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

2 Purpose 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: 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 Risk MOE = POD (mg/kg/day) Exposure (mg/kg/day) Routes considered, as appropriate  Oral, Dermal, Inhalation

5 Age Groups Assessment performed for the following age groups:  Children 1-2 years old  Children 3-5 years old  Adults 20+

6 Scope 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 Framework Source: USDA ERS

8 Region 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)

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11 Road Map Key Data Used (distributions selected)  Lawn  Golf Courses  Public Health  Home Garden Characterization Future Consideration of Survey Data

12 Lawns – 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  Percent of population hiring lawn care services Lawn Size (Vinlove and Torla 1995 and ORETF Survey)

13 Lawn Size Uniform Distribution 500 – 15,000 ft 2 Difficult to quantify  Only considers lot size minus footprint  Does not consider other structures/green space

14 Lawns: 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 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 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 Data Hose-end Sprayer  Uniform Distribution: – 49 mg/lb ai Granular Applicator  Uniform Distribution: 0.02 – 7.6 mg/lb ai

18 Lawn: 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 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 Data Duration: up to 2 and 3.5 hrs for adults and children respectively (Cumulative, EFH) Adult TC: 1,930 – 13,200 cm 2 /hr  Uniform distribution (n – 16 Vacarro) Child TC: 700 – 16,000 cm 2 /hr  Uniform distribution Vacarro (n – 16) and Black (n – 14)

21 Lawn: Post- Application Exposure Data Turf Transferable Residues (TTR)  Chemical specific dissipation data (mg/cm 2 ) 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 Lawn: Post- Application Exposure Data 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 Lawn: Post- Application Exposure Data 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, 90 th 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 Data  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 ( %) Wet palms: uniform distribution 1.4-3x higher than TTRs

25 Lawn: Post- Application Exposure Data  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 Data 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, ) An activity pattern that is easy to understand and measure

27 Golf Courses: Post- Application Exposure Data Residue transfer to skin (transfer coefficient)  Uniform distribution: 200 to 760 cm 2 /hr  Small data set (less than 10) includes walking and using a cart. Chemical-specific turf residue data

28 Public Health: Post- Application Range 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 Data An activity pattern that is easy to understand and measure  Shaker Can (n-20): uniform, mg/lb ai  Garden Duster (n-20) uniform, mg/lb ai  Small Tank Sprayer (n-20), uniform, mg/lb ai Similar issues regarding clothing as in lawn applications

30 Garden: Applicator Exposure Data  Area Treated  Ornamental Gardens: uniform, 500 to 2,000 ft 2 No data. Defined in the assessment as the area consisting of the perimeter around a median home area 2,250 sq ft 2., with a 2.5 to 8 ft border  Vegetable gardens: log-normal, 135 to 8,000 ft 2 May be easier for people to estimate than lawns

31 Garden: Post- Application Exposure 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 cm 2 /hr  Duration of garden activities: uniform, 5 to 60 min.  Chemical/regional specific residue data

32 Indoor: Inhalation Exposure Data Applicator – uniform range of inhalation exposure values for pressurized aerosol can (PHED) 0.72 – mg/lb ai Post application inhalation exposure (adults and children)  Pest Strips: – 0.11 mg/m 3 (Collins et al., 1973)  Crack and Crevice – – mg/m 3 (Gold et al., 1983) Duration of time spent indoors, and breathing rates  Up to 24 hours, at rest to moderate

33 Methods 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

Characterization Input Parameter BiasAssumptions 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

Characterization Input Parameter BiasAssumptions 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 + over estimate; - under estimate; ~ neutral

Characterization Input ParameterBiasAssumptions and Uncertainty Home Garden Applicator: spray ~20 reps, high confidence, clothing issues Home Garden Applicator: dust ~20 reps, high confidence, clothing issues 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 + over estimate; - under estimate; ~ neutral

Characterization Input ParameterBiasAssumptions 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 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. + over estimate; - under estimate; ~ neutral

38 Survey 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 Patterns 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 Survey Data: Macro Activity Patterns 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 Survey Data: Macro Activity Patterns 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 Survey Data: Macro Activity Patterns 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 Patterns Consolidated Human Activity Database (CHAD) hhtp://  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 Survey Data: Macro Activity Patterns Stochastic Human Exposure and Dose Simulation model - SHEDS  Developed by: Valerie Zartarian Jianping Xue Haluk Ozkaynak

45 bedroom sleeping living room playing lawn playing car In-transit daycare learning Exposure Rate [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 Year Winter Weekday Winter Weekend Spring Weekday Spring Weekend Summer Weekday Summer Weekend Fall Weekday Fall Weekend

47 Survey Data: Macro Activity Patterns 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)

48 EXTRA SLIDES From other presentations FOLLOW

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51 Region 11 had an applicator residue where a residue for a child should be

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53 Questions for the SAP on Residential Exposure

54 Question 1 Historically, 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 = ) 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

55 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 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) 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?

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