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To protect thousands of species from thousands of chemicals released in the environment, various risk assessment tools have been developed. Here, we link quantitative structure-activity relationships (QSARs) for response concentrations in water (LC50) to critical concentrations in organisms (C50) by a model for accumulation in lipid or non-lipid phases versus water Kpw. The model indicates that affinity for neutral body components such as storage fat yields steep Kpw-Kow relationships, whereas slopes for accumulation in polar phases such as proteins are gentle. This pattern is confirmed by LC50 QSARs for different modes of action, such as neutral versus polar narcotics and organochlorine versus organophosphor insecticides. LC50 QSARs were all between 0.00002 and 0.2Kow(-1). After calibrating the model with the intercepts and, for the first time also, with the slopes of the LC50 QSARs, critical concentrations in organisms C50 are calculated and compared to an independent validation data set. About 60% of the variability in lethal body burdens C50 is explained by the model. Explanations for differences between estimated and measured levels for 11 modes of action are discussed. In particular, relationships between the critical concentrations in organisms C50 and chemical (Kow) or species (lipid content) characteristics are specified and tested. The analysis combines different models proposed before and provides a substantial extension of the data set in comparison to previous work. Moreover, the concept is applied to species (e.g., plants, lean animals) and substances (e.g., specific modes of action) that were scarcely studied quantitatively so far.

A microcosm experiment that addressed the interaction between eutrophication processes and contaminants was analyzed using a food web model. Both direct and indirect effects of nutrient additions and a single insecticide application (chlorpyrifos) on biomass dynamics and recovery of functional groups were modeled. Direct toxicant effects on sensitive arthropods could be predicted reasonably well using concentration-response relationships from the laboratory with representative species. Model predictions showed that nutrient additions alone caused only small effects on toxicant fate and effects probably due to the relatively high dissipation rate of chlorpyrifos. Enhancement of eutrophication effects by the insecticide was relatively small and seemed to be additive. The recovery of some affected functional groups was hampered in the indoor microcosms due to their isolation from outdoor seed populations. Introducing recolonization scenarios in the model simulated dose-dependent recovery. Recolonization increased the recovering rate after exposure to the pesticide. Modeling can extend the use of microcosms as a link between laboratory and field as this allows the prediction of effects and recovery of ecosystems for concentrations that have not been experimentally tested.

Chemical management programs strive to protect human health and the environment by accurately identifying persistent, bioaccumulative, toxic substances and restricting their use in commerce. The advance of these programs is challenged by the reality that few empirical data are available for the tens of thousands of commercial substances that require evaluation. Therefore, most preliminary assessments rely on model predictions and data extrapolation. In November 2005, a workshop was held for experts from governments, industry, and academia to examine the availability and quality of in vivo fish bioconcentration and bioaccumulation data, and to propose steps to improve its prediction. The workshop focused on fish data because regulatory assessments predominantly focus on the bioconcentration of substances from water into fish, as measured using in vivo tests or predicted using computer models. In this article we review of the quantity, features, and public availability of bioconcentration, bioaccumulation, and biota–sediment accumulation data. The workshop revealed that there is significant overlap in the data contained within the various fish bioaccumulation data sources reviewed, and further, that no database contained all of the available fish bioaccumulation data. We believe that a majority of the available bioaccumulation data have been used in the development and testing of quantitative structure–activity relationships and computer models currently in use. Workshop recommendations included the publication of guidance on bioconcentration study quality, the combination of data from various sources to permit better access for modelers and assessors, and the review of chemical domains of existing models to identify areas for expansion.

An extensive study on the presence of nine organotin compounds (OTs) in a freshwater foodweb was made, using newly developed analytical procedures in order to obtain insight in accumulation and degradation processes. Tributyltin (TBT), Triphenyltin (TPT) and their degradation products were detected. Zebra mussels, eel, roach, bream, pike, perch, and pike perch and cormorant showed high OT body concentrations. At the lower trophic levels, phenyltin concentrations were higher in benthic species while butyltin concentrations were higher in pelagic species. This indicates that TBT is passed on primarily via the water, while TPT is passed on to a larger extent via the sediment. At the higher trophic levels, net bioaccumulation of TPT was greater than that of TBT, resulting in relatively higher TPT concentrations. High concentrations of biodegradation products of TBT, but not of TPT, were found in the livers of fish and birds, which indicates that TBT is more easily metabolized than TPT. A comparison with literature data of fish lethal body concentrations revealed that fish in the field may be endangered. With birds, the highest concentrations of OTs were present in liver and kidney and not in subcutaneous fat, which confirms that OTs accumulate via different mechanisms than traditional lipophilic compounds. As a whole the OT concentrations found in the foodweb may be considered to be quite alarming.