USE-EXPOSURE RELATIONSHIP OF 

PESTICIDES FOR AQUATIC RISK 

ASSESSMENT

Yuzhou Luo, Frank Spurlock, Xin Deng, Sheryl Gill, Kean Goh

Surface Water Protection Program

Department of Pesticide Regulation

California Environmental Protection Agency

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Background

 Aquatic exposure to pesticide use is required in 

pesticide evaluations for surface water protection

 DPR surface water protection program is 

developing a more consistent and transparent 

method for evaluating registration packages

 Existing modeling approaches

 Advanced models: require a large set of input data 

 Simple models: ignore spatial variability in climate, soil, 

crop, and/or topography

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Objective

 To develop a simple model to estimate aquatic 

exposure to pesticides in California field conditions

 The model is designed as a screening tool of 

pesticide evaluation for surface water protection in 

the pesticide registration processes

 Use-exposure relationship: a statistical relationship 

as an abstraction, highlighting selected 

inputs/outputs of an existing model

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Variables

 Use

 What’s used: chemical properties

 Where used: use pattern/site (scenario)

 How used: label rates and intervals

 Exposure

 Peak concentration in a certain return period

 Exposure Index (EI) from edge-of-field concentrations

 Dissolved: 1-in-3 year return 4-day moving averages

 Sediment-bound: 1-in-3 year return 10-day moving 

average

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Conceptual model

 Regular modeling approach

 Use-exposure relationship

 Direct linkage between input data to exposure

 Exposure=f(label rate, chemical property)|b

 where b is a set of scenario-specific coefficients

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Development

 Select base model and scenarios

 Select chemical properties

 Aerobic soil metabolism half-life (AERO), and organic 

carbon-normalized adsorption coefficient (KOC)

 Log-normal distribution parameterized by ~200 

common pesticides (Spurlock, 2008)

 Run stochastic simulations of the “regular approach”

 Save results as input-output (use-exposure)

 Perform regression analysis on the modeling results 

for the coefficient vector b

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Base model

 Pesticide Root-Zone Model (PRZM) is selected

 Preference of its use by USPEA for pesticide risk assessment

 Developed modeling scenarios for California

 Uses in recent projects of risk assessments for California 

(e.g., OP cumulative risk study, red-legged frog study)

 Continuous updates and supports

Format Inputs Outputs

PRZM FORTRAN program Field conditions, chemical 

properties, applications

Daily 

concentrations

Use-exposure 

relationships

Regression 

equations

Modeling scenarios, registrantsubmitted dataExposure index

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Modeling scenarios

 USEPA Tier 2 modeling scenarios for California

 USEPA (2006). Organophosphate pesticides: revised 

cumulative risk assessment

 USEPA (2008). USEPA Tier 2 crop scenarios for 

PRZM/EXAMS Shell

 USEPA (2010). Effects Determinations for the California 

Red-legged Frog and other California Listed Species 

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Processes

 Initialize PRZM model with one scenario

 Run stochastic simulations with random AERO and KOC

 Derive regression relationships between inputs and 

outputs

AERO KOC EI

AERO1 KOC1 EI1

AERO2 KOC2 EI2

... ... ...

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KOC*: a breakpoint for KOC

EI: exposure index (i.e., peak concentration)

BASE: base application rate, used to normalized the predictions

RATE: equivalent application rate based on label rates and intervals

Model: ln(EI_BASE)=b1+b2ln(AERO)+b3ln[max(KOC,KOC*)]

EI=EI_BASE*(RATE/BASE)

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Coefficients

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Discussions and future studies

 Selection of the base model 

 Modeling scenarios

 Receiving water bodies

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Thank you!

Reference: 

Luo et al., 2010. PLoS ONE, 

6(4): e18234

Contact:

Yuzhou Luo, Ph.D.

Environmental Scientist

Department of Pesticide Regulation

Cal/EPA 

1001 “I” street, Floor 3B

Sacrament, CA 95812

Email: yluo@cdpr.ca.gov

Phone: 916.445.2090

Fax: 916.445.4405

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