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A Bayesian network model to assess the public health risk associated with wet weather sewer overflows discharging into waterways

Overview of attention for article published in Water Research, April 2012
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Title
A Bayesian network model to assess the public health risk associated with wet weather sewer overflows discharging into waterways
Published in
Water Research, April 2012
DOI 10.1016/j.watres.2012.03.044
Pubmed ID
Authors

R. Goulding, N. Jayasuriya, E. Horan

Abstract

Overflows from sanitary sewers during wet weather, which occur when the hydraulic capacity of the sewer system is exceeded, are considered a potential threat to the ecological and public health of the waterways which receive these overflows. As a result, water retailers in Australia and internationally commit significant resources to manage and abate sewer overflows. However, whilst some studies have contributed to an increased understanding of the impacts and risks associated with these events, they are relatively few in number and there still is a general lack of knowledge in this area. A Bayesian network model to assess the public health risk associated with wet weather sewer overflows is presented in this paper. The Bayesian network approach is shown to provide significant benefits in the assessment of public health risks associated with wet weather sewer overflows. In particular, the ability for the model to account for the uncertainty inherent in sewer overflow events and subsequent impacts through the use of probabilities is a valuable function. In addition, the paper highlights the benefits of the probabilistic inference function of the Bayesian network in prioritising management options to minimise public health risks associated with sewer overflows.

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 79 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
United States 2 3%
Netherlands 1 1%
United Kingdom 1 1%
Ireland 1 1%
Mexico 1 1%
Canada 1 1%
Unknown 72 91%

Demographic breakdown

Readers by professional status Count As %
Student > Ph. D. Student 19 24%
Researcher 15 19%
Student > Master 11 14%
Student > Bachelor 7 9%
Lecturer 5 6%
Other 11 14%
Unknown 11 14%
Readers by discipline Count As %
Engineering 23 29%
Environmental Science 16 20%
Computer Science 5 6%
Mathematics 4 5%
Agricultural and Biological Sciences 4 5%
Other 12 15%
Unknown 15 19%