Developing Bayesian networks in managing the risk of Legionella colonisation of groundwater aeration systems. (1st April 2021)

• Record Type: Journal Article
• Authors: Yunana, Danladi; Maclaine, Stuart; Tng, Keng Han; Zappia, Luke; Bradley, Ian; Roser, David; Leslie, Greg; MacIntyre, C. Raina; Le-Clech, Pierre
• Is Part Of: Water research. Volume 193(2021)
• Publication Date: 2021-04-01
• Subjects: Risk assessment -- Groundwater -- Drinking water -- Water safety plan -- Pathogens; Water -- Pollution -- Research -- Periodicals; 363.7394
• DOI: 10.1016/j.watres.2021.116854
• Languages: English
• ISSNs: 0043-1354

Abstract

Highlights: The initial BN model quantifies the probability of high risk of Legionella at 40%. BN analysis revealed system design as the most important risk variable. The revised BN quantify probabilities of Legionella and human exposure separately. BN offer a more robust and transparentalternative to estimation of Legionella risk. 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. Graphical abstract: Image, graphical abstract