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Improved method for estimating bioconcentration/bioaccumulation factor from octanol/water partition coefficient
Author(s) -
Meylan William M.,
Howard Philip H.,
Boethling Robert S.,
Aronson Dallas,
Printup Heather,
Gouchie Sybil
Publication year - 1999
Publication title -
environmental toxicology and chemistry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 171
eISSN - 1552-8618
pISSN - 0730-7268
DOI - 10.1002/etc.5620180412
Subject(s) - bioconcentration , partition coefficient , bioaccumulation , chemistry , octanol , environmental chemistry , alkyl , correlation coefficient , ionic bonding , chromatography , organic chemistry , mathematics , statistics , ion
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 ( K ow ), 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 K ow 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 K ow and a series of correction factors if applicable; different equations apply for log K ow 1.0 to 7.0 and >7.0. For ionics, chemicals are categorized by log K ow 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 ( r 2 = 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.