Robert W. Okey

A QSAR-based biodegradability model—A QSBR

A microbial biodegradability predictive model has been developed using groups and molecular indices as molecular descriptors. The model contains 12 variables. The model was calibrated and later validated with a data set developed from biodegradation studies of acclimated activated sludge metabolism. It also successfully predicted the biodegradation rate of several other substances with one exception within the limits established by a study of a second database. This latter study was designed to evaluate the expected scatter in acclimated biodegradation rate data. The nitro group, amines, halogens and heteroatoms severely decrease the rate as do structural complexity and electronic alterations created by most substituents. The carboxyl group markedly enhances the rate but the hydroxyl group has little effect. It appears that the hydroxyl group is significant in acclimation but not in degradation. A discussion of statistical methods and software is included in this work.

Uncouplers and activated sludge—the impact on synthesis and respiration

Halogenated organics are among the most commonly‐encountered anthropogenic compounds accumulating in the environment. These compounds have many behavioral characteristics in common. Many are carcinogenic; many are toxic; almost all resist biodegradation; all undergo bioconcentration and concentration in sediments. One group of halogenated materials, the halogenated phenols, interact with microbial cell tissue and higher‐life forms in a way not seen with the other compounds in this broad category. That is, they will uncouple oxidative phosphorylation, the energy transfer process which permits the accumulation of the energy of substrate oxidation as adenosine triphosphate which is subsequently used in synthesis. The present study reports on laboratory work which serves to quantify the various results of energy uncoupling. These are an increase in oxygen use, a decrease in synthesis and a decrease in the rate of normal substrate uptake. The work is carried out utilizing four common types of organics as growt...

Molecular level studies on the origin of toxicity: Identification of key variables and selection of descriptors

Abstract A study of toxicity at the molecular level is repotted. Twenty key variables or descriptors were identified using regression and principal component analysis as determinants of relevance. Most of the key descriptors described various size and electronic characteristics tentatively identified as being associated with toxicity. Other key descriptors consisted of complex structures associated with ortho-substitution, which were also suspected to substantially increase toxicity. A model employing the twenty key variables identified was developed using neural networks (NNs). The resulting model demonstrated good predictive capability when tested on compounds not in the training set.