United States Office of Water EPA-823-R-00-008

Environmental Protection (4305) September 2000

Agency

AQUATOX FOR WINDOWS

A MODULAR FATE AND EFFECTS

MODEL FOR AQUATIC ECOSYSTEMS

RELEASE 1

VOLUME 3: MODEL VALIDATION REPORTS



	



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;+,zT2{C,AB1+>.&>;51+>z;+62{/+.&p2{^$=&,>.&/+CtONaad$8.&4;+62&I.&-$2{1+>z;+62{4!1+AB:/+.&;+1+,>z^\*(#LdF


>,;+62&-[AB2&.&4!:-$2s,?x;+62s0.&/+1+C1+;+IM,?x
gE
 1+>{4!1+AB:/+.&;+1+>KZ;+62s,-$.&/+01+/+/+2s2&=&,4!IX4!;+2&AW1+4V4!2&2&>Z762&>

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1+>Z;+62U-$2&4!2&-$0,1+-F562U4!1+AB:/+.&;+1+,>Z7.&4V4!;+.&-$;+2&CZ71+;+6Z.U/+,7 1+>1+;+1+.&/9=&,>C1+;+1+,>Z,?O_ZKL`+Anx8YT:;9;+62U<-$2&C1+=&;+2&C

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0.&-$IX1+>K|T1+,AB.&4!47.&4>,;5-$2&<,-$;+2&Cz?$,-n;+62{-$2&4!2&-$0,1+-F
	



 


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-$.&;+2&4

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;[;+1+AB2&4!8L4!:=&6|.&4s1+>|2&.&-$/+ID.&IX8L;+62

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MODEL VALIDATION REPORTS CHAPTER 3

3-1

VALIDATION OF AQUATOX 1.68 FOR PREDICTING BIOACCUMULATION

 OF PCBS IN THE LAKE ONTARIO FOOD WEB

Introduction

Certain chemicals are persistent in aquatic systems and tend to accumulate in the tissue of fish and

other aquatic organisms, sometimes to levels that make them unsafe for human or wildlife

consumption. The “bioaccumulative” compounds, such as polychlorinated biphenyls (PCBs), are

the cause of a significant number of advisories against fish consumption throughout the U.S.

Exposure to bioaccumulative compounds may be through water, contaminated sediment, or diet.

The extent to which a chemical bioaccumulates depends uponmanyfactors,such astrophic structure

(food-chain and food-web relationships), ambient water and sediment characteristics, the chemical

characteristics ofthe pollutant, and the lipid content ofthe exposed organisms. The bioaccumulation

factor(BAF)relatestissue concentration to ambient water concentration ofthe chemical. BAFs may

be useful in setting ambient water quality standards for bioaccumulative chemicals. The various

chemical and biological processes that drive bioaccumulation may vary over time. For example,

food consumption and therefore dietary exposure will vary with seasonal cycles of prey organisms.

However, for purposes ofsetting water quality standards, regulatory agencies are usually concerned

with protection based on lifetime average consumption, and thus with long-term average conditions

of the water body and of the fish that are consumed. 

Several mechanisticmodels have been developed to predictBAFs and fish tissue concentrations. For

lipophilic compounds, the octanol-water partition coefficient (KOW) is often used as an indication of

the compound’s bioaccumulative potential. Thomann et al. (1992) and Gobas (1993) developed

steady-state modelsthat rely on KOW, organic content ofthe sediment, lipid content ofthe organisms,

and position in the food web to predict tissue concentrations in the organisms. A recent paper

(Burkhard 1998) compared the Thomann and Gobas models against an observed dataset from Lake

Ontario (Oliver and Niimi 1988). The two models performed similarly for compounds with log

KOWs in the range of three to eight, and they were in good agreement with the observed data. The

models were shown to be highly sensitive to several input parameters. Sources of uncertainty also

were identified. The purpose of this study is to evaluate the performance of the AQUATOX model

using the same assumptions and dataset in so far as possible.

Model Structure

The AQUATOX model is a general ecological risk assessment model that represents the combined

environmental fate and effects of conventional pollutants,such as nutrients and sediments, and toxic

chemicals in aquatic ecosystems. It considers several trophic levels, including attached and

planktonic algae and submerged aquatic vegetation, invertebrates, and forage, bottom-feeding, and

game fish; it also represents associated organic toxicants. It can be implemented as a simple model

(indeed, it has been used to simulate an abiotic flask) or as a truly complex food-web model. Foodweb modeling is now considered necessary for bioaccumulation studies other than screening level

(Abbott et al. 1995). “Food web models provide a means for validation because they mechanistically
MODEL VALIDATION REPORTS CHAPTER 3

3-2

(1)

(2)

describe the bioaccumulation process and ascribe causality to observed relationships between biota

and sediment or water” (Connolly and Glaser 1998). 

The fate portion of the model, which is applicable especially to organic toxicants, includes:

partitioning among organisms, suspended and sedimented detritus, suspended and sedimented

inorganic sediments, and water; volatilization; hydrolysis; photolysis; ionization; and microbial

degradation. Earlier versions of AQUATOX had two modes for representing partitioning of a

pollutant, equilibrium fugacity and kinetic partitioning. However, the equilibrium fugacity mode

was found to have limited applicability and was discontinued. The effects portion of the model

includes: chronic and acute toxicity to the various organisms modeled; and indirect effects such as

release of grazing and predation pressure, increase in detritus and recycling of nutrients from killed

organisms, dissolved oxygen sag due to increased decomposition, and loss of food base for animals.

Partition Coefficients

Although AQUATOX is a kinetic model, steady-state partition coefficients for organic pollutants

are computed in order to place constraints on competitive uptake and loss processes, speeding up

computations. They are estimated from empirical regression equations and the pollutant's octanolwater partition coefficient. 

Natural organic matter isthe primary sorbentfor neutral organic pollutants. Hydrophobic chemicals

partition primarily in nonpolar organic matter (Abbott et al. 1995). Refractory detritus is relatively

nonpolar; its partition coefficient is a function of the octanol-water partition coefficient (N = 34, r

2

= 0.93; Schwarzenbach et al. 1993):

where:

KOMRefrDetr = suspended refractory detritus-water partition coefficient (L/kg); and

KOW = octanol-water partition coefficient (unitless).

This and the following equations are extended to polar compounds, following the work of Smejtek

and Wang (1993):

where:

Nondissoc = un-ionized fraction (unitless); and

IonCorr = correction factor for decreased sorption, generally 0.1 (unitless).

Partitioning of bioaccumulative chemicals on organic carbon in sediments in Lake Ontario, as

represented by the Oliver and Niimi (1988) data, exhibits a weak relationship with KOW (US EPA

1995, Burkhard 1998): 
MODEL VALIDATION REPORTS CHAPTER 3

3-3

(3)

(4)

(5)

(6)

where:

KOC = the partition coefficient for particulate organic carbon-water (L/kg).

Converting to organic matter (assuming a conversion factor of 0.526) and generalizing to include

polar compounds, this relationship is used in AQUATOX for this project, and only this project, to

represent the partitioning of chemicals between water and refractory detritus in sediments:

where:

KOMRDetrSed = sedimented refractory detritus-water partition coefficient (L/kg).

There appearsto be a dichotomy in partitioning; data in the literature suggest that labile detritus does

not take up hydrophobic compounds asrapidly asrefractory detritus. Algal cell membranes contain

polar lipids, and it is likely that this polarity is retained in the early stages of decomposition. KOC

does not remain the same upon aging, death, and decomposition, probably because of polarity

changes. In an experiment using fresh and aged algal detritus, there was a 100% increase in KOC

with aging (Koelmans et al. 1995). KOC increased as the C/N ratio increased, indicating that the

material was becoming more refractory. In another study, KOC doubled between day 2 and day 34,

probably due to deeper penetration into the organic matrix and lower polarity of the partially

decomposed material (Cornelissen et al. 1997).

Polar substrates increase the pKa of the compound (Smejtek and Wang 1993). This is represented

in the model by lowering the pH of polar particulate material by one pH unit, which changes the

dissociation accordingly. The partition equation for labile detritus (N = 3, r2 = 1.0;) is based on a

study by Koelmans et al. (1995) using fresh algal detritus:

where:

KOCLab Part = partition coefficient for suspended labile organic carbon (L/kg).

The equation is generalized to polar compounds and transformed to an organic matter partition

coefficient:

where:

KOCLabPart = partition coefficient for suspended labile organic carbon (L/kg);

KOMLabDetr = partition coefficient for suspended labile detritus (L/kg); and
MODEL VALIDATION REPORTS CHAPTER 3

3-4

(7)

(8)

(9)

0.526 = conversion factor for organic carbon to organic matter.

O’Connor and Connolly (1980; see also Ambrose et al., 1991) found that the sediment partition

coefficient is the inverse of the mass of suspended sediment, and DiToro (1985) developed a

construct to represent the relationship. However, AQUATOX models partitioning directly to organic

detritus and ignores inorganic sediments, which are seldom involved directly in sorption of neutral

organic pollutants. Therefore, the partition coefficient is not corrected for mass of sediment. 

Association of hydrophobic compounds with colloidal and dissolved organicmatter(DOM)reduces

bioavailability; such contaminants are unavailable for uptake by organisms (Stange and Swackhamer

1994, Gilek et al. 1996). Therefore, it is imperative that complexation of organic chemicals with

DOM be modeled correctly. In particular, contradictory research results can be reconciled by

considering that DOM is not homogeneous: refractory humic acids, derived from decomposition of

terrestrial and wetland organicmaterial, are quite differentfrom labile exudatesfrom algae and other

indigenous organisms. 

Humic acids exhibit high polarity and do not readily complex neutral compounds. Natural humic

acids from a Finnish lake with extensive marshes were spiked with a PCB, but a PCB-humic acid

complex could not be demonstrated (Maaret et al. 1992). In another study, Freidig et al. (1998) used

artificially prepared Aldrich humic acid to determine a humic acid-DOC partition coefficient for

several dissimilar chemicals (n = 5, r2, = 0.80), although they cautioned about extrapolation to the

field:

where:

KOCRefrDOM = refractory dissolved organic carbon distribution coefficient (L/kg).

Until a better relationship is found, we are using a generalization of their equation to include polar

compounds, transformed from organic carbon to organic matter, in AQUATOX:

where:

KOMRefrDOM = refractory dissolved organic matter distribution coefficient (L/kg).

Nonpolar lipids in algae occur in the cell contents, and it is likely that they constitute part of the

labile dissolved exudate, which may be both excreted and lysed material. Therefore, the stronger

relationship reported by Koelmans and Heugens (1998) for partitioning to algal exudate (n = 6, r2

= 0.926) is:
MODEL VALIDATION REPORTS CHAPTER 3

3-5

(10)

(11)

 which was also generalized for polar compounds and transformed for organic matter:

where:

KOCLabDOC = partition coefficient for labile dissolved organic carbon (L/kg); and

KOMLabDOM = partition coefficient for labile dissolved organic matter (L/kg).

Unfortunately, older data and modeling efforts failed to distinguish between hydrophobic compounds

that were truly dissolved and those that were complexed with DOM. For example, the PCB water

concentrationsfor Lake Ontario, reported by Oliver and Niimi (1988) and used by many subsequent

researchers, included both dissolved and DOC-complexed PCBs (a fact which they recognized).

AQUATOX distinguishes between truly dissolved and complexed compounds; therefore, the

partition coefficients may be larger than those used in older studies.

Bioaccumulation of PCBs in algae depends on solubility, hydrophobicity and molecular

configuration of the compound, and growth rate,surface area and type, and content and type of lipid

in the alga (Stange and Swackhamer 1994). Phytoplankton may double or triple in one day and

periphyton turnover may be so rapid that some PCBs will not reach equilibrium (cf. Hill and

Napolitano 1997). Therefore, one should use the term “bioaccumulation factor” (BAF) rather than

“bioconcentration factor,” which implies equilibrium (Stange and Swackhamer 1994).

Hydrophobic compounds partition to lipids in algae, but the relationship is not a simple one.

Phytoplankton lipids can range from 3 to 30% by weight (Swackhamer and Skoglund 1991), and

not all lipids are the same. Polar phospholipids occur on the surface. Hydrophobic compounds

preferentially partition to internal neutral lipids, but those are usually a minor fraction of the total

lipids, and they vary depending on growth conditions and species (Stange and Swackhamer 1994).

Algal lipids have a much stronger affinity for hydrophobic compoundsthan does octanol,so that the

algal BAFlipid > KOW (Stange and Swackhamer 1994, Koelmans et al. 1995, Sijm et al. 1998). 

For algae, the approximation to estimate the dry-weight bioaccumulation factor (r2

 = 0.87), computed

from Swackhamer & Skoglund’s (1993) study of numerous PCB congeners with a natural

phytoplankton assemblage, is:

where:

KBAlga = partition coefficient between phytoplankton and water (L/kg).

Rearranging and extending to hydrophilic and ionized compounds:
MODEL VALIDATION REPORTS CHAPTER 3

3-6

(12)

Figure 1. Partitioning to various types of organic

matter as a function of KOW. 

(13)

(14)

Comparing the results of using these coefficients, we see that they are consistent with the relative

importance of the various substrates in binding organic chemicals (Figure 1). Binding capacity of

detritus is greater than dissolved organic matter in Great Lakes waters (Stange and Swackhamer

1994, Gilek et al. 1996). In a study using Baltic Sea water, less than 7% PCBs were associated with

dissolved organic matter and most were associated with algae (Björk and Gilek 1999). In contrast,

in a study using algal exudate and a PCB, 98% of the dissolved concentration was as a dissolved

organic matter complex and only 2% was bioavailable (Koelmans and Heugens 1998).

For macrophytes, an empirical relationship reported by Gobas et al. (1991) for 9 chemicals with

LogKOWs of 4 to 8.3 (r2

 = 0.97) is used:

Again, rearranging and extending to hydrophilic and ionized compounds:

For the invertebrate bioconcentration factor, the following empirical equation is used, based

on 7 chemicals with LogKOWs ranging from 3.3 to 6.2 and bioconcentration factors for Daphnia

pulex (r2 = 0.85; Southworth et al., 1978; see also Lyman et al., 1982), converted to dry weight :
MODEL VALIDATION REPORTS CHAPTER 3

3-7

(15)

(16)

(17)

(18)

where:

KBInvertebrate = partition coefficient between invertebrates and water (L/kg); and

WetToDry = wet to dry conversion factor (unitless, default = 5).

Extending and generalizing to ionized compounds:

Fish take longer to reach equilibrium with the surrounding water; therefore, a nonequilibrium

bioconcentration factor is used. For each pollutant, a whole-fish bioconcentration factor is based on

the lipid content of the fish extended to hydrophilic chemicals(McCarty et al., 1992), with provision

for ionization:

where:

KBFish = partition coefficient between whole fish and water (L/kg);

Lipid = fraction of fish that is lipid (g lipid/g fish); and

WetToDry = wet to dry conversion factor (unitless, default = 5).

Lipid content offish is varied depending on the potential for growth as predicted by the bioenergetics

equations; the initial lipid values for the species are given. The bioconcentration factor is adjusted

for the time to reach equilibrium as a function of the clearance or elimination rate and the time of

exposure (Hawker and Connell, 1985; Connell and Hawker, 1988):

where:

BCFFish = quasi-equilibrium bioconcentration factor for fish (L/kg);

TElapsed = time elapsed since fish was first exposed (d); and

Elimination = combined clearance and biotransformation, see (40) (1/d).
MODEL VALIDATION REPORTS CHAPTER 3

3-8

Figure 2

Bioconcentration factor for fish 

as a function of time and log KOW

(19)

(20)

Nonequilibrium Kinetics

Often there is an absence of equilibrium due to growth or insufficient exposure time, metabolic

biotransformation, dietary exposure, and nonlinear relationships for very large and/or

superhydrophobic compounds(Bertelsen et al. 1998). Although it is important to have a knowledge

of equilibrium partitioning because it is an indication of the condition toward which systems tend

(Bertelsen et al. 1998), it is often impossible to determine steady-state potential due to changes in

bioavailability and physiology (Landrum 1998). PCBs may not be at steady state even in large

systems such as Lake Ontario that have been polluted over a long period of time—the challenge is

to obtain sufficient data for a kinetic model (Gobas et al. 1995). In fact, PCBs in Lake Ontario

exhibit a 25-fold disequilibrium (Cook and Burkhard 1998). 

Sorption and Desorption to Sedimented Detritus

Partitioning to sediments appears to involve rapid sorption to particle surfaces, followed by slow

movement into, and out of, organic matter and porous aggregates (Karickhoff and Morris, 1985);

therefore, attainment of equilibrium may be slow. This applies to suspended detritus compartments

as well. Because of the need to represent sorption and desorption separately in detritus, kinetic

formulations are used (Thomann and Mueller, 1987), with provision for ionization:
MODEL VALIDATION REPORTS CHAPTER 3

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(21)

(22)

(23)

where:

Sorption = rate of sorption to given detritus compartment (:g/L"d);

k1 = sorption rate constant for given compartment (L/kg"d);

Nondissoc = fraction not ionized (unitless); 

ToxicantWater = concentration of toxicant in water (:g/L);

Diff1Carrier = factor to normalize rate constant for given carrier (detritus

compartment in this case) based on all competing uptake rates

(unitless);

Diff2Carrier = factor to normalize loss rates (unitless);

Org2C = conversion factor for organic matter to carbon (= 0.526 g C/g organic

matter);

Detr = mass of each of the detritus compartments per unit volume (mg/L);

1e -6 = units conversion (kg/mg);

Desorption = rate of desorption from given sediment detritus compartment

(:g/L"d);

k2 = desorption rate constant for given compartment (1/d); and

ToxicantDetr = mass of toxicant in each of the detritus compartments (:g/L).

Because there are several processes competing for the dissolved toxicant, the rate constantsfor these

processes are normalized in order to preserve mass balance. The Diff1 factor is computed for each

direct uptake process by the various carriers, including sorption to detritus and algae, uptake by

macrophytes, and uptake across animals’ gills:

where:

RateDiff1Carrier = maximum rate constant for uptake given the concentration gradient

(L/kg"d);

Gradient1Carrier = gradient between potential and actual concentrations of toxicant in

each carrier (unitless);

kpCarrier = partition or bioconcentration factor for each carrier (L/kg);

PPBCarrier = concentration of toxicant in each carrier (:g/kg).
MODEL VALIDATION REPORTS CHAPTER 3

3-10

(24)

(25)

(26)

(27)

(28)

Likewise, the loss rate constants are normalized; the equations parallel those for uptake, with the

gradient being reversed:

where:

RateDiff2Carrier = maximum rate constant for loss given the concentration gradient

(L/kg"d); and

Gradient2Carrier = gradient between actual and potential concentrations of toxicant in

each carrier (unitless).

Desorption of the slow compartment isthe reciprocal of the reaction time, which Karickhoff

and Morris (1985) found to be a linear function of the partition coefficient, expressed in hours, over

three orders of magnitude (r

2

 = 0.87):

So k2 is taken to be:

where:

KPSed = detritus-water partition coefficient (L/kg, see Eq. (2)); and

24 = conversion from hours, as used by Karickhoff and Morris (1985), to

days.

The slow compartment may be involved in 40 to 90% of the sorption so, as a simplification, fast

desorption of the labile compartment is ignored. This compensates in part for the fact that

AQUATOX models the top layer of bottom sediments as if it were in close contact with the

overlying water column (interstitial water is not modeled at this time). 
MODEL VALIDATION REPORTS CHAPTER 3

3-11

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(30)

(31)

(32)

The sorption rate constant is set to 1200 L/kg"d in the code, representing the very fast sorption of

most chemicals.

Bioconcentration in Macrophytes and Algae

Macrophytes—As Gobas et al. (1991) have shown, submerged aquatic macrophytes take up and

release organic chemicals over a measurable period of time at rates related to the octanol-water

partition coefficient. Uptake and elimination are modeled assuming that the chemical is transported

through both aqueous and lipid phases in the plant, with rate constants using empirical equations fit

to observed data (Gobas et al., 1991), modified to account for ionization effects (Figure 3, Figure

4):

where:

UptakePlant = uptake of toxicant by plant (:g/L"d);

ClearPlant = clearance of toxicant from plant (:g/L"d);

StVarPlant = biomass of given plant (mg/L);

1 e -6 = units conversion (kg/mg);

ToxicantPlant = mass of toxicant in plant (:g/L);

k1 = sorption rate constant (L/kg"d);

k2 = elimination rate constant (1/d);

Diff1Plant = factor to normalize rate constant for given plant based on all

competing uptake rates (unitless);

KOW = octanol-water partition coefficient (unitless); and

Nondissoc = fraction of un-ionized toxicant (unitless).
MODEL VALIDATION REPORTS CHAPTER 3

3-12

Figure 3. Uptake rate constant for

macrophytes (after Gobas et al., 1991)

Figure 4. Elimination rate constant for

macrophytes (after Gobas et al., 1991)

(33)

(34)

Algae—There is probably a two-step algal bioaccumulation mechanism for hydrophobic compounds,

with rapid surface sorption of 40-90% within 24 hours and then a small,steady increase with transfer

to interior lipids for the duration of the exposure (Swackhamer and Skoglund 1991). Uptake

increases with increase in the surface area of algae (Wang et al. 1997). Therefore, the smaller the

organism the larger the uptake rate constant (Sijm et al. 1998). However, in small phytoplankton,

such asthe nannoplankton that dominate the Great lakes, a high surface to volume ratio can increase

sorption, but high growth rates can limit internal contaminant concentrations (Swackhamer and

Skoglund 1991). AQUATOX uses a generalized uptake construct, but explicitly models growth rate

and the effect on the BAFs.

The kinetics of partitioning of toxicants to algae is based on studies on PCB congeners showing

uptake to be very rapid. Sijm et al. (1998) presented data on several congeners that were used in this

study to develop the following relationship for phytoplankton (Figure 5):

Based in part on Skoglund et al. (1996}, but ignoring surface sorption and recognizing that growth

dilution is explicit in AQUATOX, the elimination rate constant (Figure 6) is computed as:
MODEL VALIDATION REPORTS CHAPTER 3

3-13

Figure 5. Algal sorption rate constant. Figure 6. Rate of elimination by algae.

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(36)

Bioaccumulation in Animals

Animals can absorb toxic organic chemicals directly from the water through their gills and from

contaminated food through their guts. Direct sorption onto the body is assumed to be negligible in

this version of AQUATOX. Reduction of body burdens of organic chemicals is accomplished

through excretion and biotransformation, which are often considered together as empirically

determined elimination rates. “Growth dilution” occurs when growth of the organism is faster than

accumulation of the toxicant. Fecal loss is important as an input to the detrital toxicant pool.

Inclusion of mortality and promotion terms is necessary for mass balance, but emphasizes the fact

that average concentrations are being modeled for any particular compartment.

Gill Sorption—An important route of exposure is by active transport through the gills (Macek et

al., 1977). This is the route that has been measured so often in bioconcentration experiments with

fish. As the organism respires, water is passed over the outer surface of the gill and blood is moved

past the inner surface. The exchange of toxicant through the gill membrane is assumed to be

facilitated by the same mechanism as the uptake of oxygen, following the approach of Fagerström

and Åsell (1973, 1975), Weininger (1978), and Thomann and Mueller (1987; see also Thomann,

1989). Therefore, the uptake rate for each animal can be calculated as a function of respiration

(Leung, 1978; Park et al., 1982):

where:

GillUptake = uptake of toxicant by gills (:g/L - d);

KUptake = uptake rate (1/d);

ToxicantWater = concentration of toxicant in water (:g/L);

Diff1Carrier = factor to normalize rate constant for given carrier (animal

compartment in this case) based on all competing uptake rates

(unitless);

WEffTox = withdrawal efficiency for toxicant by gills (unitless);
MODEL VALIDATION REPORTS CHAPTER 3

3-14

(37)

Figure 7. Piece-wise fit to observed toxicant

uptake data; modified from McKim et al., 1985.

Respiration = respiration rate (mg biomass/L"d);

O2Biomass = ratio of oxygen to organic matter (mg oxygen/mg biomass; generally

0.575);

Oxygen = concentration of dissolved oxygen (mg oxygen/L); and

WEffO2 = withdrawal efficiency for oxygen (unitless).

The oxygen uptake efficiency WEffO2 is assigned a constant value of 0.62 based on observations of

McKim et al. (1985). The toxicant uptake efficiency can be expected to have a sigmoidal

relationship to the log octanol-water partition coefficient based on aqueous and lipid transport

(Spacie and Hamelink, 1982); this is represented by an inelegant but reasonable, piece-wise fit

(Figure 7) to the data of McKim et al. (1985) using 750-g trout, corrected for ionization:

where:

LogKOW = log octanol-water partition coefficient (unitless); and

Nondissoc = fraction of toxicant that is un-ionized (unitless).
MODEL VALIDATION REPORTS CHAPTER 3

3-15

(38)

(39)

Figure 8. GutEffTox constant based on mean value

for data from Gobas et al., 1993.

Ionization decreasesthe uptake efficiency. This same algorithm is used for invertebrates. Thomann

(1989) has proposed a similar construct for these same data and a slightly different construct for

small organisms, but the scatter in the data do not seem to justify using two different constructs.

Dietary Uptake—Hydrophobic chemicals usually bioaccumulate primarily through absorption from

contaminated food. Persistent, highly hydrophobic chemicals demonstrate biomagnification or

increasing concentrations as they are passed up the food chain from one trophic level to another;

therefore, dietary exposure can be quite important (Gobas et al., 1993). Uptake from contaminated

prey can be computed as (Thomann and Mueller, 1987; Gobas, 1993):

where:

DietUptakePrey = uptake of toxicant from given prey (:g toxicant/L"d);

KDPrey = dietary uptake rate for given prey (mg prey/L"d);

PPBPrey = concentration of toxicant in given prey (:g toxicant/kg prey);

1 e-6 = units conversion (kg/mg);

GutEffTox = efficiency of sorption of toxicant from gut (unitless); and

IngestionPrey = ingestion of given prey (mg prey/L"d).

Gobas (1993) presents an empirical equation for estimating GutEffTox as a function of the octanolwater partition coefficient. However, data published by Gobas et al. (1993) suggest that there is no

trend in efficiency between LogKOW 4.5 and 7.5 (Figure 8); this is to be expected because the

digestive system has evolved to assimilate a wide variety of organic molecules. Therefore, the mean

value of 0.63 is used as a constant for small fish. Nichols et al. (1998) demonstrated that uptake is

more efficient in larger fish; therefore, a value of 0.90 is used for large game fish. Invertebrates

generally exhibit lower efficiencies;
MODEL VALIDATION REPORTS CHAPTER 3

3-16

(40)

(41)

(42)

(43)

Landrum and Robbins (1990) showed that values ranged from 0.42 to 0.24 for chemicals with log

KOWs from 4.4 to 6.7; the mean value of 0.35 is used for invertebrates in AQUATOX.

Elimination—Elimination includes both excretion and biotransformation of a toxicant by

organisms. Biotransformation is difficult to model separately and may cause underestimation of

elimination (McCarty et al., 1992). Therefore, an overall elimination rate constant is estimated and

the derived value is reported in the toxicity record. The user may then modify the value based on

observed data.

For purposes of estimating elimination, a modification of Eq. (35) is used to compute uptake,

assuming a generalized allometric relationship between respiration and the mean weight of the

animal (Thomann, 1989):

where:

k1 = uptake rate (L/kg"d);

WetWt = mean wet weight of organism (g);

1000 = units conversion (g/kg);

WEffTox = withdrawal efficiency for toxicant by gills, see Eq. (36) (unitless).

If, as Thomann (1989) assumes, lipid-normalized bioconcentration is equal to the octanol-water

partition coefficient at equilibrium and zero growth, then:

where:

k2 = elimination rate constant (1/d); and

KOW = octanol-water partition coefficient (unitless);

LipidFrac = fraction of lipid in organism (g lipid/g organism); 

Wet2Dry = wet to dry weight ratio (5); and

Nondissoc = fraction of compound un-ionized (unitless, 1.0 for PCBs).

Note that this is the only place where the lipid fraction is used in modeling bioaccumulation in

AQUATOX. This simple relationship, although weak, has been used in AQUATOX for both

invertebrates and fish (Figure 9). However, the fish curve seems to drastically underestimate

clearance at higher KOWs. Therefore, as an alternative until the formulation is changed, k2

estimates may be entered manually using as guides regression equations for Daphnia:

and small fish:
MODEL VALIDATION REPORTS CHAPTER 3

3-17

Figure 9. Elimination rate constants for Daphnia and for

10-g fish; see Thomann, 1989.

(44)

For any given time the clearance rate is:

where:

ClearAnimal = clearance rate (:g/L"d); and

ToxicantAnimal = mass of toxicant in given animal (:g/L).

Data Used for Model Evaluation

Data presented by Oliver and Niimi (1988) for various PCB congeners in Lake Ontario sediments,

water, and organisms were used to characterize pollutant distributions in that Great Lake food web.

Although the data are high quality, they are not synoptic; rather, water samples were taken in April,

1984; sediment samples were taken in May, 1981; suspended sediment samples from November to

April for 1982 to 1986; plankton (a mixture of phytoplankton and zooplankton) in July, 1982;

mysids in July, 1981 and October, 1984; benthos in June, 1985; sculpin in spring, 1986; alewives

and smelt in May, 1982; smaller smelt (not used in the present study) in April, 1986; and salmonids

in fall, 1981, and April, 1982. Furthermore, water, sediment, and plankton samples were taken from

all three major basins in Lake Ontario; but fish and benthos samples were taken from the western

Niagara Basin.

Seventy-two PCB congeners were studied by Oliver and Niimi (1988). Because of computational

load, 16 congeners were selected for use in the evaluation of AQUATOX. They were selected to

span the range of KOWs and included congeners with higher, and therefore more reliable,

concentrations. The freely dissolved concentrations were computed from the reported water

concentrations, which included both dissolved PCBs and those associated with dissolved organic

matter, using the approach of US EPA (1995). 
MODEL VALIDATION REPORTS CHAPTER 3

3-18

The feeding relationships of phytoplankton,mysids, the amphipod Diporeia,sculpin, alewife,smelt,

and salmonids were taken from Burkhard (1998, based on Flint 1986 and Gobas 1993). However,

feeding ratios are not constants; what is actually consumed in Nature depends on time-varying prey

biomass. Consumption as modeled in AQUATOX treats the preferences as weights for available

prey, mimicking the way predation changes in the real world. The assumption made by both Gobas

(1993) and Burkhard (1998) is that the plankton analyzed by Oliver and Niimi (1988) were

phytoplankton, although Oliver and Niimi state (p. 388) that a plankton sample from a depth of 10

m“would contain a mixture of phytoplankton and zooplankton, phytoplankton should predominate.”

Furthermore, Mysis relicta is treated in the models as if it is the herbivorous or next trophic level,

although mysids are opportunistic feeders and prey heavily on zooplankton (www.fw.umn.edu/

nresexotics3001/mysisrelicta.html). Therefore, cladocerans were included as the intermediate,

herbivoroustrophic level, asthey are in most implementations of AQUATOX. Feeding preferences

for both cladocerans and mysids were based on the literature. The assignment of animals to guilds

in AQUATOX is as follows: amphipods, detritivorous invertebrates; cladocerans, herbivorous

invertebrates; mysids, predatory invertebrates; sculpin, benthic fishe’ alewife, forage fish; smelt,

nominally small game fish; trout, large game fish.

Lipid content for the various organisms was based on Oliver and Niimi (1988). Using these as

constant lipid fractions is even more misleading; there is ample evidence, presented in Arts and

Wainman (1999) and elsewhere, that lipid content is a time-varying function of nutritional state at

all trophic levels. Furthermore,manyliterature valuesforlipid content(cf. Arts andWainman 1999),

even in Lake Ontario, are two or more times those reported by Oliver and Niimi (1988), allowing

for wet and dry weights. Therefore, in this study the lipid values were held constant at the reported

values with some misgivings. For comparison purposes, BAFs on a lipid-normalized and freelydissolved basis were taken from the tables in US EPA (1995) and were checked using the

computational procedure presented in that document.

Code and Parameter Changes to Facilitate Analyses

AQUATOX is more mechanistic than the implementations of the Gobas and Thomann models used

by Burkhard (1998). Several changes were made to the AQUATOX code, and even more changes

weremade to the user-supplied parameter valuesto facilitate the comparison withBurkhard’s (1998)

results. Because of the requirement that the simulations be run to steady-state with a constant

dissolved concentration, the code was changed to disable the differential equation for the dissolved

phase. Burkhard (1998) set the dissolved concentration in water to 1 ng/L for all chemicals,

primarily because there is no concentration-dependent feedback in the Gobas and Thomann models.

However, several of the congeners exhibit dioxin-like toxicity, and the model predicted high

bioaccumulation levelsthat caused chronic toxicity to be manifested over the course of a seven-year

simulation period. Therefore, the dissolved concentrations were set to congener-specific levels

calculated from the observed concentrations. 

The constant dissolved concentration constraint also removed volatilization as a loss term. The

model was parameterized so that microbial degradation was not a factor. Version 1.68, which was

the version used, does not model biotransformation separately from depuration.

Burkhard (1998) assumed well mixed conditions, but AQUATOX simulates the dynamics of the

ecosystem as well as the fate and effects of the pollutant. Stratified conditions characterize Lake
MODEL VALIDATION REPORTS CHAPTER 3

3-19

Ontario during most of the growing season. A simulation without stratification showed that the

pelagic ecosystem in this deep lake requires a well defined epilimnion in order for phytoplankton

dynamics to be represented realistically. Therefore, the simulations were driven by time-varying

epilimnetic and hypolimnetic temperatures with mean values (7.4° and 3.6°) and ranges taken from

the literature (Canada Centre for Inland Waters, 1979, p 97), and stratification was correctly

modeled.

AQUATOXmodelstime-varying lipid fractions. That function was disabled in the code so that lipid

fractions were held constant at the initial values. However, in AQUATOX, lipid fractions in animals

only affect the estimations of the k2 (elimination) parameters. Based on initial runs, the estimates

of k2 values were found to be too low at higher KOWs; therefore, they were replaced by estimated

values using (41) and (42). The result is that the model is insensitive to lipid values in all but algae.

The model was modified so that the partition coefficient for refractory detrital carbon in sediment

would be 25 times KOW using (4). This was done only in this application to facilitate the comparison.

In the first simulations the only PCBs were those in water; however, steady-state was not reached

even after 16 years. Therefore, the initial PCB concentrations in refractory detrital sediments were

set to observed values, correcting for organic carbon content in the sediment (2.7%), and greatly

shortening the time to steady-state.

In order to accommodate the numerous simulations required for the sensitivity analyses, the code

was modified to run in batch mode, and then copy the predicted BAFs at the end of the simulation

to a text file suitable for importing directly into an Excel spreadsheet. The initial simulations were

run for 16 years to determine time to steady-state. Inspection of the output suggested that seven

years was more than sufficient to achieve steady state, even with large perturbations. Therefore,

seven years was used for the standard and sensitivity simulations. Because of the time required to

run 100 simulations for the uncertainty analysis, a four-year simulation period was used for those

simulations. That was determined to be the minimum time required to reach steady state for a

chemical with a log KOW of 6.5.

Results and Discussion

AQUATOX simulations were calibrated for the Lake Ontario ecosystem with some difficulty

because the model had never before been applied to a Great Lake. In particular, bioenergetic

parameters,such asmaximumconsumption rate (CMax), minimum biomass for feeding (BMin), and

respiration rates were only approximated forsculpin and smelt using other Great Lakesspecies. The

model was run for seven years with 1972-1973 loadings repeated; and, after a transient period, the

annual patterns were stable (Figure 10). The phytoplankton and zooplankton biomass levels and

seasonal trends were similarto those in the literature (CanadaCentre forInlandWaters, 1979, Scavia

1980). Unfortunately, no biomass data were found for fish to verify the calibrations, and catch

statistics only covered lake trout, cisco and whitefish (Robertson and Scavia 1979).
MODEL VALIDATION REPORTS CHAPTER 3

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Figure 10. Seven-year simulation of Lake Ontario with predicted epilimnetic biomass patterns.

Comparison of Predicted and Measured BAFs

With the ecosystem model calibrated, AQUATOX was then applied to the 16 PCB congeners, and

contaminant concentrations and lipid-normalized BAFs were predicted. The simulations were

started with PCBs in the freely dissolved phase in water and in the refractory detrital sediments. In

general, the phytoplankton reached steady state quickly and their BAFs varied little over time.

Amphipods also attained a dynamic equilibrium quickly with seasonally varyingBAFs. Most of the

fish exhibited an approach to steady state within a year or two with seasonally varyingBAFs. It took

about four years for steady-state concentrations to be reached in the lake trout (Figure 11), and the

BAFs did not exhibit significant seasonal variations. Biomagnification in the higher trophic levels

was not well defined.
MODEL VALIDATION REPORTS CHAPTER 3

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Figure 11. Seven-year simulation of Lake Ontario with predicted bioaccumulation factors.

The contributions of the various uptake and loss processes differ from one group to another (Figure

12,Figure 13). Given the dynamics of uptake and loss, especially in the short-lived zooplankton,

and the averaging effects of disparate epilimnetic and hypolimnetic sediment concentrations, it is not

surprising that there are fluctuations in the BAFs for many compartments. Therefore, the tabulated

BAFs were taken at the end of the simulation period, March 31, which corresponded to the time

when many of the observed data were collected. 

As can be seen from Figure 14 and Table 1, some predictions are remarkably close and others are

off by factors of as much as 8.6, as in the case of sculpin. The only predictions that exhibited a

noticeably different trend were those for the phytoplankton BAFs, which diverged at lower KOWs

(Figure 14A). The irregularities in the predictions mirror the fluctuations in the observed data and

are related to the varying concentrations of PCBs in the sediments. The best predictions are for lake

trout (Figure 14G), which are the most important fish in terms of human health hazard. Mysid

BAFs are predicted very well (Figure 14B), as are smelt BAFs (Figure 14F). Phytoplankton BAFs

are underestimated (Figure 14A); this may reflect the fact that the observations are actually for

combined phytoplankton and zooplankton. Amphipods are over-estimated (Figure 14C); as will

be shown later, the model issensitive to changesin their bioenergetic parameters,such as maximum

consumption rate. The overestimates of sculpin (Figure 14D) and alewife (Figure 14E) BAFs

emphasize the sensitivity of the benthic food web as modeled. The predicted partition coefficients

for refractory detrital sediments (Figure 14H) are very close to the observed values.
MODEL VALIDATION REPORTS CHAPTER 3

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Figure 12. Predicted transfer rates for PCB

180 in Lake Ontario diatoms.

Figure 13. Predicted transfer rates for PCB

180 in Lake Ontario smelt.

A. B.

Figure 14. Observed and predicted lipid-normalized and freely dissolved BAFs for PCBs in Lake

Ontario ecosystem components.
MODEL VALIDATION REPORTS CHAPTER 3

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C. D.

E. F.

G. H.

Figure 14. continued

T
MODEL VALIDATION REPORTS CHAPTER 3

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he predicted and observed BAFs can be compared statistically by taking the ratio of the predicted

to the observed, as summarized in Table 1. A ratio of 1.0 represents perfect correspondence

between the predicted and observed BAFs. This is a quantitative summary of the results shown

diagrammatically in Figure 14, with sculpin and alewife biasing the overall results. The

bioenergetics equations for these fish most likely are not parameterized properly; additional literature

survey and calibration of the bioenergetic parameters would help correct the discrepancies. 

Table 1. Ratio of observed to predicted bioaccumulation factors.

Congener Log KOW PhytoplanktonMysids Amphipods Sculpin Alewife Smelt Trout Mean for

congeners

18 5.24 0.07 0.24 1.02 3.89 0.71 0.38 1.05

28+31 5.67 0.13 0.55 1.55 18.62 4.57 0.17 0.49 3.73

84 6.04 0.05 0.44 2.69 2.51 1.86 0.07 0.18 1.11

70+76 6.17 0.19 1.35 4.57 21.88 6.61 0.28 0.95 5.12

66 6.2 0.19 1.82 4.79 13.49 5.50 0.30 0.87 3.85

101 6.38 0.49 0.98 2.34 3.47 2.19 0.23 0.41 1.44

110 6.48 0.37 1.15 4.37 9.55 4.57 0.33 0.74 3.01

105 6.65 0.40 0.89 2.75 5.62 4.07 0.29 0.55 2.08

149 6.67 0.04 0.87 2.40 16.60 3.24 0.34 0.69 3.45

118 6.74 0.51 0.63 2.34 3.72 3.09 0.22 0.43 1.56

138 6.83 0.68 0.79 1.91 3.39 2.95 0.30 0.46 1.50

153 6.92 0.71 0.72 1.82 3.89 3.89 0.35 0.52 1.70

187+182 7.19 1.15 1.00 2.04 6.31 4.68 0.63 0.79 2.37

180 7.36 1.41 1.38 0.95 4.07 5.25 0.93 1.00 2.14

203+196 7.65 0.39 4.68 5.75 10.96 15.14 3.63 3.24 6.26

194 7.8 1.78 3.89 3.72 10.00 13.80 3.98 3.80 5.85

Grand

Mean

2.89

Mean 0.53 1.34 2.81 8.62 5.13 0.80 0.97

Std Dev 0.51 1.22 1.42 6.14 3.94 1.24 1.03

Count 16 16 16 16 16 15 16

Minimum 0.04 0.24 0.95 2.51 0.71 0.07 0.18

Median 0.39 0.93 2.37 5.97 4.32 0.30 0.62

Maximum 1.78 4.68 5.75 21.88 15.14 3.98 3.80

Skewness 1.34 2.11 0.70 0.98 1.85 2.30 2.32

Kurtosis 1.20 3.89 -0.43 -0.18 3.05 4.06 4.55

A similar tabular summary was presented by Burkhard (1998) for the Gobas and Thomann models.

It is instructive to compare the results of the three models (Figure 15). The heavy line indicates

unity for the mean and predicted ratios. This is not a rigorous comparison because AQUATOX was

only used to simulate 16 PCB congeners, in contrast to the 72 congeners simulated by Burkhard

(1998), although there is not any apparent bias in the smaller sample. Burkhard concluded that the

Gobas model seems to represent Lake Ontario bioaccumulation better than the Thomann model.

However, AQUATOX did equally as well for smelt and lake trout, and the best of the three models

for phytoplankton and mysids.
MODEL VALIDATION REPORTS CHAPTER 3

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Figure 15. Comparison of predicted/observed BAF ratios for the AQUATOX, Gobas, and

Thomann models.

(45)

Sensitivity Analysis

Following the approach of Burkhard (1998), sensitivity analyses were run on each key parameter,

using the equation:

where:

Pi = nominal value of input parameter i,

)Pi = deviation in the input parameter i,

BAFi = the BAF predicted using the nominal input values, and

)BAFi = the deviation in the predicted BAFs using the nominal and modified input

parameter i.

A sensitivity of 1.0 means that a change in a parameter results in an equal change in the BAFs

predicted by the model. A negative sign indicates that the change is opposite in direction to the

parameter change. Because the simulations were run for seven years to ensure steady-state,

sensitivity analysis for each parameter for the 16 PCB congeners took eight hours on a Pentium III
MODEL VALIDATION REPORTS CHAPTER 3

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Figure 16. Sensitivity of AQUATOX BAFs to a +10% change in KOW . 

500 mHz machine. Therefore, only the following representative parameters were analyzed:KOW, k2,

preference of amphipods for refractory detritus, Cmaxamphipod, and BMinsculpin. 

Burkhard (1998) found that Thomann’s and Gobas’ models were sensitive to variable KOW values

within a factor of +/- 2.0. As shown in Figure 16, in AQUATOX phytoplankton are highly sensitive

to KOW; furthermore, as shown in Figure 14A, the predicted algal BAF trend does not parallel the

observed trend Both the phytoplankton k1 uptake and k2 values are a direct function of the KOW

values. In contrast, none of the animals show any sensitivity, reflecting the fact that by manually

entering k2 elimination values the dependence on KOW was overridden. 

One of the key parameters is the elimination coefficient for each of the organisms. As explained

above, the k2 values can be estimated by the model, see (40). However, those estimates were found

to underestimate elimination, so simpler regression equations (41) and (42) were used to estimate

the k2 values, and those were entered manually in the toxicity parameter screens. In order to test the

sensitivity, k2 +10% values were used in the analyses (Figure 17). The sensitivity was small and

affected only the lower KOW congeners.
MODEL VALIDATION REPORTS CHAPTER 3

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Figure 17. Sensitivity of AQUATOX BAFs to a +10% change in K2

values for all organisms. 

Figure 18. Sensitivity of AQUATOX BAFs to a +10% change in

amphipod preference for refractory detrital sediments .

Amphipods provide the primary link between the sediments and the highertrophic levels. They feed

on detritus, converting some refractory detritus to labile detritus that can be assimilated or

decomposed more rapidly. Therefore, the preference of amphipods for refractory detritus was

increased by 10%, from 0.05 to 0.055 and the corresponding preference for labile detritus(primarily

freshly sedimented algae) was decreased by 10% in a sensitivity analysis. There were seemingly

random fluctuations in the animal BAFs, probably representing differences in the initial PCB

concentrations in the sediments (Figure 18).

In reviewing the literature on Lake Ontario, the observations of Landrum and Robbins(1990) on the

feeding rate of Diporeia (the common amphipod in the Great Lakes) were found to be at variance

with the maximum consumption rate (CMax) used in AQUATOX. Therefore, an analysis was
MODEL VALIDATION REPORTS CHAPTER 3

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Figure 19. Sensitivity of AQUATOX BAFs to a -77% change in

amphipod maximum consumption rate.

performed in which CMax was changed from 1.3 g/g d to 0.288 g/g d. A systematic response was

found from one trophic level to the next (Figure 19), suggesting that indeed amphipods are

important in the transfer of contaminants, that the effects are magnified at the higher trophic levels

and that the CMax value should probably be changed in future implementations. Smelt are affected

the most, probably due to amphipod biomass dropping below the minimum biomass level (BMin)

for consumption and uptake of PCBs by smelt. In this and other analyses of sensitivity to

bioenergetic parameters, interpretations of the effects onBAFs are difficult because those effects are

indirect.

Because the trophic feeding relationships seem to be important in the transfer of PCBs in the Lake

Ontario food web, the effect of changing the sculpin BMin was investigated by changing the value

from 0.01 to 0.1 mg/L. This has the effect of providing a refuge from predation for amphipods and,

to a lesser extent, zooplankton; mysids were found to respond the opposite to the fish. Because

sculpin compete to a certain extent with alewives, and both compete with and are fed on by smelt,

changing the feeding dynamics for sculpin affects the other fish species, although the sensitivities

are low (Figure 20).

In summary, changes in KOW and k2 values had little effect on the BAFs; and changes in feeding

preferencesin amphipods had no systematic effect on BAFs, but changesin CMax and BMin values

were important in regulating the transfer of PCBs in the Lake Ontario ecosystem.
MODEL VALIDATION REPORTS CHAPTER 3

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Figure 20. Sensitivity of AQUATOX BAFs to a change in sculpin

minimum biomass for feeding from 0.01 to 0.1 mg/L.

Uncertainty Analysis

Uncertaintyanalysis was conducted using the same parameter distributions as were used by Burkhard

(1998), in so far as appropriate. However, the contributions of individual parameters were not

investigated because of the computational load. Rather, the aggregate uncertainties due to all

selected input variables were analyzed simultaneously. Burkhard (1998) used a Monte Carlo

procedure with random valuestaken from normal and lognormal distributions; the simulations were

run for 100,000 iterations. Such an approach is neither desirable nor warranted for a complex model

such as AQUATOX. Instead, Latin hypercube sampling was performed for 100 iterations; that

algorithm took a random sample from each of 100 segments of the normal distributions for each

selected input parameter, ensuring that the distributions were well represented. Each of the 100

simulations was run for a four-year period to obtain steady-state. Because of the model complexity

and the length of the simulation period, the uncertainty analysis took 40.5 hours to complete on a

Pentium III 500 mHz machine. The input parameters are shown in Table 3, taken from the

AQUATOX Setup. Two parameters, Log KOW and the sediment detritus-water partition coefficient,

were converted from lognormal to normal distributions to facilitate the analysis.
MODEL VALIDATION REPORTS CHAPTER 3

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Table 3. Parameters used in uncertainty analysis of AQUATOX; Parameter 1 is the mean and

Parameter 2 is the standard deviation.

The bioenergetic parameters have a direct effect on the ecosystem components, such as the biomass

of lake trout shown in Figure 22. Given the number of parameters, the uncertainty exhibited is

surprisingly small.
MODEL VALIDATION REPORTS CHAPTER 3

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Figure 22. Effect of uncertainty on biomass of lake trout over four years; the mean and

deterministic results are very close and are bounded by +/- 1 standard deviation; the top and

bottom curves represent the high and low values out of 100 simulations. 

The low uncertainty in the trout bioenergetics contributes to the low uncertainty in the trout BAFs

(Figure 23). Because the trout had no contaminant burden at the beginning of the simulation, it took

three or more years for steady state to be reached. The seasonal fluctuations that are evident in the

short-lived animals are barely discernible in the large, long-lived trout due to the slow dietary uptake

and even slower depuration.
MODEL VALIDATION REPORTS CHAPTER 3

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Figure 23. Effect of uncertainty on the log BAFs for lake trout over a four-year period.

The 90th and 10th percentiles, representing the tails of the distributions, were computed from the

mean and standard deviations of the BAFs for the various simulated organisms. The ratios of these

percentiles were then calculated (Table 4).

Table 4. Lipid-normalized, freely dissolved log bioaccumulation factors using uncertainties.

State variable Mean Standard Deviation Ratio 90th:10th

Diatoms 6.77 0.00004 1.00

Mysids 7.84 0.36 1.13

Sculpin 8.88 0.29 1.09

Alewife 8.63 0.31 1.10

Smelt 7.78 0.36 1.12

Lake trout 8.12 0.31 1.10

Refr. sed. detritus 8.63 0.23 1.07

Labile susp. detritus 7.66 0.28 1.10
MODEL VALIDATION REPORTS CHAPTER 3

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These low ratios are misleading because manually entered k2 values desensitize the model to

changes in weights and lipid fractions in organisms. Therefore, although the ratio of 1.10 for lake

trout is for all selected input parameters, it is not directly comparable with the values of 3.63 and

3.98 for all parameters obtained with the Gobas and Thomann models (Burkhard 1998).

Conclusions

The subset of 16 PCB congeners from the 72 used by Burkhard (1998) provided an adequate basis

for evaluating the validity of AQUATOX in predicting bioaccumulation of PCBs in Lake Ontario.

Unlike the Gobas and Thoman models, AQUATOX had never been applied to such a large system,

but in general, AQUATOX gave acceptable results. It provided better fits to observed data for

phytoplankton and mysids than those provided by the Gobas (1993) and Thomann (1989) models

as implemented by Burkhard (1998), and equally acceptable results for smelt and lake trout when

compared to the Gobas model. 

Several modifications to the code were necessary to facilitate direct comparison to the Burkhard

(1998) study. Specifically, the freely dissolved contaminant concentration and lipid fractions in

organisms were held constant. A procedure for computing the sediment-water partition coefficient

was added, and capability for running in batch mode also was provided. All these features were

made options in Version 1.68 and subsequent versions.

AQUATOX seems to have been successfully calibrated to represent the Lake Ontario ecosystem.

Most predicted bioaccumulation factors were reasonably close to observed values. Phytoplankton

BAFs were underestimated according to the observed data, which may be biased by inclusion of

zooplankton; predictions were noticeably better than the Gobas and Thomann models as

implemented by Burkhard (1998). Mysid BAFs were predicted very well, in contrast to the

underestimations in the Gobas and Thomann models, which may reflect the lack of herbivorous

zooplankton in those implementations. Amphipods, sculpin, and alewife BAFs were overestimated,

suggesting that the bioenergetics parameters may not be well calibrated; indeed, changing the CMax

for amphipods to a value measured with the Lake Ontario species greatly improved the BAF

prediction. The smelt and lake trout BAF predictions were very close to the observed and were

better than the predictions of the Thomann model, and equivalent to the Gobas model results.

AQUATOX has many nonlinear relationships in the ecosystem, fate, and effects portions of the

model. Therefore, it does not exhibit one-to-one sensitivities to input parameters as do the simpler

Gobas and Thomann models. Furthermore, by manually entering estimated k2 values, the

simulations were desensitized to lipid and weight factors. The algal BAFs are relatively sensitive

to changing KOW values, although only one congener exceeded a sensitivity of 1.0 (perfect

correspondence). BAFs exhibited low sensitivities to benthic feeding relationships. The almost

negligible effect of changing preference of amphipods for labile organic matter, which is mostly

sedimented phytoplankton, was in sharp contrast to the response of Thomann’s model to a similar

preference for phytoplankton (Burkhard 1998). This emphasizes the unique capability of

AQUATOX in realistically representing ecosystemic relationships affecting contaminant fate. The

model properly represented the cascading effect that changing amphipod consumption has on BAFs

in the benthic food web. A future task should be to fine-tune the amphipod and fish bioenergetic

parameters for Great Lakes species.
MODEL VALIDATION REPORTS CHAPTER 3

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AQUATOX exhibited a low degree of uncertainty, especially compared to the Gobas and Thomann

models (Burkhard 1998). However, this is probably in part an artifact of the constraints imposed by

specifying the k2 elimination values.

In conclusion, this study has demonstrated the validity and robustness of AQUATOX in estimating

bioaccumulation factors for PCBs in Lake Ontario.

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