Heavy metals risk assessment in drinking water: An integrated probabilistic-fuzzy approach

• Published in: Journal of environmental management 2019-11, Vol.250, p.109514-109514, Article 109514
• Main Authors: Hu, Guangji, Bakhtavar, Ezzeddin, Hewage, Kasun, Mohseni, Madjid, Sadiq, Rehan
• Format: Article
• Language: English
• Subjects: Arsenic; Fuzzy inference system; Heavy metals; Probabilistic health risk assessment; Uncertainty; Water quality
• ISSN: 0301-4797; 1095-8630
• DOI: 10.1016/j.jenvman.2019.109514

Heavy metal(loids) in drinking water have long been a critical water quality concern. Chronic exposure to toxic heavy metals and metalloids (TMMs) through water ingestion can result in significant health risks to the public, while elevated concentrations of less toxic heavy metals (LTMs) can compromise the aesthetic value of water. An integrated probabilistic-fuzzy approach was developed to help water utilities assess water quality regarding heavy metal(loids) (WQHM). In probabilistic assessments, the probabilities of exceedance of health risk guidelines due to chronic exposure to TMMs and exceedance of aesthetic objectives due to elevated LTMs concentrations were quantified through Monte Carlo simulations. The probabilistic assessments can address the aleatory uncertainties due to random variations of health risk parameters. A fuzzy inference system, composed of fuzzy membership functions, operators, and rules, was used to facilitate interpreting WQHM based on the probabilities of guideline exceedance. Epistemic uncertainties due to vagueness and imprecision in linguistic variables used for describing health risks and aesthetic impacts can be reduced by fuzzy inferencing. The developed approach was applied to four water quality scenarios characterized by different combinations of TMMs and LTMs concentrations. Reasonable decisions were recommended for WQHM management under the four scenarios. The developed approach offers a useful tool for systematically assessing WQHM from a health risk mitigation perspective by addressing different types of uncertainties. •An integrated probabilistic-fuzzy approach was developed for heavy metals control.•Health risks and aesthetical impacts due to heavy metals in water were evaluated.•Aleatory and fuzzy uncertainties in water quality assessments can be reduced.