Species sensitivity distribution example

It is noteworthy that comparisons of existing assessment schemes reveal dissimilarities in the use of extrapolation methods and their input data between different jurisdictions and between prospective and retrospective assessment schemes. This is clearly apparent from, for example, a set of scientific comparisons of 5% hazardous concentration (HC5) values for different substances. Absolute HC5 values and their lower confidence values were different among the different statistical models that can be used to describe a species sensitivity distribution (SSD Wheeler et al. 2002a). As different countries have made different choices in the prescribed modeling by SSDs (regarding data quality, preferred model, etc.), it is clear that different jurisdictions may have different environmental quality criteria for the same substance. Considering the science, the absolute values could be the same in view of the fact that the assessment problem, the available extrapolation methods, and the possible set of input data are (scientifically) similar across jurisdictions. When it is possible, however, to look at the confidence intervals, the numerical differences resulting from different details in method choice become smaller because confidence intervals show overlap. 

The toxic pressure of each of the compounds in a mixture is calculated using the species sensitivity distribution (SSD) concept. In this concept, laboratory toxicity data for various species are collected from a database, for example, the USEPA s Ecotox database (USEPA 2005) or the RIVM e-toxBase (Wintersen et al. 2004), and compiled for each compound. A statistical distribution of these data, called the SSD, is derived. Each SSD describes the relationship between exposure concentration (X) and toxic pressure (Y), whereby the latter is expressed as the probably affected fraction (PAF, %) per compound (Posthuma et al. 2002). Depending on the test endpoint chosen for deriving SSDs, there is the option to derive chronic and acute toxic pressures, based on SSDN0ECs and SSDEC50s, respectively. 

The main focus of the ecological risk assessment is to minimize undesired events caused by chemicals. Species sensitivity distribution (SSD) is an example of an ecotoxicological method which is based on such events at above the no-effect level/concentration. We can assume that within a community species differ in... 

Over the last 10 years a lot of efforts have been made to implement probabilistic methods for environmental risk assessments (ERA) into regulatory decisionmaking schemes. ECOFRAME in the US (www.epa.gov/oppefedl/ecorisk) was the first and largest project in this area, the EU-project EUFRAM (vww.eufram. com) is a comparable initiative. Whereas on the effect side, several examples exist where species sensitivity distributions (SSDs) were used for regulatory decisionmaking, comparable cases on the exposure side are rare. 

Differences among species in distribution patterns of histological changes may be caused by species variations in the distribution in the nasal epithelium of chemical-metabolizing enzymes. For example, in rats exposed to methyl methacrylate, nasal lesions were shown to be caused by the carboxylesterase mediated metabolism of methyl methacrylate to methacrylic acid, an irritant and corrosive metabohte. The distribution of these enzymes in the nasal tissues of man, rat, and hamster indicated a lower rate of metabolism in man compared to rat and hamster, suggesting a lower sensitivity to methacrylate in humans (Mainwaring et al. 2001). 

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.

No differences occur if for the same scenario either experimental data or QSAR predictions of the compound-specific parameters are used. Possible influences of parameter inaccuracies are by far outweighed by the predominance of the environmental factors in this example. The realistic assumption of a phenol release rate of 10 kg/h, resulting in an exposure level of about 70 xg/l, is unlikely to conflict with the effective concentrations (> 1 mg/l). Compared with the values based on the small data set (n = 5 species), the increased width of the sensitivity distribution for the fish and macroinvertebrate community n = 29 species) yields considerably lower LC5Q estimates for the most sensitive species. Consequently, the ratio of environmental and... 

The ability to introduce the lux phenotype into different bacterial species provides a convenient method for rapidly screening in a simple and sensitive way the presence of specific bacteria and for monitoring their growth and distribution in the environment [198], Another application of transformed bacteria deals with specific susceptibility in toxicity tests the presence of agents that disrupt or kill the bacteria destroys the metabolism, thus eliminating light emission. Some examples are listed in Table 7. 

As has been pointed out earlier, the kinetic results (ft = k6/k9 independent of the nature of the sensitizer) as well as the observed product distributions obtained in photosensitized oxygenation reactions and in (chemically prepared) singlet oxygen oxygenation reactions (see p. 45, for example) leave but little doubt that 102(1Ag or 1S9+) is the common oxygenating species. ... 

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