Record Display for the EPA National Library Catalog

RECORD NUMBER: 96 OF 1643

OLS Field Name OLS Field Data
Main Title Applications of Molecular Connectivity Indexes and Multivariate Analysis in Environmental Chemistry.
Author Niemi, G. J. ; Regal, R. R. ; Veith, G. D. ;
CORP Author Minnesota Univ.-Duluth.;Environmental Research Lab.-Duluth, MN.
Year Published 1986
Report Number EPA/600/D-86/017;
Stock Number PB86-148137
Additional Subjects Environmental surveys ; Chemical compounds ; Molecular structures ; Industrial wastes ; Biochemical oxygen demand ; Partition coefficients ; Biodeterioration ; Chemical bonds ; Statistical analysis ; Forecasting ; Toxic substances ; Toxic Substance Control Act
Holdings
Library Call Number Additional Info Location Last
Modified
Checkout
Status
NTIS  PB86-148137 Most EPA libraries have a fiche copy filed under the call number shown. Check with individual libraries about paper copy. NTIS 06/21/1988
Collation 16p
Abstract
The authors have developed a data matrix of 90 variables calculated from molecular connectivity indices for 19,972 chemicals in the Toxic Substance Control Act (TSCA) inventory of industrial chemicals. The first three principal components convey generalized information on chemical structure: size, degree of branching, and number of cycles. The other components contained more specific information on branching, bonding, cyclicness, valency, and combinations of these structural attributes. Here the authors explored the use of the connectivity indices and their calculated principal components for their potential in predicting biodegradation as measured by biochemical oxygen demand (BOD) and the octanol/water partition coefficient. This approach showed promise in the prediction of biodegradation, but was of limited use in the prediction of the partition coefficient. Because it is possible to calculate the connectivity indices at a nominal cost for nearly all chemicals, the approach will prove especially useful for the identification of chemicals with similar structures and for systematically exploring where data are lacking on biological endpoints for chemicals in TSCA.