Composite samples for perfluorooctanesulfonate in fish may change contaminant threshold decisions.

Abstract

The widespread occurrence and persistence of the per- and polyfluoroalkyl substances (PFAS) in the environment may pose significant health risks to humans. Fish consumption is a direct pathway for exposure, prompting extensive monitoring. In the United States and Europe, fish consumption advisories for PFAS generally focus on perfluorooctanesulfonate (PFOS), the most frequent and abundant PFAS compound reported in fish. However, PFAS analysis is costly, leading monitoring programs to frequently use composite samples (i.e., combining different fish of the same species). This financial saving comes at an analytical cost of lost information on individual variation. It is unclear how this may influence threshold-based decisions. To test this, we used a modeling approach (i.e., Monte Carlo) based on aggregated data distributions from freshwater fish species. We simulated the measurement of PFOS for individual fish compared to composite means (three, five, and 10 fish) at different levels of error structure. These values were then compared to a typical do-not-eat threshold of 200 ng•g wet mass-1 and four metrics were estimated: (i) the probability of detecting a PFOS concentration above the threshold, (ii) the maximum simulated PFOS concentration, (iii) the number of false negative samples, and (iv) the rate of false negative samples. These results show that the probability of assigning PFOS levels above or below the threshold is shifted by masking individual value contributions in a composite sample. Because true individual contaminant loads are variable, composite sampling is a more conservative metric for contaminant thresholds. The model suggested that the greater the variability, the lower the detection probability for truly contaminated samples. Our study may help to inform sampling for monitoring programs aimed at managing human exposure to PFAS.