Aquatic toxicity, methods

As computing capabiUty has improved, the need for automated methods of determining connectivity indexes, as well as group compositions and other stmctural parameters, for existing databases of chemical species has increased in importance. New naming techniques, such as SMILES, have been proposed which can be easily translated to these indexes and parameters by computer algorithms. Discussions of the more recent work in this area are available (281,282). SMILES has been used to input Contaminant stmctures into an expert system for aquatic toxicity prediction by generating LSER parameter values (243,258). 

USEPA (1991) Methods for aquatic toxicity identification evaluations phase I toxicity characterization procedures. EPA 600/6-91/003. US Environmental Protection Agency, Environmental Research Laboratory, Duluth... 

Interpretation of the biological significance of residue levels fonnd in wildhfe Adoption of improved uniform methods of quantitation so that residne levels can be compared, and so that a time estimate of their environmental significance can be made (NRCC 1975 USEPA 1988) Reexamination of aquatic toxicity data wherein concentrations tested exceeded the solubihty of chlordane in water of 6 to 9 pg/L (WHO 1984)... 

CANMET (1997a) Review of methods for sublethal aquatic toxicity tests relevant to the Canadian metalmining industry, Aquatic Effects Technology Evaluation (AETE) Program, Project 1.2.1, Canada Centre for Mineral and Energy Technology (CANMET), Mining Association of Canada (MAC), Ottawa, Ontario, pp. 1-132. 

U.S. EPA 1993a. Methods for aquatic toxicity identification evaluations Phase... 

U.S. EPA (U.S. Environmental Protection Agency) (1993a) Methods for aquatic toxicity identification evaluations Phase II toxicity identification procedures for samples exhibiting acute and chronic toxicity, EPA-600/R-92/080. 

The pT-method can also be applied to assess liquids (untreated and treated wastewater, surface waters and groundwater). All data from aquatic toxicity tests used to detect pollutants can be integrated into this method. A general description of the pT-method is given in Chapter 3 of this volume. The present chapter specifically addresses the application of the pT-method to sediments and dredged material in order to classify and categorize the hazard associated with the degree of contamination of these matrices. 

Boxall ABA, Brown CD, Barrett KL. 2002. Higher-tier laboratory methods for assessing the aquatic toxicity of pesticides. Pest Manag Sci 58 637-648. 

Norberg-King TJ, Mount DI, Armato JR, Jersen DA, Thompson JA. 1991. Methods for aquatic toxicity identification evaluation phase I toxicity characterization procedures. 2nd ed. Duluth (MN) US Environmental Protection Agency. No. EPA/600/6-91/003. http //www.epa.gov/cgi-bin/claritgw op-Display document=clserv ORD 0235 ra nk=4 template=epa (accessed December 28, 2007). 

The EPA uses QSARs to predict a large number of ecological effects, as well as for environmental fate within the PMN process. The EPA s website (www.epa.gov) provides a valuable source of further information on all these predictive methods, as well as a database and aquatic toxicity values and detailed information on how the models have been validated. Many of the predictive models have been brought together into the EPISUITE software (see Table 19.2 for a listing of the models available). This includes the OPPT s models used for the prediction of physical and chemical properties for new chemical substances. The EPISUITE software is downloadable free of charge (www.epa.gov/oppt/exposure/docs/episuitedl.htm). This provides not only an excellent resource for the development of QSARs, but also a transparent mechanism for the assessment of PMNs. 

A9.6.4.7 The Nordic Council of Ministers issued a report (Pederson et al, 1995) entitled Environmental Hazard Classification, that includes information on data collection and interpretation, as well as a section (5.2.8) entitled QSAR estimates of water solubility and acute aquatic toxicity . This section also discusses the estimation of physicochemical properties, including log Kow For the sake of classification purposes, estimation methods are recommended for prediction of minimum acute aquatic toxicity, for ...neutral, organic, non-reactive and non-ionizable compounds such as alcohols, ketones, ethers, alkyl, and aryl halides, and can also be used for aromatic hydrocarbons, halogenated aromatic and aliphatic hydrocarbons as well as sulphides and disulphides, as cited in an earlier OECD Guidance Document (OECD, 1995). The Nordic document also includes diskettes for a computerized application of some of these methods. 

US EPA, Methods for Aquatic Toxicity Identification Evaluations, Phase I, Toxicity Characterization Procedures, Report No. USEPA-600/3-88/034, US Environmental Protection Agency, Washington, DC, 1988, pp. 8-27 and Table 8.4. 

Many test methods are available for aquatic toxicity testing (Table 1). They differ in cost, precision, complexity, and the skill needed to conduct them. Nevertheless, their objectives are similar. They are conducted to determine the relative potency among chemicals and the relative susceptibility among different species and life stages and to identify other variables that influence the overall outcome of exposure. Toxicity tests are usually conducted to meet regulatory guidelines for the use and discharge of... 

As with the daphnid toxicity tests, those using a variety of fish species, amphibians, and macroinvertebrates have long been the standbys of aquatic toxicity evaluations. Table 4.5 summarizes the species and methods used in these tests. 

Multiple linear regression (MLR) is one of the most common and simplest method for QSAR modeling. MLR has been used for the prediction of geno-toxicity [12], Daphnia magna toxicity [13], photobacterium phosphoreum toxicity [14], aquatic toxicity [15], eye irritation [16], mutagenicity, and carcinogenicity [17], An MLR model assumes that there is a linear relationship between the molecular descriptors of a compound, which is usually expressed as a feature vector x (with each descriptor as a component of this vector), and... 

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