comparison of dietary results  DEEM and Lifeline  Approximating Consumption Distributions for Probabilistic Models using SAS Simulations  Case Study  Summary Presentation Roadmap Slide 85 of 168 Slide 86 of 168 Overview Overview  Introduction  Framework for Model Comparison  Simple Example  SAS Model Approximations Slide 87 of 168 Introduction Introduction  Model Comparisons to Date:

Same inputs, same outputs   most of the time  The Olive Example Slide 88 of 168 Introduction Introduction  Dietary Exposure Models are not   or at least should not be  black boxes  Basic information  CSFII Food Diaries and FCID Recipes  are publicly available to know what people eat Slide 89 of 168 Overview Overview  Introduction  Framework for Model Comparison

Model Comparison Framework

Outputs   Calculating a 99.9th percentile

Inputs:

Food Diaries

Food Recipes ( Supplemental Slides)

 Simple Example  SAS Model Approximations Framework for Dietary Exposure Models Framework for Dietary Exposure Models Model Weight RefPop Bodyweight ( BSA)

Binning Methodology ( Consumption Diaries)

Reference Population Model Design Components ° The Reference Population is a ` sample' from the Modeled Population ( I. e., U. S. Population)

° The Reference Population and the Binning Methodology determine the Model's expected frequency of using each of the CSFII diaries ° The Model Weights project the simulated exposure days to the modeled population ° The bodyweights for the Reference Population may affect exposure if these bodyweights are used to determine consumption ( Lifeline)

Exposure Distribution for Modeled ( US) Population Slide 90 of 168 Model Comparison Framework Model Comparison Framework Equal Weights ( Random)

CARES ( Stratified)

CSFII Sampling Weights Model Weight US Population ( Natality, Mortality)

US Population US Population Modeled Population CSFII normalized Gower Dissimilarity Index Census ( PUMS) CARES Lifeline ( NHANES)

CSFII normalized RefPop Bodyweight Random ( Age, Season)

Random ( 2 Day Diaries)

Binning Methodology Natality ( NCHS)

CSFII Survey ( 2 Day)

Reference Population Lifeline Calendex Model Design Components Slide 91 of 168 Outputs: Comparing Upper Percentile of Projected Population Outputs: Comparing Upper Percentile of Projected Population Slide 92 of 168 Slide 93 of 168 Outputs: Subpopulations of Interests Outputs: Subpopulations of Interests  Subpopulations include:

Infants

Toddlers ( 1 2, 3 5 years old)

Youths ( 6 12)

Teenagers ( 13 19)

Adults ( 20 49, 50+)

Females of Childbearing Age ( 13 49)

 Addressing Acute ( 1 Day) Exposure  Not Addressing Subchronic, Chronic and Lifetime Exposure Inputs: Food Diaries USDA Consumption Survey Food Intake Individuals ( CSFII, 1994 98)

Inputs: Food Diaries USDA Consumption Survey Food Intake Individuals ( CSFII, 1994 98)

Further information is available at the web site:

www. barc. usda. gov/ bhnrc/ foodsurvey Slide 94 of 168 Features of Aggregate Exposure Models Features of Aggregate Exposure Models No  No, but OK to use DEEM CEC  Acute CEC Critical Exposure Commodities CSFII ( soon to have FCID)

FCID ( pretty recent)

FCID ( recent version)

FCID ( recent version)

Food Recipe  Seasonal Tables    % aPAD at Upper Percentile   Seasonal CDF  Lifeline No  w/ o Temporal  DEEM   Calendar Day  CARES  Residential  Calendar Day Water  Food Calendex Source /

Output Slide 95 of 168 Food Diaries & Food Recipes Food Diaries & Food Recipes RAC Residues Food Diaries Recipe Slide 96 of 168 Food Diaries & Food Recipes Food Diaries & Food Recipes Calendex/ CARES RAC Residues ` RAC' Diaries Food Diaries Recipe DEEM Calendex & CARES use the Food Recipes to convert Food diaries into RAC diaries.

These models perform the Dietary Risk Assessment and Critical Exposure Commodity ( CEC) Analyses on RACs ( e. g., Beef, Lettuce, Tomato fresh, Tomato proc, Wheat, etc.). Slide 97 of 168 Food Diaries & Food Recipes Food Diaries & Food Recipes LifeLine Lifeline uses the Food Recipes to convert RAC residues into Food residue distributions. The Lifeline model performs the Dietary Risk Assessment on Foods ( e. g., Hamburgers).

` Food' Residues RAC Residues Food Diaries Recipe Slide 98 of 168 Food Diaries & Food Recipes Food Diaries & Food Recipes LifeLine Calendex/ CARES ` Food' Residues RAC Residues ` RAC' Diaries Food Diaries Recipe Lifeline use the food recipes differently than Calendex & CARES, and also has a slightly different recipe. Models may provide different predictions due to current use of different recipes. Slide 99 of 168 Slide 100 of 168 Overview Overview  Introduction  Model Comparison Framework  Simple " 10 Diary" Example

Baseline Model: Equal Contribution from Diaries ( L2)

Different Model Weights ( D1, C1)

Stratified Sampling and/ or Frequency of Use

Lifeline Bodyweights ( L1)

Lifeline CSFII Option ( L3)

Estimating Exposure from SingleRAC Consumption Approximations

Estimating Exposure with Multiple Commodities  SAS Model Approximations Illustration: Apple Consumption Illustration: Apple Consumption This ` baseline' assumes that the ten food diaries all have the same expected contribution to the consumption distribution for this subpopulation. Slide 101 of 168 Distribution of Apple Consumption ( L2)

( 10 hypothetical 3 to 5 yr olds)

Distribution of Apple Consumption ( L2)

( 10 hypothetical 3 to 5 yr olds) Slide 102 of 168 Distribution of Apple Consumption ( D1, C1)

Variable Modeling Weights and/ or use Frequencies Distribution of Apple Consumption ( D1, C1)

Variable Modeling Weights and/ or use Frequencies Slide 103 of 168 Variable Contributions from Diaries ( D1 C1)

Different Model Weights, and/ or use Frequencies.

Variable Contributions from Diaries ( D1 C1)

Different Model Weights, and/ or use Frequencies. Slide 104 of 168 Illustration: Apple Consumption Illustration: Apple Consumption Applying Lifeline's modeled bodyweights to all diaries ( NHANES based regression for 12 groups ( gender race ethnicities). Slide 105 of 168 Accounting for Lifeline Bodyweights Accounting for Lifeline Bodyweights Shaded cells indicate synthesized diaries Slide 106 of 168 Variability due to Lifeline Bodyweights ( L1)

Variability due to Lifeline Bodyweights ( L1)

Slide 107 of 168 Variability with Lifeline Bodyweights ( L1)

Variability with Lifeline Bodyweights ( L1)

Slide 108 of 168 Comparing Model Consumption Approximations ( D1 C1, L1)

Comparing Model Consumption Approximations ( D1 C1, L1) Slide 109 of 168 Lifeline CSFII Dietary Bin Option Lifeline CSFII Dietary Bin Option Synthesized diaries Weights adjusted to reflect expected frequency of drawing diaries using CSFII option in Lifeline Slide 110 of 168 Comparing Model Consumption Approximations Comparing Model Consumption Approximations Using CSFII option in Lifeline ( L3), has the effect of weighting each diary more similar to Calendex ( D1). The Lifeline bodyweights may continue to cause differences in model predictions. Due to these two effects, this option may not provide an estimate that is closer to Calendex. Slide 111 of 168 Dietary Exposure = Consumption x Residues Dietary Exposure = Consumption x Residues Approximating Consumption Approximating Residues Approximating Dietary Exposure from Apples In this simple ` SingleRAC' scenario ( single residue = 1000 ppm, PCT= 100%), the distribution of exposure is equal  in value  to the consumption distribution for that subpopulation Slide 112 of 168 Accuracy of Model Approximations:

Fresh Tomatoes ( 1000 ppm, PCT= 100%)

Accuracy of Model Approximations:

Fresh Tomatoes ( 1000 ppm, PCT= 100%)

3to5 Yr Olds Age Group 1.02 1.10 1.09 1.13 1.17 1.11 95 10.84 9.14 11.48 10.82 9.10 10.45 99.9 CARES ( C1)

DEEMCalendex ( D1)

Lifeline ( L1)

CARES DEEMCalendex Lifeline SAS Approximations Actual Model Perce ntile Slide 113 of 168 Accuracy of Model Approximations:

Fresh Tomatoes ( 1000 ppm, PCT= 100%)

Accuracy of Model Approximations:

Fresh Tomatoes ( 1000 ppm, PCT= 100%)

20to4 9 Yr Olds Age Group 1.07 1.31 0 1.33 1.13 1.32 95 4.77 6.19 10.65 6.69 5.35 6.26 99.9 CARES ( C1)

DEEMCalendex ( D1)

Lifeline ( L1)

CARES DEEMCalendex Lifeline SAS Approximations Actual Model % ile Slide 114 of 168 Approximating Dietary Exposure with Residue Data Approximating Dietary Exposure with Residue Data Approximating Consumption Anticipated Residues Approximating Dietary Exposure from Apples The exposure distributions are harder to anticipate with more typical residue scenarios. Slide 115 of 168 Approximating Exposure with Complex Residues Approximating Exposure with Complex Residues Slide 116 of 168 Estimating Exposure with Multiple Commodities Estimating Exposure with Multiple Commodities SAS utility uses SingleRAC consumption approximations to estimate exposure at some upper ( 99.9th) percentile when pesticide is used on multiple commodities Slide 117 of 168 Estimating Exposure Estimating Exposure We estimate exposure at the 99th% to be 0.04 mg ai/ kg bwt; and calculate apples contributing 51%, bananas 32%, and olives contributing 17% of the Total exposure for this upper 1 percentile Slide 118 of 168 Slide 119 of 168 Notes on Applying SingleRAC Approximations Notes on Applying SingleRAC Approximations

Initial intent of SAS utility was to provide Lifeline user with a quick (~ 1 minute) ` a priori' estimate of exposure at 99.9th, as well as some idea of the Critical Commodities contributing at this upper percentile

Assume that simulated exposure days at the upper percentile obtain exposure from primarily one commodity

This approach underestimates aggregate dietary exposure since it does NOT account for co occurrent exposure from multiple commodities. We need to run the probabilistic ( Monte Carlo) risk assessment model to properly account for aggregate dietary exposure Slide 120 of 168 Notes on Applying SingleRAC Approximations Notes on Applying SingleRAC Approximations  If a Single Commodity provides exposure exceeding the aPAD to some expected percent of the population (> 0.1%), then the probabilistic models will also exceed the aPAD at the corresponding upper percentile ( 99.9th)

 A pesticide with 100 uses is a pesticide with 100 stories Slide 121 of 168 Overview Overview  Introduction  Model Comparison Framework  Simple Example  Constructing SAS Model Approximations for Calendex,

CARES and Lifeline Slide 122 of 168 Why Approximate Models?

Why Approximate Models?

 We highlighted several design components ( Reference Population,

Binning Method, Model Weights and Bodyweights) that explain how these models work  We developed the SAS model approximations to help us identify the effects of each design component  Hypothetical model approximations were also developed ( e. g., L2) to help us control for other factors while focusing on the effect of a specific component Approximating Calendex Approximating Calendex CSFII Sampling Weights Model Weight US Population Modeled Population CSFII normalized RefPop Bodyweight Random ( 2 Day Diaries)

Binning Methodology CSFII Survey ( 2 Day)

Reference Population Calendex Model Design Components Calendex constructs a 365 day consumption profile by randomly drawing from the 2 daily diaries that the 20,607 individuals provided. We can use the CSFII sampling weights to approximate the contribution from each diary since all diaries have the same expected frequency of use.

Since both Calendex and DEEM apply the CSFII sampling weights to project simulated exposure days to the US Population, the CEC utility from the DEEM can be used for Calendex. Slide 123 of 168 Calendex: Dietary Bin Calendex: Dietary Bin CSFII Diaries 20,607 2 1 RefPop Person 2,603 31,887 74,723 CSFII Weight Calendar Day xx 02 1 xx 02 1 xx 02 2 xx 02 2 xx 02 1 xx 02 1 365 5 4 3 2 1 xx 01 2 xx 01 1 xx 01 2 xx 01 1 xx 01 2 xx 01 1 xx 02 1 xx 02 2 xx 02 1 xx 02 2 xx 02 1 xx 02 2 . Since all diaries have the same expected frequency of use, the Model Weights reduce to CSFII sampling weights Slide 124 of 168 DEEM Calendex: Model ( CSFII) Weights DEEM Calendex: Model ( CSFII) Weights There is considerable variation in the CSFII sampling weights within age groups. Slide 125 of 168 Approximating Calendex ( D1)

Approximating Calendex ( D1)

Slide 126 of 168 Approximating Calendex ( D1, D2)

Approximating Calendex ( D1, D2)

Slide 127 of 168 Approximating CARES Approximating CARES CARES ( Stratified)

Model Weight US Population Modeled Population CSFII normalized Gower Dissimilarity Index Census ( PUMS) CARES RefPop Bodyweight Binning Methodology Reference Population Model Design Components CARES constructs a 365 day consumption profile by randomly drawing food diaries from a bin constructed by use of a Gower Dissimilarity Index.

Since the CARES reference population is a stratified sample from the US Census   PUMS, the CARES'

model weights are needed to project each simulated person day to the modeled population.

Since the Food Match Table provides the actual sequence of CSFII diaries used in each CARES run, it is not necessary to calculate the expected number of times that each diary will be used by CARES. Slide 128 of 168 CARES: Dietary Bin CARES: Dietary Bin CARES: Dietary Bin CSFII Diaries 100,000 2 1 RefPop Person 2,603 31,887 74,723 CARES Weight Calendar Day xx 02 1 xx 02 1 xx 02 2 xx 02 2 xx 02 1 xx 02 1 365 5 4 3 2 1 xx 01 2 xx 01 1 xx 01 2 xx 01 1 xx 01 2 xx 01 1 xx 02 1 xx 02 2 xx 02 1 xx 02 2 xx 02 1 xx 02 2 CARES ( bin) use a Gower Dissimilarity index to create bins from which CSFII diaries were selected for each of the 100,000 individuals in the Reference Population.

For each day, the CARES model weights sum to 241,343,436. Slide 129 of 168 CARES Variation in Frequencies of using CSFII diaries CARES Variation in Frequencies of using CSFII diaries 0 5,000 10,000 15,000 20,000 25,000 Count Infants 1to2 3to5 6to12 13to19 20to49 50plus Slide 130 of 168 Variation in CARES Model Weights Variation in CARES Model Weights Slide 131 of 168 Approximating CARES ( C1)

Approximating CARES ( C1) Slide 132 of 168 Approximating Lifeline Approximating Lifeline Equal Weights ( Random)

Model Weight US Population ( Natality, Mortality)

Modeled Population Lifeline ( NHANES)

RefPop Bodyweight Random ( Age, Season)

Binning Methodology Natality ( NCHS)

Reference Population Lifeline Model Design Components Lifeline generates its reference population by randomly drawing from the 1996 NCHS Natality statistics. All individuals are equally weighted.

Lifeline constructs lifetime consumption profiles by randomly drawing from dietary bins based on age and season.

Since consumption profiles are generated on the fly, we need to calculate the expected number of times that each diary is used to approximate the Lifeline Model.

Slide 133 of 168 Lifeline: Dietary Bin Lifeline: Dietary Bin CSFII Diaries 10,000 2 1 RefPop Person

Age 1 1 1 Lifeline Weight Day of Life xx 02 1 xx 02 1 xx 02 2 xx 02 2 xx 02 1 xx 02 1 31,390 5 4 3 2 1 xx 01 2 xx 01 1 xx 01 2 xx 01 1 xx 01 2 xx 01 1 xx 02 1 xx 02 2 xx 02 1 xx 02 2 xx 02 1 xx 02 2 All modeled individuals are equally weighted. All diaries within a dietary bin have the same expected frequency of being used by Lifeline.

Slide 134 of 168 Slide 135 of 168 Lifeline Model Approximation ( L1)

Lifeline Model Approximation ( L1)

For each subpopulation ( 3to5 yr olds), select All CSFII diaries in DietBin( s).

Match each CSFII record with different bodyweight in corresponding bodyweight bin and calculate Consumption ( grams food/ kg bwt).

Compute Lifeline Model Weight that reflects the expected number of uses for diary ( Weight= 1 for infants, Weight= 4 for 8 yr old diary, Weight= 1 for a 12 yr old diary in the 6 12 yr old subpopulation, or Weight= 2 for a 12 yr old diary in the 13 19 yr old subpopulation).

For each commodity ( RAC FF), calculate the Total Number of Consumers = SUM weights for all diaries ( w/ Consumption > 0), and then calculate the Total Number of Non

Consumers = Total Pop.   Total Consumers.

Rank the diaries by Consumption ( grams food/ kg bwt),

assign GroupID, and Calculate the Average Consumption and corresponding Weights ( sum) for each Group, C_ 1 to C_ 100 ( Supplemental Slide). Lifeline Model Weights Lifeline Model Weights Diary Model Weight i = Expected # Uses in Lifeline = Expected # Draws from DietaryBin x Prob( Selecting Diary | DietaryBin).

While each diary within a dietary bin has the same chance of being used,

we need to make adjustments since we are interested in calculating percentiles for EPA subpopulations that intersect with these Lifeline dietary bins.

E. g., if we are calculating an upper percentile for the overall US population, then we need to account for the fact that we have a disproportion number of food diaries for children.

[ About 54% ( 23,074 of 42,269) diaries were from respondents < 20 yrs old; these first 20 yrs account for approx. 23% ( 21 of 86 yrs) of the modeled lifetime.] Slide 136 of 168 Lifeline Dietary Bins   Number of CSFII Diaries Lifeline Dietary Bins   Number of CSFII Diaries Slide 137 of 168 Lifeline Dietary Bin & Model Weights Lifeline Dietary Bin & Model Weights Since Lifeline applies its own bodyweight model to calculate consumption & dietary exposure, each diary needs to be replicated to account for different permutations of bodyweights ( Lifeline Age) that may be expected in Lifeline.

Using Lifeline data, we generated bodyweight bins for various age groups to minimize the number of replications needed to account for consumption variability created by the Lifeline. Rather than creating 4 separate permutations of a 8 year old food diary with the bodyweight distributions of 8, 9, 10 and 11 year olds, we constructed the bodyweight distribution for this 8 11 year old group, and created 1 set of permutations for this bodyweight distribution. Slide 138 of 168 Lifeline Bodyweights Lifeline Bodyweights For the children subpopulations, we created 12 permutations of bodyweights ( 0th, 5th, 15th, 25th, 35th, 45th, 50th, 65th, 75th, 85th, 95th, 100th percentiles).

For adult subpopulations ( 20to49 yrs old, 50plus, Females 13to49 yrs old), we created only 5 permutations ( 10th, 30th, 50th, 70th, 90th percentiles) due to increasing size of data base.

Slide 139 of 168 Approximating Lifeline ( L1)

Approximating Lifeline ( L1) Slide 140 of 168 Slide 141 of 168 Olive Consumption ( D1, C1, L1)

Olive Consumption ( D1, C1, L1) Slide 142 of 168 CASE STUDY CASE STUDY David J. Miller David J. Miller  Background  Goals, objectives and focus of current model comparisons in OPP  Future model comparisons in OPP  Probabilistic models and past SAP Reviews  Side by Side comparison of dietary results  DEEM and Lifeline  Approximating Consumption Distributions for Probabilistic Models using SAS Simulations  Case Study  Summary Presentation Roadmap Slide 143 of 168  Background  Goals, objectives and focus of current model comparisons in OPP  Future model comparisons in OPP  Probabilistic models and past SAP Reviews  Side by Side comparison of dietary results  DEEM and Lifeline  Approximating Consumption Distributions for Probabilistic Models using SAS Simulations  Case Study  Summary Presentation Roadmap Slide 144 of 168 Slide 145 of 168 Case Study Case Study  In order to compare exposure/ risk estimates from the three models, OPP developed hypothetical case study

Exposure through food, water, and residential turf scenarios  Only food + water considered here

Widely used insecticide

Agricultural uses include fruits,

vegetables, nuts, and grains  > 100 tolerances  Registered on over 300 crops in agriculture  High usage on apples, pecans and grapes Slide 146 of 168 Case Study Case Study  As part of case study, OPP assessed exposure/ risk to two groups

Children 3 5 y. o.

Adults 20 49 y. o.

 Food

Primarily PDP data  Water

PRZM EXAMS assessments Slide 147 of 168 Case Study Case Study  Given different assumptions and databases used by each of the three models, exposure estimates would not be expected to be identical, but should be reasonably similar to each other Percent aPAD Occupied Percent aPAD Occupied CARES DEEM LL CARES DEEM LL 68 62 52 4 5 3 Food +

Water 60 52 37 2 2 1 Water 30*

29 32 1 3 1 Food 99.9th Percentile 95th Percentile ADULTS 20 49 y. o. Slide 148 of 168 Percent aPAD Occupied Percent aPAD Occupied CARES DEEM LL CARES DEEM LL 129 116 115 9 14 7 Food +

Water 89 80 78 3 3 3 Water 81*

81 77 5 12 4 Food 99.9th Percentile 95th Percentile CHILDREN 3 5 y. o. Slide 149 of 168 Slide 150 of 168 Exposure Contributors Exposure Contributors  A routine requirement for OPP is to identify crops which contribute significantly to exposure  Two methods available for this comparison

Share of total exposure

Number of High exposure events  DEEM and CARES both have this capability

Capability currently being developed for Lifeline Slide 151 of 168 Exposure Contributors Exposure Contributors  Both DEEM and CARES identified similar " top" contributors to exposure to include

Strawberries

Olives

Blueberries

Almonds

Peaches Exposure Contributors, Ranked Exposure Contributors, Ranked 7 6 6 4 Peaches 5 5 5 5 Blueberry 4 3 4 3 Strawberry juice 3 4 3 7 Almonds 2 2 2 2 Olives 1 1 1 1 Strawberries CARES DEEM CARES DEEM No. High Exp.

Events Share Total Exposure Children 3 5 y. o. Slide 152 of 168 Exposure Contributors, Ranked Exposure Contributors, Ranked 3 12 6 13 Beet Tops 5 7 5 6 Peaches 4 4 4 7 Blueberries 6 3 3 1 Olives 1 2 2 3 Almonds 2 1 1 2 Strawberries CARES DEEM CARES DEEM No. High Exp.

Events Share Total Exposure Adults 20 49 Slide 153 of 168  Background  Goals, objectives and focus of current model comparisons in OPP  Future model comparisons in OPP  Probabilistic models and past SAP Reviews  Side by Side comparison of dietary results  DEEM and Lifeline  Approximating Consumption Distributions for Probabilistic Models using SAS Simulations  Case Study  Summary Presentation Roadmap Slide 154 of 168  Background  Goals, objectives and focus of current model comparisons in OPP  Future model comparisons in OPP  Probabilistic models and past SAP Reviews  Side by Side comparison of dietary results  DEEM and Lifeline  Approximating Consumption Distributions for Probabilistic Models using SAS Simulations  Case Study  Summary Presentation Roadmap Slide 155 of 168 Slide 156 of 168 Summary Summary  OPP previously sponsored SAPmodel review sessions   for the LifeLine, CARES,

DEEM/ Calendex and SHEDS models  Each of these models have been used  and continue to be used

to assess pesticide exposure by various stakeholders Slide 157 of 168 Summary Summary Today we:

 Discussed the results of our investigation of model design features for DEEM/ Calendex,

CARES, and LifeLine  Compared 1 day dietary exposures estimates ( food +

water) for these models Slide 158 of 168 Summary Summary  We applied our SAS programs to single commodity and multicommodity case studies to demonstrate how model design impacts 1 day dietary exposure estimates  We found the 1 day dietary exposure estimates ( food and water) for the three models to be very close Slide 159 of 168 Summary Summary  We found it to be tedious and labor intensive to format large data sets for multiple models,

each designed to accept data in different formats  We found that apparently large differences in dietary exposure estimates were due to data entry errors Slide 160 of 168 Questions to SAP Questions to SAP Slide 161 of 168 Background for Questions 1.1 ( paraphrased)

Background for Questions 1.1 ( paraphrased)

 EPA determined that the 3 models investigated differ in how they:

Establish a reference population

Bin food diaries to generate longitudinal profiles

Apply model weights

Estimate body weights for individuals Slide 162 of 168 Question 1.1 Question 1.1  The SAP is asked to please comment on whether the above cited model design features reflect those most likely to result in differences in dietary [ food and water] exposure estimates based on identical data sets. If not, what other model design features are likely to cause different dietary exposure estimates? Slide 163 of 168 Question 1.2 Question 1.2  The SAP is asked to please comment on the approach taken by the Agency to develop and use SAS approximation models ( see Section IV of the background document) to attribute differences in model predictions from differences in model designs. Please suggest possible improvements or refinements to these SAS approximation models and to alternative methods for comparing model predictions. Slide 164 of 168 Question 2.1 Question 2.1  The SAP is asked to please comment on the different approaches used by the three models in developing their Reference Populations and model weights. Slide 165 of 168 Question 3.1 Question 3.1  The SAP is asked to please comment on the frequency that CSFII diaries are used by the various models. Are there any potential biases that may arise in the respective dietary exposure estimates for these models as a result of how they used CSFII records?

Considering Lifeline's current dietary bin design ( age, season), please comment with respect to the use of the CSFII survey weight option. Is either Lifeline option (

CSFIIweighted or not) generally more appropriate than the other or are there circumstances in which one might be preferable to the other? Slide 166 of 168 Background for Question 4.1 Background for Question 4.1  Two methods for estimating dietary contributors at the high end of the exposure distribution were described:

1. The Critical Exposure Contribution ( CEC) method quantifies the contribution of specific commodities ( RAC FF) to the total exposure at the upper percentiles of the exposure distribution. Slide 167 of 168 Background for Question 4.1,

Cont.

Background for Question 4.1,

Cont.

2. The ` frequency exceeded' method tabulates the frequency that consumption of a particular commodity ( RAC FF) causes exposure to exceed some level of concern. Slide 168 of 168 Question 4.1 Question 4.1  The SAP is asked to please comment on the relative merits of the two approaches described above ( CEC and frequency exceeded) for identifying significant contributors ( RAC FF) to exposure at the upper percentiles of exposure. Are there other methods or techniques which the Panel might recommend for accomplishing this important part of the dietary exposure assessment?


Tuesday,

March 25, 2003 Part III Environmental Protection Agency 40 CFR Part 141 Minor Clarification of National Primary Drinking Water Regulation for Arsenic; Final Rule VerDate Jan< 31> 2003 18: 56 Mar 24, 2003 Jkt 200001 PO 00000 Frm 00001 Fmt 4717 Sfmt 4717 E:\ FR\ FM\ 25MRR2. SGM 25MRR2 14502 Federal Register / Vol. 68, No. 57 / Tuesday, March 25, 2003 / Rules and Regulations ENVIRONMENTAL PROTECTION AGENCY 40 CFR Part 141 [ FRL   7472   5]

Minor Clarification of National Primary Drinking Water Regulation for Arsenic AGENCY: Environmental Protection Agency ( EPA). ACTION: Final rule.

SUMMARY: Today, EPA is revising the rule text in its January 2001 final rule that established the 10 parts per billion arsenic drinking water standard to express the standard as 0.010 mg/ L, in order to clarify the implementation of the original rule. DATES: This regulation is effective April 24, 2003. For purposes of judicial review, this final rule is promulgated as of 1 p. m. Eastern Time on March 25, 2003. ADDRESSES: The official public docket for this rule is located at EPA's Water Docket, in the EPA Docket Center ( EPA/ DC), EPA West, Rm B102, 1301 Constitution Avenue, NW., Washington, DC. FOR FURTHER INFORMATION CONTACT: For general information contact the EPA Safe Drinking Water Hotline at ( 800) 426   4791. The Hotline operates Monday through Friday, excluding Federal holidays, from 9 a. m. to 5: 30 p. m. ET. For technical information contact, Richard Reding, Office of Ground Water and Drinking Water ( MC   4607M), U. S. Environmental Protection Agency, 1200 Pennsylvania Avenue, NW., Washington DC 20460, ( 202) 564   4656, email: Reding. Richard@ epa. gov.

SUPPLEMENTARY INFORMATION:

I. General Information A. Who Is Regulated by This Action? Entities potentially regulated by this regulation are public water systems ( PWSs). All community and nontransient non community water systems must comply with the revised arsenic drinking water standard beginning on January 23, 2006. A community water system ( CWS) means a public water system which serves at least 15 service connections used by year round residents or regularly serves at least 25 year round residents. Non transient non community water system ( NTNCWS) means a public water system that is not a community water system and that regularly serves at least 25 of the same persons over 6 months per year. Primacy States are required to revise their programs to adopt the new arsenic standard by January 22, 2003 ( unless an extension has been granted). Categories and entities potentially regulated by this action include the following:

Category Examples of potentially regulated entities State, Tribal and Local Government ........................................................ State, Tribal or local government owned/ operated water supply systems using ground water, surface water or mixed ground water and surface water. Federal Government ................................................................................. Federally owned/ operated community water supply systems using ground water, surface water or mixed ground water and surface water. Industry ..................................................................................................... Privately owned/ operated community water supply systems using ground water, surface water or mixed ground water and surface water.

This table is not intended to be exhaustive, but rather provides a guide for readers regarding entities likely to be regulated by this action. This table lists the types of entities that EPA is now aware could potentially be regulated by this action. Other types of entities not listed in the table could also be regulated. To determine whether your facility is regulated by this action, you should carefully examine the applicability criteria in § § 141.11 and 141.62 of title 40 of the Code of Federal Regulations. If you have questions regarding the applicability of this action to a particular entity, consult the person listed in the preceding FOR FURTHER INFORMATION CONTACT section.

B. How Can I Get Copies of This Document and Other Related Information? 1. Docket. EPA has established an official public docket for this action under Docket ID No. OW   2002   0057. The official public docket consists of the documents specifically referenced in this action, any public comments received, and other information related to this action. Although a part of the official docket, the public docket does not include Confidential Business Information ( CBI) or other information whose disclosure is restricted by statute. The official public docket is the collection of materials that is available for public viewing at the Water Docket in the EPA Docket Center, ( EPA/ DC), EPA West, Room B102, 1301 Constitution Ave., NW., Washington, DC. The EPA Docket Center Public Reading Room is open from 8: 30 a. m. to 4: 30 p. m., Monday through Friday, excluding legal holidays. The telephone number for the Public Reading Room is ( 202) 566   1744, and the telephone number for the Water Docket is ( 202) 566   2426. For access to docket material, please call ( 202) 566   2426 to schedule an appointment. 2. Electronic Access. You may access this Federal Register document electronically through the EPA Internet under the `` Federal Register'' listings at http:// www. epa. gov/ fedrgstr/. An electronic version of the public docket is available through EPA's electronic public docket and comment system, EPA Dockets. You may use EPA Dockets at http:// www. epa. gov/ edocket/ to view public comments, to access the index listing of the contents of the official public docket, and to access those documents in the public docket that are available electronically. Although not all docket materials may be available electronically, you may still access any of the publicly available docket materials through the docket facility identified in section I. B. 1. Once in the system, select `` search,'' then key in the appropriate docket identification number.

II. What is EPA's Statutory Authority for This Final Rule? SDWA section 1412( b)( 12)( A) required EPA to publish a revised arsenic standard. On January 22, 2001, EPA published a final rule revising the existing arsenic drinking water standard from 50 parts per billion ( ppb) to 10 ppb, with a compliance date of January 23, 2006 ( 66 FR 6976   7066). Under EPA's regulations at 40 CFR 142.12, States that wish to maintain primary VerDate Jan< 31> 2003 18: 56 Mar 24, 2003 Jkt 200001 PO 00000 Frm 00002 Fmt 4701 Sfmt 4700 E:\ FR\ FM\ 25MRR2. SGM 25MRR2 14503 Federal Register / Vol. 68, No. 57 / Tuesday, March 25, 2003 / Rules and Regulations enforcement responsibility for drinking water standards must revise their programs to adopt new or revised Federal regulations. Today's final rule clarifies one issue raised by stakeholders concerning the standard published in January 2001.

III. What Is EPA Doing Today? Today, EPA is revising the rule text to express the new arsenic maximum contaminant level ( MCL) as 0.010 mg/ L instead of 0.01 mg/ L. EPA is making this minor regulatory amendment in response to a concern raised by a number of States and other stakeholders that State laws adopting the Federal arsenic standard as 0.01 mg/ L might allow rounding of monitoring results above 0.01 mg/ L so that the effective standard ( in consideration of rounding of results) would be 0.014 mg/ L ( or 14 ppb), not 0.010 mg/ L ( 10 ppb). These States and other stakeholders suggested that the rule text be revised to clarify the rounding issue and avoid the potential for confusion about how to evaluate compliance monitoring results that are greater than 10 ppb. In response, EPA solicited public comment on today's amendment in a proposed rulemaking that was published on December 23, 2002 ( 67 FR 78203). Although EPA considers this amendment to be a minor clarification of the intent of the January 2001 rule, EPA chose to conduct a formal rulemaking to provide a full opportunity for public comment with respect to the rounding issue.

IV. Summary of Public Comments on Today's Regulatory Change The comment period on the December 2002 proposed rule closed on January 22, 2003. Most commenters strongly supported today's action; other commenters indicated a concern. A summary of these comments follows. The comments and EPA's responses are included in the Docket for today's final rule. In expressing support for making today's clarification, some commenters requested extensions of the compliance deadlines that were specified in the January 2001 rule. EPA does not agree that an extension of the compliance deadline is necessary or appropriate. The EPA Administrator is firmly committed to maintaining the January 23, 2006, compliance date for a new arsenic standard ( 66 FR 20581, April 23, 2001). EPA also has been clear that the 2006 compliance deadline applies to all systems with arsenic levels above 10 ppb. As noted in the December 2002 proposal to clarify the rule text, every aspect of the existing final rule and all analyses supporting the rule establish 10 ppb as the new arsenic standard. In addition, EPA made clear in several contexts that rounding down monitoring results in the range of 11 to 14 ppb to 10 ppb was not allowed under the rule ( e. g., in a guidance memorandum ( EPA 2002a), in EPA's document `` Implementation Guidance for the Arsenic Rule'' ( EPA 2002b), and in the training conducted by EPA ( EPA 2002c) on the rule since its issuance). For systems that may need additional time to come into compliance with the rule for cost or technical reasons, there is an exemption process under SDWA section 1416 under which eligible systems may receive additional time, if necessary. This process was fully addressed in the January 22, 2001, rule ( 66 FR 6988). In expressing support for making today's clarification, some commenters also requested extensions of the deadlines to submit revised arsenic primacy packages that were specified in the January 2001 rule. With respect to the deadline for States or Tribes to submit primacy revision packages, because the Agency has been clear that no rounding is permitted under the Federal rule, State programs that allow systems to round compliance monitoring results that are greater than 10 ppb down to 10 ppb will not be approved. The provisions in 40 CFR 142.12, for EPA ( at the EPA regional office level) to grant extensions of the two year period for adoption of the revised arsenic regulation as appropriate on a case by case basis, are sufficient to accommodate the commenters' requests for additional time for submission or revision of primacy packages. EPA notes that States routinely request and receive extensions of their primacy deadline. One commenter believes that State and local governments should have maximum flexibility in implementing Federal regulatory requirements. The commenter does not support today's clarification because it limits the ability of State and local governments to mitigate adverse financial effects of the arsenic standard, especially for rural or low income systems. The commenter suggested States should have the flexibility to use public education at systems where arsenic levels are between 10 and 14 ppb instead of requiring compliance at 10 ppb. However, EPA does not agree that the final arsenic rule, as promulgated in January 2001, would allow the use of public education rather than compliance with the 10 ppb standard at any system where arsenic levels exceed the 10 ppb standard and are between 10 and 14 ppb. As EPA discussed in the January 22, 2001, preamble, EPA is aware of the impact that the new arsenic standard will have on certain systems. As discussed in the January 2001 final rule ( 67 FR 6992), the Agency is implementing many financial and technical assistance actions to mitigate this impact with an emphasis on assisting small systems. In addition, EPA notes that there are certain flexibilities already built into the statutory and regulatory structure. For example, the final arsenic rule discusses the flexibility for small systems to receive an extension of up to nine years to comply with the new arsenic standard through the exemption process provided in SDWA section 1416. One commenter submitted comments that were not relevant to the December 2002 proposal to revise the arsenic rule text to express the 10 ppb standard as 0.010 mg/ L instead of 0.01 mg/ L. EPA is not addressing these comments because, in the December 2002 proposal, EPA clearly informed readers that EPA was not requesting and would not respond to comment on any other issue associated with the arsenic standard or its implementation. As noted in the December 2002 proposal and in the April 17, 2002, ( 67 FR 19037) announcement of the preliminary results of EPA's review of existing drinking water standards, EPA will continue to evaluate the expert analysis, the public comment received after publication of the final rule, and other relevant information on the arsenic drinking water standard, as part of the next six year review of drinking water standards, which is to be completed in August of 2008.

V. Administrative Requirements A. Executive Order 12866: Regulatory Planning and Review Under Executive Order 12866, ( 58 FR 51735 ( October 4, 1993)) the Agency must determine whether the regulatory action is `` significant'' and therefore subject to OMB review and the requirements of the Executive Order. The Order defines `` significant regulatory action'' as one that is likely to result in a rule that may: ( 1) Have an annual effect on the economy of $ 100 million or more or adversely affect in a material way the economy, a sector of the economy, productivity, competition, jobs, the environment, public health or safety, or State, local, or Tribal governments or communities; ( 2) create a serious inconsistency or otherwise interfere with an action taken or planned by another agency; ( 3) materially alter the budgetary impact of entitlements, grants, user fees,

VerDate Jan< 31> 2003 18: 56 Mar 24, 2003 Jkt 200001 PO 00000 Frm 00003 Fmt 4701 Sfmt 4700 E:\ FR\ FM\ 25MRR2. SGM 25MRR2 14504 Federal Register / Vol. 68, No. 57 / Tuesday, March 25, 2003 / Rules and Regulations or loan programs or the rights and obligations of recipients thereof; or ( 4) raise novel legal or policy issues arising out of legal mandates, the President's priorities, or the principles set forth in the Executive Order. It has been determined that this final rule is not a `` significant regulatory action'' under the terms of Executive Order 12866 and is therefore not subject to OMB review.

B. Paperwork Reduction Act This action does not impose any new information collection burden under the provisions of the Paperwork Reduction Act, 44 U. S. C. 3501 et. seq. This final rule merely clarifies the way the 10 ppb MCL for arsenic is expressed in regulatory text. Burden means the total time, effort, or financial resources expended by persons to generate, maintain, retain, or disclose or provide information to or for a Federal agency. This includes the time needed to review instructions; develop, acquire, install, and utilize technology and systems for the purposes of collecting, validating, and verifying information, processing and maintaining information, and disclosing and providing information; adjust the existing ways to comply with any previously applicable instructions and requirements; train personnel to be able to respond to a collection of information; search data sources; complete and review the collection of information; and transmit or otherwise disclose the information. An agency may not conduct or sponsor, and a person is not required to respond to a collection of information unless it displays a currently valid OMB control number. The OMB control numbers for EPA's regulations are listed in 40 CFR part 9 and 48 CFR chapter 15.

C. Regulatory Flexibility Act The Regulatory Flexibility Act ( RFA) generally requires an agency to prepare a regulatory flexibility analysis of any rule subject to notice and comment rulemaking requirements under the Administrative Procedure Act or any other statute unless the Agency certifies that the rule will not have a significant economic impact on a substantial number of small entities. Small entities include small businesses, small organizations, and small government jurisdictions. The RFA provides default definitions for each type of small entity. It also authorizes an agency to use alternative definitions for each category of small entity, `` which are appropriate to the activities of the agency'' after proposing the alternative definition( s) in the Federal Register and taking comment. 5 U. S. C. 601( 3)   ( 5). In addition to the above, to establish an alternative small business definition, agencies must consult with the Small Business Administration's ( SBA's) Chief Counsel for Advocacy. For purposes of assessing the impacts of today's final rule on small entities, EPA considered small entities to be public water systems serving 10,000 or fewer persons. This is the cut off level specified by Congress in the 1996 Amendments to the Safe Drinking Water Act for small system flexibility provisions. In accordance with the RFA requirements, EPA proposed using this alternative definition in the Federal Register, ( 63 FR 7620, February 13, 1998), requested public comment, consulted with the Small Business Administration ( SBA), and expressed its intention to use the alternative definition for regulatory flexibility assessments under the RFA for all future drinking water regulations in the Consumer Confidence Reports regulation ( 63 FR 44511, August 19, 1998). As stated in that final rule, the alternative definition would be applied to this regulation. This final rule imposes no cost on any entities over and above those imposed by the final arsenic rule, because that rule was developed, costed, and evaluated as 10 ppb. This final rule merely clarifies the way the 10 ppb MCL is expressed in regulatory text. Therefore, after considering the economic impacts of today's final rule on small entities, I certify that this action will not have a significant economic impact on a substantial number of small entities.

D. Unfunded Mandates Reform Act Title II of the Unfunded Mandates Reform Act of 1995 ( UMRA), Public Law 104   4, establishes requirements for Federal agencies to assess the effects of their regulatory actions on State, local, and Tribal governments and the private sector. Under section 202 of the UMRA, EPA generally must prepare a written statement, including a cost benefit analysis, for proposed and final rules with `` Federal mandates'' that may result in expenditures to State, local, and Tribal governments, in the aggregate, or to the private sector, of $ 100 million or more in any one year. Before promulgating an EPA rule for which a written statement is needed, section 205 of the UMRA generally requires EPA to identify and consider a reasonable number of regulatory alternatives and adopt the least costly, most cost effective or least burdensome alternative that achieves the objectives of the rule. The provisions of section 205 do not apply when they are inconsistent with applicable law. Moreover, section 205 allows EPA to adopt an alternative other than the least costly, most cost effective or least burdensome alternative if the Administrator publishes with the final rule an explanation why that alternative was not adopted. Before EPA establishes any regulatory requirements that may significantly or uniquely affect small governments, including Tribal governments, it must have developed under section 203 of the UMRA a small government agency plan. The plan must provide for notifying potentially affected small governments, enabling officials of affected small governments to have meaningful and timely input in the development of EPA regulatory proposals with significant Federal intergovernmental mandates, and informing, educating, and advising small governments on compliance with the regulatory requirements. Today's final rule contains no Federal mandates ( under the regulatory provisions of Title II of the UMRA) for State, local, or Tribal governments or the private sector. This final rule imposes no enforceable duty on any State, local or Tribal governments or the private sector. This final rule would not change the costs to State, local, or Tribal governments as estimated in the final arsenic rule, because that rule was developed, costed, and evaluated as 10 ppb, and this final rule merely clarifies the way the 10 ppb MCL is expressed in regulatory text. Thus, today's final rule is not subject to the requirements of sections 202 and 205 of the UMRA. For the same reason, EPA has determined that this final rule contains no regulatory requirements that might significantly or uniquely affect small governments. Thus, today's final rule is not subject to the requirements of section 203 of the UMRA.

E. Executive Order 13132: Federalism Executive Order 13132, entitled `` Federalism'' ( 64 FR 43255, August 10, 1999), requires EPA to develop an accountable process to ensure `` meaningful and timely input by State and local officials in the development of regulatory policies that have federalism implications.'' `` Policies that have federalism implications'' is defined in the Executive Order to include regulations that have `` substantial direct effects on the States, on the relationship between the national government and the States, or on the distribution of power and responsibilities among the various levels of government.''

VerDate Jan< 31> 2003 18: 56 Mar 24, 2003 Jkt 200001 PO 00000 Frm 00004 Fmt 4701 Sfmt 4700 E:\ FR\ FM\ 25MRR2. SGM 25MRR2 14505 Federal Register / Vol. 68, No. 57 / Tuesday, March 25, 2003 / Rules and Regulations This final rule does not have Federalism implications. It will not have substantial direct effects on the States, on the relationship between the national government and the States, or on the distribution of power and responsibilities among the various levels of government, as specified in Executive Order 13132. There is no cost to State and local governments, and this final rule does not preempt State law. This final rule imposes no cost on any State, or local governments over and above those imposed by the final arsenic rule because that rule was developed, costed, and evaluated as 10 ppb. This final rule merely clarifies the way the 10 ppb MCL is expressed in regulatory text. Thus, Executive Order 13132 does not apply to this rule. In the spirit of Executive Order 13132, and consistent with EPA policy to promote communications between EPA and State and local governments, EPA specifically solicited comment on the proposed rule from State and local officials. EPA received no comment on Federalism issues from State or local officials.

F. Executive Order 13175: Consultation and Coordination With Indian Tribal Governments Executive Order 13175, entitled `` Consultation and Coordination with Indian Tribal Governments'' ( 65 FR 67249, ( November 9, 2000)), requires EPA to develop an accountable process to ensure `` meaningful and timely input by tribal officials in the development of regulatory policies that have tribal implications.'' `` Policies that have tribal implications'' is defined in the Executive Order to include regulations that have `` substantial direct effects on one or more Indian tribes, on the relationship between the Federal Government and the Indian tribes, or on the distribution of power and responsibilities between the Federal government and Indian tribes.'' This final rule does not have Tribal implications. It will not have substantial direct effects on Tribal governments, on the relationship between the Federal Government and Indian tribes, or on the distribution of power and responsibilities between the Federal Government and Indian tribes, as specified in Executive Order 13175. There is no cost to Tribal governments, and this final rule does not preempt Tribal law. This final rule imposes no cost on any Tribal government over and above those imposed by the final arsenic rule because that rule was developed, costed and evaluated as 10 ppb. This final rule merely clarifies the way the 10 ppb MCL is expressed in regulatory text. Thus, Executive Order 13175 does not apply to this rule. In the spirit of Executive Order 13175, and consistent with EPA policy to promote communications between EPA and Tribal governments, EPA specifically solicited comment on the proposed rule from Tribal officials. EPA received no comment from Tribal officials.

G. Executive Order 13045: Protection of Children From Environmental Health Risks and Safety Risks Executive Order 13045: `` Protection of Children from Environmental Health Risks and Safety Risks'' ( 62 FR 19885, April 23, 1997) applies to any rule that: ( 1) is determined to be economically significant as defined under Executive Order 12866, and ( 2) concerns an environmental health or safety risk that EPA has reason to believe may have a disproportionate effect on children. If the regulatory action meets both criteria, the Agency must evaluate the environmental health or safety effects of the planned rule on children, and explain why the planned regulation is preferable to other potentially effective and reasonably feasible alternatives considered by the Agency. This final rule is not subject to Executive Order 13045 because it is not economically significant as defined in Executive Order 12866, and because it does not concern an environmental health or safety risk that EPA has reason to believe may have a disproportionate effect on children. This final rule merely clarifies the way the 10 ppb MCL is expressed in regulatory text.

H. Executive Order 13211: Actions That Significantly Affect Energy Supply, Distribution, or Use This final rule is not subject to Executive Order 13211, `` Actions Concerning Regulations That Significantly Affect Energy Supply, Distribution, or Use'' ( 66 FR 28355 ( May 22, 2001)) because it is not a significant regulatory action under Executive Order 12866.

I. National Technology Transfer and Advancement Act As noted in the December 2002 proposed rule, section 12( d) of the National Technology Transfer and Advancement Act of 1995 ( NTTAA), Public Law 104   113, section 12( d) ( 15 U. S. C. 272 note), directs EPA to use voluntary consensus standards in its regulatory activities unless to do so would be inconsistent with applicable law or otherwise impractical. Voluntary consensus standards are technical standards ( e. g., material specifications, test methods, sampling procedures, and business practices) that are developed or adopted by voluntary consensus standards bodies. The NTTAA directs EPA to provide Congress, through OMB, explanations when the Agency decides not to use available and applicable voluntary consensus standards. This action does not involve technical standards. Therefore, EPA did not consider the use of any voluntary consensus standards.

J. Congressional Review Act The Congressional Review Act, 5 U. S. C. 801 et seq., as added by the Small Business Regulatory Enforcement Fairness Act of 1996, generally provides that before a rule may take effect, the agency promulgating the rule must submit a rule report, which includes a copy of the rule, to each House of the Congress and to the Comptroller General of the United States. EPA will submit a report containing this rule and other required information to the U. S. Senate, the U. S. House of Representatives, and the Comptroller General of the United States prior to publication of the rule in the Federal Register. A major rule cannot take effect until 60 days after it is published in the Federal Register. This action is not a `` major rule'' as defined by 5 U. S. C. 804( 2). This rule will be effective on April 24, 2003.

VI. References EPA 2002a `` Calculation of Compliance for the New Arsenic MCL'', Cynthia C. Dougherty memorandum, January 25, 2002. EPA 2002b `` Implementation Guidance for the Arsenic Rule'', EPA16   K   02   018, August 2002, Section I   A. 4, and Figure II   1. EPA 2002c `` Arsenic and Clarifications to Compliance and New Source Contaminants Monitoring'', Albuquerque, New Mexico, April 15   16, 2002, pp. 8   9.

List of Subjects for 40 CFR Part 141 Environmental protection, Chemicals, Indians lands, Intergovernmental relations, Radiation protection, Reporting and recordkeeping requirements, Water supply.

Dated: March 19, 2003. Christine Todd Whitman, Administrator.

For the reasons set out in the preamble, title 40, chapter 1 of the Code of Federal Regulations is amended as follows:

PART 141   NATIONAL PRIMARY DRINKING WATER REGULATIONS 1. The authority citation for part 141 continues to read as follows:

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2. Section 141.23 is amended: a. By revising the entry for arsenic in the table in ( a)( 4)( i). b. By revising footnote 15 to the table in ( k)( 1). The revisions read as follows: § 141.23 Inorganic chemical sampling and analytical requirements.

* * * * * ( a) * * * ( 4) * * * ( i) * * *

DETECTION LIMITS FOR INORGANIC CONTAMINANTS Contaminant MCL ( mg/ l) Methodology Detection limit ( mg/ 1)

* * * * * * * Arsenic ............................................. 0.010 6 Atomic Absorption; Furnace ...................................................................... 0.001 Atomic Absorption; Platform   Stabilized Temperature ............................ 0.0005 7 Atomic Absorption; Gaseous Hydride ....................................................... 0.001 ICP Mass Spectrometry ............................................................................ 0.0014 8 * * * * * * * 6 The value for arsenic is effective January 23, 2006. Until then, the MCL is 0.05 mg/ L. 7 The MDL reported for EPA Method 200.9 ( Atomic Absorption; Platform   Stabilized Temperature) was determined using a 2x concentration step during sample digestion. The MDL determined for samples analyzed using direct analyses ( i. e., no sample digestion) will be higher. Using multiple depositions, EPA 200.9 is capable of obtaining MDL of 0.0001 mg/ L. 8 Using selective ion monitoring, EPA Method 200.8 ( ICP MS) is capable of obtaining a MDL of 0.0001 mg/ L.

* * * * * ( k) * * * ( 1) * * * 15 Starting January 23, 2006, analytical methods using the ICP   AES technology, may not be used because the detection limits for these methods are 0.008 mg/ L or higher. This restriction means that the two ICP   AES methods ( EPA Method 200.7 and SM 3120 B) approved for use for the MCL of 0.05 mg/ L may not be used for compliance determinations for the revised MCL of 0.010 mg/ L. However, prior to January 23, 2006, systems may have compliance samples analyzed with these less sensitive methods.

* * * * * 3. Section 141.62( b) is amended by revising the entry ``( 16)'' for arsenic in the table to read as follows:

§ 141.62 Maximum contaminant levels for inorganic contaminants.

* * * * * ( b) * * *

Contaminant MCL ( mg/ l)

* * * * *

( 16) Arsenic .......................... 0.010 * * * * *

Subpart O   [ Amended]

4. Amend § 141.154 by revising paragraphs ( b) introductory text and ( f) to read as follows:

§ 141.154 Required additional health information.

* * * * * ( b) Ending in the report due by July 1, 2001, a system which detects arsenic at levels above 0.025 mg/ L, but below the 0.05 mg/ L, and beginning in the report due by July 1, 2002, a system that detects arsenic above 0.005 mg/ L and up to and including 0.010 mg/ L: * * * * * ( f) Beginning in the report due by July 1, 2002, and ending January 22, 2006, a community water system that detects arsenic above 0.010 mg/ L and up to and including 0.05 mg/ L must include the arsenic health effects language prescribed by Appendix A to Subpart O of this part.

5. Amend Appendix A to Subpart O by revising the entry for arsenic under `` Inorganic contaminants:'' to read as follows:

APPENDIX A TO SUBPART O   REGULATED CONTAMINANTS Contaminant ( units) Traditional MCL in mg/ L To convert for CCR, multiply by MCL in CCR units MCLG Major sources in drinking water Health effects language * * * * * * * Inorganic contaminants * * * * * * * Arsenic ( ppb) 10.010 1000 1 10. 1 0 Erosion of natural deposits; Runoff from orchards; Runoff from glass and electronics production wastes. Some people who drink water containing arsenic in excess of the MCL over many years could experience skin damage or problems with their circulatory system, and may have an increased risk of getting cancer.

* * * * * * *

1 These arsenic values are effective January 23, 2006. Until then, the MCL is 0.05 mg/ L and there is no MCLG.

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6. Amend Appendix B to Subpart Q by revising entry `` 9. Arsenic'' under `` C. Inorganic Chemicals ( IOCs)'', to read as follows:

APPENDIX B TO SUBPART Q OF PART 141   STANDARD HEALTH EFFECTS LANGUAGE FOR PUBLIC NOTIFICATION Contaminant MCLG1 mg/ L MCL2 mg/ L Standard health effects language for public notification * * * * * * * 9. Arsenic 11 ......... 0 0.010 Some people who drink water containing arsenic in excess of the MCL over many years could experience skin damage or problems with their circulatory system, and may have an increased risk of getting cancer.

* * * * * * *

Appendix B   Endnotes 1. MCLG   Maximum contaminant level goal. 2. MCL   Maximum contaminant level. * * * * * * *

11. These arsenic values are effective January 23, 2006. Until then, the MCL is 0.05 mg/ L and there is no MCLG. * * * * * [ FR Doc. 03   7048 Filed 3   24   03; 8: 45 am]

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