## FIFRA SCIENTIFIC ADVISORY PANEL Consultation October 25-26, 2011

Consultation on the Two-dimens ional Exposure Rainfall-Runoff Assessment (TERRA) Watershed Mo del 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, 2011Page 2 of 33 

### Table of Contents

1.0 OVERVIEW 1.1 INTRODUCTION .................................................................................................. 3 1.2 EPA’S ECOLOGICAL RISK ASSESS MENT PROCESS FOR METALS ............. 3 1.3 OFFICE OF PESTICID E PROGRAM AQUATIC EX POSURE 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 A SSESSMENT FOR CONVENTI ONAL USES OF COPPER

.............................................................................................................................. ...... 14 2.2 ECOLOGICAL RISK A SSESSMENT FOR ANTIMICR OBIAL 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 TH E TERRA MODEL ............................................................. 26 3.2 EPA REVIEW OF THE GOODWIN CREEK WATERSHED SCENARIO ........... 26

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

5.0 APPENDIX A ....................................................................................................... 30Page 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 statut es: 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 (FQP A). Through its regist ration and registration review programs, OPP conducts ecol ogical risk assessments to ensure that pesticides do not pose unreasonable risks to the environment. In asse ssing potential environmental exposure to a given pesticide, EPA uses data submitted by pesticide manufacturers and open lit erature as inputs to computer-based exposure models. Models are an e ssential part of the risk assessment process as they allow EPA to perform nationwide environmen tal 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 Pane l (SAP) meeting is to seek consultation from the SAP on the usefulness of the Two-dime nsional Exposure Rainfall-Runoff Assessment (TERRA) 1 model as an aquatic exposur e assessment tool for evaluati ng 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 Am erican 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 ove rview 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 analys is 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 ASSE SSMENT PROCESS FOR METALS

The EPA’s Office of Research and Developmen t (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) varia tion of metal concentrations and environmental chemistry across geographic regions; 2) backgrou nd and introduced concentrations of metals; 3) metal concentrations for essential health of or ganisms; 4) environmental fate and chemistry of metals such as speciation, pr ecipitation, and sorpti on; and 5) toxicity differences among organisms.

An ecological risk assessment has three compone nts: 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 cha nged from Two-dime nsional Runoff, Erosion, and Export (TREX) to Two-dimensional Exposure and Rainfall-Runoff Analysis (TERRA). This name change was made to avoid confusion with the Terrestr ial 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 characte ristics including those that ar e important to protect and describes the mode of action, use characteristics, chemistry, envir onmental fate, and toxicity of the pesticide and its degradation products. The el emental nature of metals requires a unique risk assessment process to account for natura l biogeochemical cycles and chemistry.

Figure 1. Risk As sessment Framework

Information on the stressor is integrated into a conceptual model to identify the routes of dissipation and exposure pathways. More importantly, this integr ated information leads to a detailed analysis plan for analyzing data and ch aracterizing risk. Figure 2 illustrates a conceptual model of the fate and transport processes for me tals. Antimicrobial uses of metals, such as embedded or sorbed metal in wood and shingles , requires a leaching pr ocess 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 ar e expected to be similar for antimicrobial and conventional uses of metals. Important transpor t processes from terrestrial environments are associated with erosion, overla nd flow (runoff), and infiltratio n. In contrast, the important transport processes in aquatic environments are advection/dispersion, deposition, and resuspension. Critical environmental chemistry para meters for metals in terrestrial and aquatic environments include ionic strength, redox poten tial, 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 de velops the exposure and toxi city profiles. The exposure profile provides an estimation of environmental c oncentrations (EECs) in aquatic and terrestrial environments based on the disposition of the pestic ide and the nature of the receiving ecosystem. Exposure assessment for metals should consider freely-dissolv ed 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 E Transport Processes Dispersion Deposition/Resuspension
Flow C Chemical Processes Sorption/Desorption Precipitation/Dissolution ComplexationPage 6 of 33 inorganic complexes, and organic complexes) , particle bound metal (s orbed, 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 tran sport models, and chemical speciation models. More importantly, the temporal and spatial variability of water and soil chemistry further complicates speciation modeling. Although selecti on of the appropriate models is dependent on the goal of the risk assessment, an exposure mo del 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-respons e profile summarizes data on the effects of the pesticide and relates them to the assessment endpoint s. By relating exposure levels to toxicity endpoints, the risk assessor can determine the effects of varying expos ure levels on ecological receptors. Development of appropriate stresso r-response relationships for aquatic organisms exposed to metals is particularly complicated b ecause metal species differ in their toxicological properties. Thus the same total pool of metal or dissolved metal con centration available under one set of environmental conditions ( e.g ., pH, DOC, hardness) can elic it 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 ecolo gical entities. This process requires risk analysis and a framework to interpret the risks in the regulatory decisi on making process. For pesticide registration decisions under FIFRA, EPA has an establis hed 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 asse ss aquatic exposure. The aquatic exposure assessment provides an estimation of the environm ental 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 concentr ations to more refined EECs.

As most pesticide registrations are national in scope, exposur e modeling in OPP must account for the wide variety of landscapes across the c ountry. However, there are circumstances where more spatially explicit exposure modeling is ne eded, 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 pred ict 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 × 10 6 liters) of water (F igure 3). The drainage area to normal volume ratio for the standard pond was derived fro m engineering specifications for farm ponds in the southeastern United States. Surface wate r-ground water interactions are not considered in the screening-level assessment . The modeling is conducted a ssuming 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 expos ure is conducted using the GEN eric E stimated E nvironmental C oncentration (GENEEC) model. GENEEC is a single runoff event meta-model of the P esticide R oot Z one M odel (PRZM) and EX posure A nalysis
M odeling S ystem (EXAMS) modeling for a cotton site in Yazoo, MS. Modeling parameters, such as maximum label application rate, average K oc , and the upper 90 th 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 fr om 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 e xposure modeling, then refi ned Tier II exposure modeling is conducted, using PRZM and EXAMS mode ls. 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 se ries of daily pesticide concentrations in the Runoff, Erosion, Spray Drift

10 ha Watershed

1 ha Standard PondPage 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, a nd 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 wa ter 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 Consid ered in PRZM/EXAMS Modeling.

With the passage of the FQPA, the EPA was tasked to routinely incorpor ate drinking water as a route of exposure into human health risk assess ments. This statutory requirement led to an assessment of available basin-s cale models as well as develo pment of a PRZM/EXAMS index water reservoir scenario for drinking water assessments (USE PA, 1998). The index reservoir scenario is modeled after the Shipman reservoi r, 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 acc ount for the percentage of watershed area in a specific crop (USEPA, 19 99) in the larger watersheds capable of sustaining a drinking water re servoir. The PRZM/EXAMS watershed modeling procedure parallels the modeling approach for aquatic exposure assessment us ed 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 acre s) 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 m 3 (1.44 ×10 8 liters). The reservoir is assumed to remain at a constant volume by assuming the watershed inflow volume is equal to reservoi r 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 expos ure assessments through a FIFRA SAP meeting (USEPA, 1998). These basin-scale models included CFWR, modifed PRZM-EXAMS linkage, RIVWQ, HSPF(BASINS), SWAT, and A nnAGNPS (see “Model Acronyms” list on the following page). OPP determine that these basi n-scale models did not have the appropriatePage 9 of 33 subroutines to address plant grow th, pesticide degradation proce sses, and agronomic practices. OPP concluded that the models required spatially-explicit da ta on watershed and hydrological properties. Additionally, model calibration appeared to be pr erequisite for watershed modeling.

Other reviews of watershe d-scale model capabilities have been conducted by the EPA’s Office of Water (OW) and HydroQaul (USEPA, 1997 and HydroQual, 2010). In th eir review of model capabilities, OW assessed various watershed models for development of Total Maximum Daily Loads (TMDL). Most of the m odels 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 rura l areas and build-up/washoff functions for urban areas. Major differences among the models were asso ciated 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 (lum ped model parameters in defined sub-basins), and fully-distributed models (lumped model para meters in defined grid sections within subbasins). All the models were capable of simula ting runoff, and some were capable of simulating soil erosion and stream sediment transport. Fe w 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 us ed for conventional pe sticides 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 specia tion models, such as the BLM model, have been used in exposure assessments for metals. Pleas e 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 Li g and ModelPage 10 of 33 1.4 EXPOSURE MODELING SPECIFIC TO ANTIMICROBIAL PESTICIDES

Unlike many conventional pesticid es which tend to enter the a quatic environment via runoff and spray drift, many antimicrobial pesticides tend to enter the aquatic environment indirectly via wastewater treatment plant efflue nts or from end-uses that lead to direct contact with surface water. The Antimicrobials Divisi on (AD) in OPP uses other mode ls to estimate concentrations of antimicrobial pesticides in aquatic environments in addition to those used for conventional pesticides. Models used to estimate surface wate r 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/Offi ce of Pollution Prevention and Toxics/Economics, Exposure and Technology Division/Exposure As sessment Branch (OCSPP/OPPT/EETD/EAB) (USEPA 2006), is a screening-leve l tool that includes models whic h estimate concentrations of chemicals released to air, surface water, and la ndfills. E-FAST consists of four models: (1) General Population and Ecological Exposure fromIndustrial Releases model; (2) Down-theDrain (DtD) model; (3) Consumer Exposure Mo dule (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 E xposure from Industrial Releases and Down-theDrain models of E-FAST also include PDM options.

The Antimicrobials Division uses the Genera l Population and Ecological Exposure from Industrial Releases model (hereinafter referred to as the Industrial Releases model) and the Down-the-Drain model. The Industrial Releas es model is designed to estimate human and ecological exposures from releases to air, surf ace 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 ingesti on 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 antimicrobial s that are released to domestic wastewater treatment plants, AD typically runs the DtD mo del using the PDM option. Input parameters required to estimate exposure of aquatic organisms to antimicrobial pesticides released to surface water downstream of domestic wa stewater treatment plants incl ude: (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 cap ita release of household wastewat ers, stream dilution factors, flow, and WWTP removal efficiency to provide both high-end and median time-averaged surface water concentrations of chemicals discharged by wastewat er treatment plants receiving household wastewaters. DtD model output that is relevant to a ssessing 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 10 th percentile of stream dilution factors) and average (50 th percentile of stream dilution fa ctors) scenarios. The Industrial Releases model of E-FAST is used to estimate human and ecological exposures from pesticides that are released to industrial wa stewater treatments plants.

Another model used by the Antimicrobials Divisi on is the Marine Antifoulant Model to Predict Environmental Concentrati ons (MAM-PEC), Version 2 . This model was developed by the Institute of Environmental Studies/IVM and De lft Hydraulics for the European Paint Makers Association (CEPE) for conducting ri sk assessments on antifouling agents. It is currently used as an assessment tool for antifoulant risk assess ments in Europe. The model provides predictions of environmental concentrations of antifouling products in six generalized “typical” marine environments: commercial harbor, estuarine harb or, marina, marina poorly flushed, open sea, and shipping lane. The model take s 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, bact erial 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 exposur e to copper resulting from its antimicrobial uses. The model is designed as a fully di stributed framework, and accounts for spatial distribution of rainfall, soil properties, hydrology, and pestic ide use across a watershed.

The TERRA model was developed using the CA SCD2 model framework and chemical fate subroutines from WASP/IPX (Velleux et al. , 2008). It is an event-ba sed, basin-watershed scale model and simulates environmental fate for contaminants in overland flow, surface soil erosion and deposition, channel flow, and sediment transport. Usi ng a raster or grid-based approach, TERRA is designed to spatially-distribute paramete rs across the watershed. The grid cell area is specified by the user a nd can range from 30 meters 2 (0.0074 acres) to 960 meters 2 (0.24 acres) (Velleux et al ., 2006; England et al. , 2007).

For each grid cell, hydrologic and soil prope rties including infiltra tion, 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 classi fication 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 simula ted 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. Ch annel flow within streams or rivers in the watershed is simulated using diffusive wave form ulation 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 roughne ss factor (Mannings N). However, the overland flow and channel flow subroutines in TERRA re quire 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-sec tion is difficult with TERRA. The hydrology model subroutines have been verified using model comparison with an alytical solutions and e xperimental data sets as well as comparison of model results with i ndependently 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, fr iction of slope, soil erodibility, crop-management factors, and soil conservation practices. Dire ction of the movement of erod ed 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 si ze, 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 pred ict chemical concentrations in overland runoff, soil, streams, and sediments. The model can also predict linear chemical partitioning for different particle sizes (sand, si lt, and clay) and for three chemi cal phases, including a dissolved phase, bound phase, and particulate phase. Advecti on 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 chemi cal concentration account s for a mass balance in each grid cell and stream flow. The TERRA model does not account for biodegradation, hydrolysis, oxidation, photolys is, volatilization, or di ssolution/ precipitation.

Model calibration has been conducted through m odification 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 su mmarized 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 fram ework allows distributio n of meteorology, soil, contaminants, and hydrological properties over a landscape. Additionally, this model design provides the capability to track cont aminant 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 s ubroutines 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 a nd bound chemical is estimated using advection, dispersion, and diffusion. Nume rical 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 groun d water fate, subsurface flow, spray drift, and surface water environmental fate parameters, including hydrolysis, photodegradation, microbial degradation, and volatilization. Agronomic factors such as pe sticide application methods ( e.g. , Tband, in-furrow, etc .) and plant growth are also not considered in the model.



### 2.0 COPPER ECOLOGIC AL EXPOSURE ASSE SSMENTS 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 fungi cides and direct water applicat ions as aquatic herbicides, algaecides, and molluscides. Othe r copper-containing conventional pe sticides are registered for use to control leeches, crustaceans, mites, and te rmites, and root growth in sewage and irrigation systems. Antimicrobial uses include antif oulants, wood preservati ves, and material preservatives. Certain copper all oys are also registered for pro duction 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 Reregist ration Eligibility Decision (RED). For antimicrobial uses of copper, environmental e xposure was assessed by OPP as part of the August 2010 Revised Addendum to the Revised Coppers RE D. EPA anticipates conducting a complete ecological risk assessment, including an endangered species assessment, for all pesticidal uses of copper through the registration review process. Th e 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 regul ates 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) unde r the Comprehensive Environmental Response, Compensation, and Liability Act (CERCLA). Envi ronmental screening levels developed by the Office of Solid Waste and Emergency Response (OSWER) have been used in the RED assessment of copper for evaluati on of some toxicity endpoints.Page 14 of 33 The OPP RED and registration review risk as sessment strategy for copper involved linking exposure models with the BLM model to produce site-specific EECs and toxicity endpoints for ionic Cu 2+ (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 Cu 2+ is complex because it forms inorganic (including CuOH + , Cu(OH) 2 0 , Cu(OH) 3 - , Cu(OH) 4 2 , CuCl +

, CuCl 2 0 , CuCl 3 - , CuHCO 3 -

, 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 co pper into surface water. Dissolved copper concentrations from exposure models were used in the BLM model along with site-specific water quality data to estimate th e EEC and toxicity for ionic Cu 2+ . This exposure modeling approach assumes chemical equilibria with no cons ideration of chemical kinetics with regard to precipitation/dissolution, complex formation, adso rption/desorption, and oxidation/reduction. Site-specific BLM estimated concentration of Cu 2+ and toxicity were used to calculate risk quotients.

Figure 5. OPP Risk Assess ment Strategy for Coppers

2.1 ECOLOGICAL RISK ASSESSMENT FOR CONVENTIONAL USES OF COPPER

An ecological risk assessment was conducted fo r copper sulfate, copper group II salts, oxides, hydroxides, and complexes used on food crops as f ungicides and bacteriocides, and directly in aquatic environments primarily for aquatic plant control. Most of thes e compounds dissociate in the environment to form ionic copper 3 (usually Cu 2+ ), and the counter ion. Throughout the risk assessment, the cupric ion (Cu 2+ ) has been evaluated as the ac tive ingredient. The aquatic

2 Inorganic and organic copper complexes replace the bound water of the hexaquoc oppe (II) ion with the ligand. Hence CuCl 2 0 is actually [Cu[H 2 0] 4 Cl 2 ] 0

. 3 Cu 2+ is shorthand for Cu[H 2 0] 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 M odel 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 Cu 2+ ion in aquatic systems.

For the aquatic risk assessment, Cu(II) compound us es on terrestrial crops were modeled using the PRZM/EXAM models for estimating Cu 2+ runoff from a 10-ha field into a standard small water body. PRZM/EXAMS input parameters for Cu 2+ are shown in Table 1.

Table 1. Chemical Input Para meters 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 25 0 C 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 m 3 /mole Default to eliminate volatilization Hydrolysis T 1/2 Stable Default due elemental nature of Cu Aqueous Photolysis 1,000,000 days De fault due elemental nature of Cu Aerobic Soil Metabolism 1,000,000 days De fault due elemental nature of Cu Anaerobic Soil Metabolism 1,000,000 days De fault 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 rang e 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 fo r the effects of majo r dissolved ions and dissolved organic carbon (DOC) present in natu ral surface waters. (These ions and DOC may reduce the toxicity of the Cu i on 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 Am bient Water Quality Criteria. In its risk assessment, OPP used the BLM model to estimate the concentration of Cu 2+ ion (copper activity)Page 17 of 33 and the toxicity of this ion in the standard small water body using site-s pecific water chemistry monitoring data from 811 USGS sites di stributed across the nation (Table 3).

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

Parameter Units Data Range for
BLM Missing
Data Low Value High Value Median Value Average Value

Temperature 0 C 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 μ g/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 Acid 2 % 10 to 60 0 10 29.15 14.90 16.69

Ca 2+ mg/L 0.204 to 120.24 0 0.95 114 37.6 40.62

Mg 2+ 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

SO 4 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

S 2- mg/L ND 811 ND ND ND ND

The PRZM/EXAMS models estimated peak, 21-day average, and 60-day average Cu 2+

concentrations for each runoff scenario. Risk quotients were derived for aquatic invertebrates and fish by comparing the copper activity de rived from runoff with the copper activity representing an aquatic invertebrate or fish LC 50 at the specific site, ba sed on water chemistry as calculated by the BLM. The resulting risk quotients (RQs) were compared to OPP’s preestablished levels of concern (L OC), and expressed as the percen tage of sites exceeding those LOCs. Equations, which were fit to the data gene rated in this process, provided an estimation of dissolved copper concentrations ba sed on application rates, and an estimate of how many of the USGS sites exceed a given endpoint at a pa rticular 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). Estim ated 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 co pper compounds are applied at a range of rates between 0.2 – 1.0 mg/L elemental copper. Averag e copper rates for algae control were 0.5 mg/L for susceptible species and 0.75 - 1.0 mg/L for resi stant 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 gi ven water body ranged betw een 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 co ntrol mollusks, leeches, and tadpole shrimp was difficult to obtain. Based on information availabl e, use rates ranged from 1.0 mg/L to as high as 2.5 mg/L elemental copper. Direct water appli cation of copper sulfate pentahydrate for snailPage 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 bodie s is for human health as well as agricultural and aquacultural reasons. Aquaculture systems rely on direct water applic ations 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 site s. Although a number of site-specific factors influence runoff concentrations, 3.4 μg/L dissolv ed Cu correlates to application rates of approximately 1.5 lb Cu/A.

2.2 ECOLOGICAL RISK ASSESSMENT FO R 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 Reregistrati on Eligibility Decision (RED): AntimicrobialUses 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 a nd shingles was conducte d by linking screening level exposure calculations with the BLM model. The BLM modeling was conducted to estimate the concentration and toxicity (LC 50 ) of Cu 2+ using water quality conditions at 811 USGS monitoring sites. The water quality conditi ons are shown in Table 3. In contrast, the exposure for antifoulant uses of copper was m odeled 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 coppe r 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 treate d wood and then transported into surface water via impervious surfaces. Leaching rates from shin gles and wood structures are used to estimate the copper loading into the surface water. Retenti on of copper in soil is not considered in these calculations.

In the residential conceptual model, a 10- ha resi dential 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 as sumed to have a treated wood deck and fence as well as telephone poles. The hydr ology of the exposure assessme nt is controlled by a single runoff event, which represents the 90 th percentile rainfall amount from the Yazoo, MS weather file (Burns et al 2007). Relevant parameters for calculati ng copper loading are shown in Table 5.Page 20 of 33 Figure 6. Conceptual Model for Assessing Exposure from Residential Us es of Antimicrobial Copper in Wood and Shingles.

Table 5. Exposure Parameters for Calculat ing Copper Loading from Shingles and Wood Treated with Copper
Parameter Value 1 Units Housing Density 9.88 houses/ha
Total Number House in Watershed 98.8 houses Rainfall event (upper 10 per centile in Missi ssippi) 0.125 m
Pond Volume 20E6 liters
Treated Wood Pole exposed surface area 4.38 m 2 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 Cu 2+ m -2 mm -1 rainfall Shingles Roof area per house 232.36 m 2 Copper emission rate (Maximum) 0.38 mg Cu 2+ m -2 mm -1 rainfall Copper emission rate (Minimum) 0.05 mg Cu 2+ m -2 mm -1 rainfall

1-Velluex et al , 2009

The total copper loading was estimated for a single 90 th percentile rainfall event. The dissolved copper concentration in the st andard 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 ha d dissolved copper concentrations exceeding the benchmark copper concentration of 3.4 μg/L. Th is assessment is based on the assumption that 100% of leached copper can move directly into the static pond. Cu re tention in soil is anPage 21 of 33 important factor controlling Cu lo ading 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 fo r Jackson, MS from 1960 to 1990 was used to estimate the daily Cu loading from copper leach ing from treated wood. Loading rates were calculated for each rainfall event using the following equation:

Total Copper Loading= rainfall event (mm)*3.2 mg/m 2 /mm rainfall*16651.75 m 2 treated wood in 10 ha

The Cu loading rates were adjusted to account fo r a house density of 98.8 houses in 10 ha. Each house has footprint of 2000 ft 2 . 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 pesticides 4 , there are no daily copper load ing 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 sta ndard pond is 26.43 μg/L. The percen t 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 re duction in the predicted dissolv ed copper concentration from transport across pervious surfaces such as soil.

4 ug Cu/L = -0.38 + 2.5358*Cu application rate (lbs Cu/A) R 2 =0.75 Percent sites exceeding LOC (Daphnia magna) = 105.53 (1-e-0.0143* ug Cu/L) R 2 = 0.9957 Percent sites exceeding LOC (Rainbow Trout) = -0.8624+0.244*ug Cu/L R 2 = 0.9865Page 22 of 33 OPP also conducted a screening-level exposure a ssessment for antifoulant uses of copper in commercial marinas. This exposure assessmen t 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 possibl e rate. A leaching rate of 50 μg Cu 2+ /cm 2 /day was used in the model. Input parameters for the MAMPEC mo del are shown in Table 6.

Table 6. Input parameters for MAMPEC model
Parameter Value 1 Units Molecular mass of copper 63.5 g/Mol
K d of copper 3.0 M 3 /kg Silt Concentration 35 g/m 3 Particular organic carbon concen tration 1.0 Gram Organic carbon/m 3 Dissolved organic carbon 2.0 g/m 3 Temperature 15 o C Salinity 30 s.e Depth Well mixed sediment top layer 0.2 M Sediment density 1000 Kg/m 3 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 m 2 Depth of harbor mouth 20 M Exchange volume in on tidal period 259000000 m 3

1-Baart et al 2008

The copper loading from ships in commercial ha rbor are shown in Table 7. The predominant source of dissolved copper in a co mmercial harbor can be attribut ed to copper release from ships at berth.

Table 7. Copper Loading from Shi ps 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/cm 2 /dayPage 23 of 33 The EECs of dissolved copper ra nged from a maximum concentr ation of 4.76 μg/l to a minimum concentration of 0.0325 μg /l (Table 8). The av erage EEC was 1.57 μg/l.

Table 8. Estimated Environmental Concentratio ns 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 MO DELING FOR METALS IN OTHER PROGRAMS OF EPA

In the EPA’s Office of Water, exposure mode ling for metals has been associated with development of Total Maximum Daily Loads (TMD L) and Water Quality Criteria (WQC). The modeling efforts for TMDL and WQC differ due to the regulator y 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 water 5

. In contrast, the modeling for water quality criteri a is designed to address the imp act 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 copper 6

.
Selection of the appropriate mode l requires a balance of the regul atory goals, available data, and resources. A review of exposure models wa s conducted to assess the capabilities for their application in the developmen t 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 In tegrated Modeling Systems ( i.e.

, BASINS, SWRRBWQ) to receiving water mode ls (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 ge nerally conducted by th e state water quality agencies.

Working with the Brake Pad Partnership, the Offi ce of Science and Techno logy 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_im paired_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%20MERCURYPage 24 of 33 Francisco Bay area (Donigan et al. , 2009). The watershed modeling was conducted using the EPA’s Hydrological Simulation Pr ogram-FORTRAN (HSPF). Importa nt characteristics of this modeling approach are summarized in Table 9.

Table 9. Summary of important characteristic s 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 miles 2 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 fl ow 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 assessmen t 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 app lied to urban areas. Approxima tely 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 an thropogenic 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 measur ed 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 addres sing the bioavailable metal. Refinement of the criteria involved the development of water e ffect ratios (WER) and water hardness regression equations are necessary to result in site-spe cific WQC. Although these methods correct WQC to site- specific water conditions, their effici ency is constrained by site-specific testing requirements ( i.e. , WER approach) and by prediction limitati ons that result from an inability to account for other critical water quality parameters ( i.e. , water hardness). Th ese 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 pred icts chemical speciation of metals as well as the acute aquatic toxicity of metal species. Fr eshwater BLM models have been developed for copper, silver, zinc, cadmium, nickel, and le ad. No saltwater BLM models have been implemented for WQC at this time. Water qua lity parameters for the model include water temperature, pH, alkalinity, , disso lved organic carbon, humic acid, Ca 2+ concentration, Mg 2+ concentration, Na + concentration, K + concentration, SO 4 2- concentration, Cl - concentration, Cu 2+

concentration and S 2- concentration. Site-specific metal t oxicity 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 a ssociated with metal contamination of surface water from mining and Superfund sites (Caruso et al. , 2008). Table 10 provi des an overview of exposure modeling efforts for metals. A notable de ficiency in modeling is the lack of watershed modeling for estimating metal loading from mine sites.

Table 10. Summary of Major Metals Mo deling 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 TREXPage 26 of 33 

### 3.0 EPA’S REVIEW OF THE TERR A MODEL AND ITS APPLICATION TO ANTIMICROBIAL US ES 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, physi cally based model. Such model capabilities may be a useful attribute for higher tiered si te-specific exposure modeling of pesticides.

 Model selection requires consideration of hard ware 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 tr ansport modeling for each grid cell in a
watershed. The size of the watershed modele d and spatial resolution of the watershed modeling is expected to c ontrol the CPU requirements.

 The reason for using a more sophisticated mode ling approach is to gain more realistic estimates of aquatic exposure. However, TE RRA is based on a framework developed for predicting non-point source pollutants in agri cultural settings, but is used in this assessment in a simulated suburban setting. As such, TERRA omits important pathways by which contaminants are introduced to aquati c environments such as storm sewers and impervious surfaces. Because individual pixels average the properties of pervious (soil) and impervious surfaces that fall within each pixe l, it is unlikely that any pixels other than pixels falling wholly within parking lots ac tually have the properties of impervious surfaces. Further, water is forced to drain in specific pathways in TERRA. Because pixels truly representing impervious surfaces are ra re in a pixilated wate rshed 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 ove r 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 st orm 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 upl and 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 ad equately 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 ×10 6 liters) (DANC) as the standard pond, dissolved copper concentrations for the risk a ssessment 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 c opper concentrations in lower order streams in the upper section of the watershed. OPP be lieves these lower order environments may yield higher dissolved copper concentrations than predicted in the reservoir.

 The simulated urban watershed was calibrate d to the hydrology and sediment loads of
Goodwin Creek. The Goodwin Creek watershe d is a predominately pastured/forested watershed (Johnson et al. , 2000). A simulated urban environment was superimposed over
the Goodwin Creek watershed in order to re present an urban cluster with spatially
distributed antimicrobial uses. This simula ted urban environment was designed with 25 to 50 feet riparian zones around streams and no st orm drainage systems. Additionally, the impervious area in each grid cell was estimat ed 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 e ffects 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/natu ral drainage characteristics are important input parameters in wate rshed 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 Predic tive Exposure Modeling. U.S. Environmental Protection Agency Office of Research and De velopment 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 scie nce and USEPA workshop review. Hydrological
Processes. 22:4011-4021

Donigian, A.S., B.R. Bicknell, and E. Wolf ram. 2009. Modeling the Contribution of Copper Brake Wear Debris to the San Francisco Bay: Ph ase 2. Submitted to the Association of Bay Area Governments and California Depa rtment of Transportation.

England Jr., J.F., M.L. Velleux, and Pierre Y. Julian. 2007. Two-dimensional simulations of extreme floods on a large watershe d. Journal of Hydrology. 347:229-241.

HydroQual, Inc. 2010. Literatu re Review: Comparison of Common Watershed Models for Rainfall, Runoff, Sediment Transport, and Contaminant Transport and Fate.

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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 XPage 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 Wa ter 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
OrchardsPage 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 XPage 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