Concentration ratios (CRs) are used to derive activity concentrations in wild plants and animals. Usually, compilations of CR values encompass a wide range of element-organism combinations, extracted from different studies with statistical information reported at varying degrees of detail. To produce a more robust estimation of distribution parameters, data from different studies are normally pooled using classical statistical methods. However, there is inherent subjectivity involved in pooling CR data in the sense that there is a tacit assumption that the CRs under any arbitrarily defined biota category belong to the same population. Here, Bayesian inference has been introduced as an alternative way of making estimates of distribution parameters of CRs. This approach, in contrast to classical methods, is more flexible and also allows us to define the various assumptions required, when combining data, in a more explicit manner. Taking selected data from the recently compiled wildlife transfer database (http://www.wildlifetransferdatabase.org/) as a working example, attempts are made to refine the pooling approaches previously used and to consider situations when empirical data are limited.

No abstract

In the framework of environmental multimedia modeling studies dedicated to environmental and health risk assessments of chemicals, the bioconcentration factor (BCF) is a parameter commonly used, especially for fish. As for neutral lipophilic substances, it is assumed that BCF is independent of exposure levels of the substances. However, for metals some studies found the inverse relationship between BCF values and aquatic exposure concentrations for various aquatic species and metals, and also high variability in BCF data. To deal with the factors determining BCF for metals, we conducted regression analyses to evaluate the inverse relationships and introduce the concept of probability density function (PDF) for Cd, Cu, Zn, Pb, and As. In the present study, for building the regression model and derive the PDF of fish BCF, two statistical approaches are applied: ordinary regression analysis to estimate a regression model that does not consider the variation in data across different fish family groups; and hierarchical Bayesian regression analysis to estimate fish group-specific regression models. The results show that the BCF ranges and PDFs estimated for metals by both statistical approaches have less uncertainty than the variation of collected BCF data (the uncertainty is reduced by 9%-61%), and thus such PDFs proved to be useful to obtain accurate model predictions for environmental and health risk assessment concerning metals.

The study is a good example of probabilistic risk assessment. The means of mean and SD posterior estimates of log BCFworm (2.06, 0.91) are the 'best estimate values'. Further risk assessment of quinoxyfen and other phenoxyquinoline fungicides and realistic representative scenarios for modelling exercises required for future authorization and post-authorization requirements can now use this value as input.