Kristina Voigt

PyHasse Software for Partial Order Analysis: Scientific Background and Description of Selected Modules

The software PyHasse is an elaborated “experimental” software for ordinal analysis of data matrices. PyHasse is based on the interpreter programming language Python. A brief introduction to the programming language Python is given and the general principles behind PyHasse are outlined. An actual overview about PyHasse (status, April 2013) is provided. Today PyHasse comprises 91 modules covering 9 different categories, such as basic Partial Order Analysis, i.e., the drawing Hasse diagrams and the calculation of some important quantities. A selection of newer or rarely used modules are discussed in detail in order to explain some principles of PyHasse. As a leading example the pollution by Lead, Cadmium, and Zinc of regions of south-western Germany is discussed.

Thematic issue on modelling human and ecological health risks

Abstract In this virtual thematic issue (VTI) the authors of 14 papers address key challenges of environmental modelling and software to support the identification of human and ecological health risks. The contributions address different pathways how environmental exposure can affect human health with a focus on models, data and software and can be divided into the following research areas: (1) modelling approaches to quantify air pollution concentrations and exposure, (2) models and methods to determine health effects of air pollution, (3) models and software to predict and quantify disease and health risks and (4) data interoperability and integration. These contributions highlight that further advances in this research field are required, especially from highly polluted regions, as so far papers from these regions are scarce. The majority of contributions covers exposure to ambient air pollution, which is a key public health risk in both industrialised and developing countries. While not a focus of this VTI, human exposure to water and soil contamination as well as exposure pathways through other environmental media are equally relevant. Further research into how modelling, software and data can support an integrated assessment of the whole Exposome is therefore essential.

PyHasse Software Features Applied on the Evaluation of Chemicals in Human Breast Milk Samples in Turkey

In this chapter we evaluate the data of 18 Organochlorine pesticides (OCPs) found in breast milk samples from 44 mothers in the Taurus Mountains in Turkey. In this approach the association of concentration levels in breast milk samples with the two confounding factors: smoking habit and habit of taking medication is the goal. For all data evaluation approaches, we applied the Hasse diagram technique and its software package, namely the PyHasse software. Special emphasis was laid on the software features “similarity” and “Local Partial Order Model” to draw further conclusions out of the data. The data analyses resulted in differences between the smoking women and those who did not smoke as well as between the medication and non-medication breast milk samples. Little differences were found comparing hormone taking mothers and mothers taking other medication.