Keywords
health risk analysis, fuzzy set, probabilistic risk assessment, triangular membership functions
Start Date
1-7-2010 12:00 AM
Abstract
Health risk analysis to the contaminated water involves the use of mechanisticmodels that include many uncertain and variable parameters. Currently, the uncertainties ofrisk analysis models are treated using statistical theories considering the randomness in datadistribution. However, not all uncertainties in data or model parameters are due torandomness. Other sources of imprecision that may lead to uncertainty include scarce orincomplete data, measurement error or subjective interpretation of available information.These kinds of uncertainties cannot be treated solely by statistical methods. This paper usesfuzzy set theory (FST) together with probability theory (PT) to incorporate uncertaintiesinto risk analysis model. Based on the form of available information, FST, PT, or acombination of both is used to incorporate parameter uncertainty and variability into riskassessment models. The pollutants concentration, cancer and non-cancer risk potencyfactors are highly uncertain parameters in risk analysis model and treated as fuzzy variableswhile the remaining model parameters are treated as random or constant function.Triangular fuzzy function (TFN) is integrated with random variables at different alpha-cutlevels to produced cumulative distribution function (CDF) of individual’s risk. Themethodology is explained through a case study related to the human health risk posed byproduced water discharge from petroleum industries.
Modeling for Uncertainty Assessment in Human Health Risk Quantification: A Fuzzy Based approach
Health risk analysis to the contaminated water involves the use of mechanisticmodels that include many uncertain and variable parameters. Currently, the uncertainties ofrisk analysis models are treated using statistical theories considering the randomness in datadistribution. However, not all uncertainties in data or model parameters are due torandomness. Other sources of imprecision that may lead to uncertainty include scarce orincomplete data, measurement error or subjective interpretation of available information.These kinds of uncertainties cannot be treated solely by statistical methods. This paper usesfuzzy set theory (FST) together with probability theory (PT) to incorporate uncertaintiesinto risk analysis model. Based on the form of available information, FST, PT, or acombination of both is used to incorporate parameter uncertainty and variability into riskassessment models. The pollutants concentration, cancer and non-cancer risk potencyfactors are highly uncertain parameters in risk analysis model and treated as fuzzy variableswhile the remaining model parameters are treated as random or constant function.Triangular fuzzy function (TFN) is integrated with random variables at different alpha-cutlevels to produced cumulative distribution function (CDF) of individual’s risk. Themethodology is explained through a case study related to the human health risk posed byproduced water discharge from petroleum industries.
