Uncertainty characterization approaches for risk assessment of DBPs in drinking water: a review.
The management of risk from disinfection by-products (DBPs) in drinking water has become a critical issue over the last three decades. The areas of concern for risk management studies include (i) human health risk from DBPs, (ii) disinfection performance, (iii) technical feasibility (maintenance, management and operation) of treatment and disinfection approaches, and (iv) cost. Human health risk assessment is typically considered to be the most important phase of the risk-based decision-making or risk management studies. The factors associated with health risk assessment and other attributes are generally prone to considerable uncertainty. Probabilistic and non-probabilistic approaches have both been employed to characterize uncertainties associated with risk assessment. The probabilistic approaches include sampling-based methods (typically Monte Carlo simulation and stratified sampling) and asymptotic (approximate) reliability analysis (first- and second-order reliability methods). Non-probabilistic approaches include interval analysis, fuzzy set theory and possibility theory. However, it is generally accepted that no single method is suitable for the entire spectrum of problems encountered in uncertainty analyses for risk assessment. Each method has its own set of advantages and limitations. In this paper, the feasibility and limitations of different uncertainty analysis approaches are outlined for risk management studies of drinking water supply systems. The findings assist in the selection of suitable approaches for uncertainty analysis in risk management studies associated with DBPs and human health risk.
Chowdhury
Shakhawat
S
Department of Civil Engineering, Queen's University, Kingston, ON, Canada. shakhawat@ce.queensu.ca
Champagne
Pascale
P
McLellan
P James
PJ
D004202:Disinfectants / Q000009:adverse effects*; D004781:Environmental Exposure / Q000592:standards / Q000706:statistics & numerical data; D017143:Fuzzy Logic; D006262:Health; D006801:Humans; D009010:Monte Carlo Method; D011336:Probability; D018570:Risk Assessment / Q000379:methods* / Q000706:statistics & numerical data; D013223:Statistics as Topic; D035501:Uncertainty; D014874:Water Pollutants, Chemical / Q000009:adverse effects*; D014881:Water Supply / Q000592:standards* / Q000706:statistics & numerical data
D004202:Disinfectants; D014874:Water Pollutants, Chemical