Moschandreas, D.J.

Suranaree University of Technology Intellectual Repository

Scenario–model–parameter:a new method of cumulative risk uncertainty analysisD.J. Moschandreas a,*, S. Karuchit baIllinois Institute of Technology, Department of Chemical and Environmental Engineering, Perlstein Hall,Room 233, 10 West 33rd Street, Chicago, IL 60616-3783, USAbSuranaree University of Technology, School of Environmental Engineering, 111 University Avenue, Suranaree Subdistrict,Muang District, Nakhon Ratchasima 30000, ThailandReceived 12 October 2001; accepted 27 March 2002AbstractThe recently developed concepts of aggregate risk and cumulative risk rectify two limitations associated with the classical riskassessment paradigm established in the early 1980s. Aggregate exposure denotes the amount of one pollutant available at the biologicalexchange boundaries from multiple routes of exposure. Cumulative risk assessment is defined as an assessment of risk from theaccumulation of a common toxic effect from all routes of exposure to multiple chemicals sharing a common mechanism of toxicity. Thus,cumulative risk constitutes an improvement over the classical risk paradigm, which treats exposures from multiple routes as independentevents associated with each specific route. Risk assessors formulate complex models and identify many realistic scenarios of exposure thatenable them to estimate risks from exposures to multiple pollutants and multiple routes. The increase in complexity of the risk assessmentprocess is likely to increase risk uncertainty. Despite evidence that scenario and model uncertainty contribute to the overall uncertainty ofcumulative risk estimates, present uncertainty analysis of risk estimates accounts only for parameter uncertainty and excludes model andscenario uncertainties. This paper provides a synopsis of the risk assessment evolution and associated uncertainty analysis methods. Thisevolution leads to the concept of the scenario–model–parameter (SMP) cumulative risk uncertainty analysis method. The SMP uncertaintyanalysis is a multiple step procedure that assesses uncertainty associated with the use of judiciously selected scenarios and models ofexposure and risk. Ultimately, the SMP uncertainty analysis method compares risk uncertainty estimates determined using all three sourcesof uncertainty with conventional risk uncertainty estimates obtained using only the parameter source. An example of applying the SMPuncertainty analysis to cumulative risk estimates from exposures to two pesticides indicates that inclusion of scenario and model sourcesincreases uncertainty of risk estimates relative to those estimated using only the parameter source. Changes in uncertainty magnitude mayaffect decisions made by risk managers.D 2002 Published by Elsevier Science Ltd.Keywords: Cumulative risk; Aggregate risk; Uncertainty analysis; Variability; Monte Carlo simulation1. IntroductionThe National Research Council (NRC) instituted theclassical risk assessment paradigm, a multiple-step proce-dure that identifies a hazard and then relates populationexposure to one agent with dose and risk (NRC, 1983).However, this conventional risk assessment practice is con-strained by the following limitations that could lead tounderestimation of risk.1. Exposures to a pollutant from multiple routes areusually treated as independent events associated with eachspecific route (EPA, 1999a). Therefore, simultaneous expo-sures experienced by one person from multiple routes over aperiod of time are not considered.2. Exposures to multiple chemicals are often treated asindividual events and the combined toxicity effect(s) ofsimultaneous exposures to multiple chemicals are notaddressed.3. Uncertainty analysis in conventional risk assessmentconsiders only parameter uncertainty. Although both of theother two types of uncertainty (scenario and model) con-tribute to overall uncertainty, they are frequently assumed0160-4120/02/$ - see front matter D 2002 Published by Elsevier Science Ltd.PII: S0160 -4120 (02 )00025 -9* Corresponding author. Tel.: +1-312-567-3040; fax: +1-312-567-8874.E-mail address: djm@iit.edu (D.J. Moschandreas).www.elsevier.com/locate/envintEnvironment International 28 (2002) 247–261negligible or ignored (Fayerweather et al., 1999). Failure toaccount for them could compromise the validity of theoutcome and conclusions reached by current methods ofestimating risk assessment.The recently developed concepts of aggregate andcumulative risks respond to the first and second limitations,respectively (EPA, 1999a, 2000). Risk assessment analysisis evolving as risk assessors formulate models that are morecomplex, identify many and more realistic scenarios ofexposure, and attain new insights that allow the practitionerto estimate risks from exposures to multiple pollutants andmultiple routes. This increase in complexity of the riskassessment process is likely to increase risk uncertainties.However, methods to estimate uncertainty associated withrisk estimates have remained unchanged. Uncertainty anal-ysis of risk estimates accounts for only parameter uncer-tainty and excludes model and scenario uncertainties. Riskanalysts have not substantiated but assume that model andscenario uncertainties are smaller than parameter uncertain-ties. In a recent treatment of uncertainty assessment ofchemical dose that the authors characterize as ‘‘introduc-tory,’’ Hertwich et al. (2000) address all three types ofuncertainties. They conclude that scenario and model uncer-tainty analysis can change dose estimates by several ordersof magnitude.Currently, a specific procedure for a quantitative analysisof scenario or model uncertainty is not available in theliterature. A general suggestion regarding analysis of modeluncertainty is that risk assessors may use different models toestimate outputs (EPA, 1992a; Hoffman and Hammonds,1994). The range of outputs can be considered as represent-ing the uncertainty range. A more focused approach thatdeals specifically with scenario and model uncertainties isknown as the distributional approach. This approach hasbeen used in analyses of uncertainty from model structureand alternative assumptions or scenarios (Fayerweather etal., 1999; Evans et al., 1994a,b). The distributional approachdivides the risk assessment into a series of decision pointscalled ‘‘nodes’’ that have alternatives. A combination ofalternatives from each node constitutes a ‘‘tree.’’ Each treehas an assigned probability or ‘‘weight’’ based on expertjudgment. This weight is attributed to the risk estimateresulting from each tree. Such results form the final riskdistribution. However, the integrity of the final distributionrelies heavily on the subjective nature of experts’ input.There are also concerns that assigning probabilities tomodels, i.e., quantifying the possibility of a model to be‘‘correct,’’ is inappropriate (Morgan and Henrion, 1990;Cullen and Frey, 1998). Although the literature does notexplicitly refer to scenario uncertainty, it is reasonable toassume that approaches and comments on model uncertaintyare applicable to scenario uncertainty.This paper responds to the need to account for changes inuncertainty magnitude when two, not one, equally validmodels and two equally plausible scenarios are used toestimate risk and uncertainty. The objective is to develop anew method that adds model and scenario uncertainty to theconventional parameter uncertainty analysis of the cumu-lative risk assessment. We call this new inclusive methodthe scenario–model–parameter (SMP) uncertainty analysis.This paper focuses on the development of the SMP uncer-tainty analysis as an integral part of the cumulative riskassessment method. We begin with a review of essentialconcepts involving exposure, dose, and risk, including thenew aggregate and cumulative risk concepts, continue witha review of uncertainty classification and uncertainty anal-ysis processes, and conclude by formulating the SMPuncertainty analysis process. We demonstrate the applica-tion of this method with results from a related paper on theuncertainty of risk estimates from exposures to chlorpyrifosand diazinon using the National Human Exposure Assess-ment Survey in Arizona (NHEXAS-AZ) database (Karuchitand Moschandreas, 2001).2. A synopsis of risk-related concepts2.1. Exposure and doseDefinitions of exposure, dose and related terms used inthis paper are those established in the EPA document‘‘Guidelines for Exposure Assessment’’ (EPA, 1992a). Thebasic structure of the flow of an agent from the outerboundary to the receptor target organ and associated defi-nitions are illustrated in Table 1 (EPA, 1992a). The onset ofthe scheme is the contact of a chemical agent with the outerboundary, which establishes an exposure. The outer boun-daries of the inhalation route are the mouth and nose, andthe outer boundary of the ingestion route is the mouth. Inthis scheme, there is no outer boundary of the dermal route,since the skin is the place where absorption takes place, andtherefore it is an absorption barrier or exchange boundary,not an outer boundary. The route-specific boundaries, withcorresponding chemical transfer process, are shown in Table2 (EPA, 1992a).The intake process commences when the chemicalmoves through the opening of the outer boundary. Theamount of the chemical after crossing the outer boundaryis called a potential dose. Inhalation dose, oral dose anddermal dose are common names for route-specific potentialdose (EPA, 1992a). Potential dose is synonymous withadministered dose. The amount that reaches the exchangeboundary is called an applied dose (see Table 1). Theuptake process takes place at the exchange boundary andinvolves absorption of the chemical through the skin orexposed tissues. The amount of chemical absorbed iscalled an absorbed dose, while the amount of chemicaltransported to an individual organ and the amount thatreaches it are called a delivered dose and a biologicallyeffective dose, respectively.Although the above dose terms signify different quanti-ties, they all have the same unit. The unit of dose has threeD.J. Moschandreas, S. Karuchit / Environment International 28 (2002) 247–261248

Scenario–model–parameter: a new method of cumulative risk uncertainty analysis

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Scenario–model–parameter: a new method of cumulative risk uncertainty analysis

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