Consultation on the Effects of Climate Change

on Pesticide Exposure Assessment Models

November 10, 2010
2

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 Exposure ..................................................................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 ModelTM (DEEMTM) .............................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 ............................................................34
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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 used 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 pressure1. Changing weather patterns may also alter the 

behavior of pesticides in the environment. OPP is seeking advice on 

approaches for determining to what extent climate change may effect 

pesticide exposure, whether current models are sufficiently robust 

(applicable) to adequately predict any changes in exposures, and how to 

identify priority areas for additional inquiry.

As an overview to the potential effects of climate change, 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 where climate change may affect pesticide use.

EPA 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 effective 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.htm
4

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 incorporate 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 Plan 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 Interagency 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 encouraged 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 report3 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 from 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-ClimateChange-Adaptation-Progress-Report.pdf

3 http://www.usda.gov/oce/climate_change/sap_2007_FinalReport.htm
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As will be evident 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 aspect 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

determining the impact that climate change may have on the magnitude 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 the inputs in 

exposure assessment models in light 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 recognizes that there are other issues involving climate change and 

pesticides that are not addressed in this consultation. These issues include

 changes 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, greater use of probabilistic 

assessment methodologies, and consideration of human exposure 

resulting from volatilization4. These areas are being or would be

better addressed in other venues and the impact of climate change 

could be incorporated directly into their 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 different climatic conditions. 

Such differences, however, would 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.

 benefits of pesticides, including the 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.
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Climate changes 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 other 

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 II 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 III 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 

summary of preliminary conclusions.

II. Description of Approach

To a large extent, the risk assessment paradigm relies on historic data, 

including data on how a pesticide is used, to evaluate current and future 

pesticide exposures. 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 many 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 any mathematical 

tool for conducting a specific type of exposure assessment. Therefore, it 

includes models implemented by computer software programs such as the 
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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 elements within the model are likely to be 

affected by climate change. For the purpose of this review, “elements”

include both inputs and parameters, where inputs could be results of a study 

or other data that represent a specific scenario and parameters are 

constants within a model that quantify linkages 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 climate 

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, the approach OPP followed to evaluate its exposure models 

with respect to climate change involves three steps.

1. An examination 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. An assessment 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 light 
8

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 studies, 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 used 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. The screening review helped guide 

the selection of case studies for a more focused consideration of the inputs 

that may be affected by climate 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 with 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 

models, 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 DEEMTM

and occupational post-application as case studies.

1. Dietary Exposure 

Several models are used to assess dietary risks, depending on the purpose 

of the specific assessment. The models used include 

o Dietary Exposure Evaluation ModelTM (DEEMTM)

o Lifeline
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o Cumulative 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 estimates 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. 

Lifeline 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, multipathway 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 

CalendexTM - 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, 

dietary exposure is represented by the following generalized equation:

BodyWeight

Residue Percent Treated) Consumption

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 in 
10

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, depending on where and when the pesticide is used, the percentage 

crop treated could either increase or decrease 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 – NHANES5” 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 closely with 

NHANES to develop systems for quickly incorporating new consumption data 

as it is made available.

d. Body Weight 

Body weight is not likely 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, the 

important inputs in DEEMTM are similar, and many cases the same, as those 

in all other dietary exposure models. Therefore, 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 drinking water and the percent 

crop treated.

2. Handler Exposure 

 

5 Centers for Disease 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 handler 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 temperature 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 this 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 outdoors.

Regardless of whether the pesticide is handled on the job or 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 major 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:

BodyWeight

Unit Exposure (Amount Handled/Day) 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 fundamentally a toxicological consideration. As an adjustment to 
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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, loading 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 amount handled per day is the number of pounds AI mixed, loaded or 

applied by an individual in a day. This value is a function of the use being 

assessed, for example a lawn treatment. In general, the amount is 

calculated as the maximum allowable application rate multiplied by the 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 weather.

c. Body Weight 

Body weight is not likely 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, which may be better addressed at a broader level along with 

toxicology-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 exposure 

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 or 
13

normal activities in and around the home. 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 children. 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 inputs of post-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:

BodyWeight

Surface Residue Transfer Coefficient Exposure Time 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 (cm2), depends 

on: 1) the amount of pesticide applied to or that drifts onto 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 influencing 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 cm2that an 

individual contacts per hour while performing a task. Like Unit Exposure in 

the handler equation, the TC is determined by the specific activity. High 

contact activities (e.g.; harvesting apples) have higher TCs than those 
14

involving low contact activities (e.g.; hoeing two week old lettuce). OPP 

believes it is unlikely that climate 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 time is the duration of the activity during which an individual may 

be exposed to residues on a given day. In occupational settings, this would 

be the hours worked per day performing a task; in residential settings, it is 

the amount of time spent in activities such as gardening or playing on a 

lawn. The duration of occupational tasks is not expected to increase as a 

result of climate change. The effect on residential activities is unclear.

d. Body Weight 

Body weight is not likely to change as a result of climate change. 

Surface Residue 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 agricultural 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

Environmental Justice 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 residues 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 models and a preliminary assessment of whether the 
15

components may be affected by climate change. A more detailed description 

of aquatic and terrestrial models can be found in the Agency’s website6. 

1. Aquatic Exposure 

OPP uses 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 

pesticide 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 risk 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, representing

a drinking water reservoir. Both models are meta-models of the Pesticide 

Root Zone Model (PRZM) and the Exposure 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 amount 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.htm
16

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 and 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 Ground 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 pesticides 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 trophic 

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 and higher tier models and even within the Tier 1 

models. The Rice Model, for example, is a simple equation relating the 
17

application rate to water concentrations given the soil and sediment partition 

coefficient, Koc 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 concentration 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 Information

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 Yes No No NA Yes

NA = not applicable

The values used for Application Rate, Number of Applications, and 

Application Intervals are all based on use directions found on pesticide labels 

in order to assess the maximum amount of pesticide that can legally be 

applied 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 the 
18

total amount of AI applied 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 approved 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 result 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 dates, 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 pesticide 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 model 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 law constant. 

Partition coefficient (Kow) is also likely to be affected by temperature.

c. Environmental Fate Properties

Table 3 lists the 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 models, 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 Aquatic 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 No 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, 

pesticide 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 affect 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 precipitation are 

likely to influence the amount of organic matter present in soil, which may 

affect the adsorption/desorption capacity of soil.

The dissipation and degradation processes of a pesticide on foliage are also 

functions of complex interactions between physical and chemical properties 

of the pesticide, photodegradation, plant uptake and wash-off by rainfall. 

Potential changes in temperature and rainfall patterns could affect these 
21

interactions, and ultimately, the level of pesticide concentrations in an 

aquatic system.

In summary, fate processes may be sensitive to temperature and moisture 

conditions; therefore, changes in temperature and precipitation due to 

climate change may alter the adsorption/desorption behaviors and 

degradation/dissipation rates of some pesticides.

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 Models

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

Meteorological 

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 likely to remain 

representative of soils on which pesticides may be applied. However, crop 

parameters and agricultural practices are likely to be influenced by changes 

in climate.

Percent 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-specific 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 within 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 scenarios, 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 1980s (NRCS, 1986). The interaction of hydrologic groups and land 

use treatment (cover) is accounted 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 dependent to some 

extent upon climatic factors such as temperature, humidity and rainfall. 

Most 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 would 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 Exposure

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 Exposure (TREX) is a spreadsheet based model that 

estimates pesticide concentrations on avian and mammalian food items. It

calculates the decay of a chemical applied to foliar surfaces 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 

foliage 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. THERPS is similar to TREX in that it uses the same underlying data set for 

determining initial pesticide residues on food items.

TerrPlant provides screening-level estimates of exposure to non-target 

plants from single pesticide 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 the 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 

one 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 subject 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 Yes 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 depend on the foliar dissipation rate, discussed 

under environmental fate properties for the aquatic exposure models. It is 

likely to be influenced by changes in temperature and rainfall patterns.

Results of TerrPlant depend on aqueous solubility, discussed under physical 

and chemical 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 spray 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 climate 

change. These inputs are largely similar to those found in the aquatic 

exposure models. Therefore, OPP did not choose any of these models for 

this consultation.

3. Spray Drift 

AgDRIFT® and AGDISP

OPP uses two models for estimating spray drift and deposition 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 for 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 exposure estimate from legal use. As 

noted previously, when such parameters are used, they can only change 

with 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® model 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 

meteorological data used in the models will need to reflect conditions in the 

field. Climate change could also affect the spray drift models indirectly, if 

conditions change substantially from those on which the models were 

parameterized. We are not reviewing these models in greater depth at this 

time, however, because the key components that may be affected by climate 

change are represented in the PRZM/EXAMS case study.

C. Summary of Screening Review

The screening review was to examine whether 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 patterns 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 chose DEEMTM 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 concerns. 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 begins to evaluate the influence of climate change to our 

exposure assessments in order to better identify any issues that need 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 any 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 timing 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 geographic conditions that currently exist across the 

country. The issue for OPP is whether climate change will affect the range of 
27

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 ModelTM (DEEMTM)

We previously identified two elements of DEEMTM 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 speeding 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 frequency 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 the 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, processing 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 

plots 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 on 
28

the label. OPP guidance on crop field trials (Guideline No. 860.1500) 

recommends 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 North 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 finds domestic commodities with over-tolerance 

residues. This indicates that the crop field trials 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 

tolerance 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 applied.

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 dinner plate than field 
29

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 label. 

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 higher residue

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 and 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 is available on the PDP web site7.

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 details.

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=STELDEV3003972
30

conditions, 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 time, 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 applied 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 

exposure 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 projected 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 Statistics 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 predicting postapplication 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 amount 
31

applied is immediately available on the surface of the foliage and that 

residues decline over time and is represented as:

t DFR(t)  DFR(0) (1D)

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 measured 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. Dissipation (D) 

With warmer and moister climates, degradation and elimination of residues 

may be hastened. Conversely, a drier climate may prolong the degradation, 

although some 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 exposure 
32

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 places into a few categories: 

pesticide behavior in the environment, pesticide use and other inputs. As for 

most pesticide behavior, most 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 physical and 

chemical processes.

Pesticide use and other inputs include pesticide use practices, of which the 

application date was the only variable identified as likely to change over time 

due to the indirect effects of changes in climate. Other factors embodied in 

the 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 temperatures ranging from 20 to 

30oC. 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 average8. 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 interaction 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 climate 

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.

Although various fate inputs are derived at temperatures ranging from 20 to 

30oC, the model can adjust the degradation/dissipation rates based on 

meteorological data. PRZM has a routine to correct temperature dependent 

degradation and dissipation rates, which is based on the Q/10 equation 

similar to Arrhenius’s equation. Also, soil temperatures could increase, and 

models can easily adapt to climate change provided underlying data are 

representative 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 fields, 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 inputs and 

parameters, and agricultural cultural practices. The soil inputs selected are 

typical values, i.e., representative soil properties as compiled from soil 

survey reports9. Soil hydrological parameters and runoff curve numbers

 

9 http://websoilsurvey.nrcs.usda.gov/app/HomePage.htm
34

developed by USDA10 are empirical parameter imbedded into crop scenarios 

for predicting direct runoff or infiltration from rainfall. The crop inputs, 

including application dates, were obtained by contacting county extension 

agent 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 ongoing 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 which 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. Although 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 Registration 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.html
35

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

assessments will significantly increase.

4. Climate Change 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 significant 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 studies 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 extent 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 example, 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 individual 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 1980s 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. Many inputs that might be affected by climate change may increase 

or decrease exposure.