## Consultation on the Effects of Climate Change

on Pesticide Exposure Assessment Models

November 10, 20102 Table of Contents
I. Background

................................ ................................ ...................... 3 II. Description of Approach

................................ ................................ .... 6 III. Screening Review

................................ ................................ ............ 8 A. Human Health Exposure Models

................................ ....................... 8 1. Dietary Exposure

................................ ................................ ......... 8 2. Handler Exposure

................................ ................................ ...... 10 3. Post

-Application Exposure

................................ .......................... 12 B. Ecological Exposure Models

................................ ........................... 14 1. Aquatic Exposure

................................ ................................ ...... 15 2. Terrestrial Expos ure ................................ ................................ .. 23 3. Spray Drift

................................ ................................ ............... 24 C. Summary of Screening Review

................................ ...................... 25 IV. Case Studies

................................ ................................ ................. 26 A. Human Health Case Studies

................................ .......................... 27 1. Dietary Exposure Evaluation Model TM (DEEM TM )

............................. 27 2. Post

- application: Agricultural Workers

................................ .......... 30 B. Ecological Case Study: PRZM/EXAMS

................................ .............. 32 1 . Physical/Chemical and Environmental Fate Properties

.................... 32 2. Pesticide Use and Other Inputs

................................ ................... 33 C. Summary and Conclusions

................................ ............................ 343



## I. Background The Office of Pesticide Programs (OPP) is seeking advice from the FIFRA Scientific Advisory Panel (SAP) on a systematic approach to determine if there is a scientific need to modify OPP’s approach to assessing exposure from pesticides in response to the effects of climate change. In this SAP, OPP is focusing on data inputs and parameters use d in its human and environmental exposure models . Changes in the Earth's climate, such as increased temperatures and frequency and severity of extreme weather events, have and are projected to continue to affect cropping patterns and/or pest pressure 1 . Changing weather patterns may also alter the behavior of pesticides in the environment. OPP is seeking advice on approaches for determin ing to what extent climate chan ge may effect pesticide exposure, whether current models are sufficiently robust (applicable) to adequately predict any changes in exposure s, and how to identify priority areas for additional inquiry

. As an overview to the potential effects of climate cha nge, the meeting will begin with three presentations on some of the broader climate issues. The first of these presentations provides an overview of climate change initiatives within EPA and across Federal Agencies , followed by two discussions on areas whe re climate change may affect pesticide use

. E PA is working to fulfill its mission to protect human health and the environment in a world in which the climate is changing. Many of the outcomes EPA is trying to attain ( e.g. ; clean air, safe drinking water, safe food) may be sensitive to changes in weather and climate. Until now, EPA has been able to assume that climate is relatively stable and future climate will mirror past climate (an assumption of “stationarity”). But with climate changing more rapidly and in unprecedented ways, the assumption of stationarity may need to change

. EPA must anticipate and plan for future changes in climate. It must “mainstream” considerations of climate change into its programs and rules to ensure they will remain effect ive under future climatic conditions. Administrator Lisa P. Jackson explicitly acknowledged this in her seven priorities for EPA’s future when she stated, “we must also recognize that climate change will affect other parts of our core mission, such as protecting air and water quality, and we must include those considerations in our future plans.”

1 http://www.usda.gov/oce/climate_change/sap_2007_FinalReport.htm4

Given this imperative, EPA’s new Strategic Plan explicitly calls for the Agency to integrate climate adaptation into its programs and rules: The Agency must incor porate the anticipated, unprecedented changes in climate into its programs and rules, drawing on the critical information and tools provided by EPA researchers, to continue to fulfill statutory, regulatory, and programmatic requirements. The Strategic Pla n also calls for EPA to collaborate with the Interagency Climate Change Adaptation Task Force and the US Global Change Research Program (USGCRP)

. On October 5, 2010, the Task Force delivered a report to the President entitled, “Progress Report of the Inte ragency Climate Change Adaption Task Force: Recommended Actions in Support of A National Climate Change Adaptation Strategy” 2

. Consistent with EPA’s efforts to “mainstream” climate adaptation, the Task Force has recommended that adaptation planning be e ncouraged and mainstreamed across the entire Federal Government, and Federal agencies implement adaptation planning. EPA is also a member of the USGCRP, which is conducting scientific research, modeling and assessments to evaluate the potential impacts of climate change on the United States and evaluating alternative adaptation strategies.
The U.S. Department of Agriculture (USDA) has been evaluating the relationship between climate change and its effects on agriculture for a number of years. A May 2008 report 3 describes a range of current and predicted effects. The effects of climate change on agriculture are already apparent and expected to continue for the foreseeable future. These effects are both direct and indirect and vary by type and intensity among regions of the country. Agricultural production , including crop and livestock, is sensitive to the direct effects of temperature and rainfall, as well as indirect effects resulting fro m increased pest pressure and pest resistance. These changes can affect the use of pesticides. The Center for Disease Control (CDC) tracks the occurrence of vector borne disease (VBD) in the U.S. In recent years, changes have been observed in the distribution and incidence of VBD, including the northward movement of Lyme disease and Eastern Equine Encephalitis. These trends are potentially associated, in part, with a changing climate and may be addressed through adaptive strategies, such as greater reliance on pesticides and integrated vector control.

2 http://www.whitehouse.gov/sites/default/files/microsites/ceq/Interagency

- Climate

Change

-Adaptation

- Progress

- Report.pdf 3 http://www.usda.gov/oce/climate_change/sap_2007_FinalReport.htm5

As will be ev ident from these presentations, OPP ’s work is only a very small part of the suite of the climate change efforts that need to be considered in the coming years. Even within the context of pesticide assessment, this SAP meeting addresses a fairly narrow asp ect of the potential effects climate change might have on the science of pesticide risk assessments . In large part, this consultation is intended to provide OPP with input to assist it in determin ing the impact that climate change may have on the magnitud e and extent of pesticide exposure and its ability to evaluate those exposures

. A team of OPP scientists and risk managers reviewed a wide range of pesticide exposure assessment inputs and models to identify th e inputs in exposure assessment model s in lig ht of present and predicted effects of climate change. As discussed in further detail in section II, OPP is initially focusing on exposure assessment tools because it has the capacity to directly address these areas

. OPP is using exposure assessment to illustrate some possible approaches for considering potential effects of climate change in pesticide risk assessment. OPP recognize s that there are other issue s involving climate change and pesticides that are not addressed in this consultation. These issue s include

 c hanges in risk assessment methods or components which could be influenced by climate change considerations , but which would encompass a significant number of other issues as well . These areas include, for example, ecosystem modeling, greate r use of probabilistic assessment methodologies, and consideration of human exposure resulting from volatilization 4 . These a reas are being or would be better addressed in other venues and the impact of climate change could be incorporated directly into th eir development.

 scientific issues which are not unique to pesticides. For example, some chemicals may exhibit different types of toxicity and may behave differently in the environment under differe nt climatic conditions

. Such differences, however, woul d not arise exclusively for pesticides, but may be similar for many chemicals in the environment. Independent of this consultation, OPP is reaching out across the Agency to begin to address these cross

- program issues.

 b enefits of pesticides, including th e control of pests that carry vector disease as well as agricultural pests. Climate change could result in conditions conducive to pest population growth, the expansion of pests into new geographical areas, and the development of resistance.

4 Volatilization was subject of a separate SAP meeting in December 2009.6

Climate chan ges may also influence the efficacy of alternative pest control practices, including non

- chemical alternatives. EPA sees this as an important area for future consideration and we plan to work with partners from other parts of the Agency, USDA, CDC and oth er experts

. This SAP meeting will focus on issues pertaining to exposure modeling. We are seeking advice to help us determine the extent our exposure estimates might be influenced by changes in the inputs values as climate changes. The background paper is organized as follows. Section I I provides a brief description of OPP’s approach for considering how OPP could evaluate the impacts of climate change on the elements to or other aspects of its exposure models

. Section I II describes the results of OPP’s screening review of most exposure models used in conventional pesticide risk assessment. Section IV describes case studies that focus on the model inputs and parameters that are potentially affected by climate change and how the results may be affected by climate change

. Section IV also provides a summ ary of preliminary conclusions. 

## II. Description of Approach

To a large extent, t he risk assessment paradigm relies on historic data, including data on how a pesticide is used, to evaluate current and futu re pesticide exposure s . An important issue for OPP is to determine whether there is a need , and if so, how to modify these models to reflect future changes in climate within the time frames consistent pesticide regulatory review cycle of at least every 15 years . To help break the effort into manageable pieces, OPP prioritized various components of risk assessment. We began by focusing on the area that is primarily within OPP’s control , exposure assessment models. Exposure models were selected because ma ny are unique to pesticide assessment, whereas hazard (toxicity) assessment tools are not solely within OPP’s purview. Methodological developments, e.g., probabilistic ecological risk assessments, should consider the effects of climate change, but are better considered in broader venues. Moreover, the more immediate manifestations of climate change , the increased likelihood and magnitude of extreme weather events , may have the greatest influence on the environmental fate and transport of pesticides.
OPP compiled a list of exposure models the Office uses in pesticide risk assessments. In this context, the term “ model ” refers to a ny mathematical tool for conducting a specific type of exposure assessment. Therefore, it includes models implemented by compu ter software programs such as the7

Dietary Exposure Evaluation Model ( DEEMTM ) and mathematical algorithms such as the ones used for worker and residential exposure assessments. For each model, the team conducted a screening review and made a preliminary determination as to whether el ements within the model are likely to be a ffected by climate change. For the purpose of this review, “elements” include both inputs and parameters , where inputs could be re sults of a study or other data that represent a s p ecific scenario and parameters are constants within a model that quantif y l inkage s between inputs and outputs of the model

. OPP selected several models as examples to solicit advice from the SAP on how to evaluate the variability and magnitude in estimated pesticide exposure, given the variability and magnitude of predicted clim ate change. OPP hypothesizes that some aspects of climate change will directly affect the fate and transport of pesticides and their residues , and therefore that exposure models which predict fate and transport may need to change to account for the impact of climate change . Our focus is primarily on increasing temperatures and changing rainfall patterns, especially the potential for more frequent occurrences of high rainfall (runoff) events. OPP also considers indirect effects of climate change; that is, effects on pesticide exposure that result because of changes in pesticide use patterns induced by changes in cropping patterns and pest pressure.
To summarize, t he approach OPP followed to evaluate its exposure models with respect to climate change involves three steps.
1. An examin ation of the exposure models to identify those elements that are likely to be affected by climate change , either directly or indirectly

. These elements include the data entered into the model and the parameters in the model. 2. A n assess ment of how critical the changes may be to the results of the model, i.e. , in magnitude , variability, uncertainty and direction. OPP is particularly concerned about the possibility that assessments would underestimate the current and future risks posed by pesticides. This step allows OPP to prioritize any needed modifications in the model or the data used in the model. 3. A determination of how best to address OPP’s priority needs: whether existing data can be parsed to account for changes, existing research will answer our questions, or whether new research or new data are needed. All models discussed in this document have been peer reviewed by the SAP. Therefore, OPP is not asking this SAP to examine the fundamental models per se, but to consider how well the existing models might perform in light8

of changed environmental conditions that may result from climate change. We are seeking advice from the SAP on the overall approach to the preliminary evaluations we have completed regarding the case st udies, as well as asking for suggestions on methods to estimate the influence of climate change on estimates of exposure both in terms of variability and absolute values

. 

## III. Screening Review For the screening review, OPP examined the exposure models use d in human health and ecological risk assessments and made preliminary evaluation as to which data inputs and model parameters are likely to be affected by changing weather patterns. Th e screening review helped guide the selection of case studies for a mo re focused consideration of the inputs that may be affected by c limate change

. The two primary criteria for selecting case studies were : 1) the likelihood that climate change may affect important elements of the exposure assessment, and 2) the frequency w ith which the model is used by OPP. OPP also placed a higher priority on models that share important inputs with other models. Based on these criteria, three models were selected, two human health models and one ecological model

. From the human health m odels , OPP selected the Dietary Exposure Evaluation ModelTM (DEEMTM) and the post

- application occupational exposure algorithm . OPP selected the Pesticide Root Zone Model/ Exposure Analysis Modeling System (PRZM/ EXAMS ) from the ecological models

. This section briefly discusses each model screened and describes the rationale for selecting certain models as the case studies. 

### A. Human Health Exposure Models For most pesticides, OPP assesses dietary, occupational and residential exposures. OPP examined the inputs to these models and selected DEEM TM and occupational post

- application as case studies. 

### 1. Dietary Exposure
S everal models are used to assess dietary risks , depending on the purpo se of the specific assessment . The models used include

o Dietary Exposure Evaluation Model TM (DEEM TM )
o Lifeline9

o C umulative and Aggregate Risk Evaluation System (CARES)
o Stochastic Human Exposure and Dose Simulation (SHEDS)
o CalendexTM. The most commonly used dietary model is DEEMTM , which provides deterministic estimate s of dietary pesticide exposure for chronic dietary risk assessments and a probabilistic assessment for acute dietary risk assessments. DEEMTM predicts exposures resulting from consumption of both food and drinking water. Life l ine and CARES are probabilistic models used to assess cumulative and aggregate exposure to pesticides. Both models predict exposure from diet, drinking water, and residential environments. SHEDS (Stochastic Human Exposure and Dose Simulation) is a physically

- based stochastic model developed to quantify exposure and dose of humans to multimedia, multi

pathway pollutants. CalendexTM is used to examine exposures to pesticides across various pathways and routes (e.g., dermal exposure through turf uses) for aggregate exposure assessment. The Agency currently uses Cale ndexTM

- Food Consumption Index Diaries to perform this aggregation

. Although the workings of each model vary, the key inputs used in dietary exposure assessment are similar or the same across the models. Dietary exposure is determined by the sum of residues in and on food and in drinking water, given consumption of water and treated foods

. Consumption, and body weight of the person consuming the food , can be varied to focus on different sub

- populations, including children

. That is, d ietary exposure is represented by the following generalized equation:

Weight Body n

Consumptio Treated) Percent Residue Exposure 

##   
 (

. Where exposure is measured as milligrams (mg) of pesticide residues per kilogram (kg) of body weight per unit of time, which is typically a day. Each of these inputs is discussed briefly below.

a. Residue Levels in Food Residue levels in food are measured in mg of pesticide per kg of food

. Climate change could affect residues both directly and indirectly. Some changes, such as more intense rainfall events, could reduce residues on food because more residues are washed off, but could increase residues in drinking water through increased loading in runoff. Indirectly, changes in10

agricultural production and pesticide use patterns could alter the level and distribution of residues on food or in drinking water. b . Percent Crop Treated
For some refined assessments, measures of residues are weighted by estimates of the percent of the commodity treated. The percent of a crop treated with a particular pesticide is influenced by market factors, agricultural production practices , and the presence of pests at economically damaging levels , as is the percent of an area treated within a watershed. Thus, d epending on where and when the pesticide is used, the percentage crop treated could either increase or dec rease as a result of climate change.

c . Consumption Values Consumption values are currently derived using data from USDA’s Continuing Survey of Food Intakes by Individuals (CFSII) , but OPP will be updating the consumption information shortly using “What We Eat in America

– NHANES 5 ” survey

. Although dietary patterns may change over time, factors unrelated to climate are expected to drive any such changes. Changes in food and water consumption would be captured as the Agency updates its inputs file using the newest data

. OPP is working cl osely with NHANES to develop systems for quickly incorporating new consumption data as it is made available

. d . Body Weight Body weight is not like ly to change as a result of climate change , but body weight is captured in the consumption survey so it is updated when new consumption data are incorporated. In summary, two important elements into dietary exposure models could be affected by climate change

-- residues and percent crop treated

. Of all of the dietary models, DEEMTM is the one most frequently used. Further, t he important inputs in DEEMTM are similar, and many cases the same, as those in all other dietary exposure models . T herefore, DEEMTM was selected as a case study. Section 4 presents a more in depth discussion of the sources of data on dietary residues found on food and d rinking water and the percent crop treated. 

### 2. Handler Exposure

5 Centers for Di sease Control and Prevention’s National Health and Nutrition Examination Survey (NHANES)11

A handler is an individual who mixes, loads and/or applies pesticides. Depending on the labeled uses, OPP routinely conducts both occupational and residential handler exposure assessments considering dermal and inhalation routes . For occupational handlers, OPP conducts assessments for a range of scenarios, including those in agriculture, structural pest control, painting, lawn care, and more. Residential hand ler assessments include residential lawn, gardens, pets, swimming pools, paints, foggers, crack & crevice, treated articles , and rodenticides

. Changes in rainfall patterns would be unlikely to affect indoor exposure

. Similarly, any influence of temperatu re increases would likely be mitigated by indoor temperature controls

. Therefore, indoor residential tools such as the Multi

- Chamber Concentration & Exposure Model (MCCEM) and the Screening Consumer Inhalation Exposure Software were not further considered in th is exploratory screening effort

. Indirectly, climate changes that result in greater pest pressure could increase indoor use . However, OPP concludes that the effects of climate change are more likely to be associated with activities that occur outdo ors. Regardless of whether the pesticide is handled on the job o r by a homeowner, exposure is based on similar factors that determine the amount of exposure associated with a task and how much pesticide is used for a specific type of treatment. The majo r inputs used to estimate handler exposure are unit exposure, the amount of pesticide handled in a day ( determined by application rate and area treated), the proportion of the pesticide entering the body and the individual’s body weight. Handler exposure is represented by the following generic equation:
Weight Body y)

Handled/Da (Amount Exposure Unit Exposure 

 where exposure is measured in mg of pesticide residue per kg of body weight per day. An additional input of handler exposure models, percent dermal absorption, may be influenced by increasing temperatures associated with climate change. Toxic effects associated with dermal exposures are ideally measured through a route specific dermal toxicology study. When a route specific study is not available, an oral toxicology study can be used, adjusted with a percent dermal absorption factor. Although percent dermal absorption is sometimes shown as an element in the exposure algorithm, it is only used in cases where an oral toxicity study is used to estimate risk. Thus, it is fundamental ly a toxicological consideration. As an adjustment to12

the available toxicology study, it is more appropriately addressed with other toxicological aspects of risk. Thus, percent dermal absorption is not considered for this exposure

- focused SAP. a. Unit Exposure Unit exposure measured in mg of residue per pound (lb) of pesticide active ingredient (AI), is the amount of pesticide with which an individual comes into contact while performing a particular task ( e.g. , mixing a powder concentrate into water, l oading a backpack sprayer, applying a pesticide with a backpack sprayer). For each task, unit exposure is largely a function of the formulation and application equipment, along with types of packaging and protective equipment. It is not likely to be affected by climate change; changing rainfall patterns would not likely to be a factor and there would be no indirect effects through changes in pesticide use patterns since values are task specific. b. Amount Handled per Day The a mount h andled per d ay is t he number of pounds AI mixed, loaded or applied by an individual in a day. This value is a fun ction of the use being assessed, for example a lawn treatment. In general , the amount is calculated as the maximum allowable application rate multiplied by th e size of the area to be treated. Climate change is not likely to affect the values OPP uses for this variable. The area treated is a function of the site (e.g. , crop) and application method; that is, it is a function of technology, not climate or weathe r. c. Body Weight Body weight is not like ly to change as a result of climate change.
In summary , the elements of the handler exposure models are unlikely to be affected by climate change. The possible exception is percent absorption into the body, whi ch may be better addressed at a broader level along with toxicolog y - related issues . Thus, OPP did not select these models for further review at this time. 

### 3. Post-Application Exposure OPP conducts both occupational and residential post

- application exposu re assessments. As with handler exposure, OPP is not evaluating climate change effects associated with indoor exposures at this time. For outdoor uses, OPP estimates exposure resulting from performing work tasks or13

normal activities in and around the hom e. Post

- application inhalation exposure is being evaluated in association with volatilization in another process. Thus, we focus on models used to assess the amount of pesticide exposure from dermal contact . Models estimate exposure from direct contact with treated surfaces as well as certain secondary exposures, such as hand

- to
- mouth exposure for ch ildren. Treated surfaces refer to surfaces, such as crops, plants, painted decks, lawns, and pets, that have pesticide residues due to direct application or indirect deposition. The major inpu ts of p ost
- application exposure are the amount of pesticide on the treated surface, the proportion of the pesticide on the treated surface that is transferred to the skin as a result of the activity being performed, and body weight. Some of the specific inputs discussed below are unique to residential post

- application exposure assessment. Post
- application exposure may be represented by the following generic equation:
Weight Body Time

Exposure t Coefficien Transfer Residue Surface Exposure    where exposure is measured in mg of pesticide residue per kg of body weight per day. As mentioned previously, an additional element of the exposure models, percent dermal absorption, may be influenced by increasing temperatures associated with climate change. However, as an adjustment to the available toxicology study, it is more appropriately addressed along with other toxicological aspects of risk.

a. Surface Residue Surface residue measured as mg of AI per square centimeter (cm 2 ), depends on : 1) the amount of pesticide appli ed to or that drifts on to the treated surface , and 2) the amount of degradation that occurs over time after an application. Climate change , including increasing temperature and shifting rainfall patterns, could affect surface residues , particularly influe ncing the rate of degradation

. The effect on surface residues could either increase or decrease as a result of climate change. b. Transfer Coefficient Transfer Coefficient (TC) is the amount of treated surface in cm 2 that an individual contacts per ho ur while performing a task. Like Unit Exposure in the handler equation, t he TC is determined by the specific activity. High contact activities (e.g.; harvesting apples) have higher TCs than those14

involving low contact activities (e.g.; hoeing two week ol d lettuce). OPP believes it i s unlikely that c limate change would alter the values used in exposure assessments

. In residential exposures, OPP is also concerned about incidental oral exposure by infants and children. These models utilize a number of parameters including surface area of the hand and frequency of hand to mouth contact or object (e.g., toys) to mouth contact. These latter parameters are unlikely to be affected by climate change.

c . Exposure Time Exposure t ime is the duration of the acti vity during which an individual may be exposed to residues on a given day . In occupational settings, this would be the h ours worked per day performing a task ; in residential settings, it is the amount of time spent in activities such as gardening or playi ng on a lawn. The duration of occupational tasks is n ot expected to increase as a result of climate change. The effect on residential activities is unclear. d . Body Weight Body weight is not like ly to change as a result of climate change. Surface Re sidue is an important component of post

- application exposure assessments and is the input most likely to be affected by climate change , both directly by conditions that influence degradation and indirectly by changes in application rate

. The a gricultural occupational post

- application assessment model was selected as a case study due to its high frequency of use by OPP and because its inputs are representative of those used in the residential assessments . Moreover, farm

- worker exposure is an important E nvi ronmental J ustice consideration

. 

### B. Ecological Exposure Models
OPP environmental exposure models estimate the potential exposure of plants and animals to pesticide residues in aquatic and terrestrial environments. The models also provide estimates of res idues in drinking water for use in the dietary exposure models

. OPP examined the inputs to these models and selected PRZM/EXAMS as a case study. Below are brief descriptions of the aquatic models screened, followed by a discussion of the components of mo dels and a preliminary assessment of whether the15

components may be affected by climate change

. A more d etailed description of aquatic and terrestrial models can be found in the Agency’s website 6

. 

### 1. Aquatic Exposure O PP use s simulation models to predict pesticide concentrations in surface and ground water for use in both human health and aquatic ecological exposure assessments. Some models are generic, in that they do not consider differences in climate, soil, topography, or crop in estimating pesticid e exposure. Other models are more refined (higher tier) and estimate concentrations under spatially and temporally specific site conditions. The results of the models can be used for assessing both acute and chronic risks to various biological endpoints. Rice Model The Tier I Rice Model is simple soil

- water partitioning model. It estimates concentration in surface water based on the amount of pesticide applied and the organic carbon partition coefficient Koc of the chemical applied into a rice paddy for a specific volume. The single, screening

- level concentration calculated with this model represents both short

- and long

- term surface water exposure and can be used for both ecological ri sk assessments for aquatic organisms and for human drinking water exposure assessments for human health risk assessment.
GENEEC2 and FIRST GENeric Estimated Environmental Concentration (GENEEC2) is a Tier I surface water model used for estimating concentrations of pesticides in a "standard farm pond" scenario following a single, large rainfall/runoff event. The FQPA Index Reservoir Screening Tool (FIRST) is a Tier I model used to estimate concentrations of pesticides in drinking water from surface water sources. Structurally, FIRST is very similar to GENNEEC2 , except the dimensions of the water body and surrounding fields are larger, represent ing a drinking water reservoir

. Both models are meta

- models of the Pesticide Root Zone Model (PRZM) and the Expos ure Analysis Modeling System (EXAMS) models

. PRZM/EXAMS The Pesticide Root Zone Model (PRZM) simulates chemical movement in unsaturated soil systems within and immediately below the plant root zone

. It is a Tier 2 model that generates estimates of the amo unt of pesticide in runoff and spray drift from an agricultural field. PRZM has capabilities for modeling the effects of soil temperature, volatilization and vapor

- phase

6 http://www.epa.gov/pesticides/science/models_db.htm16

transport, irrigation, and biotic transformation of applied pesticide. PRZM is also capable of simulating site specific transport. PRZM is often linked with the Exposure Analysis Modeling System (EXAMS), which simulates the processes that occur in a water body situated next to an agricultural field. EXAMS takes the estimates of runoff a nd spray drift generated by PRZM and estimates the concentration of a pesticide and its degradates in an aquatic environment in benthic sediment as well as in water. Each PRZM simulation is conducted for multiple years (usually 30 years) using historical meteorological data to provide a probabilistic exposure characterization for a crop scenario for aquatic and human health risk assessment.
SCI - GROW Screening Concentration In G ro und Water (SCI

- GROW) is the primary, Tier 1 model used to estimate pesticide concentrations in vulnerable ground water for drinking water risk and environmental assessments. SCI

- GROW model was developed by fitting a linear model to ground water concentrations with the Relative Index of Leaching Potential (RILP) as the independent variable; as such, SCI

- GROW is an empirical model. The RILP is a function of aerobic soil metabolism and the soil

- water partition coefficient. The ground water concentrations are taken from 90

- day average high concentrations from Prospective Ground Water studies. Currently, OPP is developing a Tier II ground water model that will include temporal (e.g. number of years of metrological data) and spatial (e.g. regional crop scenarios) inputs that could be affected by climate change. Results of this SAP review will be considered in the model development so that the impacts of climate change on the model are recognized.
KABAM The Kow - based Aquatic BioAccumulation Model ( KABAM ) is a higher tier model used to estimate bioaccumulation of hydrophobic organic pestic ides in freshwater aquatic food webs and subsequent risks to mammals and birds that consume contaminated aquatic prey

. This model is based upon work by Arnot and Gobas (2004), who parameterized it based on polychlorinated biphenyls (PCBs) and a number of hydrophobic organic pesticides (e.g., lindane, DDT) in the Great Lakes. The model relies upon a chemical’s octanol

- water partition coefficient (KOW ) to estimate uptake and elimination constants through respiration and diet of organisms in different trophi c levels of the aquatic community. The model uses Environmental Effects Concentrations (EECs) from PRZM/EXAMS to calculate pesticide levels in tissue. The aquatic exposure models differ significantly in complexity and in the inputs used between Tier 1 an d higher tier models and even within the Tier 1 models. The Rice Model, for example, is a simple equation relating the17

application rate to water concentrations given the soil and sediment partition coefficient, Ko c or Kd , on the other hand, PRZM/EXAMS is a computer model that simulates a number of processes by which pesticides move over and through soil into water. At a basic level, however, all models , except KABAM, translate an amount of pesticide applied to an area of land, given physical and chemical properties of the pesticide and the pesticide’s ability to move through the environment, into an estimate of concentration in the environment. Depending on the model, other factors can also be considered. KABAM begins with the estimate of pesticide conce ntration in the environment and estimates the amount of pesticide that may accumulate in animal tissue throughout the food chain. Because of the commonality across aquatic models, the similar components will be discussed together

.
a. Pesticide Use In form ation Table 1 presents the pesticide use information used in each of the aquatic exposure models. The inputs may include the application rate, the number of applications made, the interval in days between applications, the application method, and the depth of soil incorporation. As shown in Table 1, GENEEC, FIRST, and PRZM/EXAMS include all these variables while the Rice model and SCI

- GROW use only a subset. Table 1. Pesticide Use Information for Aquatic Models Input GENEEC FIRST Rice SCIGROW KABAM PRZM/EXAMS Application Rate Yes Yes Yes Yes NA Yes Number of Application Yes Yes No Yes NA Yes Application Intervals Yes Yes No No NA Yes Application Date Yes Yes No No NA Yes Application method Yes Yes No No NA Yes Pesticide Incorporation Depth Yes Ye s No No NA Yes NA = not applicable The values used for Application Rate, Number of Applications, and Application Intervals are all based on use dire ctions found on pesticide labels in order to assess the maximum amount of pesticide that can legally be ap plied in the shortest period of time. Label directions specify the maximum application rate and typically the minimum application interval. The number of applications may not be explicitly stated, but there may be a limit on the18

total amou nt of AI applie d in a season. In some cases, however, there are no restrictions , and OPP assesses the consequences of repeated applications of the maximum rate at the minimum interval for the entire season, which may be the entire year. These label directions must be a pproved by OPP at the time of Registration and again during Registration Review. Therefore, climate change will not, independently, result in changes to these values for the exposure assessments. Similarly, application method will not be altered as a res ult of climate change. OPP assesses application methods specifically allowed on the product label , as well as all others not strictly prohibited in order to estimate the maximum possible exposure. Application d ate s , however, may be indirectly affected by climate change if, for example, warming temperatures advance the cropping season or permit earlier infestations of pests. Climate change may also indirectly affect the incorporation depth if, for example, changing conditions induce insect pests to modify their behavior or weed seeds to germinate at deeper or shallower levels, which in turn may lead farmers to alter the placement of pesticides in the soil

. b . Physical and Chemical Properties Table 2 presents the physical and chemical properties of the pe sticide that may be included in the exposure models. With the exception of the octanol/water partition coefficient (Kow ), PRZM/EXAMS uses all the properties listed while the Tier 1 models consider a limited number of the variables. KABAM is the only mode l that considers Kow , where it is used to derive the time to reach a steady state.19

Table 2. Physical and Chemical Properties for Aquatic Models Input GENEEC FIRST Rice SCIGROW KABAM PRZM/EXAMS Molecular weight No No No No NA Yes Aqueous Solubility Yes Yes No No NA Yes Vapor Pressure No No No No NA Yes Henry’s Law Constant No No No No NA Yes Octanol/Water Partition Coefficient (Kow ) No No No No Yes No NA = not applicable Some of these properties may be altered by changes in average temperatures and in precipitation. It is well established that temperature has an effect on vapor pressure , solubility , and Henry’s l aw constant. Partition coefficient ( Kow ) is also likel y to be affected by temperature

.
c . Environmental Fate Properties Table 3 lists t he environmental fate properties that may be included in various exposure models. As with the other sets of variables, the Tier 1 models typically rely on a small subset of the environmental fate properties to estimate water concentrations. The Tier 2 mo dels, PRZM/EXAMS, rely on the whole suite of properties. The variables do not directly influence results in KABAM, but are considered indirectly through the results of PRZM/EXAMS that are inputs into KABAM.20

Table 3. Environmental Fate Properties for Aqua tic Models Input GENEEC FIRST Rice SCIGROW KABAM PRZM/EXAMS Hydrolysis Yes Yes No No NA Yes Aqueous Photolysis Yes Yes No No NA Yes Aerobic Soil metabolism Yes Yes No Yes NA Yes Anaerobic Soil Metabolism No No No No NA Yes Aerobic Aquatic Metabolism Yes Yes No No NA Yes Anaerobic Aquatic Metabolism No No No No NA Yes Soil and Sediment Partition Coefficient (Kd or Koc) Yes Yes Yes Yes Yes Yes

Foliar dissipation No No No No NA Yes Volatilization from Foliage No No No No NA Yes Plant Uptake Factor N o No No No NA Yes NA = not applicable The dissipation of a pesticide from soil and water results from abiotic degradation processes (i.e. , hydrolysis and photolysis) and biotic degradation processes (i.e. , aerobic and anaerobic metabolisms). However, pe sticide degradation in soils and water depends on complex interactions between the physical and chemical properties and these biotic and abiotic processes in soil/sediment and water under certain temperature and moisture conditions. Climate change could a ffect these fate characteristics. Soil partition coefficient (Koc) is generally influenced by the organic matter content of soil. Several researchers (Fallon et al, 2006 and Grace et al, 2006) suggested that climatic shifts in temperature and precipitati on are likely to influence the amount of organic matter present in soil, which may affect the adsorption/desorption capacity of soil. The d issipation and degradation processes of a pesticide on foliage are also functions of complex interactions between ph ysical and chemical properties of the pesticide, photodegradation, plant uptake and wash

- off by rainfall. Potential changes in temperature and rainfall patterns could affect these21

interactions , and ultimately , the level of pesticide concentrations in an a quatic system. In summary, fate processes may be sensitive to temperature and moisture conditions; therefore, c hanges in temperature and precipitation due to climate change may alter the adsorption/desorption behaviors and degradation/dissipation rates of some pesticide s. d . Other Inputs Some of the models consider other variables, which are shown in Table 4. Application Efficiency and Spray Drift Fraction may be used to determine the amount of a pesticide available for transport into surface or ground water or transported via other routes. These inputs are derived from other models discussed in Section III.B.3. KABAM also uses the results of PRZM/EXAMS , the Estimated Environmental Concentrations (EEC) , as inputs. Table 4. Other Inputs for Aquatic Mod els Input GENEEC FIRST Rice SCIGROW KABAM PRZM/EXAMS Application Efficiency 1 Yes Yes No No NA Yes Spray Drift Fraction 1 Yes Yes No No NA Yes Crop Specific Scenario No No No No NA Yes Percent Crop Area No Yes No No NA Yes Site Specific Meteorologica l data No No No No NA Yes Water Column EEC 2 No No No No Yes No Pore Water EEC 2 No No No No Yes No
1 May be estimated from spray drift models, AgDRIFT® and AGDISP

. 2 Estimated Environmental Concentrations from PRZM/EXAMS output. NA = not applicable PRZM/EXAMS models various crop

- specific scenarios, which include detailed information on soil properties (chemical, physical and hydrological), crop parameters, and agricultural practices, as well as some non

- agricultural scenarios

. These scenarios are representative of areas of major agricultural production and of areas that, because of soil types, may be particularly vulnerable to runoff into surface water or leaching into ground water.22

While climate does shape soil properties over a long period of time, existing crop scenarios cover a range of geographical areas and are l ikely to re main re presentative of soils on which pesticides may be applied

. However, c rop parameters and agricultural practices are likely to be influenced by changes in climate

. Percen t of Crop Area (PCA) is used to estimate pesticide concentrations in drinking water. Climate change may indirectly affect this variable as changing temperature and rainfall patterns induce shifts in agricultural production. PRZM/EXAM also uses site

- speci fic meteorological data, including temperature , rainfall, and rainfall intensity. Clearly, changing climate patterns could have substantial effects on local meteorological. e. Model Parameters An important consideration is the presence of algorithms wit hin the models that quantify the linkages between inputs and outputs. The PRZM model has many algorithms (approximate 100+) and several are imbedded into PRZM scenarios and many of these algorithms are temperature and precipitation sensitive. For example , the runoff curve number (CN) , an integral part of PRZM scenario s , is derived from an empirical algorithm used in hydrology for predicting direct runoff or infiltration from rainfall

. This algorithm is based on analysis of a large number of runoff events studied over the 1950s through 1 980s (NRCS, 1986). The interaction of hydrologic groups and land use treatment (cover) is accounte d for by assigning a CN value for average soil moisture condition to important soil cover complexes for fallow, cropping, and residue parts of a growing season. Many of the imbedded algorithms in the model are depen dent to some extent up on climatic fa ctors such as temperature, humidity and rainfall. M ost of the algorithms have the capabilities to incorporate changes due to climate shifting , but it is unclear if more recent data would yield significantly different algorithms , and if they would, how wou ld they affect the results of the exposure assessment

. As a result of the screening review, OPP selected PRZM/EXAMS as a case study because : 1) several variables, especially among the physical and chemical properties and the environmental fate properties, may be affected by climate change ; 2) it is commonly employed ; 3) the inputs for PRZM/EXAMS include all the inputs that are used in the Tier 1 models ; and 4) results from the review of PRZM/EXAMS can be applied to these other models

.23



### 2. Terrestrial Expos ure OPP uses Tier 1 models to estimate pesticide concentrations on food items eaten by terrestrial mammals, birds, amphibians , and reptiles. OPP also uses a Tier 1 model to assess exposures of terrestrial and semi

- aquatic plants located adjacent to areas treated with pesticides.
TREX T- HERPS and TerrPlant Terrestrial Residue Ex posure (TREX ) is a spreadsheet based model that estimate s pesticide concentrations on avian and mammalian food items. It calculates the decay of a chemical applied to foliar surfa ces for single or multiple applications. A first order decay function is used to approximate the concentration at each day after initial application , given the concentration resulting from the initial and additional applications. The concentration on fol iage is calculated based on the Kenaga nomogram (Hoerger and Kenaga, (1972) , as modified by Fletcher (1994). Terrestrial Herpetofaunal Exposure Residue Program Simulation (T

- HERPS) is a simulation model that estimates exposures to terrestrial reptiles and amphibians from pesticide use. T

HERPS is similar to TREX in that it uses the same underlying data set for determining initial pesticide residues on food items. TerrPlant provide s screening

- level estimates of exposure to non

- target plants from single pe sticide applications. The model considers habitats adjacent to treated fields, including dry terrestrial habitats and wetlands. TerrPlant considers pesticide transport through spray drift and runoff. TREX and T

- HERPS account for some dissipation from th e application site while TerrPlant is a function of the transport of the pesticide to surrounding locations. Table 5 provides a list of inputs into the terrestrial models. All of these pesticide use inputs, except for type of formulation, are included in at least on e of the aquatic models previously discussed. As noted in the discussion of aquatic models , values used in exposure assessments are based on label restrictions, which would not change due to climate change . Formulation type , as well, is subje ct to OPP approval. Thus, none of these inputs except for foliar dissipation and aqueous soluability would be influenced by climate change.24

Table 5. Pesticide Use Inputs for Terrestrial Models Input Description TREX/T- HERPS TerrPlant Application Rate Ye s Yes Number of Application Yes Yes Application Intervals Yes No Type of Formulation Yes Yes Foliar dissipation Yes No Aqueous Solubility No Yes Spray Drift Fraction No Yes Runoff Fraction No Yes
Results of the TREX model d epend on the foliar diss ipation rate, discussed under environmental fate properties for the aquatic exposure models. It is likely to be influence d by changes in temperature and rainfall patterns. Results of TerrPlant depend on aqueous solubility, discussed under physical and ch emical properties for the aquatic exposure models. Temperature is likely to affect this input. TerrPlant also uses the fraction of the pesticide transported via spray drift or runoff. The value used for spray drift may be a default value or it may be estimated. Estimation of s pray drift is discussed in the following section. Runoff is very likely to be affected by changing rainfall patterns, especially high

- intensity events. Several inputs into terrestrial models are also likely to be affected by clim ate change. These inputs are largely similar to those found in the aquatic exposure models. Therefore, OPP did not ch o ose any of these models for this consultation

. 

### 3. Spray Drift AgDRIFT® and AGDISP OPP uses two models for estimating spray drift and de position from treated areas to adjacent areas where non

- target organism may be exposed ; AgDRIFT® and AGDISP(AGricultural DISPersal)

. Both models are used for estimating drift from aerial applications through a mechanistic approach. AgDRIFT is also used fo r estimating drift from ground boom and air

- blast applications. The models cover a wide variety of application methods, equipment and techniques that are used to apply pesticides

. Climate change is not likely to significantly affect the type of equipment used.25

Pesticide use information is an input to these models , including the maximum application rates, maximum number of applications per season, and minimum application intervals permitted on the pesticide label are typically used to yield the maximum ex posure estimate from legal use . As noted previously, when such parameters are used, they can only change wit h OPP approval. Meteorological conditions, such as wind speed, atmospheric stability, relative humidity and temperature all have the potential to change in light of climate change at any given time and place. For wind speed and atmospheric stability , however, many label requirements and/or best management practices limit the conditions under which pesticide applications are made. The AgDRIFT® mo del contains empirical curves for ground and airblast applications from which spray drift is estimated. The parameters of the curves and algorithms quantify pesticide drift under a defined application scenario, given the amount applied and the application method used. If meteorological conditions change substantially and require modifications to application equipment, these parameter values may not longer accurately estimate the amount of drift. Climate change could affect the spray drift models directly because the meteorol o gical data used i n the models will need to refle ct conditions in the field. Climate change could also affect the spray drift mode l s indirectly, if conditions change s u b stant i ally fro m those o n which the models were parameterized. We are not reviewing t hese models in greater depth at this time, howeve r, b e cause the key components that may be affected by climate change are represented in the PRZ M/EXAMS case study. 

### C. Summary of Screening Review

The screening review was to examine whet her inputs and parameters may be affected by climate change. The review suggests that some of the components of OPP’s pesticide exposure assessments are may be affected by climate change, but that there are differences among the models for dietary exposure, occupational and residential exposure, and ecological exposure. Dietary exposure models are most likely to be affected by climate change indirectly, through changes in pesticide use patterns as humans and pests adapt to changes in climate. Use pattern s are unlikely to affect other exposure models, however, because OPP evaluates exposure at the limits of legal use. The occupational and residential exposure models and ecological exposure models are more likely to be directly affected by climate change,26

where temperature and changing rainfall patterns could alter the way a pesticide dissipates or degrades or moves through the environment. OPP needs to better understand the magnitude of effects climate change may have on its exposure models in order to set priorities for addressing any effects . For this presentation, OPP has selected three models for more thorough review. For dietary exposure, we cho se DEEM TM because it is most frequently used and is representative of the other dietary exposure models. We selected the post
- application exposure worker model because it is commonly used, representative of other post

- application models, and raises potential environmental justice concern s . Finally, we selected the linked PRZM/EXAMS model, which is frequently used and includes nearly all data inputs considered in other ecological exposure models. 

## IV. Case Studies In this section, OPP begin s to evaluate the influence of climate change to our exposure assessments in order to better identify any issues that n eed to be addressed and evaluate the need for any revisions in the context of the overall significance in exposure estimates

. Ultimately, OPP wants to identify any inputs and parameters that could change sufficiently during the maximum 15

- year reliance on an y given exposure assessment to the extent that they would significantly increase OPP’s exposure estimates. The importance of climate change to OPP’s exposure assessments will depend on how sensitive the model results are to changes in the input or parameter in question and the magnitude of change that may be induced by alterations in climate. In assessing the latter, it is important to bear in mind the time frame over which different aspects of the climate may change and the time period over which OPP relies on the outcome of any particular assessment. OPP reviews a pesticide’s registration at least every 15 years , and often reviews more frequently. OPP seeks to understand if the rate of change or variability in temperature and rainfall , and the timi ng of such changes will significantly increase our exposure estimates over the time for which the Program relies on an assessment

. The effects of climate change are also likely to differ by geographic area. Some areas may become hotter and dryer, whereas others may have greater rainfall and more extreme weather events. Both regionally and nationally, climate change will be gradual relative to the maximum duration between pesticide risk assessments. The Agency exposure assessments address a range of geog raphic conditions that currently exist across the country. The issue for OPP is whether climate change will affect the range of27

conditions evaluated in OPP’s models faster than OPP can update the input parameters from ongoing monitoring

. 

### A . Human Health Case Studies 1. Dietary Exposure Evaluation Model TM 

### ( DEEM TM 

### ) We previously identified two elements of DEEM TM that may be influenced by climate change: the residues found on food items or in drinking water and the percent of the crop or area around sources of drinking water that is treated with a pesticide.

a. Evaluating Pesticide Residues on Food Residues in or on food could either increase or decrease as a result of climate change. Warmer temperatures and increased rain events could reduce residues by sp eeding degradation and/or removal . Areas experiencing less rainfall could have slower degradation of pesticide residues. Moreover, in cases where climate change results in higher pest pressure, it is possible that pesticide application rates and the freq uency of application could increase, which in turn, could increase pesticide residues on food. Residues are an important element in the outcome of an exposure assessment.
In a dietary exposure assessment, OPP can use three different values to estimate th e level of residues in food: tolerance levels, field residues, and/or monitored levels

. Tolerance levels and field residues are both based on magnitude of residue field studies using maximum labeled rates while monitored levels come from measurements taken from food samples. Magnitude of residue (MOR) studies are designed to determine the maximum residues that could result in food or animal feed from the legal use of the pesticide. There are many types of MOR studies including crop field trials, process ing studies, livestock feeding studies, livestock dermal application studies, application to stored commodities, residues in foods as a result of direct treatment of irrigation water, and residues in fish. MOR studies are used to set tolerance levels as well as to estimate of residues for dietary risk assessment. This discussion will focus on crop field trials, since they are most likely to be affected by climate changes. Crop field trials are controlled studies where the pesticide is applied to test pl ots under conditions that are likely to lead to the highest residues: maximum application rate, minimum interval between applications, and minimum interval between the final application and harvest, as specified on28

the label. OPP guidance on crop field t rials (Guideline No. 860.1500) recommend s the minimum number of field trials for each crop as well as the locations for conducting the field trials. These locations represent the major growing areas of the crop , and the number of trials in each region reflect the relative amount that region produces. For example, if half of a given commodity that the US consumes is grown in the Northeast, half of the field trials should be conducted in the Northeast. The US, along with its NAFTA partners, has created a N orth American Regional Map, which specifies the regions described in the crop field trial guidance. Some of the US regions extend into Canada and Mexico. OPP uses the results of the crop field trial data to establish a tolerance level, which is generally higher than the highest residue level found in the field studies. FDA, which is responsible for tolerance enforcement of plant

- based food commodities, rarely find s domestic commodities with over

- tolerance residues. This indicates that the crop field tri als accomplish what they are designed to do ; that is, to determine the upper bound of residues from legal use of the pesticide. In an initial screening dietary exposure assessment, OPP assumes that all of the treated crops consumed in the US are at the to lerance level, which is an overestimate. Should this assessment exceed the level of concern, the first step in refining the estimate is to use the actual field residue data, as opposed to a high end estimate. Generally , the field residue data will represent a variety of growing conditions. Because the underlying studies are conducted in a wide variety of areas and conditions across the country, it is likely that they will continue to be representative even with moderate changes in climate over the coming few decades. The Agency periodically reviews the crop field trial guidance to reflect changing dietary consumption patterns and changes in the major growing areas. The last major change to the 860.1500 guidance occurred in 1996. Moreover, the pesticide use patterns in the trials represent those that would result in the highest levels of residues. Should there be a need to increase application rates or reduce the interval between application rates and harvest, the pesticide registrant would be required to submit new crop field trials so the Agency could determine whether the changing use conditions could result in increased pesticide exposure and also to determine if the current tolerance level is adequate given the change in how the pesticide can be app lied. Monitoring data are measurements of residues in and on samples of food products. The Agency frequently uses data from the USDA Pesticide Data Program (PDP) , which measures residues closer the din n er plate than field29

trial data

. These data reflect actual use patterns in the field, which could include lower application rates, less frequent applications, and longer intervals between application and harvest than is specified on the labe l. Changes in the climate could alter environmental conditions to be more favorable to pests, increasing pest pressure and inducing agricultural producers to increase the use of pesticides. Therefore, the monitoring data obtained today may not be reflective of residues in foods in the future. A key issue is the speed at which climate changes alter pest pressure compared to how frequently PDP monitoring data are updated

. Monitoring data are collected annually, although individual commodities may be sampled less frequently. By design, the commodities with high er resid ue levels are monitored more frequently than those with relatively low residue levels. In addition, USDA works closely with OPP to identify commodities that may be of particular regulatory interest and adjust the commodities that are monitored accordingly

. Residue data for commodities with the greatest potential to contribute to dietary exposure are updated regularly to account for changes in use patterns that could result from many factors including climate change. Based on the frequency of monitoring a nd the ability to modify the monitoring program to focus on commodities of interest, OPP believes that PDP data will continue to provide robust exposure estimates in light of climate change. For further detail on the frequency of monitoring by commodity i s available on the PDP web site 7

. b. Evaluating Pesticide Residues in Drinking Water Pesticide residues or concentrations in drinking water are estimated with the aquatic exposure models discussed in the Ecological Exposure sections, Sections III.B. and IV.B

. Pesticide concentrations may be affected by changes in climate, particularly temperature . Change in temperature can affect many of the physical and chemical properties as well as the environmental fate properties of pesticides

. Concentrations also depend on cropping patterns and pesticide use patterns. However, for modeling purposes, EPA relies on label restrictions, such as maximum application rate and/or minimum interval between applications, to estimate concentrations , and these values will not change without EPA approval. See Section IV.B. for more detail s .
c. Evaluating Percent Crop Treated The Percent of a Crop Treated (PCT) with a particular pesticide can change over time as a result of year

- to
- year variation in pest pressure , weather

7 http://www.ams.usda.gov/AMSv1.0/getfile?dDocName=STELDEV300397230

co nditions, and the availability of alternative pest control methods, including products exiting the market and new products entering the market. Climate change may also affect the PCT if changing conditions allow pests to spread more widely in space and ti me, leading growers to treat relatively more acreage. Changing conditions may also result in shifts in cropping patterns, bringing crops into areas with existing pest problems. Changing conditions could also result in the opposite effect, if conditions became less conducive to pests or resulted in agriculture shifting away from areas with pest problems. Currently, OPP uses PCT in refined risk assessments to estimate the proportion of a commodity that would not have residues. This refinement can be appli ed with any of the three types of residue levels used (i.e.; tolerance, field trial or monitoring) in the assessment . The results of the assessment can be sensitive to the PCT values used. OPP has two methods for estimating PCT for use in the dietary exp osure assessment. For new pesticide uses, OPP uses the PCT of the most widely used pesticide (the market leader) within a similar class ( e.g. , herbicide, insecticide, fungicide) as a proxy for the PCT of the new pesticide. This is referred to as the proj ected percent crop treated (PCTn ). This sets a very conservative upper bound, at least over a five

- year time period

. Refinements to the PCTn can occur if needed by looking at the market leader for a specific pest/crop combination which coincides with the new pesticide’s pest claims. For existing pesticide uses, OPP uses a ten

- year average of the annual PCT of the pesticide. Data on PCT, for both methods, come from surveys conducted by the National Agricultural Statisti cs Service (NASS) of the U.S. Department of Agriculture. Most crops are surveyed every other year. OPP supplements these data with data from private marketing surveys, which are often performed annually . Given climate change, these data from past use, particularly for existing pesticide uses, may not reflect future PCT. As with residue data, a key question is the speed at which changing pest and cropping patterns are expected to occur. 

### 2. Post-application : Agricultural Workers
Surface Residue was identified to be the major input for pred icting post
application exposure for agricultural workers that may be affected by climate change. For this particular model, Surface Residues are estimated as Dislodgeable Foliar Residues (DFR)

. OPP estimates DFR as a function of time in order to assess the exposure of workers who enter a field some days following a pesticide application. A basic formulation hypothesizes that some amount of the total amount31

applied is immediately available on the surface of the foliage and that residues decline over time and is represented as:
t (0) (t) 

### D) DFR DFR



  1

(
where DFR(0) is the proportion of the pesticide applied that is immediately available to be dislodged (or transferred) from the plant surface, D is the fraction of residue that dissipates daily, and t is me asured in days after treatment. Data to estimate the parameters of this function come from crop residue studies where foliage samples are collected over time following application. As with the field trials mentioned in the previous section, these studies are conducted in various geographical regions of the country to account for differences in temperature and rainfall. In assessing exposure, OPP assumes the maximum application rate permitted by the label is used. As the label rate is under OPP control, it will not change independently due to changes in climate. Thus, there would be no indirect effects of climate change on the model. The issue is whether changes in climate would affect the parameters of the equation, DFR(0) and D , such that data generated in the past would not be relevant for assessing future exposure.

a. Dislodgeable Foliar Residue (DFR(0) )
DFR(0) is the proportion of a pesticide on the treated surface available to be dislodged immediately after application. This proportion would be used to estimate the maximum expected surface residue concentration. Climate change is unlikely to have a significant impact on this parameter. It may be that higher temperatures would lead to increased volatility, reducing deposition, but this suggests that exposure assessments might be conservative over future conditions. b. D issipation ( D ) With warmer and moister climates, degradation and elimination of residues may be hastened. Conversely, a dri er climate may prolong the degradation , although som e chemicals may photodegrade faster if a drier climate is associated with increase ultraviolet light penetration

. Thus , the effects of climate change may depend on nature of changes in various geographic areas along with the inherent physical and chemical properties of the pesticide . Given that the data used by OPP are already from diverse climatic regions, climate change may not a significant impact on OPP’s exposure32

assessments for occupational and, by extension residential post

- application scenarios. 

### B. Ecological Case Study : PRZM / EXAMS The screening review identified components of the PRZM/EXAMS model that may be affected by climate change , which OPP place s into a few categories: pesticide behavior in the environment, pesticide use and other inputs

. As for most pesticide behavior, m ost of the physical and chemical properties and the environmental fate properties that are used in the model are likely to be affected , primarily due to the change in temperatures. Pesticide behavior in soils and water, however, is determined by complex interactions and the models, which were developed and parameterized , under certain meteorological conditions, may be less accurate under substantially different conditions because parameter values no longer represent physi cal and chemical processes. Pesticide use and other inputs include pesticide use practices, of which the a pplication d ate was the only variable identified as likely to change over time due to the indirect effects of changes in climate. Other factors embo died in t he crop scenarios that OPP employs and these crop parameters and typical agricultural cultural practices may change with climatic conditions. Similarly, the percent of crop area in watersheds, which is an input into the model estimating pesticide concentrations in drinking water, may change. Clearly , the meteorological variables are directly tied to climate change and the data used today may not reflect conditions in the future

. 

### 1 . Physical/ Chemical and Environmental Fate Properties Physical and chemical properties such as aqueous solubility, vapor pressure, and Henry’ law constant, have considerable impact on the rate and pathway of pesticide dissipation and degradation. Data on these properties come from studies that are often conducted at temperature s ranging from 20 to 30 oC. EPA reports that the global average surface temperature is likely to increase by 1 to 6 oC by the end of the century, with temperatures in most of North America rising more than average 8 . More of this increase will come in winter months rather than in summer. Thus the direct effect of higher temperature on these properties is likely to be manifested slowly. However, pesticide fate behavior in soils and water depends not only temperature , but also largely on a complex in teraction with physico

- chemical properties , along with abiotic and biotic processes in water, soil/sediment,

8 http://www.epa.gov/climatechange/science/futuretc.html#projections

.33

and foliage under certain temperatures and moisture conditions. There is a possibility that these properties will be affected to some extent by cl imate change, but it would be difficult to predict the overall impact on exposure assessment. For example, changes in temperature and precipitation due to climate change may alter the soil partitioning behavior (Kd or Koc ) of pesticide. Pesticide adsorption to soil depends on both the chemical properties of a pesticide (e.g. water solubility) and properties of the soil (e.g., organic matter and clay contents, pH, permeability). For most pesticides, organic matter is the most important soil constituent controlling the degree of pesticide retention. Temperature also induces the metabolic degradation rate of soil organic matter that may affect the retention capacity of applied pesticide. The interaction between partitioning, dissipation and degradation of pesticide in various media is a very complex process , and it would be a challenge to isolate a dominant pathway driven by climate change. Bloomfield et al, (2006) concluded that the effects of climate change on pesticide fate and transport is likely to be very unpredictable and would difficult to estimate. Al though various fate inputs are derived at temperatures ranging from 20 to 30 oC, the model can adjust the degradation/dissipation rates based on meteorological data. PRZM has a routine to correct tem perature dependent degradation and dissipation rates, which is based on the Q/10 equation similar to Arrhenius’s equation. Also, soil temperatures could increase , and model s can easily adapt to climate change provided underlying data are repre sentative of the observed changes. However, having quality data on soil temperature could be important.
OPP concludes that environmental fate characteristics are likely to change as a result of climate change. It is unclear if the changes would be significant over a fifteen year period the maximum period between reviews of any pesticide. It is also unclear if there are specific geographic areas that are more likely to be affected by climate change

. 

### 2 . Pesticide Use and Other Inputs PRZM/EXAMS uses a suite of field s , orchard crop and non

- agriculture scenarios that are representative of areas of major agricultural crops grown and non

- agriculture activities. Each scenario contains detailed information on soil properties (chemical, physical and hydrological), crop inpu ts and parameters, and agricultural cultural practices. The soil inputs selected are typical values, i.e., representative soil properties as compiled from soil survey reports 9

. Soil hydrological parameters and runoff curve numbers

9 http://websoilsurvey.nrcs.usda.gov/app/HomePage.htm34

developed by USDA 10 are empirical parameter imbedded into crop scenario s for predicting direct runoff or infiltration from rainfall

. The crop inputs , including application dates, were obtained by contacting county extension a gent of crop interest. Crop Scenarios are likely to change over time. It is unclear whether significant changes in cropping would occur during a 15

- year pesticide review cycle. However, OPP currently adds new scenarios and update existing ones on an ong oing basis to ensure that the scenarios remain current with respect to current agricultural practices and weather patterns in the U.S. Percent of Crop Area (PCA) may change, but for most crops, OPP uses conservative default values. For the crops for whic h there is an established PCA, the model can accommodate an alternative PCA, consistent with data collected by USDA. PRZM/EXAMS uses meteorological data files containing measurements taken at 237 weather stations located throughout the United States for a period extending from 1961 to 1990. OPP plans to acquire updated meteorological data files on an ongoing basis to ensure that the weather files remain current with respect to current agricultural practices and weather patterns in the United States. Alth ough updated meteorological data will better reflect changing climatic conditions since 1990 , it is not clear how best to use the full dataset of historic data to predict exposure in light of climate change. The most profound changes resulting from climate change may be in the distribution of certain weather events. 

### C . Summary and Conclusions
1

. Climate change is occurring and is projected to continue to occur. These changes have and will affect pest pressure. The changes in pest pressure are likely to affect how pesticide are used and will be used in the future. Changes in use patterns will affect the exposure to pesticides.
2. Provided up

- to
- date inputs , such as food residue and weather data, the current models used for conventional pesticides can, to a large extent, continue to provide reliable predictions of pesticide exposure for proposed and existing pesticide uses, even though climate change will alter the environmental conditions under which such uses occur in the future.
3

. Under the Regi stration Review Program, all pesticides are reevaluated at least every 15 years. It is not clear if the rate of change in

10 http://www.mi.nrcs.usda.gov/technical/engineering/neh.html35

weather patterns will occur at a pace such that uncertainty in our exposure assessments will significantly increase

. 4

. Climate Chan ge will alter the kinds of pests and where they are found and will alter agricultural practices (e.g.; where crops are grown)

-- both of which could indirectly affect pesticide exposure and risks. We need to consider the pace of such changes

-- whether si gnificant changes will occur at a rate faster than the maximum time between risk assessments under EPA’s Registration Review program. Such an understanding would be useful in deciding how climate change might affect the currency of EPA’s pesticide exposure assessments.
5

. EPA currently collects data on pesticide properties

-- especially environmental fate, dislodgeable foliar residues and crop residue trials

-- from studies performed in diverse geographic areas, reflecting the wide variety of conditions that pesticide users might encounter. We recognize that as a consequence of climate change, environmental conditions at a test site will not necessarily be the same as those which existed in the past, raising the possibility that prior st udies may not predict what would happen in the future. Therefore, we need to consider whether the variability attributable to climate change would be greater than the variability that exists across test sites throughout the country and therefore the exten t to which EPA can continue to rely on predictions derived from existing data intentionally collected from across the country.
6

. As OPP acquires updated weather data, it is unclear how these data could be best used in light of climate change. For exam ple, should the more recent weather data be weighted more heavily than older weather data or might there be a need to consider incorporative a predictive component to account for predicted changes in climate. Given the 15 year maximum between reviews of i ndividual chemicals, such modeling may not be needed

. 7. Many model parameters and algorithms that could reasonably be affected by climate change. Some of the imbedded model parameters (e.g. CN) were developed using data from studies conducting in the 1 980s and earlier. It is unclear if more recent data would yield significantly different parameters and if they would, to what extent, would they be likely to affect the results of the exposure assessment.
8

. M any inputs that might be affected by climate change may increase or decrease exposure

.