Characterization factors

The characterization factor KyQp was introduced by research personnel from the Universal Oil Products Company. 

From these observations, the characterization factor KfjQp (or was defined for pure components using only their boiling points and their densities , ... 

To extend the applicability of the characterization factor to the complex mixtures of hydrocarbons found in petroleum fractions, it was necessary to introduce the concept of a mean average boiling point temperature to a petroleum cut. This is calculated from the distillation curves, either ASTM or TBP. The volume average boiling point (VABP) is derived from the cut point temperatures for 10, 20, 50, 80 or 90% for the sample in question. In the above formula, VABP replaces the boiling point for the pure component. 

In this manner, the KuQp of a petroieum cut can be calcuiated quickly from readily avkilable data, i. e., the specific gravity and the distillation curve. The A //np value is between 10 and 13 and defines the chemical nature of the cut as it will for the pure components. The characterization factor is extremely Va luable and widely used in refining although the discriminatory character of the Kuqp is less than that obtained by more modern physical methods described in 3.2 and 3.3. 

If the boiling temperature is not known, it is somewhat risky to estimate it. One could, if the Watson characterization factor is known, use the following... 

Watson characterization factor log = common logarithm (base 10)... 

K y = Watson characterization factor 5 = standard specific gravity... 

Watson characterization factor Tu = normal boiling point... 

Three frequently specified properties are density—specific gravity—API gravity, characterization factor, and sulfur content (2,6,7). The API (American Petroleum Institute) gravity is a measure of density or specific gravity (sp gr) ... 

The Watson characterization factor has also been used as a measure of the chemical character of a cmde oil or its fractions ... 

For a wide-hoiling-range material such as cmde oil, the boiling point is taken as an average of the five temperatures at which 10, 30, 50, 70, and 90% of the material is vaporized. A highly paraffinic cmde oil can have a characterization factor as high as 13, whereas a highly naphthenic cmde oil can be as low as 10.5, and the breakpoint between the two types of cmde oil is approximately 12. 

Once the indicator is defined, a model can be developed that predicts the indicator value as a function of an emission. Such models are normally simple linear models defined by characterization factors. If an emission is niuitiplied by a characterization factor, an indicator value is obtained. 

The sum of indicator values obtained when multiplying all emissions assigned to that impact category by their respective characterization factors is called the category indicator result. The indicator moles of may, for instance, be the sum of contributions from sulfur dioxide, nitrogen oxides, and hydrogen chloride, and there is a characterization factor for each of them relative to the indicator. 

Another relationship used to indicate the crude type is the Watson characterization factor. The factor also relates the mid-boiling point of the crude or a fraction to the specific gravity. 

Life cycle impact characterization factors may be missing for a lot of additives. 

Those possible explanations are investigated in this chapter. We will shortly describe the LCA methodology in Sect. 2. We will review case studies on plastics and printed matter/paper in Sect. 3. In Sect. 4 we will address the data situation for LCI databases and LCIA characterization factors. In Sect. 5 we will come to some conclusions and recommendations. 

The data situation for additives in LCIA seems to be somewhat better than for the LCI [4]. Characterization factors exist for a number of additives and for a number of impact categories. Nevertheless, the fist is nowhere near complete. Especially for the impact categories of human toxicity and ecotoxicity, impact factors are missing. Approaches exist to calculate such factors based on substance characteristics. In this volume, LCIA factors are derived for a large number of additives based on such approaches [5]. The lack of such factors, therefore, seems to be less of a problem for including additives in LCA case studies than the lack of LCI data. 

As mentioned above, there are characterization factors for a number of different impact categories, e.g. acidification, eutrophication, climate change, human toxicity and ecotoxicity. However, characterization factors are missing for many additives, especially for human toxicity and ecotoxicity, which makes it difficult to assess the potential impact that a product will cause during its entire life cycle. A major reason that characterization factors are often missing is the lack of data regarding substance properties, such as physical chemical properties and toxicity. 

In a study by Andersson et al. [30], the possibilities to use quantitative structure-activity relationship (QSAR) models to predict physical chemical and ecotoxico-logical properties of approximately 200 different plastic additives have been assessed. Physical chemical properties were predicted with the U.S. Environmental Protection Agency Estimation Program Interface (EPI) Suite, Version 3.20. Aquatic ecotoxicity data were calculated by QSAR models in the Toxicity Estimation Software Tool (T.E.S.T.), version 3.3, from U.S. Environmental Protection Agency, as described by Rahmberg et al. [31]. To evaluate the applicability of the QSAR-based characterization factors, they were compared to experiment-based characterization factors for the same substances taken from the USEtox organics database [32], This was done for 39 plastic additives for which experiment-based characterization factors were already available. 

In a first attempt to derive characterization factors with QSARs, the entire dataset of plastics additives was included, and aquatic ecotoxicity was predicted for two different trophic levels. This generated characterization factors that did not correspond well with the ones derived from experimental data [30]. Hardly surprising, but a clear indication that two trophic levels are unsufficient. A second attempt to derive characterization factors with QSARs are currently being performed [31]. In this second attempt, substances that are difficult to model in QSAR models have been removed from the dataset and the ecotoxicity has been predicted for three different trophic levels instead of two. However, results have not yet been obtained from this second attempt. If the results show that it is possible to derive reliable characterization factors by the use of QSARs, the current data gap regarding characterization factors for human toxicity and ecotoxicity could be... 

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