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Voigt, K. ; Brüggemann, R.* ; Scherb, H. ; Cok, I.* ; Mazimnci, B.* ; Mazmanci, A.* ; Turgut, C.* ; Schramm, K.-W.

Similarities of environmental health data of persistent organic pollutants in three countries analyzed by the pyHasse software.

In: Proceedings (25th Conference EnviroInfo 2011). Aachen: Shaker, 2011. 137-144 (Innovations in Sharing Environmental Observations and Information)

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Chemicals, such as Persistent Organic Pollutants (POPs), introduced into the environment by human activity may disrupt the endocrine system of animals including fish and wildlife as well as humans and produce adverse effects because of the crucial role hormones play in controlling development. POPs are detected worldwide. In order to evaluate the data on numerous studies on POPs sound mathematical and statistical data evaluation methods are needed. The data analysis method demonstrated in this paper, is based on the theory of partially ordered sets and provides a generalized ranking. Partial order is a discipline of Discrete Mathematics and one may consider partial order as an example of mathematics without arithmetic. The data analysis is performed with the free available software package PyHasse, written by the second author, which provides apart from the calculation of Hasse diagrams many features, such as for example the similarity analysis applied in this paper. Studies on POPs were performed in Denmark (1997-2001) and Finland (1997-1999) as well as in Turkey in 2010. In our data analysis approach we investigated data sets of breast milk samples of women in Denmark and Finland which contained detectable levels of 20 Persistent Organic Pollutants (POPs). These results have already been published by Voigt et al, 2010. In a study performed in the Taurus Mountains area in Turkey the same 20 POPs were detected in breast milk samples. The question arises whether the above mentioned methodology of partial orders can find differences or similarities among these countries. Applying the sub-routine Similarity of the PyHasse software the similarities between data sets can be identified. The combinations are for the similarity analyses: Turkey with Denmark, Turkey with Finland, and Denmark with Finland. In the similarity analysis different types of relations are distinguished which quantify the similarities of the two compared data sets. The highest degree of similarity can be found comparing Denmark with Finland. However, there is also some similarity regarding Turkey – Denmark and Turkey – Finland. This means that the breast milk samples in all three countries are similarly contaminated with respect to their quality the 20 POPs looked upon. 137 - 144

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