Developing Bayesian networks in managing the risk of Legionella colonisation of groundwater aeration systems

Danladi Yunana; Stuart Maclaine; Keng Han Tng; Luke Zappia; Ian Bradley; David Roser; Greg Leslie; C Raina MacIntyre; Pierre Le-Clech

doi:10.1016/j.watres.2021.116854

Developing Bayesian networks in managing the risk of Legionella colonisation of groundwater aeration systems

Water Res. 2021 Apr 1:193:116854. doi: 10.1016/j.watres.2021.116854. Epub 2021 Jan 19.

Authors

Danladi Yunana¹, Stuart Maclaine¹, Keng Han Tng¹, Luke Zappia², Ian Bradley², David Roser³, Greg Leslie¹, C Raina MacIntyre⁴, Pierre Le-Clech⁵

Affiliations

¹ UNESCO Centre for Membrane Science and Technology, School of Chemical Engineering, University of New South Wales (UNSW), Sydney, NSW2052, Australia.
² Water Corporation of Western Australia, WCWA, Leederville, WA6007, Australia.
³ Water Research Centre (WRC), Civil and Environmental Engineering, UNSW, Sydney, Australia.
⁴ The Biosecurity Program, The Kirby Institute, UNSW Medicine, UNSW, Sydney, Australia.
⁵ UNESCO Centre for Membrane Science and Technology, School of Chemical Engineering, University of New South Wales (UNSW), Sydney, NSW2052, Australia. Electronic address: p.le-clech@unsw.edu.au.

PMID: 33550171
DOI: 10.1016/j.watres.2021.116854

Abstract

An Australian water utility has developed a Legionella High Level Risk Assessment (LHLRA) which provides a semi-qualitative assessment of the risk of Legionella proliferation and human exposure in engineered water systems using a combination of empirical observation and expert knowledge. Expanding on this LHLRA, we propose two iterative Bayesian network (BN) models to reduce uncertainty and allow for a probabilistic representation of the mechanistic interaction of the variables, built using data from 25 groundwater treatment plants. The risk of Legionella exposure in groundwater aeration units was quantified as a function of five critical areas including hydraulic conditions, nutrient availability and growth, water quality, system design (and maintenance), and location and access. First, the mechanistic relationship of the variables was conceptually mapped into a fishbone diagram, parameterised deterministically using an expert elicited weighted scoring system and translated into BN. The "sensitivity to findings" analysis of the BN indicated that system design was the most influential variable while elemental accumulation thresholds were the least influential variable for Legionella exposure. The diagnostic inference was used in high and low-risk scenarios to demonstrate the capabilities of the BNs to examine probable causes for diverse conditions. Subsequently, the causal relationship of Legionella growth and human exposure were improved through a conceptual bowtie representation. Finally, an improved model developed the predictors of Legionella growth and the risk of human exposure through the interaction of operational, water quality monitoring, operational parameters, and asset conditions. The use of BNs modelling based on risk estimation and improved functional decision outputs offer a complementary and more transparent alternative approach to quantitative analysis of uncertainties than the current LHLRA.

Keywords: Drinking water; Groundwater; Pathogens; Risk assessment; Water safety plan.

MeSH terms

Australia
Bayes Theorem
Groundwater*
Humans
Legionella*
Risk Assessment
Water Microbiology
Water Quality
Water Supply