What is it about?
This research study adopted a novel approach in the form of Bayesian Networks to model urban water quality to better investigate the factors that influence risks to human health. The application of Bayesian Networks was found to enhance the integration of quantitative and qualitative spatially distributed data for analysing the influence of environmental and anthropogenic factors using three surrogate (easy to measure) indicators of human health risk.
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Why is it important?
The study specific objectives were to identify the key environmental and anthropogenic factors which pose risks to human health and to model potential health risks across a region to identify areas of high risk. The initial identification of high risk areas in turn can be subjected to more in-depth investigations instead of the entire region, saving time and resources. The outcomes of this study are expected to enhance informed decision making for human health risk mitigation for improving urban liveability.
Perspectives
The study investigated the use of surrogate indicators with the aid of Bayesian Network modelling to evaluate potential human health risks due to degradation of water quality.
Professor Ashantha Goonetilleke
Queensland University of Technology
Read the Original
This page is a summary of: Use of surrogate indicators for the evaluation of potential health risks due to poor urban water quality: A Bayesian Network approach, Environmental Pollution, February 2018, Elsevier,
DOI: 10.1016/j.envpol.2017.10.076.
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