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Exposure Modeling Public Meeting (EMPM)

October 28, 2014

Location: Environmental Protection Agency, Office of Pesticide Programs (OPP), One Potomac

Yard (South Building), First Floor Conference Center (S‐1204/6), 2777 S. Crystal Drive, Arlington,

VA 22202.

Agenda

Time Presentation Title Presenter(s)

9:00 – 9:10 Welcome and Introductions

Opening Remarks

A Note on Spatial Aquatic Modeling (SAM)

Melanie Biscoe, Chris Koper

Jim Cowles

Nelson Thurman

Morning Presentations

9:10 – 9:40 Ecological modeling: Exploring the Effects of

Pesticides at the Population Level

Andrew Kanarek – Environmental Fate

and Effects Division, Office of Pesticide

Programs

9:40 – 10:10 Modeling Population and Community Effects of

Herbicide Drift

Jane Tang – Bayer Crop Science

10:10 – 10:40 Modeling Rodenticide Efficacy in Controlling

Norway Rat Populations

Alex Francisco – Arcadis

10:40 – 10:55 Break

10:55 – 11:25 Population Modeling of Endangered Species Valery Forbes – University of Nebraska‐

Lincoln

11:25 – 11:55 Overview of the Terrestrial Investigation Model

(v3.0)

Kris Garber – Environmental Fate and

Effects Division, Office of Pesticide

Programs

11:55 – 1:00 Lunch

Afternoon Presentations

1:00 – 1:30 A Comparison of PRZM3 and PRZM5 Simulations:

Evaluation Across a Range of Standard

Scenarios and Chemical Properties  

Lauren Padilla –  Stone Environmental,

Inc.

1:30 – 2:00 Methods for predicting atrazine peak

concentrations of non‐daily surface water

monitoring

Wenlin Chen – Syngenta

Jeremy Aldworth – RTI Interantional

2:00 – 2:30 Overview of Statistical Methods to Quantify

Uncertainty of Pesticide Concentrations in

Surface Water Monitoring Data

James Hetrick – Environmental Fate and

Effects Division, Office of Pesticide

Programs

2:30 – 2:45 Break

2:45 – 3:15 SWMM/AGRO Model Michael Winchell –  Stone

Environmental, Inc.

3:15 – 3:45 Improved Urban Modeling Approach to Evaluate

Pesticide Product for Impacts to Surface Waters

in California

Yuzhou Luo – California Department of

Pesticide Regulation

3:45‐4:15 Guidance for addressing unextracted residues in

laboratory studies

Gregory Orrick – Environmental Fate and

Effects Division, Office of Pesticide

Programs

4:15‐4:30 Remaining Questions and Meeting Wrap‐up Chris Koper and Mark Corbin
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Abstracts

Ecological modeling: Exploring the effects of pesticides at the population level

Andrew Kanarek, USEPA/OPP/Environmental Fate and Effects Division

Abstract: Ecological risk assessment focusing on impacts to individual organisms often leave uncertain the effects

at higher levels of biological organization – for example, whether the population or colony can persist or is trapped

in an extinction vortex, its ability to cope with other stressors, and how ecosystem services may be impaired. To

investigate such questions, ecological factors such as adaptive behavior, population structure, density

dependence, exposure patterns, landscape structure, and species interactions need to be taken into account.

Additionally, to make a model suitable for basing environmental decisions on, the choice of model structure and

complexity should be justified, endpoints directly relevant for regulatory risk assessments need to be considered,

uncertainty in parameters and output incorporated and communicated, and most importantly, to demonstrate

that the model is a sufficiently good representation of the real population so that insights gained from the model

can be transferred to the natural world with reasonable confidence. In this presentation, we will explore the

rationale along with a suite of tools for addressing the aforementioned challenges in developing population‐level

mechanistic effects models.

Modelling population and community effects of herbicide drift

Thorsten Schad*, Jette Reeg+

, Andreas Solga*, Thomas Preuss*, Katrin Körner+, Jane Tang*, Tilghman Hall*, Florian

Jeltsch+

*Bayer CropScience, +University of Potsdam, Plant Ecology and Nature Conservation

Abstract: Current risk assessment focuses on individual‐level effects of non‐target terrestrial plants (NTTP). They

do not take intra‐ or interspecific interactions into account.  The combination of a model addressing

spatiotemporally varying herbicide exposure and individual‐level risk at landscape‐scales (‘Xplicit’) with a plant

community model (‘IBC‐grass’) will allow to integrate NTTP risk with the occurrence of plant species at different

spatial scales. With this approach potential herbicide drift exposure effects at the level of plant populations and

communities are investigated. Direct (e.g., increased mortality) and indirect effects (e.g. changes in competition)

are predicted using individual‐level effect patterns obtained from Xplicit and using ecotoxicological standard

studies. By further investigations and validation, this approach can provide a useful tool for predicting and

quantifying potential effects of herbicides on NTTP populations and communities.  These types of analyses can be

used to assess potential risk to endangered plants and/or the habitat of other endangered species.   

Modeling Rodenticide Efficacy in Controlling Norway Rat Populations

Alex Francisco1*, Michele Buonanduci2

, and Carolyn Meyer3

1

ARCADIS, Walnut Creek, CA, 2ARCADIS, Chelmsford, MA, 3ARCADIS, Lakewood, CO

Abstract: Rodenticide modes of toxicity vary from neurotoxins (e.g., bromethalin) to anticoagulants (e.g., warfarin)

and have differing efficacy. We developed population viability analysis (PVA) models to estimate the effect of

rodenticide efficacy differences on commensal Norway rat (Rattus norvegicus) population reduction and rat

infestation days in the US. We evaluated two reasonable usage scenarios: (1) 25% bromethalin use and 75%

warfarin use, and (2) 75% bromethalin use and 25% warfarin use, and evaluated effects of warfarin resistance

under these scenarios. We structured PVA models as birth‐flow, single‐sex, age‐class models, and included

demographic stochasticity to reflect the influence of small populations in single units of a multi‐unit residential

housing area or city block of houses. We calibrated PVA models to available literature, and included dispersal

between units and immigration from an outside source into the population to reflect literature‐based recovery

rates. Modeling predicts that 75% warfarin use may increase infestation days between 107% and 125%.

Extrapolated across US residences reporting rodent infestations, 75% warfarin use may increase rat infestation

days between 17 and 25 million. These results indicate the rodenticide purchasing choices consumers make can

affect the level of rat infestations across the US.
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Population Modeling of Endangered Species

Forbes, VE (University of Nebraska‐Lincoln), Brain, R (Syngenta Crop Protection, LLC), Edwards, D (BASF

Corporation), Galic, N (University of Nebraska‐Lincoln), Hall, T (Bayer CropScience), Honegger, J (Monsanto),

Meyer, C (ARCADIS), Moore, DRJ (Intrinsik Environmental Sciences), Nacci, D (UE EPA), Pastorok, R (Integral

Consulting, Inc.), Preuss, TG (Bayer CropScience), Railsback, SF (Humboldt State University), Salice, C (Towson

University), Sibly, RM (University of Reading, Reading, UK), Tenhumberg, B (University of Nebraska‐Lincoln),

Thorbek, P (Syngenta Crop Protection), Wang, M (WSC Scientific GmbH ).

Abstract: In April 2014, CropLife American held its 2014 Science Forum “Population Modeling of Endangered

Species”.  The aim of the forum was to gain a better understanding of the current status of population models and

how they could be used in ecological risk assessments for threatened and endangered species potentially exposed

to pesticides in the United States. The forum recommendations are intended to assist the relevant government

agencies with implementation of population modeling in future endangered species risk assessments for

pesticides. The forum included keynote presentations that provided an overview of current practices, highlighted

the main findings of a recent National Academy of Sciences report and its implications, reviewed the main

categories of existing population models and the types of risk expressions that can be produced as model outputs,

and provided examples of how population models are currently being used in different legislative contexts. We

recommend for immediate action: the formation of a multipartite advisory committee to achieve consensus on

moving forward; more systematic collection, digitization, and dissemination of data for listed species; the adoption

of the newest developments in Good Modeling Practice; comprehensive review of existing population models and

their applicability for listed species assessments; and case studies for a few well‐tested models for particular

species to demonstrate proof of concept. We recommend that models developed for listed species assessments:

provide quantitative risk estimates; incorporate realistic variability in environmental and demographic factors;

integrate complex patterns of exposure and effects; and use baseline conditions that include present factors that

have caused the species to be listed (e.g., habitat loss, invasive species) or have resulted in positive management

action. We recommend for further research and development: quantitative analysis of the causes of species

listings to guide model development; systematic assessment of the relative role of toxicity and other factors in

driving pesticide risk; additional study of how interactions between density dependence and pesticides influence

risk; and development of pragmatic approaches to assessing indirect effects of pesticides on listed species.  

Overview of the Terrestrial Investigation Model (v3.0)

Kris Garber, USEPA/OPP/Environmental Fate and Effects Division

Abstract: The Terrestrial Investigation Model (TIM) can be used to determine probabilities associated with

different magnitudes of avian mortality following a pesticide application.  The model may be used to simulate

generic or specific species (e.g., an endangered species). TIM estimates exposures of birds to pesticides through

multiple routes, including diet, drinking water, inhalation and dermal. The model simulates thousands of birds with

varying exposures and sensitivities to the simulated pesticide. Those results are used to determine probabilities of

mortality to individuals within a flock. Versions 1 and 2 of TIM have been discussed at past Scientific Advisory Panel

meetings. Major changes to the current version include: inclusion of spray drift transport to edge habitats and a

revised approach to quantifying exposure through drinking water. This presentation will provide an overview of

TIM (v. 3.0) and present example applications of the model.

A Comparison of PRZM3 and PRZM5 Simulations: Evaluation Across a Range of Standard Scenarios and Chemical

Properties

Lauren Padilla1

, Michael Winchell1, Russell Jones2, Scott Jackson3

1

Stone Environmental, Inc., 2Bayer CropScience, 3BASF Corporation

Abstract: The US EPA Office of Pesticide Programs has recently released the Surface Water Concentration

Calculator (SWCC) for use in regulatory Tier II aquatic exposure modeling. The SWCC replaces the PRZM/EXAMS 5

(PE5) modeling tool for estimating aquatic pesticide exposure. With this change in model platform comes a change

in the field‐scale pesticide runoff and erosion model from PRZM3 to PRZM5. Presentations at recent EMPM

meetings have provided examples of comparisons of water body EECs between SWCC simulations and PE5

simulations, but have not provided explicit comparisons of the environmental process predictions coming directly

from the field models. This presentation will focus specifically on the similarities and differences in the predictions

of field‐scale processes between PRZM3 and PRZM5. This will include time series and cumulative distribution
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comparisons of the primary output fluxes of runoff, sediment, soluble pesticide, and sorbed pesticide loads. In

addition, other model processes, including photolysis and volatilization, will be examined in order to determine

whether changes in the model structure have resulted in any changes in model predictions. These model

comparisons will be conducted for several different hypothetical pesticides with a range of degradation rates and

sorption characteristics, as well as for several different standard crop scenarios representing a range of climate and

agronomic conditions.

Methods for predicting atrazine peak concentrations of non‐daily surface water monitoring

Wenlin Chen*1

, Paul Mosquin2, Jeremy Aldworth2, Clint Truman1, Chris Harbourt3

1

Syngenta Crop Protection, LLC; 2RTI International; 3Wwaterborne Environmental, Inc.  

Abstract: Daily sampling for water quality monitoring at multiple sites is operationally challenging. Consequently,

most monitored datasets do not have daily samples. We use bias factors (BFs) to address the following question: If

less frequent monitoring datasets are used to estimate exposure, what is the probability or uncertainty of missing

potential peak concentrations and/or maximum rolling average concentrations? Bias factors estimate the error in

predicting the true peak concentration and/or maximum rolling average concentration from non‐daily samples or

sampling frequencies. We will present BFs calculated from two datasets representing multiple site‐year‐chemical

combinations for selected sampling frequencies and relate them to watershed characteristics (hydrology, size).

Calculated BF results will be compared to measured data for selected site‐year‐chemical combinations. We will

also demonstrate how PRZM‐Hybrid output can be used along with calculated BFs and less than daily monitoring

data to estimate daily chemographs. This work demonstrates how lower frequency monitoring data can be

coupled with model output to estimate potential maximum shorter duration concentrations to address water

monitoring management needs.

Overview of Statistical Methods to Quantify Uncertainty of Pesticide Concentrations in Surface Water

Monitoring Data

Charles Peck, Frank T. Farruggia, R. David Jones, James K. Wolf, Nelson C. Thurman, James A. Hetrick

USEPA/OPP/Environmental Fate and Effects Division

Abstract: Monitoring data, in addition to environmental fate data and modeling, can be used to estimate and

characterize environmental exposures in pesticide ecological and human‐health risk assessments. Pesticide

concentrations from surface water monitoring data, however, are expected to underestimate "true" pesticide

concentrations because of the difficulty in capturing peak concentrations. This presentation will provide an

overview of statistical methods to account for uncertainty in quantifying pesticide concentrations from surface

water monitoring data.

SWMM/AGRO Model  

Scott Jackson, BASF and Michael Winchell, Stone Environmental, Inc.

Abstract: The PWG has developed and conceptualized the application of a novel approach for simulating fate and

transport of outdoor applied pesticides in a residential urban environment based on EPA's SWMM model and the

Canadian Environmental Modelling Centre’s AGRO model. One of the critical factors that facilitated the

development of this urban modeling approach was the extensive monitoring of high density, high pyrethroid use

urban watersheds in California (Oki and Haver, 2011). The Aliso Viejo neighborhood that was selected from that

study proved to be invaluable in both providing a better understanding of the off‐target transport of residential

applied pyrethroids, as well as serving as the basis for developing an urban residential model scenario and multiple

associated regional parameterizations. The focus of this presentation is the development of an end user tool that

allows easy use of the SWMM/AGRO models as well as the standard scenarios developed for urban modeling.

Improved Urban Modeling Approach to Evaluate Pesticide Product for Impacts to Surface Waters in California

Yuzhou Luo, Nan Singhasemanon, Kean S. Goh

California Department of Pesticide Regulation, Surface Water Protection Program

Abstract: California Department of Pesticide Regulation (CDPR)’s Surface Water Protection Program (SWPP) is

developing a more consistent and transparent modeling approach for the evaluation of pesticide products

submitted for registration in California. Recently a new module for urban pesticide uses is proposed

(http://www.cdpr.ca.gov/docs/emon/surfwtr/review.htm). The module is developed based on the USEPA
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modeling approaches for effects determinations of selected urban pesticide uses

(http://www.epa.gov/espp/litstatus/effects/redleg‐frog/index.html). USEPA’s tier‐2 models (PRZM3/EXAMS) and

modeling scenario for “San Francisco impervious surface” are generally accepted in the CDPR development, while

the landscape description and simulation design are significantly improved to represent reasonable worst‐case

conditions of pesticide aquatic exposure in California urban areas. Higher building density, more paved area, and

prescheduled lawn irrigation and its induced dry‐weather runoff are considered. Urban drainage area is modeled

as four (4) types of surfaces by water sources (precipitation and irrigation) and permeability (pervious and

impervious). Yields of water, sediment, and pesticide from modeled surfaces are routed into one receiving water

body for risk characterization. More discussions on the model capability, limitations, further development, and

incorporation to the new USEPA model set of PRZM5/VVWM are also provided.

Guidance for addressing unextracted residues in laboratory studies

Gregory Orrick, Cheryl Sutton, Katrina White, James Hetrick, R. David Jones, Mohammed Ruhman, Mah Shamim

Abstract: EPA’s Environmental Fate and Effects Division has developed guidance to assist in evaluating residues

that remain associated with soil or sediment following extraction (i.e., unextracted residues) in laboratory‐based

pesticide studies submitted to support pesticide registration. This document clarifies EFED’s current perspective on

unextracted residues, including the adequacy of extraction solvent systems, and provides an evaluation procedure

for studies submitted to EPA when such residues have been identified. The procedure discusses lines of evidence

for reducing uncertainty in the availability of unextracted residues using minimal to no additional data.

Implementing this guidance will result in a more consistent and robust approach for evaluating unextracted

residues in exposure and risk assessments.