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The BIOWIN biodegradation models were evaluated for their suitability for regulatory purposes. BIOWIN includes the linear and non-linear BIODEG and MITI models for estimating the probability of rapid aerobic biodegradation and an expert survey model for primary and ultimate biodegradation estimation. Experimental biodegradation data for 110 newly notified substances were compared with the estimations of the different models. The models were applied separately and in combinations to determine which model(s) showed the best performance. The results of this study were compared with the results of other validation studies and other biodegradation models. The BIOWIN models predict not-readily biodegradable substances with high accuracy in contrast to ready biodegradability. In view of the high environmental concern of persistent chemicals and in view of the large number of not-readily biodegradable chemicals compared to the readily ones, a model is preferred that gives a minimum of false positives without a corresponding high percentage false negatives. A combination of the BIOWIN models (BIOWIN2 or BIOWIN6) showed the highest predictive value for not-readily biodegradability. However, the highest score for overall predictivity with lowest percentage false predictions was achieved by applying BIOWIN3 (pass level 2.75) and BIOWIN6. [ABSTRACT FROM AUTHOR]

Environmental risk limits (ERLs) for individual congeners of polychlorinated biphenyls (PCB 77, 105, 118, 126, 153, 156, 157, and 169) are derived. After lipid normalization, toxicity data for birds, mammals, and aquatic organisms were converted to equivalent concentrations in soil or sediment organic carbon (OC). Accumulation in the food chain was taken into account. Field-derived data on the environmental fate of PCBs, e.g., biomagnification factors and biota-to-sediment accumulation factors, were used in the calculations. The variability in these data was incorporated by using probabilistic techniques. Parameters that are difficult to measure for these hydrophobic compounds, such as the bioconcentration factor or the sediment/water partition coefficient, were avoided where possible. Probability distributions for various species were combined per congener when statistically appropriate; ERLs were based on the fifth percentile of these combined distributions. Congener patterns occurring in various sediments and invertebrates in The Netherlands were used for determining a mixture ERL for non- and mono-ortho PCBs. The PCB 118 was selected as a guiding congener. If the concentration of PCB 118 is less than 5 Mu g/kg OC, Dutch ecosystems are assumed to be protected for effects of the whole mixture of non- and mono-ortho-substituted PCBs. Concentrations associated with adverse effects in field studies were comparable to concentrations that would result if all congeners would be present at the ERLlevel. [ABSTRACT FROM AUTHOR]

A model for risk assessment was built for simultaneous, congener-specific PCB bioaccumulation from sediment to fish to otters (Lutra lutra). Toxic equivalence factors (TEFs) were used to sum individual congeners in otters to a toxic equivalent concentration (TEQ) relative to 2,3,7,8-tetrachlorodibenzo-p-dioxin. Microbial dechlorination and burial in sediments and bioaccumulation are modeled to predict time trends of PCB concentrations in otters. Effects of accumulated PCBs on otters were assessed for vitamin A metabolism and reproduction, taking model uncertainty into account. Three locations in The Netherlands were modeled with PCB levels in sediment of 1 to 171 pg TEQ/g organic carbon (OC). Almost 100% reduction in litter size was predicted for the most polluted area in 1996. Due to large associated uncertainty, a period of 25 to 80 years may be needed for recovery of otter vitamin A levels and litter size at this site. Calculated median sediment quality criteria (SQC) range between 1 and 12 pg TEQ/g OC, depending on the chosen effect criterion. Uncertainty in calculated effects and SQCs is substantial and is mainly caused by uncertainty in PCB congener 126 accumulation.

A simplified food web with three trophic levels is designed: plants and invertebrates at the first, small birds and mammals at the second, and birds and beasts of prey at the third trophic level. Exposure of top predators via separate food chains is analyzed. However, most top predator species are exposed via more than one food chain (food web). Therefore, a species-specific approach is followed too, for which four bird of prey species and two beast of prey species with different food choices are selected: sparrow hawk, kestrel, barn owl, little owl, badger, and weasel. The most critical food chains for secondary poisoning of top predators are soil --> worm/insect --> bird --> bird of prey for dichlorodiphenyltrichloroethane (DDT), and soil --> worm --> bird/mammal --> bird of prey for cadmium (Cd). The risk for the selected top predator species is much lower than the risk based on these critical food chains because the critical food chains constitute a minor part of their food webs. Species feeding on birds (sparrow hawk) and small carnivorous mammals (barn owl) are exposed to DDT and Cd to a much higher extent than species mainly feeding on small herbivorous mammals (kestrel and weasel). It is recommended to include exposure via the pathways soil --> worm/insect --> bird/mammal --> top predator in procedures for derivation of environmental quality objectives for persistent and highly lipophilic compounds.

In previous studies, the risk of toxicant accumulation in food chains was used to calculate quality criteria for surface water and soil. A simple algorithm was used to calculate maximum permissable concentrations [MPC = no-observed-effect concentration/bioconcentration factor(NOEC/BCF)]. These studies were limited to simple food chains. This study presents a method to calculate MPCs for more complex food webs of predators. The previous method is expanded. First, toxicity data (NOECs) for several compounds were corrected for differences between laboratory animals and animals in the wild. Second, for each compound, it was assumed these NOECs were a sample of a log-logistic distribution of mammalian and avian NOECs. Third, bioaccumulation factors (BAFs) for major food items of predators were collected and were assumed to derive from different log-logistic distributions of BAFs. Fourth, MPCs for each compound were calculated using Monte Carlo sampling from NOEC and BAF distributions. An uncertainty analysis for cadmium was performed to identify the most uncertain parameters of the model. Model analysis indicated that most of the prediction uncertainty of the model can be ascribed to uncertainty of species sensitivity as expressed by NOECs. A very small proportion of model uncertainty is contributed by BAFs from food webs. Correction factors for the conversion of NOECs from laboratory conditions to the field have some influence on the final value of MPC5, but the total prediction uncertainty of the MPC is quite large. It is concluded that the uncertainty in species sensitivity is quite large. To avoid unethical toxicity testing with mammalian or avian predators, it cannot be avoided to use this uncertainty in the method proposed to calculate MPC distributions. The fifth percentile of the MPC is suggested as a safe value for top predators.