• Sampling times 



• Kind of sample (e.g., composite vs. grab, cooked vs. raw; fillet 

 vs. whole organism) 



• Sample replication strategy 



• Analytical protocols, including detection limits 



• Statistical treatment of data. 



Because the complexity and specific features of a sampling design will 

 depend on the objectives of the exposure assessment, no single design 

 Ccm be recommended here. Nevertheless, some basic steps in the study 

 design process can be summarized as follows: 



• Define concise objectives of the study and'any hypotheses to 

 be tested. 



• Define spatial and temporal characteristics of fisheries relative 

 to harvesting activities (e.g., seasonality, catch or consumption 

 rates, species composition, size ranges, demersal vs. pelagic 

 species). 



• Define harvesting activities and methods of preparing food for 

 consumption that potentially affect exposure to contaminants. 



• Define kinds of samples to be collected (species, type of tissue, 

 mode of preparation) and variables to be measured, based on 

 a preliminary exposure analysis. 



• Evaluate alternative statistical models for testing hypotheses 

 about spatial and temporal changes in measured variables. 

 Select an appropriate model. 



• When possible, use stratified random sampling for each fish 

 and shellfish species, where the different strata represent dif- 

 ferent habitat types or kinds of harvest areas that may infiuence 

 the degree of tissue contamination. 



• When practical, specify equal numbers of randomly allocated 

 samples for each stratum/treatment combination (e.g., habitat 

 type in combination with species or season). 



• Include samples from a relatively uncontaminated reference or 

 control area to help define local contamination problems. 



• Perform preliminary sampling or analyze available data to 

 evaluate the adequacy of alternative sampling strategies (e.g., 

 composite samples vs. tissue from individual organisms) and 

 statistical power as a function of the number of replicate 

 samples. 



• Develop a QA/QC program that covers: sample collection and 

 handling; chain of custody; data quality specifications; analyti- 

 cal methods and detection limits; data coding; data QA/QC 

 steps to assess precision, accuracy, and completeness; 

 database management specifications; data reporting require- 

 ments; and performance audits. 



• Define data analysis steps, including statistical tests, data plots, 

 summary tables, and uncertainty analysis. 



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