### Section 10.0 Farm Food Chain and Terrestrial Food Web Data



### 10-1 

# 10.0 Farm Food Chain and Terrestrial Food Web Data

This section presents the data compilation strategies and methods implemented to develop the representative national data set for the Farm Food Chain and Terrestrial Food Web Modules. Data were collected to quantify parameters required to develop exposure profiles for receptors in these respective food webs. These parameters include bioconcentration factors, biotransfer factors, partitioning coefficients, and ingestion rates that the modules use to estimate movement of constituents through food webs. The Farm Food Chain Module is used to assess exposure to humans that may occur through consumption of beef, milk, and agricultural products, while the Terrestrial Food Web Module is used to assess exposure to wildlife through ingestion of typical terrestrial prey items.

Because many of the parameters required for the farm food chain also were applied to the terrestrial food web, the databases that were developed contain the necessary parameters for both food webs. The selected receptors and their respective prey preferences constitute the primary differences between the farm food chain and the terrestrial food web; however, many of the exposure relationships are similar. Two general databases were generated for these food webs: a chemical-specific database and an exposure-related database. The chemical-specific parameters vary depending on the constituent evaluated, whereas the exposure-related database consists of parameters that represent uptake and deposition rates, empirical correction factors, and adjustment fractions to account for reductions in potential exposures. Sections within thisreport were constructed using the two database categories (i.e., chemical-specific and exposurerelated). In reviewing the methods used to develop the chemical-specific database and the key uncertainties (Sections 10.3 and 10.4), however, disc ussions are further categorized by food web. For this documentation, the overlapping parameters between the farm food chain and the terrestrial food web are reviewed within the farm food chain discussions, while the parameters specific to the terrestrial food web are the only parameters reviewed in discussions of the terrestrial food web.



## 10.1 Parameters Collected

This section presents the parameters included in each database. Parameters included in the chemical-specific database are presented in Table 10-1, and exposure-related parameters are presented in Table 10-2. A brief descripti on of each parameter is provided for clarity.

### Section 10.0 Farm Food Chain and Terrestrial Food Web Data



### 10-2

Table 10-1. Farm Food Chain and Terrestria l Food Web Chemical-Specific Parameters

Parameter Unit Description Module ChemBa_{ item } d/kg These biotransfer factors quantify the fraction of constituents transferred from food stocks (e.g., vegetation) into beef and milk products FFC

ChemBs fraction Fraction of contaminant in soil that is bioavailable relative to vegetation FFC and TFW

ChemBr_{ plant category } [μg/g DW plant]/ [μg/g soil] Fraction of constituents in soil taken up into plant tissues. Assessed for the following plant categories: exposed fruits, exposed vegetables, forage, grain, protected fruits, protected vegetables, roots, silage FFC and TFW

ChemBv_ecf_plant unitless Empirical correction factor for air-toplant biotransfer factor FFC and TFW

ChemBv_{ plant category } [μg/g DW plant]/ [μg/g air] Fraction of constituents in air taken up into plant tissues. Assessed for the following plant categories: exposed fruits, exposed vegetables, forage, silage FFC and TFW

ChemHLC (atm m 3 )/mol Henry’s law constant FFC and TFW ChemKd L/kg Soil/pore water partition coefficient FFC and TFW ChemKoc mL/g Organic carbon partition coefficient FFC and TFW ChemKow N/A Octanol-water partition coefficient FFC and TFW ChemkpPar_{ plant category } 1/yr Loss coefficient of particle-bound contaminants on plant surfaces. Assessed for the following plant categories: exposed fruits, exposed vegetables, forage, silage FFC and TFW

ChemkpVap_{ plant category } 1/yr Loss coefficient of vapor-phase constituents on plant surfaces. Assessed for the following plant categories: exposed fruits, exposed vegetables, forage, silage FFC and TFW

ChemRCF [μg/g WW plant]/ [μg/mL sl water] Fraction of constituents in soil pore water taken up into root tissue FFC and TFW

ChemBAF{ prey type } unitless Bioaccumulation factors for small birds, herbivorous vertebrates, small herpetofauna, invertebrates, small mammals, omnivorous vertebrates, and earthworms TFW

FFC = Farm Food Chain TFW=Terrestrial Food Web.

### Section 10.0 Farm Food Chain and Terrestrial Food Web Data



### 10-3 Table 10-2. Farm Food Chain and Terrestrial Food Web Exposure-Related Parameters

Parameter Unit Description Module
F{ item }_{ cattle type } fraction Fraction of food stock (e.g., forage, grain, silage) grown in contaminated soil. Identified for beef and dairy cattle FFC

Qp_{ item }_{ cattle type } kg DW/d Consumption rate of food stock (e.g., forage, grain, silage) by cattle. Assessed for beef and dairy cattle FFC

Qs_{ cattle type } kg/d Consumption rate of soil by cattle. Assessed for beef and dairy cattle FFC

Qw_{ cattle type } L/d Consumption rate of water by cattle. Assessed for beef and dairy cattle FFC

Fw_{ plant category } unitless Fraction of wet deposition that adheres to plant surfaces. Assessed for the following plant categories: exposed fruits, exposed vegetables, forage, silage FFC and TFW

MAF{ plant category } percentage Moisture adjustment factor used to convert plant tissue data from dry weight to wet weight. Assessed for the following plant categories: exposed fruits, exposed vegetables, wet leaf, protected fruits, protected vegetables, roots FFC and TFW

rho_leaf g/L FW Leaf density FFC and TFW
Rp_{ plant category } unitless The fraction of deposition that intercepts plant surfaces. Assessed for the following plant categories: exposed fruits, exposed vegetables, forage, silage FFC and TFW

tp_{ plant category } yr Duration of plant exposure to deposition on an annual basis. Assessed for the following plant categories: exposed fruits, exposed vegetables, forage, silage FFC and TFW

VapDdv cm/s Vapor-phase dry deposition velocity FFC and TFW
VGag_{ plant category } unitless Empirical correction factor that accounts for volumetric differences among aboveground vegetation parts. Assessed for the following plant categories: exposed fruits, exposed vegetables, forage, silage, root. FFC and TFW

VGbg_root unitless Empirical correction factor that accounts for volumetric differences among roots of different plant categories FFC and TFW

Yp_{ plant category } kg DW/m 2 Crop yield for plant categories: exposed fruit, exposed vegetables, forage, silage FFC and TFW

FFC = Farm Food Chain TFW=Terrestrial Food Web.

### Section 10.0 Farm Food Chain and Terrestrial Food Web Data



### 10-4 10.1.1 Chemical-Specific Database

The chemical-specific parameters used in the farm food chain and the terrestrial food web are provided in Table 10-1. The terrestrial food web uses the same chemical-specific parameters as the farm food chain, with the exception of biot ransfer and bioavailability factors that are used to quantify uptake in farm food chain receptors (i.e., ChemBa and ChemBs). In the terrestrial food web, these parameters were not applied; rather, parameters were included to characterize the uptake of constituents into various terrestrial prey items (e.g., ChemBAF{ prey type }).

10.1.2 Exposure-Related Database

The exposure-related parameters in the farm food chain and the terrestrial food web are provided in Table 10-2. The exposure-related parameters for the terrestrial food web are the same as the farm food web parameters, except that the parameters specific to cattle type, which are relevant only to human exposure, are not included in the terrestrial food web (i.e., F{ item }_{ cattle type }, Qp_{ item }_{ cattle type }, Qs_{ cattle type }, and Qw_{ cattle type }).



## 10.2 Data Sources

This section presents the primary data sources identified to develop the databases and information by food web type rather than by database type. In general, data were compiled for the farm food chain and terrestrial food web database from data reviews, as well as the primary literature.

10.2.1 Farm Food Chain

The majority of the information for the farm food chain database was take from two U.S. Environmental Protection Agency (EPA) documents:

(1) Methodology for Assessing Health Risks Associated with Multiple Exposure Pathways to Combustor Emissions (U.S. EPA, 1998) and

(2) Parameter Guidance Document (U.S. EPA, 1997a).

Although both were draft documents currently under review, they were the preferred data sources because they were developed in accordance with EPA’s policies and standards. All available data from these two sources were compiled, and then the remaining data gaps were filled using values derived in data base development for the 1995 Hazardous Waste Identification Rule (HWIR) (Research Triangle Institute [RTI], 1995) and other more current primary references. The data sources consulted to assemble the databases are presented in Tables 10-3 and 10-4 for chemical-specific and exposure-related parameters, respectively.

### Section 10.0 Farm Food Chain and Terrestrial Food Web Data



### 10-5 Table 10-3. Data Sources for Farm Food Chain Chemical-Specific Parameters

ChemBa_{ item } Dioxin and Organics Travis, C.C., and A.D. Arms. 1988. Bioconcentra tion of Organics in Beef, Milk, and Vegetation. Environmental Science and Technology , 22:271.

Mercury and Metals Baes, C.F., III, R.D. Sharp, A.L. Sjoreen, and R.W. Shor. 1984. A Review and Analysis of Parameters for Assessing Transport of Environmentally Re leased Radionuclides Through Agriculture. Oak Ridge National Laboratory, ORNL-5786. Oak Ridge, TN.

U.S. EPA (Environmental Protection Agency). 1997a. Parameter Guidance Document. EPA, National Center for Environmental Assessment, NCEA-0238.

ChemBs U.S. EPA (Environmental Protection Agency). 1997a. Parameter Guidance Document. EPA, National Center for Environmental Assessment, NCEA-0238.
ChemBr_{ plant category } Organics Travis, C.C., and A.D. Arms. 1988. Bioconcentra tion or Organics in Beef, Milk, and Vegetation. Environmental Science and Technology , 22:271.

Mercury U.S. EPA (Environmental Protection Agency). 1997d. Mercury Study Report to Congress. Volume III - Fate and Transport of Mercury in the Environment. EPA 452/ R-97/005. Office of Air Quality Planning and Standards and Office of Research and Development, Washington, DC.

Metals Baes, C.F., III, R.D. Sharp, A.L. Sjoreen, and R.W. S hor. 1984. A Review and Analysis of Parameters for Assessing Transport of Environmentally Released Ra dionuclides Through Agriculture. ORNL-5786. Oak Ridge National Laboratory, Oak Ridge, TN. September. pp. 1–150.

U.S. EPA (Environmental Protection Agency). 1999. Es timating Risk from the Use of Agricultural Fertilizers (Draft Report). Office of Solid Waste, Wa shington, DC. August. Available online at http://www.epa.gov/epaoswer/hazwaste/recycle/fertiliz/risk/index.htm
ChemBv_ecf_plant U.S. EPA (Environmental Protection Agency). 1998. Methodology for Assessing Health Risks Associated with Multiple Exposure Pathways to Combustor Emissions. EPA, National Center for Environmental Assessment, NCEA-0238.
ChemBv_{ plant category } U.S. EPA (Environmental Protection Agency). 1997a. Parameter Guidance Document. EPA, National Center for Environmental Assessment, NCEA-0238.

Simonich, S.L., and R.A. Hites, 1994. Vegetation-atmo sphere partitioning of polycyclic aromatic hydrocarbons. Environmental Science and Technology 28:939-943.
ChemkpPar_{ plant category } U.S. EPA (Environmental Protection Agency). 1997a. Parameter Guidance Document. EPA, National Center for Environmental Assessment, NCEA-0238.
(continued)

### Table 10-3. (continued) Section 10.0 Farm Food Chain and Terrestrial Food Web Data



### 10-6

ChemkpVap_{ plant category } U.S. EPA (Environmental Protection Agency). 1998. Methodology for Assessing Health Risks Associated with Multiple Exposure Pathways to Combustor Emissions. EPA, National Center for Environmental Assessment, NCEA-0238.

ChemRCF RTI (Research Triangle Institute). 1995. Technical Support Document for the Hazardous Waste Identification Rule: Risk Assessment for Human and Ecological Receptors, Volumes 1 and 2, 1995. Prepared for U.S. Environmental Protection Agency, Office of Solid Wa ste. Prepared by Research Triangle Institute.

U.S. EPA (Environmental Protection Agency). 1997a. Parameter Guidance Document. EPA, National Center for Environmental Assessment, NCEA-0238.



### Table 10-4. Data Sources for Farm Food Chain Exposure-Related Parameters

F{ item }_{ cattle type } U.S. EPA (Environmental Protection Agency). 1997a. Parameter Guidance Document. EPA, National Center for Environmental Assessment, NCEA-0238.

Fw_{ plant category } U.S. EPA (Environmental Protection Agency). 1997a. Parameter Guidance Document. EPA, National Center for Environmental Assessment, NCEA-0238.

MAF{ plant category } U.S. EPA (Environmental Protection Agency). 1997a. Parameter Guidance Document. EPA, National Center for Environmental Assessment, NCEA-0238.

Qp_{ item }_{ cattle type } U.S. EPA (Environmental Protection Agency). 1997a. Parameter Guidance Document. EPA, National Center for Environmental Assessment, NCEA-0238.

Qs_{ cattle type } U.S. EPA (Environmental Protection Agency). 1997a. Parameter Guidance Document. EPA, National Center for Environmental Assessment, NCEA-0238.

Qw_{ cattle type } Harris B., Jr., and H.H. Van Horn. 1992. Water and Its Importance to Animals. Circular 1017, Dairy Production Guide, Florida Cooperative Extension Service. Available online at http://ha mmock.ifas.ufl.edu/txt/fairs/2607.

University of Nebraska Cooperative Extension. 1998a. Water Requirements for Beef Cattle . Cooperative Extension, Institute of Agriculture and Natural Resources, Un iversity of Nebraska-Lincoln. Available online at http://www. ianr.unl.edu/pubs /beef/g372.htm.

University of Nebraska Cooperative Extension. 1998b. Water Quality and Require ments for Dairy Cattle. Cooperative Extension, Institute of Agriculture and Natural Resources, University of Ne braska-Lincoln. Available online at http://www.ianr.unl.e du/pubs/dairy/g1138.htm.

USDA (U.S. Department of Agriculture. 1998. Statistical Highlights 1997-98: Livestock. USDA National Agri cultural Statistics Service. Available online at http://www. usda.gov/nass/pubs/stath igh/1998/lv-ca-sl.htm.

rho_leaf U.S. EPA (Environmental Protection Agency). 1998. Methodology for Assessing Health Risks Associated with Multiple Exposure Pathways to Combustor Emissions. EPA, National Center for Environmental Assessment, NCEA-0238.
(continued)

### Table 10-4. (continued) Section 10.0 Farm Food Chain and Terrestrial Food Web Data



### 10-7 Rp_{ plant category } U.S. EPA (Environmental Protection Agency). 1997a. Parameter Guidance Document. EPA, National Center for Environmental Assessment, NCEA-0238.

tp_{ plant category } U.S. EPA (Environmental Protection Agency). 1997a. Parameter Guidance Document. EPA, National Center for Environmental Assessment, NCEA-0238.

VapDdv U.S. EPA (Environmental Protection Agency). 1998. Methodology for Assessing Health Risks Associated with Multiple Exposure Pathways to Combustor Emissions. EPA, National Center for Environmental Assessment, NCEA-0238.

VGag_{ plant category } U.S. EPA (Environmental Protection Agency). 1997a. Parameter Guidance Document. EPA, National Center for Environmental Assessment, NCEA-0238.
VGbg_root U.S. EPA (Environmental Protection Agency). 1997a. Parameter Guidance Document. EPA, National Center for Environmental Assessment, NCEA-0238.

Yp_{ plant category } U.S. EPA (Environmental Protection Agency). 1997a. Parameter Guidance Document. EPA, National Center for Environmental Assessment, NCEA-0238.



### 10.2.2 Terrestrial Food Web

Terrestrial food web parameters not included in the farm food chain discussion consisted of chemical uptake factors for terrestrial prey items. The search strategy implemented for identifying terrestrial uptake factors consisted of two basic steps: surveying current compendia and databases and conducting a focused online literature search to identify key resources specific to constituents and prey items.



## # Survey of current compendia and databases : This step consisted of identifying databases compiled by other government agencies outside EPA (e.g., the National Oceanic and Atmospheric Administration [NOAA]) and private laboratories and ongoing database development at RTI in support of other Office of Solid Waste (OSW) projects. The survey of compendia helped to identify key review papers and current work being conducted on particular receptors and constituents. In addition, EPA and other government Web sites were searched to identify new or updated databases that could be easily accessed.



## # Focused online literature searches : The survey results identified papers on uptake factors that were used to inform and focus the online literature search. Online searching primarily used the Toxline/Medline reference databases.

The key sources identified to collect uptake data for the terrestrial food web are presented in Table 10-5. To quantify uptake into terrestrial plants, the farm food chain parameters for ChemBr forage parameters were adopted.

### Section 10.0 Farm Food Chain and Terrestrial Food Web Data



### 10-8 Table 10-5. Data Sources for Terrestrial Food Web Chemical-Specific Parameters

Vertebrates 1 Garten, C.T., Jr., and J.R. Trabalka. 1983. Evaluation of mode ls for predicting terrestrial food chain behavior of xenobiotic s. Environmental Science and Technology 590-595. Rowe, C.L., O.M. Kinney, A.P. Fiori, and J.D. Congdon. 1996. Oral deformities in tadpoles ( Rana catesbeiana ) associated with coal ash deposition: Eff ects on grazing ability and growth. Freshwater Biology 36:723-730. Sample, B.E., J.J. Beauchamp, R.A. E froymson, and G.W. Suter, II. 1998. Development and Validation of Bioaccumulation Models for Small Mammals. ES/ER/TM-219. Oak Ridge National Laboratory, Oak Ridge, TN. RTI (Research Triangle Institute). 1995. Technical Support Document for the Ha zardous Waste Identification Rule: Risk Assessment for Human and Ecological Receptors - Volumes I and II. Prepared for the Office of Solid Waste, U.S. Environmental Protection Agency under Subcontract No. 801-01.

Earthworms Sample, B.E., J.J. Beauchamp, R.A. Efroymson, G.W. Suter, II, and T.L. Ashwood. 1998. Development and Validation of Bioaccumulation Models for Earthworms. ES/ER/TM-220. Oak Ridge National Laboratory, Oak Ridge, TN. U.S. EPA (Environmental Protection Agency). 1997b. Risk Assessment for the Waste Technologies Industries (WTI) Hazardous Waste Incineration Facility. Volume VI: Screening Ecological Risk Assessment . EPA-905-R-97-002f. Region 5 Waste, Pesticides and Toxics Division. Chicago, IL.
Van Gestel, C.A.M. and W. Ma. 1988. Toxicity and bioaccumulation of chlorophenols in earthworms, in relation to bioavailability in soil. Ecotoxicology and Environmental Safety 15:289-297.

Invertebrates U.S. DOE (Department of Energy). 1998b. Biota Sediment Accumulation Fact ors for Invertebrates: Review and Recommendations for the Oak Ridge Reservation . BJC/OR-112. Oak Ridge, TN. Wu, L., J. Chen, K.K. Tanji, and G.S. Ba nuelos. 1995. Distribution and biomagnifi cation of selenium in a restored upland grassland contaminated by selenium from agricultural drain water. Environmental Toxicology and Chemistry 14(4):733-742.

1 Vertebrates include small mammals, small birds, small herpetofauna, omnivorous ve rtebrates, and herbivorous vertebrates.



## 10.3 Methodology

The review of methods implemented for developing these databases was first organized around the two types of data generated (i.e., chemical-specific and exposure-related); within these descriptors, methods are presented for the farm food chain and the terrestrial food web. Because parameters unique to the terrestrial food web comprise chemical-specific parameters only, a discussion of the terrestrial food web was not necessary under the exposure-related database. Methods are presented on a parameter-specific basis. The final values adopted into the databases are presented in Appendix 10A for the chemical-specific database and Appendix 10B for the exposure-related database. Note that some chemical-specific parameters included in the database are not included in the methods discussion. These parameters include ChemHLC, ChemKow, ChemKd, and ChemKoc. Values for these parameters were supplied by EPA’s Office of Research and Development (ORD), Athens, Georgia.

10.3.1 Chemical-Specific Database

This section presents the methods used to identify chemical-specific data for the Farm Food Chain and Terrestrial Food Web Modules. Parameters related to each food web are discussed separately. The farm food chain parameters are reviewed in Section 10.3.1.1 and the terrestrial food web parameters are reviewed in Section 10.3.1.2.

### Section 10.0 Farm Food Chain and Terrestrial Food Web Data

1 The algorithms presented below for calculating the beef (Ba beef ) and milk (Ba milk ) biotransfer factors for organics, respectively, are based on the work by Travis and Arms, 1988. The Agency has reevaluated these models and developed alternatives using some of the original data and more recent studies. This work is presented in the Ecological Soil Screening Level Guidance - Draft (EPA, 2000). In addition, OSW and the EPA Office of Research and Development are conducting a joint investigation of Travis and Arms to continue the development of alternative methods to predict biotransfer factors. Based on the results of that research, these models may be revised in future versions of the 3MRA modeling system.



### 10-9 (10-1)

(10-2) 10.3.1.1 Database Compilation and Processing for Farm Food Chain

10.3.1.1.1 Biotransfer Factors

. 1 

### Biotransfer factors - ChemBa_beef and ChemBa_milk, and ChemBa_water - are specific to cattle type (e.g., beef and dairy). The Farm Food Chain Module uses biotransfer factors to calculate the total contaminant concentration in beef and milk resulting from consumption of contaminated vegetation and drinking water. Contaminant concentrations in plant tissue are presented in mg contaminant per kg DW of vegetative matter, and the contaminant concentration in drinking water (either surface or groundwater) refers to the dissolved fraction. Typically, experimentally de rived biotransfer factors are preferred, but in cases where sufficient data are unavailable, biotransfer factors are estimated based on log K
ow 

### . For organic compounds, the following approach was implemented to develop biotransfer factors. The octanol-water partition coefficient, K
ow 

### , can be used to estimate biotransfer factors for beef and milk in the absence of experimental data. Travis and Arms (1988) derived empirical relationships between K
ow 

### and biotransfer factors for beef and milk to produce estimates of ChemBa that predict whole-beef and whole-milk concentrations on a freshweight basis given dietary uptake of organic contaminants in term s of mass ingested/d. Equations 10-1 and 10-2 were used to estimate the biotransfer of constituents into milk and beef.

where

ChemBa_milk = biotransfer factors from plant to milk. ChemBa_beef = biotransfer factors from plant to beef. K ow 

### = octanol-water partition coefficient.

These correlations were derived from experimental uptake data for milk and beef. Correlations derived for milk were based on 28 organic constituents with K
ow 

### values ranging from 2.8 to 6.5. For beef, correlations were based on 36 organic constituents with log K
ow 

### values ranging from 1.3 to 6.9. The equations were applied to organics and special constituents with log K
ow 

### values

### Section 10.0 Farm Food Chain and Terrestrial Food Web Data



### 10-10 within the respective ranges. Constituents with K
ow 

### values outside the range were assigned the default value of 1 d/kg animal tissue.

The equations used for organics were inappropriate for dioxins because the correlations tend to overestimate the transfer of dioxins to milk and beef. For dioxins, the milk biotransfer factor of 1E-02 d/kg animal tissue was taken fr om U.S. EPA (1998). This value was based on measured biotransfer factors and was considered more representative than predicted values. The milk biotransfer factor was converted to a b eef biotransfer factor based on the assumed fat content in beef and milk. Dioxins tend to bioconcentrate equally in the fat of beef and milk. For this reason, the excretion of dioxins in milk is expected to be matched by the dilution of the contaminant in beef tissue. Assuming that milk is 3.5 percent fat and beef is 19 percent fat, the biotransfer factor for beef would be 5.4, times hi gher (i.e., 19/3.5) than for milk. Therefore, the Ba beef 

### was calculated by multiplying Ba milk 

### by 5.4, resulting in a biotransfer factor of 5.4E-02 d/kg animal tissue. This value was adopted as the beef biotransfer factor for dioxins.

Beef and milk biotransfer factors for metals were adopted from Baes et al. (1984). Baes et al. (1984) developed regression equations to predict the transfer of metals into beef and milk in cattle. The total mercury biotransfer factors recommended in U.S. EPA (1997d) were adopted for this analysis. For other metals, where data were insufficient to develop a biotransfer factor, the default value of 2E-02 d/kg of tissue was used.

10.3.1.1.2 Bioavailability Fraction

. The bioavailability fraction for soil, ChemBs, accounts for the bioavailability of a contaminant in the soil relative to its bioavailability in vegetation. This parameter indicates the fraction of the total chemical concentration in soil that is predicted to be in a chemical form that is not bioavailable. In the absence of data regarding constituent bioavailability in these respective media, this factor was assigned a default value of 1, as recommended by U.S. EPA (1997a).

For dioxins, a mean value derived from a reported range was adopted for this parameter. The fraction of dioxin that remains bioavailable as it moves from soil to plant ranged from 0.5 to 0.8; the mean of 0.65 was adopted for this analysis (U.S. EPA, 1997a). This data range was derived from studies reporting the oral bioavailab ility of dioxins in rats fed dioxin-contaminated soil. Because of the lack of data reporting th e likely bioavailability of dioxin in soil, it was concluded that rat exposures were a reasonable surrogate for cattle.

10.3.1.1.3 Soil-to-Plant Bioconcentration Factor and Root Concentration Factor

. The soil-to-plant bioconcentration factor (ChemBr) was calculated from the ratio of contaminant concentrations in plant tissues (on a dry weight basis) to those in the soil. This parameter accounts for uptake through the roots into all plant tissues above- and belowground. In contrast, the root concentration factor (RCF) is the ratio of contaminant concentrations in the root tissue of plants (on a fresh weight basis) to those in the soil. The significant difference between these uptake factors is that RCFs are based only on the concentration of constituents in the root tissue. Some contaminants, such as dioxins, are taken up in plant roots and not translocated to aboveground tissues; hence, an RCF uptake factor is more appropriate. The methods for deriving these plant uptake factors are discussed separately here.

### Section 10.0 Farm Food Chain and Terrestrial Food Web Data



### 10-11 Soil-to-Plant Biotransfer Factor

Different approaches were used for metals, organics, and dioxins. For metals, an extensive uptake database was used to generate uptake factors for various plant categories. For organics, uptake factors were calculated based on an empirical relationship between uptake and log K
ow 

### . Dioxin uptake is addressed in the RCF discussion.

For metals, values for this parameter were adopted from a plant uptake database developed by RTI for Estimating Risk from the Use of Agricultural Fertilizers, draft (U.S. EPA, 1999). The database was compiled using field and greenhouse studies of terrestrial plants exposed to metals in which concentrations of metals in stems, roots, leaves, and fruits were measured. The geometric mean of the compiled uptake factors was the final value adopted for this analysis. The plant uptake database was compiled from data identified from the following primary sources:



## # Risk-Based Concentrations for Arsenic Cadmium, and Lead in Inorganic Commercial Fertilizers (California Department of Food and Agriculture [CDFA], 1998)



## # Oak Ridge National Laboratory’s Methods and Tools for Estimation of the Exposure of Terrestrial Wildlife to Contaminants (Sample et al., 1997)



## # Oak Ridge National Laboratory’s Empirical Models for the Uptake of Inorganic Chemicals from Soil by Plants (U.S. DOE, 1998a)



## # Chaney and Ryan’s Risk Based Standards for Arsenic, Lead, and Cadmium in Urban Soils (1994).

In developing this database, RTI evaluated plant uptake values used in similar assessments, including the analysis conducted for EPA’s Standards for the Use or Disposal of Sewage Sludge (40 Code of Federal Register [CFR] Part 503). Uptake factors derived for the Part 503 standards are specific to sewage sludge matrices and as such are less applicable for estimating uptake for this analysis. Uptake data from sewage sludge application were not included in the database because the phytoavailability of metals is significantly lower in sewage sludge-amended soils. The metal adsorption capacity of sewage sludge (presumed to have different characteristics thanindustrial sludge, such as more organic carbon) results in increased metal adsorption in sludgeamended soils and, thus, a decreased availability of metals for plant uptake (U.S. DOE, 1998a; Chaney and Ryan, 1994). This is assumed to occur due to the presence of hydrous iron, manganese, and aluminum oxides within the biosolids matrix. Therefore, considerable binding capacity of sewage sludge may result in uptake factors that are biased toward the low end of the range; these factors may not adequately represent typical terrestrial soil matrices.

The plant uptake database includes measured uptake values for commercial agriculturalcrops as well as for native plant species. The crop data are generally taken from fertilizeramended soil, while the native species data are more likely exposed to nonamended soil. Furthermore, some of the data are derived from greenhouse or “pot” studies, while some data are from field studies. Comparative studies have shown that plant uptake rates can be significantly

### Section 10.0 Farm Food Chain and Terrestrial Food Web Data

2 The algorithm shown for calculating the soil-to-plant bi otransfer factor for organics (Br) is taken from Travis and Arms, 1988. The Agency has reevaluated this model and developed an alternative model using some of the original data and more recent studies. This model is presented in the Ecological Soil Screening Level Guidance - Draft (EPA, 2000), and will be considered for fu ture versions of the 3MRA modeling system.



### 10-12 (10-3) higher in greenhouse and pot studies as opposed to field studies (U.S. DOE, 1998a; CDFA, 1998; Chaney and Ryan, 1994). Consideration was given to including each of these types of data in the database. Both amended and nonamended soil data are considered appropriate because both conditions are assumed under the exposure scenarios modeled by the 3MRA modeling system. Regarding field versus gree nhouse data, exclusion of greenhouse data reduces the amount of useful data and, because the level of uncertainty in pot studies has not been quantified for uptake studies, it is more desirable to work with a larger data set within the constraints of the uncertainty. Therefore, bot h field and greenhouse data have been included. The inclusion of greenhouse data introduces uncertainty and likely alters the true distribution for uptake factors relative to field exposures, because greenhouse data tend to result in more conservative uptake values. The data derived from these literature searches were categorized into the respective plant categories: vegetables (i.e., protected and exposed), fruits (i.e., protected and exposed), forage, grain, root, and silage.

For mercury and methylmercury, values were taken from U.S. EPA (1997d). Data were derived from experiments conducted under reasonable garden conditions, with edible portions of plants grown in control soil and sludge-amended soil. The values were averaged and applied across the various plant types. Insufficient data were identified to develop mercury uptake factors for specific plant categories.

For organics, uptake from soil and transport to aboveground plant parts depend on the solubility of a chemical in water, which is inversely proportional to the octanol-water partition coefficient, K
ow 

### . Travis and Arms (1988) developed an empirical relationship from field experiments on 29 organic chemicals to estimate uptake factors. 2 

### Values were calculated using Equation 10-3 as follows:

For dioxin and furan congeners, uptake into plants through the roots was assessed using root concentration factors (RCFs). The approach is outlined in the following section.

Root Concentration Factors (RCFs)

The RCF is an alternative estimator of the soil-to-plant uptake factor, which is used for root crops. The RCF is defined as the ratio of the contaminant concentration in roots (fresh weight) to that in the soil pore water ([μg contaminant/g plant root FW]/[μg contaminant/mL pore water]). For metals and mercury translocated across the whole plant, ChemBr uptake values for plants are more appropriate; however, in some cases (e.g., dioxins) most of the constituent remains in plant roots and is not translocated to other tissues. For these constituents, an RCF is a more appropriate uptake factor. For dioxins, the EPA-recommended RCF of 5,200 was used (U.S. EPA, 1997a). These values were calculated using Equation 10-4, as follows:

### Section 10.0 Farm Food Chain and Terrestrial Food Web Data



### 10-13 (10-4)

(10-5)

(10-6)

(10-7) This relationship also was used to derive RCFs for organic and special constituents with log K
ow 

### values less than or equal to 2. For constituents with log K
ow 

### values greater than 2, the empirical relationship in Equation 10-5 was applied (U.S. EPA, 1997a), as follows:

10.3.1.1.4 Mass-Based Air-to-Plant Biotransfer Factor and Empirical Correction Factor

. The parameter ChemBv was used to estimate the plant contaminant concentration due to air-to-plant transfer. This parameter was calculated from the ratio of the contaminant concentrations in aerial plant parts (on a dry weight basis) to the concentrations in the air as vapor. Air-to-plant transfer is likely to be im portant for exposed produce but not for protected produce or root crops, because only aboveground exposed vegetation encounters deposition from the air. With the exception of mercury, values for metals are set equal to zero because metals do not exist in the vapor phase.

There are few experimental determinations of ChemBv. Simonich and Hites (1994) determined an air-to-plant biotransfer factor of 4.7E+4 for benzo(a)pyrene. All other special chemicals were given a default of 1. Mercury a nd dioxin values were taken from the geometric mean of measured values presented in U.S. EPA (1997a).

For organic constituents, air-to-plant biotransfer factors were derived according to Equation 10-6 as recommended by U.S. EPA (1998).

The volumetric transfer factor (Bvol) is correlated with the octanol-water partition coefficient (K
ow 

### ), Henry’s law constant (H), ideal gas constant (R), and ambient temperature (T). The result of these calculations (Bvol) is transformed into a mass-based biotransfer factor considering leaf density, air density, and percentage of moisture in the leaf. Bv is calculated using Equation 10-7, as follows:

### Section 10.0 Farm Food Chain and Terrestrial Food Web Data



### 10-14 (10-8) where

D air 

### = density of air (constant at 1.19 g/L) Bvol = biotransfer factor ([μg/L FW leaf]/[μg/L air]) MAF leaf 

### = moisture content in leaf (percentage)
D leaf 

### = density of leaf (g/L FW) Bv ecf 

### = empirical correction factor for Bv (unitless). plant 

### For organics, the air-to-plant biotransfer factors calculated using this approach are divided by the empirical correction factor (Bv_ ecf_plant) of 100. Predicted values generated using these equations usually result in overly conservative estimates of air-to-plant biotransfer factors. In some cases, the difference can be up to 40 times higher than measured biotransfer factors (McCrady and Maggard, 1993, as cited in U. S. EPA, 1998). Other research has indicated that field-derived biotransfer factors were about 2 to 5 times lower than the values generated using equation 10-7 (Lober, 1995, as cited in U.S. EPA, 1998). Therefore, the U.S. EPA (1998) recommended correction factor of 100 was applied in this analysis. The rationale for using the correction factor is based on evidence derived fr om dioxin exposures, but this correction factor also was applied to ChemBv values derived for organic constituents.

10.3.1.1.5 Plant Surface Loss Coefficients

. This parameter is used to account for environmental processes that could reduce the concentrations of constituents deposited onto plants. The dynamics of deposition change depending on whether the constituent is in a vapor or particulate form. The two variables required ar e kpPar for particulate deposition and kpVap for vapor deposition. The methods applied to derive these parameters are presented separately.

Particulate Deposition

The plant surface loss coefficient of particle-bound contaminants, kpPar, is used by the module to calculate plant concentration due to direct deposition. This coefficient reflects the rate of loss of deposited constituents from the plant surface due to environmental processes (i.e., wind removal, water removal, and growth dilution). The coefficient is directly related to the environmental half-life (Equation 10-8) (Miller and Hoffman, 1983, as cited in U.S. EPA, 1997a):

In this equation, the t 1/2 

### value (environmental half-life in days) is strongly influenced by local conditions related to wind and precipitation. Because these data were not available on a site-specific and chemical-specific basis, this equation could not be applied. Rather, the EPArecommended default value of 18.07 yr -1 

### was used for all constituents, with the exception of mercury and dioxin (U.S. EPA, 1997a).

For mercury and dioxin, values recommended in U.S. EPA (1997a) were applied. Parameters of 40.41 and 27.06 yr -1 

### for mercury and dioxin, respectively, were adopted for this

### Section 10.0 Farm Food Chain and Terrestrial Food Web Data



### 10-15 analysis. These values were derived as the mean of a data range that were assumed to be lognormally distributed (U.S. EPA, 1997a).

Vapor Deposition

The plant surface loss coefficient of vapor-phase contaminants, kpVap, was used as a variable in calculating plant tissue concentrations due to vapor deposition. The degradation of constituents on the plant’s surface is due to environmental processes similar to the processes mentioned earlier. For organics, dioxins, and special constituents, this coefficient was derived by taking the midpoint of the range of 58 to 180.7 yr -1 

### reported in U.S. EPA (1998), which resulted in a kpVap of 119.35 yr -1 

### . For metals and mercury constituents, this vapor-related parameter was not required; hence, in the database, a placeholder value of 1 was substituted.

10.3.1.2 Database Compilation and Processing for Terrestrial Food Web

10.3.1.2.1 Bioaccumulation Factors

. The only parameters exclusive to the terrestrial food web were the chemical-specific bioaccumulation factors (referred to herein as uptake factors) for prey items (ChemBAF). Following a short review of data selection guidelines that were implemented in identifying appropriate uptake factors, the parameters are presented on a prey-specific basis: vertebrates, worms, and invertebrates.

Two general guidelines were used to identify uptake factor studies. These guidelines were not used as strict data standards; rather, they were used to increase the level of confidence and consistency in the values used in the database.

1. Field studies were preferred to laborator y exposures. Because field studies tend to more accurately represent the types of exposures and uptake that occur in natural systems, field-generated uptake factors are regarded with higher confidence than those estimated from laboratory exposure data. For example, uptake factors measured during laboratory exposures may overestimate uptake because biota may be exposed to a more bioavailable form of the constituent. In contrast, processes such as degradation and sorption that occur under field conditions tend to reduce the uptake of constituents (e.g., by limiting bioavailability). Both field and laboratory exposures were used because available field study data are not adequate to develop all of the uptake factors for this analysis. Pooling both types of values, however, introduces some uncertainty into the database.

2. For use in the 3MRA modeling system, the following units for the uptake values were required. In terrestrial ecosystems, uptake factors were in units of kg soil (dry weight)/kg tissue (wet weight) for most prey items. These results were calculated by the ratio of mg constituent/kg body weight (wet weight) to mg constituent/kg soil (dry weight).

### Section 10.0 Farm Food Chain and Terrestrial Food Web Data



### 10-16 Vertebrate Uptake Factors

Terrestrial vertebrate prey receptors included small mammals, small birds, small herpetofauna, omnivorous vertebrates, and herbivorous vertebrates. Similar approaches were implemented to identify uptake factors for all categories of vertebrates. A few key sources were identified that reported uptake factors for vertebrates. In addition to the primary literature, an additional compendium reference was used that reported uptake factors for metals and 2,3,7,8- tetrachlorodibenzo- p -dioxin (2,3,7,8-TCDD) in small mammals representing various dietary preferences (i.e., insectivores, herbivores, and omnivores) (Sample, Beauchamp, Efroymson, and Suter, 1998). Sample, Beauchamp, Efroymson, and Suter (1998) provided data for a general category of small mammals. The median uptake factor generated for small mammals was applied in this analysis. Sample, Beachamp, Efroymson, and Suter (1998) developed mammalian uptake factors as screening estimates, and, as such, these values do not reflect national distributions for these parameters. These uptake factors were derived from relatively large databases, however, and the values represent some of the variability in uptake seen across different species and dietary habits.

Significant data gaps remained for categories of vertebrates other than small mammals. The values identified for small mammals were, therefore, used for other categories of vertebrates. The variability associated with rates and physiological regulation of uptake introduces considerable uncertainty in applying small mammal uptake factors to other vertebratecategories. Nevertheless, these values serve as the most reasonable placeholders until exposureroute and species-specific uptake factors become available.

Additional uptake factors for small herpetofauna were identified through primary literature searches. The uptake factors indicating sediment exposures to amphibians that were added to the database are presented in Table 10-6. Because sediments were the only media exposure pathway identified for herpetofauna, these uptake factors were adopted into the database until further soil-based uptake factors are identified. There is uncertainty with applying sediment uptake exposures to represent soil uptake; however, these values act as reasonable placeholders until more representative data are identified.

Table 10-6. Uptake Factors Identified for Small Amphibians

Constituent Species Lab/Field Uptake Factors Source
Arsenic Tadpole Field 0.10 Rowe et al., 1996
Cadmium Tadpole Field 0.58 Rowe et al., 1996
Chromium Tadpole Field 0.04 Rowe et al., 1996
Lead Tadpole Field 0.17 Rowe et al., 1996
Selenium Tadpole Field 0.83 Rowe et al., 1996

### Section 10.0 Farm Food Chain and Terrestrial Food Web Data



### 10-17 (10-9) Earthworms

Measured uptake factors for earthworms were identified in both the primary literature and review compendia. Sample, Beauchamp, Efroymson, Suter, and Ashwood (1998) reported uptake factors for metals in earthworms. Median uptake factors were adopted from this source for the following constituents: arsenic, cadmium, chromium, mercury, nickel, lead, selenium, zinc, barium, beryllium, silver, and vanadium (see Appendix 10A). Worm tissue concentrations in Sample, Beauchamp, Efroymson, Suter, and Ashwood (1998) were reported on a dry weight basis and, as such, had to be converted to wet weight to be consistent with 3MRA modeling system requirements. These values were converted assuming an 84 percent moisture content in earthworms (Sample et al., 1997).

Additional values for 2,3,7,8-TCDD and pentachlorophenol (PCP) identified through primary literature searches are presented in Table 10-7. Final uptake factors for 2,3,7,8-TCDD and PCP were generated by taking the geometric mean of values presented in Table 10-7.

Table 10-7. Uptake Factors Identified for Earthworms

Constituent Species Lab/Field Uptake Factors N Source
2,3,7,8-TCDD Not specified Not specified 15, 9.4, 0.68, 0.64, 0.47 5 U.S. EPA, 1997c

PCP E. andrei Lab 0.85, 0.54 2 Van Gestel and Ma, 1988

PCP L. rubellus Lab 0.64, 1.28 2 Van Gestel and Ma, 1988

PCP Lumbricus caliginosa Both 0.03, 0.02, 0.04, 0.14 4 Haimi et al., 1992



### For organics, current methods of estimating uptake into earthworms were identified; however, their use in the Terrestrial Food Web Module has not been reviewed. The literature reports numerous algorithms to calculate uptake into earthworms; however, the changes in soil composition, water content, and chemical equilibria between different soils makes applying one uptake equation for all soils somewhat unsatisfactory. A widely used method of estimating uptake summarized by Jager (1998) also has been applied in other EPA screening ecological risk assessments (U.S. EPA, 1997b). The method addresses uptake into earthworms as a function of the partitioning between soil and pore water. Concentrations of constituents in pore water are correlated with concentrations in earthworm tissues. Jager (1998) presents the simplified bioconcentration relationship for earthworms (Equation 10-9), as follows:

where

BCF = bioconcentration factor (L/kg worm tissue) F water 

### = concentration of constituent in the pore water (mg/L)

### Section 10.0 Farm Food Chain and Terrestrial Food Web Data



### 10-18 F lipid 

### = fraction of lipid in earthworm tissue (unitless) K ow 

### = octanol-water partition coefficient for the constituent P worm 

### = bulk density of the worm tissue.

This equation results in uptake factors based on pore water exposures in units of L/kg tissue. The Terrestrial Food Web Module requires exposures for earthworms to be converted into units of kg soil/kg tissue. Therefore, earthworm uptake factors based on pore water exposures need to be converted into soil exposure estimates using a soil-water partition coefficient (K p 

### ). The parameter K p 

### uses the relationship between the fraction of organic carbon (f

oc 

### ) in the soil and the constituent-specific K
oc 

### to estimate the partitioning of constituents from the soil phase to the pore water phase. Given that site-specific f
oc 

### data are available, estimation of earthworm uptake would be best represented on a site-specific basis. In addition, an earthworm uptake factor based on site-specific f
oc 

### would better reflect site-specific conditions, increasing the resolution of exposure to terrestrial receptors. Using an uptake algorithm for earthworms would fill some of the remaining data gaps for this prey item.

Invertebrates

Uptake factors for invertebrate prey items were the least well-represented in the database. Invertebrate prey species include taxa such as terrestrial and flying arthropods and terrestrial mollusks. The invertebrate prey item category excludes earthworms because they are considered as a separate prey item in the database. The data collection effort evaluated peer-reviewed approaches and data to estimate invertebrate uptake. The most relevant database identified was developed by Oak Ridge National Laboratories (ORNL): a biota-sediment accumulation factor (BSAF) database for aquatic insects (U.S. DOE, 1998b).

The BSAF database contains metal uptake factors primarily for emergent insects that spend a portion of their life in water (usually larval) before metamorphosis into terrestrial insects (e.g., mayflies). The data address uptake of arsenic, cadmium, chromium, mercury, nickel, lead, and zinc. The analysis done by Oak Ridge compared BSAF uptake values measured in aquatic insects to a smaller data set of measured concentrations in terrestrial flying insects. Their findings indicate a correlation between BSAFs in aquatic insects and accumulation in terrestrial insects. The data suggest that BSAFs for benthic aquatic insects can be used to estimate uptake in emergent insects. Some of the comparisons done between aquatic and emergent insect uptake factors indicate that aquatic insect uptake factors tend to underestimate uptake in emergent insects; therefore, the more conservative 90th percentile of the data was recommended for screening-level assessments. The recommended values were adopted as uptake factors for invertebrates in the Terrestrial Food Web Module. Because these data were presented in terms of dry weight, they were converted to wet weight assuming an 80 percent moisture content for aquatic insects (Sample et al., 1997).

Uptake factors for invertebrates were reported for depurated insects, nondepurated insects, and both combined. The category that combined both types of data were used for this analysis. Typically, uptake factors based on de purated organisms are preferred, but given the lack of data for this category, the variability in this parameter was better represented by combined data.

### Section 10.0 Farm Food Chain and Terrestrial Food Web Data



### 10-19 Uptake factors for selenium in terrestrial insects based on soil concentrations were derived from an additional source (Wu et al., 1995) . A summary of the selenium uptake factors identified and incorporated into the database for terrestrial invertebrates is presented in Table 10-8. The geometric mean of the reported data was used in the Terrestrial Food Web Module (see Appendix 10A for final values).

Table 10-8. Uptake Factors Identified for Invertebrates

Constituent Species Lab/Field Uptake Factors N Source
Selenium Grasshopper Field 1.0, 1.0, 1.9, 4.0, 0.11, 11, 0.21 7 Wu et al., 1995
Selenium Mantis Field 1.1, 0.78, 4.0, 9.5, 0.23, 29, 0.53 7 Wu et al., 1995



### 10.3.1.3 Assumptions and Uncertainties for Chemical-Specific Database

. This section reviews the key overall uncertainties as well as the parameter-specific uncertainties associated with the chemical-specific database developed in support of the Farm Food Chain and Terrestrial Food Web Modules. Discussions are outlined for the specific food webs.

10.3.1.3.1 Farm Food Chain Database

Variability

The data that were gathered did not consistently represent national distributions of parameters. Instead, the data selected were those recommended by EPA and typically used for diverse risk assessment applications. These data are generally thought to be representative under most regional and climatic conditions. The fact that recommended central tendency values do not always represent regional or national distributions introduces some uncertainty into the data identified.

Biotransfer Factors

The recommended default values used for milk and beef biotransfer factors have a great deal of uncertainty (U.S. EPA, 1997a). Default values were derived based primarily on the biotransfer of pesticides data. Evidence suggests that these data may not be appropriate for dioxins or for organics with log K
ow 

### values outside the prescribed range for milk (log K
ow 

### range 2.8 to 6.5) and beef (log K
ow 

### range 1.3 to 6.9). Experimentally derived values are uncertain to the extent that conditions of the study differ greatly from expected conditions in the field. Within the constraints of these uncertainties, these values are the standard for estimating biotransfer relationships in the farm food chain and are accepted as reasonable estimates for risk assessment applications.

### Section 10.0 Farm Food Chain and Terrestrial Food Web Data



### 10-20 Bioavailability Fraction

The bioavailability fraction has some uncertainty associated with use of the default estimate because some chemicals may be much less bioavailable from soil than from plant tissues. Adopting the default of 1 introduces significant conservatism in this parameter.

Soil-to-Plant Bioconcentration Factor

Soil-to-plant bioconcentration factors are strongly influenced by the chemical and physical properties of the soil as well as the plant species. Experimental uptake factors for metals derived from the plant uptake database were, in some cases, higher than previously applied uptake factors calculated using correlations developed by Baes et al. (1984). This was probably an artifact of including both field a nd greenhouse studies. In spite of the added conservatism generated by including greenhouse studies, the plant uptake database was preferred because it contains measured uptake factors that more likely reflect typical exposure scenarios for terrestrial plants.

Plant Surface Loss Coefficient

Estimated values for the plant surface loss coefficient for particle-bound contaminants were taken from U.S. EPA (1997a) even though locally measured values are preferable because of the strong influences of local conditions. U.S. EPA (1997a) noted that there was no speciation provided for mercury in the data used to derive this coefficient; therefore, the assumption was made that the values for each mercury species (total, elemental, and methyl) were equal. In addition, the plant surface loss coefficient for vapor-phase contaminants is uncertain because it is based on limited data.

10.3.1.3.2 Terrestrial Food Web Database

Data Gaps and Default Values

The large number of data gaps in the terrestrial food web data contribute to the uncertainty in estimating exposures to upper-trophic-level consumers in the terrestrial ecosystem. A default uptake factor of 1 was a pplied when uptake factors were not available. The default value may be high for some constituents that do not significantly bioaccumulate. For example, food chain impacts associated with the transfer of constituents to higher trophic levels are not expected to occur for volatile compounds and semivolatile compounds (e.g., vinyl chloride, methylene chloride, trichloroethylene). Typically, compounds with log K
ow 

### values less than 3.5 do not bioaccumulate to a significant degree. Moreover, many of these compounds volatilize before they are taken up into biota. Further, some of these compounds can be metabolized by enzymatic detoxification mechanisms. In these cases, the default of 1 may overestimate uptake into the tissue. For other compounds, such as benzo(a)pyrene, which have a higher log K
ow 

### value and may bioaccumulate to a more significant degree in prey items, the default value of 1 may underestimate uptake. Data gaps introduce uncertainty into the analysis by limiting the ability to capture the true nature of ingestion exposure for some receptors.

### Section 10.0 Farm Food Chain and Terrestrial Food Web Data



### 10-21 Parameter Variability

Uptake factors used in the terrestrial food web are not derived from national or regional distributions and, therefore, do not necessarily reflect the variability inherent in these parameters. Rather, the data identified represent median point estimates of uptake for each prey item category. Uptake factors for a particular prey item vary across species, dietary preferences, seasonal resource requirements, and climatic conditions. For instance, Sample, Beauchamp, Efroymson, and Suter (1998) indicated that vert ebrates of varying dietary preferences (i.e., herbivores, omnivores, insectivores) accumulate contaminants to different degrees. There is uncertainty associated with the uptake factors derived for prey items in the representative national data set because the variability in parameters is not represented in a central tendency value.

Vertebrate Uptake Factors

For vertebrate prey items, values identified for small mammals also were applied to other vertebrate prey items. The variability associated with rates and physiological regulation of uptake introduces considerable uncertainty in applying small mammal uptake factors to other vertebrate categories. Nevertheless, these values serve as reasonable placeholders until exposure-route and species-specific uptake factors become available.

Invertebrate Uptake Factors

For invertebrates, uptake factors based on sediment exposure were adopted in the absence of data quantifying exposure through the soil. This approach introduces uncertainty because these exposure pathways are not equivalent. The primary literature reports uptake factors on various terrestrial insects such as beetles, especially for metals; however, these data are difficult (i.e., costly) to locate through traditional search methods because they are generally found as secondary assessments conducted within larger site-specific risk analyses. The uptake factors adopted from the Oak Ridge work (U.S. DOE, 1998b) represent the best alternative in the midst of current data limitations.

Amphibian Uptake Factors

As indicated in the discussion of uncertainty for invertebrates uptake factors, amphibian uptake factors identified during primary literature searches were based on sediment exposures rather than the preferred exposure medium of soil (Canton and Sloof, 1982). The fact that amphibians are semiaquatic, spending some of their developmental stages in water, explains the abundance of uptake data associated with sedime nt exposures. Because only sediment exposuredata were identified for herpetofauna, these uptake factors were adopted until further soilderived uptake factors are identified; however, there is uncertainty with applying sediment exposures to estimate soil uptake values.

The uptake data for amphibians were used to generate uptake factors for the prey category of small herpetofauna. This prey category consists of both reptile and amphibian prey items; however, the uptake factors identified in the primary literature were derived from studies

### Section 10.0 Farm Food Chain and Terrestrial Food Web Data



### 10-22 exposing amphibians. Because only amphibians are represented in the uptake data, there is uncertainty that the uptake factors adequately represent the range for reptiles.

Finally, the values identified for small herpetofauna represent only the tadpole life stage. Given the differences that occur during amphibian metamorphosis, uptake rates and tissue burden could change significantly from the tadpole to the adult life stage. The uptake factors identified do not address this source of potential variability.

10.3.2 Exposure-Related Parameters

This section presents the methods used to identify exposure-related data for the Farm Food Chain and Terrestrial Food Web Modules. Because the same parameters are used for the farm food chain and the terrestrial food web, discussions are combined.

10.3.2.1 Database Compilation and Processing for Farm Food Chain and Terrestrial Food Web

10.3.2.1.1 Consumption Rate of Food Items, Soil, and Water by Cattle

Plant Consumption Rate

The consumption of grain, forage, and silage (Qp) by beef and dairy cattle, expressed as a daily rate, were adopted from U.S. EPA (1997a). Cattle ingestion rates of seven types of grain, two types of forage, and two types of silage for both beef and dairy cattle were used to derive this parameter. For beef cattle, the average grain, forage, and silage consumption rates are 0.47, 8.8, and 2.5 kg DW/d, respectively. For dairy cattle, the average grain, forage, and silage consumption rates are 2.6, 11, and 3.3 kg DW/d, respectively. These measured values represent average consumption rates for various vegetation types (i.e., grain, forage, and silage) and should be used as defaults in the absence of site-specific values.

Soil Consumption Rate

The consumption of soil (Qs) by livestock, expressed as a daily rate, was adopted from U.S. EPA (1997a). Soil consumption rates were used to estimate uptake of constituents into animal tissues from inadvertent consumption of soil while feeding. Reported soil ingestion bycattle varies from 1 to 18 percent of dry matter intake (U.S. EPA, 1997a). The EPArecommended average soil ingestion rates of 0.39 kg DW/d for beef cattle and 0.41 kg DW/d for dairy cattle were adopted for this analysis. These values approximate 3 percent of the total dry matter intake.

Water Consumption Rates

Water consumption rates (Q
w 

### ) vary according to many factors, such as breed, body size, air temperature, humidity, and moisture content of feed (Harris and Van Horn, 1992). In general, dairy cattle consume 2 to 4 pounds of water for each pound of feed (dry weight) consumed and an additional 3 to 5 pounds of water per pound of milk produced (Harris and Van Horn, 1992). Rations high in salt or protein increase water intake. Total water intakes consist of direct water

### Section 10.0 Farm Food Chain and Terrestrial Food Web Data



### 10-23 ingestion and water intake through feed stocks containing moisture. For instance, feeds such as silage, green chop, or pasture have a high moisture content and contribute to overall water intake, while grains and hays have a low moisture content (University of Nebraska Cooperative Extension, 1998a).

In beef cattle, the average daily water intake of a 1,200-pound cow ranges from 9.5 gal/d (36 L/d) in December and January to 23.0 gal/d (87 L/d) in July (University of Nebraska Cooperative Extension, 1998a). Table 10-9 summarizes water consumption rates of beef cattle across different body weights. For beef cattle, the water intake value of 14.0 gal/d (53 L/d) was used for this analysis. This value was derived as the average water intake on an annual basis of a 1,200-pound beef cow. The water consumption rate was appropriate because it reflected a fully mature beef cow, and it accounted for the annual variation of water intake in this farm food chain receptor.
In dairy cattle, a value of 24.2 gallons of water per day (92 L/d) was derived for this analysis by taking the average of data measured by Harris and Van Horn (1992) (Table 10-10). These data reflect the variability in water consumption of dairy cows across different temperatures and milk production rates. This value falls within the water consumption ranges reported for other cow breeds by the University of Nebraska Cooperative Extension (1998b). In these data, the average daily water intake of Jersey cows producing 30 pounds of milk per day ranged from 13.0 to 15.5 gallons of water per day (49 to 59 L/d). The average daily water intake of Ayrshire, Brown Swiss, and Holstein cows producing up to 100 pounds of milk per day ranged from 48.0 to 52.0 gallons (182 to 202 L/d) of water per day (University of Nebraska Cooperative Extension, 1998b).

Table 10-9. Daily Water Intake of Beef Cattle (gal/d)

Month Average Maximum Temperature Body Weight of Finishing Cattle
600 lb 800 lb 1,000 lb 1,200 lb January 36 °F 5.5 7.0 8.5 9.5 February 40 °F 6.0 7.5 9.0 10.0

March 50 °F 6.5 8.0 9.5 10.5 April 64 °F 8.0 9.5 11.0 12.5 May 73 °F 9.0 11.0 13.0 14.5

June 78 °F 9.5 12.0 14.0 16.0 July 90 °F 14.5 17.5 20.5 23.0 August 88 °F 14.0 17.0 20.0 22.5 September 78 °F 9.5 12.0 14.0 16.0

October 68 °F 8.5 10.0 12.0 14.0

November 52 °F 6.5 8.0 10.0 10.5

December 38 °F 6.0 7.0 8.5 9.5
Source: University of Nebras ka Cooperative Extension, 1998a.

### Section 10.0 Farm Food Chain and Terrestrial Food Web Data



### 10-24 10.3.2.1.2 Fraction of Food Items Grown in Contaminated Soil

. The fraction of each food item (i.e., forage, grain, silage) (F) is used in the Farm Food Chain Module to account for the dietary fraction of contaminated cattle feed. This parameter varies depending on sites, regions, or, moreover, resource availability. A conservative default value of 1 is assumed in the absence of data available to indicate that so me portion of the feed used for livestock is not contaminated. No data were located regarding the origin of feed stocks for livestock; hence, a default value of 1 was adopted from U.S. EPA (1997a).

10.3.2.1.3 Fraction of Wet Deposition that Adheres to Plants

. The fraction of wet deposition (Fw) that adheres to a plant as a result of direct deposition was used in the calculation of plant contaminant concentrations. All contam inants that contact plant surfaces due to dry deposition are assumed to remain on the plant surfaces until removed by weathering. Not all

Table 10-10. Daily Water Intake of Milk-Producing Dairy Cows (gal/d)

Breed Volume of Milk Produced Mean Daytime Temperature, degrees F

Reference Not Specified 10-40 °F 50 °F 60 °F 70 °F 80 °F 90 °F Jersey 30 lb/d 13 to 15.5 -- -- -- -- -- -- University of Nebraska, 1998b Guernsey 30 lb/d 13.8 to 16 -- -- -- -- -- -- University of Nebraska, 1998b Ayrshire, Brown Swiss, Holstein 30 lb/d 14.5 to 17 -- -- -- -- -- -- University of Nebraska, 1998b Ayrshire, Brown Swiss, Holstein 50 lb/d 24 to 27 -- -- -- -- -- -- University of Nebraska, 1998b Ayrshire, Brown Swiss, Holstein 80 lb/d 38 to 42 -- -- -- -- -- -- University of Nebraska, 1998b Ayrshire, Brown Swiss, Holstein 100 lb/d 48 to 52 -- -- -- -- -- -- University of Nebraska, 1998b 1,400 lb cow 40 lb/d -- 15.5 17.2 18.9 20.6 22.4 24.1 Harris & Van Horn, 1992 1,400 lb cow 60 lb/d -- 18.4 20.1 21.9 23.6 25.3 27 Harris & Van Horn, 1992 1,400 lb cow 80 lb/d -- 21.4 23.1 24.8 26.5 28.3 30 Harris & Van Horn, 1992 1,400 lb cow 100 lb/d -- 24.3 26 27.7 29.5 31.2 32.9 Harris & Van Horn, 1992



### contaminant material that intercepts plant surfaces from wet deposition adheres; rather, a portion is assumed to wash off almost immediately. This parameter is a measure of the amount of wet deposition that adheres to the plant surface. The EPA recommends a value of 0.6 for the fraction of wet deposition that adheres to plant surfaces based on data collected during simulated precipitation studies conducted by Hoffman et al. (1992, as cited in U.S. EPA, 1997a).

### Section 10.0 Farm Food Chain and Terrestrial Food Web Data



### 10-25 (10-10) 10.3.2.1.4 Moisture Adjustment Factor to Convert Dry Weight to Wet Weight

. The moisture adjustment factor (MAF) is used in the Farm Food Chain Module to convert a given quantity of plant material from a fresh weight basis to a dry weight basis or vice versa. The EPA-recommended moisture contents (as percentages) were adopted for the following plant categories: 92 percent for exposed vegetables, 80 percent for protected vegetables, 87 percent for root vegetables, 85 percent for exposed fruits , and 90 percent for protected fruits (U.S. EPA, 1997a).

10.3.2.1.5 Leaf Density

. The plant density (rho_leaf) of 770 g/L provided in U.S. EPA (in press) was used for this analysis. This plant density value reflects the density reported in grasses. This density more adequately represents the types of vegetation seen in the farm food chain than other available values.

10.3.2.1.6 Interception Fraction for Plant

. The interception fraction (Rp) for plants is the fraction of contaminant particles deposited on the edible portion of plants. Atmospheric deposition is a potential route of exposure for unprotected produce and forage but not for root crops or protected produce (including grains). Plant interception fractions were adopted from U.S. EPA (1997a) for the following plant categories: 0.052 for exposed fruits and vegetables, 0.47 for forage, and 0.44 for silage. Baes et al. (1984) estimated these average interception fractions by taking into account the following vari ables: recommended field spacing of plants, average plant/fruit sizes, growth, and exposure durations.

10.3.2.1.7 Length of Plant Exposure to Deposition

. This parameter quantifies the duration of plant exposure (t p 

### ) to the deposition of constituents per harvest in years. The parameter only accounts for the edible portion of plants; therefore, this parameter is only applied to exposed produce and forage, not to root crops or protected produce (including grains). Values used in this analysis were taken from U.S. EPA (1997a). Values were derived from data collected by the South Coast Air Quality Manage ment District (SCAQMD) on average growing seasons and from data collected by Belcher and Travis (1989) on the average time between hay harvests (as cited in U.S. EPA, 1997a). The values for different plant categories are as follows: 0.123 yr for exposed vegetables, exposed fruits, a nd forage and 0.164 yr for silage. Insufficient data were available to estimate exposure duration for other plant categories, and the default t p 

### value of 0.164 yr was used.

10.3.2.1.8 Vapor-Phase Dry Deposition Velocity

. The dry deposition velocity (VapDvd) for vapor-phase contaminants can be derived using Equation 10-10 (U.S. EPA, in press) as follows:

where

R
a 

### = atmospheric resistance, a function of vertical turbulent transport (s/cm)

### Section 10.0 Farm Food Chain and Terrestrial Food Web Data



### 10-26 R b 

### = surface boundary layer resistance, a function of molecular diffusivity (s/cm)

R c 

### = plant canopy/leaf resistance, a function of vegetative density, stomatal uptake, surface effects, and humidity (s/cm).

Atmospheric resistance values, however, are difficult to assign and data are limited. Calculated VapDvd values range from 0.01 cm/sec to 1 cm/s (Smith et al., 1995; Trapp and Matthies, 1995; and Sehmel, 1980 as cited in U.S. EPA, in press). The EPA-recommended default value of 1 cm/s was used for this analysis (U.S. EPA, in press). As apparent from the range of values indicated across different studies, this parameter is a conservative estimate of the velocity of dry vapor deposition.

10.3.2.1.9 Empirical Correction Factor for Plants

. Different correction factors were applied for aboveground and belowground plant uptake values. Each is described separately here.

Aboveground Correction Factors

An empirical correction factor (VG ag 

### ) was used to account for volumetric differences in aboveground plant tissues in exposed fruits and vegetables, forage, and silage. Values to quantify this parameter were taken from U.S. EPA (1997a) for the following plant categories: 0.01 for exposed fruits and vegetables, 1.0 for forage, and 0.5 for silage. The parameter VG ag 

### isa correction factor used in calculating the contaminant concentration in plant tissue due to air-toplant transfer for organic chemicals. Vg ag 

### adjusts for differences in air-to-plant uptake between thin leaves and bulky fruits (U.S. EPA, 1997a). This adjustment, which can be estimated as the surface-area-to-volume ratio, is necessary because lipophilic organic chemicals transferred from the air tend to remain localized in the surface portions of the plant rather than being distributed to the interior portions.

Belowground Correction Factors

The empirical correction factor for belowground vegetation (Vg bg 

### ) is analogous to Vg ag 

### and adjusts the belowground constituent concentrations for differences in root volume. The recommended default value of 0.01 was used in this analysis (U.S. EPA, 1997a).

10.3.2.1.10 Crop Yield for Plants

. The yield or standing crop biomass (Yp) values of the edible portion of exposed fruits and vegetables, forage, and silage as recommended by U.S. EPA (1997a) were used to quantify this parameter. Crop yield was only assessed for vegetation likely to be exposed through direct atmospheric deposition (aboveground plant categories). As summarized in U.S. EPA (1997a), data reflecting average crop productivity for unprotected produce, forage, and silage at the county level were used to quantify this parameter. The following national average crop yields were used for this analysis: 0.18 for exposed vegetables, 0.09 for exposed fruits, 0.31 for forage, and 0.31 for silage (Baes et al., 1984; Shor et al., 1982; and Travis, 1989, as cited in U.S. EPA, 1997a).

10.3.2.2 Assumptions and Uncertainties for Exposure-Related Parameters

. In this section, the uncertainties associated with the development of the exposure-related database are

### Section 10.0 Farm Food Chain and Terrestrial Food Web Data



### 10-27 reviewed. The uncertainty discussion is organized by parameter. Only parameters indicating key uncertainties are discussed.

10.3.2.2.1 Consumption Rate of Food Items, Soil, and Water by Cattle

. Most of the EPA-recommended values for consumption rates of plants, soil, and water are default values that estimate national averages for these parameters. There are accepted limitations associated with the use of national averages that do not reflect the variability inherent in consumption rates. The consumption rates of food, water, and soil vary with region, physical conditions, climate, breeds, and local agricultural practices. Therefore, there is uncertainty associated with adopting average point estimates for these parameters; however, these values are currently the accepted standard for determining farm food chain exposures.

10.3.2.2.2 Fraction of Food Item Grown in Contaminated Soil

. The default value of 1 used for the fraction of food items grown in contaminated soil assumes that the entire cattle diet is equally contaminated. This assumption generates a conservative estimate of exposure because in many cases, some portion of the diet may not contain contaminants. Some uncertainty is introduced by assuming that a receptor’s entire diet is derived from contaminated food stocks.

10.3.2.2.3 Fraction of Wet Deposition that Adheres to Plants

. The primary uncertainty associated with this parameter is the paucity of data available to derive the value. The default parameter was derived using one study. The scarcity of relevant data generates low confidence in the default value generated. In spite of evidence suggesting that water acts to wash contaminants from the surface of the plant, the fraction of constituents that are actually removed has not been well-quantified.

10.3.2.2.4 Moisture Adjustment Factor to Convert Dry Weight to Wet Weight

. Based on the narrow range of MAF values across various plant groups, limited variability is reflected in this parameter; however, considerable variability within each plant type is apparent. This evidence suggests that site-specific data could possibly vary significantly from default values (U.S. EPA, 1997a). Because the default moisture adjustment was derived as a central tendency value, there is considerable uncertainty that it represents the true variability within plant categories. Lacking site-specific data, the MAF is a reasonable estimate for plant categories, and it is generally accepted for risk assessment applications.

10.3.2.2.5 Interception Fraction for Plant

. Available data indicate that interception fractions vary across vegetation types. Methods presented in U.S. EPA (1997a) suggest that sitespecific interception fractions are preferred. This analysis is not site-specific, however. Rather, data that reflect national distributions are preferred. No suitable data were identified that represent national distributions for specific plant categories; therefore, default values were applied. Using a single value for all vegetation types introduces some uncertainty into the analysis.

10.3.2.2.6 Length of Plant Exposure to Deposition

. The default values adopted to estimate the length of vegetation exposure to deposition have considerable uncertainty due to substantial regional variability in the duration of the growing season, insufficient data available to derive default values, and lack of knowledge of the true representativeness of these data (U.S. EPA, 1997a). This parameter is highly dependent on regional conditions, growing practices, and

### Section 10.0 Farm Food Chain and Terrestrial Food Web Data



### 10-28 crop types. Because these factors were not accounted for in the derivation of this parameter, there is uncertainty in the values used.

10.3.2.2.7 Empirical Correction Factors for Plants

. In deriving default values for this parameter, all organic chemicals are assumed to be equally lipophilic, even though organic chemicals differ with regard to their lipophilicity. This simplification contributes to the uncertainty associated with the default values, particularly for the less lipophilic organics that may have limited distribution into the interior plant tissues. This leads to conservative correction factors for less lipophilic constituents (U.S. EPA, 1997a).

10.3.2.2.8 Crop Yield

. For crop yield, there is considerable uncertainty associated with the use of average values because crop yield varies significantly from one region to another, depending on growing conditions. The average values, however, are based on very extensive databases that capture a large amount of the national variability.


## 10.4 Quality Assurance and Quality Control

The QA/QC functions for data collection of farm food chain parameters can be thought of in terms of ensuring that data have been interpreted and extracted correctly (technical QA/QC) and verifying that data have been entered correctly (data entry QA/QC). The QA/QC activities implemented for the Terrestrial Food Web and Farm Food Chain Module data collection are described in the following sections.

10.4.1 Technical QA/QC

Sources used to obtain data for the Terrestrial Food Web and Farm Food Chain Modules are generally accepted as authoritative references. Although both U.S. EPA (in press) and U.S. EPA (1997a) are draft documents currently under review, they were the preferred data sources because they were developed in accordance with EPA’s policies and standards. Sources consulted to fill the remaining data gaps were reviewed and approved by the technical lead staff member for the data collection task. Once all the data were entered into the database, a second technical staff member manually verified correct data interpretation and accurate data entry. Primary data sources used to derive database s for HWIR95 also were reviewed and verified before being included in the database.

10.4.2 Data Entry QA/QC

Data entry was performed by hardcopy data entry. A QA/QC check of all values was performed by a second staff member to ensure that the numbers had been correctly entered into the database.



## 10.5 References

Baes, C.F., III, R.D. Sharp, A.L. Sjoreen, and R.W. Shor. 1984. A Review and Analysis of Parameters for Assessing Transport of Environmentally Released Radionuclides through Agriculture. Oak Ridge National Laborat ory, ORNL--5786. Oak Ridge, TN.

### Section 10.0 Farm Food Chain and Terrestrial Food Web Data



### 10-29 Canton, J.H., and W. Sloof. 1982. Toxicity and accumulation studies of cadmium with freshwater organisms of different trophic levels. Ecotoxicology and Environmental Safety 6:113-128.

CDFA (California Department of Food and Agriculture). 1998. Development of Risk-Based Concentrations for Arsenic, Cadmium, and Lead in Inorganic Commercial Fertilizers

. Prepared by Foster and Wheeler Envir onmental Corporation, Sacramento, CA.

Chaney, R.L., and J.A. Ryan. 1994. Risk Based Standards for Arsenic, Lead, and Cadmium in Urban Soils . DECHEMA Deutsche Gesellschaft fur Chemisches Apparatewessen, Chemische Technik und Biotechnologie e. V., Frankfurt am Main.

Garten, C.T., Jr., and J.R. Trabalka. 1983. Eval uation of models for predicting terrestrial food chain behavior of xenobiotics. Environmental Science and Technology 590-595.

Haimi, J., J. Salminen, V. Huhta, J. Knuutinen, and H. Palm. 1992. Bioaccumulation of organochlorine compounds in earthworms. Soil Biol. Biochem. 24(12):1699-1703.

Harris, B., Jr., and H.H. Van Horn. 1992. Water and Its Importance to Animals. Circular 1017, Dairy Production Guide, Florida Cooperative Extension Service. Available online at http://hammock.ifas.ufl.edu/txt/fairs/2607.

Jager, T. 1998. Mechanistic approach for estimating bioconcentration of organic chemicals in earthworms ( Oligochaeta ). Environmental Toxicology and Chemistry 7(10):2080-2090.

Rowe, C.L., O.M. Kinney, A.P. Fiori, and J.D. Congdon. 1996. Oral deformities in tadpoles ( Rana catesbeiana ) associated with coal ash deposition: Effects on grazing ability and growth. Freshwater Biology 36:723-730.

RTI (Research Triangle Institute). 1995. Technical Support Document for the Hazardous Waste Identification Rule: Risk Assessment for Human and Ecological Receptors, Volumes 1 and 2, 1995. Prepared for U.S. Environmental Protection Agency, Office of Solid Waste. Prepared by Research Triangle Institute.

Sample, B.E., J.J. Beauchamp, R.A. Efroymson, G.W. Suter, II, and T.L. Ashwood. 1998. Development and Validation of Bioaccumulation Models for Earthworms . ES/ER/TM220. Oak Ridge National Laboratory, Oak Ridge, TN.

Sample, B.E., J.J. Beauchamp, R.A. Efroymson, and G.W. Suter, II. 1998. Development and Validation of Bioaccumulation Models for Small Mammals . ES/ER/TM-219. Oak Ridge National Laboratory, Oak Ridge, TN.

Sample, B.E., M.S. Aplin, R.A. Efroymson, G.W. Suter, II, and C.J.E. Welsh. 1997. Methods and Tools for Estimation of the Exposure of Terrestrial Wildlife to Contaminants. Oak Ridge National Laboratory, Oak Ridge, TN.

### Section 10.0 Farm Food Chain and Terrestrial Food Web Data



### 10-30 Simonich, S. L. and R. A. Hites. 1994. Vegetation-atmosphere partitioning of polycyclic aromatic hydrocarbons. Environmental Science and Technology 28:939-943.

Travis, C.C., and A.D. Arms. 1988. Biocenconcentration of Organics in Beef, Milk, and Vegetation. Environmental Science and Technology , 22:271.

University of Nebraska Cooperative Extension. 1998a. Water Requirements for Beef Cattle. Cooperative Extension. Institute of Agriculture and Natural Resources, University of Nebraska-Lincoln. Available online at http://www.ianr.unl.edu/pubs/beef/g372.htm.

University of Nebraska Cooperative Extension. 1998b. Water Quality and Requirements for Dairy Cattle. Cooperative Extension, Institute of Agriculture and Natural Resources, University of Nebraska-Lincoln. Available online at http://www.ianr.unl.edu/pubs/dairy/g1138.htm.

USDA (U.S. Department of Agriculture). 1998. Statistical Highlights 1997-98: Livestock. USDA National Agricultural Statistics Service. Available online at http://www.usda.gov/nass/pubs/stathigh/1998/lv-ca-sl.htm.

U.S. DOE (Department of Energy). 1998a. Empirical Models for the Uptake of Inorganic Chemicals from Soil by Plants. BJC/OR-133. Prepared for U.S. Department of Energy, Oak Ridge National Laboratory, Oak Ridge, TN.

U.S. DOE (Department of Energy). 1998b. Biota Sediment Accumulation Factors for Invertebrates: Review and Recommendations for the Oak Ridge Reservation . BJC/OR112. Oak Ridge, TN.

U.S. EPA (Environmental Protection Agency). 1998. Methodology for Assessing Health Risks Associated With Multiple Exposure Pathways to Combustor Emissions. EPA, National Center for Environmental Assessment, NCEA-0238.

U.S. EPA (Environmental Protection Agency). 2000. Ecological Soil Screening Level Guidance - Draft. Office of Emergency and Remedial Response. July, 2000.

U.S. EPA (Environmental Protection Agency). 2000b. Exposure and Human Health Reassessment of 2,3,7,8-Tetrachlorodibenzo- p-Dioxin (TCDD) and Related Compounds. Part I: Estimating Exposure to Dioxin-Like Compounds. Volume 4: Site-Specific Assessment Procedures. Draft. Exposure Assessment and Risk Characterization Group, Office of Research and Development, Washington, DC. September.

U.S. EPA (Environmental Protection Agency). 1997. Mercury Study Report to Congress. Volume III - Fate and Transport of Mercury in the Environment. EPA 452/R-97/005. Office of Air Quality Planning and Standards and Office of Research and development, Washington, DC.

### Section 10.0 Farm Food Chain and Terrestrial Food Web Data



### 10-31 U.S. EPA (Environmental Protection Agency). 1999. Estimating Risk from the Use of Agricultural Fertilizers (Draft Report). Offi ce of Solid Waste, Washington, DC. August. Web site at http://www.epa.gov/epaoswer/hazwaste/recycle/fertiliz/risk/index.htm.

U.S. EPA (Environmental Protection Agency). 1997a. Parameter Guidance Document. EPA, National Center for Environmental Assessment, NCEA-0238.

U.S. EPA (Environmental Protection Agency). 1997b. Risk Assessment for the Waste Technologies Industries (WTI) Hazardous Waste Incineration Facility. Volume VI: Screening Ecological Risk Assessment. EPA-905-R-97-002f. Region 5 Waste, Pesticides and Toxics Division. Chicago, IL.

U.S. EPA (Environmental Protection Agency). 1997c. Protocol for Screening Level Ecological Risk Assessment at Hazardous Waste Combustion Facilities . EPA/R-6-096-003. Office of Solid Waste, Dallas, MN.

Van Gestel, C.A.M., and W. Ma. 1988. Toxicity and bioaccumulation of chlorophenols in earthworms, in relation to bioavailability in soil. Ecotoxicology and Environmental Safety 15:289-297.

Wu, L., J. Chen, K.K. Tanji, and G.S. Banue los. 1995. Distribution and biomagnification of selenium in a restored upland grassland contaminated by selenium from agricultural drain water. Environmental Toxicology and Chemistry 14(4):733-742.

### Section 10.0 Farm Food Chain and Terrestrial Food Web Data



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### Appendix 10A Chemical-Specific Databa se Developed for the FFC and TFW Modules



### 10-33 

# Appendix 10A

Chemical-Specific Database Developed for the FFC and TFW Modules

10-1. Farm Food Chain and Terrestrial Food Web Chemical-Specific Parameters . . . . . . . . 10-2 10-2. Farm Food Chain and Terrestrial Food Web Exposure-Related Parameters . . . . . . . . 10-3 10-3. Data Sources for Farm Food Chain Chemical-Specific Parameters . . . . . . . . . . . . . . . 10-5 10-4. Data Sources for Farm Food Chain Exposure-Related Parameters . . . . . . . . . . . . . . . . 10-6 10-5. Data Sources for Terrestrial Food Web Chemical-Specific Parameters . . . . . . . . . . . . 10-8 10-6. Uptake Factors Identified for Small Amphibians . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-16 10-7. Uptake Factors Identified for Earthworms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-17 10-8. Uptake Factors Identified for Invertebrates . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-19 10-9. Daily Water Intake of Beef Cattle (gal/d) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-23 10-10. Daily Water Intake of Milk-Producing Dairy Cows (gal/d) . . . . . . . . . . . . . . . . . . . 10-24 Table 10A-1. Beef and Milk Biotransfer Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-35 Table 10A-2. Soil-to-Plant Bioconcentration Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-38 Table 10A-3. Root Crop Bio-uptake Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-41 Table 10A-4. Air-to-Plant Biotransfer Factors . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-44 Table 10A-5. Plant Surface Loss Coefficients – Particulate . . . . . . . . . . . . . . . . . . . . . . . . 10-47 Table 10A-6. Plant Surface Loss Coefficients – Vapor . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10-49

### Appendix 10A Chemical-Specific Databa se Developed for the FFC and TFW Modules



### 10-34 This page intentionally left blank.

### Appendix 10A Chemical-Specific Databa se Developed for the FFC and TFW Modules



### 10-35Table 10A-1. Beef and Milk Biotransfer FactorsConstituent CASChemTypeChem Ba_beef(d/kg)Chem Ba_milk(d/kg) Chem Ba ReferenceChemBs(fraction) ChemBs ReferenceTCDD, 2,3,7,8- 1746-01-6 D 0.054 0.01 U.S. EPA, 1998 0.65 U.S. EPA, 2000bMercury 7439-97-6 Hg 0.0077 0.0026 U.S. EPA, 1997d 1 U.S. EPA, 1997aMercury (elemental) 7439-97-6e Hg 0.00770.0026 U.S. EPA, 1997d 1 U.S. EPA, 1997aMethyl mercury 7439-97-6m Hg 0.0077 0.0026 U.S. EPA, 1997d 1 U.S. EPA, 1997aAntimony 7440-36-0 M 0.001 0.0001 Baes et al., 1984 1 U.S. EPA, 1997aArsenic 7440-38-2 M 0.002 0.00006 Baes et al., 1984 1 U.S. EPA, 1997aBarium 7440-39-3 M 0.00015 0.00035 Baes et al., 1984 1 U.S. EPA, 1997aBeryllium 7440-41-7 M 0.001 0.0000009 Baes et al., 1984 1 U.S. EPA, 1997aCadmium 7440-43-9 M 0.00055 0.001 Baes et al., 1984 1 U.S. EPA, 1997aChromium (total) 7440-47-3 M 0.0055 0.0015 Baes et al., 1984 1 U.S. EPA, 1997aChromium III (insolublesalts)16065-83-1 M 0.0055 0.0015 Baes et al., 1984 1 U.S. EPA, 1997aChromium VI 18540-29-9 M 0.0055 0.0015 Baes et al., 1984 1 U.S. EPA, 1997aLead 7439-92-1 M 0.0003 0.00025 Baes et al., 1984 1 U.S. EPA, 1997aNickel 7440-02-0 M 0.006 0.001 Baes et al., 1984 1 U.S. EPA, 1997aSelenium 7783-79-1 M 0.015 0.004 Baes et al., 1984 1 U.S. EPA, 1997aSilver 7440-22-4 M 0.003 0.02 Baes et al., 1984 1 U.S. EPA, 1997aThallium 7446-18-6 M 0.04 0.002 Baes et al., 1984 1 U.S. EPA, 1997aVanadium 7440-62-2 M 0.0025 0.00002 Baes et al., 1984 1 U.S. EPA, 1997aZinc 7440-66-6 M 0.1 0.01 Baes et al., 1984 1 U.S. EPA, 1997aAcetonitrile 75-05-8 O calculated calculated Calculated based on Travisand Arms, 19881 U.S. EPA, 1997aAcrylonitrile 107-13-1 O calculated calculated Calculated based on Travisand Arms, 19881 U.S. EPA, 1997a(continued)

### Appendix 10A Chemical-Specific Databa se Developed for the FFC and TFW Modules



### 10-36Table 10A-1. (continued)Constituent CASChemTypeChem Ba_beef(d/kg)Chem Ba_milk(d/kg) Chem Ba ReferenceChemBs(fraction) ChemBs ReferenceAniline 62-53-3 O calculated calculated Calculated based on Travisand Arms, 19881 U.S. EPA, 1997aBenzene 71-43-2 O calculated calculated Calculated based on Travisand Arms, 19881 U.S. EPA, 1997aCarbon disulfide 75-15-0 O calculated calculated Calculated based on Travisand Arms, 19881 U.S. EPA, 1997aChlorobenzene 108-90-7 O calculated calculated Calculated based on Travisand Arms, 19881 U.S. EPA, 1997aChloroform 67-66-3 O calculated calculated Calculated based on Travisand Arms, 19881 U.S. EPA, 1997aDichlorophenoxyaceticacid, 2,4- (2,4-D)94-75-7 O calculated calculated Calculated based on Travisand Arms, 19881 U.S. EPA, 1997aEthylene dibromide 106-93-4 O calculated calculated Calculated based on Travisand Arms, 19881 U.S. EPA, 1997aHexachloro-1,3-butadiene87-68-3 O calculated calculated Calculated based on Travisand Arms, 19881 U.S. EPA, 1997aMethoxychlor 72-43-5 O calculated calculated Calculated based on Travisand Arms, 19881 U.S. EPA, 1997aMethyl ethyl ketone 78-93-3 O calculated calculated Calculated based on Travisand Arms, 19881 U.S. EPA, 1997aMethyl methacrylate 80-62-6 O calculated calculated Calculated based on Travisand Arms, 19881 U.S. EPA, 1997aMethylene chloride 75-09-2 O calculated calculated Calculated based on Travisand Arms, 19881 U.S. EPA, 1997aNitrobenzene 98-95-3 O calculated calculated Calculated based on Travisand Arms, 19881 U.S. EPA, 1997aPentachlorophenol 87-86-5 O calculated calculated Calculated based on Travisand Arms, 19881 U.S. EPA, 1997a(continued)

### Appendix 10A Chemical-Specific Databa se Developed for the FFC and TFW Modules



### 10-37Table 10A-1. (continued)Constituent CASChemTypeChem Ba_beef(d/kg)Chem Ba_milk(d/kg) Chem Ba ReferenceChemBs(fraction) ChemBs ReferencePhenol 108-95-2 O calculated calculated Calculated based on Travisand Arms, 19881 U.S. EPA, 1997aPyridine 110-86-1 O calculated calculated Calculated based on Travisand Arms, 19881 U.S. EPA, 1997aTetrachloroethylene 127-18-4 O calculated calculated Calculated based on Travisand Arms, 19881 U.S. EPA, 1997aThiram 137-26-8 O calculated calculated Calculated based on Travisand Arms, 19881 U.S. EPA, 1997aToluene 108-88-3 O calculated calculated Calculated based on Travisand Arms, 19881 U.S. EPA, 1997aTrichloroethane, 1,1,1- 71-55-6 O calculated calculated Calculated based on Travisand Arms, 19881 U.S. EPA, 1997aTrichloroethylene 79-01-6 O calculated calculated Calculated based on Travisand Arms, 19881 U.S. EPA, 1997aVinyl chloride 75-01-4 O calculated calculated Calculated based on Travisand Arms, 19881 U.S. EPA, 1997aBenzo(a)pyrene 50-32-8 S calculated calculated Calculated based on Travisand Arms, 19881 U.S. EPA, 1997aBis(2-ethylhexyl)phthalate117-81-7 S calculated calculated Calculated based on Travisand Arms, 19881 U.S. EPA, 1997aDibenz(a,h)anthracene 53-70-3 S calculated calculated Calculated based on Travisand Arms, 19881 U.S. EPA, 1997a

### Appendix 10A Chemical-Specific Databa se Developed for the FFC and TFW Modules



### 10-38Table 10A-2. Soil-to-Plant Bioconcentration FactorsConstituent CASChemTypeChemBr_exfruit(ug/g DWplant)/(ug/g soil)ChemBr_Exveg(ug/g DWplant)/(ug/g soil)ChemBr_profruit(ug/g DWplant)/(ug/g soil)ChemBr_proveg(ug/g DWplant)/(ug/g soil)ChemBr_forage(ug/g DWplant)/(ug/g soil)ChemBr_grain(ug/g DWplant)/(ug/g soil)ChemBr_silage(ug/g DWplant)/(ug/g soil) ReferenceTCDD, 2,3,7,8- 1746-01-6 D NA NANA NA NA NA NA NA for dioxinsMercury 7439-97-6 Hg 1.80E-02 1.00E-01 1.40E-02 1.40E-02 2.50E-01 8.90E-02 7.80E-01 U.S. EPA, 1997dMercury (elemental) 7439-97-6e Hg 1.80E-02 1.00E-01 1.40E-02 1.40E-02 2.50E-01 8.90E-02 7.80E-01 U.S. EPA, 1997dMethyl mercury 7439-97-6m Hg 2.30E-02 2.30E-02 3.30E-02 3.30E-02 2.50E-01 1.90E-02 7.80E-01 U.S. EPA, 1997dAntimony 7440-36-0 M 2.00E-01 2.00E-01 2.00E-01 2.00E-01 2.00E-01 2.00E-01 2.00E-01 Baes et al., 1984Arsenic 7440-38-2 M 4.00E-02 3.20E-03 NO DATA NO DATA 7.10E-02 4.00E-02 1.80E-02 Calculated from U.S.EPA, 1999Barium 7440-39-3 M 1.50E-01 1.50E-01 1.50E-01 1.50E-01 1.50E-01 1.50E-01 1.50E-01 Baes et al., 1984Beryllium 7440-41-7 M 1.00E-02 1.00E-02 1.00E-02 1.00E-02 1.00E-02 1.00E-02 1.00E-02 Baes et al., 1984Cadmium 7440-43-9 M 5.50E-01 6.00E-01 NO DATA NO DATA 3.10E-01 5.50E-02 2.10E-01 Calculated from U.S.EPA, 1999Chromium (total) 7440-47-3 M 7.50E-03 5.70E-04 NO DATA NO DATA 1.90E-03 8.50E-05 9.30E-03 Calculated from U.S.EPA, 1999Chromium III (insolublesalts)16065-83-1 M 7.50E-03 5.70E-04 NO DATA NO DATA 1.90E-03 8.50E-05 9.30E-03 Calculated from U.S.EPA, 1999Chromium VI 18540-29-9 M 7.50E-03 5.70E-04 NO DATA NO DATA 1.90E-03 8.50E-05 9.30E-03 Calculated from U.S.EPA, 1999Lead 7439-92-1 M 1.50E-01 3.80E-02 NO DATA NO DATA 8.30E-02 7.40E-03 6.70E-02 Calculated from U.S.EPA, 1999Nickel 7440-02-0 M 6.00E-02 5.10E-02 6.00E-02 6.00E-02 4.30E-02 7.00E-02 1.70E-01 Calculated from U.S.EPA, 1999Selenium 7783-79-1 M 2.50E-02 2.50E-02 2.50E-02 2.50E-02 2.50E-02 2.50E-02 2.50E-02 Baes et al., 1984Silver 7440-22-4 M 4.00E-01 4.00E-01 NO DATA NO DATA 4.00E-01 4.00E-01 4.00E-01 Baes et al., 1984Thallium 7446-18-6 M 4.00E-03 4.00E-03 4.00E-03 4.00E-03 4.00E-03 4.00E-03 4.00E-03 Baes et al., 1984Vanadium 7440-62-2 M 5.50E-03 5.50E-03 5.50E-03 5.50E-03 4.80E-03 5.50E-03 5.50E-03 Baes et al., 1984(continued)

### Appendix 10A Chemical-Specific Databa se Developed for the FFC and TFW Modules



### 10-39Table 10A-2. (continued)Constituent CASChemTypeChemBr_exfruit(ug/g DWplant)/(ug/g soil)ChemBr_Exveg(ug/g DWplant)/(ug/g soil)ChemBr_profruit(ug/g DWplant)/(ug/g soil)ChemBr_proveg(ug/g DWplant)/(ug/g soil)ChemBr_forage(ug/g DWplant)/(ug/g soil)ChemBr_grain(ug/g DWplant)/(ug/g soil)ChemBr_silage(ug/g DWplant)/(ug/g soil) ReferenceZinc 7440-66-6 M 1.50E+00 3.70E-01 NO DATA NO DATA 2.90E-01 1.80E-01 1.60E+00 Calculated from U.S.EPA, 1999Acetonitrile 75-05-8 O calculated calculated calculated calculated calculated calculated calculated Calculated based onTravis and Arms, 1988Acrylonitrile 107-13-1 O calculated calculated calculated calculated calculated calculated calculated Calculated based onTravis and Arms, 1988Aniline 62-53-3 O calculated calculated calculated calculated calculated calculated calculated Calculated based onTravis and Arms, 1988Benzene 71-43-2 O calculated calculated calculated calculated calculated calculated calculated Calculated based onTravis and Arms, 1988Carbon disulfide 75-15-0 O calculated calculated calculated calculated calculated calculated calculated Calculated based onTravis and Arms, 1988Chlorobenzene 108-90-7 O calculated calculated calculated calculated calculated calculated calculated Calculated based onTravis and Arms, 1988Chloroform 67-66-3 O calculated calculated calculated calculated calculated calculated calculated Calculated based onTravis and Arms, 1988Dichlorophenoxyaceticacid, 2,4- (2,4-D)94-75-7 O calculated calculated calculated calculated calculated calculated calculated Calculated based onTravis and Arms, 1988Ethylene dibromide 106-93-4 O calculated calculated calculated calculated calculated calculated calculated Calculated based onTravis and Arms, 1988Hexachloro-1,3-butadiene 87-68-3 O calculated calculated calculatedcalculated calculated calculated calculated Calculated based onTravis and Arms, 1988Methoxychlor 72-43-5 O calculated calculated calculated calculated calculated calculated calculated Calculated based onTravis and Arms, 1988Methyl ethyl ketone 78-93-3 O calculated calculated calculated calculated calculated calculated calculated Calculated based onTravis and Arms, 1988Methyl methacrylate 80-62-6 O calculated calculated calculated calculated calculated calculated calculated Calculated based onTravis and Arms, 1988(continued)

### Appendix 10A Chemical-Specific Databa se Developed for the FFC and TFW Modules



### 10-40Table 10A-2. (continued)Constituent CASChemTypeChemBr_exfruit(ug/g DWplant)/(ug/g soil)ChemBr_Exveg(ug/g DWplant)/(ug/g soil)ChemBr_profruit(ug/g DWplant)/(ug/g soil)ChemBr_proveg(ug/g DWplant)/(ug/g soil)ChemBr_forage(ug/g DWplant)/(ug/g soil)ChemBr_grain(ug/g DWplant)/(ug/g soil)ChemBr_silage(ug/g DWplant)/(ug/g soil) ReferenceMethylene chloride 75-09-2 O calculated calculated calculated calculated calculated calculated calculated Calculated based onTravis and Arms, 1988Nitrobenzene 98-95-3 O calculated calculated calculated calculated calculated calculated calculated Calculated based onTravis and Arms, 1988Pentachlorophenol 87-86-5 O calculated calculated calculated calculated calculated calculated calculated Calculated based onTravis and Arms, 1988Phenol 108-95-2 O calculated calculated calculated calculated calculated calculated calculated Calculated based onTravis and Arms, 1988Pyridine 110-86-1 O calculated calculated calculated calculated calculated calculated calculated Calculated based onTravis and Arms, 1988Tetrachloroethylene 127-18-4 O calculated calculated calculated calculated calculated calculated calculated Calculated based onTravis and Arms, 1988Thiram 137-26-8 O calculated calculated calculated calculated calculated calculated calculated Calculated based onTravis and Arms, 1988Toluene 108-88-3 O calculated calculated calculated calculated calculated calculated calculated Calculated based onTravis and Arms, 1988Trichloroethane, 1,1,1- 71-55-6 O calculated calculated calculatedcalculated calculated calculated calculated Calculated based onTravis and Arms, 1988Trichloroethylene 79-01-6 O calculated calculated calculated calculated calculated calculated calculated Calculated based onTravis and Arms, 1988Vinyl chloride 75-01-4 O calculated calculated calculated calculated calculated calculated calculated Calculated based onTravis and Arms, 1988Benzo(a)pyrene 50-32-8 S calculated calculated calculated calculated calculated calculated calculated Calculated based onTravis and Arms, 1988Bis(2-ethylhexyl)phthalate117-81-7 S calculated calculated calculated calculated calculated calculated calculated Calculated based onTravis and Arms, 1988Dibenz(a,h)anthracene 53-70-3 S calculated calculated calculated calculated calculated calculated calculated Calculated based onTravis and Arms, 1988

### Appendix 10A Chemical-Specific Databa se Developed for the FFC and TFW Modules



### 10-41 Table 10A-3. Root Crop Bio-uptake Factors

Constituent CAS Chem Type ChemRCF (ug/g WW plant)/(ug/mL

soil water) ChemRCF Reference ChemBr_root (ug/g DW

plant)/ (ug/g soil) ChemBr_Root Reference TCDD, 2,3,7,8- 1746-01-6 D 5.20E+03 U.S. EPA, 1997a NA NA for dioxins

Mercury 7439-97-6 Hg NA NA for metals 3.50E-02 U.S. EPA, 1997d Mercury (elemental) 7439-97-6e Hg NA NA for metals 3.50E-02 U.S. EPA, 1997d Methyl mercury 7439-97-6m Hg NA NA for metals 9.90E-02 U.S. EPA, 1997d Antimony 7440-36-0 M NA NA for metals 3.00E-02 Baes et al., 1984
Arsenic 7440-38-2 M NA NA for metals 6.70E-02 Calculated from U.S. EPA, 1999 Barium 7440-39-3 M NA NA for metals 1.50E-02 Baes et al., 1984 Beryllium 7440-41-7 M NA NA for metals 1.50E-03 Baes et al., 1984
Cadmium 7440-43-9 M NA NA for metals 4.00E-01 Calculated from U.S. EPA, 1999 Chromium (total) 7440-47-3 M NA NA for metals 6.60E-04 Calculated from U.S. EPA, 1999
Chromium III (insoluble salts) 16065-83-1 M NA NA for metals 6.60E-04 Calculated from U.S. EPA, 1999
Chromium VI 18540-29-9 M NA NA for metals 6.60E-04 Calculated from U.S. EPA, 1999
Lead 7439-92-1 M NA NA for metals 3.00E-02 Calculated from U.S. EPA, 1999
Nickel 7440-02-0 M NA NA for metals 2.70E-02 Calculated from U.S. EPA, 1999 Selenium 7783-79-1 M NA NA for metals 2.50E-02 Baes et al., 1984 Silver 7440-22-4 M NA NA for metals 1.00E-01 Baes et al., 1984 Thallium 7446-18-6 M NA NA for metals 4.00E-04 Baes et al., 1984 Vanadium 7440-62-2 M NA NA for metals 3.00E-03 Baes et al., 1984
Zinc 7440-66-6 M NA NA for metals 2.30E-01 Calculated from U.S. EPA, 1999 (continued)

### Appendix 10A Chemical-Specific Databa se Developed for the FFC and TFW Modules



### 10-42 Table 10A-3. (continued)

Constituent CAS Chem Type ChemRCF (ug/g WW plant)/(ug/mL

soil water) ChemRCF Reference ChemBr_root (ug/g DW

plant)/ (ug/g soil) ChemBr_Root Reference
Acetonitrile 75-05-8 O calculated Calculated based on U.S. EPA, 1997a NA NA for organics

Acrylonitrile 107-13-1 O calculated Calculated based on U.S. EPA, 1997a NA NA for organics

Aniline 62-53-3 O calculated Calculated based on U.S. EPA, 1997a NA NA for organics

Benzene 71-43-2 O calculated Calculated based on U.S. EPA, 1997a NA NA for organics

Carbon disulfide 75-15-0 O calculated Calculated based on U.S. EPA, 1997a NA NA for organics

Chlorobenzene 108-90-7 O calculated Calculated based on U.S. EPA, 1997a NA NA for organics

Chloroform 67-66-3 O calculated Calculated based on U.S. EPA, 1997a NA NA for organics

Dichlorophenoxyacetic acid, 2,4- (2,4-D) 94-75-7 O calculated Calculated based on U.S. EPA, 1997a NA NA for organics

Ethylene dibromide 106-93-4 O calculated Calculated based on U.S. EPA, 1997a NA NA for organics

Hexachloro-1,3- butadiene 87-68-3 O calculated Calculated based on U.S. EPA, 1997a NA NA for organics

Methoxychlor 72-43-5 O calculated Calculated based on U.S. EPA, 1997a NA NA for organics

Methyl ethyl ketone 78-93-3 O calculated Calculated based on U.S. EPA, 1997a NA NA for organics

Methyl methacrylate 80-62-6 O calculated Calculated based on U.S. EPA, 1997a NA NA for organics

Methylene chloride 75-09-2 O calculated Calculated based on U.S. EPA, 1997a NA NA for organics

(continued)

### Appendix 10A Chemical-Specific Databa se Developed for the FFC and TFW Modules



### 10-43 Table 10A-3. (continued)

Constituent CAS Chem Type ChemRCF (ug/g WW plant)/(ug/mL

soil water) ChemRCF Reference ChemBr_root (ug/g DW

plant)/ (ug/g soil) ChemBr_Root Reference
Nitrobenzene 98-95-3 O calculated Calculated based on U.S. EPA, 1997a NA NA for organics

Pentachlorophenol 87-86-5 O calculated Calculated based on U.S. EPA, 1997a NA NA for organics

Phenol 108-95-2 O calculated Calculated based on U.S. EPA, 1997a NA NA for organics

Pyridine 110-86-1 O calculated Calculated based on U.S. EPA, 1997a NA NA for organics

Tetrachloroethylene 127-18-4 O calculated Calculated based on U.S. EPA, 1997a NA NA for organics

Thiram 137-26-8 O calculated Calculated based on U.S. EPA, 1997a NA NA for organics

Toluene 108-88-3 O calculated Calculated based on U.S. EPA, 1997a NA NA for organics

Trichloroethane, 1,1,1- 71-55-6 O calculated Calculated based on U.S. EPA, 1997a NA NA for organics

Trichloroethylene 79-01-6 O calculated Calculated based on U.S. EPA, 1997a NA NA for organics

Vinyl chloride 75-01-4 O calculated Calculated based on U.S. EPA, 1997a NA NA for organics

Benzo(a)pyrene 50-32-8 S calculated Calculated based on U.S. EPA, 1997a NA NA for organics

Bis(2-ethylhexyl) phthalate 117-81-7 S calculated Calculated based on U.S. EPA, 1997a NA NA for organics

Dibenz(a,h)anthracene 53-70-3 S calculated Calculated based on U.S. EPA, 1997a NA NA for organics

### Appendix 10A Chemical-Specific Databa se Developed for the FFC and TFW Modules



### 10-44Table 10A-4. Air-to-Plant Biotransfer FactorsConstituent CASChemTypeChemBv_exfruit(ug/g DWplant)/(ug/g air)ChemBv_exveg (ug/gDW plant)/(ug/g air)ChemBv_forage (ug/gDW plant)/(ug/g air)ChemBv_silage (ug/gDW plant)/(ug/g air)Chem_BvReferenceChemBv_ecf_plant(unitless)ChemBv_ecf_plantReferenceTCDD, 2,3,7,8- 1746-01-6 D 6.55E+04 6.55E+04 6.55E+04 6.55E+04 U.S. EPA, 1997a NA NAMercury 7439-97-6 Hg 1.97E+04 1.97E+04 1.80E+04 1.80E+04 U.S. EPA, 1997a NA NA for metalsMercury (elemental) 7439-97-6e Hg 1.97E+04 1.97E+04 1.80E+04 1.80E+04 U.S. EPA, 1997a NA NA for metalsMethyl mercury 7439-97-6m Hg 3.30E+03 3.30E+03 5.00E+03 5.00E+03 U.S. EPA, 1997a NA NA for metalsAntimony 7440-36-0 M NA NA NA NA NA for metals NA NA for metalsArsenic 7440-38-2 M NA NA NA NA NAfor metals NA NA for metalsBarium 7440-39-3 M NA NA NA NA NA for metals NA NA for metalsBeryllium 7440-41-7 M NA NA NA NA NA for metals NA NA for metalsCadmium 7440-43-9 M NA NA NA NA NA for metals NA NA for metalsChromium (total) 7440-47-3 M NA NA NANA NA for metals NA NA for metalsChromium III (insolublesalts)16065-83-1 M NA NA NA NA NA for metals NA NA for metalsChromium VI 18540-29-9 M NA NA NA NA NA for metals NA NA for metalsLead 7439-92-1 M NA NA NA NA NA for metals NA NA for metalsNickel 7440-02-0 M NA NA NA NA NA for metals NA NA for metalsSelenium 7783-79-1 M NA NA NA NA NAfor metals NA NA for metalsSilver 7440-22-4 M NA NA NA NA NA for metals NA NA for metalsThallium 7446-18-6 M NA NA NA NA NA for metals NA NA for metalsVanadium 7440-62-2 M NA NA NA NA NAfor metals NA NA for metalsZinc 7440-66-6 M NA NA NA NA NA for metals NA NA for metalsAcetonitrile 75-05-8 O calculated calculated calculated calculated Calculated basedon U.S. EPA, 1998100 U.S. EPA, 1998Acrylonitrile 107-13-1 O calculated calculated calculated calculated Calculated basedon U.S. EPA, 1998100 U.S. EPA, 1998(continued)

### Appendix 10A Chemical-Specific Databa se Developed for the FFC and TFW Modules



### 10-45Table 10A-4. (continued)Constituent CASChemTypeChemBv_exfruit(ug/g DWplant)/(ug/g air)ChemBv_exveg (ug/gDW plant)/(ug/g air)ChemBv_forage (ug/gDW plant)/(ug/g air)ChemBv_silage (ug/gDW plant)/(ug/g air)Chem_BvReferenceChemBv_ecf_plant(unitless)ChemBv_ecf_plantReferenceAniline 62-53-3 O calculated calculated calculated calculated Calculated basedon U.S. EPA, 1998100 U.S. EPA, 1998Benzene 71-43-2 O calculated calculated calculated calculated Calculated basedon U.S. EPA, 1998100 U.S. EPA, 1998Carbon disulfide 75-15-0 O calculated calculated calculated calculated Calculated basedon U.S. EPA, 1998100 U.S. EPA, 1998Chlorobenzene 108-90-7 O calculated calculated calculated calculated Calculated basedon U.S. EPA, 1998100 U.S. EPA, 1998Chloroform 67-66-3 O calculated calculated calculated calculated Calculated basedon U.S. EPA, 1998100 U.S. EPA, 1998Dichlorophenoxyaceticacid, 2,4- (2,4-D)94-75-7 O calculated calculated calculated calculated Calculated basedon U.S. EPA, 1998100 U.S. EPA, 1998Ethylene dibromide 106-93-4 O calculated calculated calculated calculated Calculated basedon U.S. EPA, 1998100 U.S. EPA, 1998Hexachloro-1,3-butadiene 87-68-3 O calculated calculated calculated calculated Calculated basedon U.S. EPA, 1998100 U.S. EPA, 1998Methoxychlor 72-43-5 O calculated calculated calculated calculated Calculated basedon U.S. EPA, 1998100 U.S. EPA, 1998Methyl ethyl ketone 78-93-3 O calculated calculated calculated calculated Calculated basedon U.S. EPA, 1998100 U.S. EPA, 1998Methyl methacrylate 80-62-6 O calculated calculated calculated calculated Calculated basedon U.S. EPA, 1998100 U.S. EPA, 1998Methylene chloride 75-09-2 O calculated calculated calculated calculated Calculated basedon U.S. EPA, 1998100 U.S. EPA, 1998Nitrobenzene 98-95-3 O calculated calculatedcalculated calculated Calculated basedon U.S. EPA, 1998100 U.S. EPA, 1998(continued)

### Appendix 10A Chemical-Specific Databa se Developed for the FFC and TFW Modules



### 10-46Table 10A-4. (continued)Constituent CASChemTypeChemBv_exfruit(ug/g DWplant)/(ug/g air)ChemBv_exveg (ug/gDW plant)/(ug/g air)ChemBv_forage (ug/gDW plant)/(ug/g air)ChemBv_silage (ug/gDW plant)/(ug/g air)Chem_BvReferenceChemBv_ecf_plant(unitless)ChemBv_ecf_plantReferencePentachlorophenol 87-86-5 O calculated calculated calculated calculated Calculated basedon U.S. EPA, 1998100 U.S. EPA, 1998Phenol 108-95-2 O calculated calculated calculated calculated Calculated basedon U.S. EPA, 1998100 U.S. EPA, 1998Pyridine 110-86-1 O calculated calculated calculated calculated Calculated basedon U.S. EPA, 1998100 U.S. EPA, 1998Tetrachloroethylene 127-18-4 O calculated calculated calculated calculated Calculated basedon U.S. EPA, 1998100 U.S. EPA, 1998Thiram 137-26-8 O calculated calculated calculated calculated Calculated basedon U.S. EPA, 1998100 U.S. EPA, 1998Toluene 108-88-3 O calculated calculated calculated calculated Calculated basedon U.S. EPA, 1998100 U.S. EPA, 1998Trichloroethane, 1,1,1- 71-55-6 O calculated calculated calculated calculated Calculated basedon U.S. EPA, 1998100 U.S. EPA, 1998Trichloroethylene 79-01-6 O calculated calculated calculated calculated Calculated basedon U.S. EPA, 1998100 U.S. EPA, 1998Vinyl chloride 75-01-4 O calculated calculated calculated calculated Calculated basedon U.S. EPA, 1998100 U.S. EPA, 1998Benzo(a)pyrene 50-32-8 S 4.70E+04 4.70E+04 4.70E+04 4.70E+04 Simonich andHites, 1994100 U.S. EPA, 1998Bis(2-ethylhexyl)phthalate117-81-7 S calculated calculated calculated calculated Calculated basedon U.S. EPA, 1998100 U.S. EPA, 1998Dibenz(a,h)anthracene 53-70-3 S calculated calculated calculated calculated Calculated basedon U.S. EPA, 1998100 U.S. EPA, 1998

### Appendix 10A Chemical-Specific Databa se Developed for the FFC and TFW Modules



### 10-47 Table 10A-5. Plant Surface Loss Coefficients – Particulate

Constituent CAS Chem Type Chem kpPar_ exfruit (1/yr) Chem kpPar_ exveg (1/yr) Chem kpPar_ forage (1/yr) Chem kpPar_ silage (1/yr) Reference TCDD, 2,3,7,8- 1746-01-6 D 27.06 27. 06 27.06 27.06 U.S. EPA, 1997a Mercury 7439-97-6 Hg 40.41 40.41 40.41 40.41 U.S. EPA, 1997a Mercury (elemental) 7439-97-6e Hg 40. 41 40.41 40.41 40.41 U.S. EPA, 1997a Methyl mercury 7439-97- 6m Hg 40.41 40.41 40.41 40.41 U.S. EPA, 1997a

Antimony 7440-36-0 M 18.07 18.07 18.07 18.07 U.S. EPA, 1997a Arsenic 7440-38-2 M 18.07 18.07 18.07 18.07 U.S. EPA, 1997a Barium 7440-39-3 M 18.07 18.07 18.07 18.07 U.S. EPA, 1997a Beryllium 7440-41-7 M 18.07 18.07 18.07 18.07 U.S. EPA, 1997a Cadmium 7440-43-9 M 18.07 18.07 18.07 18.07 U.S. EPA, 1997a Chromium (total) 7440-47-3 M 18.07 18.07 18.07 18.07 U.S. EPA, 1997a
Chromium III (insoluble salts) 16065-83-1 M 18.07 18.07 18.07 18.07 U.S. EPA, 1997a

Chromium VI 18540-29-9 M 18.07 18.07 18.07 18.07 U.S. EPA, 1997a Lead 7439-92-1 M 18.07 18.07 18.07 18.07 U.S. EPA, 1997a Nickel 7440-02-0 M 18.07 18.07 18.07 18.07 U.S. EPA, 1997a
Selenium 7783-79-1 M 18.07 18.07 18.07 18.07 U.S. EPA, 1997a Silver 7440-22-4 M 18.07 18.07 18.07 18.07 U.S. EPA, 1997a
Thallium 7446-18-6 M 18.07 18.07 18.07 18.07 U.S. EPA, 1997a Vanadium 7440-62-2 M 18.07 18.07 18.07 18.07 U.S. EPA, 1997a Zinc 7440-66-6 M 18.07 18.07 18.07 18.07 U.S. EPA, 1997a Acetonitrile 75-05-8 O 18.07 18.07 18.07 18.07 U.S. EPA, 1997a Acrylonitrile 107-13-1 O 18.07 18. 07 18.07 18.07 U.S. EPA, 1997a Aniline 62-53-3 O 18.07 18.07 18.07 18.07 U.S. EPA, 1997a Benzene 71-43-2 O 18.07 18.07 18.07 18.07 U.S. EPA, 1997a Carbon disulfide 75-15-0 O 18.07 18.07 18.07 18.07 U.S. EPA, 1997a Chlorobenzene 108-90-7 O 18.07 18.07 18.07 18.07 U.S. EPA, 1997a Chloroform 67-66-3 O 18.07 18.07 18.07 18.07 U.S. EPA, 1997a Dichlorophenoxyacetic acid, 2,4- (2,4-D) 94-75-7 O 18.07 18.07 18.07 18.07 U.S. EPA, 1997a

Ethylene dibromide 106-93-4 O 18.07 18.07 18.07 18.07 U.S. EPA, 1997a Hexachloro-1,3- butadiene 87-68-3 O 18.07 18.07 18.07 18.07 U.S. EPA, 1997a

Methoxychlor 72-43-5 O 18.07 18.07 18.07 18.07 U.S. EPA, 1997a Methyl ethyl ketone 78-93-3 O 18. 07 18.07 18.07 18.07 U.S. EPA, 1997a Methyl methacrylate 80-62-6 O 18.07 18.07 18.07 18.07 U.S. EPA, 1997a Methylene chloride 75-09-2 O 18.07 18.07 18.07 18.07 U.S. EPA, 1997a (continued)

### Appendix 10A Chemical-Specific Databa se Developed for the FFC and TFW Modules



### 10-48 Table 10A-5. (continued)

Constituent CAS Chem Type Chem kpPar_ exfruit (1/yr) Chem kpPar_ exveg (1/yr) Chem kpPar_ forage (1/yr) Chem kpPar_ silage (1/yr) Reference Nitrobenzene 98-95-3 O 18.07 18.07 18.07 18.07 U.S. EPA, 1997a Pentachlorophenol 87-86-5 O 18.07 18. 07 18.07 18.07 U.S. EPA, 1997a Phenol 108-95-2 O 18.07 18.07 18.07 18.07 U.S. EPA, 1997a Pyridine 110-86-1 O 18.07 18.07 18.07 18.07 U.S. EPA, 1997a Tetrachloroethylene 127-18-4 O 18.07 18.07 18.07 18.07 U.S. EPA, 1997a Thiram 137-26-8 O 18.07 18.07 18.07 18.07 U.S. EPA, 1997a Toluene 108-88-3 O 18.07 18.07 18.07 18.07 U.S. EPA, 1997a Trichloroethane, 1,1,1- 71-55-6 O 18.07 18.07 18.07 18.07 U.S. EPA, 1997a Trichloroethylene 79-01-6 O 18.07 18.07 18.07 18.07 U.S. EPA, 1997a Vinyl chloride 75-01-4 O 18.07 18.07 18.07 18.07 U.S. EPA, 1997a Benzo(a)pyrene 50-32-8 S 18.07 18. 07 18.07 18.07 U.S. EPA, 1997a Bis(2-ethylhexyl) phthalate 117-81-7 S 18.07 18.07 18.07 18.07 U.S. EPA, 1997a

Dibenz(a,h)anthracene 53-70-3 S 18. 07 18.07 18.07 18.07 U.S. EPA, 1997a

### Appendix 10A Chemical-Specific Databa se Developed for the FFC and TFW Modules



### 10-49 Table 10A-6. Plant Surface Loss Coefficients – Vapor

Constituent CAS Chem Type Chem kpVap_ exfruit (1/yr) Chem kpVap_

exveg (1/yr) Chem kpVap_ forage (1/yr) Chem kpVap_

silage (1/yr) Reference TCDD, 2,3,7,8- 1746-01-6 D 119.35 119. 35 119.35 119.35 U.S. EPA, 1998 Mercury 7439-97-6 Hg NA NA NA NA NA for metals Mercury (elemental) 7439-97-6e Hg NA NA NA NA NA for metals Methyl mercury 7439-97-6m Hg NA NA NA NA NA for metals Antimony 7440-36-0 M NA NA NA NA NA for metals
Arsenic 7440-38-2 M NA NA NA NA NA for metals
Barium 7440-39-3 M NA NA NA NA NA for metals Beryllium 7440-41-7 M NA NA NA NA NA for metals
Cadmium 7440-43-9 M NA NA NA NA NA for metals Chromium (total) 7440-47-3 M NA NA NA NA NA for metals Chromium III (insoluble salts) 16065-83-1 M NA NA NA NA NA for metals

Chromium VI 18540-29-9 M NA NA NA NA NA for metals
Lead 7439-92-1 M NA NA NA NA NA for metals
Nickel 7440-02-0 M NA NA NA NA NA for metals
Selenium 7783-79-1 M NA NA NA NA NA for metals
Silver 7440-22-4 M NA NA NA NA NA for metals
Thallium 7446-18-6 M NA NA NA NA NA for metals
Vanadium 7440-62-2 M NA NA NA NA NA for metals
Zinc 7440-66-6 M NA NA NA NA NA for metals Acetonitrile 75-05-8 O 119.35 119.35 119.35 119.35 U.S. EPA, 1998 Acrylonitrile 107-13-1 O 119.35 119. 35 119.35 119.35 U.S. EPA, 1998 Aniline 62-53-3 O 119.35 119.35 119.35 119.35 U.S. EPA, 1998 Benzene 71-43-2 O 119.35 119.35 119.35 119.35 U.S. EPA, 1998
Carbon disulfide 75-15-0 O 119.35 119.35 119.35 119.35 U.S. EPA, 1998 Chlorobenzene 108-90-7 O 119.35 119.35 119.35 119.35 U.S. EPA, 1998 Chloroform 67-66-3 O 119.35 119.35 119.35 119.35 U.S. EPA, 1998 Dichlorophenoxyacetic acid, 2,4- (2,4-D) 94-75-7 O 119.35 119.35 119.35 119.35 U.S. EPA, 1998

Ethylene dibromide 106-93-4 O 119.35 119.35 119.35 119.35 U.S. EPA, 1998 Hexachloro-1,3- butadiene 87-68-3 O 119.35 119.35 119.35 119.35 U.S. EPA, 1998

Methoxychlor 72-43-5 O 119.35 119.35 119.35 119.35 U.S. EPA, 1998 Methyl ethyl ketone 78-93-3 O 119. 35 119.35 119.35 119.35 U.S. EPA, 1998 Methyl methacrylate 80-62-6 O 119.35 119.35 119.35 119.35 U.S. EPA, 1998 Methylene chloride 75-09-2 O 119.35 119.35 119.35 119.35 U.S. EPA, 1998 (continued)

### Appendix 10A Chemical-Specific Databa se Developed for the FFC and TFW Modules



### 10-50 Table 10A-6. (continued)

Constituent CAS Chem Type Chem kpVap_ exfruit (1/yr) Chem kpVap_

exveg (1/yr) Chem kpVap_ forage (1/yr) Chem kpVap_

silage (1/yr) Reference Nitrobenzene 98-95-3 O 119.35 119.35 119.35 119.35 U.S. EPA, 1998 Pentachlorophenol 87-86-5 O 119.35 119. 35 119.35 119.35 U.S. EPA, 1998 Phenol 108-95-2 O 119.35 119.35 119.35 119.35 U.S. EPA, 1998 Pyridine 110-86-1 O 119.35 119.35 119.35 119.35 U.S. EPA, 1998 Tetrachloroethylene 127-18-4 O 119.35 119.35 119.35 119.35 U.S. EPA, 1998 Thiram 137-26-8 O 119.35 119.35 119.35 119.35 U.S. EPA, 1998 Toluene 108-88-3 O 119.35 119.35 119.35 119.35 U.S. EPA, 1998 Trichloroethane, 1,1,1- 71-55-6 O 119.35 119.35 119.35 119.35 U.S. EPA, 1998 Trichloroethylene 79-01-6 O 119.35 119.35 119.35 119.35 U.S. EPA, 1998 Vinyl chloride 75-01-4 O 119.35 119.35 119.35 119.35 U.S. EPA, 1998 Benzo(a)pyrene 50-32-8 S 119.35 119. 35 119.35 119.35 U.S. EPA, 1998 Bis(2-ethylhexyl) phthalate 117-81-7 S 119.35 119.35 119.35 119.35 U.S. EPA, 1998

Dibenz(a,h)anthracene 53-70-3 S 119. 35 119.35 119.35 119.35 U.S. EPA, 1998

### Appendix 10B Exposure-Related Databa se Developed for the FFC and TFW Modules



### 10-51 

# Appendix 10B

Exposure-Related Database Developed for the FFC and TFW Modules

Table 10B-1. Exposure-Related Database Developed for the FFC and TFW Modules . . . . 10-53

### Appendix 10B Exposure-Related Databa se Developed for the FFC and TFW Modules



### 10-52 This page intentionally left blank.

### Appendix 10B Exposure-Related Databa se Developed for the FFC and TFW Modules



### 10-53 Table 10B-1. Exposure-Related Database Developed for the FFC and TFW Modules

Data_Group_Name Variable_N ame Units Central_Tendency terrestrial foodweb Bv_ecf_plant unitless 100 terrestrial foodweb Rp_forage unitless 0.47 terrestrial foodweb MAFroot percent 87 terrestrial foodweb VGag_exfruit unitless 0.01 terrestrial foodweb VapDdv cm/sec 1 terrestrial foodweb tp_silage y 0.16 terrestrial foodweb tp_forage y 0.12 terrestrial foodweb tp_exveg y 0.123 terrestrial foodweb tp_exfruit y 0.123 terrestrial foodweb Rp_silage unitless 0.44 terrestrial foodweb Fw_silage unitless 0.6 terrestrial foodweb Fw_forage unitless 0.6 terrestrial foodweb Yp_silage kg DW/m2 0.31 terrestrial foodweb Yp_forage kg DW/m2 0.31 terrestrial foodweb Yp_exveg kg DW/m2 0.18 terrestrial foodweb Yp_exfruit kg DW/m2 0.09 terrestrial foodweb VGbg_root unitless 0.01 terrestrial foodweb VGag_silage unitless 0.5 terrestrial foodweb VGag_forage unitless 1 terrestrial foodweb Fw_exveg unitless 0.6 terrestrial foodweb Fw_exfruit unitless 0.6 terrestrial foodweb Rp_exveg unitless 0.05 terrestrial foodweb MAFexveg percent 92 terrestrial foodweb MAFforage percent 92 terrestrial foodweb MAFgrain percent 90
terrestrial foodweb MAFleaf unitless 85 terrestrial foodweb MAFexfruit percent 85 terrestrial foodweb VGag_exveg unitless 0.01 terrestrial foodweb rho_leaf g/L FW 770 terrestrial foodweb Rp_exfruit unitless 0.052 terrestrial foodweb MAFsilage percent 92

### Appendix 10B Exposure-Related Databa se Developed for the FFC and TFW Modules



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