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A method was developed to determine trace concentrations of a range of individual PCB congeners in biological samples (serum, food and faeces) using GC-MS, to prepare a mass balance of PCBs in humans. A simple method for the analysis of PCBs in human serum, which excluded an extraction step, was first employed. Results indicated that the recoveries of 13C12 PCB spikes were variable. A soxhlet extraction step was added and was found to be efficient and reproducible. A quality control routine and method validation results are presented. In batch tests of the methods presented it was found that the serum analysis method gave within batch mean 13C12 spike recoveries of 98 – 120% and standard deviations between 6 and 20%. The food/faeces analysis method gave within-batch mean 13C12 spike recoveries of 88 – 100%, and within batch standard deviations between 4 and 12%. The batch to batch mean recovery for serum analysis was 100%, with an RSD of 9% for high spikes and 10% for low spikes. For food/faeces analysis the batch to batch average recovery was 110%, with an RSD of 5% for high spikes and 9% for low spikes.

The statistical treatment of data-sets from environmental pollutant studies in which different measurements are combined to produce averages or comparative factors (e.g., transfer coefficients (TCs), input–output balance values) are considered here, with particular reference to the analysis of data from input–output balance studies of pollutants such as PCBs in animals and humans. Many methods of statistical analysis ignore the fact that all measurements are subject to error, and generally assume that the normal distribution applies to all data-sets, which is commonly inappropriate for environmental (and particularly biological system) data. Considerably different estimations can be obtained by applying different, commonly used, statistical methods, as shown in a simulation study presented here and when applied to data from an input–output balance study of PCBs in humans. Alternative average and combined factor estimators for the treatment of data from these types of studies that give considerable advantages in terms of bias and the ease of assessment of accuracy are proposed.