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• KABAM Version 1.0 User's Guide and Technical Documentation - Appendix A - Description of Bioaccumulation Model

User's Guide and Technical Documentation
KABAM Version 1.0
(K_ow (based) Aquatic BioAccumulation Model)

Appendix A
Description of Bioaccumulation Model

On this page

• A.1 Calculation of Fraction of Chemical in the Water Column That Is Freely Dissolved (Φ)
• A.2 Calculation of Chemical Concentration in Sediment
• A.3 Calculation of Respiration Uptake (k₁) and Elimination (k₂) Rate Constants
• A.4 Calculation of Growth Rate Constant
• A.5 Calculation of Dietary Uptake (k_D) Rate Constant
• A.6 Calculation of Dietary Elimination (k_E) Rate Constant
  • A.6.1 Parameters Affecting G_F
  • A.6.2 Parameters Affecting E_D
  • A.6.3 Parameters Affecting K_GB
• A.7 Overall Sensitivity of Body Concentration of Chemical (C_B) to Individual Input Parameters
  • A.7.1. First Sensitivity Analysis
  • A.7.2. Second Sensitivity Analysis
  • A.7.3. Third Sensitivity Analysis

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• KABAM User's Guide Home Page

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Appendix A. Description of Bioaccumulation Model

The bioaccumulation portion of KABAM is based on the model published by Arnot and Gobas (2004). The purpose of this model is to estimate chemical concentrations (C_B) and BCF and BAF values for aquatic ecosystems. Conceptually, each aquatic organism is assumed to be a single compartment. Chemicals enter the organism 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 organism 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 incorporated into one equation that is used to define the concentration of the chemical in organism tissues (Equation A1, see Table A1). As uptake constants (i.e., k₁ and k_D) increase, so does the estimated pesticide concentration in an organism. As elimination constants increase (i.e., k₂, k_E, k_G and k_M), estimated pesticide concentrations in an organism decrease. However, for respiration and diet, processes of uptake and elimination are linked. Therefore, factors that would influence uptake constants would also influence elimination constants, so these cannot be considered independently. In addition, as the freely dissolved fraction of pesticide in the water (φ) decreases, so do estimated pesticide concentrations in organisms. Rate constants defining the uptake of a chemical through respiration (k₁) and diet (k_D) and the elimination of a chemical through respiration (k₂) 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 Gobas (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 the pesticide is distributed homogenously throughout organisms. The third assumption is that the effects of chemical partitioning into egg and sperm cells on chemical mass in parents is not considered as a loss pathway. 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 chemical 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 reasonable 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 increasing order of hierarchy, these trophic levels include:

1. phytoplankton
2. zooplankton
3. benthic invertebrates
4. filter feeders
5. small (juvenile) fish
6. medium sized fish, and
7. large fish.

Concentrations in organisms are first calculated at the lowest level of the aquatic food chain (phytoplankton). Pesticide concentrations are then calculated for zooplankton, including consideration that the diet of zooplankton includes phytoplankton, which contain pesticide residues. Tissue residues are calculated for the next five trophic levels based on their diets of organisms from lower trophic levels.

C_B = [k₁ * (m₀ * Φ * C_WTO + m_P * C_WDP) + k_D *Σ (P_i * C_Di)] / (k₂ + k_E + k_G + k_M)

Table A1
Equation A1: Calculation of Pesticide Tissue Residue (C_B) for Single Trophic Levels and Its Associated Parameters
(Arnot and Gobas 2004)

Parameter Symbol	Definition	Value	Units
CB	pesticide concentration in the organism	calculated	g/kg (wet weight)
CBD	pesticide concentration in the organism originating from uptake through diet CBD = CB when k1 = 0	calculated	g/kg (wet weight)
CBR	pesticide concentration in the organism originating from uptake through respiration CBR= CB when kD = 0	calculated	g/kg (wet weight)
CDi	concentration of pesticide in i (prey item)	calculated	g/kg (wet weight)
CS	concentration of the chemical in sediment (dry weight of sediment)	Equation A4	g/(kg (dry) sediment)
CWDP	freely dissolved pesticide concentration in pore water of sediment	input parameter (from PRZM/EXAMS)	g/L
CWTO	total pesticide concentration in water column above the sediment	input parameter (from PRZM/EXAMS)	g/L
k1	pesticide uptake rate constant through respiratory area (i.e., gills, skin)	Equation A5	L/kg*d
k2	rate constant for elimination of the pesticide through the respiratory area (i.e., gills, skin)	Equation A6	d-1
kD	pesticide uptake rate constant for uptake through ingestion of food	Animals: Equation A8 Phytoplankton: 0	kg food/(kg org*day)
kE	rate constant for elimination of the pesticide through excretion of contaminated feces	Animals: Equation A9 Phytoplankton: 0	d-1
kG	organism growth rate constant	Animals: Equation A7 Phytoplankton: 0.1	d-1
kM	rate constant for pesticide metabolic transformation	0	d-1
mo	fraction of respiratory ventilation involving overlying water	1 - mp	none
mp	fraction of respiratory ventilation that involves pore-water of sediment	≤ 5%; 0 for organisms with no contact with pore water	none
Pi	fraction of diet containing i (prey item)	user defined	none
Φ	fraction of the overlying water concentration of the pesticide that is freely dissolved and can be absorbed via membrane diffusion	Equation A2	none

• A.1 Calculation of Fraction of Chemical in the Water Column That Is Freely Dissolved (Φ)

  Aquatic ecosystems contain organic matter suspended in the water column. This suspended organic matter is defined as dissolved organic carbon (DOC) and particulate organic carbon (POC). It is assumed that once chemicals partition to organic carbon, they are no longer bioavailable to aquatic organisms. The fraction of chemical in the water column that is freely dissolved (Φ), 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 water 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 relationships are defined using proportionality constants (α_POC and α_DOC) that are defined from the scientific literature.

  Equation A2

  Φ = 1 / [1 + (X_POC * α_POC * K_OW) + (X_DOC * α_DOC * K_OW) ]

  Table A2
  Equation A2: Derivation of Available Pesticide Fraction in Water (Φ) and Its Associated Parameters
  (Arnot and Gobas 2004)

  Parameter Symbol	Definition	Value	Units
  XPOC	concentration of particulate organic carbon in water	user defined	kg/L
  XDOC	concentration of dissolved organic carbon in water	user defined	kg/L
  KOW	octanol water partition coefficient	user defined	none
  Φ	fraction of the overlying water concentration of the pesticide that is freely dissolved and can be absorbed via membrane diffusion	calculated	none
  αPOC	Proportionality constant to describe the similarity of phase partitioning of POC in relation to octanol	0.35	none
  αDOC	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 calculating the fraction of bioavailable 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 bioavailable 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 generated using the approach described by Arnot and Gobas (2004).

  Equation A3

  F = 1 / [1 + (X_POC * α_POC * K_OW) + (X_DOC * α_DOC * K_OW) + (X_biota * 0.436 * K_OW^0.907)]

  Table A3
  Equation A3: Derivation of Available Pesticide Fraction in Water (F) by EXAMS and Its Associated Parameters

  Parameter 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
  KOW	octanol water partition coefficient	user defined	none
  OCSS	percent organic carbon in suspended sediment	user defined	%
  XSS	concentration of suspended sediments in water column	user defined	kg/m3
  XPOC	concentration of particulate organic carbon in water	XSS*OCSS	kg/m3
  XDOC	concentration of dissolved organic carbon in water	user defined	kg/m3
  Xbiota	concentration of biota in water	user defined	kg/m3
  αPOC	Proportionality constant to describe the similarity of phase partitioning of POC in relation to octanol	0.35	none
  αDOC	Proportionality constant to describe the similarity of phase partitioning of DOC in relation to octanol	0.074	none

  

  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.

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• A.2 Calculation of Chemical Concentration in Sediment

  Since it is possible for aquatic 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 consistent with EXAMS for calculating the fraction of pesticide sorbed to sediment in the benthic column.

  Equation A4

  C_S = C_SOC * OC

  where: C_SOC = C_WDP * K_OC

  Table A4
  Equation A4: Derivation of Pesticide Concentration in the Solid Portion of the Sediment (C_S)
  (Arnot and Gobas 2004)

  Parameter Symbol	Definition	Value	Units
  CS	concentration of the chemical in sediment (dry weight of sediment)	calculated	g/(kg (dry) sediment)
  CSOC	normalized (for OC content) pesticide concentration in sediment	calculated	g/(kg OC)
  CWDP	freely dissolved pesticide concentration in pore water	input parameter (from PRZM/EXAMS)	g/L
  KOC	organic carbon partition coefficient	user defined	L/kg OC
  OC	percent organic carbon in sediment	user defined	%

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• A.3 Calculation of Respiration Uptake (k₁) and Elimination (k₂) Rate Constants

  The respiratory uptake constant (k₁) is calculated differently for phytoplankton (Equation A5.1) and for animals (Equation A5.2). For phytoplankton, k₁ is dependent upon the K_OW of the chemical as well as 2 constants (A and B) that describe chemical uptake resistance through the aqueous and organic phases (respectively) of the plant. If A and B are kept constant at 6x10^-5 and 5.5, respectively as recommended by Arnot and Gobas (2004), k₁ for phytoplankton increases with increasing K_OW, ranging by a factor of 10 from Log K_OW 4-8 (Figure A2).

  For animals, k₁ is dependent upon the chemical uptake efficiency 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 k₁ 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 k₁. The value of k₁ decreases by 50%, as the C_OX value increases from 5 to 10 mg/L (Figure A3). The value of k₁ is also strongly influenced by the body weight of the organism, with decreasing k₁ observed with increasing 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 k₁ value spans 3 orders of magnitude (Figure A3).

  Equation A5.1

  For phytoplankton: k₁ = 1 / (A + B/K_OW)

  Equation A5.2

  For animals: k₁ = (E_W * G_V) / W_B

  where: E_W = [1.85 + 155 / K_OW]^-1

  G_V = 1400 * (W_B^0.65 / C_OX)

  Table A5
  Equations 5.1 and 5.2: Associated with the Derivation of Pesticide Clearance through the Respiratory (gill) System (k₁) and Associated Parameters
  (Arnot and Gobas 2004)

  Parameter Symbol	Definition	Value	Units
  A	constant related to the resistance to pesticide uptake through the aqueous phase of plant	6.0 x 10-5 (default)	days
  B	constant related to the resistance to pesticide uptake through the organic phase of plant	5.5 (default)	days
  Cox	concentration of dissolved oxygen	User input	(mg O2)/L
  EW	pesticide uptake efficiency by gills (fraction)	calculated	none
  GV	ventilation rate of fish, invertebrates, zooplankton	calculated	L/d
  k1	pesticide uptake rate constant through respiratory area (i.e., gills, skin)	calculated	L/kg*d
  KOW	octanol water partition coefficient	user defined	none
  WB	wet weight of the organism	user defined	kg

  

  Figure A2
  Relationship between K_ow and k₁ for phytoplankton.

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  Figure A3
  Relationship between C_OX, W_B and k₁ for aquatic animals.

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  The elimination rate constant for the respiratory system (k₂) is related to the respiratory uptake constants (k₁). This is because both constants are influenced by the same processes related to respiration. The value of k₂ is also influenced by the partitioning 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 between lipid and water is directly related to the octanol-water partition coefficient. It is also assumed that the chemical partitioning between NLOM and water can be related to the octanol-water partition coefficient using a proportionality constant (β) (Equation A6, Table A6).

  Equation A6

  k₂ = k₁ / k_BW

  where: K_BW = V_LB * K_OW + V_NB * β * K_OW + V_WB

  Table A6
  Equation A6: Involved in the Derivation of the Respiratory Elimination Rate Constant (k₂) and Associated Parameters
  (Arnot and Gobas 2004)

  Parameter Symbol	Definition	Value	Units
  k1	pesticide uptake rate constant for chemical uptake through respiratory area (i.e., gills, skin, membrane permeation)	calculated (Equation 5)	L/kg*d
  k2	rate constant for elimination of the pesticide through the respiratory area (i.e., gills, skin, membrane permeation)	calculated	d-1
  KBW	organism-water partition coefficient (based on wet weight)	calculated	none
  KOW	octanol water partition coefficient	user defined	none
  VLB	lipid fraction of organism	user defined	(kg lipid)/(kg organism wet weight)
  VNB	NLOM (Non Lipid Organic Matter) fraction of animals NLOC (Non Lipid Organic Carbon) of plants	user defined	kg NLOM/(kg organism wet weight)
  VWB	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 (k₂) 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 are considered in terms of ranges applicable to KABAM, K_OW has the greatest influence on the determination of k₂ for phytoplankton. When Log K_OW values are changed from 4 to 8, the k₂ value decreases by three orders of magnitude (Figure A4). The lipid fraction of the organism (V_LB) and the non-lipid organic carbon fraction (V_NB) influence k₂, with decreases in k₂ 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 k₂. Likewise, as V_NB decreases, an increase in V_LB has a greater effect on k₂ (Figure A4). When the lipid fraction of organism (V_LB) is increased from 0.5 to 3%, the elimination rate constant through respiration, i.e., k₂, decreases by > 30% (Figure A5). A change in V_NB from 5% to 20% results in a decrease in the value of k₂ that is > 50% (Figure A5). The water content (V_WB) of an organism has a negligible (< 0.5%) effect on k₂.

  

  Figure A4
  Influence of Log K_OW on k₂ (for phytoplankton) at different % lipid (V_LB ).
  (with V_NB = 8%)

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  Figure A5
  Influence of % lipid (V_LB) on k₂ (for phytoplankton) at different % NLOM (V_NB) at Log K_ow 4.
  Note that for all Log K_ow values from 4-8, the curves follow a similar trend for different V_LB and V_NB, but differ in magnitude of k₂.

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  To determine elimination constant from respiration (k₂) 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 influence on the determination of k₂. When Log K_OW values are changed from 4 to 8, the k₂ value decreases by 4 orders of magnitude (Figures A6 and A7). Body weight also influences k₂, with k₂ values decreasing by 2 orders of magnitude as the body weight is increased from 1x10^-7 to 1 kg (this is a range considered 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 k₂, with decreases in k₂ observed as these two values increase. When V_LB is increased from 1 to 5%, k₂ decreases by 84% (Figure A7). When V_NB is increased from 15 to 40%, a 14% decrease in k₂ is observed. The water content (V_WB) of an organism has a negligible (< 0.5%) effect on k₂. Increasing the concentration of the dissolved oxygen (C_OX) value from 5 to 10 mg/L results in a decrease of 50% in the value of k₂.

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  Figure A6
  Influence of Log K_OW on k₂ (for animals) at different body weights (W_B).
  (with V_LB = 5%, V_NB = 20%, V_WB = 75% and C_OX = 10mg/L)

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  Figure A7
  Influence of Log K_OW on k₂ (for animals) at different % lipid composition (V_LB).
  (with W_B = 1 kg, V_NB = 20%, V_WB = 75% and C_OX = 10 mg/L)

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• A.4 Calculation of Growth Rate Constant

  Equations A7.1 and A7.2 provide an approximation of growth of aquatic organisms based on weight and temperature (Table A7). Comparing the results of the two equations indicates that higher temperatures result in higher 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 assumed that if the water temperature (T) < 17.5 °C (midpoint between 10 and 25 °C), equation A.7.1 is used and if T ≥ 17.5 ° C, equation A.7.2 is used.

  Equation A7.1

  k_G = 0.0005 * W_B^-0.2 (T ≈ 10 °C)

  Equation A7.2

  k_G = 0.00251 * W_B^-0.2 (T ≈ 25 °C)

  Table A7
  Equations A7.1 and A7.2: Involving the Derivation of the Growth Rate Constant (k_G) and Associated Parameters
  (Arnot and Gobas 2004).

  Parameter Symbol	Definition	Value	Units
  kG	organism growth rate constant	calculated	d-1
  T	temperature	user defined	°C
  WB	wet weight of the organism	user defined	kg

  

  Figure A8
  Relationship between body weight (W_B) and k_G at 2 different temperatures.

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• A.5 Calculation of Dietary Uptake (k_D) Rate Constant

  In determining uptake and elimination 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 gastrointestinal tract (GIT) of the organisms and the organism itself. Since phytoplankton do not consume other organisms, 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 pesticide transfer efficiency (E_D). The feeding rate is different for filter feeders compared to other aquatic organisms. Empirical dietary pesticide transfer efficiency (E_D) values vary from 0-100%. Variability in E_D has been attributed to various factors, including sorption coefficients of chemicals, composition of diet, and digestibility of diet. Based on several different observations, it is assumed by Arnot and Gobas (2004) that this value can be related to K_OW (Equation A8, Table A8).

  Equation A8

  k_D = E_D * (G_D / W_B)

  where: E_D = (3.0x10^-7 * K_OW + 2.0)^-1

  For animals (except filter feeders): G_D = 0.022 * W_B^0.85 * exp(0.06 * T)

  For filter feeders: G_D = G_V * C_SS * σ

  G_V = 1400 * (W_B^0.65 / C_OX)

  Table A8
  Equation A8: Involving the Derivation of the Pesticide Clearance Rate Constant through Diet (k_D) and Associated Parameters
  (Arnot and Gobas 2004)

  Parameter Symbol	Definition	Value	Units
  Cox	concentration of dissolved oxygen	user defined	(mg O2)/L
  CSS	concentration of suspended solids	user defined	kg/L
  ED	dietary pesticide transfer efficiency	calculated	%
  GD	feeding rate of organism	calculated	kg/d
  GV	ventilation rate of gills	calculated	L/d
  kD	pesticide uptake rate constant for uptake through ingestion of food	calculated	kg food/(kg org*day)
  KOW	octanol water partition coefficient	user defined	none
  T	temperature	user defined	°C
  WB	wet weight of the organism	user defined	kg
  σ	efficiency of scavenging of particles absorbed from water	100	%

  For aquatic organisms (non-filter feeders), the dietary uptake constant (k_D) is derived using 3 input parameters: octanol-water 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 this value up to an order of magnitude (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 °C (Figure A10).

  

  Figure A9
  Relationship between Log K_OW and k_D (for non-filter feeders) at different body weights (W_B).
  (T=10 °C)

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  Figure A10
  Relationship between Log K_OW and k_D (for non-filter feeders) at different water temperatures.
  (W_B=1 kg)

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  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 Table 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 Quality Assessment (NAWQA) data for streams indicate that suspended sediment concentrations 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 magnitude (Figure A12). Increases in W_B from 1x10^-4 to 1x10^-2 kg (which is a reasonable range of weights for filter 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.

  

  Figure A11
  Relationship between Log K_OW and filter feeder k_D with different W_B values.

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  Figure A12
  Relationship between Log K_OW and filter feeder k_D with different C_ss values.

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• A.6 Calculation of Dietary Elimination (k_E) Rate Constant

  The rate constant for elimination of the pesticide 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 the 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 non-filter feeding aquatic animals, k_E is calculated in a similar manner, with the exception of the method of calculating the feeding 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 increase 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 parameters used to derive G_F, E_D, and K_GB are explored below.

  Equation A9

  k_E = G_F * E_D * (K_GB / W_B)

  where: E_D = (3.0x10^-7 * K_OW + 2.0)^-1

  K_GB = (V_LG * K_OW + V_NG * β * K_OW + V_WG) / (V_LB * K_OW + V_NB * β * K_OW + V_WB)

  V_LG = [(1-ε_L) * V_LD] / [(1-ε_L) * V_LD + (1-ε_N) * V_ND + (1-ε_W) * V_WD]

  V_NG = [(1-ε_N) * V_ND] / [(1-ε_L) * V_LD + (1-ε_N) * V_ND + (1-ε_W) * V_WD]

  V_WG = [(1-ε_W) * V_WD] / [(1-ε_L) * V_LD + (1-ε_N) * V_ND + (1-ε_W) * V_WD]

  G_F = [(1-ε_L) * V_LD + (1-ε_N) * V_ND + (1-ε_W) * V_WD] * G_D

  For animals (except filter feeders): G_D = 0.022 * W_B^0.85 * exp(0.06 * T)

  For filter feeders: G_D = G_V * C_SS * σ

  G_V = 1400 * (W_B^0.65 / C_OX)

  Table A9
  Equation A9: Involving the Derivation of the Fecal Elimination Rate Constant (k_E) and Associated Parameters
  (Arnot and Gobas 2004)

  Parameter Symbol	Definition	Value	Units
  Cox	concentration of dissolved oxygen	calculated	(mg O2)/L
  CSS	concentration of suspended solids	user defined	kg/L
  ED	dietary pesticide transfer efficiency	calculated	%
  GD	feeding rate of organism	calculated	kg/d
  GF	egestion rate of fecal matter	calculated	(kg feces)/(kg organism)*d
  GV	ventilation rate of gills	calculated	L/d
  kE	rate constant for elimination of the pesticide through excretion of contaminated feces	for animals: calculated for plants: 0	d-1
  KGB	partition coefficient of the pesticide between the gastro-intestinal tract and the organism	calculated	none
  KOW	octanol water partition coefficient	user defined	none
  T	temperature	user defined	°C
  VLB	lipid fraction of organism	user defined	(kg lipid)/(kg organism wet weight)
  VLD	overall lipid content of diet	user defined	kg/kg
  VLG	lipid content in the gut	calculated	(kg lipid)/(kg digesta wet weight)
  VNB	NLOM (Non Lipid Organic Matter) fraction of animals NLOC (Non Lipid Organic Carbon) of plants	user defined	kg NLOM/(kg organism wet weight)
  VND	overall NLOM content of diet	user defined	kg/kg
  VNG	NLOM content in the gut	calculated	(kg NLOM)/(kg digesta wet weight)
  VWB	water content of the organism	user defined	kg water/(kg organism wet weight)
  VWD	overall water content of diet	user defined	kg/kg
  VWG	water content in the gut	calculated	(kg water)/(kg digesta wet weight)
  WB	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
  εL	dietary assimilation rate of lipids	fish: 92% aquatic inverts: 75% zooplankton: 72%	%
  εN	dietary assimilation rate of NLOM	fish: 60% aquatic inverts: 75% zooplankton: 72%	%
  εW	dietary assimilation rate of water	freshwater organisms: 25%	%
  σ	efficiency of scavenging of particles absorbed from water	100	%

  • A.6.1 Parameters Affecting G_F

    The fecal egestion rate G_F is calculated using the feeding 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 temperature increase, so does the feeding rate of the aquatic animal (Figure A.13). An order of magnitude increase 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.

    

    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 parameters: 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).

    

    Figure A14
    Relationship between C_ox and G_D (filter feeders) with different W_B values.

    ---

    

    Figure A15
    Relationship between scavenging efficiency and G_D (filter feeders) with different W_B values.

    ---

    

    Figure A16
    Relationship between concentration of suspended solids (C_ss) 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.

    

    Figure A17
    Relationship between G_D and G_F with different lipid, NLOM, and water compositions in the diet
    (V_LD, V_ND, and V_WD, respectively). Dietary assimilation rates for 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 invertebrates, and zooplankton, the differences 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 fecal egestion rate (G_F), when compared to effects of the feeding rate (G_D) on G_F (Figure A.18). As with the composition of the 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.

    

    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, and 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).

    

    Figure A19
    Relationship between dietary 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, using 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 particles 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 expected to have less of an influence on G_F when compared to G_D and the parameters used to derive G_D.

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  • 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).

    

    Figure A20
    Dietary transfer efficiency (E_D) vs. Log K_ow

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  • 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:

    V_LG = [(1 - ε_L) * V_LD * G_D] / G_F

    V_NG = [(1 - ε_N) * V_ND * G_D] / G_F

    V_WG = [(1 - ε_W) * V_WD * G_D] / G_F

    Using these simplified equations, changes in the feeding rate of the organism (G_D), the fecal egestion rate (G_F), diet composition, and dietary assimilation 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 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 (approximately 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 water 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 attributed to lipid and NLOM (Figure A.27). For invertebrates, dietary assimilation efficiencies vary significantly, leading to uncertainty in assigning one value to this parameter. Since hydrophobic chemicals are not likely to be stored in the water of organism tissues, it is assumed that this route is not significant to bioaccumulation.

    

    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%.

    ---

    

    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.

    ---

    

    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.

    ---

    

    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.

    ---

    

    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%.

    ---

    

    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%.

    ---

    

    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%.

    ---

    The partitioning of a chemical between the gastrointestinal tract (GIT) and the organism is described by K_GB. This partition coefficient is determined 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 magnitude 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 magnitude 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.

    

    Figure A28
    Relationship between lipid content of the gut (V_LG) and K_GB,
    with different body compositions (V_LB, V_NB, and V_WB).

    ---

    

    Figure A29
    Relationship between NLOM content of the gut (V_NG) and K_GB,
    with different body compositions (V_LB, V_NB, and V_WB).

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• A.7 Overall Sensitivity of Body Concentration of Chemical (C_B) to Individual Input Parameters

  • A.7.1. First Sensitivity Analysis

    In order to understand the influence of input parameters on model predictions 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 parameter is defined in Table A10. Diets of each trophic level were varied according to the definitions in Table A11. Uniform distributions were used to allow unbiased selection of values from set ranges. Once parameter assumptions were assigned, a Monte Carlo simulation was carried out using Crystal Ball 2000. In this simulation, 10,000 trials were conducted with randomly selected 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 contribution 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, concentration of POC in the water column (X_POC) and Log K_OW contribute the greatest variability in the predicted C_B values (38, 28, and 22%, respectively).

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  • 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 parameters 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 defined 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 trophic 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 that 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 fixed in KABAM, including the constant related to the resistance to pesticide uptake through the aqueous phase of plant (A), proportionality constant expressing 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 variability in estimates of C_B.

    Table A10
    Parameters and Associated Assumptions Used for First and Second Sensitivity Analysis of KABAM

    Parameter	Parameter Description	Trophic Level	Minimum of Range	Maximum of Range	Source/Comments
    A	Constant related to the resistance to pesticide uptake through the aqueous phase of plant	Phytoplankton	1x10-5	1x10-4	In Arnot and Gobas 2004, this value is set to a constant of 6.0x10-5 days. This value was varied by an order of magnitude around the reported constant value to understand the influence of this parameter on estimates of bioaccumulation. The reasonable range of values for this parameter is unknown.
    B	Constant related to the resistance to pesticide uptake through the organic phase of plant	Phytoplankton	1	10	In 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 reported constant value to understand the influence of this parameter on estimates of bioaccumulation. The reasonable range of values for this parameter is unknown.
    COX	Concentration of dissolved oxygen (mg O2/L)	All	4	12	Minimum is based on 60% of saturation of water with 6 mg/L as saturation (in 30 °C water). Maximum is based on solubility limit of oxygen in cold water (5 °C; see USGS 2008a).
    CSS	Concentration of suspended solids (kg/L)	All	2.0x10-6	5.0x10-4	Based on 5th and 95th percentiles of approximately 38,000 measurements of suspended sediment concentrations in surface waters of the US provided by NAWQA (USGS 2008b).
    CWTO	Total pesticide concentration in water column above the sediment	All	0.1	100	Assumed to be reasonable range for EECs expected from PRZM/EXAMS modeling.
    CWTP	Freely dissolved pesticide concentration in pore water of sediment	All	0.1	100	Assumed to be reasonable range for EECs expected from PRZM/EXAMS modeling.
    Log Kow	Log of octanol-water partition coefficient	All	4	8	Assumption that bioaccumulation model can be used for chemicals with Log Kow 4-8.
    Koc	Organic carbon partition coefficient	All	3.5x103	3.5 x107	Determined based on assumption that Koc can be estimated as 0.35*Kow. In sensitivity analysis, Koc is linked directly to KOW in order to avoid error in selection of inconsistent values for these parameters.
    mp	Fraction of respiratory ventilation that involves pore-water of sediment	Zooplankton	0	1	Based on full range of parameter values.
    		Benthic Inv.	0	1	Based on full range of parameter values.
    		Filter Feeders	0	1	Based on full range of parameter values.
    		Small Fish	0	1	Based on full range of parameter values.
    		Medium Fish	0	1	Based on full range of parameter values.
    		Large Fish	0	1	Based on full range of parameter values.
    OC	Percent organic carbon in sediment	All	1%	10%	In the OPP standard pond used in EXAMS, the default value for this parameter is 4%. This parameter value is varied by 1 order of magnitude around the OPP standard pond value.
    T	Temperature (°C)	All	1	30	Reasonable range of values for this parameter in the environment.
    VLB	Lipid fraction of organism	Phytoplankton	0.5	2.0	See Table C1 of Appendix C.
    		Zooplankton	1.0	4.0	See Table C2 of Appendix C.
    		Benthic Inv.	0.5	12	See Tables C4-C9 of Appendix C.
    		Filter Feeders	0.4	4	See Tables C13-C15 of Appendix C.
    		Fish	0.5	8	See Table C19 of Appendix C.
    VNB	NLOM (Non Lipid Organic Matter) fraction of animals NLOC (Non Lipid Organic Carbon) of plants	All	-	-	Set to equal 1-VLB-VWB
    VWB	Water content of the organism	Phytoplankton	0.85	0.95	Assume 5% deviation from mean (i.e., 90%).
    		Zooplankton	0.74	0.96	See Section C.2. of Appendix C.
    		Benthic Inv.	0.69	0.83	See Table C3 of Appendix C.
    		Filter Feeders	0.78	0.93	See Table C12 of Appendix C.
    		Fish	0.71	0.80	See Table C18 of Appendix C.
    WB	Wet weight (kg) of the organism	Phytoplankton	-	-	Not a necessary parameter for phytoplankton.
    		Zooplankton	1x10-9	1x10-7	See Section C.2. of Appendix C.
    		Benthic Inv.	5x10-6	2x10-3	See Table C11 of Appendix C.
    		Filter Feeders	2x10-4	1x10-2	See Section C.5 of Appendix C.
    		Small Fish	1x10-3	5x10-2	See Table C16 of Appendix C.
    		Medium Fish	5x10-3	0.6	See Table C17 of Appendix C.
    		Large Fish	0.25	3.6	See Section C.5 of Appendix C.
    XPOC	Concentration of particulate organic carbon in water (kg/L)	All	2.0x10-6	5.0x10-4	Based on 5th and 95th percentiles of approximately 38,000 measurements of suspended sediment concentrations in surface waters of the US provided by NAWQA (USGS 2008b).
    XDOC	Concentration of dissolved organic carbon in water (kg/L)	All	5.0x10-7	5.0x10-5	In the OPP standard pond used in EXAMS, the default value for this parameter is 5.0x10-6. This parameter value is varied by 2 orders of magnitude around the OPP standard pond value.
    β	Proportionality constant expressing the sorption capacity of NLOM or NLOC to that of octanol	All	0	1	Designed to represent all values equal to or less than the partitioning of a chemical between octanol and water.
    εL	Dietary assimilation rate of lipids	Animals	0	1	Based on full range of parameter values.
    εN	Dietary assimilation rate of NLOM	Animals	0	1	Based on full range of parameter values.
    εW	Dietary assimilation rate of water	Animals	0	1	Based on full range of parameter values.
    σ	Efficiency of scavenging of particles absorbed from water	Filter Feeders	0	1	Based on full range of parameter values.

    Table A11
    Dietary Assumptions of Aquatic Trophic Levels Used for Sensitivity Analysis of KABAM

    Trophic Level	Organism in diet	Minimum Value	Maximum Value	Comments
    Zooplankton	Phytoplankton	100%	100%	-
    Benthic Invertebrates	Sediment	0	50%	-
    	Phytoplankton	0	50%	-
    	Zooplankton	-	-	Set to 1- (% diet attributed to sediment + % diet attributed to phytoplankton)
    Filter Feeder	Sediment	0	33%	-
    	Phytoplankton	0	33%	-
    	Zooplankton	0	33%	-
    	Benthic invertebrates	-	-	Set to 1- (% diet attributed to sediment + % diet attributed to phytoplankton + % diet attributed to zooplankton)
    Small Fish	Phytoplankton	0	50%	-
    	Zooplankton	0	50%	-
    	Benthic invertebrates	-	-	Set to 1- (% diet attributed to phytoplankton + % diet attributed to zooplankton)
    Medium Fish	Zooplankton	0	50%	-
    	Benthic invertebrates	0	50%	-
    	Small fish	-	-	Set to 1- (% diet attributed to zooplankton + % diet attributed to benthic invertebrates)
    Large Fish	Small fish	0	100%	It is assumed that large fish consume only smaller fish
    	Medium Fish	-	-	Set to 1- (% diet attributed to small fish)

    Table A12
    Second Sensitivity Analysis Results: Contribution to Variance of Specific Variables to C_B Values of Phytoplankton at Different Log K_ow Values

    Variable	4	5	6	7	8
    A	≤ 0.1%	≤ 0.1%	0.7%	9.3%	18.2%
    VLB	≤ 0.1%	≤ 0.1%	≤ 0.1%	0.2%	≤ 0.1%
    VWB	5.9%	3.4%	2.3%	0.2%	≤ 0.1%
    Water Column EEC	59.6%	46.1%	44.9%	45.7%	43.0%
    XPOC	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 Results: Contribution to Variance of Specific Variables to C_B Values of Zooplankton at Different Log K_ow Values

    Variable	4	5	6	7	8
    COX	≤ 0.1%	≤ 0.1%	0.2%	2.5%	4.3%
    mP	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%
    VLB	1.2%	0.4%	0.6%	0.6%	≤ 0.1%
    VWB	20.8%	14.1%	8.8%	0.6%	0.8%
    Water Column EEC	11.2%	2.2%	0.3%	≤ 0.1%	≤ 0.1%
    WB	≤ 0.1%	≤ 0.1%	≤ 0.1%	0.5%	0.6%
    XPOC	1.7%	2.5%	0.8%	≤ 0.1%	≤ 0.1%
    β	32.5%	20.2%	8.4%	1.7%	≤ 0.1%
    εL	≤ 0.1%	≤ 0.1%	≤ 0.1%	0.2%	≤ 0.1%
    εN	≤ 0.1%	≤ 0.1%	0.5%	1.1%	0.3%
    Total	99.3%	99.6%	99.1%	95.0%	99.1%

    Table A14
    Second Sensitivity Analysis Results: Contribution to Variance of Specific Variables to C_B Values of Benthic Invertebrates at Different Log K_ow Values

    Variable	4	5	6	7	8
    Characteristics of prey*	≤ 0.1%	1.2%	12.0%	12.0%	6.0%
    COX	≤ 0.1%	≤ 0.1%	≤ 0.1%	1.3%	1.6%
    Diet composition	≤ 0.1%	0.2%	1.2%	1.0%	8.6%
    mP	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%
    VLB	4.0%	2.5%	1.6%	1.0%	≤ 0.1%
    VWB	3.6%	1.5%	1.1%	0.6%	≤ 0.1%
    Water Column EEC	14.4%	1.9%	≤ 0.1%	≤ 0.1%	≤ 0.1%
    XPOC	2.5%	2.7%	0.5%	≤ 0.1%	≤ 0.1%
    β	32.6%	21.5%	7.6%	0.4%	≤ 0.1%
    εL	≤ 0.1%	≤ 0.1%	0.7%	1.2%	≤ 0.1%
    εN	≤ 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 Results: Contribution to Variance of Specific Variables to C_B Values of Filter Feeders at Different Log K_ow Values

    Variable	4	5	6	7	8
    Characteristics of prey*	≤ 0.1%	1.5%	11.8%	11.7%	2.5%
    COX	≤ 0.1%	≤ 0.1%	≤ 0.1%	2.0%	3.8%
    CSS	0.2%	1.4%	2.4%	3.2%	8.1%
    Diet composition	≤ 0.1%	≤ 0.1%	0.3%	0.6%	1.6%
    mP	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%
    VLB	1.5%	1.5%	1.3%	0.4%	≤ 0.1%
    VWB	11.1%	7.0%	4.3%	2.8%	0.4%
    Water Column EEC	10.6%	1.8%	≤ 0.1%	≤ 0.1%	≤ 0.1%
    WB	≤ 0.1%	≤ 0.1%	≤ 0.1%	≤ 0.1%	0.3%
    XPOC	2.0%	2.2%	0.2%	≤ 0.1%	≤ 0.1%
    β	42.0%	30.7%	13.7%	1.5%	≤ 0.1%
    εL	≤ 0.1%	0.4%	2.6%	2.7%	0.5%
    εN	≤ 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.

    Table A16
    Second Sensitivity Analysis Results: Contribution to Variance of Specific Variables to C_B Values of Small Fish at Different Log K_ow Values

    Variable	4	5	6	7	8
    Characteristics of prey*	≤ 0.1%	3.3%	16.1%	18.2%	9.2%
    COX	0.2%	0.2%	≤ 0.1%	1.3%	2.3%
    Diet composition	≤ 0.1%	0.6%	2.2%	2.9%	2.7%
    mP	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%
    VLB	2.5%	1.3%	1.3%	0.8%	0.3%
    VWB	1.5%	1.0%	0.3%	0.2%	≤ 0.1%
    Water Column EEC	11.6%	1.5%	0.2%	≤ 0.1%	≤ 0.1%
    XPOC	2.0%	2.6%	0.5%	≤ 0.1%	≤ 0.1%
    β	45.7%	34.3%	13.8%	1.7%	0.2%
    εL	≤ 0.1%	1.2%	4.4%	3.4%	0.9%
    εN	≤ 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 Results: Contribution to Variance of Specific Variables to C_B Values of Medium Fish at Different Log K_ow Values

    Variable	4	5	6	7	8
    Characteristics of prey*	≤ 0.1%	2.7%	17.3%	21.0%	10.0%
    COX	0.3%	≤ 0.1%	≤ 0.1%	1.1%	2.4%
    Diet composition	≤ 0.1%	0.2%	0.8%	0.6%	≤ 0.1%
    mP	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%
    VLB	0.4%	0.2%	0.2%	0.4%	≤ 0.1%
    VWB	15.7%	9.1%	5.0%	4.2%	0.5%
    Water Column EEC	7.7%	1.1%	0.2%	≤ 0.1%	≤ 0.1%
    WB	≤ 0.1%	0.2%	≤ 0.1%	≤ 0.1%	≤ 0.1%
    XPOC	1.2%	1.7%	0.5%	≤ 0.1%	≤ 0.1%
    β	51.2%	43.5%	20.9%	4.3%	0.5%
    εL	0.2%	1.4%	2.2%	1.9%	0.3%
    εN	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.

    Table A18
    Second Sensitivity Analysis Results: Contribution to Variance of Specific Variables to C_B Values of Large Fish at Different Log K_ow Values

    Variable	4	5	6	7	8
    Characteristics of prey*	≤ 0.1%	5.8%	22.8%	23.1%	9.7%
    COX	0.9%	0.9%	≤ 0.1%	0.9%	2.1%
    Diet composition	0.3%	0.8%	0.9%	0.3%	≤ 0.1%
    mP	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%
    VLB	1.8%	0.9%	1.3%	1.1%	≤ 0.1%
    VWB	1.1%	0.5%	0.5%	0.6%	0.2%
    Water Column EEC	9.3%	1.2%	≤ 0.1%	≤ 0.1%	≤ 0.1%
    XPOC	1.4%	1.7%	0.3%	≤ 0.1%	≤ 0.1%
    β	52.0%	38.8%	13.8%	1.7%	≤ 0.1%
    εL	≤ 0.1%	0.9%	1.7%	2.2%	0.4%
    εN	2.0%	10.5%	15.3%	12.8%	4.3%
    Total	98.6%	98.9%	99.0%	99.0%	98.4%

    ^*m_P, body composition, etc.

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  • A.7.3. Third Sensitivity Analysis

    A third sensitivity analysis was conducted to explore the influences of KABAM input parameters that are controlled by the user (including chemical specific inputs and ecosystem inputs), with the fixed parameters unchanged. In this 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 defined in Table A19. Diets of each trophic level were varied according to the definitions in Table A11.

    The contributions of individual chemical specific and ecosystem input 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 trophic levels of KABAM are provided in Tables A20-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 addition, these tables indicate that the relative importance of individual parameters to estimates of C_B change with Log K_OW.

    This sensitivity analysis indicates that several parameters contribute >10% of variance in C_B of one or more trophic levels. These include: water column EEC, pore water EEC, particulate organic carbon (X_POC), sediment organic carbon (OC), concentration 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 with the standard pond used in EXAMS.

    One notable observation resulting from this sensitivity analysis is that at Log K_OW 7 and 8, benthic invertebrate diet composed 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 estimated 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.

    Table A19
    Parameters and Associated Assumptions Used for Third Sensitivity Analysis of KABAM

    Parameter	Parameter Description	Trophic Level	Minimum of Range	Maximum of Range	Source/Comments
    COX	Concentration of dissolved oxygen (mg O2/L)	All	4	12	Minimum is based on 60% of saturation of water with 6 mg/L as saturation (in 30 °C water). Maximum is based on solubility limit of oxygen in cold water (5 °C; see USGS 2008a).
    CSS	Concentration of suspended solids (kg/L)	All	2.0x10-6	5.0x10-4	Based on 5th and 95th percentiles of approximately 38,000 measurements of suspended sediment concentrations in surface waters of the US provided by NAWQA (USGS 2008b).
    CWTO	Total pesticide concentration in water column above the sediment	All	0.1	100	Assumed to be reasonable range for EECs expected from PRZM/EXAMS modeling.
    CWTP	Freely dissolved pesticide concentration in pore water of sediment	All	0.1	100	Assumed to be reasonable range for EECs expected from PRZM/EXAMS modeling.
    Koc	Organic carbon partition coefficient	All	3.5x103	3.5 x107	Determined based on assumption that Koc can be estimated as 0.35*Kow. In sensitivity analysis, Koc is linked directly to KOW in order to avoid error in selection of inconsistent values for these parameters.
    mp	Fraction of respiratory ventilation that involves pore-water of sediment	Zooplankton	0	0.05	Based on default parameter values (0 or 0.05).
    		Benthic Inv.	0	0.05	Based on default parameter values (0 or 0.05).
    		Filter Feeders	0	0.05	Based on default parameter values (0 or 0.05).
    		Small Fish	0	0.05	Based on default parameter values (0 or 0.05).
    		Medium Fish	0	0.05	Based on default parameter values (0 or 0.05).
    		Large Fish	0	0.05	Based on default parameter values (0 or 0.05).
    OC	Percent organic carbon in sediment	All	1%	10%	In the OPP standard pond used in EXAMS, the default value for this parameter is 4%. This parameter value is varied by one order of magnitude around the OPP standard pond value.
    T	Temperature (°C)	All	1	30	Reasonable range of values for this parameter in the environment.
    VLB	Lipid fraction of organism	Phytoplankton	0.5	2.0	See Table C1 of Appendix C.
    		Zooplankton	1.0	4.0	See Table C2 of Appendix C.
    		Benthic Inv.	0.5	12	See Tables C4-C9 of Appendix C.
    		Filter Feeders	0.4	4	See Tables C13-C15 of Appendix C.
    		Fish	0.5	8	See Table C19 of Appendix C.
    VNB	NLOM (Non Lipid Organic Matter) fraction of animals NLOC (Non Lipid Organic Carbon) of plants	All	-	-	Set to equal 1-VLB-VWB
    VWB	Water content of the organism	Phytoplankton	0.85	0.95	Assume 5% deviation from mean (i.e., 90%).
    		Zooplankton	0.74	0.96	See Section C.2. of Appendix C.
    		Benthic Inv.	0.69	0.83	See Table C3 of Appendix C.
    		Filter Feeders	0.78	0.93	See Table C12 of Appendix C.
    		Fish	0.71	0.80	See Table C18 of Appendix C.
    WB	Wet weight (kg) of the organism at t	Phytoplankton	-	-	Not a necessary parameter for phytoplankton.
    		Zooplankton	1x10-9	1x10-7	See Section C.2. of Appendix C.
    		Benthic Inv.	5x10-6	2x10-3	See Table C11 of Appendix C.
    		Filter Feeders	2x10-4	1x10-2	See Section C.5 of Appendix C.
    		Small Fish	1x10-3	5x10-2	See Table C16 of Appendix C.
    		Medium Fish	5x10-3	0.6	See Table C17 of Appendix C.
    		Large Fish	0.25	3.6	See Section C.5 of Appendix C.
    XPOC	Concentration of particulate organic carbon in water (kg/L)	All	2.0x10-6	5.0x10-4	Based on 5th and 95th percentiles of approximately 38,000 measurements of suspended sediment concentrations in surface waters of the US provided by NAWQA (USGS 2008b).
    XDOC	Concentration of dissolved organic carbon in water (kg/L)	All	5.0x10-7	5.0x10-5	In the OPP standard pond used in EXAMS, the default value for this parameter is 5.0x10-6. This parameter value is varied by two orders of magnitude around the OPP standard pond value.

    Table A20
    Third Sensitivity Analysis Results: Contributions to Variance of Specific Variables to C_B Values of Phytoplankton at Different Log K_ow Values

    Variable	4	5	6	7	8
    VLB	0.7%	0.4%	0.3%	< 0.1%	< 0.1%
    VWB	7.6%	5.1%	3.3%	0.5%	< 0.1%
    Water Column EEC	80.1%	55.9%	51.8%	52.9%	52.8%
    XPOC	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: Contributions to Variance of Specific Variables to C_B Values of Zooplankton at Different Log K_ow Values

    Variable	4	5	6	7	8
    COX	< 0.1%	< 0.1%	< 0.1%	0.6%	3.1%
    mP	< 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%
    VLB	12.3%	12.7%	13.3%	7.2%	1.1%
    VWB	1.3%	0.8%	0.9%	0.4%	< 0.1%
    Water Column EEC	75.0%	48.0%	16.0%	0.9%	< 0.1%
    WB	< 0.1%	< 0.1%	< 0.1%	< 0.1%	0.6%
    XPOC	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: Contributions to Variance of Specific Variables to C_B Values of Benthic Invertebrates at Different Log K_ow Values

    Variable	4	5	6	7	8
    Characteristics of prey*	< 0.1%	< 0.1%	< 0.1%	0.2%	< 0.1%
    COX	< 0.1%	0.5%	2.2%	0.2%	< 0.1%
    Diet composition	< 0.1%	1.2%	21.1%	34.4%	36.5%
    mP	< 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%
    VLB	31.1%	38.5%	20.0%	6.3%	0.8%
    VWB	< 0.1%	< 0.1%	0.5%	0.2%	< 0.1%
    Water Column EEC	57.8%	27.9%	1.7%	< 0.1%	< 0.1%
    XPOC	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.

    Table A23
    Third Sensitivity Analysis Results: Contributions to Variance of Specific Variables to C_B Values of Filter Feeders at Different Log K_ow Values

    Variable	4	5	6	7	8
    Characteristics of prey*	< 0.1%	2.1%	7.7%	14.8%	5.3%
    COX	< 0.1%	< 0.1%	0.5%	< 0.1%	1.3%
    CSS	0.3%	12.6%	12.5%	6.3%	13.0%
    Diet composition	< 0.1%	0.8%	2.9%	2.5%	7.6%
    mP	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%
    VLB	24.2%	24.0%	16.5%	9.5%	4.0%
    VWB	0.6%	< 0.1%	0.4%	0.4%	< 0.1%
    Water Column EEC	64.8%	26.7%	1.4%	< 0.1%	< 0.1%
    WB	< 0.1%	< 0.1%	< 0.1%	0.2%	< 0.1%
    XPOC	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: Contributions to Variance of Specific Variables to C_B Values of Small Fish at Different Log K_ow Values

    Variable	4	5	6	7	8
    Characteristics of prey*	< 0.1%	3.1%	21.0%	32.6%	28.6%
    COX	< 0.1%	2.1%	5.6%	0.6%	< 0.1%
    Diet composition	< 0.1%	1.1%	6.8%	9.4%	10.0%
    mP	< 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%
    VLB	28.7%	25.3%	13.4%	4.8%	0.5%
    VWB	< 0.1%	0.2%	< 0.1%	< 0.1%	< 0.1%
    Water Column EEC	61.8%	34.4%	2.7%	< 0.1%	< 0.1%
    WB	< 0.1%	0.4%	0.7%	0.2%	< 0.1%
    XPOC	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.

    Table A25
    Third Sensitivity Analysis Results: Contributions to Variance of Specific Variables to C_B Values of Medium Fish at Different Log K_ow Values

    Variable	4	5	6	7	8
    Characteristics of prey*	< 0.1%	4.9%	25.2%	38.4%	30.7%
    COX	< 0.1%	3.9%	8.6%	0.9%	< 0.1%
    Diet composition	< 0.1%	0.3%	2.7%	2.6%	1.4%
    mP	< 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%
    VLB	19.3%	14.0%	6.6%	3.0%	0.3%
    VWB	2.4%	1.8%	1.4%	0.4%	< 0.1%
    Water Column EEC	68.4%	35.5%	2.4%	< 0.1%	< 0.1%
    WB	< 0.1%	0.4%	0.4%	< 0.1%	< 0.1%
    XPOC	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 K_ow Values

    Variable	4	5	6	7	8
    Characteristics of prey*	0.2%	5.3%	24.5%	38.0%	30.7%
    COX	< 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%
    VLB	27.5%	19.3%	11.6%	8.0%	1.5%
    VWB	< 0.1%	0.2%	< 0.1%	< 0.1%	< 0.1%
    Water Column EEC	62.6%	28.8%	2.2%	< 0.1%	< 0.1%
    WB	< 0.1%	0.4%	< 0.1%	< 0.1%	< 0.1%
    XPOC	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.

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