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Discrete mathematical data analysis approach: A valuable assessment method for sustainable chemistry

Authors
Journal
The Science of The Total Environment
0048-9697
Publisher
Elsevier
Publication Date
Identifiers
DOI: 10.1016/j.scitotenv.2013.02.098
Keywords
  • Sustainable Chemistry
  • Persistent Organic Pollutants (Pops)
  • Partial Order
  • Hasse Diagram Technique
  • Pyhasse Software
  • Chemometrics
Disciplines
  • Chemistry
  • Design
  • Ecology
  • Geography
  • Philosophy

Abstract

Abstract Sustainable/Green Chemistry is a chemical philosophy encouraging the design of products and processes that reduce or eliminate the use and generation of hazardous substances. In this respect, metrical scientific disciplines like Chemometrics are important, because they indicate criteria for chemicals being hazardous or not. We demonstrated that sustainable principles in the disciplines Green Chemistry, Green Engineering, and Sustainability in Information Technology have main aspects in common. The use of non-hazardous chemicals or the more efficient use of chemical substances is one of these aspects. We take a closer look on the topic of the hazards of chemical substances. Our research focuses on data analyses concerning environmental chemicals named Persistent Organic Pollutants (POPs), which are found all over the world and pose a large risk to environment as well as to humans. The evaluation of the data is a major step in the elucidation of the danger of these chemicals. The data analysis method demonstrated here, is based on the theory of partially ordered sets and provides a generalized ranking. In our approach we investigate data sets of breast milk samples of women in Denmark, Finland, and Turkey which contained measurable levels of 20 POPs. The goal is twofold: On the one side the hazardous chemicals are to be identified and on the other side possible differences among the three nations should be detected, because in that case possible different uptake mechanisms may be supposed. The data analysis is performed by the free available software package PyHasse, written by the third author. We conclude that the data analysis method can well be applied for distinguishing between more or less dangerous existing chemicals. Furthermore, it should be used in sustainable chemistry in the same manner for detecting more and less sustainable chemicals.

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