Bayesian Network and System Thinking modelling to manage water quality related health risks from extreme events

The efforts of modern civilization to create an environment to meet human aspirations have successfully resulted in constant improvements of our lifestyle, but it has increased risks to human and ecological health. This situation has motivated many scientists throughout the world to analyze the environmental factors that can affect our health or ecology and to calculate the levels of risk. In Bangladesh development activities and utilization of the river pose a great threat to the health of the existing natural environmental system, particularly for the important river Buriganga of the capital city, Dhaka, due to the pollution of the river water. A study was carried out to observe the ecological health hazards of the Buriganga river and their risk to human health. Several random samples of water were collected from different spots on the river from September to December 2006. The samples were analyzed to determine water quality and associated environmental health risks. The study revealed that the water is high in biological oxygen demand (BOD), chemical oxygen demand (COD), phosphate (PO4 -3), ammonia, organic matters and nutrients, etc. It also revealed huge environmental health risks and possible ecological disruption of this river. Finally, the research recommends a sustainable policy framework on how the pollution could significantly be reduced by using different appropriate measures. Key words: Environmental health risk, ecological disruption, sustainable policy, water quality, Buriganga river, Bangladesh doi: 10.3126/hn.v3i0.1915 Hydro Nepal:Journal of Water, Energy and Environment Issue No. 3, July 2008. Page: 25-28

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Assessing the impacts of extreme weather events on potable water quality: the value to managers of a highly participatory, integrated modelling approach

H2Open Journal ◽

10.2166/h2oj.2019.024 ◽

2018 ◽

Vol 2 (1) ◽

pp. 9-24

Author(s):

Edoardo Bertone ◽

Oz Sahin ◽

Russell Richards ◽

Anne Roiko

Keyword(s):

Water Quality ◽

Decision Support ◽

Extreme Events ◽

Drinking Water Treatment ◽

Extreme Weather ◽

Integrated Modelling ◽

Extreme Weather Events ◽

Weather Events ◽

Sd Model

Abstract A decision support tool was created to estimate the treatment efficiency of an Australian drinking water treatment system based on different combinations of extreme weather events and long-term changes. To deal with uncertainties, missing data, and nonlinear behaviours, a Bayesian network (BN) was coupled with a system dynamics (SD) model. The preliminary conceptual structures of these models were developed through stakeholders' consultation. The BN model could rank extreme events, and combinations of them, based on the severity of their impact on health-related water quality. The SD model, in turn, was used to run a long-term estimation of extreme events' impacts by including temporal factors such as increased water demand and customer feedback. The integration of the two models was performed through a combined Monte Carlo–fuzzy logic approach which allowed to take the BN's outputs as inputs for the SD model. The final product is a participatory, multidisciplinary decision support system allowing for robust, sustainable long-term water resources management under uncertain conditions for a specific location.

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