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We derive equations for the effective concentration giving 10% inhibition (EC10) with 95% confidence limits for probit (log-normal), Weibull, and logistic dose-response models on the basis of experimentally derived median effective concentrations (EC50s) and the curve slope at the central point (50% inhibition). For illustration, data from closed, freshwater algal assays are analyzed using the green alga Pseudokirchneriella subcapitata with growth rate as the response parameter. Dose-response regressions for four test chemicals (tetraethylammonium bromide, musculamine, benzonitrile, and 4-4-(trifluoromethyl)phenoxy-phenol) with ranges of representative slopes at 50% response (0.54-2.62) and EC50s (2.20-357 mg/L) were selected. Reference EC50s and EC10s with 95% confidence limits using probit or Weibull models are calculated by nonlinear regression on the whole dataset using a dose-response regression program with variance weighting and proper inverse estimation. The Weibull model provides the best fit to the data for all four chemicals. Predicted EC10s (95% confidence limits) from our derived equations are quite accurate; for example, with 4-4-(trifluoromethyl)phenoxy-phenol and the probit model, we obtain 1.40 (1.22-1.61) mg/L versus 1.40 (1.20- 1.64) mg/L obtained from the nonlinear regression program. The main advantage of the approach is that EC10 or ECx (where x = 1-99) can be predicted from well-determined responses around EC20 to EC80 without experimental data in the low- or high-response range. Problems with the estimation of confidence interval for EClow,x (concentration predicted to cause x% inhibition) from algal growth inhibition also are addressed. Large confidence intervals may be the result of experimental error and lack of a well-defined reference response value.

A closed-system algal toxicity test with no headspace was applied to evaluate the toxicity of 11 aldehydes and two nitriles to Raphidocelis subcapitata. Algal growth rate and dissolved oxygen (DO) production were used as the test endpoints. Compared to literature data, our test results based on the endpoint of DO production is 2.5 to 257 times more sensitive than the conventional algal batch tests. In addition, our analyses show that different relative toxicity relationships may be observed when different test methods or different endpoints are applied. The test alga was found to be quite sensitive to aldehydes, and a quantitative structure-activity relationship (QSAR) relationship was established based on the n-octanol/water partition coefficient (log Kow). This study also evaluated the combined effects of aldehydes and nitriles. Four synergistic joint actions related to malononitrile were identified. On the other hand, most of the combined effects between aldehydes and acetonitrile were antagonistic. In general, we find that greater-than-additive effects mainly are associated with toxicants displaying flat dose-response curves, and less-than-additive effects may be related to certain steep-slope chemicals. Model analyses show that the above mixture toxicity behavior may be due to response addition or response multiplication joint action modes.

Polychlorinated biphenyl (PCB) congeners were analyzed in four deep, dated sediment cores from the Ashtabula River (OH, USA), for the purpose of identifying relevant PCB sources and congener patterns. The time span for three of the cores is from the mid 1960s to 1998, whereas the fourth has a time span of six years. The total PCB concentrations are in the range of 0.4 to 6.8 microg/g dry weight, with the highest concentrations observed in samples from the 1970s. A factor analysis (FA) model with nonnegative constraints was used to investigate the sources and patterns of PCBs. Additionally, a new model, based on a least squares method, was developed to identify possible patterns of anaerobic dechlorination of PCBs in the sediments, and to quantify the relevant dechlorination pathways. Both models were validated successfully either by artificially created data sets (FA model) or by using laboratory data from the literature (dechlorination model). The FA model revealed two significant sources. The first was identified as a slightly altered Aroclor 1248. The second did not resemble any Aroclor closely, but was very similar to the overall average congener profile of all samples. Simulation of anaerobic dechlorination on an Aroclor 1248 profile from the literature, according to dechlorination activities H/H', as defined in the literature, yielded a congener profile very similar to that of the second pattern. This indicates the likelihood of anaerobic dechlorination of PCBs in Ashtabula River sediments.

The joint toxicity of nonylamine and decylamine and of atrazine and decylamine was evaluated in assays with the green alga Selenastrum capricornutum based on an isobologram method. In this method, curves of constant response, isoboles, are plotted versus concentrations of two toxicants. The response parameter was growth rate based on biomass, and several response levels were used. Dose-response curves were developed for dilution series using fixed ratios between concentrations in toxic units of the compounds. Probit and Weibull dose-response curves were then determined by nonlinear regression. A model for isoboles for partially similar action was used when applicable. The no-effect concentration (NEC or EC0) was estimated based on a newly proposed model containing median effective concentration (EC50) and EC0 as explicit variables. Results show that nonylamine and decylamine are nearly concentration additive at EC50 and EC10 (similarity parameter lambda = 0.70-0.76) and to a lesser extent at EC0. By contrast, the mixtures of atrazine and decylamine show antagonism in that atrazine acts as an antidote to decylamine. The shapes of these isoboles are independent of response level. The EC50 values (mg/L) for chemicals acting singly were 0.090 (nonylamine), 0.039 to 0.044 (decylamine), and 0.225 (atrazine). In order to determine NEC effectively, the level of inhibition must be fairly low, with observed growth rates between 0.6 and 1.0 times the average growth rate of the controls.