Quantitative modeling of environmental fate and impact of commercial chemicals

Present laws in many countries require that all commercial chemicals be assessed for their environmental behavior and hazards. Because there are a large number of chemicals currently in common use (approximately 100,000) and new chemicals are being registered at a very high rate (1,000 per year), it is obvious that our human and material resources are insufficient to obtain experimentally even basic information about environmental fate and effects for all these chemicals. Thus, it is necessary to develop quantitative models that will accurately and rapidly predict environmental behavior for large sets of chemicals. During the last decade, a considerable effort has been made to develop methods that use molecular characteristics to describe environmental behavior of organic pollutants. Thus far, molecular connectivity indexes have been shown to be the most successful structural property for describing and predicting soil sorption coefficients, association coefficients with dissolved humic substances, Henry's law constants, bioconcentration factors in aquatic organisms and vegetation, biodegradation rates, and fish acute toxicity. The general quantitative model, based on the first-order molecular connectivity index (MCI), has been developed for the accurate estimation of soil sorption coefficients for predominantly hydrophobic chemicals: polycyclic aromatic hydrocarbons (PAHs), polychlorinated biphenyls (PCBs), halogenated hydrocarbons, and phenols. It is possible to extend this model for many classes of agricultural chemicals. A similar model, based on the second-order valence MCI, has been developed for fish bioconcentration factors of PCBs, chlorinated diphenyl oxides, halogenated hydrocarbons, PAHs, and phenols, plus substituted benzenes. Furthermore, a prototype expert system has been developed for classifying untested chemicals as readily or not readily biodegradable, based on their chemical structures. Finally, using a standardized data base for fish acute toxicity, we have obtained a simple linear correlation between the valence zero-order MCI and the 96-h LC50 data for a large group of 150 structurally diverse commercial chemicals.

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