Figure 3.4 Species sensitivity distributions using

FIGURE 1.3 Graphical illustration of the use of uncertainty factors (UFs) in extrapolation from a single deterministic effect measure and the estimation of UFs from a species-sensitivity distribution. The y-axis scale on the left graphic is intentionally omitted. 

FIGURE 3.3 Species sensitivity distributions (SSDs) for dichloroaniline (DCA) and nonylphenol (NP) using fish estimates from ECOSAR and ICE (Asfaw et al. 2004) using fathead minnow (fhm) as the surrogate species. Note SSDs not including ECOSAR values for fish, daphnids, and algae are noted as -fhm, whereas SSDs including these values are noted as -fhme. Source Asfaw et al. (2004). 

Figure 5.8 Interpretation of the concept of (acute) toxic pressure (multisubstance probably affected fraction of species (msPAF), based on species sensitivity distributions (SSDs) made from EC50s) using fish species census data from a large monitoring data set. The predicted msPAF (X-axis) is linearly associated to the observed impact of local mixtures on fish assemblages (species loss) in Ohio surface waters (approx. 700 sampling sites). (Study data from Posthuma and De Zwart [2006])...

Figure 5.10 Difference in the dose-effect models for humans and species assemblages (species sensitivity distribution [SSD], right). Threshold-type curves are used for many compounds it is assumed that below a certain daily intake there will be no effects. Nonthreshold chemicals (i.e. certain types of carcinogens) lead to increased probability of cancer, and for this a linear model is assumed in the relevant concentration range. Species sensitivities are assumed to follow a non-linear curve (the SSD), relating the exposure to the fraction of species affected, with a maximum of 100% of the species affected.

Figure 9.4 Risk assessment for an aquatic environment based on a probabilistic procedure into which the concept of varying sensitivity in multispecies communities is incorporated (Nendza, Volmer and Klein, 1990). Exposure and effects are determined separately from experimental or, if not available, QSAR data. Physico-chemical data and information on bioaccumulation and biotransformation are the input for computer simulations of transport and distribution processes that estimate the concentrations of a potential contaminant in a selected river scenario, using, for example, the EXAMS model (Bums, Cline and Lassiter, 1982). For the effects assessment, the log-normal sensitivity distribution is calculated from ecotoxicological data and the effective concentrations for the most sensitive species are determined. The exposure concentrations and toxicity data are then compared by analysis of variance to give a measure of risk for the environment. Modified from Nendza, Volmer and Klein (1990) with kind permission from Kluwer Academic Publishers, Dordrecht.

Figure 9.5 Sensitivity distribution functions estimated from experimental (left curve) and QSAR data (right curve). Acute toxicity data for five fish species were used for the estimate resulting in almost identical distribution functions. Reproduced from Nendza, Volmer and Klein (1990) with kind permission from Kluwer Academic Publishers, Dordrecht.

Aluminum is ordinarily used. Ryles(31) commented on the properties of aluminum species as has Arnson(32), Lauzon(33), Schecher(34), and Baes and Mesmer (35). The distribution of aluminum species is very sensitive to pH (Figure 4). 

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