### 1 of 123 

# User’s Guide and Technical Documentation

KABAM version 1.0
(K OW 

# (based) A quatic B ioA ccumulation M odel)

April 7, 2009

Environmental Fate and E ffects Division (EFED) Office of Pesticide Programs U.S. Environmental Protection Agency Washington, D.C.

### 2 of 123 Acknowledgements

Author

Kristina Garber

Technical Reviewers

Brian Anderson Lawrence Burkhard Paige Doelling Keith Sappington Thomas Steeger

Editorial Reviewer

Karen McCormack

QA/QC Officer

Nick Mastrota

QC Testers

Oak Ridge National Laboratories

### 3 of 123 Table of Contents

1. Introduction................................................................................................................

................. 5 1.1. Model Description .........................................................................................................

...... 5 1.2. When to use this model....................................................................................................

.... 6 1.3. Conceptual model ..........................................................................................................

...... 6 1.4. Model Application .........................................................................................................

...... 7

2. Input parameters............................................................................................................

.............. 8 2.1. Chemical Specific Inputs ..................................................................................................

... 8 2.2. Ecosystem Inputs ..........................................................................................................

..... 13

3. Parameters & Calculations...................................................................................................

..... 17

4. Model Results ...............................................................................................................

............ 18

5. Assessing pesticide concen trations in fish tissues for human consumption............................. 24

6. Model assumptions, limitations, and uncertainties................................................................... 24

Appendix A. Description of bioaccumulation model ................................................................... 26 A.1. Calculation of fraction of chemical in the water column that is freely dissolved ( Ф )...... 28 A.2. Calculation of chemical concentration in sediment .......................................................... 30 A.3. Calculation of respiration uptake (k 1 

### ) and elimination (k 2 

### ) rate constants ....................... 30 A.4. Calculation of growth rate constant .................................................................................. 37 A.5. Calculation of dietary uptake (k D 

### ) rate constant ............................................................... 38 A.6. Calculation of dietary elimination (k E 

### ) rate constant........................................................ 43 A.7. Overall sensitivity of body concentration of chemical (C B 

### ) to individual input parameters

............................................................................................................................... .................... 57

Appendix B. Explanation of defaults and alternativ e values representing abio tic characteristics of aquatic ecosystem ..............................................................................................................

........... 72 B.1. Particulate organic carbon (X POC 

### ) and dissolved organic carbon (X DOC 

### ) ......................... 72 B.2. Concentration of dissolved oxygen (C OX 

### )......................................................................... 72 B.3. Water temperature (T).....................................................................................................

.. 73 B.4. Concentration of suspended solids (C SS 

### ).......................................................................... 74 B.5. Sediment organic carbon (OC).......................................................................................... 74

Appendix C. Explanation of default values re presenting biotic charac teristics of aquatic ecosystem, including food web structure...................................................................................... 75 C.1. Phytoplankton.............................................................................................................

....... 75 C.2. Zooplankton ...............................................................................................................

....... 78 C.3. Benthic invertebrates.....................................................................................................

.... 79 C.4. Filter feeders............................................................................................................

.......... 84 C.5. Fish (small, medium and large sizes)................................................................................ 86

### 4 of 123 Appendix D. Selection of mammal species of concern and correspondi ng biological parameters

............................................................................................................................... ........................ 91 D.1. Descriptions of mammal species....................................................................................... 91 D.2. Determination of mammalian default parameters for KABAM ....................................... 92

Appendix E. Selection of bird species of c oncern and corresponding biological parameters...... 97 E.1. Bird family descriptions ..................................................................................................

.. 97 E.2. Detailed conceptual model .............................................................................................. 102 E.3. Determination of daily food intake.................................................................................. 103 E.4. Definition of default parameters to represent birds in KABAM..................................... 104

Appendix F. Description of equations used to ca lculate the BCF, BAF, BMF, and BSAF values.

............................................................................................................................... ...................... 107 F.1. Bioconcentration..........................................................................................................

.... 107 F.2. Bioaccumulation...........................................................................................................

... 108 F.3. Biomagnification ..........................................................................................................

... 109

Appendix G. Description of equations used to calculate dietary-based and dose-based EECs, toxicity values, and RQs for mammals and bird s consuming contaminated aquatic organisms 110 G.1. Food ingestion rates ......................................................................................................

.. 110 G.2. Drinking water intake rates............................................................................................. 110 G.3. Dose-based EECs...........................................................................................................

. 111 G.4. Dietary-based EECs ........................................................................................................ 111 G.5. Adjusted dose-based toxicity values............................................................................... 111

Appendix H. Methods for estimating metabolism rate constant (k M 

### ) ........................................ 113 H.1. Use of Equation A1........................................................................................................

. 113 H.2. Use of Arnot et al. 2008.................................................................................................. 114 H.3. Assumptions and uncertainties........................................................................................ 114

Appendix I. References Cited...................................................................................................

.. 116

### 5 of 123 1. Introduction

1.1. Model Description

KABAM (K OW 

### (based) A quatic B ioA ccumulation M odel) is used to estimate potential bioaccumulation of hydrophobic organic pesticid es in freshwater aquatic food webs and subsequent risks to mammals a nd birds via consumption of cont aminated aquatic prey. This model can also be used to estimate pesticide concentrations in fish tissues consumed by humans. The model was designed for use by the U. S. Environmental Protection Agency Office of Pesticide Programs’ Environmental Fate and E ffects Division (EFED) scientists. KABAM is composed of two parts: 1) a bioaccumulation model estimating pesticide concentrations in aquatic organisms and 2) a risk component transl ating exposure and toxicological effects of a pesticide into risk estimates for mammals and birds consuming contaminated aquatic prey.

The bioaccumulation portion of KABAM is base d on an aquatic food web bioaccumulation model published by Arnot and Goba s (2004). This model was originally published in 1993 by Gobas (Gobas 1993) and was modified by Arno t and Gobas (2004). The Arnot and Gobas (2004) model was selected for estimating pes ticide bioaccumulation based on the following reasons: 1) the Gobas 1993 model underlying the Arnot and Gobas 2004 version is generally accepted by the scientific community as a reasonable approach for estimating bioaccumulation of persistent hydrophobic organic compounds in aquatic systems (Burkhard 1998); 2) the 1993 version of the model has been used by EPA for regulatory purposes (USEPA 1995, 2000, 2003); and 3) both Gobas 1993 and Arnot and Gobas 2004 have been published in peer-reviewed literature. Although originally developed and applied to the Gr eat Lakes ecosystem for modeling PCBs and selected pesticides, this model has b een applied and validated for other ecosystems, including the Hudson river, Fox river/Green Bay and Bayou D’Indie in Louisiana (USEPA 2003, Burkhard 2003). A detailed description of the Ar not and Gobas (2004) model is available in Appendix A.

The bioaccumulation portion of KABAM relies on a pesticide’s octanol-water partition coefficient (K OW 

### ) to estimate uptake and elimination c onstants through respiration and diet of aquatic organisms in different trophic levels. Pest icide tissue concentrations in aquatic organisms are calculated for different trophic levels of a food web through diet and respiration.

In the risk component of KABAM, pesticide concentrations in aquatic organisms are used to estimate dose- and dietary-based exposures and associated risk quotients for mammals and birds consuming aquatic organisms. The methods used in the risk component of KABAM are consistent with EFED’s current modeling approach for assessing risks to terrestrial mammals and birds described in USEPA 2004a, as implemente d in the T-REX model (version 1.4.1; USEPA 2008a).

### 6 of 123 1.2. When to Use this Model

KABAM should be used for pesticides havi ng all of the following characteristics:

 The pesticide is a non-ionic, organic chemical.

 The Log K OW 

### value is between 4 and 8.

 The pesticide has the potential to reach aquatic habitats.

1.3. Conceptual Model

Conceptually, KABAM represents a freshwater a quatic ecosystem. This ecosystem receives runoff and spray drift containing pesticides from sites where pesticides are applied. The aquatic ecosystem incorporates seven food web compone nts to describe the trophic transfer of a pesticide in an aquatic food web. These include, in increasing order of trophic level within the food web: phytoplankton, zooplankt on, benthic invertebrates, filter feeders, small fish, medium fish and large fish. These components are referre d to within this Use r’s Guide as “trophic levels.” They are not intended to represent discrete trophic levels, but rather generic levels of an aquatic food web ( e.g., primary producers, primary consum ers, secondary consumers, and predators). KABAM also evaluates potential exposures and risks to mammals and birds that feed upon aquatic animals containing pesticide residu es accumulated through the aquatic food web (Figure I).

Figure I. Conceptual model depictin g aquatic food web of KABAM. A rrows depict direction of trophic transfer of bioaccumulated pesticides from low er levels to higher levels of the food web. Sedimen t Benthic invertebrates Ph y to p lankton

Zoo p lankton Small fish

Filter feeders Medium fish Lar g e fish Water column Mammals Birds

### 7 of 123 KABAM can represent a specific ecosystem, as defined by the model user. The ecosystem can be defined by abiotic ( e.g., water temperature, % organic car bon in sediment) and biotic input parameters ( e.g., body weights of aquatic animals, feeding preferen ces of fish, birds, and mammals). The model user can modify these parameters to match the characteristics of ecosystems relevant to a specific mesocosm study or a field study.

For general use, the default model ecosystem fo r KABAM is defined as the EFED standard pond scenario for the Exposure Analysis Modeling System (EXAMS). The standard pond has two compartments: a water column and a benthic area. The water column is 20,000,000 liters in volume and the benthic area has a volume of 500,000 liters. The standard pond receives pesticides in runoff (dissolved in water and so rbed onto eroded soil) and spray drift from a 10-ha treatment field that is immediately adjacent to the pond. The treatment field is represented by various scenarios using the Pesticide Root Z one Model (PRZM). The meteorological data corresponding to the selected PR ZM scenario can influence the runoff of a pesticide into the standard pond and also the water te mperature of the pond environment.

The default biotic portions of KA BAM are designed to be represen tative of organisms from the seven trophic levels defined above . Mammals and birds of con cern are defined by considering species of mammals and birds relevant to the United States which rely upon aquatic ecosystems for their food sources.

1.4. Model Application

The application of KABAM is referred to in th is User’s Guide as the “KABAM tool.” The KABAM tool is implemented in Microsoft ® 

### Excel 2003. This software program was chosen as an operating platform because it is availabl e to EFED users and to the public. Excel is a commonly used spreadsheet program that most scie ntists are familiar with. Computers suitable for running the software programs necessary for this tool require no additional hardware.

Once the KABAM tool is opened, the “Model De scription” worksheet is displayed. This worksheet contains the version information, a brie f model description, and a list of references. Across the bottom of this Excel window are se veral worksheet tabs indicating the various portions of the KABAM tool, including “Chemi cal Specific Inputs,” “Ecosystem Inputs,” “Parameters & Calculations,” and “Results .” The requirements a nd functions of these worksheets are explained in more detail below.

The overall format of the KABAM tool was developed for ease of use. Tables embedded in the worksheets were designed for clarity of informa tion and for eventual cut and paste from Excel into a Microsoft ® 

### Word document containing a risk assessment. Where necessary, comments are provided for guidance on selecting input paramete rs. For more detailed information than is contained in the spreadsheet concerning the mode l, input parameters, calculations, and results, this guidance document should be utilized.

### 8 of 123 2. Input Parameters

Two types of input parameters are required to run KABAM: thos e that are specific to the pesticide and those that define the aquatic ecosystem, including the mammals and birds of concern. These input parameters are distinguishe d by two worksheets that are titled “Chemical Specific Inputs” and “Ecosystem Inputs.”

To run the model, the user is only required to in put chemical specific values since default values are already inserted into the a ppropriate locations fo r ecosystem input parameters. These default values allow the user to run KABAM with reason able and reliable parameters; however, the user can select other parameters to explore bioaccumulation of a chemical and associated potential risk to mammals and birds that consume aquatic animals. Guidance for altering input parameters from the default values is pr ovided in this User’s Guide.

2.1. Chemical Specific Inputs

The “Chemical Specific Inputs” worksheet contains three tables for the user to input data. Tables 1 and 3 require the user to input chemical-specific valu es. Table 2 contains default values that do not require user inputs, but are designed to allow the user flexibility in the case that chemicalspecific data are available for uptake and depur ation rate constants in aquatic organisms.

Table 1

Table 1 requires inputs related to the chemistr y and estimated environmental concentrations (EECs) of the pesticide. Required inputs include: 1) pesticide name, 2) Log K OW 

### , 3) organic carbon partition coefficient (K OC 

### ), 4) sediment pore water concen trations of pesticide residues (Pore water EEC), and 5) aqueous concentration of pesticide residue s (water column EEC). This table contains no default values. Th e user should input values for ea ch of these parameters in the “Value” column of Table 1.

The titles of several tables displayed in the KABAM tool are designed to automatically insert the pesticide name as entered in Table 1.

Of all parameters incorporated into KABAM, Log K OW 

### has the greatest influence on estimates of bioaccumulation in aquatic organisms ( see section A.7 of Appendix A). As a result, this parameter is the most important for estimating potential e xposures of mammals and birds to pesticides through cons umption of contaminated aquatic organisms. Estimates of Log K OW 

### can be obtained for a pesticide from acceptable or supplemental registrant-submitted studies (OPPTS Guidelines 830.7550, 830.7560, 830.7570) and from sc ientific literature. One useful source for locating Log K OW 

### data in the scientific literature is Sangster (2007). Before using data from this database, the scientist should review the original citation and determine whether the data are acceptable or supplemental. If no measured values of Log K OW 

### are available, this value can be estimated using EPI Suite software that includes KOWIN (USEPA 2009), which considers contributions of th e molecule’s individual fragments to the overall Log K OW 

### . If a range of Log K OW 

### values is available, it is suggested that the model user input the high and low Log K OW 

### values separately in order to bracket the bi oaccumulation potential and its associated risks.

### 9 of 123 Bioaccumulation potential increases as Log K OW 

### increases. General guidance for evaluating measured and estimated Log K OW 

### data is available in Appendix B of USEPA 2003.

In Table 1 of the KABAM tool (reproduced below), K OW 

### is automatically calculated as 10 to the power of the Log K OW 

### value that is entered by the model user. The K OW 

### is used to estimate uptake and clearance rate constants that define the concentrations of the pesticide in the tissues of the aquatic organisms.

K OC 

### data can be obtained from registrant-submitted studies (OPPTS Guidelines 835.1230, 835.1240) or from the scientific literature. As the K OC 

### value of a chemical increases, the estimated accumulation of a chemical also increases. The user should select the K OC 

### value input into PRZM/EXAMS for deriving aquatic and benthic EECs. Input parameter guidance for PRZM/EXAMS indicates that the K OC 

### parameter value should be calculated as “the average K OC 

### from batch experiments” (USEPA 2002). If no scientifically valid estimates of K OC 

### are available, this parameter value can be estimated as 0.35*K OW 

### (USEPA 2004b).

Table 1. Chemical characteristics of Pesticide X.

Characteristic Value Guidance

Pesticide Name Pesticide X Required input

Log K OW 5 Required input Enter value from acceptable or supplemental study submitted by registrant or available in scientific literature.

K OW 100000 No input necessary. This value is calculated automati cally from the Log K OW value entered above.

K OC (L/kg OC) 25000 Required input Input value used in PRZM/EXAMS to derive EECs. Follow input parameter guidance for deriving this parameter value (USEPA 2002). Time to steady state (T S ; days) 30 No input necessary. This value is calculated automati cally from the Log K OW value entered above.

Pore water EEC (μg/L) 5 Required input Enter value generated by PRZM/EXAMS benthic file. PRZM/EXAMS EEC represents the freely dissolved co ncentration of the pesticide in the pore water of the sediment. The ap propriate averaging period of the EEC is dependent on the specific pesticide being modeled and is based on the time it takes for the chemical to reach steady state. Select the EEC generated by PRZM/EXAMS which has an averaging period closest to the time to steady state cal culated above. In cases where the time to steady state exceeds 365 days , the user should select the EEC representing the average of yearly averages. The peak EEC should not be used.

Water Column EEC (μg/L) 6 Required input Enter value generated by PRZM/EXAMS water column file. PRZM/EXAMS EEC represents the fr eely dissolved concentration of the pesticide in the water column. Th e appropriate averaging period of the EEC is dependent on the specific pesticide being modeled and is based on the time it takes for the ch emical to reach steady state. The averaging period used for the water column EEC should be the same as the one selected for the pore wa ter EEC (discussed above).
Note: Table 1 of this User’s Guide contains ex ample data for chemical specific characteristics.

### 10 of 123 The time to steady state (T S 

### ; in days) is also calculated automatically by the KABAM tool according to Equation 1 (Hawker and Connell 1988). This equation is consistent with recommendations provided in USEPA and OE CD guidelines for fish BCF studies for determining the time to reach steady state (U SEPA 1996, OECD 1996). It should be noted that there is uncertainty in using this equation for chemicals with Log K OW 

### >6, since this falls outside of the range of data used to de rive this relationship. Alternativel y, the time to steady state can be defined using empirical data from available BCF studies that were sufficient to define steady state. This information can be used to supplement the calculated T S 

### value.

24 31

. 55 * ) 10 54

. 6 (

. 1 3 

### +

= − OW S 

### K x T Equation

EECs from PRZM (v3.12.2, May 2005) and EXAM S (v2.98.4.6, April 2005) (coupled with the input shell pe5.pl, dated Aug 2007) are used in the KAB AM tool. EECs generated by PRZM/EXAMS represent the freely dissolved concentration of the pest icide in the surface and pore water of the standard pond. The bioaccumu lation portion of KABAM assumes that the aquatic environment is at steady st ate. Because the time to reach st eady state is pesticide specific, the appropriate averaging period of the EEC sh ould be determined on a chemical by chemical basis. Generally, the time to reach st eady state can be re lated to the Log K OW 

### of a chemical, with increasing time required as the Log K OW 

### increases. Therefore, it is not relevant to use short-term (peak) estimates of pesticides in the aquatic environment. The EEC used to represent the concentration of the pesticide in the pore and surface waters of the aquatic habitat should be selected so that the averaging period ( i.e., 4-d, 21-d, 90-d, 1 year), is co nsistent with the time to steady state estimated for that chemical. For example, a chemical with a Log K OW 

### = 5 would have an estimated time to steady state value of 30 days. Since the sta ndard output file from PRZM/EXAMS does not include a 30-d average, th e next closest averaging period would be selected (either 21 or 60 days). Therefore, th e EEC represented by the 21-day average would be selected for this chemical. In cases where th e time to steady state exceeds 365 days, the user should select the EEC representing the yearly EEC.

In cases where multiple uses of a single pesticide are possible ( e.g., cotton, corn, apples), EECs from the different uses can be modeled to allo w for an understanding of the bioaccumulation and associated risks associat ed with different uses.

Table 2

KABAM automatically calculates uptake and el imination constants through respiration (k 1 

### and k 2 

### , respectively) and diet (k D 

### and k E 

### , respectively). In using the model with its default parameters in place, it is assumed that the elimination of the pesticide from aquatic organisms through metabolism does not occur ( i.e., metabolism rate constant k M 

### = 0).

In Table 2 (reproduced below) of the KABAM t ool, the model user can enter measured rate constants for uptake and elimination constants. These data can be obtained from acceptable or supplemental studies submitted by the registrant or from the literature. For example, k 1 

### and k 2 

### rate constants for fish can be obtained from pesticide BCF studies submitted for the fish (OPPTS

### 11 of 123 Guideline 850.1730). However, caution should be us ed when altering rate constants. For example, the k 2 

### from a bioconcentration study typically represents a total elimination half life. However, the k 2 

### in KABAM represents elimination from the gills. Therefore, incorporation of a measured k 2 

### into KABAM without consideration of ot her elimination pathways may result in erroneous results. In order to run the mode l, it is not necessary for the user to alter the default values inserted into Table 2. If the model user alters the parameters in Table 2 of the KABAM tool, they will be highlighted yellow.

Table 2. Input parameters for rate constants. "cal culated" indicates that model will calculate rate constant.

Trophic level k 1 (L/kg*d) k 2 (d -1 ) k D(kg-food/kgorg/d) k E (d -1 ) k M * (d -1 ) phytoplankton calculated calculated 0* 0* 0 zooplankton calculated calculated calculated calculated 0 benthic invertebrates calculated calculated calculated calculated 0 filter feeders calculated calculated calculated calculated 0 small fish calculated calculated calculated calculated 0 medium fish calculated calculated calculated calculated 0 large fish calculated calculated calculated calculated 0 * Default value is 0. k 1 and k 2 represent the uptake and elimination cons tants respectively, through respiration. k D and k E represent the uptake and elimination constants, respectively, through diet. k M represents the metabolism rate constant.



### The model user should exercise caution when using a value of k M 

### >0, as this approach will decrease predicted EECs and RQs for mammals and birds. Initially, k M 

### should be set to 0 as a screen. The assumption that there is no metabolism of the pesticide within aquatic organisms is conservative. If no metabolism is observed in available fish BCF studies, then k M 

### should not be altered. In cases where metabolism occurs, this assumption can result in overestimates of pesticide accumulation in tissues of aquatic or ganisms. In cases where the model user has evidence to indicate that metabolism may occur in fish ( i.e., from BCF studies) and RQ values exceed LOCs, then the user can estimate k M 

### using approaches described in Appendix H. This will allow the model user to characterize eff ects of metabolism on bioaccumulation in aquatic ecosystems and associated risks to mammals and birds consuming aquatic organisms.

Table 3

To calculate risk quotients, us er-supplied avian and mammalia n toxicity endpoints should be entered into Table 3 (reproduced below). Acceptable or supplemental registrant-submitted or open literature studies should be used to defi ne the effects of the pesticide on birds and mammals. Required input data include: avian acute oral LD 50 

### (OPPTS Guideline 850.2100), avian subacute dietary LC 50 

### (OPPTS Guideline 850.2200), avian reproduction (expressed as a NOAEC or a NOAEL) (OPPTS Guideline 850.2300), mammalian acute oral LD 50 

### (OPPTS Guideline 870.1100), or subacute dietary LC 50 

### (if available), and mammalian reproduction NOAEC or NOAEL (OPPTS Guideline 870.3800).

### 12 of 123 Table 3. Mammalian and avian toxicity data for Pesticide X. These are required inputs.

Animal Measure of effect (units) Value Species If selected species is "other," enter body weight (in kg) here.

LD 50 (mg/kg-bw) 50 mallard duck

LC 50 (mg/kg-diet) 500 Northern bobwhite quail
NAOEC (mg/kg-diet) 10 mallard duck Avian

Mineau Scaling Factor 1.15 Default value for all species is 1.15 (for chemical specific values, see Mineau et al. 1996).

LD 50 (mg/kg-bw) 50 other 1.2

LC 50 (mg/kg-diet) N/A other
Chronic Endpoint 10 Mammalian

units of chronic endpoint * ppm laboratory rat

*ppm = mg/kg-diet Note: Table 3 of this User’s Guide contains exam ple data for chemical specific characteristics.



### In the appropriate cell under th e “value” column of Table 3, th e user should input the lowest (most sensitive) available toxicity data for each toxicity endpoint. If an endpoint value is not discrete ( i.e., contains a “>” symbol), the whole number should be entered as a discrete value, keeping in mind that all resulting risk quotient (R Q) values derived using this endpoint are “<”. For the chronic mammalian data, the user must also select the units of the value. The user should select units from the drop down menu as either “ppm” or “mg/kg-bw.”

Under the “species” column, the user should use the drop down menu to select the appropriate test species associated with the toxicity value en tered in the adjacent cell in the “value” column. If the test species is not one of the options av ailable in the drop down lis t, the model user should select “other” as the test species. If “other” is selected, the user must enter the body weight (in kg) of the test species. In the case that “other ” is selected as the test species, a message will appear in the spreadsheet below Table 3 to alert th e user of the need to enter the body weight of the test species. These data should be obtained from the study report if possible (time weighted average of control animals). Alternatively, refe rence body weight values may be obtained from a variety of sources, including U.S. EPA 1993 a nd Dunning 1984. Failure to enter the body weight of the test species when it is entered as “other ” will prevent calculation of risk quotients that correspond to that endpoint.

If available, the model user should enter chemi cal-specific data to represent the avian scaling factor (see Mineau et al. 1996). If no chemical specific data are available, the default value of 1.15 should be entered. This value is used to adjust avian dose-based toxicity values based on the weight of the species of concern ( e.g., herons) as described in th e T-REX User’s Guide (USEPA 2008a) and in Appendix G.

### 13 of 123 2.2. Ecosystem Inputs

In order to estimate pesticide concentrations in tissues of aquatic organi sms, biotic and abiotic characteristics of the model aquatic ecosystem must be defined. In addition, the mammals and birds consuming aquatic organisms are also defined as ecosystem inputs.

To run KABAM, it is not necessary to al ter any of the default parameters in the “Ecosystem Inputs” worksheet.

If the model user alters default parameter val ues, they will be highlighted yellow in the KABAM tool.

It may be necessary for the mode l user to incorporat e alternate ecosystem input values if the modeling incorporates EECs from a source other than PRZM/EXAMS ( e.g., from a mesocosm study). In that case, the model us er should enter parameter values that correspond to the specific water body used.

Table 4

Abiotic characteristics of the aquatic ecosystem that are necessary for KABAM are defined in Table 4 (reproduced below) of th e model tool. These characteris tics include: concentrations of particulate organic carbon (X POC 

### ), dissolved organic carbon (X DOC 

### ), dissolved oxygen (C OX 

### ) and suspended solids (C SS 

### ), water temperature (T), and % or ganic carbon (OC) content of the sediment. The model tool is populated with de fault values for these parameters, which can be altered based on the needs of the model user. Default values relevant to the abiotic characteristics of the aquatic ecosystem are desi gned to be consistent with the OPP standard pond scenario used in EXAMS. Brief explanations for these default values as well as guidance on selecting alternative values are provided in Appendix B.

Table 4. Abiotic characteristics of the model aquatic ecosystem. Characteristic (symbol; units) Value Guidance* Concentration of Particulate Organic Carbon (X POC ; kg OC/ L) 0
Concentration of Dissolved Organic Carbon (X DOC ; kg OC/L) 0 When using EECs generated by PRZM/EXAMS, use a value of “0” for both POC and DOC.

Concentration of Dissolved Oxygen (C OX ; mg O 2 /L) 5.0 Default value is 5.0 mg O 2 /L when using EECs generated by PRZM/EXAMS.

Water Temperature (T; o C) 15 Value is defined by the average water temperature of the EXAMS pond when using EECs generated by PRZM/EXAMS. Model user should consult output file of EXAMS to define this value. Concentration of Suspended Solids (C SS ; kg/L) 3.00E-05 Default value is 3.00x10 -5 kg/L when using EECs generated by PRZM/EXAMS. Sediment Organic Carbon (OC; %) 4.0% Default value is 4.0% when using EECs generated by PRZM/EXAMS. *When using pesticide concentrations from monitoring data or mesocosm studies, consult Appendix B of the User’s Guide for specific gu idance on selecting values for these parameters.

### 14 of 123 Table 5

Necessary biotic components of the aquatic ecosy stem define characteristics of the sediment and water column biota. These include body weights and body compositions, specifically % lipids, % NLOM (non-lipid organic matter) , and % water. These values are defined for the seven trophic levels of the aquatic ecosystem (phytoplankton, zooplankton, benthic invertebrates, filter feeders, small fish, medium fish and large fish) mode led by KABAM in Table 5 (reproduced below) of the tool. Default values for these biotic paramete rs are displayed in Tabl e 5 below. A description of how these default parameters were selected is available in Appendix C . In addition, Table 5 allows the model user to define whether organi sms within each trophic level respire pore water. If yes, it is assumed that 5% of the total resp ired water is from pore water. Default assumptions related to respiration of pore water for each trophic level are depicted in Table 5 below.

Table 5. Characteristics of aquatic biota of the model ecosystem.

Trophic level Wet Weight

(kg) % lipids % NLOM % Water Do organisms in trophic level respire

some pore water?

Sediment* N/A 0.0% 4.0% 96.0% N/A Phytoplankton N/A 2.0% 8.0% 90.0% no Zooplankton 1.0E-07 3.0% 12.0% 85.0% no Benthic invertebrates 1.0E-04 3.0% 21.0% 76.0% yes Filter feeders 1.0E-03 2.0% 13.0% 85.0% yes Small fish 1.0E-02 4.0% 23.0% 73.0% yes Medium fish 1.0E-01 4.0% 23.0% 73.0% yes Large fish 1.0E+00 4.0% 23.0% 73.0% no *Note that sediment is not a trophic level. It is included in this table because it is consumed by aquatic organisms of the KABAM food web. N/A = not applicable



### Table 6

Table 6 (reproduced below) of the KABAM tool allows the model user to define the diet composition of each of the trophic levels of the a quatic ecosystem. The aquatic trophic levels are assigned a hierarchy, which is relevant to the as signment of diet composition. The order of the trophic levels, in increasing hi erarchy, is as follows: phytopl ankton, zooplankton, benthic invertebrates, filter feeders, small fish, medium fish, and large fish. The diet of each aquatic trophic level is composed of sediment or wate r column biota from lower trophic levels. The KABAM tool does not allow the mode l user to assign a portion of the diet of one organism to its own trophic level or to trophic leve ls that are higher. The default values defining the diet of each trophic level are in Table 6 below. An explan ation of how these default parameters were determined is available in Appendix C

.

### 15 of 123 Note that the total diet of each organism within the aquatic food web should equal 100%. If the total diet ≠ 100%, an error message will appear under Table 6.

Table 6. Diets of aquatic biota of the model ecosystem.

Diet for:

Trophic Level in diet Zoo plankton Benthic Invertebrates Filter Feeder Small Fish Medium Fish Large Fish

Sediment* 0.0% 34.0% 34.0% 0.0% 0.0% 0.0% Phytoplankton 100.0% 33.0% 33.0% 0.0% 0.0% 0.0% Zooplankton 33.0% 33.0% 50.0% 0.0% 0.0% Benthic invertebrates 0.0% 50.0% 50.0% 0.0% Filter feeders 0% 0% 0.0% Small fish 50.0% 0.0%
Medium fish 100.0%

Total 100.0% 100.0% 100.0% 100.0% 100.0% 100.0% *Note that sediment is not a trophic level. It is included in this table because it is consumed by aquatic organisms of the KABAM food web.



### Table 7

Table 7 (reproduced below) of the KABAM tool a llows the model user to define the mammalian and avian species of concern, as well as their body weights. Species are considered to be of concern for pesticide exposures through consumption of residues in freshwater aquatic animals that serve as prey.

For mammals, default species include the fog shrew ( Sorex sonomae ), the water shrew ( S. palustris ), the rice rat ( Oryzomys palustris ), the star-nosed mole ( Condylura cristata ), the American mink ( Neovison vison ), and the Northern river otter ( Lontra canadensis ). For birds, default species include sandpipers, rails, herons, kingfisher, ducks, grebes, ibis, rails, cormorants, osprey, cranes, bald eagles ( Haliaeetus leucocephalus ) and pelicans. Descriptions of how mammalian and avian species were selected, in cluding their body weights, are provided in Appendices D and E, respectively. These appendices also provide descrip tions of the species themselves as well as justifications for defau lt parameters used to represent the species in KABAM ( i.e., body weight and diet).

The selected body weight value influences estima tes of pesticide exposu re through differential consumption of contaminated food items, as well as dose-based toxicity values. Therefore, the magnitude of the body weight parameter has an effect on the magnitude of the dose-based RQ. For mammals, higher body weight values result in higher dose-based RQs (keeping the diet constant). As a result, default body weight values for the fog shrew, water shrew, rice rat, and star-nosed mole were selected as higher values of re levant ranges in orde r to represent size classes that would be most vulnerable to e xposures through bioaccumulation. In order to bound the risk of accumulated residues to mink and rive r otter, the lowest and highest body weights of these species were selected as defaults. For bird s, higher body weight results in lower RQs. In order to bound the risk of accumulated residues to birds, the lowest and highest body weights of

### 16 of 123 birds with the same diet were selected as defa ults. The user can alter the assigned body weights to represent the low and high end of possible we ights in order to bound the potential RQs for a particular species. Additional data on body wei ghts of species of mammals and birds are provided in Appendices D and E, respectively.

Table 7. Identification of mamm als and birds feeding on aquatic biota of the model ecosystem.

Mammal/Bird # Name Body weight (kg)
Mammal 1 Fog/Water shrew 0.018 Mammal 2 Rice Rat/Star-nosed mole 0.085 Mammal 3 Small mink 0.450 Mammal 4 Large mink 1.800 Mammal 5 Small river otter 5.000 Mammal 6 Large river otter 15.000

Bird 1 Sandpipers 0.02 Bird 2 Cranes 6.7 Bird 3 Rails 0.07 Bird 4 Herons 2.90 Bird 5 Small osprey 1.25 Bird 6 White pelican 7.50



### Tables 8 and 9

Tables 8 and 9 (reproduced below) of the KABAM t ool allow the model user to define the diet composition of the mammals and birds of concern that are defined in Table 7. The animal names entered in Table 7 will appear at the heads of the columns of Tables 8 and 9. The diet of each mammal and bird species is attributed to a por tion of each trophic level of the aquatic ecosystem. Justifications for the default diets for each mamm al and bird species are provided in Appendices D and E, respectively. Note that the total diet of each mammal and bird should equal 100%. If not, an error message will appear under Table 8 or 9.

Table 8. Diets of mammals feeding on aquatic biota of the model ecosystem.
Diet for:
Trophic level in diet Fog/Water Shrew RiceRat/Starnosed mole Small Mink Lar g e Min k Small River Otter Large River Otter
Phytoplankton 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Zooplankton 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Benthic invertebrates 100.0% 34.0% 0.0% 0.0% 0.0% 0.0% Filter feeders 0.0% 33.0% 0.0% 0.0% 0.0% 0.0% Small fish 0.0% 33.0% 0.0% 0.0% 0.0% 0.0% Medium fish 0.0% 0.0% 100.0% 100.0% 100.0% 0.0% Large fish 0.0% 0.0% 0.0% 0.0% 0.0% 100.0%

Total 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%

### 17 of 123 Table 9. Diets of birds feeding on aquatic biota of the model ecosystem.
Diet for: Trophic level in diet Sandpipers Cranes Rails Herons Small Osprey White pelican Phytoplankton 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Zooplankton 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% Benthic invertebrates 33.0% 33.0% 50.0% 50.0% 0.0% 0.0% Filter feeders 33.0% 33.0% 0.0% 0.0% 0.0% 0.0% Small fish 34.0% 0.0% 50.0% 0.0% 0.0% 0.0% Medium fish 0.0% 34.0% 0.0% 50.0% 100.0% 0.0% Large fish 0.0% 0.0% 0.0% 0.0% 0.0% 100.0%

Total 100.0% 100.0% 100.0% 100.0% 100.0% 100.0%



### If the model user chooses to alter the default diet of a mammal or bird, the model user should consider the daily food intake for determining appropriate aquatic trophic levels to include within an animal’s diet. The user should verify that the weight of an individual dietary item does not greatly exceed the daily food in take of the mammal or bird. This will prevent the user from simulating a bird or mammal that consumes prey that are much larger than could be reasonably consumed. This can be determined using allometr ic equations for estimating daily food intake, as described in Appendices D and E. In addition, these appendices c ontain data defi ning the daily food intake for several species of birds and mammals.

Pesticide exposures to mammals and birds th rough consumption of contaminated aquatic organisms are determined by weighing the exposur e concentration by the contribution of each food item to the total diet. While this approa ch is reasonable for chronic exposures, it may underestimate acute exposures resulting from consumption of larger trophic level organisms within short periods of time. In order to explore high-end exposur e concentrations and subsequent risks resulting from acute exposures , the model user can set the highest aquatic trophic level consumed by a bird or mammal to 100%. For example, high-end acute exposures of cranes (which consume benthic invertebrates, f ilter feeders, and medium fish) to a pesticide could be assessed by setting the cran e diet to 100% of medium fish.

3. Parameters & Calculations

Also included in the KABAM tool is a tabulari zed summary of the relevant parameters for the bioaccumulation portion of KABAM. This summary is included in a separate worksheet, titled “Parameters & Calculations” (Table 10 of the KABAM tool) and represents values used to calculate pesticide tissue concentrations for the trophic leve ls of the aquatic ecosystem. This worksheet is locked (read only) in the KABAM tool and cannot be altered by the model user; however, this worksheet can be printed by the mode l user or copied into a risk assessment as a model output. A full description of the parameters contained in Table 10 of the KABAM tool as well as the equations used to calculate th ese parameters can be found in Appendix A.

### 18 of 123 4. Model Results

The final outputs of KABAM include Biocon centration Factors (BCFs), Bioaccumulation Factors (BAFs), Biomagnification Factors (B MFs), Biota-Sediment Accumulation Factors (BSAFs), estimates of pesticide concentrations in tissues of aquatic organisms, and RQ values for mammals and birds consuming contaminated aquatic organisms.

Note that the “results” worksheet of KABAM is locked (read only) and cannot be altered by the model user, with the exception of format changes ( e.g. , number of decimal places). Also, the KABAM tool does not automatically account for si gnificant figures. The format of numerical values in the Tool can be alte red by the user to increase or decrease the number of decimal places.

Table 11 and Figure 1

Table 11 ( reproduced below ) of the KABAM tool reports pesticide concentrations in tissues of aquatic organisms on both a total bo dy weight and lipid normalized ba sis. The table also reports contributions of the pesticide concentration in tissue from respiration and from diet. These values are useful for understanding the dominant uptake route of the pes ticide that influences bioaccumulation. Figure 1 (reproduced below) of the KABAM tool graphically represents the relative contributions of pes ticide uptake through di et and through respir ation to the overall concentrations of the pesticide in the ti ssues of the different aquatic animals.

Table 11. Estimated concentrations of Pesticide X in ecosystem components.

Ecosystem Component Total concentration

(μg/kg-ww) Lipid normalized concentration (μg/kg-lipid) Contribution due to diet (μg/kg-ww) Contribution due to respiration (μg/kg-ww)
Water (total)* 6 N/A N/A N/A Water (freely dissolved)* 6 N/A N/A N/A Sediment (pore water)* 5 N/A N/A N/A Sediment (in solid)** 5,000 N/A N/A N/A Phytoplankton 27,298 1364913 N/A 27,298.25 Zooplankton 21,065 702157 651.72 20,412.98 Benthic Invertebrates 23,678 789265 1,812.95 21,865.01 Filter Feeders 15,549 777440 1,167.92 14,380.88 Small Fish 34,713 867830 7,246.79 27,466.40 Medium Fish 41,050 1026242 14,492.66 26,557.01 Large Fish 56,332 1408297 30,795.48 25,536.39 * Units: μg/L; **Units: μg/kg-dw Note: Table 11 of this User’s Guide contains example re sults based on example chemical specific data entered in Tables 1 and 3.

### 19 of 123 Fi g ure 1. Total Pesticide Concentration per trophic level 0 10000 20000 30000 40000 50000 60000

Zooplankton Benthic Invertebrates Filter Feeders Small Fish Medium Fish Large Fish
Trophic LevelConcentration (μg/kg)

Contribution due to Respiration (μg/kg-ww)
Contribution due to Diet (μg/kg-ww)
Note: Figure 1 of this User’s Guide contains example re sults based on example chemical-specific data entered in Tables 1 and 3.



### Tables 12 and 13

BCF, BAF, BMF and BSAF are calculated by KABAM ( Tables 12 and 13 ). These terms are intended to provide a relative m easure of the pesticide concentration in an organism to the pesticide concentration in sources ( i.e., the environment and the diet) of that pesticide. Appendix F contains the equations used to calculate BCF, BAF, BMF and BSAF.

Table 12. Total BCF and BAF values of Pesticide X in aquatic trophic levels.

Trophic Level Total BCF (μg/kg-ww)/(μg/L) Total BAF (μg/kg-ww)/(μg/L)
Phytoplankton 4801 4550 Zooplankton 3421 3511 Benthic Invertebrates 3705 3946 Filter Feeders 2435 2591 Small Fish 4766 5786 Medium Fish 4766 6842 Large Fish 4806 9389 Note: Table 12 of this User’s Guide contains example re sults based on example chemical specific data entered in Tables 1 and 3.

### 20 of 123 Table 13. Lipid-normalized BCF, BAF, BMF and BS AF values of Pesticide X in aquatic trophic levels.

Trophic Level BCF(μg/kglipid)/(μg/L) BAF(μg/kglipid)/(μg/L) BMF(μg/kglipid)/(μg/kglipid) BSAF(μg/kglipid)/(μg/kgOC) Phytoplankton 240045 227485 N/A 11 Zooplankton 114028 117026 0.51 6 Benthic Invertebrates 123488 131544 1.16 6 Filter Feeders 121769 129573 1.14 6 Small Fish 119142 144638 1.16 7 Medium Fish 119142 171040 1.24 8 Large Fish 120143 234716 1.37 11 Note: Table 13 of this User’s Guide contains example re sults based on example chemical specific data entered in Tables 1 and 3.



### Tables 14, 15, and 16

Tables 14, 15, and 16 (reproduced below) of the KABAM tool summarize the estimated exposure values, mammal and bird toxicity values and resulting RQ values, respectively, used to estimate potential risks to mammals and birds that consume aquatic animals contaminated with pesticides accumulated thr ough the aquatic food chain.

Table 14 uses the mammalian and avian body weight s (entered by the mode l user) to calculate the dry food ingestion and drinking water intake rates according to allometric equations specific to mammals and birds. The wet food intake is calculated using the dry food intake and the % water of the diet. Dose-based EECs represent the sum of pesticide in take through diet and through drinking water, accounting for pesticide con centrations in diet items and in water and food and water intake rates. Di etary-based EECs represent the su m of pesticide intake through diet only, without consideration of species specific intake rates or body weights. Descriptions of the equations used to calculate food intake rates, water inta ke rates, dose-based EECs, and dietary-based EECs are av ailable in Appendix G.

### 21 of 123 Table 14. Calculation of EECs for mammals and birds consuming fish contaminated by Pesticide X. Biological Parameters EECs (pesticide intake)

Wildlife Species Body Weight

(kg) Dry Food Ingestion Rate (kg-dry

food/kgbw/day) Wet Food Ingestion Rate (kg-wet

food/kgbw/day) Drinking Water Intake (L/d) Dose Based(mg/kgbw/d) Dietary Based (ppm)

Mammalian
Fog/water shrew 0.02 0.140 0.585 0.003 13.857 23.68

Rice rat/star-nosed mole 0.1 0.107 0.484 0.011 11.921 24.64

Small mink 0.5 0.079 0.293 0.048 12.041 41.05

Large mink 1.8 0.062 0.229 0.168 9.408 41.05

Small river otter 5.0 0.052 0.191 0.421 7.844 41.05

Large river otter 15.0 0.042 0.157 1.133 8.852 56.33

Avian
Sandpipers 0.0 0.228 1.034 0.004 25.5861 24.75

Cranes 6.7 0.030 0.136 0.211 3.6561 26.90

Rails 0.1 0.147 0.577 0.010 16.8571 29.20

Herons 2.9 0.040 0.157 0.120 5.0943 32.36

Small osprey 1.3 0.054 0.199 0.069 8.1859 41.05

White pelican 7.5 0.029 0.107 0.228 6.0108 56.33

Note: Table 14 of this User’s Guide contains example re sults based on example chemical specific data entered in Tables 1 and 3.



### Table 15 (reproduced below) of the KABAM tool summarizes the acute and chronic, dose-based and dietary-based toxicity valu es representing effects of a pe sticide to mammals and birds. Dietary-based toxicity values are taken directly from user inputs in Table 3, without adjustment. Available dose-based toxicity values are adjust ed for the weights of the animal tested ( e.g., laboratory rat, mallard duck) and of the anim al for which the risks are being assessed ( e.g., mink, bald eagle). Methods for adjusting toxicity va lues are consistent with those used by T-REX (USEPA 2008a). A full descriptio n of the methodology for adjusting dose-based toxicity values is provided in Appendix G.

### 22 of 123 Table 15. Calculation of toxicity values for mammals and birds consuming fish contaminated by Pesticide X. Toxicity Values
Acute Chronic Wildlife Species Dose Based (mg/kg-bw) Dietary Based (mg/kg-diet) Dose Based (mg/kg-bw) Dietary Based (mg/kg-diet) Mammalian Fog/water shrew 142.87 N/A 1.05 10

Rice rat/star-nosed mole 96.92 N/A 0.71 10

Small mink 63.89 N/A 0.47 10
Large mink 45.18 N/A 0.33 10

Small river otter 35.00 N/A 0.26 10
Large river otter 26.59 N/A 0.20 10

Avian Sandpipers 25.96 500.00 N/A 100

Cranes 62.10 500.00 N/A 100

Rails 31.33 500.00 N/A 100

Herons 54.77 500.00 N/A 100
Small osprey 48.27 500.00 N/A 100
White pelican 63.16 500.00 N/A 100

Note: Table 15 of this User’s Guide contains example re sults based on example chemical specific data entered in Tables 1 and 3.



### Table 16 (reproduced below) of the KABAM tool presents RQs, which are the ratio of exposure concentrates to effects values. RQ values ar e then compared to Agency levels of concern (LOCs) for non-listed and listed mammals and bi rds. For acute exposur es, the LOC is 0.5 for (non-listed) birds and mammals and 0.1 for federa lly-listed threatened and endangered (listed) species of mammals and birds. For chronic ris k, the LOC is 1.0 for all species (non- listed and listed) mammals and birds (USEPA 2004). RQ valu es that exceed their respective LOC values appear in red and bold in Table 16.

Dose-based and dietary-based RQs are not equivale nt. Dietary-based RQs are calculated by directly comparing the concentration of a pesticid e administered to experimental animals in the diet in a toxicity study to the concentration estimated in select ed food items. These RQs do not account for the fact that smaller-sized animals n eed to consume more food relative to their body weight than larger animals. The dose-based RQs account for these factors by incorporating the

### 23 of 123 ingestion rate-adjusted exposure from the various food items to the different weight classes of assessed animals and the weight clas s-scaled toxicity endpoints.

Table 16. Calculation of RQ values for mammals and birds consuming fish contaminated by Pesticide X. Acute Chronic Wildlife Species Dose Based Dietary Based Dose Based Dietary Based

Mammalian Fog/water shrew 0.097 N/A 13.198 2.368

Rice rat/star-nosed mole 0.123 N/A 16.737 2.464

Small mink 0.188 N/A 25.643 4.105
Large mink 0.208 N/A 28.335 4.105

Small river otter 0.224 N/A 30.498 4.105
Large river otter 0.333 N/A 45.296 5.633

Avian Sandpipers 0.986 0.049 N/A 0.247

Cranes 0.059 0.054 N/A 0.269

Rails 0.538 0.058 N/A 0.292

Herons 0.093 0.065 N/A 0.324
Small osprey 0.170 0.082 N/A 0.410
White pelican 0.095 0.113 N/A 0.563

Note: Table 16 of this User’s Guide contains example re sults based on example chemical specific data entered in Tables 1 and 3.



### EECs and RQs for birds are based on the selected body weight of the bird as well as its diet. Default values for birds were designed to repr esent birds on the low and high end of weights with three different diets. Birds consuming benthic invertebrates, filter feeders, and fish include sandpipers, ducks and cranes (default birds 1 and 2, which are named sandpipers and cranes, respectively). Birds consuming benthic invertebra tes and fish include belted kingfisher, rails, ibis, grebes, double-breasted cormorants, bitterns , egrets, and herons (default birds 3 and 4, which are named rails and herons, respectively). Birds consuming fish include osprey, bald eagles, and the white pelican (default birds 5 and 6, which are named small osprey and white pelican, respectively). In the case that RQs exceed the LOC for both birds within a feeding group, then it can be assumed that RQs would exceed the LOC for all of the birds within the feeding category since bird s on the low and high end of the we ight ranges have RQs of concern. In the case that RQs exceed the LOC for the defa ult bird with the high body weight of a feeding

### 24 of 123 category ( i.e., birds 2, 4, and 6), the model user can refine the EECs and RQs to be representative of specific bird species within a feeding category by entering specific body weights of individual species of concern. Appendix E contains species spec ific data on feeding habits and body weights of over 40 species of birds, including some listed species, which consume aquatic animals from freshwater habitats.

5. Assessing Pesticide Concentrations in Fish Tissues for Human Consumption

It is possible to use KABAM to de rive pesticide concentrations in edible tissues of fish that are relevant to assessments of pesticide risks to human health. Current default values described above for % lipid content of fish applies to th e whole fish; however, not all fish tissues are consumed by humans. Therefore, it is necessary to modify the output of the pesticide tissue concentration to account for a lower % lipid composition of edible tissues. This can be accomplished by entering in all the relevant de fault input parameters for KABAM as defined above. It may be necessary to explore different body weights of the large fish, based on those that would be expected to be consumed by humans.

The relevant output is the lipid normalized concentr ation of the pesticide in the large fish (Table 11). This value can be convert ed to the total pes ticide concentration in edible tissues by multiplying by the % lipid content of the edible ti ssues. The default value for lipid content in edible tissue of the large fish is 3%, based on USEPA 2003. The resulting value represents the concentration of pesticide in fish tissue (in μg/kg-ww) potentially consumed by humans. This value can then be used in conj unction with fish consumption rate s to characterize risks of a pesticide to humans cons uming contaminated fish.

6. Model Assumptions, Limitations, and Uncertainties

There are several key assumptions and resulting un certainties associated with modeling pesticide concentrations in tissues of aquatic organisms. The assumptions involve the equations of the model itself and the parameterization of those underlying equations. Appendix A describes the assumptions associated with the equations of the bioaccumulation model. In order to explore uncertainties associated with specific parame ters and their influenc es on model outputs, a sensitivity analysis was conducted (see section A7 of Appendix A). This was used to define the parameters that have the greatest influence on model outputs ( e.g. , K OW 

### , water column, and pore water EECs). Appendices B and C describe the parameterization of the model, including the associated assumptions.

In addition, the use of PRZM/EXAMS for derivi ng EECs in the surface a nd pore waters of the aquatic ecosystem introduces the assumptions a nd uncertainties associated with PRZM and EXAMS to KABAM.

One major assumption associated with KABAM concerns the model’s assumed steady state. Given the episodic nature of pe sticide applications, sporadic peak exposures to aquatic organisms would be expected. For a chemical with a Log K OW 

### of approximately 5, comparison

### 25 of 123 of the fish tissue EECs predic ted using the steady state and dynamic bioaccumulation modeling with PRZM/EXAMS/Arnot and Gobas indicates pr edictions are similar (USEPA 2008b) when a 60-d average was selected for water and sediment concentrations as input to the steady state model. This result suggests that steady-stat e bioaccumulation modeling can provide useful predictions of bioaccumulation pot ential even with highly dynamic exposures, provided proper consideration of the averaging period associated with water and sediment concentrations is made.

As discussed above, in using KABAM with default settings, it is assumed th at the elimination of the pesticide from aquatic organisms through metabolism does not occur, i.e., the metabolism rate constant (k M 

### ) = 0. In cases where pesticide metabolis m does occur, this could overestimate pesticide bioaccumulation. Appendix H of this guide provides methods for estimating k M 

### for fish using empirical data provided for specific chemi cals (from BCF studies). This approach can be used to characterize effects of metabo lism, but should be used with caution.

The Arnot and Gobas (2004) model is genera lly appropriate for chemicals with Log K OW 

### value ≥ 4 to ≤ 8. Uncertainty increases as the value incr eases above 8 because the model has generally been validated using chemicals with Log K OW 

### values within the range of 4 - 8. Making predictions for a chemical with a Log K OW 

### > 8 leads to uncertainty in model outputs because predictions are based upon extrapol ations in its s ubroutines.

For chemicals with Log K ow 

### < 4, exposure from food becomes insignificant because uptake and depuration across the gills controls the residue in the organism. Thus, there is no need to run a food web model for these chemicals. In these cases, available BCF data are sufficient to predict residues in the aquatic species.

It is assumed that there is no predation w ithin a trophic level of the aquatic food web ( e.g., medium fish cannot prey upon medium fish). It is also assumed that mammals and birds only consume organisms from the aquatic system.

### 26 of 123 Appendix A. Description of Bioaccumulation Model

The bioaccumulation portion of KABAM is ba sed on the model published by Arnot and Gobas (2004). The purpose of this model is to estimate ch emical concentrations (C B 

### ) and BCF and BAF values for aquatic ecosystems. Conceptua lly, each aquatic organism is assumed to be a single compartment. Chemicals enter the organi sm through respiration and diet and leave the organism through respiration and fecal egestion. The chemical concentration in the organism can also be influenced by the growth of the or ganism as well as metabolism of the chemical within the organism. These processes that define uptake and loss of the chemical from aquatic organisms are described by rate constants and are in corporated into one equation that is used to define the concentration of the chemical in or ganism tissues (Equation A1, see Table A1). As uptake constants ( i.e. , k 1 

### and k D 

### ) increase, so does the estimated pesticide concentration in an organism. As elimination constants increase ( i.e. , k 2 

### , k E 

### , k G 

### and k M 

### ), estimated pesticide concentrations in an organism decrease. Howeve r, for respiration and diet, processes of uptake and elimination are linked. Theref ore, factors that would influen ce uptake constants would also influence elimination constants, so these cannot be considered independen tly. In addition, as the freely dissolved fraction of pesticide in the water ( Ф ) decreases, so do estimated pesticide concentrations in organisms. Rate consta nts defining the uptake of a chemical through respiration (k 1 

### ) and diet (k D 

### ) and the elimination of a chemical through respiration (k 2 

### ) and fecal excretion (k E 

### ) as well as growth dilution of a chemical (k G 

### ) are estimated separately using equations A5-A9, which are described below. Parameter definitions and abbreviations are consistent with those published by Arnot and Goba s (2004) in order to ensure consistency with the publication and transparent methodology used in KABAM.

Use of Equation A1 involves several assumptions. The first assumption is that the organism is at steady state. The second assumption is that th e pesticide is distribu ted homogenously throughout organisms. The third assumption is that the eff ects of chemical partitioning into egg and sperm cells on chemical mass in parents is not considered as a loss pa thway. The fourth assumption is that when data are lacking to define the metabolism rate constant for a chemical, it is assumed that metabolism does not occur and that the elimination rate constant for metabolism (k M 

### ) is 0.

Uptake and elimination of a chemical from an organism is influenced by the body composition of the model organism. Body composition includes lipid, non-lipid organic matter (NLOM; e.g., carbohydrates and protein), and water. Chemicals are expected to partition differently to these components of an organism. Partitioning of a chem ical into these components is related to the octanol-water partition coefficient (K OW 

### ). It is assumed that octanol is a surrogate for the lipid fraction of an organism. It is also assumed that the partitioning of a chemical to NLOM is less than to octanol, but that there is a relationship between the partitioning of the chemical to NLOM and to octanol and that octanol serves as a reason able surrogate for estimating this parameter.

Arnot and Gobas (2004) recommend that equation A1 be applied to an aquatic food web with seven trophic levels. In incr easing order of hier archy, these trophic le vels include: 1) phytoplankton, 2) zooplankton, 3) benthic invertebrates, 4) filter feeders, 5) small (juvenile) fish, 6) medium sized fish, and 7) large fish. Concentr ations in organisms are first calculated at the lowest level of the aquatic food chain (phytopla nkton). Pesticide con centrations are then calculated for zooplankton, including considerati on that the diet of zooplankton includes

### 27 of 123 phytoplankton, which contain pesticid e residues. Tissue residues are calculated for the next five trophic levels based on their diets of organisms from lower trophic levels.

Table A1. Equation A1, calculatio n of pesticide tissue residue (C B ) for single trophic levels and its associated parameters (Arnot and Gobas 2004).

M G E Di i D WDP P WTO B 

### k k k k C P k C m C m k C A Eq + + + Σ + + Φ

=

2 0 1 

### ) * ( * ) * * * ( * 1

.

Parameters: Symbol Definition Value Units

C B pesticide concentration in the organism calculated g/kg (wet weight)

C BD pesticide concentration in the organism originating from uptake through diet; C BD = C B when k 1 = 0 calculated g/kg (wet weight)

C BR pesticide concentration in the organism originating from uptake through respiration; C BR = C B when k D = 0 calculated g/kg (wet weight)

C Di concentration of pesticide in i (prey item) calculated g/kg (wet weight)

C S concentration of the chemical in sediment (dry weight of sediment) Equation A4 g/(kg (dry) sediment)

C WDP freely dissolved pesticide concentration in pore water of sediment input parameter (from PRZM/EXAMS) g/L

C WTO total pesticide concentration in water column above the sediment input parameter (from PRZM/EXAMS) g/L

k 1 pesticide uptake rate constant through respiratory area ( i.e., gills, skin) Equation A5 L/kg*d

k 2 rate constant for elimination of the pesticide through the respiratory area ( i.e., gills, skin) Equation A6 d -1

k D pesticide uptake rate constant for uptake through ingestion of food Animals: Equation A8; Phytoplankton: 0 kg food/ (kg org*day)

k E rate constant for elimination of the pesticide through excretion of contaminated feces Animals: Equation A9; Phytoplankton: 0 d -1

k G organism growth rate constant Animals: Equation A7; Phytoplankton: 0.1 d -1

k M rate constant for pesticide metabolic transformation 0 d -1

m o fraction of respiratory ventilation involving overlying water 1 - m p none

m

p fraction of respiratory ventilation that involves pore-water of sediment ≤ 5%; 0 for organisms with no contact with pore
water none

P i fraction of diet containing i (prey item) user defined none

Ф fraction of the overlying water concentration of the pesticide that is freely dissolved and can be absorbed via membrane diffusion Equation A2 none

### 28 of 123 A.1. Calculation of Fraction of Chemical in the Water Column That Is Freely Dissolved ( Ф )



### Aquatic ecosystems contain organic matter susp ended in the water column. This suspended organic matter is defined as dissolved orga nic carbon (DOC) and part iculate organic carbon (POC). It is assumed that once chemicals partition to orga nic carbon, they are no longer bioavailable to aquatic organisms. The fraction of chemical in the water column that is freely dissolved ( Ф ), and thus bioavailable for uptake by aquatic animals is estimated according to Equation A2 (Table A2). This equation assumes that equilibrium exists between the pesticide concentration in the water and in the organic carbon in the wate r column. Equation A2 assumes that partitioning between POC and water and DOC and water can be related to partitioning of the chemical between octanol and water. These re lationships are defined using proportionality constants ( α POC 

### and α DOC 

### ) that are defined from the scientific literature.

Table A2. Equation A2, derivation of available pesticide fraction in water ( Ф ) and its associated parameters (Arnot and Gobas 2004).



### ) * * ( ) * * ( 1 1 2

. OW DOC DOC OW POC POC 

### K X K X A Eq
α α + + = Φ

Parameters: Symbol Definition Value Units

X POC concentration of particulate organic carbon in water user defined kg/L

X DOC concentration of dissolved organic carbon in water user defined kg/L

K OW octanol water partition coefficient user defined none

Ф fraction of the overlying water concentration of the pesticide that is freely dissolved and can be absorbed via membrane diffusion calculated none

α POC Proportionality constant to describe the similarity of phase partitioning of POC in relation to octanol 0.35 none

α DOC Proportionality constant to describe the similarity of phase partitioning of DOC in relation to octanol 0.08 none



### The Arnot and Gobas (2004) approach for calculati ng the fraction of bioava ilable pesticide in the water column (Equation A2) is different from the approach used in EPA’s Exposure Analysis Modeling System (EXAMS) (Equation A3, Table A3). The major difference is that the approach employed by EXAMS accounts for decreases in bioa vailable pesticide in the water column due to sorption to biota. The values for α DOC 

### for the two approaches are also slightly different. Despite these different approaches, for chemicals with Log K OW 

### values 4-8, the fractions of bioavailable pesticide in the water column estimated by the two approaches differ by <0.02 (Figure A1). Therefore, utilizing water column EECs generated by EXAMS is still consistent with the results that are gene rated using the approach desc ribed by Arnot and Gobas (2004).

### 29 of 123 Table A3. Equation A3, derivation of available pesticid e fraction in water (F) by EXAMS and its associated parameters.


# ( ) 907

. 0 

### * 436

. 0 * ) * * ( ) * * ( 1 1 3

.
OW biota OW DOC DOC OW POC POC 

### K X K X K X F A Eq + + + = α α

Parameters: Symbol Definition Value Units

F fraction of the overlying water concentration of the pesticide that is freely dissolved and can be absorbed via membrane diffusion calculated none

K OW octanol water partition coefficient user defined none
OC SS percent organic carbon in suspended sediment user defined %

X SS concentration of suspended sediments in water column user defined kg/m 3

X POC concentration of particulate organic carbon in water X SS *OC SS kg/m 3

X DOC concentration of dissolved organic carbon in water user defined kg/m 3

X biota concentration of biota in water user defined kg/m 3

α POC Proportionality constant to describe the similarity of phase partitioning of POC in relation to octanol 0.35 none

α DOC Proportionality constant to describe the similarity of phase partitioning of DOC in relation to octanol 0.074 none

0.0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.0

4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0
Log Kowbioavailable fraction of pesticide in water column Arnot and Gobas (2004)
EXAMS

Figure A1. Fraction of bioavailable pesticide in water column estimated using approaches of Arnot and Gobas (2004) and EXAMS. Values for OC SS , X SS , X DOC and X biota are consistent with OPP standard pond scenario used in EXAMS.

### 30 of 123 A.2. Calculation of Chemical Concentration in Sediment



### Since it is possible for aquati c organisms to be exposed to chemicals through consumption of contaminated sediment, it is necessary to estimate the concentration of the chemical of concern in the sediment. This is accomplished using Equation A4 (Table A4), which uses the concentration of the chemical in the pore water, the chemical K OC, 

### and the organic carbon content of the sediment. This approach is cons istent with EXAMS for calculating the fraction of pesticide sorbed to sediment in the benthic column.

Table A4. Derivation of pest icide concentration in the so lid portion of the sediment (C S ) (Arnot and Gobas 2004).


### OC C C A Eq SOC S 

### * 4

. =

OC WDP SOC 

### K C C Where * : =

Parameters: Symbol Definition Value Units

C S concentration of the chemical in sediment (dry weight of sediment) calculated g/(kg (dry) sediment)

C SOC normalized (for OC content) pesticide concentration in sediment calculated g/(kg OC)

C WDP freely dissolved pesticide concentration in pore water input parameter (from PRZM/EXAMS) g/L

K OC organic carbon partition coefficient user defined L/kg OC

OC percent organic carbon in sediment user defined %



### A.3. Calculation of Respiration Uptake (k 1 

### ) and Elimination (k 2 

### ) Rate Constants



### The respiratory uptake constant (k 1 

### ) is calculated differently for phytoplankton (Equation A5.1) and for animals (Equation A5.2). For phytoplankton, k 1 

### is dependent upon the K OW 

### of the chemical as well as 2 constants (A and B) that describe chemical upt ake resistance through the aqueous and organic phases (respectively) of th e plant. If A and B are kept constant at 6x10 -5 

### and 5.5, respectively as recomme nded by Arnot and Gobas (2004), k 1 

### for phytoplankton increases with increasing K OW 

### , ranging by a factor of 10 from Log K OW 

### 4-8 (Figure A2).

For animals, k 1 

### is dependent upon the chemical uptake effi ciency of the gills, the ventilation rate, and the body weight of the organism. The uptake efficiency of the gills is determined by the K OW 

### of the chemical, while the ventilation rate of the organism is determined by an allometric equation that is influenced by the body weight of that organism and the concentration of oxygen in the water column (C OX 

### ) (Table A5). When C OX 

### and organism body weight are kept constant, Log K OW 

### has little effect on the k 1 

### for animals (0.8% change from Log K OW 

### 4-8). When Log K OW 

### and body weight are kept constant, C OX 

### strongly affects k 1 

### . The value of k 1 

### decreases by 50%, as the C OX 

### value increases from 5 to 10 mg/L (Figure A3). The value of k 1 

### is also strongly influenced by the body weight of the organism, with decreasing k 1 

### observed with increasing

### 31 of 123 body weight. As the body weight of the organism increases from 1x10 -7 

### to 10 kg (a relevant range for aquatic organisms, see Appendix C), the k 1 

### value spans 3 orders of magnitude (Figure A3).

Table A5. Equations associated with the derivation of pesticide clearance thro ugh the respiratory (gill) system (k 1 ) and associated parameters (Arnot and Gobas 2004).

OW 

### K B A k ton phytoplank For A Eq
+ = 1 : 1

. 5

. 1

B V W 

### W G E k animals For A Eq * : 2

. 5

. 1 

### =

1 

### 155 85

. 1 : − 

### ⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛
+ =

OW W 

### K E Where

⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

=

OX B V 

### C W G 65

. 0


### * 1400

Parameters Symbol Definition Value Units

A constant related to the resistance to pesticide uptake through the aqueous phase of plant 6.0x10 -5 (default) days

B constant related to the resistance to pesticide uptake through the organic phase of plant 5.5 (default) days

C ox concentration of dissolved oxygen User input (mg O 2 )/L

E W pesticide uptake efficiency by gills (fraction) calculated none
G V ventilation rate of fish, invertebrates, zooplankton calculated L/d

k 1 pesticide uptake rate constant through respiratory area ( i.e., gills, skin) calculated L/kg*d

K OW octanol water partition coefficient user defined none

W B wet weight of the organism user defined kg

### 32 of 123 1.E+00 1.E+01 1.E+02 1.E+03 1.E+04 1.E+05

4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0
LogKowk1

Figure A2. Relationship between Kow and k 1 for phytoplankton.

1.0E+00 1.0E+01 1.0E+02 1.0E+03 1.0E+04 1.0E+05 1.0E+06 1.0E+07

0 2 4 6 8 10 Cox (mg 02/L)k1 WB = 1 kg WB = 1e-1 kg WB = 1e-2 kg WB = 1e-3 kg WB = 1e-4 kg WB = 1e-5 kg WB = 1e-6 kg WB = 1e-7 kg

Figure A3. Relationship between C OX , W B and k 1 for aquatic animals.

### 33 of 123 The elimination rate constant for the respiratory system (k 2 

### ) is related to the respiratory uptake constants (k 1 

### ). This is because both constants are influenced by the same processes related to respiration. The value of k 2 

### is also influenced by the partiti oning of the chemical between the organisms and the water. The organism-water partition coefficient (K BW 

### ) is determined by the body composition of the organism ( i.e., lipid, NLOM, and water) and the K OW 

### of the chemical. It is assumed that the partitioning of the chemical be tween lipid and water is directly related to the octanol-water partition coefficien t. It is also assumed that the chemical partitioning between NLOM and water can be related to the octanol-wa ter partition coefficient using a proportionality constant ( β ) (Equation A6, Table A6).

Table A6. Equations involved in the derivation of the respiratory elimination rate constant (k 2 ) and associated parameters (A rnot and Gobas 2004).

BW 

### k

k k A Eq 1 2 

### 6

. =

WB OW NB OW LB BW 

### V K V K V K Where + + = * * * : β

Parameters:
Symbol Definition Value Units

k 1 pesticide uptake rate constant for chemical uptake through respiratory area ( i.e., gills, skin, membrane permeation) calculated (Equation 5) L/kg*d

k 2 rate constant for elimination of the pesticide through the respiratory area ( i.e., gills, skin, membrane permeation) calculated d -1

K BW organism-water partition coefficient (based on wet weight) calculated none
K OW octanol water partition coefficient user defined none

V LB lipid fraction of organism user defined (kg lipid)/ (kg organism wet weight)

V NB NLOM (Non Lipid Organic Matter) fraction of animals, NLOC (Non Lipid Organic Carbon) of plants user defined kg NLOM/ (kg organism wet weight)

V WB water content of the organism user defined kg water/ (kg organism wet weight)

β proportionality constant expressing the sorption capacity of NLOM or NLOC to that of octanol Phytoplankton: 0.35; Animals:0.035 none



### Elimination rate constants for phytoplankton (k 2 

### ) are calculated using seven parameters, including: K OW 

### , V LB 

### , V NB 

### , V WB, 

### A, B, and β (defined above in Table A6). The parameters A, B and β are all constants. If the other parameters ar e considered in terms of ranges applicable to KABAM, K OW 

### has the greatest influence on the determination of k 2 

### for phytoplankton. When Log K OW 

### values are changed from 4 to 8, the k 2 

### value decreases by three orders of magnitude (Figure A4). The lipid fr action of the organism (V LB 

### ) and the non-lipid or ganic carbon fraction (V NB 

### ) influence k 2 

### , with decreases in k 2 

### observed as V LB 

### and V NB 

### increase. These two parameters are related. As V LB 

### decreases, an increase in V NB 

### has a greater effect on k 2 

### . Likewise, as V NB

### 34 of 123 decreases, an increase in V LB 

### has a greater effect on k 2 

### (Figure A4). When the lipid fraction of organism (V LB 

### ) is increased from 0.5 to 3%, the elimin ation rate constant through respiration, i.e., k 2, 

### decreases by >30% (Figure A5). A change in V NB 

### from 5% to 20% results in a decrease in the value of k 2 

### that is >50% (Figure A5 ). The water content (V WB 

### ) of an organism has a negligible (<0.5%) effect on k 2


### .

1.0E-03 1.0E-02 1.0E-01 1.0E+00 1.0E+01

4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0
LogKowk2 VLB=2.5%

VLB=2.0%

VLB=1.5%

VLB=1.0%

VLB=0.5%

Figure A4. Influence of Log K OW on k 2 (for phytoplankton) at different % lipid (V LB ) (with V NB = 8%).

### 35 of 123 1.0E+00 1.0E+01

0.0% 0.5% 1.0% 1.5% 2.0% 2.5% 3.0%
% lipidk2 VNB=20%

VNB=15%

VNB = 10%

VNB=5%

Figure A5. Influence of % lipid (V LB ) on k 2 (for phytoplankton) at different % NLOM (V NB ) at Log Kow 4. Note that for all Log Kow values from 4-8, th e curves follow a similar trend for different V LB and V NB , but differ in magnitude of k 2 . 

### To determine elimination constant from respiration (k 2 

### ) for animals, seven input parameters are required: K OW 

### , W B 

### , V LB 

### , V NB 

### , V WB, 

### C OX 

### , and β (all of which are defined in Table A6). The parameter β is a constant representing the proportionality of the sorption of a chemical to NLOM to the K OW 

### of that chemical. If the other parameters are considered in terms of ranges applicable to KABAM, the octanol-water partition coefficient (K OW 

### ) and the water content of the organism (V WB 

### ) have the greatest influen ce on the determination of k 2 

### . When Log K OW 

### values are changed from 4 to 8, the k 2 

### value decreases by 4 orders of ma gnitude (Figures A6 and A7). Body weight also influences k 2 

### , with k 2 

### values decreasing by 2 orde rs of magnitude as the body weight is increased from 1x10 -7 

### to 1 kg (this is a range consider ed relevant to aquatic animals, see Appendix C for more information) (Figure A6). The lipid fraction of the organism (V LB 

### ) and the non-lipid organic matter fraction (V NB 

### ) influence k 2 

### , with decreases in k 2 

### observed as these two values increase. When V LB 

### is increased from 1 to 5%, k 2 

### decreases by 84% (Figure A7). When V NB 

### is increased from 15 to 40%, a 14% decrease in k 2 

### is observed. The water content (V WB 

### ) of an organism has a negligible (<0.5%) effect on k 2 

### . Increasing the c oncentration of the dissolved oxygen (C OX 

### ) value from 5 to 10 mg/L results in a decrease of 50% in the value of k 2


### .

### 36 of 123 1.0E-06 1.0E-05 1.0E-04 1.0E-03 1.0E-02 1.0E-01 1.0E+00 1.0E+01 1.0E+02

4.0 5.0 6.0 7.0 8.0
Log Kowk2 WB = 10 kg WB = 1 kg WB = 1e-1 kg WB = 1e-2 kg WB = 1e-3 kg WB = 1e-4 kg WB = 1e-5 kg

WB = 1e-6 WB = 1e-7 kg

Figure A6. Influence of Log K OW on k 2 (for animals) at different body weights (W B ) (with V LB = 5%, V NB = 20%, V WB = 75% and C OX = 10mg/L).

1.0E-05 1.0E-04 1.0E-03 1.0E-02 1.0E-01 1.0E+00

4.0 5.0 6.0 7.0 8.0
Log Kowk2 VLB = 5%

VLB = 4%

VLB = 3%

VLB = 2%

VLB = 1%

Figure A7. Influence of Log K OW on k 2 (for animals) at differe nt % lipid composition (V LB ) (with W B = 1 kg, V NB = 20%, V WB = 75% and C OX = 10 mg/L).

### 37 of 123 A.4. Calculation of Growth Rate Constant



### Equations A7.1 and A7.2 provide an approxima tion of growth of aquatic organisms based on weight and temperature (Table A7). Comparing the results of the two e quations indicates that higher temperatures result in hi gher growth rate constant (k G 

### ) values (Figure A8). With both equations, as body weight increases, k G 

### decreases. There is some uncertainty associated with these equations, since growth rate can be influenced by additional factors, including species and prey availability. For KABAM, it is assume d that if the water temperature (T) < 17.5 o 

### C (midpoint between 10 and 25 o 

### C), equation A.7.1 is used and if T ≥ 17.5 o 

### C, equation A.7.2 is used.

Table A7. Equations involving the derivat ion of the growth rate constant (k G ) and associated parameters (Arnot and Gobas 2004). 

### ) 10 ( * 0005

. 0 1

. 7

. 2

. 0 

### C T W k A Eq o B G 

### ≈

= −



### ) 25 ( * 00251

. 0 2

. 7

. 2

. 0 

### C T W k A Eq o B G 

### ≈

= −

Parameters: Symbol Definition Value Units

k G organism growth rate constant calculated d -1

T temperature user defined o C

W B wet weight of the organism user defined kg

1.0E-04 1.0E-03 1.0E-02 1.0E-01 1.0E+00

-7 -6 -5 -4 -3 -2 -1 0 1
Log W B (in kg)kG T=10oC

T=20oC

Figure A8. Relationship between body weight (W B ) and k G at 2 different temperatures.

### 38 of 123 A.5. Calculation of Dietary Uptake (k D 

### ) Rate Constant



### In determining uptake and eliminat ion rate constants related to dietary sources of chemicals (k D 

### and k E 

### , respectively), it is assumed that aquatic organisms are represented by a 2-phase model that includes the gastrointestin al tract (GIT) of the organisms and the organism itself. Since phytoplankton do not consume other organi sms, a dietary uptake constant (k D 

### ) is not a relevant rate constant, and the elimination rate constant due to fecal elimination (k E 

### ) is considered insignificant in plants. Therefore, k D 

### and k E 

### are only calculated for animals.

The dietary uptake constant for a chemical in animals is influenced by the weight of the organism, the feeding rate of the organism (G D 

### ), and the dietary pestic ide transfer efficiency (E D 

### ). The feeding rate is different for filter feeders compared to other aquatic organisms. Empirical dietary pesticid e transfer efficiency (E D 

### ) values vary from 0-100%. Variability in E D 

### has been attributed to various factors, includi ng sorption coefficients of chemicals, composition of diet, and digestibility of diet. Based on seve ral different observations, it is assumed by Arnot and Gobas (2004) that this value can be related to K OW 

### (Equation A8, Table A8).

### 39 of 123 Table A8. Equations involving the d erivation of the pesticide clearan ce rate constant through diet (k D ) and associated parameters (A rnot and Gobas 2004).

B D D D 

### W G E k A Eq * 8

. =

1 7 

### ) 0

. 2 * 10 0

. 3 ( : − − 

### + = OW D 

### K x E Where

) * 06

. 0 exp( * * 022

. 0 : ) ( 85

. 0 

### T W G feeders filter except animals For B D 

### =

σ 

### * * : SS V D 

### C G G feeders filter For =
⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

=

OX B V 

### C W G 65

. 0


### * 1400

Parameters: Symbol Definition Value Units

C ox concentration of dissolved oxygen user defined (mg O 2 )/L

C SS concentration of suspended solids user defined kg/L

E D dietary pesticide transfer efficiency calculated %

G D feeding rate of organism calculated kg/d

G V ventilation rate of gills calculated L/d

k D pesticide uptake rate constant for uptake through ingestion of food calculated kg food/(kg org*day)

K OW octanol water partition coefficient user defined none

T temperature user defined o C

W B wet weight of the organism user defined kg

σ efficiency of scavenging of particles absorbed from water 100 %



### For aquatic organisms (non-filter feed ers), the dietary uptake constant (k D 

### ) is derived using 3 input parameters: octanol-wa ter partition coefficient (K OW 

### ), the weight of the organism (W B 

### ), and temperature (T). For chemicals with Log K OW 

### ranging 4-5.5, changes in Log K OW 

### cause little (<4%) effects to the value of k D 

### ; however, for chemicals with Log K OW 

### <5.5, increases in Log K OW 

### can result in decreases in th is value up to an order of magn itude (Figure A9). Increases in W B 

### from 1x10 -7 

### to 1 kg result in an order of magnitude decrease in k D 

### (Figure A9). Temperature also affects k D 

### , with an observed increase in k D 

### of 65% when the temperature is increased from 2.5 to 20 o 

### C (Figure A10).

### 40 of 123 1.0E-04 1.0E-03 1.0E-02 1.0E-01 1.0E+00

4.0 5.0 6.0 7.0 8.0 Log KowkD WB=10 kg
WB=1 kg
WB=1e-1 kg
WB=1e-2 kg
WB=1e-3 kg
WB=1e-4 kg
WB=1e-5 kg
WB=1e-6 kg
WB=1e-7 kg

Figure A9. Relationship between Log K OW and k D (for non-filter feeders) at different body weights (W B ) (T=10 o C).

### 41 of 123 1.0E-04 1.0E-03 1.0E-02 1.0E-01 1.0E+00

4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0 Log KowkD T=20oC

T=15oC

T=10oC

T=5oC

Figure A10. Relationship between Log K OW and k D (for non-filter feeders) at different water temperatures (W B =1 kg).



### For filter feeders, the dietary uptake constant (k D 

### ) is derived using five input parameters: K OW 

### , W B 

### , C OX 

### , C SS 

### and σ (all of which are defined in Tabl e A.8). For chemicals with Log K OW 

### values ranging 4-5.5, changes in Log K OW 

### cause little (<5%) effects to the value of k D 

### ; however, as the Log K OW 

### increases from 6 to 8, the k D 

### for filter feeders decreases by an order of magnitude (Figure A11). Available National Water Qual ity Assessment (NAWQA ) data for streams indicate that suspended sediment concentra tions range 1-281 mg/L (USGS 2008b). If the concentration of suspended sediments (C SS 

### ) is increased from 1x10 -6 

### to 1x10 -4 

### kg/L, k D 

### for filter feeders increases by 2 orders of magn itude (Figure A12). Increases in W B 

### from 1x10 -4 

### to 1x10 -2 

### kg (which is a reasonable range of weights for f ilter feeders; see Appendix C) result in an 80% decrease in k D 

### (Figure A11). Increases in oxygen concentration (C OX 

### ) from 2 to 8 mg/L results in a decrease in k D 

### of 80% for filter feeders. Changes in the scavenging efficiency ( σ ) of filter feeders result in proportional changes to the k D 

### value. For example, a 25% decrease in σ results in a 25% decrease in k D


### .

### 42 of 123 1.0E-04 1.0E-03 1.0E-02 1.0E-01

4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0
Log KowkD

WB = 1e-4 kg WB = 1e-3 kg

WB = 1e-2

Figure A11. Relationship between Log K OW and filter feeder k D with different W B values.

1.0E-05 1.0E-04 1.0E-03 1.0E-02 1.0E-01 1.0E+00

4.0 4.5 5.0 5.5 6.0 6.5 7.0 7.5 8.0
Log KowkD

Css = 1e-6 kg/L Css = 1e-5 kg/L Css = 1e-4 kg/L

Figure A12. Relationship between Log K OW and filter feeder k D with different Css values.

### 43 of 123 A.6. Calculation of Dietary Elimination (k E 

### ) Rate Constant



### The rate constant for elimination of the pest icide through excretion of contaminated feces (k E 

### ) is calculated using the fecal egestion rate (G F 

### ), the dietary pesticide transfer efficiency (E D 

### ), the partition coefficient of the pesticide between th e gastro-intestinal tract and the organism (K GB 

### ), and the body weight of the organism (W B 

### ) (Equation A9, Table A9). For filter-feeding and nonfilter feeding aquatic animals, k E 

### is calculated in a similar manner, with the exception of the method of calculating the feed ing rate of an organism (G D 

### ). An order of magnitude increase in either G F 

### , E D, 

### or K GB 

### results in an order of magnitude in crease in fecal egestion rate constant (k E 

### ). An order of magnitude increase in W B 

### results in an order of magnitude decrease in k E


### . Effects of changes in individual input paramete rs used to derive G F 

### , E D, 

### and K GB 

### are explored below.

Table A9. Equations involving the derivation of the fecal elimination rate constant (k E ) and associated parameters (Arnot and Gobas 2004).

B GB D F E 

### W K E G k A Eq * * 9

. =

1 7 

### ) 0

. 2 * 10 0

. 3 ( : − − 

### + = OW D 

### K x E Where 

# ( ) ( )
WB OW NB OW LB WG OW NG OW LG GB 

### V K V K V V K V K V K + + + +

= * * * * * * β β



# ( ) () ( ) ( )
WD W ND N LD L LD L LG 

### V V V V V * 1 * 1 * 1 * 1

ε ε ε ε

− +

− +

− −

=


# ( ) () ( ) ( )
WD W ND N LD L ND N NG 

### V V V V V * 1 * 1 * 1 * 1

ε ε ε ε

− +

− +

− −

=


# ( ) () ( ) ( )
WD W ND N LD L WD W WG 

### V V V V V * 1 * 1 * 1 * 1

ε ε ε ε

− +

− +

− −

=



# ( ) ( ) ( ) 

# [ ]
D WD W ND N LD L F 

### G V V V G * * 1 * 1 * 1 ε ε ε

− +

− +

− =

) * 06

. 0 exp( * * 022

. 0 : ) ( 85

. 0 

### T W G feeders filter except animals For B D 

### =

σ * * : SS V D 

### C G G feeders filter For =
⎟ ⎟ ⎠ ⎞ ⎜ ⎜ ⎝ ⎛

=

OX B V 

### C W G 65

. 0


### * 1400

Parameters:
Symbol Definition Value Units
C ox concentration of dissolved oxygen calculated (mg O 2 )/L

C SS concentration of suspended solids user defined kg/L

### 44 of 123 E D dietary pesticide transfer efficiency calculated %

G D feeding rate of organism calculated kg/d

G F egestion rate of fecal matter calculated (kg feces)/(kg organism)*d

G V ventilation rate of gills calculated L/d

k E rate constant for elimination of the pesticide through excretion of contaminated feces for animals: calculated for plants: 0 d -1

K GB partition coefficient of the pesticide between the gastrointestinal tract and the organism calculated none

K OW octanol water partition coefficient user defined none

T temperature user defined o C

V LB lipid fraction of organism user defined (kg lipid)/ (kg organism wet weight)

V LD overall lipid content of diet user defined kg/kg

V LG lipid content in the gut calculated (kg lipid)/(kg digesta wet

weight)

V NB NLOM (Non Lipid Organic Matter) fraction of animals, NLOC (Non Lipid Organic Carbon) of plants user defined kg NLOM/ (kg organism wet weight)

V ND overall NLOM content of diet user defined kg/kg

V NG NLOM content in the gut calculated (kg NLOM)/(kg digesta wet

weight)

V WB water content of the organism user defined kg water/ (kg organism wet weight)

V WD overall water content of diet user defined kg/kg

V WG water content in the gut calculated (kg water)/(kg digesta wet

weight)

W B wet weight of the organism user defined kg

β proportionality constant expressing the sorption capacity of NLOM to that of octanol 0.035 for animals none

ε L dietary assimilation rate of lipids fish: 92%; aquatic inverts: 75%;

zooplankton: 72% %

ε N dietary assimilation rate of NLOM fish: 60%; aquatic inverts: 75%;

zooplankton: 72% %

ε W dietary assimilation rate of water freshwater organisms: 25% %

σ efficiency of scavenging of particles absorbed from water 100 %

### 45 of 123 A.6.1. Parameters Affecting G F


### The fecal egestion rate G F 

### is calculated using the feed ing rate of the organism (G D 

### ; kg/day), the dietary assimilation rates for lipids, NLOM, and water ( ε L 

### , ε N 

### and ε W 

### , respectively) as well as the contents of the diet (V LD 

### , V ND, 

### and V WD 

### ).

For non-filter feeders, the feeding rate (G D 

### ) is calculated using body weight and temperature. Generally, as body weight and te mperature increase, so does th e feeding rate of the aquatic animal (Figure A.13). An order of magnitude in crease in body weight leads to an order of magnitude increase in the feeding rate of the organism. An order of magnitude increase in temperature leads to a 40% increase in the feeding rate of non-filter feeders.

1.0E-07 1.0E-06 1.0E-05 1.0E-04 1.0E-03 1.0E-02 1.0E-01 1.0E+00

0 5 10 15 20 25 30 Temperature (oC)GD(kg/d) WB = 1e-6 kg WB = 1e-3 kg WB = 1 e2 kg

Figure A13. Relationship between temperature and G D (non-filter feeders) with different W B values.



### For filter feeders, the feeding rate (G D 

### ) is calculated using four para meters: the concentration of dissolved oxygen in the water (C OX 

### ), body weight (W B 

### ), the concentration of suspended solids (C SS 

### ), and the scavenging efficiency of particles absorbed from water ( σ ). As with non-filter feeders, increases in body weight of filter feeders leads to increases in G D 

### (Figures A14-A16). An order of magnitude increase in body weight leads to an 80% increase in feeding rate (G D 

### ). An increase in dissolved oxygen in the water (C OX 

### ) from 2 to 10 mg/L results in a decrease in G D 

### of 80% (Figure A14). Decreases in scavenging efficiency lead to proportional decreases in G D 

### , with every 10% decrease in scavenging efficiency ( σ ), leading to a 10% decrease in G D 

### (Figure A15). An order of magnitude increase in the concentration of suspended solids (C SS 

### ) leads to an order of magnitude increase in G D 

### (Figure A16).

### 46 of 123 1.0E-07 1.0E-06 1.0E-05 1.0E-04

2 3 4 5 6 7 8 9 10 Cox (mg/L)GD (kg/d) WB = 1e-4 kg WB = 1e-3 kg WB = 1e-2 kg

Figure A14. Relationship between Cox and G D (filter feeders) with different W B values.

1.0E-07 1.0E-06 1.0E-05 1.0E-04

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 scavenging efficiencyGD (kg/d) WB = 1e-4 kg WB = 1e-3 kg WB = 1e-2 kg

Figure A15. Relationship between scavenging efficiency and G D (filter feeders) with different W B values.

### 47 of 123 1.0E-07 1.0E-06 1.0E-05 1.0E-04 1.0E-03

1.00E-06 1.00E-05 1.00E-04 Css (kg/L)GD (kg/d)

WB = 1e-4 kg WB = 1e-3 kg WB = 1e-2 kg

Figure A16. Relationship between concentration of suspended solids (Css) and G D (filter feeders) with different W B values.



### The fecal egestion rate (G F 

### ) is calculated using the following parameters: ε L 

### , ε N 

### , ε W 

### , V LD , 

### V ND, 

### V WD 

### as well as G D 

### , which is discussed above. When the default dietary assimilation rates for lipids, NLOM, and water ( ε L 

### , ε N 

### , and ε W 

### , respectively) are used, changes in lipid, NLOM and water composition of the diet (V LD 

### , V ND 

### and V WD, 

### respectively) have little effect on G F 

### , when compared to effects of G D 

### on G F 

### (Figure A.17). This indicates that as the feeding rate of an animal (G D 

### ) increases so does its fecal egestion rate (G F 

### ), while the composition of the animal’s diet has little effect on the fecal egestion rate.

### 48 of 123 1.0E-06 1.0E-05 1.0E-04 1.0E-03 1.0E-02 1.0E-01 1.0E+00

1.0E-05 1.0E-04 1.0E-03 1.0E-02 1.0E-01 1.0E+00 GD (kg/day)GF (kg feces/kg organism)

VLD 1%, VND 10%, VWD 89%

VLD 1%, VND 20%, VWD 79%

VLD 10%, VND 10%, VWD 80%

VLD 10%, VND 20%, VWD 70%

Figure A17. Relationship between G D and G F with different lipid, NLOM, and wa ter compositions in the diet (V LD , V ND , and V WD , respectively). Dietary assimilation rates fo r lipids, NLOM, and water are set to default values used to represent fish (see Table A9).



### When setting the lipid, NLOM, and water composition (V LD 

### , V ND 

### and V WD, 

### respectively) of diet equal for fish, aquatic invertebra tes, and zooplankton, the differen ces in dietary assimilation rates for lipids, NLOM, and water ( ε L 

### , ε N 

### , and ε W 

### , respectively) of these three groups of animals have little effect on the f ecal egestion rate (G F 

### ), when compared to eff ects of the feeding rate (G D 

### ) on G F 

### (Figure A.18). As w ith the composition of th e diet, changes in G D 

### result in greater effects on G F 

### when compared to changes in the assimilation efficiencies of lipid, NLOM, and water.

### 49 of 123 1.0E-08 1.0E-07 1.0E-06 1.0E-05 1.0E-04 1.0E-03 1.0E-02 1.0E-01 1.0E+00

1.0E-07 1.0E-06 1.0E-05 1.0E-04 1.0E-03 1.0E-02 1.0E-01 1.0E+00

GDGF

Fish Aquatic invertebrates Zooplankton

Figure A18. Relationship between G D and G F with dietary assimilation rates for lipids, NLOM, and water set to default values for fish, aquatic invertebrates, an d zooplankton (see Table A9). Lipid, NLOM, and water compositions in the diet (V LD , V ND , and V WD , respectively) are set to 1, 20, and 79%, respectively.



### When V LD, 

### V ND, 

### V WD 

### are equal ( i.e., 33.33%) and when G D 

### is set to a constant number, variations of ε L 

### , ε N, 

### and ε W 

### result in equivalent effects to G F 

### , with G F 

### decreasing as assimilation efficiency increases ( i.e., the organism eliminates less as its digestion [assimilation efficiency] becomes more efficient). If assimilation efficiency is set to 1 for lipid, NLOM, and water, the fecal elimination rate of the organism is 0 kg feces/kg org (Figure A.19).

### 50 of 123 0 0.0001 0.0002 0.0003 0.0004

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

ExGF change in EL change in EN change in EW

Figure A19. Relationship between di etary assimilation efficiencies ( 

### ε L 

### , ε N 

### , and ε W 

### ) and G F (V LD =V ND =V WD =33.33%; G D = 1.0x10 -3 kg/day). Each dietary efficiency rate was altered independent of the others, with the others set to 1.



### In summary, changes in the feeding rate of the organism (G D 

### ) have the greatest effect on the fecal egestion rate of the organism (G F 

### ). G D 

### is calculated for non-filter feeders using body weight and temperature. G D 

### is calculated for filter feeders, us ing the concentration of dissolved oxygen in the water (C OX 

### ), body weight (W B 

### ), the concentration of suspended solids (C SS 

### ), and the efficiency of scavenging of pa rticles absorbed from water ( σ ). Changes in these parameter values would be expected to have the greatest influence on G F 

### , which would result in influences on rate constant for pesticide elimination through excretion (k E 

### ). Changes in the composition of the diet and the dietary assimilation rates are exp ected to have less of an influence on G F 

### when compared to G D 

### and the parameters used to derive G D


### .

A.6.2. Parameters Affecting E D


### The efficiency of dietary pesticide transfer (E D 

### ) is based only upon the K OW 

### of the pesticide. As K OW 

### increases, E D 

### decreases. For chemicals with Log K OW 

### 4-8, the efficiency of dietary pesticide transfer is 50-3% (Figure A.20).

### 51 of 123 0.0 0.1 0.2 0.3 0.4 0.5 0.6

4 4.5 5 5.5 6 6.5 7 7.5 8
Log KowED

Figure A.20. Dietary transfer efficiency (E D ) vs. Log Kow.



### A.6.3. Parameters Affecting K GB


### The equations used to calculate the contents of the gut ( i.e., V LG, 

### V NG, 

### and V WG 

### ) listed in Table A.9 can be simplified as follows, using the equation for G F 

### : 

# ( )

F D LD L LG 

### G G V V * * 1 ε

−

=


# ( )

F D ND N NG 

### G G V V * * 1 ε

−

=


# ( )

F D WD W WG 

### G G V V * * 1 ε

−

=

Using these simplified equations, changes in the feeding rate of the organism (G D 

### ), the fecal egestion rate (G F 

### ), diet composition, and dietary assimila tion of lipid, NLOM, and water can be explored to understand effects on these parameters on estimations of the lipid composition of the gut. If ε L 

### , ε N 

### , and ε W 

### are all equal and V LD 

### , V ND, 

### and V WD 

### are all equal, V LG 

### , V NG, 

### and V WG 

### are equal. Changes in feeding rate (G D 

### ) and fecal egestion rate (G F 

### ) do not affect V LG 

### , V NG 

### or V WG 

### (Figure A.21). As would be expected, changes in V LD 

### result in effects to V LG 

### , with an order of magnitude increase in V LD, 

### resulting in an order of magnitude increase in V LG 

### (Figure A.22). Also, changes in V ND 

### result in effects to V NG 

### , with an increase in V ND 

### from 10 to 20%, resulting

### 52 of 123 in a 50% increase in V NG 

### (Figure A.23). An order of magnitude increase in V WD 

### (keeping V LD 

### and V ND 

### constant) results in slight (a pproximately 10%) decreases in V LG 

### and V NG 

### and slight (2%) increases in V WG 

### (Figure A.24). A decrease in ε L 

### from 0.9 to 0.1, results in an order of magnitude increase in the lipid content of the gut (V LG 

### ) (Figure A.25), but only slight (<2%) changes to the NLOM and wa ter contents of the gut (V NG 

### and V WG 

### , respectively). A decrease in

ε N 

### from 0.9 to 0.1 results in an order of magnitude increase in the NLOM content of the gut (V NG 

### ) (Figure A.26), as well as decreases in the gut composition attributed to lipid and water. A decrease in ε W 

### from 0.9 to 0.1 results in a 50% increase in the water content of the gut (V WG 

### ), as well as an 80% decrease in the gut composition at tributed to lipid and NLOM (Figure A.27). For invertebrates, dietary assimila tion efficiencies vary significantly, leading to uncertainty in assigning one value to th is parameter. Since hydrophobic chemical s are not likely to be stored in the water of organism tissues, it is assumed that th is route is not significa nt to bioaccumulation.

1.00E-03 1.00E-02 1.00E-01 1.00E+00

1.E-07 1.E-06 1.E-05 1.E-04 1.E-03 1.E-02 1.E-01 1.E+00

GDVXG VLG
VNG
VWG

Figure A21. Relationship between gut contents (V LG , V NG , and V WG ) and G D . ε L , ε N , and ε W are set to defaults for fish (Table A9). V LD = 1%, V ND = 20%, and V WD = 79%.

### 53 of 123 1.0E-03 1.0E-02 1.0E-01 1.0E+00

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1

VLDVXG VLG
VNG
VWG

Figure A22. Relationship between gut contents (V LG , V NG , and V WG ) and V LD . ε L , ε N , and ε W are set to defaults for fish (Table A9). V ND = 20%, V WD = 1-V ND -V LD .

1.0E-03 1.0E-02 1.0E-01 1.0E+00

0.1 0.12 0.14 0.16 0.18 0.2 0.22 0.24 0.26 0.28 0.3

VNDVXG

VLG
VNG
VWG

Figure A23. Relationship between gut contents (V LG , V NG , and V WG ) and V ND . ε L , ε N , and ε W are set to defaults for fish (Table A9). V LD = 1%, V WD = 1-V ND -V LD .

### 54 of 123 1.0E-03 1.0E-02 1.0E-01 1.0E+00

0.75 0.76 0.77 0.78 0.79 0.8 0.81 0.82 0.83 0.84 0.85

VWDVXG

VLG
VNG
VWG

Figure A24. Relationship between gut contents (V LG , V NG , and V WG ) and V WD . ε L , ε N , and ε W are set to defaults for fish (Table A9). V LD = 1%, V ND = 20%. Note that V LD +V ND +V WD does not equal 100%, except when V WD = 0.79.

1.0E-03 1.0E-02 1.0E-01 1.0E+00

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

ELVXG

VLG
VNG
VWG

Figure A25. Relationship between gut contents (V LG , V NG , and V WG ) and ε L . ε N , and ε W are set to defaults for fish (Table A9). V LD = 1%, V ND = 20%, V WD = 79%.

### 55 of 123 1.0E-03 1.0E-02 1.0E-01 1.0E+00

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

ENVXG

VLG
VNG
VWG

Figure A26. Relationship between gut contents (V LG , V NG , and V WG ) and ε N . ε L , and ε W are set to defaults for fish (Table A9). V LD = 1%, V ND = 20%, V WD = 79%.

1.0E-03 1.0E-02 1.0E-01 1.0E+00

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

EWVXG

VLG
VNG
VWG

Figure A27. Relationship between gut contents (V LG , V NG , and V WG ) and ε W . ε L , and ε N are set to defaults for fish (Table A9). V LD = 1%, V ND = 20%, V WD = 79%.

### 56 of 123 The partitioning of a chemical between the gastrointestinal tract (GIT) and the organism is described by K GB 

### . This partition coefficient is determ ined using the contents of the gut (V LG 

### , V

NG, 

### and V WG 

### ), the contents of the organism’s body (V LB 

### , V NB 

### and V WB 

### ) as well as the octanol water partition coefficient (K OW 

### ) (Table A.9). An order of magnit ude increase in the lipid content of the body (V LB 

### ) results in an order of magnitude decrease in K GB 

### (Figures A.28 and A.29). An order of magnitude increase in the NLOM content of the body (V NB 

### ) results in a decrease in K GB 

### of approximately 20%. An order of magnitude increase in the lipid content of the gut (V LG 

### ) results in a 50% increase in K GB 

### (Figure A.28). An order of ma gnitude increase in the NLOM content of the gut (V NG 

### ), results in an increase in K GB 

### of 70% (Figure A.29). Changes in the Log K OW 

### of a chemical from 4 to 8 do not alter K GB


### .

1.0E-02 1.0E-01 1.0E+00 1.0E+01

0 0.002 0.004 0.006 0.008 0.01 0.012

VLGkGB

VLB=0.01,VNB=0.20, VWB=0.79

VLB=0.10, VNB=0.20, VWB=0.70

VLB=0.01, VNB=0.30, VWB=0.69

Figure A28. Relationship betw een lipid content of the gut (V LG ) and K GB , with different body compositions (V LB , V NB , and V WB ).

### 57 of 123 1.0E-02 1.0E-01 1.0E+00 1.0E+01

0 0.1 0.2 0.3 0.4 0.5 0.6

VNGkGB

VLB=0.01,VNB=0.20, VWB=0.79

VLB=0.10, VNB=0.20, VWB=0.70

VLB=0.01, VNB=0.30, VWB=0.69

Figure A29. Relationship betw een NLOM content of the gut (V NG ) and K GB , with different body compositions (V LB , V NB , and V WB ).



### A.7. Overall Sensitivity of Body Concentration of Chemical (C B 

### ) to Individual Input Parameters



### A.7.1. First Sensitivity Analysis

In order to understa nd the influence of inpu t parameters on model pr edictions of pesticide concentrations in tissue of aquatic organisms (C B 

### ), a sensitivity analysis was conducted. Parameters were assigned uniform distributions and assumptions of ranges based on data in the scientific literature. The range for each parame ter is defined in Table A10. Diets of each trophic level were varied according to the definiti ons in Table A11. Uniform distributions were used to allow unbiased selection of values fr om set ranges. Once parameter assumptions were assigned, a Monte Carlo simulation wa s carried out using Crystal Ball 2000. In this simulation, 10,000 trials were conducted with randomly select ed parameter values resulting in predicted pesticide concentrations in each of the seven trophic levels. The sensitivity of the model to specific parameters was defined by the contribu tion of each parameter to the variance of the estimation of pesticide concentrations in each of the trophic levels.

The results of this analysis indicate that of all the variables in the model, the Log K OW 

### contributes the most to variability (<75% of total) in estimates of C B 

### for all animal trophic levels. For phytoplankton, the water column EEC, con centration of POC in the water column (X POC 

### )

### 58 of 123 and Log K OW 

### contribute the greatest vari ability in the predicted C B 

### values (38, 28, and 22%, respectively).

A.7.2. Second Sensitivity Analysis

Based on the results of the first sensitivity analysis, a second analysis was conducted where the influence of individual para meters on variability in C B 

### was examined, with fixed Log K OW 

### values. In the second sensitivity analysis, the Log K OW 

### was set to values of 4, 5, 6, 7, and 8 and a Monte Carlo simulation (10,000 trials) was run for each Log K OW 

### value. Parameters were assigned uniform distributions and assumptions of ranges based on data in the scientific literature. The range for each parameter is de fined in Table A10 (with the exception of Log K OW 

### ). Diets of each trophic level were varied according to the definitions in Table A11.

The contributions of individual parameters at Log K OW 

### values of 4, 5, 6, 7 and 8 to the variability in the pesticide tissue concentration (C B 

### ) of the seven aquatic trop hic levels of KABAM are provided in Tables A12-A18. The results of this sensitivity analysis indicate that parameters have different relative importance in estimating C B 

### for the seven trophic levels ( e.g., the water column EEC contributes the most variability to the phytoplankton C B 

### , while the pore water EEC and fraction of respiratory ventilation th at involves pore-water of sediment (m P 

### ) value contribute the most variance to the zooplankton C B 

### ). In addition, these tables indicate that the relative importance of individual parameters to estimates of C B 

### change with Log K OW 

### . It should be noted that several parameters in the Arnot and Gobas (2004) model are linked ( e.g. , m P 

### and m O 

### , diet composition, V LB 

### , V NB 

### , and V WB 

### ). Therefore, sensitivity of C B 

### predictions to one parameter implies sensitivity of the predictions to the linked parameters.

This sensitivity analysis also indicates that some parameters that are fi xed in KABAM, including the constant related to the resi stance to pesticide upt ake through the aqueous phase of plant (A), proportionality constant expres sing the sorption capacity of NLOM to that of octanol ( β ), and m P 

### (set to either 0 or 0.05), can contribute > 10% of total variabilit y in estimates of C B


### .59 of 123Table A10. Parameters and associated assumptions usedfor first and second sensitivity analysis of KABAM.ParameterParameter DescriptionTrophic LevelMinimumof RangeMaximumof RangeSource/CommentsAConstant related to the resistance topesticide uptake through the aqueousphase of plantPhytoplankton1x10-51x10-4In Arnot and Gobas 2004, this value is set to a constant of 6.0x10-5days. This value was varied by an order of magnitude around thereported constant value to understand the influence of this parameteron estimates of bioaccumulation. The reasonable range of values forthis parameter is unknown.BConstant related to the resistance topesticide uptake through the organicphase of plantPhytoplankton110In Arnot and Gobas 2004, this value is set to a constant of 5.5 days.This value was varied by an order of magnitude around the reportedconstant value to understand the influence of this parameter onestimates of bioaccumulation. Thereasonable range of values forthis parameter is unknown.COXConcentration of dissolved oxygen(mg O2/L)All412Minimum is based on 60% of saturation of water with 6 mg/L assaturation (in 30oC water). Maximum is based on solubility limit ofoxygen in cold water (5oC; see USGS 2008a).CSSConcentration of suspended solids(kg/L)All2.0x10-65.0x10-4Based on 5thand 95thpercentiles of approximately 38,000measurements of suspended sediment concentrations in surfacewaters of the US provided by NAWQA (USGS 2008b).CWTOTotal pesticide concentration inwater column above the sedimentAll0.1100Assumed to be reasonable range for EECs expected fromPRZM/EXAMS modeling.CWTPFreely dissolved pesticideconcentration in pore water ofsedimentAll0.1100Assumed to be reasonable range for EECs expected fromPRZM/EXAMS modeling.Log KowLog of octanol-water partitioncoefficientAll48Assumption that bioaccumulation model can be used for chemicalswith Log Kow 4-8.KocOrganic carbon partition coefficientAll3.5x1033.5 x107Determined based on assumption that Koc can be estimated as0.35*Kow. In sensitivity analysis, Koc is linked directly to KOWinorder to avoid error in selection of inconsistent values for theseparameters.Zooplankton01Based on full range of parameter values.Benthic Inv.01Based on full range of parameter values.Filter Feeders01Based on full range of parameter values.Small Fish01Based on full range of parameter values.Medium Fish01Based on full range of parameter values.mpFraction of respiratory ventilationthat involves pore-water of sedimentLarge Fish01Based on full range of parameter values.OCPercent organic carbon in sedimentAll1%10%In the OPP standard pond used in EXAMS, the default value for thisparameter is 4%. This parameter value is varied by 1 order of60 of 123Table A10. Parameters and associated assumptions usedfor first and second sensitivity analysis of KABAM.ParameterParameter DescriptionTrophic LevelMinimumof RangeMaximumof RangeSource/Commentsmagnitude around the OPP standard pond value.TTemperature (oC)All130Reasonable range of values for this parameter in the environment.Phytoplankton0.52.0See Table C1 of Appendix C.Zooplankton1.04.0See Table C2 of Appendix C.Benthic Inv.0.512See Tables C4-C9 of Appendix C.Filter Feeders0.44See Tables C13-C15 of Appendix C.VLBLipid fraction of organismFish0.58See Table C19 of Appendix C.VNBNLOM (Non Lipid Organic Matter)fraction of animals, NLOC (NonLipid Organic Carbon) of plantsAll--Set to equal 1-VLB-VWBPhytoplankton0.850.95Assume 5% deviation from mean (i.e.,90%).Zooplankton0.740.96See Section C.2. of Appendix C.Benthic Inv.0.690.83See Table C3 of Appendix C.Filter Feeders0.780.93See Table C12 of Appendix C.VWBWater content of the organismFish0.710.80See Table C18 of Appendix C.Phytoplankton--Not a necessary parameter for phytoplankton.Zooplankton1x10-91x10-7See Section C.2. of Appendix C.Benthic Inv.5x10-62x10-3See Table C11 of Appendix C.Filter Feeders2x10-41x10-2See Section C.5 of Appendix C.Small Fish1x10-35x10-2See Table C16 of Appendix C.Medium Fish5x10-30.6See Table C17 of Appendix C.WBWet weight (kg) of the organismLarge Fish0.253.6See Section C.5 of Appendix C.XPOCConcentration of particulate organiccarbon in water (kg/L)All2.0x10-65.0x10-4Based on 5thand 95thpercentiles of approximately 38,000measurements of suspended sediment concentrations in surfacewaters of the US provided by NAWQA (USGS 2008b).XDOCConcentration of dissolved organiccarbon in water (kg/L)All5.0x10-75.0x10-5In the OPP standard pond used in EXAMS, the default value for thisparameter is 5.0x10-6. This parameter value is varied by 2 orders ofmagnitude around the OPP standard pond value.βProportionality constant expressingthe sorption capacity of NLOM orNLOC to that of octanolAll01Designed to represent all values equal to or less than the partitioningof a chemical between octanol and water.61 of 123Table A10. Parameters and associated assumptions usedfor first and second sensitivity analysis of KABAM.ParameterParameter DescriptionTrophic LevelMinimumof RangeMaximumof RangeSource/CommentsεLDietary assimilation rate of lipidsAnimals01Based on full range of parameter values.εNDietary assimilation rate of NLOMAnimals01Based on full range of parameter values.εWDietary assimilation rate of waterAnimals01Based on full range of parameter values.σEfficiency of scavenging of particlesabsorbed from waterFilter Feeders01Based on full range of parameter values.Table A11. Dietary assumptions of aquatic trophic levels used for sensitivity analysis of KABAM.Trophic LevelOrganism in dietMinimumValueMaximum ValueCommentsZooplanktonPhytoplankton100%100%-Sediment050%-Phytoplankton050%-BenthicInvertebratesZooplankton--Set to 1- (% diet attributed to sediment + % diet attributed to phytoplankton)Sediment033%-Phytoplankton033%-Zooplankton033%-Filter FeederBenthicinvertebrates--Set to 1- (% diet attributed to sediment + % diet attributed to phytoplankton+%diet attributed to zooplankton)Phytoplankton050%-Zooplankton050%-Small FishBenthicinvertebrates--Set to 1- (% diet attributed to phytoplankton+% diet attributed to zooplankton)Zooplankton050%-Benthicinvertebrates050%-Medium FishSmall fish--Set to 1- (% diet attributed to zooplankton + % diet attributed to benthicinvertebrates)Small fish0100%It is assumed that large fish consume only smaller fishLarge FishMedium Fish--Set to 1- (% diet attributed to small fish)

### 62 of 123 Table A12. Second sensitivity analysis resu lts: Contribution to variance of specific variables to C B values of phytoplankton at different Log Kow values.
Variable 4 5 6 7 8 A ≤ 0.1% ≤ 0.1% 0.7% 9.3% 18.2%

V LB ≤ 0.1% ≤ 0.1% ≤ 0.1% 0.2% ≤ 0.1%

V WB 5.9% 3.4% 2.3% 0.2% ≤ 0.1%
Water Column EEC 59.6% 46.1% 44.9% 45.7% 43.0%

X POC 7.0% 30.9% 39.1% 41.7% 38.0% β 26.7% 18.6% 12.0% 2.1% ≤ 0.1%

Total 99.2% 99.0% 99.0% 99.2% 99.2%

Table A13. Second sensitivity analysis resu lts: Contribution to variance of specific variables to C B values of zooplankton at different Log Kow values. Variable 4 5 6 7 8
C OX ≤ 0.1% ≤ 0.1% 0.2% 2.5% 4.3%

m P 4.3% 23.0% 37.4% 39.5% 36.1%
Pore Water EEC 27.6% 37.2% 40.8% 40.6% 37.4%
T ≤ 0.1% ≤ 0.1% 1.3% 7.7% 19.6%

V LB 1.2% 0.4% 0.6% 0.6% ≤ 0.1%

V WB 20.8% 14.1% 8.8% 0.6% 0.8%
Water Column EEC 11.2% 2.2% 0.3% ≤ 0.1% ≤ 0.1%

W B ≤ 0.1% ≤ 0.1% ≤ 0.1% 0.5% 0.6%

X POC 1.7% 2.5% 0.8% ≤ 0.1% ≤ 0.1% β 32.5% 20.2% 8.4% 1.7% ≤ 0.1%

ε L ≤ 0.1% ≤ 0.1% ≤ 0.1% 0.2% ≤ 0.1%

ε N ≤ 0.1% ≤ 0.1% 0.5% 1.1% 0.3%

Total 99.3% 99.6% 99.1% 95.0% 99.1%

### 63 of 123 Table A14. Second sensitivity analysis resu lts: Contribution to variance of specific variables to C B values of benthic invertebrates at different Log Kow values. Variable 4 5 6 7 8 Characteristics of prey* ≤ 0.1% 1.2% 12.0% 12.0% 6.0%
C OX ≤ 0.1% ≤ 0.1% ≤ 0.1% 1.3% 1.6% Diet composition ≤ 0.1% 0.2% 1.2% 1.0% 8.6%

m P 4.9% 16.0% 5.3% ≤ 0.1% ≤ 0.1%
OC ≤ 0.1% ≤ 0.1% 0.9% ≤ 0.1% ≤ 0.1%
Pore Water EEC 37.2% 51.1% 61.4% 61.1% 59.4%
T ≤ 0.1% ≤ 0.1% 1.0% 8.2% 16.1%

V LB 4.0% 2.5% 1.6% 1.0% ≤ 0.1%

V WB 3.6% 1.5% 1.1% 0.6% ≤ 0.1%
Water Column EEC 14.4% 1.9% ≤ 0.1% ≤ 0.1% ≤ 0.1%

X POC 2.5% 2.7% 0.5% ≤ 0.1% ≤ 0.1% β 32.6% 21.5% 7.6% 0.4% ≤ 0.1%

ε L ≤ 0.1% ≤ 0.1% 0.7% 1.2% ≤ 0.1%

ε N ≤ 0.1% 0.5% 5.8% 5.6% ≤ 0.1%

Total 99.2% 99.1% 99.1% 92.4% 91.7%
*m P , body composition, etc.

Table A15. Second sensitivity analysis resu lts: Contribution to variance of specific variables to C B values of filter feeders at different Log Kow values. Variable 4 5 6 7 8 Characteristics of prey* ≤ 0.1% 1.5% 11.8% 11.7% 2.5%
C OX ≤ 0.1% ≤ 0.1% ≤ 0.1% 2.0% 3.8%

C SS 0.2% 1.4% 2.4% 3.2% 8.1% Diet composition ≤ 0.1% ≤ 0.1% 0.3% 0.6% 1.6%

m P 3.1% 6.9% ≤ 0.1% 0.5% 0.7%
OC ≤ 0.1% ≤ 0.1% 0.9% 2.0% 4.1%
Pore Water EEC 28.6% 42.0% 52.2% 50.5% 42.3%
T ≤ 0.1% ≤ 0.1% 1.9% 13.0% 25.1%

V LB 1.5% 1.5% 1.3% 0.4% ≤ 0.1%

V WB 11.1% 7.0% 4.3% 2.8% 0.4%
Water Column EEC 10.6% 1.8% ≤ 0.1% ≤ 0.1% ≤ 0.1%

W B ≤ 0.1% ≤ 0.1% ≤ 0.1% ≤ 0.1% 0.3%

X POC 2.0% 2.2% 0.2% ≤ 0.1% ≤ 0.1% β 42.0% 30.7% 13.7% 1.5% ≤ 0.1%

ε L ≤ 0.1% 0.4% 2.6% 2.7% 0.5%

ε N ≤ 0.1% 1.8% 5.3% 4.6% 1.2%

σ ≤ 0.1% 1.9% 2.1% 3.5% 7.8%

Total 99.1% 99.1% 99.0% 99.0% 98.4%
*m P , body composition, etc.

### 64 of 123 Table A16. Second sensitivity analysis resu lts: Contribution to variance of specific variables to C B values of small fish at different Log Kow values. Variable 4 5 6 7 8 Characteristics of prey* ≤ 0.1% 3.3% 16.1% 18.2% 9.2%
C OX 0.2% 0.2% ≤ 0.1% 1.3% 2.3% Diet composition ≤ 0.1% 0.6% 2.2% 2.9% 2.7%

m P 3.9% 4.4% 0.5% ≤ 0.1% 0.6%
OC ≤ 0.1% ≤ 0.1% 0.5% 1.5% 2.6%
Pore Water EEC 31.8% 45.3% 52.1% 53.0% 51.6%
T ≤ 0.1% 0.4% 0.8% 9.8% 27.7%

V LB 2.5% 1.3% 1.3% 0.8% 0.3%

V WB 1.5% 1.0% 0.3% 0.2% ≤ 0.1%
Water Column EEC 11.6% 1.5% 0.2% ≤ 0.1% ≤ 0.1%

X POC 2.0% 2.6% 0.5% ≤ 0.1% ≤ 0.1% β 45.7% 34.3% 13.8% 1.7% 0.2%

ε L ≤ 0.1% 1.2% 4.4% 3.4% 0.9%

ε N ≤ 0.1% 2.9% 6.8% 6.1% 0.8%

Total 99.2% 99.0% 99.5% 98.9% 98.9%
*m P , body composition, etc.

Table A17. Second sensitivity analysis resu lts: Contribution to variance of specific variables to C B values of medium fish at different Log Kow values. Variable 4 5 6 7 8 Characteristics of prey* ≤ 0.1% 2.7% 17.3% 21.0% 10.0%
C OX 0.3% ≤ 0.1% ≤ 0.1% 1.1% 2.4% Diet composition ≤ 0.1% 0.2% 0.8% 0.6% ≤ 0.1%

m P 1.5% 1.5% ≤ 0.1% ≤ 0.1% 0.2%
OC ≤ 0.1% ≤ 0.1% 0.4% 1.3% 1.9%
Pore Water EEC 20.5% 33.1% 43.3% 48.5% 49.0%
T 0.2% ≤ 0.1% 1.0% 11.1% 33.1%

V LB 0.4% 0.2% 0.2% 0.4% ≤ 0.1%

V WB 15.7% 9.1% 5.0% 4.2% 0.5%
Water Column EEC 7.7% 1.1% 0.2% ≤ 0.1% ≤ 0.1%

W B ≤ 0.1% 0.2% ≤ 0.1% ≤ 0.1% ≤ 0.1%

X POC 1.2% 1.7% 0.5% ≤ 0.1% ≤ 0.1% β 51.2% 43.5% 20.9% 4.3% 0.5%

ε L 0.2% 1.4% 2.2% 1.9% 0.3%

ε N 0.6% 3.4% 7.4% 4.7% 0.7%

Total 99.5% 98.1% 99.2% 99.1% 98.6%
*m P , body composition, etc.

### 65 of 123 Table A18. Second sensitivity analysis resu lts: Contribution to variance of specific variables to C B values of large fish at different Log Kow values. Variable 4 5 6 7 8 Characteristics of prey* ≤ 0.1% 5.8% 22.8% 23.1% 9.7%
C OX 0.9% 0.9% ≤ 0.1% 0.9% 2.1% Diet composition 0.3% 0.8% 0.9% 0.3% ≤ 0.1%

m P 1.4% 0.3% ≤ 0.1% ≤ 0.1% ≤ 0.1%
OC ≤ 0.1% ≤ 0.1% 0.4% 1.1% 1.7%
Pore Water EEC 27.0% 35.1% 41.3% 45.1% 44.9%
T 1.4% 1.5% 0.7% 10.1% 35.1%

V LB 1.8% 0.9% 1.3% 1.1% ≤ 0.1%

V WB 1.1% 0.5% 0.5% 0.6% 0.2%
Water Column EEC 9.3% 1.2% ≤ 0.1% ≤ 0.1% ≤ 0.1%

X POC 1.4% 1.7% 0.3% ≤ 0.1% ≤ 0.1% β 52.0% 38.8% 13.8% 1.7% ≤ 0.1%

ε L ≤ 0.1% 0.9% 1.7% 2.2% 0.4%

ε N 2.0% 10.5% 15.3% 12.8% 4.3%

Total 98.6% 98.9% 99.0% 99.0% 98.4% *mP, body composition, etc.



### A.7.3. Third Sensitivity Analysis

A third sensitivity analysis was conducted to explore the influen ces of KABAM input parameters that are controlled by the user (including chem ical specific inputs and ecosystem inputs), with the fixed parameters unchanged. In th is sensitivity analysis, the Log K OW 

### was set to values of 4, 5, 6, 7, and 8, and a Monte Carlo simu lation (10,000 trials) was run for each Log K OW 

### value. Parameters were assigned uniform distributions and assumptions of ranges based on data in the scientific literature. The range for each parame ter is defined in Table A19. Diets of each trophic level were varied according to the definitions in Table A11.

The contributions of individual chemical speci fic and ecosystem input parameters at Log K OW 

### values of 4, 5, 6, 7, and 8 to the variab ility in the pesticide tissue concentration (C B 

### ) of the seven aquatic trophic levels of KA BAM are provided in Tables A 20-A26. As with the second sensitivity analysis, the results of this analysis indicate that parameters have different relative importance in estimating C B 

### for the seven trophic levels. In a ddition, these tables indicate that the relative importance of individua l parameters to estimates of C B 

### change with Log K OW


### . This sensitivity analysis indicates that severa l parameters contribute >10% of variance in C B 

### of one or more trophic levels. These include: wa ter column EEC, pore water EEC, particulate organic carbon (X POC 

### ), sediment organic carbon (OC), c oncentration of suspended solids (C SS 

### ), water temperature (T), lipid composition (V LB 

### ), diet composition, and characteristics of prey (including body composition, diet composition and m P 

### ). Several of these parameters, including X POC 

### , OC, and C SS 

### have default values that were selected to be consistent w ith the standard pond used in EXAMS.

### 66 of 123 One notable observation resulting from this sensitivity analysis is that at Log K OW 

### 7 and 8, benthic invertebrate diet com posed of sediment contributes ≥ 25% of the variance in C B 

### of all three size classes of fish. This indicates that the proportion of the benthic invertebrate diet attributed to sediment can influence the estima ted pesticide concentrations in fish tissues.

As indicated above, several parameters in the Arnot and Gobas (2004) model are linked ( e.g., m P 

### and m O 

### , diet composition, V LB 

### , V NB 

### and V WB 

### ). Therefore, sensitivity of C B 

### predictions to one parameter implies sensitivity of the predictions to the linked parameters.67 of 123Table A19. Parameters and associated assumptions used for third sensitivity analysis of KABAM.ParameterParameter DescriptionTrophic LevelMinimumof RangeMaximumof RangeSource/CommentsCOXConcentration of dissolved oxygen(mg O2/L)All412Minimum is based on 60% of saturation of water with 6 mg/L assaturation (in 30oC water). Maximum is based on solubility limit ofoxygen in cold water (5oC; see USGS 2008a).CSSConcentration of suspended solids(kg/L)All2.0x10-65.0x10-4Based on 5thand 95thpercentiles of approximately 38,000measurements of suspended sediment concentrations in surfacewaters of the US provided by NAWQA (USGS 2008b).CWTOTotal pesticide concentration inwater column above the sedimentAll0.1100Assumed to be reasonable range for EECs expected fromPRZM/EXAMS modeling.CWTPFreely dissolved pesticideconcentration in pore water ofsedimentAll0.1100Assumed to be reasonable range for EECs expected fromPRZM/EXAMS modeling.KocOrganic carbon partition coefficientAll3.5x1033.5 x107Determined based on assumption that Koc can be estimated as0.35*Kow. In sensitivity analysis, Koc is linked directly to KOWinorder to avoid error in selection of inconsistent values for theseparameters.Zooplankton00.05Based on default parameter values (0 or 0.05).Benthic Inv.00.05Based on default parameter values (0 or 0.05).Filter Feeders00.05Based on default parameter values (0 or 0.05).Small Fish00.05Based on default parameter values (0 or 0.05).Medium Fish00.05Based on default parameter values (0 or 0.05).mpFraction of respiratory ventilationthat involves pore-water of sedimentLarge Fish00.05Based on default parameter values (0 or 0.05).OCPercent organic carbon in sedimentAll1%10%In the OPP standard pond used in EXAMS, the default value for thisparameter is 4%. This parameter value is varied by one order ofmagnitude around the OPP standard pond value.TTemperature (oC)All130Reasonable range of values for this parameter in the environment.Phytoplankton0.52.0See Table C1 of Appendix C.Zooplankton1.04.0See Table C2 of Appendix C.Benthic Inv.0.512See Tables C4-C9 of Appendix C.Filter Feeders0.44See Tables C13-C15 of Appendix C.VLBLipid fraction of organismFish0.58See Table C19 of Appendix C.VNBNLOM (Non Lipid Organic Matter)fraction of animals, NLOC (NonLipid Organic Carbon) of plantsAll--Set to equal 1-VLB-VWB68 of 123Table A19. Parameters and associated assumptions used for third sensitivity analysis of KABAM.ParameterParameter DescriptionTrophic LevelMinimumof RangeMaximumof RangeSource/CommentsPhytoplankton0.850.95Assume 5% deviation from mean (i.e.,90%).Zooplankton0.740.96See Section C.2. of Appendix C.Benthic Inv.0.690.83See Table C3 of Appendix C.Filter Feeders0.780.93See Table C12 of Appendix C.VWBWater content of the organismFish0.710.80See Table C18 of Appendix C.Phytoplankton--Not a necessary parameter for phytoplankton.Zooplankton1x10-91x10-7See Section C.2. of Appendix C.Benthic Inv.5x10-62x10-3See Table C11 of Appendix C.Filter Feeders2x10-41x10-2See Section C.5 of Appendix C.Small Fish1x10-35x10-2See Table C16 of Appendix C.Medium Fish5x10-30.6See Table C17 of Appendix C.WBWet weight (kg) of the organism at tLarge Fish0.253.6See Section C.5 of Appendix C.XPOCConcentration of particulate organiccarbon in water (kg/L)All2.0x10-65.0x10-4Based on 5thand 95thpercentiles of approximately 38,000measurements of suspended sediment concentrations in surfacewaters of the US provided by NAWQA (USGS 2008b).XDOCConcentration of dissolved organiccarbon in water (kg/L)All5.0x10-75.0x10-5In the OPP standard pond used in EXAMS, the default value for thisparameter is 5.0x10-6. This parameter value is varied by two ordersof magnitude around the OPP standard pond value.

### 69 of 123 Table A20. Third sensitivity analysis results: Cont ributions to variance of specific variables to C B values of phytopla nkton at different Log Kow values.
Variable 4 5 6 7 8

V LB 0.7% 0.4% 0.3% <0.1% <0.1%

V WB 7.6% 5.1% 3.3% 0.5% <0.1% Water Column EEC 80.1% 55.9% 51.8% 52.9% 52.8%

X POC 11.3% 38.0% 44.2% 46.1% 46.8%

Total 99.7% 99.4% 99.6% 99.5% 99.6%

Table A21. Third sensitivity analysis results: Cont ributions to variance of specific variables to C B values of zooplankton at different Log Kow values.
Variable 4 5 6 7 8

C OX <0.1% <0.1% <0.1% 0.6% 3.1%

m P <0.1% 2.2% 22.2% 44.0% 40.2%
Pore Water EEC <0.1% 2.2% 22.8% 42.0% 38.8%
T <0.1% <0.1% <0.1% 3.0% 15.5%

V LB 12.3% 12.7% 13.3% 7.2% 1.1%

V WB 1.3% 0.8% 0.9% 0.4% <0.1% Water Column EEC 75.0% 48.0% 16.0% 0.9% <0.1%

W B <0.1% <0.1% <0.1% <0.1% 0.6%

X POC 10.7% 33.5% 24.2% 1.5% <0.1%

Total 99.3% 99.4% 99.4% 99.6% 99.3%

Table A22. Third sensitivity analysis results: Cont ributions to variance of specific variables to C B values of benthic invertebrates at different Log Kow values.
Variable 4 5 6 7 8 Characteristics of prey* <0.1% <0.1% <0.1% 0.2% <0.1%

C OX <0.1% 0.5% 2.2% 0.2% <0.1% Diet composition <0.1% 1.2% 21.1% 34.4% 36.5%

m P <0.1% 0.8% 0.3% <0.1% <0.1%
OC <0.1% 0.8% 11.6% 16.6% 18.6%
Pore Water EEC 0.2% 6.0% 35.1% 41.8% 42.0%
T <0.1% 1.3% 4.0% <0.1% 1.7%

V LB 31.1% 38.5% 20.0% 6.3% 0.8%

V WB <0.1% <0.1% 0.5% 0.2% <0.1% Water Column EEC 57.8% 27.9% 1.7% <0.1% <0.1%

X POC 7.4% 22.3% 3.0% <0.1% <0.1%

Total 96.5% 99.3% 99.5% 99.7% 99.6% *m P , body composition, etc.

### 70 of 123 Table A23. Third sensitivity analysis results: Cont ributions to variance of specific variables to C B values of filter feeders at different Log Kow values.
Variable 4 5 6 7 8 Characteristics of prey* <0.1% 2.1% 7.7% 14.8% 5.3%

C OX <0.1% <0.1% 0.5% <0.1% 1.3%

C SS 0.3% 12.6% 12.5% 6.3% 13.0% Diet composition <0.1% 0.8% 2.9% 2.5% 7.6%

m P 0.2% 0.3% <0.1% <0.1% <0.1%
OC <0.1% 1.9% 15.6% 19.7% 18.4%
Pore Water EEC 0.2% 8.2% 39.5% 45.4% 39.2%
T <0.1% <0.1% 0.9% 1.0% 10.8%

V LB 24.2% 24.0% 16.5% 9.5% 4.0%

V WB 0.6% <0.1% 0.4% 0.4% <0.1% Water Column EEC 64.8% 26.7% 1.4% <0.1% <0.1%

W B <0.1% <0.1% <0.1% 0.2% <0.1%

X POC 9.2% 22.6% 1.6% <0.1% <0.1%

Total 99.5% 99.2% 99.5% 99.8% 99.6% *m P , body composition, etc.

Table A24. Third sensitivity analysis results: Cont ributions to variance of specific variables to C B values of small fish at different Log Kow values.
Variable 4 5 6 7 8 Characteristics of prey* <0.1% 3.1% 21.0% 32.6% 28.6%

C OX <0.1% 2.1% 5.6% 0.6% <0.1% Diet composition <0.1% 1.1% 6.8% 9.4% 10.0%

m P <0.1% 0.7% <0.1% <0.1% <0.1%
OC <0.1% <0.1% 6.6% 13.3% 14.5%
Pore Water EEC <0.1% 2.4% 27.4% 38.6% 39.4%
T <0.1% 4.2% 11.2% <0.1% 6.5%

V LB 28.7% 25.3% 13.4% 4.8% 0.5%

V WB <0.1% 0.2% <0.1% <0.1% <0.1% Water Column EEC 61.8% 34.4% 2.7% <0.1% <0.1%

W B <0.1% 0.4% 0.7% 0.2% <0.1%

X POC 8.7% 25.4% 4.3% <0.1% <0.1%

Total 99.2% 99.3% 99.7% 99.5% 99.5% *m P , body composition, etc.

### 71 of 123 Table A25. Third sensitivity analysis results: Cont ributions to variance of specific variables to C B values of medium fish at different Log Kow values.
Variable 4 5 6 7 8 Characteristics of prey* <0.1% 4.9% 25.2% 38.4% 30.7%

C OX <0.1% 3.9% 8.6% 0.9% <0.1% Diet composition <0.1% 0.3% 2.7% 2.6% 1.4%

m P <0.1% 0.2% <0.1% <0.1% <0.1%
OC <0.1% <0.1% 6.7% 14.0% 14.6%
Pore Water EEC 0.2% 3.0% 27.0% 39.7% 41.2%
T 0.2% 9.7% 14.8% 0.2% 11.1%

V LB 19.3% 14.0% 6.6% 3.0% 0.3%

V WB 2.4% 1.8% 1.4% 0.4% <0.1% Water Column EEC 68.4% 35.5% 2.4% <0.1% <0.1%

W B <0.1% 0.4% 0.4% <0.1% <0.1%

X POC 8.8% 25.8% 3.8% <0.1% <0.1%

Total 99.3% 99.5% 99.6% 99.2% 99.3% *m P , body composition, etc.

Table A26. Third sensitivity analysis results: Contribution to variance of specific variables to C B values of large fish at different Log Kow values.
Variable 4 5 6 7 8 Characteristics of prey* 0.2% 5.3% 24.5% 38.0% 30.7%

C OX <0.1% 6.6% 9.7% 1.1% <0.1% Diet composition <0.1% 0.4% 2.4% 2.4% <0.1%
OC <0.1% <0.1% 5.5% 13.3% 13.0%
Pore Water EEC <0.1% 2.3% 22.7% 36.4% 38.1%
T 0.2% 15.9% 17.2% 0.3% 16.0%

V LB 27.5% 19.3% 11.6% 8.0% 1.5%

V WB <0.1% 0.2% <0.1% <0.1% <0.1% Water Column EEC 62.6% 28.8% 2.2% <0.1% <0.1%

W B <0.1% 0.4% <0.1% <0.1% <0.1%

X POC 7.9% 19.9% 3.6% <0.1% <0.1%

Total 98.4% 99.1% 99.4% 99.5% 99.3% *m P , body composition, etc.

### 72 of 123 Appendix B. Explanation of Defaults and Alternative Values Representing Abiotic Characteristics of Aquatic Ecosystem



### Abiotic characteristics of the aquatic ecosystem that are necessary for KABAM are defined in Table 4 of the model tool. Th ese characteristics include concen trations of particulate organic carbon (X POC 

### ), dissolved organic carbon (X DOC 

### ), dissolved oxygen (C OX 

### ), suspended solids (C SS 

### ), water temperature (T), and % organic carbon (OC) content of the sediment. The model tool is populated with default values for thes e parameters, which can be altered based on the needs of the model user. Default values are ba sed on the abiotic characteristics of the aquatic ecosystem and are designed to be consistent with the OPP standard pond scenario used in EXAMS. Brief explanations for these default valu es as well as guidance on selecting alternative values are provided below for each parameter.

B.1. Particulate Organic Carbon (X POC 

### ) and Dissolved Organic Carbon (X DOC 

### )



### X POC 

### and X DOC 

### are entered by the model user in units of kg OC/L. These parameters are relevant to estimating the available pesticide fraction in water ( Ф ). The greater the value of either of these parameters, the less pesticide is available in water. Less available pesticide results in lower concentrations of pesticide in tissues of aquatic organisms.

The estimated environmental concentrations (EECs) generated by PRZM/EXAMS in the water column and pore water represent the available concen tration of the pesticide in water. Therefore, a default value of “0” is assumed for both X POC 

### and X DOC 

### . As a result, the pesticide concentration available in the water is equal to th e PRZM/EXAMS EEC input in Table 1.

It may be necessary for the model user to incorporate alternat e values for the X POC 

### and X DOC 

### parameters if the modeling incorporates EEC s from a source other than PRZM/EXAMS. For example, if the exposure concentrations are avai lable from monitoring data or mesocosm studies, X POC 

### and X DOC 

### specific to the monitoring study may be us ed. In that case, if the empirical exposure concentrations correspond to the total water column ( i.e., unfiltered), the model user would want to enter X POC 

### and X DOC 

### values that correspond to th e specific water body used. If given a range of available values, the user should consider that use of lower X POC 

### and X DOC 

### values will result in more conservative estimates of pesticide accumulation in the aquatic food web.

B.2. Concentration of Dissolved Oxygen (C OX 

### )



### The C OX 

### parameter influences the ventilatio n rate of aquatic animals. As C OX 

### decreases, the gill ventilation rate of aquatic animals increases (be cause animals need to take in more water to acquire the amount of oxygen they require). With an increase in th e gill ventilation rate, the rate constants for pesticide uptake (k 1 

### ) and elimination (k 2 

### ) through respirati on both increase. Although the increase in k 1 

### leads to an increase in pes ticide uptake, the increase in k 2 

### also leads to an increase in pesticide elimin ation. The net effect is a decrea se in pesticide concentration in

### 73 of 123 aquatic organism tissues. Therefore, a decrease in the value of C OX 

### results in a decrease in pesticide concentrations in tissues of aquatic organisms.

C OX 

### is entered by the model user in units of mg O 2 

### /L. The default value for this parameter is 5.0 mg O 2 

### /L, based on the OPP standard pond. This c oncentration does not re present the highest possible value for C OX 

### ( i.e., the limit of solubility of oxygen) and is not expected to result in the most conservative estimates of pesticide in aquatic animal tissues. However, it is consistent with the OPP standard pond which is used to derive EECs.

The model user could expl ore the influence of C OX 

### on the predictions of pesticide tissue concentrations in aquatic organi sms by selecting a higher value, for example the solubility of oxygen (potential ra nge: 6-12 mg O 2 

### /L). To determine the solubility of oxygen in water at specific temperatures and pr essures, see USGS 2008a.

It may be necessary for the model us er to incorporate an alternate C OX 

### value if the modeling incorporates EECs from a source other than PRZM /EXAMS. In that case, the model user should enter a C OX 

### value that corres ponds to the specific water body used.

B.3. Water Temperature (T)



### The water temperature parameter influences calcu lation of the growth d ilution rate constant (k G 

### ), the pesticide uptake rate constant through diet (k D 

### ), and the pesticide elimination rate constant through excretion of feces (k E 

### ). The growth diluti on rate constant (k G 

### ) is dependent on whether the temperature is above or below 17.5 o 

### C. The growth dilution rate co nstant is higher when the temperature is above 17.5 o 

### C compared to when the temperature is below 17.5 o 

### C (Figure A.8). Temperature affects the pesticide uptake rate through the dietary uptake rate constant (k D 

### ) by changing the feeding rate of the animal (G D 

### ). An increase in temperature results in an increase in the feeding rate, and with that, an increase in th e pesticide uptake constant for the diet (Figure A.10). The fecal egestion rate constant (k E 

### ) is affected by temperature by changing the feeding rate (G D 

### ) as well as the f ecal egestion rate (G F 

### ) of the animal. An increase in temperature results in an increase in the feeding rate (Figure A.13) , and with that, an increase in the fecal egestion rate. The increase in the fecal egestio n rate results in an increase in the pesticide rate constant for pesticide elimination through excretion. In su mmary, increase in temperature results in an increase in k D 

### , k E 

### , and k G 

### . Although k G 

### and k E 

### represent processes ( i.e., pesticide elimination/dilution) that compete with k D 

### ( i.e., pesticide uptake), the net increase in the two processes (uptake and elimination/dilution) does not cancel e ach other out.

The water temperature of the EXAMS’ pond varies based on th e selected PRZM scenario. Therefore, the model user should select the wate r temperature based on the PRZM scenario used for deriving EECs. If the modeling incorporates EECs from a source other than PRZM/EXAMS, the water temperature relevant to the other EECs should be utilized.

### 74 of 123 B.4. Concentration of Suspended Solids (C SS 

### )



### The concentration of suspended solids (C SS 

### ) is relevant to filter feeders only. C SS 

### influences the calculation of the rate constants fo r pesticide uptake through diet (k D 

### ) and pesticide elimination through excretion of feces (k E 

### ). An increase in C SS 

### leads to an increase in the feeding rate of filter feeders (G D 

### ) which in turn results in an increase in the pesticide upt ake through diet (k D 

### ). An increase in C SS 

### also leads to an increase in the f ecal egestion rate of filter feeders (G F 

### ) and an increase in the pesticide eliminatio n through excretion of fecal matter (k E 

### ). Although k D 

### and k E 

### represent competing processes, the net increas e in the two does not cancel each other out.

C SS 

### is entered by the model user in units of kg/L. The default value for this parameter is 3.00x10 -5 

### kg/L, based on the OPP standard pond. If the modeling incorporates EECs from a source other than PRZM/EXAMS, a C SS 

### value relevant to the ot her EECs should be utilized.

B.5. Sediment Organic Carbon (OC)



### Sediment organic carbon (OC) is a parameter th at influences organisms that consume sediment. As OC increases, the concentration of the pesticide in the solid component of the sediment increases to the extent that the pesticide sorbs to organic matter. As the pesticide concentration in sediment increases, the pesticide concentratio n in organisms that consume sediment also increases.

OC is entered by the model user as % of the dr y weight of the sediment. The default value for this parameter is 4.0%, based on the OPP sta ndard pond. If the mode ling incorporates EECs from a source other than PRZM/EXAMS, an OC value relevant to the other EECs should be utilized.

### 75 of 123 Appendix C. Explanation of Default Values Representing Biotic Characteristics of the Aquatic Ecosystem, Including Food Web Structure



### The seven trophic levels of the aquatic ecosys tem of KABAM are phyto plankton, zooplankton, benthic invertebrates, filter f eeders, small fish, medium fish, and large fish. In KABAM, each trophic level is defined by its % lipid, % Non Lipid Organic Ma tter (NLOM), % water, body weight, and diet. Each of these trophic levels is described with in this Appendix, with emphasis on the information relevant to KABAM and explanat ions of default parameters used to define these trophic levels (in Tables 5 and 6 of the KABAM tool). If th e model user wishes to explore the influences of changes in parameter valu es representing the aqua tic food web on EECs and RQs for birds and mammals, this can be accomplis hed by altering parame ter values within the range of reported values for a specific parameter.

Although the % water composition of an aquatic organism does not influence the bioaccumulation of a chemical in that organism (see Appendix A), it is an important consideration for the definition of % lipid and the percent non-lipid organic matter (% NLOM). Often, tissue analysis results and body weight data in the scientific lite rature are reported on a dry weight basis. For KABAM, input parame ters for body composition are entered on a wet weight basis. Therefore, % water composition is discussed in the sections below since it is necessary to understand the water composition of an organism in order to translate the reported data into input parameters for KABAM.

Lipid composition of an organism can influe nce the bioaccumulation of a chemical (See Appendix A), with higher lipid composition lead ing to higher accumulation. Since KABAM is intended for use in ecological risk assessments of pesticides with the potential to bioaccumulate in aquatic ecosystems, it is necessary for this to ol to serve as a conser vative representation of bioaccumulation. Default parameter values for % lipid were selected from the open literature and are intended to represent the high-end of available data (75 th 

### -90 th 

### percentiles).

C.1. Phytoplankton



### Phytoplankton are microscopic auto trophic aquatic organisms that derive their nutrition from photosynthesis. Groups of fres hwater phytoplankton include al gae (green, yellow-green and golden-brown), cyanobacteria (blue-green alg ae), diatoms and dinofla gellates. Phytoplankton can be unicellular, colonial, or filamentous. Thes e organisms have limited mobility that is based on water movements; however, some are able to move via flagella. An aquatic habitat will generally contain an assemblage of phytoplanktonic species that vary in proportion over time and space (Wetzel 1983).

For parameterization of KABAM, it is necessary to define the % water, % lipid and % NLOM contents of phytoplankton. The body weight is not a necessary input for phytoplankton, nor is the diet composition since these organism s do not consume other organisms.

### 76 of 123 Since it is assumed that phytoplan kton are present in the water co lumn of the aquatic ecosystem where photosynthesis can occur, it is assumed that phytoplankton do not reside in benthic sediment and do not respire pore water. This shou ld be indicated in Table 5 of the KABAM tool ( i.e., “no” should be entered in the column titled: “Do organisms in trophic level respire some pore water?”).

Aquatic plant tissues are composed of approxi mately 90% water by weight (Hannan and Dorris 1970; Raven et al. 1999, Sladecek and Sladeckova 1963). The default parameter for the water composition of phytoplankton is 90%.



### Reported % lipid values for phytopla nkton vary from 2-27% of dry weig ht. If it is assumed that phytoplankton are composed of 90% water, then this range of lipid compositions is equivalent to 0.2-2.7% on a wet weight ba sis (Table C1). For KABAM, the default parameter for % lipid of phytoplankton was selected as 2% to represent a high-end estimate (75 th 

### to 90 th 

### percentile of data in Table C1) of this parameter.



### The wet weight of an organism is the sum of the water, lipid, and NLOM content. If the water content of phytoplankton is 90% of the wet wei ght, and the % lipid is known (2%), the NLOM content of phytoplankton is the % remaining afte r subtracting the water and lipid content from 100%. Therefore, the default parameter for the NLOM composition of phytoplankton is 8%.

### 77 of 123 Table C1. Percent lipid composition of freshwater phyt oplankton (under culture conditions) reported in the scientific literature.

Species Mean% Lipid (dry weight basis) Mean% Lipid (wet weight basis) Source

Not stated Not stated 0.5 Oliver and Niimi 1988 Anabaena sp. 6.8 (±0.4) 0.68* Stange and Swackhamer 1994 Anabaena sp. 5.3 (±2.4) 0.53* Stange and Swackhamer 1994 Anabaena sp. 2.2 (±0.2) 0.22* Stange and Swackhamer 1994 Chamydomonas reinhardtii 10.8 (±6.2) 1.08* Lürling and Van Donk 1997 Chlamydomonas applanata 18.2 1.82* Shifrin and Chisholm 1981 Chlamydomonas applanata 16 1.60* Shifrin and Chisholm 1981 Chlorella ellipsoidea 13.5 1.35* Shifrin and Chisholm 1981 Chlorella pyrenoidosa 13.4 1.34* Shifrin and Chisholm 1981 Chlorella pyrenoidosa 14.4 1.44* Shifrin and Chisholm 1981 Chlorella pyrenoidosa 16.4 1.64* Shifrin and Chisholm 1981 Chlorella pyrenoidosa 16 1.60* Shifrin and Chisholm 1981 Chlorella vulgaris 12.5 1.25* Shifrin and Chisholm 1981 Chlorella vulgaris 13 1.30* Shifrin and Chisholm 1981 Cryptomonas pyrenoidifera 8.5 (±5.1) 0.85* Lürling and Van Donk 1997 Microcystis aeruginosa 5.8 (±2.3) 0.58* Lürling and Van Donk 1997 Nannochloris sp. 20.2 2.02* Shifrin and Chisholm 1981 Nitzschia palea 22.2 2.22* Shifrin and Chisholm 1981 Oocystis polymorpha 12.6 1.26* Shifrin and Chisholm 1981 Ourococcus sp. 27 2.70* Shifrin and Chisholm 1981
Scenedesmus acutus 6.4 (±2.5) 0.64* Lürling and Van Donk 1997 Scenedesmus obliquus 19 1.90* Shifrin and Chisholm 1981 Selanastrum gracile 20.8 2.08* Shifrin and Chisholm 1981 Selenasrum capricornutum 19.5 (±0.2) 1.95* Stange and Swackhamer 1994 Selenasrum capricornutum 16.0 (±0.3) 1.60* Stange and Swackhamer 1994 Selenasrum capricornutum 8.0 (±0.9) 0.80* Stange and Swackhamer 1994 Synedra sp. 7.5 (±1.6) 0.75* Stange and Swackhamer 1994 Synedra sp. 13.7 (±0.7) 1.37* Stange and Swackhamer 1994 Synedra sp. 11.7 (±4.5) 1.17* Stange and Swackhamer 1994 Synedra ulna 23 2.30* Shifrin and Chisholm 1981

Average 1.4
75 th percentile 1.8
90 th percentile 2.1 *Calculated from reported % lipid based on dry weight and assumption that algae wet weight is 90% water.

### 78 of 123 C.2. Zooplankton



### Zooplankton are aquatic animals that are suspende d in water. This group is primarily composed of rotifers, cladocera and cope pods, but also include s protozoa and insects at immature life stages. Species of zooplankton primarily c onsume phytoplankton but also consume detritus, bacteria, yeast, and other (smaller) zooplankton (Wetzel 1983). For parameterization of KABAM, zooplankton is represented by herbi vorous species that have a diet composed 100% of phytoplankton.



### Since it is assumed that zooplankton are presen t in the water column of the aquatic ecosystem and do not reside in th e benthic sediment, it is assumed th at zooplankton do not respire pore water. This should be indicated in Table 5 of the KABAM tool ( i.e., “no” should be entered in the column titled: “Do organisms in tr ophic level respire some pore water?”).

Beers (1966) reported water compositions of se veral groups of marine zooplankton inhabiting the Atlantic Ocean. Average % water compos ition of these groups ranged 74-96%, with an average % water composition of 86% correspon ding to copepods. Based on this information, the default % water composition of zooplankton is 85%. 

### This value is used to translate dry weight data into equivale nt wet weight values.

Reported mean % lipid values for zooplankton vary from 6.4-24.3% of dry weight. If it is assumed that zooplankton are composed of 85% wa ter, then this range of lipid compositions is equivalent to 0.96-3.6% on a wet weight basi s (Table C2). Based on this information, the default % lipid for zooplankton is se t to 3% to represent a high-end (75 th 

### to 90 th 

### percentile) estimate of this parameter.



### The wet weight of an organism is the sum of the water, lipid, and NLOM content. If the water content of zooplankton is 85% of the wet weight, and the % lipid is known (3%), then NLOM content of zooplankton is the % remaining after subtracting the water and lipid content from 100%. Therefore, the default parameter for the NL OM composition of zooplankton is 12%.



### Wright (1958) provided biomass data for two species of zooplankton ( Daphnia longispina and D. pulex ) in a reservoir in Montana, where the average body wei ght of zooplankton was 1.3x10 -7 

### kg-wet weight (assuming 85% water content; range 0.9-1.6x10 -7 

### kg-wet weight). Acharya et al. (2005) provided dry body weights for Bosmina freyi that translate to approximately 0.3-3x10 -8 

### kg-wet weight (assuming 85% water content). Jeppesen et al. ( 2004) provided body weight data for Daphnia sp. that translate to approximately 0.67-3.3x10 -7 

### kg-wet weight (assuming 85% water content). Based on this information, the default weight for zooplankton is set to 1x10 -7 

### kg-wet weight, 

### with the intention of bei ng a representative weight of species of zooplankton.

### 79 of 123 Table C2. Percent lipid composition of freshwater zooplankton reported in th e scientific literature.

Zooplankton identification Mean% Lipid (dry weight basis) Mean% Lipid (wet weight basis) Source

Daphnia magna (cladoceran) 6.4-19.7 0.96-3.0* McKee and Knowles 1987 Unspecified 6.7* 1.0±0.33 Morrison et al. 1997 Mostly cladocerans, also copepods and rotifers 10.8 (±3.6) 1.6* Mitra et al. 2007

Mostly cladocerans, also copepods and rotifers 12.1 (±3.0) 1.8* Mitra et al. 2007

Mostly cladocerans, also copepods and rotifers 12.2 (±2.4) 1.8* Mitra et al. 2007

Leptodora kindtii 13.1 (±1.0) ** 2.0 Vijverberg and Frank 1976 Mostly cladocerans, also copepods and rotifers 13.7 (±1.9) 2.1* Mitra et al. 2007

Mostly cladocerans, also copepods and rotifers 13.9 (±1.9) 2.1* Mitra et al. 2007

Mostly cladocerans, also copepods and rotifers 14.6 (±1.0) 2.2* Mitra et al. 2007

Cyclopodia 15.9 (±1.8) ** 2.4 Vijverberg and Frank 1976 Chydorus sphaericus 18.5 (±2.8) ** 2.8 Vijverberg and Frank 1976 Bosmina coregoni 20.5 (±1.9) ** 3.1 Vijverberg and Frank 1976 Eurytemora affinus 23.6 (±2.7) ** 3.5 Vijverberg and Frank 1976 Daphnia hyalina 24.3 (±5.3)** 3.6 Vijverberg and Frank 1976

Average 2.3
75 th percentile 2.9
90 th percentile 3.3 *Calculated from reported % lipid based on dry weight and assumption that zooplankton wet weight is 85%
water. **Expressed as % of total organic matter attributed to lipid. It is assumed that this is equivalent to a dry weight basis.



### C.3. Benthic invertebrates



### The benthic invertebrate trophic level includes an imals that inhabit the sediments of aquatic habitats. Benthic invertebrates include a di verse group of animals, including crustaceans ( e.g., crayfish, amphipods), aquatic worms ( e.g., oligochaetes), aquatic insect larvae ( e.g., Diptera, caddisflies, beetles, mayflies and dragonflies), protozoa, snails, a nd nematodes. Different species of benthic invertebrates have a variety of feeding strategies, in cluding herbiovory, detritivory, and predation upon other benthic inve rtebrates (Covich et al. 1999). In order to represent all of these feeding strategies with th e benthic invertebrate trophic level of KABAM, it is assumed that benthic invertebrates consume organic matter fro m sediment, phytoplankt on, and zooplankton in equal quantities. Therefore, the default diet compositio n of benthic invertebrates is 34% sediment, 33% phytoplankton, and 33% zooplankton.



### Since it is assumed that benthic invertebrates are present in the benthic compartment of the aquatic ecosystem, it is assumed that benthic i nvertebrates respire sediment pore water. This

### 80 of 123 should be indicated in Tabl e 5 of the KABAM tool ( i.e., “yes” should be entered in the column titled: “Do organisms in trophic le vel respire some pore water?”).

Available water composition data for benthic i nvertebrates include a range of 69-83% water (Table C3). The average value of the available data is 76%. Based on this average, the default value for KABAM representing the % wat er of benthic invertebrates is 76%.

Table C3. Water composition (%) of benthic invertebrates reported in the scientific literature. Organism Mean% Water Source Crayfish ( Orconectus propinquus ) 69 Gewurtz et al. 2000 Hyalella azteca 72.5 Lotufo et al. 2001 Mayfly larvae 73 Gewurtz et al. 2000 Diporeia sp., 73.1 Lotufo et al. 2001 Crayfish (Astacus fluviatilis) 80.0 Sidwell 1981 Lumbriculus variegatus ( oligochaete) 81 Liebig et al. 2005 Crayfish (Astacus, Orconectus and Procambarus) 82.5* USDA 2005 *excluding the shell



### Lipid data are available for vari ous freshwater crustaceans, olig ochaetes, and insect larvae. These values indicate a wide range (approximately 1 to 10% of wet weight) of lipid composition of benthic invertebrates (Tables C4-C9). The default lipid composition for benthic invertebrates is 3%. 

### This value was selected to be representative of a high-end value (75 th 

### percentile) of available lipid compositions for freshwater benthic invertebrates (Table C10).

Table C4. Lipid composition (%) of Hyalella azteca (a freshwater crustacean) reported in the scientific literature.

Source Mean% Lipid (dry weight basis) Mean% Lipid (wet weight basis) Lotufo et al. 2001 2.4* 0.66±0.03 Lotufo et al. 2001 3.2* 0.88±0.04 Lotufo et al. 2001 6.3* 1.73±0.25 Lotufo et al. 2001 6.5* 1.79±0.41 Lotufo et al. 2000 6.9 (±0.9) 1.9* Lotufo et al. 2000 7.0 (±1.1) 1.9* Lotufo et al. 2000 7.2 (±0.8) 2.0* Kane Driscoll and Landrum 1997 7.5 (±1.5) 2.1* Lotufo et al. 2000 7.5 (±0.9) 2.1* Lotufo et al. 2000 7.7 (±1.5) 2.1* Kane Driscoll et al. 1997 8.2 (±0.7) 2.3* Kane Driscoll et al. 1997 8.4(±0.7) 2.3*

Average 6.6 1.8
75 th percentile 7.6 2.1
90 th percentile 8.2 2.3 *Calculated from reported % lipid and assumption that dry:wet weight ratio for H. azteca is 0.275 (based on Lotufo et al. 2001).

### 81 of 123 Table C5. Lipid composition (%) of freshwater crayfish (crustaceans) rep orted in the scientific literature.

Genus Mean% Lipid (wet weight basis) Source

Astacus fluviatilis 0.5 Sidwell 1981
Orconectus 0.86 (±0.11) White et al. 1998 Astacus, Orconectus and Procambarus 1.0 USDA 2005 Undefined 1.9 (±0.47) Morrison et al. 1997 Orconectes propinquus 2.4 (±0.26) Morrison et al. 2000
Orconectes 2.52 (±0.16) Gewurtz et al. 2000
Procambarus 2.95 (±1.25) Lin et al. 2004
Procambarus 3.02 (±1.29) Lin et al. 2004

Average 1.9
75 th percentile 2.6
90 th percentile 3.0

Table C6. Lipid composition (%) of Diporeia sp. (freshwater crustaceans) reported in the scientific literature.

Source Mean% Lipid (dry weight basis) Mean% Lipid (wet weight basis) Landrum et al. 2007 10.78 (±1.5) 2.9* Landrum et al. 2007 11.97 (±0.38) 3.2* Landrum et al. 2007 17.1 (±0.64) 4.6* Kane Driscoll et al. 1997 20.1 (±4.6) 5.4* Kukkonen et al. 2004 20.4* 5.5±0.7 Kane Driscoll et al. 1997 21.3 (±6.7) 5.7* Lotufo et al. 2001 22.2* 5.97±0.75 Kukkonen et al. 2004 23.0* 6.2±1.4 Lotufo et al. 2001 23.3* 6.27±1.21 Lotufo et al. 2000 23.7 (±8.5) 6.4* Lotufo et al. 2000 23.9 (±6.3) 6.4* Kane Driscoll and Landrum 1997 27.2 (±1.3) 7.3* Lotufo et al. 2001 40.3* 10.85±0.62 Lotufo et al. 2001 43.1* 11.59±1.18

Average 23.5 6.3
75 th percentile 23.9 6.4
90 th percentile 36.4 9.8 *Calculated from reported % lipid and assumption that dry:wet weight ratio for Diporeia sp. is 0.269 (based on Lotufo et al. 2001).

### 82 of 123 Table C7. Lipid composition (%) of Lumbriculus variegatus (a freshwater oligoc haete) reported in the scientific literature.

Source Mean% Lipid (dry weight basis) Mean% Lipid (wet weight basis) Croce et al. 2005 5.8* 1.1±0.1 Kukkonen et al. 2004 6.3* 1.2±0.13 Liebig et al. 2005 8 (±0.4) 1.5* Kukkonen et al. 2004 7.9 1.5±0.19 Kukkonen and Landrum 1994 9.2 (±0.9) 1.7* Fisk et al. 1998 10.5* 2.0±0.2 Kukkonen and Landrum 1994 11.1 (±1.4) 2.1* Fisk et al. 1998 12.1* 2.3±0.2 Fisk et al. 1998 13.2* 2.5±0.3 Kukkonen and Landrum 1994 13.2 (±4.3) 2.5* Fisk et al. 1998 15.3* 2.9±0.3 Fisk et al. 1998 17.9* 3.4±0.8 Fisk et al. 1998 18.9* 3.6±0.8 Fisk et al. 1998 19.5* 3.7±0.6

Average 12.1 2.3
75 th percentile 14.8 2.8
90 th percentile 18.6 3.5 *Calculated from reported % lipid and assumption that water composition of L. variegatus is 81% (Liebig et al. 2005).

Table C8. Lipid composition (%) of other freshwater oligochaetes reported in the scientific literature.
Organism Identification Mean% Lipid (dry weight basis) Mean% Lipid (wet weight basis) Source
Tubifex tubifex and Limnodrilus hoffmeisteri 5.3* 1* Oliver and Niimi 1988
Ilyodrilus templetoni** 5.85 (±2.28) 1.1* Lu et al. 2003 Ilyodrilus templetoni** 6.11 (±0.55) 1.2* Lu et al. 2003 Ilyodrilus templetoni** 6.72 (±1.59) 1.3* Lu et al. 2003 Ilyodrilus templetoni** 7.44 (±1.33) 1.4* Lu et al. 2003 Ilyodrilus templetoni** 7.35 (±1.26) 1.4* Lu et al. 2003 Ilyodrilus templetoni** 8.82 (±1.60) 1.7* Lu et al. 2003 Oligochaete 9.5 (±1.0) 1.8* Landrum et al. 2007 Limnodrilus sp. 11.93 (±0.16) 2.3* Jonker et al. 2004 Oligochaete 12.8 (±1.8) 2.4* Landrum et al. 2007

Average 8.2 1.6
75 th percentile 9.3 1.8
90 th percentile 12.0 2.3 *Calculated from reported % lipid and assumption that water composition of L. variegatus is 81% (Liebig et al. 2005). **Mean of values for I. templetoni is 1.4% (wet weight). When this va lue is used in calculating the mean and percentile values for the group of oligochaet es, the mean is 1.8% (wet weight). The 75 th and 90 th percentiles are 2.2 and 2.4, respectively.

### 83 of 123 Table C9. Lipid composition (%) of freshwater insect larvae reported in the scientific literature.

Organism Identification Mean% Lipid (wet weight basis) Source

Chironomus riparius 0.6 Leonards et al. 1997 Hexagenia limbata (mayfly larvae) 1.5 (±0.05) Morrison et al. 2000
H. limbata and H. rigida 1.50 (±0.052) Gewurtz et al. 2000 Caddisfly larvae 1.7 Morrison et al. 1997 Mayfly larvae 2.0 (±0.25) Morrison et al. 1997

Average 1.5
75 th percentile 1.7
90 th percentile 1.9

Table C10. Mean lipid composition (%, wet weight basis) of freshwater benthic invertebrates from data in Tables C4-C9.

Benthic Invertebrate Mean 75 th
Percentile 90 th
Percentile Insect Larvae 1.5 1.7 1.9 Hyalella azteca (a freshwater crustacean) 1.8 2.1 2.3 Freshwater oligochaetes (excluding L. variegatus ) 1.8 2.2 2.4 Crayfish (freshwater crustaceans) 1.9 2.6 3.0 Lumbriculus variegatus (a freshwater oligochaete) 2.3 2.8 3.5 Diporeia sp. (freshwater crustaceans) 6.3 6.4 9.8

Mean 2.6 3.0 3.8



### The wet weight of an organism is the sum of the wa ter, lipid, and NLOM content. By default, if the water content of benthic i nvertebrates is 76% of the wet weight, and the % lipid is known (default = 3%), the NLOM content of benthic i nvertebrates is the % remaining after subtracting the water and lipid content from 100%. Therefore, the default parameter for the NLOM composition of benthic invertebrates is 21%.



### The benthic invertebrate trophic level is compos ed of a wide variety of taxonomic groups. The body weights of organisms within this group can vary by orders of magnitude (Table C11). The default weight for benthic invertebrates is set to 1x10 -4 

### kg-wet weight, 

### with the intention of being representative of a midpoint weight of species of benthic invertebrates.

Table C11. Body weights (wet) of freshwater benthic invertebrates repo rted in the scie ntific literature. Benthic Invertebrate Weight (kg) Source Amphipods 0.05x10 -4 Leonards et al. 1997 Mayfly larvae 0.16x10 -4 * Morrison et al. 1997 Chironomids 0.24x10 -4 Leonards et al. 1997 Caddisfly larvae 0.32x10 -4 * Morrison et al. 1997 Snails 0.82x10 -4 Leonards et al. 1997 Crayfish 18.0x10 -4 Morrison et al. 1997 *converted from reported dry weight to wet weight assuming 75% water content.

### 84 of 123 C.4. Filter Feeders



### Filter feeders are benthic invertebrates that ar e distinguished by their feeding habits. These organisms feed by straining water and extracting organic material su ch as detritus and plankton. Examples of freshwater filter feeders include mollusks. For KABAM, it is assumed that filter feeders consume materials suspended in th e water column, including phytoplankton, zooplankton, and detritus. It is also assumed that filter feeders consume suspended sediment incidentally. The default composition of the diet of this trophic level is 34% sediment, 33% phytoplankton, and 33% zooplankton.



### Since it is assumed that filter feeders are present in the benthic sediment compartment of the aquatic ecosystem, it is also assumed that filter feeders respire sediment pore water. This should be indicated in Table 5 of the KABAM tool ( i.e., “yes” should be entered in the column titled: “Do organisms in trophic level respire some pore water?”).

According to available data, water composition of freshwater mollusks ranges 78-93% (Table C12). The default water content of filter feeders is set to 85%, 

### based on the midpoint of the range of available data.

Table C12. Water composition (%) of freshwater mollusks reported in the scientific literature. Identification Mean % water* Source Corbicula strata (freshwater clam) 77.6 Sidwell 1981 Corbicula japonica (freshwater clam) 79.8 Sidwell 1981
Corbicula sandai (freshwater clam) 80.0 Sidwell 1981 Corbicula fluminea (freshwater clam) 81.4 Sidwell 1981 lamellibrancha clams (subclass) 82 USDA 2005 Corbicula leana (freshwater clam) 82.1 Sidwell 1981 Dreissena polymorpha ( zebra mussel) 87 Bervoets et al. 2005 D. polymorpha 88-93 Hendriks et al. 1998
Anodonta anatine (mussels) 90.6-92.8 Hyötyläinen et al. 2002 *It is assumed that this does not include the shell.



### Data on lipid content are available for several speci es of freshwater mollusks. These values range 0.4-4% of wet weight (Tables C13 - C15). The default lipid compositio n for filter feeders is 2%. 

### This value was selected to be representative of a high end (75 th 

### percentile of Dreissena sp. and Corbicula sp .) value of available lipid compositions for freshwater mollusks.

### 85 of 123 Table C13. Percent lipid composition of Dreissena sp. (freshwater mollusks) reported in the scientific literature.
Species Mean % Lipid (dry weight basis) Mean % Lipid (wet weight basis) Source
D. polymorpha 4.3* 0.55 Bervoets et al. 2005 D. polymorpha 14 1 Hendriks et al. 1998 D. polymorpha 8.5* 1.1 Kwon et al. 2006 D. polymorpha 9.1 1.2* Becker van Slooten and Tarradellas 1994 D. bugensis 9.0 (±1.4) 1.2* Marvin et al. 2002 D. bugensis 10 (±0.5) 1.3 Marvin et al. 2002 D. polymorpha 11 (±0.6) 1.4* Marvin et al. 2002 D. polymorpha 10.8* 1.4 (±0.1) Kwon et al. 2006 D. polymorpha 11.5* 1.5 (±0.1) Kwon et al. 2006 D. polymorpha 12.3* 1.6 (±0.1) Kwon et al. 2006 D. polymorpha 12 (±4.4) 1.6* Marvin et al. 2002 D. polymorpha 17 2 Hendriks et al. 1998 D. polymorpha 18 2 Hendriks et al. 1998

Average 11.3 1.4
75 th percentile 12.3 1.6
90 th percentile 16.4 1.9



### * Calculated from reported % lipid and assumption that water composition of D. polymorpha is 87% (Bervoets et al. 2005).

Table C14. Percent lipid composition of Corbicula sp. (freshwater clams) reported in the scientific literature.
Species Mean % Lipid (wet weight basis) Source

C. leana 1.1 Sidwell 1981 C. japonica 1.2* Kang et al. 2002 C. japonica 1.2 Sidwell 1981 C. fluminea 1.5 Sidwell 1981
C. sandai 2.4 Sidwell 1981
C. strata 4.0 Sidwell 1981

Average 1.9 75 th percentile 2.2 90 th percentile 3.2 *Based on reported mean lipid content of 5.8% dry weight and 80% moisture content reported for this species by Sidwell 1981.

### 86 of 123 Table C15. Percent lipid composition of other freshwater filter feeders repor ted in the scientific literature.

Identification Mean % Lipid (dry weight basis) Mean % Lipid (wet weight basis) Source
Sphaerium striantium (fingernail clam) 8.7 0.36 Rice and White 1987 Elliptio complanata 3.2 (±1.2) 0.48* Marvin et al. 2002
Anodonta anatine (mussels) 11.2 (±0.8) 0.81 Hyötyläinen et al. 2002
Anodonta anatine (mussels) 12.2 (±0.7) 0.98 Hyötyläinen et al. 2002 Lamellibrancha (clams) 5.5 1.0 USDA 2005
Anodonta anatine (mussels) 10.9 (±0.6) 1.02 Hyötyläinen et al. 2002
Anodonta anatine (mussels) 11.3 (±0.9) 1.05 Hyötyläinen et al. 2002 *Calculated using assumption that filter feeders are 85% water.



### The wet weight of an organism is the sum of the wa ter, lipid, and NLOM content. By default, if the water content of filter feeders is 85% of th e wet weight and the % lipid is known (default = 2%), the NLOM content of filter feeders is the % remaining after subtracting the water and lipid content from 100%. Therefore, the default parameter for the NLOM composition of filter feeders is 13%.



### Reported wet weights of various sp ecies of mollusks range 0.2-12 x10 -3 

### kg. Mean wet weights of Dreissena polymorpha have been reported as 0.41 ± 0.26 x10 -3 

### kg (Van Haelst et al. 1996). Wet weights of C. fluminea ranged approximately 0.2-2 x10 -3 

### kg (Andrès et al. 1999, Vidal et al. 2002). Hyötyläinen et al. (2002) reported wet weights of Anodonta anatine tissue as ranging 4.5- 12.1 x10 -3 

### kg. Based on this information, the default weight of filter feeders is set to 1 x10 -3 

### kg, 

### with the intention of being a re presentative weight of mollusks.

C.5. Fish (Small, Medium and Large Sizes)



### There are hundreds of species of fish inhabiting fresh waters of the United States and Canada, including ponds, lakes, streams, and rivers. Species of bluegill and other sunfish ( Lepomis spp. ), bass ( Micropterus spp. ), and crappie ( Pomoxis spp .) are common inhab itants of fresh warm water ponds, lakes, and streams distributed throughout the continental United States (Page and Burr 1991, Carlander 1977). As described below, these species were used to define default parameters for the small, medium, and large fish in KABAM. Although there are many other species of fish in ponds of the U.S. ( e.g., perch, minnows), sunfish, crappie, and bass were considered representative of fish that are found in freshwaters of the U.S., and thus suitable for models for defining input parameters for use in KABAM.

Several bird and mammal species ( e.g., belted kingfisher [ Megaceryle alcyon ], northern river otter) consume amphibians, in addition to fish. For KABAM, it is assumed that the default fish also represent, i.e ., serve as surrogates for, aquatic-phase amphibians, such as salamanders and frogs. This assumption is consistent with OPP’ s policy in which exposure and effects data for fish are assumed to be representative of aquatic-phase amphibians (USEPA 2004).

Default parameters for small fish in KABAM are designed to represent the young-of-year (YOY), i.e., fish that have hatched within the year, befo re January 1 of the next year, of sunfish, bass and crappie. YOY of these species consume copepods, cla docerans, rotifers ( i.e.,

### 87 of 123 zooplankton), chironomid larv ae, and mayfly larvae ( i.e., benthic inverteb rates) (Carlander 1977). Average body weights of YOY of sunfish, ba ss, and crappie are prov ided in Table C16. For KABAM, it is assumed that the small fish weighs 0.01 kg and its diet is 50% zooplankton and 50% bent hic invertebrates.

Table C16. Average body weights for young of the year fish (Source: Carlander 1977).

Species (scientific name) Average body weight (kg) Green sunfish ( Lepomis cyanellus ) 0.001-0.01 Pumpkinseed ( L. gibbosus ) 0.002 Warmouth ( L. gulosus ) <0.011 Bluegill ( L. macrochirus ) 0.0001-0.05 Redear sunfish ( L. microlophus ) 0.0006-0.04 Largemouth bass ( Micropterus salmoides ) 0.0002-0.02 White crappie ( Pomoxis annularis ) 0.0002-0.01 Black crappie ( P. nigromaculatus ) 0.0005-0.02



### The medium fish in KABAM is designed to repres ent adult sunfish and crappie. These fish reach sexual maturity between ages 1 and 3, with lifespans ≥ 6 years. Their diets include insects, insect larvae, crustaceans, snails, and other fish (Car lander 1977). Mature fish range in weight, 0.005- 0.579 kg, depending upon their age (Table C17; data from Carlander 1977). Although mature fish display a wide range of weights, most species weigh approximately 0.1 kg as adults. For KABAM, it is assumed that the medium-sized fish weighs 0.1 kg and its diet is 50% benthic invertebrates and 50% small fish.

Table C17. Average body weights (in kg) of medium fish at different ages.

Species (scientific name) 1 yr 2 yr 3 yr 4 yr 5 yr 6 yr 7 yr 8 yr

Green sunfish ( Lepomis cyanellus ) 0.01 0.024 0.048 0.086 0.086 0.132 - - Pumpkinseed ( L. gibbosus ) 0.005 0.034 0.034 0.063 0.099 0.157 0.157 0.157 Warmouth ( L. gulosus ) 0.011 0.046 0.046 0.085 0.163 0.163 - - Bluegill ( L. macrochirus ) 0.014 0.052 0.052 0.090 0.141 0.141 0.208 0.208 Redear sunfish ( L. microlophus ) 0.026 0.081 0.125 0.187 0.187 0.265 - - White crappie ( Pomoxis annularis ) 0.031 0.085 0.123 0.181 0.346 0.346 0.579 0.579 Black crappie ( P. nigromaculatus ) 0.037 0.097 0.143 0.210 0.289 0.363 0.468 0.468 - Indicates data were not available



### The large fish in KABAM is designed to represent the largemouth bass ( Micropterus salmoides ), which is a predatory fish commonly found in wa rm waters throughout th e continental United States. It is also designed to be representati ve of large predatory fish that are consumed by mammals and birds. The diet of largemouth ba ss is composed primarily of fish, including sunfish, crappie, perch, shad and smaller-sized largemouth bass. Largemouth bass will also consume crayfish, especially when no other fi sh are available. Largemouth bass become sexually mature between ages 2-5, with a lif espan reaching beyond 10 years. Adult largemouth bass weigh 0.25-3.6 kg, depending upon their age (Carlander 1977). For KABAM, it is assumed that the large fish weighs 1 k g, and consumes 100% medium-sized fish.

### 88 of 123 Since small and medium fish consume benthic inve rtebrates, it is assumed that these fish are sometimes present in the benthic compartment of the aquatic ecosystem. Therefore, it is assumed that small and medium fish respire some pore water. It is assumed that medium fish are predominantly present in the water column of the aquatic ecosystem, where they are consumed by large fish. It is assumed that large fish do no t respire pore water. This should be indicated in Table 5 of the KABAM tool ( i.e., “yes” should be entered for sm all and medium fish and “no” should be entered for large fish in the column ti tled: “Do organisms in tr ophic level respire some pore water?”).

Available water composition data for Lepomis sp., Pomoxis sp., and Micropterus sp . include a range of 71-80% water (Table C18). Although wa ter composition data were not available for largemouth bass, data do exist for smallmouth bass ( M. dolomieu ) and are used as a surrogate for largemouth bass. The average valu e of the available data is 73%. Based on this average, the default value for KABAM representing the % water of all fish is 73%.

Table C18. Water composition data fo r fish relevant to small, medium, an d large default fish of KABAM.

Species (scientific name) Reported Body Weight (kg) Corresponding Default fish % water Source
Black crappie ( Pomoxis nigromaculatus ) 0.102 (±0.007) Medium 70.7 (±0.29) Sethajintanin et al. 2004 Smallmouth bass ( Micropterus dolomieu ) 0.277 (±0.0901) Medium-Large 71.1 (±1.26) Sethajintanin et al. 2004 Smallmouth bass ( Micropterus dolomieu ) 0.870 (±0.0685) Medium-Large 71.3 (±1.76) Sethajintanin et al. 2004 Smallmouth bass ( Micropterus dolomieu ) 0.326 (±0.177) Medium-Large 71.9 (±1.44) Sethajintanin et al. 2004 Black crappie ( Pomoxis nigromaculatus ) 0.148 (±0.021) Medium 71.9 (±0.84) Sethajintanin et al. 2004 Smallmouth bass ( Micropterus dolomieu ) 0.395 (±0.222) Medium-Large 72.0 (±0.99) Sethajintanin et al. 2004 Black crappie ( Pomoxis nigromaculatus ) 0.114 (±0.016) Medium 72.1 (±0.87) Sethajintanin et al. 2004 Black crappie ( Pomoxis nigromaculatus ) 0.111 (±0.015) Medium 72.6 (±0.38) Sethajintanin et al. 2004 Black crappie ( Pomoxis nigromaculatus ) 0.0798 (±0.012) Medium 73.2 (±0.59) Sethajintanin et al. 2004 Smallmouth bass ( Micropterus dolomieu ) 0.154 (±0.0647) Medium 73.4 (±2.11) Sethajintanin et al. 2004 Bluegill ( L. macrochirus ) Not reported unknown 79.5 Sidwell 1981



### Lipid content of fish reported in the literature varies widely for Lepomis sp., Pomoxis sp., and Micropterus sp. from 0.5-8% on a wet weight basis, w ith an average value of 2.9% and a 75 th 

### percentile of 4.0% (Table C19). Table C19 in cludes lipid composition da ta for wild-caught and laboratory-reared Lepomis sp., Pomoxis sp., and Micropterus sp. Several lipid content values available in the literature cannot be related to the weights of the fish analyzed due to a lack of information included in the indivi dual studies. Thus, these lipid contents cannot be related to one of KABAM’s default fish. Based on this and the data in Table C19, the default lipid composition for all three fish is set to 4%, to be representative of a high-end value.

### 89 of 123 Table C19. Lipid composition data fo r fish relevant to small, medium, an d large default fish of KABAM.

Species (scientific name) Reported Body Weight (kg) Corresponding Default fish % Lipid (wet weight) Source

Green sunfish ( Lepomis cyanellus ) Not reported Unknown 0.5-2 Price and Birge 2006

Bluegill ( L. macrochirus ) 0.012 (±0.0012) Small 0.72 (±0.46) Liber et al. 1999
Largemouth bass ( Micropterus salmoides ) Not reported Small (defined based on length data) 0.89 (±0.19)* Miranda and Hubbard 1994

Largemouth bass ( Micropterus salmoides ) Not reported Small (defined based on length data) 0.95(±0.26)* Miranda and Hubbard 1994

Largemouth bass ( Micropterus salmoides ) Not reported Small (defined based on length data) 0.97 (±0.18)* Miranda and Hubbard 1994
White crappie ( Pomoxis annularis ) Not Reported Assume medium (spawning fish) 1 Neuman and Murphy 1992 Longear sunfish

( L. megalotis ) Not reported Unknown 1-2 Price and Birge 2006
Bluegill ( L. macrochirus ) Not reported Unknown 1-3 Price and Birge 2006 Largemouth bass ( Micropterus salmoides ) Not reported Unknown 1-5 Price and Birge 2006

Largemouth bass ( Micropterus salmoides ) Not reported Small (defined based on length data) 1.3 (±0.29)* Miranda and Hubbard 1994

Largemouth bass ( Micropterus salmoides ) Not reported Small (defined based on length data) 1.3 (±0.24)* Miranda and Hubbard 1994

Largemouth bass ( Micropterus salmoides ) Not reported Small (defined based on length data) 1.6 (±0.47)* Miranda and Hubbard 1994

Bluegill ( L. macrochirus ) Not reported (juveniles) Presume small 1.7* Fischer et al. 1998

Bluegill ( L. macrochirus ) Not reported (adult males) Presume medium 1.8* Fischer et al. 1998

Smallmouth bass ( Micropterus dolomieu ) Not reported Unknown 1.90 Kay et al. 2005
White crappie ( Pomoxis annularis ) Not Reported Assume medium (spawning fish) 2 Neuman and Murphy 1992
Bluegill ( L. macrochirus ) Not reported (adult females) Presume medium 2.1* Fischer et al. 1998
Black crappie ( Pomoxis nigromaculatus ) 0.114 (±0.016) Medium 2.19 (±0.51) Sethajintanin et al. 2004 Bluegill ( L. macrochirus ) Not reported Unknown 2.3 Sidwell 1981 Black crappie ( Pomoxis nigromaculatus ) 0.111 (±0.015) Medium 2.54 (±1.85) Sethajintanin et al. 2004 Smallmouth bass ( Micropterus dolomieu ) Not reported Unknown 2.70 Kay et al. 2005
Black crappie ( Pomoxis nigromaculatus ) 0.148 (±0.021) Medium 2.82 (±0.36) Sethajintanin et al. 2004 White crappie ( Pomoxis annularis ) Not Reported Assume medium (spawning fish) 3 Neuman and Murphy 1992 Smallmouth bass ( Micropterus dolomieu ) 0.395 (±0.222) Medium-Large 3.09 (±1.08) Sethajintanin et al. 2004

### 90 of 123 Black crappie ( Pomoxis nigromaculatus ) 0.0798 (±0.012) Medium 3.11 (±1.63) Sethajintanin et al. 2004 Black crappie ( Pomoxis nigromaculatus ) 0.102 (±0.007) Medium 3.15 (±0.40) Sethajintanin et al. 2004

Smallmouth bass ( Micropterus dolomieu ) Not reported Medium-Large (defined based on length data) 3.3 (±0.3) Kwon et al. 2006

Smallmouth bass ( Micropterus dolomieu ) 0.154 (±0.0647) Medium 3.33 (±1.8) Sethajintanin et al. 2004 Smallmouth bass ( Micropterus dolomieu ) 0.326 (±0.177) Medium-Large 4.17 (±1.34) Sethajintanin et al. 2004 Smallmouth bass ( Micropterus dolomieu ) 0.870 (±0.0685) Medium-Large 4.93 (±0.33) Sethajintanin et al. 2004 White crappie ( Pomoxis annularis ) Not Reported Assume medium (spawning fish) 5 Neuman and Murphy 1992 White crappie ( Pomoxis annularis ) Not Reported Assume medium (spawning fish) 5 Neuman and Murphy 1992 Smallmouth bass ( Micropterus dolomieu ) 0.277 (±0.0901) Medium-Large 5.03 (±0.358) Sethajintanin et al. 2004

Smallmouth bass ( Micropterus dolomieu ) Not reported Medium-Large (defined based on length data) 5.5 (±0.4) Kwon et al. 2006

Smallmouth bass ( Micropterus dolomieu ) Not reported Medium-Large (defined based on length data) 5.6 (±0.2) Kwon et al. 2006

Smallmouth bass ( Micropterus dolomieu ) Not reported Medium-Large (defined based on length data) 5.8 (±0.4) Kwon et al. 2006

White crappie ( Pomoxis annularis ) Not Reported Assume medium (spawning fish) 6 Neuman and Murphy 1992 Bluegill ( L. macrochirus ) 0.00972 (±0.00276) Small 7.9 (±0.14) Carr et al. 1997

Average 2.9
75 th percentile 4.0 90 th percentile 5.5 *Calculated from reported dry weight assu ming that fish = 73% water (Table C18).



### The wet weight of an organism is the sum of the wa ter, lipid, and NLOM content. By default, if the water content of fish is 73% of the wet weig ht, and the % lipid is known (default = 4%), the NLOM content of fish is the % remaining afte r subtracting the water and lipid content from 100%. Therefore, the default parameter for NLOM composition is 23%.

### 91 of 123 Appendix D. Selection of Mammal Species of Concern and Corresponding Biological Parameters



### Mammal species of concern were defined for us e as default species in KABAM. Mammals were considered to be of concern for pesticide e xposures through aquatic bioaccumulation if their diets incorporated freshwater aquatic animal s. Specific species were identified using a Field Guide to Mammals of North America (Reid 2006). This guide contains information on the ranges, taxonomy, habits, feeding preferences, and habitats of mammals located in the continental United States, Canada, and Alaska.

A review of this source identified six species of mammals that consume aquatic animals. These include the American water shrew ( Sorex palustris ), the fog shrew ( Sorex sonomae ), the starnosed mole ( Condylura cristata ), the marsh rice rat ( Oryzomys palustris ), the American mink ( Mustela vison ), and the Northern river otter ( Lontra canadensis ). Additional references were sought to obtain data on the body weights and feeding preferences of these mammals. These species are used in KABAM to represent mammal s of concern for risks of pesticide exposures through aquatic bioaccumulation. De scriptions of these species are provided below. Information from these species descriptions were used to de fine the default parameters used to represent mammals in the KABAM tool.

D.1. Descriptions of Mammal Species



### D.1.1. American Water Shrew ( Sorex palustris )

The distribution of the American water shre w includes Canada, Alas ka, and areas of the continental United States, including the We st Coast, Rocky Mount ains, Great Lakes, Appalachian, and New England areas. These shrews inhabit areas boarding fast and slow moving streams, marshes, creeks, and ponds. This species is primarily insectivorous, consuming aquatic invertebrates, such as stonefly nymphs, mayflies, caddisflies, and diptera. The American water shrew is also known to consume other animals, including fish, salamanders, leaches, and dead mice. Documented body weights range 0. 008-0.018 kg, with males weighing more than females (Beneski and Stinson 1987).

D.1.2. Fog Shrew ( Sorex sonomae )

Fog shrews inhabit parts of Or egon and California on the Pacific Coast in the “fog belt.” This species is found in marshes, near streams, and in forests. Their diet includes insects, earthworms, centipedes, slugs, snails , and amphibians. Their weight ranges 0.0055-0.015 kg (Reid 2006, Smithsonian 2008).

D.1.3. Marsh Rice Rat ( Oryzomys palustris )

Marsh rice rats are distributed in states along th e Gulf of Mexico and East Coast of the United States. This species inhabits wetlands, marshes, swamps, meadows, and areas along streams. Its diet includes insects, fiddler cr abs, snails, fish, clams, arthro pods, wetland plants, seeds, fungus,

### 92 of 123 baby turtles, bird eggs, and carrion (of mammals and birds). Their weight ranges 0.045-0.080 kg (Wolfe 1982).

Six subspecies of O. palustris have been recognized (Wolfe 1982). One of these subspecies, Oryzomys palustris natator has been federally listed as enda ngered since 1991. This subspecies is known to occur in Florida and has a designated critical habitat (USFWS 1993).

D.1.4. Star-nosed Mole ( Condylura cristata )

The star-nosed mole is distributed throughout th e Eastern and Great Lakes regions of the United States and Canada. It inhabits marshy areas and streams. The diet of this species includes aquatic annelids, aquatic insects, small fish, mollusks, crustaceans, grubs, and earthworms. Reported body weights range 0.034-0.085 kg. The we ights of these animals do not differ by sex but by location within their geogr aphic distribution range, with smaller animals being observed in the southern pa rts of the range ( e.g., Tennessee) (Petersen and Yates 1980, Reid 2006, Smithsonian 2008).

D.1.5. American Mink ( Mustela vison )

The American mink is distributed throughout the United States an d Canada, except in the dry areas of Arizona, Nevada, California, Utah, and Texas. Mink inhabit wetlands and marshes. Their diet is composed mostly of fish, amphibians (frogs), cr ustaceans (crayfish and crabs), muskrats, and other small mammals. They will also consume squirrels, birds, bird eggs, reptiles, aquatic insects, earthworms, a nd snails if given the opportunity . Individual body weights vary based on range and sex, with females weighing less than males. Documented body weights of this species range 0.45-1.8 kg (Larivière 1999, USEPA 1993).

D.1.6. Northern River Otter ( Lontra canadensis )

The historical distributi on of the Northern river otter includ es most of the United States and Canada. The current distribution of this species in the United States includes states bordering the Gulf of Mexico and Great Lakes, the East Coas t, New England, the West Coast and Alaska, as well as Canada (Larivière and Walton 1998, Reid 2006). Northern river otters inhabit lakes, swamps, marshes, streams, and ponds . The diet of this species is primarily fish, but also includes frogs, crayfish, small mollusks, reptiles, birds, and fruits. Body weight s range 5-15 kg, with males weighing more than females (U SEPA 1993, Larivi ère and Walton 1998).

D.2. Determination of Mammalian Default Parameters for KABAM



### Tables 7 and 8 of the KABAM t ool allow the user to identify six mammal species of concern, their body weights and their diet s. For the purpose of KABAM, mammalian species of concern include those that consume aquatic animals. Based on the information above, relevant species in the United States include the American water sh rew, the fog shrew, the star-nosed mole, the marsh rice rat, the American mink, and the Northern river otter. A detailed version (with specific mammals identified) of the conceptual model of th e aquatic ecosystem depict ed in Figure I of the

### 93 of 123 User’s Guide is provided in Fi gure D1. Default values represen ting the body weights and diets of these mammals are described below.

Figure D1. Detailed conceptual model depicting aquatic food web, with mammals included. Arrows depict direction of trophic transfer of bioaccu mulated pesticides from lower levels to higher levels of the food web.



### D.2.1. Identification of Default Body Weights for Mammalian Species

Body weight and diet are the pa rameters that distinguish on e mammalian species from another within KABAM. Two pairs of species have similar body weights and diets, such that they can be grouped together. These pairs are 1) the American water shrew and the fog shrew; and 2) the star-nosed mole and the marsh rice rat. The American mink and the nor thern river otter are Sediment Benthic invertebrates: Crayfish, chironomid larvae, mayfly larvae, snails Phytoplankton: Algae, cyanobacteria, diatoms, dinofla g ellates

Zooplankton: Cladocera, copepods, rotifers Small fish: Young of the year, tad p oles

Filter feeders: Clams

, mussels Medium fish: Sunfish, bluegills, largemouth bass, frogs Large fish: Largemouth bass Water column Northern River
otter American min k Rice rat, star-nosed mole Fog and water shrews

### 94 of 123 sufficiently different in body wei ghts to distinguish them as se parate default species in the model.

The selected body weight value influences the estimates of pes ticide exposure through consumption of contaminated food items, as well as dose-adjusted toxicity values. Therefore, the magnitude of the body weight parameter has an e ffect on the magnitude of the RQ. Since higher body weight values result in higher dose-based RQs, the higher body weight values were selected to represent the four groups of mammals used in KABAM. In order to bound risk estimates for the two heaviest species of mammals ( i.e ., American mink and Northern river otter), default parameters are set to the mi nimum and maximum body we ights. The following values are suggested for inclusion in Table 7 of the KABAM tool to represent mammals 1-6:

Mammal # Name Body weight (kg) Mammal 1 Fog/Water Shrew 0.018 Mammal 2 Rice Rat/Star-nosed mole 0.085 Mammal 3 Small Mink 0.450 Mammal 4 Large Mink 1.800 Mammal 5 Small River Otter 5.000 Mammal 6 Large River Otter 15.000



### D.2.2. Determination of Daily Food Intake

If the weight of a food item ( i.e., aquatic trophic level) is less than that of the amount of food consumed by the mammal in one day, then the food item is a reasonable assignment. In order to determine whether or not a particular trophic level is relevant to a mammal, the daily food intake is estimated.

The dry food intake per day (F dry 

### , kg/day) for a mammal can be calculated according to Equation D1 (USEPA 1993). This value can be converte d to represent food intake per day on a wet weight basis (F wet 

### , kg/day) by assuming that the diet of an organism is 75% water (Equation D2, see Appendix C for % water of aquatic organisms).

822

. 0 

### * 0687

. 0

. 1 BW F D Equation dry 

### =



### ) (% 1 . 2 diet of water

F F D Equation dry

wet



### − =

The resulting wet food intakes per day for th e mammalian species of concern for KABAM are provided in Table D1. This table presents food intake per day for each species based on the low and high ranges of the body weights. These wet food intakes can be used to assign appropriate aquatic animals to the default diets of these mammals.

### 95 of 123 Table D1. Low- and high-end body weights and estima ted food intake per day of mammals which consume aquatic animals.

Species Body Weight (kg) Dry Food Intake per day (kg) Wet Food Intake per day (kg) Percent Body Weight Consumed Daily Shrew (Water and Fog) 0.006 0.001 0.004 67% Shrew (Water and Fog) 0.018 0.003 0.010 56% Rice Rat, Star-nosed mole 0.034 0.004 0.017 50% Rice Rat, Star-nosed mole 0.085 0.009 0.036 42%
Mink 0.450 0.036 0.143 31%
Mink 1.800 0.111 0.446 25%
River Otter 5.000 0.258 1.032 20%
River Otter 15.000 0.636 2.545 17%



### D.2.3. Definition of Default Diet s of Mammals for use in KABAM

Water/fog shrew

The diets of the American water and fog shrews (s ee section D.1) include species that would be classified as benthic invertebrates ( e.g., stonefly nymphs, mayflies, and snails) and fish ( e.g., fish and amphibians) according to the trophic levels of KABAM. However, since these species are primarily insectivorous, the default diet is assigned as 100% benthic invertebrates.



### Based on the daily food intake for these two species (Table E.1), it is reasonable to assume that these shrews can consume organisms in the small fi sh category. If interested in the potential acute risk to water/fog shrews from pesticides through consumption of fish/amphibians, the model user could define the diet of these mammals as 100% small fish. Since this represents a higher trophic level in the aquati c ecosystem than the benthic inve rtebrates, this assumption may result in a higher RQ.

Rice rat/star-nosed mole

The diets of the rice rat and the star-nosed mole (see section D.1) include species that would be classified as benthic invertebrates ( e.g., arthropods, snails), filter feeders ( e.g., clams) and fish according to the trophic levels of KABAM. Base d on the daily food intake for these two species (Table E.1), it is reasonable to assume that indi viduals of these species could consume organisms in the small fish category. Since no data are available to de fine feeding preferences of these two species, for the purpose of KABAM, the de fault diet composition of these mammals is equally distributed among th ese three trophic levels ( i.e., 34% benthic invertebrates, 33% filter feeders, and 33% small fish).



### American mink

The diet of the American mink (see section D.1) includes species that would be classified as benthic invertebrates ( e.g., crayfish) and small/medium-sized fish according to the trophic levels of KABAM. Based on the daily food intake for this species (Table E.1), it is reasonable to

### 96 of 123 assume that these mammals could consume organi sms in the small and medium fish category. The default diet for this mammal is 100% medium fish.



### Northern river otter

The diet of the Northern river ot ter (see section D.1) is primarily fish, but also includes species that would be classified as benthic invertebrates ( e.g., crayfish) according to the trophic levels of KABAM. Based on the daily food intake for this sp ecies (Table D.1), it is reasonable to assume that these mammals may consume organisms in th e small, medium, and large fish categories. According to USEPA 1993, river otters have b een documented as including various fish that would be classified in diffe rent trophic levels of KABAM , including sunfish and bass. Therefore, the default diet for this mammal is 100% medium fish for the small otter and 100% large fish for the large otter.

### 97 of 123 Appendix E. Selection of Bird Species of Concern and Corresponding Biological Parameters



### Bird species of concern were id entified in order to define defa ult parameters (for body weight and diet composition) to represen t birds in KABAM. Bird species were considered to be of concern for pesticide exposures through aquatic bioaccumulation if their diets incorporated freshwater aquatic animals. Sp ecific species were identified using th e Smithsonian handbooks’ Birds of North America (Eastern and Western Regions) (Alsop 2001a and 2001b). These handbooks contain information on the ranges, ta xonomy, habits, feeding preferences, and habitats of birds located in the continental United States, Canada, and Alaska.

A review of this source identified over 40 bird species of concern that fall into 11 families. These families include: Accipitridae (eagles, hawks and kites), Alcedinidae (kingfisher), Anatidae (ducks), Ardeidae (herons, egrets and bitterns ), Gruidae (cranes), Pelecanidae (pelicans), Phalacrocoracidae (cormorants), Podicipedidae (grebes), Rallidae (rails), Scolopacidae (sandpiper) and Threskiornithidae (ibis). Descrip tions of these families are provided below.

It should be noted that this revi ew was not intended to be inclusiv e of every relevant species or family of birds inhabiting North America. Rather, the intention of this review was to identify birds that may consume aquatic animals containi ng pesticides that bioaccumulate in aquatic ecosystems. Information from identified bird spec ies and families was used to define the default parameters representing birds in the KABAM tool. These defa ult parameters are described below.

E.1. Bird Family Descriptions



### E.1.1. Accipitridae (Eagles, Hawks and Kites)

Most species of this family pr ey upon terrestrial rodents; how ever, several rely upon aquatic animals for their diet (Table E1). These species include the osprey ( Pandion haliaetus ) and the bald eagle ( Haliaeetus leucocephalus ) (Alsop 2001a and 2001b). Ospreys fly over freshwater and saltwater areas and catch fish from the surf ace of the water using thei r feet. Body weights of osprey range from 1.25 to 2.0 kg (USEPA 1993). Bald eagles eat fish, rodents, birds, and carrion. Body weights of adult bald eagles ra nge 3.0 – 5.8 kg (USEPA 1993). An additional member of this family, the snail kite ( Rostrhamus sociabilis ), has a subspecies that is federally listed as endangered (USFWS 2008). This specie s is known to occur in wetlands of Florida, where the bird eats snails (Alsop 2001a). The average body weight of this bird is 0.38 kg (Dunning 1984).

Table E1. Body weights and diets of species of Accipitridae that prey upon aquatic animals.
Species (scientific name) Body weight (kg) Diet
Snail kite ( Rostrhamus sociabilis ) 0.38 1 snails 2 Osprey ( Pandion haliaetus ) 1.25-2.00 3 fish 3 Bald eagle ( Haliaeetus leucocephalus ) 3.00 – 5.80 3 fish, rodents, birds, and carrion 3

1 Dunning 1984; 2 Alsop 2001a and 2001b; 3 USEPA 1993

### 98 of 123 E.1.2. Alcedinidae (kingfisher)

One species of this family, the belted kingfisher ( Ceryle alcyon ) is widely distributed throughout North America, inhabiting freshwater areas such as lakes, rivers, and ponds, as well as marine coastal areas. This species feeds primarily up on fish, but its diet also includes amphibians, insects, and crayfish. Body weights of this sp ecies range 0.13-0.22 kg (USEPA 1993; Table E2).

Table E2. Body weights and diets of species of Alcedinidae that prey upon aquatic animals.
Species (scientific name) Body weight (kg) Diet

Belted kingfisher ( Ceryle alcyon ) 0.13-0.22 1 primarily fish, but also amphibians, insects and crayfish 1

1 USEPA 1993



### E.1.3. Anatidae (Ducks)

There are many species of ducks th at are widely distribut ed in North America (Table E3). Ducks predominantly inhabit freshwater areas such as lakes, rivers, wetlands, and ponds. Their diets include a wide variety of aquatic organisms, such as aquatic insects, insect larvae, snails, amphibians, fish, crayfish, mollusks, plankt on, and aquatic plants (Alsop 2001a and 2001b). Body weights of ducks vary based on the species, with a range of 0.3-2.0 kg for ducks inhabiting freshwater areas (Dunning 1984).

Table E3. Body weights and diets of species of Anatidae that prey upon aquatic animals.
Species (scientific name) Body weight (kg) 1 Diet 2

Cinnamon teal ( Anas cyanoptera) 0.36-0.41 seeds, aquatic insects, rice, algae, snails,
crustaceans Bufflehead ( Bucephala alboela) 0.30-0.55 aquatic insects and insect larvae, snails, small fish, seeds Wood duck ( Aix sponsa) 0.64-0.91 plants, animals, snails, tadpoles, salamanders Hooded merganser ( Lophodytes cuculatus) 0.54-0.91 fish, crustaceans, aquatic insects, aquatic animals Lesser scaup ( Aythya affinis) 0.54-1.05 plants and animals Common goldeneye ( Bucephala clangula) 0.80-1.40 mollusks, crustaceans, insects, aquatic plants
Mallard ( Anas platyrhynchos) 0.72-1.58 plants, insects, mollusks, crustaceans Red-breasted merganser ( Mergus serrator) 0.91-1.31 fish
Common merganser ( Mergus merganser) 1.05-2.05 small fish, mollusks, crustaceans, aquatic insects, some plants

1 Dunning 1984; 2 Alsop 2001a and 2001b



### E.1.4. Ardeidae (Herons, Egrets and Bitterns)

This family includes species of herons, bitterns and egrets, several of which inhabit waters of North America (Table E4). Their habitats include fres hwater areas such as lakes, rivers, ponds, wetlands, and streams, as well as marine coastal areas. These birds wade through water to spear

### 99 of 123 their food with their beaks. Their diets include fish, crustaceans, amphibians, snakes, crayfish, and insects (Alsop 2001a and 2001b, US EPA 1993). Indi viduals of this fam ily range in weight from 0.08 to 2.9 kg, depending upon the species (Dunning 1984, USEPA 1993).

Table E4. Body weights and diets of species of Ardeidae that prey upon aquatic animals.
Species (scientific name) Body weight (kg) 1 Diet 2

Least bittern ( Ixobrychus exilis) 0.08-0.09 fish, insects Green heron ( Butorides virescens) 0.21 2 fish, aquatic invertebrates Snowy egret ( Egretta thula) 0.35-0.40 crustaceans, insects, fish Little blue heron ( Egretta caerulea) 0.32-0.45 small vertebrates, crustaceans, large insects
American bittern ( Botaurus lentiginosus) 0.52-1.07 frogs, small eels, small fish, snakes, salamanders, crayfish, small rodents, water bugs Yellow-crowned night heron ( Nyctanassa violacea) 0.72-0.85 crustaceans, fish, shellfish
Black crowned night heron ( Nycticorax nycticorax) 0.73-1.01 fish, mollusks, small rodents, frogs, snakes, crustaceans, plants, eggs, birds Great egret ( Ardea alba) 0.80-1.07 fish, frogs, snakes, cr ayfish, large insects Great blue heron ( Ardea herodias) 1.87-2.88 fish, other aquatic animals

1 Dunning 1984; 2 Alsop 2001a and 2001b



### E.1.5. Gruidae (Cranes)

Cranes inhabit freshwater wetlands and marshe s. These birds eat fish, frogs, small mammals, mollusks, crustaceans, and plants (Als op 2001a and 2001b). Two species of cranes, i.e ., the whooping crane ( Grus americana ) and the Mississippi sandhill crane ( Grus canadensis pulla ), are federally listed as endangered (USFWS 2008). Body weights of the whooping crane and sandhill crane range 2.5-6.7 kg (Dunning 1984) (Table E5).

Table E5. Body weights and diets of species of Gruidae that prey upon aquatic animals.
Species (scientific name) Body weight (kg) 1 Diet 2

Whooping crane ( Grus americana ) 5.44-6.36 fish, frogs, small mammals, mollusks, crustaceans, and plants Sandhill crane ( Grus canadensis ) 2.45-6.70 plants and animals

1 Dunning 1984; 2 Alsop 2001a and 2001b



### E.1.6. Pelecanidae (Pelicans)

There is one species of pelican that inhabits fr eshwater aquatic habitats of North America: the American white pelican ( Pelecanus erythrorhynchos ). Their habitats include freshwater areas such as lakes, rivers, ponds, wetla nds and streams, as well as marine coastal areas. The diet of these birds includes fish (Alsop 2001a and 2001b). The average weight of the American white pelican is 7.5 kg (Dunning 1984) (Table E6).

### 100 of 123 Table E6. Body weights and diets of species of Pelecanidae that prey upon aquatic animals.
Species (scientific name) Body weight (kg) Diet
American white pelican ( Pelecanus erythrorhynchos ) 7.5 1 fish 2

1 Dunning 1984; 2 Alsop 2001a and 2001b



### E.1.7. Phalacrocoracidae (Cormorants)

Of the species of cormorants inhabiting No rth America, the double-breasted cormorant ( Phalacrocorax auritus ) is the most widespread, inhabiting freshwater areas such as lakes, rivers, ponds, as well as marine coastal areas. Cormor ants dive for their pr ey, which includes fish, crustaceans, and amphibians (Alsop 2001a an d 2001b). The average weight of the doublebreasted cormorant is 1.8 kg (Dunning 1984).

Table E7. Body weights and diets of species of Ph alacrocoracidae that prey upon aquatic animals.
Species (scientific name) Body weight (kg) Diet

Double-breasted cormorant ( Phalacrocorax auritus ) 1.60-2.04 1 fish, crustaceans and amphibians 2

1 Dunning 1984; 2 Alsop 2001a and 2001b



### E.1.8. Podicipedidae (Grebes)

Several species of grebes reside in the continental United States (Table E8). Their habitats include freshwater areas such as lakes, rivers , ponds, wetlands, and streams, as well as marine areas. These birds forage for aquatic insects, cr ustaceans, and fish by diving underwater (Alsop 2001a and 2001b). They range in weight 0. 2-1.8 kg (Alsop 2001a and 2001b, Dunning 1984).

Table E8. Body weights and diets of species of Podicipedidae that prey upon aquatic animals.
Species (scientific name) Body weight (kg) 1 Diet 2
Eared grebe ( Podiceps nigricollis) 0.22-0.37 aquatic insects Pied-billed grebe ( Podilymbus podiceps) 0.34-0.55 aquatic insects, small fish, crustaceans

Horned grebe ( Podiceps auritus) 0.33-0.53 fish, crustaceans, aquatic insects Western grebe ( Aechmorphorus occidentalis) 0.80-1.82 fish

Clark's grebe ( Aechmophorus clarkia) 1.50 2 fish

1 Dunning 1984; 2 Alsop 2001a and 2001b



### E.1.9. Rallidae (Rails)

Rail species inhabit freshwater areas such as lake s, rivers, ponds, wetlands and streams as well as saltwater marshes of North America. These species feed upon crustaceans, aquatic insects, snails, fish, and plants (Alsop 2001a and 2001b). Individuals of this family range in weight from 0.07 to 0.49 kg (Dunning 1984) (Table E9). One sp ecies from this family, the clapper rail ( Rallus longirostris ) is federally listed as an endangered species and is known to occur in Arizona, California, Nevada, an d Utah (USFWS 2008).

### 101 of 123 Table E9. Body weights and diets of species of Rallidae that prey upon aquatic animals.
Species (scientific name) Body weight (kg) 1 Diet 2

Sora ( Porzana carolina) 0.08 plants, insects, spider s, small crustaceans, snails
Virginia rail ( Rallus limicola) 0.07-0.12 insects (primarily), worms, crustaceans, small fish
King rail ( Rallus elegans) 0.25-0.49 plants, aquatic invertebrates, aquatic vertebrates
Clapper rail ( Rallus longirostris ) 0.25-0.35 crabs, crustaceans, worms, amphibians, reptiles, mollusks, small fish, aquatic insects

1 Dunning 1984; 2 Alsop 2001a and 2001b



### E.1.10. Scolopacidae (Sandpipers)

Many species of sandpipers inhabit freshwater aquatic habitats of North America (Table E10). These habitats include lakes, rivers, ponds, wetl ands, and streams. Their diets include aquatic invertebrates, insects, crus taceans, small fish, amphibian s, and mollusks (Alsop 2001a and 2001b). Body weights of sandpipers range 0.02- 0.70 kg (Dunning 1984).

Table E10. Body weights and diets of species of Scolopacidae that prey upon aquatic animals.
Species (scientific name) Body weight (kg) 1 Diet 2
Least sandpiper ( Calidris minutilla) 0.02 2 insects and larvae, crustaceans Spotted sandpiper ( Actitis macularia) 0.03-0.06 invertebrates, small fish Wilson’s phalarope ( Phalaropus tricolor) 0.07 2 larvae, crustaceans, seeds

Greater yellow legs ( Tringa melanoleca) 0.12-0.22 small fish, insects and larvae, crabs, snails

Willet ( Catoptrophorus semipalmatus) 0.22 aquatic insects, mollusks, small fish Long-billed curlew ( Numenius americanus) 0.57-0.70 aquatic insects, larvae, mollusks, crustaceans, small amphibians

1 Dunning 1984; 2 Alsop 2001a and 2001b



### E.1.11. Threskiornithidae (Ibis)

Ibis inhabit freshwater areas such as lakes, rivers, ponds, wetlands, and streams, as well as marine coastal areas of North America. These species are wading birds that feed upon crayfish, aquatic invertebrates, fish, and frogs (Alsop 2001a and 2001b). Individuals of this family range in weight from 0.4 to 1.3 kg (Dunning 1984) (Table E11).

Table E11. Body weights and diets of species of Th reskiornithidae that prey upon aquatic animals.
Species (scientific name) Body weight (kg) 1 Diet 2
White-faced ibis ( Plegadis chihi) 0.43-0.81 crayfish, aquatic invertebrates, fish, frogs White ibis ( Eudocimus albus ) 0.59-1.28 not stated

1 Dunning 1984; 2 Alsop 2001a and 2001b

### 102 of 123 E.2. Detailed Conceptual Model



### A detailed version of the conceptual model of th e aquatic ecosystem depicted in Figure I of the User’s Guide, with specific birds identified, is provided in Figure E1.

Figure E1. Detailed conceptual model depicting aquati c food web of KABAM. Ar rows depict direction of trophic transfer of bioaccumulated pesticides from lower levels to higher levels of the food web. Sediment Benthic invertebrates: Crayfish, chironomid larvae, mayfly larvae, snails Phytoplankton: Algae, cyanobacteria, diatoms, dinofla g ellates Zooplankton: Cladocera, copepods, rotifers Small fish: Young of the
y ear, tad p oles

Filter feeders: Clams

, mussels Medium fish: Sunfish, bluegills, largemouth bass, frogs Large fish: Largemouth bass Water column Belted kingfisher, cormorant, grebes, ibis, herons, rails Ducks,
cranes, sandpipers Bald eagle,
osprey, pelican

### 103 of 123 E.3. Determination of Daily Food Intake



### If the weight of a food item ( i.e., aquatic trophic level) is less than that of the amount of food consumed by the bird in one day, then the fo od item is a reasonable assignment. In order to determine whether or not a particul ar trophic level is rele vant to a bird, the da ily food intake is estimated.

The dry food intake per day (F dry 

### , kg/day) for a bird can be calc ulated according to Equation E1 (USEPA 1993). This value can be converted to represent food intake per day on a wet weight basis (F wet 

### , kg/day) by assuming that the diet of an organism is 75% wa ter (Equation E2, see Appendix C for % water of aquatic organisms).

651

. 0 

### * 0582

. 0

. 1 BW F E Equation dry 

### =



### ) (% 1 . 2 diet of water

F F E Equation dry

wet



### − =

Of the bird families described above, body weight s range 0.02-7.5 kg. The resulting wet food intakes per day for birds of concern for KABAM ar e provided in Table E12. This table presents food intake values per day for each species ba sed on body weight. These wet food intakes can be used to assign appropriate aquatic animal s to the default diets of these birds.

Table E12. Body weig hts representative of birds that consume aquatic animals and corresponding daily dry and wet food intakes.

Family or species Body weight range (kg) Dry Food Intake per day (kg) Wet Food Intake per day (kg)
Sandpipers 0.02- 0.70 0.005-0.046 0.018-0.185 ducks 0.30-2.00 0.027-0.091 0.106-0.366

cranes 2.45-6.70 0.104-0.201 0.417-0.803 belted kingfisher 0.13-0.22 0.015-0.022 0.062-0.087 rails 0.07-0.49 0.010-0.037 0.041-0.146 ibis 0.43-1.28 0.034-0.068 0.134-0.273 grebes 0.22-1.82 0.022-0.086 0.087-0.344
Double-breasted cormorant 1.8 0.085 0.341

Bitterns, egrets, herons 0.08-2.90 0.011-0.116 0.045-0.466

osprey 1.25-2.00 0.067-0.091 0.269-0.366 Bald eagle 3.00 – 5.80 0.119-0.183 0.476-0.731 white pelican 7.5 0.216 0.864

### 104 of 123 E.4. Definition of Default Paramet ers to Represent Birds in KABAM



### Based on the species descriptions above, birds can be divided into three groups based on their diets. The three diets include: 1) filter feeder s, benthic invertebrates and fish, 2) benthic invertebrates and fish and 3) fish . (Table E13). These three diets were used to define the default parameters representing birds in KABAM (Tab le E14), which are described below.

Table E13. Summary of diets and body weights of fam ilies of birds defined as consuming aquatic animals. Diet Family or species Body weight range (kg)
Sandpipers 0.02- 0.70

Ducks 0.30-2.00 Filter feeders, benthic invertebrates, fish Cranes 2.45-6.70 Belted kingfisher 0.13-0.22 Rails 0.07-0.49 Ibis 0.43-1.28 Grebes 0.22-1.82 Double-breasted cormorant 1.80 Benthic invertebrates and fish

Bitterns, egrets, herons 0.08-2.90 Osprey 1.25-2.00 Bald eagle 3.00 – 5.80 Fish White pelican 7.50

Table E14. Default body weights and diet para meters for use in KABAM to represent birds.

Bird

# Bird Name Relevant Families/species Default weight

(kg) Default diet

1 Sandpipers Sandpipers, ducks, cranes 0.02 33% benthic invertebrates 33% filter feeders

34% small fish

2 Cranes Sandpipers, ducks, cranes 6.70 33% benthic invertebrates 33% filter feeders 34% medium fish

3 Rails Belted kingfisher, rails, ibis, grebes, double-breasted cormorant, bitterns, egrets, herons 0.07 50% benthic invertebrates 50% small fish

4 Herons Belted kingfisher, rails, ibis, grebes, double-breasted cormorant, bitterns, egrets, herons 2.90 50% benthic invertebrates 50% medium fish

5 Small Osprey Osprey, bald eagle, white pelican 1.25 100% medium fish

6 White pelican Osprey, bald eagle, white pelican 7.50 100% large fish

### 105 of 123 E.4.1. Birds Consuming Benthic Invert ebrates, Filter Feeders, and Fish

Because sandpipers, ducks, and cranes share similar diets ( i.e., benthic invertebrates, filter feeders, and fish), they are considered as a group for defining input parameters for KABAM. Two of the default birds in KABAM (# 1 and 2) represent birds with a similar diet.

Comparison of the daily wet food consumption for sa ndpipers (Table E12) to the weight of small and medium fish in KABAM (0.01 and 0.1 kg, re spectively) indicates that not all of these species would be expected to consume medium-s ized fish. Therefore, it is assumed that sandpipers consume small fish. All species of cr anes are expected to be able to consume a medium-sized (0.1 kg) fish in one day. Therefor e, it is assumed that the diet of cranes is composed of medium-sized fish. Since the rela tive proportion of benthic invertebrates, filter feeders and fish within the diet s of these species is unknown, it is assumed that these prey items compose an equal share of the diet of these birds.

The 1 st 

### default bird in KABAM has a diet of 33% benthic invertebrates, 33% filter feeders and 34% small fish. This bird is intended to repr esent the low end of birds that consume benthic invertebrates, filter feeders, and small fish. Therefore, the default body weight of 0.02 kg was selected because it is consistent with the lowest body weight of birds that have this diet (Table E13).

The 2 nd 

### default bird in KABAM has a diet of 33% be nthic invertebrates, 33% filter feeders, and 34% medium fish. This bird is intended to repr esent the high end of birds that consume benthic invertebrates, filter feeders, and medium-sized fi sh. Therefore, the default body weight of 6.7 kg was selected (Table E14).

It should be noted that pesticide EECs and subs equent RQs for sandpipers, ducks, and cranes are bound by KABAM’s default birds 1 and 2. RQs fo r these two default bi rds are intended to represent birds with similar size and feeding habits as sandpipers, ducks, and cranes. These EECs and RQs can be refined by the model user to repr esent a specific bird species by entering specific body weights of individual specie s of concern and the appropriate species composition of their diet.

E.4.2. Birds Consuming Benthic Invertebrates and Fish

Because belted kingfisher, rails, ibis, grebes, double-breasted cormorants , bitterns, egrets, and herons share similar diets ( i.e., benthic invertebrates and fish), they are considered as a group for defining input parameters for KABAM. Two of the default birds in KABAM (# 3 and 4) represent birds with a similar diet.

Comparison of the daily wet food consumption fo r small rails, small grebes, and the belted kingfisher (Table E12) to the weight of small and medium-sized fish in KABAM (0.01 and 0.1 kg, respectively) indicates that not all of these speci es would be expected to consume medium fish. Therefore, it is assumed that some of th ese species consume small fish. Species of rails, ibis, grebes, bitterns, egrets, her ons and the double-breasted cormoran t are expected to be able to consume a 0.1 kg fish per day. Therefore, it is assu med that the diet of these species is composed

### 106 of 123 of medium-sized fish. Since the relative proportion of benthic i nvertebrates and fish within the diets of these species is unknown, it is assumed that these prey items compose an equal share of the diet of these birds.

The 3 rd 

### default bird in KABAM has a diet of 50% benthic invertebrates and 50% small fish. This bird is intended to represent the low end of birds that consume be nthic invertebrates and small-sized fish. Therefore, the default body we ight of 0.07 kg was selected because it is consistent with the lowest body weight of bi rds that have this di et (Table E13).

The 4 th 

### default bird in KABAM has a diet of 50% benthi c invertebrates and 50% medium fish. This bird is intended to repres ent the high end of bi rds that consume benthic invertebrates and medium-sized fish. Therefore, the default body weight of 2.9 kg was selected (Table E14).

It should be noted that pesticide EECs and su bsequent RQs for belted kingfisher, rails, ibis, grebes, double-breasted cormorants, bitterns, egrets, and herons ar e bounded for KABAM’s default birds 3 and 4. RQs for these two default bi rds are intended to represent birds with similar sizes and feeding habits. These EECs and RQs can be refined for specific bird species by entering specific body weights of individual specie s of concern and entering the appropriate diet.

E.4.3. Birds Consuming Fish

Because osprey, bald eagles, and wh ite pelicans share similar diets ( i.e., fish), they are considered as a group for defi ning input parameters for KABAM. Two of the default birds in KABAM (# 5 and 6) represent birds with a similar diet.

Comparison of the daily wet food consumption fo r the lower end body weight (1.25 kg) of these birds to the weight of medium and large fish in KABAM (0.1 and 1.0 kg, respectively) indicates that the lower weight individuals of these bird species are able to consume medium fish, but unlikely to consume large fish. Therefore, it is assumed that the diet of default bird #5 (named osprey), can be represented by 100% medium-sized fish. Comp arison of the daily wet food consumption (0.86 kg/day) for the higher end body we ight (7.5 kg) of these birds to the weight of large fish in KABAM (1.0 kg) indicates that the higher weight indi viduals of these bird species are likely to consume large fish. Therefore, it is assumed that the diet of default bird #6 (named white pelican), can be represented by 100% large-sized fish.

In order to bound EECs and RQs fo r these three birds, the lowest and highest body weights were selected to represent KABAM’s default birds 5 and 6, respectively, in KABAM. These EECs and RQs can be refined for specific bird species by entering specific body weights of individual species of concern.

### 107 of 123 Appendix F. Description of Equations Used to Calculate the BCF, BAF, BMF, and BSAF Values



### Bioconcentration, bioaccumulation, and biomagnification factors are calculated in the “results” worksheet of the KABAM tool using data from the “parameters & calculations” worksheet. The equations for these calculations are described below.

F.1. Bioconcentration



### Bioconcentration is a measure of the amount of pesticide residue in an organism’s tissue relative to the concentration in the organism’s envir onment (USEPA 2008c). This includes pesticide uptake through respiration and co ntact, not through dietary sources . Bioconcentration factors (BCFs) are calculated by considering pesticide tissue concentrations with respect to environmental pesticide concentrations. BCF valu es >1 indicate that the concentration in the organism is greater than that of the medium ( e.g. , soil or water) from which the pesticide was taken. BCFs can be calcu lated on a total organism basis or nor malized to the lipid content of the organism.

KABAM calculates the total (body weight) BCFs of a chemical for each aquatic organism according to Equation F1 

### (USEPA 2003). C BCF 

### is calculated using equation A1 (see Table A.1 of Appendix A for a full description) where C B 

### = C BCF 

### , when k D 

### = k E 

### = k M 

### = k G 

### = 0. The units of total BCF values are expressed as: (μg pesticid e/kg wet weight)/(μg pest icide/L water). Total BCF values account for the total amount of the pesticide in the water ( i.e., C WTO 

### ).

WTO BCF 

### C C BCF Total F Equation =

. 1

M G E Di i D WDP P WTO B 

### k k k k C P k C m C m k C A Eq + + + Σ + + Φ

=

2 0 1 

### ) * ( * ) * * * ( * 1

.

KABAM also calculates the lipid-normalized BCF s of a chemical for each aquatic organism according to Equation F2 

### (USEPA 2003). The units of lipid normalized BCF values are expressed as: (μg pesticide/kg lipid)/(μg pesticide/L water). V LB 

### represents the fraction of lipid in the body of the organism for which the BCF is being derived. Lipid normalized BCF values account for the pesticide concentration that is freely dissolved in the water ( i.e., C WTO 

### * Φ ).

Φ ⎟

⎠ ⎞ ⎜ ⎝ ⎛

= * . 2

WTO LB BCF



### C V C

BCF normalized Lipid F Equation

### 108 of 123 F.2. Bioaccumulation



### Bioaccumulation is the net uptake of a pesticid e from the environment by all possible routes ( e.g., respiration, diet, de rmal) from any source ( e.g., water, sediment, and other organisms) (Spacie et al . 1995). Bioaccumulation factors (BAF) ar e calculated by considering pesticide tissue concentrations with respect to environmen tal pesticide concentrations. BAF values >1 indicate that the accumulation in the organism is greater than that of the medium ( e.g. , soil or water) from which the pesticide was taken. Thes e factors can be calculated on a total organism basis or normalized to the lip id content of the organism.

KABAM calculates the total BAFs of a chem ical for each aquatic organism according to Equation F3 

### (USEPA 2003). The units of total BAF values are expr essed as: (μg pesticide/kg wet weight)/(μg pesticide/L water). C B 

### is calculated according to Equation A1 

### . Total BAF values account for the total amount of the pesticide in the water ( i.e., C WTO 

### ).

WTO

B 

### C

C BAF Total F Equation =

. 3

Lipid-normalized BAFs of a chemical are calcul ated for each aquatic organism according to Equation F4 

### (USEPA 2003). The units of lipid normalized BAF values are expressed as: (μg pesticide/kg lipid)/(μg pesticide/L water). The variable C B 

### is calculated according to Equation A1 

### . The variable V LB 

### represents the fraction of lipid in the body of the organism for which the BCF is being derived. Lipid normalized BAF valu es account for the pestic ide concentration that is freely dissolved in the water ( i.e., C WTO 

### * Φ ).

Φ ⎟ ⎠ ⎞ ⎜ ⎝ ⎛

= * . 4

WTO LB B



### C V

C

BAF normalized Lipid F Equation

Accumulation factors are also deri ved by considering pesticide tissu e concentrations with respect to pesticide concentrations in sediment. Bi ota-sediment accumulation factors (BSAFs) are calculated by dividing the lipid normalized concentr ation of a chemical in an organism by the chemical concentration in the sediment (dry weig ht), normalized to the organic carbon content of the sediment ( Equation F5 

### ) (USEPA 2003). The variable C SOC 

### represents the pesticide concentration in the sediment, normalized to the or ganic carbon content of the sediment (units of g/kg OC).

SOC LB B



### C V C

BSAF F Equation ⎟ ⎠ ⎞ ⎜ ⎝ ⎛

=

. 5

### 109 of 123 F.3. Biomagnification



### Biomagnification is the increase of a pesticide concentration in the tissue of an organism compared to the tissue concentrations of its prey (USEPA 2008b). Bi omagnification factors (BMFs) are calculated by considering lipid normaliz ed pesticide tissue concentrations within an organism with respect to the lipi d normalized concentrations of th at pesticide in the prey of the organism. Factors >1 indicate the occurrence of biomagnification.

KABAM calculates the BMFs of a chemical for each aquatic organism according to Equation F6 

### (USEPA 2003). The units of BMF values are expressed as: (μg pe sticide/kg lipid)/(μg pesticide/kg lipid). The variable C B 

### is calculated according to Equation A1 

### . V LB 

### represents the fraction of lipid in the body of the organi sm for which the BMF is being derived. P i 

### represents the fraction of diet cont aining prey item i. C Di 

### represents the concentra tion of the pesticide in prey item i and V LBi 

### represents the fraction of lipid in the body of the prey item i. It should be noted that although KABAM allows aquatic organi sms to consume sediment, uptake of pesticide through consumption of sediment is not considered in the calculation of BMFs in the model tool.



# ∑ ⎟ ⎠ ⎞ ⎜ ⎝ ⎛ ⎟ ⎠ ⎞ ⎜ ⎝ ⎛

=

LBi Di i LB

B



### V

C P V

C

BMF F Equation * . 6

### 110 of 123 Appendix G. Description of Equations Used to Calculate Dietary-Based and Dose-Based EECs, Toxicity Values, and RQs for Mammals and Birds Consuming Contaminated Aquatic Organisms



### Exposures of birds and mammals to pesticides a ccumulated in tissues of aquatic organisms are calculated by the KABAM tool. Relevant toxicity data are also calculated by KABAM based on input data from toxicity studies for birds and mammals. The equations used to estimate exposure and to adjust toxicity values and to calculate RQs depicted in Tables 14-15 of the KABAM tool are described below.

G.1. Food Ingestion Rates



### Dry food ingestion rates (FI dry 

### ) are estimated for mammals and bi rds using allometric equations that relate food intake with body weight ( Equations G1 and G2 

### , respectively). FI is calculated in kg dry food/kg-bw day and BW is animal body weight in kg.

Food intake (FI) values are conve rted from food dry weight/kg- bw day to food wet weight/day using the wet weight of the assign ed diet of each mammal and bird ( Equation G3 

### ). The variable P i 

### represents the fraction of diet of the mammal or bird containing pr ey item i (an aquatic organism). The variable V WBi 

### represents the fraction of water in the body of the prey item i.



# ( ) 

# ∑

− =

WBi i dry

wet 

### V P FI FI G Equation * 1 . 3

G.2. Drinking Water Intake Rates



### Drinking water intake rates (DW) for ma mmals and birds are calculated based on Equations G4 and G5 

### (USEPA 1993); where BW represents the body weight (in kg) of th e animal for which the drinking water intake is being assessed. Resulting units of DW are L/day. 

# ( ) ) ( * 099

. 0

. 4 90

. 0 

### mammals BW DW G Equation = 

# ( ) ) ( * 059

. 0

. 5 67

. 0 

### birds BW DW G Equation = 

### ) ( * 0582

. 0

. 2 651

. 0 

### birds BW BW FI G Equation dry 

### = ) ( * 0687

. 0

. 1 822

. 0 

### mammals BW BW FI G Equation dry 

### =

### 111 of 123 G.3. Dose-based EECs



### Dose-based EECs are estimated assuming that pest icide intake is a func tion of the amount of pesticide contained in the food and drinking water of an animal. The dose-based EEC is derived according to Equation G6 

### . In this equation, pesticide inta ke through food is calculated as the sum of the products of the fraction of each prey item in the diet (P i 

### ) and the pesticide tissue residue concentration fo r each prey item (C Bi 

### ; μ g/kg-ww). The sum of the pesticide residues ingested through food is converted into units of mg pesticide/kg food. This value is then multiplied by the intake rate for wet food (kg food/ kg-bw day). The resulting value is in units of mg pesticide/kg-bw day. Pesticide intake through drinking water is calculated by multiplying the concentration of the pesticide in water (C WTO 

### , mg/L) by the water intake (DW in units of L/d) and dividing by the bodyweight of th e mammal or bird of concern. This results in units of mg pesticide/kg-bw day. The sum of pesticide intake through diet and throu gh drinking water is the dose-based EEC.

G.4. Dietary-based EECs



### Dietary-based EECs are estimated assuming that pesticide intake is a function of the amount of pesticide contained in the food of an animal. This differs from the dose-based EECs in that pesticide exposure through drinking water is not considered. In addition, the dietary-based exposure value is not adjusted fo r the relative amount of food consumed per day by animals of different sizes. The dietary- based EEC is derived according to Equation G7 

### . In this equation, the pesticide intake through food is calculated as the sum of the pr oducts of the fraction of each prey item in the diet (P i 

### ) and the pesticide tissue residue concentration for each prey item (C Bi; 

### μ g/kg-ww).

G.5. Adjusted Dose-based Toxicity Values



### Available dose-based toxicity values are adjust ed for the weights of the animal tested ( e.g., laboratory rat, mallard duck) and of the anim al for which the risks are being assessed ( e.g., mink, bald eagle). These adjustments are ma de for mammals and birds according to Equations G8 and G9 

### , respectively (USEPA 2006). In these equa tions, AT = adjusted toxicity value; LD 50 

### or NOAEL = endpoint reported by toxi city study; TW = body weight of tested animal (350g rat; 1580g mallard, 178 g Northern bobwhite quail or weight defined by the model user for an alternative species); AW = body weight of assessed animal; x = Mineau scaling factor. Chemical specific values for x may be located in Mineau et al. 1996). If no chemical specific data are available, the default value of 1.15 should be used for this parameter. Methods for adjusting toxicity values are consistent with those used by T-REX (USEPA 2008a). 

# ( ) BW DW C FI C P EEC based Dose G Equation WTO
wet Bi i 

## * * *

. 6 + = − 

# ∑

( ) 

# ∑

= − Bi i 

## C P EEC based Dietary G Equation *

. 7

### 112 of 123 

# ( ) ) (

. 8 25

. 0

50 

### mammals AW TW NOAEL or LD AT G Equation ⎟ ⎠ ⎞ ⎜ ⎝ ⎛

=

( ) 

### ) (

. 9 1

50 

### birds TW AW LD AT G Equation x

− 

### ⎟ ⎠ ⎞ ⎜ ⎝ ⎛

=

### 113 of 123 Appendix H. Methods for Estimati ng Metabolism Rate Constant (k M 

### )



### Generally, chemical-specific data are not available to determine the metabolism rate constant (k M 

### ) for aquatic organisms. However, this parame ter can be estimated using data from available bioconcentration factor (BCF) studies, in combination with estimated rate constants. Two separate approaches can be employed to estimate k M 

### . The first utilizes Equation A1 from Arnot and Gobas 2004. The second utilizes a method desc ribed by Arnot et al. 2008. These approaches are described below.

H.1. Use of Equation A1



### In this approach, Equation A1 (see Table A1 of Appendix A) is rearranged to solve for k M 

### (Equation H1). In a BCF study, fish are fed unc ontaminated food; therefore, uptake through the dietary pathway is assumed to be negligible. As a result, it is assumed that k D 

### = 0. BCF studies with fish involve water-only exposures, so fish do not respire pore water. As a result, m O 

### = 1 and m P 

### = 0. To calculate k M 

### , the model user should use the m easured concentration of pesticide in the test water. In this case, it is assumed that this value represents the freely dissolved pesticide in the water, and therefore, Ф = 1. Based on these assumptions, Equation H1 can be restated as Equation H2.

M G E Di i D WDP P WTO B 

### k k k k C P k C m C m k C A Eq + + + Σ + + Φ

=

2 0 1 

### ) * ( * ) * * * ( * 1

. (From Appendix A)



### ) ( ) * ( * ) * * * ( * 1

. 2 0 1 G E
B Di i D WDP P WTO M 

### k k k C C P k C m C m k k H Eq + +

− Σ + + Φ

=

G E
B

WTO M 

### k k k C

C k k H Eq

− − − = 2 1 

### ) ( * 2

.

Equation H2 can be used to estimate k M 

### from available data from a BCF study.

 

### Empirical estimates of k 1 

### (L/kg*d), total pesticide con centration in fish tissues (C B 

### ; g/kgww) and C WTO 

### (g/L) from the BCF study should be entered into this equation.

 

### k 2 

### (d -1 

### ) is calculated as k 1(empirical) 

### /K BW 

### (see Table A6 of Appendix A). To calculate K BW 

### , it is necessary to have estimates of % lip id, % non-lipid organic matter (NLOM), and % water of the test fish (V LB 

### , V NB 

### and V WB 

### , respectively).

o If % lipid data are not available for the test fish, this approach should not be used and it should be assumed that k M 

### = 0.

o 

### If % lipid data are available, but % NLOM and % water are not available, it can be assumed that the fish are 73% water and that % NLOM is equal to 100-73-% lipid.

 

### k E 

### (d -1 

### ) can be estimated using the KABAM tool. The model user should use the large fish of KABAM to calculate k E


### .

### 114 of 123 o 

### Body weight of the fish and water temp erature should be set to mean reported values from the study. If body weight data are not available for the test fish, this approach should not be used and it should be assumed that k M 

### = 0.

o 

### This constant is influenced by the % lipid, % NLOM, and % water of the diet (V LD 

### , V ND 

### and V WD 

### , respectively). Calculation of th is constant requires input of diet of the large fish to be 100% medium fish (Table 6 of KABAM tool). If data are available from the BCF study report to define the % lipi d, % NLOM, and % water of the feed of the test fish, the data should be entered in the appropriate columns of Table 5 of the KABAM tool fo r the medium fish. Otherwise, if these data are not available, the % lipid, % NL OM, and % water of the medium fish can be set to the default values of 4, 23, and 73%, respectively.

 

### k G 

### (d -1 

### ) can be estimated from empirical data on body weight over the study period. If k G 

### cannot be estimated, the model user can use k G 

### from the large fish.

H.2. Use of Arnot et al. 2008



### In this approach, it is assumed that the elimin ation rate constant meas ured during the BCF study (k T 

### ) is the sum of elimination through respirati on, fecal elimination and metabolism of the pesticide by the fish as well as growth dilu tion (Equation H3, Arnot et al. 2008). Equation H3 can be rearranged into Equation H4, to solve for k M


### .

M G E T 

### k k k k k H Eq + + + = 2 

### 3

.

G E T M 

### k k k k k H Eq

− − − = 2 

### 4

.
Equation H4 can be used to estimate k M 

### from available data from a BCF study.

 

### k T 

### (d -1 

### ) is the total elimination rate constant estimated from the de puration period of the BCF study.

 

### As with the first approach, k 2 

### (d -1 

### ) can be calculated as k 1(empirical) 

### /K BW 

### (see table A6 of Appendix A).

 

### k E 

### (d -1 

### ) can be estimated using the KABAM tool . See discussion above on how to derive this constant value.

 

### k G 

### (d -1 

### ) can be estimated from empirical data on body weight over the study period. If k G 

### cannot be estimated, the model user can use k G 

### from the large fish.

H.3. Assumptions and Uncertainties



### If k M 

### is calculated as a negative value, it should be assumed that no biotransformation of the chemical occurs and k M 

### should be set to 0 in Table 2 of the KABAM tool. Since a negative biotransformation rate would indica te that the organism is creati ng the pesticide, it is assumed that this is not possible for a pesticide.

There is some uncertainty in using the model estimated k G 

### value (using Equation A7), as it may differ from the growth rate of th e test species of the BCF study.

### 115 of 123 The first approach involves use of total pesticide concentration in fish tissues (C B 

### ; g/kg-ww) and C WTO 

### (g/L). It would be appropriate to enter mean values for these parameters into equation H2. However, variability in these parame ters can influence predictions of k M 

### . Therefore, the model user should explore variability asso ciated with these values by cons idering standard deviation, as well as minimum and maximum values for these parameters. This will result in a range of relevant k M 

### values.

Both approaches involve use of fish body composition data (V LB 

### , V NB 

### , and V WB 

### ). It would be appropriate to use mean values to calculate K BW 

### (and ultimately k 2 

### ). However, variability in these parameters can influence predictions of k M 

### . Therefore, the model user should explore variability associated with these values by cons idering standard deviation, as well as minimum and maximum values for these parameters. This approach will result in a range of relevant k M 

### values.

Both approaches involve using the KABAM tool to calculate k E 

### . This involves the use of diet composition data (V LD 

### , V ND 

### , and V WD 

### ). In the case that data are not available from the study report to define the % lipid, % NLOM, and % water of the diet of the test fish, there is uncertainty in using default values for these parame ters, as they may differ from the diet of the test species of the BCF study.

### 116 of 123 Appendix I. References Cited



### Acharya, K., Jack, J.D. and P.A. Bukaveckas. 2005. Dietary effects on life history traits of riverine Bosmina . 2005. Freshwater Biology, 50: 965-975.

Alsop, III, F.J. 2001a. Birds of North Ameri ca: East. 1st edition. Smithsonian Handbooks. DK Publishing, Inc. New York.

Alsop, III, F.J. 2001b. Birds of North America: West. 1st edition. Smithsonian Handbooks. DK Publishing, Inc. New York.

Andrès, S., Baudrimont, M., Lapaquellerie, Y., Ri beyre, F., Maillet, N., Latouche, C. and A. Boudou. 1999. Field transplanta tion of the freshwater bivalve Crobicula fluminea along a polymetallic contamination gradient (River Lot, France): I. geochemical characteristics of the sampling sites and cadmium and zinc bioaccumula tion kinetics. Environmental Toxicology and Chemistry, 18(11): 2462-2471.

Arnot, J.A. and F.A.P.C. Gobas. 2004. A food we b bioaccumulation model for organic chemicals in aquatic ecosystems. Environmental Toxi cology and Chemistry, 23 (10): 2343-2355.

Arnot, J.A., Macay, D. and M. Bonnell. 2008. Estimating metabolic biotransformation rates in fish from laboratory data. Environmenta l Toxicology and Chemistry, 27(2): 341-351.

Becker van Slooten, K. and J. Tarradellas. 1994. Accumulation, depuration and growth effects of tributyltin in th e freshwater bivalve Dreissena polymorpha under field conditions. Environmental Toxicology and Chemistry, 13 (5): 755-762.

Beers, J.R. 1966. Studies on the chemical co mposition of the major zooplankton groups in the Sargasso Sea off Bermuda. Limnology and Oceanography, 11 (4): 520-528.

Beneski, J.T. and D.W. Stinson. 1987. Sorex palustris . Mammalian Species (Publication of the American Society of Mammalogists), No. 296, p. 1-6.

Bervoets, L., Voets, J., Covaci, A., Chu, S., Qada h, D., Smolders, R., Schepens, P. and R. Blust. 2005. Use of transplanted zebra mussels ( Driessena polymorpha ) to assess the bioavailability of microcontaminants in Flemish surface waters . Environmental Science and Technology, 39: 1492-1505.

Burkhard, L.P. 1998. Comparison of two mode ls for predicting bioaccumulation of hydrophobic organic chemicals in a Great La kes food web. Environmental Toxi cology and Chemistry, 17 (3), 383-393.

Burkhard L.P., Endicott, D.D., Cook, P.M, Sappington, K.G., and E. L. Winchester. 2003. Evaluation of two methods for pr ediction of bioaccumulation factors. Environmental Science and Technology, 37:4626-4634.

### 117 of 123 Carr, K.H., Coyle, G.T. and R.A. Ki merle. 1997. Bioconcentration of [ 14 

### C]butyl benzyl phthalate in bluegill sunfish ( Lepomis macrochirus ). Environmental Toxicology and Chemistry, 16 (10): 2200-2203.

Charlander, K.D. 1977. Handbook of freshwater fi shery biology, Volume 2. The Iowa State University Press, Ames, IA.

Covich, A.P., Palmer, M.A. and T. A. Crowl. 1999. The role of benthic invertebra te species in freshwater ecosystems; zoobenthic species in fluence energy flows and nutrient cycling. BioScience, 49(2): 119-127.

Croce, V., De Angelis, S., Patrolecco, L., Po llesello, S., S. Valsecchi. 2005. Uptake and accumulation of sediment-associated 4-nonyl phenol in a benthic invertebrate ( Lumbriculus variegatus , freshwater oligochaete). Environmental Toxicology and Chemistry, 24 (5): 1165- 1171.

Dunning, J.B. 1984. Body weights of 686 species of North American Birds. Western Bird Banding Association. Monograph number 1. May 1984.

Fischer, R.U., Congdon, J.D., Difonz o, C.D. and J.M. Aho. 1998. Lipid and reproductive cycles of bluegills ( Lepomis macrochirus ) exposed to 35 years of elevat ed environmental temperatures. Journal of Thermal Biology, 23 (6): 359-367.

Fisk, A.T., Wiens, S.C., Webster, G.R.B., Bergma n, Å., D.C.G. Muir. 1998. Accumulation and depuration of sediment-sorbed C 12 

### – and C 16 

### – Polychlorinated alkanes by oligochaetes ( Lumbriculus variegatus ). Environmental Toxicology and Chemistry, 17 (10): 2019-2026.

Gewurtz, S.B., Lazar, R. and G.D. Haffner. 2000. Comparison of polycyclic aromatic hydrocarbon and polychlorinated biphenyl dynamics in benthic invertebrates of Lake Erie, USA. Environmental Toxicology and Chemistry, 19 (12): 2943-2950.

Gobas, F.A.P.C. 1993. A mode l for predicting the bioaccu mulation of hydrophobic organic chemicals in aquatic food webs: application to Lake Ontario. Ecologi cal Modeling, 69:1-17.

Hakwer, D.W. and D.W.Connell. 1988. Influe nce of partition coefficient of lipophilic compounds on bioconcentration kinetics with fish. Water Research, 22(6), 701-707.

Hannan, H.H. and T.C. Dorris. 1970. Succe ssion of a macrophyte community in a constant temperature river. Limnology and Oceanography, 15(3), 442-453.

Hendriks, A.J, Pieters, H. and J. DeBoer . 1998. Accumulation of metals, polycyclic (halogenated) aromatic hydrocarbon s, and biocides in zebra mussel and eel from the Rhine and Meuse Rivers. Environmental Toxico logy and Chemistry, 17(10): 1885-1898.

### 118 of 123 Hyötyläinen, T., Karels, A. and A. Oikari. 2002. Assessment of bioavailability and effects of chemicals due to remediation actions with caging mussels ( Anodonta anatina ) at a creosotecontaminated lake sediment si te. Water Research, 36: 4497-4504.

Jeppesen, E., Jensen, J.P., SØndergaard, M ., Fenger-GrØn, M., Bramm, M.E., Sandby, K., MØller, P.H., and H.U. Rasmussen. 2004. Imp act of fish predation on cladoceran body weight distribution and zooplankton graz ing in lakes during winter. Fr eshwater biology, 49, 432-447.

Jonker, M.T.O., Hoenderboom, A.M., and A.A. Koelmans. 2004. Effects of sedimentary sootlike materials on bioaccumulation and sorption of polychlorinated biphenyls. Environmental Toxicology and Chemistry, 23 (11): 2563-2570.

Kane Driscoll, S. and P.F. Landrum. 1997. A comparison of equilibrium partitioning and critical body residue approaches for predicting toxicity of sedi ment-associated fluoranthene to freshwater amphipods. Environmental Toxi cology and Chemistry, 16 (10): 2179-2186.

Kane Driscoll, S., Harkey, G.A. and P.F. Landr um. 1997. Accumulation and toxicokinetics of fluoranthene in sediment bioassa ys with freshwater amphipods. Environmental Toxicology and Chemistry, 16 (4): 742-753.

Kang, Y.-S., Yamamuro, M., Masunaga, S. and J. Nakanishi. 2006. Speci fic biomagnification of polychlorinated dibenzo- p -dioxins and dibenzofur ans in tufted ducks ( Aythya fuligula ), common cormorants ( Phalacrocorax carbo ) and their prey from Lake Shinji, Japan. Chemosphere, 46: 1373-1382.

Kay, D.P., Blankenship, A.L., Coady, K.K., Nei gh, A.M., Zwiernik, M.J., Millsap, S.D., Strause, K., Park, C., Bradley, P., Newsted, J.L., Jone s, P.D. and J.P. Gies y. 2005. Differential accumulation of polychlorinated biphenyl congene rs in the aquatic food web at the Kalamazoo River superfund site, Michigan. Environm ental Science and Technology, 39: 5964-5974.

Kukkonen, J., and P.F. Landrum. 1994. Toxicoki netics and toxicity of sediment-associated pyrene to Lumbriculus variegatus (oligochaeta). Environmenta l Toxicology and Chemistry, 13 (9): 1457-1468.

Kukkonen, J.V.K., Landrum, P.F., Mitra, S., Go ssiaux, D.C., Gunnarsson, J. and D.Weston. 2004. The role of desorption for describing the bioavailability of sel ect polycyclic aromatic hydrocarbon and polychlorinated biphenyl congeners for seven la boratory-spiked sediments. Environmental Toxicology and Chemistry, 23 (8): 1842-1851.

Kwon, T.-D., Fisher, S.W., Kim, G.W., Hwang, H. and J.-E. Kim. 2006. Trophic transfer and biotransformation of polychlorinated biphenyls in zebra mussel, round goby, and smallmouth bass in Lake Erie, USA. Environmental Toxicology and Chemistry, 25(4): 1068-1078.

Landrum, P.F., Robinson, S.D., Gossiaux, D.C., Y ou, J., Lydy, M.J., Mitra, S. and T.E.M. Ten Hulscher. 2007. Predicting bioavailability of sediment-associated organic contaminants for Diporeia spp. and oligochaetes. Environmental Science and Technology, 41: 6442-6447.

### 119 of 123 Larivière, S. and L.R.Walton. 1998. Lontra canadensis . Mammalian Species (Publication of the American Society of Mammalogists), No. 587, 1-8.

Larivière, S. 1999. Mustela vison . Mammalian Species (Publication of the American Society of Mammalogists), No. 608, 1-9.

Leonards, P.E.G., Zierikzee, Y., Brinkman, U.A.T., Co fino, W.P., Van Str aalen. N.M., and B. Van Hattum. 1997. Environmental Toxi cology and Chemistry, 16(9): 1807-1815.

Liber, K., Gangl, J.A., Corry, T.D., Heinis, L.J and F.S. Stay. 1999. Lethality and bioaccumulation of 4-nonylphenol in bluegill sunfish in littoral enclosures. Environmental Toxicology and Chemistry, 18 (3): 394-400.

Liebig, M., Egler, P., Oehlmann, J. and T. Knacker. 2005. Bioaccumulation of 14 

### C-17 α

ethinylestradiol by th e aquatic oligochaete Lumbriculus variegatus in spiked artificial sediment. Chemosphere, 59: 271-280.

Lin, H.-I, Berzins, D.W., Myers, L., George, W.J ., Abdelghani, A. and K.H. Watanabe. 2004. A Bayesian approach to parameter estimation for a crayfish ( Procambarus spp. ) bioaccumulation model. Environmental Toxicology and Chemistry, 23 (9): 2259-2266.

Lotufo, G.R., Landrum, P.F., Gedeon, M.L., Tigue , E.A., and L.R. Herche. 2000. Comparative toxicity and toxicokinetics of DDT and its major metabolites in freshwater amphipods. Environmental Toxicology and Chemistry, 19 (2): 368-379.

Lotufo, G.R., Landrum, P.F., and M.L. Gedeon. 2001. Toxicity and bioaccumulation of DDT in freshwater amphipods in exposures to spiked sediments. Environmental Toxicology and Chemistry, 20 (4): 810-825.

Lu, X., Reible, D.D., Fleeger, J.W., and Y. Chai. 2003. Bioavailability of desorption-resistant phenanthrene to the oligochaete Ilyodrilus templetoni . Environmental Toxicology and Chemistry, 22 (1): 153-160.

Lürling, M. and E. Van Donk. 1997 . Life history consequences for Daphnia pulex feeding on nutrient-limited phytoplankton. Fr eshwater Biology, 38, 693-709.

Marvin, C.H., Howell, E.T., Kloic, T.M. and E.J. Reiner. 2002. Polychlorinated dibenzo- p

dioxins and dioxinlike polychlori nated biphenyls in sediments and mussels at three sites in the lower Great Lakes, North America. Enviro nmental Toxicology and Chemistry, 21 (9): 1908- 1921.

McKee, M.J. and C.O. Knowles. 1987. Levels of protein, RNA, DNA, glycogen and lipid during growth and development of Daphnia magna Straus (Crustacea: Cl adocera). Freshwater Biology, 18: 341-351.

### 120 of 123 Mineau, P., Collins, B.T. and A. Baril. 1996. On the use of scaling factors to improve interspecies extrapolation of acu te toxicity in birds. Regulatory Toxicology and Pharmacology, 24: 24-29.

Miranda, L.E. and W.D. Hubbard. 1994. Lengt h-dependent winter survival and lipid composition of age 0 largemouth bass in Bay Springs Reservoir, Mississippi . Transactions of the American Fisheries Society, 123: 80-87.

Mitra, G., Mukhopadhyay, P.K. and S.Ayyappan. 2007. Biochemical composition of zooplankton community grown in freshwater ea rthen ponds: nutritional im plication in nursery rearing of fish larvae and early juveniles. Aqua culture, 272, 346-360.

Morrison, H.A., Gobas, F.A.P.C., Lazar, R. , Whittle, D.M., and G.D. Haffner. 1997. Development and verification of a benthic/pelagic food web bioaccumulation model for PCB congeners in Western Lake Erie. Envir onmental Science and Technology, 31: 3267-3273.

Morrison, H.A., Whittle, D.M. and G.D. Haffner. 2000. The relative importance of species invasions and sediment disturba nce in regulating chemical dynami cs in western Lake Erie. Ecological Modeling, 125: 279-294.

Neumann, R.M. and B.R. Murphy. 1992. Seasona l relationships of relative weight to body composition in white crappie, Pomoxis annularis Rafinesque. Aquaculture and fisheries Management, 23: 243-251.

OECD. 1996. Guidelines for testing of chemical s: Bioconcentration: fl ow-through Fish test, 305. Organization for Economic Co-operation a nd Development. Adopted June 14, 1996.

Oliver, B.G. and A.J. Niimi. 1988. Tr ophodynamic analysis of polychlorinated biphenyl congeners and other chlorinated hydrocarbons in the Lake Ontario ecosystem. Environmental Science and Technology, 22: 388-397.

Page, L.M. and B.M. Burr. 1991. A field guide to freshwater fishes; North America, North of Mexico. The Peterson Field Guide Series . Houghton Mifflin Company, Boston.

Petersen, K.E. and T.L. Yates. 1980. Condylura crisata . Mammalian Species (Publication of the American Society of Mammalogists), No. 129, p. 1-4.

Price, D.J. and W.J. Birge. 2006. Use of sunf ish and stoneroller minnows as real-time in situ biomonitors of PCB contamination in freshw ater streams. Environmental Science and Technology, 40: 4388-4393.

Raven, P.H., Evert, R.F. and S.E. Eichhorn. 1999. Biology of Plants. Sixth Edition. W.H. Freeman and Company/Worth Publishers. New York.

Reid, F.A. 2006. A field guide to mammals of North America. 4 th 

### edition. Peterson Field Guides. Houghton Mifflin Company, Boston.

### 121 of 123 Rice, C.P. and D.S. White. 1987. PCB availab ility assessment of river dredging using caged clams and fish. Environmental T oxicology and Chemistry, 6: 259-274.

Sangster, J. 2007. LOGKOW© A databank of eval uated octanol-water partition coefficients (Log P). Available online at: http://logkow.cisti .nrc.ca/logkow/index.jsp. Accessed 29 January 2007.

Sethajintanin, D., Johnson, E.R., Loper, B. R. and K.A. Anderson. 2004. Bioaccumulation profiles of chemical contaminants in fish fr om the Lower Willamette River, Portland Harbor, Oregon. Archives of Environmental C ontamination and Toxicology, 46: 114-123.

Shifrin, N.S. and S. W. Chisholm. 1981. P hytoplankton lipids: inters pecific differences and effects of nitrate, silicate and light-dark cycles. Jour nal of Phycology 17(4): 374-384.

Sidwell, V. 1981. Chemical and nutritional co mposition of finfishes, whales, crustaceans, mollusks, and their products. National Oceanic and Atmosphe ric Administration Technical Memorandum, NMFS F/SEC-11.

Sladecek, V. and A. Sladeckova. 1963. Relationshi p between wet weight and dry weight of the periphyton. Limnology and Oceanography, 8 (2): 309-311.

Smithsonian. 2008. North American Mammals. Smithsonian Museum of Natural History. Accessed online 22-August-2008 at: http://www.mnh.si.edu/mna/search_name.cfm.

Spacie, A., L. S. McCarty and G. M. Rand. 1995. Bioaccumulation and bioavailability in multiphase systems. In G. M. Rand, editor, Fundamentals of Aquatic Toxicology: Effects, Environmental Fate and Risk Assessment 2nd Ed ition. Taylor and Francis, Washington DC.

Stange, K. and D.L. Swackhamer. 1994. Fa ctors affecting phytopla nkton species-specific differences in accumulation of 40 polychlorinated biphenyls (PCBs). Environmental Toxicology and Chemistry, 13 (11): 1849-1860.

USDA. 2005. USDA National Nutrient Database for Standard Reference, Release 18. Nutrient data laboratory Home Page. United States Depa rtment of Agriculture, Agricultural Research Service. Available onl ine at: http://www.nal. usda.gov/fnic/foodcomp.

USEPA. 1993. Wildlife Exposure factors hand book. United States Environmental Protection Agency. Office of Research a nd Development. EPA/600/R-93/187.

USEPA. 1995. Great Lakes Wa ter Quality Initiative Technical Support Document for the Procedure to Determine Bioaccumulation Factors. United States En vironmental Protection Agency. Office of Water. Wa shington, DC. EPA/820/B-95/005.

### 122 of 123 USEPA. 1996. Ecological effects test guidelin es: OPPTS 850.1730, Fish BCF. United States Environmental Protection Agency. Prevention, Pesticides and Toxic Substances. EPA 712-C-96- 129.

USEPA. 2000. Methodology for Deriving Ambient Water Quality Criteria for the Protection of Human Health (2000). United States Enviro nmental Protection Agency. Office of Water, Washington, DC. EPA-822-B-00-004.

USEPA. 2002. Guidance for Selecting Input Para meters in Modeling the Environmental Fate and Transport of Pesticides, Version II. US E nvironmental Protection Agency, Washington DC. Online at: http://www.epa.gov/oppefed1/mode ls water/input_guidance2_28_02.htm.

USEPA. 2003. Methodology for Deriving Ambient Water Quality Criteria for the Protection of Human Health (2000). Techni cal Support Document Volume 2: Development of National Bioaccumulation Factors. United States Enviro nmental Protection Agency. Office of Water, Washington, DC. EPA-822-R-03-030.

USEPA. 2004a. Overview of the Ecological Risk Assessment Process in the Office of Pesticide Programs. U.S. Environmental Protection Agency , Office of Prevention, Pesticides and Toxic Substances, Office of Pesticide Programs , Washington DC. 100 pp. January 23, 2004.

USEPA. 2004b. Exposure Analys is Modeling System (EXAMS): User Manual and System Documentation. EPA/600/R-00/081, Revision G (May 2004). U.S. Environmental Protection Agency, National Exposure Research Laboratory, Athens, Georgia.

USEPA. 2008a. User’s Guide: T-REX Version 1. 4.1 (Terrestrial Residue Exposure model). United States Environmental Protection Agency . Environmental Fate and Effects Division.

USEPA. 2008b. White paper on methods for asse ssing ecological risks of pesticides with persistent, bioaccumulative and toxic character istics. Submitted to the FIFRA Scientific Advisory Panel for review and comment. Un ited States Environmental Protection Agency, Environmental Fate a nd Effects Division.

USEPA. 2008c. Terms of Environment: Glossary, Abbreviations and Acr onyms. United States Environmental Protection Agency . Accessed 8-September-2008, at: http://www.epa.gov/OCEPATERMS/.

USEPA. 2009. EPI Suite TM 

### v4.0. United States Environmenta l Protection Agency. Available for download at: http://www.epa.gov/op pt/exposure/pubs/episuitedl.htm.

USFWS. 1993. Endangered and Thr eatened Wildlife and Plants: Desi gnation of critical Habitat for the Silver Rice Rat. United States Fish and Wildlife Service. 50 CFR Part 17.

USFWS. 2008. Endangered Species Program. United States Fish and Wildlife Program. Accessed online 23-July-2008. http://www.fws .gov/endangered/wildlife.html#Species

### 123 of 123 USGS. 2008a. Dissolved oxygen saturation tables. United States Geologi cal Survey. Available online at: http://water.usgs.gov/software/DOTABLES/.

USGS. 2008b. National Water-Quality Assessment Program. United States Geological Survey. http://water.usgs.gov/nawqa/.

Van Haelst, A.G., Zhao, Q., Van Der Wielen, F.W.M., Govers, H.A. J. and P. De Voogt. 1996. Determination of bioconcentration factors of eight tetrachlorobenzyltoluenes in the zebra mussel Dreissena polymorpha . Ecotoxicology and Environm ental Safety, 34: 35-42.

Vidal, M.-L., Bassères, A. and J.-F. Narbonne . 2002. Seasonal variations of pollution biomarkers in two populations of Corbicula fluminea (Müller). Comp arative Biology and Physiology Part C, 131: 133-151.

Vijverberg, J. and T.H. Frank. 1976. The chemical composition and energy contents of copepods and cladocerans in relation to their size. Fr eshwater Biology, 6: 333-345.

Wetzel, R.G. 1983. Limnology. Second Edition. Saunders College Publishing, Harcourt Brace College Publishers, Fort Worth.

White, P.A., Rasmussen, J.B. and C. Blaise. 19 98. Genotoxic substanc es in the St. Lawrence system II: extracts of fish and macroinvertebra tes from the St. Lawrence and Saguenay Rivers, Canada. Environmental Toxicology and Chemistry, 17 (2): 304-316.

Wolfe, J.L. 1982. Oryzomys palustris . Mammalian Species (Publication of the American Society of Mammalogists), No. 176, p. 1-5.

Wright, J.C. 1958. The limnology of Canyon Ferry Reservoir, I. phyt oplankton-zooplankton relationships in the euphotic zone during September and October 1956. Limnology and Oceanography, 3 (2): 150-159.