No abstract

No abstract

The concentrations and removals of 16 fragrance materials (EMs) were measured in 17 U.S. and European wastewater treatment plants between 1997 and 2000 and were compared to predicted values. The average FM profile and concentrations in U.S. and European influent were similar. The average FM profile in primary effluent was similar to the average influent profile; however, the concentration of FMs was reduced by 14.6-50.6% in primary effluent. The average FM profile in final effluent was significantly different from the primary effluent profile and was a function of the design of the wastewater treatment plant. In general, the removal of sorptive, nonbiodegradable FMs was correlated with the removal of total suspended solids in the plant, while the removal of nonsorptive, biodegradable FMs was correlated with 5-day Biological Oxidation Demand removal in the plant. The overall plant removal (primary + secondary treatment) of FMs ranged from 87.8 to 99.9% for activated sludge plants, 58.6-99.8% for carousel plants, 88.9-99.9% for oxidation ditch plants, 71.3-98.6% for trickling filter plants, 80.8-99.9% for a rotating biological contactor plant, and 96.7-99.9% for lagoons. The average concentration of FMs in final effluent ranged from the limit of quantitation (1-3 ng/L) to 8 microg/L. Measured FM removal and concentrations were compared to predicted values, which were based on industry volume, per capita water use, octanol-water partition coefficient, and biodegradability.

Whether or not a given chemical substance is readily biodegradable is an important piece of information in risk screening for both new and existing chemicals. Despite the relatively low cost of Organization for Economic Cooperation and Development tests, data are often unavailable and biodegradability must be estimated. In this paper, we focus on the predictive value of selected Biowin models and model batteries using Bayesian analysis. Posterior probabilities, calculated based on performance with the model training sets using Bayes' theorem, were closely matched by actual performance with an expanded set of 374 premanufacture notice (PMN) substances. Further analysis suggested that a simple battery consisting of Biowin3 (survey ultimate biodegradation model) and Biowin5 (Ministry of International Trade and Industry [MITI] linear model) would have enhanced predictive power in comparison to individual models. Application of the battery to PMN substances showed that performance matched expectation. This approach significantly reduced both false positives for ready biodegradability and the overall misclassification rate. Similar results were obtained for a set of 63 pharmaceuticals using a battery consisting of Biowin3 and Biowin6 (MITI nonlinear model). Biodegradation data for PMNs tested in multiple ready tests or both inherent and ready biodegradation tests yielded additional insights that may be useful in risk screening.

No abstract