Organochlorine pesticides in the environment and humans: Necessity for comparative data evaluation


Voigt K., Brüggemann R., Hagen S., Çok İ., Mazmancı B., Mazmancı M. A., ...More

Simulation in Umwelt- und Geowissenschaften, Workshop , Leipzig, Germany, 1 - 02 June 2013, vol.146, pp.9-22

  • Publication Type: Conference Paper / Full Text
  • Volume: 146
  • City: Leipzig
  • Country: Germany
  • Page Numbers: pp.9-22
  • Gazi University Affiliated: Yes

Abstract

Over the last 30 years, endocrine disruption research has shown how chemicals in our environment can profoundly affect development, growth, maturation, and reproduction by mimicking hormones or interacting with hormone receptors. The effect of environmental contaminants on health is a major concern because exposure is associated with a number of diseases, including cancer, diabetes, and infertility. In a recently performed monitoring project, 18 OCPs (Organochlorine pesticides) in samples of soils as well as in human breast milk were analysed in different regions in the Taurus Mountains in Turkey. The soil samples were taken in seven different geographical heights. At each height only one sample was retained. Concerning the breast milk samples, five different heights were considered. At each height a different number (from 3 to 14) of human breast milk samples was analysed. The aim of our data evaluation approach now is to find out whether there are conformities between the environmental soil samples and the human breast milk samples. An appropriate data analysis method to find out such conformities and differences is the discrete mathematical method Hasse diagram technique. The software package used is the PyHasse software. This software is written in Python by the second author and it is under constant development. It comprises several modules which are of great support also in the data evaluation of environmental health data. In this presentation we will apply the main Hasse Diagram Technique Module (mHDCl2_7), the Similarity Analysis for the comparison of two data matrices, similarity10_1 and CombiSimilarity6. The latter reveals there is high evidence that milk- and soil pollution are similar.