This effort will include samples for components of the food web and its surroundings (i.e., phyto- 
and zoo-plankton, benthic invertebrates, forage fishes, and piscivorous fishes), sediments, and 
water column. In addition, ancillary data such as lipid contents and organic carbon contents will 
be measured. For the purposes of evaluating extrapolation procedures, existing data sets will be 
used and when necessary, high quality measurements will be performed in this investigation. 
2. Determination of Metabolism Rates. 
» 
Mechanistic models for predicting chemical residues in aquatic food webs historically have 
included a rate constant for the metabolic loss of chemical in organisms (Gobas 1993, Thomann 
et al. 1992). Although biotransformation of most all compounds occurs in fishes, common 
modeling practices to date set the metabolic loss rate to zero because many PBTs are thought to 
have rates of metabolism so slow that they can be considered non-metabolizable (although 
metabolism rates have not been measured for nearly all the PBTs). In the absence of measured 
metabolism rates, the default modeling assumption of "no metabolism" is used in modeling 
exercises. This practice of tacitly assuming that metabolism is not important for PBTs has 
largely come about because nearly all the modeling exercises have been perfoimed for PCBs, a 
class of chemicals that bioaccumulate to a degree that suggests extremely low rates of 
metabolism. Consistent with this assumption, the validation efforts to date have shown that the 
food web models have excellent predictive ability for PCBs. 
This research effort will evaluate the use of field data to infer and deduce information about 
metabolic rates for PBTs. The approach for determining rates of metabolism involves the use of 
mechanistic food web models together with high quality field data. With these models and the 
high quality field data, the models can be solved for the rate of metabolism for the chemical of 
interest. In essence, the difference between the model prediction using no metabolism and the 
actual field data is accounted for by the metabolic rate loss parameter if the model parameters are 
set to accurately predict bioaccumulation for non-metabolized congeners. This research effort 
will define for the approach the data quality requirements, and the range of metabolic rates and 
bioaccumulation potential for which the methodology will work. 
3. Bioaccumulation Models for Fish ELSs. 
If metabolism rates can be effectively determined from field data as proposed, the final step in 
bioaccumulation model development for risk assessments involving metabolizable PBTs will be 
the development of empirical and mechanistic bioaccumulation models for fish during ELSs. 
This is a four step process: 1) develop models for bioaccumulation of PBTs (with varying rates 
of metabolism) by female fish, 2) develop maternal transfer models (empirical to PB-TK) to 
predict bioaccumulation by embryos, 3) develop post-spawning bioaccumulation models 
(empirical to mechanistic) to predict uptake and elimination of PBTs (with varying rates of 
metabolism) by fish at different stages of development, and 4) determine inter-species 
differences in ELS bioaccumulation and strategies for inter-species extrapolation of ELS 
dosimetry data. Step 1 is part of the general bioaccumulation model development and step 2 has 
a foundation in field data (e.g., Guiney et al. 1996) and PB-TK models developed (e.g., Nichols 
et al. 1997). Step 3 is an essential element of research on risks of PAHs to fish ELSs under 
project B4. Thus, over time projects B2 and B4 will have an increasing degree of shared research 
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