Abstract-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 Ͼ7.0. 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 ϭ 0.73) and mean error (0.48) for log BCF (n ϭ 694) indicate that the new method is a significantly better fit to existing data than other methods.

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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N,N-Diethyl-m-toluamide's (DEET) commercial use as an insect repellent and other reported uses are reviewed. Evidence that DEET is reaching the environment mainly from consumer use of DEET-containing insect repellent includes studies reporting higher concentrations of DEET in surface water and wastewater samples during the summer months, the presence of DEET in on-site septic tank effluent at concentrations similar to that reported in wastewater treatment plant (WWTP) influent, and changes in WWTP effluent concentrations before and after the introduction of a DEET replacement in Germany. Its detected concentrations in influent and effluent of WWTP and surface water worldwide are reviewed and correlations between DEET usage and wastewater effluent concentrations are analyzed. The removability during wastewater treatment is also evaluated. A correlation between commercial DEET use in a metropolitan area and concentrations in WWTP effluents was assessed, and 2 different models were used to predict DEET concentrations in rivers and streams throughout the United States. Ecological toxicity data are reviewed for acute studies and for chronic values that are available for Daphnia magna and algae. The ecological risk of DEET usage is evaluated by examining the relationship of the expected dose/response to observed concentrations.

A new predictive model for determining quantitative primary biodegradation half-lives of individual petroleum hydrocarbons has been developed. This model uses a fragment-based approach similar to that of several other biodegradation models, such as those within the Biodegradation Probability Program (BIOWIN) estimation program. In the present study, a half-life in days is estimated using multiple linear regression against counts of 31 distinct molecular fragments. The model was developed using a data set consisting of 175 compounds with environmentally relevant experimental data that was divided into training and validation sets. The original fragments from the Ministry of International Trade and Industry BIOWIN model were used initially as structural descriptors and additional fragments were then added to better describe the ring systems found in petroleum hydrocarbons and to adjust for nonlinearity within the experimental data. The training and validation sets had r2 values of 0.91 and 0.81, respectively.