Bis(4-chlorophenyl) sulfone (BCPS) concentrations found in Austrian freshwater fish and cormorants (Phalacrocorax carbo sinensis) indicating trophic transport and biomagnification - A screening pilot-study.

Bis(4-chlorophenyl) sulfone (BCPS, CAS No. 80-07-9) is used as monomer for the production of several groups of polymers like polysulphones and polyethersulphones. Residual amounts of monomer remain in the polymer matrix and might migrate out from the polymer matrix. In the present study, freshwater fish and fish-eating birds were examined. Following fish species (top predators) were collected at two Austrian locations: Sander lucioperca, Silurus glanis, and Lota lota. Whole fish samples were analysed for BCPS. Levels in freshwater fish ranged between 1.3 and 9.3 ng/g fat. In addition, breast muscle and liver samples from six cormorants (Phalacrocorax carbo sinensis) were investigated. BCPS levels in cormorants breast muscle were in the range of 4.3-40 ng/g fat (mean: 16.3 ng/g fat, n = 6) and 28-86 ng/g fat (mean: 53.5 ng/g fat, n = 6) in the liver samples. BCPS concentration in liver was 3.3-fold higher than in muscle tissue. One of the cormorants had ingested fish with a BCPS level of 5.5 ng/g fat; BCPS levels in the cormorant were 23 ng/g fat (breast muscle) and 28 ng/g fat (liver), suggesting biomagnification values (BMF) of 4.2 (fish/breast muscle) and 5.1 (fish/liver), respectively. A BMF value higher than 1 can be considered as an indication for very high biomagnification. Comparing the BCPS concentrations of cormorants' breast muscle from 2019 (mean: 16 ng/g fat) to the concentrations from 2001 to 2005 (mean: 8.9 ng/g fat), indicates that BCPS levels might be increasing in Europe.

Uncertainty of testing methods--what do we (want to) know?

It is important to stimulate innovation for regulatory testing methods. Scrutinizing the knowledge of (un)certainty of data from actual standard in vivo methods could foster the interest in new testing approaches. Since standard in vivo data often are used as reference data for model development, improved uncertainty accountability also would support the validation of new in vitro and in silico methods, as well as the definition of acceptance criteria for the new methods. Hazard and risk estimates, transparent for their uncertainty, could further support the 3Rs, since they may help focus additional information requirements on aspects of highest uncertainty. Here we provide an overview on the various types of uncertainties in quantitative and qualitative terms and suggest improving this knowledge base. We also reference principle concepts on how to use uncertainty information for improved hazard characterization and development of new testing methods.