FIFRA SCIENTIFIC ADVISORY PANEL 

Consultation 

October 25-26, 2011 

Consultation on the Two-dimensional Exposure Rainfall-Runoff 

Assessment (TERRA) Watershed Model and Its Use in the FIFRA 

Ecological Risk Assessment for Antimicrobial Uses of Copper 

U.S. Environmental Protection Agency 

Office of Pesticide Programs 

Washington, DC 

September 28, 2011   
Page 2 of 33 

Table of Contents 

1.0 OVERVIEW

1.1 INTRODUCTION .................................................................................................. 3

1.2 EPA’S ECOLOGICAL RISK ASSESSMENT PROCESS FOR METALS ............. 3

1.3 OFFICE OF PESTICIDE PROGRAM AQUATIC EXPOSURE MODELS ............ 6

1.4 EXPOSURE MODELING SPECIFIC TO ANTIMICROBIAL PESTICIDES ........ 10

1.5 CHACTERISTICS OF THE TERRA MODEL ...................................................... 11

2.0 COPPER ECOLOGICAL EXPOSURE ASSESSMENTS CONDUCTED BY EPA

2.1 ECOLOGICAL RISK ASSESSMENT FOR CONVENTIONAL USES OF COPPER

 .................................................................................................................................... 14

2.2 ECOLOGICAL RISK ASSESSMENT FOR ANTIMICROBIAL USES OF COPPER

 .................................................................................................................................... 19

2.3 AQUATIC EXPOSURE MODELING FOR METALS IN OTHER PROGRAMS OF 

EPA ............................................................................................................................. 23

3.0 EPA’S REVIEW OF THE TERRA MODEL AND ITS APPLICATION TO 

ANTIMICROBIAL USES OF COPPER

3.1 EPA REVIEW OF THE TERRA MODEL ............................................................. 26

3.2 EPA REVIEW OF THE GOODWIN CREEK WATERSHED SCENARIO ........... 26

4.0 REFERENCES ..................................................................................................... 28

5.0 APPENDIX A ....................................................................................................... 30
Page 3 of 33 

1.0 OVERVIEW

The U.S. Environmental Protection Agency’s (EPA) Office of Pesticide Programs (OPP) 

regulates pesticides under two major federal statutes: the Federal Insecticide, Rodenticide, and 

Fungicide Act (FIFRA) and the Federal Food and Drug Act (FFDCA), both of which have been 

amended by the Food Quality Protection Act (FQPA). Through its registration and registration 

review programs, OPP conducts ecological risk assessments to ensure that pesticides do not pose 

unreasonable risks to the environment. In assessing potential environmental exposure to a given 

pesticide, EPA uses data submitted by pesticide manufacturers and open literature as inputs to 

computer-based exposure models. Models are an essential part of the risk assessment process as 

they allow EPA to perform nationwide environmental exposure assessments in the absence of 

wide-spread spatially explicit monitoring data for each chemical. 

1.1 INTRODUCTION 

The purpose of the FIFRA Scientific Advisory Panel (SAP) meeting is to seek consultation from 

the SAP on the usefulness of the Two-dimensional Exposure Rainfall-Runoff Assessment 

(TERRA)1

 model as an aquatic exposure assessment tool for evaluating the antimicrobial uses of 

copper. The TERRA model is a watershed model that has been developed and is being proposed 

for use in such a copper assessment by the American Chemistry Council (ACC), a group of 

pesticide registrants. In this document, OPP will provide an overview of the aquatic exposure 

assessment process as well as the exposure models that are currently utilized to develop these 

assessments. OPP will also provide an overview of the most recent aquatic exposure 

assessments for the EPA registered uses of copper. As part of this document, OPP will present 

an analysis of the TERRA model. The analysis will include an overview of the TERRA model 

capabilities and its application in assessing aquatic exposure from antimicrobial uses of copper. 

1.2 EPA’S ECOLOGICAL RISK ASSESSMENT PROCESS FOR METALS 

The EPA’s Office of Research and Development (ORD) has developed a Framework for Metal 

Risk Assessment (USEPA, 2002). According to this framework, risk assessments for metals 

should account for the following factors: 1) variation of metal concentrations and environmental 

chemistry across geographic regions; 2) background and introduced concentrations of metals; 

3) metal concentrations for essential health of organisms; 4) environmental fate and chemistry of 

metals such as speciation, precipitation, and sorption; and 5) toxicity differences among 

organisms. 

An ecological risk assessment has three components: problem formulation, analysis, and risk 

characterization (Figure 1) (USEPA, 1992). The problem formulation identifies the assessment 1

 Please note that the name of the model under review was recently changed from Two-dimensional Runoff, 

Erosion, and Export (TREX) to Two-dimensional Exposure and Rainfall-Runoff Analysis (TERRA). This name 

change was made to avoid confusion with the Terrestrial Residue Exposure (T-REX) model that OPP uses to 

estimate pesticide concentrations for avian and mammalian food items ( USEPA, 2008). 
Page 4 of 33 

endpoints and ecological characteristics including those that are important to protect and 

describes the mode of action, use characteristics, chemistry, environmental fate, and toxicity of 

the pesticide and its degradation products. The elemental nature of metals requires a unique risk 

assessment process to account for natural biogeochemical cycles and chemistry. 

Figure 1. Risk Assessment Framework

Information on the stressor is integrated into a conceptual model to identify the routes of 

dissipation and exposure pathways. More importantly, this integrated information leads to a 

detailed analysis plan for analyzing data and characterizing risk. Figure 2 illustrates a conceptual 

model of the fate and transport processes for metals. Antimicrobial uses of metals, such as 

embedded or sorbed metal in wood and shingles, requires a leaching process to account for 

environmental exposure. In contrast, conventional pesticide uses of metals, such as in fungicides 

for crops or algaecides applied to water, have direct environmental exposures through 

applications to foliage, water, and soil. Once the metal is transported to or applied to soil, the 

environmental chemistry and transport processes are expected to be similar for antimicrobial and 

conventional uses of metals. Important transport processes from terrestrial environments are 

associated with erosion, overland flow (runoff), and infiltration. In contrast, the important 

transport processes in aquatic environments are advection/dispersion, deposition, and resuspension. Critical environmental chemistry parameters for metals in terrestrial and aquatic 

environments include ionic strength, redox potential, ambient metal concentration, inorganic 

ligands, organic ligands, and soil/sediment mineralogy. 
Page 5 of 33 

Figure 2. Conceptual Model for Metal Exposure Assessment

In the analysis phase, the risk assessor develops the exposure and toxicity profiles. The exposure 

profile provides an estimation of environmental concentrations (EECs) in aquatic and terrestrial 

environments based on the disposition of the pesticide and the nature of the receiving ecosystem. 

Exposure assessment for metals should consider freely-dissolved metal in water (free ion, 

Transport Processes

Overland Flow

Erosion

Infiltration

Deposition/Resuspension

Surface Water

Terrestrial

Use Site

Application Rate

Leaching Rate

Aquatic

Use Site

Application Rate

Leaching Rate

Chemical Processes

Sorption/Desorption

Precipitation/Dissolution

Complexation

Sediment

ETransport Processes

Dispersion

Deposition/Resuspension

Flow

CChemical Processes

Sorption/Desorption

Precipitation/Dissolution

Complexation
Page 6 of 33 

inorganic complexes, and organic complexes), particle bound metal (sorbed, precipitated, and 

incorporated into mineral phases), and metals in air. The importance of each phase is dependent 

on the metal as well as the source of the metal. 

Exposure modeling of metals is complicated because it requires a linkage of hydrodynamic 

models, sediment transport models, chemical transport models, and chemical speciation models. 

More importantly, the temporal and spatial variability of water and soil chemistry further 

complicates speciation modeling. Although selection of the appropriate models is dependent on 

the goal of the risk assessment, an exposure model for metals should have the capability to 

account for soil:water partitioning. This capability is important in determining the freely -

dissolved concentration of metal. 

The second component of the analysis phase is the effects assessment and development of 

stressor-response profiles. The stressor-response profile summarizes data on the effects of the 

pesticide and relates them to the assessment endpoints. By relating exposure levels to toxicity 

endpoints, the risk assessor can determine the effects of varying exposure levels on ecological 

receptors. Development of appropriate stressor-response relationships for aquatic organisms 

exposed to metals is particularly complicated because metal species differ in their toxicological 

properties. Thus the same total pool of metal or dissolved metal concentration available under 

one set of environmental conditions (e.g., pH, DOC, hardness) can elicit different effect levels 

under a different set of environmental conditions. 

The final phase, risk characterization, integrates the analyses from the ecological effects and 

exposure assessments and quantifies risk to ecological entities. This process requires risk 

analysis and a framework to interpret the risks in the regulatory decision making process. For 

pesticide registration decisions under FIFRA, EPA has an established approach for calculating 

estimates of risk and interpreting these risk levels (USEPA, 2004). 

1.3 OFFICE OF PESTICIDE PROGRAM AQUATIC EXPOSURE MODELS 

OPP employs a tiered modeling strategy to assess aquatic exposure. The aquatic exposure 

assessment provides an estimation of the environmental concentrations (EEC) of pesticides and 

their degradation products in surface water. For conventional pesticides, a two-tiered screening 

modeling approach is used to determine exposure in aquatic environments. To use this approach, 

each refinement requires incorporation of additional site-specific data, progressing from 

conservative or high-end concentrations to more refined EECs. 

 

As most pesticide registrations are national in scope, exposure modeling in OPP must account 

for the wide variety of landscapes across the country. However, there are circumstances where 

more spatially explicit exposure modeling is needed, such as the exposure assessment for an 

endangered species with a well-defined habitat. In these cases, modeling is conducted to 

represent predictions of pesticide concentrations for use sites near the endangered species 

habitat. 
Page 7 of 33 

OPP’s screening models are designed to predict runoff and erosion of pesticides from 

representive use sites with high runoff potential. The screening model scenario is a 10-hectare 

(25-acre) field, draining into a 2-meter deep, 1-hectare static (no flow) pond, which is assumed to 

contain a constant volume (20 × 106

 liters) of water (Figure 3). The drainage area to normal 

volume ratio for the standard pond was derived from engineering specifications for farm ponds 

in the southeastern United States. Surface water-ground water interactions are not considered in 

the screening-level assessment. The modeling is conducted assuming 100% of the 10-hectare 

field is treated with the pesticide at the maximum label application rate. 

Figure 3. Screening-Level Ecological Exposure Scenario

In EPA’s Office of Pesticide Programs, Tier I modeling for aquatic exposure is conducted using 

the GENeric Estimated Environmental Concentration (GENEEC) model. GENEEC is a single 

runoff event meta-model of the Pesticide Root Zone Model (PRZM) and EXposure Analysis 

Modeling System (EXAMS) modeling for a cotton site in Yazoo, MS. Modeling parameters, 

such as maximum label application rate, average Koc, and the upper 90th percentile confidence 

limit of the mean aerobic soil metabolism half-life, are used in the modeling to predict EECs 

(Figure 4). The GENEEC model predicts EECs from a single-runoff event. Based on the single 

runoff event, a daily peak, 21-day average, 60-day average, and 90-day average EECs are 

predicted. If no risks of concern are found using the Tier 1 screening exposure estimates, then 

there is a high probability for low risk from the pesticide use. 

If there are risks identified using Tier I exposure modeling, then refined Tier II exposure 

modeling is conducted, using PRZM and EXAMS models. Tier II modeling incorporates more 

spatially-explicit data for pesticide use areas, including 30 years of meteorological data, soil 

properties (organic carbon content, bulk density, water holding capacity, etc.), and application 

timing. Tier II modeling predicts a 30-year time series of daily pesticide concentrations in the 

Runoff, Erosion,

Spray Drift

10 ha Watershed

1 ha Standard Pond
Page 8 of 33 

standard pond. The 1 in 10 year daily peak, 1 in 10 year 21-day average, 1 in 10 year 60-day 

average, 1 in 10 year 90-day average, 1 in 10 year average annual, and average 30-year annual 

concentration are derived from the time series. For both modeling tiers, spray drift estimates 

from AgDrift and AGDISP may be incorporated into the surface water modeling. More 

information on this and other models used by OPP is available on the EPA’s website at 

http://www.epa.gov/oppefed1/models/water/index.htm. 

Figure 4. Parameters Considered in PRZM/EXAMS Modeling.

With the passage of the FQPA, the EPA was tasked to routinely incorporate drinking water as a 

route of exposure into human health risk assessments. This statutory requirement led to an 

assessment of available basin-scale models as well as development of a PRZM/EXAMS index 

water reservoir scenario for drinking water assessments (USEPA, 1998). The index reservoir 

scenario is modeled after the Shipman reservoir, which represents an actual drinking water 

reservoir with high vulnerability for atrazine contamination. Development of the index reservoir 

also promoted development of percent crop area (PCA) factors, which account for the percentage 

of watershed area in a specific crop (USEPA, 1999) in the larger watersheds capable of 

sustaining a drinking water reservoir. The PRZM/EXAMS watershed modeling procedure 

parallels the modeling approach for aquatic exposure assessment used in ecological assessments 

except for differences in drainage area to normal volume ratio and reservoir outflow. The 

reservoir watershed is a 172.8 hectares (427 acres) field draining into a 5.26 hectare (13 acres) 

reservoir with a mean depth of 2.7 m for a normal capacity of 144,320 m3

 (1.44 ×108 liters). The 

reservoir is assumed to remain at a constant volume by assuming the watershed inflow volume is 

equal to reservoir outflow volume. 

In 1998, the OPP conducted a review of six basin-scale models to determine model capabilities 

and their potential utility in pesticide human health exposure assessments through a FIFRA SAP 

meeting (USEPA, 1998). These basin-scale models included CFWR, modifed PRZM-EXAMS 

linkage, RIVWQ, HSPF(BASINS), SWAT, and AnnAGNPS (see “Model Acronyms” list on the 

following page). OPP determine that these basin-scale models did not have the appropriate 
Page 9 of 33 

subroutines to address plant growth, pesticide degradation processes, and agronomic practices. 

OPP concluded that the models required spatially-explicit data on watershed and hydrological 

properties. Additionally, model calibration appeared to be prerequisite for watershed modeling. 

Other reviews of watershed-scale model capabilities 

have been conducted by the EPA’s Office of Water 

(OW) and HydroQaul (USEPA, 1997 and 

HydroQual, 2010). In their review of model 

capabilities, OW assessed various watershed models 

for development of Total Maximum Daily Loads 

(TMDL). Most of the models considered in the 

review had a hydrology component for runoff 

(curve number equation) and stream-flow, an 

erosion (Universal Soil Loss Equation) component, 

and a water quality component for pollutant 

loadings. Pollutant loadings were controlled by 

loading functions for rural areas and build-up/washoff functions for urban areas. Major differences 

among the models were associated with the spatial 

resolution, number of physical and chemical 

processes included in the model, and the 

computational requirements. 

The HydroQual watershed model review included 

an evaluation of TR-55, HEC-1, HEC-HMS, HSPF, 

KINEROS, SWAT, SHETRAN, MIKE SHE, 

SMDR, CASC2D, GSSHA, and TERRA (aka,

TREX). These models represent a range of 

watershed model frameworks, including lumped 

parameter models, semi-distributed models (lumped model parameters in defined sub-basins), 

and fully-distributed models (lumped model parameters in defined grid sections within subbasins). All the models were capable of simulating runoff, and some were capable of simulating 

soil erosion and stream sediment transport. Few of the models had capabilities to simulate 

chemical transport. The watershed model reviews by OPP, OW, and HydroQual indicate 

variable model capabilities among the models. 

Other aquatic exposure models used for conventional pesticides include the Tier 1 Rice Model 

and the Biotic Ligand Model (BLM). The Tier 1 Rice model is used to assess pesticide 

concentrations in rice paddies. Chemical speciation models, such as the BLM model, have been 

used in exposure assessments for metals. Please refer to Section 2.3 for more explanation on the 

BLM model. More information on the models used by OPP is available on the EPA’s website at 

http://www.epa.gov/oppefed1/models/water/index.htm. 

 

Model Acronyms

CFWR: Conceptual Flowing Water with 

Reservoir Screening Model 

PRZM-EXAMS: Pesticide Root Zone 

Model- Exposure Analysis Modeling System 

RIVWQ: River Water Quality 

HSPF(BASINS): Hydrological Simulation 

Program-FORTRAN 

SWAT: Soil and Water Assessment Tool 

AnnAGNPS: Agricultural Nonpoint Source 

Pollution Model 

TR-55: Technical Release 55 

HEC-1: Hydrologic Engineering Center 1 

HEC-HMS: Hydrologic Model System 

KINEROS: Kinematic Runoff and Erosion 

Model 

SHETRAN: System Hydrology Europe 

Transport 

MIKE SHE: System Hydrology Europe 

SMDR: Soil Moisture, Distribution, and 

Routing 

CASC2D: Cascade of Planes in 2-

Dimensions 

GSSHA: Gridded Surface/Subsurface 

Hydrologic Analysis 

TERRA: Two-dimensional Exposure 

Rainfall-Runoff Assessment 

BLM: Biotic Ligand Model 
Page 10 of 33 

1.4 EXPOSURE MODELING SPECIFIC TO ANTIMICROBIAL PESTICIDES 

Unlike many conventional pesticides which tend to enter the aquatic environment via runoff and 

spray drift, many antimicrobial pesticides tend to enter the aquatic environment indirectly via 

wastewater treatment plant effluents or from end-uses that lead to direct contact with surface 

water. The Antimicrobials Division (AD) in OPP uses other models to estimate concentrations 

of antimicrobial pesticides in aquatic environments in addition to those used for conventional 

pesticides. Models used to estimate surface water concentrations of antimicrobial pesticides and 

associated exposures to aquatic organisms include two of four models included in the Exposure 

and Fate Assessment Screening Tool (E-FAST) and the Marine Antifoulant Model to Predict 

Environmental Concentrations (MAM-PEC). E-FAST, Version 2.0, developed by the Office of 

Chemical Safety and Pollution Prevention/Office of Pollution Prevention and Toxics/Economics, 

Exposure and Technology Division/Exposure Assessment Branch (OCSPP/OPPT/EETD/EAB) 

(USEPA 2006), is a screening-level tool that includes models which estimate concentrations of 

chemicals released to air, surface water, and landfills. E-FAST consists of four models: (1) 

General Population and Ecological Exposure from Industrial Releases model; (2) Down-theDrain (DtD) model; (3) Consumer Exposure Module (CEM); and (4) Probabilistic Dilution 

Model (PDM). In addition to the PDM model of E-FAST, which can be run as a stand-alone 

model, the General Population and Ecological Exposure from Industrial Releases and Down-theDrain models of E-FAST also include PDM options. 

The Antimicrobials Division uses the General Population and Ecological Exposure from 

Industrial Releases model (hereinafter referred to as the Industrial Releases model) and the 

Down-the-Drain model. The Industrial Releases model is designed to estimate human and 

ecological exposures from releases to air, surface water, and land as a result of manufacturing, 

processing, and industrial use of chemical substances; this model is also applicable for chemicals 

released from some commercial uses, such as laundries, dry cleaners, and car washes. The 

Down-the-Drain (D-t-D) model is designed to estimate surface water concentrations of 

chemicals downstream of domestic wastewater treatment facilities that receive wastewater 

primarily from residential, commercial, and institutional establishments. The D-t-D model 

estimates exposures to aquatic organisms and to humans from ingestion of drinking water and 

fish based on concentrations in surface water downstream of domestic wastewater treatment 

plants. The Industrial Releases and the D-t-D models have an option to run the Probabilistic 

Dilution Model (PDM). PDM estimates the number of days per year that concentrations of 

concern (COC) for aquatic organisms are exceeded based on estimated surface water 

concentrations immediately downstream of wastewater treatment plants. 

To assess exposures from uses of antimicrobials that are released to domestic wastewater 

treatment plants, AD typically runs the DtD model using the PDM option. Input parameters 

required to estimate exposure of aquatic organisms to antimicrobial pesticides released to surface 

water downstream of domestic wastewater treatment plants include: (1) volume of antimicrobial 

pesticide entering domestic wastewater treatment plants; (2) percent removal of antimicrobial 

pesticide during wastewater treatment; and (3) concentrations of concern (COC) based on 

ecotoxicity endpoints for aquatic animals and plants. Concentrations of concern for aquatic life 

are based on ecotoxicity endpoints for aquatic animals and plants, and include uncertainty 

factors, as appropriate. 
Page 11 of 33 

The DtD model uses daily per capita release of household wastewaters, stream dilution factors, 

flow, and WWTP removal efficiency to provide both high-end and median time-averaged 

surface water concentrations of chemicals discharged by wastewater treatment plants receiving 

household wastewaters. DtD model output that is relevant to assessing exposures of aquatic 

organisms to antimicrobial pesticides are number of days per year and percent of year that COCs 

for aquatic animals and plants are exceeded for both high-end (upper 10th percentile of stream 

dilution factors) and average (50th percentile of stream dilution factors) scenarios. The Industrial 

Releases model of E-FAST is used to estimate human and ecological exposures from pesticides 

that are released to industrial wastewater treatments plants. 

 Another model used by the Antimicrobials Division is the Marine Antifoulant Model to Predict 

Environmental Concentrations (MAM-PEC), Version 2 . This model was developed by the 

Institute of Environmental Studies/IVM and Delft Hydraulics for the European Paint Makers 

Association (CEPE) for conducting risk assessments on antifouling agents. It is currently used 

as an assessment tool for antifoulant risk assessments in Europe. The model provides predictions 

of environmental concentrations of antifouling products in six generalized “typical” marine 

environments: commercial harbor, estuarine harbor, marina, marina poorly flushed, open sea, 

and shipping lane. The model takes into account emission factors (e.g., leaching rates, shipping 

intensities, residence times, underwater hull surface areas), compound-related properties and 

processes (e.g., volatilization, speciation, hydrolysis, photolysis, bacterial degradation), and 

properties and processes related to the specific environment (e.g., currents, tides, salinity, DOC, 

suspended matter load). 

1.5 CHACTERISTICS OF THE TERRA MODEL 

The TERRA watershed model is proposed by the American Chemistry Council (ACC) as a 

refined model for estimating environmental exposure to copper resulting from its antimicrobial 

uses. The model is designed as a fully distributed framework, and accounts for spatial 

distribution of rainfall, soil properties, hydrology, and pesticide use across a watershed. 

The TERRA model was developed using the CASCD2 model framework and chemical fate 

subroutines from WASP/IPX (Velleux et al., 2008). It is an event-based, basin-watershed scale 

model and simulates environmental fate for contaminants in overland flow, surface soil erosion 

and deposition, channel flow, and sediment transport. Using a raster or grid-based approach, 

TERRA is designed to spatially-distribute parameters across the watershed. The grid cell area is 

specified by the user and can range from 30 meters2 (0.0074 acres) to 960 meters2

 (0.24 acres) 

(Velleux et al., 2006; England et al., 2007). 

For each grid cell, hydrologic and soil properties including infiltration, overland flow, and 

channel flow can be assigned (Julians et al., 1995). The infiltration rate is determined using the 

Green and Ampt equation, which includes such parameters as hydraulic conductivity and soil 

porosity. These parameters can be obtained from the soil textural classification or GIS-overlays 

of soils data (i.e., STATSGO or SURRGO). The model has no subsurface flow subroutines to 

allow estimation of base flow into surface water. 

In the TERRA model, overland flow is simulated using a two-dimensional diffusive wave 

approximation equation to predict runoff water flow direction and discharge rate from each cell. 
Page 12 of 33 

Data requirements for estimating overland flow include raster maps of topography, retention 

storage depth, and surface roughness coefficients. Channel flow within streams or rivers in the 

watershed is simulated using diffusive wave formulation in one dimension. Data required for 

channel flow predictions include designation of drainage cell (grid cells within a channel), 

channel geometry (width and depth), and roughness factor (Mannings N). However, the overland 

flow and channel flow subroutines in TERRA require small time steps (5 to 60 seconds) for 

numerical stability, and modeling the hydrology of narrow channels with substantial changes in 

bed slope or channel cross-section is difficult with TERRA. The hydrology model subroutines 

have been verified using model comparison with analytical solutions and experimental data sets 

as well as comparison of model results with independently calibrated watershed modeling (Julian 

et al., 1995). 

The surface erosion subroutines in TERRA are based on modified USLE sediment transport 

equations for rill and sheet erosion (Johnson et al., 2000). The sediment transport subroutines 

account for overland discharge, friction of slope, soil erodibility, crop-management factors, and 

soil conservation practices. Direction of the movement of eroded soil between grid cells is 

dependent on overland flow direction. In the erosion subroutine of TERRA, the erosion process 

accounts for particle-size distribution of sand, silt, and clay. Additionally, each grid cell has a 

sediment trapping efficiency for each particle size, which is dependent on grid cell size, settling 

velocity for each particle size, and flow velocity. According to Rojas 2008, erosion estimates are 

optimized at grid cell sizes of < 150 meters. 

The chemical transport and fate subroutines in TERRA account for chemical partitioning, 

advection-diffusion, erosion, deposition, infiltration/transmission loss, and mass transfer loses. 

These subroutines have been evaluated for metals (Cu, Cd, and Zn) and total suspended sediment 

in the California Gulch watershed near Leadville, CO (Velleux et al., 2008). The chemical 

transport and fate subroutines in TERRA can predict chemical concentrations in overland runoff, 

soil, streams, and sediments. The model can also predict linear chemical partitioning for 

different particle sizes (sand, silt, and clay) and for three chemical phases, including a dissolved 

phase, bound phase, and particulate phase. Advection and dispersion is assumed for all chemical 

phases. Erosion and deposition is assumed for the bound phase and corresponds to transport for 

the different particle sizes. Infiltration is assumed for the dissolved and bound phases and is 

linked to the water infiltration flux. The chemical concentration accounts for a mass balance in 

each grid cell and stream flow. The TERRA model does not account for biodegradation, 

hydrolysis, oxidation, photolysis, volatilization, or dissolution/ precipitation. 

Model calibration has been conducted through modification of hydraulic conductivity, Mannings 

N, soil erodibility factor, land cover factor, and the chemical partitioning coefficients (Velleux et 

al., 2008 and Rojas et al., 2008). 

A table of model capabilities for TERRA is summarized in Appendix A. The TERRA model has 

sophisticated modeling subroutines (Green and Ampt and diffusive wave) for assessing surface 

water hydrology. Most notably, the grid-cell framework allows distribution of meteorology, soil, 

contaminants, and hydrological properties over a landscape. Additionally, this model design 

provides the capability to track contaminant loads into surface water. 
Page 13 of 33 

The erosion subroutines in TERRA are based on a modified USLE erosion model in which 

predicted erosion is based on overland flow, slope, USDA soil erodibility factor, USDA cropmanagement factor, and USDA soil conservation practices. The erosion model also provides a 

sediment trapping efficiency. The erosion subroutines account for suspension and deposition of 

sand, silt, and clay in both the field and stream channel. The only environmental fate subroutine 

is a linear partitioning model. With TERRA, chemical mass balance is maintained, and an 

estimation of contaminant partitioning on sand, silt, and clay is provided. Transmission loss of a 

chemical from infiltration of dissolved and bound chemical is estimated using advection, 

dispersion, and diffusion. Numerical stability of erosion and surface water hydrology is 

dependent on selection of the proper grid size. Computational requirements (computational time 

and storage requirements) are dependent on the grid size. 

Features not considered in TERRA include ground water fate, subsurface flow, spray drift, and 

surface water environmental fate parameters, including hydrolysis, photodegradation, microbial 

degradation, and volatilization. Agronomic factors such as pesticide application methods (e.g., Tband, in-furrow, etc.) and plant growth are also not considered in the model. 

2.0 COPPER ECOLOGICAL EXPOSURE ASSESSMENTS CONDUCTED 

BY EPA

Copper pesticides are registered for use as both conventional pesticides in mainly agricultural 

settings and as antimicrobial pesticides in mainly commercial and residential settings. 

Agricultural uses include fungicides and direct water applications as aquatic herbicides, 

algaecides, and molluscides. Other copper-containing conventional pesticides are registered for 

use to control leeches, crustaceans, mites, and termites, and root growth in sewage and irrigation 

systems. Antimicrobial uses include antifoulants, wood preservatives, and material 

preservatives. Certain copper alloys are also registered for production of antimicrobial products 

such as doorknobs, kickplates, and bedrails. 

The most recent environmental risk assessment by OPP for conventional uses of copper was 

completed for the July 2006 Copper Reregistration Eligibility Decision (RED). For 

antimicrobial uses of copper, environmental exposure was assessed by OPP as part of the August 

2010 Revised Addendum to the Revised Coppers RED. EPA anticipates conducting a complete 

ecological risk assessment, including an endangered species assessment, for all pesticidal uses of 

copper through the registration review process. The final work plan for the registration review of 

copper was published in March 2011. 

Because copper is both ubiquitous and toxic, it is also regulated by other EPA programs. For 

example, the Office of Water (OW), which regulates copper under the Clean Water Act (CWA), 

released draft Ambient Aquatic Life Criteria for copper in 2003. In terrestrial ecosystems, 

copper is a contaminant of concern (COC) under the Comprehensive Environmental Response, 

Compensation, and Liability Act (CERCLA). Environmental screening levels developed by the 

Office of Solid Waste and Emergency Response (OSWER) have been used in the RED 

assessment of copper for evaluation of some toxicity endpoints. 
Page 14 of 33 

The OPP RED and registration review risk assessment strategy for copper involved linking 

exposure models with the BLM model to produce site-specific EECs and toxicity endpoints for 

ionic Cu2+ (Figure 5). Because copper is a metallic element, it will continue to cycle via 

biogeochemical mechanisms in the environment after it is released. The chemistry of Cu2+ is 

complex because it forms inorganic (including CuOH+

, Cu(OH)2

0

, Cu(OH)3

-

, Cu(OH)4

2

, CuCl+, 

CuCl2

0

, CuCl3

-

, CuHCO3

-

, etc.) species and soluble complexes with organic ligands (e.g., DTPA, 

EDTA, HEDTA, humic acids, and fulvic acids)2

 in solution. Exposure models such as PRZMEXAMS have been used to model runoff of copper into surface water. Dissolved copper 

concentrations from exposure models were used in the BLM model along with site-specific 

water quality data to estimate the EEC and toxicity for ionic Cu2+. This exposure modeling 

approach assumes chemical equilibria with no consideration of chemical kinetics with regard to 

precipitation/dissolution, complex formation, adsorption/desorption, and oxidation/reduction. 

Site-specific BLM estimated concentration of Cu2+ and toxicity were used to calculate risk 

quotients. 

Figure 5. OPP Risk Assessment Strategy for Coppers

2.1 ECOLOGICAL RISK ASSESSMENT FOR CONVENTIONAL USES OF COPPER 

An ecological risk assessment was conducted for copper sulfate, copper group II salts, oxides, 

hydroxides, and complexes used on food crops as fungicides and bacteriocides, and directly in 

aquatic environments primarily for aquatic plant control. Most of these compounds dissociate in 

the environment to form ionic copper3

 (usually Cu2+), and the counter ion. Throughout the risk 

assessment, the cupric ion (Cu 2+) has been evaluated as the active ingredient. The aquatic 

 2

 Inorganic and organic copper complexes replace the bound water of the hexaquocoppe (II) ion with the ligand. Hence CuCl2

0

 is actually 

[Cu[H20]4Cl2]

0

. 3

 Cu2+ is shorthand for Cu[H20]6

2+, the hexaquocopper (II) ion.
Page 15 of 33 

portion of the OPP assessment uses a standard OPP Environmental Fate and Effects Division 

(EFED) tool, the Pesticide Root Zone Model and Exposure Analysis Modeling System 

(PRZM/EXAMS) to estimate runoff, and an OW tool, the Biotic Ligand Model (BLM), to 

estimate the potential toxicity and concentration of the Cu2+ ion in aquatic systems. 

For the aquatic risk assessment, Cu(II) compound uses on terrestrial crops were modeled using 

the PRZM/EXAM models for estimating Cu2+ runoff from a 10-ha field into a standard small 

water body. PRZM/EXAMS input parameters for Cu2+ are shown in Table 1. 

Table 1. Chemical Input Parameters for PRZM/EXAMS Modeling 

Parameters Input Value and Units Source of Information 

Soil Partition Coefficients 400 L/kg sand soil 

3600 L/kg clay soil Jones and Hetrick, 1991 

Molecular Weight 63.54 g/mole Atomic Weight of Cu 

Solubility in Water 5892 mg/L at 250C Solubility of Cu(OH)2 with fixed water 

chemistry 

Vapor Pressure 1.00E-15 Torr Default to eliminate volatilization 

Henry’s Law Constant 1.00E-15 atm m3/mole Default to eliminate volatilization 

Hydrolysis T1/2 Stable Default due elemental nature of Cu 

Aqueous Photolysis 1,000,000 days Default due elemental nature of Cu 

Aerobic Soil Metabolism 1,000,000 days Default due elemental nature of Cu 

Anaerobic Soil Metabolism 1,000,000 days Default due elemental nature of Cu 

Aerobic Aquatic Metabolism 1,000,000 days 

10 days (Dissipation Half-Life) 

A 10 day aquatic field dissipation 

half-life was used to estimate the 21-day 

average concentration. 

For modeling conventional uses of copper, a range of crop scenarios with diverse geography was 

selected to ensure a representation of crop-climate-agronomic combinations (Table 2). 
Page 16 of 33 

Table 2. Crop Scenarios Modeled for Conventional Copper 

Crop State

Maximum 

Application 

Rate 

(kg Cu/ha)

Average 

Application 

Rate 

(kg Cu/ha)

Number of 

Application

First 

Application 

Date

Application 

Intervals 

(days)

Alfalfa 

CA 0.59 ND 4 10-May 30 

MN 0.59 ND 4 10-May 30 

NC 0.59 ND 4 10-May 30 

TX 0.59 ND 4 10-May 30 

Almond CA 11.87 2.58 2 1-March 210 

Apples 

NC 19 1.79 4 25-March 7 

PA 19 2.24 4 25-March 7 

OR 19 4.25 4 1-May 7 

Citrus 

CA 15.43 3.81 4 15-October 7 

FL 15.43 2.24 4 15-May 7 

Corn 

CA 1.18 ND 4 8-May 7 

FL 1.18 ND 4 15-November 7 

IL 1.18 ND 4 1-June 7 

NC 1.18 ND 4 11-May 7 

OH 1.18 ND 4 1-June 7 

Filberts CA 35.62 ND 2 10-November 14 

Grapes CA 4.75 0.67 4 1-February 7 

Peaches GA 14.84 3.70 2 1-April 240 

Potatoes 

ID 3.56 0.78 4 30-May 7 

ME 3.56 0.90 4 30-May 7 

Turf 

FL 6.10 ND 2 1-June 30 

PA 6.10 ND 2 1-June 30 

Walnut CA 29.68 3.47 4 7-March 7 

To assess the speciation and consequent toxicity of conventional uses of copper, OPP used the 

Biotic Ligand Model (BLM), which accounts for the effects of major dissolved ions and 

dissolved organic carbon (DOC) present in natural surface waters. (These ions and DOC may 

reduce the toxicity of the Cu ion by binding to it and making it biologically unavailable.) The 

BLM, which has been reviewed and approved by the Science Advisory Board (SAB), has been 

used by EPA’s Office of Water to establish Ambient Water Quality Criteria. In its risk 

assessment, OPP used the BLM model to estimate the concentration of Cu2+ ion (copper activity) 
Page 17 of 33 

and the toxicity of this ion in the standard small water body using site-specific water chemistry 

monitoring data from 811 USGS sites distributed across the nation (Table 3). 

Table 3. Summary of USGS Water Quality Data Used in BLM model 

Parameter Units Data Range for 

BLM 

Missing 

Data Low Value High Value Median 

Value 

Average 

Value 

Temperature 0C 10 to 25 0 10 24.5 17.4 17.2 

pH 4.9 to 9.2 0 5.05 9.2 8.0 7.85 

Cu μg/L 363 1.0 51.4 1.2 2.61 

DOC mg/L 0.05 to 29.65 0 0.2 29.2 3.00 4.06 

Humic Acid2 % 10 to 60 0 10 29.15 14.90 16.69 

Ca2+ mg/L 0.204 to 120.24 0 0.95 114 37.6 40.62 

Mg2+ mg/L 0.024 to 51.9 0 0.18 51.8 10.6 13.02 

Na+ mg/L 0.16 to 236.9 0 0.88 190 10.3 21.09 

K+ mg/L 0.039 to 278.4 0 0.09 18 2.2 2.93 

SO4

2- mg/L 0.096 to 278.4 0 0.10 270 26.1 44.46 

Cl- mg/L 0.32 to 279.72 0 0.32 266 11 22.66 

Alkalinity mg/L 1.99 to 360 0 2.0 311 116 120.61 

S2- mg/L ND 811 ND ND ND ND 

The PRZM/EXAMS models estimated peak, 21-day average, and 60-day average Cu2+

concentrations for each runoff scenario. Risk quotients were derived for aquatic invertebrates 

and fish by comparing the copper activity derived from runoff with the copper activity 

representing an aquatic invertebrate or fish LC50 at the specific site, based on water chemistry as 

calculated by the BLM. The resulting risk quotients (RQs) were compared to OPP’s preestablished levels of concern (LOC), and expressed as the percentage of sites exceeding those 

LOCs. Equations, which were fit to the data generated in this process, provided an estimation of 

dissolved copper concentrations based on application rates, and an estimate of how many of the 

USGS sites exceed a given endpoint at a particular dissolved copper concentration. 

Aquatic assessments for copper addressed both direct water applications and runoff. In all cases, 

the estimated concentration of copper was used as an input for the BLM to determine site 

specific Cu 2+ concentration, and this concentration was compared to the site specific toxicity 

values to determine risk quotients (RQs). Estimated peak runoff of Cu concentrations ranged 

from 2 parts per billion (μg/L) to 365 μg/L, depending on the application rate, timing of 

application, meteorological conditions, soil type, and cropping practices associated with the 

particular scenario (Table 4). 
Page 18 of 33 

Table 4. PRZM/EXAMS Dissolved Copper Estimated Environmental Concentration 

Crop State

1-in-10 year Concentration 

(ug/L)

First Year Concentration 

(ug/L) Accumulation 

Slope 

for Peak Conc. 

(ug/L/year)

Low Kd High Kd Low Kd High Kd

Peak 21-day Peak 21-day Peak 21-day Peak 21-day

Alfalfa 

CA 14.2 13.1 2.9 0.4 1.8 0.7 1.4 0.1 0.46 

MN 27.1 3.2 3.1 0.7 1.8 0.5 1.4 0.1 0.98 

NC 52.5 4.3 6.5 1.3 1.8 0.6 1.4 0.1 1.87 

TX 84.5 7.8 6.1 0.9 2.9 0.7 1.4 0.1 0.52 

Almond CA 216.7 18.9 54.8 7.4 31.4 10.9 27 1.7 6.83 

Apples 

NC 1848 159.9 631.6 123.1 71.7 36 45.6 5.8 73.9 

PA 3529 256.3 817.9 168.4 350 119 365 26.8 119.92 

OR 1317 125 431.7 102.8 70.6 34.5 44 5.7 46 

Citrus 

CA 349.2 324.9 70.4 11.6 57.4 32.5 35.6 4.6 10.87 

FL 1766 104.7 382.7 58.8 57.4 23.8 35.8 4.6 64.78 

Corn 

CA 167 17.1 42.8 8.3 13.2 4.4 9.2 0.8 5.75 

FL 202.2 10.2 34.8 5.1 4.4 1.9 2.7 0.3 7.54 

IL 286.2 22.2 50.4 10.9 22.4 8.4 19.5 1.6 9.70 

NC 234.9 15.8 45.5 8.8 9.3 4.1 8.1 1.0 8.26 

OH 255.8 18.9 53.5 10.5 18.4 6.1 15.3 1.3 8.26 

Filberts CA 2768 279.9 415.2 198 162 68 81.6 11.1 13.53 

Grapes CA 110.7 3.6 23.1 4.2 17.6 3.0 11.0 1.4 3.46 

Peaches GA 381.5 32.7 277.5 30.5 25.6 8.2 27.3 1.8 12.95 

Potatoes 

ID 122.7 13 53 9.1 13.2 5.9 8.3 1.1 4.22 

ME 662.5 63.4 139.6 38.1 54.9 22.8 46.2 4.9 23.92 

Turf 

FL 579.8 64.4 152.1 25.5 45.8 14.1 10.7 1.0 26.34 

PA 839.1 66.6 177.1 24.5 60.9 16 11.9 1.4 19.45 

Walnut CA 1057 103 267.9 48.3 110 55.2 68.8 68.7 33.97 

For algaecide and aquatic herbicide uses, the copper compounds are applied at a range of rates 

between 0.2 – 1.0 mg/L elemental copper. Average copper rates for algae control were 0.5 mg/L 

for susceptible species and 0.75 - 1.0 mg/L for resistant species. Average copper rates for weed 

control were 0.2 - 0.5 mg/L for susceptible species and up to 1.0 mg/L for resistant species. The 

average number of annual applications to a given water body ranged between two and four, with 

none of these applications being made to more than 25 - 33% of a given water body at any one 

time. Generally, applications are made at an interval of approximately two weeks. 

Information regarding the use of copper to control mollusks, leeches, and tadpole shrimp was 

difficult to obtain. Based on information available, use rates ranged from 1.0 mg/L to as high as 

2.5 mg/L elemental copper. Direct water application of copper sulfate pentahydrate for snail 
Page 19 of 33 

control was found to be within the 1.0 to 1.5 mg/L concentration. Control of snails and leeches 

in ponds and other associated quiescent water bodies is for human health as well as agricultural 

and aquacultural reasons. Aquaculture systems rely on direct water applications for the control 

of snails and tadpole shrimp at rates of 1.0 to 2.5 mg/L concentration of copper 

Dissolved copper concentrations should be 3.4 μg/L or less in order to prevent aquatic 

invertebrate LOC exceedances at 95% of the sites. Although a number of site-specific factors 

influence runoff concentrations, 3.4 μg/L dissolved Cu correlates to application rates of 

approximately 1.5 lb Cu/A. 

2.2 ECOLOGICAL RISK ASSESSMENT FOR ANTIMICROBIAL USES OF COPPER 

The most recent risk assessment of antimicrobial uses of copper was completed for the “Revised 

Addendum to the 2009 Revised Copper Reregistration Eligibility Decision (RED): Antimicrobial 

Uses of Copper” issued in September 2010. For this assessment, EPA conducted a screeninglevel assessment for the following antimicrobial copper use scenarios: preserved wood in a 

residential community (wood decks, wood fences, etc.) and roofing shingles in a residential 

community. 

The exposure modeling for wood preservatives and shingles was conducted by linking screening 

level exposure calculations with the BLM model. The BLM modeling was conducted to 

estimate the concentration and toxicity (LC50) of Cu2+ using water quality conditions at 811 

USGS monitoring sites. The water quality conditions are shown in Table 3. In contrast, the 

exposure for antifoulant uses of copper was modeled using MAM-PEC with no linkage to the 

BLM model because a saltwater BLM has not been adopted for regulatory use. 

The exposure calculations for antimicrobial copper in shingles and wood were conducted using a 

residential conceptual model as depicted in Figure 6. This exposure scenario assumes that 

copper can be released from shingles and treated wood and then transported into surface water 

via impervious surfaces. Leaching rates from shingles and wood structures are used to estimate 

the copper loading into the surface water. Retention of copper in soil is not considered in these 

calculations. 

In the residential conceptual model, a 10- ha residential watershed is assumed to drain into 1-ha 

standard pond. The 10-ha watershed is assumed to be populated with single family homes at a 

density of 4 houses per acre, and each house is assumed to have a treated wood deck and fence as 

well as telephone poles. The hydrology of the exposure assessment is controlled by a single 

runoff event, which represents the 90th percentile rainfall amount from the Yazoo, MS weather 

file (Burns et al 2007). Relevant parameters for calculating copper loading are shown in Table 5. 
Page 20 of 33 

Figure 6. Conceptual Model for Assessing Exposure from Residential Uses of Antimicrobial Copper 

in Wood and Shingles.

Table 5. Exposure Parameters for Calculating Copper Loading from Shingles and Wood 

Treated with Copper

Parameter Value1 Units 

Housing Density 9.88 houses/ha 

Total Number House in Watershed 98.8 houses 

Rainfall event (upper 10 percentile in Mississippi) 0.125 m 

Pond Volume 20E6 liters 

Treated Wood 

Pole exposed surface area 4.38 m2

Deck exposed surface area 17.84 m 2

Wood fence exposed surface area 146.32 m 2

Exposed surface area for decks in 10 Ha 1762.59 m 2

Exposed surface area for wood fences in 10 Ha 14456.42 m 2

Exposed surface area for poles in 10 Ha 432.74 m 2

Total exposed surface area for decks, fences and poles 16651.75 m 2

Copper emission rate for treated wood 3.2 mg Cu2+ m

-2

 mm

-1 rainfall 

Shingles

Roof area per house 232.36 m2

Copper emission rate (Maximum) 0.38 mg Cu2+ m

-2

 mm

-1 rainfall 

Copper emission rate (Minimum) 0.05 mg Cu2+ m-2 mm-1 rainfall 

1-Velluex et al, 2009 

The total copper loading was estimated for a single 90th percentile rainfall event. The dissolved 

copper concentration in the standard pond ranged from 326 to 792 μg/L for treated wood and 

7.17 to 54.6 μg/L for shingles. 

The Tier 1 modeling for antimicrobial copper had dissolved copper concentrations exceeding the 

benchmark copper concentration of 3.4 μg/L. This assessment is based on the assumption that 

100% of leached copper can move directly into the static pond. Cu retention in soil is an 
Page 21 of 33 

important factor controlling Cu loading into surface water (Donigan et al., 2009). An additional 

analysis was conducted to assess the impact of Cu sorption to soil for antimicrobial uses on 

treated wood. The 30-year weather data for Jackson, MS from 1960 to 1990 was used to 

estimate the daily Cu loading from copper leaching from treated wood. Loading rates were 

calculated for each rainfall event using the following equation: 

Total Copper Loading= rainfall event (mm)*3.2 mg/m2/mm rainfall*16651.75 m2 treated wood in 10 ha 

The Cu loading rates were adjusted to account for a house density of 98.8 houses in 10 ha. Each 

house has footprint of 2000 ft2. The total area in soil was estimated at 8.165 hectares (10 ha 

watershed minus 1.835 house foundation area). This area was used to calculate the Cu loading 

rate (kg Cu/ha or lbs Cu/A). Using regression equations developed in the risk assessment for 

conventional Cu copper pesticides4

, there are no daily copper loading events which can cause a 

5% exceedance of LOCs at the 811 USGS sites. 

30 year daily time series (Jackson, MS)

Cu Concentration (ug/L)

0

1

2

3

4

Concentration for 5%of Sites Exceed Acute Daphnia LOC

Figure 7. Daily rainfall converted to a copper application rate based on the copper treated wood scenario 

and a 30-year daily rainfall time series for Jackson, MS.

Another consideration is the annual application from copper leaching from treated wood. The 

highest leaching rate (1.031 lbs Cu/A) was associated with a rainfall event on October 4, 1964. 

The annual application rate is 10.57 lbs Cu/A. Using regression models, the estimated peak 

copper concentration in the standard pond is 26.43 μg/L. The percent site exceeding the LOC is 

33% (268 USGS sites) for Daphnia magna and 6% (49 USGS sites) for rainbow trout. A 

comparison of the Tier 1 AD exposure assessment and the additional analysis as described 

above, showed a substantial reduction in the predicted dissolved copper concentration from 

transport across pervious surfaces such as soil. 

 4

 ug Cu/L = -0.38 + 2.5358*Cu application rate (lbs Cu/A) R2=0.75

 Percent sites exceeding LOC (Daphnia magna) = 105.53(1-e-0.0143* ug Cu/L) R2 = 0.9957

 Percent sites exceeding LOC (Rainbow Trout) = -0.8624+0.244*ug Cu/L R2

 = 0.9865 
Page 22 of 33 

OPP also conducted a screening-level exposure assessment for antifoulant uses of copper in 

commercial marinas. This exposure assessment was conducted using the MAM-PEC (version 2) 

model. The commercial harbor scenario assumes that all ships were treated with copper and 

copper would leach at the highest possible rate. A leaching rate of 50 μg Cu2+ /cm2

/day was 

used in the model. Input parameters for the MAMPEC model are shown in Table 6. 

Table 6. Input parameters for MAMPEC model 

Parameter Value1 Units 

Molecular mass of copper 63.5 g/Mol 

Kd of copper 3.0 M3/kg 

Silt Concentration 35 g/m3

Particular organic carbon concentration 1.0 Gram Organic carbon/m3

Dissolved organic carbon 2.0 g/m3

Temperature 15 oC 

Salinity 30 s.e 

Depth Well mixed sediment top layer 0.2 M 

Sediment density 1000 Kg/m3

Fraction organic carbon in sediment 0.003 

Net sedimentation velocity 1 m/day 

pH 7.5 

Chlorophyll 3.0 μg/l 

Width of the Harbor 2000 M 

Length of the Harbor 10000 M 

Width of the river 500 M 

Depth of harbor 20 M 

Length of open harbor mouth 5000 M 

Flow 1.5 m/s 

Tidal height 1.5 M 

Tidal Period 12.41 H 

Exchange surface 100000 m2

Depth of harbor mouth 20 M 

Exchange volume in on tidal period 259000000 m3

1-Baart et al 2008 

The copper loading from ships in commercial harbor are shown in Table 7. The predominant 

source of dissolved copper in a commercial harbor can be attributed to copper release from ships 

at berth. 

Table 7. Copper Loading from Ships in a Commercial Harbor 

Copper Loading Value Units 

Total copper load 244612.5 g/d 

Copper loading from moving ships 19464 g/d 

Copper loading from ships at berth 2225148.5 g/d 

Copper leaching rate ships at berth 50 μg/cm2/day 
Page 23 of 33 

The EECs of dissolved copper ranged from a maximum concentration of 4.76 μg/l to a minimum 

concentration of 0.0325 μg/l (Table 8). The average EEC was 1.57 μg/l. 

Table 8. Estimated Environmental Concentrations for copper as antifoulant in an inland 

marina 

Distribution Statistics 

Total 

Concentration 

(μg/l) 

Dissolved 

(μg/l) 

Suspended 

solid 

(μg/g dw) 

Sediment 

After one 

year 

(μg/g) dw 

Maximum 9.76 4.76 143 8.84 

Average 3.12 1.57 47.0 2.91 

Median 0.418 0.204 6.11 0.378 

Minimum 0.0667 0.0325 .976 0.0604 

2.3 AQUATIC EXPOSURE MODELING FOR METALS IN OTHER PROGRAMS OF 

EPA 

In the EPA’s Office of Water, exposure modeling for metals has been associated with 

development of Total Maximum Daily Loads (TMDL) and Water Quality Criteria (WQC). The 

modeling efforts for TMDL and WQC differ due to the regulatory objectives. TMDL modeling 

is designed to provide loading estimates of metals into surface waters (USEPA, 1997). As of 

January 2011, there were 348 TMDLs approved for copper in surface water5. In contrast, the 

modeling for water quality criteria is designed to address the impact of water quality on metal 

toxicity to aquatic organisms. As of January 2011, there were 769 river and stream segments 

listed as impaired (i.e., not meeting their water quality standards) for copper6. 

Selection of the appropriate model requires a balance of the regulatory goals, available data, and 

resources. A review of exposure models was conducted to assess the capabilities for their 

application in the development of TMDLs (USEPA, 1997). The models ranged in complexity 

from watershed scale models (i.e., USGS Regression Method, AGNPS, HSPF, SWEEWQ) to 

field scale models (i.e., CREAMS/GLEAMS, WEPP) to Integrated Modeling Systems (i.e., 

BASINS, SWRRBWQ) to receiving water models (RIVMOD, EXAMS II, HSPF, PLUME). 

The review showed that model capabilities varied substantially among models. Because there is 

no standardized exposure modeling procedure for development of TMDLs, the review highlights 

the importance in understanding the capabilities of models and their application in developing 

TMDLs. For the TMDL program, modeling is generally conducted by the state water quality 

agencies. 

Working with the Brake Pad Partnership, the Office of Science and Technology in the Office of 

Water developed a watershed modeling assessment of copper release from brake pads in the San 

 5 http://iaspub.epa.gov/tmdl_waters10/attains_impaired_waters.control?p_cause_name=COPPER 6

http://iaspub.epa.gov/tmdl_waters10/attains_nation.tmdl_pollutant_detail?p_pollutant_group_id=706&p_pollutant_

group_name=METALS%20(OTHER%20THAN%20MERCURY
Page 24 of 33 

Francisco Bay area (Donigan et al., 2009). The watershed modeling was conducted using the 

EPA’s Hydrological Simulation Program-FORTRAN (HSPF). Important characteristics of this 

modeling approach are summarized in Table 9. 

Table 9. Summary of important characteristics of the watershed modeling assessment of 

copper release from brake pads in the San Francisco Bay area 

Model Parameters Modeling Approach 

Watershed Boundaries Twenty-two sub-watersheds in San Francisco Bay Area. 

Hydrography National Hydrography Database (NHD) was used to define the stream network. 

Greatest length stream within each sub-watershed was selected as the representative 

stream segment. 

Major reservoirs with drainage area ≥ 20 miles2

 were excluded. 

Stream attributes including length, endpoint elevation, slope, and drainage area were 

calculated using the BASINS manual delineation tool with National Elevation Dataset 

(NED) data. 

Meteorology Hourly time step for precipitation. 

20 NCDC weather stations from the BASINS national database were used for 

precipitation in sub-watersheds. 

Potential evaporation was calculated from measured pan evaporation. 

Soils SSURGO soil data was used to distribute soil properties. 

Soil textural classification was used to assign SCS hydrologic soil groups. 

Mean slope was determined using 30-meter digital elevation model (DEM) from USGS 

Seamless data. 

Land Use National Land Cover Database (NLCD) was used to define land use patterns. 

Land uses were aggregated into model categories of agriculture, developed/landscape, 

forest, grassland, shrub/wooded, and wetlands. Within each model category, there were 

different NLCD sub-categories to represent different agriculture crops, urban areas, etc. 

Impervious areas in urban land uses were designated for each sub-category. 

Irrigation Irrigation was considered for urban and agricultural land use categories. 

Irrigation amounts were calculated to compensate for evaporation/transpiration losses 

from lawns and agricultural crops. 

Copper Sources Brake pad and non-brake pad copper sources were modeled. 

Brake pad sources include road way brake pad dust and wet and dry deposition to 

various land use categories. 

High, median, low ranges of copper loadings from brake pads were modeled to address 

variability. 

Calibration Comparison of measured and simulated flow duration curves for selected sub-watersheds 

were used for hydrology calibration and quality assurance. 

Suspended sediment and copper concentrations for selected watershed were used to 

calibrate accumulation and wash-off fractions of copper. 

Calibration simulation time 1981-1995 

Simulation Time 2003 

The San Francisco Bay watershed assessment predicted that approximately 240,000 kg (540,000 

lbs) of copper was released from anthropogenic activity. The single largest source of copper was 

associated with pesticide applied to urban areas. Approximately 100,000 kg of copper (42% of 

anthropogenic copper released) was released from urban areas, with brake pad wear accounting 

for approximately 87,000 kg of copper (36% of anthropogenic copper). This assessment showed 

that a substantial amount of copper was released into air from brake pads. 
Page 25 of 33 

Unlike TMDL modeling approaches for predicting metal loadings into surface water, the water 

quality criteria requires a measured concentration of dissolved metal in surface water. Early 

water quality criteria using total recoverable metal and acid extractable metal were unable to 

provide a consistent approach for determining the impact from site-specific water quality impacts 

on metal toxicity (Paquin et al., 2002). This inability to predict metal toxicity effects led to a 

refinement of the water quality criteria for addressing the bioavailable metal. Refinement of the 

criteria involved the development of water effect ratios (WER) and water hardness regression 

equations are necessary to result in site-specific WQC. Although these methods correct WQC to 

site- specific water conditions, their efficiency is constrained by site-specific testing 

requirements (i.e., WER approach) and by prediction limitations that result from an inability to 

account for other critical water quality parameters (i.e., water hardness). These deficiencies led 

to development and adoption of a chemical speciation modeling approach, involving models 

such as the Biotic Ligand Model (BLM), for predicting site specific WQC for copper. 

The BLM is a chemical equilibrium model that predicts chemical speciation of metals as well as 

the acute aquatic toxicity of metal species. Freshwater BLM models have been developed for 

copper, silver, zinc, cadmium, nickel, and lead. No saltwater BLM models have been 

implemented for WQC at this time. Water quality parameters for the model include water 

temperature, pH, alkalinity, , dissolved organic carbon, humic acid, Ca2+ concentration, Mg2+

concentration, Na+ concentration, K+ concentration, SO4 

2- concentration, Cl-

 concentration, Cu2+

concentration and S2- concentration. Site-specific metal toxicity values can be back-calculated 

using all the water quality data except the metal. Organic ligand speciation is computed using 

the Windermere Humic Aqueous Model.

Other modeling efforts for metals have been associated with metal contamination of surface 

water from mining and Superfund sites (Caruso et al., 2008). Table 10 provides an overview of 

exposure modeling efforts for metals. A notable deficiency in modeling is the lack of watershed 

modeling for estimating metal loading from mine sites. 

Table 10. Summary of Major Metals Modeling Efforts for Mining Sites. 

Site 

Speciation 

Model Stream Model 

Watershed 

Model 

Leadville/California Gulch Superfund 

Site 

Biotic Ligand Model 

(BLM) 

 

Upper Ten Mile Creek Superfund Site WASP 

Summitville Mine Superfund Site META4 WASP4 

North Fork Clear Creek part of Clear 

Creek Superfund Site 

META4 WASP4 

Mineral Creek and Summitville Mine 

Superfund Site 

 One Dimensional 

Transport with 

Equilibrium (ODEQ) 

Red Mountain Creek, CO ODEQ 

North Fork Clear Creek part of Clear 

Creek Superfund Site 

Visual MINTEQ 

BLM 

 

Camp Shelby Watershed, MS CTT&F 

California Gulch Superfund Site TREX 
Page 26 of 33 

3.0 EPA’S REVIEW OF THE TERRA MODEL AND ITS APPLICATION 

TO ANTIMICROBIAL USES OF COPPER 

OPP reviewed the American Chemistry Council’s use of the TERRA model in estimating 

environmental copper concentrations from antimicrobial uses of copper (Velleux et al., 2009). A 

summary of our findings is described below. 

3.1 EPA REVIEW OF THE TERRA MODEL 

 The TERRA model is a fully distributed, physically based model. Such model capabilities 

may be a useful attribute for higher tiered site-specific exposure modeling of pesticides.

 Model selection requires consideration of hardware availability, availability of trained 

personnel, long-term commitment to the model, in-house expertise, and acceptance of the 

model (USEPA, 1997). The CPU requirements for TERRA are high, requiring 

hydrodynamic sediment transport and chemical transport modeling for each grid cell in a 

watershed. The size of the watershed modeled and spatial resolution of the watershed 

modeling is expected to control the CPU requirements. 

 The reason for using a more sophisticated modeling approach is to gain more realistic 

estimates of aquatic exposure. However, TERRA is based on a framework developed for 

predicting non-point source pollutants in agricultural settings, but is used in this 

assessment in a simulated suburban setting. As such, TERRA omits important pathways 

by which contaminants are introduced to aquatic environments such as storm sewers and 

impervious surfaces. Because individual pixels average the properties of pervious (soil) 

and impervious surfaces that fall within each pixel, it is unlikely that any pixels other than 

pixels falling wholly within parking lots actually have the properties of impervious 

surfaces. Further, water is forced to drain in specific pathways in TERRA. Because pixels 

truly representing impervious surfaces are rare in a pixilated watershed at pixel sizes 

larger than the impervious surfaces in the watershed (e.g., streets, sidewalks, driveways, 

etc.), very little water will pass to a water body that has not passed over a pervious surface 

(soil). In reality, much more water would be expected to be conveyed by impervious 

surfaces because engineered environments are typically constructed to convey much of the 

water that falls on the landscape over or through impervious surfaces such as roof gutters, 

downspouts, driveways, street gutters, and storm sewers in order to prevent erosion. 

3.2 EPA REVIEW OF THE GOODWIN CREEK WATERSHED SCENARIO

 One watershed, Goodwin Creek, MS, was selected for the copper exposure assessment. 

The watershed was selected because it has been extensively studied by USDA’s 

Agricultural Research Service (ARS) for upland erosion, in-stream sediment transport, 
Page 27 of 33 

and watershed hydrology (Johnson et al., 2000). There was no comparison of the relative 

runoff vulnerability of the Goodwin Creek with other watersheds in the United States. 

Thus, this modeling simulation may not adequately represent the most vulnerable 

watersheds to antimicrobial copper impacts in the United States.

 Although the Goodwin Creek watershed scenario was designed to produce the same 

drainage area (10 ha) to normal capacity (20 ×106 liters) (DANC) as the standard pond, 

dissolved copper concentrations for the risk assessment were predicted in the reservoir at 

the terminus of the watershed. Given the distributed framework of the TERRA model, 

there is an opportunity to assess dissolved copper concentrations in lower order streams in 

the upper section of the watershed. OPP believes these lower order environments may 

yield higher dissolved copper concentrations than predicted in the reservoir. 

 The simulated urban watershed was calibrated to the hydrology and sediment loads of 

Goodwin Creek. The Goodwin Creek watershed is a predominately pastured/forested 

watershed (Johnson et al., 2000). A simulated urban environment was superimposed over 

the Goodwin Creek watershed in order to represent an urban cluster with spatially 

distributed antimicrobial uses. This simulated urban environment was designed with 25 to 

50 feet riparian zones around streams and no storm drainage systems. Additionally, the 

impervious area in each grid cell was estimated as a percent correction to account for 

impervious surfaces such as roads, walkways, and driveways. In this assessment, there 

was no attempt to simulate storm sewers effects on copper transport from different use 

sites such as house shingles or treated wood. The watershed modeling assessment of 

copper release from brake pads in the San Francisco Bay area showed that impervious 

areas substantially contributed to loading of anthropogenic copper into surface waters 

(Donigian et al., 2009). This loading was attributed to the build-up and wash-off copper 

on impervious surfaces as well as to the retention of copper in pervious areas (i.e., soil). 

Impervious surfaces and sewer systems/natural drainage characteristics are important 

input parameters in watershed modeling (USEPA, 1997).

 Because the Goodwin Creek watershed is a predominately forested watershed, the 

calibration processes may not adequately represent urban hydrologic and chemical 

transport processes. 

 
Page 28 of 33 

4.0 REFERENCES

Baart, A., and van Hattum, B. 2008. User manual- Quick Guide, MAMPEC version 2.0. Institute 

od Environmental Studies (IVM), Amesterdam, Netherlands. 

Burns, L.A., Suarez, L.A., and Prieto, L.M. 2007. United States Meteorological Data: Daily and 

Hourly Files to Support Predictive Exposure Modeling. U.S. Environmental Protection Agency 

Office of Research and Development Washington, DC. 

http://www.epa.gov/ceampubl/tools/metdata/index.html

Carsuso, B.S., T.J.Cox, R.L Runkel, M.L. Velleux, K.E. Bencala, D.K. Nordstorm, P.Y. Julian, 

B.A. Butler, C.N. Alpers, A. Marion, and K.S. Smith. 2008. Metals fate and transport modeling 

in streams and watersheds: state of the science and USEPA workshop review. Hydrological 

Processes. 22:4011-4021 

Donigian, A.S., B.R. Bicknell, and E. Wolfram. 2009. Modeling the Contribution of Copper 

Brake Wear Debris to the San Francisco Bay: Phase 2. Submitted to the Association of Bay Area 

Governments and California Department of Transportation. 

England Jr., J.F., M.L. Velleux, and Pierre Y. Julian. 2007. Two-dimensional simulations of 

extreme floods on a large watershed. Journal of Hydrology. 347:229-241. 

HydroQual, Inc. 2010. Literature Review: Comparison of Common Watershed Models for 

Rainfall, Runoff, Sediment Transport, and Contaminant Transport and Fate. 

Johnson, B.E., P.Y. Julian, D.K. Molnar, and C. C. Watson. 2000. The Two-Dimensional 

Upland Erosion Model CASC2D-SED. Journal of the American Water Resources Association. 

36: 31-42. 

Julian, P.Y., B. Saghafian, and F. L. Ogden. 1995. Raster-Based Hydrologic Modeling of 

Spatially-Varied Surface Runoff. Water Resource Bulletin. 31:523-536. 

Paquin, P.R., J.W. Gorsuch, S. Apte, G.E. Batley, K.L. Bowles, P.G.C. Campbell, C.G. Delos, 

D.M. Di Toro, R.L. Dwyers, F. Galvez, R. W. Gensemer, G.G. Gross, C. Hogstand, C.R. 

Janssen, J.C. McGeer, R.B. Naddy, R.C. Playle, R.C. Santone, U. Schnider, W.A. Stubblefield, 

C.M. Wood, and K.B. Wu. 2002. The biotic ligand model: a historical overview. Comparative 

Biochemistry and Physiology. Part C 133: 3-35. 

Rojas, R., M. Velleux, P.Y. Julian, and B. E. Johnson. 2008. Grid scale effects on watershed soil 

erosion models. Journal of Hydrologic Engineering. 13:9 793-802. 

United States Environmental Protection Agency. 2007. Framework for Metal Risk Assessment. 

USEPA 120/R-07/001 

United States Environmental Protection Agency. 1992. Risk Assessment Forum: Framework for 

Ecological Risk Assessment. EPA/630/R-92/001. 
Page 29 of 33 

United States Environmental Protection Agency. 1998. FIFRA Scientific Advisory Panel: 

Proposed Methods for Basin-Scale Estimation of Pesticide Concentrations in Flowing Water and 

Reservoirs. http:www.epa.gov/scipoly/sap/tools/atozindex/drinkrisk.htm. 

United States Environmental Protection Agency. 1999. FIFRA Scientific Advisory Panel: Use of 

Watershed-derived Percent Crop Areas as Refinement in FQPA Drinking Water Exposure 

Assessments for Tolerance Reassessments. http:www.epa.gov/scipoly/sap/tools 

/atozindex/drinkrisk.htm. 

United States Environmental Protection Agency. 1997. Compendium of Tools for Watershed 

Assessment and TMDL Development. EPA841-B-97-006. 

United States Environmental Protection Agency. 2008. Terrestrial Residue Exposure Model 

Version 1.4.1. www.epa.gov/oppefed1/models/terrestrial/trex. 

United States Environmental Protection Agency. 2004. Overview of the Ecological Risk 

Assessment Process in the Office of Pesticide Programs, U.S. Environmental Protection Agency. 

www.epa.gov/oppefead1/endanger/consultation/ecorisk-overview. 

Velleux, M.L., J. F. England Jr., and P. Y. Julian. 2008. TREX: Spatially distributed model to 

assess watershed contaminate transport and fate. Science of the Total Environment. 404:113-

128. 

Velleux, M.L., P. Paquin, A. Redman, and R. Santore. 2009. Copper Transport and Fate 

Simulations and Evaluation of Estimated Environmental Concentrations for Copper-Based 

Antimicrobial Compounds. Prepared by HydroQual Inc. Submitted by American Chemistry 

Council Copper Task Force. MRID 48561401. 
Page 30 of 33 

5.0 APPENDIX A 

Table 1: Model Capabilities of TERRA

GENERAL MODEL CAPABILITIES 

Model Purpose 

Runoff X 

Erosion X 

Leaching X 

Receiving Surface Water Body X 

Receiving Ground Water 

Program Language 

Fortran X 

C 

Source Code Available X 

Availability 

Commercial 

Public X 

Internet Availability X 

Documentation 

User Manual Current X 

Example Input Files X 

Example Output Files X 

Pre/Post Processing 

Input Preprocessor 

Output Postprocessor 

Software Compatibility 

 Statistical X 

 Spreadsheet X 

 Graphical X 

Geography Referenced Databases 

Crops X 

Management X 

Soils X 

Meteorology X 

GIS Interface 

 Reach file linkage 

 Drinking Water linkage 

 ARC View X 

 ARC Info X 

 ARCINFO/ARCVIEW Compatible X 

Scale 

Microplot X 

Field X 

Watershed X 

River Basin X 

Modeling Approach 

Deterministic X 

Stochastic 

 Weather Generator Built-In 

Validation 

Peer-Reviewed 

 Internal X 

 External X 

Test Case Validation 

Sensitivity Analysis 

Simulation Period 

Event X 

Continuous 

Simulation Time Step 

< 1 Day X 

Day X 

>1 Day X 
Page 31 of 33 

FIELD SIMULATION 

General 

Mass Balance X 

Solution Technique 

 Numerical X 

 Analytical 

Multiple Pesticides X 

Transformation Products Considered X 

Pesticide Type 

Organic 

Inorganic X 

Hydrophobic 

Hydrophilic 

Pesticide Application 

Banded 

Broadcast 

Foliar 

Soil Incorporation 

Chemigation 

Multiple Application 

 Same Method 

 Different Method 

Pesticide Dissipation Processes 

Foliar Interception 

Foliar Washoff 

Foliar Degradation 

Plant Uptake 

Soil Degradation 

Soil Sorption 

 Linear X 

 Non-Linear 

Volatilization 

Transport to Ground Water 

Transport to Subsurface Lateral Flow 

Transport into Surface Water X 

Spray Drift 

Soil Physical/Chemical Properties 

Soil Chemical Properties (pH, redox, organic matter) 

Soil Physical Properties X 

Use of Multiple Soil Types X 

Soil Hydrologic Properties 

Precipitation (rain/snow) X 

Evapotranspiration 

Infiltration (tipping bucket, Richard Equation) X 

Runoff (Curve Number, kinematic wave) X 

Subsurface Lateral Flow 

Multiple Subbasins X 

Surface Water- Ground Water Interactions 

Crop Management 

Irrigation (drip, furrow, sprinkler) 

Tillage Practices (e.g., conservation, no-till, reduced) X 

Buffer Strips X 

Grassed Waterways X 

Terraces X 

Tile Drains 

Crop Growth 

Biomass Production 

Ground Cover 

Heat Units 

Leaf Area Index (LAI) 

Root Growth 

Rooting Depth 

Yields 

Crop Simulation 

Agricultural Crops 

 Orchards 
Page 32 of 33 

 Pastures/rangeland 

 Row Crops 

 Small Grains 

 Turf 

 Fallow 

 Rotation 

Forest 

Urban 

GROUND WATER SIMULATION (AQUIFER) 

GENERAL 

Mass Balance 

Solution Technique 

 Numerical 

 Analytical 

Multiple Pesticides per Simulation 

Transformation Products 

Time Dimensionality 

Steady-State 

Transient Flow 

Transport Processes 

Advection 

Dispersion 

Diffusion 

Ground Water Dissipation Processes 

Degradation 

 Kinetic Model 

Sorption 

 Linear 

 Non-linear 

Transport into Surface Water 

SURFACE WATER SIMULATION 

Overland Erosion (USLE, MUSLE, Onstad-Foster, SLOSS) X 

Sediment Deposition X 

Distribution of Sediment Size X 

Surface Water Representation 

Mass Balance X 

Solution Type 

 Numerical X 

 Analytical 

Multiple Pesticide per Simulation X 

Transformation Products Considered X 

Moveable Sediment Bed X 

Spatial Dimensionality 

Lake X 

River X 

Reservoir X 

1-D longitude Sections X 

2-D horizontal Sections X 

Branched Sections X 

Vertical Stratification 

Time Dimensionality 

Steady-State X 

Dynamic 

Pesticide Dissipation Processes in Surface Water 

Abiotic Degradation 

 Hydrolysis 

 Photolysis 

Microbial Degradation 

Sediment Sorption 

 Linear X 

 Non-Linear 

Volatilization 

Sediment Transport 

 Settling X 

 Resuspension X 

 Burial X 
Page 33 of 33 

OUTPUT 

Field 

 Temporally Dependent X 

 Spatially Dependent X 

Ground Water 

 Temporally Dependent 

 Spatially Dependent 

Surface Water 

 Temporally Dependent X 

 Spatially Dependent X