Improved method for estimating bioconcentration/bioaccumulation factor from octanol/water partition coefficient
A compound's bioconcentration factor (BCF) is the most commonly used indicator of its tendency to accumulate in aquatic organisms from the surrounding medium. Because it is expensive to measure, the BCF is generally estimated from the octanol/water partition coefficient (Kow), but currently use...
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Published in | Environmental toxicology and chemistry Vol. 18; no. 4; pp. 664 - 672 |
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Main Authors | , , , , , |
Format | Journal Article |
Language | English |
Published |
Hoboken
Wiley Periodicals, Inc
01.04.1999
SETAC |
Subjects | |
Online Access | Get full text |
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Summary: | A compound's bioconcentration factor (BCF) is the most commonly used indicator of its tendency to accumulate in aquatic organisms from the surrounding medium. Because it is expensive to measure, the BCF is generally estimated from the octanol/water partition coefficient (Kow), but currently used regression equations were developed from small data sets that do not adequately represent the wide range of chemical substances now subject to review. To develop an improved method, we collected BCF data in a file that contained information on measured BCFs and other key experimental details for 694 chemicals. Log BCF was then regressed against log Kow and chemicals with significant deviations from the line of best fit were analyzed by chemical structure. The resulting algorithm classifies a substance as either nonionic or ionic, the latter group including carboxylic acids, sulfonic acids and their salts, and quaternary N compounds. Log BCF for nonionics is estimated from log Kow and a series of correction factors if applicable; different equations apply for log Kow 1.0 to 7.0 and >7.0. For ionics, chemicals are categorized by log Kow and a log BCF in the range 0.5 to 1.75 is assigned. Organometallics, nonionics with long alkyl chains, and aromatic azo compounds receive special treatment. The correlation coefficient (r2 = 0.73) and mean error (0.48) for log BCF (n = 694) indicate that the new method is a significantly better fit to existing data than other methods. |
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Bibliography: | ark:/67375/WNG-7CBNZZKX-H ArticleID:ETC5620180412 istex:C5DB443BAAE095EE37D107BAB8843AFAD39CECBE ObjectType-Article-2 SourceType-Scholarly Journals-1 ObjectType-Feature-1 content type line 23 ObjectType-Article-1 ObjectType-Feature-2 |
ISSN: | 0730-7268 1552-8618 |
DOI: | 10.1002/etc.5620180412 |