1 Residential/ Non- occupational Exposure Assessment Jeff Evans Biologist Health Effects Division Office of Pesticide Programs

1

Residential/Residential/

Non- occupational Non- occupational Exposure Exposure

AssessmentAssessment

Jeff EvansBiologistHealth Effects DivisionOffice of Pesticide Programs

2

PurposePurpose To present our use of a calendar based model

(Calendex™), to address the temporal aspects of OP pesticide use

Approach is similar to the OP case study presented to SAP (12/7-8/00)

To discuss the data used in our cumulative residential exposure assessment

To discuss with the Panel:

Use of distributions of the available data

Additional ways to incorporate survey data and other pesticide use in future assessments

3

Residential OP Assessment: UsesResidential OP Assessment: Uses Indoor use: DDVP (crack and crevice, pest strips)

Pet use: DDVP and Tetrachlorvinphos (spray/dip, collars) – currently only qualitatively assessed

Home Lawns: Bensulide, Malathion, Trichlorfon

Golf Course: Acephate, Bensulide, Fenamiphos, Malathion, Trichlorfon

Home Garden: Acephate and Disulfoton (ornamental) , Malathion (ornamental and

edible food)

Public Health: Fenthion, Malathion, Naled

4

Expression of Residential RiskExpression of Residential Risk

MOE = POD (mg/kg/day)

Exposure (mg/kg/day)

Routes considered, as appropriate

Oral, Dermal, Inhalation

5

Age GroupsAge Groups

Assessment performed for the following age groups:

Children 1-2 years old

Children 3-5 years old

Adults 20+

6

ScopeScope Assessments conducted for 12 distinct geographical

regions, reflecting climate & pest pressure differences

One region split into two residential assessments

Includes remaining residential OPs that have significant exposure and appropriate exposure data

Pet products not quantified

Only screening level SOPs available at this time

Regional FrameworkRegional Framework

Source: USDA ERS

8

Region 5 – Eastern UplandsRegion 5 – Eastern Uplands Lawn: DDVP, Malathion,

Trichlorfon

Golf Course: Acephate, Bensulide, Fenamiphos, Malathion, Trichlorfon

Ornamental Gardens: Acephate, Disulfoton, Malathion

Home Garden: Malathion

Indoor: DDVP (pest strips and crack and crevice treatments)

9

10

11

Road MapRoad Map

Key Data Used (distributions selected)

Lawn

Golf Courses

Public Health

Home Garden

Characterization

Future Consideration of Survey Data

12

Lawns – Use InformationLawns – Use Information

National Home & Garden Pesticide Use Survey (NHGPUS 1991)

percent of households using a given pesticide – regional distinctions

Treated lawns based on regions using the National Garden Survey 1996-1997 Percent of population hiring lawn care services

Lawn Size (Vinlove and Torla 1995 and ORETF Survey)

13

Lawn SizeLawn Size

Uniform Distribution 500 – 15,000 ft2

Difficult to quantify

Only considers lot size minus footprint

Does not consider other structures/green space

14

Lawns: Use InformationLawns: Use Information Label

site/pest relationships

application rates

State Cooperative Extension services

Timing of applications to control common pests

Comparative Insecticide Effectiveness for Major Pest Insects of Turf in the United States

15

Lawn: Applicator Exposure DataLawn: Applicator Exposure Data Data source: ORETF

Application Type:

Granular push-type rotary spreaders

Hose-end sprayer – ready to use and one requiring the user to add the concentrate

Clothing types:

Range of clothing

Short-sleeved shirt, short pants and long-sleeved shirt, long pants

16

Lawn: Applicator ExposureLawn: Applicator Exposure

Unit Exposure (UE)

mg of exposure/amount of active ingredient (a.i.) used

• UE x ai/sq ft x area treated

• Divided by body weight

17

Lawn: Applicator Exposure DataLawn: Applicator Exposure Data

Hose-end Sprayer

Uniform Distribution: 0.017 – 49 mg/lb ai

Granular Applicator

Uniform Distribution: 0.02 – 7.6 mg/lb ai

18

Lawn: Applicator Exposure DataLawn: Applicator Exposure Data Well understood activity pattern

Easy to measure and develop distributions

However, selected a uniform distribution that:

• Reflects range of clothing that can be worn

• Survey data suggest that clothing worn while applying pesticides changes as growing season progresses

– seasonal changes are only based on formulation type not equipment used

– Hose-end includes both “mix you own” and “ready to use”

19

Lawn: Post- Application Exposure DataLawn: Post- Application Exposure Data Difficult activity pattern to determine what is

representative

Residue transfer to skin (transfer coefficient)

Choreographed Activities of Adults Measured Using Biological Monitoring, (Vacarro 1996)

• Crawling, football, Frisbee

Non-Scripted Activities of Children Measured Using Fluorescent Tracers, (Black 1993)

• Mostly solitary play with toys and books. Also activities such as cartwheels

20

Lawn: Post- Application Exposure Lawn: Post- Application Exposure DataData

Duration: up to 2 and 3.5 hrs for adults and children respectively (Cumulative, EFH)

Adult TC: 1,930 – 13,200 cm2/hr

Uniform distribution (n – 16 Vacarro)

Child TC: 700 – 16,000 cm2/hr

Uniform distribution Vacarro (n – 16) and Black (n – 14)

21


Turf Transferable Residues (TTR)

Chemical specific dissipation data (mg/cm2)

• Uniform distribution selected for each day’s residues

– Each day includes a range of values instead of mean

– First day values include “as soon as dry” up to 8 hours after application

– Watering in and not watering in

– Other days include potential for rainfall

22


Non-Dietary Ingestion (Hand-to-Mouth)

Most challenging activity pattern to assess

Hand-to-mouth frequency of events, (Reed 1999)

Adjust lawn residue data (TTR) to account for saliva wetted hands, (Clothier 2000)

Saliva extraction e.g., (Camann 1995)

23


Hand-to-mouth frequency of events (Reed 1999)

Children in day-care (n-20) at home (n-10)

Uniform distribution: 0.4 to 26 events/hr

Mean 9.5, median 8.5, 90th percentile 20

• Issue: indoors vs. outdoors, active vs. quiet play

• Freeman et al., 2001: outdoors (~2-3x less than indoors)

– Small subset (4 out of 19)

24

Lawn: Post- Application Exposure Lawn: Post- Application Exposure DataData Lawn residue data to account for saliva

wetted hands (Clothier 2000)

• Compared wet hand efficiency vs. dry hand efficiency (cyfluthrin, chlorthalonil and chlorpyrifos)

• Dry hand transfer efficiency is similar to TTR measurements (0.9 to 3%) for 2 chemicals

– Chlorpyrifos much lower overall (0.05 - 0.15%)

• Wet palms: uniform distribution 1.4-3x higher than TTRs

25


Saliva extraction (uniform: 10 to 50%)

• 50% removal by saliva wetted sponges – vigorous (Camann et al., 1995)

• 20 – 40% hands rinsed with water/Ethanol and water/Isopropanol (Fenske and Lu, 1994)

• ~10 – 22% soil removal from hands to account for possible residue/soil matrix (Kissel et al., 1998)

26

Golf Courses: Post- Application Exposure Golf Courses: Post- Application Exposure DataData

Percent of individuals participating in golf, 1992 Golf Course Operations by the Center for Golf Course Management

Number of hours playing golf

Percent of Golf Courses Applying Selected Pesticides (Doane GolfTrak, 1998-1999)

An activity pattern that is easy to understand and measure

27

Golf Courses: Post- Application Golf Courses: Post- Application Exposure DataExposure Data

Residue transfer to skin (transfer coefficient)

Uniform distribution: 200 to 760 cm2/hr

Small data set (less than 10) includes walking and using a cart.

Chemical-specific turf residue data

28

Public Health: Post- ApplicationPublic Health: Post- ApplicationRange of residues that deposit onto lawns is based on a

percent of public health use application rate (3.8 to ~30%) using values presented in Tietze et al., 1994 and the Spray drift model, AgDrift

Once an estimate of deposition is made the post application is assessed in the same way that lawn chemicals are

Estimates of % population based on percent of homes having lawns

Timing and pesticide used based on personal communication and publications prepared by organizations such as the Florida Coordinating Council of Mosquito Control

29

Garden : Applicator Exposure Garden : Applicator Exposure DataData

An activity pattern that is easy to understand and measure

Shaker Can (n-20): uniform, 0.0034-0.356 mg/lb ai

Garden Duster (n-20) uniform, 7.99-1375.4 mg/lb ai

Small Tank Sprayer (n-20), uniform, 7.99-354.4 mg/lb ai

Similar issues regarding clothing as in lawn applications

30

Garden: Applicator Exposure Garden: Applicator Exposure DataData

Area Treated

Ornamental Gardens: uniform, 500 to 2,000 ft2

• No data. Defined in the assessment as the area consisting of the perimeter around a median home area 2,250 sq ft2., with a 2.5 to 8 ft border

Vegetable gardens: log-normal, 135 to 8,000 ft2

• May be easier for people to estimate than lawns

31

Garden: Post- Application Garden: Post- Application ExposureExposure

Post-application dermal exposure

An easily defined activity in agriculture

Home gardens are more difficult due to wide variety of crops grown (fruits and vegetables) and a wide variety of activities

• Uniform distribution of 100 to 5,000 cm2/hr

Duration of garden activities: uniform, 5 to 60 min.

Chemical/regional specific residue data

32

Indoor: Inhalation Exposure DataIndoor: Inhalation Exposure Data Applicator – uniform range of inhalation exposure

values for pressurized aerosol can (PHED)

• 0.72 – 2.499 mg/lb ai

Post application inhalation exposure (adults and children)

Pest Strips: 0.005 – 0.11 mg/m3

(Collins et al., 1973)

Crack and Crevice – 0.075 – 0.548 mg/m3

(Gold et al., 1983)

Duration of time spent indoors, and breathing rates Up to 24 hours, at rest to moderate

33

Methods SummaryMethods Summary All available data considered

e.g.,Lawn residue data available for all compounds and made regional adjustments where feasible

Addressed a variety of activity patterns Some more straight forward : Application

Some more difficult : Hand-to-Mouth

Tended to use uniform distributions when presented with scenarios that had confounding variables

CharacterizationCharacterizationInput

ParameterBias

Assumptions and Uncertainty

Lawn Applicator: hose-end

~ 60 replicates, high confidence – issues re: clothing and percent of users for “mix your own” and ‘ready-to-use”

Lawn Applicator: rotary

~ 30 reps, high confidence, clothing issues

Lawn Size ~ Reasonable considering equipment used, may be a slight underestimate in areas that have larger lawns (Midwest)

Dermal Contact Transfer

- to +

Adults: activities appear to be representative, but distributions may be reflective of study design rather than actual activities

Children: Includes above scripted activities and a range of non scripted activities. Study is based on a non-toxic substance (not a pesticide), high transfer efficiency (6%)

+ over estimate; - under estimate; ~ neutral

CharacterizationCharacterization

Input Parameter

Bias Assumptions and Uncertainty

Turf Residues: dermal

~ Reflects a range of high values (e.g., immediately after sprays dry to values influenced by rainfall)

Turf Residues: hand-to-mouth

~ to +

Based on surrogate data

Frequency ~ to +

Based on video-observations of children, indoor scenarios

Duration on Lawn

~ For children the value is time spent outdoors in addition to time on lawns – Does not account for survey responses of individuals that did not play on lawns or go outside

Public Health: Drift

~ Distribution of aerial and ground equipment values

Population Exposed

~ to +

Assumed large % of population based on those having lawns. Minimal exposure


CharacterizationCharacterization

Input Parameter

Bias Assumptions and Uncertainty

Home Garden Applicator: spray


Home Garden Applicator:

dust


Home Garden Applicator: granular

~ chemical specific, high confidence, clothing issues

Garden Area Treated: ornamentals

~ to +

assumes all plants are treated

Vegetable ~ well studied variable for individual crops, but not for multiple crops and activities.

Recognize it’s a highly variable exposure scenario


CharacterizationCharacterizationInput

ParameterBias

Assumptions and Uncertainty

Frequency of Applications

~ to + based on generic insecticides, not chemical specific

Post Application garden

~ to + assumes all plants are treated

Residues ~ regional and chemical specific

Indoor Air ~ chemical specific data

Duration ~ rest to light activity - established values

duration 0 - 24 hours

Population Exposed

~ to + values based on use of all pest strips, not just those containing DDVP

Use patterns for all scenarios

~ based on percent of households using that particular pesticide.


38

Survey DataSurvey Data

Overview of our use of survey data to address use and co-occurrence

Future considerations:

Use of existing macro activity pattern data

• SHEDS example

Upcoming pesticide use survey

39

Survey Data: Macro Activity Survey Data: Macro Activity PatternsPatterns

Human Activity Patterns

Calendar based models present an opportunity to consider an individual’s macro activity patterns that can lead to exposure to one or more chemicals

Macro Activity Patterns are broadly defined as where individuals spend their time

• In the garden

• Driving to work

40


Our Basic Approach (Independence/Dependence)

Identify households based on reported use of an OP for a given scenario (e.g., NHGPUS)

• 6% of households in Region 5 use lawn chemical A

Identify the time individuals spend on lawns or other locations

• In the Exposure Factors Handbook, there are recommended values taken from surveys such as the National Human Activity Pattern Survey (NHAPS)

41


STEP 1: Calculate Exposure from Food for Individual #1 on a given day (Food Exposure(from DEEM™))

STEP 2: Select Residential Treatments for Individual #1 on a given day

Specific to region, time and demographics of individual

• Were pesticides applied in/around home?

• If so, which treatments?

– And how much, how often, during what time frame, with what frequency, and by whom?

Repeat Step 2 until all relevant residential uses are addressed

42


Co-occurrence is driven by random probabilities (% households being treated)

(6% lawn use) x (10% crack and crevice) = 0.6%

However, once a household is selected, the probability of being on the lawn is 1 because:

We used a distribution of time spent on the lawn based only on individuals who were actually on lawns

Does not account for individual responses indicating they did not spend time on lawns

43

Survey Data: Macro Activity PatternsSurvey Data: Macro Activity Patterns

Consolidated Human Activity Database (CHAD) hhtp://www.epa.gov/chadnet1

Compilation of pre-existing human activity surveys collected at the national, state and city level

• Review questionnaires and individual responses

• Develop daily activity patterns for an individual based on responses to the questionnaires

• Most surveys are cross-sectional rather than longitudinal

44


Stochastic Human Exposure and Dose Simulation model - SHEDS

Developed by:

• Valerie Zartarian

• Jianping Xue

• Haluk Ozkaynak

45

bedroomsleeping

living roomplaying

lawnplaying

carIn-transit

daycarelearning

Exp

osur

e R

ate

[ug/

min

]

Time (min)

etc...

Macro-activities

46

8 CHAD diaries simulate a person’s year in specified age-gender cohort

1 person from each of 4 seasons

1 person from each of 2 day categories (weekend and weekday)

Fix 5 weekday diaries and 2 weekend diaries

Repeat 7 day activity patterns within each season

Day of Year1 36090 180 270

WinterWeekday

WinterWeekend

SpringWeekday

SpringWeekend

SummerWeekday

SummerWeekend

FallWeekday

FallWeekend

47


Residential Exposure Joint Venture (REJV)

Longitudinal survey data addressing the application pesticides in and around households

• When and where applications are made

• Multiple applications made in one day

• What they wore while making those applications

• Demographic information (children)

53

Questions for Questions for the SAP on the SAP on Residential Residential

ExposureExposure

54

Question 1Question 1Historically, the Agency has relied on means (primarily arithmetic or geometric) from residue and exposure studies for key input variables in exposure assessments. The recent development of calendar based models and others having features to incorporate distributions of exposure values has presented the Agency an opportunity to consider using all available data points from existing exposure and residue studies. In the Cumulative Risk Assessment Case study presented to the FIFRA Scientific Advisory Panel in September, 2000, most of the exposure variables were presented as uniform distributions. The exceptions were for variables that are reasonably well established , such as exposure durations taken from the Agency’s Exposure Factors Handbook. The data used in the Case Study and in the preliminary CRA, are believed to be from well conducted studies of generally high quality. However, these data sets tend to be small (e.g., n = 10 - 30) and are being used to address wide variety of exposure situations. The uniform distribution appears to be most appropriate for these relatively small data sets because it relies on easily established values such as the minimum and maximum and provides the most conservative estimate of the standard deviation ([email protected]).

55

Question 1 (continued)Question 1 (continued)

Does the Panel have any additional comments or thoughts on OPP’s use of the uniform distribution in general or on OPP’s

selection of the uniform distribution for the specific parameters chosen? What criteria, if any, would the SAP recommend for developing parametric input distributions from available data?

Under what circumstances, if any, would it be appropriate to use available data empirically? Does the Panel have any

recommendations on how sensitivity analyses could be performed to determine if the assumption of a uniform distribution

is responsible for a majority of the risk at the tails of the exposure distribution.

56

Question 2Question 2

The use of calendar based models also allows exposure assessors to consider exposure from a variety of sources from the same or from different chemicals. Longitudinal survey data such as the National Human Activity Pattern Survey (NHAPS) are available for consideration by HED for use in future assessments. In addition, from a practical standpoint, the use of such survey data ensures combinations of exposure do not come from unrealistic random combinations that current models may produce (e.g., activities adding up more than 24 hours in a day).

57

Question 2 (continued)Question 2 (continued)The use of calendar based models provides an opportunity to explore the potential for the co-occurrence of multiple sources of exposures from residential pathways. In the cumulative assessment, OPP used summary statistics from sources such as the Exposure Factors Handbook (EFH) regarding the time spent indoors, time spent on lawns and time spent at other outdoor locations. In the preliminary assessment, we assumed these activities were stochastically independent. OPP is currently evaluating data in the EFH such as data from the National Human Activity Pattern Survey (NHAPS) to determine if it can directly incorporate (i.e., empirically) information on an individual’s activity patterns over a full day from this database to account for the likelihood and duration that an individual might be exposed to a pesticide through various activities over the course of a day.

Please comment on whether and how OPP might directly incorporate NHAPS (or similar time use data) into the software to better account for variation in activities across individuals?

58

Documents

1 Residential/ Non- occupational Exposure Assessment Jeff Evans Biologist Health Effects Division Office of Pesticide Programs