No abstract

to imply that both samples have the same origin, since this is unlikely; however, the analysis shows that the sample was definitely a No. 6 fuel oil. ConclusionWe have recorded infrared spectra of over 50 different petroleum samples; absorptivities of 21 bands between 650 and 1200 cm™1 can be used to characterize the samples. Stored in a computer file, the 21 absorptivities of every known sample can be compared with those of any unknown sample at a later date. Using the ratios of known to unknown absorptivities, an unknown can be matched to a known in a matter of seconds.The method of analysis presented here is rapid and, for the 50 plus samples we have studied, it provides an unambiguous identification for petroleum products, eliminating the need for adding tracer materials to petroleum products or for other methods of analysis. Ideally, for the method to be used on a worldwide scale, infrared spectra of all possible petroleum samples would have to be measured and the absorptivities of the selected bands stored in a computer file. This could all be accomplished within a relatively short period of time. In addition, to identify samples taken from natural waters, the effects of weathering on the infrared spectra of samples have to be known.Currently, we are investigating these effects and will report on them shortly.

No abstract

No abstract

The use of water quality models in three management contexts is described: screening, planning, and design. Screening models are illustrated by describing a methodology applicable to large areas of land and another applied to a large number of chemicals. The application of models in a planning context is illustrated by a local planning agency's use in developing nonpoint source control strategies and by the federal government's use in assessing potential exposure to a pesticide. Development of design criteria is illustrated by summarizing the long history of model use to develop effluent limits in the Holston River basin (Tennessee).