Organic chemicals, bioconcentration factor range

Experimentally measured bioconcentration factors (BCFs), which provide an indication of the tendency of a chemical to partition to the fatty tissue of organisms, have been found to range between 10 and 100 for trichloroethylene in fish (Kawasaki 1980 Kenaga 1980 Neely et al. 1974 Veith et al. 1980). Barrows et al. (1980) estimated a value of 17 for bluegill sunfish. Somewhat lower BCFs were determined by Saisho et al. (1994) for blue mussel (4.52) and killifish (2.71). These numbers are suggestive of a low tendency to bioaccumulate. 

Metabolic transformation of the absorbed substance by the organism results in a reduction in the BCF below the value that is expected from the chemical s ability to partition into the lipids of the organism s tissues. Figure 9.4 illustrates this by showing the bioconcentration factors of some metabolizable chlorodibenzo-p-dioxins together with those of a range of non-metabolizable substances. 

Numerous factors influence the bioaccumulation of uranium, such as the chemical and physical form of the uranium the season of the year and other climatic factors such as temperature, age of the organism, specific tissue or organs involved and the specific characteristics of the local ecosystem, such as total suspended and dissolved solids. Bioconcentration factors for uranium have been measured by several investigators in various aquatic organisms. Mahon (1982) measured bioconcentration factors of 1,576 and 459 in algae and plankton, respectively. Horikoshi et al. (1981) determined bioconcentration factors in several species of bacteria that ranged from 2,794 to 354,000. However, bioconcentration by the bacteria represented adsorption onto the cell surfaces of the bacteria rather than true biological uptake. 

The availability of reliable measurements or estimates of water solubility, octanol-water partition coefficient, bioconcentration factor, rate constants and the like allows one to make qualitative judgements or, through the use of mathematical simulation models such as EPA s EXAMS (19), quantitative calculations of environmental distribution and persistence. In the qualitative use, Swann and coworkers (20) classified chemical mobility in soil based upon reversed-phase HPLC retention data which in turn is related to S. The approximate water solubility equivalents in this first-estimate classification, with chemical examples, are in Table II. This classification holds for chemicals whose primary adsorption in soil is to organic matter, and excludes those chemicals (such as paraquat) which bind ionically to the soil mineral fraction. A recent tabulation of pesticides found in groundwater had 11 entries, 8 of which represented compounds with water solubilities in excess of 200 ppm with the remaining three falling in the range of 3.5 to 52 ppm (21). 

This study was undertaken to test the ability of our previous molecular connectivity models to accurately predict the soil sorption coefficients, bioconcentration factors, and acute toxicities in fish of polycyclic aromatic hydrocarbons (PAHs), alkylbenzenes, alkenylbenzenes, chlorobenzenes, polychlorinated biphenyls, chlorinated alkanes and alkenes, heterocyclic arid substituted PAHs, and halogenated phenols. Tests performed on large groups of such compounds clearly demonstrate that these simple nonempirical models accurately predict the soil sorption coefficients, bioconcentration factors, and acute toxicities in fish of the above compounds. Moreover, they outperform traditional empirical models based on 1-octanol/ water partition coefficients or water solubilities in accuracy, speed, and range of applicability. These results show that the molecular connectivity models are a very accurate predictive tool for the soil sorption coefficients, bioconcentration factors, and acute toxicities in fish of a wide range of organic chemicals and that it can be confidently used to rank potentially hazardous chemicals and thus to create a priority testing list. ... 

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